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NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/cloud_sdg.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1: Synthetic Data Generation and Training Workflow with Warehouse Sim Ready Assets\n",
"\n",
"This notebook is the first part of the SDG and Training Workflow. We will be focusing on generating Synthetic Data for our use case\n",
"\n",
"A high level overview of the steps:\n",
"* Pulling Isaac Sim Docker Container \n",
"* Using Replicator API for Data Generation with Domain Randomization\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"\n",
"This notebook shows provides an overview of generating synthetic data using Warehouse Sim Ready assets with Isaac Sim and Omniverse Replicator. We will generate data for the `palletjack` class of objects. \n",
"\n",
"1. [Set up Isaac Sim via Docker Container](#head-1)\n",
"2. [Generate Data for Detecting Palletjacks](#head-2)\n",
"3. [Deeper dive into SDG script](#head-3)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Set up Isaac Sim: Docker Container Installation <a class=\"anchor\" id=\"head-1\"></a>\n",
"\n",
"### This step can be skipped if the Isaac Sim Docker container has already been set up on your Cloud/Remote Instance\n",
"\n",
"* Follow the [instructions](https://docs.omniverse.nvidia.com/isaacsim/2022.2.1/install_container.html) for Isaac Sim Container Installation\n",
"* Ensure that `docker run` command on Step 7 works as expected and you are able to enter the container. \n",
"\n",
"We will use `./python.sh` in the container to run our SDG script. Please make sure you exit the container before running the next cells "
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## 2. Generate Data for Detecting Palletjacks <a class=\"anchor\" id=\"head-2\"></a>\n",
"\n",
"* We can find the Palletjack USDs in the Warehouse Sim Ready asset collection (`http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks`)\n",
"* First, we will mount our current local directory while running the docker container. This will ensure that we can run our scripts inside the Isaac Sim container. Data generated in the container will also be saved in this mounted directory."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/karma/Downloads/getting_started_v4.0.1/notebooks/tao_launcher_starter_kit/detectnet_v2/sdg_and_training/sdg-and-training/palletjack_sdg\n"
]
}
],
"source": [
"import os\n",
"\n",
"# This is the directory which will be mounted into the Isaac Sim container. Make sure <path_to_repo_cloned> is updated correctly\n",
"# os.environ[\"MOUNT_DIR\"]=os.path.join(<path_where_repo_cloned>, \"palletjack_sdg\")\n",
"os.environ[\"LOCAL_PROJECT_DIR\"]=os.path.dirname(os.getcwd())\n",
"os.environ[\"MOUNT_DIR\"] = os.path.join(os.getenv(\"LOCAL_PROJECT_DIR\"), \"palletjack_sdg\")\n",
"print(os.getenv(\"MOUNT_DIR\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"# Make sure the MOUNT_DIR location is correct, it shold have the scripts needed for SDG there\n",
"\n",
"!docker run --name isaac-sim --entrypoint bash -it --gpus all -e \"ACCEPT_EULA=Y\" --rm --network=host \\\n",
" -v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache/Kit:rw \\\n",
" -v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \\\n",
" -v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \\\n",
" -v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \\\n",
" -v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \\\n",
" -v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \\\n",
" -v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \\\n",
" -v ~/docker/isaac-sim/documents:/root/Documents:rw \\\n",
" -v $MOUNT_DIR:/isaac-sim/palletjack_sdg \\\n",
" nvcr.io/nvidia/isaac-sim:2022.2.1 \\\n",
" ./palletjack_sdg/palletjack_datagen.sh\n",
" \n",
"# Make sure $MOUNT_DIR is set correctly from the cell above"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The data generation will begin in `headless` mode. We will be generating 5k images and using a 90:10 split for training and validation. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Once the data generation is complete, list the folders in the data directory\n",
"\n",
"!ls -rlt $MOUNT_DIR/palletjack_data\n",
"\n",
"# There hould be 3 folders -> 1. distractors_warehouse 2. distractors_additional 3. no_distractors "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Deeper Dive into SDG Script <a class=\"anchor\" id=\"head-3\"></a>\n",
"\n",
"* The `standalone_palletjack_sdg.py` is the Python script which runs and generates data in headless mode inside the container.\n",
"* The overall flow of the script is similar to the `standalone_examples/replicator/offline_generation.py` file provided as a starting point with Isaac Sim\n",
"\n",
"\n",
"* We will be carrying out specific randomizations targeted to our use case. Some of them are:\n",
" * Camera Pose Randomization -> Should be similar to a robot perspective in the scene\n",
" * Palletjack Color Randomization -> To ensure model is robust to variations in Palletjack colors\n",
" * Distractors Pose Randomization -> To enable the model to *focus* on the right object (Our object of interest: Palletjack)\n",
" * Lighting Randomization-> Model robust to lights and reflections/shadows in the scene\n",
" * Floor and Wall Texture Randomization -> Model more robust to changes in background textures and features <br> <br>\n",
" \n",
" \n",
"* We are only interested in the `palletjack` object class, all other semantics are removed from the stage with the `update_semantics()` function\n",
"\n",
"* You can use a model of your own choice to train with this data (Pytorch/Tensorflow or other frameworks)\n",
"\n",
"* The data is written in the KITTI Format, this allows seamless integration with TAO to train a model. Refer to `training/cloud_train.ipynb` notebook (Part 2) for training with TAO\n"
]
}
],
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"name": "python3"
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|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/training/cloud_train.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2: Synthetic Data Generation and Training Workflow with Warehouse Sim Ready Assets\n",
"\n",
"This notebook is the second part of the SDG and Training Workflow. Here, we will be focusing on training an Object Detection Network with TAO toolkit\n",
"\n",
"A high level overview of the steps:\n",
"* Pulling TAO Docker Container\n",
"* Training Detectnet_v2 model with Data generated in Part 1\n",
"* Visualizing Model Performance on Sample Real World Data\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"\n",
"This notebook shows an example usecase of Object Detection using DetectNet_v2 in the Train Adapt Optimize (TAO) Toolkit. We will train the model with Synthetic Data generated from Omniverse Replicator.\n",
"\n",
"1. [Set up TAO via Docker container](#head-1)\n",
"2. [Download Pretrained model](#head-2)\n",
"3. [Convert Dataset to TFRecords for TAO](#head-3)\n",
"4. [Provide training specification](#head-4)\n",
"5. [Run TAO training](#head-5)\n",
"6. [Evaluate trained model](#head-6)\n",
"7. [Visualize Model Predictions on Real World Data](#head-7)\n",
"8. [Next Steps](#head-8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Set up TAO via Docker Container <a class=\"anchor\" id=\"head-1\"></a>\n",
"\n",
"* We will follow the [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#running-tao-toolkit) for using TAO toolkit. Make sure that the pre-requisite steps are completed (installing `docker`, `nvidia container toolkit` and `docker login nvcr.io`)\n",
"\n",
"* The docker container being used for training will be pulled in the cells below, make sure you have completed the pre-requisite steps and `docker login nvcr.io` to allow pulling of the container from NGC\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"%env DOCKER_REGISTRY=nvcr.io\n",
"%env DOCKER_NAME=nvidia/tao/tao-toolkit\n",
"%env DOCKER_TAG=4.0.0-tf1.15.5 ## for TensorFlow docker\n",
"\n",
"%env DOCKER_CONTAINER=nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Download Pretrained Model <a class=\"anchor\" id=\"head-2\"></a>\n",
"\n",
"* We will use the `detectnet_v2` Object Detection model with a `resnet18` backbone\n",
"* Make sure the `LOCAL_PROJECT_DIR` environment variable has the path of this cloned repository in the cell below\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# os.environ[\"LOCAL_PROJECT_DIR\"] = \"<LOCAL_PATH_OF_CLONED_REPO>\"\n",
"os.environ[\"LOCAL_PROJECT_DIR\"] = os.path.dirname(os.path.dirname(os.getcwd())) # This is the location of the root of the cloned repo\n",
"print(os.environ[\"LOCAL_PROJECT_DIR\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!wget --quiet --show-progress --progress=bar:force:noscroll --auth-no-challenge --no-check-certificate \\\n",
" https://api.ngc.nvidia.com/v2/models/nvidia/tao/pretrained_detectnet_v2/versions/resnet18/files/resnet18.hdf5 \\\n",
" -P $LOCAL_PROJECT_DIR/cloud/training/tao/pretrained_model/"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## 3. Convert Dataset to TFRecords for TAO <a class=\"anchor\" id=\"head-3\"></a>\n",
"\n",
"* The `Detectnet_v2` model in TAO expects data in the form of TFRecords for training. \n",
"* We can convert the KITTI Format Dataset generated from Part 1 with the `detectnet_v2 dataset_convert` tool provided with TAO toolkit\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack warehouse distractors dataset\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_warehouse && rm -rf $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_warehouse/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/cloud/training/tao/specs/tfrecords/distractors_warehouse.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_warehouse/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack with additional distractors\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_additional && rm -rf $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_additional/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/cloud/training/tao/specs/tfrecords/distractors_additional.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_additional/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for kitti trainval dataset\")\n",
"# !mkdir -p $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse && rm -rf $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse/*\n",
"!mkdir -p $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/no_distractors && rm -rf $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/no_distractors/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/cloud/training/tao/specs/tfrecords/no_distractors.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/tfrecords/no_distractors/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Provide Training Specification File <a class=\"anchor\" id=\"head-4\"></a>\n",
"\n",
"* The spec file for training with TAO is provided under `$LOCAL_PROJECT_DIR/specs/training/resnet18_distractors.txt`\n",
"* The tfrecords and the synthetic data generated in the previous steps are provided under the `dataset_config` parameter of the file\n",
"* Other parameters like `augmentation_config`, `model_config`, `postprocessing_config` can be adjusted. Refer to [this](https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/detectnet_v2.html) for a detailed guideline on adjusting the parameters in the spec file\n",
"* For training our model to detect `palletjacks` this `spec` file provided can be used directly\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!cat $LOCAL_PROJECT_DIR/cloud/training/tao/specs/training/resnet18_distractors.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters can be set in the `spec` file. Adjust batch size parameter depending on the VRAM of your GPU \n",
"\n",
"* You can increase the number of epochs, the number of false positives in real world images keeps decreasing (mAP does not change much after ~250 epochs and usually results in the best trained model for the given dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Run TAO Training <a class=\"anchor\" id=\"head-5\"></a>\n",
"\n",
"* The `$LOCAL_PROJECT_DIR` will be mounted to the TAO docker for training, this contains all the data, pretrained model and spec files (training and inference) needed\n",
"\n",
"#### Ensure that no `_warning.json` file exists in the `$LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords` sub-folders (`distractors_additional`, `ditractors_warehouse` and `no_distractors`)\n",
"* Delete the `_warning.json` files before beginning training\n",
"* TAO training won't begin if the structure of the `tfrecords` folder directories is not as expected "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Setting up env variables for cleaner command line commands.\n",
"%env KEY=tlt_encode\n",
"%env NUM_GPUS=1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* TAO Training can be stopped and resumed (`checkpoint_interval` parameter specified in the `spec` file)\n",
"* Tensorboard visualization can be used with TAO [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tensorboard_visualization.html#visualizing-using-tensorboard). \n",
"* The `$RESULTS_DIR` parameter is the folder where the `$LOCAL_PROJECT_DIR/cloud/training/tao/detectnet_v2/resnet18_palletjack` folder which is specified with the `-i` flag in the command below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 train -e /workspace/tao-experiments/cloud/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -r /workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Evaluate Trained Model <a class=\"anchor\" id=\"head-6\"></a>\n",
"\n",
"* While generating the `tfrecords` part of the total data generated was kept as a validation set (14% of total data)\n",
"* We will run our model evaluation on this data to obtain metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 evaluate -e /workspace/tao-experiments/cloud/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -m /workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt \\\n",
" -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Visualize Model Performance on Real World Data <a class=\"anchor\" id=\"head-7\"></a>\n",
"\n",
"* Lets visualize the model predictions on a few sample real world images next\n",
"* We will use palletjack images in a warehouse from the `LOCO` dataset to understand if the model is capable of performing real world detections\n",
"* Additional images can be placed under the `loco_palletjacks` folder of this project. The input folder is specified with the `-i` flag in the command below "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 inference -e /workspace/tao-experiments/cloud/training/tao/specs/inference/new_inference_specs.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack/test_loco \\\n",
" -i /workspace/tao-experiments/images/loco_palletjacks \\\n",
" -k $KEY"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image \n",
"\n",
"results_dir = os.path.join(os.environ[\"LOCAL_PROJECT_DIR\"], \"cloud/training/tao/detectnet_v2/resnet18_palletjack/test_loco/images_annotated\")\n",
"# pil_img = Image(filename=os.path.join(os.getenv(\"LOCAL_PROJECT_DIR\"), 'detecnet_v2/july_resnet18_trials/new_pellet_distractors_10k/test_loco/images_annotated/1564562568.298206.jpg'))\n",
" \n",
"image_names = [\"1564562568.298206.jpg\", \"1564562628.517229.jpg\", \"1564562843.0618184.jpg\", \"593768,3659.jpg\", \"516447400,977.jpg\"] \n",
" \n",
"images = [Image(filename = os.path.join(results_dir, image_name)) for image_name in image_names]\n",
"\n",
"display(*images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next Steps <a class=\"anchor\" id=\"head-8\"></a>\n",
"\n",
"#### Generating Synthetic Data for your use case:\n",
"\n",
"* Make changes in the Domain Randomization under the Synthetic Data Generation script (`palletjack_sdg/standalone_palletjack_sdg.py`\n",
"* Add additional objects of interest in the scene (similar to how `palletjacks` are added, you can add `forklifts`, `ladders` etc.) to generate data\n",
"* Use [different](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#downloading-the-models) models for training with TAO (for object detection, you can use `YOLO`, `SSD`, `EfficientDet`) \n",
"* Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html). These can be used for training a model of your own framework and choice\n",
"* Exploring the option of using `Synthetic + Real` data for training a network. Can be particularly useful for generating more data around particular corner cases\n",
"\n",
"\n",
"#### Deploying Trained Models:\n",
"\n",
"* After obtaining satisfactory results with the training process, you can further optimize your model for deployment with the help of Pruning and QAT.\n",
"* TAO models can directly be deployed on Jetson with Isaac ROS or Deepstream which ensures your end-to-end pipeline being optimized (data acquisition -> model inference -> results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "f23a2831654361cfd8b219e05b5055fdda3e37fe5c0b020e6226f740844c300a"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/training/tao/specs/training/resnet18_distractors.txt | random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_warehouse/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_additional/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/cloud/training/tao/tfrecords/no_distractors/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
}
image_extension: "png"
target_class_mapping {
key: "palletjack"
value: "palletjack"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 960
output_image_height: 544
min_bbox_width: 20.0
min_bbox_height: 20.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 0.5
zoom_max: 1.5
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "palletjack"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15000000596
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 20
}
}
}
}
model_config {
pretrained_model_file: "/workspace/tao-experiments/cloud/training/tao/pretrained_model/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
arch: "resnet"
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 5
minimum_detection_ground_truth_overlap {
key: "palletjack"
value: 0.5
}
evaluation_box_config {
key: "palletjack"
value {
minimum_height: 25
maximum_height: 9999
minimum_width: 25
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "palletjack"
class_weight: 1.0
coverage_foreground_weight: 0.0500000007451
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 1.0
}
}
enable_autoweighting: true
max_objective_weight: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 32
num_epochs: 100
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-06
max_learning_rate: 5e-04
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
visualizer{
enabled: true
num_images: 10
scalar_logging_frequency: 10
infrequent_logging_frequency: 5
target_class_config {
key: "palletjack"
value: {
coverage_threshold: 0.005
}
}
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "palletjack"
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.400000154972
}
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/training/tao/specs/inference/new_inference_specs.txt | inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "palletjack"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 32
gpu_index: 0
# model handler config
tlt_config{
model: "/workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"palletjack"
value: {
confidence_model: "aggregate_cov"
output_map: "palletjack"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.005
clustering_algorithm: DBSCAN
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
dbscan_confidence_threshold: 0.9
minimum_bounding_box_height: 20
}
}
}
classwise_bbox_handler_config{
key:"default"
value: {
confidence_model: "aggregate_cov"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 20
}
}
}
}
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/training/tao/specs/tfrecords/distractors_warehouse.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/training/tao/specs/tfrecords/no_distractors.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/cloud/training/tao/specs/tfrecords/distractors_additional.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/local_train.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Synthetic Data Generation and Training Workflow with Warehouse Sim Ready Assets\n",
"\n",
"This notebook is the second part of the SDG and Training Workflow. Here, we will be focusing on training an Object Detection Network with TAO toolkit\n",
"\n",
"A high level overview of the steps:\n",
"* Pulling TAO Docker Container\n",
"* Training Detectnet_v2 model with generated Synthetic Data \n",
"* Visualizing Model Performance on Sample Real World Data\n",
"\n",
"`This notebook is very similar to the cloud training notebook, only mounted directories and paths for the docker containers are changed. The data, model and training, evaluation and inference steps are identical` "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### If Isaac Sim is installed locally, ensure that data generation is complete. Run the `generate_data.sh` script in this folder. Ensure the path to Isaac Sim is set correctly in the script (`ISAAC_SIM_PATH` corresponds to where Isaac Sim is installed locally on your workstation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"\n",
"This notebook shows an example usecase of Object Detection using DetectNet_v2 in the Train Adapt Optimize (TAO) Toolkit. We will train the model with Synthetic Data generated previously.\n",
"\n",
"1. [Set up TAO via Docker container](#head-1)\n",
"2. [Download Pretrained model](#head-2)\n",
"3. [Convert Dataset to TFRecords for TAO](#head-3)\n",
"4. [Provide training specification](#head-4)\n",
"5. [Run TAO training](#head-5)\n",
"6. [Evaluate trained model](#head-6)\n",
"7. [Visualize Model Predictions on Real World Data](#head-7)\n",
"8. [Next Steps](#head-8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Set up TAO via Docker Container <a class=\"anchor\" id=\"head-1\"></a>\n",
"\n",
"* We will follow the pre-requisites section of [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#running-tao-toolkit) for using TAO toolkit. Make sure that the pre-requisite steps are completed (installing `docker`, `nvidia container toolkit` and `docker login nvcr.io`)\n",
"\n",
"* The docker container being used for training will be pulled in the cells below, make sure you have completed the pre-requisite steps and `docker login nvcr.io` to allow pulling of the container from NGC\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: DOCKER_REGISTRY=nvcr.io\n",
"env: DOCKER_NAME=nvidia/tao/tao-toolkit\n",
"env: DOCKER_TAG=4.0.0-tf1.15.5 ## for TensorFlow docker\n",
"env: DOCKER_CONTAINER=nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5\n"
]
}
],
"source": [
"import os\n",
"%env DOCKER_REGISTRY=nvcr.io\n",
"%env DOCKER_NAME=nvidia/tao/tao-toolkit\n",
"%env DOCKER_TAG=4.0.0-tf1.15.5 ## for TensorFlow docker\n",
"\n",
"%env DOCKER_CONTAINER=nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Download Pretrained Model <a class=\"anchor\" id=\"head-2\"></a>\n",
"\n",
"* We will use the `detectnet_v2` Object Detection model with a `resnet18` backbone\n",
"* Make sure the `LOCAL_PROJECT_DIR` environment variable has the path of this cloned repository in the cell below\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# os.environ[\"LOCAL_PROJECT_DIR\"] = \"<LOCAL_PATH_OF_CLONED_REPO>\"\n",
"os.environ[\"LOCAL_PROJECT_DIR\"] = os.path.dirname(os.getcwd()) # This is the location of the root of the cloned repo\n",
"print(os.environ[\"LOCAL_PROJECT_DIR\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!wget --quiet --show-progress --progress=bar:force:noscroll --auth-no-challenge --no-check-certificate \\\n",
" https://api.ngc.nvidia.com/v2/models/nvidia/tao/pretrained_detectnet_v2/versions/resnet18/files/resnet18.hdf5 \\\n",
" -P $LOCAL_PROJECT_DIR/local/training/tao/pretrained_model/"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## 3. Convert Dataset to TFRecords for TAO <a class=\"anchor\" id=\"head-3\"></a>\n",
"\n",
"* The `Detectnet_v2` model in TAO expects data in the form of TFRecords for training. \n",
"* We can convert the KITTI Format Dataset generated from Part 1 with the `detectnet_v2 dataset_convert` tool provided with TAO toolkit\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack warehouse distractors dataset\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_warehouse && rm -rf $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_warehouse/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/local/training/tao/specs/tfrecords/distractors_warehouse.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/tfrecords/distractors_warehouse/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack with additional distractors\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_additional && rm -rf $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_additional/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/local/training/tao/specs/tfrecords/distractors_additional.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/tfrecords/distractors_additional/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for kitti trainval dataset\")\n",
"# !mkdir -p $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse && rm -rf $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse/*\n",
"!mkdir -p $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/no_distractors && rm -rf $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/no_distractors/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/local/training/tao/specs/tfrecords/no_distractors.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/tfrecords/no_distractors/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Provide Training Specification File <a class=\"anchor\" id=\"head-4\"></a>\n",
"\n",
"* The spec file for training with TAO is provided under `$LOCAL_PROJECT_DIR/specs/training/resnet18_distractors.txt`\n",
"* The tfrecords and the synthetic data generated in the previous steps are provided under the `dataset_config` parameter of the file\n",
"* Other parameters like `augmentation_config`, `model_config`, `postprocessing_config` can be adjusted. Refer to [this](https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/detectnet_v2.html) for a detailed guideline on adjusting the parameters in the spec file\n",
"* For training our model to detect `palletjacks` this `spec` file provided can be used directly\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!cat $LOCAL_PROJECT_DIR/local/training/tao/specs/training/resnet18_distractors.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters can be set in the `spec` file. Adjust batch size parameter depending on the VRAM of your GPU \n",
"\n",
"* You can increase the number of epochs, the number of false positives in real world images keeps decreasing (mAP does not change much after ~250 epochs and usually results in the best trained model for the given dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Run TAO Training <a class=\"anchor\" id=\"head-5\"></a>\n",
"\n",
"* The `$LOCAL_PROJECT_DIR` will be mounted to the TAO docker for training, this contains all the data, pretrained model and spec files (training and inference) needed\n",
"\n",
"#### Ensure that no `_warning.json` file exists in the `$LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords` sub-folders (`distractors_additional`, `ditractors_warehouse` and `no_distractors`)\n",
"* Delete the `_warning.json` files before beginning training\n",
"* TAO training won't begin if the structure of the `tfrecords` folder directories is not as expected "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Setting up env variables for cleaner command line commands.\n",
"%env KEY=tlt_encode\n",
"%env NUM_GPUS=1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* TAO Training can be stopped and resumed (`checkpoint_interval` parameter specified in the `spec` file)\n",
"* Tensorboard visualization can be used with TAO [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tensorboard_visualization.html#visualizing-using-tensorboard). \n",
"* The `$RESULTS_DIR` parameter is the folder where the `$LOCAL_PROJECT_DIR/local/training/tao/detectnet_v2/resnet18_palletjack` folder which is specified with the `-i` flag in the command below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 train -e /workspace/tao-experiments/local/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -r /workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Evaluate Trained Model <a class=\"anchor\" id=\"head-6\"></a>\n",
"\n",
"* While generating the `tfrecords` part of the total data generated was kept as a validation set (14% of total data)\n",
"* We will run our model evaluation on this data to obtain metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 evaluate -e /workspace/tao-experiments/local/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -m /workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt \\\n",
" -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Visualize Model Performance on Real World Data <a class=\"anchor\" id=\"head-7\"></a>\n",
"\n",
"* Lets visualize the model predictions on a few sample real world images next\n",
"* We will use palletjack images in a warehouse from the `LOCO` dataset to understand if the model is capable of performing real world detections\n",
"* Additional images can be placed under the `loco_palletjacks` folder of this project. The input folder is specified with the `-i` flag in the command below "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 inference -e /workspace/tao-experiments/local/training/tao/specs/inference/new_inference_specs.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack/5k_model_synthetic \\\n",
" -i /workspace/tao-experiments/images/sample_synthetic \\\n",
" -k $KEY"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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h828f78p/pW/r0Ul7pyW9yW2kZwGrKKGOYMRjmtS5nW4ij2kkgd6i1mUjlDoc8cRZLdjGO4q/o12kSLGmUkQ9DWrcXcttaFRjB4rlmLpdiWIHfnNaxjcmR3dpPC8oeeIPJ2NZniHwg2pS/b7WdU2jLRetSaeXmRWYgPXQzJHFZLJuZj3A71V7Eo53SbmXSYfIgjRSepIya0P7Y1Uc748emwU144ZjkKQaWFdp2hSfrTaTHsU5r+/uXzLJn0AXFRia53DqcHit1bRGG4kA+lT2lqjEhoxj1NLRBuY7T3F0oBiJK/3RWlZ2jtGq569fUVswwJCm1FA9cCmTTQ2kTSYHHpUc19CkiVSIIhufoOpqrPqSRrlRn0Oa56/1a4uHKopC+lZzvKw+Yn8TTUO4OR5Daud+doz61ZaW3kRldCjfwketR2sM0ModUB2nPNX9Te3vokeCARTj7+DwTXEtjRmK5O4qenrSAd+RirH2ObGWXml+yyAfdGRWT3LRXAyMnikbpVn7LIRyMHtQbObgbc00BUABUjvSHHTvVv7JJ/dpBZz/AN0UuUCpyFxmhsbhjPvmrDWlwHACZBpv2W4B+4PpRYCL27Gl3HGMCpBbXGMbB+dK1pcY4jH507BYh5wffpSqCOT+FTG0uMn5R+dH2Wc/wgfjTsBDkg+1OIPVRTzaTHjAz9aeLafnhcfWlYCHJ5J7U3JI4HBqx9kmIyAM0Ja3AXoKGBXUcd6eBnICipvs02OVFL9lm6gAfjRyjKrRj8acoKjnFWPs0wPQc+9BtZSMbVx9aGmNFdXGaNoYEmpls5snAX8aDZy9yBTSJZWKHGep7Uo+cfMvIqc2lx224+tM+y3IbotOwFZ42BJx+NNzg8/nV82k5/u5NRtYzei0OLArKxDZHFKHUk7eDU32G4HOFNNFjcEbgVU/WlawDfNyNu7NU7jSYZ8sjMjHrjpVxtOuu23d35609LW7AAOzP1oFYyYNAjWTfK5cZ6EVsIqooVRgClFtdYJO0Y9DQLe4I5CnPpTKvYCB1FNKc5/Spfs8w+8qj8aQwTeg/OlYVyHbxmmhQc471P5M2PuD86RraYqMKOfeiwyAjjk5qN+nTipmguFGNo/OmNBOByox35pWAqEYzg8U8ZwCOtIVJbGKeFPGPyp6CG9cdqUjjAGR3p4Tkn9KQocj3oAbGpyRipyuxh3piABiBUrLxk0NgPBzSEUvH4UmeehoQDT7DiopkDgqwyp6jsakKgk9QKaSQKoRzetaG9nEl3bgtC/JXun/ANaptE1xlxaXLZHRHJ6exrrJ0BsoQQDuU8GuK1bRntszxDMJ6gfwmrTJaOkmYKDnpVQzIwIZgB0rFsNWYW/2W5JYfwOT+lULx389gWPWqIOk06F7ae8DOZI3QFM9ua0oJliuI9+AG45rndK1NW3RTN8+3Cn1rbmgW4RUbIbAx7GgZ2sR2xLx27U9OOTg1gaRqTW0o0+9bMi8Ix71vFhkdaCivehioI7GsOVEkZw8gQd2NdBKQ8VcvqkRktbpQOdpIxTEb8DxDQXt42JATaGrD0YsutRjOflYVr6QP+KQi9fKNY+mEDVox068fhQIbqmBqT1QI/eD61paon/EzbPQjis4giQfXrSYDLJSdUXPYmrEkG7WZJgOFjxj1NR2ny6mpx1OK0Ol5ce6igDz0jFxJ/vn+dTL92opB/pEvb5z/OpVHFADJDnNT6Y224P0qB8DpU+mY+1HPpQCO30g/wCjAZqXVgP7GvMc/ujUWkAeQTVjUcHSrwdMxN/KmDPOIjjBxVjOVqtD90fSrX8HAqQK8lbmiH9xjPG/JrEkx6Vs6LxAw55amgR3lsf3S/SrK81VtQPs6nParSgkZPFWhk8XX0pt+C1o4PA2nJp0XAyeaS9INlJ6bT/KkIz4/ks4MncdvWtu2wbZB7VhxjZZwj7wx1rctD/oqfSmgJU4PFWB0qqo5ParQORimA4UuByTx6UYz0NLg+lMBVx6UY9aU4FGMnNIAIpe1GPal20gGc0N06U4qBmm7RtppANJOMUw5BANSbR6mmMOQfekwPC9WXbrF8PS4f8AnXpPhdydHtAQD+7HNed66gTXtRUdrh/516B4TOdEtP8Ac60kMzfGe5roiP5SYhgiuahDBUD/AHsc103jCUQ3IZhx5WT+dcxDIspRgeD0pkmheuBFbqC2QcnnpXSeD59FbX7ay1bUJ7KS46XPm7V24+6Sememax5rq3hs7cTWgfc20nPJrM8Wi2nvLQW9p5BEZyCc5qhddD17xLqfw/0VhDFq109yBn/Q5/MH4npXH/8ACYaS02RqGobAeNzdvzrzH7IxOOAR7UjWsi9W61OnYfvbtnqqeONG87cbu9CZ6Z/+vWtZ+PvCfCXVxqbA90wMfnXifkuO9J5L9zmhOPYPe7n0bD4++HzRoPtV0MD+MnJ+tTL44+HxORfTqfdjXzglrI57CrqaLeSwCRdrL2GaXu9hfvP5j6Al8ZeANmf7WuCf9h+f5Vi33jbwwzL/AGdql6n97zcV4i2nXCsUZRuFSx6HeypuVUK9M5qko9g9/wDmPXE8cJDL5lvrfXpuAp0nxBvWJQazFtP94DP8q8sTwhq7xmRIFZfXdUcnhbWoiA1qTx2NVe2yDlfVnp6+LpMEHVoCM5OQKafGErEt/aNuw7cCvJ30q8UENGcjtURtblB0qHFdh+8ewf8ACaXCoR9utTn/AGRT08c3Ij2i4tGHrgV415E/HGaPLmHrUpR7DvI9lXxvdA4EtoSfYf40q+OLpWJ3WZx/n1rxjE4HfFAMwJO40uWIXkezDx5crk7bRh16/wD16d/wnVyz5MdqR7NXi5knAHzGkMkoH3jS5IheR7WPH1zz/o1uP+BmlHjiXr9lt/8Avs14r502PvnNAuJhxvNHKh3Z7P8A8JzPyfs0OfTfTV8dTMCfscIPbDmvGxPP3Y4o+0TA53kY9DRyoOZns8PjyZd2bONvbzKevxC+YbtPBJ7CSvFvtU4OQ5pRdTjnefzo5EF2e2r8QAFz/Zx59ZKcfiCgGP7NPPfzK8Q+1znjzH+maX7Zc9N7Y9M0ciDmZ7e3j+PHOnOPo4/wpP8AhYEAODYSjH+2K8T+23IGN7Y+tIL25xw7/nS5EHMz25fiBbgZayl4/wBoUP8AESzA3Gxn9fvCvE/t1zj77fnQb+47MQKfIhc0j2uP4kacT81pcj8qWb4h2zn91YTY9SRXiQvrgNncaeNSuAPvt+dU7MS5l1PaV8e2oXmxnz/vCpE+INoFObGcnt8wrxMardAEeY350f2rdAffJqeRFczPbG8f2LgbrO4HsCDSjx5YFMm0uR9cV4n/AGvcj5d7Yp39sXY/5aN+dHIh8zPax47sNp/0a5/HFRSeNLCeONRb3Aw2SSB0rxn+2rru5wO1OGt3ajAds0cqFzM9vXxvpCcgTj/tnUg8caMSGzcZHX93Xhg1m5B++TSrrdyM/N+lHKg5me6/8JxozEgPN9PLo/4TPRSMtLKP+2ZrwtdcuVOQf0pTrlzn7x59hRyIOZnu3/Ca6CQFNxIPTMZoHjDQVORdMP8Atma8I/tu4BHOcdOKU67OeuPwFHKg5me8R+NvD4uIhPdkxZ+b5SOK218a+BwpInB4zjJyf1r5pbWZWPIFKusyA5xVxUURJzezPoA+L9AmclL4IpPCsp4p6+K9DwQNSjyPUEV4CNflAHyj8qcPEUoHzIpP0qHFXL5me9DxTowODqMWPxpR4n0ZjzqMO3Hqa8EHiOXOfLXNJ/wkUv8AcU/hRyIOZnvn/CRaKDxqUHPQbqcviLRVP/ISgz/vV4AfEMpI+QD14pR4glznYpH0o5A5mfQf/CQaORn+0rbP+/Sx69o5yDqlsP8AgdfPh8Rv/cUZ9qP+EjfoY1PvS5A5mfQp1nR1xjVbU7u3mCmjWtL3HbqVqfX94K+fR4kPP+joc/WnJ4lVHDNbIw7ijlDnZ76msaWzEjUbX/v4KX+1dLU86na7v+uorw2HxfaxsN+lRyAHu+M1d/4TjSuAfDsR9T5lNQ8w52e1f2npzYJ1K1z7Sij+1dP3YW/tj7+aK8Z/4TnR/MyfDybPQSVXfxppzMxXQUXPTEvSj2fmHOz3FdQsGbJv7bB/6aCmjUbNel3B/wB/B/jXgLeJbdmJWz2gnjLUg8SQZ/49QR9f/rUcnmHOz6C+22YQN9tt8+nmCmfb7MjP2qHP/XQV8/f8JFbnn7KBn3pT4kt/LH+i5P8AvUez8w52fQCX1nncbqD/AL+D/GornU7SNNy3UJOeBvFeBf2/bE/8e+D65qGfV4JQNsZX1OaOQXOz3w6wF+USR8c8MMVag1GCSEM88W5v9oV86DU0HOWP1Y1YXWYQANp/OqaRKlLqfRUdxBu/18X/AH2Kd9ohZwfPj/77FfOv9tW2SGjJPqDQNZttv+rYH/eqeUvnZ9GSTQhQTPHz/timiaPcv75OenzivnU6xa5yI3PH96lOtW3ACSf99UuV9w5j6IkdXPMqjH+2KcDGI+Zk/wC+hXzp/bFqxO7zB9Gpf7XtR/z1z/vdP1ocH3Fzn0VHJEz7ElTcO+4V5Rr2nXviHxlqENn5bBcAuW+UYHrXGR6zYpIN5nK5yQD/APXq5e6zBfKW09ZbZCu1yGwW+tOMbPUHMuXXhW+0jTmu7t4FBOFCvkt9KwniWQgydOtOt3ldcNI7KvRWYkCq90JGGxGwO5rchu59CeAW/wCKNsAR/DXUVyfgJ8eD7EZzha6jzPWmJEmc0hYUzzO1N35pjHlqbkUwtyaTcR3oAfkClByeelM3UBgaAMbxMwSKM9eayYnaRF3DgDoK0PFDZgiA5yxzVO0jzEpxxiouUW4EyuO1TlwPlWow2RhRgU8AKMmmIVU7mng547UzO76U4tgYoAdkA4pM/NxUZck8Uu4KKAHkgfWo3bPSmF8nigZxQAoXnJPNSAnFIBx0ozTACaQc80A5zmlxxigEcnqin+0iTzyMU0uUlVj0qTVP+QgR71WuN3TkntWXUs318SW0aKmDkDBFK3ie3XqDXIf2bfEbltWIbv61UudD1RxxA/NdUVoYS5k9DuJNbS5jXbGwGeuKtjXbaEYfIOPSsywtTBptrHMvzgDd9awvE9tcvqMYt0kZNvOwcU7BqdcfEljk/vKU+IrE/wDLYZ9K4IWVyIgDbyZxVSTTr4zRlInBLAdKdhe8egz6tbSzxYbgc5xVoa3p/Q3MYI9TWK9qqhF2chOa4yexnN/KTDIw3HBxUpDdz0v+3dPH/L1H+dPXWbFv+XhPzrzI2E/RIHHrxVjRNNupdTxJE4Qc4PSm0Jcx3DajbPf5SYFRVw6taIMGYZ9KxI7fyZpQqjIrkJmvDfTb2lCBjxjFJIppnpy6vZMM/aEx9aRtVsycC4Qk15lM8yKWHmADvip/DUtxdasyzlnjC5wRTsTd3O5gvYJJpSJFwD1PFWvttvwfOj/76Fc/eRItjdFUO4KcY61wVuk5YsyznB96Vrjd+h7ALu3YH9/GBjn5q5XUHSW8kZGDLngiuQkM3JVJhx6GtzT4ybOMvuD45BrKreKuVBvZjTcJBOXcgAVvaBqqX7sIwcJxn1rl9Rt5Zv3UaEyN0GOtdH4Q8M3tiDNcsFD8hBWMHJvQqSOwikyMU2WKSYEIvBq/baezEDFaAtkiAXH1rpUSDm9O0Lyrk3EwDEdPStYQ7pcdquy8AIgpECxcnk1VhmJrjLAIgqck4zUmmt51vjIJFN19TNGhA4U1T0w7SV3Y9KwnuaGjPbeYdrY9qp/2VLE4aSJgvZiOKuzApg7uPWtO0vbee1aO9mfC/dA71pB6GbIdPtQoUD610c9uZ7LylOxsDBrM0qKJ8usuMdFPWtw5CHbyccUSeo0jHg05lYmR+ntT5ljiHystV7q6lidlYn6VRYNId1UhM0VYn5scU8SEDIODVQHEeC/bpTUkwepNMRofbZAMHNVZpDOcAfnT1cP1FRv8smR2paDMu6WWMnj8qxbmaRxsU5aupbEz4fHNZWoaeIY2e3QbjVAeZq7Rkqq5HcUimJiQU2OO/rUlpdtaXKy7VcDhlYdRTbyaCW6MtvGUU87T2Neb0N+o0k0FOB3qMPxxTwTgevrWbKFxxTucjFRMWz1pwfPfpSACDmlwe3NMEmRgfnQGKnJanqA/B6AUhXP1pN57Um444PBpoBuMfWj5s4HegnPGc01iVU4brVgP9z1pemc1EG4HXPc0pf0oEPHIGeKdyOMdaiLY70bzyOaAuPBOSBQcgjjim565FHNAx/Uil29c0zPvQWPTNAAwwMDrTiD0FNLEqeaQGgVx+SO9M69aTIYnqBRkg8c0DH8dKCvGRTGPHApVLDk0AKF560uBuyaYW2ngcGgkk8UMLjyBScdDTSSe/SkBGMd6BjivAxS7c9eKjJI4zzQGOKLCH4XpmmsuWpCQOD1oORyOtAmOYZHNJs7mmnPANIXIBzQAp5wKNvUE8CmcnnHTtQevFAwK81E4GG+lPB5Oc1G/3GxQMyGzuznr6VIoJGOlMAPmEH1qcIDwDU2Acq/KQScVGcHgE5pwHFBXnjigCT7PLGu9kOw9GpX+6OOa1bok6NGccjGR7VlOV+XPSmrEsReeKA2Bx1FKCACelRn5ec5HagB2SRTGz60u5QOozTW5AxQFzSnx9is2A+8p/nVGQBsqyjB4IPQ1p3SY0XTGAwdr/wAxWc+GAGee9UK5yusaN9mJngUmE8le61kKqyn74Jr0W3VGuYhIAylgCD3rC8XeFlsJH1CwGLYnMkf9w+o9qpMTRyTIySbhkEdK67SZpLywjkkP7wHaD9K5mEvNE8QXc46V0ukwSRabEGXY+S2PxqhG9dWn2oOWAFwrDDCrMGqskawToTKvGc1H9ojkV3HDqRxVa4ljMqkjDsSTQI0Y9WhkmFuTtkboD3qnc8NLk8c1mXPyahbt38xcfnWpdAjzM9TQgNTTkC+FgFORsb+tZelWqyajDcGRAqE5Unk1saXg+GmUEZwxx+dctp8RPiSzyMjc2R26UwLmr4XUzgcetUJAN9X9b41LbjoeKoSY+WkwG2h26iozxk1o8fbJv90ZrNtgTqac4Ga0XaOK6uXkYKiJkmgDz6Uj7VL6b2/nUkYOOaidlknkYdGYkfnU0WF+8CaAGSjjOKm00/6TyOMVDL69vSpNOGbv2xQB3Gj4MPWrd/zp1wo7xtn8qpaMAI2Oc1fvF3WFweg8ts5+lMDzOA/KAatDgfhVWLPAxVwKDHnPNSBWc81s6If3Lcjhqxn6nNbGikeW4xzkUwO7s/8Aj2T6VcUg4qrZHNspb07VbXBHAxVoZNGKber/AKFJ/umljxk57U6c5gb6UCMmEhbOALzxzW5Zn/RU78VhwkNZRGPgZI5rash/oq0gLMY65qcdKgTqeasp1FUA5QAuacckZBpAOaMEc0APA4pDRnIFGPSgBQfenKMim4A6U7nGKAENJtpSCe9GSF96AGnpTDjPIp340xsgZ7j1pMDxTxKCPEepZ4/fsRXc+EG/4kVrxziuJ8UA/wDCS6iD/wA9j/Kux8GsToduPQkfrUoZB4wRZbpcjK+Vgj8a5aJAhUKOAcAV0/jRZDKoj4YxEA/jXK229EjEnLd6sk25NPmuVt3BBVW+UE1H4h0+Q63Ajjapi+Vs9aL9ESytXjd97Md2G6VVvSwubZnmwVTqx7VVxbHY+EvhddeJ9MmvVvI4ETKqMZLt6e1cRd6a0UjpnlSQfqDiu18NeOpfDeg3ltBqERa44RR1jPqK5Oe5juW4mTd1PPU0aDuZH2V88kfWgWrE4zWh5Ib/AJapg+9SRWu9gvnRD3JqNBlKC2cHBrotMgb7PhvXjFJZaDLcD5Lu0GP70uK1YtGvYxsW6sTj0m/+tTQXOdu7Z1upOc88VbtC0dvtY8Zq9LoF68zEz2h+ktQSaXc24JeSAj0EuapCbNKC/kjtgglwM9KuG/DhVeYcjmuYIdMqWX8Gpu+Q4III9jV3ROpYvWUTS7GBBPB9ax5YXYkkcVcYNnkjP1qIhy3BH4GpdhpszzCx7c1F5LAZxxWl5bk5wMfWkEMnXAqNB3MxoH9PwqNoG5IFapic5+UUxoJOy8UmkMyjC2Kabd/StQwSYPy0nkOf4aVguZfksO1HkH05rTELkcoaQxt2U8e1FguZvlMBjaaTyW/u1piKTH3Tz3o8l+6GiwXM0xEfw9aTymz93HtWn5L9ShwPal8pv7lOyAzPJOc7TS+UT2rQMbDotOVWwPlPPtSsDZR8r5emaXycDGDWgUz/AAkGlETd1osCM3yePu5o8nj7taOw5+7x9KQRn0+lFgM3yjn7vSmmPP8AD+NabRN0xRs4xtosFzLMX+zzSBOPu8Vp7M5yvNIEA7cUWFcz1i5PFKYvQVpCMdl5oZBt+7zRYLmbsGOVzS+UAOV6Vf2AH7tK6j+7TsFzL8sEnigRDHStDyx120uwY6UrDuZ/kDFOESnsKveWMjA4p3lqTjbRYLlMW64GQKf9lUkAKK0BEgx64qVVUkcdKrlFczVsFL9AKvW2gvcuFRFJ6jJrStLZXmUYBz2r0bwl4OF7fxu7DYVzjPSq5UJM89tvBNzcxFxHEqrx8xqrN4TljyHEYI96+k18Faf5CxsWyDnjgVzPi3wYlvbia3H7lR8xA5FFkWfPkuleX1UDmq7WKgA4Ga7LUbELJgD5T6isqWyxnAzUuJNznGtR6cCmG3AHTArYkhAbkVAYlPGODRyhczPs6E80ptVrQMKgdKXy1x0pOIXM3yB0ppgFaRjQdBSGJcfdANTYZneQM9KDEB2rQ8gYDUGFCBRYDO8kd6DEM1oGJM8dKb5S56UWAoeVSeX2A4rQ8n2pvlAnBFFgKIj4o8rNX/JXPTikMQ6flRYVyh5dHlnsKv8Akjg00winYCls4pNlXTAuOtHkj8KVhlPy84zSmOrogGaDCKLAUdnbpSeXg1c8oAH1pDDRYCpspNnNW/Jz0pRAMc9aLMRT8onoK6Xw3f2emRzPeact6SuI1ZsBTWSkXPStvQNFuNWE6wvFGsYyzSNgCmkJlu51+0vNOFvbaRHaS7svIDnI9BWHPOkGCwyT7Vvz+HZtNsvtE1zbNk4CI2SaxHgV3+YD8a0JZ7p4HIHhOxxxlc10gauX8FE/8IrZ5P8ACa6INTBE26l3AVCGo3ZoHYmLDr2pNw7Gog3rS7hmgB+4ZpcjsaiLUgJNMDO1yPzo48ttUHJqrERsAXhAKn1tsxRgHvzVaH5kXAqRllWyAByacOuO1IihF9qGcY6cUCJMgZpjHPTmmbiTjsaNwVcDk0AO3hB700fMOc4pqoc5bmnjk0ALxjApVU4pMgdaN2aYDy2MCkBHem4IGTyaVee1CAdkEinZwKbkDtSFsLwKGCOV1DJ1I/WkxmVTnnNR3rltUYDj5qsouZVOMmsr6mnQ6GBAYFZj2pWVSpO6rENmWgXfkHHSmS2cgYeWhIHWuqL0MyrcAARY9atlVC5xye9MuYCiwgryTzVh4m4Cg0XFYptH/Fx7VG8eMHjNX/shdQSSKqy28wkCqrFc9adwKrDN6gx2qw8K/wB0c0xoSt/GOc4q2Y3LnAouIq+QAQoxU0MQVj049BUjWpClsnNR20cgckgge9DYynCitqEgxwetWpLSEgDykP1FNtQovpSetWySzYUde9JBcptaRYw0SH2wKbBbwxSFkiVWx1AxV2WFlTcX7VBblnLHafqadwKltGHlkBAxnvUr20WcLEmD/s0W4/ey/XoKmAZ8gAj3oEV3gicHEaE4x92uenixd7B2OABXSzQyRRlgxP0rmZGJu9xyDnNZ1H0ZpG52mjaBBAFnlVWkYZGa3orHMgVV4zVXSleaCI/7IrpoLUIAxPNEWkidyJLdLeMEjmqVwNoLnua0b1xFHuJ/CsaSYOpLHjtVRdxWI3dU5FUp72KNsbhmob+4ZItyDPasK4mUKxP3j1psDUv50ntTtkGfQVS07icZ/Ws2ysZ5GeSPd5Y5NXbZwkgwDnPU1zTNEbc8JaInd07VSiDeZ14FXjMfLwADxVTdk5OM1pTZMi+Lgqyng4rqLC+S4tgxYZHBriRIM9c10droky26ypcgswBC44/Orkl1JV+hNqPlNNuHJqh83YYrQMAhU+cRuFV/tFtn5siqWwMhCFuTQqFTx1qUzQg9Tj1qI3G05C8e9FwJ1kMa5I4qvJMz5bHyiopLmeU7VX5fpSC3vLrEcQB+pxigB6EyGrsVurp+8JHsRVF9O1C0I5VvdT0qVGvEj/eybh70XA8aErKmNoYdORQ8kLoPLUo4+8O34Ve0rUksC6z2yTQOMMrDn8Kz7nyGmdoFKxknC+lef0NhM/LmkJJUYzTQ2OB0oAx1PWs2ikPJJXGeaA2BjFN64OeO9KSOuaEMNx3U4klee1R4P3qAeo5piHhmxml3nB6UxWyOetJ15xmiwXFPLZzS4BGaYR/+qlUnODj2q9AQ8jApBnoaTOCMCg5IJFK4wJ6Z7U4HBz2pqnIFL06NQA4HHWk3ZJ9O1JkkAZxTSfzpASFiPSgZHX6004xz1pA3OM0wHl/SkUgKAKTOGA9aCQSQDigB4GfSm5ycHijjHGaTIHJoExxHvRnnimZ7ZwDSkkY5oGKSeD3oBweAaTk9eKOcYHWgB2TnnpTCcdOlAODzSY6gUAOHTikyT+FAOBgmmkjOBQAp6Z5oySaTOOO1AJ/CgBxbkelIxGMH8qbj8qQsB/SgLC8A80fdbrxTc+lGQMZPNAATjnHWmNyKdz36UwkYPpQBmEfvD9akwMZGc1Hj94cetTr05HSpGNXcTg9aG3Z57UoGZDkU7aMkn9aYGgLyCXRWiWTE8RwV9RWdu3IpHXvVd4c3AlB/hwcVOgAWmIGJIIFM3bR0zTzzxSbdoOeuaVgInAZs9MUoO7rwaUrz6Gl2gd+RQhM1rpsaDpWQeQ4/Ws4ptIPati8Abw1oz9eZefxrHbrweaolEkRH2iHHeRePxrW8VJu8O3+xTxHms22hMt3DggEMDmtzxLGT4e1DBHMDYq0imeV+G4vtFxcuRzHDuFbglbbx0UVneFEH+nMOptiKtQSjc69SByKbILKSuZDIGOOMirTMHlRic9qrZCncOh6ipVOXD9gcVI2ht9lbq2fGQJV/nWxesGkkI71i35Pnw5J2l149Oa2pV8tZAB9KZJraSobQDjPIauesABrVswOAH/pXQ6M2fD7fR/wrG0hVN2zMvK9KoCLXJQdZkjA+6Ac+tZ75IX613V3p8F7o1w/loJg3EhHPFcPMNp29e4pMGRw/8hVMetL4iPl2V5g/eUDPrzSQnOqRcY+YUzxQjPbzlBwuCR+NAHHpjIFWk6VUU7T15qdJF6ZFABIM0kMhglDDrTmYEcdagyQRSA7/AERs24bPXrVXxhO8ekoiSFfMkw2D1GKf4cfdZg4xxVPxoWNvaDtvOfyqhnLRetWs/LVVDVoAbetSIifjirulXGyQx/3jwaosOaWBzHdRvnowpoZ6hpzKbZM+lXgeeOlZ+nHNovvV9R6H86pCJ4z8xAouOYWPtTUznpUjLujINMDGtiradDgbcMc1u2RH2VMc5rDgbfZqNoXDEfWtrTyPsi4FKwFpBk4qwnAqBByanTOM1QEgOTTxjHvUYPHvT8nFAC9uBRxmk+bFABHtQA7OD0pxz64NNHqOaXJNACikbHFLzmkJ74pANKjFQv7mpifxqN/pmgDxnxcNvizUQB1cH9K6zwUQdHj553tXL+NQB4vv+Mfd/lXSeCGP9kc9BIaSQxfGLhJoic4ERJ/OuTilWcIyjAPNdb4vG64gU9DEePXmuRRFjwoGFzVEG1LOtvYQvNZM8TMVDk9TVLXmivBBJFB5BVMHJzmteaIaj4ds4UmjRopSw3N1qN9Ka7En71NsKAuVPX6UNjObtdBvLtWeMAIvUk1ah8O3e7OUwD/eronvI4NN8uPbDDHje571dhIZFbsRnNYTk0axSZzX/CP3eDyhx6mmjQL0EH5T9DXXYHJ7UoXIFZqbKcUcf/YF+ATtT/vql/sS9wMRqf8AgQrryAfpSgYqudi5Ecd/Y18B/qufYimjSL7vCfzFdmCDzjmjHzdKPaMFBHGjR77JAgbI9xSPo+obSBbNxzgEV2uBzS455pe0YciOITSr4Dm2kH40p0vUARi3krtsBj0xihgM0e1YcqOMi028GQ0EgqRbC7HPkSEGuvAAPTrTsAZ460/aMORHHCwuixPkS/lQ1ldqeIJcfSuxwo4AoxweOlHtGLlRxotLkDmKX8qctpOucxSAfSuwwO4zRgUKow5UcgbWbkCOTj2pgtbjH+rf34rssAnNLtUEHGKPaBynGCCdcDy39uKTyZ8/cf64Ndthe3NB255GKfOLlOKMU+B8kgHqRQY5gp+RvyrtSqng9KAikcjrR7QOVHClJMkBXP4UoSYcYb6YruSqj+EfhTtiDHyij2o+U4cLI3Zh+FKI5h1DY+ldz5akjKinKkY52jNHtBcpw/ky5PysPqKUJIOmc/Su3fa3bmmGNMZwKfOLlOJPme5/Cm7HJ6H8q7fyosY2D8qXyYx/CtHtA5TiBGQp4I/CkZGx7fSu1MMfTav5UhhiOPkXjtihVA5DjAvfse+KGQk5549q7EwQnjy1/KobiCERkCNcngcUOoHIckyZ5FIE4xx+VdclnbIgUxrn6Uv2K3P/ACyjP4Ue0DlOMLEdgfwpFlbkbV/Kuy+wW3P7pP8AvmkOnWpb/Up+VHtA5Tji7j+FKVZn2jKp+Vdh/Z9qWz5CH8KZ/ZdoW5hT8qOcOQ5Brl17Ifwp8F28rkbVBHXiusOm2ZPNvGfwo/s206rAgz7Ue0DkMCO5eM5UCtTS/GOsaRcLLaXAUj1GRj0xVw6da5P7pfpik/s20yMwrVKqJwOgg+NGvooEtnYSY7kMCfyNZmsfFPxHqsTQ7raCFvvLEnX8TzWf/ZdmoP7kc9aP7MswQRCvFHtQ5DHl1y6uD8+38qYb6RhyBW3/AGbadoVyaUadajH7peOKPbD5DnWl38si/lTcKQMxCukGnW3Qxg03+zYAfu0e1DlOc2oDkwilZEYgeUoFdCdNg/uUh0y3YY2YNHtRcpzvlx5I8oUbIsnMS810P9l24b7vSj+zIP7uR6Ue0HymEPspGPs4OPelH2XH/HqPzrc/sq364x7Un9l256Cj2iDlOfZLbPEWKb5duRzF+tdENKgOODTf7JgGByRRzoOU53yrfJxH+tL5Fr12c+ma6D+ybf3FDaVBzgGjnQchgiG1AyYyR9acYrDGfKfP+9W0dJg6ZOKG0eA5xkCl7RC5Tn5GsY87oZCMdmpLa70EXCG6t7ox5+YI/Jrak0KCQYJb6ZqufDFoWyWkHtmn7RD5DZN58JPKGNI1YyY+9vbr/wB9YpkF78KuS9jqgPbDH/Gsn/hFrPafvfXNKPDNkpGC3T9aftUS4S7nQGX4WLGQtprCsRkDcf8AGudvX8IksbSHUVPo7ZqZtAgIAMj8Uz/hHYf+ejgdqftExqL6synGk/wCcAeuKi22DDG6XPtXZaN8PI9SlEs88qWy9SOCxrbPwv0jPy3F0Of71WncGjzB0tFOVLn1zSFLU4+Z8GvUG+F2lnJF1chvqKYfhXpxGBe3QPrxxQxHmH+jI332/EU9QPLcI7bT6Guh8Z+EIPDEdq0V084nJA3DkEVm6JqsWjwzPJYx3Uj8IX/hpJiaIdIi82dhJn5VJANUrtWkcfMQAT+NdCniIXyGJbCKDHJZB1rnry4WNs7cknjFWSe4+Dcr4Wss5+7xXQA1z3hBnPhix3jHycVv5wKBIdu460A0054pM8c0xkhNGeOtRk+lJu5oAlz+lAaow2TR3piKGscxL9ait1xGD04qXVf9Uv1pqf6tQPSpuVYkL8Y60znpSFse2KaCZDgZA9aBDixztXk9zTkUIOeppwXbjFKf1pgNzzg9acuFHvTRwR3NBYemKADqeTTgMAU3HPoKcCOPSgBScUZIPFA5OO1HU8dKQ7BupMZByccUvHemMWcEIOPWmCOVvSf7TccY3VYhyk6N1APT1qO8gCX7szZ54p0U0YnQnIANZJal30O0ivT5KnyW6U8X7dBA35UsU0flr7gVKHjArpjsZ9Sjd3BkMRaJlG7mrolXaMKT+FUr6SPcnPeriyrtHPamK4pkyM7D+VNznPymlM35Uw3AHU0wKjR/6ersCBjHNaAAAzisi5uSL6P5uKvrcMV60gHs6k4I4oyuOBUXmsfpR5h2+9AGWk/k6jIXUqCfSrn9oRAE+ntVBizam2WyMdKvrGh6gUxDG1GE9Tx9KZ9uiCkK3P0p8kUZ/gH5VE0a4+6BigCKxmAeQ+pq6bqIDk4rKtWAnkGe/arWQT0oGWjew7Tlh0rkbm4R9QYqCfmxXSOEKHAFcvcDF+cHb81ZzVx30PXtFjjjsoGx/ADWu1wu0kGue05nFjCCf4RWscRW5kPJxSSEmZl7ctLJhicDtVR2BTg/KOtRXEpLlj0z1rMvL8KpjQ59cVpsBDqepxqvlx8noaqJB50alhw3as64JZiT371paaxMXUnHQmkBt2kwtbGSCOMcjjFYcQYTYxjmrXmyKG5wKymvnE+Au0A9fWsaiLidCrHGM1EwbJyKrLN5iqckGnvOUYHrTpikWUtnYZGfpXQWVxdx2ixGT5VHGByKwbbU7iPd5bxNkdGHSta0lZYQZZUBbrg1qyCfezuWY5+pqMxiRsYFTwFSTtwRTriJ48Mu38KBkUUAzgj8KVkXfg8j0qEXMwbcR0pj3+/OVANICZ0Zm2px7VG9ndqcqWH0NQf2jtO1ee2av2l5FDCxnYsT3JouO5nGC9QlfOdQeozUyC4VDuk3D0NX7a+065RpJOgOME1LcyQXcOyKEDHRhSuGh4cL3YipJCrgdM0ycwyNugyFbse1aFrqsUNk1ld2yTRY+UgYZT61llVDFl+6elcbNhMYOM4xS5yPXNJnaecUdwTwKgYrbenekUdKf8vJHSm8d6SGKpwOT1pozux2FKeRmlwM+tOwC8Ck3cgY59qCcDDUDJFFgEbIFGVBGepobjHfNJjJGaYDgTv46UjHJ9KC2eP1o4NACdwe1Ox8uO9AztwMfWgDAzQAYz359aXHvTRz2oBOMdcGiwC4IoJ3EYFKo75/Ckxz70AHOPXNCqFyT1NKenp60HGPegA5FBHHvSd8GlyQOaAEOO/WgcjrS4OeKCOc4oYCDrjNNJ54NLtPX1oAxkCkgAcHNDDjI60Ek0g9McUXAX6/lSYHXv6Uqjvjij+I4p2AQn0GKQ/LjHWlH3uaDyeKADrz3pvbkYpQWxj9aQg4zmgBSoxx0pCAG+7Sk8cUhPr1oARjk9OKaQCMZNOOcUhIx0OaAKTwhWOQfrSAKDkHFWmIbFQvF3GMUhjflVsj8aQ8njp3zQp3LjFNbK0mwEPAOBilB/Ad6Tf8vFNU54OMUXAcSFb1NLnPWmjhiD0p/AwR0piGnHbrSAZHJp+MEkCm8DJ7U0B0Eyb/AArpS8fLJL/M1keRk81riInwvYOXyrTSAD05NVUUE46mtLEjbKIpcRgHHzVseIIt2gXyjOfs7fyrPt49lyh9GrY11CdDvR3+zt/KmNs8x8JxD7DeSAYPkNRbACRz3K9am8IrvsLoZ/5YtUqW4jjUg/w0jNCEBAMD5SKSR2hxg/KSDSkgDDenWoLxyqqOo45pJjZJcS754T6uv866K7GJJNucVyBcGaInPDr/ADrsbxgxfIxx1qhGhoeDobgg8F6ytJdBeMrnG7gVr+HudBcjqS4rnLT/AJCMQwRiQUwOzF1bxabcxPMqvkkKepyMV51qlzHZXCROjs2wMSo4rW8RXQtdZKEE5QGucvr03FyNgwuMHNADhqkaTrMqsSpzjFLqGuR3kVyvkMDIm0VIllJ9pSLcpLkc4q3/AGNILoxbkyBnNIDiFQseasRwKQOKW6jMV9NGeqMRxT0oAili2fdJBqvkg81clzjPao4YfOmC+tIDodH1y2sbcI6MSR27Go/EOrQajp6Rxo3mK+7J9KLHQ2uxjgYrQHhqTJ+6cLVA0cZHvY9cVYETY+8aYoxIwx0JFWlPyUgKjNIvXkUROplXdwAealkwTTrW0Nyxx/DzQNHbWHiLTordY2dgw4PFb9pfWlxEHS5hAPq4BriLTw7NPD5isMVuWOi28KAXFksxHuRVJgzp45ID0uoM/wDXQVds4YLq7SB7iPD5+64J6VzqabpgOf7I/wDIhqSOz0+OdJItNeKQdHEhOKYizdW1raWjhbgYimKkk4q5pciTWeUdXCnBKnIrkVt2u9Iu0n3OrXJxzzxWt4Pt3tYbtAjJGZAUB+nNFwOmQYPFTpUMSnPtU6jAoAeB6DmndDSA4p2RTAQjjmkAOSacT7UYJHShAAHPFPxgE0g+lLntjigBMZHpTSMmnDjtRyRQgGkACoj3FSMOMc1EwPpQB5F46QL4uvMNnKof0rf8C86URuPEpyKxPHi48Wzn1iT+Va3gNs6fMvpLUgWPGiHzbfacExNg+nNcbAsggAlPz967XxmwjNs56FGzXHQypcRhkzzTEy3YiCKzvBIC8siARZP3T3NVbV3srgx+c2yQAMc0rP5XUE/SoL0FihHcUMEL4luJlvRY7/3MahsDuT611uh3AudIt33ZbbtP4V59cRywzlZgd/Xmus8KXGbWWIfwkECsZo0idNkggcUBu3NMHPWlX71c+xsSZ9aCxBANNzg5xRvz9aLiF4xS5JNMzSg4HJ4NO4WJFY85xQTTARSgnnFJiJC/0o5z1+lR7iOTTs8ZoAXJz15pS/NMPA5NAJweKdxku4EYoz+dMBzSA/Nz07UCH5yc0E4IpvVqVjx1xRcY7cBQWximbgcHrQTg5oESKcUbh360wHPPShiNxp3CxIDgUu/jOKi54zSg89OtK4rEm/AzSbuQTTM889KAwxTAm3+/SnCUHiq45pAabETlyDQXyvvUB5NLnPHNJMdiYPjrR5mR7VCD2oyeRQFiXeOmaTPr0qEcHHrSlgBTAeGw1VJ7jN0qdhyalLFck9MVVjG7LkZLc0XAsGYEcVJFxyTwaqpBubc3A9KtZAOB0qbhYlbGeKQHI+lM5yOaTPfP1qrhYkz3pM80wE0Drg96VxD9w+gFN3UmQvBo5ouA7IxRuBApnWjPP0p3HYfnA6U3OTx3pPqaOR0NILCkikLHHTFIck80ufx4pALuzyKTdnNNBwuKTqRjtTEOLds0oOByaYx+YYoLev5UAO3e1AI/xpuelKSSOKEwHEjOaTOKb05pGPzU7gPJ4wDQfXNMbsQabyT14pAPBB70pwKZ06UhY/jRcY8sBSbt3emE+tJnHSiwD8/nSkjB6Uwtk01jj/GlcB5z26UE4HOKZvyOO1NPzcngCnqAu8Ennip4xna5Hy/zq/oXh99SmWaYFbQev8Z9K7n7DaNAIjbx7AMAba3pwb3IbOctvE4hRI5LcBVGBsrWs9esbrAMyo3oxqC48L2dwD5ZaI57Gse68EXn3ra5Rz6MMGt9jNq52seJFDIwYdsHNSbPwrzKSw8QaRl1im+X/nmxIptv431qzbbcBWA6rIpBpcwtSf4tp/o+mf77fyrjNF02yvopWvNQS0CfdBGWY+wq9478X/27DZRm3ETRZOQepIrA0WWG01K2vbmMTLG4Yoe4pFGvf2OnaVaRmK+Mtw54iKYOPWsN1Bfc2K1vFOqwa9rn2+3g8qMIEVT1471iTxGbGWKgdcVVyWj3XweC3hayJOcrXQhawPBihfCthg8FK6IVQokZWmY59qsEZ6CmMooGyDFIV96lIGKaQMigQznOBTse+aCO9KMelAijqfMS/WoUcLGB3qXVciJQvXNRwJhFzycdaksUIWOWP0FS8Yx0oAHSjOMetUSL1BNA6fTvTe+aXHbtQMCefU0hGRTunTrSjrk0AN6jGcUH2oPXpS4wOaLiALjBNBcLwBmmktJgL09acoC/WgBoViMt19KeMY4p2OMmkJCgmgEczfD/AExu5zzUUQ/0lBgYyOtSXmDetzgVXjb/AElcdM1C3L6HbxDES1MBkcVSiciJee1Tq7Z4roWxmQahHhVP+0KsojBFHtVS+c/u8881ajc7R9KYhxVxxnimshNP8ymmUUAZl0pF3Fx3q8MhORVa5kT7XFntVwSI1ADRz60bsHpkGpRtxkUmBn60DMZjjUuByRWihyvJxVZ0/wCJgOO1XtinqKBEW8D8KZI425xk1OY161FIq7eMUDMy3ZRcSfLwat5Gc44qrbxn7RIferRiOOtAhkjLtPrXMXEmL4nGcsOa6ZgApOOlcvOx+2twAM1MkV0PUtNkaaCFd38IHNX764iigKvKoCjnmsrw9C9xHHgfw9fSuZ8dasYNQbTraRTsUGVlPQntSTsJK5s6prml3VtHb2ZJlB+Y4rn5HJcjqawdHWXzy2MKe56muh+VVz3pXuBXZOQzcn07Vcskcozg8elVJnJGAMCtHSTGkbiTJyOAKoCUmPZ85ycVjOB5zHHeteWM4OAOayZRtYjvnpUSLiCzsj4Bq+Xd1AC9R6Vj+cY51+XOK3oJtyhgmBRAUiK3hO8tkD2NacJycFeaUbNgIXc1Ptf9blhxWpBPHNKmAOKnM0753SE0yRRu+U5Paq8ksqqRtzUsAuJpEGAeaiiiZh80mM+tRF2LHcMVIN2z5QxFFgJ1gjhQlpdze1Z13NKON3y+lStvzyDVu1uZIvl2I4PZlzQMy4nkIACZBq9Gk+BjeoHoa6C1tNPlHmTOA5/hHFPuo4Y4R9nbJP8AD1pDseIG73Nh4FZSME9DUJRSflPHatOXU4rrTo7ae1QSR8LKvBxWYOM9q4pM2iL1IB4peAMnmkAyuBTcZ4qCh554FNxluaUkDgUc546UAJnA96MnOKccEe4pBk84FCYhNoP+FOABo6Z9qUt8p4HFDYxAARg9TTWXjFKOSKUHnHWkgG8YxigDinAgnBpcgH6U7juNHAPPFKCfWkPJHalyQvA+tFxAQM56UAd6TOeo60A54xTAUhTyKF5oBz9BS4I69KAG0Drg0d8dqVWwcGgAII6Gg5wMHn1pScnp0pB09KAFwDxnpSfjxSgd6Q5JGMYoAT8eKUjGDikOQf6UobPGOKAAtjoKQkZwaCM9e1LjigBoPTBxRyo9c0uMdcUgOATQAA+tIDwTSg5zngU3GBQIe2KQjAx/Om9qdkjHFKwxh460vUewpTz149qbnbxmmA0n1p235dw6UDBBwOtGOM96AI9vXrSEZYVJk44/KmZBPOKTGQtH6VG/TnNWScnA4qGRAVKjOT3qbAQbge9KCM44JqF0aOUE55pw45JyadgH55PGaXGR1PvRk54xingHaKokM8DBzUZ+uakC/h70m3HI/OhDOhHzeELEDkrcSf1qrDgAnvVqCMx+EbeYZw12y8/Q1WUYyRWqEyRQfPQ9twzXRa1GDpF6DyPIf+VYMQ+ZD710WqqzaVdDBz5D9PoadhM8q8GYNlcDnmFv51YK/wCirgnOOKg8FqWt5R2MbjFWBzbJg9BSsQV85Taw5xVS9bCqp/OrbHcAT1xVK8BK8jipGQlSXT0yK6t385CcmuVxwK37Jyykd9tNCOm8Nn/iTy4HO56563BGoxc/8tB1+tdB4Z50yVfSRhXOrj+015wPNxx9aoBPFyK+sjjBEY5rnZo/LYMozn1rqPFf/ITQEc+WOT1rn5k3259VOaQF2Jib2Ak4JI6VsNzeMQf4KwrZsz25HByK2iSL7GeDGaBtHBapkavc8YPmGkjGBT9W/wCQvdf79RoeKQhJDkUticXa0knA/pSWZxdp9aYHcaK2WbitwbT1JrA0Pl2xzW91GOnFMbPKmG25kUdnYfrU496jmGLuYE5xI386kXkUgI2GDWhonM0n0FUJRk471d0UYmk+lAXO/wBKG61GRj0rSXA471maST9kAPNacZGORirQEyg/XNOdcIelNUEcn8KdIx8o4FFxGHYw+RaTAncGmLVu6Yv+iDHHNY1ovlQTNnO6Qn6VsaZIfsx+vFAGgvUetSqT+dQqcnjipl7UAPBpwxnpTB79KfnA9qoB3WjNID6d6DSAXPWnZz6Uyl4oADnOKORxkUvA60hPemAjCmsMU7IPNMkIIpAeTePl2+J5M9TCn8q0vATEWFwmP+WvX8Kz/iBuHicDHW3Q/qaueAiPIugTz5g4/CoGafjAE/ZTtyAjAmuNRFjACDaPau68WxTPpJeIfIv329M9K4O1jlSICVtzCquJlqKZoFlUIrb12ncM4qlcnJjx1HStzSmtRDei62ZMBEW4dGrEucKkeRnHWkxGZf3Mt3dGWf8A1mAOmOK2PDtwYbxUzhZFIrM1a9W/vBMsYjAQJtHtUlg/lywuTt2nOazlsXFnfI4K8HIpRJjkVnwPghgcg1YLfMPSsGtTVFrfxzQxJXHpVYSD1p3m81IycccZzxTiwxjNV/MGBSCUZwaALROAMUmSAMHmq/m4PXIpTJ6mgC1uyKTdVcyfLweRSCbOKBFsnI60b+cVW83nrxS+YCDk4osBZ3Ad6QN26VX8wrweaPNyRg0xllmwaQHI61AZuR3o83HAoFcsZAHrTh0GarmUAYzzSeYBkZNFwLBYZ9qOOxqs84A5pY5wR9aAuWiQBgGm7+D+lV2k5JBpQ5x14pATBuTnvSg471AZMDIOc0nmDdVIRaDdu9M34OKhEvvSebnofrVWAslj+FN3kcVGZOKQSDGaSAno3deah87pmmmUDnFDAsBunNB6Z71AJVIoMuGpWYxLh96LH/ePNOjwgx2qt5u+YtxxxUwcYxxSAsbsqMUE8c1D5mMDP5UGTAPNAEpIOKdgAdar+Zgc04yjHNKwE2cn+tN3Y46moRJjvShwOSaaQiUnn60u4A9fwqEv3FNL/SqsguWM5HXgUm7t0FQ7xik8ygLk3JODxSgkCoPM4xQknGTQBPuz+FN34PSoWlBJxxTvMBHrQA8HJ4pQR+PeofM54PFL5oB44qWCJD1OaARuqBpRng80qsfxpgTE5PtRuGOtRFx9KQsO/WkFiXd1JpAwzknmoy+F5NJvBXmmFibPP9KMiod47daQt+VMCUtzxQDjvUW8UB80mBKxBHXmmqwBPFRFsH2pu/B4NKwExIJPWkHcZ/Ooi+DwaPNp2AmBC9avaHaw6nrMdnI+FwXYDuBWLqf2q00c3/l/ui2xST1Jq98N2MniF5XyWMJzW1ON9yJSPU44UhRUjXaijAUdqfjA4pScHpQW9ua6ErGdwXNTRrxUSmp0OKoVx20cj+dU7rSbG9BFxbRyZ9V5q7kE5o9sUrIdzxv4qeHtN0iPTZbOLy2ldgwzxwK4u1hebZFEpZ2ICqO5r0b4zbTFo8W795ukbbntivPbFpkdDCG80H5dvXNQ0Mku7G4sJhFdReXJjOM5qlPMYyAADmtC9F2Lgm9EonPJEuc1Wcqoy2MnsaaJZ7l4NX/ilLA56p09K6AdqwvB2D4T0/3jrdwM1Qh3400jnrT+MD1phoGMYVGRzUrVGRwaYDfxpVz6ijHGKVQKBFHU87E5701BlV+lGqnCp65pIv8AVjPpSKHnr60EcCgcdKUAd6aJYBeOlOxxRkCm5yKADOOtNLZFHABJNRFy52x/nSGPaQIPf0pVRpMFzx6UqRAAluT61Lg446UAIo2rgUvA+tA+Uc0YpgIoJPNDDOfSnelMkJII7YoEcveHN63f+lVlkCShmHQ81YuR/prjHfrTGt/Nyq8sahJ3L6GgniK2CAAMCOKX/hKbcHAznOOazv8AhHb1lyoQg+pqu/ha/LqVC9eua6UtDB3udBNqhnVP3ZAz1NTNr1rCQryAN6VG9i8awqeSAAcVi6hod3LetJHHlDTsPU2v+EjtuoOR64pRr9uWxmufbRL0rjyxx70QaJcrOhdTgGnYWpuyagkk6HHAqc6xbIdpOCKriy/e4x0FYtxp073THY3XrSsM6NddtuRuNB1u2Y8Mc1zrWEvTYfyp9rpsxuBuUhfpRYFc2F1CNrwuSQAOtStrUGf9YPzqolj/AKxQuax5NKl85m2MRnpQkDudF/bMDciVeOxpjapGxwrgj2rnm0ybHyxtzUun6bKrkndtPY02hK5rw36qzOwwuac2s2w43gmqk9hJLZOidT6Vkw6DfGYKkQJPvSGzoW1i2KHLDGOtYpdJrvco4Jpt1ol5b8SKB9DxUltb+Xs9utJjSZ6noUtraaO880gjRFySTjtXl98ILjUri5BLmRyw+laOrX3nWCW6ycZGQKzIY84wOKzaZUXYkhcxuNv5VqIxdOR17VnhVVwepq5FvbvhacUJkpKrwx/CpYJdo3DoD0quyc5BP1NWIIi3QZNUImafzMkKRWcwO8k9c1srZsUO47fpWc9vtYgEk5rOSuXEii2s214Fbnr3rdaK2it1YE5A6YrLt7XdNkHFdJBGCi7gGxTirEtmZHPtbIQ7fpV15VVVKYJxzWmqQDkoPoRTlt7YkExgfStAM2CUOrbsJ9e9NWcMeByPWttrW1cghB0qePTYmXKqM1NwMhIo5wPMQZ9RViOFI/uLkehrXS0iC4aLJHcVOlrH5YCgEUnIaRjGFJD/AKoEt7VAbUCfHllT2yK6BbPy7gSJtCgY21ZZAeSKnmKscu8TW8gIIPtUghkkXcAcn0Fbj2SStlwCKeltHEPkyKOZBY+dDcAsyyRAH1HBFMbj3FXtQ1GLU0V2tkjnUYZk/iql1WuJloUfd6/Sk288Gg4GKXjvUsoRRjI/WlxtX+tAGKXAxzSQxhB7072zmgAA8mjHJx+FUwEz82MUhAzwead+HNIc5GQBQgAMOnegjHK0uAB0oGBx0oYhFJK9MUYwOTnNKcY9aQjoe1Awz7Uvt/KjK5HNLnA6UxCHsPWlIAUYNJx1P5UE56jAoGAHWg5I460pxigH2oATOVwOtIRzwadwB9aXgcYoAQDvmk5JyeBR9T+FL36fjQAm33o3YFLuwcUY4yaAE3EnGaM44xg0m7PT86ack89aAHjkYoHHBNAGBx1pGBOMUAByaNuGyaTvg0ozgD9aVwBh8p96aPenEEYpO/FMAUHFNOSacCfwo6E8UANJz07UEZ78inYwf6UnA/GgAA45PSm89+hpx9KOOhoAY2emcmmsuTyfyp+MHikY5OMc0AMwB6Uwjnr1qTBJGRSSAcYoAruCetQgAdRVpvunNVgB3oYCr97p9acxOOOKaCc0/k4z2pDE4zgk80meoYU8r+dIfU4oTEjrbJS/w5twQDjUm/kaobQDtI/Gr1ixPw8RBwRqR/lVRfTvWyCQMuACBxmumvTnTJ+DkwN/6Ca5tx+6O2ulmUtpzAngwn/0GqIZ5J4HyWcY42SA/lUq5W1TiovBAIldR02vzVgDFuM9PWpYFZhnay+nNVb350Hr/KrjptCkcjHSqd4AEXBxUgVZGO0Z6etbFpLtIwe3WsafBTOK0YXxEM8DApknT6DdbI5V9WzjPtWapA1BMYz5uf1qDSZD5rbTT1J+2Iw5/eDr9aaAs+LRu1WIgZ/dDmudzlmQHIIrpfFSg6lAB3irmjlZgKAJbI4uIR3DCt4sDe4PXYa5qaU286FRyORWtpl69zfEuBkIcmi4HJ6uANXuCO7UyI5WjUXMmpzuwxljSR8DFA0Nk6mktTi5U+9Ok6Ypttj7SmPWgR2mgZ85vpXQBeQPzrA0M/viuD04NdCnykZFMZ5ZdDF/Pj/no386kXpTb0f8TO6H/TVunbmnKMpSERv+tXNK/wBe30qpIOOKt6TzcsCe1A0jvNGJNoM9RWoud3tWVowBtzWunoapAx6nnmlfITg8U7aOnalb/VEUxGNEoWGcqd2ZDkelamlM5tzn16Vl2+0R3ATr5pzmtbSh/o7cc5oA0FJIqdDwPWoE+lTx8D3oGSc07qB7UnTtTt3HApiF+lKDTNwpQcdKAHgcGj0pobP0p1FgA0hzn2oBAo7cGgAI7+tRkCpCRzTGYHNIDyr4iLjxLCextF/man8A5xeD/aWk+I6Ea/aPng2uPyY0zwA2J70ZP8OKQztNfcJ4S1NSM5RDn0wwrzKCdbiPzE6V3/ie9MGkm125Fz8p+g5rhI4kiBVFCj0FUJlq0sZL3zfLKjy4zIc+grKvDnZ2zWpaXktmZDH/AMtEKH6Gsu+XmPjg0mBV1j7J9qT7HxGI1Df73eqxJ8hRVjV7JLG5SKOXzA0avn0J7VVbPkD6VLGdi13BbFIy6glQRz7Up1CFiMSrg+9cSZnOCck4xzR5r4FZOFylI7YahCW4lX06083sIxiZOfeuH81gaXzWHrS9mPmZ2v25OnmLTkvE/vqfTmuI85sd/wA6FnZWU88Gj2YKTO+80cnePao/taAkBxn61yi63cD+FfyqlLcNJIznqxyaOQXOdwLxOm8Z+tH2tMfeH51wwnYDGT+dJ9oc/wAR/Oj2Ycx3f2oLyWFL9q4zuGK4T7TIQcs350oupCPvN+dPkDmO7+0jru4pftK4yDn+lcILqTH3j+dAu5Vzh2596Xsw5zujchsEEUq3S5xkVwv2yT+8350C9l/vN+dHIHMd4LnnOePWlNwCQc1wYvZAfvtz7077dMORI3HvS9mHOdyJ1PWgTDGQ1cMb6Y/8tH/Onfb5u8jfnR7MXMdz9owOufWk+08cn8K4gahOOBI3PvSnUJ26yN9c0ezHznbfaCp68Uouc/jXEf2jOx5lal/tGYf8tX/A0+QOc7b7QMYzUiOW5FcN/aM2Mea1aWn6rDFCfOlfeT9eKOUOc6d5iow3HpSfaB0z1rl9S1VHSP7PM2c5JqiNSn6+a2aXILmO1M+7gnmk+0AdM/WuL/tS4HWU80o1W4GP3px6U+QfMdl5/GM01rnYCSa47+1Z2/5aGhtTmbAMhxT5Q5zsElGMZ9805ZRGODn1rjf7Un7SEUv9qT9DIaXIHMdmLgc80Gf1Oa43+1JwMCSj+1rhf46XILmOzFxwcEYpBcc9a4watcYwJDS/2vcdN9HIPmO0+0DdjOc0plHfNcfDq8olUyP8vfHerx1yDYwy+e1HIHMdB9ox3xSG4GPU1xp1efcfm+lKNYuOPnH5UcguY7H7SMcmkM4JBJ4rkBrEwGdwNN/tifuwNHIx8yOvM/ocik8/PfmuTGtzgYBWg6zN6gGlyMOY6wSle9DTDHBrk/7buMEZFINauMdRxVcrByOtExUY3ClE/brn0rkv7amI6iga1MDgEUuQOZHWGcCl+0Hbwa5I61MTgkUn9tXBxyMCjkDmOtM+7ocGkM5J61yh1qYHtR/bU2eMUcgcx1ZnzjmnfafT865P+2puRgUf21P1GKORj5jqxcHPWk+0GsCPWo9g3MQ3fAqCfWnMp8vBT3oUA5kdN9oHrSCfIxnFcr/bMxGCFo/teQ46UcguY6oT8daBMPXNcqdZmz2oGsyg8Yp8guY6vzwAR3qheamYBhMFiPyrEOrysOoFQQuZGd2YmnGAcxozTa5daJDLdif+zvMPluRhC3t+tdl8Mwf7Zc7uPJIxXIXXie8v/DtpojRKltbPuDKDlj2z+ddf8MBjWJc9fJNbRM5Hqp5PWl7ZNLgd6cAMnitAQ1eTjNTqDjrUQWpkGBTCw+kLKiM7sFRQWZj2Ap+AK4/4g6ybLSk02A4uL35cjsnekxpHlfjHW5tf12S9Zj9mUmO3B/ug1DoF9Hpup295Iu9YnDbfWma5CsEdvGqnAGM+9L4esI9S1W3tZXMccjhWb0FQnqDLnibW/wC3dbkvEj8tGAVV+lYk0XmNuPQVv+K9KtNG117O0kMkaKDknJBPaufnnkACRqCT1qxHvXhDnwnpxxj930rdH1rE8IJs8JaeP+mfWts8HmnYmIo6ZpCDigcGndRQURc460wgipDxmmGgQhzSjOKQ8mlxxQBnau2Amaji+ZQT0p+rAHZmiLpikUSLgUp4BpN2M9iKRj8tAgPr2pryBRx19Kb5rP8AKg/GnrGFOW5agCMRNKcvwPSp1RU4ApSeMmm4OeOlMLjs57U4cDikAoyucY5oAcB60h56UlKDgUCDHGe9I2MGlyc0ybhaAOZn+a8dQOc1o2enTuyMgB9u9JaWHnX43NtXdya77S00PS5dwnCzlesp6fSkmUZ0Wj3flAm2k6elJ9hbOCp4610j+INLVtou0Y5xheaRryyzu3LzzmrUmDsc4+nF5E+U1KdNJ/hrXk1KwDczx/nTTqNmRuE8Z9g1aXIMY6WR/CaP7K/2a2vtUDKGEikH3pvn2+MmVB9WFO4GI2mYlyV6UracBztrWa8swwzcxZ/3hTGvrTtPH9AwouK5lHTVH8PNIbADt+laR1Cz/wCeyfnQb+yH/LdD+NAXMiKwIlbjipGsQf4f0rSF/Zjkyp+dMbUrJTgyJ+dCC9jN+wgcf0prWSrnA5+lai3tg3SZM/WnefYnnzU/Oi4XMaCzC5GKtQaVPO/7qInnk9qvLLaMTsdSfarkOoRxJtDgClfsAun6FaBW+2QrJJngNyAKqavpWnxwP9l0pQ//AD124C1qwatZIPnnUN3JNQ32p2dxEY1uU2n3rPW5opI8lv02XrKB0NOt43kxgcV0d54Zee6aaOaMo3ODVdtOa2k2F0yBzg1WhLKotkXrycVMkDMRtU4qeJYCwUuCfSrq4AwooQivHYr1lPPoKsoEixtUDFAUk804pTBIbLMQrYrGaUtKfXNbUseYyAKxYkRrvazfxYNRItCwiZLncucV0lu8mwe9ath4btpYUmMznjsK1P7PsbNd7kcDHzmkpWFymErtj7tTxSEP93ir6LauzbXT86k+z2/USKSfQ1fMTYS2t0mySfpSEsjFRnipAipyHA/Gmm4hBwXXNK4EsM7AEYq3CRjng1QFxGDlWUU8Th25kGfapauNM0waKpxy46ODVlWJGTioaLTH0UZoqSrnzabkTR7XiVXHdeM/hUeBg9qmubhLudphEsZPZahxwa52EUKOBS5LUh4GfSjHGe1SWHzFs9qcCMUDk9aYU9zQA7nHSlHynGM5oAGODTcnGenvQApyDnpSg7uaM/LyKBx3oAQcdaTkt7Ubsk0ADtnHeqAU9Peg9QQaQqBzmjIz60kAHJ7ClB7UDGPxo4AoAUjpSHoBS565pG6YNMBQCKTDc0BvfilHI5oAQAkAk0vIo7EdqT7tAAfmPXrSZ5wKecYHXNHA+tADVyTyB6UpPHpSk4IApDgnpQFhCMDjpSY4BpSARwaUnAHf6UrhYQEjpRg53Ypd2QMUckimAhoxxTiAeD1pvbGaVgE64GaMEZzzS8A9hSE5P0pgIQaOvI/WlIJFHQdOKQC/eAz1puCTxS4wMCl4Bznr2pgNOOx+tITz0oxzz0oOR2zQADpTeSSccU4L3pOD1NADT1ppH40/p7mm/QUmBDISEJAqsO+avOMj8KogHkGgYq8jNSFTjjvTFHGM8U8NhcnpSsAgJHuTQWAwO9KGz1HFNblsmqSEdXpYI8AS88/2kSP++RVWMlm5A4q7ooVvAdxGRll1Dd+gqGKP5xlfrWqE3disg8s/Tmup8vdpeMHJh4/75rnniDQsVzzXWRIHsYgO8IHP+7VJkyR4n4IH+kyR/wC1Jn8qEctCFzlfen+Ck2anMmeksg/Soo0YpyMcmpkK4rEhR6Yqner8gOelWTJtAUj2qpdEiPOM1CK3RTnOIQMc1oRDMSnP8NZ8/EQx6VfiO+Be521ZFi3o7jzJB3q1GSbsH0cfzrP0cn7TKM8elXEyl0COcOP50BY0/E4P9pWxz/yzwa5+4Ta4cCug8T/8f1s3qhNYs/QHHXtQwM3UMs0Jx6CnW9y2nXwLpkspGDU80e94Mc4YZH41L4kjCSWzBRycZFKwWOYvMm9f35pUPGKn1pVj1ZlQYGxcVBH0560AJIDjNNtcfaUye9PkHFMgGLhPrQB1umXaW0pLk4xxWqutwZBIOK5nIABzQDjOKaYGPduJNSuZEB2vKzDPuaeh4qGY/wClyfWpk6dKQDJM59qsaV/x8n6VWlxVjSji856YoC52enahFaw7Wzz0q+NZiZs4rmzkgccVIgIOaoDqF1iI9s0/+1EcbMda56Nj1xVqP76n3oAvxhAs4XrvzmrthdiJTHjkmqFtgi5BHzBuPepLcZlQZx8w5pgdKoIwc1OvaoAMAc8VMhx1NMCTHPtTu+PSkzkDHandqAFGMUnUYIozg5FKDnkUXABgHGOKQ5HSnYyOKQHB56VQCUv4UnBPtSnNIY09KRgCKkwDTWyFoEeZ/EgD7fp59Ymx+dVPAR/0q+HfC4q58SXie+0+NXBljRt6jqAemaz/AAO23Urle5jBH51AHQeMAzWVqR13kZ/CuOs45o4ispycnBz1Fdj4vuEFrax5HmbixXuBjrXIW1ytyjMoIwcc1oiWywoUI2eSQcVnagdscZ71e5KkAdOaoaid0SGpY0UL+0mtJEWcjc6CQYPY1C3MFS3z3Ujo10HDbBs3D+Htj2qIf6ioGRgDA9adtz0pfLc4wpp3lSDHymgY3A+tGM9qd5cn9007yn/umgZGAKTbUhjf+6aTY/8AdNILDCoFAAxT9jg/dNG1gehoExu32oC5PSnBWB6UEEfwmi4DdoPFG0elLg+lLg56GgBu0DtRsBpxBxRj2NFwG7RQFHpxSkY5xSZoCwbRShR1wKXnril6UCG7falCgjpS84o7UAN2CnbR+FFFACBRz3oKrj3p3p6Ue1ACbR2FLsFGQOvFLvj7uv50rgN2D60uzmnh4+nmJ+dBaM8eYv50wGbBg0bBT9yA/fX86A0ZP31/OgQzaPSk2AdqflP+eif99CjKf31/OhsZHsB7cUbARin5445owM0wGBMHFG3nFP70HOaQEezHFGwCnk0A4oCw3aCKTYBTwfbiigLEe0GjYMVIBRmgCPYMUCP86caX6UAR+X7UFakJ7YpKAsMKUbKdmlzQAwIOlBjFPzSZ5oGN8s9qTbUmcUig9RQIZspQo706gCmA3ZjvQY896fnNHFLUYzYAOtJt9Kk60YOaBDNnFGypB6UcD6UARiOmlKn601gDTAhI5q9aACJz2qowq3an/R2HamgN+/8AEFlceErHSLa02TwvukmwOevT65rqfhb/AMhabJ/5Y1zGpL4dh8JacLH5tWc5uG5yB3z2rqfhWAdUuD/0xqokyPUcHmlAIpxAz3oHJxmrEgGSamQGolA3dasIBjigYM4RGdyFVRkk9hXjt7dSeI/E8151Vn8m3HbaO9dp8QtWa00mPTLZiLm9OGx2jHWs/wABaKryG+lQeXCNsee7etS9RmB8T9Oi0qy0W2RAGIYuR1JxXF6bHNLIEt1ZpmbChepNd/8AGNh5mkE/e+euJ8P350zUYb4RiQxPkL60rDY2/s7q0uTHeRvHN1ZX61TkZY0yxAPatjxJq765qst+0Qi3gKqZzgCsGaAS4Zifl5qiD6A8IKR4S04Hp5VbJBrI8IYPhPTuv+qraI461QIZyKORS496TFAxrDmmEU9hxSdRQIZzSr9KO/WlAoAy9XyDGT0zQhyoApmtn54gTx6UxHZ0AjH40iiZ2VF5PNCo0uC/C+nrRHFt5Y5ap9vQ9qAGqgThRS/WlJ44oGMUxMTG7qaccKeOlID3x9KbyeDQA4tkYFIODSgYpCRkDvQIcMdCaXHPTimcLz/OmvcLGKGBZRMiorm4t4FOWDN6Vn3OqMF2JwO/rWRJOZXPJ561DdikjWt5jcXYAbaCe1ZXiFVj1DabveSvTPSrFi480Y69KrzaZE87ySbiWYnJqVItRKFtcrDIjGc/Ka2JfESygAs3pxVM6HvUtHGdo7mmG203T0LXl2rN/dWquJohluUllLZem+aoPWT6ZqhqHjDTbLMNnaGWTuW6Vz0vie7mlLbVjB7DmplWSGo3O/j1Hy4QoD49zUdxfmVAoVj+NcAdfuyCPNbHUimjxDOp+WVs1n9YK5Ds3WdxhY2OenemJDeK2fLb361ysHiq+gbKznb6EZq2vjfUnOPMQH/dqliF1IcDpSbskARNUuL6JQ2F/PNc/ZeItXu51TzkOT0C4ro0kvnUBmB9wKHiojVO5V8zUdxbHOeKRpb0n52JIq/I9+kJZSMgcHFcPd+LL63uZEkdDgnjbRHExb0CVKx1azXgPyHrTnuNYcHa2F+lcjbeO5olCiFGY9yKfJ41vJOMIq+gqnVQrHVQ3uoRP887gd8VPJqV6yFRPJ9c1wcniWZv48e1NPiOXs/tip9sPkOraW8xlrmUHP8AepkV1c95JMA9c1zQ8QXDAbsEVs6Zr1jKrJeExFe+MihVr6Byo3G1i9dMCSTj3rInuLl5SzSSc9s1sWkNtqUe6zu4yPdsGpjpckXDBWHtzmnzMaSZT0CR31WNSzbWPOa9CSLOMCuQ0u28nUoiykc16BGibAT6VrTbZlNWZTW3OMVOlrnHFSyTxRrkkCs+41uOIEAZPbFbCND7LFj5qpSNptmxbCFyawrvWJpsgNtB96yJdUtoFMkjmRvQc1m2NHWTeIL2RlhtGZI84+Xqal8WHUrLR4pgcA4HJ5ya4i28TyNfRCODCbug5JrsPEt1LqemRBsoFxwTzUcxSOVt7jVZ3UJMw9cnGa3oIr1VAZmz3INVLKwcoCD2602S1n+0MizsCenzGqJaNKaW9SP5PMb6Gs1v7VdiY45GP1pw07UfKPl6iBx/ETWHcXOpWzkLqbHBwdppN2A3El1tAoNu3H+1VhdT1mD5vsbFuwzXLjV9RUEi/kBPqc09Nd1JG5vSxx/EKFIXKdUfEGtjAOlMf93NTDXdeA/5BlwB+NcxH4l1RWAScNj/AGa0bbxhrrM0afvWx90R5NF0Oxsx+K9fjfBs5DjorIa0R4x1o4UaI5Y9wrVyL+KdYE2JoXBB6eXg1aT4g6hApOQpHHKf/Wo0BHDeb5q/dCsOuBSg8DPWmSgPKXHyhudo7UbTz85rkZqh2dwxjml7YPSmbSP4zS7SeN5qSh3cHFL1bngU3y2wD5nFIFYHlzihgOY4AAFLwRnHSmmMkj5jmlKEZ+bmi4IXnqPyoKkHkc1H5ZzneaXYWJ/eEimhseByOKM8Yx1qMKSOWNJ5RB4c80NCJTyORS8A4AqPyv8AbajyvmzvNTdiJM4pHJ59Kb5fqx+lN8vjG4+xqkMkAwM0rc4A6daj8kkH5iTR5ZzncfzpgSEDpxmkBJPTj2pnlc5LGk2bernH1pAPLHPSlBzk9B2qCdlgQuzkj602JxOgZWbBpgWVLdadnPTrUIiy3LNx707yeThiPfNAD/WlzgdPxqHyz/eP1zTvK4++1AD+pPrSZx2pgjyD8xHbNAh45c/nQA/B7DilGccimGIg8MfxpPLP99s0mA89cD86X3xk1H5ZPVjj60vlEfxtigBTg9BSfQUhi6AOcUeXkY3mmA8k8Dgmjvg9KYYyB985pvlns5oAeSQT6Uc9cU0Rnu5z60GI9d5oAU/N247UuccZpvlkDJc03yyDkseaAH55INIAeuKQxkAfMc0CNsZLGgYEYpAeKUozDJY8U0xsRw/FAhsnKkj8qon371daN8fe4qmw+f8AGkA5Fx1NPK7gMEcdqapDcdxT9o6UAIueSfypTgU4rjGBzTWBGOPyqgOt0FseB7wcZ+3A/otEZZ1O0ZPpSeHBnwxqC4yqzqSPyqwo2x5Xg1ohNiSBlgI2847V1NmP9Bt+M/ux/KuZYt9kd+S2K6iw40+2PT5F/lVITPD/AAckyeLZgD+5aWUEHseaSVJlnVkP7rLBs/Wr3hRMeLLpQcsLqYfoahZdqyZzhXbj8alomxScFhz1qvcLtiGTV8qHjBHBqrcgCHB7VmVsUJ1zBkDtVm0OYfwqvOp+y5z0FT2uBAPXFUiCfRmP2qYcbs1fU4usH+8P51Q0jm+mHQHnNXywF4MjjeP51QGp4mz9ptT6pWLMTsJrd8Tj9/ZnHGw1hTfdx2oAgI5jAOMkVqXMAmuoQ65UA4z61mMoGxucjFbEwPnxc44zQBxniJQNafH9xapxYHWrviIltaY9P3a1SiOKQCyH0qOL/Xoc96kkqJM+cv1oA6rS7aO4uNsnIA/Ot1dKtlIO3j6VzmmTSRXIKEZPAzXWJ57AcpTsB51qiLDrN2ijAWQgUxOVGKl1kP8A25dh/veZ2qKMHbgUgGSfrU2mn/TB9KikwKm08kXYwMnFMDttHtY7iN2kGfQVrJpsBP3BWNoc8oLRqg6d631ebP3F/OqQwTT4NwwgxU/2GHYcjntTY3mDcIuPrUxkm2EeWvvzTAxrYMzXIJ+VWwDWrpcEbgsQCQay4Q/mXTYATIwPetHSpZArhY8jPXNAGwCM1YGO1UVklB/1QP41Ks0ygnyf1piLgxmnZqos8uM+R+tL9olKj90efelcCx1zTh71WWeQdYj+dO+0v/zxb86YE+RnPSkJHpUIuX6mFhn3pjXLj/liwoAsgc8ilY+1VxcsMgxNmkNyc/6pqALCkY5pkzBELHOBycVELs5/1LflUNzOZYHQRuNykCgDyLxPeLd+KbyfkAkAbvQCovD2ox2GuxTSljEQVcKMkir3izSlsJrdyD5kuclu+KxNOh87UEjU4cnC/WoHY6vV5hq+sPPawymCG3AYsOgz1/WspYliB2AAGuq0nTZbTTdcaZmYtaFRuHTrXHWUU0MbLK245yvOeKpMVi2pxn0xVHUUH2cHpg4xV+PBzv6kVTvl/wBC2k55oYjM1HUH1F4mdAvlRiIAegqsf9Rk1b1K7gupITBGEVIlRgBjJHeqvBgI9qko76HwXC9rBL9rfLxqxAUcZFSHwXFj/j6fP+6K2rC/hm0y0Zc4MK9varAuULZyfyosFzEh8CQSKS15Jn0CipR8PoGQt9ukBH+wK6C2vI1z1/Krq3kflnGfyp2Hc42T4foqkjUGPtsqm3gognF7/wCOV3T3cRjHXP0rMe4TceeM0WQcxy58GMRxdDP+7QngeSU8XijH+zXTfaY8feqe2uowSCaEK5zH/CvZ/lAv0yf9ion8AXaBibyIge1d59qi2qcn8qZPcQ7Tg9qdkFzgV8F3IRh58WT3waafBV2P+XiL8jXafaIx/EKT7REeN4pWQXOOHge9J/10GKlPgq6AI82D612K3UQGN3NNNzHnlhSsFzi18FXok5mgKkUh8D6hnIe3x9a7cXEfdhS/ao92N9OwXOH/AOEJ1ANy0BHoDR/whOogZHkHPbNd0lxH1Lin/aoj/EKLBc4P/hCr/kBITj/ap3/CE6hk5SD2+au8W4T+8KebqP8AvgUcornnr+D76IAtHFz6NUI8N3RzmGPj3rvbm5j/ALw5qkZEPG4UrAccfDVxjiJM/Woz4bu/+eMfH+1XamRAPvDFNLoVOCKLAYXhnQ4Idehm1OGH7IiknzBuBOOOK9Y8IWnhATyym30kTdF8yFFIHtkV5xJKMdQKhMqZzkE1lKF5JmU6d5KV9j3aa18IXf3rHR5h03G3jYfnio7vTPCgsJY003SQCpAAtU6/lXBeGB/xKFIP8bc1tjJ6mqs2XZspr4W8PKgH9n6b9fIFdVY+FvB62saPpOjtjB5t4+T+IrB2mnZzxWVOgoO97mFLD+zk5XudXJ4c8JyJtfRtGK/9e0X+FcR4l8L+GJtTQ22lacFUDcIogoP5cVacfIewrHNzFuOJF6881rKHOrM1qQc1a9jgde8IvJrtwdP02NbU42CPgYxWYfBupn7umt+BFepC4jPO9akW4jH8S/nWijYqKsrHlDeDdUH/ADDHP4igeCdWIyNKf8xXryTxY5ZSfrVpJI+MMtPlKueLnwRq5GRpL4+oo/4QfWNuTpMnPoea9t82PONy5pTJH03L+dPlC54ifBOrAAf2TKPU5o/4QzUlbnTJa9rMke3G4c1DJNGerDik4hc8UbwjqIZsabJ1qeLwddMg36fKH716y0iZJ3A/jSq6Y+8tHKFzyU+DLnPFhNTG8G3g+7p8/HtXsaunHzjjpzUokjLffGfrRygzxNvBupFvl0y4P0Wmt4M1UDK6TdEn0SvdYpUTP7z9asCYAf6z9aOULnz6fB2tg4/sW79/kpR4L1sgZ0e7x/uV9BG4U8mT9acs6/8APT9aOUOY+fB4M1g8HR7of8BqQ+DNUwCNHuie42V9A+cpP+s/WkaZQf8AWfrRyjufPw8HaoQd2k3QPoUo/wCEO1LtpN1x/s17/wCcpP8ArP1o85MYMh/OjlFc8B/4Q7UhkHSbrj/ZpP8AhENR/wCgTdH/AIDX0B50ef8AWfrQJYx/y0/WjlHc+fj4P1IDJ0i7/BCaQ+ENTHH9lXX12GvoQSp/z0/WlDxjrJ+tPlFc+ev+EQ1ED/kF3XH+xQPCOon/AJhV3nH9w19C+bGej8UodAPv8/Wlyhc+d/8AhFNSHH9k3fT/AJ5mmnwxqI66Td/9+zX0QZE4+ekLof4h+dHKFz53PhnUDGT/AGTeD/tmaYfDF+uM6Vef9+jX0X5if3/1oDpnIbmnyjufNF7pMtmP39rLEWGQHXFVLYD7O471678T5InktV3KW8ts4ryKDPkOe9TYDf1Twza6X4T03VRfCW5uzloQR8o/+tXV/CoA6ndHH/LHrXDX/h7ULHQrTU7p08q4P7tN2SM813HwnYC+vCzAfuhjNOJDPVME9KMHpTDKhOdwo8wdd1WCJAPmqUOqI0jkBFBZiewFVRKP7wrmPHGtG301dLtnBuLzg8/dTvSbGcpdX1z4k8QS3KKSZH8qAei5r1fS7FdO02G2UfcX5vc964vwPpiJM122NsI2Rj1Pc13QlU/xCkgueWfGE4utIB6bX/nXKeG7O3u9VtoLxttu8gDnOOPrXVfF7a99pHchX78Vx1nBcXcyW8ClpHOFUUxNml4ut9Pstdlg08q9ugGCrbhnHIzWTPq86KkaQwhQOPlqTVLCfTrk21woWUYJAOahiezQkXAckDjFFyT174dX91f+HmNyykxyFFCjAArsD06VxHw1aAeGpngLCJZjkv611/2uEr/rU/76FUCZKeD0ph61A19AOs0f/fQphvYOvnJj/eFAXLVNJx2qub6AHPnJ/wB9Cm/bYXGRKn/fQosO5ZyeeKUZqmL6Af8ALZM/7woGo23H7+P/AL6FAXKmsR75I92AKfCAFAAAAFVNVvYWuIlE8Zx1ANKl5B/z1Q46/MKQNl8EHg0pOPrVQXcBwfMXn/aFPN1GRxIn/fQpiuT5Ocmgtx0qubmLHMif99CgXMRXIkQj/eFAyYMSMc0oYDjpVc3EeOJF9vmFN89S2GZST70guWDKOQOaQNkelVnnjiXczKPbPWqFzqQAwp/KgC/cXap/FzWRPfOxODz9aoT3quS2c1Smu9qFmPHYVLkUkXzOSPmbmojcKoJDYP8AOucvNUlwRHGxwOMUujTXc0jmaFwvYtWM5NIuKR2Om3IikWVlyM5I9a0rjUJpZ96RRrGOi4rmPttvAR51wqnP3ScVfm8SaWgRRdJXG51L6aG6USTxFqWoPpM7Rz+UEXhIxiuGs9VeS2lFzHmQD5T611mq+INJfSJYY51eaX5VVRXPWmmpaWrefGWlkHG7jFa0+d6Mmbh0Oclbc7MepNRGQDHGT2qW8t54ZSJF2gnj6VXCgdeKbi7iTBizGkCetDyhMZqB52clRkUuUTZI8mB0qW1Yu5z07VVC+p5q1bfKR6USQJnY+HIUNwj+/WvQVVAPlAwO9eeaVcrbxIWYIo6mumt/EFi5H+kAKOlc1m2acyOim2raycD7prwXVYS+qXLsxwXPFetXev2C28vm3KouDnmvJ9SuYp72R4clC3B9a6IKwSkmisqqq5HBpPN/OmgMxwelXbLTprmXZDE0jHsBWhkVQrSDnpVy1spJpAkMTOx6ADNdPZ+DnUJJfS7FPJRev0rpba3gs4xHaRBR645qo029xOVjm9O8KScSXxES9QgPNX9Xs7Oz0jZbQKCOrEZJFb627TNlzmqfiCBItFmdmVSOAD1NbckYrQIyucZZBhKCmV+hxXoOmLObRDvY+5Oa4fS4TIQxO0DvXd6fq2n2lpHGZ0JA5HevOavI0ujRtWdZlZueeuK2JtTKR/eAwOtcw+t2zN8ko56AVm3OrYLMW4967qDaWpnUszfutV3cByTWLc6sASN/PSsC41dUXLMT+NYt1rWWwmMdxW7qGaR0V1qcjjAbA+tQwI12AsbbiT+Fco+pSzvtRCWPIArp9B+2W1oXdChb+8OgrnqVtC1E2NJVdP1BWlUMU7Vt3viG5ukKRxIqqeOM5rkrrU4Ycu7F2Izhap/8JM0WGS0JX/aauZVJt6F8qO20rWNRnaSFYkZgMjjFcdrXjzXbLVpbeOG3jaI4yyZNVLTxdPFqsUmwKScEKePpWV4p1Q6jrcs3lLHkDp3roU3YnlTNF/iD4hkXBmg+giArV0q48Q62m5haQxDrIRjArz7fz6UPdzlPK86Xy/7oYgUKTJcbHrP2rQNLIOqatFI46rGcn8hTj468AtE0Ys7tnAwJRGev5148qKvanHAFPnaHY9ATxPYfaS1jLKgzxuTkCtKLxSLJjcafqP78jDZTr+deb2L4kPpWrCAT8w61EqrGonZL4t1S5l86aYOf9wCt2w8YTiGKKaxsrlAclXTr9a4BLi3hh/1g3DtUD6uFOBkd8ilGpIHE6EndhsAZ9KXILcdKQHn29KOg4pvUBd3ANJnjvSj1/KjOcikgAMCaUsDSBcYOKB8ucDJoYxyt14oHfPNN68il3f4VNhhuHTFITjFB4X0pS444+tUAFiVzijr3oB5wTxSHkc0AKSBn0pFPtS8N06UhyPemJgSR2Jp2Rim7jzzS9AR60Ag3MB7UKTnpxR39cetGeM96Bin5sDPSmnGcH0604A9aQjj3oAo3o3KDngdRUlq6GMBeKZcgiN+e1V9PDYOScdqANTdngmn54PFRJg8nt2qTuOPxoEG4HtSjrQc53dqX7pBx1pWGIfrSHIOD0owMk0pODwOKYCEjOBzS0m7PUYpdxHaiwCZAIGKN2DikJBb3oxxmgY76jFJxnNKoODmkPT3oEKcHj0ppOc8dKUDHI5+tKeKAEAxRgdaUj0o9vWgBOv0pflP4UcDigY9aAEOPx9aQ88ZxS56kikzxzTAT8TQSOtL2qNulIBhYEnrVPAMxPSrhGW9MVXIxKT1zSYBjnIpw5bim8gYoQbaLgPA5ppH+1TjwOOlBXGM00wOx8KoD4S1gk/Ms0ePfpSrnIzyPSp/BcH2jwl4hxjMckbZ/z9KLdQrlj0rZImxJIqmzYKMZFdFpi5021z/cWsNwjQy5BxtNbejnOnW3uB/OrBnj/hX5fG19gcC8lBH/AH1UEi4ecHGBK38zVrw6jR+PtTTsL6XP5moLgAXF2vpM/wDM1m2TcpMDtBXr7VUn+aElj26VZOUXI6A9KguEV4M9Ki4FKcYss9BinWYYwKcdRRMf9DKkcYp9nzbp6YqgJNHLC9mHvWoj2iXDG7kKID1FZelkf2lJz0HNSX7gTMGHGQaAOg151l+yNGx2jPUdaxbhgQe1aOp6ha30Fn9mmVyowwA6VmyjBJPpT3EMJxFGxGT2ralz5kJPbg1hyEmNMVsyk7rc5+uaEgOM1/J1iTIIwABn0qnH0q/4mz/a+TxlBUunaFd3+myXkW3Ck7UP3nx1xQwMx+9Qpnzl+tTSHrkVAg/ejHrSA6GDiRT0rs7QhoUPtXExnp7V2WmndZxHGARVIZwOvfL4gvB/t/0qCMYHFWvEYz4lvFA53D8eKnTw/q4i8w2EwQLuJx2pMRlyDmpLAkXa1HJ1PNSWRxeIaAOs0qbF0gY4zXWr8wzXD2rhLhCOoPeu1ifKKcYyKaGWYsZFSHGGHtUS5GKl52np0qkIw7fJmu2z8u4cVq6Rt/ebemayYlxdXZDA5xlfStLSGBaQng02Brj71TLioAeaeGyPehASnAFOUqfpUW4d6ercYpgSYUdKD2pM0mc84pAPIHrTSozzzSMeBxSFuMUAOHWkwM5zQOetIelADk56cmmvxzSK23pUd9MY7C4lQfNHGzD6gUbAef8AxHDGax+bIGTj0rlNGH/E0hI+8HBFTXOoTXxae4Yu7ZJyen0qTwtEk/iGFc8ctj1xUDuej3hlXSr8xruzbtu+mK85trtboOVU5U4Oa6Ky1aZdT1+C5fANu0caE8DsKwUhjiyEUDPXHemmSyTYX5FU73DWhPQZq6pI6EVT1AhbJwDmmwRl6hBbQND5Dht0QZ8HOG9KijXcij+8cVLf2DWBg3OHM0QlGO2e1IqlYoSOMsOfxqSj1eKBLS1gt0yVjjCg/hTlxjkUkpJZMn+EfypgbBpiL9rjJ4q9HjBFZ1q2Sa0IiNtUArquwcd6zJowJW4GK03I8vpWbdNiTPrSYDCqkfdFS223fyoyarGQgU+B8zDmkgNYhTDxjillC4B2jkVGrARkYNDSZjB/CqApSKm88CkwmegqOZsSEA0zzfl6c0ATFRnhRj3pcJnGBUHmHHJ5p3mgCkBMwTjCjijYmR8ozUAfPNPEvXB5ouBOUQfwj8qVRH/dFVi5HXrThIRzTAsqE7KMUp2ZwFB9areaRyaXzevPPYUgGTqhyMCoPLT0FOdyTimg9jSuAgjjz90UFFweBS7u1NY/KTQBVljQjpVAvGJduMHPFX5CNp96zJeLhG96lgejeFFB0Vf99q3AtYvhMZ0UEf32re20IBpXikCcdKlxS49KAK8sYMTA56Vy7wQqxwgrrJRmNvpXHPLhm+tMGS+XEw+4KURRdNgxVcTDHWno46irQjQiji4wgq7FBF/zzWs6JxtAq/FIBgZpoGWBbQgZ8sUG2hzkxrilL4GaZv8AypiEaGL+4Kge3i5+Uc1KXByaiJ5oGQm2jJ5XNOW2iA4QUvbNPGM0CFW3i/uU77NHuzsFKDjFP3DrQMcltCDnYM1L5UWMbaiEgpfM5FArj/s8ORmMGpBFHjGwVD5vOc04SDHBoAl8mLP3aa0MQOSgzSeYPWmvID3oAcIoyM7aTy4/7opu8Ypnm+9AEvlR91pRFFj7tQ+bxmnLIOOaAJvKi/u0pijPBX9ai8ylEg9aAJPLjGPlpRFH/dqLzQSeaC/vQBIYkAxj9ab5Mf8Ad/Wm78d6b5oHemBJ5cfZf1o8qPH3f1qIy9gRTvNGOvNIEedfFANE9o8UJK7SGftmvN4eLdvXmvVviZLu0GIZwfNrylcLbMSelQyiS7/th9PtZLxbj7GciAyfdx7V3HwxRXvLvf0EYxiuN1LxLd6vpdjYSIi29qMLtH3u3Ndj8NiBc3WDyUGQKESz0gpGD0/Wl8tD2P51WMuCeeaPOAHX8a0BE8pghieSQ7UUZYk9BXnW59Z1iW7AJEr+XAD2XpW14s1JmtYtOhfElwfn9lqbwvp6I/2hvuxDagPr61DGdLZ2ENlbxxKMbRyc9TVoLGF6H86hZ+PSommx3qhI8/8AigEGpaWU4Ox88+9c7p941hdR3Mf3kORW18SJfN1PTjnJEbD9aydJjtpNSgW8IEG4b/TFSNkOqX8uo3kt1Ljc+OB6VX/s+e7AMSEgcmtDXhaLqsostv2YYCY6VlyXFxDIvlTugPHymmIju9S1XS4vscVxNBEx3FFOAT61RbWtQY/NdSn/AIFTtULPMpZ2ZiOpNUtuOlS2x8qLP9r3qni4l59Wpx1rUD/y8yfnVTaO9BWldhyotrrmoL0uJPzpTrmoEf8AHzJge9UtvNGzvRzMLIs/2vfE5+0Sf99UHV70kkzOT9arBQc0hT35pczCyLP9rXuM+fJn/epf7Wvs58+TP+9UAUAUhXNF2Fi1/bV/nJuJM/Whtb1Bhj7RIfxqoVoxyAaLsOVF8a9qKJtWdse5pg1zUeSbhz+NUiOelG00XYWRf/t3UtuPtD4HI5po13UAf+PiTP1qlimkY7UXCyND+3tQL83MntzS/wDCQahnInfP1rOK8ZpMYouFjS/4SHUB/wAtm596RvEF8RjzWxWfgde9GKLgaa+I78YAcY+lPHinUlPyy4+grI20YzQMs3GoTXMplkOXPeofPY8nrTQvFLgdqVkFzb8Iava6T4mtby+thcxIT8h5Gexr0PXtQPiG6W6tYXTI4jRc/wAq8h24wR1r1DwD8Tm8OWMtrNZwSysPknY7SK0hZESuncxNVjnNo5EbGSPrkc1yR1B1yCK9Y1fx34YuYbmQ25kvplJbYnBY+9eQy7XkZwMAnOKJJGg9r4sAcU0XxH8NRBRmpFhD8ms9BDhenuKeuoMp9qfFZox5p8tlEg4PNFkBHLqs0sYjLttHQCovtkm0AOw+lIYlU9KjwFzgUuVBce928n32Zvqc0qXaqOetQkqTjFMIGPpT5UBd+3AflVyz8R3lkpFtN5ee+OtY3XikAAosgOgHi7VdxJvGY+4qVfGurL1uR/3zXNkDtTccUwsdWPHusKMLdAcY+4Kz59fubxy91cPK/uePyrFAp20ChgaTazcBdqzMo9BQNauQuBJissKDnNLtwOKnlQHQ2PiRrZSJAHb+8e1Wz4njlXEqnBrkgoPWlC84qrAdJNrNnKAMH8ar/arFmy27HesPaAaUrtHWgDq7PxBZ2JPkwJuA4bHNU7vxPfXEhJnIQn7o4ArnselGMjmocEx3ZrnWpmPJBFRSanK/BbA9KzNtKBxT5EBp2t7suo5CR94Ve1ycPf78Y3KK59cqQa37/TZ7/S4tRgIdUXD460+VBczPNB70vmIB161QA496XbS5QLrSgcD86TzxVLafWjGMc0cqEasF8kX8GaujV4cYZTXPhe2aRlI7mpdNMd2b7arDjIj59TVdr5HPJwCaxiG9aX5sDmnyIGz1wEE80vXjPeocgZB6etOLDIxxWVyx5wOlGeaaMY9TSkAemTSuCHgnvSZAyBTc9h2po46mgZICM9etHB7UwnjA/OheUHPemA7gnBo4zjoabwuSKaGyc8imgJO5yeKQkAY7mm7+SDQxAA54oAcp4znHtTgwK5qLI6jrQTxzTAkyaU59ajDDopFODjmkwH5GOaaTnoaYfr1pdwH0oFYcCdxA6etKenFRb+wpxb37UNhYr3R/dMags+nXpT7wt5TlcZxxVHS5SCwbp70rgbikBfenhhjk1XEi5GefpS+Ypb0pjuWN64zmk3ckVGXUjGaaZVGBxmmBMSD9aFfGeOKiWQDpim+byfegRMD83T8KM557VEJV7mjzBuAzxQBJkBqXOai8wbiARQJFIOTQO5NvzgdqA3WoQ4Pf8KDLhuelAEuQBjtSgg8iot4x14pyMvTNAD+c+1PJGP61GXXgZ5pPMAPagCTcDwaRsen41H5i4JyM03zQOM9aLgiRWwDTWI600OCeophcdCR9KYEhPft60zOab5ijjNMZ1PANIZJu7dai2jc2DyaTcA+AafjDc9+lAhmO2CeaVuOO1Pwck+lMGCDu6+lIB6jK5oJOADQOgx+VPVd7Ae/ShAjvfBEefC3iRFOCRET+tMVdhA4wal8IHy/DmvgnG8RDP506OPDqAMg10LYGSSoWtZCgx8pxWpovOm2n0H86y5JDHG69eK1NDObC2/z3qkSzyrRlK/EPV0xg/b3/AJmqt6v+l3yn5cTuM/ia0LAFfifrI7fbSf1NZWosRrGoBScC4fg/Ws5IgojdGOeR2NQTqTCxWrG47SrdahmVhESBxUDKcjA2Jz1xRZHNqmDSuM2LEYyBTLH/AI9UPemDZNpqj+05TzkjGam1AASMPaoNOz/abHPbpUurBjJlVLH2qgTKmmROZXAVgN2RkdavyEHd7etaNsyCCJnXlUGarzmOdt0K/WlsJsz3lMcQfAOO1aGt3EttpcNyuMsB07ZrPkUFCG79RWlcQm7s7aIruwVOB7U7gc14mSRLm3kf7zwBq9C8OMz2tlDsXatspBx3wK4jxlHi9tUPB8jH0rvfC+3+yNLOcDyACcVM1dESPM9WQpq16nA2zsMD61nLnzV+tbPiOPy/EWpLngTsc1jr/rh9aaKibcSk7R19q7i0ULbxKBgADiqOh6NY3FjDcSKzS5znNdF9iiUDaDj61SKPLvEzCPxPcuB0ZD/KvXNKRkaSSU7g8YKjPQYryjxggXxNcqOBhP5V61p4Pkwkk4aBeR9Kzn0IkrniU3DyDH8bfzpbNiLlfrT7obbicekjAH8TTLIZukB45qykdFaAPdRrnqRXaINqKM9uKq6f4astsF0ryF9uSCeK3o9PQqBzTQylHjvmpjjbj1FXU05B0Y06SwQA5bHFUJnKQY+03Yz82MVf0g7nkyDmqlsoGo3UZY7zwPTFdPY6HFAnmiVmLqCRjgUAiEAk9aerDGc81d/s9R/EaVdOXP3qaYFRfmxk1KMDvVoaeAfvUv2Hj71JsZUHNGcDOat/YSMkPzQ1iRzuouBVyTxmkY8fSrYsdx5ak+wHd97imIqAmhifWrf2EnJzTWsnCcmlcCuOD1FQ3/zadcg45hf+Rq4LU4zmorq1ZrScA/8ALJj+hoYzwYHMOAe1a3gvjxLD1+63NZUO0oOOCefzr0Dw54ZkFxa38cqIrD7gHJqQOcvxt8V6krHtx+lU7aKSOWXexYMcrz2rR16JrXxpqED4LBQc/gDVC2u1uJZEClShxz3qkSaultBHeo1wFMeDkEZ7Vk36hbN++DWrplhJqd/FaRMqu+eT2wM/0rN1JGS3mDEYRip/A4psaMO6huIvLNyGw6Bo8nPy9qlyDbQj064+tR3VzPd+V53SNAicY+UUsbZjA9KgZ6nIxwh/2B/Kmb8nPFXP7MnliiYEcxr/ACqP+ybnGABx70wH2b5Y4rVhb5PeqVtplzGNzbQO2DWjBaShcHrTuIjkJEZrLvGKkHrmtqa0k2kZ5rJuLCcrwBwaGBQ380+J8SgilNhchchc4PrTlsrkEHaKQGlGxwcntSByYvoeaRIXDAmlWMgMMUwKN0xD57YqAtmrV1bSHBUZqt9mnAzsPNAw3cU3fTvss542Hmk+zT/3DSAUPgcHmnbx+NMFvMBkJS+TOf4DmgB2/AzShyOc5pPs9wR/qzS/ZLk/wGgQu7NJupws7o/8smP0pRZXZBxC2BTAg3At15p2aedOvM5Fu5z3xSjTr4f8u8hz7UgIs4OaaW4NT/2degc20n5U7+zL4pj7LIaAM+QgqRisySNiU/3ua6qw8N6rqVwttDaOGYE5bgD3rUj+F/iF9odLdVDZJ8wZqWrgy34PUjRQD/fOK6HGelJpXhbU9LshbtArkEnKuKunStTxxaH8xRYWpUAoI44qydJ1bI/0Pj/fH+NH9mapz/oTfmKYylNxCw9q4KUkSNj1r0h9L1RkIFk3TuRXMSeDNaLsfsh7mgTOcyfxqZGOBzUh068UsGt3BU4IxQlpcYyY2/KncCxC/IGKvq2MZqlHbzKBlDVyKKZgD5Z/KqQFnzO1Rl+MU9bafH+ralNrP/zzP5UwsRZ7UbhUos59ufLP5UGzn/55tQBCCenalU881L9juCOImP0pws7gE/ujQMjDUu/A6U/7LPjJjamvBKvVDTERmXvSiX0qNo5B1UimZK9RSuFiYy9ulPWU4qqWGeakXkcfpQIs+YetRNLzQEdsgKaBa3BOfLY/hRcYNJxTPNHrUjWs5/5Ztz7UxbS4PHkv+VMLB5nGc0qS96T7JcjgQt+VOFpMASYm+mKAF832oDnNKLafHETD6igW85IHltk+1K4CeZR5h705rafIAhf8qQ204/5ZN+VACeaTTTKQeKebWfH+qY/hSfZLj/nkx/CmFhgkNL5nHej7LcZx5T/lQbefHMb4/wB2lcDi/iM7vpEGOAJK81jA+zMTXpHxIimi0q2LxsAZMAkcV5wn/HqRnrUMZc1fWLO70nTbG2tRG1uv7x8AbjXT/D5tslycYworltVXSE0+wSxO66K5uDz1rqvAEbzS3YRGJVB90UITR2xm568VHJdLDGzucKoyacbS4JA8mT/vk1z/AIkluIni04IVkm5YHghaoEijatLqN7LfSDl22xg9lrr7F1giWMdh+ZrK0nTpmjVkgcoowuFyK11tLlW/1Ev/AHwaSBl4Sbh1qN2NOjtpwv8Aqn/75NL5Ewz+6f8A75NVcEjzjx8d2pWQxyEOPzrItIZLiZIkGXY4Ara+IEMkOq2IlRk3IcZ+tYtvPJbTpNFwynINSNj9Qt2tJjDJjzF602K6sYFxdW7SMehB6VFdyzT3DzSnLuck0+LS575d0IUheuWxVakmbqTxTXCtDEUTHAJzVXYPSuig0TUbnd5FjLNs4You7FSr4Z1otxpNyWHUeXUjRzGwEdKTy16gV1q+F9VBy+lXIz/sU5/Dl+Bj+yrgH/coGcjs/Om+WD2rrX8O6gMY02f/AL4qu+halnjTJ+f9ilYDmwgA6c0CMHtzW6dE1UZxpdz/AN+zSLomrH/mGXHP/TM07BYw9oycilKrngVtf2Hquf8AkHT5/wBymNpOoRvg2MoPf5aVgsY5QE4xSGIYOB0rZ/s+42hmt5B9VNOFqoO1xg9wRSAw/KzxjmlMYxkjmteWKJGCqRn0FV2guB92BiP92ldIdmZ/l8ZxSFMnkVoLbXUmQls5+iGmPBMp2vGQfTFHMgsyjsA6Ck8v2q9skH/LM/iKiZmXjaKLoLMqeX7UpjBAxU5kYfwj8qTedpO0UXQWZDtHek2DNS+ZtByopA+7sPpRdCsN2A54pAoBxU2SOdvNIWH92jmQcrISnpSAVNknnZj8KbkrzsougsN2ikKjk9qc0u0fdoEwwBsyKd0Fhu1aliCqecc1GZh1MfFKZgRkR8etAWL6FFIORTJ3Qjg1TWfJx5L56dDQ8u07WQg0DsDAGoWBAxUgk9uKQ4btSuIrkEUAbqn2pjoaBsX+E0XGQ+WCaNnapwUJ+6c1MkQkHAOaGxFPy6BGD1OKuGEDpSG2G0mlzBZlQJil24PWrBhC4xTWUDrRcCDYPWmdOKsHB703Yp707gQ55p4GRUwhiPV6URxK3+souBBtzgnrSstTlID/AMtKNkX/AD0oAqKvJpSpH0qdljHR6jO0A80wIs5pQBTsLjOaQgDGCKAEIzXQeG/EzaCs0b2y3MEo5Rz0NYDA5wDxQFJBoCxavZY7q5edI0i3knanQVWKkjFIAw4FKN2OtAAVApuBjJpMN3NG1jQAdTQeaCDSYbsKAEzipBtHXmmeW+elGwgcdaAPURIM88n0pzOAc/lVWVxDIUJBx3BqMzDkbq5WjQ0N3GScUwyDGO9UfOHXdz3FKs6sPvdKLAXhJgdaQPyRnHpVL7QpHBGKVZxydwpjL3mDpmm+YACAaomZc/ewKT7QFAyw+lFgNAPnvSB+aom6BB5x6UeeuAN1A0X9+R1/GkyBx1ql9oUchh6daBcLj71FxFwycfLxS+ZxjpVH7SOcsPpTTcAkncBx0poDQ85QvT60nnkjpxWf9oXj5uaUXKgfewBQBoCQE88Cqt5qEVuAWb6AdTVO41GGKJm8wHFYH2v7TMzMenak2y4R5nY2ZdclwfKjA54yaiPiCZG2tGDmszeoJz0qvJKouELEDHrSV2bypwSOi/thZIWMy7MdKIQpQMs6881nb4pEzgGlSaNMgx5FNI5ZI1o3Qtxdpz2zzSGZRIV+2ISOmTWWLmCPn7PikFxA7E/Zsn1NVYmxri4Qsf8ASkOOozSCdAM/akx9ay/OhPS1CnvinGWHHNsM55NA7GkLhSPlukxnrmnG4QHBu0/A1kiSIHAgwvfFOE0J/wCXYEUNjNIzA5xdLgUhuI1wPtaZ9jWf58ajBt+PSmiWHgNbCkKxp+fHk4uV/Cj7QgIzdLz61medDn/j2AxS+ZD1NuOadwsaf2mM9LtAc9c08XEW3m8Q496y1nt0JzZgj69KTzrfORage1AzWFwi9bpOemTSi4Vjj7QmfQGskz2+Ri1FKLmENlbXafUUxGn9riDYN2mR1BNH2tCcLdRj8axZLu08wb7XcW9qcJrYc/ZhigLGv9oUnIuYx75pouBz/pKfnWS01sTkW3FIZ7c/8u1AWNj7Ru6XKce9MNyeSLhDmsoyw9RDSCa26NbnNFxmuZhj/j5j9+aTziePPX86yDLBn/UUv2i37QYoA1RMARm4QfjWtEoOCTniuTFxbgjdCSK6uAgxoRwpUYpATdzTRweQM4p/XHPNIFG7OKLANwQQcdas2687iKjAweRn+lXbeLcB9c00B2HhdUXQNeDDO1IyP1qGKYJxu57Vb8MpjRdeGM5gU/zrMjRgCQMnsK3QmaJIKHnORWpoJP8AZkD44BP86zooyLfO3DEcg1o+H2K6TFt/vsT+dUiWea2g/wCLpaxgdbpax9YyuvamMf8ALy/863Ixj4r6txgG5SsLX22eJdTU/wDPwamWoiuAHUj9KrykpGVYcDvU7xBwSpxjvVeR227WGD296ysBUmRWsmYE4NRWBVrZQKstG39nyEeh4qpYLi1QZzg807AyawymrMD/AHasau0sfzRHDY4zUNgN2s7R1K5NT60ck5GMUxGjEWNpCx5LIM/lUSwzRBgsLPjkACo1crpsJXIKoOaaupXSjidqljFhs7zUpSqWjwgHlnGABXTwQRWtsBtywGCTWDH4iv7dDmRZB6MtMvfEt01uUiRY5uoYcimhGX45GL+0c94T/Oux8LPIbbTIm/1RtwRkVwOv3c2oW2n3E+PN2Mre+DU1h4o1Wyt44IbhQka7VygJH402rieqGeKRjxPqY6fvz/IViLxKvfmrd3PLczyTzOXlkO5mPc1TH+tFCBI9W8N4Okw8Y61t9RisHwv/AMgiL6mt7r0qijy/xqMeJZj3MaH9K9Q8P+Z5kDSnMTQLsA+leZeOOPExBH3oVNWtI8W3On6M9ns3zquyCYt/q19Pw7VDVyXqYWpDGo3Yxx574H/AjUFngXcZP94UshJ5YknOSabbY+1IT0zTKR7LpufsUOP7orTTpWZpZH9nW+P7grUi+715qkNky8gc051Bj5pFAx0pzAeXTJOKhYLrc4br/Cfau7t8fZo+ONozXBhhHrlwhQfMchq7q2bNrH/uigCXGaeFwvSkQE4pwBB60DADilPIyKDzS59KADpzScmlIOCaTqaAFVc9TS7cng8UoPXn8abxng8UAKO+RgCo3wafk9KjY8+1ACYzxTbpBFYzk8/un/kaePvADrTNS4sJxnrE/wD6CaAPnWDAiOB3P869x8MxY0mzkOCfLBrxCDBi46ZOPzr3jw+B/Y1iARkwqaAPMPGBA+IeogAZKJn/AL5FZqRIshZQNx64rW8cLt+Id8cY/dpz/wABFYdpDJFcSu75Rvu80Lcll+1uJrO4We3YrKp+Uiq12TNbTKeS/X61q6JcW9prFvcXI3Qxtlhj2qldFHmuGQYR5Cy+wJ4qpCRhanqP9oG3XYFFvCIR7471VjXKe+RWhrf2ES2y2IAKwgT4/v8AeqEI/d5PrUFo92twTbRNyMxr1+lSqB9KitSTaQknP7tefwFSjjOTTGSxsAMVMrAEelVQ3UU5pelAieRupFVZBlTmlaTmoXlyDigBmOMDgUHjoKTcetIzk96AH8dfWm54OaTPrTS/GMUCA8ijBPWkBJoLYpDA9fekHJpNwzk0vmUALx0pyDPGKiDZOfWpVP8A+umBOAAMVIg4z3qJTmplJHJFAE6gYHFTRqBUcbZI9KmGMjmi4iZEH5VIMHtTEPrUoOTTAUKM9KlVAOwpoIqQNgUmK50HhhQLmcgdEH866auO8G6hHeXl7Gg/1ajn15rshQUhMUuKKKZVhMUYpaKAsJSY5606koE0cdrEYj1GfHds/mKxnUdMYFb+vcX8h9QP5Vgv0PtQQRYw3AFWIV56VB75q1CO9MZajUAdKlxzUaMAKeG9aAHDHTFOwPSmZ5pQR0pgKAAaXj0FNJHajIFAhHxg1UlwasO3BqnM3BoGUrhsHpWbKwNXbl8AjrWTK2SalgPZgTxU8DAnArPZ+OTVy1cevNAG7bEHFakIGKyLUjANa0R4qkImIX0FOwoAO3mmAjNO4oAUKuScCmsoz0pc00nn2oGIQDSqF9BSE0gNAiXA9KUKp7UgPHtRuwc0ALtX0FG1fSjNGfWmAm0elLtX0o/Gk6UAec/GXjw3ZjAH+kcflXiqgm1bHWvZvjOR/wAI7ZHubg/yrxkZFmzL1ArNjN3xN4VsvD2kaPcxX5nur2PzJIyR8vAPHtziu7+Di5XU2wONgzXnWu+E9T0Kw02/1CeORb5N0Sq2SgwDg/nXpHwbCm31Q9PmT+VCJZ6dJLFbQvPMwWOJS7segArySJ5fE3ieW8wWM8m2IH+GMdP0rq/iHqvk6dFo8TDzLw5lweVjH+NO8B6OsFmdQdfvDZCD2A6mrGdfaW8drbRwRABEXAwKthRio1qQc85osFw2gCozGCenFS5zTWoBHjvxnfOsaIuFH7pyT+Irn9AmsbfXrGXUVX7IjgvuGRW78ZCBrWi7P+eT9f8AermtK0ebWdRtbCOUK0pxubsKVhknjC+0+88Q3U2mKotSQF2jAPqQK52cybhsZgD/AHTWt4j0k6Fq1xpzOJGix8w6HNZ0GsS2kL7LWKT3Ycii5LPa/hKc+DTnljOwJPU13Wwda5L4dWk9t4OtpbhY0e4JlCp2BrrQeKaQkw2j0pCikdOadmkNOxVyMoOpFN2LUh5ppFAajdvBFULyZFIDEbVp2oailom0cue3pXK31/JIDhsA9SaTYIXU9Qjab5TgDsK5+51D7yqc+wqtdXUbSFTJnnGF6ms2/wBUt7CJsBWmx8q56fWoci0hmqX8sCZJVWPTJrkbm7ldyyE5Y8uetQz3M19clpHaSRjwP6Ct/T9CZ4le8IjTqQetYymVYx9Ot5pr6LarSfNya7dhDCm6ZlB9O9ZzahaWf+j2MQG3jfVGe+AJeZ9xPesJastOxrS6k7ZjtwEjA645NZc7wxku5Bb1PesufUzgmPgetZUlzJOeT+NNRYXNS7vg2VjAxWawZjmmBwPemvMyjgVaTC5LtCjJximl1bgVW3sx+Y1LHyQFH41RIvl7utSJCBgYzUyQ7hljU42rwMVLYWARbYssB0qtEmXJIG2rLAP1NRvtBxmkMR2RfTP8qrSOXztFOZd54yRWvo3h271ZiYk2wj70jdBVIT8zDWAyHmtOz0S5u1BhgJTu7cCuqtvDVnZTqZWaZh1DdM1smMOgRQFHYDirUerJv2OXtvD1pCoaZTLIO3YVpx2EJVR5KBPQLW1b6eHjwfvZ61Lc6VcBAlttyR1Y9KOeKHYqQvZWSlnSFAByWArz7WXhutUmkiVdhb5SoxXXXHga+uWMk1/uOclT0qL/AIQOdRu+0r+IpOaZVji0tgTwoqwtrGvLqMV1CeE5lb5pBj6VVuNCnjJJwFqbisYBWHOCg/KoLjyiQscY46tWv/YlwW4I+pqy+hxJAHUs8ndafMhWOdhtQxyEzV1oooYf4QT1qZ4LlE4hZVHoKzLiVpAVYdKL3C1hxuIU5RdxqrLc7m9BUHIYqATj0pjFgeRzTSEyRpMjioiSeSaMnvSZx71QmgDE0pbFKsUrfdQn8KsR6bcOA2xsepougUSrls8U4Ix5rSTSZepxj3qxb6XK8wWNd/POKlzQ+Uy/IyAcYA60eXHniu9g0aGRQJIUwB0xWjbaZb7lQQRbRx90VDqFJHl/2diMrG5+iml+xTOu5YXC+u017PLFb2tmVEcaqfRRXM3F1vYhVAXsAKPaBynnZtX6lSAenFL9jZuiMT7Cu2JUOCUBx0p6uM/dAp+1JcThzZSDqjAe4qaOzVlBPFdZduGjwVBA7VzN1OWmbbwB6VSncOUoyx4chaaIT61LnJ96eiA43HFVdkkXkL60n2Yk5zxVg7QeKUscYFK4Mq+Uc4FOMDE4U5IqVWxngZqSE7W+vWi7EMisZDkswHtTXtiGxmrzOAvWokQsCQadwPQv7JtnJMkZPvmnDR7PGBF+prVawv1yBYXTfSI1H5F6kmH0+7Qf7URrm9rB7NG3JLsZ/wDY9n/zy/U00aTac4h68dTWkSQfmVlPuDS4AXJI/A0+YfKZy6VYrn90D+Jpf7Ks16Qj8zV5gNueKTsPehg7FL+yrH/n3H5mkXSrM9bVKukbmwDT8AHHemIpf2TZAZ+zIaadLsyAfJXHSrwGTzmg5XtxSuMonSbPA/0dMUq6XZdPs6GrhyR35o2/Lng0xFT+ybMjP2dOKadKsjgG2Q1fGab+NFyimNKsCeLaOgaVZZwbdPyq4MUoPOcZNMVig2k6cxw9nER6YoGjaYgwtjCD/u1ebHXBJpMYOe9MFoUf7F03OfsUX5Uh0XSyRmxhOPVa0ei570n4Uh8zKf8AZGnqARZwjHQbaP7Ksgci0iz/ALtXByKcpz9femIoDSbJnz9mj+mKDpFgpI+yR5PtV4jnPalOcDmi7FYoLpNiAcWsXHXil/sqxx/x7Rg+uKurzk0p4oQFL+y7I9bWP8qDplnkgW6KPYVb9O9CAknNFgKX9mWfX7LH+VPXSrNTzBGee4q6flxgUpA/+tRYCmul2ALf6NEc+1N/siwLbvssfHt0q6fagE44pgUzpVhj/jzj/Km/2ZZAjNrGQPUVe3ZOOhpGO4UwKf8AZdiWJFrED7Cg6RYMP+PZOKtqTmgkLSYFI6Pp/U2kZ/CmtpGnEcWsdXSccjNBOeTQBnnSLAn/AI9o+B2FNOjafyPsqE960CcUdD70hmcdG08jH2VM/jSHRLAgZt14+taPuDk96QtnNAGd/Ymn5H7hQ3rk006JY9oAMe5rSOAQDSEkGgDLGh2ByDH+GanEaxgKowF4FWyOc45qGQZbgU0JiLllB6Uds4p6jKAA96VUwMGmIaoLYx1rWtosKOlUYE/e5Hatm1TcRgAZqkgOj8Nt5en6wB0NsM/rVS3/ANYNo4HrV3Qo91rq65IP2b/GqNvuOMd61QmaOxZRyecdKs6AynTFHcOw/WoIsKAp+9jrUmhHFkR28xqpEs83vLlLT4w3sTA5kuIwDjpnHX86xfE0qweK9QVv4rjA/IV0N4qn4u6juI4liIz24WsXxRGsnivUgyjiYY/IUmIptGVBA/KoZSrRkFcH0qy+6Jc9V/WoXVZEJ71jbUaKwyNPm4yMGs7Tc/Zlycnca1FAFjMrehrM004tAvUBjTdySW0OzV9yjnaam1Z8qD6jmorPP9tqOAGGMVf1yCMMqOdoK800AyBt2mRD/Zqk8biQDBA96txBY7CMISVA4JoN010QhAAXjOKTAiKcFcZqBoyDzVud1gUM5A7D3qo91Gs6oR1Iz7UhspawVNnZ88guMflWbGOhrV8RyQ+ZBBF/ACW/GsqPgVSESyYIFVjjzAferDHj3qufvD1zQM9S8KEnSIseproOgzXPeElZtIUAdCa6MqwGOfyqgPMvHIx4kGR1gUj9awYxk10Pj8bfEkPHW3HP41z8Q44PNIQsmabAcXCn3pZKZC2LhMHHNIpHsukE/wBnW4P9wVqx+grJ0bnSrdsHlBzWtFkYqkDLI6dKV+VNNU8GlcHHvQScU+9dbnVU+U9TXb2bf6NHjptFcTL5v9uT7T8o+8DXc2Y/0SE+qDNMZYA6c0HrRnn2oHJoAXPtmlHTHSjGKO9AC+2KXgcUdKb14A5oAUt6dO9ITzijP4UDBH9aAFA9qY3Xg1Jzt9qiIA5oAF++DUWoEGCXJ48tv5GpV689BUF82IJTjI8tj+hoA+fIRthf2Jx+de6eG2zodjkc+SvNeFRMfJbPck/rXufhnP8AYNju6mJaQHnfjgh/iBf/AO5H/wCg1gw3ImmeLaVKevetrxu4PxGvhjnYgP8A3yKzFVNwYYz3poll3TrQ3+owWe7YZW27vSoJ4tk11BnmCQoffBqSKWSG4jltyVkQ5Uj1qF2eQzO3LucsffPNNgZ2v6fbWNxbtBNva4hEsgz91vSqaDNop+tGqRlbrfnIZRxTEk/0YDNSUj2+zP8AoNv/ANcl/kKlyT9Khscmxt/+ua/yFTk44zTGNyRmm7ieadjjrRsNAhhc96Zkj8alKcdKaVJ4xQBGDk0jEg8VLsIHSmmPn2pARZPWk3ADkVLspNg6UARkkEUwyc4qYp3pnl+tADM+tN3EEU5kwvGaPK560AKuSeelTLTRHgDFSqmKAHLyKlUk456U1EOOtTKhApgTRn5eKsI2ahReOOamReMUhEwNSKajVMcYqRVOaYEgOFyay77Vo2t5IofvHgt7VeuX8u3kb0FckxJzSYmju/hxIHvb4DHEa/zNeiDpXk/gPUrfTtVuBdSCOOWIKGPTIOea9Mg1SxuF3RXcLA/7YoQ00XaKrm9th1uIh/wMU37faj/l6h/77FO5XMi1RVU6haj/AJeof+/gpv8Aadn/AM/cH/fwUXQuZFykqp/aVnj/AI+4P+/gqGTWtOi5a8iPsrZ/lRdMXMjC19wb+QemB+lYLmr99c/aruWYfdZsge1UHHzUxDV+btVuIYFQRrzVhAcVQEoIp+7FRA07HFIY/fzS7+ajz60YyaYEu+gv71Hjt6UmOKAEdxzzVOeTIIzViToaoT8AmkBn3UmM81lyTAGrF2/Jx1rMkJznvUsCZpAT1q7aMMgZrHDHfya0bQkkADigDpbNhxitaPpWLZD5Aa14zxVICwpA6089OKhHSng0xDs03I3YoFMY80DHscjigHt3pmTQDzQBPnpS5qLNOBpiHg0u6os80uaAH55ozk0zNBNFxnm/xlI/sPT15/15/lXjoO22b6V6/wDGX/kD6bz1mOPfivIJeLdh6Cs2Mbe6zf6qbdb25kmFunlxBjkIvoK9d+D0kVtpeszykJFGVdifQLmvEoj+9ArufD2r3Fpo13pcKjbeMGkfuFHYUIlnR24uPFPimS5bJNzJhAf4Ix/9avXreCO2to7eEbY41CivPvBV1o+mrLdXl7HHNjYik9BXVnxZoQPOpQ8nHWrQrm4vFSA+hrBHivQx/wAxKGnDxVooxnUIgT707hc3SeOKax4rF/4SzRM7f7Riz9elI3inRccahEx9jSuFzzn4xEHXdFJYf6l+n+9XKw3dxYGC5tWdZgfkZeoNbfxR1Kx1bWtNaymWVIYiGI9Saz9D1G20/U7O6uU3xQtuK4zU3HcxNQluby8kubx3aeQ5dn6mqRj4IJwK2/Empxavrt1ewIY4pD8qkdqw5Iy8inOF9qYmfSXhgj/hGNNHTFuorYDDHWuM0HxJpMGgWMT3yCRIQpBOOlaB8W6Kg+bUIvzqkSjpNw4oyK5hvG2gxkhtSiGKiHj3w4eTqkQNFx3Or471HLIIkLHoBmufXxnoMoDLqcJX1zUf/CceHDnfqUWPSk2NM53XdYdbmRlPOTgHtXJT3V3cxtLc3GyIdTnAqzqviPTJri4uEmVwzEqo71xt/fNqMoRp/LiJ6A8D61lJmiSJr/W2JMOmoxA4aUDJ/Co9O0O71AmS5cxRdSzn5jWjZ6lpWhWDxwyGd3OTx1rHvfEUl4Spby0z91f61lJtlKxvNd6XoyiKziE046ufX61mXF9cXhLyykjP3R0FRaJb6bqMx/tHUVsoB1Pc13dhafD62i2jVUbP3i75z+lChfcOc8/a4YH5OWNVXjnkcltxFenapdeBbTSpGsryDzwPlC8k1xJ1fTiT+9Ge3FPksLmMF42Xg5qEnbxU9zeRPIxVuCaotMC/BFCiyrlgHJwOtKYnI5qOKREO4tzT5L5T9aLMCRbcDlzxUysifdFUvtIcj5qv2rWK8zTZx2FLlYm7D1LuMqpIHXFI0Up6A1oJqdigADKFPYClGqWWSfMUelHKTzGZIksX3gRmoi3bFatxdQ3gRYju9gKiXTW6uce1Q2kaIrwOcgBR+Nd/p2sWthpEcOSZCMlVHSuPS2SPG38anXg96XNbYTjc2ZNTlnlLRjbnitWwVxGGkJ3Vh2ERkkXjOOtdLCAiYxisZVG9ylFI1rD/AFZyKthfqKq6cPkOT1q6TzinEoaw4GKjJOcZqUjsTTCuDVCZVmXPFVZLYOmD0rRdMnmosDbimIyprZdm0AADpWdNBtzgc1vvECenNVpLYseBUse5gHgfpUBtINxdokLHuRWtPZlSTiqLIQ2DU8zQrFcJEgISGMZ6kKM1zuuwp5ilUCk9wOtdKy89a57XRtkSqjJg0ZMdpG3LD8KvRWyIowi5FMT5sZ4NTg4GBWzZBZjAJBwBU6BQCCOTVVZMdTzUyGSQgKhOeOKhjQ+X7gxz9Kt6Ulwk+Qp8o98Vp6ZoYdle4Bx12d/xrXuIljCqgAC9B6VJRWjXB6dauRBLdC78VAZUiTL4ArMvL4ynC/d6UgWg6/vDcN1O3tWYwDH0qRskdeKjyBSdwvcjaPmmEjI7GpcgnHemkLkjr70risUrs4iJzXPMgMrGujvIt0fyjJFYN5E0ah+etbQFIpyHDDbio8k9TSt0JNNHNbmRIvTk015Qp2jmgnimquW560aAKCwOQOtSx7i4yOKVV7VOq+WMn0pAMdjnAFOUMIDnimrlpKW4bEeBT0A+qEmweGNTCVs5yT+NUgverCDaOuTX5420evZE+5X4ZFP1GaDbWr/etYW+qCmKamBzwKFWqR2k/vFyopTaDo1yf32nxE/7BK/yNUZ/BOkSqRA9xbnthtw/Wt5T1pc1vDMcTBWUiHTi+hxs3gKdVZrTUY5G7LKm39RWHc+GvEFny2nrMv8AegcN+nWvT88U3cVPDEGuqnnVaOkkmQ6K6Hjsn2mCTbNbtE3pINuKA8oGBGD+Net3UMNypS5hSZPR1zXNX3hSwkUtaM1s5PY7l/I16FHOactJqxEqVtjiDJN0Ma80peXAHlgD61cvLCexlKTLnHRh0NVjz7V69OpGpFSi9DJqxHvk7IMe5pN8uP8AVrUpB/Ckbj3rVWExm6YfwLilVpemwCn9hS/ezSYIjJlH8K49KQvNxlFp5xxSg889qobQwtIf4Vx6UhaTONi0/bnPvTsdOeKQiMPMP4Fpd8n9xeadjpg0YYc0AM3S4+4OaA0w52JTxnnNBOfpTTBjPMlH8C0b5SchFBqQZ6daUAdDQFiLdJjOxc04PcD+BRUw+Udc5pTzS1Ar75j/AALSgzf3VFS44peMZpoRCDOOdi4PvSAz8/IvtU2flxilALLmmFiHdL/cWkYyqOUXBqfnbmo5WyACcUAR5prtwT1IpOpNDD5MdsHNAFb7eATkDilW+R1PTNZzY+bt6UkCDkn1oEavno3zcU9Z0x05qooGMcCpOKBlgyp6UnmITmoffFLxSsBLvTOTz6Ub0+pqJgMUAYA/WmA9pU6AUEDqehqJgM+tWFTfjPHFNDbIwvIxUmATgCpBHt5wTUqpuAJFNEjrePAzjrWtbpgA45FVreIADFaEaAJnBzVIDY0Bysl6mOGgINQbT5x2gYBqfQVzPc5P/LE1CZQ7Hy+g6mtESy3EwZTkYIo0k4ifHAMp4piTDywNvNN0ZsrNk8+aatCZw2qfL8W7/jr5J/Rax/FjY8XajgD/AFi/+gitnWh/xdq590i/ktYXi8hfGeoqTwGQ/wDjoqZCI8lExKM56GoJ4gVJX8Ku53xbXGDgcGqjRNEGKnIz0rLqMigUPZTq33gGyD34rG0tf9F6chjW5aqJba4PQhW479KxdLYm0Ix0Y07iJ9OG7xHbjbx1NaHiaPa4JHDLxmqmmoD4kiGOSpAFafihP3sY/wBjFAjGjuIv7LSISAuBgjvWeLmSHIBx6GoEtpPtJHRQasTx78qpw3cGkyrFeW4kmkQyNu2miV/9LLtzyDVV2aM4PBpwYyIXY0ITJNXmjuL4yp0IGDVeM9KbN1GPzp8Y4waYh78rVR+v41bbhaqvjPIoGbdrf3cEIWG4kjX0VsVOurX4H/H7Pj/frIRpAgwwp4aQdHH40tQDU7ma7uleeVpWC4DMc8UyPIBqKYkyDcc+9SxnpnpTQA549qiiP+kJ9alfnJxxVdSfNUjgg0MR1cOtahDGscdzIqqMAA8CrH/CRaqANt4+a58PN6rTg8+3Hy0rsDpE8S6tz/pj57ZFSf8ACTauR/x+H8q5pZLgf3eaeZbgAYVaExnVW1xNLexySEl5IyW9zSxeKNVjhWNLgALwPlqpYPK1zYtj5vKIbHTpWMJbkSSDauNxx+dUwOrHi3WOM3S8f7ApR4t1foLgYH+xXKCW6AwUBp4nucfcFTzMdjqh4t1XH+sQ477Kl/4S7VBj54/++K5FZrrP+qB/Gn+fdf8APIUXE0dYPFupg43IT/uUo8X6n6x/98VyRuLpcfuh+fSnLPcjkwfrRzBY6z/hL9RA58o+2ylXxhqm37kH5VyZuLgYxB+tJ9pucf6jr3zS5h2OvHjXUwMbIMf7tNPjTUMD93CT3+XpXI/aLnp5BNNNzcdDbEY9TTuwsdcvjXUNp/dQH3xUN9431BrKeMQwDdGV3Y6ZFcqbqcZHkEVFNPPJE6+QTkGi7FYwkPG2vRNM8aXlhZW0AhiZYlAXNeabijnjkGtqO7JRTsJ4p3HYfqepSav4qudQmVUklOCo9hgfyqO2tmjvnlLko/RaqB/+JqrMpG7tV21uvOvzblMbOQfWriyWa9rOtldxTkZ2Nux61Wkk82a6mIA81y+B2yelOlGYyc5x+tU45y9s7kbecYNNiM7VFzKre2KpJnAUetaGpHdGpHY1Stk3kZ7GsxnqVr4lkFpChgT5EC9fapR4hk6mEfnXIR3hVAPJfIFS/wBoH+KF6V2M6oeIyDn7PkemaePE2G4t8j3auR/tEkN+5fP0pDqPB/dP9MUuZhY69fE2Tn7Pj23Uh8TgHH2YnPfdXI/2kD/yxkH4Un2/OcxP+VPmY7HXHxOoP/HsSP8AepreKExxat/30K5MXoPVGH4VKtwjrRzAdL/wlUQHNq+PTcKa/i+AE/6JJ7fMK5d5echGP0qpcXaRAeYrDPtRdhY7qz1830MssVm+yM/OWYACh9eI6W6k/wDXUVh+G5xdeE9edQV8vofwrCyXVWJJ7bqmc2jqw+GVZNt2O3/tttvNqMn0lFPGuEDm0/8AIgrhQDkgggegr0XRdG0+TwurzaRIsjrlg7Hc/uPSlGbka1sHGkrtkKaxM5Cx2JZmPAEg5/SqUnjS0guZLea0mSaNtrKccGq0OjLpfi7SJLeVmtriXKqx5XHY1ka/pvneJdSlLlczdK0TuctamoW5XudH/wAJ1YJkG2mPpjFSr4707A3QTk+2K4saNuAzMevYVMNGGT++OPpRcxsztl8c6d2tpwKlXx5p2P8Aj3m+vFcMukkE4lOKcmksP+WvWi4ju/8AhPNPH/LtP+lO/wCE904EA28+O5GK4X+ynB5moGkyc/vePTFHMFjt7rxtp9xbPEkMoJ6MayBr1o7YXdn6Vgf2VJgfvv0oXSZFPEowfahsDf8A7YgxwSDT4tZjXgSMD9awRpb5x5vH0oGmyjjzam4nE6RtcQ8eax/E0xtajx/rGrBGnzEY8zpStp8jqAJOTSuS4m2dXj8kusjAA9c9ajOsxZIEjelY76fMEVBKMCmf2ZKM/vOtAchtf2vH90yPn61pad4p0+ytvKkWV5Cck+lcl/Zso/5a0h0uXtKD71SYKJ3y+OdNAxsmOKT/AITjTc5MctcD/Zcw480flSNpU3/PWq5irHoS+O9KBwUm/Kpj8QNIQf6qcj6V5sdLmI/1n6U06XN2lH40uZjPSl+ImjEcw3IH+6KQ/EXRs/6u44/2a81bS5sf6wYqP+yJWPMgxRzMD0w/EfRweYbj8hR/wsvQ16x3Wf8AdrzH+xZT1mx6cU06JIcgzc+tPmCx6efidoSk5juuP9mtC28WLfW6XFrpd68D/dfbwa8QvLYWCMZJN6j0Fe6+DSG8EaRjn9wG/U00xEcmuz5x/ZF3j/PtWfda9MSf+JVcgeldVIMe1ZF11OOKtjOUuNZkbJ+wTqe2apPqbhCz2cyqOS2OK6GWMlulZ+pRkafN2+Q5qWMyP7etiBhGxVuDxLaxkbo3x6iuQXBz6561MuMDmpuI76HxrYRYHkTHHpV9PH2mqozBP+GK83GNvvQDkZp8zFY9MHxC0wHH2a5P4Cl/4WFpbLkW9zn0wK8yzkdaUHIAo5mFj0w/EHTVAP2e4OfpTT8QdO721x9MCvNsn6U0FgD60XCx6YfH+mgf8e8/6Uf8LC0wH/j3uPyFeaA9jS84zS5gPTR8QtLx/qLjA65Apf8AhYelYP7i4z6YFeXk7j1pu0N0OKrmCx6efiLpeP8AU3B/AUD4jaURzBcA+mBXl7fL34phPXpS5mFj1M/EfSgARBckfQUn/Cx9JJz5Fxj6CvK92D14pD35o5mFje+I3iu08QwWcNrDJGICWLP3Jrz6fH2dvpV7UQWKntWfMM2r59KBlC3G6ZR711ttJHYWElw+N5GEBrB0Wy+0XK56Hr9Kva/NmZYI+ETr7mgDGlmlllZ2kYljk8035iTljn60d8U4YNAWAM6jh2x9TQZJic+Y350vtipAq4NAWRDl853tn607fL18xs+uTSkYNPVQRQFiaAkKoJzzWpM7Lbq2OBWYqgbcVosxMSq3QU0IiV94DHionnKTJGFJ3d6m4zgYpnCn7wDHpVNCM66ErzMfMbHpmoiH6GRvbk1YuDhzUBIPFQ2MjYNuzk5NJg96k78UYzSuOxAW56nj3oBYjAqXYo60cUXAiO4dGpuXHRqlKijYMUxkJLHqxpCD61MY80wjGQaAGZbGM8GmsOegp+2gLzijQLDOPQD8KAD61KI/U8Umw0DsRnrRUm09xSbPagBvNBBzUmzjmnqmaAK+CKO9SuhDUzafSgLCDJOM1taRp8Nw/wC/UkYzisqGPdKBjit/Sm/0kj2rObsikjZhghgXbHGq/hSyjIz0NPJI4/OopT6VyXbZqtiIrz0p61Huz0P1qeBGkcAdz0pu4ja0qHbGHI61tR4/OqVrEscSqB061eUAdsmstLlWNTTuc+tXj14FZFtfQWwZXcZ+tT/2xbEffH51pFEsvMAc9qb/AEqmupW74PmAH609b2ArnzF596rYCYrz7VGQOajN5Fj5XU/jTftUeTlhRuJj9pPakKALSidCpO4YqGSZCcbhjNAEFwuflrOnt8jgc1qMVI4qvJjnPNQ0UYUsRU9KwtZADrnBNdfLErAmuP8AEieXMoXPSiO4MyvMAXA61YgjV4y7yfMP4ahtLSa6mWOJCxPf0rq7Dw1HGEacmRj19K6EjFmVp2mTXrfKMKT94+ldlp+l29mqBV3OP4jU0cENpHkBUUVRutZVSUgwf9s1EtC0jZaVIjlyB71kX+oxBv3bFmBrJkvJXOXck+uarvKC2TWb1KSJp7iSR+WOKiJzwKi8zLcHNNMuBxnmgGiYt2ppx2HNMZxjIwKDJnApAh5OR70wjPJP40AgrxSFs4FOwDXuY1Ow9SKpatABpbMMfLzV8WaSvvbrS3sAksZkAyNhxWkGQ0cZawSXbbU6e9aw8PsCuZMnviodJfyztx3rqVAZVbsauU2nYFFGCNCK/dIYeppn9hyZO2RR+FdOI1xjGRSeXHnHT2qFMfKjlf7DmzxOOvpQ2jT5G2UN65rppFXnGP8AGoQOenFPmCyMFNJmUgmRfpUv9ku5CsPxroUjQ8ED1p4Ee4YFLmYrHtPXFSKTwKjAweetSrwRXwjPTJVOKlQE1FwelSg5FZsQ8e1Ln2pFxjg0YJ4qQAk9RSM3TIpSCBTTzQAxzlaqy8D3q0w4qtKMg561pETOZ16TZbklQQK5P7SueUPNdbryZt2yK5EjJwK+nyv+Gc1Ql85CMbTmkEqk9Dx2xTVXuOtOCnOcV6pkKJUHJX8KUOpz8p/Km7Q1OUHqKoBd6d0zR5ijA2E0hz9acRwMmldiEMidkP5UnmKeqGjB5pQpCgHrTsOwySRR0Q1Cb1dxBQ5qyR8pz0qnLa7vmWQp64FCExwvVyflNAu1GeD+VVHt5gvEp+uKgFteZ/1xP4VVhamj9tUHhWzS/bOvynI9qzTbXm7/AI+CPYAVVujeQHctwc+m0VWgjbF9jJKn8qPtwPY8e1crJfakmMEnnrtFM/tLVepUk+yCgV2dWb7PY/lQL0jsa5I6jqik4Lf98igalqoUbl+b12CgLnWPf4XO09KyF8SbpSgRsg46VmXV7qqojxuHz1GwZFVreOSOQySgjuaTQ0zvLeczQI5yNwzTpMHpVfTzusYWB4I4qywBHPU0FEQQZpjL8h/WpzgHkVG2cH6UCZiOACR70W4+8D1zRJne2cdaLY8tg9TQhFxTlKeMHpyaav3fpTxkY96LjFHB55pRwaFHJIpx6E+vamFxO/TilLgnHSjJGOOKTAz0oENYEn6VZRlwuWAFQHjtUkOnw3ylZS3HTBxTWoy6FDEcjAqWGPcw9KqiyS0dYoy+AO5q9AD94fSnawi7BHxwOSa0Au1RUNugIVehq0Yjj3qrCLmiDN5JnGDE2afLGnllY0CUmiKftrDjmNqp+e4lKM2RVoTNC3jUgkc4FU9HBBnGMEympIZwq4XJJo03h5ueS+TVoRw+vkx/FiT3gjP8q5/xgAPGWoEjtGf/AB0Vu+JTj4pIf+nWPNY/jZdnjG7JGVMcbf8AjtTIViVFWWAHrwOagmVkDA/Mo71bWJTCrxnBKg0yRipZZABnuOlZsqxStY/NiuPL/unn1rE0hP3Eg4yHIrobOJlacx9Oc1z+nxkxTyqcESkYoJLmlgr4utlOCNpxWp4oXbdRE8BlPH41Q0shvENo/AbJFavjFQJbcE9U/rTsDOQlyHyBUTSB8eZwR3FWpF46VVmiyOKkRVuYt4BY/QiqoBjbB+7VnzPLBWTlarzBQ4KklT2oGiKbAxjpT4m4pswBxhSPrQgxwKaAlb7tVXHPNW2+7VaTtQIsxwAqDv7U826n+Oo0Ee0fepwCZ5LUhoimj8qTG7NSx4AqKUDzBgk/WpY+AaYMVzxVcfMwGcc1PKarcbh6UxGgLUDrL+tSC04z5v61GkcRjzvb6mpFSP8A56GkMkWzLDAlx+NKbNwCPO/GhViC/wCtNKUiK/65hSsB0OmK8b2absny+vrxWG1pIzuRLj5z39629MQGSx2SEhUPPrWNIkQnmzOVO9uPxqmAhs5uB52PxpyWM+cCf8zTRHD1+0n8aeqw44uiKkdyT7BOOPPH50v9n3OM/aMA+9NCxZ5uyDRsi5/0xqLAObT5zgrcYHcHvQLK4H/Lfn605UiZcm8Ylf0pj+UkbMLosew9aVgQojnUcPvx1phuscBjkdahguFMkahjuZsYpupj7POoiU/MMsRRYpIuBlYgieT6YqMsmSPtUgOemKt2JM9spXaccYxzVwWxJ5Vc/SmDRjEoM5vXH4VGxOOLpvyreNtwMohP0qF7Uj+FPyoJOKvAqXDBTuA71NaSb0+aTBFSa9D5GpFcAbkDcUzSwMMdoPPemBLtBuYW8zcc4rSgCiQbfve3Wop4wFRgqrhuoFMtLcx6g0/mfK/GKqIi/OGVcjgVWmx9lkxg8dRV6dgi8nIPas5l220y9jk1TEUbpt8GB6VBZcOQeDVqzj89iGGQvFQhlF3Lt+6vArMdje3S7QReIuRwD2p3mT4Aa7jOParltbQyWsb+UuWUEn1qQ2URGfKXFIEZ4kmH/L1Hz14o3yYz9oTPuKvGyj4/dLTTaoAQIxQO5R3SsQTcx5PbFPC3BPEqH04qV7RFBYRAEDGaW1DINkg+aiwERE3O9QQO4qJuBuI2mtPYMH3qlcx5PBpWAfA4aEE8H0qlqVs07JjPFWoD8uO9PkGSeelAzT8OW3keC9fTP3sc+mRXR6NpenWmj2pVUuVgXzS2M75SOlY2iKT4S18Z6qoz+FdRoVlax6Vbw2Epmbygzyv9yMkcmnY3ptqB5shlk1JTMgV2uBuXHT5+leg6h4/XT9Vaz/sqWQRlUDjoa4m6t4bfWglvefax56ky4xltwzXtDW8ZcEwx5wM/KOeKiC3sdmKlHljzI8xeV5/ibaliwTzlZI+y5Gara3g+I9RH/TY5H4CrsuW+LgGeBcKAMdOBVbXIWXxLqLdQZv6Cqj1ObE7Qt2KijA4FS4GOBSKhJ49KEd2420M4x4H5U8AYJpRngBaeN/8AdAFIZHg59BS4I5qUFh2FOJPTaKaEQ4I7daVOM1MCx/hpNxz9ymMjxlqUqcjFSjP92nAt/dpCIuopBgsBzU2Dk4H4VWmuHiYAoOaAHsCKTn0pvntgHZxS+ec/c4osK48jHNIenpTWmJPKGkMzHonFNAPCnrTSMHpTTKw/ho845yUosFxzdT3po/Kk884yqGjziR92nYYh5ODSY5pHnwpJSqyXkhOQM1LEWzwSKY2NuKijnea4EZGKuPb7QSaBpnK+IeLZ+M8d69t8EgDwLo3977MP514v4lGLST6CvZfBjY8EaOPS2H9a0iBrXL4FZNw25sVeuWJPWqDp82TViKrxjk1lasMaZc9z5ZxWxICFNY+rE/2dcH/YNJjPPlXpxj2qwAMDIphHPFSr6VIhQp6Clx0AFPA45pMkGkAm3kilCjOPzpcg80oJPFADcZ6CjbnoKdz0HFHQelA0N24pCOKeF79qGUcc0WCxEw5HWgrkVLjAx1pvbgZoAhK561GU71P3xSFR+FIRXIG7pSbOKnZcjpmkx60yjG1AkMB2xWbNzEV7mtnUVDSgFexrFY5cAnjNMlm1pnl2GnS3Mh2tt+UVSgs/t5eVyRzmtfRYtJ1O2ms9Qd0ckGNgcAVMujvpzuofzI/4XHcUAYzaNGCOaibTI0POa3Wj9KjMOTyOKBmGLFC2MNUq2Ce+K1TDjtR5XGccUXEZbadE3TOaUWES8ZNaXl80wx4pAZFxGIZEVTx71aJVoxnPvUF8u25XnNWI+RirQMjIAIC9KhaDdMrk8DtU0hwcDtUDySCZEVeD1NUxEjWsb5J61G2nL1DVejj+XJp/l8e9ZjMr7ARyG6U37A5/iFayxEnil8vPFLQZi/YXPegae+OtbLRAY571etNLkumGBtj7u3ApN2CxzK2LH8a2dK8L3Et1FLOoSHOSD1NdHBp9laHEQ86Trvbt9BWjGD5iH0NZuZagNt/AmhXTyPOZ45GOQEbAqU/C/QpCf312Po9bNrJ/pBbAIFbkL7gMsAT0HenzFJHFn4U6CQP9KvAP94c10Vj8I/Ccduhkt5pmI5LSGtWY7YucZJxXS2agWUfP8IqFJsqxyLfC7wgQqrpXTqfMbJ/Wue1j4XeGhefuftNuuPuRvx+teqRAF+a5/VwDfNtOR61XMxpHn4+GXhxsZnvRj0cVJ/wrLwz/AM977/vsV1uzByaCp5pc4rHKL8MvC/e5vuv9/wD+tSj4Y+Flx/pV8eez/wD1q6jb1xSbfWlzMVkc5/wrDwiT81zff9/P/rUi/C3wju5ub0j08z/61dJ360gDE5AoU2Fjn0+GPhCJiRPeZPrJ/wDWqa3+H3hKzfzI7i7z7t/9atl+T70xwcAkUpSuOxRfwn4dEZKz3J9Of/rVSg8L6G8oW4ludg6YPJrVfIU81Ei4kBzk1Ogxo8H+FBkma656cnj9Kli8L+FoXEiSXJYdyT/hU/l9DmnFRnrVXFYR9M8PwxOUnn3AZGQa4nV47gqxtbhlXP5iuuu4/wBwx7CuclXc5HUDtWbSKTOOkkvwSWlOaj8y8IBMprcv7Ta+5elZ5iPTHFITKhlu8/6w59c0ede5yJWx061dSB3Hyr09qsJpsjDnApisZX2m9XgSkfjSi41Ej5blx6VuppMe3c5yRU0djFu4TNArGGg1R12i5l5969A8PeDzcWMdxd6hOzuM7V6Cs63siRkJwK9C0DaNNRSBgcCnHViaMx/ClmqAC4n9zmsm/wDDqQypsvXCd8jmu4lCmsPVlXeoIrTlQJnPvpdpHGM3Ejkdz3rMvvDWmam6NNLKMehran2hcYFQKnQ4qbJDM+18LaXp8Ra3nm3E9Cc1bCKibQxPpkVaKj0qMgY9xTuFigdNjvpdl1cyhB2WrI8J6PtBFzce4zUyYLYq0CuOTSYFB/CmkHb/AKTcYPvTD4R0nBBurnmtIuuetRmYAHLfjRZDuZMnhGwjGUvZj7ECqk3heCNCyXTnHqoroDIDgKwNRzSKsZHek7AcXJYhHIDcA1GbQf3q0JyDM31qDgjFRYCBbT/b4py2yjJLc1Y2gjrSnAINAXK/luB1FRyRyujJkYNWytMYk59qWqCxiWuhyQsS0qtznp0rTEZVAoOcVN370ZBB4waq4DAjA5LCkKsx5YU8jnI5NAGeMdKTAjETu6qv3jW2PCl4qoz3EWWGcYNZ0TKJ03cDIr0FFzCuDn5aSZJx/wDwjM6P806N9Aa5/VpZ9Mu/IMQbvuzXpjR5GcYrh/Ett5l+WAHAArUZ69h81MrEcHNIBnipQvzDNfBNnoirUo4GKYBzUg4qGIcven0g6UvSpAB70089KcKMcdaAIWJxUMgGPerBFV5s+lXETOb1sEW7+lcd0PWu010D7M/8q4vbg/Svpsp+BnLV0Y4EmndTwaQdOKMEY5xXsWM0OAwcHoaUd6Xr170AYOM0xsAcr6+9AxjkUo4B44o28UWJGkDijOPpQQemKUDPQimAE5HFIcdCKcRkcUBcknOKLARlMinLGPSpApI44p2ccYpAQ+WM5K1malCN6YXg1tFcjFQTQrIvIziqBnNmIntS+T02itg2KAc5/A00WK+jY+tO4rGV5J6kUnk1rCxXkHdjHXNIbCMg/ex9aLhYyfJAOR0FVr6E/YZWAxgVpDRY45vNSa4/3WfIqZtPjlUo27aeoouIm0P5tFtj22d6vNjb1qOxgS3tVhjGEXgD0qbaeRQUN2ljnNNYcEVKVOPamOMDJHagTMCX/WPx3pluDkgdOwqW4RvNb0zUUH32HahCL6KQlSAYpkfAqQcUWAVRg/Wl46HmkPXilA470wE6DinYBpR79qQ5zkUDGt6VpaTGGLjnIwazm+761raAAzzZPOAaqKAddLi5XPUjAq3bpsQZHWob5c3cOO5INXYY+fpVMRZhAByPwq6vzLnHJqCGMiMN71MpI70IRc0lSl8xA/5ZtWQD5h57+la1hkXYYddpFULaBWX5Ths8irsItW1vsAbBwal06NfMuSeDvH8qsABYwB19KowEi5mA4O7pVoTOD8WgJ8UIMd7SM/qazvG6hvFs3fMMf8q0PGYKfEmyY/xWaEfmao+NgD4pc+sCf1qZCQ+KB0t1MZz8oyppzlHDqww2B8pqS3aSO2Td8wKDkdqJFSZZHIGQOo61BZRsEaGe57oV/Kue08czj/pqa6jTMrcyK3cEVzmnn97eEjlZiKCGW9OTPiaxOPl3Gtjxmoa5tjtHCf1qjZS2Uep28kk5E6t8sQHWr3ipxO0DhSAFIpgcxJGCtU5lwOlaLL3HTFVZYRISOhqGFjFuR2xQseV5GamuEKk8cUIMgYpDKt4hVUJNRR4IqzqOfLT61Xixj3qkIe2cYFVnznmrTZA/rVV+tAGnEjeShwOlSBcjkClg/wBQh9qcSM0guUL1cSrx1FJEe1SXwPmJ9KZGB1JpgJIOuelQAYkUe9WJKgAxKpPrQFjdWIqPugn0qQREgfKOevFTpgKPXFOHWgCJYmB+4v5U7yCSPkX8qsrwRmrAUHpTQBZqFltgV4wenaoDbFnc7FPzHt71ciDC5gxjbzmpY1BLf7xpsCiLbnlE59qUWxzxCpHuK0CozyOakUKR0FTYZQW1OP8AVKD9KX7L/wBMkz7CtHjGR3o7dBTsKxmNAyjCxpk8dKgNndEkHyMD/ZrXMbF1ULyTVr+z5VPKGiwI5EaW1rdi4kdSXb5Qo6VNOjNeImzdkc8Vqa3ZTxfYSRgPcBc10ujaTDHdOLlVdsjH0osUmef2v2m01J08l/IPcjit+KUPxxn0rvpNAsgS4UkHtXG65bx2mqwJFEFDqScUWBshKg8d6idM9amGMc01uBkCmBxHikbdTjweDF/Wq+lYKnjnNWfE5STU12tkrGAw9DmqmlECTb6mkxM2LkERA+4qpa3DG/MJj+UDIb1q3dukcQBPJPAqKJkMqgY39xVIll50EpAY1QkJMM2cgDIFXJ84Xae/aqsnOV9eDTYkFlALXRWmcZd8nP8AKseAhnfjrW9rziC3itkHRcmucjJVWPqKzKPQbCM/2db9MbB/KpymBinacinS7U8cxL/KrBjxjgUxlLZn6Um0jirZjx9BSGNcj1pWAz5ozs4PFVxHtuSfatO4QCM1WWMNMc9cUwY3bwP1qtNGfMPHGOK1PK46VVkiLSlQTjFSBmwxkDHBqR1I61JEmMjinMvpRYDc0OLf4T18YPKqOPpWxFq+g6b4ehhiF7NKsIH2dUOC+O9J4DQNBqCsNyll4/CuyWNBgBV4/wBkU7NmsaiSs0eKQo8V3FLJbTRp5wcjyzx82a9f/wCEr0J5AftjbSBn903H6VceCJ1w6KwznBUUv2aEqAYY8dvkFKMGjaviY1Uk1sebRzQ3nxWiurfe1vLcgq5UgHA/+tU+rweZ4k1IdhLXoQtYBIriJAynIIUDFcNdZbxJqYx0kFUoWMatZTsktimtiQuc9qzlHzEehro/LHl8+lYggAJ5J570mjEF4p+cinGLb0FKEB5OamwxmcNzTs889akEY25/SkaMKOOKQhM45oBpOpoFNXGO3YxQG5zSAZwM04CmwE3Y5qGVPMHP4VNtyeaawOcUgsU2t5AflkI9qZ9mmx/rGP41eVSW+tTGLFOwrGSbeYj/AFjfnVa6juI1wkjZ7c1u+SCPcVUvohsBHqKaEYxt9TA+WZTz6mk+y6oR/wAfC5Pua2UaMj744pGaL++PwqgMgWGqc5vF+gYigWGp7v8Aj6GP941rboyOGFNeSNFJZwAOpNFhmdJpt4ZMrdjb3BJqZLeWBTudSB3FTpNDLykqtj0NJIUwQHBNJpBYSxHmX6jA5ramgPl1l6VGf7RXPaujmTKYpWEcL4ohZbCViOABXrfhEg+C9Ix/z7LXmHi+P/iWzYOCAOv1r07wjgeDtKXP/LstVEDSddxOTUTR561ZC5NMkwoJNXYDKnT5jnpWTqyD+y7nj+A1tTkdKzNVAGk3Pp5ZpMDzkLzyPwqZUHXBpqr+tW4Uz16VAERXBpdvORx/WrXlL3HFIURaYyrsOaXaFz3qYqc5FIV75oERFc4waXbzjNP74A4qWNM544oGVsZOKUofSrXligpn2oAqAeo5pSB06VZMakYpvlrnpmiwFVhwOKawBq0UHTFN8vmkBW24HNNwcZHPpVlox9ad5a7emKYGBqZCyjPXbXNTuQ5wea6PXFAnGGAO2uclA5YUAT2ZngnXzFIB9a6rTtSIGwncncGuTNzJLsL8EcDjtXRaFErwSbu5yKBG/wDZUukLW5G7+7mqEkbI21wVOemKkAkt3V4yVI9K2La7sdSURXyBJhwsg4oC5z7jPakxggVtajos1moljBlg/vrzWSUB5pDImXAyaYy4H1qcpk89MUwjcaLiMPUSPtS/SlXdwOTmjU+bpAeMClEmNrD86YMQrzjNIHRWCMcMelLu3MTTTCpnRznIq7iNONcoAKUpzSxp+7GM1rafp0Tusl222PP3R1NZtlIzYbaW4mWOJSzt2FbreE7j7MJI5Yyw+9uOAKvR3VrbF1s7dUPTd61L58kiBSx2ntWUp2NIxM6DSrOwBeUC5l7Z+6v0qtJK8rHd0zwB0FaF1gLt6GobO2FzOIt2O5NYyqN6GnKR2sbNJ1rWisLjbuWByOo4qvNc2WnrhXUv/Kt6w8S6emnxCe8jDYwc9qm47EEFhPIucMuOuang85JcbWJHGcUp8YaOjlBdpgdeKjPjXR48nz1P0FDkx2RLPLJujViwywrvLUFbSMHj5RXmN1430mW4hSElmZgM7a9MtT5trEW5yAacG+opWHzLI8eI+D61y+tGSC9CLuJ2849a6mbJXCttxXHeIfEem6PqghvCxdlyNozWknpoJFf7TOABtY/hSfaLjdjY5H0rOfxzpO47Ukx64pjeONOXBWKRvU1ldjNUS3ZO4Kaj8+4Z/uPn2FZx8a2A+5E+TTv+E005f+WcnrRzMNC+Zrjbny3x64qP7RcDjY1Uz4208rgpJg1G/jXTioHlSUrsNC6Z5w+NrZprXFxj7rVRXxjp2STHJQ3jPTsECJxxwTRdhoXDPcgnMbYPNR+ZcL0U1W/4TLTyo/dvQvjHTznMUgPuKE2MuCW7ZcsrGk8+4C4w2apf8JpYqTiJitIfGmnkEeRJRqGhPNPdCMhg3NZbSPuOQc+1TyeMbBo9ogbJ9e1ZZ8QW28nyzg0rsLIsSq0i/d4qJbcqOEpDrttwFTiga9BjGyldhoTKjKcAfpQRJngHBqD/AISG2B/1Rx61KvijT4SN8BYUaiZdtLeRwS3A960fJAhIjALYrBk8X2P8EMmPTFJD4st1kD+UwHoTVapCuaghvlUt84GOcV33hzP9lReZw23kGvN5PHdgEYGCXd2A6V3/AIavlvtIilUbdwziiF+fUUrG++xI2cDJ9K53UWluPnA2gda2ifLiIzkmub8SalHpVqjyA7GNdEmSii0MkjY7UyWyuk52kD1zWHN4xgYERKwPrVUeLyOSr8H1rJSuM3SZwMc5qJnm568VQHi2ywMo7N7dqb/wl1uxOIiB71m5Ms0R5vXkHvS5mIxlsms9fFlnzmJwR29aP+Evs1/gP1Ao5mGhpGC62gbjmo5LO6Knr09ao/8ACYWpYblYenFDeMLUqcBye/FNNhoMuIbxMoPM/ClWz1Ew7jvVD68Ug8X2ZfkN9cUN4xtJN29XPpgU9Q0KMsUkbkM3TvUfzcY602fXrWVtwXFQLrVvuORx2pXYOxcVXUYPftS4eoV1u2CklecccUf23aEhmyv4VOotB7lgai3EHk5FK2sWRXBXn1xTDqtngHH4Yp3Y9BzSYwc80u4kHP6U1tRsPbP0qWPVdPHXHvxRqLQYDzjNSI3zbad/aWm8crn6ULquljLM6qRx0o1DQQkLNGDj7w616TAgNumDxgV5wut6SLmIHDEsMZFelW7CW1TZhcgVpBESGMq8+lcR4kkCagAfTiu9ePjCda4zxHdadaX6pcIDIV6kVo46XCJ6iuODUg5xioldTgVKpHWvz9noj1wOtSL9KjWpFPrUsB9KKaOuacCKkBelJkGgk4pe3PWgCN+vSoJOlTtUMnSqiBz+tgeQ3HOK4sjGa7bWebduK4xuDivpso/hs5qm4wdKdjBo6NzTgeM+teyYiEdsUY6jGKkGO3WkxwcGqSEN7GlUheMdaMZ9qUDnjrTGIeTikxgcU8Cl25BA4qWmCQ3BZeOtKqYGak28UoXPGKpC2GgZ4HBowA3PNO24JakHXNMVwIz7Um30FOIz05p3QdcUrDIyBjGKTbjBNPx1zQeetADOMHikOFHAp2ciozycmgQxvmphT5fpU23Awv41G3CkGgB8f3RSuCB0p0X3AegpW/Si4xmN2OaGGQRTiMYPej+H60A0c5ccTPz3qO3B8xu9TXXFw4I78VBDkSNimiTRQDbyeKeFwM0yMZ+lTAfiKoYgU5z+lO70YPXpS7c5oATGRwaBgdacB8vvTOTSEDKK1tA2+fOD/dFZPTFauiH97Ke4Aq4gXLyPdeQjaQM5JFX4o8Ad81VugXlhwcc1qRJ09qpgSYwoFG3HtUhXpSleOe1ICfTP+QipHTaeD9Kp7j9o3DAx2q3poxqSHPUEfpVdYwZTwCB1q0xMvxsWAPXNZ8bEajc+5HStK3CDjPas2zYprF6jYI4watEs4Tx0SPiHpbd/sa5/NqreMoS3iWN2Aw1sh/U1N8SIN/jnSQZGXfaj5l7YJqLxtE1zqcCGQqxs05H1NSxIsWzf6Ogddo2jr0pJINwbYdpxwfWnWJD2io3UIBz1NP8ALZSdh4x0NZlFTTsfbGSUYO0/SuatohFdXi/9NTXUWyLNelXBVvaubgTF9fgjBWWqJY+zhC+LNNlHOWwRXReMuJLXjqDmsm2Rf7csOfmVxWt4xGfs2R2J60xHNlOOaqTKQTgYFXCflHOagk+ZenFSO5k3Cfumz+dQxgrirVwCI39KuaVpf2na8gOz+dKwGNqdu6WUU+PlZ8VRhGa6/wAZRxw6LaKihVEuDge1chFTESSA4HpVVx82BVt/udeaqPy1IZtWql4YwOpFXxpFyeQlVtKGHgOfSu+RAVU8dKaQHnGsWc1rJF5q4DDIqnGOK6fx0uG08j0cfyrmE6Y6UCFkHFVx/rAMd6sSfd5qvn94CPWkxo6mBDKFVR8xq6NMuh/BTdGIN7FwCPeu2VVA5AJqkNnILp1wOfLzVlNPuAMbPxrqQidQop+BjgCnYk5HyZY7qJMYP8WfSpIoy8pQckk4qxqis2swlWCYXn3qeyVBqMRx3NKw0QCyn67DT1sLjuvFdQsYPO0VMI1IPyiq5RnLLYzn+CpPsMpAGw10wUE9AKcET0pWFc5Y2s8LCVV+ZOQajOtagG52H/gNdY8SOpUgYIwaz/7CtjwC2BRYZzN1c3mqSWqTbcQyBxhcV0f263tL1POdV38jPFP/ALJgty0i5Jx3rA8SReZNZsOoyKWwHWnxFZH5Vmjz/vVyWtP/AGjqsDW6l/LBDEVnrZnFbGgW+L6TK8BOtJNisQCymxzGaR7OZVPyHj2rrxEgGSBUckKsrAAYIxiqsM8I1UH+1bnzAdxfvUenllvU2KWPpXReOtNi03WLdI2LeZGXJP1rL0O1W71OO3ZtiyHG8dqkB+rJNFdWxdMKxx1zToLfZeC4yeRjFdTrXhyCz0g3H2hneNgFBHXNcrBNKdR8hlxHjIPvVIlmkz+XtJGRmqx2/aHcjhm3fSrRVXZEJxlgMntVe9jEV3PCrBlQ4VgetNoEUNanea68zBCsMKexxWYCRGas3l+9wsMBVQlvkKR1OetVwP3TVAz03TUxpVocf8sV/lVk4I5qCyf/AIlFjgf8sE/lUhkJGelAxzc8AcUmzBximh+QAanU89qYFaaJjETtzULREyjaOwrSYbkNRRp+/OeDiiwiEQORgiokh2zPuH8JrV2A81XnQByR6UrAYccDkk7c805rd+u01pQRgpkd6k8vigZseAkZBfgjGSprsq5fwinlm65HO2up4qkJh0pw6UDFFMQtcJLF/wAVNqhAzlwa7vI4rDt9PV9Wv5MfeYc0MDFeJiCFyMisoIQTxzmu7XTkPUda5JFALZHRiP1qGMrBW4OCaNpPUc1fC46c0u0ZyRRYDP2HPSnbMjpV3aCSTxTsAelFgKIhI6Ic0eVgY21oduBQBkYIpWAoeSCOV5FIY8dVxWgAM9Kq3PD88e9AEJQdBSFAB059cUbwOKN9CBBsXGQOaXH5U3dilLYNMYu0Z9KgmjDMBtyKm3/lSFgT9e1Ait9mXH3P0pDbD/nmKvxYYgYqwIV9KLAY32Zc8JxTZbNXUq0YOexFbnkp6UnlAc4oGYUdjFGu1IgvtiphbIFLbAMj0rX8lT2oMCY6UMDI0+PbqAyOa3SCetU7eEC+4rTK44xzQhHG+MoydLm5wcD+dei+EAD4Q0n/AK9lzXA+NFxpkxPoP516H4SQDwppmBgfZ14qkI1sYGe1VpxkVbfjiqMx5NUwKMnLH26Vm6wMaTdHPSM1qMKzNZX/AIlVzn+4aVwOCRN+MCrsale3SmWqbgRirmwDoakCFs9u9NEbk9KshN3bgVKqADpVAU2iIHSovLbB4rQdM8YqPZ2pWGU/KIxxUqxkDipvLJapUTj2osBW8s454pNpxjFXGQbuKCvI4p2EUihB+tIUIHAq4yDPSm7BSsMqbCR0ppjJPSrZTFN2c5NFhFN4z1xRtO3BBFWfL3HmlEfqKBnGeIG2XIGOq1gSttAPvXR+KsC7jGedtc1LglQ3TvSCxLNdi6uEKptCrtxXTaCf9HcAfxda5y7ktXvFFomECgZ9TXS6Bn7K49WoEarfMw5FQyR/NkH8al4zk1G3APNAGxoesy2TCKQebAeCrc1uXHh3T9fiM+kyrDcY+aI8AmuNTPBHWtCzu5bWQSRyMrA9QapWFsUr7TrrTpmiuoXjZTxnofpVMjPIFej22tWOswi01iJGH8MvpWLrPg6a0V7nTm+02nUYOWAo5RpnmuqE/alGOcU5ACRuwBS6wCmoKp+UgdCKbsd2Cg80gIzjzGx0ph81rpAvCdzT9mJNp4Oa0JYNPiCgXhL45AWqsI1rKL/QjIBlgcA1J5z5xnn1qG0DSWm23LPH0JUd6lWJ4uGjcnv8prnqOxtBXLlqmOTmr0NzA9ykHmAMxxWW93MkRVLaUnoAENY5N+ZPMS3nBBzkIeK5nzNm9kjqPE0KaYIj5255O3pXKS6pPCxeFypPGQabeHVLxxJNDdORwN0Zqi1pfEkG1uB/2yP+FNQE2RyXMrkl3LMTnJNQG4kAPOfrU5sb4n/jyuf+/RqQaXdALutp1+qGrskIorJKT8o61ZEUuAWY1dWwmTjyZB/wA1I1tKU/1UnH+yaljSIbOIteQ5OMOvP419I2TYs4iP7o/lXz1p1vI19b5jfb5g7dq9/s5ozYxESLjaOM0kyGWXJIOa8T+KbFfFESq2CIRyO9eyGYFjlx7c14l8WDJ/wlELRgsvkAHbzzVxVyjkGmcH79Hnuo5c81RxM3/LNz/wABNJiccFXB/wB01fIiGy/9ol7OcUfaJf8AnofzqgFmA+5J/wB8ml8uc/8ALOX/AL4NHIhXL4uJNpG+m+dJ/fNVRa3mP+Paf/v23+FBt7xSM28//fs/4U+RDuyz58meHo85wPvmqzW90Dzbz/8Afs/4Un2e73Y+zzZ9PLP+FHIhXLQmfOd9L9okPV6qG2ux1t5x/wBsz/hTTBdDH7ibn/YPP6UciC5c86QfxmkM0h/5aGqZjuAvzQzD6oaBFcEHEUp/4AafIO7LfnSA/wCsOaDPJn75qoLe5zgwTf8AfB/woFvdEnEE2P8ArmaORCuy350n9/mm/aJMffOaq/Z7k/8ALGbnp8hp32a4C5MEo/4AaORBcn+0SAcuajaeQ/xGkFtOV3CGX/vg08WlxjJgl/74NHKguIJJP71TxO3cn6UxbW4bgW8px6Iamitrgf8ALvL/AN8GpaBE6g4zjNe1+B4yuhwZHJXvXj9laTSuN0Eo7/d617V4UBXSogylDjoR0rF35rDZvuu5a8/+Ji40WMknIk4r0QgEAbua4D4qWsp8NhlVmO/OFGTWqQRPGmuJB/HTftEh4Dmq7W90eTbTgH1jNN+z3S9YJhj/AKZmr5ES2W/tEh/jzSfaJP75qq0F0P8AlhN/37NIbe6AyYJgB1Ow0+RCuXBcy/8APQ0faZAOGrPCyg/cf/vk0hEmcFX/AO+TR7NBdl/7Q/8Afo+1S4xuqiN3o3/fJpuXJwM/lRyILsv/AGiT+9QJ34O6qILg4JOfpSBmJxzxRyId2XzO/XNJ9pk9apbnHekG48ZzT5UK7L5uZDxu60huJOm6qIJB680bmzS5EMv/AGmT1pDdyDnNUixC8mkyx6mnyIC2L2TOc0fbZD3/AAqoCPUH8acCuM5GaOVCLQupG74p6yu/fAqugBXIx+dTRgBuevpUuKGX7FHe8hAx98da9/0yIfYYsnB2Dj8K8W0K1WeRJscBgBXtunqBaoD/AHRWSb5vITRNtCHPWvLviP8AZo9WgdmIZk7CvUWDFjyNo6V498VXC6vbLnB29K2Wo1se7BAOozUoT0phPenqeK/O2emPB5wadTeaenHFSxDgPSnjrTB1608DPNSAZwaM80uOaaaAEbH4VDJwKlbkdKhf7vSqQmYmrrutnUHBx1riypDHJFdvqufsz4HauKZTk19Lk79xo56g0Ln39aVuDilXqSKU5717RiIo+bNLtBHApVwB1peWFUriG7Rnr7Uu3B9qXbgYp4XccmmIYB70uCOlPK9aMcc0wuAOeuKKXAAPrSAce9AhBkNnFBOWyBTz64puOc4pgAU7T60u315pVGAf50MflzmkMYe4FNY889aU5b0FJjHBpANx60mBin49aQqM5oCzGEAHgUzGTUw46mmsARxTsxioCRg4pWXcOvSlTlOlLg460WYDVXC4zTcc44qTB280Y70CZz14m2Z+OM1WgwJTV6/GLh+ABmqkZUyEkUWEaEa8E1IFBPBpifcGTzUoUjmqQCAHvTgwFBBox3702MQHJ4pCAcU7GO1JtFSITH5Vp6MuZXHcis7jHetXQ1LXEg9FzVxGadzEyyQegPNacS8dKr3SEmDOevSr8aBVHrTYg25HShlymTUuAAfSo5FOMDvQITTWI1GMcdD/ACqGMFJiTz81S2OEv4iTgc/yqHdtmOw5AJzVoTLokw/AqnAAdUuj7L/KpUbc24nGe1OskjbULvf8p2qR71ZLOA+IyhfGuhkcg2x/maj8WY/tuzOOtkv8zU/xII/4SzQyOP3LDP41X8VnGraf3zYr/M1LQkTRxq2nB+/l8EdRTbRJ0twZD5gI+93xWDr2rzaJaac1tGG+0KQ4Y8cVt6HrVvq9kZI0ZHT5XXqAagtD7aETXuVbB9a5iMOdV1FSOfM5rrYlMeoCROTjketcoCf7a1EkEAv3qiWaEKA6pZvjo61oeMk2x2rHvkCqMZAv7Q9t6/zrR8Z/6u2wOh6/hQI5x0+ReBVeZCPcVbOPKXvQ4BTkdqlgYsi7lK+9dVZIscKKo4xXNSptJI4Ga6myIeFW6EjpTGY3jgj+xLcDH+vH8q4mHjiu28bL/wASWJumJhj3riYuTSESSA4qq/Jq03TFVpODQBv6TjdAT2Ir0BAdoJPNee6Vj9xz3FehRcRr9KENnK+PAdmn/V65RFGMnrXWeOuYbH/fbH5VyiZFMED/AHcGq+fnA96syHg1W/jH1pMDtNG4vIjjI967ZOcc1w2jN/pcI6jjNd0gGBj0qooZJz61IF71EB/Op14XrTaJOa1qMf2zbEsV+U496nsW/wBOiBHeotbER1W3EzELjg+9T2hIvoh2LYpIZ0qn5hxUp68VGvHWn4qwHAUoGaaOe9SDBFIQmDQvA+tLx0zTGPHWi4DJjujYd8VhX1mb25towwXGTmtuT7jHviqPW9g+hpDuV10Jhx5q/lVyw05rSaSQuCCMDjpWgMCjjpmiwXG9KYTTyO1MIx1zikB5l8SEP9t2Ttn5oCB6daxvDiFtdtgOzZ/Kuk+JkYEulSDqfMU/pXO+GCB4gt/qaTBHdeK0Z/D7ydNjqT+dcErp5xXI39cV6J4lAbwzdj/ZB/UV5sIB9rWbnOMUxMvOdycHmqzkYYDBIHNTOoSPg1WiTyw/Ocg5Jp3Ap3V1bS2FpbxxBZ4d/muFA3ZPH1qmBmFqtzw2kWnWskb7rpy/nLn7vPFQxjKMMVIz0my/5A2n4P8AywX+VPPPQcVHYfNotgR/zwUVKRgUrhcRcMwA4qyqktxVZVw4xV2FAT15podyRYsxnntUaREz5x2FXUT5CPamrHi4H+7TEIqflUE8Y3fQGtAR+3FV5o+n0NAGfbR/ugcVOUyAMU+1T90OKmKYbNFgLuiN5Lzc4BxW4LrNc3buYZDjuKti6b6UIDb+04NOFzWGLk5p32lqdxG2LrHUUacd9xcHjlqyPtWPXFamincZT3NCYGntrhI4i0khPTzG/nXfjqM1xkMYbzMD/lo386TAYIuMUFO1WvLOMU0x5OMUgKwhxzRsx1FWSnak8vJx+tAyDaCKNuRU/lil2cdKAIAmODTHhR/vDNW/LxQEByO1AFP7Mg/hFL9mQ/wirYT2oEdAFP7KmfuCkNsh/hq7szRsHNAFL7LHj7o9KabVCelXQmR0pdg9OaAKa26oc0/ZgcdasMnOCKTy8c4oAr4yetBXmpvLGc0uzHJFAFcJk0oTNWNgpyxn0oApwx4vxnoRWgY6rxJi+HPatDbz7UAcX43QjSZz7AfrXfeFGA8K6X7W61xHjpcaNOfZf5iur8OSbPDGnLn/AJYLTQjcmkAzWfLMu4D86huLr5j81Z5ucsSTTbAvNMoJrL1iYHTLkZ6ocUs9yAhGayNRnElpIueo6UgM6xTdGT71bEWTUOmLmI+xq+qd8U0gIVQg4xT/ACznAqYL6UFQB0p2Ag8sg9KPKB5I5qwFGc+tJt5NAFfyxT1i+Xtin7MmpAmR1pWAi2Umw+lT7elBUYxTAr+WDzTSnNWNtIF70wK5jHpTDGSOBVnYD96kKYz6dqkCsY8DpTCmTmrG3vTWUmgDhfF/y38QwM+X1rlnG91UnAJxXVeMAP7Rj65Edcu6l8ADkkCpuBYvre1tr5Y7WXzFCjJ966TQSTZv7NXN3unNpt8IXkV22hjjtmuj0MH7C3H8VMDUOcexpjN2pc4PtTJMfSkMlh5PFWQpA6cVVt6vBQcelMQRuV4rZ0rxBcaewCMGiz8yN0NY2A2QOPemhSAAOtNOwWM74g6ja6n4ht5rW2EP7nD4/iOetYIfaQ69vWrOvhv7Vj3KV+QYz35qjPkD5etK4A7bnycVOmnzXI81BkD3qoCeC3WnGSVHAVmCn0NMR7J8P7MReFgJYF3mQ5JGeK6lLSFXDNCnr90V4HF4jlsrcWwu7lAOfkcio38WXRGBf3eP+uprNxuWpNHvOo6naWBCzIoPYKoNVYdbsJ5kiSPlj3QCvBpPEN1I25rydj6lyaWPxHdI4YXcwI9Gp8qDnZ9I4Af7q5x/dFROBuHAJ7fKK+eT4rvmck391jt+8NPHiy743Xt0cf8ATQ1PIHOz6FKAkF1U4/2ajlijZDlFP/Aa+fv+EvvT/wAvtz/38NDeL9Q6Lf3OP980uQfOz31oYHUBoUO3plRT2toGi2iGPn/ZFfPo8X6gCP8ATrj/AL7qQ+MtQxxfXGf9+jkH7Rnux0q1X5jEox0+XFL/AGfEAGUMD25NeDf8Jnqmc/b7gj0L0n/CZ6oTxfXA/wCB0ezQudnvwtEx3qjJolvcSl5IwzD1Ga8P/wCEy1PdzfXH/fdSL4z1IHP9oXPPo1Hs0P2jPa/7BsiQDAnHbbUbeHbJixECD/gNeOjxndgc6hdE467qP+EzvVGFvrnb3G7rRyC5z2WPRrNJlzbxk44G2rqWNuhO23jHH9wV4YfG2ogZS/uSfTdUTeN9WBBW/uOO26nyhzs97ECDkRr6fdFK0aAfcXGP7orwMeOdaxg6lcYPbNIfGuqnbi/uBj/ap2FznvWyNs/u0P8AwEUxoot5Zo0wBySorwqPxxqqjm/nP409vG+pPyb6f3GaXKPnPchbxH5hGuCP7opDbx8YiX/vkV4W3jnVQgVL+f8AOmr461lRj+0Zz6c0+UOY96W1ibO6JGHcFBQ1vCo2+THg9tgrwj/hPNYOP9Pn9+aF8davn/j+mx7npRYOY9y8mIPtEaDPbaKXyo1J/doex+UV4aPHeqh8/bZsD3o/4TvViOL+cH60WFzHuX2eLGDCn02ikFvAwP7mM8f3RXhv/CdauQc38+760i+OdYGP+JhPge9HKHMe5C2hC8Qpj/dFMNvEgP7pDn/ZFeHv441ZlIN/Pg+9L/wnGr42/b5/XINKwuZnuixR7QfKTP8AuCmNFCD80af98ivDf+E31YZxqM+fXNJ/wm+rFj/xMZ8fWjlDmZ7o0EJ+URqD7KKdHiIhV44rwoeOdYwc6lPTT421Yn/kIzfWjlHzM98ZmHc/Wo3UzgCT5sdiM14R/wAJtrBxu1Kcj0zQfGmq5LLqM+frTsLmZ7NqGoafpyr9p2L/AHQEBqnBr2mXUqRRbWc8AGMCvGLrxJfXZUzXUkhH97mo4tduY5VcTupXpiqsDbPoTyowgJjTn/ZFQXEMTId0UZHcFRivFP8AhNNTIC/2jPj606Pxrfp969lYehqGmF2eti0tlJ/cx4/3BTxZWZcN9mhJHqgryc+PLsrj7RID7GhvHV2EAFxJ+NLlYcx602m2YclrW3+nlioGsLHd/wAedvz/ANMxXln/AAnl/wBrl/xpR47vVbInbpg0+Vj5j1P+z7BV4srf/v2KjGlWIbP2O3zjp5YrzT/hP77O3zzgdDTf+E9vE5M7k0crHzHpUum2IQn7Fb8DJ/disKW+0PeY/wCy4vlPJ2AVyE3ji7m3A3LkEemKyjrspJLSDk5zimkJyPULKHStSj3RabAEU94xVgaHpg/5cLf/AL4FeZ2fjG60+ErDNwT0xVv/AITy9LAmUH8Klxd9AUmd/wD2Bo4znT4Ppsp48P6Tz/oFvz1yleff8J3c7tzSA+vFS/8ACfz5Pz7s+wp8rDnO3HhzSFb/AJB9v+CU4+GtIYfNp9uPolcUvj5wQTg+tXbT4jWodTcxSlOjBRzRysOc6YeFdE5zp0JPY4py+F9GHH9nwgewqnY/FDwiilbyw1Ase6qCP51oR/FL4fBsyWGqYH/TPj/0KnyD5iRdFsICvk26oF6AVoiWVMYY46YFUP8AhaXw8ZvmtNTVSOMR/wD16q3XxO8EEP8AZob4Nj5VdMc/nR7IOdGv5swYqHPNYup+HtP1OczX0HnyYwCx6VzVx8QrZpWaMOoPTIqsfH8hJUMvtlaPZtBzHvIApy8VCok61KrEdRX5yz1SVaeeCKiHJqUfpUsQoqQCmAYpwOTUgLml9aQ9qXHpSAjbFQvytTNUMnQ1SEY+qcW7ZrjJPvHn8K7XUx+5b6Vxsi/MSOea+lyf4Wc1XcYo4xSYOacMjmlAJPFe4jEQD8qXaPenbOvNOANMBAvNL+HSlPIxSgZOD1piE9yaQDmnlRSoABQOwzgckVmX2rwWkwjCtI57L2r0/wALWGjppE2qXKxuY9wcyjIjA9jXnumWcOJb+ZQd7s+CPfgCtOWyE7XsRabdSak+2O1lQf33GF/OuiTQ4BFvuda0+HjJUvkj9axJLie6coP3cI7L6Vj3tpEbyOIE/vOOtSI7ifQ9Ot7OO5n1+COCQ4R1j3A/rVc2XhpSN/ifPpttzXP31pKuk28duw3IeN3IrOF3eW6MJrNJAO6UAdl9n8J/w+ILmRvRbY/4Upg8KJCs0uqX5VjhdsQyf0rlIdUsm4YmJ+4da1lMc9isiFWQnhhyKBamqG8Eg/Nf6uT7RD/4mkZfBB/5fNb59FH/AMTWKVUDoM0oUU7j1NpYfBJjZ/tWt4TrnH+FNVvAo583W2z6j/61Z0SjyJSDiqpUA8c0+YDfWTwQnAbWsfSng+CZFbb/AGzxyQOCawI8YORxVmIARvkdqOYLF8S+CnbAl1yJMffKgqP0JqR/D0V1bPc6Hq0OpRoCzQldkoHsvf8ASsWMDYOKlheS2uEuIGMcqHKuvBFDYHN3hV7h9uc5wQRjBp+haNdavqhtrdC2xDJI+OEUdzW9r0C6tENUt4Qt4hxcxoPv/wC1j1rpvAtxa2nw+1bUGRY5DJIrt/EflG0fr/OmkmF9Gzz0fNI3lk7AcA461OiueAVb0HQ1HAAkagelWUUNkd8flU21BET7kOHUr9aO/qDUm5iuGYn61XdHDFl4+lN7DJxk/WkPJ+lVRdyg4dMj1FTR3COcA49c1Ih/UVr+HVU30gOeU4/Osvjbnrmtfw4Ab5uedn9auIzo7lcGLI/iFXBH3HFQ3KMTHn+8MVeRDVskh8vOD0qORQuQOTV7aMVVkXOTSAqwri8iJwOelUjuS6OzncTWjCv+mxZ6Zqk8TCWWRjjDEKPaqiDLUCFhlvxqK2JTVJyTkbRj2q1bvAlu2/JfHHvUVsFl1GcAc+WCatEM4P4mHb4j0JwODGw/WoPFRH27TWI62Y/nU/xOHl6xoB9nGT9RVfxKD9r0sk5BtBUsDlvGaFdL0glsglse3FbXw68pNKuVJG9mJwe9c94uuXmtrCF49ix52t2NdX4KtoR4ctzt+dlYk55zk1DKiaJBj1BSoJRuoHauUlbPiDUVzxkEV164g1ONHOVPQ1yN8gi8UalsOQcHFUiXuXI8m6tD3Mi/zrV8ZrmCHHGGrHU4uLRu4kX+dbfjL/j0hJz9/igDnAv7lT2xTCTtPcU9DujAz2pjgAEdKkRny42nHJz0rp7IDyEPsK5l+SSBXQW2UgTnBx0poZm+NMnQVJ7TriuHiHGa7PxcznQwCcr5q1xsJPFAIkYYHNVnxVpvrVV/vc1LGbelfdhPcHoa9Egx5S/SvOdNzsiI9a9DgGI1PqKpAzm/HI/0ax/66N/KuRQZ79K7Dxx/x5Wn/XQ/yrkUA2+9DEI4wvFVRkyAY71ZkFVzjePrSYHYaPxdwe5rvF+6MVwejEC6gDdOK76NcqKqLGxyjH41MoGMiolAHfNS9uM4piOc11401W0MgyKntgRfR4P8XFRa+fK1GzfZv7Yqe3Um9ib/AGulAHSDnBxzT9vOaEXgGpmUEZFUDIgPypw9e1DAUgGD14pCHEc0xuvSnnGOtV2JyeaQCzn9w+B2rIurnyLi0KpuLuF+ma05jmMrnqKzoJIrqa2U5BV8jI7igDbCkdRTScZ4olkAO0GnRRiTknpTsA3ORSNnHNKwAcgU0j35NAHBfE1V+z6W3fe4/Suc8IIsniOEN0CMa6r4moo07TCOolYH8q5Xwcf+KlhXoCjj9KkZ6B4hQ/8ACM3hxwI/6ivLhNMuoIuB5RFeta3H5nhW9GeTH/UV5WTF9o8otiTqBTEy2wDkKTwTSXkMcOoTRREtEB8pPfimyKcAD8KS5gls7lreZSkyjJBPQEU2Bl3GnG30+2vjJuNyW+TH3cGoYfut61JNa3CW8dyx/wBHlLCPnPI61FBwrVAz0vTY86Jp5HeFasGE9KTSlz4f085/5YCrJHGBzQBWjhbzAK0beH24qtGp80GtSHAOaEMesYCYwKjCn7SB/s1aAGCc8VBGP9J/4DVWETbcDlar3C8gY7VfxletV7oAAcUAUrMDyV455qZk9utFmo8gcd6sFM96AKhTB+tKFJqSQYb2pMZHWkA0DANPAoGe9LwMUgE7VvaD92QH2rDUfNW5ofAk/DFMDZHBrlbWIhHJHJdv511PtWHbrmNuP4j/ADosIhMeKaY++KtsnFRlcjNAyqU596ULnnFWAgo2DPp70CIPL745oCHuOKnKEnjml8s+n6UDuQbDSCM5xVnyiBkjmp7W2Mj5KnAoFcp/Z2x6Uht2x0rbNsc/dphtzn7tAXRkC3OOBSfZmzwK2xbZH3af9kORxTsFzC+zMDwKX7I+RxzW+LTBzto+y/7NFh3Ofa1cdqYbZivArpPsZYfdppsiOq0WC5zbW7jtxR5DD+HiuiNoOm2k+ygjpRYDnTFg5PFORMGrt3GFmIFRpHyOKQFAgC9Bq5xUUsYW9/Cpe9IRyfjoZ0Ob22n9RW1orkeHLDnjyVrG8dEf2LPnvtH6itbSdq+H7BR08laYx0z9feoAMtip3wXApQg2kmmBmXWc9azJVaX5O5rTuxnIxVeyjD30a4zuNICbStOkMTZOea0xp7+hBrb06zAjI21f+yA1ohHL/wBnvjgUHT5B1rqBadaU2oI6UWA5Y6c+OKBpz46V1RtBikNoDxRYDlBp0hPcGnjTX710/wBkFPFpRYDljYP6Gj+znPSup+yADGKPsg64p2C5yn9nNn1NH9muR0rqxaD0o+xjjiiwXOT/ALNfHpikbTXNdYbQc8UgswOopWC5yLaY57Uh0t/SuuNpjtS/ZBRYSZ4X4+tWt9TjDd4649gx2qg+YkAAeteifFeLytct+eGh4H4158ZTC6SJ99WBHHesmrFEmoadfabqCxX6lZSobrng12nhbTmutJklHTzMYrkNUu9SvNT83U1kWfaPldduB24r0LwZrGj6d4baK8uQLlpWYRgc4oQmxf7GkLcZ/Kq1xpTxZY5OK2NH8WWWoXEqSW0iouce9a0uoaNIvzW7j8aqyBO5xUcRRwMc/StyLRtRkiEospthGQ23jFS3b6dKUFlC0cmeWJ7VZe8vzb+SLuYR4wV3cYqOeK3KUTGK7SR3HWmKxU7vSori9igZgXBPtWbJftK2EO32rGUm9i7JFDxNP52rwkYysYBrPCljtB/Gm30vm3oYDgHA96GLA56VtFaESGSLsbB5INWEu44l2NAHJ6EmqrHcferkNh9oQSb0BHYmqIMnUmR7klI9gx0FUwtaF9bmO5KFwxHdariLnmiwyuV5oK1ZMQHvR5WaQFUqQBQQSOBVnyuetIYulAFbaaUKRz2qwYcHFHlCgCvt70Yz9aseV70oiGOtAFfac0u3BqbyhR5YzSAgI7Um2rGwHNJ5Y9aYEGO9AHGamEQzzQYvSgCE55xRjIFTeWMdaXywcUAQ0mOan8sU0xDNAEOB+NJ3qfygueaPLGKAIaAvPWpfLo8sUARFcc0gBNWPLGOaQRCi4EPalGM9KlMY/Ck2ZNAERHejBqbYKTZSAizmkPWphHmlMQIyaLgQUYBNTCMdulJ5YB6UwIu9H8qlEYBzSiNTnNK4EOORgUpxxxU3lj8KQxg/SmBGcYFJipDGPWjYKQDCBimEYNTeWM0hQE0XAixzS471JsoCZFO4EYAzzSEZNS+WDxQUAFFwIgKTBqTZQI6LgMpO/WpPKH40nl8UARlaMVIF4o28e9AEdHGKeUzRs96AGnmgrinbQelGw5oAaBxQBz9KeY+etJt49TQA2l6U4L0ppX3oAZjnOaXGaXbx0pcYNMD695Ap4JxTAcnFPr81Z7A4DmnjpTc5pwApMQ9eeSKcODTRTgeakBw9aTtSjmjoOakBjfdqB6nOahfvVoDLviixsXUsO4HeuUn1zw/CQkmj3YYntIef1rq9Qz5LYriL1IzJygJB7ivpMnl7rRy1i5/a2jMBjRbsD/rof8afFqeiEFjpF7gdjJ/9eqyAYHHalHUgYr30zAsSaz4eiBMmmagi+u//AOvVu0n8I6kiqL++064Y4BnTch/L+pFZFyqGEiQAj6VJCqvbhdoK4xjFNPUDa1TwxfaZbG7DR3dmAGE8HOB6kf1GRWMDkgqQa0tJ1m90N/8ARJN1ufv20hyjeuPQ/Sl1pLJ9moaZH5dtMf3sP/PJ+/4VTsBmk8dPxphlSFSZGCj1Na2laNc6xOsMAwBgu56KPWsHxJor/wDCTz6X5jfZrYKXI43EjP8AWpsJtm3ZaxYf2YsKt5iSFvNHY1l6g4tY7a2jwTIC3HpWdc20Om2aCFCsecdaXWY5d+m+Q+C0XDVbegupeQYXA9KoX65u7VwMYbGagF1qlsf38SzJ/eXg02e/iuGgYo8ex/m3CsubUo6FhmzTHOKrPGGHIqdZUlsA8TBkzwQajOdtUBkw2kc9xOJFBGa0DZKmkyW8JKAnIK9qgthi8nGDWkCfszDHU0wsc67alYICZBLH0G/mpRqzxKDPbMP9w5q3qgC2ZYjgEVOIVkiTKjoKAQ6yu4LqKXyn5C5II6Uxfm5BBHrU1rbRxSuVUAsuDiufSwuWab7PcMhVzgE0h3OggUHmrMY3bvYVzdrqF7YzeXexF4z/ABgciuktnWVSyH5WXg00IiiUGIGnHA4xTohhKaTzwOKBDdJu3t9alQDIaPOD7UfbwPD1/ZxqFWW5Mhxx1/8A1VBZfJ4hBxwYzWbaE+RfBxyJjt+lVFiYyIADAFXIAN/TtVWPliOlXIBsfryaHuCIj97HrQVwD3pzD5zmkxkUDIYkDDBXjNI9mjA4+U1NCDtOPWpNuep/KhgZaQzqpZCcA4roPB88r608Tx4xETms+EHDqOgNbfhgD+2P+2bU4jOrukB8o853CrqLhelV7lcIjH1FXlXcoParZJFtAU1XkXBNXGA96qy85qRkFuAL2Mt03daimRJLuZmbCBiRxU8I2TqSM81TvctdTEAgbquJLGvGZVDAfgKbp6tHqVyjdfKGfzrQ04Bcyt29ahDG41u4kxhGj4960Qmef/FMbrnQHI6SOMj6iq/iU5/shsdbX/CrPxWBX+wW3EYmf+lVPEeGi0c5PFvg/pUSBFdNOsPEunf2ffbonhBaGReDmt3SNFg020htIXfaicOeST3zUOhRxNa4bBJRvwrVt4XQJ5TnBjzg81BXQz9gbVI4pTz2b1rkNRiMPiy/Rjk7QR+ldfNmXUo1YbWA4rkNWJHiu63feMYB/SmQyUHEttnH+sX+db3jQE6dGAcfPnNYMg+e3I6l1/nXQeL2b+y4yFBw3WmBzEQ+QcAZFMnyFJzxToTmFcjnFRy9PSoCxTYkxtjrXSWig28Z29VFc1ISqNjGa6azJe1i25HyiqQGP4vBXRD05kWuJjziu48XxqNAY/xeYtcPCMACkxokbpVWT71W2BweaquOcUAzX04kLHyMZr0a2IaFCOmK8304ApH25r0OyOIEGT0oQjD8bn/QLTA580/yrj0GBXY+NQP7NtTnpMf5VxyHimwQ2T7vSq/8X41akxjjiqv8XPSpYHW6OCbmD8K9BThQMdBXnulP+/twh44zXoERwgGeSO9VEbJRmplPA9ahB2j61JGM45zVEnP+IzKL2zkiXLBsY9RVq0w19F15YVD4iWT7VZiFgGLcGrdoP9JjyMvuHSgDplUEAAU9l4zQq7V680NnsaYELdfakFK3vSdKAKeq3y6fp810yFxEu4qO9cgvxHtup0yTB7eYOK6XxCf+JDfc5/ctXjYgZ7cuOMCk2M9EX4jWRX5tLc/9tBQPiFphUEaQ646EOOK8xAOOtBJxjJpcwWPTY/iBZGVVNjOS52ggg4rsY9QSCJQ6/McfL35rxbTon+0Qe7Kf1r262iRpBIUBYAYJHNUncLCsRuLY60w49KkkB3momJxjvTA474lxPcWOk28CbppLrYoHckVynhuCWx8ZRWtwm2WMOrqe3Fek6zbLcXul7jgpISp9DgVga9YxW/j3R7mI5kuI2WXI64B5/wA+lZuSUrE82tjf1cE+F7xVHLR4/UV5NNABdiY5LJxXrt8pbQZ8nA2/1FeQ3jSprDhWzBvINUMt7iFD+nOKfe3zalqL3sqKruoBA6cDFQk7iBn5fepLkxfbMQf6vaMfXHNDYjCMtw0KxsG+zozbCVwM96WLABNWZ9TkuLOHT2RQls7lSOpyc81VjyFapKPVdJI/4R/TRjrAKsEVX0UE+HNNJ/54irbcUAJCP3oyPwrSjCg9Kzo1Pmg1pRLz1zTQEw24PHaoYiBccjjbxUyrx1qqjZu8D0pgXxtI6VBdEbR7VOFxxn8ap30gjUbiKBDLIj7OPqassR+FVLD5rdWHQk5q0/cUARScMOKZTip70AY60hjRyc9qcD6Uu3npSY5xQAmTnFbOjPtR8Vj7cdTWhp8gjV6QG6J/es62B8o4/vn+dAnBIqzZRFoMkdzTERlahYEHoa0jB7VG0BPUUAZ4zngUOCVIAq/5A9KDB14poZ5/rtlq0mtM9u0wh2DAV8DNQWtpqXmH7T9owB8uJO9ehyWqZfcueBjioLa1RmYvGq46U+S4uRbnILpt8zhxHdvHjpvNalvpd5s4huwT/tmupihzb89KsWQxEwJJG7ihQDlOW/s2+I+WC7yPVzzSf2fqJdVa3uQvfD//AF67PHFNY8/hTVMlxOPXS78XAIgu1ixzl+9Ol0q8lQrCt3vH/TXrXVkl4uXwO9NtYHR2ZfwOe1NwDkOXtdH1NY9stvdsQevmVJJpF+VwIrtW7Yc12CiakLyBwG7mhQVhchxs+k61tAghuR65k/8Ar1XsNI8Rx6xazTLOtsJB5gMmRj35r0PGRRtFHs/MXs1e5DtGT60yRQFPFSnG6mS/dNJo0OfuziY1HH97NS3f+tNQocEetSxlS8bbd5PpUZnA5zTdVYI+7Pass3GAOaQGV43l8zSJR9MfnWzpD50GxHbyV/lXKeK7kPp0oz2H866jRR/xIbEnr5K0CLaLk8inyjbHT44i1FwCFxVDMa6JyabpJB1a3yM/NTrrqRSaTn+1bcDg7utSDPQLIDYeKuDGelU7POw5PerYFaokXj0pQB6UmM07GO9MQnAPSjjOcUnfNLjNIBeMdKUAemabj3pcH1pgO4z0o4xjFNx70ooAXgDpSEc9OtJznrSkH1oADgdqMDHSjmkwT1pAKAOmKTj0pCMHrRz60xo8W+MJzr9mNuAIM5rzuC4S1vLe4dA4ikV9p74OcV6B8XjnxHbKDnEGTntXm1xwozWUtyjX8TeJJPFOvvqLwLCuwRogOcKPWqdmGeXIHSs+HG/itnSJIldzI4Hpms5Ow0kb2jLLab3YcOOBWk8+eWbA+tYD6yiArH+BNU21J5Sct1rJuTKSR1C6vFbnIG89qo3mt3N2SPN2J/dWsA3J9aia5560lEZpPOSevWpYgWOCcZGWPoKzrU+c+48gfqat3lyLaHy8jc33v8K0jEhsq3colvU2jCjgfSnSMSc5yKpRN5synPWrVyfLQEc+1aEjM4OR60jo5k+XOCOaOqjnBNTrfPCPLEaEe4qgIDHECCzHPpSMkYOAaguZSZSzYH0qAy+9S2MuMqA9aQGP3qmZhjGcUGYZxSux2Lm6HPc0m6Hdk5qmZR2pomzRcLF7fBnvRvg/utVPzR60eaPWlqKxcLxds0geDuGqp5oNNMoHIp3HYvq1uezCpfJhbo+PxrL80UnnEkc0XCxpSJAn8eagLw+hqoZPegSc9aAsXA9ueobNOBtwD96qPmAd6QS8nNArF5ngPY0B4M96o+Z78UeYDkUDsXjJb8fepN0I5waomSjzCR1oFYubod2cHFJuh7A/nVTzPekEnPWmFi4ZIT2NNMkP91qrb8HFBbFAFrfEeoOKTzYc9Gqr5nvSbx+NAWLfnQjghqXzoP7rZqoWyKUHAoCxbEtv3Vqd51sBwrVRLDsaTzB1zQFi6ZrcAbVbPemmWEr0aqnmUF+4oCxaEsXYGgSRHscVV8wHpSbx60BYt74Q3fFAaIHviqm7ml3460rBYt74jwM0mY89TVQPzSh89DTsFi0WjPrmgmP1NVMnqaN/BGaLBYtZj9TS4j4OaqB+OtO3cHmlYVi3iIHqaMQ5+8ap7jjrRu6UWCxoLDCcfPUqWUcpAEgGazQ3oaUSEc55+tMLGq+mKgy0oqlIkStgPmoPMPXefzpA2RzSAmKxf3uaTMGMFjTFwR60h2dDjNAEoW3OfnNAFtu5c/lUG5FOKRpEXtQFi0Ete8jflUkdvZyN+8uTGPXbmqsbiTqOlK3B6UXA1xp+gnAOssCe/l9P0rUt9A8IynM3ivy8Dn911rkOuTjikwPxppiO0Ph7wbuJTxdwPWKs640bw7Ed0HiNZB6GOucNJiq5hmjPaWUbkR36yD6VXMEWcCdTVXHNAwKQWLJijXI81ab5cWc+YKqMx3U059aQWPr5ZgT0NTIwb2NRgDPSnhRycV+bs9glGMU/2qIZFSK1SxDwBS4J5pqkdKfwKkBc+tL14pCPm60oOBSAYx7VC4Jzip2qFxgGqQjL1A7YXJ7DtXn8+oQzlypIK/eyK9BvQDGfQ1wuqWab/lULub5sCvosn6nPVJoZI5UDJIpyOxqUr2I5rHvdEjS1klgLK6rkc1Hbw6oLZJY52Ylfusc176ZzmpdkJAWNS2pzbqQMDFZX2y5MEkV3BtboGHStSyJ+yoMVSswJiOMDvUsTYV4T9yQYamkEU0/Kme4qhHTeF73+w0vpZSZYEjyNp54rko72fU57nULg7pbiUuR6DoB9AKpQXswu9ThjYlHjztP0qXSB/oEZzk8/zoF5lfWFd4Agb5SaW7HOljOSI8fpU2qIGtj1B9ahuWURaaw5YcVT2F1NHZ+INZGsRKZ7cFV2scEetbmOBisjWgVNu/TDjmsrallxtOU6NJb27mIFsgr2NZG3V9PGWmWeFe7jNdIpJtsY4NUtQUmxkUe386bQjGi1X7PO8tzE2H/udq6KwuoL2xlaBgwXrngismNI5pZFeMHEfpWhBbQxaNdmNNu6Mk49cUK42QajGZLB/TrU0IHkp67RWGLS5j09ZVmYxsMlSc1d0/U/MZbe4j2Pj5W7NQmJGxbqN7c9uTWbbKRPcD/brWt1PmngdKzoMfbLlcYw3SqsBK8AmgdCKTQQsFy1pIxZSpZPb1FWoB171nabvPid4sfKu4D8qaGaMYyD9TimkdfSnxoRkHsTSlcmhgUIsprsXP8ACf5VTt4z/p56bHyR+NXHOzWoG6ZB/lVSEHztROTy2T+dNIhkcYBOQKtxD5qrRDkircYwwx2oKQ0p81N24qcj5jTccdKLDK8Rzu+tTbaitwSG6YDGrG0k5NMCtCctIP8AarY8PNs1dT3KNzWRbpmSTjjdWvovy6onGQQRTQHYznfEhFaCcRAjPSs6XIhU+hrRhIMeM9qbEMcEjI/Kq0gHT1q24wuKruMtSAhXhwfesyQlrqRXJyzEitcD51HbNZmogRSuygg9qqJLHoXLCIHgU2OXZqbD+HyuPzqGwkJDOeuOtV7Z3n1h89ozj860Qjmfiqc2mhNtz/pLD+VZXiN9sGkMw6wkY/KtH4ql/wCzNJ2DLpckj8qxvFDyHRdCldcOUcEe+BUyBG/okMVxpdo6jDbGyw78mtS08wPHGWVhsPHesbwmkkugWMwJRnD5U9OprZt1DTRNLCVO0jcKgroVb1Fk1C3eI4dR8wIriNZz/wAJdc7uDsFdreRJDfxTQ5LdwTmuM8QfN4ukfGN0IyKohisfkiOOdy/zrovFRb+x0yONwxXNykiGLaCfmBx+NdR4qGdFj/4CcUAcnAf3K/So5xx0wadFkQrTZVZl3VNgKUgARvWuktCBZwkjHyjj8K5mXO1gOtdLYBnsocjPyjmmtAOe8YanF5I03a3nORJnsBXLRLgVreMv+RiK4+7ClZUWaGBJIPlxiqbhgfWrzEBaqSEE0gNHTmXYi5+YHpXoNpkWqEZOBzXmmnP/AKSB6mvTrEFoFH94DimhnMeL9Tt7ryrCNW82Jt7t25HSucQfLx+dW9dff4iuxgYD7fyFVwBtobEQyKNuarN1xVqU4qq2CfapA6fQ3Vprfa3IIr0aPBQE15X4dYG8CHoGBr1KM/uwAO1VEGSgbmqxGoUcdagjBXgircanrWgjnfFERaayWNgjMc7j2q5Y8TxgD5tw5qr4sRSbLziVTdyw7VZsvlljC8gMMH1paAjqhg9aHGCPekAND5brQBC5qMt7U5xtqI+1AGbr4xoN8QfmWIsK8ssm822YsA248ivVtaUtot6AhdjCwCqOSa8ttbbUYI+dMvBz/wA8jzUspDf7Mt9xHlkcZ60NpUHACED1zXQ2GqW9vZhL/wALXs8wPMgQ9PyqlqV79om32Wi3lvDjlWjJ5oApWyiO6jVcYUjGfrXslr6EY+UV4pHbXjTowsrrlxx5Z45r2y2y20+qDP5VSEwlA3GoWx6E1LJncahfIGaGIz9V/wCPnTiCciTn9KxfE/y+M9BU8Y8ytTXby2sGsJ72ZYYfMxvb1rn9c1Wx1fxpopsbtJ1QNuKjp7Vg0/aJk21OpvjnQLkjrs4/OvHZpozevE5IkZiQDXrerGRfC98yHDLCWB+leRyxiWYTNguO9bDZZPCBajU4mK9GAoVjnJpv/LUuRQ1oBHNd2b2FvapDi6jkdpZMfeB6VTTo36Vcmt9PTS4Z45c3rzOJEz0XtxVNTw1SM9S0Rj/wjemnk/uR/WrWc1U0MlfDGmkH/lj/AFNWjzzTHckg5kX61eWQK1ZyAqwYVMXPUmgC/wCevK89Kz4LgfazyelQyzFSTntWZFckXQ+bgincDqhcAis7Vn3JG4ONuagFz8vXmqmoz7oBg80rgWtGnKW7BmJBcke1ahuRgk1zVhPiEDIFWWnIJ+bNFwNqOUSucHIAqYcnFZekyiR3A7DmtXnPFAAAaaTlqeCRkVGwPagYvBqxan5Wz61XAyOetWbcYVqBE45Yc81u2Kn7MvOTWGg+YGty2YCFRQIsnimEA5pDJTC/fNMB+BSHGDTPMFNaTg00MJnRCQ/cDoKhSaJGJVcn1IqR5PvYxkgdahhCrkudx9hVhcuK+62BAz3pLGQGN+MHd0qsjlohtbAp8I8oNzkk5pgX9/NIzjPPpVFrtEYqW59Kimv1BCjdkjqBTvYRd+yRXIVpAxx0wcVajcrKyBeABzWPbak5b5QSPRuKtJeYdsrkn0pOVxXNYNyOtVrhpGuFC4CjnJNV1vePumobx2uk2I5jyMEii4XNMTOvIXd7g1Y3jbmsO0kuI0WLzA5B5JHatMyYU0xoeWBP40yVwEPNQGQ561DPNhDzWbGZd5KPOI70yN13VRu7j98ee9RJcnPWoAh16XB4POK54yNt61qapN5jlfbrWSRgYpMZz/iF82sikE8D+dd3oQDaBYEdPJWuA8REi2m5x8or0Hw0pbw7p2T/AMsFoRJrxrharXRq9t2LmqFwc1QzIuxxnNN0n/kKwZ/vc0t2RkgCmaV/yFIOP4qEB6DZsvlmrYYd6zLYtg8irIcg1oiC4GWlYqRVZXPXvSmRs9aYycFR1oyKrFj60eYfWkBZyM804EVV8w9M0oc+tAizkUbhiq+8g9aUv70wJxjOaCy56VX3n1o3n1oAsFvakBFQbye9G85xmgCfIx0pAw6moNx9aNzYxmgEeM/Fth/wksIByfIGR6V5tcg4Ga9B+KwP/CVBj/zwUV57dMSorGW5ZFEMMeaJGYHApIfvHNDck0gG+Yx79KfFMUbceaaB3pu3miyGWZLrcpwMVDGsk0iomSzHAFMIya2dLtMfvCvzHv8A3VosJsvWqJYwByMhenufWsC+u2uLhjk4zV7VL4yfu4ztReAKyMDvQBo2X3kx1rTuPlRdwAFZllw6HNX7s+bGFB4piI2JJB7fzqyhs2YvNKQ2MAAVSI+UA00rnimBS1Bk+0nymLL61VyTTpcCVvrTaRQhNKWNHSgigBCSaM0YoxQMM4oyaMUUAGcUZNFGKADJ6UlLiigAyaMmkpaADJo5pKKAFzSZoopgFLk0lLQAZpKOlLigAVsNmntLkYxTKSlYAozRRTAXJFLvJptFABmiiikAUUUUwFpKKKAClyaSigAzRRRQAZNAoooAKUEikooAKXNJRQAuaTNLSUALmjJ6ik4pc4FIRKk20YxUbvubNNoosAvOKT60UdaYyxbE56VM/BqO1x61I/XPeoZDI+SKQ5FOY85puc0AJzjmm96fSHGcUwE+lJnDUuAKacA0DGNyabSnrRTGfYRPSng8dOajDDIp/uK/NmesSgZGTTgQRjFRqeKkXFSwADvUgNNHy/jSjmpEOAzR2IoHPFKRSuBGxqNxxUxA5zULdKpAZ14MxmuJ1dgrLnIy3au5uvuN9K4jWkBC7hn5q+iyX7Rz1kLcc2T4Gfkxik04E2UeBg4qSTixbjnZ3pumgmzjI9K+hRzkWpRf6K7fxAfnUGjXLTRtBJjenIPqKvXy/wCjP9K57TrkprMaLgFiVNGiEdPg01wBnNSY7A1NbWM99KsFugaQ9ATimNo5u3ULqeoEAgGIkGptAZpNMQkDG4/zrbh8FeIFv7l3toPLaIhT5o5NZ2labd6XbG0vo1jnViSqtnj8KaRKWozUwRasRjiqU5zbWRwBhhXouh+GdM1bR5JbwTMzMVwjYwKh1rwn4cttFKQyTLNbDeuXySfQ1V9A2OYXO0Vma2AIIz6OK148NGGHQjis7Wx/ogI6hgRUjNSEbrQ8dgap3Y/0WQd8VftgBYe5UGqlyp+zyem2gRl24InPfMdbMEfmaZMg7xn+VZMKk3SgdPKNbVup+wyKDjKH+VAGKmH0T6KRTbyBn0yOSEfOqj71SWq7dFYYztBNW7dRLpyd9ycila4CaFdG8gVm/wBah2OPeoosDULlRnOeao+EbrzdavIM4wucfTitAc6tcnu3Ipgi5F3FZ2mNjxbJE3GMnP4VpxrnrWTaHHjcKOjJ/SqQzZUDc/8AvGkK9akRfmfP940jkK3PFHUDHusR6rbZ5yeCTUe6M3F8oZQRycH3rr/DGn2Wo+IsXkCzhLdnRWGQGBH+Jrs4/DehyrNMuk24aQEN8mCaqxMkeLQuoblx+dXEeMY+Ydat6xp9rbag0cFusSf3arrAikYQce1IEI8i7yCw/OgOg6uDTzChY5QGm+QmMhRTKK9uUQy/MOWzUwkXPDD86bFCmZMqOtSrCmM7B+VKwitC4WSTJHJrV0dgdUiAYZJPGfas+OJfNkBA9av6XGqapAQB97rTQzsLonylGKuxkrGoHTFV7snykzirEZ+UfSmxEh5HJqLbnoKV2+Unqaar5H86QxQmHGPWsPUzIb+Y4O3OAK3QcMOfwrJvzm4YZH3uaqJDIVYR2xjGN7DrVe1BTVV75jNKpieQspOR2ptuT/agfGDsIq0yTmvighGl6c3Q/av6Vi+KVB0LQ2J5yw4+lbnxOO/RdO4P/H1/7LWRrSLPomjq3QM1KQ0anhcTjRLNo8OmXAXoeprdsLlHaEkFD8wIbisjw6wt9PgQo4RXbDAcVr2Lwy+Qr4JLsOazKuUtVi8m6jlU8E8iuF8QyB/GbMvTyRXc6lG8Nypx+7LdCelcT4jiVfFilQNrQDmrIZFMT9njOcYNdP4lH/EkQ85wprl5v+PVa6jxFlvDqMOoC4/KgDlIMmBT2olmEER27Sx6BqW3P7hcGq1/EJYc45BqbiKPnalK7tJFbqMcYPWpYr3VPIk/0lYig+RQuc1SjLK5Q8j1rrtMjjewgLRISF6460x2PPdWmuJrhJ7yTdK6/wAqrLcoo6mu/wDFttD/AMI3cSfZo96FSHxyvNcFBEpXpSsCA3KHgGoHkUng1bkiTb90VTkiAagCaw3i6DIwB65NdHaaxq73Bi+1RxCMZUletZOlQRkB2B3E4r0O0tLc2kf+jx5x1KgmhDPNpbkyXk0k8gMjMSx9TTvtMeOtdb4vsLRdOt7hLaNJfN2F1GCRjvXKJChXlRQBE88RH3hVdmDZwRVqS2jx90VVeEBuODRcVy5pM9xBdB4NgYnBZugrqE13WvtyW/2qEof+WmzgVn6LaQo8bMmd2DzXpEFlbMik20I4/uCqQM5rT9Z1i4u5bZ5IPLjHEpXrVq21bV5hOJZIY/Lzswv3jXTLbQ5x9nj+u0VbjtoQMeRH/wB8inYRwcuv3IszPqkQf5wAMdqr6vr2pWLxy2UKC2+Us7Dkc1u+PNlvYWz+QuBIMgDG4VJbRQXQQyQKY32/IwoA7ESxMiOZoyGUHIPtTWeMfxrj60z7LblV2wIABxxUFxFBgKsS5+lUMHkj3Eb1/Okyg/iX86gFrCx+aNfrS/ZbfnMS/lSELcTRpA7+YBtGRz3rmJPE19GxUzISOB8orpDZ27cGFSPSuJ1KyiS/mVE2gMeM9KmRSNJPFt+gwJkH/ABSv4p1CUFXljx/uiuf+yqT3x9aBaJxnOPrUXA3k8T3a5CugB6giul0vXLW5tzNuAK4DqT0NefrYrI4Vck/Wut8O6NYqJDJCXfjqTiqTBnRzSRN8yuOeetQM6YxvH502S0t95xCoxUT2sA/5YrVCOT+KAjbQdO+ZSftPTPtXF+GAP8AhJ7PLBeTyfpXZ/Eq3jj8PWTpEARcgE/ga4vw0it4htA4ypYg/lSA9W1cxHwnqREq5Fu2Bnqa8ccS/aIyrYUffFevapawDwrqJWFciByD6YFeRNOiMiP96QZFUInVhuJxmgkmckfdx+tImOme9SXMTW1y0Drh9oJ+hoYirNpnk6fFqXnAmeVk8vHTFVE/iqZ7C7W1W9K4tHkKISf4h14qFTjNQUeo6DtHhbTvmGfK9fergKnncKoaBBE/hjTspz5ec+vJq61tF/czQMdlf7wx9aR5An8QppgjP8AqrfRxJbStjGEPf2oYIgu7pFOPMGcetZb3UJliCSqW7gHpWPp401rRheGR5yxwAT0qxJDoYAMcEyt/FknipTNHTs7G9HMCuQ4I+tNmkLYGc1ysZhXWIltpHaDuM10YVQykUXIlGxHbuVLDPQ8VaMuRgt+tU4olZ2J9exqYwR46HP1oJNvw6++efnoo/nXQc5/pXOeGIUjuLojglR/OuiOCetUhi7SaQjtTww2YplAheg7VYteY2qtjjrVi2OIz9aAZYjxkD9a1ImKxishDlhWsn+rFUIcXqMyc05iMc1Ex9uKQCiX1pHlIB9KZ1pGwAapAMlnZSeMjA6VELzKbVVqbf3CWsTSuGKKvO0ViaZqk91dOjBdpGVA64qnJIuMG02dHDIfKAqUzHb3qCLHl5okcomQpbHYVRJGSxumzna2Oasqi5HLVmyvI8ikKUGeR3rSSVMDO6khMkWCMEkbufeljVVZlBbAoEqA9TTI3RZH5JzQ7AWAi8ct+dEjCKPIDMR6VH5yjuTTZJUZCMsM0CH2kzs5YnH+zVxpSM1RtVXGcfjU7njNNDFeYZNUbq6whp8r8mqdwTtPFZyAwLq5P2hs1ELnHGSKju/8AXHg1AagYSXAlmYDtUTHjn8abGMzt9KbMCelSwOb8TMBZzY5Jx/OvSfCwDeG9MIzzbqelea+JBizlB5zivT/Cq58M6cwPHkLTiDNWTkVnXGccCtCYgdOlZ854PpWgjGuvvH6U3SiqapCznAByaddc9O3ek0obtUhBUHnv3pIZ16XcC5xIM/WpRdx5zvFQiJevlp+VSqi9Ni/lVkE32uIY+cc037ZBn/WD86XYo/gX8qYEXd9xfypjHm7hz9+kN3Dj74/OmsoPRV/KgIOMqv5UAON5CP46eLuHGd4qLaOu0flTwox91cfSgB32uHrvo+2Q4+9SbR/dX8qNo/ur+VMA+2wk/e5pftkWPvUhQZ+6Pypdo/ur+VAhv2yLON1Kt5ERndS7e21fypu32H5UABvYuu6gX0JB5P4Uuz2X8qTaR0C/lSGjxj4mzRy+J2MZOBEq81wVxjA9q774nHPil9wAPlJ0rgbjgCsnuUNtxktn0phGGPpToSea9a8DaLplx4Ot55bKGSaV3Lu65PBxQlcDyMq3XHFBjfGccV6/qOm6fHJIUsYQFGAAtctJZW+4kxoPoKTVhnI2dqZpfnUhF6n19q2LyQ20Jhj5cjLAdvap7mSOBGdVGF4QD+I+tXdIsf3JkuQGlkOWJ7e1AjjZIpWJLIeaaIZD/Aa9ElsYtufLX24pLa2tkcbokJ9xTA4m2QqVDAg+lXLlmjTKjJNW9aCrrJWNQq4BAFMitpLlxHGu9uwoEVV+ZQSOTUZZjJsUDA6mrEimNyhGCOKiV080L/Ee1ArmXLDI0h+U9ab9nlJ4QmvRfscH2eMiJRhRnjrUIt4hz5a49hQVc8/MMgPKGkMUn9016ItvCwx5aflSSWsHP7lOfagLnnnkyddpo8l/7prtJ7aLjEa/lVJ4Iv7gFK47nL+W2fumjyZP7prpfIjznYKmjgQcbR+VFwucr5L/AN000IxHAJruIbaDoYlP1FaNpZ2vmDMKH/gNMVzzpbadsbYXbPoKlGmXzDItJsf7te4aVY2uxf8AR4v++a6BrS3W3P7mPpx8vSr5Rcx81SWlxEcSRMv1FRiNx1Br1XxbbwqJSI0B74FcLKiAcKKzeg7mL5bHoM0CGT+6a2LdEaYfKK0hDHjlRSuFzlfJkPO00vkP3U107QJjhRSCFDyVH0ouO5zHkSY5U0eS/wDdNdP5Kc5UDjio2jjx90UuYVznPJf+6aDE4/hNdFHEmcbaJ4owVOwU1ILnONGwGSpxTQD6V094kY05jsA59KwkUAEU7gVsH0pMH0q5sHWkKjOcUXHcqYNGDnpVsoM0m2i4XK2D6UmPareABTMD0ouBXwfSjBqxgelGBRcCvijGasbemaAgp3C5XwfSjB9KmI59qlhUHNK4XKm0+lKFNXyq46UoRccAUuYVzP2mk2N/dNaRVQBwKNoxijmC5nbG9KXy3HO2tFFA7Ch1APHSjmC5nCJz0WnrazMMiNiD7V1GkQxOhyq5HqK6G1hiDjKJj6VS1Fc85WxuW+7C7fQU46bejn7NLj/dr2iytoNgAiT/AL5FWZYI8Y2Jj6Cr5Rc54SbWYfejZfqKaYXHVTXqms28H/PJB+FcrcxwhjhB9cVL0KuckVI6jFJWjqKqOmOtZ1JMoKWkoJoAs22OeOalc5xUdqSQRUhx3FQySMnijjtxQ1HHFMQhBFNJHTvUgOTgjFNKkHNMYzNBweO1Kc+lN9qAGMPmoA6UAHOBUoCx8scn0pjPrYRkd6mUMKZ3FSr0znpX5uz1x6nPbBp445FMU5p4wOlQxD/fvTlIHNMHJ4p4xUgKDSk8YNJj0oxmkIaeaibn2qVuelRP0qkBRuTlTiuP1obouV/iFdhcD5Sa5PWf9V7Zr6LJvtHPWehG4zYnv8tR6VxZR4Pr/Opwu6xIIz8lQ6SSbFcjkEivoUc1yW8GbZ/pXC2z7fFcQX5cyD+Vd7c5Fq5HXFedpJ/xWtuoyfnBotqI9H2gdOat2E0VtdCSZnVMclOo+lQbWA5H5dqawODTKbNqDxxoEYe0R7ySQKT80f8AWuYtryLUrm4u4N5iZ+C3Ws4jGqM4HJUipfDIX7JOACf3xz7U07knTR+LL/Q9LkjtYoJMEtiVSR+hFZtx4t1jV9IMs1laIHJQ+RGcgfmaZeoDbyAtjis+3vZbLRXkhwzB8BT3qkhN66mrDFiBAM4ArO15M6eSM5DDoKWPxBewx5udEkAIyGj5pG8UWxify0Ky44SZMUrDNOx5sBkYJQHkVU1KXybKViH27D90ZqxZX0d7aF0kjL4y6qfu0s1xOkYaJUZccnrRYVzmdDaWW4Xe7SAIRnGK6q3GbSQcj5TWZDq1xNPGhSIKVJ4XmtGNnltmycbhQMzLFH/syVSpyN3BFW7EH7DFlSOO4xTLG7le2l3YDRsV4HXFWbS5lubdWYjPsKLAch4MEsXja9WWCVI9rfM6kA8+tdI0bf2rORGdpH3h0rI0rxLqN94zuNHuRCtvFu2sq4bj3rdE8sV6YQQUwT70wWxNEhVjnislEkTxxDIsTGPy/vBeOhrXeaRiBkc+1ZzaldReJrWxDKYpEyeOehoA14xl3Pq1NnjJ5AJqSBOWOT1pZ98Q+VqQzOjvNQ07U4Z7FnjmI2lguRtPXOae/ifxQmsTwHV38jbkDy1zn8qiur24jljZH6nn6VSuX26zI6nhk6e+KtMlj2uZbqQSTOXboSam7CqlmqhMs2WJzmrxUHvQCGkc0bcAnrTgOoNKB74osO5XiA3v35qfHoKjhz5sgx3qc4HT8qEMrouJ3q9prBNRgOMjd0qmozcMBWhpqg6jAP8AaoA6y+2+UpGalQ/u1+lR6iu23FORj5afSmyRHPGM5pikUrEs3TFNz+dTcolU7mC9z3qhqFtIJXHXnCmricMpz3qDUJgt5MM5PQD3xVxIZjrCIFKMcyE8kVJbIo1OIKcgo3Wo/JbcRIeT3zUtmCNWiXqArYP4VdxHP/FCPb4btGxz9qGP++TXP6pj+wdJJBI83Ga6b4qf8itbn0ul/ka5u93HwrpxAyRP3+hpMSN7w9cRHT4od/ziY5BqdbOG41S2jkLKu5iCpweKg8OxxyaaokRSRcEe+MCmXF/baVqaeZdCKTzD5QfkYrNspdi1rDmGdI2JZN2A1cP4lQp4niUHIMIIrtdauAI1M+BuOQR0NcFrUyv4ggOekW0U+ZA0LMGNl1xzXU62Fbw4oJ/gXp9K58Wsk9ttjXLema6PVwv/AAjojXBYIBgHocU7knJQDEQ9PWlYcNxmmxMUtdwA49aU+Y277g/OspTSfLZs7KWClUp+1coxTbWrtta/R90Ylwnl3RAz6ius0nLafAc9FrBv1SNRJMfujqnp+NbWiWup3tjE2n2bSxY4Plse/fFCq/3X93/BL+o/9PYf+Bf/AGo3xSc+GL3HcL/MV55AcYHavUNW8OeIrnSZoZNInkjcAFIIzvPPbPFc5H4A1zAI8O62M+sajFP2v91/d/wRfUP+nsP/AAL/AO1OYkGVyKpv1ruh4C108f2Bqn0IjH9aYfhvr7n/AJF/Vfrvh/8AiqXtP7r+7/gj+o/9PYf+Bf8A2pzWmk+UMHBzxXotgcWMeTklRWbbfDTxCqgLo14vP8csWf8A0Kuhh8K+J7aBEFjEdo+6XDEfkaFU/uv7v+CH1D/p7D/wL/7U5jxef+JRGM8CZePwNckinb611/iqG5t7AR6pEIwsq8RAhwfx7VzafY9vAn/HFDq/3X93/BBYL/p7D/wL/wC1Kkgwv0qlIfmrUlNjj5vtA+m2qrDTN2f9L/DbS9p/df3f8En6jr/Fh/4F/wDam/pOXNsOMcV6TDjy1+ledaeiqkRhBOQMb/64rtoV1rylKiwwR331Uav91/d/wSngf+nsP/Av/tTZGMe9ToeOtYg/tz008/8AfdSWN3ff2qbK8W2/1Hmgwhv72O/41ftVdJpq/l/wRPAS5ZSjOEuVXaUruyt5LuUPG0rR2lo4UOVl4UjOadZkySKzDaeDt9KZ42VzYWxQfP5vH1pbRnZ4/OGJABuFaHAdY7lIxyORxVV2zinSkkDgjioicYB6UDFz3FA603vinA+goEKe+K4/UlzqFwOnzGuwJGORzXIaiB/aM5z/AB1Mhoo7QPxp3l5wMU4rlqv2VsWYOy8CoSKHWtmIl3MPmNdHocBCzSZxyABWaAMcitzSBttW46tVpEsJfvnmmEcZJqWXG81G+MdM1Qjkvibj/hFbU4z/AKUv4cGuD8MceJbJjjAYj9DXoHxLXPg+JsdLlK8+8NYHiOy9d+P0NSM9Y1UA+FNTwefsr/yrxsRh0RmAJUAg+lex6jn/AIRbUuCf9Gk/lXjLo7GFlYhVHI9aYiwgy2egqe4me4lMspy+0LnHYVW5HTrWrrV1a3c9q1pF5Yjt1SQbQNz9z70AYUlzevbpA+/7IjkoCPl3d+ahTvmrcuqSnThphjXy0lMu/vnHSqinIJqRnqXh3/kVtNP/AEz/AKmtBm3L04rO8OEnwnpw/wCmZH6mtDHy4A4oGIoyM1jaozSRy9vlNbQwFzisTUAzRTbQTkEAUMcdzm/Dql9TYiHzSqfd9OetdGJplaaP+zsgHAHFcpY3Vzpc5lt1KvjadwzVxvEGqkk5Xkf3KzTsdFSN3dGTZkjUgAOd5z+ddSDkqe2K5mxSQ3yEoeSTnFdMM5QU0ZzGQNukYe9WDnHbFV4fvtxgZqycYAqjI1/D+fOn/wB0fzrdOc+lYPh4jz58ddo5/Gt7HNMAx2pxHSgdKO1ACYqxbkeWeO9QdunNWLb7rfWmJkqYZhWqn3BispR84Na0Z+QfSmIRuRiomOOKlbgVETkUAN6U1ydpp3So5CQpoQEc0KyoVcAoRgg1TSytbd90KRRnGCRWhIyKo3NjIqhPHHINwYkjsKuxV3axcjYLGPmBp4yeaoo3lRIvHXpWjEMqDVElaTInXbjPvV6PGecdKq3H7uUNipI7gsSAgJpAWwoPXFRxg+Y+QKRZZP7lM+1YlbIA6d6QFjb9KZIDsOAM1IJmZAQmagkuSu5SoyB0phYW3zg5p8j4Wo7c5QmiU5Xmn0ArTSc1VlyUPNSyHLHPaomA2Hr0rKQznrsZlPOKr44Jq1ej9+2Kqn0xUDK6kec3rinscqaYoAlb1pzd/SkwOZ8TkLZTMQegxXqHhYf8Unpfb/RlNeW+KebKYA+n869T8L/L4T0odf8ARkqoiZemAOaz5/TFaMg5rMuMgk5qmBk3XHFJpJ/4m1vz/FS3Wc5pNHIGrQ8ZO7j8qS3A7EVIoqJSQamXpWqIY7Ix1NIOe9IwOMCnKDimCQjYzSZGKHz6UmcCkMXvTxjuaiycU4ZxQSPGKd9KaCT6UfMD2pjHZzRwOc00g9qXBFADiRScD1pOfak5PakApIoOKbhs9qMMfSgaPFvieWPiyUfw+WuK4S4yCK7f4mTL/wAJhMrSLkIo61ydjd21pqtrczhJYoZA7ISMMB2rOW5RTjVkJ3KVyO4r2HwA+zwRa56BpP515hrerf23rVzfCIRJK2VQdgBgV6V4QYp4DtWxjJYfqaEITVJgYZCOrH1rlbuTBMaZLEZPsK2tZuBBEucY9PWuTub+5lnaGHCsR8+0ZwPrSYC2kYnuDc3LCOGLhFbuatHXIIGwiFz+QrEeOVoixYk54FReQ2fmPapA6NfFAdkjNuME4zmrz3VvHtYyoM/7VcikQTkjpUEjFj15FO4GpqMyTamXRgV4GRU1teNZTCVAMgcZrKteJF45q3dg7QE61QBJK0sjOeSxyaYka+dvPWkUHAz1qPD+dkH5QKQHY/2lYLbqDcxhgoyM9KrNqtljidPwNcQ7ESNz3pC3PWlcDuP7Ts0YD7SnPvQ+qWZX/j4T864bcc9aQEnqaYHWz6lbPjEi/gapSXkHI8wGsDJ7E00kg+9IZvC7iz98Yqwl/bDrIK5rr3pN3GKAOvj1O1BGZBV611iyVhuuEANcHmk3HpnigVj2jTfEmjxqPM1CFT6E1tt4u8O/Zz/xN7fOOm7mvns4I6DP0puAOw/KtFMXKekeKNe0y5Di2uVkzxx3rj2uYm5BFZBPpSZqHqUa0FzGs+Swq8L+Du4rm8nNOKsOoIpWA6H7db/36T7fBj74rnueaNvHNKwHQm/gI/1gqI3kRPDZNYWcHGatWEsMN3G83KA9KGgNU3McZw52n0pst9CxUBs4NRanC95cGaFSyt6DpWS8bxvsYEEdqSQWNy6vYHsWVXG7pisgSADrVc5FJzVWGWzIKbuHrVejmiwE4fnFL5lQDrSnJosImLims/NRcigkkdKLDJQ3ejIqPFO2t2U0CF3Dv2pwcVGVb0prAqcEEGgCQsNxqRJFUYJ61VPvS9uKLDLomTGOtAuEAPPFUs4pOaLCsXRcJ60CdSetU6TvS5UFi99oUd6Dco30qiRigYp8qCx0Gl6nb2xPmtjPStqLxDpySDc7bR1IFcLRmmtBNHrNn4w0RFw1zj04qaTxroRIH2sEH0U15BminzMOU9B1XxRpVyjC3kZmzxkYrmZdTjY5DViYoxSHYt3k6TY21TpcGigYmKUjnFFFAye3cJnJp7TK1VcigEUrCJi4PegsCOKho7U7BYl3e9KZOAM1DSZosFiYsPWmckYpopevSgB+8IuFHPrTCCeSaU8CkzxQB9fDnkipEwKrpIeeDmpkcGvzdnrk49c04HOMVGpAqQDjjrUCHdOtOHNMHXmpOAKlgKBRnBoBo4NIQh9ahfpUrHHWo3PFUgKVwSIz71x+qyxXGYSzIQeuK7G45WuV1s7LR2CAnI7V7+TX5mc9fYpi/gW38kh87cZA4qKzu4ra22EsxBJzitC2CtYKzKMlOmKr6Sge2bKjhz1r6VHKPivtOndY715ordv9Y6LyBXLatoemQ+MoL/RdQklsECtiX7+7uBwOPwrsrmNDbuCgIKntXmEylfF9mASFMqjb2607h1PRTqsBOQHxn0qT7ZC4ABIOM9KtuiH5tgGPQVAxC5+UflSY2YAO3ViS2Vwal8OPDBDdxmZdxmzhjiq8ny6qD1zk4p+lada3guzcREsJOMHBpRYkbGpKTasy9cdQaxfuaM46Nvz9avvpEdohkhkl24xsZsis8bjpFwByytxWl9BNanTW+Wt4yx5KiqOtxI+mSlkUkDg45qrbX+twwxedpYljCjDRnkim32sJPYSwy2lxBKR/GvFRcZc0u0t009ZI4lRpI8MR3qBvD8aIxgupozgnaDxU+k3ltNYRxJKDIi/MvpWmhSaEmNlY47GquBxdn55ukWFwXwcbq3rKS+xsuIU2AcOp61mWMBhvEYjBYmuiiyLZfai4HPw6vBYR3Rnhk+8cADvS6RrtkdlnJJ5M7EsA/TFaVjFHJJfxyxq6sRwRWPoGh2v265nuVE7q2EDfwigEY2m5j+KcrDlJAxD44IxXYuD/AGkoxxtNcsbmW0+Isdgsai2PKjv0z1roLi3uG1VWt7naXBI3dFptAnc1CnzrWNfqU8Y6awHJTH86tE6vA3ziKf0xxVO5lmn8Sac89sYGUYAJ60WGdPa/Nu/3qLofLilswD5n1p9yPk9aAOc1Acpgcg1UmwdTHJ5j6Vf1NcKhB5z0qpOpbUoRjgxdaEyWQWm4FtxzzxWljjms2yUh3yc5bitULgc1QxVQHBHSlkgEq4yRT1GV4p44NAWM+KGVJnVZM49anPnL/BnHenxDF3IPUVZwfSgZn+cqzZbIJHT0rQ0yWOTUYArrndwCeagMSG55AORVi1tITfwyFMMrDpQI7HUgRbIOtOUfu19MVHcnda+2KdGSsC/SmwGvjJNRkgGg56CmsMkDNQxkqDkNWfqO77ZLMI8KmOfXitGIAAVl6xJJJf8A2Zj+6wCFH86uJLIUQmFriRSB/DnvUdof+JrBg5BVv5UXsxZ0RWJRF2gUtuNt3atjBO7+VaWJMf4ngN4RRu63KkflWC0PneEbRi2Ntx/Q10PxKwfBrgdRcIf1rEgw3gyPI5+0D+VZVZNRdioJF3QbZJNN81WKyC427h9BXpFv4f0doIZZ9Ot7ibbkyTRhz+vSvONAEz6a7xSD5bjGwjg8CvWbUk2cJYAHbzivBzivUp0IuDs2bwihHtLV1CtbQkDoCg4rOvNH0qbJk021cjuYxWsaqXHSvlVi67lfnf3m8YpnMTeG9GLECxRAeuxiv9azZvCGlmKSOMSxq3o5P866eQ8mq7j1rshjcQted/eX7OHY8w17w5Ho9gzw3BkjDAYcYI5rEdgsZY9ua7jxoCdHlJxncvH/AAKqPgLw6dd8So86FtPssSzZ6M38K/nz9Aa+oyqtOtFym7jxVNRwlNL+af5QLVh4S07R9Di1zxTEZZpRm205hgD0L+/t2/lVl8datOwi0xY4IVOAkaABR6VD8TNdOseIZYIpibeAbFCn8657SLyGOySMuN6k7vavXZ5XNdm/qHjXxPYWcl1LcHyY8E7MVjR/F3Vy8YE05BcdcdM1W17WdPutGurJJmaZwBhV4H41xtrZ/vIsh2UuMnp3pDR9AP4k1F5A+cZx8oJxXFeKfiDrGlasyZk8nYMBJCMGujU9N/AxXmvj2VJ7xmQEAYHPfFIGaEPxE1K7UkPc9e8xre0/VtZKJdR3jo7fNguTXmOm8Q59DXeadqlrHYxK8ihgORRcEz02fTLL4jeCbgXsCrf2+VLJ13AZB/Gvn26tnsbuW0c5MTFc+tfQnwp1CC9j1hYWz5cke4fUH/CvFPGNqbTxXqULAArO4/U1Vge5zUwqmw596vSDmqkg+bIqRHa6Mmba1YgD5RXoUJzEp9q8/wBJkxZ2pI6IK7SLU7URqN3OKpAzTHpWbkjxV/25f+z09dUts4LVSF9C3iPzQ3yiz2/jvqKr+H1X6ndgf+Xv+CX/ALaQ+NdzaXBtYhvN4PoaZaBwUMjbnwBu9ab4uuobnSkWNuRICT6VHZMEhRQ28BQQ3rWjOE6qViUX3FMAOKghukuQdgJ2jmpgTtANCAcOKdjpTRgnmnE5HemgA81mTaPBPO8pyGbrg1pjgdKci73wKTAzoPD1tI43M2B6GtVNGgVcKcAdBVpECAAfjUo6d6aQXKP9kRYIyeas21sLaDylORnOTVgHikJzTsBQmGJKYentUk33yADURPFIDm/iQoPgosf4J4yfzrzfw2f+KjsM/wDPTj8q9K+IKh/A93wTtKH/AMerzHQG269p5Of9cKlgewXvPhnUl7m1k/lXixnEYhRlJ3jGfSvabr/kX9SGMf6NJ/6Ca8ZCKyIWx0GKYhykKTknFat5phsZLfMiv50Il47A9qylAJC9qutNL5iGUsdq4Xd6UdAKl3d2i6atkIf9K80yGTH8PpWcpBXjpWlfJZ/2bHL/AMvpkIP+5/nFZS9DioGeqeGufCenYP8AAf5mtLnpWb4ZIHhHTmx1Qj9TWkTTsAn4dKzJQC59M1phu9VJLYEkjp1osNMypLKJmJxyetQNYp6ZrUaMKpOeagqbFXKiWaKflGKe8W1lIPSrJxUUvamJlOL75Ge9WOq8jpVWI/OwGetW85XmgRqaAR9omwP4RW/3rA0FR9pmJ/uiug6fWhDFGKD1zSZx0FHPemIU5zU9ufkYe9RDGKkt+jg0xEyHLCtaMYjHpWSnUVqxn92KYhWHvUJJ59BUpqNqAGHjmopD3z17VIxqJj8poAiklMjA+V0GOtNGME+WKUkFqKfMMiILNzH+tXY5pAANgx9aiT7wyKsKPQU+YB+PNALxjjkc0KgiYtGg3Hqc09V9KcOKLgQtc3KjiAN9GqAzynIOng7uvzVe9KPai4iut9dABVssAf7dLlndnaBVLdeanJqNmxRcZGZNigKoA9BUMkregpztj+lQM3NJyATkkZ709lxHSqCcGpWH7s1LA5W+BFwaqHnODzVzUObk1UPGallFdADMwpzdODTV5mc+1PI+XpmkTc5fxMMWM3fgfzr1Hwzx4V0vPT7MleYeJV/0Gf8A3R/OvT/DZz4U0r/r1T+VUgZdl61QuB1zV+XgVnT1TEZN3wSRTdHGdWgB4G7r+FOuhg03SiRq1vjruI/ShDOxHXrUykYqBfpUyk1oQO79aUdOtRnOelSDOCMUDQ1uaac8ZNKxIpnNMQ/IIxmjgcA005xSDJoAk49aZc3K2trNcMCUiRnYL1wBnijnsKqav/yBL/jH+jSf+gmpm7RbRrQgp1YQls2l97S/Uqx+JlljV00jVnRxlWW2yCPUc0k/iu3s8C60zVoCTx5luFP6tWh4flEXhyO7YZW2s0OD67RisCz8NHVHbUteke4uZjuWMMQqL2FZRjUcU+b8Ed9arhKdSUFR2bXxy6Nrt5F2Hx1oA3efb6mOcfJEn/xdSp488Kj78OsEegSP/wCLpq+FtITAWxU/ViaSfQNKhtbh2s4U2xsctxjiny1P5/wRn7fCf8+P/J5f5Grb+JtCuofOt9B8STx9N8duGH5hqhm8c+FLKURXOk61HJjOyWNVOPoXrhtOBOnRLuJ6nAPvXRaRZW0gdpreORuOXGTRyVP5/wAECxGE/wCfH/k8v8guvE3wuur1rq88MSz3DfeeaGNs/gZMVDdeI/hXPCYo/B8TEnkC3ijP5q2a83+IISLxTNHEixKoHyqMDpXO6ZZ/2lqcUU10tvCDl5GbGB7e9Q4VP5vwRX1jCf8APj/yeX+R2eu+FtC1Vprjw5Y6razNkiLyQ8f6HIFW9Hml0zw1b6bPbzeZEx3MqfKeT64pNId9G1ZWivBJZuf3YLZLLW74n0+4vlR7GURxTD53/u0+Sp/N+CF9Ywn/AD4/8nl/kcNq9ybyXes8CRr8se98ZPv71UsrH7PE5kljaZ+XYNxTZ7O2t5GnluxKkRKIqjqR3qIX8Wz5mC/Wo5Z/zfgh/WMJ/wA+P/J5f5Dmsxg4uIB9XqBrRRkG8tQfQy1HdXltsVY5ASfvY7VSMkZJINHLP+b8EH1jCf8APj/yeX+Re+ytj5buy57mX/61NOmHbzd2uT6yVTEqluB0p8rb4QSMc0cs/wCb8EL6xhP+fH/k8v8AItxaeFkB+12v4Sc1O1luYAXFvknAG/rWXFw61fhUG6gZuvmLx+NJqok3zfgjSjVwlSpGDo7tL45dWl28yJkKSMjYG1ipx7VA8iiQoOuKt3GPtU2P77fzqq6jJOOT3rVO8U2cNeChVnBbJtfc2v0M9vvHOOtMOBRJnefrTevWgzHdaMY700gg5pR0oAXpzQw6YpD14ooEFIQOtGfWg0AFJjvQaKBhSEE9KDmjOeKAECljgcmlMbjgqc1qWUCpGZDwx6V0sEtqlsg+wEsBy2OTRcDB0N7CEP8AbIS7E/KcdKt6lcWBaLyoiT1OB2rTlurNTn7C/TnC9ay7i5iafzY4NuOikUriHRSWFxkiy2Y45FTBLIMp+yJx7VZ01BLbea9vI+T1C1MZLcZzbyAj/ZoAtQafp08av9jiyfarcehadtx9kjOeelO0ySCWDasTqQehHStyCNAn3cUAZn9kW0cQ8uIKuOgrG1DRbCUEzREY6kV2WECdKzrlEY8rSA8/fT9EyR5koP1oXTdFZMG4cE1tTW1m106bWBB9KhNtZLxsIwfSncLmWNJ0huVum/GmjR9Ob/l76VrG3s2xgY7dKa1pYduPfFFwuZa6NZ7vkuA496kn0G1DKUuAMjlTV0WtoOFfH4UjWtqz7vN575ouBm/2BGRnzk6+tB0EAfJIh9ia0fslsp4mpfssHUTCgB9po1sUHCFxWnDpdsEwVXPsKZpcERYgSAnqa3IbRCvXv1pXC5nHSbUx58pScelYeo6BbsTIind6CuzFooHWsnU7PdbvtmKH1z0podzjn0UZ5jaozoeP4G57VpfYps8XzfXNILS6XrfnP1pgZUmiEHhX96aNFY8fN+VbBtrs/KLzk9zTxb36DH26Mj3ApXFcwf7FYcZI/CnJobsPvH8q3Eivy243EfsDUoF+qNiWEk07hc5x9FkU4yT+FRnSWGeT+VdUDeKBloSfYUn+lk7tkJ7nNFx3OVOlsM8nikOnYGSTXWI02CXgiOT09Kad+Cfs6EelFwucp/Z/XLH8qT+z/wDbP5V1u0MMfZU5pTBHtw9sOaVxXOdtdKt5ThmYn0HFai+HbSRDy4Y9DmtCKCLeMW2MdOa6mzsLZ4Y2MQzjPNLUVzza88PtbHKyFl+lZ/2FQeXxXsqadZMP3kSnjHNczrWi6fHchkjHPpTKPPXs9oyHBqM2smccV2TabZMBlGBFNbTLPPcfWi4XONNtIO1H2aT0rs/7Lss9Tik/su1A607hc437NJjOKcLSQjNdb/ZEBHEn50v9kQDpKPai4XOP+zyelJ9nl/u1150qIAgMOadHpYjYMGRsUXC5x3kS/wBw00qy5yDXoIDLGY1SEgjGSoqvY6Fb3NwVuCD3+Wi4XOE6UHOOQfyr0a1trTR753htYJ1/hEq5rY/4SpyMf2XYYHQeXVaBc9fT2qQAE5BqIDDDFSqOnrX5sz1yULxTlziminjOBUAPBBFOApo9acDmpEO6UE45oooAaTUbGpWx0qJhgHFNAU7kfKfWuO1h7tRJuRfs57967KY5HTiub1wL9hfd0r3cm+NnPX2MeHV5IoESTT52TGN6DINS6ZqFtDEVkLISxOCtalkR/ZqY+7s4FVdMhjkilDxqw3nqM19LZnMSyXFrNA+24jPB43V51PAT4jgkHO2ZT+tejXGmWZjdvIRSASCvHNcMSF1WNiekgx+dFmJbnoDj1OKglHr0q0STUEy/KeKtlSOYn2jWowBzg8Vf0EEi6Lf89OKzbr5dZiOeeeta2h53XIHTcKlIlGjeKfsr+uK52MGTSrrAAKtnjvXT3CE2z4646VzVrtbT70cYPJ9jWiQnudJYAnTrds5zGPwqPWSf7IucjPyd6n0r/kGW3Of3Y5o1dM6TdcdIzU2GZ2i28B0mKRYlWQrhmA5NRt4Zs0zJby3EDkE/I/Gan0Yl9EgIHr/OtYAGHGOcUCOFto7rz4tk2TuIG7tW/YtfhCl0sZjGcMlZdvuW7TpkSkV0iLm1ye1OwzGs9QS3vrpZYJCGxjA6YqrpWqiLWJbZgEhlJYM3GDW7YKP7RnBxkoDyKgtLCzunuBNbo5WQgEjpSA5TWYlj+IVndowdSVGVOR6V1csf+nQH0yK5zWLO30zxTZR24ZY5cNhjkA5rqHU+fbk8nOKY1orFto+VrF10bNa0qQnqxWt5lxg1h+JgBd6U/YS4/lTQXNqxH+s9c1PcABPem6cAxk781LdKQhoYHO6muEQkc5qnOMXttzk7av6kCIRn1rOdsX1oTyNvekiWR2ibZn781rKu4c1nWwAlYZ/irVVTt4qxiQjipSBjpUcDZ3D0PIqcjBAwBmgZUU5vGwOdverYBIziq541ED/Yq4nTmgCsV/0oD1FW4VxcRnPQ1WlXF3GR1xVg/IQ3THOaBHUSqDY/NxkUikNboc9qypdTRbHJYk7eBSxaiGtYzj+GiTBGj1o4zVJL5WFPFwDyDUXGXlPIrLv43k1SSTGAqgA/hV1J1yAfWqOtT4u2hjIB2gkitIkyM1Nx3FueatRf8fVq2eMt/KqRLLjt71ahkG6DPXca0JMz4i8+D5vaaM/rWDbE/wDCGZHa5H8hW54/fd4Ku2xnbJGf/Hq52wnSXwOzqxKi4Gc/QVjW+Blw3NbwwZBp1w2zcizg5HrgV6xZNvsYWxjK9DXk3gy7gbTb0bm4uBnI9q9X05lfT4WU5UrxXzmdL/Zo+p0RLJqpc9KtmqtyMivk1ubQ3MuQc1BIvHvViT7xFQSgiuqJqcd42GNGlPqy5+uRW2jL4D+HC4G6/vh5kjZ6Mw/oMD65rE8bZ/saXuAy/wDoVaHiwSeJPDYls90htolYRjrtx6V9dkb/AHbDG/7nT/xS/KJ5PPI0zySuTvc7ifesA7jK+12Azzg10TqhBIOeKqWuiSXUTTK2AWNey7njox7hAtsxXr61QjZiFy7DDDGD711OpaFLbaPcT7s7Fz0rlIzkD1BH86RUWe7KcxIT/dGfyrz7x4FN2eP4Vrv4mzbxnH8A/lXBeOwPMzjqBTCRzGn/AOpI96sK5LYz0qvp+PJI966C20CSe3WbcQG6UrXBI774FXPl63rsRICPbxSk+m0kf+zGuQ+JEkUvji/mixtdyeP510PhaFvCek6hdNl7q8ARO2FFcHq073V/JLIQzseTV30sEtTJcZJNVJOtXpBjNUZsg0hHZ6Xk6Zak/wBwVoJ6VnaQA2m2w5+4K6eHRiyKxbHtTQGYOOKIyf7R5/55f1rbXROcFqrJpYGu/Zyf+XbeP++sVlVXw+q/U78D/wAvf8Ev/bTI1o/6A+enH86uWYQW8Ww5XYMU/wATaeLTSfMzkFgDRYmKS2iMabU2jFanAamjo+JiPUVqAN3o0+zEEZzzu5q4Y6aAqqC3HenhSF4FWVjGcjinCIHrTAqhWYjirkEOxcnrTkQKM1JkY5oAFz3FSDNMU89adkZxQA4CkPHalFBIxmmBRlB3nmoW4FTTY8xutQkg5zQIwvHALeBdRPXAX/0IV5NpLMusaeV6idP51654x58D6oPRB/MV4/YHGoWjH+GVDx9ahjPa51J0LUOn+okzn6GvE5YjIkLKcFR+de1SkHRL8EH5oHH/AI6a8VkmMYgG0kMME+lMRKM5HrWxf30eorZKUKvbwCIn1xWOGwcitfUrBLCHT5RJv+1QeYf9k+lMDEu0zGG9CRVFOjVo3K5gcfjWbD0bNZ3KPTdCS5/4RLTXtpVBCt8rDg8mpf7YEMgivomt27P1U/jVTQLxo/CdgkdtK5UMMjgHk064v9QKMP7GR4+++QGncRtRyLLGrxsro3RlOQaV/umuJtDqGl6lFIGEMMrbpYAcqAfSt268SadbyrDLMVZhkErxQOxalOFOKrgndnFL5ySwh42DI3IYHINRhiPrQwJe/qKil6inhs1HKR8tICghxIw96uKBs5qlGQZXHfNXAoK/SgDW0Af6TN838I/nW/nJxmuf0DH2ifP90fzrdzigZJjPejH/AOum5PWlzgincVh/QDipIDw31qPtTrc/foEToPmFasJ/d5rKj6/jWrFnyxxxTAU1G/I9xUjcjFQvxxTEREjvUL+hp8h9OlQ5zSGOA49aUDgUi9OtC8mgCWMEGrKKMfWoUHSrCr6U0IeOBRjml/SkNABijOKXHFIeOtMYhNQSHrzT3bH0qu7Z6UgGMcnFMC5680409B+lAD4l44pZztQ/SpFXjNRXPzIeKGBy18f35Oeardc5qxfD/SCarr0PpUjRWXiV/WlJwtIBiV6RiQppWCxz3iIbrKcZ/h/rXpnhcj/hFNKbpm2SvNNdINrMCOClej+GH/4pXSva2SmhGlL7Vn3AGDxVuV8EntWfPJyasDMucc0mlL/xMocdc9qZcvyaXSD/AMTOEg8g0kB2IHTJqZQCMZquDnGRUwOFrQljwPen/jTE6U7v0oEMbGQKTgd6CTnpQOnvTADj14oyMdaTPrSdRQA8emaqawP+JJf8/wDLvJ/6CatL9KqawWGiX+eR9mkx/wB8mpqfA/R/kzpwv+8U/wDFH/0pC2cbw/DaF8EfaDEgPqNo/wADVDUdWvbaddP0exN1cqgJaTiNBjue9S2ep+f4Q0ayyMQyISP8/U1q9ycYzSp/AvRfkgxX+8VP8Uv/AEpnHXGj+M77y3k16C23j544V+57VkXfw+1q8kl+1eIWljAyCxbn8M16OaZJnymyB0NVYwucFpkJt9Mgj6tGu3Prium0FmeKXeecjFYSDEWB0zWzoYI80fSgXU8v+IqAeLrps5OF5/CuN2mSQJnrXX+P1b/hLLtmOfu4/Kubi+ycCQOZD6dKyluUS2kk5uQGnciIYXnpXsPg2/g1fS20+5OWA2nmvJYTBEjLDE248knvXX/Ddp5NdYqrCNBucmnFie+hzXiPSG0XXLqx+by0clM9waxLhchQK63x7fxX3ie5eNt235eOnFcZNMGx60pbjQ4JApy0hPHanLcW6EEKTj1qmTk0gBzjFILF9dRCKwWEAE5pHvGuWQBQoHYVVCMQeKsraSxspZCARkUAWYR+9ANXIhm7t9pP+sX+dVI+JAPwNW4fku4Md5F/nUy+B+j/ACN8L/vFP/FH/wBKQ64AFzL/ANdG/nVNoyZWYnjHSrlzzcS+vmN/OqbSNvMe3gDk1UVaC9F+ROJ/3ip/il/6UzPkHzmmYFPYfOaTHegyG8YpRjpR9aDQICBSAgUcCl96BiHFApeM03GKAFwOfWkwAKUikxQAYH41NaopuUJxgHoah96A2DwaAOlSGFwZJJQpB4AqxbXuy4/ez/u8VyW9v7x/Oje/979aVgO3mvbR1I80A9qypfs4JIlBJ9653ewHLGk3n1NFhnc22v2lpbJBG7HaPSk/tZZvmBHNcNuI6Gl85wchiPxosB6HHqsNpGGEgLHrSt4sQKAGGPc1521xIw5c0zLH1osKx3s/jNhgKy9Kz5PF93IxEW1R6kZrlRGxIqzHFhTkiiwjUGt3Zm3s6sSeeKbJqtwzE7hz7VmqAD94cU5mXPWmBfXVLkfxD8qG1S5xjI9+KpB1HOac0ibeDRYCx/adxnqPypw1GdicY/KqJZT3qaApvAzzSAsvezLjIANA1GYZ6flVe5dBJjdk1BuHrQBr2mtXNqS6bckYyRV1PFl+gx+7x6ba5zcM9fwpSwPelYLHRt4y1IrtCQj8Kq3PiK8uECvt2+gFY24etJvHTNMC8NQlx2oF9J6A1S3DFIJB+NAF7+0JDxgUjahIOoBqluBoLe9IC3/aL8HFOGpyDtWeTnmlBBpgaJ1OTGAKYdUl6f1qiWAPWmswagC+upSk8j9ad/aEo9frms4NinCXigRfOoyY6n86P7Vm67j+dZ+4HvRkUDNKPWJkI4z9TWqnjS+iVUSKIBfWuXUj1pd1AjopfGmpOpAEa56kVmnWbuRiWmYknPNZxIpBQM0jqtzn/WE03+1rk9ZKzy2Dx1oBHPSlYDR/te6H/LTP1ApDq95jHmD8hWeWyfejPNFgL/8AbF2Du3D/AL5pn9r3e4/OMH2qlnPSgEUCLw1m727SV/75pP7ZuwcblA+lUhijK85oGXW1u7ORlfyp0euXceSpwx71nYFIAM0wNE6rdbN2/vUP9q3GCCxqAqPLzmotvHWgD61QgnOanVsng1WC4xjrUyKe1fnTPZJgeenFSL61EpIHIqUFccVmIcP0p+PSmA8cU5c1ID8+1JnNJSgZ/CkIKib2qUimMfzpoCnODtwe9c9rgxpkx9BXRTjKn1NYWsxM+lzqB8xXFe9kz/eM56690g0wbtKh/wBzrUej8pNk5w5FT6Sn/EogBPRMVBowOLpTyfNOK+oRyl6ZR5Tg9Cprza4AivuecOMY7816dKmYXB9DXmlzk3KlhgBxx+NAdT0NQSqnHYVFOMZq0qjy0I7qKhnHPFMbOOvP+QupOMDIrY0IZe4HuD/Osq9Uf2uBgg55zWzoQBuLhccDHP51KEbfkLOvlmZYtw++/QVjDQ7SC2vVXX7KYsOiHpW1PEGgcZ7VxNhCpbUo8KPoPrWqE9zsdLj8vTIFBDgLgMOhpdRj8zTrlSMZjNM0BmOjW43H5Vx+tXLksLWXIB+Q9fpSKMLw8Q2hJznDNWwozGOwxWToT+bpLsECgOeF6Vtov7gcdqloTOKPy6jsBz+9zmunRMWfXFc3cx7dXk4wQ2TXTxAtZAnuKYIqWoI1Rh3MQ/nUmnRqLi7UHkSk063XGorkcmPrRp8eNRvsj+IUWA5fximzxBpRHc8n8a6GUBZbbBwC+KyfGkROo6S46K3P5iteZSUt2x0mFFgL0iZx6ZrE8Uxnbp7dhNXQyjKj61jeKk/0CzbHSYUAaekDAk61ZuR8jcVX0gcPkVeuFzGaYzl9SH7nJPesq5GJbU474zW1qa/uOfWse5ODZk8qXoVhMW0x50g77q11HFZNmALmb038VtIAcUwIbNCZpu3NW2Xp61DaxlZ5hV0LTCxnSDGpR5HVDzV7bxVC6BXVrQA/eDVqY4pDKc4IuYc96kuELQOB1xTboYngOe+KtFcjnpTAymtpEtACSTitG3tWayjyf4au3Ea/ZQQOMU2zQi0Q+1KSEiuls4XhjUvlsm0A1dRQelBXn3qEhjEVsVS1NI1uDIjZk2jitVFIWsfVF23G7GDjjPetIksjEhlCRsAB0JqYpHFNDtYkbsVArgwjOAfapGZd9uApIDVoSZnjoZ8GX+ASNyf+hCueslDeDpRjAEw6fQV0fjUZ8Gaht/2P/QhXO6ed3hKUA8ecv9KyrP3WXDc2fBSRw2F5Gu1cyq2D3r1LT8f2fDjpj+teWeEoVktbzem4Bl/lXqWmKE0yBV6AH+dfPZyk8Kn5nQi1VW4zg1aNVrj7tfILc2huZcg+bmoXzip3PzYFQyfdNdMTU47xrj+xJzznco/8eFZXh3xJNplwmwg7BtKno6+la3jQf8SKf6r/AOhCuBkfD5HHHavq8k+BjxrthKf+KX5QOs1Xwpbas02o6DOolkJd7OQ4IPfbWLo8E9tZGC8geGdHbcjrg9ahstZmtpQzMwC/ddTgiuvtdbsdZtgl+A+OFuIx8y/WvfTPIscx4hH/ABTt8P8Apka8qRikZPuOfxr2jxRo9ynh28e1X7VC0J2tHyT+FeLxMPKbPXByD1FDQR3PrLSfB2k3Oj2E0q3G6S2RyRJgZKg1418WrCHTfEtzZWu8QpHGwDHOMjJr37w+nneGdIYsw/0KHn/gArwz40gJ4xum3ceRF1+lDHI8808DyyT0zXqvhnTRc6RHc3L+VaRD7x6v7CuU8D+Ff7RiOp6oDDpStkA8NOR2HtXTa1rJvZ1srJFht4htSNeiD/GpDoUPE+uGO2keLoo2Io/hHvXA+aXJb1rrvEcSQeGZVA+YuhLdzzXFRMNvWhkj5OnX6VRmPzVbkOBVGQ5agLHbaKP9AtSDjKivQ7X/AFCemK840M7dPtyT/DXoely/aLeNQeSQM1cQsXMc1mf8zV/24/8As9dRd6I0OrWum28yyTzoXO75QMVQ/wCEcv8A/hPPsX7ozf2Z5v3+Nvm4649azq/Z9V+p24Ffxf8ABL/205vxlkaCcc/OOKo6axmsomVdo2jjGK7bxJ4A17VNN+z2n2YOWBy8mAMfhVWz+HXiaKBRP9hMgABKSnH8q0bOJFmE5hRsfwin5J7VsQ+E9VjiRWWHIGDh6k/4RbUx/BF/38poGYoByBinhSQcmtceF9T7xxf9/KX/AIRnU8/6uP8A7+CnoFjG5ApR71sDwzqfeKL/AL+Cj/hGdU/55R/9/BQBk4GaePpWkfDmqKeYVP0kFL/wj+qdrcfjIKQWM7p2ox0rR/sHUhwYB+DihtC1BE3vCFUHklxwKYGDccPxUDg4960dXtDY3SRNIjFl3fLWexFDAxfFp/4ozVVx/wAss/qK8asyReW+P+ei/wAxXsvizDeENUHbyT/MV41a4+1W/wD10X+dQwR7c4I0e8/64Pj/AL5NeMDHkx7scjivZs50i6H/AExb/wBBrxWWESxwNkgryMUCZIMFhnpVmVJUEXmb9m3MYb09vaqq9a0tRv0vLewjEPlvbR+WzZ+9TuCM2ZvkK9yMVRERiGWI56VctFFzrMNsxyHfp7VJ4ihhtdTEMK7VCZIz3qOpZ3fhps+DrAscj5//AEI1Vn121aRooiXK5BParHhc58EWW4AY3j9TXH5Kzygf32/nWdSTS0OvDUY1HqaQdHuPMaRjls4J4q9Nd6ZOGhubaIq3BOBWBubkZpoOeDWKqO56E8LTatYnhlk0HVVsw5ewuTmMk/dNdGMHnOfpXMa84k0e3Kj5omUg/hWtZXG63jbJwyg10p3R40o8rsawPrTJBkKTUX2uPox2+5qvLdea5RXU+9BNiOM4uH471eQ/LnFZSOIpMA5PrWjFIGTigRr6M5F1Jg8FelborA0bH2h85yV6VvA4NMZIDinDkU0c8Yp+OKAHD3p8HV/rTcAde9EbYLU0STg8itWE7ohWOGyQO2a14ThABVAPYkCq8jcmpWPrVdzQIic84qLOTTnJOaaMYJ70AKTxjNPjH41D1NWYxxmkMmQc1aXgVBEPepxTEBOaAKKOnFMAPAqJm71IfeoHOQcUmMYxJ+lQse1OLcdaaOuaLgKq8+1TRrznNMQdzVhRx6UAKQAKp3DfIatO20d6ozD5Cc0MDnrw5mOKrcg4x+NWLr/XH+dQ+oqRlQZ896jlYrkKM1MoAlcd6a+BmkBzWs7vs8xbIyhr0bw45/4RfTBkZ+zIK8+14/6LMFGTsrudBfb4b03/AK91/lTQjSmbg89azbiTOfarE0hwcms6eTr61TYFWVgT9am0n/kJxZHc/wAqrEbj71b0nC6lFxk5/pSW4WOuTripsccVACAelSK3tWpLJVX3p30PFMDEjgU6gQjcHrTR0680jk+lJzjoc0AO+tOC+9MzyODUg5GMUwsAUE1V1gAaJf8AP/LtJ/6Cat96o6wSdEv+P+XeT/0E1NT4H6P8mb4X/eKf+KP/AKUjkrZprS3t0LEK8SSIe33RXXabfR39sGDDePvL6GqVvYJfeHbFCMMLeMq3odorn5o73RrsM2VbPDDowqab9xei/JBiv94qf4pf+lM7rHHWmyLlDz2rN07V4b2MDOJf4hWg7EqcCrMDiVDMCqIzYJ+6Ca29CR0aXzEdOBjcuM10PgspHpLtxuM7A8dan1uTdfxttHKYxjHenYo8C8fOW8W3owcAqP0rlkXdOoHWun8dyM/jC8iVGLFgFUDJJx2rY8N/DuUouoeIpPslrjcLfPzv9fSsmgMfQtHudYmWKzhLAcNIfuj6muxuri08FaLJY2ribUJh+8fPQmmar4ptrSL+ztDgWGBBtBQYAri7h3lkaWVy7nkknrRsBnXB3pJI5/eHJJrPihtSfnd2P5VFNcylnG44zUAZuxqRmkGs4wpWJeepPNRh42Y7V57cVTAJFakMKiJSByaAKs0jRKMDrTfts7YBfp0qW8XlRVXywO9AF233O69SavRDbewK3GHX+dVLAhZkJ6VdYK+oQMD1kU/rSl8D9H+Rvhf94p/4o/8ApSHSgtdzBR1dv5002blT8yjPvTbgn7TLjtI386hEcrtu52UR+Bei/IWK/wB4qf4pf+lMyriMxTsjHn61DjP8Ve4fD3SNPm0Gae4soZpXfG6RNxA/Guol0bRoYvNksLYKP4jGOKTkZpHzRt/2v1o25/ir6O+yeGV48iyYdTlBVwaJpLxiZNOtSrDIIiHIo5mFkfMu31ajb23D86+mBoGl5DHTrY/WMVINB0nzFI0y0z/1yFLnHY+Y9g9f1oCj+8K+nH8PaOeW021Jz0EYpE0LSQMnS7T2HlCjmDlR8ybB/e/Wk2j+9X00fD+kAlv7LtA3r5Qpf7C0nZgaZaf9+hRzBynzKVGMbv1pNo/vD86+mDoOkAZ/sy1+vlCmL4f0p+DplqB7RinzBynzVtX+8PzoKqP4v1r6THhzSEk3DTLTJ/6ZCgeHNGMhb+zLTd6+WKOYLHzVgev60uBnG4fnX0oPDukHAOm2uB/0yFA8N6OCWGm2mfeIUcwWPmo4H8QpOO5FfSyeH9HjJzptoc+sQpW0PSWGBptpgdvKFPmCx80jGeop4PuK+kxoOkqhxplpz/0yFH9haW20nTLTjp+6FHMFj5uJOByMfWpIxyST+tfRx0PTDz/Z9tn2jFIuiaWnTTrYk9/LFTzC5T5yjVS3PQ1N5SZ/+vX0UdH0tQP+JdbE/wDXMU0aNphO7+z7YE/9MxTuLlPnvyEEW7jmoCi5xX0WdH00KEGn23X/AJ5ik/sfTgTmwtiPeMUXCx86hFyCakkiVWHBB9hX0QNJ07AP9n2ox0AjFKNLsQD/AKDbf9+xRcOU+dTEDzjNOEYAzgV9EjStPz/x5W4H/XMU1tH0/J22Vv8A9+xRzBY+dmjGeR+lKEG7p+Yr6EbSrHjFpb9f+eYpf7PsCT/oVtx38sUXCx88EAdjj6UAA9Afyr6G/s+wBH+g2+f+uYp502wZctZW/wD37FFxWPnfb2wfwFLs44Vv++a+gv7NsVHy2duM9/LFOWwsBwbWA/8AbMUXCx89BScYU4/3TS7CxPyn/vk19CPYWYxi0gA7YQUCysyhH2WAn12Ci4cp89FCDgqR+BpfLbtG/H+ya+g5LOyhiMsltCAP4ig4rM/tXQwWz5Oemdn/ANai4WPDTG56xv8A98GkETkEiNyB32mvaH8Q6THLs+xCTnqEAFS2uq2GpO0ENhGnrlBzTEeIpGWbGCfwqXyVxXvEWjWKpu+xQ+/yCnLo2m5yLG3yf+mYpXHyngYh5x2pxjGCBiveH0fTs/8AHlb5HcIKadJ0vGfsNvnP9wUXHyng/l8Dil8vccCvdhpWmLwbG35/2BSHStNHSxtwDycIOaXMHKeFBBkjjjvSEAdePrXun9k6az5Fjbf98CkbRNKkIDWEBI/2KdxWPCdooKjrXvA0TSxgixt+PVKT+xdMzk2Fv/3wKLhyngzbR9aTbivejoemE5Gn24P+4Kf/AGLpZXB0+2z67BRcdjwNeKd8o4AGe/Ne7HRtKGB9gtv++KaNE0sOx/s625/2BSuLlPCSMHjAzTCPzr3ltF0gqAdPt8/7lN/sbSyP+PC2wP8AYFO4WPCM44pyqCM9a9zOiaW5wdPt8f7lKNE0sHH9n2+B22UXHY8KI98e1Lt5zXuf9haW3JsLfH+5Spommtn/AEG3x6bKVxWO7XhupqdCTUEZyelWF56DivzxnsEq80/GKYtPHqazEOHSnjgUzOfanjOOlSxC9frS5/Om07rxQAhyBUZIp7ZHFRk+tCArTNz7CsbWV3aXcEZ+7W1L901wnie/mFylsZTHERkqONxr28ndqrMK2qsza0XJ0iE4xgECmaGuJL1T/wA9qydD1uK2txbXEg2g4THb61oWOp2Fk1w006qJH3DvX1fQ5WjalUFGHtXmt4v+knByA/OfrXotvfW+oRs1rIJAOu3tXn9wpM0pI6Of50CO/jH7lD6qP5U24TAzirFuB9lhH+wv8qSZSRimxs4XU8r4ijG3hu9bWgKDc3WOcEZ/Ws7V7cxeLLTOSHFanh9U/tC9ChsDHWpSBG86fuyPauIt0CXepKOD+ld7sB4IznrXIwqsfiHVIyu6NRyPatUiXubHhv5tFg/H+ZrSuFJgk7/Kf5Vm6PKWss2UAEAJAyec1db7Y0T/ALsZwe/akO5heHfl0u4U9pK6GIf6OuPSuf08gQ3H2ZflVvn+tadr/aptl3pEUOcHvigW5yesXq2niEQsv+tAO6uwtwTpykjBArCvbW3ubsi4t1aZF3Z9BWxALp7QMu0REYHrQNAiH7fbn1Qils1xq12PoaZGt1HcwBgCzZCH2pw+0jVJUjVROVySfSkgMnxkg3aexOB5uK0LhNtpCxPAlWq+vqy20Emoorxh/l29jTozMbIscG33DGfWmM15gRHWP4pBOixEj7sqkVpOL0R5cIfxqlrCz/2S7XCq0CkEgdc9qQi7onzK+euBWlPH+7NYmhfaJXcwkCPYM59a05lvvLILRkfSnYZg6udsBHv1rGuivkWbA5zJjFbt+cQn7TGCvcCsvVZLZ7bT2t4vLVZQCPelERDbJtu5lxyGrajTgGsheNUuFzyG9K24E+UZ6VYxtupN5Lx2FXCmKrwA/wBoSHsVFXtuR05qQMm9j/4mVi3cM38q0wgxzVK+Ui/sSP75FaQSmBn3agSQHH8dWiuQc1HfDaIjj+MVZK8c0wHyMPseAM8Utqm20jPtT3j/ANG6fw020ytqnoRSYh4OCeKCc4/nSEEtTWBB9qjYZOnTrWbquWuVDYxt4FX0+Zc9KzfEEyrdW6ooB8rJP41pEllNBvbaBjFTMjRzwZbI3dqitZAAVx87dKnkQrJbSbgcSCtCSl4zTPgvU8f3VP8A48K5jRct4RlRhyHX+ldX4vYt4R1Pj/lmD+ork9CyfCs4BPBU/wAqxrfAy47mz4ShaSO+cSsu0qNor1DSVZdKhVm3EA8/jXlvhMT7NQMRQj5dwNeo6OXbSIDIAGwcgfWvns3X+xpnQty9Vafoc1ZqrP0Ir5Fbm0NzNfhiahfnmppR8xqvKa6YmpyfjXnQrg9sp/6EK8+kxuJ616B4zP8AxIZ8DuvX/fFefyn5sYxX1eSfw2GM/wB0pf4pflErSHOcHt0rY8PkCzfH/PQ1juCGz2rY0BgbR/USHj8q91HjnQ2+pT6d+9SXZGOWDfd/GqmreFfDnjDMwdNL1F8Hzo/uP7kdDVPXhu8PagMceQ1ec6P4gu9Ji2Kxnt8cxP2+npTuVE+t9DtmttDsLb7T53kW6R+aowHAGM1wPj/wTbav4jXV9Uv1Gnqiq1ugw0hHbPpXY+G5c+ENGmMm1ZbVGVceozj9a8s+Kmuyad4inhG8/uEZQTxyKp7DZneJfEoSNLW1VY0jG2KJeAgrD0G4Zp5S5LM/OTXM2kst20kkrlnJzW/oQxehRnOKlC3L/ilmbw9N8vAZf51w0XSu98WZXwzc8Y+ZP51wEZwtDYh8rYBA6Yqm3WrEmcZqA9aQztNGP/Eqt+P4K7Tw7Lth4+8jA4ridFP/ABKrc9PlrrvDznLAnv0qogauvTa1qOpW9/aSwiWEFQjcZzXOjxLf2niITao5tJwn2UsW7Z3da6/qc15x8UPmlt27hEP15cVFVfD6r9TtwL/i/wCCX/tp6e97PBp8t158jlVBX94cc9619G0q61HTYLyTxLsaZQ2xcfL7dRXmc94958NLKfed3lqrn1xxzWLIuo2ejfa4NUmWOGPfsU8Y9qai1Jt7HM5xcUktT3gaBtGG8TNn6j/4qkOhsMCPxMfxYf8AxVeXeAtQ/tzQ5bqdmd1m2fMeeldX9mhx90VpYzudOmivjDeJ2/Bh/wDFU7+xWH/M0Sf99D/4quVFvCD92lFtEf4BTsgudR/Yz4/5GqQf8CH/AMVSf2S4PHixvxYf/FVzBtIjkFOPSmmytTgGPke5osB1H9kuD/yNTH8R/wDFUf2W44/4Sth+X+Nc0LWHpsFPFrB/cFFgN86XMv3fFY59cf40h0mSQqJvFhdQwOzIwf1rEW0gJP7sUn2O35/djmgDIl1NNW+J40fUb2Gw+zxlY3ByLgdsHp/+quk1HTNOsURotSS4difkGM4A9jXkPje2jtfipociriOURZGf9oirwu/7V8eXOiDzIordSzSK3OMDj9aT8io2vqbWvTG78FanLtA3W7H9a8dgYCaA4z86/wAxXtOtW0dr4L1WCIYRbZseteLR5WSA4H3l/mKnYUrX0PcI1/4llwOv7lv/AEGvE5pXjSHYuQeG9q9utsvYSjPJhP8A6DXijOiRJvYDPFMkQH5smtjU7GODTdMuo2VjdRsXGc4IP/16x1xnmrc9rNb28E0gIimBMRz1A60CLvh6eCaSa1FuDPE/mCbAzgjGM1ja1J9o1i5JP3Dtz9K6DwzEsdvfXJXktjd9BXJtIJpppf77E/rUMpHf+Hpdng6yTJ6uAPXk1iR2xczsV/eLIV2+vPWtLQyD4bswD0duPxqhI7R6jcOsuPnIxnpzWU0d+Ebu0h76PMGUmSPae4JqldW7WsoXqCMhgOKtyX02MF1/Oqpd5SWaQsBxis+WNtDt559SDVSf7KYHpgVb01iLOEd9oqhq5H9nov8AeYCr9mAtrGMdutbrY8ee7H6mrT6dIkSFpNw6Vm6fZXaXccjoyoDhs96vXomaBkhYqx53A4IqnbLdx3MbvM7qpztJJBoINWDQZ9Z1e7VL1LSFE3KX/iOOgqvp/wBotIdkrEtuPJPWqt3FPcXLzCVlDfwg8CpraOSOII7l8HOTSK93ltY63w5N5tzLnqFB/WulWuT8LH/TZxj/AJZj+ddeBxyKtEijgU72pBQuKYh45FJHyz0AU2MEyPnpQgJwMHjnFaAu4IEXzZFQngZOKz0yDXHa5cTjVpHKMyBsKD0Ap3EejNKGUEYIPQiq8jAcZrmNHl102Pmo9uImOUjkBPHrWkt5qcZzNa2kuOoRiM0XAvZyTzSdPYVStbq6uLiRp7ZIE/gCtmrbEDgdTQA5evNWogQM1WT3q1F19qALMYGPapfTFMQZGafwR15pgLxmmk9aXimMxFMQ1zioHP1pzniomPPFIY0jJOKeq4Ipq89qlVMnpSAkQVKOBTUFObhfegCGZsnAqlPnYc46VYdsscVWn5Q0Ajn7nmU1CMEc9qmuMiY1EOnvUlFYYMjEU1hkc0o++wFDDjGMUgOe8QDbZzkdNldfoL/8U5pw3Z/cLXH6/k2c+f7ldXov/IB0/jH7hf5VSJLkz8HmqEhyx7Val5J68VTZec0xkeeevNXdMGNRi/z2qtswelW9MA/tCIEHrzQtwZ1CEnjIqZFz3qID2wKmj47VqiGSDgc0pIx1pOmcjrUbNzgCmICSSOaO2CabyB0ozgdKQD/pUgPvUIbnGKkBx2oAefrVHV+NFvxn/l2k/wDQTVstx0qlq5/4kt/xj/R5P/QTSqfA/R/kzfC/7xT/AMUf/SkO0c40Sw/69o//AEEVZubaG6iMUyBkNVNI/wCQJY4H/LvH/wCgireSO1KHwL0X5IMU/wDaKn+KX/pTOQ1HSZtJk8+2YvHnqOq1oaXrqzjypzh8cHsa3XG5MFcg1zer+H2djPY/I3UoP6VRgdf4Ugls7OVJ+CZSwA7g1Z1xGku4pAPlVCOnvWV4BuJJdIne6kZ5Vm2/N1AA6U7xy+pTNYQaaCPOJDt0Cj1NPoNHLXy6FoGpz6xLGs+oynILHOz6VwPiDxZqGsXe0l47fPbvVXxQs1nrc0Elw0skRwzZrmLm6m3D94cemaykxm19rKcbDUc1zvibap+tYqTyMCS5P41s6Y6PF5bDLH1qGwMJkLMxA700DBIxW1d6eYJfMTJUnkUzy45F3IoHqKXMUkZiK5PCk5q7FFdOuM7RVgJjHAqZWC96hyY+UgXTXkOZH5q7BpkKnJXcPenLdIoGQDSvqXGBgfSpu2VZFYRhNRCqMAdqsxIpvUJyMMMfnVNZTJeBw3zetTBj/aEQJJPmL/OtX8D9H+ReG/3in/ij/wClIbdEC6lH+2f501byZMIDxRc/8fkuf77fzp6fZhGdxbd9KcPhXogxX+8VP8Uv/SmbNh4uvtJsxDa3gjycspANLdeOdWuo9k1+sif3SoFcddACdtudvvUG0mmc51Y8T3Z6yRf981pp8Q9ahjWNL9AijAAQVwQGBR15pAd4vxH1sf8AL8Dj1UUo+JOuYz9tT/vgVwPJPSlwaVkO7O9/4WXrZH/H6g/7Zik/4WVre7m8Tj/YFcEVwaUCnZBc70fEfWmGDfIO/wBwU/8A4WNrWOL+MA/9MxXn4WjaaVkK7PQF+IWrknOoLnHdBSj4i6sq5/tGPPYbBXnxB96aQR2p6DTZ6CPiRrIz/p0Z/wC2Ypo+JGtkkm9jH/bMVwAGOaOaLILnfH4j62FwL6M85z5Yph+JWttj/S0BH/TMVwXNOHWiwHbH4ja0Wz9tB/4AKF+IuuLyLxcH/YFcRg8igjtRYLncn4k64f8Al8Qf9sxR/wALH1wf8vqn/gArhenFLRYLs7sfEjWif+PtAP8AcFIfiVrq8Ldxk57oK4bbSFaLBc7hviTrjf8AL4ox6IKG+I+uN1vF49EFcOFpQtFgO2PxI1ztdr/3wKa3xE1piG+2YP8AuiuMxgfSkxRYR2Z+IuuEYN5n/gApf+Fi63xm9GR/sCuLIwaQg0WA7Y/EfXWO43g9BhBUZ+IeuNx9vI+iCuM57UpU4osB2B+IWuDn7d/44KQePtbwV+3nHX7orj8cdaTHvTsB2H/Cf62el+wx32CgePtcIwdQb/vgVyNLiiwHXHx5rbcHUGx/uioz441rdkak49torlcUoHqM0gOo/wCE21rYAdTlyPYUDxtrQBH9pyc/7IrmCo+lAXFKwHRXHi/VriHy5dQlkQ/wnpVH+2rjgeb09RWX9RSYPNMDTOtTg5MpJ+lWLXxHdWziSO4Knv71g4yOlJimFjs/+Fh6yF2pdLt/3KcPiHrTDBvFA9kFcVxikxzSsB2reP8AWHwBegDv8gpG8easxz9sA/4AK4zpS9uaLAdj/wAJ3qxJP2xc9vkFB8eat1+2Lnv8grjaMZ7UcqGdgPHurAEi5XP+4KX/AIT/AFgkn7UuD6IK472NLjiiwjsT8QdZGcXSHjoYwcUn/CwNZ4zdqR6FBXHEUmM07DOzHxE1kZ23CD6xg00/EHWj/wAvYzn+4K4/A70gGDRYDsT4+1gjm5TrniMU0ePdXByLsZ9dlcjzzSbQaVkB1w8e6urH/SVOf9gUo8e6uB/x8r9Sgrkcc00rzRZCOyXx7rHX7Umf+uYp3/CfavgYu0HPXyxXGDNHJ7UWQHZN491j/n8TPtGKX/hP9Xxj7Wg+kYri14zTj0p2A+s42I7VOjnqRgVGB0qVR82M1+cs9klQipeuOaiAFPXIHrUMQ/tUnOB60wdOlPXrUMQvXrS45ppzigjoRQArcjmojjpUuKjYd+poQFeXGCDXJeJ7SzuYUdyPtCnCc4NdbN9wiuE8TKyX0Z7EcEc17OUfxjGt8Ja8P/2XZ2pF2YROT8xcZBFWbNNFmnujMLVk3fJuIAx7VkeHtD/tuSd5ZWjjTgEDqa0YPCkUtxcW6zlPJxk7clhX1iucjFt57SDXNllJFHbtwQDgE1l6xo0+nsxkmgk85yyiN8kDPeuntPC2nWq4eIzN2ZjjmuNuY1j+1hjykpUZPbNAjt7e7tBBCpuoc7BxvHpTpbq24AuYv++xUNvoGmSWcDm0UsUBJyeTimzeH9MB+W1UfQn/ABqr6DM7WbZz4v0UyAbJF+Ur3p+jpt17VIgfuvx+dQarN5PirRBz5cR2gelVrfS/7V8S6m32mWDa5IMfXrU9RLc7MKeuDXJkf8VLq6gY3RGtJPDuoRBTDrcw56OCf61lYdfFN9Gzbj5OC2OvStE9AY3w9c61HpmLO0jntw569c/nWuNV1ePPn6QQO5DVneGW1uPT5vsCwPAspGJOua2xqHiCLl9JST1ZX/8Ar1NxHN6Zeqk15DHFJ+8OS3ZTW3Br9pHbrHLHMrKMHC5BrF0+4kV9Q3W53yNl8dErch8Q2cNlHDNaTgquC3lAg07j6GVNd281880ZYI0O3LDHNaWnX1udCCNcL565Gw9etY2qX9jeahbPACIghEg245rXsRo50XBaIXWDgE/N7UXBE8c0bSadmVd3zBsnpU5UL4iXkfPD1B61RFhYulnllEkjEON3NOuPD0A1yGCO4lRGQnKtyKQDvF0BfRd5U4SQGoLBS/h+XI4DAin6tob2mmzTG+nlRMExyd6q2cN5NpE0lvOqxKAWjbvVAdEwzbKcdgaoa0N3h+4HbaD+tMMeuLZI3mW7oUGAeuKqajcat/Y0yzWcRhC/M6tyBRYCz4ZkyrDGMqOa6Cdf3fTtXP8AhdTsVsY3RiuklGYyPahjOW1ZQYHHaufuV3WkC5xidTk10eqjMDDsK5y9400e0qmlEQ65jkGu3aRnByvP4VowRaikeRIjZ7Gqj5PiKdQOqqSfwroYEwgqmMy4p7qK8YNDul28qPSpzqVwn+sspMeoFWQpGqg4xuj61oKuBQBz11qULy28jRSx+W+TuHWtGLV7CT/lqV/3lIo1lcfYmIHE4rSe3hkyWgjJ91FAGXf3Vu8UZjnRj5g4B7Vf3R44kU5HY5qhrVhbR2HmJCqtvUEqO1SHQLEESRLIpxnhzQgNNji2I74qvbORaofanklrcD0GKSDAtFGKbELuzzTXfmkzg4AzTH5bmsxksbg96y9Zy90mRwE4NX0Ayaz9Zz5keO61pEmRVimUN/tDvV6SRWFucYG8Y4rMiUGQfrV951byx2VxirJE8V4bwnqQx/yyz+tcdoBUeFb3HUbP6V13ic7vCup4/wCeJ/mK5Lw6N3he+GOqr/Ksa3wsuD1NLwlcmObUF8l3yi8r2r1TRZBLpELhSuc8HtzXlPg+dY72/Rn27ol49ea9X0ZxJpURVgRyMj614Gbf7mvU6Fq7l41VmGQatVVn6GvkFubQ3M2TAzVdgT1qefG4ioiBiumJscl41B/sG49in/oQrzyQnfk9K9E8an/iQXPHHyc/8CFeczOoPrX1OSX5GGMt9Tp/4pflEjbhjn0rU8P7Wt5uMfPx+VZD9Aa1fD7HyZweP3nT8K+gR45V8dXM0GiQrBK0fmy7HA/iXFefIhMbADnFdz4/Z106z2j5TMc/lXEwzlAcgdOaTBbn0foPiaC08KaRZyW0zPBbIpIYYPArzz4q3seq6ot5HE0aGBVAJyTitvTedLsz6wqf0rl/GuRsPYJwPxouORxNpctA2FG7Paug0uLVbhybOSKJiOp521gWBV7tAwGK7nRYGEbOpxnigSMDxE2u6fbx2moX8dzBcc4Cjt+Fc/GOOhrofGj7L62jLFv3ZbntzXPwzhT8wpCHOuRg/lVYjDHirckynotVncelAHTeHLmSe1eJ9u2E4UCu38Ppy7HntXn/AIYfJnQjHQg16Zo0XlWqsP4uaqIzY44P51598SUVwx6bbdSPruIr0ANntXB/ENHdJwEZj9lXgDJ/1lFX7Pqv1O7A/wDL3/BL/wBtDSvn+E6bgciRh+G6mFI4/Ct0sTeZH5ByTT9EDf8ACqmDBwfMbgjnGaoXtwsHhCZ4kcKY9pBHUk81ZwG/8I4wPC1zIejXRA/75Fd7gZOa5H4ZW5t/A1vlSrTSvJyO2cf0rrsEimMNvpS9+aT8DR8x7UWAWkPB6dadz9RQc/jQIQYz0qRR+dMAJ5qTBBFACr05p2ABQPU0v9aAPLfiSVTx34ak6D5Of+B1HoZEfxg1lWxl4Gx+Sml+KvHivw4/oV/9DFCL9n+N7AgBbiH/ANp//WosNHZa8AfDGrAf8+sn8q8LjORCSehX+de8ayufD2qA/wDPrJ/KvBjgLER935ahge62GHsm9DF/7LXiTwrLAm/kg5H517bp2DahR08r/wBlrxO584QxGFec/N9M0AJjtWjdX73dhY2jRgLaBgjdzn/9VZ+TjpWpfLbf2XprwqBOQ3nkNz7cU7CL8EgtvCdxIByd3J9elcVCDhia6XUp/K8JJFnmWQj9a5m3Jw3pUDR3uggf8I/ak9PMb+dZcsKvqV06PkmViQB05rU0U48OW/oHb+dQW0ccV1cSEktI2SPSsqjO/C3u2Z7LvLEAHHXC1WiYfMAa24GjskuCvLyHIJ5xWILcI7nfkjmsjqctSrq7b1to/wC82a2YV2xquOAKxL395fWikjgZrbi5xXQtjy5bsshA2OAaBAucdqeg5NSUyBq2oI4WmG3bcQq4/CtG3xwcnNPYbnJNKwyXw1C8V5OWAwyAD866cNx0OawtGIE7/StvPcVQiQHFOU88VFnFO6UwH5PSiJwGf1poP402Nv3sgNCEywrFjgVzl7qEEepXEU8Pm7W7V0MfPtXJarYSy6pcSwMAWbkGhiOu028tb60ElupVR8uwj7uO1QajdeQUjjXMjnge1VtGih0rS9rSbpWO5/c+1PtI2u7w3cqnA4UHtTA0I14GRg0oPz8ilYcUKCcYoAmXg1aiHSqqdauRA9aALC+lO70xQfWnZwKaACeahY09jnpULGi4CE9qYetK3t1oUZwO9IByr8w4qZQRTVXtzUoWqQh44HNRykhTUp6VWlNAyEkc8VDN/qzxUxPWo5v9UcYzUsZzlx/rm4qIkbT61Nc8StmoSARnFTcCsMbmNIcdTSgHewxTWBFAXOd8QEi2uOM/LXWaP/yL+nf9e6/yrldeGbe4H+xXU6Ln+wLDnP7hf5UxE0mT0HWoOAKsuOaYUz2HNMCHb61a04Fb6IgfxVHs9KsWR23UZPGDQgOj549KmjBIPYVWVs84qZW7VoS2SMx9cimqMmmjmn52DpTEIxIpnO7rQzHuKYW9iKAJBkHrUgqDNSbsDFMB+TjnrVPV+dFvz/07yf8AoJqzkgdKp6s3/Elvuv8Ax7yf+gmpqfA/R/kzfC/7xT/xR/8ASkO0kkaLYc8fZ4//AEEVb3Z6VQ0kn+xrHIOPs8f/AKCKuZ9ARRT+Bei/JCxX+8VP8Uv/AEpjiSe9Nz6/nSFj6Uhbg9aoxQ7wa5js9UVQC/2ng1oavIStsWYCQbvrXBefLDLcLG7JmQ5CnHermlStJdHezMdvUnNK5R5p4xUf8JPfkfe3cn14rkLhSZABXXeLif8AhJb9s9ZOR6cVyU2TMNvWs5DCJSEanQ3clu5KcGnRqVVsjmqj8OakDbsdVLv5dydyNxn0qS+s2gPmw/6pu4rABrb0nUk4t7hsxnhSe1JoCuZOOWwRVWS5fccNxW/f6E8kZltxu74Fc3LG8UhSRSCOuaSQxftMn97im+fJnrUecUgOKqyA1rJsSKxq+uHvoWxz5i/zrOszkqa0Qp+127Kesi5/Opn8L9GbYX/eKf8Aij/6UiO54vJR/wBNG/nSraSsPMCEj2ouc/a5O58xv50wzSiTaGYD2pw+FeiDFf7xU/xS/wDSmUp4j5rA9ajEZAq4wh8w+ZOqsfWm5tx/y8D8qdjnK/lntTfL5xVrdbf89xmj/RD1uMfQU7AVvKx3puzBqyXtQRmb9KTdak/67P4UWAg8vI4pfLyetWQbILk3GD6Ypm+16iX9KVgK/l9RmlERx1qYyW3aX9KPMtQp/e8+mKLAQCPI9KQx1ZD2p/5a/pS5tAufPGfTFFgsVfLOKDFkdass9qB/r/wxTd9t/wA9c0WAriHNJ5farBlt+0lNMtv135NAEIjPrQ0ealEtv3Y0vmW3/PQ/lRYCIxggUvl8VJ5tt/fNHnW/940WAj8rpSGPmpPPt/7xpfPtz/EaLAR+XgClKcVL59qerH8qBNaf3zRYCHZ2oMdT+fZj+JuaY09t/CzUWAjKUhTJp/n2/Ymneda4yWNAEPl470pTNP8AOtyfvHNHm2+eWNFgIvLHrR5YNSebABw1Hmw/3qAGeUOvelEeKkEtuOrc00zwD+I0WAZtpSmKcZoBjBJpfPt8ck5pAN2c0uz1NKJ4PUik8+AnJJzRYBCnNBQetO823/vGk8639SaLBYj2YoKipDNbdiSaPMtuu407AQiPjApdg21N5ttn7xp2+1IyGNAFbZ60vlirG+zI+81O32QHMjflS1Aq7Bik284q6r2GRukYD6Zq5F/YJcLNPcYHJKr1o1AxygNG3C108Q8EqpM1xfEnsAf8Ktr/AMK8WLm61FmP+y3+FMDjNpNIU9K7Fj4BC/Jcagx+h/wqrdP4OER+zTXrPjjcDQBzBQkUpXFaI/s1m3I8pXsCMVEwts8bsH1pXApbe9BXnNXQtsD1OKaRb9icUXAp7cUbBVofZz3ajNt3LUXArBfSlKjFWD9lz1bNITb/AN4/jTArBO9G3FT7rcDhj+VOX7Oe5oA+rOp46U+Mcmo96g8U9cbga/OWeyTqe1SA8VEvSpVqGIeOmacppo4zTlqWA4HFLx2NNJpfpUiAnioieakPSoyeOaaAglwc1Tn0mzvbaZ7hCWCkqR24q7JjBzT4kDWU4HPyH+Ve1k6Tr6mNb4Wcx4Y0qC40p5BPNGxkYfKat2emut/dot7ICuPmzyal8GjOi89RKwNWbLH9uagvZduPyr6yxx62I/7P1EEeVqJ+jLXB3byWlzeQzKksjycuRXqZXHNeb6zHnVL1z/C/AqZaajTszurAf8S239RGvP4USrUmnLnS7U+sYzT5046VQM4nXl/4qbSecDeM/nVzQgR4j1NccE/1qj4oJXXtMI67x/Ornh/LeJr3JGdvIpCW512MrXDXS7fGM67sZjOa74L8lcNfpjxs28dYz/KtAe5p+Cctp10uOk5/lXUBTuHOK5jwMM29+D2nrrCuOtFinscFErJfa4pPG7P6mus01d+i224A/u+4rmbpooNW1YOwUuOAT3rqtJX/AIkltkf8shRYRy3iGzaS8thDEq5jJOBjoa0bXTrJtBSf7MhnCE7++as3yg39pkcmN1qSzT/inn9lf+tFkIx5NItnsrOVlZXklw7A9jU9zo0cGsW0MNzNGJATuJyRVgAnw7ayHqJgf1q7fqDrdgw9GGPwqbDMnW7K/sdFubgX7TxxjLJIOoqv4ek+0eH7x2GMqDW/4jt/P8OX8ZOMwnFc/wCF0xol3D/dh/pTSA6CIZ0uM/8ATMfyrP1JCfD94Qf+WRzWnaITpMRJ/gFU79N+g34HXyGx+VMGUvC8iusUanKrCMfXiullUbDxXJ+DmP7oYyPJ6/jXXyj5elFgOY1VR9nkx1rlLw4sGzj7wrr9UH7iXI7VyV4u+xl/ChCZMXDa+3q0akGukhXMYJrloGJ1iIZ+9EK66NMxrTGVWwNSh68qRWkoGeaz5uNStffNamKYGVrYAtYW/uzLWuEz1NZmvLjSyR1Dqf1rVUgIGPA60m0twM/WFzprg9iD+tW0AMKn1Wq2qyxNpswD5bggAdeacmp2At491wAdgyCpyDilzoQoAKED0pqEC3XJqKC/s3jY+eqnpg8UyO6tzFjzkyD0zQ5LuMkJz347GmlSRk5oVo3OUdSPY1KSDjNRe4Eca8/WqOuApLAwH8HX8a01wCMCqOsgySQ/LnC9fxrSImZcY431YSNSqsf7wqvjYwUnrVnG6Fdv94VZAeJB/wAUtqmP+eBrkPDLn/hHL4Ef8s1Ndh4iXf4W1T2t2PHtXGeGTnw3enP/ACyWoqL3WVDc1PB4U3+oKwUkwjGfrXq2hosejxKo4yf515J4OVJ9YvVfORb5GD716xoChNIjUEnBPX614Ob/AO5nTFamnVafoasmqs+NpxXxq3No7mZLgsajbGKllGc+tRMRjmumJscp41H/ABT11nH8GP8AvsV5pKuJuea9J8auDoF0B6oOP94V51Mo8zua+pyP4JBjv90pf4pflErnOcdq1vDwzHcdcbx/KstwAcjpV7RLuK2aVJOA7A59K9+55BU+IC/8SyybsJm/lXAqMoe3FehfERP+JHZOuCpuOCPTFeeqPlJ9qL3Etz23TP8AkD2PHP2dM/lXNeN/uRHp+7OPfmuj0r59FsWXgG3Tr9K5zxvzFHjOQh/nQUzhLJ8XacV6LoisbEYHy+tecWLAXsefWvQ4r0af4VuLvaPkU7fr0pknC+I7z7drc8gbKIdi/QVQRQee1QHk5YnJ5P1qeLnAANSwJwFAqvJwcVYOAtVnxuoBG74ZG64lB/uj+detWCoLSML/AHRXknhg4u5eP4R/OvWrE/6JFgc4q4jZaC4PFc7qryx64xVvm+y9xnjdXTL0zjmue1RC2uOMZP2LjA/26mr9n1X6nbgf+Xv+CX/tpn2viK3j0kxzySeYsu1gF9a3L1Bb6XHMb5Qr44ABwK5D+wHutIu7oylD54wu30rXvrVxp8cO1s7Rzj2rTY4DbtvEGmw20YF6pUDA4xk1Y/4SjTApH2wlsenSvNrbR9Tm05HNlKGV+F2HkZ61s2fhi4urOSaSUxOvAjZTkkVV0Ox1v/CX6dsz9pPy/e+XrSr4v0zlmumw33Plrz1dJ1HyizWk6vnGzYckZ60NomonCi1m2LznYeTnpSuFj0H/AIS2wJ8pZ3Mo+bG3tTH8aacsXnbpTHnbkAcGuGi0jUjeiU2U+GQq3y421WbRNWbR5rVbCct525PkPzDNAJHoi+LdPDCEyS+fjdjAwRTj410ph5jTS+SOCSAMNXCjSNWXW4J/7Om8vydrnb901RPhrW20S5iGnT+c1xuRMcsPWnoDR6Wni3SiwgeaVbgkbRjqD3q94jv4YYLZLS9YMfm3Lgj6GvMzoOrHxHb3BsJRbxRASPt4Bx0rpJ7dyg3K6rjjcOtJhY43x1rsmq6vppmjBlspArOn8fIP51q6ld29z8U9Ev7V8rKArA8EHBGDXN+K7WW0vo5ZEIjklDI3Y+tXZGlg8Z6LeTQMkDzR+W2PvDIpID1TU/m0LVBjrayf+g14I2AiD2Fe+6t/yBdVH/TtL/I14A+BHH34FS9xnuek/wDHjGx4JhB/8dryG9ZIooi3GePxzXrek5FlDjn9yP8A0GvKL9EmWMMmQM/nmmIpAc1duLKW2tLW5fHl3G4Jg9cdahEfy5x0qxNdSTWdtaNgx25Yx8cjPWlcRV1kyNpNmAjGNGbc3YE9Kx7b7rAmtHUdQmFobDAMZbeTjkH0rOtjlXGMYqRo7rRF/wCKai9pG/nVRM+fIo67jVvRP+RYjx/z1as23iubqeZLcElWOaxqM9HCdSaVCSw74rNfO5hzxWm2m6tID5cbMB1INQf8I5rshOLQtx2cVMVc2nKK6mJkS6xCSQVVfWt6HHPrVa28JataXDT3MMarGpJXeCRWhHbSRqrsuFboa3R5jd2SrhlxTh1xSwxNK6ogyx4q22m3EYBdQAfelcnUWE4UdqmIHaoQgTakjqpJ4GauTW8kEYkZflPGRRcon0sYuG9MVsFgvQVkaVG5ZpAMritTjgj9aaFYkV84zS7z2puxj0WlWNuuKYhwfmi3UGWQ9aiaRUbDOB9alsxuklxjA700wZaHXpzWZNZyNPI+3gmrpuYlbb5qfnU639iAFknjVvQmi6FYy4rOV+GBC+9aEZ8kBQOBTpNQslbaZ0UnoDxmpSo27jwpGcmgVmML7jUqH0qjJe2qtgzoD0609dSslIDXUefrTugsaKckVcj+Uc1lxalZsfluYz7Zq4L60B2/aI+mfvUXQ7F0En6UhNNRt6hlPB70E+tAhuetRtz3pxPNMxluOlACAc5qRVoUVKq47VSAVV5qUCkRaeBxTAa3SqrnnrU8xqswz3qWFhjZJzTWXchz0p4BWlcHy+KQHNXnE59B0qsenFWr/i4NVsYUkikxkEZJkekYYGadAAJn9/WnTDCkmkI5nXP+Pa4/3K6bSGP9h2PHSBf5VzOvKDaTn0Wul0jnRrE/9MF/lTQFsENyRg0nWnYAoxzTAQjB9qktVLXUYH96mZyTU1oP9KTB700BvDgjipRnNQqc1PGADnFaEtEijnmlwaOKD1oERMKYQcUrnnvTCfXNMBeScVKBnrUIJzxUvYUAKeDxVHVmP9j33T/j3k/9BNXScZzVHVjnR744/wCXeT/0E1NT4H6P8mb4X/eKf+KP/pSF0kk6NY/9e8f/AKCKuk1S0kn+x7HjH+jx/wDoIq3nk8Gin8C9F+SKxX+8VP8AFL/0piMeOtIR8vpSknuKQtkEYqjnOSuOLucf7Z5q5pR/0k+pFVLvIvLgHrvNW9HA+2AHkYqRnmvi/P8Awkl/nn5+PyrkpCVlBHWut8WHdr96xBB8w1yTSbZ84zis2McJG2tmq7ZLHipmYuSema17PT4JLZHYEsetK40jCCmkGQ1a5tIv7Q8sjCHtV9tNtgPuUrlONhdC1tkK29w/GflY1u6j4ft9XjMiMEmA6jvXMSWsEXzYAA55rX0LxApnFowOF+6xpkWOUvLCayuGhmQqwPBPeq2w5r1HVNNg1eAMVHmr0NcjcaZHbyNG8eGHr3oGULMABR147VfRwt5b5ycyKv61VRAk2AOlXI1DXUBOMh1/nUz+F+jNsL/Hp/4o/wDpSGXBzdy8f8tG/nTlngX5WjJPsetNuP8Aj8lPTDt/On/YkaPzfOQH0zzTg/dXogxX+8VP8Uv/AEpmFe/PcsVGBnpVbBz3raeJAxGMn1ppjTrtGaTkY2MfH1pOa2PKT+4KQwpjO0Uc4zIOTSc1q+Qmfuik8lP7op8wWMzBIowc1qCFM/dFO8lB/CKOYDJwaXacdDWr5Uefu0+KJS4+UYzQpCZj7T6GkIIPIr0fQ7K3LLugRs9cjNdethZrbHNpAcd9grRK4rnhW09cGkII65ruvEkMKTkrCqkn+EYFcxNEGBwBUt2GZdLgnsakdGjbkVetZonG1lAbFK47GbtPoaUqR2NbggQgfKMU2RI8fdFK4GKFPvRgjtWoY0A5ApCqgcqKOYRlkH0NFW55EHyoMmkhtyZAW6U7jKuDjkUmK07xFWEYAFZ3emIbRT8DFHFADKKdRQMbRTutAFAhKSncelGKAEpKdxS44zQAzFLj2pRyasx4A5FAFXacdKNp9DV7jjinfLgcc0riuUNpx0NG0+hrQ49KTA9KLhcobT6GgqR1FXxj0FMdA4waLjTKQ60uCBnFPeJkNSRSgja9FxkKxuwyFJoeNkxvUj61r2MnkvuVQw703WJln2nGGHtRcm5mpE8n+rjZv90ZpRbzA5MTj6iuy8FyCS0ni2ruVs5xyavaxGnksdoyOvFVYVzzoqynkH8abVq+UCY4qrSKHDgU2ndqTvQBZhxipT04qKPgdKl7ZqGIYRjvR2oYj8aaTxTFYTOKQ89qCwA60hYEUx2EPWkOcGncY96KAGDOOaUEg5oI5owaAPrfy1wMinpHjpS4yKcATX5zc9gWPOMmpF601QRT1Hb1qGIkpRzz2poAHAp4GKlgLilHApPpTh3qRCcVGwBNPIx0pjYpoCFxmrFkqtazAn+E/wAqrtwDnrVrT1zBIc44Nezk/wDvHyMq3wMxfBpDaVOP7s7D+VT6eP8AieaiduAQtQ+DhiyvE9LlqtWIzrV/gYGVr644rmiy15l4kk2azdxKuBwc+vFeoleK8x8WxsPEdyMcFFIA+lKQnud7poxpNn/1yX+VSzj5feotFGdFsyf+eQqzOp2cVSWhbOA8WAprWmN/tj+dXdAYHxbchU4aInNUvGhK6jpxH97+tXvDwx4okB6mI4qSYrU7VVytcHrIMfjVTk5KY/SvQVGB0rgvECbfF0LHunH5VYPcu+Aw23UgTyJ/8a7EgAHPSuS8BAbtUXHzCUH+dddKpaNgvU0Ip6I8+1CygvNe1J2MgRR5ij1I/pXZaOM6Ha5/551zmoErrd4hXaTb4/Sum0UH+wrb/rnTEUNRT/S7B8dd4/SnWQJ8PTDnIVx/OpdTHz6a3bzGB/KjTsNoVxg95BQIzYDv8HRP3En9a0L4H+09MfuSf5Cqtqm7wYQP4XP86vXv+s0l/Uj+lIZPqq50e85x+5Y/pXI+DBvsLwE5zCf512uopv0y6X1iYfpXFeBzkXSDp5LfzpAdPZDdpEeOy1VlXfpd2pHWJs/lV3S/m0dSewI/WqHmf6NcJnrG38jTuDMDwHL5jqGP3Ubj8a7uQfLXn/gUFLxRj+FhXfSdKYHPav8A6l65O7XOnXHPIXrXX6qv7mUe1cjcANZXC/7JqAK9tuN9ZyMMHywM12kWREtchactaE/3VFdhFzCKpMdtCrdDGpWZ9zWqFzWXekLd2RzjMmK2FUAVQjK14E6RKB6qf1FdqCDAgwMFR/KuQ1pc6TPgc8H/AMeFdZEc2sJ9Y1/lXg57KUaK5Wa0yGXAY4FZl3tI+6M/StSUcms24ySfavkoTle9zshsY8yoAcKOfamQwxkFmUH8KlnwTQAAvHFdiqS7l2KM0MYk4Ude3FXAvyj6VWn/ANYKsiVCg57CvfyecpcybOaulZChcc5rO1NyHQk9BWhvU1m6yMCNwcjFe+jjZnhhJ14NXVwsIx6iqNuu5yxHHtVt2CoAOckVoSP1ws3hvU19bZ/5VwngxVXQNRwDlo8nJ69a7zV2B0DUVx/y7P8Ayrh/ByqNFv8Av+44/Ws6j91ocS14Jt3/AOEhvvLmKM9tkgjI4Ir1/wAO+YNHQSMGYMeR9a8i8Hu48TzLDGGb7M3BPbIr13w8zNpS7lCnceAfevBzX/cvmdMDUNVZulWqqz18atzaG5nSHBNUrhiuT61bnOCTWfPICOa66aN0ct4wkB0SdQO6/wDoQrgpgd5rsvFcudLmT12/+hCuNkkG7pmvqsmVoyFjf9zp/wCKX5RK0pwBzTYDtO4ZzmkmGXPpULTGCKWTblU5xXr1LtaHkpoZ4kuZJdKjhaTKrJuC+nFcorKEb6Gti8vU1C38lkK5IO4HpVP+z4ypAZuacL21F1PX9KIbRLBlOc26fyrnPHAP2aPb1KMK6LRE2aHp6jnECjn6Vg+N13W0YB6qwPtWgNnnNn/x+R85wa6DxBqB/sa0sEb7zeY49hXOw7Le4SRiSFNPu7k3tzvGcAYH0oEiqc7h2qxCVDZY0NbFsYOKctrgct+lIZJIVPQ1Wk4PFWDAAOXP5VC8A7OfyoFY2PDWBeuCf4f6163YD/RosntXjukzw2LvI4ZywGMV6p4d1WDVNMWWEEbTsIbqCKuI2bgODXP6vJNHrrPFLtk+x9cZ/jrezyOKwdUyddOFzizH/odTW+z6r9TtwP8Ay9/wS/8AbSSwvpl0iWW7beY5CCVAHFRza6QE/fEp/srziobXJ0W+3RsR5p4x7VkIj71HlsTtzgA1Zwo7O31EvHEylirDIJpsmpSxLKRGx2nqDS2IzaWv7vBAAxj2qUKQlwCmQW449qQCjUZzPGNp3MOOaF1KXyidr4DY6050Y3dr8hwF5OPamqhMTfKRh/Sgoct/PI8g5BABqQalLug4OCOmetPSM5kJU5K+lRhHL252Hr6UMQz7e5iuDhsqeualW+f7Tagg4kX1pCjiO9BQ7m9qkZGN7pb7GwqkE46cUxMZHfObO/OSCjkAntWRqtzez2saXMjmMD5Pl21rvFJHZaomwlnfK8daj12KQabbSOjBdgGT0zSY0eV+OXnay0pJMeXvbYe/arGsXqzp4TR45FMcijc3RuV6U74hqf7K0c9CJXH8qdrzZ0/wYW5PnD+a00B6VqwH9i6pn/n1k/ka+f1+ZIx24r6B1ttmiaqwH/LrJ/Kvn6POIh7rSYHvuhWaT6ZubcPLiXAHfNLb/BvQ7myikn1LUi5yx8t1Uc/hVrQfk02Tjny467iw5sIs/wB2vAzjG1aCXs3Y6KUUzgl+EHhofILzVfr5y/8AxNJe/Cbw3FppKXF+jwKzB965bvzxXbC7ha9kt1kUyx8soPIzRqZzpV1/1yb+VeDHNMXzK8jodKFr2PE/FPgvTtG8BXl9BcSzTNdR48wDp6V5dANu8V7j46fHwvvMjkXER/UV4chC7sd6+twNadWm3LuclRJWsdtohDeG0HcSNU+kIjshV1jVmO8kdapaOX/4ROQrwwkfFa3ghDcafKZlB8uT5QR1romiqcmtDoLLRrWKRvL1bYjnkSDOK3LbQvN/49tZt2PQ7QP8az5fK84ssYXPBAFRtYW8jAvEee4OKIzsDiT6t4ct0m8m81lEd16gAZ/WsW28I2CPtOtSTKO2RgCuK1iwni1q5QzS7c5UsxPFK9+1tCqpbNKeh2E5qr3I2PQU8NaOmCmqusqnPzYwajl0VySE1W1KnorGvM5Ly9Z3Ma3aKegOeKgEuqPIqhrgFuMnNMR6SPBl1cybvt1r5nUcnitiDwrf3StFcanbmMD+Ac5rzKEX+mn7Te3BlhQcjeaS48TrLAy2QuLeQjhhJii4HoF1p9z4fuIrWNjdC4OQ3TbWlJ4X1K2iUQvHNubf85xjNeQ2up6jcXcUcmoXLdgS5OK9R0e2vvssTz31ywAwA0hNNNDLsFlqKq4kFuCpx9+pBZaw/EUNvt9d9J5ECZbLFu5JNTwMdvyMwH1qrisUz4Z1m5kDSG1UE9mPSprzwTNKjL/bDREgZVVxn9azfEb3kelXE1rezwyryNjnmvOotT8QXrkvqF0GUcmQmpYHol34VvbYxrbXMcqAcu/BzTD4cvTiQ3dtuHO3k152+o65GdrX10wzjjNSx+KLmBQkgZ2HUlyKm9gO+k0dZ5t8upRgrgECOp7nRLHUwvn+IrmNFGPLRcCvLrvXbi4k8xJpI/VQ5xVddSvCCRczY/3zT5mB6z/wjtjEqi11ZHjPXzRzUg8H3F2Ge0vbeXFeRLfXPIM8h+rGtrw7He3OpRmG6uEj3fPskIzS5hnpJ8FXGxWN9AjgcgDNU7zwslvHvk1IbgeVVeT+tWoIyi7Pnwe7Ek1FfI4UbDkCmpDsdLaqFtIVByAoqTHNR2zbrWIjuo5/CpeK1RkxmBQFxTgOaeq5NMQiL+dSqKco9qeq0wADig/KKdSNgCgCtKcnpUOOM1MxyTUe3mgBoG+nldsZx6U5V5qSQfuyPakByl8v+kNVY8Cr96B554qo6kITUsZTiH75sU6cDyyeKSH/AF7Yp9xny+BSEcrrwIt5zz9yuk0jP9jWJP8AzwX+Vc7rxP2O54/5Z9a6HSAW0Oxwf+WCfypoZd70uPalxjOaeg4NMQzGBnvU1mP9LSmlRge1OtQBcr65poDcVasIDjFQIR1NWBjrWhFx/I6CkOaXtRxTAgcnOKZg45FPYjPuKQ0AIOKeCccmmk5ppPpmgBzGqmrH/iT33/XvJ/6CasZqpqpP9kXv/XvJ/wCgmpqfA/R/kzfCf7xT/wAUf/SkP0kn+x7LP/PvHz/wEVb3epqjpLf8Siyzn/UJ/wCgirZPOaKfwL0X5IrFf7xU/wAUv/SmK3IpCTg4xTScjoaaTnoDTOc5m651C5zwd9TadII7kHOBg1DdD/T7n/epIHxMoFIZ534mYvrV4ef9Ya5htqz5IyK6fxKQdXuv981zQZVuQX6VnIaEZerAYB6Vt6fMDbKg6gc1jzuHPyjjFX9LwFYGpHce8bm+EoU7QeavSyts7DjvScAcdPWq9yymM4NFhuTe5mXTyyyFWbC+1T200NsBt69z3qvNjdVcketIGzstI1+NnEDtgnoTWxe2sd5CzgfP2xXmquV5zg+tdZoOu7gttMRv6Kx71RBlyxtFelH4xU8IzdxHoN4/nS6swk1iUnBye1RxPtuoBt6yKP1qZ/C/RnRhf94p/wCKP/pSGXnN1N2+dv51WK4HU1ZucG6mHTMjfzqs75ZlC8AdaIfCvRBiv94qf4pf+lMjN5GOMcikN8nZeaz2OWPPem8U+UxND7YCelN+2DPAqjkigGjlAum7HTFN+1jOMVUJ4o7U7AXPtY9KX7YO61SzQTRYC6b0f3eaVL4KwbHSs80daLAdXYeLFs3G63yPrWw3xIQQFF0/Jx1L155RVJ2FY39T8RtqLBjEE9qyzdls8DFVKSla4yyLkc7lzmoS3zZXimg0ZpWGXItQeNcHmg3zN1FUqKLCLYvG9BTZLpnGOgqtRRYB6vtbPephdMCDxkVX6UlOwyxLcvKoBxUJPSkowaBCk0Y5pME0YIoAU+9GeKME9qNjelAB2pM0uxvSl8t/SgBM8UmTThG/PFAjagBoNKWpfLal8s+tADM807zGxgUeWfUUbPegBfNcDrR5z880bOmTSbR60AL5rHqc0ec/rSYB70hCg0WAd5zEYzxR5rnvSYX1oGBRYA81iKbmncYPFHy4oAck8kY+ViKR5nl+8cmm5BHSgECgCza6hdWefs8rIT1Ip0uq3swPmTu2euTVTcPSl3e1AWEZmc5JyaTmlyRRu5zQAnNHOaXOaTNADt7Yx2pN74xmkyTSigBdx9aNx9aTvQQMZFACHJNKAfWk6UZoAfscjNOHGd1NWQgUjvuAAoEOL98UbiwqPtSqeTQB9dryMA1Kv1qsofbUq5zzX5wz2CcE1JkkCokPHNSioYh4HenelMz0xT+TUgKKdnNNA5p2OaQhD9ajY81IajPXmhAQyZxirmmD924xVGQ4zWhpPzKw7Zr2ModsQjOt8DMHweRjU0B+7dNmrunjOuagvTG0/pVPwkCb7WF4+W4OR+Jq/ZA/8JBqA6DYpH6V9gcKNUivMfGCj/hI5Vz1iX+Venkc15t4zUjxAfTyhSkJ7nZ6B82gWZzn92OauzL8vSqPhf5vDloSMHbitOYYQ8U+mhcjz3xwoFxYN0+fGT9am0HP/CVkf9MeaZ49G0WTY/jqTw+x/wCEpQZ5aIioFHc75fu9Oa4PxWu3xLaseMpXfKDiuF8YqP7dsyx421oge5P4Dbdf6svb5D/Ou1IrhfD9zaaHd3U0szSfaAPkjT7uK3j4ssBg+Xcn6R07A3oY2tHHiWcY624ro9DP/Ehtz/sVxuuagl3qr3NsHQNGEw4wa0dK8baZZ2UWnS2t8Z0UgukWUP40hRehr6+3lWdhIe0+P0pdGydDuRjndJ1+lZuqa/Z3lpaxrDODHKH+dMDpT9O8Q2kVpcxPBOWYtjavA4qhk2kESeC58nBBarN0xNho7g8hlrmLDxdY6fpUujy2V7JNJuxLHHlBn1NaFz4gtG0qwi+z3G6Fly23g4pAdhJH5kEiHupH6VwXgRR9uuEz/wAs2GPxrp/+EptMORaXXK8ZSuO8P6lHo+oGUwSTrKWUiMZIzSEdpo4/4k/XPLD9az4hnzR2KkY/CodL8QwwWTw/Yrp/mYgqvrVWDWVVnxaTsTnt0pNAZfglgL8DOfvc16BJ92vPNDuE0m68zynmy5JVOozXU/8ACSJJn/iXXgHutMZHqwzFIOnBrk9pa1u1A5EZNdBeamtyjr9nljyP4hWLGEFvdt5gz5ZAXHWgRnQP+7sz0wB/OuyhYmMHoDXEwuv2O3kJGOnX3rsrc5hXHpSjuW3oQal81xY4ONs4Oa3A3y9qwr7iS0PX98orcAqySrqozpNx7KD+orqLQ7tPtm9YlP6VzWoLu0y4H+wTXRafzpdof+mK/wAhXhZ4v3KNaYS96zZu9aco4NULgDaeK+OgdkNjHkUGWhsYqRxl80xjwa6olmfKT5p4PWgwSbAeelE5/ecVcRyYlyB0r6HJl70jnxGyKixzZweaq6rzFEpOD71sL71l606LLApAJINfRI4mZcbGMgDvVrpDux3Apixq0uF71O4Vk2qfunmtCRdROdGvhjrbv/KuI8FZOl3ysf8AlgT/ADrub8j+y7sY627j9DXC+CfmsL44/wCWDcfnWc9mVDcseC5RF4tcsDhrZ1459K9g8PSLJpoKZxuPUe9ePeC3A8aLkhM279a9h0Ag2LbSCNx6V4WaJfUn6o6UapqrOeOelWjVS46GvjFuaw3Mm4yT7Vm3B7Vo3BzkHrWZccGu2mbo5DxWo/s6U/7v8xXGucHANdp4qx/Zc3sV/wDQhXFMRnjvX1OUfCxY5f7HTf8Ael+UStM3z8dDWPqF9s862ZSAy5BrVuCA+AK5/UlJnLY4xXss8YqRMTzVoFiMZ5Peq0ON2OlXFA79qaLR6vopzoOnnP8AywWsPxqpFpGQf4GJFbWhD/iQ2GOnkisXxuMWkPqQy0wkeXSORnNMhlYScVJIAOvNRRD94PSglF9ZGbFSh2I680kadDTytKxRGzt0qJjnrUzcfSoXwDxTsIbv44Neg/Dx2axvFBztlBx9RXnh9q734bn93qa47oc0IDvVfoN1YupGX+2neJ2V1tM5z/t1sgKR0zXKeLVO27KOyEWQOVOD/rKVb7Pqv1O7A/8AL3/BL/201dO1C9Ol3HncyBjsBAG4VBHe6hwVt5SwGSB2rH8Gq1x4HvZ5Xd5Y5WCuzEkcCsPU9cvLOKB7S5dSQd4Y9ao4D0mK+vmhSTyMZOME1P8AbNQ3OnkZPX738qqaZoEd7pVtNLeXSmSMOyq3cirK+FYMY+33pOeDvp6gSC71APGWh+U9SG6VIs19lh9n+YHIG8c0xPDFqHJN3dkEdN9Ivhi0CgfabtjnO4ydvSjUdyQ3l9sb9xjA5O+kW7vWFufLwxONu7rTf+EashIzLLc8jH+sqQeHLMqoL3GF/wCmlMBZLm9WO62xgsO+7pQt3eiSyJhX5hyu/rTR4csxuHmT5J6+ZTv+EZ08N/y3x6eZQIbJd3nkXv7hSyn5fn6VFqd7dX+lw2rRIAAD8r96sjw1YnGWnz6+ZR/wjGnqWKmcbvSTpSsM8c8R6vLfpFps6MHs52+Zu46Vb1TVEuLrw5puwb7OdAWB+9kj/CtX4p6NZaSdIks42WSZisrE5LYxU3xD0jT9M1vwxLZwCJ5mXzCOrYK4NCQHoOsRmTR9UjBILWsg/wDHTXz5CcCH2K19ISojrIkgyjKQc+hFfPE9qbHUpLacFRHKdue4zxSYLc9/0Qj7FJjvEldrazR2+kiaUhY44yzE+gryfwL4ofXJ761aFEWCJMFep5xXqW22k0MxXpxbyIUf6Gvms7s7J9zqo7nHwTxQ6x/biB2+3ShXiB+6Ogrr9RbdpF0R08lv5Vyps7eNY7Y6qot4X3J8nJHpXRNcW9xoFybeQyRrEw3EEZ4rxa1m4tHRJ+6zz3x0M/C6+55E0X8xXh+MNxXt/jf/AJJjqPHHmQn/AMeWvEwMk19Xl2lN+v8Akck9kdfoKeb4WZc8tKwBrqfCcCWtvJCzqAsZYk9zmuc8OKD4Z9zO1TTyvCUZG2kHHJrukTE7dQkmGGMntT3KovJ6dq5SDU7hIQrToRjr3pYtVuPMAkYOgGAT1rG+pZHr8X/EyLDkOgNUIwBjpmruouJ5yytvQKMECquwr95cV0RMZXFwODThyQDzSAc9uKeqsCOOtUIp+IIkk0oqw43CuOs4g87KfQ12uuZWwUKCS7hcYrlreBrTUVWUEBs9ulTIpDdMONVt+cfvAK9hgnaDZG7jlcgV5GII7a9hkScM7SD5cdOa7xXtjcsZbxt5AwQ3AqUUjqzcjbyMn6U0ykYIGM9qxDqNnEAPtg3AdM1BNq0TOAJ1GP8Aaqrgy/fXccZ2uQQ5wB71X3KLh8gdBxisr+14hdjKbyTj61dfL3TkcAgHHpTRDLIZSwwBx7V5z4oiW28RXYC8Ph/zFehIMGvP/FLSXniK5EcTEIFUYX0FDsJJmGXFPWTavBphtbgHHlSfTYaeYiyjAxjrU6Fagr5JPeuw8KzyQKGhPzZyc1x4hIGfmYj+6M10Xhm8jiZo52aL5vlLLgVLLieiPqbEhtm0kcjPeozennIDE8jmsl72yKEm5JYdBnrSLf2ICnzwZG420kNvQ9AsnzZQk/3BU+TnPaq1sFjtYkXIAUdfpUu49jxXQjCW5KDxUqHiqwYgCplbFUST54p4NRK1OzTGPBqN36g0FyBUDNzSAUnrQMYyT0pmT0xSg8Z70XAmjGeaklx5ZpiHaM96bK3yE5pgc/dgGc9KrS/6s/Sp7jBmbmoJRmNuahjM+33ec+4jrUlycR8GmQcysafc/wCq5pAcvrx/0K5B7pj9a6LRxjRLEA/8sF/lXN+ITt0+54/g/rXR6Ic6JYn/AKYLz+FCEXwMnmp0Xio0G7HvVhF6+1UBC68ZIxTrTi5Qilk+UUy14uU56GmgN9Rkc1Mo6YqFeec08N6mtCCTDdMcUpBx1pgkbPfFDyDpQMbtyeaQ5zTgwoyPWgBpHtTSM9KfkHvTM0CsN5qpqmf7HveP+XeT/wBBNWx1qtq3OkX3/XvJ/wCgmlU+B+j/ACZvhf8AeKf+KP8A6UhNKz/Y9ln/AJ94/wD0EVcIqrpP/IHsfa3j/wDQRVrbRT+Bei/JFYr/AHip/il/6UxpHHpTACOae3XHPNIRxgA1VzA5e9O3ULkE55yfyqC2ZvtKkY4PFTX6bdRuCe5H8qhtgPtSfXFS2B5/4jx/bN2e+81zirG10okOE710PiID+2bv3kNc4YvNuAmdoPes2MmSaJHdhGCPepY9QiQ7Vj2j2qiVCbgDkA1NHao8e/npmpGaf9q22wfIwx61Vur2GRMQgg981mnYWxzVhLR3AKqeaB6EW5m6mmEHNWWtmjOG4pYUG/mgG0VxGxxwQKeke2QMHxg5rQvFAgBAqKxktN+2ZMk+poJuSo7SShmOT6mrkJAu4c9d6/zqv8n2nEeMdsVPGu+7t+o2yL/Oifwv0Zvhf94p/wCKP/pSEuFH2ubn+Nv51XkKgFQecVPcc3kw7b2/nUKwgMzHkkUofCvRBiv94qf4pf8ApTMZ1bcRjvSBG9KtuAGIpuR7VRhcq+W3pS+W3pVnOKNw6E0wuV/Lf0oELmrHmDpxTd4yeeKQXIvJajyGqbzV6Ck84Y4oAi+znPWjyDnk1J5+O1J54OeKAE+z+9J5HHPWn+fx0phlPpQAeSKXyV9ab5rEE0nmMKAH+SB2oMK5pnmOKQM1MCURL3o8pAahy2etB3GgCby0Hak2pmogGz1ppBz1oAnwh5o/djtUO00mDQBNuTFLvXHtUW00hXFAEvmIO1J5i56UzaaTbmgCTzRnpQZec1HtxSlaBjjMfSjzWzTdooC5NAC+a1J5h9aNo6GjaB0FAg3k9TSbqXaMUoUdKB3GZPrRk4qTaOmKTAxyKBXGEk0VJtBFIADQFxn0owaft5ooHcZzRzTzQMYxQFxoB9KMZp4yOKOlADMGgrTzjikzQIaVIFGDT+nNGQTxQMZtNG3j3p/SkJ5FAhoU0m3nFP3HFJkCgBNppQnrRuJoJyMUAJtpdo9aXPFJ9KBht5o2ilHTmkwc4oEORVHJ5FSbov7maaEOOtHlnmkApZMY28UgdBxto8vjJNJ5Yx15oEfXAXvT1XNRr0qRWORxX5wz2SQDFSDpmmjkU4HAxipEOB4p4NMHIpwqWA6lzikHpSjmpAOMe9Rt1xUhphXmmhFWXOD6VoaLli3HeqMx+Q1mz/bXwlrczwtnkxDJr1cqlbEIzq/CyXwpE6a5rybSP32f1NasEJXxBdccmMZH5VxVgt/Nql3BHe3UUqnLugO5vrRbpey6jK5vtQWQLjJyGavsUzitoekbGwflJ/CvOvG9vOdaVhE5jEQ+cLxn0zVv+zb51LHVNTGfc1kazcalaEae17cSQbd37xeT+NDZL3O08IeZJ4ZtyR90kfrWrOTtxg1xHhrQJtT0g3A1i6hUSECOLgVrzeHJwu3+17wj1J5/nVRs0XK5i+O4WeC0bIXDcA1DoRLeK7Zx3TGB9Ki8WWEljpUKm6luGL9X5xUNpHdnV7NbOVY7iQDDnoBip6iieox9Kjv/AAvpeqvDc3sbl4xwQ5ArDW28Tqx26haE9OU/+tVPVtc8V6N9mWS6s3EjY+WPPFaIJM1hYWOjeIoUsh/x9RncrnPT0rZKg/wgY9q4CC58ReIPEhMVzbCazi+UhdoAP/662DZ+McD/AE62/Mf4UXF0KfigBdfXIAzAD0rofD7E+HYhhdvzc4964fXY9Vh1VV1RleZo8hkPBFXtNuNfGkn7E6C3UkKG6n6VII6HxGzCy09FVcfaVzx2q3pIUPqCiNSC3UD2rlNQTxOmmx3V9AjQqRtAYZGenFWNOj8UAzpAIxLkGQbhxnpVDNPwxn+wNViKj5ZHHT2qSSNZPCtmxQfK6kce9c/ov9vYv4rRcxoxE4LD73fFSCTxBNpEbJGEsFbuRnrQM7xQTg4BOPSvO/BZceJrlSAVzIAD25rb3+LvLDRRAgj5QSvSsTw4ZbjVXjtmFrf7m8zcM896BdTr9EObW5RQPlmYHiqVou24bAwcniq2kWetA3ccd/EgWU7srncfWqqQap9qIjuow2TyRQxGPoBMXiORDwRM/BrvWfcp5rgdG3jxIUfDSrMys3qfWu3mmWCJnkYKq/eJ7UrDM7Us+VIe+09aTwdptheWF3Nd2yzOJDHlhkBcUy8mjntmkR1aMqec1gaHqN5FBqMFrePDEsTSEp2agDsNb8P6PZaLuh02FdhypA6Guet5f3a4GBXORavql3pa/bdSuJyWP33OK0oLK4lgUrORkZqebULF+/JY2pXoJlJrdH+7iuRuLK6gijZrksGkC4PbPetQaNqW4Y1dwB6L/wDXqrhY1b7cbC4GP+WZrd0s50ezP/TFf5VxVxomqm0mJ1lyNhO0r1/Wux0UH+wrEMckQqDXjZ2/3Ct3NKZNN1qjcj5Kvy8n6VQuTxivjI7nZDYzyMCoXxjpViUYqs44NdMTQz5+XyOtW1GI0+naqk4w+KvREeUn0r6LJnaUjnxGyBTk81ma9HuWFlxkZGa0jyxxVHWDiCEYyWbAr6JHEzNiDRxl2znHBpquw6d+tSSI8aAE8ChRjpjFWSXbqNW0u63HH7h//QTXn/gdt1hd+phbP5mu/vONPuPQwtn/AL5Nef8AgbH2K8zk5hYAVEti47ljwcobxxEGUMDC/X6V7F4fQR2UgUcbzXjPhaMv40tULMi+W/T6GvZPDqeXaSruY5fPNeDma/2OXqdCvsbB6VTuKuGqdzyM18ctzWG5jzk5NZ9wCV561o3DckVQnHUetdlM6DkPFII0mYe6/wDoQriCRmu48Vf8gm4z1BUD/voV5nHdXcEgjuITJno8f9a+nyh6SDGf7nTv/NL8ok85G48cis5rGXUpJ0hyfKQscc5PYVJd3sj3DW8UDl+7MMAVqaFdQ6WzRTdJT/rOnNe2jxtDjVV42IdHRl6hhgirSSIBksPfmu08ZWqN4bluPL+cMu2QD1PrXm+z5TnNUNHtvh87vDmnsDwYRisbxuCLCM56Bq1PCox4U0wf9MB/M1meN0zpifRv5UA0eVyMCSc1HGT5gppGSeOKfEVzgdaYkaUTADk07dyeR+dZoVnJUGn+RIOp4+tSMtt7Gom5781WIbPU0nOepoEWACTXoHw2TFvqUp3Dc6qMjrgVx2iBRM4dQ2R3r1nSVVbCJURV45AGKaA0RknGeK5bxWCUvv8Arx4/7+CurUDNcv4qBIvQOM2H/tSlUfw+q/U7sD/y9/wS/wDbSp4C/wCRC1Md/Ob+QrktXdX0+L93gr1PrzXWeAhjwLqeRz5rf+giuU1Z5Dp8SFdqjofWtVucB7JoEgOhWZHeFePwrSUnfWT4bI/sK0zz+6UfpWwoGaLgI2c0hPHWnbAMnJpu3nrQ2Aoye9Ozx1NAQetGwEnmpuAgz17VJ070zaAeSalVA2DzRcBRg80uTmghEXc7BV/vMcCmxzWkg/4/LcY/vSgUOSRSjc81+MQzbaKep81vw6VN8TcHVvCWTn5gf1Wtzxz4Xj8UQ2KW2vaXB9ncswkmHINL4r8NWmv3mizDxDp0EWnkF90gJfBHTn2rN14LqUqcjoJRlm9DXm+s6XbXrutxGGKseRwRXpEt/o2SRren/U3Cj+tcjqn9mHUGFvqVpN5vI8uUEA0vaRlsw5Gg8B6RpmnX17JZ+f5zwqHEjZHXtXr1kgewjVwGGDwR715x4Z0yW0u7iYr8jx4BznPNek6eP9CQdhn+dfN53JONzooppsia1gD5EKf98ior5FXTLkKoUeU3QY7VakP7yoL4/wDEvucc/um/lXzcJNyVzol8LPMvG/8AyTHVR6PCf/H1rxEZya9w8bZPw01j6wf+hrXh2cNzX3GXO9N37/5HHU0sdx4aGfDQP/TZq17rR1vtFLRYNyj7iM9FrJ8LwmTwvnP/AC8Nit3TbxvMlgifa+CjHGc12z2IhuYA0a635wML3zU6aNcOSH3bcdR1rpltDjmZie/ArStrNI1zliT1rCzN1sYukaBdJbBoIJZgGyTjvWpc+HdU1qSJ/IjthH8p8zjdTbrWdT0248iynWOIjdtZcnNU28YeIEBEksJB7iOtVciTRa/4V/qhgkzNbCTnacnFWrHw9f2kawzWySuowZB0NY48Z69ni7jx6GIUo8a+IWO2Oe3Of70VDTYlJI2ToN9JLgWq4HI+YVBfaDfy2c1stnGssylPMYDC571nXvjLX7CxkuY5bZmVckGPANc6vxa8SSEI8dlknqUNQqbWo+dMkX4R6uqD/iZWZfnpuyKyx4U1CFnjlMe5TjIPWry/FHxF5m0R2Y56iM5rT83VL2L7U9yoaQbioQd6bbBJI54eGrrkgoT9aevhy8BDErx0Oa6GK1vJVy10QT22DilNndjOLstg/wB2pbaGkmYkPhq/MyOpB2nOc11M/gnWdRsMwX8UDyLyxzVnT7O4HBumI+lZzeJNbtr2ezi1GPyojhT5YJ+lODkxSSLemeE9X0W3MVzL9tJ/ijB4/OryaDcMcmzcZ5yeKyR4g13b8upsrnqTGp/pWFe/EDxPZXzwvexzIh6NEBn8hTkpBGUTthpWoQE+VDCgx1Y5rn9R8EXd7BIyTwIznLEJ0rmZfiBr0zl/tKRk9dq1D/wmuuspDai3PoopJMbsdvZaENBj8uJFcFRlsck+prL12ye6ijbZtKtnJWuYbxNrMnW+c/hWpp17qOrN5ElyWOM4PFKTY0kQJpcqtkjjHFS2uizS3aEqSAcnHari2+okYUNnOBWlpdvqsURMykE/rTg2EkjsLYt9nj3tlgoBNTZNVLQOltGJOWxzVlTjvzXXHY5pEqk96kVsEVCG5qQY7nmqJsTg07fmoN/ajd70wJHfjrUJbrmmMxPemmgCQHPXNKOWqPOFp8eTyaBlncNuM1FKx8s0gamSt+6NAGNKcyN9ahlGIzj0qVyDIail/wBWecVLAowDEzZp9zzEeOKZDxK496luP9UaQHJeIuNOuR22D+ddFoQzoVh/1xWub8QCSa1lt4Y97yLgZOMc10OjTx2ml2dvOyiSOJVIBzyKaA3o0xx6VLswtUU1G3xyxH4VIdUtQD8xJ+lUIfKAQc1FC/lzA45qtLqtsSfmP0xSWeo2c18kUsvlq38TcUhnSwCWRFfbgHtV1IcYJHPpVK3vrCJNiXiOB6mriahZnpOpNWmTYlxg420ySP2oS8t2JxJk/Sh7yAZYyAKO5ouBEYyDxQVbHTihtRsl5NzHn61A+rWKAYuoznrz0o5gJSjUbSO1RjU7Nl3faE25xmkbUrJDg3MYJ7E0XAkwemKp6sP+JPfD/p3k/wDQTT/7Y04HBvIRj/aqrqmsaa2k3kaXkLSPA6qA3JJU1NR+4/R/kdGF/wB4p/4o/wDpSLGj8aPZDB/494//AEEVewSeKyNM13S4dJs4pb2FXSBFZS3IIUcVYPiDRlPOoQY9d1FOXuL0X5IMV/vFT/FL/wBKZeKkjBHFNZGH0qk3iPR+P+Jhb/8AfdIfEuhgZOqW4z/tVVznMLVmI1OZcdhz+FUoyUcMOo5qXUL+0v8AVZ5bOcSxYA3r0NVZpUhjLM4VR1JPak2M4PXCRqNwTyS5Nc7teafap5NburzrPeTyKwKljtI7isAMwlyOD61AClCoOetaluP9ABx2NZxztINaMMgWzCnHSgDJUHzenetmOVFiUbxms262fLs698Vbt9nkAleaQEk1xGoyAGPvTIplfjy1B9ahufK3jJxx2FEDwhuXwKBWLUkHmRnLnb1xVK3sTNPlsiNTkn19q0rd4rkMqtgDknFXY0jhi82QYVfuL6+5oAzsbZ9oXaB2q1ED9qgx/fXj8aqmQzXRcnrVxMJdQDPJdf50pv3X6M6ML/vFP/FH/wBKRHOP9Lm/32/nVfdL5jcYQCrM4/0qZufvt/Oq0kqkMmfmxRD4V6IMV/vFT/FL/wBKZmN8zHnmm7cCjJ3GkJaqMBSPzpMAGk59KQ5oAXj0pOKMGkwaBCkDFL2xTcGlwTQMPrSHg0YOaCpoAX6Uh96NuKCKBB2pc8UzBBxTgKADPFJnFG3vRt5pjEzmk74p23rSYoACfSjqaWj60AIWwcUEijjOaU8DAFACA8UHOKAMUZNABuOMUmSOgp3QZoBoATnHNHPvTutHQ0ANI4o5xS8UuaAGgGnc0Cjp1pCAA0hyv1p2e1J70AHPejbxR1pR3oAQA9KMUuaOMUAJjijbnvRg0duKAAKM8mggZ9qKXIxQMQLilI9aQGjNAg/CkI74pxFAoGHak255FKeaTJ4AoAdgYptLmm9eaBC4HpQQMUc0hDZoAUHjGKMjvQOTjFIQelAxcZ+lIRijBAzmk5PemAAke9PB70wA96cAc9eKBDs0uenpRsX+9ShUJ5NIBpOTTalCR+ppCIuozmgD6yDqOalRweab5S8Yx9KkCDt1r85Z7JIjAjrT856VEqYPBp6gioESrTh19KjWngd88VIDs4FO4603GODS/jUiA5xTG5OakPSo29qYFeToRVnRztlfB5x1qCRcgk1NpGBO3Neplb/2iJFT4WZmhuR401pe+BzWtIxXxJG3GGgI6VlaUNvj7WFPUop/lWrfHZrNlj+IEV9mjh6Gi306VxfikPL4gtwQMNbkY/Ou1IzXHeIl/wCKlsRjrG39aGhD/AEhbSLhT0WU4rppR8hrlPAny297H0Cy11chG0jNCKeqOG8eK50+HajH5+cDNZ1hL/xUOkfK3I9Pau+lnuFdI18oRH7xYjP61l6/eiL7MkEsBcuBhcFqdhI6GPpXM+MF3S2Ddtxro4ATGM+lc94tGPsbejkfpTBlHwQP+Kk1IjP+qGT+Nd5XAeCBnxFqB6/uh/MV32cHmkPocL41G3VbZsnBiIP51ueFTu8MpnBAd8fnWL444vLQn+KNv51q+Dsf8I0OeBIwH50JCRb8THHhmZv7pQ/+PCpNJdTqlzgnJRD9eKPEQz4cuQRxtH8xUOlEDWJgP4rdDVAV/DQI1LXYsj/WZ/PNOsmL+DXLH7jn/wBCpvh0bPFOux9iVP8AOnWIz4Svk9JXH60AdDauXggbP8C/yrgvDLf8VvdqQMiSTn1ya7jTfmsLUjp5a/yri9ARV8d3uDyJX4/GgFudDpJ/0zVE/uz/AONY97K0LXEiN8wJwPetnTARreqpjrIp/Q1z1xKZrqdCMFZSMfjSYM4q/vr2ytb29tVJvASyjODuqKy8V/EC8so4l8MW1wjL9+SYAsPfLVb1ZMfbgePmrZ0yQCytWJHKCkmCMUaz4+CCD/hGNNRB2M4OP/HquaNN4i8nVf7Y0qys4Ps/ytbtksffk1vSYZ8gcUl9xod7njMdFyrHJQGQaarFQqmQ7auxw+JVh+UoI2GRj0qKMFtFtRwMs2fzruoGVrSHj/lmB+lQtWVZWuYc00y6TbrcQt5iOpdh0JrcGoDj9xIMiqWtbP7NbAwQ6mrb7Ng55I61pYgklv1a2lUwPgoR+lbWh86Faf7n9TWIArQkBf4T1ra0Ft2g2v8Aun+Zrx86X7guCLMnOapzDmrsveqUvIJxXxa3OyGxnzZ5qq/3fSrcvUiqzgYrpgaGZcH97VmJsRoPaqt0QH96tRA+QhPccCvocn+NnPX+EUH5uTyaj1EDEbkZZelSKnzjH40moRloF2/eFfRo4mYt1IZFC/iabHnbTZVaSbGOKkH3Cvcd6u5JfnIe0lXGSYmB/I1534FyLW9J6eWwr0IndaSDHPln+VeeeBBuhvCf+ebColsVHck8NNK3jazSMgMQ3J+hr2fw9vFvOJCCQ+BivFfDrunjexZELnLcD6GvavD0jSQ3G5CmH4Brw8yX+xSN18RsN0qnPwpq43Sqc+MGvjFudEDJnXknNZ83StCbGTVGYV2UzdHIeK1xpE591/8AQhXBSnbyBk16B4sB/sWc46bf/QhXnk8g5x3r6fJ3pIeN/wBzp/4pflEmS4s4rbcSrXTHAVugHrWLqUsYVYmORu3PtH6Cob1ZgRKn3Rwapys7wqp5z09a9s8Wxva1cTS+GEZp3WCYgJExzmuYiXAx3Nb/AImAitLG17xxA49KydKsbzUbkQWVpLcyAgssa7iBnrTGj1jRiltpNpaCQMYogC2Me9ZXjTYbGKPd8xVm49KsWNpq5vbofZTFHEBzKMZ+lVPENhf3sUeyLewQr1x1pgzyl1xmi3GX56Yq5qenXWnTtFcIA69SpyOagt1+Ukjr0oEh1su6TmtPy12gVm2oIlNaqoz9BSGVpIlweBiqbxKOgrQmRkHNUZMk+1AiWzkeB8ocGux0rxZqTSwWZSDb90OQc1xkOAa09NGdQtye0g/nU3GeiLfaiWwZIh9ErJ124uJFuTK6km12nC4+XdmtJcLwfxrL1rmO4wf+XXj/AL6pVPs+q/U7MCv4v+CX/tpQ8KXVxbaDd28TjynkO4EdcisXVHc2yxsflU8Utjqs1jaPDEFwzZOabLLFcx7ZVJ79a6UjhPSdIu7u30q3hjmGAgPKZxxV5r6/6i4ycf3BXBw+K7iKGOPah2AKM+gp/wDwml3kjyocdhmk4jO3/tDUcYacfXYKZ/aGo5/4+OP9wVxL+M7wrxHADUf/AAmN6HGEgxjnjmlyiO+OoX5UgXDfXYKel3qDKAbpvwUCvPz4xvsMMQknp8tL/wAJjfDBBh/75o5RnoIur7OGnP5Cj7bfqD/pMgH4V59/wm2pkHPkZ/3aUeM9RbaC0OO/yUcoHqOhaaniK6lttYaSe3jTeq7ivP4Vvp4E8KTxfurTzFHBK3Dn+tcP8MNZutX1PVoZypCWwYBRjvzXpmnPbyTu9pHsi2ANgYG6vl83q1YTfJJqx2UopxMVvh74YU5XTiD/ANdnP8zUcngTwyEIOm5+srf411UpqrK2eK8COKrv7b+86VFHIz+CvC1splfTYkUd2c4/nWJqnhrw6bB5ra0jGOjRuf8AGun14iJra4liaW2jfMigZwPUiuemzPb6jdQwmG0kP7pSMdOpxXdRq1GuZzf3lJI1vDvh21sIY7uC4uWMluMo8mVHToK7iwH+hrmuY0Fi2mQenkgCunsDm0H1NZ4qcp0nzO+pE1ZjZAPNqK85spx/0zb+VSy/62mXQ/0Sb/rm38q8yO6E9jzTxqM/DbW/YwH/AMfFeFnG417r4xG74c68OwEB/wDH1rwvIJPrX3GW/wAJ+v8AkclXU6nw2ZDprIrMI/MJ68V0Gho/2mVgRjJBrA8MnGmP/wBdTwa7HT4Q8bPZQZLHlc45+td8iIGnEN5GB071qSSLHahwQSKwPP1KDIGnk46/OKabvUhHtNmpzzgt0rJI2uU9dIuNQMqO2AgHXFZbIe5JH+9VrVr2X+0khmgEb+WCAg4xVKa4EWCwOPWtU7GMkAjQdAfzpTEByAc+xqmdSJh86O1kkiGcsMc1VTxFA/8AywnHr8nSncmxe1Bf+JfJwzHA4JzXFuCNQ2bcENjFdhPew3FmwidtzYxlTVbVpLe4FrFBb5uwwLS7cZx70nMpRZzT7hdMp4IODXrelxqdItRjnywM1y+q6Roi6bJeHIv3Awiv3+ldhoyxPpdoxkwDGuQT0OOlZyaZauPFvgbVPJ6VYt7QBwH6dzVr/R0beGHHqaV5YpF5K+5DVDLRLFbgSAAhVJ7VwE0Spq92oB/1jH9a7ltiwF0uFyO26vN59Xgjvp2m3YZiAw5yc1rTIqGqrDrnpXGa+6tqk7A/LuGPyrfXUwCALW5K54Pl9axdRsp7y8lnit5yHbIUxnitGzFGKGFOHHJrSm8Oaotu0/2UhVG4ksOBWaqODtcZPtUl6k8ciswXNdJ4cOdQBU42ryawRpGoCMSpZTFT0IWtvwzbXUVxIZ1eEdAHGM1DRaZ3dqyu2CfmrQeXIAHGKyraNIj5jSLmr0c0W/dwy9yD0oihs0sg457UoPpUasHxjpjinqewrpjsc8tyZWyRTwQTUa06qEPB79qGbAphbFMd+1ACg880hbpg0zcQOc80AgnNAEmTUycDNVxkmpM4/CgCTd61FK37smgnNRzEBDzQIzX4brTJP9U1PPJNRvxE3H50hlCDmZsmrMw/dEVBB80xGMc1anXMftSGc9PaxT3yiUE8dAa0zbRRIu2ML2qoy/8AEwAPpxWyEV1XPT1poRCY0VASB04qGRVAxgc+1X5FXAA7VTnxjHr3piM2cAHjFQ7Y2YEjOO9SSrjmoD8ufWpGTrJg/e+gq3DcP0DNWatXbfOeemaYGxDMVXPmtu+tY3i+8m/4R2fbLIrbl2lW561fTPXvWH4rkH9jugcZ3jjNAWOD/tC8wFN1Mf8AgZqOW+u+ALmUfRqjzlxxUcpw44oEWDqV6FWMXk21TkAPTJNQvHbL3UzH3c1CwAamtSuIc11MeDK//fRqSylka+tgXcjzV/iPqKq96sWXOoW2P+eq/wAxUz+F+jOjC/7xT/xR/wDSkF87C/ufmbiVu/uaq72B+8T+NT35xqFzn/nq38zVUGnD4V6IMV/Hqf4pf+lMeXNHmZPrUfJNWLW3W4kKvJsUdTVGB2fhCULpcig/xmtm+aOaxmjkwdyEVkaZqHh7T7YRpLLgD5vl70+513Q2hbaZ96j5RtxmndAchLxFj0FZ+cSVoXEsEhPlbsdcEVRIy4AB/Kpuh2uOLfKfWpoWzFhhSrbSPEW2Nj1waVImC/dIx6ik2gswKpxgU2VzHGAG61J5ZPUVDeIVRQeKEIryOX5Jot4XmlCKPqfQUkUTTSCNBkmujsLJIo8D7g5d+7H2qrgSWdqkcWduIl9f4jWbqV+Z5dkR+QfrUurapvH2e3ICLwSP5VjKcHJpAaVuTvBNaUG03MRPJ3jj8azbb5mFaMKN9pgOP4xz+NKfwv0Zvhf94p/4o/8ApSCfAuJeON5/nVUoF3MF+Y9zV24H+kSZ/vH+dUWR9zkn5e1EPhXogxX+8VP8Uv8A0pmayjccU3AFNYtvPPejcT1NMwHcE8mkIGabuNN5z1pgSU00gzRg0ALSkDFMwaMGgB3SjINMxk07FAhSRScE0m2kxQAvHegkdqMUYoAMijPNIRzS4oAM8UnU0vtR07UDEoPTmnYyOnNGDigQ00HilxRjFMYlLwaacjtQAfxoAdxjFJjFB6dKOcUAGaKAPWjmgBKdmkwcUoGOtAAT6UgPrRRtoAO9Ge1LikwaQhaCPWjBpdtACcenNABoxzSlSaAFHTFGBmgrg0uwnoeKAGjvRwe1O24zShAAc9aAGEAdqBzTwgP0p6w55HSgCHml61OLfPXNO+zD3oGVccZoOat/Zx36UghB6c0AVaU4yKuC3XFL9nTApXEUKXnvVv7OM8UogXqeadwKeaDyau+So4KCjyl7KOKVwKZFJjtVwoo7UhQbegouBTxzThx2NWdq4xilCjoBRcLlbA9KTnoBVrywOtGPQUXAq4akAOelXNg7ikKjHtRcD6wB+YU5Ce9RhRvqRRjPNfnLR7RKMCnKc0xRn3p4PNQSOX0pRSZOcU4DvSAd1HWgYHNJyfpS1IgJ9aYw96fjvTDndjNNDIn6EVNpP/HwwHcVE/3TT9KP+llfavSyv/eYmdT4WZ1ofL+JF8v963zz9BWnq7eXe6fJ283b+eKyF4+Jr5ySbT+laviEHybRweUuAa+2RwmsclfeuV19c+ItOwByrCuqycZrlvEH/Iw6T7lqGJlLwblb/VIv7r/1rrnHymuO8L/J4o1aPP8AESfzrsHPymhbFdDivH0Ec2jIHBP7zjBrn9Mt0hvtFCx7cOu45610vjnI0MMDgiQVz1gxabSyTzvQ8/Wp1uCPVIx1rn/FihrW3PpIcflW4jYySe9YviYh7NMc7X/pV3FJGR4Ib/ioL5T1EP8AUV3ZPPXmvPvBb/8AFS6gSwH7kY/MV3RmQfxikVvE4vx3J/xMLWMcnyifpzWp4OkU+GGycETMKwfG3za3byKco0O3IPfNanhGaKLQpEZhkXB/pQmSjoNfwdAux/0z/qKp6SR/bkQJ4ezU/wAqk1q8hOiXg3jPl8CszTb2FNZsHaQBTZAfjTAuaQyp421Vf7yg/pS2B/4kOpx46TS4/KqOn30EfjG+mZxskQAH8qXT9StU07VEZ/mMshX8RTuM6PSpAdJtSP8AnmK5fSAE8b3bDqWY1o6Lq1smkWqPJ8yrhqwbPUbeDxrPKzfupCQppbglqdTYSY1/UwTyQh/SueuI2S+vGPeUkVfh1a0TxDfPuOx41A+oqpeSbjLMg4ZsigGjldbi+a7cdOpH4Vc05UbSbbHXbVTW3byrkgjkCrGjNnS4e/H5UmwRqR4xyabqLA6JejP/ACz/AK1GXIOOlM1CQHQr0Drs6/jSLMiME6JaMP8Aa/nXXW0+/TYJUGW8sHHvjpXIQsT4fs1A5y3NdJp7EaVAp6+WBkUloD2MO48Vangxz+G2aPP9484/Cm3vji7gvBCmjwquwN88hyPaq9xoWsyAy22pSSIXPyM5GKzrrTp7fWoLe6LNM+3dg7jjNNszNk+Ob4xM39mWwwDxuavSvDc/2vw3Y3BjWMyR7ii9Ac1wR0iA3Nxc4xiMqExweK7vwspXwzZqw2kKRj05NePm93R1NYbGhLiqkn3D6VblqrL0r4yO51w2Mybhvaq7/dIFWZvvVVfoa6YGhlXYwxOK4u58Q+Io9SuLWC5t1iT/AFayJyR6V212OtchNBC995s0Jco2Rg9K+gyfWq0YV/hE/tjxUtpDci4g3SNtKiMZH1ro9PudUOnyjU5kln3/ACsgwNuKwLezv7nU1VrmODT2YKGPJTPU4rrrnSrTS4I4YNTa/Jbc7nBC+gBFfSqNjiZRXATH8fvThFtjZiOaicgynFWI3LRkMcjFXYklCnyWA67D/KvPPAUbh9QJYeXtYKK9KhjXyck/wH+Veb+AfuaiTkfM2M+lRLYqJhPc3drqouLWZopI87HXqDXr3wpv7zUNJ1CS9naaVZwNzfSvJvssl7qAtoMB3JAycAGvX/hhYyadoVzDOq+d5vzsvevCzJ/7JI3izuT0qncYxmrjdKpz9DXxq3N4GTP3qjKOKvz9M1Sk56CuumbI5PxaQdFuPX5f/QhXnl1GN+a9E8XADRbg8Z+X/wBCFee3SlnJAJr6bJ9pFY3/AHOn/il+USk6g2rhgSD1xWTsh/tCJGciNWB4NazRPPatEhw7HFZsGjz3GqyWKsodBksegFe6eMWfFTh74Y6CMCsvTNRvdMkaWyupbeRl2lomwSPStLxLH5F0sOdxWMAmsexia4uIoU+87BaaEi7LrOt3s7O+qXpJHOZiKs6bb6jqCXEsusy28UI6yzH5m9BXd6Z4c8NXdo169hqd1M0ZilmxiMEcfLiuM1a1t7eKaHT5JDZQsd0cuM7qJXRSjcpaJp8eua1HYXl3JHFJuLyg5PAzXYp8PfDsRKjWLxh7KP8ACuD0q4eG9SSJ9jgHBru9J1mOYCO5O2T+92NcWKnVjrA68NGnJWkVL3wFp1tbGfTb+eZ+wlxg1zbpJa7lYbXXg5rsdU1CXSNRtZHO6zlG1lHb3qt4i0oSR/aocNwC4Hp60sPWnJe+LEUorWJxM80kvD9qqMOc1oTIME+lUHPOK7b3OMkhHvWhYn/TYcH+MfzrNiyDWjpw33sK9cuKRSO/Dc1n6tgx3H/Xt/7NV9V2rhetZ2rBjFcY6i2/rSqfZ9V+p24L/l7/AIJf+2mX4etIJbO6eWFZCGwCw6cVlalHGifJwQa2/DufsF3yPvj+VYmpKBkjrzXSjzz0Gx07T2s7djZQE+WuSUBJ4q0thZB/+PK3x6eWKbYSL9hg/wCua/yqcyZ6iobY7ANPsWP/AB5wY9PLFSDTLLacWdvz38sUkUmee1Tq5LYFFx2IhpliMf6HAMeiCmNYWgPFrB/37FWSeTk80wuBz3ouwsQrYWYyptYef9gUHT7Qf8u0OP8AcFSqwPsaXORjvSuwN/wVbwW+qzNDEkZaPBKrjPNd8UVE+VQM9cVwng4/8TRwf+ef9a72T7lfF567Yn5HZS+FFKXNVJDVyXoaptwc140TpRA4ypyM1zXiI/6FIMnGDxXTSfdrl/EOBaP9K7KHxItD/C+tWdykNgkhM6wnK49K7nTv+PMfU1474IP/ABVg6f6iT+Vew6b/AMefXPzGu3HUlCi2upjNtsSUgSU2f/j2lH+wf5U+Rf3maZP/AMe8g/2D/KvEiN7Hm3i4Z+H/AIhA/uQn/wAfFeD5+c+te8+Kf+RC8RL38uH/ANCFeCSMQ1fdZav3T/rscNR6nV+GyfsLAHI3mugmu7hbKGGJniKyZLKcZrnPDJAsJM9fM/pXTpEbmyC7gOfSuybsKJ1D6Pqw2NtLblBJLiq0+h6u0gAjIB9XFa9nd3IsYdzkgIBzUy3d2wLeYMj7vFY8yNbMwV8O6u0vMaNxwS4zWfdaBfzRmCaF4oy3zOBk/hVx/FuqxOVPkgqxBwlRDxtqxzuMPthKtRe5LaMy58M3UD2i6YVkhikDbScMw75FbzaFq0wEiWewkcjIArNk8VX7kMfKDjoQvNNTxXqyjaLttv8Auim4sSkjSHhjVNpJhTLcH5xTH8IanKrLFHAjlflaRuM/hWXdeKtWjtmkS6O/jqBXPt8QPExv2t2vowgPXyxxSUWVzHQf8Ky8Qtfx3k93YsqD7iM39RW7beGdShhKyJHyeMPXDaf8Q/FD3i2zXyOjtjmJc16dpl3dyWqNczF5CPmOMVM9NxxMSbwnqsj7iYAmRxvPNWX8J6icCF7ZVA7sc1vPcSY4kIpoups/6w1FyznbjwZeSW5jW5ijkZSNwzXPJ8IdWEsUjaxbkq27/VtzXX63e38Fqk9tclGRxuyM5Brmj4s1s3rob5ggHQKP8K2hdrQzm0tzpj4d1IrtcxlgMBg2BQnhXUWj+eaEMfc4rnf+Ei1Rx/x+yfpXIa54x8Rx6lJAmrSpEhwFUD/Cn7Nk8yPSbvwTqV7bSwJewQ+YpUkAnArn4/g9IjxkayCynJ/d8fzrhD4r14qcatdBvUPSReKdcLYOq3RH+/T5WhXPYB4PuUt1Rr1CBx0qwvgpig3Xib+uSteRx+JNZzn+07kf8Drq/C95qGrXBe71G4dY8ZBc81m9CkjqZ/B0znC36Ln26VHF4ZbS0kZrrziw7dq0fKQDcCc/WmyH9ww5596IzVxtOxBHwAPQYqZevtVZT2zip0PHNda2MJbk2eKC1R7qUPxVokczVGW55pXb0poOetADic8UvYYqMMCaXdzgUASr9ad61GCVHPFKWzmgBeajl5U0Bs0yRjg0gKpUevNRS4WJuakyefWo5BmM0gKNsR5rmrsoJhPaqVvgTP061dmy0RA4oAx8j7dnjpWrtPlJnk5rIAB1AAdMVu4BgU00O5GQWPpiqsw61ayc1Wn5piM+YHpmqxUDrVqUENz61C4XGfSpAREG3PerEXGMfjUcQymRTk+V8HpTAfd3v2WLK43Y4rjdYneaBy7Eknk1v30nmykDlV4rA1dgLYgL1NK4HOAfvOtMlx5oxT8fvBTJf9auDQICOaYw/Khz8xFBOaBDMYqaxz/aFsf+mq/zFRE1NY5a/tv+uq/zFTP4X6M6ML/vFP8AxR/9KQzUP+Qhc/8AXVv5mqnSrWoZ/tG5B/56t/M1WPoRTh8K9EGK/j1P8Uv/AEpiVPakgt9KgqdUMYDZ6iqMDXF7PPp8OnQWqEbt2UXLOaivNIvrZIzNbMm4Z+n1pbGSS0WCeF8SB+v1rrvH0cumQaciSk+fFvkPvS5Rplr4Z39rYWF7FLpVtczO/wDrZlDEDHQV1+21uXOyzhV/QIK8y0DWodJcpOCUkw2QORXc6P4g0+9v0jgmBZxjBGDXi432vM7bHp4bk5fM2U02NlwYI8emKo6noNvNZupiQMR8pA6GtW4uhbRh2YKgPzE0439tdIRDIsmOpXtXnp1I+9c6eVPc8Ju5J7K7dGRQyEjpWdc3Mt9Iu4DPYAV2vjvSPJvRdRqQko5x61x8EO18D7x6n0FfRYasqlNSPJr0+SbRbsbZI1xnA/5aP/Si/wBRCgww8KOB9KSZpRb7YY2KjqQOtZTxyg5ZG/EGt9zKwhcFjT0KAHI5qLac8g0/y3UBtrYPtVCaNWxiMj4HGFzWiuYZ7cN/E6j9aq6SD5h/3Ku433UOedrj+dTP4X6M2wv+8U/8Uf8A0pDJz+/k/wB8/wA6pSTbnZAvQdavTKxuJAoJO88D61G1lPtYiIgmiHwr0QYr+PU/xS/9KZiLCrOcmrEdjHIeG+tNSGUysoQ5XrxV21gm3N8jAD1FMwSH/wBkWnk7vMYt2FVzp8I6E+9aDKyD5hjPSmYG08imgKX2CA560n2CAetXgny5PWmsgJ7UCKhs7fPGcUfYbdhwTmrWxWHWkVQpNAFb7DB6Gg2EA9asPg4IajjuaAKv2GAdc0GygHQGrWB0NICAaYalc2UA4ApDaQ+hqzkcj86FdOhIpC1IPscO33pDawdcGrOQaYWUrkEAUDIfssJGdvFILWAnoanA/KnEoDnODQBWFpBnoaDawk5wathVP484pCgTrwKAKv2WHOCDR9khLcLxU8m0Hng0blPcZp6AV/skXHy/rS/ZIv7pqcOucZFODqOhGaLoCEWMBXIU5pv2GHPQ1Y38cGkLjOMjP1pAVjYw54B/OmfY4gcYJq799sA80hxk4Ip6AVFtIQcHJp5sYMcg1N5fOdw/OnqgPUjNGgyqtrb9Npp32O3BB2k/jVkoG6EDFNwEODSERmztgg+Q7qjNrAP4TVo47HmjysnkU1YWpVFvAF+5zSrBC3VKseTT1hwOOo9KNBlb7JD12Uq2sOMlOKsbTn7rZ+lIQ/ZXPf7poug1Ifs0HaOkEEQP+rFTgMpzsf8A75NBRyuQj/8AfJpaAV2hhK8R4ppjhGMx1OIpSf8AVye3ymkMUmcmNvf5TTuAzy4Ovl0ZhzgIAKkWJsZ2Pg9MKaTyHOdsTn/gJo0Cwu+PGVj6U0yL/wA8hUnkyBT+7cEDn5TSeTJ0EchP+6aQDNyP1QU5SijBjFKYZAMiGT3+U0eTLg/upOuPumnoFgBjx/qwKcpjA5QUeU4HEchP+6aPIkxu8uT6bTSAXdGf+Wa00lOojWjypjgiGQ/8ANHky9TFJ/3yaAGfL/cFOJUDAiFO8mXI/dSZ/wBw0eTOW/1EmP8AcNADBt7xj8qjIA/gFWTbXJGRby4/3DSNa3PANvJ/3yaQWK3TJ2LSbwSMRrVoWdwRj7NLz/smmmzutxH2WUEf7JoCxATuPKihiFHRfyq0LO6B5t5P++TQbK5ZSBayH6KaLgUw27qBinAjH3RVhdNu+f8ARJv++TT/AOzrzHFnMcf7NGg7H0msTDGWqVVZetPUjtS5J61+ctnsXGqSDzU+4FeKYOKcVHGKkQ7Geaf05FMGQMdqUZxUgOGfWnimAYpwBzUiFpjcGnH6005poCKTNO0wYveT2prfSnWBAvB616GXP/aIkz+FmbcAJ8TbcA8va/0NafiYkaOzjqjqf1rM1EiP4jaY/d4Sv6GtbXk83R7gegB/UV9ujz1sXkkzEjDuoP6Vy/iViNc0Zx0EhB/MV0Vo2bSE9vLX+Vc/4nYpfaW2P+WwA/MU2MzNDfZ421FDxnca66Rzg81x+nYX4gXKH+JCf0rspI+CaUWO2hyfjBTLpDKTwWGK4x5JoIdMKtgmZRnuK7jxWMaM74zhga44o0kGnMwH+uHT61LEtz0COdtmNxJqnqEjPbkHn0zWnFapg8VU1aBIrCRwCSO9aJDkctoJaPX7og/ej/rXTSTtzgmqHhnQr/7dPqE1qRayx4jJPJP0rqP7PAXmE/lSC+hwGuyLLLGCGLpnae1O0It5cqgnO/OBW1reh3lwLc21mz/M24ggYpvhDTZ7ea+S6i2yfLhSQcUWEmQakZDYT5DY2HNZkBYvZ/KdxjwtdzqFqJdPuoY4wXeIgDjrjisuDTbhZ9HfyVxBGVlG4ccUWHc5lBK+rvEqMXAzinW8csiXO1GO1iG4711NtZGPxjPckx+W0XTPIqXTI47aXUllMaiWVmQFhyKHESOSsop5bdTGrFc44FZ0yyLrkcGwmbcPlPWu+0CD7NpKRXGxXEjEAketZM2g39x8Qob+BITbhAeXGemKLDbMtYLhdSaNo23bM4xzWu2RaBCCCAOK35dMvP8AhII7nyl8rySpO4daqX+jX4WSQQAKTwS4p2Bs4PXD5aTDnkCrHh9t2mIMYAJrQ1LwrqeorJHaLE0+zKo0gG41Lo/hLxBYaZsu7ONJAxJ2zKf60rCRDPgkAVBd/wDIGuwTjEf9a3j4X1l1DJZqc+sqj+tUNa0TUbDQLuW8gWJSoUfvA2efak0y7o56GQf2JaHHy5atzTZxNpcLqeCCB+dYMGF0a2U8DcRitXRcJpkag8Bm/nUoCcatfWy4g0Mzw7jhxL1P0rF1XxN/Zespe3Gjqs5jC4d/u5/CuosWItyATgMa5Pxbo82v+IY7KKURM0KtuYehpsguDxhem5NoNHi8508zbv7V6V4dvJL7w/aXEsAhdlOUHQYOK89/sCQ68moCdfKig8krjkn/ACa9F0RDHpEKEg4B6fWvKzZfuLmtPZlqUc1Wm4FWpOtVJjkYr4tbnVAz5QCaqPgCrcnWqriuiBqZV7jJrz/U7nVIL+QW3kiFiB8wya9AvuSRXIXEatcyE8YNe9lH8U58R8JnLe6yLgQmaAHbkkJ2rf8ADVxcXOjztcOpdZyo2jHaqaWyGTzMHLLg1qabAtvp7RRDjzNxr6dKxxFpV+bJ71LjZlR6U5F3L5hHTtUavuY/yqxdTQhVmjGOm0/yrzbwSxF3eoDxucE16TCTtGDxtrzfwl8uq3ajH+seoltqUjAvmZLlmV2VgxIKnB616r8HZWk0vVN0kj4mT77ZIyDXlOp/LcvnAwT/ADr1D4KyK+m6uFOcTp/I14eY/wC6SNobnqLdKpz9DVxulUp8kEGvjFudEDLn7881Tk7irk/eqbDNdcDdHKeLsDQ7hcd1P/jwrgJvvH0J7V6D4vx/Ytzz3X/0MV5sZQ7SL3RiDX0uTdSscv8AY6f+KX5REhBEmccZqzo0Yl1m9uep2gVUL+XC79wD1q14aO9p3z2HFe+jxTK8Ur/xMckfwAVgWcjQXglVtoTJBrpfFceL2J+zLiufjs5ZbS5nRCUj647n2ovYEetfDO6YeBZJnkaUGeQFcE4Pv/8AWrzTU50Zrkhv3jTNuGfeu6+GwvE8D3kQRo42uWbupPA6GvNL9JEvZ/MRlJkb7xz3qm7ol3TKCOd5Oce4q/a3TwjaHLhv73aqPlupORgGrMUZ2xtkYJxWbV0aRdjVlnkmRVkdmA6AnOK6jw1qKTQHT7h8uB+73d19K5qS2aPlfmApIZZIplkjO10O5T6GuWUbHbGSkh/iXRZtOvWmidjayn5f9k+lc6yvnrmvVnSDXtGAcffHzAHlWrzbULWSxvZbaQfMhxn1962pzuclSnysigGV5rY0KPdq0A4wMt+VZUH3OD3rovDdrvnknx9wYH41qQjqN3qaz9RGFnHrb/1rTRcdSKztT5FxzjFvyf8AgVTU+z6r9TtwX/L3/BL/ANtMzw4SbK8B/wCen9KydUcMCB1B5Favhc5hvVP9/OaxdUYqWB55PNbs4D0OxcGxgGMZjXH5Vcw7DhSfwrEeKeSzsnhCtsjHy5x2o/tzUrUnfakDGCV5FQ2M6CNH4Gwj8KbftNDaPJDkMgznHQZrm4/Fc4QLuXg5+Yc1NJ4ollilR/LxKu04HalcZ0saPJCr5chlDZpnlPjkHFcxF4nu4YY4UaNooxhcjmnt4llmJBdVY9sYouJnSiJl4AIz71UsTP8AaLuB8ny3ypPoax21y5O0mUZHcAVX/tKdbl547kiSQbWPUEUXJuen+D1K6q2/vGec137fcFeR/DnUZrvxVJFNJvAtiw/MV66/3BXx2fO9dW7HbR+FFKTnNU3HNXZQaqSAg14sTqRXk+70rlvEQH2R/aupkJxXLeJVP2OTnBIrsw/xos5jwOMeL1z0MMn8q9j03m0/4Ea8e8FPu8WwDABWGXPvxXsOnf8AHn1/iNelmP8AAMpjpOWpkgHkyf7p/lTpP9ZSSf6pvpXz6A808UAN4J8Rj/pjF/6EK8DlHz89q9+8RBj4M8SLj/lgn868BbJfnr3r7rLf4TOCp8TOl8OOFsZAccv079K63T5A0W3muL0EkW8gyMB/6V09m8lvCZSPkdsDJrsqLQcNDvrLm0jzggKOlTqwB44rno9YkggTMY24AzmpDrhK/cBx71y8jNuZGDrzJDrNzECMht2PqM1kGbn73NQ+JNSabxQ86oyBo1yMZ3GsltSeTzA0bQun97g11Q2MZI1vtcectIoPuasJOnB3jn3rlpLWJrUzGYmY/ME25zVOJJ5CPJjmJHOFQkitESdbfzOLYleQCM/Sse6S3m1eB4mLrIVDilS61T7O8T6ZdOWG3f5TVmgTW86CZJInHO11Kn8jSaGjV0yMQeKYYyuF87ge1ez2pCwHHWvGNOneXWbViSSGyK9Fh1qdVCMi4Hc1hU1NYs6nfgc+lIsgJOawW1piuV25NTJqZZQSygkVi0aXL2qhW0udSQMLuye2K85WeOW9fy5EcY6qc11Wuapt0DUQXQ7rdlA75Irx638yIqYWYPjjb1roo6Iwqq53RvLaN/LkuI0cdQzciuM1eYTanO4YMC3BHQipJdN1WRzI+n3rM3JfyG5/Smf2JrDjKaVfMDzkQNXRcyKIbGeafG+DU9xo2p2cfm3enXlvHnG+WEqM/U1DFbyysBEpdjwFA5NJ2KLlu4ZsGvRPBVmJrPz/ADmU7yGQdwK89XSNWjOX0u9GP+mDV6p4VlsdO8MwRhZmv5OZPMXaI+elY1FoaQep1PAAHp1qKUbYziqbaghGGkAqtcajlxHGd2epxXPGEmzVssKcsc4qUE44qtGSPpUocCvQicstybdmjdnv0qInuKN2OtWSSM3vSbsdOaYGHpzQDgcUgHjApc85qPOKcGoESbj+FNLU3OKazAc0ASZ6jNROw9abncM54o4/GgCI7t3tSS48k+tSHHX1pk+PJOOoFIaM+1XMz59auy8RHscVn2kjfbCuM561ozDbExoAxlOdSGemK3MfulxWIOdRXtxW8o/cL0IzTQEbAAcDrVObC81dc9eKpTjg8ZoEZ0zZ7c+tVz1xjirMyjrmq7DK+9IYIxB4PFOlk2RswqANgn3pk7FlAHSgCnKxJOOtY+rN+4/Gtg45FZOs/wDHuq5781OoHPDmTmmsP3op6j95TZBiUVRIyVRuPPPekAGOvNKfmc00nmhgJjBqay/4/wC3IP8Ay1X+YqHtU1lxf23/AF1X+YqZ/C/Rm+F/3in/AIo/+lIiv/8AkI3JP/PVv5mq/JNW70D+0Ln/AK6t/M1WIBpw+FeiDFfx6n+KX/pTGYzjFX2iJiTg4xVRFAYfWteDU3t9+6FXLLtXI+7VGBWRz5JVeACCTXXeP5BPZ6TIHz+5wc/QVx0MhZvL27ix6Vv634jTWbO3sxpawm2GDJuyTx+lFwSOceRmVST0GKu6LfNY6zaXOcbJBk+1UZOowMCmg4Hv1rOcU00zeGh9A3iLPZSfKHBXdg1UsLmwMaxWwRZCPmUDvT9DuRfaJZygcPEM/Wq0kN5DdtIkEEVuDgsB82K+cStzQZ6sXdXK/inTjfaJME/1ijcteU28KpcpHMQqlgGNe3hQ6EdRivF/F+nyadrk8eTsY7l+ldeW1Hd02c+LhePMdXC9ptEcLxbR0AIq0sMDYVo4zn2FeUCR1OQxH0NTpf3Mf3biQY/2q9nlPOueyWek2hXL2kLE9MoKtzWGnww/PaQ46Y2CvHo/E2sREbb6QY6Z5qQ+KtYf716x+oqeVlXRrToi67dKiBEBOAB0pbdohOC6nJwF+tZ9ndPNO80rFnYZJ9TUwdlubfC/ekXP51cl7r9H+ReHf+0U/wDFH/0pDpZGF3Jt4w5/nURluTJ95tvrSzE/a5ex3t/OnfbSP3exeKcPhXohYr/eKn+KX/pTPTvCGhWEmgRzy26vNKcs5HStW78PWlygUQooHdRiuC03xhf6dpq2sUkO0HgMtTv8QdTCkKbcHGOFqWmZJo61PCuiqczQpKRxhj0qwvhbQHYY0+HA615x/wAJfeNncsZPfk1ZtPHV/a5CLEB3B5pcrDmR6D/wiehsONNh/I0z/hEtDUY/syInuea4wfEbUTn5bfA9qP8AhY2obgSlvj0xSsyro7H/AIRLRMj/AIl0QHpzQPCGic406Pn3NcafiRqDEYitx+FC/EfUlGfLtyfcU+Viujr28H6GH2/2dFj8ajPhDRC3Onx9exNcn/wsfUCxLR25B6+1NPxGvif9XB+Aoswujsf+ER0XJJ06PnoKa3hPRhhfsEW76VyD/Ei+AGI4A2OuKQ/EW+OAI4BxyaXvD0Ow/wCEU0VFJ/s+H8qRfDOikHGnQf8AfNcf/wALEvMBRHD9TQPiHdheIoc5709RXR158LaQemnw/lSr4W0YYZtPh47Yrjf+Fg32/IEOPTFOPxDvSeUhPtRysLo6xvDWlMT/AKDEM+3Smx+GtKQnFlFn6VyX/CwbvduKQ4+lM/4T+9ZshYR+FFmF0dqfDukgE/YYs9+KZ/wj2lN0sYv++a43/hYF8SQyw49hTf8AhP78dBBj6UrMLo7UeHdKBO6ygP8AwGgeHtKByunwZ9dtcWfH16U58jjrxUq/ES7Rc+VEfYCjlYcyOyPh3SsACxgA9AtMPh/S1xmyhIHbbXIf8LEuWJxFEM/pULeP75gciH8qdmF0d2ui6YI9osIAD/s0xtG00NxYwAnvtrgm8eX56GEf8BpP+E6vyRmSP/vmjlYro73+w9MPWyi/FaX+xdL35FhBn1215+fHd8ScvGP+A0v/AAnV9jPmR/8AfNFmF0d+dF01m/48oMnr8lPGh6eoGLSLPb5eled/8J5fjBDx/wDfNSL461D75uFyeOlFmHMjvzpFkAV+yw/98imjRrJV/wCPWIn/AHa8+fxxqR5+0qPwpz+OtSK/LcIPU7aqwro9AGlWRYMLWEevyjmntp1mTk2sX/fNedReN9RRSDOjZOT8tOPjm/aM/vY1J6YFLlY00d//AGfZluIIv++RS/YbQcC3jA9lHNeaf8JjqWTm5/IU5fF1/j/j6PPtRZg2j037DajnyIwOw2imtBECCqID0+6K8zfxdfcYvHOKjPi2+LZNyxo5WJNHpVybW2XdKkY+qiq63tjwFWL5unyivMrnxDd3B/eXDN9ahXWbgMD9oP5U+ULnrWY1G7ykP/ARWRdapbiTb9njODzxXCt4huGXm6kH41XbU3OSZ2JPenyibPT7K4t75jthQBR/dFaiQQAcRp/3yK8osfFEtiu1VDZPJNasfj64TIEMRz2NPlBM9C8mLlfLjwfRRR5MPQxx4HqorzxPH1zE+7yYnJ7HoKhufHt9OvlrFFGvcrnJpcluo7npZihHIjTJ77RTCsW7AVM/7oryeTxNfOPmuXA7VH/wkl93upM/WlZj5ket7YlxgLx7ClEcbH7qZP8AsivIv7duT1uJOT69KDr900mTczEj/aphdHrvloD/AKtfypBFHnJjXA/2RXkh8RXu0L9rm496a/iG9yMXk3/fVKwuY9caOMq3yLz/ALNNjij3HCrz7V5M3iG97XUv0zTP+EgvCeLqUfjRYLnrkiKWHIxSEoRwF+teRNr12Sd11N/31Tf7amxj7RKf+BGk0w5j1l5IUbaCu7vz0oMkEZ3PIvPcmvIxqkm4v50mf940kmrNJjeznt96izC560moWcz7UdHZRyR2qZbi3LBd6AnnGRmvHF1J1GFZlHscZo/tAht4L59d3NHKFz2R/LQcMpz6MKaJYcbvMX/voV48NSdVyJJPpuNIdSkIGXfI5+9S5Rcx9Rrnp3pQTUYm5JANOVyTX54ewTA5NSA96hLDFSLyKkCTPpSim9KUdKkQ/wBKUdeuaQDIxmlAxxSEB4pp6U85PWo2oAjc8YpLI4vFGOTSuPeszUblrSFpQGOP7vWuzBT5K0ZEyV0GufL430ZzwcEZzW9rIX+x7vLrlYz3rz5dfinl3TaZPI/QOzDIpsmpWcgKvYXTg9QZP/r19t7eFtzlVCp2O+025g/se0d54hmMZy461g+Lbq1eXTylzG2yYM205wK5tZdHCgHSZz7ed/8AXqzb6nptsrxpohYOOS0m7+dL28e4/q830Zbspo5PiB9pjkVoJF2hs8ZxXZzSRoMPKi/VhXnyX9jG4aPSXVuxEnSlfVYWciTTpWbt89CqxRboya2N3xJJDJpUkayqSWHCtXJqUFnZsMDEo6/WrpvYJ4WhWyeJyOHLggVl3oNvYIXAIU9MihzT1Rn7GSeqPSop4duPOj5/2hVLWpo/7JuPLljZwvADA15wfGdjbNsaAuwGPlbIp1l4qtb+9FnFavGZFJyeacal9Cp0dLo6jQ7WfV7Hfd6/dWyIdqQxy7QBWhc6NbwRqF8Q3zOWwAbjNec6l4pbQ51txbrJv+YE9jWdJ46uXk8wW0e72Bpuo0Sqd0ej61ZHTxb/AGbWbyTfnfmbOPTpU+l6Hp9xDHLdajcK8i5LebjJry9fFutXkgjSzLIxwdsZOB9ai1u68Qy3KW1lbzy2uByikgHvTU2yZU+U7TSIIrzxHrdk19M0FpGzW7iXlz/WtIaNYC302SW8mEkr4uMy9s/pXnQ0nXWIaC3kil4yxcL/AFrQstL1mIzSahMvlyJsUb9xU+tPmYlA9Hk0fwgL0QSal++K7v8AX9qoW2neFBcXovtRUIr4hPnY4rhpvDP+hQwPfr5xbllGeKibwlAZCn2+Ugdfl6/rUuo0WqaZ2tgfCEdu66jqMiyeYdg8zqnasi5n8PHxbbLBqMq6WcB3MhBH4+lY0+hW88qtO8yLEuxdvGRTm8PWqRgmGWTnOWbrT9o+pSpLzPSLnw9p6a3YQRz3RguEZsiXOcDIxWXrXhqwzKhnvCVPygzGs9/E2uT3djJb2sCfY08tSRnIxjJ5qG41TXdRuZTMUj9xH1+lV7VC9hN7Io3mlW+ngG2kuVcR7i5kPWl0nRINR043Fxc3byMTn98cVYmh1C4tGjnnLK3GAgBqTTUurKzNtADtJ6sM1LrIpUJdh6eH7aLPl3V6px0E5xU6aUPIMRubh0PZnzQEvySWPP0pwj1Dk78A9OKXtUx/V5D3sIoNNMakkRnK5PeptITZaY7bjio0S58iYzH5dnX8arWs02GSNxtDdKrmVrkum07HQac26F8dnIrmPFOrzaLr0d3HGJGWFRtbpgmum03/AI9W7ksSa5DxrZT6hqiQWyGSZo12oPrTMZLU2Brs664lgIVEcsPmFs8g16H4daZ9HieYAMScY9M15gdNvh4jt7lYgYY7fZI+ehweK9L8LyTvoqCdNrAkD3GeK83NbfVzSnszTkAJNVZeFq3Ie1VZiNtfEdTqgZsv3iKqTZxVuT7xqnM2RxXRA1Mu8zzXKSI0t86LjJNdZddDxXKybPtdxvJ6djiveyn+KYYj4SDMxn+VxsA5Ge9XvDzXX2G5+0/MTP8AIf8AZrIa1mN9DNGQIsYYE10eiJK9pc+bgBHG3HpivqEcRfWQCMr3NQOMEEHmn55zipEXcp45NWQ9ydGxGoB7V554RUDWrpPWV679QcDHpXBeFAB4hux2Mr/hWcloVFmDqK7dSYkBgHPB7816V8FpBLZ62wUKPPQbR/wKvNtaJiu5cD7rn+dehfA991rrnAH72I4B9Q1eHmP+6TN4bs9abpVOYjaauN0qncdK+MW50QMqYcmqr9OKtzdSKrNXVA3RynjDjQrnjn5P/QhXmd9iC9LYwGPNem+MQf7CuSfVP/QhXm2qIZHcDlga+lybVsvHf7lT/wAUvyiU7yUJZP3LEACtXw+Cjyx4A+UHmueWYy3MMJ5w4zXTWZxqswHA2ivePEsQeIbGW9WIRAGQHGfQVjyqI5DZWsqLFEP3jE/ebua7eWGBdNvJJJP3scDOqqRnFecwXNtIpd7X5j33VMtXoaRtFWe5KJrxZJlttXmiijXcAshUMfpWLc3NxcP+9laQjua0JBET8sSgH3qncxZb5Rge1WmZyJ7XyJYCkow3rmmR20huAIlLIrde2KqKrIwq5FdvGMIMA8HmgEzembbt243NVGVgZTgYqkL2fPbGMV0XhCDR9WvZ7fWZTAiRF42DhAxHbNRKN0a05curJNI1MadMjvzC+FcenvWh4p0RNTtftVqv75Fyu3+NfSuOe6B3IOFyQM+ldf4U1YXFubCWT97GMxFv4l9Kws4O5vdVI6HA2o5K85z0r0LSrX7HYohHzkZP1rD8R6Kun3f26EEQzP8AOOytXRwkrGmTkECuiMrnLKLRZ5P1rM1M/LcD1t8H/vqtEPnOaqXaiaWWP+9blf1pVH8Pqv1OzAr+L/gl/wC2mV4cHlx3i9yQf0rF1dThiw+XJFatjK2majJazqAZI+Oep7VdQeHpkC388qsTlkUZwa1vc4TX06ZJdMtZOFHlgc/SpGu4omxuyfRRnNMtNW8B2SRrNLcybPuhwcfkK2U8c+DokCxXXkIO0cH/ANagDNXRptZt2Z1sbJgcD7V8rkeo4q5YfD2KONmbVrKVnGMsuQPpUU3ibwld6jFMNSmkjCnzMrgj061Yk8Y+Do48DUb1UHGRCcD9Keghx+Hxt1DS6lpLJnptwSKmXwPaOHZZtMIAzg5zUVr4r8C3Mqol9ezSHpvQ4/lU8XjvwasrRLLcsfunERxRoBRs/C8U371Ibfb0wTkVb/4RuSNGk8mx2DsE5/lUGn+MfDdjFLBNNOoEpMe1N2VNOufHHhUnBu7zPYGIinoKx0nhDS1s9XecLbqTGV/drg16AwygrzTwb4m0jVdf+x2LymURFyGTaMV6W/8Aqwa+O4g/jq3Y7aKtFFSU4qpJkn2q3KOTk1Uk6mvCidSIHxg/SuU8SH/QpfpxXUydOa5fxKCbOUD0rsw/xos5XwSQPGEGepil/wDQa9l005tP+BGvE/A86f8ACbQR5+bypeP+A17Xpn/Hqf8AfNepmKtRdzGTHyn94OKRvuH6U98b+aaSCK+cQzzbX+fCHiYDtbr/ADr5/BBIPHIr6D1kZ8K+KT1/0cV889l+gr7rK9ab/rocFRe8dFoOPs8vu39K7vw9bTXDLGYFlhwSARnmvPtEuVjhdCfmLjAr0bw3qkunRbkCsrjBU121LocFc2k0i48yQPZIY+NgwDQ+hXUvMenYHsAKH8WTxgKsMeSeXyeKlk8T34h3xNHgc9Kx5zZQ7EH9j6hCvyWDZxxkCuc1zwDq+tX41Dz7S23IEMc2c5HfgVbm8d647EtLEgXjCp1qq3jTVSxZmiJPXKVor9DJtDdL8JajoeqQ3c8tnPGilcRkk5/EV2SanFbIR9mkywx8qgVwtx4jvrogyMo9NoxUS6/qKuC1xuA6BgDRZhdHZSak0hzHbXJx1xWB4h8Nap4v1CGeEQW8UUWzM2Q3X2FZ7+KNTggkaN4y2CR8nesSD4l+JYt3z2ufRoqFCV7j5kdJovgjUNC1RLq8ntbmLaVCxgnn8a6R9OmmOY7YAde1cx4d8Xaxrcw+2GABTk7EwAK7L+1igwCMetZVG7lxRSGl3Bx+5AHpkUv9jXRyzIgx0Gae+szKcRbM57ip4L+4mUNuXnqMdKzuXsYur+Eb3V9PNtFLDAzEHc5J4H0rBHw01awljvLbUrQzQsGVSpAJFdhq2rX1gsHlMmJCQSVzXOv4p1f7W0a3CbMdPLGa6KcZWMZzXU6SHU9X8gfaoCJv4vKGV/CrcWr3wTi3mP8AwGuUj8Q6hwomGfoK5rVPHniC11CaK3ukRVOB+7BNDpzGpxO91vTNa8WWTWEk62doWDMZFyzY/lWLY/C+80zUorqLVYpdhzs2Yri/+E88SOCH1FiD1/dqKavjDXQPl1OYAdOlXyySJurnscen6k8hDEEkc/PVeTRblSxYJ19a8st/F2vLKpXUpDk85ArvdA8S6heWkhuZVkYNgNjHFZSi0aJouvodyzqfMRVBzyaS4gW1njikkVpCM/L6VDc6xc8/MQvYgdadZzC4lLt88m3lj2qYN8wSRZUnHIp46Uxeppy88d670zmYueKXOetJ7Uo549KBC496TnHFJnmlGcUwCgcUZ5oznpQMXPOSaD0pD1prHmgQAZHXFBJz2o6nHakI5oEOJB7VDPjymp571FNzE1IClZpidmq/Ljy29az7Tmduehq/IP3ZoGZHW/B9RW9arttAD65rA4/tEfSuig5t/bNNCIXqjMeoq/KQuRWdcMOQKGBTk4NVm4OalnZgDtIz71XG4/e/SpYxhyDxVeVyXOasOcA1VY56nmlcGRSZxlc1j6vxCM+tbJOR0rG1cMYR9aAMVPvE4/GmSD94DUqAhQ2e/NMk+8p9askjIAY4qP8ACpH6n3puKAGEGpbPm/tv+uq/zFNIAHWn2Y/0+2/66r/MVM/hfozfC/7xT/xR/wDSkMvuNQuf+urfzNVhVu+H+nXP/XVv5mmW8BuJQg496UPhXogxX+8VP8Uv/SmRRg+Yp981Zll8xyQ/JHPFao0BBGHjuSTjutY91A9pKyOORVGBX3yRSblJBHQirUD3S28hVG8tvvNj+tQbwFDYresfEyW+hy6cbVXZ8hWPQZoGnYwTkikBz1qYDjFIY89OKRakj1r4eXwuPDccOfmt2KkenJNa+q2l3dZMd35cWOUx1ri/hhP5d/eWZIxIgkX6iu216IvFE4dgFbkA9a+fxEXDENLqepQknBFmzZvs6FhztxXHfEDS/tFit4q5aM4OPSuj0u4E0bIWzsNWb62jvLGa3fpIpHNYRk6NZSNWuaLR4AyANimFRmrWp2j2V7LbuPmRsVVQZNfTxldXR40otSsxMDNKFBPWrAt02gk81HLEqnjrTuS0algMvgf3avphbiAFhneP51nWKlWBB7VoCJXuYGOcq4P60T+F+jNcL/Hp/wCKP/pSGS83svPG8/zqYwW23cJl346elRTKPtch/wBs/wA6DbP5u/B24pw+Fei/IMV/vFT/ABS/9KZlTkrOwDEjNRlnPc1YnUtKQB+lN8tivvSZzkILA9TS/N61J5ZB96XZxk0hkJLA9TijJ96kIOelMxz0pgN3EDqaD06mn7cnpSMp6YpiIsn1NO5p2wijYaQDCTnrRk+tO2HvSlT6UAR89c0HPHJqTYaTb2AoAZk+vFKDg08p6U3YelMALGlU8Umwj6UoU56UDAn3pOfWl25owaAG5op20+lBWkIbnHSlycdaXbQUOOlADM89aM4+tG0g4NKFIzQAhwcetL1o2t6Uu00AJt7k0Gl2njmk2HtRYAHSl96Pm9KDmgBOM9ad1FN2nilwRQAvFGQKQD0o560AKeTRwaTB79aMHpRcAxxSCnNkDFM5IoAUUopuwnvS4oAcAcGilUYFNxk5oAceKMjtTTmkoAfj16UhPNAyR60oX1oAMgDmlzx70beKMe9IQlBXIHqKULnjNBU+ppjEB9aXAxnFG3NIRnvSAMbhxSdOlBBXpSEcUABGaQUvQdab/OgBxNJmjBIzSgc4/OgQdu1GRRjtmk2Yx70AfWoA9KMAUE5PpScbq/Oj2R+3JyKfgjikx707JwKkB1O6DOKaDTh1qQHD6Uo5PNJwR6UvGcUhCnrTGHNOPsaQ9KAIX4rK1TJt2JGeK1Xz1qpLbLduIXYqrcEr1row6vUSGmkcZvVFPB60zcpyc13H/CFacTn7VdHPuP8ACkXwRpa7j590c9tw/wAK+ujhJ2NljaS7nDKwLZApPmZjziu5bwbpMKSSSXNyqKNzEuMAD8K8R1zx6i6nONGjU2KEpG04yz478dqPqs0xPMKS0PRrKxjfLEscCrNzbKtszgYwK8v0Lx/rt/qEViIrdYnB3Mq8gCtCTxjqaTXkDmPyoCdvHWtvZ8q1RzyxSctGdHKrKM7jWJrZb7P6n61B4Y8RXXibXYtL+zJHuBZpd/AA9q0/EMCwJNE3JjJAK9DXM6M4O7O5VoSjocBIcMcciul8F20L3Ut/NM4Nv8qoo65rl5WClueB2rqPBDeba3xCMFUgliMA1vqldGK5W7M35bS0kuTK0IlbHHmIDirETRxr8sEagdMIBVfzgzHBxSrOhGDIqj3NZ+0my3RpxWxcW8AGCnHtxTFuI0BCRgDOcbjVXz0IJBVvcc0izRMu7zEwOvPSiU6iCEaT6IuLfKQQIBx3LGmi/wBrc26EH1JNVYmaZC0KtKoOMxoSP0qFriCKRkllVZB1Rzgio5qrVzXlop9DUW7jMqyeQgYD0oe7UuWaJB6HFZf2u3B4uIs/74pX1G2VMNPHwO5qOaoPkp9DWW/2/L5cbA+q5py3jF9wijwO2Kq29rdzxrNFZzvE4yrrGSGHsanWyuyjEWdz/wB+mqmq1uom6Se6JpL12wBFGPXim/2lNnG2PjjlaaLW8dAfss+Og/dmsu81C0sZzb3lwtvMOdsnymhqqlsCnR7o0Tfyngbc9uKUalOhAwmPXFYzavparn+07b6b6a+s6aFUtexAEcEHrVRhU6kznTXU2ZNXuFYnKc/7NMGr3W4qGXZ1wRWEda01zhbyMn0zTxqVmwCiZdx96fvIiMoPqbNxrk0cThYkfKnIJNefanqV49yHSd4syAkI2O9dXK+6Ntg3YHOK4vUcLPz0yD+tXTlJuzM60Y2uj2rRsixUH8/WsPX9Ui0XxHFfTBiiQgHb15ra0cgaemDkev4Vx3xAciVcKWYxAcDPeu1Hlz3OnTW7X7XHagOXuU8xDjjGO9d54ccyaUjEY5IxXj6GRdb0f90+GttpyvTg16v4RnE+kEAEGORlORXnZn/u7LhsbcmMGqcnINW5O9U5RwTXxH2jqgZ9xgHBqk4JHFXZgTVJxjmuiBqihdjCmuU8hrjVpIgcepP0rrLs8ZrlTL5etzykZCqOM9eK93KP4xhX+EYiH97hgRHj8a2dF3JbXCnneQR7Vg+e6yOBCSso6+lbWgTSSfaVZMbNoU+tfVqxwM0BbsASTmnx5O4qOcYFWSSqADqafFGAG4xxiqJKCnaAPzrg/Co3eI73bkDzn/rXoscOduR3rz7wsp/4Sm/HQC4cAfnUscTnde4u5yefnb+deg/A45t9dOAP3kXH4NXE63at9pvJeCiSMf1rtfgiylddCqAN0RP4hq8LMbLCzR1QuetnpVOfqfSrZ71UnHBGcV8UtzeBmyj5jVV2GOKtS5BNV8eorpibo5fxkM+H7o+mz/0MV5vdMHmf1z0r0nxpgeHrn3K/+hrXmk5xcOAf4iOK+kyZlY5N4Kn/AIpflEzorUJdvL054resUY3k1xMGWGKPe7EYHHbNZypk4HJ96teLtbCafaaLDhVVQ85Xuewr3KkmtEeVTimzCtLk6x4sM8zFYGVsqG4CAcCtDS/Bqaik1xJcSQQM58hQMkr6msLTedTihiJ3TsI8j0PWvSNVvYtN0SfyBtMMeyP69KuCCdjyjUmis7+WCyuGmjjbbvIxk1El6xwHUEj2psUBDkydfemlQz4GOKoxFvLgyADaBx2FdDp2lxCyja4U72AJHpWXpdgb7WrWDGVzvP4c10+oh7aB2wPm+UGnYLmDeyRktHbQhFHAI5JrPYGUDjDr14rQVhGW9QOtV5d7xiZeGzg+9S0UmVS2BjPNEN3LZzxXMBxJGcimPFMzbsAE0hjfaQR+NKyZVmnc9QtLq38QaWJAitG4w6ddrdxVlNLlRQI0YgDjHavP/COsHS9SNvKT9muDhjn7jdjXo95qU9tZFImYMeOOlctSc6UrI6VGNSNyiFI4PrUWP9P/AO2X9atiGRYEYKZGx/DWLc3s0NwzshgkUbMH5s961c1K3qv1NcHFx9rf+SX/ALaYutxSXGpTzQxu6w4ViO1ZWyY5by5AOpJU1vNqriVhJ8wk6hRjNZsNxc6jqraaj7QM4J9K6lY84TTNDl1Xc8csaqhwwY8/lW9B4KtcKbmd5fZRgVl6RdW+iXs/nwySyN8oZT0H0ruLS7hvLZZoXDIe/pUgUYfD2k2oOyxRjjq/NUPFN3Zador2QtkElyu2MKgwvvXS4y3rmvM9fu5L7XbpmfKRt5aDsAKBFfS/JTLNncq4x61OtqYoC8Z2kt0qjE2zDHjB5rRE7y8xr8gPJPelqM0LLRLi6RZli3J9etQ29lLLqBhMBJXOQ3FdTpt7mziHl4KrjjpVee+UamrCE5C44GM0wudB8NtONt4v837OqZt2UsDXtWf3YrxLwz4ls9I1dbm/WRY9pQbBnk16G/xE8NRriW+eP/ehb+gr5jOcHWq1VKEbqx10pLkSOglGcmqsg5rnJfiZ4R5xrA+nkv8A4VnTfFDwsvTUWb0xA/8AhXjxwGJ/kZ0KS7nWS4xXLeIj/ojn0qhJ8U/DRBxNcMewER5rm9b+JOlXMLRQ21w+e5GK68PgcRzr3WN1Yx3Zj+AS3/C00GcjyZsf98mvoTS+bU/7xr55+HUsVz8TrSWFWUSxS8Me+019C6aCtuw/2zXo5wrUbeRjGXNqh8p/e4p4wetV5iTPgVOuMDNfKo26HAagm7w74mUYJa1bj8DXzk6hVXnnAr6Q1BAdB8QovBazl5/A184bcBcHsK+5yr+G/kcFTct6af36+u6vRdJVjaK5Ixk159pUDyzkr0Xkmu10u+MVp5BQYDHDZruqaoUHYvnEjk5GM1b3BIdqPyRgVnW6/aXIEgAzjPpWRd+I0s9QNvJGwMbbSP61zcjbN/aJIox3wTf9puAx3EY7g+mKrXmqJ5kZgYsv8YxV+AeGEubr+0YrmR3k3o8YJGD9KxLiK0S5kMEV19nJPlmRcEiupHO9RDqs244fHPSnJq0yyAscjuDUBtsqGAPJ6UfZXBI8qTPpsNMDTutVt5rXYjOrn2rGYbnG3Gama0k7xSg9hsNRi2uISHaGUJ/eZCBSuFmdh4BgaeW4XB+UCuza3Pm7G4OcVx3gf7RHNctbzKnAJDd67R/NlPmSyrv9hWM43NoysJNp80HJHB7iprBHG/cCB2zUclyzKIzcjAHrUUl35e0LOjEDHBFZ+zKczA+Il5PDaacsMrJmR+R9BXE2urz282+VjKCMHd1rpfGourtbIQxvOqFmPlqW2/XFcYqmQ49a6YaIwlqyeTUbhpGYTPgnIGelVHJkJYsSx6k1qJoGrOiuml3bo3IZYiQR60v/AAjWsOSRpV6Mf9MTVpkmIcqTTkY461rNomqwozzaXdrGoyWaIgAVSNr5jgLwT0p3HYdasDIPrXo3h6IxeHJJtu55pcL7CuFTQdaiIcaTflCMh1gYjHrmvS9HNpFoMNu0rRTRj50fggmsZmkSmWII8xjgdjV7RZVk1KVE6GLOKgubOGVg4lOB1ANO02O2t75Xid/MZSACc8VlCGt2W5aGyufSndhTR8oPPNKCD9K7VsczQ48UdDxRkA03eOeRRcQpGOaAc4NJ5g/OjfjgkUx2FbPpQPbik3g980plX1FACDJNK44zxmmmRRjJpjSrgkmi4rEinPJoyc8nioBOrAEGkaYdyM0XAmLDvmoLhgIjgGmmfIxuFVrm6VYiN/Wk2OwyzbM7HrV6ZyIjWNp90DPIuRj1zWhPOvkHnt600Jooqc6kMnjHWuhhcCLAOK5JLpf7Qxu4x1zXTRuqWYlY496YgmcA5as+duScde1WGmjmIIIJ9AaqzMCuc80mMpuw3Zx+FRc4OBgCllbJz+lML/KAeKkYx2GMjrVYnJqwx/KoGyc0WEMIGKxtXH7sdetbLKQM1k6qAY1ye9FxGQo/c8+vWq7j94KsqQEAI6GiZE80BORjNWIqMuSabjmpWwCRTWGO3WgQxl7ipLND9ut+P+Wq/wA6QgDvU9of9NgHHEi/zqJ/C/RnRhf94p/4o/8ApSK96v8Ap9x/11b+dW9KjBc5yd3AqO9Ia8nG0AiRv51teHbSJ44biUAxrOFbPbmnD4V6IMV/Hqf4pf8ApTFtZWiuHgbJA5FZviRVZIpQOTkE1ua7bx2fiaWK3bdCVDZz3Pas26s5dSRbaIZcnIBqjA5frDUsMYI9yKllspIH8pioOcHmrNxps9j5TSDAkXK4NJjIrVY23iR9p7U6VRGBkjnoRURRhJgcmoJwwXr3pAkdR4LvFtPFFm5PEhMZ/GvYbmNWBR1BGa+ebCd7W6hnBOYnD/lX0TFIL7S7W7XpLGr/AJivGzSnZqaPQwb0aM/y0i4jQLzzgVKThcmkKlGyTkVU1W++yxh14GMCvOS52jtOA8daNI16t3bxblcYbb61x6WMyHLxOP8AgJr0qWf7fZzKuWeMbiKwLPVoo5mWVtvb5hXvYOXucjex52Jj73MjmVQ9SCB71XnUAkd69IiFjdrhxA+ee1RT6Dpdz1tlz6qcV121MOZKNrHF2ZAXJPQVeQv59vt/vjd+dNngjt76WKIYRTgA1LHtSeEZxlx/OnL4H6P8gw3+8U/8Uf8A0pDZh/pb5GTvP86ebqUNszgU2T/j7kI/vn+dTmS0EZBVvMpw+FeiHiv94qf4pf8ApTM8wxuWL3ManPQmjy7bj/TIh+NY92oFzIRkZNV8nOaZjym8Utcj/SYz+NJttT/y8p+dYOWJzTTmgOU3mW2BA+0pz700rbE8XC8VhjNHNMLG1m2/57rSj7L1+0KaxME0mDQFjaP2U/8ALdaVPshOPtKj61ic0YoCxvbbPnddIMe9IFtD/wAvKfnWFg4owc0WFY3xHZZwb1B6011sMDF2p5rCIpcHFFgsbe2yJwboAY7igiyGMXAPvisPmigdjbIsQR/pOfwpM2QH/Hzz9KxcGgCgLG2PsRH+vP5UD7D/AM9z+VYoYjilRsHNArG0FtSP9aaRjYjA88+/tWSZj0qKlYLGzusgOJSfwpPMtMf6w1jjNAJzTCxsE2WM+ZTfMsv+ehrKJxSUDsa4lsgP9YaUyWX/AD0NY1L1oFY2PMscZMjcdKBLYDnzG/Kseg0DsbLy6cMFXcj6Ugm00jmR8n2rHooCxtrLpgALyP8AQCmm4009N+PesaiiwrGt59h/tYoFxYDru5rIoosOxrNdWHGFf3o+0WIPR6yu9HFFhWNgXGm9WWT8Kc1xpZP3ZRWLijFFgsbX2nSgB8s2fwp32rSeN6TYz/DisT0o/lQFjpIb/wANjAkt7zHcj/8AXV2PUPBQB32d6T/P9a47FG0U7oLHWSap4TxiKxvPQZPT9aptqOiDcYrSb0GT/wDXrnwMUvFAWNxb7Szy0EoyOgNKb7Sz0tZPbmsEZ9aXcfWkFjoIr/RVP762nI/2Tz/OrcuqeGxCRDpUpbsXbBz+dcoD3p+47PegVjVfUbIfdsyB9ahF9bn/AJYms1ifWhOTQOxqi7gI/wBTzSG8gBwIefc1QBxk0DrmkIvm9h3fLb8emab9sh7wA1S69DQRjtQMu/a4h/ywGKT7bHgYgUHvVT5ttIVpAXPtqZOIVo+3rnHkrVIAjmkOaYi6L5CT+5Wj7cv/ADxWqHOMik5AosOx9f7lwASKQfeFRrCVPWpBHznNfnB7DJR1p64qEA1Ih4x3qWIlFLn1po6U6pAUc0YxSgdKAMHrSEKfTNIad16UxjQAw9Peq6nbOrd81O3HNU52KMCOtdOGdqsfUUtjfSXcoyecVIrgDrXN/bphg57U1tUaPJZ1H1avvYyujgluTeN5ZV8E62YMmT7JJt29elfJshzEuM9Oa+n7nXLSaB4Z5UMcilXXPY14T4m8Lppd47adMtxaOSVX+JPb3p3MpRd7mHpd1NanzbZhHJ03d8V03hzQNZ8Wm8FisXlJgSTSthdx7D3rlUsrpztELrnuRxXrfgDbovheS2WUF3mLuTx6f4VUUnuV0OC8T+G73wbcWyXFzAbidS6/Z3OUx61UOuajcwqJ7qRznvVzxxM974suZZHLDaqr7DFZSJGFALfd6VM4p6CpVJI9W+E2nafdzajeXUcNxcxKqrHIobYD3xXa+J4mfSvJtLcZLfdij/wrxfwJq+o6VqV7LZyKEkQK+4Z+ldyvivVjn/S09fuCpsrWOj2jvc4/UtB8W3k7RWNhdvET1+7z7k1jazY6lp0y2F/A8VzGgJUnPXvkV6WPFOrnn7aq/RBVw6DaeJtHfVdWvG+0B/KUqAOKlQS2CpUlLVnFeDPHVt4Y0Gawu9GN3M8jFZiR0PbkVz+jlpNQumAOJG37B7k8VqeM9JttDu1tbMs8ZAOWOTmsLTLlrW6aQHEnGKqWqM4yfNdn0T4TsTpXh2yt/L2SMPMkBXnJ5r58+IFvcQfEDWftClWeXeme6kcEV6N/wmWuvEhF6oOBx5Yrk/FIk8SXYvLy4H2lE2htoAYehoixTu3c4WBljf5gOalupTNAVB3HGOP0p76dOsu0qG915rX0bTGjuIrp7eSRo3DIoXIyOmaqy3Emz6F8JW11pvgvRrO7DRzx2y71PVSR0/Ctgy8Ebj+deVr4j8YXOWXzAD0ygqVdQ8VSDMl2Ix+ApuRbdz0ZifODKx+nrXzr8WJ5bj4hX5lj2+WiImR1GOtehPeasFzNrwjHcBhXHeMNLsNQ33za0Zr8IB8/IYDtQpGckedwKhYlgOOlWXufNeKMdIxiqxt5N2NpPoRViKzcLuEbFj3xTdmCJ45FSRWPY9a0byJroq6HjsaymtLjBO3tXr/g/wAPaG+jaL9vh8ySQF3JYjJzUcqLu7HJaBqEGgWk9rfb/MlORxnArKu7j7bO32VGl54AHNWvH0cFv4uu4rQFbZSBGCaytOe7tH821cpKSADUezV7mqxEuXlPZrfxBa6bZIg025f5Rl898Vm6l4isI9Qt9TnsJMKmAjHgVs6I++xKvgtxu+uK5j4kKPIhOAoMZ6D3qzKT1NdfGsB27NKYmYZQFuSPbivQvBt499onntafZv3jAJnOfevH4iTq3hhQp2Nbdx/s16/4MnE2kSqAQY52XmvOzO/1dmkNrm7IaqSelW5BVWWvhup1QM+ZcmqjIM1emOTVVgFyTXRE0Mu7Tgkdq8/1eXU/7aaKwSEh1+YydBXf3TcNXD6vcTWktxPBFvkVOBjNe5lC/famNf4DOF5rwZonFqpXtjrXVeC5r65+2i88vA27dgxg1yrS3ck+mS+Q22cfvfl+6f6V0vgp7kalrEMsZSNSpjJH3utfWJHAzrTHl/p0qRwEiOfSnqmSKWZAQfRRViGW7KyRfL0rzDQWceOr+IR/L9pfLZ6cmvUbRcxodp615rpCbPHWosTjddMNo+pqGNGNrlwY7u8t9uQ8rgn2zXYfBVIo5NcjiBG0xbvf71cR4jSX+2L/AG5wsrV2nwRJabXS3X9z/wCzV4eZR/2WZ003d/I9ePSqc2cVbbpVWc18UtzeBmT8N1quSBVibGSTVV2UKfSuqBujmfGjZ0C6A/2P/QxXl88h+2S88hzXpHi4k6Dc9+V/9CFeT39xLBfT4iJ/eEDvmvo8oVjTGa4Omv70vyibH2mKxs5dRmAKR/Ko/vN6Vw1xdtczNK0m5mOSSa+hLLTNKbR7OC40e2fZGrMJF3Zb1q2ltpcQxFpFgvsIRXXLM6UJtSRyrCStY8C8OuP7ftjySgY5xx0rovEV0W08ITwzjPNem6+0P9h3SQ2NvGQud0cYB4NeQ6rMJrUeoO6uzCYqNdNo5cRSdNmU0SvESeSBmqiWjKqSzNsjY9RyRV9R/o67fvNwazZN4JQsdq9ATXVc50jd8NQCHVJLgThlWMqoxzzWj4guC9rCoPBc5rE0aZYbkkPlmTB5q3qUomiQg5w1O9wsT2L2UWnXlw21rkJsRWGeveq5sLiSx2qnmyEAjZ0JNNe3jSzgZCS0vWuk0wWun+FIAZg9zJdAMvdR6UpbMcUro5uTwr4ihjDyacQO2JVJP4ZrNuNN1KCXypLGcMfRCf5V6fprvPfz3PmOQCFUE9K3BczAY3mvKnj3F25T0fqqa0Z4f/Zt9v4sbk8dom/wru/DU9ze6d9lvLedZoeMyoRuWu6juZcH94w/GiSaRwSTnA71jUzDnVuUqGG5Opn2VzZ20BS5QEZwuD+lc9rekW+r6nKpnkt4vL8/cDjbj5fyqkJxLq6u7Hark4q9c3Aup7jn5WtthPtuFd1KN0pPuv1Loyf71f3Jf+2nONBEluuw7lTgMTyai0+3gs/tGoM26452VLcxzSWTJaRh3LAAZ6CqN0k9gqwXAALjPByK7TyL6EE03myGRhhicmul0CRY59oJ2SJ69xXOtHHMAQ2DWlp8skEihFyUHU80wO4gkRiDvA+teTXZ26jdIeolbPvzXXNqt+GKxWhLZ4JBxWDdaLqdzdyXPkBS7bjnjmi4mjOiOX5xtrUtXV1VATtByeOtS2+kTREvNsTA6dasGYAbIwDjjIpDOg0iRIbA73A+c4B60ks0c+phvlKCPBPvWEizOcsXPoAasw28zHARhnvTES6syCaDY+R14pus3AjVcckjn2qC5gMciZznNR62wUKOenPFMdzFlfLZJ5zmq7uSakcjHFQMMUrBcUE1HK3tTs4FROeamw7nY/CttvxB049/Lm5Pb5DX0hpp/cOP9s181/C+a3g8e2Mt1cRQQiKUGSVgqglD3NfQ2i6lZ3FvK8N5BInmEbkkBFeJnMZSptRVzopSSVmXpoyZ844qQAAjNVpb2LzceYv1zTvttuuN0qY+tfKKjU/lZ08ytucfeox0jXQvU2c39a+bAxwv0FfROo6zp9nY6nHdXKRPNBKiA/xE5xXzv5b8Ajt1r7XLYShB8ytscdRaI0tGEhnYoCQByBXd+CrVdTv7mKe1EkcODz1ya4XTL99JdmRVbeMHNemfDS4aX7Xe4Us5wQK7pkxO8i8OaRHjbp0Sg9eDVhdD0rO5tJs3OMFngDHH1NaUNyGTJ6gc5FSNesg6Aj6VCKaKYaK3TZHbJGi/dVIhx9K5vxj4XvvGulwWtkkcUsE3meZMNg245FZ3ib4rXHh/Wm059BSXC7lk87bkfTFYZ+OeohcR6FaqPeUmrSJuhYvhF4jtLmCdWspBFKrsFlJyAfpXbzW2qG6LjTnC4xworhx8edVU8aHZ/wDfw09vj5q4Gf7Fsj9JGoauNOx18Om6ok/NrKVzzuAqvrXhvWNY0q5022gEZnXAeb7oNco3xz1V5ARo9lkc58xqF+PGt7sDRdPH1djUqDQ3JFE/B7xfaPFLHPayqHAkWGXBx36jmuiT4f329jLaTkf745qppnxp1q61JLV9Lsdkh4ZCw212tt4n1q9j3pYQKDzksf8AGm3YVrmJZ+B4owwnspV4wMvVWb4fbiTHaSdf+en/ANeuzg1TU3QPLFbr7AH/ABq6t+38aKT7VPMhcpj+HtAk0G3eKG1O6QYYsQa4e6+CGo3N1LOmuWse+QuF8o8Anp1rvvFXiG+0Xw9c6np8EMzW6hmSXOMV5hJ8cfEEkY2aZpcbEZB2scfrVpBseiQaFq9nbJbEJII0ChkfAOKSPRNdMm8fc7DfmvMX+NXisggQ6WD6+Sf8agb41eMhgK2nx/7sH/16l0lIanY9Yk8OaxcwyQFfLWZShcvnbnviuSPwLaIIT4jJkUhsfZ/0zmuU/wCF1eNcH/SrMH2gFC/GDxk5Ba/gHri3WqUOUTk2exappusrpkdtprIHjUKZJDgECsK88M3NyYpprUNcFcSsg4JrhIfip4un3Br6EgDj9wK7Xwb4i1rW42nvblWRCR8iAZNRJDTGR+Fp1JAtz/hWto3hvy7xZJbVVyCAx7VuRTSyNk4AzUst0wAOVGO4oSGeX30N3baldQA7lSVgGHfmq+b7H3q1L5915cMx58xvx5qtncua6IrQzbKZa+x94cUn+mg53DFXe3TFBwRx0p2JuZ/+nf3h9aCLw/xZ/Gr6p7cCgL+FFguUNt2OjYPtQVuz/FWiEBxR5e446UWC5mlbvGS3WkZLvJG41pmOkC8DNFhGV5Fz2cimC0uSTmRs/Wtgxj1pDH05osO5ki2ud2PMOKcNOklOWbI75rT8vBNOwcZNFguUodJWMEqoGetD+HY5wcmQZ64atBWI5qzFdMgxgUJBcwx4QtQwYmUH13VrppmxBGHfYOxrShulc8gYqyoikPUfWqsIwhpBE2+JscfrTJdFunOQy49K6UWo58twaXy5YzRYDj28PX+CfkP44qu+iagpwUXj/arty7gcioHKt160rBc4tdGvs/6sEd+aauiamJNxhG0npXZK6jOCDU8MuT8w4osFzip9MvzEyRwqCRjLDpWPL4Z1Ija8fmj/AGTzXrAhifk4NNa0hPbH40cornkZ8L3gwDZy49aP+EXnHP2eQN9a9ZNogb5WB+tH2b/ZU07AeMN4S1RmJ8uPGeMtihvCGpBefJz/AL9eytaw/wAUK1RubKHqIaVhnkp8I6mOMQ/g9Oh8Mahbuk7iMJGwZsN2HNejSWzAnahA96o3isLWcED/AFbfyqJr3X6G2FX7+n/ij/6UjhZvDF/cSvNH5e2Ri4y/Y81qWWkz2Xh+eOd0Escu8bT1HpXR2+fskI2j/Vr29qz9ZBSJGb5Ys/NRD4V6IeK/j1P8Uv8A0pnK3dwZLlSwIZVxTrVrmebbZSKkuD8zdqoXs5luJZFyFzhfpWp4WtzNLPO7hFQBRk9TVHOUz4UvS+Z7qMgnJIyaq6g0qXghllLiMAKD6V3n+i4OZ4/++q5LxEtsmoxyQurqU+bb61DvcpNWM2FT529hximS2pml2oM5pglY7mz9KVL37PkkckUNu2g42uao8LXt3bI1rCMY5JPWvWfB/mx+Fre2uVbzoQUI9u1choNzutoQr5JUZ5rs7S9eC3ZYyvPXivGxlZyXJJHo0KaXvIklIEhXFV57WKb/AFygj0NRXF/MXJBFVjeTsTlxXnJPodTLUWm20MbeTEFzycd65LxDpcs6vHDYKSejKK6Rb24xgEcVDJdTM/LVtSnKErkySaseYnRr6I/vEaI57mtC30jWHXNrKW9g5rsrtWnt2XI5FYOn3M9ne7FkwG4I7V6cMVKSOR0YnPSQywXbpPnzB97PrUyBWmhO3JDj+da1hpb614ju4mVmOM5B6V0F/wCD7WxhaRJpN8SFyOoJAzXo6ypXfZ/kzmoJLFQX96P/AKUjh5gRdP2+Y/zqcaezjzt67SOOa6ZfCNndwRzvNMGkUOQvqRmhPA9sjFmvZ8AcAmtIRfIvRfkRiX/tFT/FL/0pnns9m7TN9aj+wOOmK2r1BBdyxAEBWIGepqrvrBzdy1FWM42TikNifUVpFvQ03OTyaXOx8qM77E2aT7Gc4rRPWhgeafOxcqMxrRx2xSG1fGcZrSI460nOetPnYcqMryXHagQtnla0yAaMDFPnYuUzDCwHSm+S2elamBgEU0AA8mnzi5TP8hh2pPKbuK0jg0h2kUczDlM8Qt3FHkk9qv4X1pQFzmjnFymeIj6U0wN6GtPalDBCMZo52PlMv7O3pR5DDtWntT16UBVo52LlMzyG9KPs7CtFgoGBUJYL3pqTFYpeQ/pTChXrVx5lHSq0jF+TVpkkdBoxmjFMQUGlwKTFACUtBFLigBAOeaDS4ooGJmkp2KMetMBtHTrTscUmPWgQUcUYpcUgEoyKXHNGAKACijp2pT9KADdnjpTScUuPWgjnFAAOaXIpMYpQB3oATPGKQcc07AzxRtGaAEJ70u7jFLjFBFADcE0qjBpw6Uo6e9Ai5CIWAB/E1bNraMOHA/GsgO2cCn7j0qbCNJLSJ3O1gAKc2mgnhxWYJG7EjFPFxKp4YigC+dMkI+Uj8aY+nSgdBn0qv9rmOMu3tzUovphn5s5pAL9hmAPyZxUTWkmT8lSfb5um7ilF9J6UAQeQwzlCKb5Y6FTVn7Sc5x1o+0MScqPyouwPq3Oc800HtQxPajBr85PZJAcin9OaiUkcGpFPakIdnnjpT6YuKd0qWIcKUcGkp3WkAGmnjtTz0AppzigCJumM81m6iSkDlTggdRWkfeq0yqeCOPet6H8RClsefy3dzKx3XLsCeOcVXDPyC2fqc16QLe34PkR5x12ij7PB/wA8Y/8AvkV97CPuo4ZbnmE4kwWDD6Cse4KyfeyT9OleyNDAAf3Kf98CqTxxKCFt48H/AGBV2JPH2ZGwjZ9sLUkbyBfLgZz7BTXq37tT/qY/wQU54o5oXTaE3DGVUA1SRLWh4LrcatIZJCVk6ZYdayIbK5upRHBFJIf9lTgV9DNo+lbFE9rFMR1MgzmrEUNnGD5VrEn+6gFFhKJ5FpmnHTIQnzmRuX+Q1pJH5rfLHOzH+7Ga9WgaMEnaoP0FT/L1/lUuJpc8rTTdTIzDpt04/wBpNufzrWgj1e20O6tZrXyufMXLcg13xIbrk1FLbRMpJhDduaXKFzxe8urO/Um7lVnHDZNZ9hYW19qUcUcqxW4OZJCCSBXsjaFphfd/ZdoWPUtEDU1tpdrAcJZ2yrnosYFHII4Fx4cg4kvbiQjj5VI/pWZd634ftWYQ6bLc4H3pCa9jXTrMjP2S3A/65iqsumWe8n7Nbgenlj/CmoibPGR4wVEP2XR4I8dyM0q+LNVlOIvLhB/hWLNeyCzsl6WkA9hGKDHahsG3jH/ABT5QPMLa61O9hBa/uBnrtG3FTfYpcfvrmaT/AHiTXqsIhC4RIwPZRUhx2Vcf7op8ozyB9MjPKq5PoFJrFvLErMRscn3jNe8bBjoBVa5hViSQuT7UuUDwYWknG9JCvtGac9i/aG4/79GvaGgVR7D0poWMDo2aLAeMLYzrk/Zbkj/rka07S+vLW1hUW10vkMSp8s9K9V3gfdQ/iamTe4HQUuUex5Lfyx6iRJ9guGk/2ojnNUtO0rVZ7+EW+i30qq4YkQkDH1PFezyNKj8MoqSGW5HSbjtinyknJP4k0zw48lvfaXqcbHDMSoP9axNT8eeHL3U4Lr+z76aOEYETqME/nXqQ09bli8+yQ/7a5qVdNtoxgRQZxx+6GKdgZ5zD8Q7C5Blg8M38qoMb1ThfYeld78PvEFvr2jT3EGny2SRzmMpIOWOM5/Ws2XRPFMu6OHXLKCAsSqrbdB+VTWuheMrWMpb+I7IITnBtR/hXLjKDrUXBGtN23O2kcYOKpyygDrXPjSPGZGG8Q2P4Wn/1qjbQvFr8N4itMe1oP8K+a/sKte/MjdVIrY1JZwDVV5JHRiqkqOSQOlUf+EY8SP8A6zxLD9BaCnnwr4haDyh4rWNT122Yrro5G7+/L7hSxCS0KV3cgqTnGO9cpEmoal4lS2tDD5T/AH3cHhR1NdjD8Of38U1/4iursI24xiPYrexGTxXTxaZDENsIiiX/AGUAr0sJlqoT5ua5lUrOStY8q1u18R2Oo/Z9KtYbuFRncy8k1reB4/EL3V+dZsUgjCr5RUYye9eirZLGeJQM+gqSS3MgANwcDsBXqowM1ItoyRz2prwMQVx05rQFoB1mJoktVfgTMv0qrisZ9rG5RBjHNeWwzrbfEC8hLDJujg+teyxWgjjCiQ8dzXOSfDvw7Lqz6jIblrppPNz5nAb6VLY0jza80XVNa1rUorSAAFyfMk4X863fhVZ3Wg6hq8GqGOPeECSBvlbGe/411uoeBLS9u5J01vUbZXABjhYAVnv8M7AqE/tvUz9StceKwyr0nTva5pCXK7naG/tSvFxD/wB9iqdxf2y5JniH1cVyf/CqNK/6Cuo89fmX/Cm/8Ko0T+LUtUJ/31/wrwf9X0n8f4f8E2Va3Q1LrVLVScTxn2DVWjuhdhzC6kIMsc1U/wCFWaGvAvtT+vmL/hTB8MNIjyE1PV1U9Qsyj+ldNLJIL4pFPEdkc74l1OGXTpoFcF22gDPoQa4mGf7NJPI/mSNJkjahOK9Vi+Gfh23lEhS8uGU5Jlnxn8sVqp4X0WMYTSrc/wC+u4/rXoUMH7Bv2djo+tUKlBUqyldNvTl6pd/Q4Ww8bO9mgezCOi7SZJMFvfpTbnxyYAW+x7yO0ZLH+Vd1J4Z0ZwV/syAZ7qgFVT4N0IPu/swnnn96+KmWAhJ3cV+Jf1rCrrP/AMkPNr74iX88csEWhOEkXYWYknB79K5WaOWeIny5BkfdKmvc28JaLgldLT8XasTWPAcN9bFbACzmH3SGYj8a2o4f2XwJL7zKrVwVT4uf/wAkPG447iJzm3mOPRDVe4trqaTi2mAP/TM16L/wqnWiQZPEACnqI1Ymrlv8Mb6IBDrLOp/idSTW9qnl+JkvqH9//wAkPMbeznhlD+RMO2dhq+8EzRlfLfB5+6a9I/4VkxU79Wnz2wCKqzfDHUGjP2fW9rHoHjbgUWqeX4h/sH9//wAkPPrVZo5AssM21TxhDxU0JuW1ASrbuIU6bxgk/jXbRfCvUMfv/EL7fSNGz+ZrVs/h1Fan5r+aU+shLAn6Gi1Ty/EF9Q/v/wDkhxkWrXlopWNJF3HJ2qDS/wDCVXsQ2/Zrp3PfyxgV6Mvgu1x8zJ9RGKlHgvTurE/gg5rF4dt3aX4mvtcGtE5/+SHklx4o8Ru5+zmeNe2YAf6VA3iXxW6kNcT46HFuOf0r2QeEdLHWP/x0U4+E9NwAIFGP9gVX1b+7H7mHt8J3n/5IeIWNtqk119ocsqICW81gpP0FbtkLi5hnRVcy7MIuOTjBx/OvU18JaX3hXH+6KzJPhxpb363cepalCQ24JG67VPftV+zm2ttH5iVfC04z9mptyi1ry21t216HBQ201vDs2Mkp52sMEVafRbrUrPypbUZPRyfu+9ekJ4P0lZTI5lmfu0jc/pV5dH09AAN+B23VvY808ftfAeop/rLyMDPRRmuks9AntolRZEIHB+Xk+9ehR6fpqfwE/VjUottPAwsS80WA4NtLuVyTNtHsuaoTRSjITe/vsr1D7PYYHyoPwp2LJegT8FFFgPH5NOupj80DkEddtCaKi4L2zZ/3TXsDPbheApPpiqsu2VvugfQUrAebJp8MaAi3fnsFNSmy4x9nkA9NprvjEvORSfu8HcOKBnluvWxiFuVhMeTjkdaxNVQ7M5H513nj0xrDYunzAM2cdq4G8mjlQqwOe3NNEmG0LgE4wKiK8c1cd2k+XdlR0BqvIvOT0pMCse9QvxVhiAcZFROCTxyfQDNAzS0XS7jVDJFbqnyj5mboK9b8O6bDp+jRQwrgr98+p9a858JalcaOty8ljmGUjLyDGMeleteH1bV9GW9toZJI8ncUQ4U+lCjcBVDdcn3p5BYdan8gglSCD6EYp8dtLK5SON3I67VJrVQSJbOB8cPZw2UiTR5uJRiE46V5oQxr1f4gae2y3E9lcYOdjCM/erz+y0+W6vBDHDvKn5gRwPrWU0OJV0/RLrUHBVD5ZPWvUvCWnnS7YwAAZ9KhsrYQwIpRVIGMKK2LHIbCgn6ColHQuLOkFyEUcN07VbEy+SCehrPjnHlLlRQ9wrDG8CsEjRnkvxZAXxdbSJjElmpI99xrhJAc5x1rvfirNCfFFnCrB3SzG78WNcM3YEgCtkZsrEGkINTkgHqPzppZf7w/OmLQhDNnoafGHdwB0p3ynoR+dKp2HIcfnTBM3PDtpJ/wk+npt3b5MDnGa9+sYXjhG9QuBwFOcV8/aBq8dn4h0+4uCCiSDge/Fe6R67ZRFEadCGPVecVhO5tE2eidOtG3jB4NZr6mqyqBkg85x0qK51+K352sT796heY7DPGQY+C9aTPH2Vjj6V84LzGn0FfQ2raxb6loV/aTr5EVxbtGZG525HpXz6uyPKZ3BTtDY6it4O5nJEXSmn5jzVgqMZ2nB6fKaasZP8Lc9PlNXYgr7GB4q3bw+aSM4PpR5Tj+B/8Avk1NFFJkMsUuexCH/Ck7hcmijlgIPbvXrnw+YDRSR3kJzXlDSTFcSQynHUiM17D4CtbZfCluy3CmV8sY+hHJ61nKLLTR0glbd8p49adK5K8/MKpGYo+CQADj6VZLrtCK2c9TSSK0OHmS8F/cF0BjMh21cgtDIowcE9q3lsPPkdgMDPUirkdhbxYIUlveumK0MZM53+y5D2pw02Qj7tdQEU9hSGMZ6cVdibnMf2XMT070HSpicYFdPtXPHWlCDrRygcv/AGTOPSlTSZy3TiunEa+lIYz60WC5zh0mUf0o/smQEkjFdAUI6mozIQcYzRygYY0mTtS/2Q+BxW6rZPYU48kdKOUDn/7KcA0p0pwOlb/HtSkAjGRRYDnv7KmwOKX+yZcZArfxgepNKu7PSiwjA/sqXsMU8aZOuME1u5OTkUqscU7DMVbC5H3XIp/2O7x/rM1sbM0BRnk0AYptrroxzTPsEzEdfet0xg96j246GgRinS5s/KP1pBY3SnpW6AcUvPeiwGMsFyvY/nTgkwblCfxrULHd92l3Z42kfhTsBmh1A+aFj75pPPjXpE4rTKpj7p/Km7V7Jn8KVgKAuo+6PTJJLeRcEOPpWiUQ/wDLP9KURD/nkKLDMgra5PzSflVO+tLaWyuVhLGVo2Cg8ZOOK6Ty0/54g0jQQPjMajHtScbqxdObhNTW6af3NP8AQ4KNL+O3ji/s3JRQuftC84FUNT0+81G2MUlk6DqCJl4r0r7HAw/1YxSGwtzxsNYqi0rcz/D/ACO2eOpTk5SoRu9d59f+3jxV/BepSH5XKr6ELn/0KpIfCeowx7Nzlc89P8a9lOnW/dM1H/Zdv2U/nR7GX8z/AA/yF9bo/wDPiP3z/wDkjyT/AIRe5kAHlzHHTMi1Wm8HXkoAHmL9dp/rXsH9lQ4ONw96X+x4uMS/pQ6Uv5n+H+QfW6P/AD4j98//AJI8ZbwNqBHyvtHpgf8AxVSW/gu+ik3Oqy+oKj/4qvYDog/56Zo/sU/wuKPZS/mf4f5B9bo/8+I/fP8A+SPKZdNuLEblhlhYfxIpYfpTI/E13Zr5Ul8Gwf47Vs16ydHkOQWUiqsnhtHYsYIXJ/vLWUsKp/E3+H+RpHMIR2ox++f/AMkeXv4rZ2y94np/qGFA8Vr1a7jP/bJq9KbwlC2P9Eg/75FRt4Shz/x4QcdggrL6jDz/AA/yL/tJf8+Y/fP/AOSPOf8AhKogMrdAZ7+UT/Sm/wDCUw5y1109ImH9K9FPhWNcj7BFz/sCoX8KLz/oKH6IKFgqff8AITzNf8+Y/fP/AOSPP5PEsTHi824/6Zt/hUCsb2CW9huCYoj+8cRnivQH8LBTn7Cv/fupo9FjFm9p9lMaSfe+TFVHBxWzf4f5CeYw/wCfMfvn/wDJHD+Gpplv55rKU3ErABhjZj867K8m1S5tXibStu+MqG+0KeoxmnaL4Qi024kaGU5lI4YdK7tvDtu8cY/tCNXCjdnGK6o0ZW5eZ/h/kZLHU4zU1Qjda7z6f9vHHWcDRWVvG4w6xqrD0IFSNFlTkcV1TaBaRyKn2+NiRyR0FPXw7ZEMW1GMY6AYroSskjzpzdSo5vdtv723+p8z67dynXLxcn5ZCB7Vn/a5BxnpXp3ir4c3FtdzXtrcJdLK5OxVO4Vy7eDNTYEiyl/75rCUdTVSZzf2uQjk0faZCepro/8AhCtWz8to5H0oHg3Wgf8Ajxkx64qOUOdnOG5YdzR9qbuTXSr4P1gMA1hJz3xTv+EN1YsQbCTj/Zpcocxy/wBpb1NL9pYDjNdT/wAIRrBGRYP+RpR4J1jbn7BJn/dp8gczOS898nrTTPJ2J/KutHgnVyM/YXxnGcVJ/wAIRq/T7C/1xT5Q5zjfPk6ZNIZpa7L/AIQfWD/y5N6dKd/wg+r/APPkfpiiwuY4nzpPelE0mMZrtT4C1hif9E47UH4e60ScWwHtTsHMcX5rnvUvnYTvmuxX4b6yWP7kYHtQvw31oj/VKP6Ucouc4nzXweTTfNfHeu7Pwy1jqVQUL8M9W5yIxT5Q5jhVlel81/U13f8AwrLUyB80YH61IPhfqRYgyxrnpmjlDmPP/NY9c0hYsMYr0IfDDUCSPPi/GpD8Lb4jIuYh7UWFzHm+3mgqTwK9HHwtvNpzdRA54zTh8LLw4AvIfqBTsxXPNQho2fjXpo+Ftygw93Gc9CBTk+FVzn5ryLB6YFHLIGzzHZmjYa9NPwsdOGv1z7LQPhfuH/IQGf8Adp8shcx5js54pdh7jn0r00fDDBA/tBf++aenwuB5OoDPpso5Zdh8x5eUNGz2r1H/AIVapb/kI/8AjlB+FyZH/Ew6dflo5WHMeXBDRsPevVB8LrfodRbr/dp3/CqrY8nUmGf9mjlYcx5TsOaNhr1ofCa0Cg/2o5OfSlPwmtNwzqbkD2o5ZBzHkgU45oEeRXrR+FFiP+YlJ19KB8K7DvqEo56Yo5WHMeS7DnpS7DivWz8K7BumpSgfSj/hVWnDOdSlx9KfIw5keSFCR0o8s168vwt0wc/2hMR6Yp4+FmlHB+3zfTFLkYuY8f8ALPpSbM9q9h/4VbpJwft8wpV+F2kFhm+mA7k4o5WO547sxR5Z9K9jPwy0bH/H5Nn1yKVfhpouf+Puf0ySKORhc8cMR9KAhz0r2j/hWuhBirTzMP8AeqRfhp4cxxLLnvlqORhc8UMRPajymxyK9s/4Vr4c3Z8yU/R6cPh34dTr5mD1y9HIwbPETGfSjy2xXtw+HvhlASInb0y9KPAXhpzh4iP+B0crFzHiAjxzTguO1e1t4B8MRx4RNze7VB/wg/h0J80PP+9ScWO545sI5qVW77Aa9dPgrw9gYt8gf7dRt4R8Ox9bQ/g1HKwPLFkjxgxipFkt+hiwa9QXwd4dIH+it68tSN4Y8Mom02r9eecmlyMDzgNYY5Ug09U09iPnAJ9a9BHhzwqUGbdgM+ppf+Ed8J9rZuPVjRyMDgxb2BU4kXP1qRLW328MhNd5/wAI/wCFAMtAwA9GNB0Twvn/AI9mx6hjRygeo5dugpV3A5wanI4pO1fnFz2BoY1MtMAFPA5qWA8Y6dKXJBx1pKcAOtSAopwpByaXvxSEH1pD35pTmm9eooAY2CKrzYBHerDCqsxw1b0P4kfUTHCTCjijzeeBTV5WlIHpX6BD4UcEtxpk9RVaZuuBVoqPao22c5NVYRnKh75zU8S5z1qfMfalR1UcLiqEVJYCeiilS2O3mrMkmeQAKhLtjkigB0FsqvljV1I0FUELZyWqZXb+9SGWjsU4FJvQJjiqpLZ+9TGcgHBoAkmdc8VB5uDTHZsdOKYCeMUAXVuTjG00x5dwzUSuTwaawJ+6DQAjznpmofMJ685p/kueq805bViSKNQHwy47VK1xjv8AhTEtip65qyIMjkDNAFYTyHpULtKwwQa0Bb+xFBt+nBoAyzHKeKets56mtL7MMZzigw88GiwFBbMZBPP1q3DAiADjrTmjIXpQEfAp2FcbLFCXycflTAUT7oqRomY5IpnkHNAFuGVdue5pZZyBwKijQqMU2UMOOaYEyzMcc4rUsfmBOaxB265rVsCV5yazqPQqBoucdqrucHkU9nyc1E+Sa5OZ3NWiNpD1FRmSTH3qc4IqFl960jJk2Q5pGx1qBpnz1PFOOMcVCy89a2i2RKwpmfP3jU0Dsx5Y4qqQAetTQMozzW1iC8rCgtiq4kXOM04yCmIk84gdaptdP5hHNTFhiqjEbyetIB7Xbhu9WrGZpJSD0rOcAt0q5YD94KznojSJsNnFQOxz0qw3K4zVeQY4rk5nc1sQs5B6A1G0p/uilc9aj3VopMlobJLgdKpPO+eCasSFsHiqLbt3UVtEhlpZGIzninece9VuQOT+tIXPNWK5aMgIxUBfHaovMGeuKYzDnLU0iWyfzKTz1FVS69mqInJyHFVYm5fE647UvnpiqKuAeWqTIznNAFoSIRjj8qMg9MVWDjtzThJweKALHA4yKdke1U/MOSdtL5hPXAFIZbAJBxtpdp6FlzVMTIPvSAfjSi4j/vigZb8kH7xB+lL5aj2qus6nnfTzcRlfvUyR5WMcZHNNxCTjIzVczRE9RRuTqMflQBP5UDdaXyIcDmq6yDPY1KJR3pjJBBA3B6UotoD0NRiUdj+lP8xfSkIDbxf3qQW0frSsy9RUbO2fv4osA82UZ53n86qanpL3WnTRWl0IbhhhHIyAatrIcfeFAlC9SKLAcS/gnxHPGEl8RWzKOxt8/wBKi/4VtfSL++1Wyb/tgRXctdLnig3IxzjFFgOBb4TvISf7VtU9hAaa3wmyMf2xbD3Fsf8AGu+N0gFN+1JiiwHCJ8KjGu0azb57H7L/APXqX/hWt+nMXiCBfpa//XrtvtKZ6jNPFzEP4qdgOPsvh55N1Fc32r/bWjbKxtBtT8Rnmu/sZdStrP7PbT2ccS/dAjxt+gqiLqM/xc1YSZCnFAD7qG6upA13cQuyjGVTFW7aW+i2rFdRhAPu+WKz2ZDzuP500TmP7r0+YLFrUXu7seXPcrsHIUIOKwYPDGl2qsIY1BdiztjljV6W95BJzUZ1JAcEflRuFiufD9iCecfQ02LRIUlLpK69sVZa9hkxuB/CpYZojkBDUtDRlnwzEOft1yCeeop//CMwEc31x+lbPmLx8tO8xf7lTyod2cxd+CfD9xP9pvoBdT7du9+uPwqqPBvhhWwNLiaumuJRvGIqg8xg2BDVJIRjf8IZ4dRDjS7fHutCeEvDxHGj2f8A37rWbzG+9n6CpVZkAAjNOyEZQ8K6EpGNHtMD/YqQ+G9A6Notn+EQrT3vjG2gu/8AdosgM4eFvDso2nRrQj/rngirUPhXRk+7Z4x0+c1ZjlYOPkq4krf3aTiik2VRpFkgx5Z4H941HJomnycPbqw9ya0GOeqUAgf8s6nlXYLszG0LTNm1rRCvoc4pr2FguFFhagD0iWtKWQiM/JWcZnB5jFUkhNjVsbMf8uVvn/rkOKPsVruB+zQf9+1/woadgM7DgUw3J25CZqtBFpYLdSMQQ/8Afsf4U4rCP+WMYP8AuCqP2yXHEdKbmY4xGPxpWQF3KYwIo8f7gp0NrbKd628at6hQKoebOBk4q1bySuDuxSaQFn7NASSYUyf9mneVCB/qk/BaYDJ7YoYuPSlYY/KdAoxSYTPQYqqWcMcYo8xxwSKtIllrah6CmkAHtVcSN/epNxz1piJyuecCk2qP4KiMjAcGjzGAzkUDJspn7hpdiH2qESHHakeVu4FAD2hj55FM+zxHnjNIp3dRS+WCODQA37PH0yM1E1uAOGFTNDzwahZTkigCMQEnhqXytp5PNG1ugJoPmehpWAlXC4AP504yKO9V9zZwRzS/eGOlADzOobk80vnL2IqLyhn7ympVg4HIpgG4EdcUB1PXrSmFuelQmJ88jFADmemln7DNKUyOoFCq4/ioECyPnkVLuBXlTmmHzAeuacry4xwKAG5KnjP0qJ5ZAOhqYiXqXAP0qB94PL0wuRm6ZQQQSaRLpuu00pTJzupgjkzwwAoFcm+0P1wRTDcyg9aTy3JwXzR5JHU5phcd58xGd2KjMrnktUnlcdetNaJR1Y0WC40TzZ+/VhJZCuSeaqvtwcE5pEaTjGaLAWnnkXng1E15ID0prI2eTj1qKRSehpWAet9Lk/KKQ6i6tytVSm0cOc56VExC9yaLBc0P7QZhxgGkGoSBuvNUFYetKQM8kikO5qpdSnkkVbSVmTO4VjxYHG85Natui+Vy1RIuOpKJnGORml+1nPXnvULRjPDVG0Q5yTU3LaJzqGD1H40w6l15FVWgXBPJqs8KZ78+9UiHoan9prjBAzQNUX2NY7RRj+I/nSLboR941ViTbjv43b/VoauGaEgZUc1z0Nuu7hz+dXXQgACSmTc1RLD2QUefBg5RayD5mPvGmN5u05PHvVCL0moW6P8AdWmf2rCTgACsCbdvIzmoSrZ4JqSjozqMWegpTqMWOormmDKMknNMIZv4mosFzpm1CPHX8KY2oIOg/WuZ+fOdx4p678ZL4osK50P9pjPX9aQ6qmOT09a50lgPvGm/Pj79Azo/7VVhUbaiT0PHasJN/QNmht6jOTQJs3FvzztYY704amyD+lYCFuoJqXcxHemM1W1VmHODUDam4PQVmOzDkHFCb3x60CuaP9qS54IxTl1dgOVzWYYWySxoEJI6UC0NM6tkdKjOpEjPSqHlMvFL5fHUUCLZ1Tn72KcdRVx948VmiHnJ5HpTvJUnjIoC5cN8wH3jUZ1CTHOai8gY5Y0ht1A65piY83zN1LUgvpRwCaasP+z0p7JwAFFMEMN+/Qs1Ma9kOMMal2DAyo57UmIlPzJQMga9lzyW/OmfbHB/iye1TN5Sg4WoCVJyBTQhReup6k+9SJfHcCSagGN2dlPYKo4UHNMRaXUR03EU/wC3K/IbFUVUE52jBp/lx45/Si4Fw3IIzvBpDdjI+aqhWLbwcGmKq7x3HvRcDRW6BHByKGunB7kelQxuq9E/Gn+ZuBwKdwHfamAyAc0hvG64NNIYDPFIxZeeDSQxReNn7rU8XxH8JxVcyEkA4pC/+0KoC0Lxs9CKU3TVUEncsKd5i4JLfhSuBMbmQ4xmmGeUkZY0iyIBy1LlG53UWEBnYHnOKTz/AK4oJjYYJpRFHt5kxTGN+0NnqaU3DYyDULbVO0SAmnLC8nQjFFgD7TIM89eaGun9P1pDA3PJzUbQSZ5PFSA5r1hwM/4VG182cYP1p4tQ38RzSixOchhiiwiM3LHACmgyv/dqb7KwzzTTbP8A38GjlGRiXB5B/Opd6txk5qI27HPOaTyWU8g0co7l1ZyExhTjrUTSKWwVU+9QGFjxuOKTydoyGzUqIXHG1gdskc0j2aMOBioxuUn1pySvzkk03ELkDWBIJ3ACqxsZi5wPlrR8xgcYyKcZcAnFTyhzHpZORSKwBpu9c9aA4Y1+Y2PbJh81SLgY9KiQgA4p4FSA/APNKKQYApwPfFSAnOOKctJTqQhePWkJpcZFJ3xQBE1VZ2H61bYe9VZFBNb0HaaExm8bQKb5hPekIHYUzGDmvvqTvFHDJajixPNNYnGadx+FDVoSVvmJ4NSKMDmnrGCakVPWgCBlzjik2cZAqyU4xikCc4o1AgVD6U9YyKnVO+KmVO+KYFPySTyCaBbEc4q9sANOwATRYDP+zPjpxTlszx8vNXd20UglwaAIFtcfwinLB64qYy5pvmYPSgBoh57U7ysUpkPYUhmboBzQAbcdMUqhgaPNbHQUhlc/w0wJMkdMUwtTN7H2pCTQA/Primls9MU0j3pp9QaAHFj6AU3J9aXr1ppwPei4h4U4znNN+bNHJ4BxSAY75oAmUNimsjHvQHYdaQyN6UXAVIW9RWhajaOTVFZDjnFXLZsjrUT2KiWmz2qJ8/3qeenWoZAD9a5nY1InbHeoi3Jp7Cmlc9DTiSRMWzwOKict7YqwRULHrW8SGRckYqWPA5zTd3tQpGa2IZNkZoz6mmArnrTt3PtTEBY4qEgluuKkyCDzVZ2w/BpASFPVjV2zCq+Rms3eQe5q3aOSRxispvQ0ibW4Y61BIVJ600ZxTHJrm6mo1ymajJTPGaa7kc4pm8jk1SZIOUwciqkjIDwKldjz6VUkIreDM2PyCO1RENngCmcA9SKN2D96tCBwjJb5lFK8MZHrTMnP3s0vbrTQDDHGBwOKgk2A8CpmI7UwhO4OaYrEaY3Z7VOCCDzmoykTdadsQDAI4oAbuYHAxinbjgDP5UwlRwDTOp4P0oAe8vltgjrSrKCOlQvETz1NNVZM8jGO9AyXCMeSKXbERjBP0pFib2qRY2HpigByLGBznFPPl4yM0mz1pdmOlFhEYVd2SaXKnIBNJgEjg1JsBxgUxDNuD941KowKQDbSg0DFz9acqZOc4pM5OKQj3oAk2E9M00qR65oBfPXilweuaAGgY65p/lqw6mmkA96QDac5OaAB7YE8GmtBxjNP3E+tO28dTzSbGVjEM4z+lN8rb7irOzJ5prIM53Z9qm4WI1Cg4CKacQvTYKAMHgUZ5p3AkiUA/cWrqFdn+rFVIuWAPWr8Y+XpUykUkMPPAQYpojIOSoxU5TnrSbQRUqQWK0saMPuDNVDEV4EQxVxyFJwapSTkEhck1opEtDwqJy6gYoil3SEL0PSqrGRj833fSp7dWVsj9adxGgF+XpSEHHpTVLHqaftz3pFFK43b/lJAqLc/941daPLdaayArjaPrTuS0VvnI+8aaVdhjeam8s9M0oXHei4EIjf1p20jqTU424wKbxn6UXAamdw5xVtX4HNVlPPSpVJx0FAycSUoY5qEOcdKTzCKQD5slDzVAoc/eNWZHJHFViOetNBYaYiRgtmhlZANuMfSpewxTdwBxQwIhuOM07ywTmlIWjIXv0ouITy/birMG1RtxUW4bQc1IsgFFwLG5c4oO085qBXyacDuoGRsg3Hmm7BnrTiMNSgAnpVIljDxSEE9ql2jNKuKYiuQc4AoC9sVYAFGwGgZBgjoKMEdqnCAd6Tb70ARIxU8rUg56AUvbmjb6UCI3B7U3yznJNTFTjrSY45pgQHcvOKRZDjkVLjPHamMuOmKQwDjPIpxVSvFNVR+NP24XrQBCYQW4qTyzjg00g561IvTGaBDSj9ck0xiy/eyKlYlVqBn3HmgBjHdSiFmGRkU9QAQTUomXGM0AUjvBPJqWJCc/Mc0MpZs4FKAVbpTAMHHJyaY2N3zc1MZR/cpplX0oEVvKG4tzg0h4PpipH5HFNRRnpTuIbvweTn8KDIS3Q1IcDIxzSHbjpmkMhcux+9ioyhz1qfOeiigj2GaYiuUNOUOuOcVKPSnbRnJoAZkntmoyFU81YO0Dio2ZTxikBTkwScDiosADBFXiEHYZphjTqcUikUtvHAzSjPoeKthYx2pSIz0GDQBBEuX9DWomQmPSoIIwXGelXgFUYrKRrEr7m6VHKxFWv3eCc4pjCMDNJDbKJkKqe9QF89atSeV071XeNCevStIoybGjaxzighfU04Rr60MgxjBxVE3GIdr5ByPSpnmGRjtTVRQcY4p5RR0poQq3TY6U43LbSDTQAB0HNI6qV6igCi6l5CTSbZM4AyKkdNrdRT1VttBRAQx4IyaQhuflqxlgeMUHOPf0pCKZjYnIHSlOf7tWiMLx+VR7SfxouKxBgEZK80hiVhkjBqfy8GjYcZ60AVtmzoM087iozUzDNM2j1oAYuM9Kl4brSFeBt/KnA8DjmncCJ1x1FAYdBUhwTzk01lGM5xTuIaTuHvTRGPU0p3AYFKCwHTmi4gWHJyTTZoP7poaR+y80LK/dQKLlEXlMBwDSKr561Z38HkA0Bgx7UXFYh3EdaC4xwOaV2Vu1R7hSuFh4DnGDxUwU47VAGboOtPzJ0p3AHLdqjyW5qZWI4K047CPu/Wi4FcqT2BzUTRKB71fAixkDNMEaAdKLhYztozwTT9hbg1baFB0HJqMqR0p3FYri2y/3+KkaAj7pBpfLPUimhec5xQBGYXHUcUjIwHAqwCehPFSAKetAFJTMtOWZlJyDmruzgYqOSFj9aLhYg3u3P50FicU4wSKfXvUEscq5wKaYWH9OcUBB/F1qAGQ8HOAakXeSTzzRcdh7ABcZxUZIAwTnNSYJ7cUvl8fdoAZHHv5PApCACQpNSBXz0NOELnsaOYVisVYDvTdjtj73NWWhb1Jp2xgozmnzAUwrIc9/U1aW5cLjmkMb7sEU4QuByeKOYByztnqQKf5oPUj8ajETc8j6UeXzihMRKZB1GKYJGGcHimGFt3A6U4Qy8ZxQ2Mcszc5o87vmgxtjB6U3yyG9RSuMGlIPB4oE7dODQYyR6CkWPJ5496OYQjzFhjIFVpLllIUDj1q00a56jjrTfs6O2duKLjsUd7McnJqxBGThiePSnmBM/T0oJCDj9KLiY5gBUZ5yOTSn5uacmM4Y0XEekeUo5pdi4p7be9GRnjpX5fc90QL2zUikik4zzTxgVIC04Gm8Yp64IGKlgLn2petOApCMUhCe1KaPek3UAMf3qrIcECrLVWlAB9BWtL4kKSGmPI4NCxjBzUoXKg5zxSha/QKPwI4ZbkXljpSGEGrIFIQR6VqSRLEB0qQRDHSgAg808E4xQIYY/yphiqU8imZ55NMBoiYVKEZcc0gPQZp/XoelADChzTghxSE4NOz3zSAjdD1pqoc9KlZgBmmZ59aYC7cDmm4J6VJu45NJn0FADQnrSlRThilCj1oGMxxTcgdal2qOpNJtU9qQER6U0qDzmpioppUDtTEQnG2kzipGXI6VGVANAAQSOtIQRSk46UAkUAIp70hY54p4bmgDmgBuT3pzcinYb8aYSQcEUgFXntV62OPrVNcehq3D7ComXEssQR1qJiKViCOlRHjpXM07mg1iD25qN27AUpBJqNg2KuJLEJJqI5709t/pUWT3FbRIYbsUobHWmHHenqRjFaogXOeQKMEmgMB3oyB0piAg9KjZOfWpMDGc1Gc5pNjEIarVvkNk1WHoTVqAc4zWUy4l3ccU1uadjI60xhXNfU1ImPtmoiRT3JBzUZ+tUmSRu3HNVHYZ5qxJn1qsxwea3gRIZkd6YXGacWX0phPHArUzAOo7UvmLz8pqPDZwF4p2wt1OMUwHfJjpTSUx1oSM7jk8U8oMcEUCK58rONxyaXC46mneSMjGDSsjdiBQBAVJ6Zpq7t2CtWGRyvWhImC/MaBjMkdRxTg5J6UMg75P0oHptoAcDxnFCu2elODegFKuRzgUwHqST0pSxXtSDge9DE4oERs53AdKcHJHWk4PUYNIWAOMUwJM5XmgKOpJpFxjJzUoC+9ADNuOlOAb1p+32pACO1K4CbGHelAxTtxHalzQBGcE9KeAD2oLDNJu59KBkgQHtS7TkfKKRTk8GpgnHFZSZSRCy47U3ywecVKyN603DDNJSHYjMQ7Cm+X7VKS3TFQsz7qpMViaFArDir0dZ0Rbfya0oRxk8VE2VEccZ45phBqRsdqY1ZKRTRTkUDNUXdEPQ1blOCeay5H+fkY5reDuZtE4kBqaMk4zVJSM8VOjueMg1qQXgcd6Gb0PFQrkjmlJxSKFZ+M5NQtchRxQynPtUTIuckUCuJ9qOad5/HQ81GQoHCnNIM555FOwrkomJGBwaDPjIzk0zr0GBSHBHOKBj1lJNWBLxVZI1PbFTqMY5oAl8wkUhkOelNxk0qgDqaBiu5C1VLnNWZANtViAOR1oFcUsSKMkA0zc2elISwHSmBJ15NR4A5NG49utG7nDChkjgfxqwoyBVcFeOKmRlpFDyp3cGlRTzzSqRmnoATk0wG4NAQmpOM9KOaZI3BpcUpPoKQc0wAjijp3pcDNBAoAaeaATjpTtvHFJgCgBMH8KX6CjJoBxxRcAxRsyM0YzStkDigCMxnFRlCKnD8dKaTnrRcCvtZm4OBSsDjipDjtTT0oERYbOM08ZBpxHFKFGOaAGO27gUgUgepqTaAOlHHUUAQNGxOTSbDirBHHvTAG9KYEQGOtPC9zTth64o2k9aAGMp5xULA5q0FHemsoIpAVCCR0FMAYfWrZUdOtJtUD60xNFfewHNLnnlakKqTyaXYOtAIiBX0oOG6ZxUu0YIpvk+lAEWwg5H5UEngYqUQkH7xpfLwaAK4xzTW+lWPKOeKQxDbigLFRiM5xzTWJ6/pVkxAGkMYJoGisOaUKBxmpfJ5wBQIsHvmkBJCpFTkMFGKjjUr0qXBrJo0QxSccio5dxzipzkDBqGQZHORQkDKBVic00IwHNWWTB4oCDHJrTYzZWTfjOM08ElsEGpdnHpSbaYrChQw5pSvHTikEfzU8KfrQIbTCC3bipAGzzS7evGKAK/lj8afgAU7y8tyaUxkUDK7ox6E0ig+9WdvakCjGMUAQgr0zSELng9KlMA9ajeH06UCG/LzzQNvY0eX65oMfXFAxDg9+KbsXNI0bZ4FKEZR60gsKUGaaRxgU7D4yOtIrMCcincSGgEcUoAOcrT9zE4A6UhLDHHFFxkZA9Kbn0FTdeMUwg0ARkjHSmnLVNtGOnSlAH4UAVyuTyOKMZ6CpyueQKRUXPWgCBo8mlEWR0qcoM8c0hU5xQBA0bD603Y/BxVllPHNJtIoEQgEDO2jLY244qcgkAAUzaR1FMEiPB44pwzjODUgYDgjmk8xSDkUgGgnPSjjNL8p74oIHrQAwqOeuKb5MbcDNSkAdSKT5exouFiMQKD1NBt/m4apuMYzQo+mKLgQ+Ww4zzT1VujHmpNgJyTTSqg0XAbuIGAelNwSOgNOxyMUpAGPWmIhKYHC1GWC9VqySopP3R6/madwKxZRyRTQyjncamZY88YqPahPBouIjEh5AJp4lYc5pwCL6ZNOCxj0zSKIxNz1/A0vnHd83OPSn7I26gUuyIdqAsJ5y4GBzUbSKTyac6qTwBURjBbpSAnVkxgfnQCvp0qERnHAOKX5s45piJ9+48cClJYjAFV/nU9CaQySKM45zTAsA8YIpuQcnoKZ5pwc8UzdmlcdhxYHvUROMkH6U9evSggZPH407iK5mccd6YZ265q0UjyBimeWm4nGAKBldbgg5INToQRksAPejYhBwOab5akcilcQuV/vc0bhnBpAgDZ5ApVBLYxn3oFY9M25HJpQmO9R4kPOaXa2a/Mj3SVc5z2p45qNCyjpTxkd6kQ7bzUi00cmnAcVLAk69KSjtQeOMUhACaTvTu1IwoAaelVLoFk49atnpVW5UlODWlJ2khMcgIhXvxUq49KWAD7OmeSBUmPQV+hUf4aOCW40LxzTWA7ZqYZ70hHpWpJX70/gjpTihHOKCD6UAMYCmYyM9KlI9qawJ7UANVKcABSgE08IM0CISPmxTvLJHpTyOad9aBkDRHB+amiEjuamYZoGRQA0RgD0NJ5Z65qQimkgUAM2nHXmjYeueadnmjNAAF96a27tS5NBLD0xQA0BiORQQxGc0u4nvSHpwaAGYPWkx7ZFBb3zSAk+uKBC8elN4BzS4Jagg44FACMw9KTcOMijYe9BXHBH40AODjrQXHpUZHPFIBnikMn3+3FWIm9qphSKtQA9SaiRUSx1prcCn/SonBPeud7mhGWqIk9hxTyp65FRsGx1q0JjGbsaiODUjqaj2n1rSJDE4CkUKOelIQTT1HHWtSBMHPSlABpw9M0h60xAANtMKnPFPAxRgetAEewg1Ztgd1Q4X15qxAuTwaymjSJd4AqNzntUgQ7aayY4rmsaXK0hGORUBJq00Yz61E6jHAqkhFZs/nVSRGZs5q668cdKgZDW8DNlYBs8gU5c+gFPIoCGtUQJmjtxTsCkIA5FMCPGTyajc44wc1YJHcUyQjrQIrrkAk5pRnOaUsf7uaUMSDxQA8McYxQckelQNI3TNKrtwM8UAScKc9aRifSkZ2xkmmFieposA9H2N0qYS5qvvy3A5qVCPYGmBICMcigsuMEUlLgelMBpI4ANRlx+VPIGfam7AadxCxsMdakyM0iIMHAp4A4yKTGJ5nPBo8w+tKy88U3bQA8THpR5nPpTQqgetGBSAUkE05cDGRSLt/GpF96GA9MZ46VaH3RxVdCueKtKpIrCZrEjamZx61OUqJh3qCrEDMM9KjdvarBGeRUL9elUmSxkTfPnFXlY45zVSIDdV+IDGamYRANTGJxUzAHmo2z0FZpallCbJJwMVnPGdxJBrUkVgST1qmxwT81dMEZSKoyBwlTxBhzigk59qkXrjJ5rYgerN6GkYv6U8YHQ80/aCPSpGVS7jkimGUkdPxqaRR6ZFMC56AUwISJDzijDjBK/rU4Q96XaR1oEVdz5GRSnceMVOxx0ApjOc4xRYBEyOtShu1NQ5PSpQMDtRYaAHFL1pMEt7VKFFIZESaYQ3XFWNoxUTj5uG/CglkeDjOKb9BTnbaMDJpiSHup/KqAXBz92k2HNOM3OKUENyaAG7MHpmnrjHA5oLDpmgAe9IY9WxUgkxUXApVAY0wJgxJyaUE0mzC5zQvvTJHA80oOTScY605QCBTAQg4oxxTiaM8HFADcE8UuCBg0nJ74pScdR+NABg4pMHFLgmkwemaAFAIWk3NigKRQcngcUAM+8aCMCkOVpN3HWlYBcZpuzFBJxwaaCfXimIXaTS7GA60mc96kDZHWgBpDYppUips+9MLdqBjMmlyexpCcdqUeuKBAN3XPWl69aXnHAo59KADBxTCMZqTp2pjZHOKYDP4uKaVBOaXPNJuyetABgD/wDVQFJNKCaQg5oAMYPNJ1FBB6mmlgO5oAdQR3pm72pck9jSEIetBHrT+DTXXjg0AREHuaacCn4ORk0j7TQAzg85pCQDnNOxg8YxSoFPJANJjQ5MMODin7Dt605UU4wMUNuB4qS0M2erVAy5b71St15pGVSOOtNCZWMfcNxTdgHVqm2qOKbtHU1RIzamc7ulKpQHrTiEbAApPLHHrSANwJ4NAIJoCU7bTANnemlWGMCnHIHvTQ7e9ArDSjfhRtOOtOo2k/SgBhTA60AEg4pSMY64o3DqO1ACDPr0oI4o3bjTsDHSkBHtxyTTdpzxUoII5FLgdaEMjAAU88mjapoYgHpzSb+cd6GAYFBjB5owaXHFFgI9pGaXZxTwOAaTFACCIY6ikMNKTzSE8cHmgBBECPpQYgOg604FcdeaQtii4rA0eBTDGozxTw/qetBII4ouMi2gdM0mOc4qbOOKRu2BRcLEXG7mlxkjinle/emc59aBABljxxRwDjGadx2zS4BJyaEBHt46U0qvXFSsg7GjYSOtAEOz2oZMAZFPMZz1NHlnu1AMjMYPRabsABO0ipjkd6b5hxTEQbBimlSDgGrPmdttIduScZpWGVMOpwGJp2SOSDVjcmTkUoEZHSkBD5nAO3ml83I5UVLhTnC8U3ap7VQhu9ePlFB2HsM0pVBwBThGp6daQEbRoQOBUZtVPOcCrBjANNI6cUwsRiAKc8EUbQT0FTqqkdKd5a8EUXGQqq9cChlQqeAAalCkE5pDGDnFAiqUHHpTki3VOEwcCgRkdOtMRGYj2NJ5RXnNTDIPPFGVPQ0hkJiJPQe1HkbupAAqQrnkNTQrY61QhptFz1yTTGtQOQCKsYkB4GRQWk6baQyosO0nNI0PWrDFs8jAppf1oCxX+znGe9KYW44+tThuacW44/GgCm0Bzmo2jYMOKvEk9qaATwVzQ0BS4GeORSA4bI4q6Yefu1E0POcdKQWPRsZx04owM5pmcjOKUZxmvzE9oeMU4AVGM08cUgF6Gng5xTepxTx1pMQo560vQ+tJjFJupAOFB/SlBHFITzQAxsL0qCTDD0FWG+lQSYJq6e6E2WIGAhFSbgeRVWNsR+9SKXr9Bw7vSicM9yYZPHFOK8Uiqx9acYyOtdBmRkHoKUKc5zTvKY9KXYRzQBGaZsJ5zU20+lJt5oAjCY9aUrTwtKQM9KQEBGKQfSpW2igFQPemBHgimFWJ6VIzAcio95zRYBdrYpuKfn61GQQcg0AIxx3o68g0FOOtAU546UAIfbNGexqQACgr7UAR9qbyT3qTbxnFKQcUWC5XIAPSlzkcU8xknmgRg07AN/GkYkVJsweKQrx0pWAi3n0ppLN1qRgey0gRieSBQAzk4FOAA7U/ZilwfSkAisM8irMRHFV1QnqKniGamSKiWM1E/wBKmIA7GmnpnrXOzUgI7VGRUzcGmEZqkiWRFNwphQ+lTnI4FRNnPWtYkMiCDPJp4UE4FLt9qXDVoiBNoHXmm4ANDj1P5UmcdqYAVHc1ER83FTnpkVGVGck0mAAAHPFW4GUdqqqFyMVZhwCBUSLiXlII4pj5x2p6lQBxTJDnNcrepqQMME1Ec5p7Ht0qN8etXFiZG5wKrO2eBzUzviqskhzxW0TNiEc9KQuewAxTS/GRTcbs1qZilznqKbnJxzTSCDjFIcjoKYEgAGcnNRMw3cCnrgnrSOAOwoAiy2eacN3rgVGWG7GDS8Z60ASEYHrQGHPam5GMZpu7NIBxUsMBs0woV5INJuxyCfwpfPI9TTARc5yFP41MqE8k1GJieopVdj0FICyqj1pXHvUauR95ad5inPGKoBMMOnNLg56UCRc0eaPWiwh6r704L6mmLKCcZpxb0NFhjiegFJt96aDnHNOGaBChB19KAmeKXcuehpQV685oGN2sOlAB70/cMe9KCAaQEsS+3FXFHy+1VY3WrCSgCsZmkQIIqN6eZBk5qJpB1qCrjSOaiZCeakaQEcUwkY4zTQmxsajPUVeiwOpzVFMbqtRkAdKJAi1gY4FMYnHFJuGOM00t1yDUobKszdaoO2TjFXpec/LVNlIJOK3iZsaOOgp4ye3NR5IBpVZsjg1oSShG64Bp25hxikWQ8YU075ieRSGROS31700bVPWpSmeopjKgOCKdhBvUd6NwIqMgE8Cgq54xgUASU0r8w5pnluOaVt5GB0oAcxVGx3pygvzioFhO7J5q0hIGFFIYoDdhSEMehqQZ6kCjOOMUgIW3gVGAw61YZuORUROT71SQhmCO9NMjLyOal4z0FNJoAYX3DJXHvQoPtTmUsMZphVs8GgY7ZzT0X3qPJA9Kmj45pCHeWWpQu01IDkZo4xQMbkg9KXnHpS0DiqJEFO3DGMUgxmpAgPNMCPrTiTjA6U/yx7GlMYHNAEPJo+tS7OOtBTAoEQ4J6UoH1p+OKM0AKM9KRh+JoG/rSknHNAEOOeaaUJ5xU2ec5o38e1AyHyzim7Djipt/qKTd6UXEQqn507bg1JkHNGBQMYQTzRtOeaeTzSmgBuwdzRtHrTto9aAOaBDRmnDPpTh7U8e3FAEeOOlBXjmpGGaCPU0AVinWmbOat7A1MKHPSgCvtb0oXd3FWQhpDHigCAjI5FN2DPSp9nHWkCHuaAICmO1JtYdBVrYKTYBQBWKmo3yBVwoKieOgCqXzgYNI2OMg1MVx2prAHpmmBGFA7GlwvYkUHd0zTCrZ5NSxol6d6cW+X3qPJwKNxpFXGscnim7jmhi+TwCKibf6CixI88mmlucU1XORxS8+lMQuQDxSEleaXknpShST/SmAzec0Zb1p3lHOaURZHB7UCEDn604nPbFN2EGnY5oABHjkHNG1gc04E7cUZbuaAGEnGMcCkBXutSgjHNBx6UgIhtAoODinEAjpilKrigZEVXOQ2Kay8YBqQxDsaUIB1pgQ7eADTfKI/HvU5jzikKHoDxSAhwy96UsT2FS+WPelMYAxgmgCKkzn608plsHNJtxxtNAEbZ3dqRl9KlAwT8pzShCeooAhCjGBTSnarOzvjFL5XtmkBU8onrQIyOKseWQOnFGzvigCvtIPWlywqbYSelLsb0FFgIMnHNLz2FStHnjFL5TAUxEIU4570FenFS+WxOM9KUqxyOKBkXl5pwTjHepFU4zikMZzRYBoWnNGrAZpQnWkKGgBPs6tSGzX1pSGzTgx7tigVhn2Mdc0C0UHORTyx7E012bpjimA4WkRoNoi1BvkHQmn7n4yaBDjboB1FR+QvoaUliOpFJgnuaAE+zr3NOEC+tNwT0zScg0AP8hMYJpnkqTw9Bz74pM8c0wDYo+UNSkJ2zQMZH9KQjGaQwIXd6mmkjPAoUnmlAzxQAxsZyKQNg96m8rI/CmLFgEGgBpbvQAp5xTzHu6CkKEcdBQIb5ag96XbjABqTyQec0bV55oATcw4NG7ntmkKg9DzTjEOuaYmNZQSCcUwwrjtipPK5600xHnBxQNEQiHUUjRgDPvUuwgY7UpwRgrSGVyu09qUOoJzUjKDxg1H5XscUAOMg7fjSHGRwMU3YRRz6dKTA7YyArwKQSMegpVVQPWggA5xX5oeyPDkj3p2c81Fu44pQ2KVgJgcVIDjqaqmTHWneYAvWlygWN3ekyBUIkG3rzTGl5yTS5RFoNxRvquJgAMHimtOo5zT5WBYZqqyyBe9VbjVLWAHzLiNT6E81h3/AIktgpEbgntngV00cPUnJKKIlJLc6+2KNGMkA1fQW0UTzz3KRxoOcnk/hXlS+NbeyiJkuEdv7qnOKzLn4krIfltGkPYGvusPBxppM4ZtN6HrFprdtdXPlx5C54ZhitbchONwJrwpNY8Y6w3/ABK9ImRf7yxED8zxXe+A9G8TWN9PeeIJCwkUBI2fcV/pXRYlHcFfzoKZ61YMgPYCmFgx9qLAVmQ9jUZQ1aJU9KYxyKQisEbikKNnrVggnpTCAOpoAgMZzkmgqM9amJX0phIz92gCJ0GOtR7QD3Jq1gEelIQuKYEGCegpNhParPy0FRikBX2cUvCgU8g0gQmgCPgdqDzU3ljvTGAXtSAaOeKGGaN5xQzE9BTGIF4x3o2MDSEsKcNxoAQpk0hGOOKkMZNNMfWgREQDQAPSl2EE4o5oGKcZHFL9BQoyeelGM0MBdpNSxjmo8HvUqKOMmoZSZL1HNNbGOKkxxx0qNjjp1rGxZDITkcUwqTT2LVGx45NNIlsY4wOtR98k0rMM9M1Gf0rRCHkqCeaNwxTdox1oAFWQG/mkDc4xSkZ9hSdD0pgSbR0qJwAakFMbr7UAQ4we9WrdTxmoC2DjFWYixFZz2LiXVX5PUUw554p8YISh+OlcctzboVXHJqBz7VYdfU1CwA96uKJZXfvkVXaPnpVtzVdyd2a6IGbINuByKjYsM46VKeTTWHPQ1qZkIZzS5P41LjFNye4oAYFbPFSYyOQKcp46Up74HNAFdsHotIFz1FSENu6UgQk/NTATb3prADtzUnQ80jMM9KEhEY6YxilxnnAoJJOdtAJPTigAAwCakTIXpimduualjB7g0DHDNBTdnIqRQPSggYpoCAgdAOaaEYnkCpwo9KXaCaYhixAHr1p+0flTgop+0Y4BpDGBcDNKB6mnlSB0puSe1MBjNgcDJpRnNPCetO2Y7UhDQvNKBilAIp2CfpSGKi7qsKMCok496lBrOSNExpXPemMtSMcVHyeazsNjMD8ab9aec56U0+9WhCDGatRc9qroMnpVqPjqKUgRJ+FNfkGnZGc1HISQcVAyvL0NU26nrVtgADk1Xbg9K2gQyLbngipFUCm54pwqxEmQB70ZpoK9M807j0oAa3XrSFVI5FOPA6imk++RTuIjChTkA08H2zSbscYzRuHuKBj2Rce9NZAKRWU9WNPwvY0gI1QetSheOKAv5U5eBQAY4oIHpTgKTAPFADGC9aZlSc1MQOlR7QT0oQhmFP40FAaft5z2pwUUwIdozTTHzmpjgE96ZkZ6cUgGFRTx0pcAnik5oAkA4oHvSIefpTx1oAcNuKQ4zUgHHNNC80wExiin7KTYc9eKoQIcU/PSjHbFAHNAC9KYcnrUhGaTApCIjkGkzjnHNSlRSYGOaBjN+frQRwKMc9KAe1MQwkD60nUcUsgB6GkGcUhhwKNozSZ70hJoAdtGaU4OCBUeGJp4UigBQKdjimHOeKXd0BoAcuF5IpQynqKZ1HWlAIoAkBFGKaMkUoBzTAWm4PQ0vOaD1pgNyw6Uu5s0c0c5qRBk56UwkmpBz2pQKYEQ9utKAeak2jNKVyOtAEBNBPFO2c0vlnHWgCMNik3DPNOaM1GI2B5oAaxBPFNP0qQoAelIV9qAICDTMDqanZCO1NMXHegBnH92mnHpUqxnpStECeBSQEGNxNNaIHoSKseTj6UhjbtTAqiE0FSOtT7HB6Uu04wRQBXIGMij5h3qx5Rppj5xQBGq5P3qcI/enbOKTBA96QhpjIHFCpxTsE/Sl5FMBuwCgrkcfjTvwpMknigLCBe2KQDnpmlYMelIN1ADsAnkU1gmKDuFID0+WgBeAPY0cCl3KOopML1FAAAM9sUmAOe1ACk5NOO3oOtIYwgdqMt2pxXim7cDk0AAJHJ5oJJpCuKKYhOSeRQMjsKkUDrTSeOKAE570bhig89BTNpPagCTtzimcE9eaYVbGKYYn6CgCXcBQGOajWJzSmNxx1oAfwSBnOad5foajWJgc9KkAIFAhwCgc0oxmm47HPvTscfLzQOwhHcUmCAcjNG1hSjOeelABx2HWgKCM0vrxQMjgUALsBGCKR4V2jigk9M036t7UAMK9eKAnFKOKXPtSAj8vFKAO4p/6imMCegouA07ckYo4HpQEGOlG3H4UxMQkduM00qCeOaNhzkU4RsBQAwpk80CPPWn7COR1pQCcdc0DuRbcHNIc56VPn2pB0JoEQleOlKqAHk07k5pQgxmmAwkjIH4U0Enr2qXauc0pVSKAGh+MYprOx6L1qY7R0xRnFAEO1iORmmGE85OKs9B9aZtB5JyaBEIUDFPVcVLsXGeKacA9qQxpjB700pye5qQ7cdcUm4EcEcU7AMx6dKFQ5Jxin5XPqKV2UEYoBELKc4xTCSh5FTF+vp3pMoe1AiBTntT1C84HNKVB9vpS7cZxSuOxt/2hH/e5oOoJjg815O194u5xpsv18ql+2+MNgzpkwzxnZ1r5b+w3/Mej9ZXY9WN8uM7qZ/aUf8Aeryxbzxmw40+QKOOY6cLrxiOmluf+2dNZF3kJ4nyPUTqMZHBJ/CpLeb7SxA6CvLlufHII2aY/wBTHwKkWTx7nd9inHrhcV0UslpRd5u5MsQ2tD1K5JjhLoOAM8mucudduo1YARqAepNcjNpvjrUWHm21wAOAC+B/OrUXwx8Q3xzf6jFApH3dxYiuuWV4aX2TFVp9ye88ZNEDuvI1PTEYrAufGpkbajzvj34ruNN+E2hWqhrya4un6n5torp7Dw3oWmgfZdKtkPTcU3H8zVwy+hDXlG6s31PHLSPxBrUu+206ZlPQlcD8zWzF8M/EGpMGvryG0THALbj+Q/xr13O3AX5QOBgYprHA5rrjCMdkZu7PPbL4U6VbYN3cz3TY5A+UZrpLHw7pmnf8eunwpgdSuT+Zrc3A846U0n2rRMQ6C4mRQN2B7cVZS8YHk5qlk5o5zSA0heA8GnC7WsrLCnAnGTQM1ftKmkMy+tZoJFLuPQmgDS8xf71KCrZzWejju1SrIB3zQBaOKaVz3FQeZnvT1figCTyyT1pvlEdaXzSO4FJ5oP8AFQA5Ux1pxUAdaj8xacJRjigBSKUISPT1pvmj0pTID3pAJ1xzSGME0bgT0oLj1pgJsGelG0Y4o3r/AHqC60AMOAaFNBYGhXXBoAfnNNbFN35qJmA70ASFgOtMLCoywPfimFsdTQBISe3FCsw61GCT3pFBBxnJoAlDHPPNSq/ziohxUsYyetRIpFjJx1qN8gE9TUhUKKYxwPesL6mpEWJqFxmnscmmE4FUQyM0zLMcEVIxHTFR7jk1rElgVOetL2oz6mkLLj1qkSGTQQ2OlM3Hml3YHU1Qhwpr57UobvimGQbuaQIUA96swrzzVYNk8Gp4jg9azkaRLwb5QDSM3vTOMCkYcda5mtTQY3vzULHGeOKlbpzUL1cRMifkcVA/PWrBNV35PStombIw2DgDmmlj3qULjkVE3ByK0IExuo70qnFPwGHXmmA0D0pSM/WhUIPWnFeMigCIoe5pAvvTyDnmmjk4oEIRk0ECl6UmMjrQMbtxRszyKeBkUoFADFQDHc1Ko5pByfengAEYosAoXvmjH50459KNpIqgGU4D0p+yjIB9qQCBceuakA4pocGnBjjigQH60h4NBz3pucUwHCjk96TJPSgD0pALjPWgEdKCjHvxShfSkMemB3p+c1GqnFSheOlS0UmNOaaWwMCpdgxTGQdqmxRHjJpMc808jApoOe9ADl44FSqfWoRgHipFalICTcR24ppf8KMk9+lIwDAZqUBDJ35qu+R3qaTA6GoTgnrWiJZGPenYA5zS9PpQMN7VYhQOc08BqTjtS5oAaVOKaOlKz9eaTjrQIMmlxj3poJozx1oARkBPShUI5FJ5nzgDn1p5cd6BjxT1II5qEMM04N6GgCXAFNzzxSZp2RjNAheMc1HnDfWn5GKYx9OtAwJHrQDmouc/dp4Y49KYDiF9CKhL4OAM1JuycE0hAIIAxQIRfXGKcCM8igHA9aQEGgCUAdhSg4NMDED1p3JHpQBOB60mVqNQygDOaXBzTAkJ7dqTNJjHU0owe9AhTnPSlBNITgdaQOKAHnNNzziml8Umc8g0wFYZNNYEClyfWgmgBvbikxkUpzTdwA65oATBxQBnrSGTnrSb8d6QDwgHIpCM0zzPemFx+NAEoBFO5qMPxTgwxTAcopwBJ7U0MMdaMnOQcikAvfgUoz+NJls0B2BPFMQ8Nx0pQT+FN3jHNL5i0DHD6UhOKA2e9L60ANJo34NLtJ6UbfagQbgRSFgOBS47UmM0ABORSDPNKenFJ6A0wAZHvUgORUZIFIXAFIBzNgcio9+enSguG7UbqAAjPemFWI4oJFJux3oAjOQaCx6UGTtnNM8wA9KAHbj2oLNTQ8Z7mkJGT81ADvMNHmtUZxjg5po+90OKAJvOx1FBk46VHgEUmR/e5pgS+bxk0bxjtUXGMdqQEDtSAm3CkypHvUe4HPrS5GOhpAAI60bh3pCfak4PBHPamA7IPSkBAPSk4HApD0460CJcqeTTCo7HNM5OOKXBoGOGO9BCHoeaZg+ppVPzdKBNCMoxzQCAvSnkj0qJgcE44oCwoIPXil6dKjC5WgLjvQIfzikJx2pQOOvNNxQMUHPFGKQjPSjaevNADvujtSA54ppUn1pQmOmRQCHEheelAZG70woc85NAj5PBoGx4IyRSgr3B4pu0ijBwSRQICy54o4NJszzSFGAoFYlwMdaTAqLD9smgLLjpQBKNuadtGOBxUQRxwacAwHANAx4UA9aOM03DY6UYY44oAUr6HrSY4zjFOw2OhphDEdCaBjCcNz1ppNP8pic7ad5RyMrQIj685o2g9zUhQhvu0pQ+lAEX8WO1BOOgqQxtnpSGM496AI2IIzQThak8k5FJ5J/CgRGv60oPp3pxjYdxSbCvegBCwpufTPtTymQKaYiTwaAuHIHNNAp4hOBk5NL5RPGDQO5EUyOKaAwFWBC1KIT0xQBVOfWgA4xmrPktnpxS+Q3HFAFQKQetKQehq0YGpDCeM0DsVw3HfikB/OrBi496TyFJFBLIC2VqMnNW/JA5zTTGMjpigCmevtQBirZgUg8ik+zoccgUtRlXmg8/WrZgTGAaaIFznNMRUbd6UgLEirbQn+HpTRFQBB3+nWn54yKmEA79KTyCCefpQB1flUhQcCnktik6Dk1jc1sKEXGKTZg0obHagv8ASjmDlHdBy1IDz1P51HuP1pN7dcUuYdiUmkByeaYWOM0m8joKlzHYmUA9aXO0dKg8xh0pvmOalzHYnzzzTWGag3Of8aQM4HPNCYWJscYxRtyarmVx3xTRO2OOa1TJZa2gUmARVQzymmmWXA7VRJdwuOTSErgY7VnmVwetKJXxQBeL8UxmHfmqnmv600yv60AXBJ6VIspzis7zZOcUomkGORQM0hJ2xzR5x7ECs1p3PRqRZXP8VAGl5nPXml84Vnh3/vVGWfOC1AjVWWnGbAxniswOQOXpjSE/xGgZq+djvQJiayt5xjJp+5wPvmmBpmUjHNDy/L1rM34H3/1oMm7o1AjS8zIoLms8Ocfep2846mgZcJI70BsHNUSzeppV3etIC/vwc5FQu+ec4qAZznNI3J60CJxJxjINNMgzywqqyj3pFiBPJoGXRKPWlD/NnNVggWlCcUAXA+RyakicBvWqaqQOTmpYx83WokUi+0oxmoXlB4zTCxximn3NYPc0At71GeRmlOD1PNMJ7VSJYNn15pnPenEg0wkfhWiJFNAY4wRTA2WwKkAz9askbkDOKAcY5yacQBQBjmqEABPWmN1wBUynim0hkQwGq5EucVCCoPbNToeetZy2LRaC8U1lxSgZHWgjnrWFtTQruD2yajbI4xVg9aYw96pEsqsCTnFREHNWjioWOOa1iQyDn0phHPSpWPtTSc1oiSM9OlJnHOKeWABpu8Z6UxCq59KCx9KN9IX4oAbuckZXFAzk/LTt4J56U8MmOooAhIPpSBX9KmO3rmkyOoNAEQEh6inhW7in7xjNLvB470AAVs9qdh854xTQakBBouAzMmegxT9z46CnZFKCB3p3AizK3QCgiT0FP8wDil35oAYA3enYbFO3AjmngbsUAM2nHNCxDOSTUm3OKkUADk0XAjCDtSlBjgc088UmRtoAaEPc0bSDxS9+aCeeDSGKFJ704KfXmgMM9aUsOxqWNDWDA5zUTK3PNSF+uTTCe+RUjGEMP4qiKn+9Uu7PFJx60xDVB/vVMisB1pi47GpVIz1qWhhtOc5pCp28mn9O9MPTrUoZBIgPUmoGjGepqy2KhfAxzWiJZFs5xk04RAjqaUMOadketWIBGB600xk9zTwwzjNLuoAhMP8AtGlKdOTUh5NAwT1oAhZQASSfwphDMRtyBVnavc04Kp6EUAVljNOEYzk5qwAo6mnEJ2oEV9o96dtHvU2FxS7VoGRg47UDipMUmKAGUEYxgUu00hHPpQA089BRgelPyMYpRtFAEWPbikJOeBUpKij5fagRHuPoKOD+NPwv1pRjsKAISmenFOUEU/bk04KMUwAZxUg6ZqIA9jUg47ikAu7uBQM9MUodad+VO4DSGx0pnI7VKTx1pCQTgGmmAzJ7im9alwpHWk24HWgQylx+VLgUhxnrQAzBNNKGpcelGTnFAEG059KbszVjIxTMLj3oGRGLPtTfKqUgjvTgePmFICAxnHFKF45qfj1o259M0CIQmOtPwKUjHWm4zTAUHHSnAgcmmhcd6PWgRJlT1oBU9KYBSgc07gSAD0pwB9eKYDQMjvQMkwQc5pN2KbuNIR3zQIU89uKQLzSjjvTh+dADCoOKUjnOKfj8KTjPFAxgX1o2e1PBzwaDhe9K4iPaMUbQRzQXBNGSe1FxjNqn8KQgA8CnHI7UmfUUXERsqn+GotgqfIyaaVoAiKr0wKQItSmPI60zy6YDSi54oxilEY65pCD2oAQio9uafzTR1oANoU47UhxxinbwT0pwIPagBnB+tOGOlO+X0p2Rn7tADAFxzQAhOaf8rHGKCi9aQCYU9KQqopxXBFNKj0oACFAHNGB6ikOPSm4xmgCTCjk0u1fSohxxil5pgSFFxQUBGM0zJNHOKAFaMD0pvlikO6jJ9aAF2gUvlg9xTSp7U0llzQBIIwoo2A1FvI4xS7yaBEgT3oKjHUVFu2nqeaN5B5JpDJCtKB0qLJOcEijzDjFMLE+0d6NoNQB29/pSh2znBoETlRnFRlMcZppZmowxoAcAF780vGOTTQhPJPNGOeTQFh+0EZyaUAU3cKQygUDHbec0hPOOc0gmHqaY0uO/NAWH8+9AByKj8084NJvYjOcUCJCrE9aTY3Umot7Z+9Tg+eC1IY8KT/FRg+tM3ehoLY5zk0xDiMH71KB3zmoixI603ecdaQFgMDxg0meoxxVcuemTRuPOWoAnO3pil4zyBVUMSetGTnGeKGBaJGegzTSwHTAqAcnIY00t6HincLFoNz2pC+OarAk96UAEc9aLgWd/IO4Uu7IzuqngE45pcLjqRQBaGD/FRkAfe/WqYC/3j9aeyr6mgGWd428tigFO71W2A8ZNNMYB69O1MC05TH3sU0mIdG5qs0Zx7UeUR9aQFgvERyeKTfCMAc1AIxjvRsXODmgRL5kWenIprSRnoKaYhSGJAAcYoGP82MjjtR5q1FsU+tJ5YyKAJTMo4pDOvYUzap6c00KpJyDQA7zsnJFPW4UDBHNRGMehpGQelKwjrTJTS5PamnAP1pM54rmbOgmB4A70HA7VDuahmPc1DkFiXK00tUW4mjIHQ1LkOw7efSjdxzTC3PX8KbvzUuQ7Epb8qbuNRbsnGeaUnoKnmGSl8CmNNURbtmonPvTUgaHPISCaZ5+KhZs554phKKOTzXTB6GTLH2j0ppuCTgGqvmJ260hlUdOK0ETtM2emKjaeQHtUDS7uc8UmQe5pAiU3RU0guSSagKBz04pVVB7UAWhcGnCbNVlxke1S8DJpgSF8ccUBmGcHpUJb/Ip4JPagB5lPrTC59aMEn2ppGD6mgRICdvNBc9qQH5elRyEkelAEgc9e1DSE9T0pn8IpOAaAHBi1OMmwYFMyKCwHWgZMsx45p/nsB1qsPXqKcD60xE5mNKsxHfnFVzwc05WBBPGaQE/nsRxSGRj7Goy+O1NaTJznpRcZMHOOTQJQCOaqGTPFKD+NMC95y44NHnA1UDcYApVJzigRbE3HWpo5st7VTX3qaPtjrUSLRe8zuajaU54/OmDimkc1iyxxY+vNMLHHWg+tN600iRd350mfWlIxTc57VohD1bA4oD475NNxQQKskeZKQymmcdKPpTEP83pzS7u5NR4pSMCgZKpBIqdT6CqyEY96lQ8+1ZSLRaDHHBpc981ECT2pcMO9YN6mgMxJ61GXJNSYBGDTWAA4qkiWQtk+1Rtx3pzBiaiIOa0iSDEkdaj565pTg9KQ8CtSBvGaCozR1oxz1piDHagjjmlAo6dqBjCuf8aTYO5pzHGMUhOTxzQICoA60nFGT3FLjvQAgAHSk6HAp+KXaM+9ADVBB61IGPrSAZNOC+tAChm/vUbsg0YHQUY68UDEHPNPXNMDYOKkDYGBQIcFxnmnZI4zUaljml79aAHhjzyaXJ9aTbx1oC0DAM396jefWlKjNGFH0p3EG85zRnI60hx2pQvFIYqn1NG4im96XHvSGKTmmMcdaCM0mASaAD1waaM59qOhpaQCqDnrUy1CDg08NikxoeevWkzSZpNwPapSGMeojjvUzYIqJwKtIkbgUYAFG2lNUIbxn3oDEUuMcilwCMUgEJJXrzSDOKUgY4ozjqaYC57Uc5pKUHk8UAITilD+9I2KTA9KAHh6NxxTcUoHrQA/dxS7jTQQe1GaAFJyD1pufxp3QU09aYDgaTvikzSYNIB3tSgZpqjkU/bTAaRjvS5I5FG31NIaQDg/Jpd2RyaYAOlOAHrTEPA70Ae9HbFGCeaAFC80ZPrTcEHrR3oAcWPTtQCKacUnekA/I9aNwxTfekzTAk3+hppNICOeKXqOlMBC3ocU0k5pSAKOxOKLiI2JP4Ug+tPPPbim7cjpSuMOnegk+vFJto2mi4B3pQaT60dqLgBfml3g+tNxkjHWl2+vWi4AJB/tUu7JHWkAx1pQKdxCgt2zShjSgYpw+lAxmT6UpZhilxxR1ouAgZs5xQXYHinGm4oEG9qUSMKM47Uo96YC+Y3rRupOO1FAC5bPWkdnpc0e1ICIuR2pwkalI5oAoAN7U1mbOc0pI9aaTQA0yEe9NMnPQ04r7YpMY7UrgRmQj1pPMPPWnEUgAouA0S47GjzD1wacBxTScnii4BnPP6UhzignGaAwPancQhzSZYfhSn1HWmncDTTAkV2B5p4YE4yKgIPQ5pAACCKdwLWM9DTdpznNRA8cHFBz60XAkySfvc0uD/eqICjBHei4E+0DkmlwD2qvvZe9L5pouBYAWjaMcVXEpzUgkz2ouA8p3GKTb7UzfzmjfxSuA8jA5FNH0phkYj1pu5ieKLgWOMdKTA9Kr7mPBNL8/ai4Epx6UAA9RUY9zTs+9AWHbAfSlEWfSoy3FJubHHWmBK0YHem+UPWmfMeKdgqKLgP+UdqQMOwqP/epNyg45ouFiTr3pD1xTN4AppdivFK4iX8aOMdaiye/NJx60ATErgUmU9BUQxjFGRmgCQFOvFAVG5zUQAIpTwPancCXbHjrQEjPSofpSZNICUpFS+VGfrUJYDilBCqeaLgTeWvamsiDpTQ4xgUBqADy1PNJ8maC+DgmkJU9DSAUolBjTHamkjOM80hA6bqYDxHGepFIYUPQ1GV7ZpMHGM0ASCEDIB4pBAv96kxxjPIpp4FMZL5SjmnCNM8VCCV5NIX5pMRP5SZJBpPJWod2TS7vfFFwJDEKPLBHbimZyfvZoyAeaLgSBFzR5cYqJjzwaTcTQFifYBQF+tQMx7n8qVXYDGaLgS7QGp2wHtUYlI69aUSk+1O4rEmz2o8oHqKYJj+FN85s4HSgCQwJ6UhhQComlY85wKa0jHABouMnECg9qaYF6giodzjqTik3NnvQIn8vHTmkaMY6VF5jY4Bpd7sMCgDdLUbsrUmwUhQdT3riaZ0ERkJGKNwp+wEcCgxgVPKx3InyfamZxxmpzGpHNJ5IHSpaGmQ5NNzz1qwIB36UGEZ4qHEdytuINM85qt+Rik8gemaVmBQaRiSelQySOeF61ptbqO1QNbDPFUoiZlBWByzE0EsCM81de265qPyFB5rqhHQyZXAB4FG3aTVnygp4FOMYrSwrlIpzT1XnAqVkUNQpHpTsFxp+UcioG+97VcKhu3FROo3fd49KLCuMTAI71OMcYpqIucYqfj0osFyAjnpSqcHBp7Y3dKcqr1xTsFxpIIqPIDYqf5SOhAphQE/do5RjC3tTSffrU5jXOcUGNP7tFhEHBHWkBPOepqx5ankLTvJXqRRygVD7cmlHTLVb8pOwpTEh7dKLDKy5P0pwqwqIucL9aeFTGdvNFgKbAEcU5UqyY1x93JFOSIY6YzRYCuVP4io2QkVf8tQKhePJosIqFACM4pwXipWhBPJqRI4wMHmiwyFRSrjNTlUxQFTPC0ARjBqVOMetAVfSnqeelS0UmPGdue9IeR708OSMdKXqOlRyjuRbfWkGAalJBH3aMAc0+ULkZINKRjsacMenNO3cdM07CuRYPpxScip88dKTgnpVWEVnB3ZA4NPHTGKnBHftSZGc4xTsIh5zjFOIPU1KCSaXrSsBCoOenFSoD6UuWBqROetRJFpigkU4k4zT1UHnFKSB2rJou5Ac01gc1P0OTSM2egppCbKjBgehqFicVcYmoXwa0USGVDkU0evarBIPAFIAuORV2EQhgfakJ5qbYvXFN2Ac07CGihsipkTnpUmxepp2AoHJOe9KFNXcLj7oppIxwoosBUHy9qKtBgeqimYU5wMUrARZPrSg4NTbFI6YNAjXGOtFguRDryadkGniMZ6UvlD0osFxARQQSeKXy8U9VAGMc0WAjK0oGaCgJ60oCr3osAdDgUoKg+pqXCAfd5pAiHqMUWAZ5gPagtin4QHpTWIOMLRYBNw65oxzTgg7mnbFz1osBF75pQSKlKoBjqaTgdhilYLjM0Zp+QeQKNwB6UWGR59qTvUmeeBSNzRYCOkNSDFKTg9KLBcjwTjilKnFSK/tTgeaVh3I+celJt4zU/A5oyveiwXK5Ixg0wnmrJAPaoXAByKaQrjO1N/lSn2pR1p2FcbggUA1NgEU0r6UWGQ9zScZyalIIHSkUZ7UWFcjyc9KcC2cYqTIPUUh68UWC40ZPUUuO2KASOAKcM0WAQKcdKXafQ0ozmpgOKLAQAe1Lt9jUvPrQc460AREHsppCG9KlzxQckdaLAQYI7U4BiOlSHI96TcaLDI/mHVaUMfrSsc96bnnpQID260u00pB6hqCzY55oAbjnOKd0pu4560nNAEoIUU4niouTT8GgBSaaTTu1GD1osAg96Wl5608EYzinYRHQMVJkn+GjZntTsBHxS04RkZp2B6UWAj/AAoPpUuB7UwgEUNAREDvSFvanlT6A0gT0pWAbinY4pCuO9Nxz1NKwXFKc9KbtzTxuz1pStFguR7e9ByTTwuaXbxTsAwClHNOCAikC4FFgDmnZ9qD6YoVQaAEyPWgc9OtLtHek2jtRYAxxRS7TjOaAv5U7AIRkYpNpqTaPWl2rzyaBEe0gUpXin7AR1NKEHqaAIsY6UYzUhQDnmgKOuaLDIqQj0NTFUJwMil8kbfvUrCK5AApPf0qx5Oe9NMPXBp2GVyaQmpzCM9aQxoMcmlYCDt0pBUxCA8CgBR/DRYCDHPtQRg57VPgY6UhVaLCK+MnHaneX0p+0dhSgZ60WAiIPYUm0nqanwBxRtXuKAINh9aaUAOanK+5pNgNMCDHPSnc8cGpGVAMAmkCr6miwEeC3bFJjB68mpmAA65pmwE5yadgIipPUU3Hsas+VzwTQYfrmlYCvzxxxS7z3FT+R0GaTyGz0pARZbn0pBk1Y8naPWk2iiwEQVu9KVIANS+XjvTWiJPWgCPPtRyOc0/yjQYqYDMZ70bfen+X3FJtOaQBj3xQDtPWlMRJxkUnlECmA4tikY5GM0BD3pChagBhyO/SkK55GM1KYycUnlN1AoAiCnHNBUj0xUhiemMj+lACDJoCFscYqVBjqKnVgo+7QIqeU2KGibjFXTKp4xTSFNAyptYUYz0PNTlST04pChPamBEAcdqTbk1J5RAzzSGNiaQXImQZpCvepSpB6UgBzyKYiIrjkGjaeual2U0qck9qQEZ9zTcYp5U0uMigCM+uacPm6GnFMjmmhdvIoANvOM0u3JwaXB7Dn0owfxoQDGQZ5alC9s07aSKbtINACFT3OaMDNBYg464oGetOwXFAGOtNK4PWnbc5pNh7miwCKgBPzUjJ/tUYOelIeD2pWAUDk0pHvSbaNpoYCgdeaCnFIBzSnA9aQChSO9BA6bqTOBn3oG3Oc5p2ABgEgUAbuMYpSMHOaRWwc96ABovfmmbCpyDUwYdzTSAwyKYIVTxk00sfTik5HPNG72oBih8D7v0oM+CQFoPQ5qPjGepoBHUABuKCB0pc0hrkNxMDOKXAxzSEZoJC+9SwF4PQUYz9KQvkegoDAj3pDF2470fWm5x7mkBJNJoB3ANNxzTue1GPl9TSsMYcE1GyDdgVKQcZFMZTirSJZXcA1XZRuq08ZJ4qMw9ya3gQyvjFOIz1p/lEnHaneVWpJWMQNKsYA6dKsCPHajbmgCEgGmhR1xVhY+elIYj60rgV8DqBSquatLBwDTjEAcmi4FQxgnmnKMce1WPLFKEGaYivsGaUR5YVOU7A80u3j3oGQ+Xzg0bPxqfyyetOWMdOgosBXVQp5604rVnYo600KMdOKQyDaOuKCo6YqZl560m3mgCMJ6AU/aMUpGDTWOBRYAOFpQc1Geo9Kf14HFAgK475qNlNS7eOaTy88k8UwICAKTHcCpWQdhSAH8KQDVQbuakwoFB2/WlULnmgBTgDgZpFBFSYUDijIpFAoz1pwHODTcY5FKBSYAcdQKOp5FHSk5A4oAeQB2o4xTGJx1oGf/1UAP60YB70z5h0o2s3B4FAhGdRxTdwNSCNV7ZNLgDHFMBoJFODjPQUn1pQM/WgBxcE05Gx2pm3nNSIM81DKRZUcChx7UgJK+lBJ24rM0uNJBFRlsDjpRnjmkKk1SJIpH4NQhWbk9PSp2UDtTTngCrRLItoFIFx1qXYabjnrVkjcc4o4B56U/HrTTyc9qAFV+elKxBpu3JpSB60AMPJ60EYpcikOadgAD2pwA9KYCewpwDA80xARSdOgyaeeaQED3pAJnnpinA0ZA5o3UwAkUjEhcign1pDyKQEOSWp6gt3oGM9KkRcDNIZIFzjmlAHOaj2kn71Pzx60wEIGeKTaKULS9e1AhpHFKMGjBz7UYApgHSlxkZxSgdeKcRxikxjOgpNwxTtoxSBT0xSATAI60nGetOIINR+XzmkMdtX60nfmlGMcUhKmgQZHUU4e1JlaNw6UgHc0jdaUMPxpMg0DE/GmFMnnpT8+1KGApgM2U0rxipcg9qQjvTER7SKdjnpS80mWoAa3NM6HjvUuAaTANMQmAR15pAmMc07AU80hGelIYBRmn7dwpEGOtPL7aAE2gdaDTQxbnFPoAaM56UhzUmc01iBQA3IFLuAHvTcZppVuwpgPY800kDrTWHbNNPpSAM5zQMjGOtOXFPxzxQAwAnvT9lC5yeKeKLAN8sE0nlADANOI70hOKYhFGKlCjFRqfanDcegoAXAHSjnFKEJ9qUDBNACYzSqKU4HSgHI5oEKetJuPSjHFLkZxTAADtpQMUYIPFL9aLgNduOlQEkVPuGKjcD8aQyMZPNLkhuaO9OwN3SgA4FLwTxxRxnGKXHFMQwZBxTthPSjnFCnFFhiFCOtHToKkU5604xgmgCHbk07aKUjBo4JpCG4yMUbcHjpTsAHNHemA0IDQVAFPoGPWiwEVKDxxUoA6VGyYPFACkZppXjinZ6U7OelCAiG7OKkWmkc0uc0AGcmjdjtQDQenAoAYWFKHyaY1IDSGT+aQtMLHntUfbrScnvQIec49aZnk5FODcYpCc0MBuB1oznilGT1pcA0ANIHbrRg4zmnBQKQigBoo5HalH5UZNACFSSKUD1pNxzilzxQAEc9OKT2pRnFIf5UwE8s/WmhRg07Ppmjk/w80ANCDvRt5p4VsZNOUHnNADRxS80/jpimhuKAEK0hBJ4p2cdqQY/GgBpz0PNLjinAcZxSHp1oAbjFLhcZ70YIpooAXjoKRhxkdacDxQcEcUARYBNIRzT8E9qMNjpQAnvSZIHFOI5o2igBu/LYIxTvl96CoxSY44oAXAx1pAfSjHqaAAO9AhSecdqTrTgO1IBjmgYmKNue9OBzSEZ6UAMxzwaf3pVXtin7elAhh5oGcYzT9nNGwdqBjM9qXjPNJtOfekKnmgLDsZzxTQoGRQNw6HilzjtQAYX0ppUEfSnA0vFAiLy800rip+/FLwR0phYrlT6UgQg59asFFpNuKQEG3nmg8cdasGMHmmmPigCH8KaWqVkOKaUOKAIjtznFKMelP8ok9KcsBz1pkjAo6YxTggxUoj4wabsNIsYY1PPSoynsDUx6Y7mjgCgTZW2gDBFISAOlWSu7txTDF7ChiRDkdSuM0o29wKeyc0mwEdKLDYfJ6c03YmeB+VPC88dKXaQeBmgVhAiYOFxSpApxTsflS9OCeKAF+xZ5AyPamm22joafuYDAY0okb15oAgaFT2NAhTOc1Z8zHUZoOxvrTAplUB96aQAcbasSRg4wOajMbDpikNG32xQQacq96NjHrXGbjA2OtJnJqXy+elLsPZaVguRc5xinheKlEZ9KcIiTRYCAR0oj55qz5XbpQsR9KLAQBMdKNhqyECmgrnoKEgKmw9zSMmKtbR1xTWX2q0gKTKw6Uw+4zVtl44qBly2K1SIZAFz3oPJx3qbZik2cniquSMxmgR80uCO3JqRUOM1QDNq46UzZkZ6CpynNG3NKwEOCBjNGM1NsXPrS5UdqAIBEc5PWl2HgVMeeTTWcgcCmBEU2t0pRgcgUpBbrTwAAOKQDcNjpRzinM+eBUZ6ZpjDPcnIo39qaKQjPSkAu8UxpMUCM5oMeenSkA0yFjRweKdtx0FKqAnmiwCAeoqQFQvvS4GMYpOB2zTATdnrSFmHpilb5RnpmkALUxDTk8YpAuBU232pdoHvSGQDOeBT1GGHFPOB0GDRxikApoCCjd6UoYUgHhQBTSPTpSbgKTcx6UADkKOmab1GcYp2OxpTtAx3oAYAWqRYwo5NNQHrT/l7nmmAmP7tOC5HNBcDoKRpDjFADtoFM28gUnmHdmjee3WmA4qBzTd2OgpRk9acV9qAIyxp8YY9eKcqDqakUZ4FQxokXgZPWhjQOmOtBGOtTYsjOOwzTcHvTtwHSkJNAhGGRUbY9ac5OfaoyRn3q0SwLYFRs3HHWnn6VGeTVCEyfrQM5p4GOtLkUAIo55pxj9DRuA70jOMcZoAQp+dG3vTTIcdOaYS59qoRLgAdaaSPXmmCMnqeaXbtPSgBScdKQA59qN3JzSFjj5elIAILdTSjgVFufOcUm5qALAwetNfAHBpiE0pjJOc8UAOTBqXcAPemIoU5NOyCaLAGTnpSg4NJk5OKQAmgB5IFIH9OKTp160gGKAuLu55pR70YzShaBgXwcUDmgDmlIIPFJiFAA96Xk00c8UDg9c0WAX6mkODS8GkOM8UDG9qdgHtxSEc9eKD7GgAaNWHpTDD0O7inDPPPFJ65NKwCcdM0u04o4FKTnpQA3OOtJkd+KQ5H0pp6c1QEgOaU5ApqsoHBp28HigQZz1pO9GAaAoHegY4rSbTkUBwBzS+ap70gGMCelKoAA45pcr60hcUwHH1ph560F17mjco5zSAeoFOGDn0qPeuDzQGBHWgB5amkg4pNyjimll7GgB2PSjOOlIG96XimAzvRsyM04A9aWgBuyjBp2cGlB74oEJg9qUZFKGx2pMj1pAKVzRtAFLnjBozTAVVAp4wDUZxijJFAEjEAYFMHNJuH40qnHNK4CNmgDink56UnOfamIAc0gPzc0oBpCaBknXvRkHjNREccU5VxyTSAUrnpSMop4YCo3bLdaYEYB3ZA6U4YzmlD9scU0nnpQA4g9qQ5JpCSMYpN5zTuA4DnHalx70zf60m6i4hwbbml880zrSbD17UrjH+ZntS7qiwc08HAyaAFzz0pwx1NIrDPpQcUxD/lK9aBx0pvHNAYDigBTRzmkHfJpcjGKAEK5py8e1GecUue1ACsAw5pu3il3dqaSQetADSvNKOOtGT7UtAxm0MeaQxjpTz0pACaQhmwCmkVIQRTaBjCMCnDB60p5puOtMQ/FITg00kgUZI5xQA7PPSkxQGHXFGaQCEHNNxzTt+DR5g6YpgM57U4L70u7dSFsdaQAQc8UnQ8il3UZFMABzQATQGHYUpbFACYpc0m7uBxTW6ZxSAeGwKQ460zfSg5HSmAuMn3pehpM4ppJOKQEmfypCuaaCRRuOfamgF2tnrQT/s0Bz1oyDSAYeDS49KXAJo7UXAQg/lTsU0mhW4560ALgGjB7UgPPNOHXimA3GaKXkU3JFIBNpOec0YxjHWnYNITgZoAQ5oHvRnoaM4PFADuB9KODxTSe2KKAHgnPWnc4qJWxTt2KAH49aCKbnOc0mcU7gPAHel2+1R5PrQGoAkAHtS4Wo92R6UmaAJCoI7UwjHSikIxQAdPrSZNO460FQaADdzQHHejHGDSFfagBxcE8GkPaoyvPFDH0oAeaQDNN3cUBgKAJNvtSgEcUzzQKPNyfegQ/pxTSMim+Zz0NKZOcbTQAmym7eeuadk55FGCegoGIVxQRnvijDntTSj5oFYQimGpNjHqRTCjYxmgAHTFHb3pPLOOTSeWT/EaAH7Qe9N6cU0pz1NKYsnIzRcVhQyg9eaXzB1OMUzysdqPKBHIpjHbwe4BpA4z1ppj4OBShcjJ4pAIZVB65o8wH1pdgGcDIpduOnFAjpRFntTvJ7kVEtyeOKQzsetcrOgseWMUuwelVhO1KJyDyeKVxFnbk8UpWoBOAMk077SPrQBMBx0pdntUIuBjNBuhQBNt9qQrke1RG64GKUPu/GmhjiBjiomGKlJ4qNmAFUhELAnoKj8sgZY80+SQAVXac56VoiGS7ARxS4VeKg8/BFSeYMZNUANjPApecUzfnHFPDZ+lAhNtMIYnFOZjn2qNpCDTAcFI6mmqwJxSGRe7VCJ1VyRSAnIy1HJOKrtc/N0pBMwNO4Fk5UZpMlqj884xjk01ZdvalcZNz6U05zzTPtB9Kb5pJ5oEShcninbBmofMIwcUvnY5xQBIU9OtATsajExOcdaQud1MCYgYAHJpFUdai8w0GQ5HU0DJSRnANJnsPzpgGeT+Qp6jHbFIBQq9W5IoLDPFJgmkY88CmApOOc00yccU07m7U0ZzQIduZvapVWoCxzgUodx0zzSAlICjBNNDAVGC5owxpDJAcnNGSDUfzCjD9DQBIXA6UqtmoxEzHGamEBUDFAATkUBQD1prIwPJ5ppViefyoAm+XtRtB+tRBDilEbn2piJAFzyaMoCaTyWo8l80xjgy/SjzBTDEc8mlMfTBpASbhTkJPGaYIwOpqRAB0qWMkB2jims3ByaQ7s0hXuagoYzelN3kU5kzTCmKYCGQ4qPeacVpNhPGaaJYx5GNM6d6lMeDTVizVCGgnHWjv1qTy+etHl5NOwEe4ZpSx5xT/ACBRs2jrTsBCC38XWjcT3qQoCcZ5pBFjvQIZvINIWY/SpDD2zTxCPWhgV9xPSjJAqwYlzikKKBgUrAQZJFHPTNTCPPalEaA807DIlY+nSlMjZ4FTBUH1p48sD3piKuXp67s9KmBUnAWnZ5oAjCN1zS4NPIJ6UpGBQBHgk0c546VIvNOKjrQBDz6UuWHepABQQM0ARZJpCTU+FzimlB6UmBDnHOaA2OamAUdRzSEL6UDIsnrSZ96kIXk0wFc4HWgBrZNJ261MQMVGRRYLjCxBoJYignaeRSbyc8UWFcUZzyaU5GKFDHntSgHoeaLDGEnnrTdxPvTz1pjZ69qLCuRlm7Uo39cGlDACpQ4IoAYGb8qCWoaXnAFAJNIYzLZxQufpTmIxTRycUAPCk9DTtp6EUqALTxgmgRHtHpShM049sGjdzTGIsXPtTjHwT2pfM4NNLgjrxQBFznrSgGnYBNPCkjmgBmDT0OOKeF9qcEBOKLCGHI6Ub1II6GnOmBTQo7iiwDOaASKl+XFMODQAm7PFNJwc0/aD0pu3nGKLALknFAYk04JmnqgHFFgEBB6igin7eKa2BigQ1gAOvNC5oIDGjBA4osMcMjrTj0pmDigZxzQIlBpDj0puT0pelAxCOM0Hp1pc4oIz9aAG4JpoGKecjpRgE80gGHNHPSnsMGk4HWmA0rmkMZxxTt2BigNkelAEe09xRgipc0h54NAEdLilOFNNznpQA8LwM0hA7UwODxnmnc44oEBX5h6UuKTnFGeKBi8A8UYy1GCacPloANvpRj86N2KTJzQAAHNLyOlAFGKBAc0AHinA80pwaYDCvfNGSMd6f1FNKkikAYz7UmTxzSgEU09aYC+pNN7cUvbNAGc0DEANNI9KU5B4pM4bpQAw9cGg+lSFgeMUw4NIQgPFDZxxSEc0wuRgc0AP6mlKg9qYGBPB/Cng4oAMYOKAMnpSg560dSKAFK8cCkCkZp2SKTeM0wGkYOaaRnin5zRuwOlIBoB6dqNvan5HpSYoAaVGKQDAp54o4xkUANxnrS7OKOCev4UvQdaAEIPSmlakB4pCeM0wGY4oyAOlKDntRgGkA3rS5K0u3BNKcelFgG5zSFeKU47CgjA4oAQimjIbinHgUgHPHNAClvfk0uSaaVzSbGHQ0wHcmgqaQAg8ml5z9aQCEe3FDU4AY5pcCgCIYpe1PwD0AoyDnigCLPHFLuHenjaB0oAUnpQA3cPwpeD3pxUYpoHJ9KADbRjjGDTun0pegoAYRjpSEHPSn54wKMYNADMnilw2evFOxSYx1oAbtbuaX5getLx6UdO3NMQds0daXIx0oDAHmgAxz60CMEZNLuFITkYzQMPLX0pNgz707PFJ1NACbBShRRjI4NHNAgKgdKMH2NLnGM0h45HWgYhB7ijv0NG71prToi5PBoAXOKdyRVRb+2lcxhvmHtUpkPVQSKAJ8eophqMzt/dNAm9qBDiAe1Jt7U8OpHQ0uA3egCMp/wDqo2kkc9KeE5pMEHrQMbg0mDTwSaQ57UEjQpxTvLBFGSKD096BiFMcDFNKEY45p+CRTifagRuiBfWgwpUmc80hYda5mjcZ5C9zTTCMdakZ+aa2SAKVgIvK9DTvJAqRUwetOCge9IZB5fNOEQ+tTBeeaCMCiwEXlqPrT9vFGADzS544FNIBM9qafpmnhT6U1hiqEQuoIqsUUNzip5PpTPLySWrREsiKAnNPwMUjDHANIFJqriEMgzgU9TTNgB6UEknHQUIBzOuCM81AxBqQIAck5owo6DNMCHYCelNMI3fdq0Fx2xSPnGaQhi269SKd5SClU5470pB+tAEflpjJppQdhxT8YPPJp4FFgIPKBoEYHQVOaTOKLAR7eOlMKjPvTyxbpSEHNADNoB60mRn1pWBzinIuO1MCPaT0GKlRMLmndeO9ByBigBAABnvTugoVfWlZRQA0HjNIckYIpeKbuwKAEOT9KaeKdnjNMPzN7UAGQDQDntS4BNHXikA4MBRuwPrSDHShRSGNyc09clv60mDn0FLuxwBTsBJuxwKXeWGB+dRqGZhnipgoApiGbT3NPC06mmTnCjNAC7akVcLk1GCRyeKRpgBigCQsByajeXkYpmSaT3oAd8x5JoJOaaC3UUvNIBw9zT1bApg+YUo+Xg1LKJc5HWkPIphkVTijfkYAqRing0x2O72px6U0DByTTBjSeKQmlb6009KYhrPio/MYnrTyBjpUR471QmPV+MmpN/HFRAe9HeqES7jjim5J60lKMY96AHYA5xSjHpUZJIpQpzycUgHbgO1JvJzgcUY5pQaYDTkmgjFSYA6mkz2oAaGwaGYGnMvHFR8YoAcuM80EgdaaKXg0AL5igDFLu4qMAAnApxBNAC723dcU8OMc1Fg07HrQA8Se2KBLn1xTdvNKcDigCQTLyMUbhjNQE5zTckd6ALAIJzTt4IqsWIBqEyN2NAF4svHIppYGqe5s5NOD/wB3rQFyc4Pf8KbtAOR1pgPf1p27aOaAHb8c0hbNQtJzxmkDE4OaAJGHPNAO09OKYXNJv/WgCbzMjINN3t+FRM2KaJSAB2oAm9yajLFvpSebuzgUIGz0ouA9Ys84qTy8DrTkbikYluKAGcHjFIFOaXBFKv6UmMDHnrQEAPrTi3ak5H0pAO24FASk3ED1oDZBzQA7Z700qN3FKrc4o70wDaOlNMa59qk70EUCItgzwaeCwo8vBzTqYD1PqKcD1pi5FLnBpAOzxTRzR70A460AG0GmFcZqQHmncfjQBCBxTivpTttIRg0AKq5pwGPemhscU7OKYh3WmMueacOelB60gGYFKBxmnYpCAaYCYDdaTA704DHSkZM0gGZweKNxoZTxQtMABJOKXODRt4owelIBd4oLCk7Him8g5oGOOTTcjGDRQDx7UAB56UCgHFB+maAEPrRml2gjHSkCY60AHBNLs5oAwaXcB2oERtH7U4elPDZGKTA4FAxAM0pUdMU4ADpSngdKBDdvPtS47Um4Higk5HegBdg/GkKYpwpwI6UAM2nvSlT1p5IJoJoAjGe4oFOOD35ppOaYxOc0u7jmmjNKPcUABPHWk6U05pfwoEJuxQDRtPrSD0JoAXOT1ppPOKdimkcnFAAcdqTbkUowaUCgBmOxprIKkYHNMOehpAReVg5HWnjI96UdaXpQAowDRRig4oGFJ+FGSD7UqnigQBqCRikPPTrSgYFFgAUuaB1oPHWmAhx60EDsaMCjFIBuOaXHNOIBpMe9ACE0EN2HFBQ5607HvQAgBAwRSDIpwJ6U7FAxmaaDu49KcVyaTAouIQr6Uu3FHGeKXJHWhgMZT+FJtxUuQRTSKAG5oB9aXYSOtGOKAG45oyRTxSHigBO3PenZAFN4xR7igBd3HSkB5pSKZkUAOxR0pOvQ0vbNMAz+VNPHehgfpSHilYCMzKODwKeJAw+U0hQN2pQiqOBTAcGyKcCM4NNA/Okwc5pAO3YPtSk5po9xTzj6UBcbkUZoxzkHilwMUABxik4PvSgelBFMA2gjikxTs8UdqAGEYNKCT1OaUjg03JUUCY7n6U4D3qM/Wgk0AiQgGmAGmgkdKeDyOKQxPqKNobqBSsw/OkBUmmIaYY+uxc+uKQx56Ej2qQml3dO1AEO11PIzTCMGrWR3ppA70DIl6VIEzwDSbF7UoDA57UBYArDrSEGnq2OtP6mgCLABxijHGakZeeKQDtQAzaKAOafj2xTQpHQ0CAgikxnrThnGKDRYDcycYFAHNKDjpRvH41ztGwgUZp231pgl/Omly1AEnApQwNRgE4z0p6iiwDh1pc0nHWmk5OKAHHH1ozz0wKTp/jTSwz60IB7PULmnHcRgCmlTnJppARNmmH0JNTlaibANWiSPaO1J0OSaGcA+pphYt2xVWEOJzwBRsXPJ5pArHG6n7ABmgQpVB9aQAt2oJ7ijfgE4oAXyyTnNG1dpwaiaYsMU0SHpQA8jB46UocYxUZLMPao87TRcCfKk8daXcB3qMHI4pwWi4A7egqMBiefyqXAHJphYk0wFCCjGegpQDijdg0WAbt70hYAYHWlZsLk0xPU0AOBI6CjJpQTSdaAGOSOnWnckZJ5p2BmjA/CgCMg8YpdhCnNScAcUoG7qeKBEDDHemjj1qwQmetBA7UDIlUmnFeOBTwVA5oL57cUWAZsIox69KcX7AZNNCMxy1OwCH5unSnADsOaeAFoMgXoKQCBcDLUFsDApCWkNOWMY5pDEClhk5xTwu3pSkgCms/HFACtg9aYVGPekLMacoOaYCiMn6UGMKD3pS+0GomckimIcSAMUBc0xR/E3FSjBGRSYCEgdO1MPzt7U/buJ4pAArVNhkawZbknNT7Ng4py4xnikLge9KwyM5ZsCkbKj1pxJ7VGc8miwAR3JpjEA4pCT3qMEls9qaQh2TnFDL3NBao3Yn6VQiT5VxzThVfPFLvPamBI+TQBjpSLuJzTicUgHg0pfmoGl2jgZNAkL0wJ6XBpiMAD3NP8AMBHNAClOOaTIApu8etG3NAg3ccdKQJu70/aO1KOB1xTAaExxS4A4oz1pOKQCgDpSEEg8UucEU7fx06UARjgc04EE5oxxTT04oAez+lRMxHOM0ucc0hOe3SmMbyetKMAdacFJ5oZD+FIQxsEYzUbAnoMVLtGaY3c4oAaRgcmhSBzQWycYo25+tADi3TFIWLUoTHNO49KTKGbM+1Gz0p4OOD1pT3xQAzy+BSCM1KDxz1pGbnikA3ZmmeWM808mk69TQAgQA4AqVRjrUecUpcmmBIQMdaUjGD3qHkjvTlBI60CJCM0YC9DSKOPWlZMjpQAnBpQM8U3BGOKcNxOBQA8qMU3j8RTh05FIcZ9KAG9+nNOIFGOaB1waBhjPQ08AY60zHOacT0oEDZ6UgHNHfNGQKYDqQj3o3Zo6ikAuDTSDTwfXpRwcmmA3kUq5BzTwBzSYBpALn16UnBNIPekyc4oAeR7UlISacMUxBzSZ55p24Uh5oAXODzS4zzim96evTikAhHejI704nAwajJz0FAClc03bjmnA+9IzYHFADCaCxqPJzzQeKAHEnr60ZHemZNHJBoAf260g4qPLZwacDxQA7NAJ9KQ8DihWNAEm3NJt5pN+O1Ln1pgNJwfUU7hqaQCOKAMGkMUjninZ5INNBpWYDGTQAbfel+YcmlBBXg5pDmgBpb5skU/cKQAGm4weKBEmcUMc9KbnikDjNAxwBFSYyKYenSgPigQu3n3oPrTS4pu6mA4ilzke9M3GhuvFK4xwAoxk8VGHOcECl3EdKBClDQVHegMaduyeRTGNxj6UnHalIzzTaBCHjpSZ96U8ikxzSATBpp+X6085xxR160hjM56ijtThz1oI9KLgIPajtS4NLimIj6n2p3bg07GegpNpoAbgUoU+tOApRn04oAQD1pWFGD1pSKLgR8ilFOIPYUh+lADc80cetKACaQ4B4pgLuAoDZBpMZ600HtSAdzupc0m2lAx0NIANJkCjJ7im55pgP4pDg0mfWlJzQAY96aSc04Nik3ZPSiwCjpTjytJnFA+tMBp+lHGacw70zB60AKQKbxmjd60Bs8YpAGQT1ppABp2wYzRx3FADRx1p3GKRh6UuPemAh6Um3NLtNABJ9qQBtApCfanlcU0g5zQADp70cZwKT3xSZx0piJOPWkIGOBQDkc0ufypDEo/WnHpTcEc0WAAelDE9AaMUbSelMBODTh6UgUjFKB3pMBQBSFc96DS8Y5p2BjNu4c0AVJxgUmOeKAGYAoODTuKTbzQIMjpikUDNO2nvQF54NAWE2knilCHigZB5qQE0DGFKTac1IWpS3sM0AQ7CDxSHPTFTqwPanHB7UAQjHTilPHSlKAn0pNoHGaAG5OKUHkGlx7UlAMQ5LUgbBxTsZzzSFaBWELe9MDc08gCoyMmgLG71xQRmlHWnYyPesTYYE5p6rjmg/WkJNSA4mgEk9ai75Y8Uebj7ooAmOMU0kAcmomkYngUgQnknilcCfch6mjcAOKYEwOBzTsED3oAcW4qMvS4701gO+KpCI2YkVE3PWpWORiomX1q0IAq0ZAppzxzR61RIu/iml80hxnmkLCgBRkGkJzwKQAseDTshMd6AGv8AKKaFLe1OJGcnrQWzwKBijA70jLnpSY9acOOKBCKKcSaAQDimMPTigB+7Jx2pM9cUxSTxnj1p4wOBTAN4z0NIWpCP1puCTtH40AICXfnoOlPAGc0gXHHWnBefSkAGjkinfhSZAH0poBAp4pTjNMDkmm5/OgCTim5NNOT1pUU0APAX8qCQRgUmzGSaZkngcUwAkLmnR/N3wKBCD1PHrSl1UbVFICX5B6Co2kJ6cVCWJPNKATTAUsScU5Uz1pyR4GTT+2BSsFxV4FG7HemgkcU0igB+etIAOtAU4p2OOaAuIBxSjHc00kdaaVyc0AOLLTT0460m3mnBfmp2AXYCvPWnFQBS7e9Mb0oATdgcUwtig5pMetIB3mfLRnnijaAKAaQ7ikeppM5FIxwCai380WAeQD3pu0AUgbOadg5xigBMCmOMipMZOKUjimIhVABk07A7U8HjGKQcUxCqmaikUscA1IWpe3FAEGznmlwAMZpzcim7cnIoAQY3U/BYcUoQE5Ip4wKBkYTHWpeFpM0h5oEG7J4pCcdOTTgKCBmgAUZ60/b71EW2ikMh9KAJcc9aQkDvUYYnpTtm7BJoAaXOcU5cnjFAAU08NmgBvl+tKMDvTiM1GeaAHl/amMxNAzRgd6AEIOaQgHihm44pM5GSaBiYwcAUYwaQuSeKcc0AOyB1pOvsKAMDJoPSkAzGTnNHU0p5ppzg0hgSeuaaMscZoweOaUA9qLCEIIHWnKMgZNG3Jp+3HFNILigY7UbcDpTh0pSRgCmAoAx0oC4oBxTiOKYDRhc0ofNMOQaM4pAPLCkDAHGaYSc03B5PWgCZnGCBUZfnBo528Uhjz1pAO34Gc0gcE0zZimlD+FMRPuAo3Co8HFJtPWkMnVgfwpSajzgUm75sY4oAfnp6U4etMyTTh7UAP6UmD2pOcZo3ZHFACjI70Z96CDim4GaAuLknvSkcBqaAfSpOSKLANJyM5pRnNNUYJHanAYPWmIUcmlJ4+lIKXFIADc0qt+VJkDgUZxRcB7HimU4fdFJgj3oAOQMimNnrUoGeRRjjkUAQ4FNJ7VKy5PFNKdaAGEc9KMil6DFJimAEZpp4NLmlJoGNyR70AjOaCuO9JSESHB/xpQB0pi9MUu7mmAYweKDxTs0cZ5pANx8uRTtgYc8mlwBxS/dFADQmwYpxxikLelAwRQMM5FJzn2paCMnNAEcyOVzG2DUSCfdhwMeoqyDk47UoosAhzt45pMkY4qXFHUEUCIhScHpTmX0pMYoGH1oJGcUgPNJgZoAUjjOKAO9OGMUvFAhoGcilXjg0YwaTPPFMBWOOlRng9afTce1AAvpQeKOtFIBD0pM/LTgPypCueBQA0nIpuD3p2wjin4yOKAEBpeBRtpMHNAACc8Ub/WjaetIVNACbu4NKGGPekx3pCvpQA7JoJJFNFOHoaADmlz6ml68UzndQA4nPam5GaXBxSEe1MBaYcbqUEjjFLwRSsAbsUgIPSl4ApCuR6UWAM5HWmmnbcUuMDnpSAZRk0p69OKOozTAOozS4pOcUoGKYCH0pBkHrTiCBTMcUgJASaU/SowOacDjqeKAAjmm7O4qReeRQaAGYOMGgjPWnZpeMCgBmBRjjFKw9Kbu7UAG04owaUMewpf50AN5x3pQMCl5pcZoAZtFIVOeKk24PFNKHrQA0DjmlHvSZK0E+tABS5NMP1pR7d6dwF4zSg4NIB+dKR3FIBdxopOlByOlMBcUpHGKaMjml/CgBSPejANANJnnkUAGBnGaWgkUpxx60ABOVxSEdx0pc8cYpuTQIXHelxTN1Lu9KBjvw5ppIo3YxxRuHpQFxCeM05ScUcUnANADi3OKaabkCkL470gH59TS/e6dKi3jgmlDjnrTEP78UmDjrSCRcY70bwOlACE+tIcGnb8npSZXPSkM3uAvAzQT3qEzegpu52PtWLNSUso6momlJ4UUoiLd6lSILzxSsBAI3btUqwkD1qbijeAevNFguN2YFGMdaaZMmjcxFADwR3pDIBUeD1zSFc9KYhxck01gDijOBTC5osAMcdKhdj605m7VCzHPSqQgLc0A0meKaTg9KtMQ7qMk0i4J56Ui5Y88CjGfamIezAcCmAEnJpQp4p2MDmkAzHfNH4VKqgdKQrznvQBGCxpRz1NLjvSY9aAF+nJoKlupozgdKaWIpgGMfhRnFABPWgqcZJoAQkmpMADH5mkWPauTSnkdKADtSqpPJoAyOlLuxxQAvGKjOKdnJpNuT0piIyAaNuWxjFThQB05pCQq89TQMYEA96cXAXAHNOCEr6CgIPWkBHyRzQE2nJqU4T61AzmQ4UUwFdxjC1HtJqRIiSBUvlAfWkBAI89BzU6oFHNOUYFMZqYCsRjFMJNITS4oEHOcU4LzzQB+dKKBgSc8UnbmlIPajBY8UriGU4Gl2ml9qYw4ANInOSeKQnil6ACgQpYUh+lDcAUmc+1ADeD1pAA1OwM0/GBnFIY3ZjrSFAee9SDJ+lB4oAhYZGAKhKENz0qzke1MZ1BxnNAEOznjpTskU7cCaG5GBQMj39aTeM0bG6UCInvTEG8dBThQIsU7bxwKAGEHApMnpUhGKaQSaBDDx9aFJxzTwuaXAGaAGZJFA4NIX9BTNrsOvNAyf5eKCVH0qvtfPJp4Q7eaQCmX0FM3FqkCqR0pwC56UWAYqFqd5QHJNPBFKzACgBnApRgikJHUUmaAFODRiig9PSmIUHHGaC1Rk4+tHJHFAyTd0phbOcU3B70YCrQAvWmEe9LuPamsuTnpSuAmcDinAk03ae1PUYoAd1GKQg04EA0FsUJAJtpGGPrSck5PSnhelAEZAz0p64p2zJoCYJFMQYyaXaRSgYFLx3oGJikOKecN0pMcUAIvJp/vTDwRilyTTACMnpSbQT0pQeaUYBpANIx2phxUhOT7UzbzmgQ5R2oPvTgCFzijHrQMi6GjcKUkdMUzAApAOzTWJzwaAC1GBnFMBQe2afgUzG2ng80AKB2p+OKQYzzxUgXIoAj2kjOacABS42ijjFAhcgCm8Gm55NIKQyReeKUihRgZ70vTrQITGD0pMEH2p+eKQjIoAAeaUgEUADNOwDxSAj2jFHWnMoWm8igB4AxzS9qaSGXFOXAGKYDQcUuc0Y5JpMcUAIeBmg9M0uKaxwMUAA5FIwB7UA470H1FAEZU0bTTzzzig80AMwaQj2p4OfwpCPSgBoBI44pSmDTsnNLyeMU0A0AGlxg9KXaQc07OOtADR976UFqQjccg0mcUgArnmm7ewJp2cilHSgY0CQHggilJZeop4HHNJwe9MQ1Tls4p5PNJwDRmgBaM4HWk60E8UgEyT3peo5pozS9yKBgyikK+lKAfwoGfwoEAz0pTnGcUnmc4NLuzQAhPHXmgUEZzSH2OKYC5FGOvNNGR2p2RQA3GDkUuB1owCaTp0pAGPQ0GjoaQ0AGOfWjODmlXkdKCBQAm4E0dRSEDNGMHHagBfxoLUg4zS7e9AC9qCopMGjHqaYCFcUoOBmkxS4wOlAAOtLilGAKOKAEoxQKUUAMI703FScGkIwRQA3GKUelKRmkH6UAOGMYpDgUvXpSEmgBuMUlOpO9ACA04EZpKCMUAKSD2pDikHHalwCeKAGcGlxnpTiuabigBRx1p3BFNpwOKAG4NABH0p4PPvSnHGRzSAYckdKQrTiKTFADOQfalGTTutGB24oAPc0ZpDSheM0ANLH86Ax6ZpSpo25+tADT9KQgnrUm2k2D15ouAwrxShADmlIPQimjrQA7kHpRS+9IRk0CFxTTx9aUgjnPFMGcng0DFzRuozgUn4UCHdKN1NBIpcjPNAx3HAoHJpOCetFAhwFGOetNBOaXJxjNMYhXJ4NJtOacOnPWjdgc9aLgNIx3owOKD81N7UCJOMYpOCvSm5oGO1ADiMdqjODUn0oJBHIpAQgjOMZo3nJxwKk2KelNKc80DGkjOKXcB0o2dTimlO4NAh+/2pd/HSosFTSjJOM0wubax469qkAUc0hOaOmDWJqPzgcUGT3pgX8qdtAoAbuLGnBacDijPSkMbtwacF45o3gZIpPMoEKRgdajznIFIWzSEnFAAxxgGkIGKTPOaazetADWHPWoipJqU9aYfSqQg2Y5JpCBkk0meaPcmmIM+1KOtN59Kd0FUAN7Ck/jpC3PFLtx9aBDxwaTd6DikY0nUUALgGjjpSZ6cYp+0evJoAaBnijaAKcBilC85osIZgnpQvJ5pT+VA+lOwxcZ69KAMe9KAW600t1xQIUn86TaSM4pVQk5NSDAoAYseOaeVFKDjpzSEDrTAQgtwo/GjCqPenbtoqMnncfypMY/k9elNdwoqJpGYkDpTQjN16UxA2W96kRCBk8U5UCrjvTu3NIYA0E96TcBxjNB5+lO4hhcnik7U7AB96ay5I9aAI8nIqVT60m3FGD27UXGSDA5pQcCoMHdk5FSA5pAOPzD2pV4FNA3HFS8AcUAIQSKaeKeWwM1Ex7mmAgG5ue1PwAM0iAkZNO6GgRGw55pNpJqcJnk01sDjrQA3aBzSFsc0E0mwt3oAaZcDAFNIY8561KIwDmn4FAyoyMTikEJJ5NXNlG31pWAhWLFKwC9qkOD0NIcAUwIscZxSdulOJ6UtADQOKXHFOpjtimINtNIwfWlGc4pcCgCMqSaSQBV5PNSZ5PFQn53z2HSkAoUClB/SgntRtoAQ4BpMg0Ec07bkUAN3AcCg0uKUA44oAaF6U4rxSgEnmnlaAIQpz7UoFPJGfSmswBoAMH8KCD3pFlB49KC26gBpAPWkPHSnAEnGKkWL16UAQHJ47UqocZNWNi0xgy8UAM2DHSjaDUmM8Cjb+QoGRbPyo208ntinKoNAiIITyaNvapv6UY9BTAh2mnAHOacFpwTigCM5xxQMjmpdtMYY460AMLHNKKFGTzUhXC8UgIzkDIpAx6U4daUrQAAZNGMUKvOad1BHegYxs9aaDmpMetMYY6UxCigkZpAexoxkUgHjGMUhOaQrxxSDNJjGlTyaUJnrS5INKCeQaaELs4pAuDmn5O2mFiPegY7aCKbwDQM4p2OOaAADmpFJ6ColODipFJoAU89KRuFpTSFfegQwAZpRRtGM0qrxSGOzSA5PNGKXbjtQIcuKdj0puPlzQrcYoAeq85p4GBTARTgRSARgKaRxxUmc9KaVJoAjHpSjil24pCSKAAUhBzQDkZo5xzQAE4NIeetB5IpSOKYDdopOlHNFAC8AUgpCM0nI4oAdSDFN5FLt3CgB2RSjjrTSnGaTBxx0pgPLAUmd3SoyuacowQaLgPC4pMcUb8dKN2aAGbeaXpRu/KlLA0gE3cYoyKQ+3ak60AOzk0c9KbnFOHY0DHDng9aQkA0ZzmkApiDcoPpS7lPQ0pAx0phUelADweOKQgHvimeVgcE0oVx3yPekA4rgU1uOaeN3RhSnkUARgnPtSls9qAKTtTAD9aTvzTsjimu1FwF2dwaQgijdgcUgkz1FIAxk0Y5xRuoBPpQAuCAaTI+lLkHpSbeaAAn1pQxxzTWBFJSAf9KTtSemKdiqQAD7UEd6Ucijbx70AN5pc/L0pSOeRRtOc0gG5NHenEfhTaAAfpTuM1HnPFOzimAp4NCnIpu4mk3YoAkJHpTTxQMtQc96QDd3PtSjrSsMjpUeSDwKYElGAaaHBPpSluBQAYAPWl7UhBNC4xzQAmBmncEUYyeDTRx3oAf8AhSEA9KM5FGCOlACdKDgnikyM80oxmkABsHBpx5pMelLRYBgzTvwpetIevNADM89KXOBzRjmgK2aAEzS54oIOKKAE3HNKCKCuaMBelFgF3A9aDim9DTgQOtACY5oK5PSl6nijpxQIYV4o56d6fnHagYoGRkkUZ5p+KQgdqADg0EBsUh6UgOOtMBce1GMjmj+dLn1FAhmMUuKXApCDSGGMZpRzSYyKMYpgBU9aOelKCeeaXtQA38aaevSlxSbh0pABwaFxS554pe1AhvI+lOGCcUCk47UAO9qMA03n1pVOKBikDvSFc9KUmk3cjigBpX1pNox05qTdk4xQVyfagVjVGc07jjPSjdjgUhP41kaCg88UoHrzTOe1OBxSGOPApm7ilJz9KTGcUAJn24oyfSl4FJnnNDGIRzTsHFJk0mRSAMc0hXuaXPHFNJ5561QiNjxUZ5OBUjDJpCAKYhmMYpeD1p2B0NJjmmIT6CkPvTiMHg0YyaYhoGe1L260pbHA/OmZoGGCTR3xQCaMGmIO9OLc8CmbcnFPUCmAZPfrSZJNSdsCgDimAwKSafgDrSZAFAIzzQIcOfpTcbaC3NJkk8UgFLEUvUc0z7p9TSgMetFgHq2RxSM2O9NLBR1qIvk07jHiQmkKs2KRBnB7VMD0AoARYwp5p+O1CrzS4IoENoOOlL25pufUUAITtpAaCfxoBxSGLtOOetIKcPmFAwKADaaXAAoyOtN3560wF470wjJ4FSYz3o4pCEUY4p2Rnik709Ru+lMYz2NIy7sCpMAfWkBy2e1ADgoFKMUYzQQaBCFvSmhT3NKRRkLgZoAayAHOabvzwKVnyKTb6GgBc04EVGQQCaAxNAEm7HWoZSScKevWn4z7UFRmkMZwoAFBb86GHNNIpgBfv3pQcimYx1pFODQIkJIFIemaN1NYk4A6UwDNJuJp2ABxSc9qQCAdc0mAT0p4GaQnB96AGng47U7IxSBc04LxQAxjk470u3jFOChTk801pADkUDHbfWkxg+1N80daTeSSaAJeBimO56CmgMWxUqx5oAgx2NIVzVrylFRXGFQAdW4oEQQrncfyqwiA4GKSNAiACpOmMUDFKgcU0nBoLUnUigQAn0px5FIO+KN3HvQABcDnpTeowKVm96TP4UwDaMCk5BxSlhmkyTQAo4oLACk5FNpAOBwafnIqPI4FBb349KAJCewNMPWmhqQsOh60DHcDmlyfwpobt2p27IxQA0nnNLnjmmFWNOFFwHA8Uh4570ZGMCkoELnJxSY5NKKD7UDG45AzT+KjIO+nkHigQpWgelOWkwKBiHANIMUpxnmm5oELjikA5wacvNHTNAC4wKXPFN5PalwcYoGISDinD2ppB6U4UrgA4Y0mc9/wo789KTGOlFwHZxjinA803PrSj1oEL0PNGTQB3pcUgFBzxRSj9aMZpgKAMUqnFNwenalA780gH5xSE+9GDjNJigA7UYBFKMYo5HSkAwptpAM8VITTeO1NAMx8xoIxxTzim4FMBAB0pGHpTmXvTSaQCHNJRmm470wHHBHFKCOKi56Z4pwBoAfSd8U1gfWnAcCncBvengigYJ+lK3tSAbgUbadnijNIY3HHApuD3qXb0IpCKYhoHr0pMflT8ZpQMduaYDNuaQLinZw1HWgBNvpR0yMUA0vIPqKQCEnFJzinMM8d6bhhx1oGL2pQSaT60DNAgY0m7A96digoCKYDdw9aMCk2jpRn2pAHemMATjtT+KaRgUAMIIHFIW9KeRTMc0AGfSng0hXikHHSgB4xRnmm9qUNQApNAA/Gk3c0Ag0AO6UU3Jz7Uc5pgSUgzQpB607j1oAATjmgNnijpRkdqQCHpTNwp5FRkc0wAgdqQn1pwHNLigCMHmhu1PK45pg5PrSAcKXr1pAaXGRQAmSBSfWnYHQ008HrTACueabyOtODYODS0mAm4YpoBzzTinORSDPegBec8UbcjrQTzSgn86YADg/ypS2eDQfQ0n0pANJzSjApRj0oI54NABnGKQn0pSM0oHNAAD60ZGeaUfrRx1xTAa1JuNK3Xim8jNIB+4UDBpmc0uKAFNIfam5NL3oAUYPWggA0U0uM/MOaAFzTs+9N6getHFAC980uMmk+hzSBsGkApGKTgilzQcdqoBPrSYoIzijoKADbzSfMreoo3cGl3ZpCDNIeelL16mlC0xjcelGcHmnYNIT2IoAbuBPTBpwPajAxSFeOKQCkUwqaUHFLzQIZyOopwOeMdadtpuDTAO2MHNHSlAINJyDSAX2o6HNGcc0cUAJzS7unFJxnpR2+tIYuQTRnFJ0pp56VQjaAyaOBxUeSTx1p3Pcc1kaDqDjIxSKDindaQDScH1oz70uPWkbmgYhNHUUED8aXtSAaOOTRk0Z9KD+lMLiHAFNPWnbaXjIzQK5GTnim7evNSNgnIppGTTQhnagA55p+3mkOAPemA3jNJ1oxQQc9eKYhjDnFOwFHrSkYpCpxSAOvbFKOhppyOtHJpgO4zilyMcUzaepNG7sKYCg9zS7/AEpMYHpSgUxCck0pGPrS59KaSaAFHqaTdnpSdaCcdKAHDC8k00y54FG0tTxHgdOaBkIRjT1jUDPU1JjA4ppbAxQAoFSqB6UxBxzTxweKYh4pGpN3pSEH1oAYeTTcGnZ/Smkkn0oAQkikGe9KBmkIzQA7IxnNJuOaTb608YA6UDGNmnLzSkZx60oTv0oEOwPWjKgUwn3pdhC9aBjhgnmnb+cCo1HGaduAoAGPHvQh2005LegpduO/WkA/dQW9TzTTwBRjPSmIY8hHFRlznip/KzyaQIuelAEC7ic4xVhBgc0vAGKTjjiiwCHJHFIRtFP5A4FNHOc0gEySOKT3PWlPBpNwNMBPfvSDrQTRuyPagBM5pAPWkJpMn8KAFJAPFHuaRQQeafwueaAG56U4DIpCy1H5u3oKAJSMdab8vrULSk8U35jQBNvUDANJ5tRAE09UP4UANZyelNCkmpliOakVAM+tAyFYie1SLEAeal+6KZuJ6UxD1UCgH34pvXvSnAGKAFJzULIWlyegqTntQc5pAIRSE8UMCB700g0wAcmnZHamCnjFACMTRjig0ZpANPH1pOaTPNLmmAHr7UFsdKTmlxQAnmUBs/Wl2UmMHmkMO9BoNKRQA08Ux809l6EUY4pMCJWIPNSqaTaDTwAO9ADu1OKgimkigHPemIULzk0MuOaeCDQSKAGbeKbjnpUgIxSBgCfSgBNpzmlIJoJ79qQvQMTOOKMccUNzzSAnNACEEdaVenIpeGNLSAAM9KUjOaXcAKYzCgBVHNOJ9ajBJpwGTzQIXIJpoBLU4gCgZFFhijkUYBxSgGlxikIAozRjANKPcUh5PFAAMmnAYHXNM6D3pRnigB2R+NLkCmlcGngdqAAH1oDZ60pSlxx0oAP5UHGKSjGaBCelLg0YxS0hiFcnimFSCOOKlyc+9B9qaAjIpOcU+mkUwE9qaMelOBwKacUANPejIxS9qaRQAjAUo9qPakzj2oAUJ6nilI2/SkDY4zSk+opAIuKU8DikGKccYpgIDQRmkJ4GKXOBmgBc+lLjPem7sU7INACge9ANJnFN5J4pgKeeKbt560ueaO/NIA20vGaXnFNwaYC8jkUm71FFHSgAJ9aQUp54pMUgFzRnFNJ7UitmgB5NN2+9ISSKQMfWgBW4HvTRyKCc0oWmA0jHNGaU8jFIB1pALwaTFKBRzgmmA3HFIRxTvemkUgE7UoFJilBI4pAPx6GnDBFR08U0AuAaCMHg0lGTTAcBjqOKMik3cc0uMigAP1o69qMECjccDIx9KAG7STS9D708HNIR3oAY3Wkx7flSmgHBoAQLmnBTxSYI5FLuOPegBOM01l/GlPHNJuzxSAbilAI69KXtxS9qAEx70EUdKcCTxigBmCBxRu554p/FIQCOlMBp60dqHUqBjmlDKy+9IBAaMmn7RxSbQTQAitxg9aUmkK4J9KXgGgBc5pQMjmgY60cZzTAGX0pvNOzxmk680gGGjPrTiOfajYDQA3FLt56UoBFAGRmgBCPrSYzT+MYzRt9TxQBFt70VLjNMIoATpzSHkUuMHg0YH40gGjNLmndqZTAXI7daM470YzQU96AEPJoA70DIpMmmIXOOtG7JxSc96ToKQx5PTFHNMBFKTnpTuAuMc0buOKM0YxQAvy4pAD60mcHpxS8YyOKQgyc80ZzRjGRTMUDJCaC1NBIBzzScHk0CHDB60EZxTSAPrS59aBhg5NH06U4NRwfxoAPrzTcU7B70nP4UAayjingcil24FGDWZQdDSZpwUkc0EEg0gE4xTCOKdtpQuKBkZFLsJxipdo7ClPFAEWzFN21JyTQFxTsIiIprZqwVFMxg0AQKuTzTyuKVz+dNPNMBrdOOaaDgE4pwX1pcAe9MRHjPIBoxipxjHSmH9KYEXWinNwKb1NABjPWmnAFSYOPak2DNAEa8+1SqqqM96ULjrQcfhTsIa3zUEYpQQD1pGcDmgBBz2owO9RvKTwKZ5hxjvQBKxxwKRF3Go1yeTUgbHSkMsKFHalI4xUAmxwRShzxVICQjNIsYA5oDDtT8jHNAhvPalzig4xSEjHWkMXj1oPIppbFCv+VMBQBnOKdgN9aaKXJz6UAJwO1AFAxnrSs+B7UAIFy1KygVH523pTd7E8mkBJuHTFDHjFR7s0Z7UAOGM1JuqENninggd6YDsUhIHWkaQY4qFpM0AShwaTzQD1qLnFJjikBLvyakDgDiqoBBpwHc9aBFgyrgU0PuNMVS1TRoF6mmA05pdxFOPNJgEcUAN3dqBkU7GOTSUAN69c00881IWGKieTHSgA20AZqMyk8UnmECkMkZlWozKOgqJmPryaAp9OtAEjSHFM3E9+KesJPtTlg5oAhGTzmlCsanMWBUiqFFAisIjnnpUwQDtUuBijbTAhK85pdtSFQaQ+1ACAcUh64FKOOTThg0ANwTzQB2qTgcim46mgBhIB4oP604gUwnApgOXrmlJGKZuxQCTSAVvmHvUbZA5qQAUFf0oAiAO3pS808DJqM5aTA6UAO2Emjbk9akBxTSwA+tAEZX5uBS7ORS7smnFhnjrTAQpxxSbSMGnB6M5pDGn2pNpPan4x1oJGaAI9h6mgg0/J/CjFAiPHFJipiKjYflQMb0pu7Bpw9KQx7qAGFu9OB5FPEXHNAiNAhNx7U/NKIwKUKKAI84pAacV64poXJoGLu4pQcCmkYOKBnNIBwFAWlHSg8mgAK8daAuaVTTh19qYDSv60MoAp5IpGYHigBqrg0oIx6U3JzilPpQAE80pI4zTduOlLjJ5pCFBp4PFMxx0oBoAepzS4wc00cc0tAA2DS02lOMUgHZpQTkVFTuopASg0A5PNMHFO96YDqCCRSZyKO/FIA56Uo5pDSj7uTTEIAc0pI9aTPSgqaQwPFITkcUMuKbTACc03nrT+nNDYxQBGf0o6daCfakPXpTAM01uaXBJpPamAgFOoBo96kBehoI7il60hz0pgJtxTiDj2pueadnC0AN2+tIMinZ70m7OcjigBetKOKZ3pcnOaAHEc8U088A04H1pD7UwAA07HFIDiloAMUFQaCePemE4oAX7p9qSgnIoUjvSYAAKZxn2p5OKaTmgBOopopcn1pQKAE6migijNAwB/KlzSD1o/CgQZz1oPFJgUUwCk6cGlxk5pSuD1oAaRScd6Ug0mfWkAZp4PHNNz7UuKAFyKM/nSD3oPNMBwNOHJqIcdKcHx9aQEm4Z5pOvSmt0poIHtTAkIB9qBkD1FN5xxSqeKADI9KMA0v4Um3HNIAzzikxxzQQRShhj3pgJgUm3nPrTsg0gJosACjjvSEjFIeeKVgHhaTnNN+YfSnE0wDvSkCkIpSRjkUgD9aayBunBp3bjvSD1NADBIV4YfjUm4HGBRgEfWmFChyn5UAL1ppXnkcU4ODweKXrQAwA4604E0d6UEZoAMg9qQmnYAHWm8dqYDSaAxFOAzTSBQwHdaMkHmkHOKdt4pAJuo+tFBznimIX6GkPSlGadwaBkQoAz0qTHXFN5GMigQmDQFHU0uRgijqKAEK0hBp2Dxig55pWGR4OcUFc06jb6UxDKTjNPI6UmBnmgBpHPPSk6U8ikxxzQAg680EHOaNp7UuTQAjHJpMYPelOTSmkMaCe9BYYxQV59aTBoAAcZyaUkYpuDj6UEcZI5pMQd/el3YPNJggZoU7qBi7xnGaXIz7UBRnpSbQDxTuA8nn2pMgDrzTcHPPWkJPWgLm9kk808YUc0gIo5NQUBcHpSbv1pcDvRgUmAnJpwwB70YxSBcGkA7OTxTWzRS5pgNwcc0hLU4nI6Ug680AABNGOKUn8qaW4oACvtSbAc00yelAZs80wEK5PNAUClIpCaaEIaaaXknml20wIiuTz0pAmDzUhODTC34UAO6D2pvGaTdQCT1pgKKCc9KQk04JxzQIj2nNMdcVNnkYoIHU8mkBUMbHrkUbakdjzUWTimMkH5UmfQ0zJpyqTQAtKuT06UgXn1qUdMYoAVc496eMimAnt+dO3d6LiBmxyahZ9zU98sMiogp3E0MZIoJHWnquOtNUcc0pyRgdKAHbsU0t+FHtSNigBwPamM3agEmkxg0ACruyTQ3GaeBmkZR3NACLgjpS4pAwAxSeZjpSYDulBHoaZuyetISc+1ADsDvRhSRig5H0pVUn6UANJOfakw2eKnEYxzTtmOlOwEKoW5qUR077oo34GaAAADil6UzfxTCSRQA9nAFR+b+lNYHHNR9807gSPMTTC5PemsCaApzSAduPTNNOOgp2xmpwjx1FAEWD1pdhNT7R6Uh9hQA0RDjNS4VfSmqM8mmPnPNMROCKTdUcZ3D3p+MUALRTc0obAoAdjH0qNpBng0jyHtUABJoAl80bgBTtxPamJGc5NSgYFAAcYpAfSmnJ4zS4xzTAk3UE9ab70wk0ASHHtTO/FN69KUcUgHCPIyTRgCjnpTTkUAO6nijPNMCnGRTugoAG+VfrTlQKme9R5JOTTs8igBSOaaQD0pHc55oHrQAmwk04R45p6nmkJGOtAw2DFISFHFNZ/yph6cUAPeTP0pm7Io65FKq8j0pAKmad3ozsBpBnHvTEKetRstOyc4NDetACKMjmnBcUzdzwKfnIoAX8acOBxTB1zT+MDmkMKbxzinbhg0zcBmmAvvTORRvyOKCeMUCGk0hPNI3pSZOaBj19e1O49aaORik780rCHjFL0NM7inUDDIzQVpOpo3UAONIck0ueAahlfYQfU0ATUvGaaG4zSZoES4oK47U0E4FO3HNACDrzSg84pRzSEDpQAYxS4PFFKfagBMYo4o6jkUZFIBwHpThnFItOHWkAgpTz3oNHQ0AJjnilB7Gg0wk5xQIdnHbinBsjmm4JxSNxQMccdqYQBSFsj3oJpgKKSkORzmjPFMAFIfbrQPbrTvrQA0DI5pu3FPo75oAYTg8Uo5oZcmkycUwHLwaD19KAcimM2D1pAOxnrRkCk3ZHvSUWAUt60hYdqOTQaAFUgig89KaOtOJyMUAAPr1pcc0maOtMBTkcUZI5ppye9HIFIB2aZmjdSGgBTx0ooFKeaACmnPendulJQA3pS9BzRQcHvQAnNBGee9JyPwpRyKAAdMUueMGk+lL2oAMdqKbmnA+vegBOgoApTwaO3FMBufxFGPypD1oHNIBRinD0puOKUHjFMAIx0pPXNPwMU09M0AIPpRjPBp45FIRzQA05UetNzk81LjNMK/lQAqA/hUmMCo8FelODnoRQwHYzSUo570vXFADCST7UvTtSsuRwab8xpABFGKXigLxmmA3aKMYp+M9aAuM0AM7U0jvUuMU3qKQDAafwRTSMfWlK5PBpgOBHpxSEEGkzgc0D2ORSAMcZp3H5UgOeKcPegBjgMDxUYJTg5x61MRQFpgNGGXI5pOnPamtGd2UODTRMQSr8H1oAmwCKaVI5Aozx14p27gUAN70jZ60/ilOD9aAIQeadk0OuO9IKAF5pelHvS54oEAGKcDkUlJk9KBjiTSHOeaUHjrzRtzznmgBDgj0puCOlPIyOf0pDkDNAhm4r16UbxS49aTGOTQMNxpc+lMxzxSknIzQKw4nNI3NGeKQ560AhCR0FKQMcUg4OcdaXPr0oGJnnik6j0pcd6QA9utIAOBxSYz0pQP71GCOlACbaO1L2oweuKAG/hSnrS59qTg/WgBCBzSAYpSuT1owQKGIb9OtKDg0pX0pMED3oQxxpMdqQk96TvmmI3VOe1ODelMFOBFZljutJkZwKaW7CkyRSAlyKQtuFRjkipAvFIBMgUcGkbtTSQBxQFh+4dKYWqMtupN2cCmA9pM005JoHvR9KAF4ApA3GKbjNLimgFyOaOlMLZ4oLY60xDi2KTdzgU0mk3HNAhT6U3Axijk+9AHNUAmCTTsHvTscdaXbxzQA0ED60MSaUAUuM0hjPu00tTyO1NI9KLAQEEnnmgREt7VYCAj0pwAAoEQrF6inbMGpC3bFRs3SgA2AUnWgknAppJxinYBw64p23FMHH1pc0APyADTGIOPWikA4pgKpAFJuwMGjHbFMKn1pAPD0EcVGx2ikLHGc0XGSAjvQzjFQbjQDxQIkMp7UwuTS7DjOKBCzUDIyzZ4p6ISM1KsQGQalGFHSgCIRk96ftCgZ60pYKeKaTzk0AKq85P5U8YzTAS30p4piFDcYp3JFIPpTwOKAIyhHWo24NWD0qF1JpARbsmlDZFJ5eelSLGB9KAI2ywxSiMt1qTjOKGcAcUAMKACjIxwKQksKAuKBjlNG4U36UY4xQIXf8A/qpu7FKF4zRszTAVWxyaax3A08pTSOKBkaNsapWaq7A789KlyWAoEP6UZFNxmlIx0oAUgHigKBScil5osA7gGmlic4NI2elN6HjrTAdxR2x3oGTS5AFAAeOtNPJoJ45pFBzSAcOOAOadgD603PNITQA4sF6ikZ8imHn6UAYOaAHBjTGY7sU5s9cUijIyaAFHPFKRijgHNKxxQA3ZzTsACmeZzTHZn4ouMc0nPFM3GkxwKUflSAQsc0oyRTiuOlO4VaAEQbetKT2FMdyOKEBIzTsIC2TtznHWnjkVXjP75896sA4OBSGDDmgDuacRmjtTENxk8U7GBzSAYFOzjrQMaRSA880E+lJzSEKTTdppxGPrQOlMYwnHFMJOfapCPlphXmkAh+lL3pvQ0c55oAeDjvSE0mcd6Rj3FMB27HNLv6VFknk0oGaAJVOacvemDgcU4EkUgFPSopk3xEdxUvWg0gIbZt8Q9RwanxzVWLMU5TseaujGM0wEC4oAxSnJpGPGe4oEOXrS8HrUcbZXmnigBcd6ZuBOBSk1CpG7NICcZpSDgU3NOB9aQCqDTvem5pSaAFLCkJ4pnc0tAh2egoB5o7UbeaAHD3oYgikAoYdqYEZWheBz1pSDmkoGKaQDNLjijPamAmKAR0NGecUlAC9PpSE5FAakOaAFB45NJQpxnNJnPSi4C5HrTCMkGlOKTBxQAbiDTt2elM70uf0oAXJ7UEZFJS5/KgBvQ0p4owKXGaBiE0dhSYwaOlOwheppeTTQPSjJzRYBe9KRn2pB196XlTzSAT8KXGTRkCg80AL7ZpmCDSnikLZ4NAARx6ilwce9IMU4YoAbRjFONIOe9MBMZGKTBFLmnAgikAwe/WlzikP3uKUdaAF60hANB4PFHNADSKaSR2qTjvRtoAaCMU5SBmkwKMYNMB2efal6imYpw6YFAMM4pA3tzS5ApwAxQA2lGKNp7UmDz60AL160hFAyOKUUAMwc8daXeRwwp+MdKMBhzQDEz6UHp70hTb0NJkjgigEOHNHQU3dilBBoAeDmjHNIODxRn86AEOB9aQjilPrSUANz2PNKM0EcUooAXCmmcqeBxTu2aBmkAZwKMHApMnPNL16GgBVYHijOO3FNxgUYoAXNDbWGCM00kr2o3A07gRFWjbI5X0pyyAnAP4VJxio2RWORwaAJh7ilGB2zVcuyEbunrUyOrcg0ADJmmY2nGKlPSmsAfrQAmOetJtPpRTgaAADsaORS/hQT2piuIp96Unpk1Gwx04oBPekBIO9OHeo84pQwNAC0cY6U3v7UvtQMZnnpSbvanlfemEc0AISOgpeMUwHBpQ2e9AhfbtQc4pB9aXdz7UhiA8Y7U4DNNIHakDEUxDyM0gGDS5yKPagBSOc4pu4/hS5o69aQwyCen40mPak5Bz2o3jnrTEL1pRgU3ORR1HXNIBTg9KQY+tB4AxSYOeKBgeaYcDpQc9qYWwc0wN7cKQnJ61FktUi1mUO6cUvJGOlAxml3A9qQCDjpSmTAwOtNPWkxk/SkIQknrTec0/GaOKBjcfhSbTnipO1N96BhjnmhhzRyD7Umcnk0wEPXikzxilxjPNIfemIaTg8U3BPNL3zS4FMQ0U4LTsD0p4wPpTEMCUoXFKz+lNHXnpQAoIFBOelJjJo6UAJindMUwmgsfrTAU9aT8KTOec0vagAowT9KXikzgUAG096janFiabgmgBqk/hTjmnhcdaa2BmgBowKB1oyopDMq0DHhSKQsq9etQGYmm5JoAlaXniojIe9GOaQISc4pPUBN2etKcnAqRYd1TCNUHqaaQECRmpVjAqRRSNkmmADGMU7pwKbjBpf4c0WEBI9KjaTkgUuC3APFKIwBz1oAYASc0p5IFOOFFNBwKQE4AC8UmBmo9/GKN3rTAl7UbzjApgJPenqMUwFwSKftzSDnrRn8KQCEADNRsw6U4nd3phWgCNiT0pduKeFoxQAzGDS44zTj04pAppDGqDUm3IoxS5AoEIQAKQ4AFBNRscUwH5o70gPHvRnnrQAyRAee9LGMLQTmmHINAD8jPWgvnp0puab7GgB4PNOpowBSZxTAeTx703NIT6U0g5oAeWwKbmkIJxTgvFABnilPbFNbC0m/IpDHjr70pXnFIrjNBfBoEKABSkgVHu4xSM/GaBiySDGAKQPxUK8kk96fjFIBS2aMn1pvPejGBQA6k60djRgimAdKM96AM9adsoEKG7U1jSYpcDgGgAC5604KccUD2p4PNFxkKpiVuKkHFKRzmm9TikAbuacD3NJspRQAE9qDz0pCcU0t0FMBeVNIW70dTTGB9aQhQ2aUEk4pikUoPcUALmgtg9OajJweaQnNAx5OaQ0AYFIKAAcjinAcUgFOpAJgYpOKDxSfe4oAkHTOaN2KQHAAPWkPtTAkBxyKXOTUQPpUgoAhm+V1erCNwKjmXdFSwt+6WgRIz4qJmZjgdKGBJPpT1GBxQMUDaAtOAwKTrTh6UhCZyah24c1N1pMZpACHIp2aYF5NKBx70AODGlBNAHSlCnPWgAFOxS7cDNLn2oEGKWjPFHamAgzSN1p+cimHg0XATHFJilzxR79qdwGkgDmm55px56UgHFIYcCkNKcUjjvTuAw9elHPBoVzS5oAGG5TjrSLnA4o69KO3JoAb1+tOB/KjFN70gFbrSUpJ6Ugb2pgKcUCjr1ox1NAB/OgHnFIT6070pAHPPtSYyKd0pTjFUA1envSEe3FLt5oxjikAntQOeDTiBgUmOeDTACtLnj3pO/NGcGgBDnFMIqQkdRTTjtSAAfanZFNxg5FOosAznOKUHGaXvRxTAa3LZFAPrQ3WgdMUgFGaDTc4OKdmmgAcGlC57030BFOB5xQAvX/CkK+hoPJoBPQ0WAb1460mCOtOOM07qM0IBgxTqMA0nQ0AIR9acuQKBnvRuIoAeOPpTd2c0BgcUjDHK0AAHp1pQMmmr1z0p2elADttJgg8UFuaN2M0AJ+FBOe1OzxkDik+lAEZUHkHFHTg8VJj2oKZ60AMGVPtS9eRTtu0daaTjigA6GlBpMZo9qABmOKbnNKVOKAnSgBucUvfinkDFAUUgG/Wk5zwakKgU0j0pgIDgUZyOlHQ0oPFKwC8GkKAnikyQaeME+lICEjtThinFcnrzTdpU8jimgDrxjioWjZWLIce1TdOaMZ5pgNS4Ujaww1SHDAVDJCGXJGPeoQ0sRwcslIC5t560mDUayBhwadvweaBWJRSbc03fkcCnBxtphYaVNMPBqY+xphWgLCH0ApCOKORRu45pDDOBS5zSd+lJz0oAeDnrSGggjvRnFMRGyUbe/en4zSFSDQwsJgZ5pdvekA5pSKQwOM9KaRTyCBUZJ/CmIcOadnt2qIHtTgeOtAC8Uc8ik5FG6kMXvjFMZQw4NOz3pCM0ARkMO+aASDjNSFc0hXPNMQgfAOadu4z1phHpS57UmMduB6imsqtRx9TQRyBRcDWApw4o70hPpWZQ7NGeKaOhJpN2aQDt3HFIDk5pDnpShR+NFwHUnT60E+1JmgB24YppYYoqPBzyaBjiSetJml20gHoadgFzxSE5pcUhxnimIBSg4PFAGeKdjjpVCG59qTdk+1DH3pgb0oAkJFG7FRjgZNGST0pgSd6aXpoc0wnJ5pATAd+1NZx0FNBJGAaTBA96BCjnr0pwPPFCoeM0/YB9aYDSTQEPelJA601pQO9AC4AprOoPFRPOBwOahLk896QFkzADnrULTZGBUWdxNAQ0xiNIe9IMnrUiwtnOOKkWEnrQBDtqRVJFTCMDinbeKBEYiJOTUoUKMYo7U4cjpTAKGApC3FG6gAPtRtwOaNwFIzcUAGfWjO7joO9RA5Oe1SjgUDHDAHFMJ55pMntTGOOTQIHbJxTR0oXLVKsfTNIBgHc09RzmlPFOApgNyM4qUDH0pNuOlOOcYoAaSc0McDAPSkJwOOtJx9aAAAnmlNGRikPHegBOtLjFAI7U1noGPPrRuGKiaQgClySM0CHE8cVEGJankZoVQBQAd6aVJqU4ApM0ARnJNIRgZp7e1ITnrQA3HFM6VITgU0Y5JNAADk4pdtMLgUCTjrQBIeOKaDlue1RmQ5zmmlz2ouBNlc0FgDUO4mj+lAEm/n2oLUwDPNPCEmgBpGcZzS54p+yjy8n2oGRfWnYP4VJ5dKUA4oAi25ph5b2FSv8oqOkAnelBNGM0vTigBPrSk4oxkUm0s2O1FgHClPalxig4H1poQnPpRn0pwpCKAG46Y60MMkU4jAoWgBegoJ4oNNzxikMQsc4pM4NJxnNJndzQBKH4phY0mcGmluKAHZ9aaSAaM5FJjg0XAcG4pDzSYIpy9KAEHFBB5oIwM0oOaAGlelJwKcTzTCTmgBfwopaAMDmgBRxQSafgAdabQAh5FN4BzTj60gXmgAyCPpRn9aNuDRg0AAGKkBppFKOlIBxPykUxGxxRmkXlvagCUHPalHTmowDn2qTgCmITODShqBikPFIB+6kPXNM7+1P/lQAo+lKBSCnHOARQAc09TSAd6eBgUAAbk56UnSl60gzSAduAAoLA8U09KTnFAh/HBoIBFM3Uu7FMYpwRTSCO9AJpfenYBnQ5o7cUHk03NABzSHgc0ZxQxpARkYO4fjTuvTrSEZX2oXimAvGM0cFRSHk4FCnPFAC9CKRvakJyaXOBQA3NBpQPzpNvGaADOOpoz2pp6ZpccZFAAelOU460zpTh933oQx+c0ZxxTBkcinZB602IcCaBzTScUm/mkAuTmjNJnPNGM0xjiOOlN5xmjtzTvaiwhgHODRg9RTm+b6imgmgBR6dqeMCmnpS9qAAjHSjPGKXPejtmkA3GRzSH9adxTT+lABwRzQKQc9qcQT0oATOKODS4NNNMBScUDmijHHBoACM9KeOAM9aYD60Z54oAfjmmk5PIozkU4HPUc0AJjjNJTup5oxQAwrzQScU7GaUqKAI9/PSjPNKV5pCCO1IADjGO9Ge1N46igDuaAHbiKXORnpTMc8cUYIHWgCTdilDHNRBiKQswPpTAn3dsUhwah3t3pC7UATbTjg4ozjg1AXk9aPmY85ouBNvAHrSeYo71HszRs9RigB7SjHvSCX0FKIvl4NKsYB60gAyEjpTdx9KlCLQUFAEIZutO3N1qTb7UmAPrTAZls5xRufNOJHHtQQM560gELnHIoDMeo4pSBS9+vFADCfahXFOIB4FNKDsaAFLe/HpTSQe9GcdRQRmkBCY8HcnBFILgA4bg1NgrTJIY3X0NMA3g9D+FPWQfxVU2vEeDkVKrK/XrQBZDrmnBx61Eo+Xil29DQA9yG70yl2g8Um00wAmjrzSMMGgZoEPz0yaCe+KZz1pc8UAOI702lJpCeKQxueacTS96aRzTACDim07J6UtADB1pep9KUjNJ0NAAetIeTil4PWk4HTtSABx0GaTn0pQ3OKdjFAmMpwOODRtwTxScUAI2M8daY2Qc9qkpcA4pjIwxAzSZ5qUoKjxg80gNjnjFBxS54zTC1ZlB1NGMCgdeKdjvQIBwORS57UY4pfrSGNpCaOtKKYBgg0lOzkYpAADmgBuCTnFGMU7oKQHPWmAh57Ug+UZp/40xulAgz3pS+aTGaQqRTAY+TxQMAccUpU+tKB7UAJ0HNBbilKk8YoVPWqAbnNAXPOKcQFNMaQDjv7UAScDikZ1H1quXOT7Uxsk5z1oAsNcDtUZmPWosYzmo+ScdqQE5kJFMJJpyJxineUT2oAg5zinBamEOalWEKOBRYRDHFk4qwEUcd6dtxQBzzVJAHCimM3p0pxAJpuAT1oAQA0p45pCeeKQk/WgBQeacDzTRn0pcGgBDyaPajGKax2+9ACk4PNGeKYOmT1pw6UAIQM+9O68Ug54xUgwaQDQvekZSR0qTHHWmk+nQUwBF4+7ilbApAxxSdTzRYAALHJ6VIP0puaOepoAfmm5OORRz1NJgmgBpbmlHI4o245qRVA69aAI9p7UhHqKkYYHXFNyaQxlJt54qQYx700sM0CI9mWzin496QvjikL07gP25pGO2mGQimF89aVxkobuelNMo6CotxOc0mD9aYhxl9Kb5hzzSFSR70qxHr3oAQycVG7GpWQCkVCTkdBSAjUEjmpAOMUu3B9qcCM0ARle9Lt4qXb60pHNAEaoTUgQAUDijdzTAXpmlDYpuaQZoAdnrRuzQAKNtAC55AzSngUgUZ602VwoxSAiJLtnsKO1IOBijnNAxQSKACTQAS1P7UAGMU3dzml3ZFN4xTEL5mDSKctSEZpQKAJAcGlz15qMZ6igg0gH5AzTN3NJxS4HUUDFzmgnFNxxQc4pANpOaFBBOaU0wGnIPFA65pT0pMZpAOzigUqgHvzTsA0xDDmmgkcdqkxxTCBQMMkj60q8Ck7UoHSgBCab0AqTbnmjaKAGgHGaXORzS44prEYOKAFDZNIpqME08YxQApNIp4pGFIaBEoIozUS88U8dKBjs8UoNNxim7qAJDg9qbgZ4pCeKFoAkU4FOBGMZoA4pOO1AATikzz1paQdM0gF6n2p/UVHxSjJosA8Z61ID8tNwNuKVeBzQIUNg+1OB55pvXpS0AHIJ96M4p3BHWgr7UhDQT3PFA69aCPagjFAwP603Ocine9Jt700Ao6UhJozt4NJmmAhJ21DG+XINSHI5qBsrKCBwaQExPPHSgNk4NJz2oHfJpgOIwKjORz2p24EH1oU5BB6UAIOtBHNIMgkHpTgc9aAGkY6c04dKOhpDjdxQAvQUm4GlYc0mM0wEzmkFGCPoaToaAFI7UoGKQnmjmkA4Z7mkNIDilHWgBT0poWlOc9aQdadgF6CnZphyaVBg89KAHjBoGO9GOKQ4oAD7U0jnOKdShaQCds0Z/OkAPIox6UwF54pRQOaO+KAEPBo/CkxilHpQAA57cUZwaXGKaeKAAjrijFLyelB6UANwcZo6+1OzxSdeaQCUoJXtRRyT0oAXPJweKX9abjHSlA4oAdkHFKDj3FICKKAAH1pwI60zOBSbucHpTAkIzTegpMnGe1G7mgBhHPFDCn0mAfrQAwDI5pcdaXbg803vSAU4o470YHrQMUwHbBnim7QO1ODGl3bhjFADOB/jScZpxxim4pALup2QRz3qPGPrSmgBeVPB/Cjdn2NITmkPsaYEgPrTs5FQZORTgQKQEu7ikHJzSdqT1oACM9KUj2pMEdKC350AJThg9qQHPQc0biDQAYGcdKMd6D145pcihAJt/CjGKdkHFIc4AoATOetMZafg8UmAaAI8Co3iDcjg1Ky4pucUgI1Zoz8/Spw+RxzTSu6mFCnKfiKYE4IoOeopiSBuvB9KeTxg0CEIzzSds07p0pPbr60wFXOMGjPPHSjFJnFIAOM0g60c5OaTkdaBjvrSUucmjHrQAg45p2cmk7YoxTAMHnpSgUAcUozQIaQOtN2YOalwMUhFAyP2xS7cYp2Dn0ox0pCEIxSYyOlSY7EUgFMCPbijHFO6Hmk70XGN5HekI7U7ANIR9aVwNMnApBzShMkGnYAqBiAcZpQRQTSDrSBDi2aYx5xS8UYNACAU6nYxSdaBiYxRgUo60m00WAQ5pp7VKFyPemlfWmIZzilAz0pcFiaco2rTsA3bz0xSH0p5b35qMtiiwAFyaUgADmmGbHSoWcnvTAnaUDpULTEk8VExppNFwJC/50zdmkGSaeIz1oAb17U8H2pwj4pwQd6LAR4z2pyRFj2qVUGDmpAMYxRYQ1YgOtPwBTd3PJpCwzTGL3pc8UwEmlBAHvTEGSaCaUg4pjcDpRcBGbIIpgO0UoGPrQEOaAADJoAyadjPSndKYAoFGeOKByeelMZsdDzSARmA4AyaQLnk9aUADnvQRnmgBuOaXBpehoB9DQADjtzTl6803IweeKTdSAezALSA4qItubHanFwq5pjJPrS471AkhY5zxTjL2pATZGKUMuMmqxkJpC5oAnLjOO1HmdgeKgyTS0wJxJkUeYAah5HFIQTSAleSmb+MUmw04R8c0IBu4kU3nvUm3mjbQBHnI4pQmetPCgU5iNtFgIWTPSjZnpQ3zNgVIBjpTsAzaBSgAGlI5pRQIQgZ6UYxnmlJpOxNAEL561IvypimL875pz8L8tAEbEk4Bp2eKFQjmgDB60WAcozz2pcnvS0be9ADc0Djg0/ae1IRzQA3BzxThnGMc0YGacPcUAMHSjnNP2gc0YAyTQA0nA4quW3v3wKfI+3JpijApMYvelHXNHSlWgCRB1NI3FKPQ1G5OcdqBDS2DQBntRjpmnZpgGO1OUdqTP50o+tADselNYUucdaaTnmkAYApRSA5OKQ0DA8mjFKCMUnagBh604AECkxmnDimAEUiinhs+1Ic5zSsIMDrRmgg460AetMBpOaaefrT8CkCUgGkYFKPelZfakHSgYoOKM/pSUhzQAuaYRup/bimjkUCGYxRnFP9s00/pSGN3dsUE84pRRjJpgIM88U7dilxgGkAzSEORstzS7eTQi88U8daBjAhqRVAoozQA4HikyBTN2DSbuaYEnejtikB4oyMCkADg0/oRxTR15pCaBEmc0mcGkHFPFADxwPWgc03Py0o9aAFHX2qUcrUVPB96AGnhjQeBz1pX68VGx5xQApo3cUwHJpSM0ADHjNNFLyDjHFGBmmAHpiopBke9THGKaR7UARqdy5796djJpq/K2PWpO9AEbDFKD7U489KQEA8iiwCMTuoDZPNDdM+hpp4OaAHnpxQORTc8fWjPApgPJ49aTFAPNOxk9KLgNxmmkc1JwecU09aAG7eM0YPSnDrTiMigCLGTTgPelIx9aQdaAA8im85p3NNzzzQAo6U7kCmjrQTgcUAOB5pSeOKYuTTs0AL1FIMg/Sgc9KcKQB15pOCaXjPHakJ7incAB5wRSHrS5z2pMkZ70gFIyM00dacDntTSDTAcOKQgNTQacM9KADbS9OO9APHSgmgBOCKDSc0c0ABGRxQMg5pMmgNg0gHbs0mSKPxo6UwHEcZpN3bFJuoA70gAn5sZopCOc0oUetMBwOabn0p2B60goAAeaUkUlL+tAWFx60wrmnZ5ozzQAwDJ5p/GMYppxng07aT1NACfhSD2p3tQRQA360ucnikzxzSUAHFIOtOPSmg/NQAEYpMY6U4nsaTHNADcHrmnDB60Fcd6Mc0rAGCOnSgNnOetKDgY5pCAe1Ah4Py0h5Occ0wZU9c08HcaBhkD60hNL/F9aNvNMBM4FLSYIpRQADrS5zxTSaUc9eKQCg84NBGOhoOKQnBxTAQ8ioyu1ialzk0m3I680hDAQaXkU0rg04ZFADSgJ9D60oYrw4/GnZ7U7bkc80xjcA9OaOlBGDShiOoosAnelyDQfag9KQC9cY4pCoIo5AzRnP1piExgUoyaUgnvmikMTig8cjoaPWhR78UxXHLj0pwGfpTQfU0u7AoGKwHpSfypdwo470AN70H2pSM0nA+tAgyRjNNbg9sUvWkIHSgYgPPXpQT6UmB3pcUhCdKQnIx2peM0h6UMZq57YpBzS9KUYH41AxpFIaf1OBRtwKQEfNOFBHHWkHSgB26jdg4xSHFJtJ5FAC5Gc4pwzQq469aUsAKYxDxSZzSFwKY0gzxTES521G0gFRNL71EXY0ASGXJ4qJmJOaKO3Wi4CFiaQmjBbpTxESelAEX3vrUiRE1YSEL1HNOIUGnYCNIQOtPwB2pC3PFIeaYgJAoBpuM803JxRcCQv6U3cfWgAmniPvRcCMk0gyTUpGBSKue1AAOlOUYOTS8UnJpgOLelRsDmnAClyAeT0pAN25Oe1KBzTTKoHtTDL6UAS4Hao2YD61E0xAyKhMjP9PWncCcyZOAT9aNwAxUAJFKAc0DJt/FJ5mKYwwfajBI6UgFMp7Um845o8sijYcdKBCbyaRjgVJ5RHNN8stxQMReBmo5W3EDPFT+VgGoTGXlVe1ADl+6MdKftzVhYgAB2pwUAYFFhFURndTtlT7QKAueooGRBadtqTA5owAOaYhu3Ap+3pSL604c0ABwKa3IoY80080AHGOlJkY4pCcHHrS9KYB1FRyNtGO5pzttUmo0Rj879fSgByLtHPU08kDFNPFN5JoAex44pM8UY5pCOaAAnNNc44BpccZpigliT0FAEijAwKaPmb2oZsDilXOOnWgBx6Y7VGBhs1IB2pduaAGgEtmn/SgjsDTumKQCH3puOaVjQnNAwIxQeKD703vQIXJNMc9qeOlQyNg0ARE7n46CnCmgbfrQTipKH9TxTlFRg807caYhzHtUZOTSnrzTOhxigQu/LClzzTKBnFMCQNS7u4qPPGKKQx+4saXIApgOTTu9ACj2pfwpqnmpD0oAZinAUUHpigQY4+tJilApe/FMBB1peaQ8NQaBiGjNA5NOIxQA3qaUcjFNPSjNIB/amHrxRk0Y9aBAD60nc8UoBNOGDQAzGOlNHepGWgJxQMYeCMCkKk1JtFLxt5oEQhaM1KQMUxh6UgEBBp4wAcVHjNLkjnNAD+wPejJJxSKRjmnjA5pjDPFNzQetMOelIBxIPHSk603mlBoAf+FLTRSniiwhQxpe2TTO/FSL6UAOFOzUePm46U6gB3HSl3YOKaKcVzSAXrQTimnrTXOaYEgbIpODUa9cVKOlACY29qaTg1MTwPWoyKAEzxnvTWyBkUvQUoww5pgKv3c0jLgU3pnFKr54NICNuOachJFK2DUedjc0wJD1pjCpMg9+aa3PSgBBjGCKbjHFKOmKCPWgBnI6U40u0EcUntQAHpmlDY4pPakKnrQA7OOKUHmowaUc0wH9elLnsKYGxSbsHNADiSeMU2gnNISelAD/AEpNuSaRW45pVJxQAY/OgjPWl3c80vBpAMGQacD60oA703HOKYEncGgjjNN5FL/OgBG4pKRjkUA4pDFHB5pQc9aCCaQ5FAhx6U0kjrS4yPejb60ANIJFOBpMgfSkIPagBxyDS9uKZuHQ0/HHXigBucUUE9B3oGaADBPekI9BS54oPHHrTAb0o70oGKQ0AH1FKMjntSckA0ueKQC444pcU36U4HPFMBRRyKB6UvBoATikDbT7UH0oIxQAo5o2+1C5xgUoyetACFSBxSj60uaQjjNAC57U09RQOOtKw60ANIzSYx3oIINBP40AIc9O1GOBSmigBu0n60Ed6eORmgjimAz60D6UH2pRmkFwA4BpSOf6UZOKTrQAYox+Ypc8e9A6nNADdxHWl3Z6UMOcUxlOeKQEgz35oxyaZk55qRcYpgN2nFHennPSmkfnQAlHXrS/hRwaQCBeM0c9acvBx2o28cUANJpKeVyPem4x15pgGOxpcY70hOMd80gINADjSZxwRTuAKDgjjrQIaRxxQG7GnLwaCBQITGRxSD0pdp60mSKAAe1LmkxmlFBQo6YoGMnNBOMUY70CF7cUlLmgEEdKBCY5pOhzTj703v7UWGhQeeaDzTNxz0pQc55pAOFBApATzmmluetMAOOtA55pc8daaOppMYhXBoB9aU8Gm9+elMDXJ56U0nPFFFZjFBweaQkk0UUAIFOcU/HI7UUUABwKNwFFFAEby44qJpeaKKQDN5PvR1HNFFMBhHFAGe3FFFAxQCe1OSLPUcUUU0IlVAopQdtFFMQjPzSdeaKKaAMDrSDNFFACigLzzRRQA4D0pc84oooAaTluacCT0oooAO9JnAoooAiaQ1E7GiigBv60hGBmiigBoRnPI4qQREcYoooAcsRPbkU5Y+5oooAd5Yp4QDNFFACbc07aAM0UUwGsRihV4oopADgKuagi5loopgWuTmlPAGaKKAGdT7CnFqKKQDd2TSHmiigByU7dgY70UUwIi4zg0E4WiigBqDJ3H8KkIwMnGKKKAITiST2FOJPaiigA2nHNPVB0xRRTARsA4FIU59qKKAGSMFG0U1SBRRQA1juOO1PPA4oopAKDgYp4OOtFFMBFOaceTRRSATGRmgciiigBGOKbkk0UUgFLgVVd8vRRSGMJyaTOfaiigB4GaUHJoooEKW4qMtzRRTAOSadjNFFAxAuDxTgtFFADlWnY5oopAJjB4petFFMQuKUDmiimAppPeiigBD1pT3oopAGMUYoooAYx7UdqKKBjhgCkPJoopiH444pmMGiigB3OKXOKKKBjCaTGaKKQC4pGHFFFIRGDg80hPNFFMYqknpUu6iikwGZ5NLRRSQDcUoB6UUUxDh1pvU0UUAOoyQaKKAJAfag96KKAHJTiR0NFFAAeDSH1xRRQA0gDkUqNgUUUAPzkZpgPJ9KKKGAlKOnSiigBGyCKQiiimAgNNYZFFFACRsQNpp4PFFFACClx3oooABwKa2M5FFFDAMjrinCiigYzHPSl5oopiGk0nWiimAopxXNFFADdpzxQCRRRQAuflPPNCk+lFFIB3U0Z7Y5oooAM9qeBniiigBCnFJtx0oooAUA4ox70UUgE6HHan8YoopgMZRSDOcUUUAIVyMilUEUUUgFBGRS8dKKKYDSO1J9aKKQCgZph4NFFMBwIxRnjnmiigBQexoA54oooAcOKdiiigBtB5FFFAAMgYozRRQwFyOKcAaKKQDSDQw70UUwGg5ox3oopgGAaUDmiigBccUbcjFFFIBmMHBooooEKAOnrTdtFFMYgIzTupxRRSANpNHQ80UUALgUbccg80UUCAvjqKTcKKKAQuOKCOmTRRQHUTpzS7iKKKQxQwakzRRTAMg8dKaVz1oooAPmp4HeiigQdqXGfrRRQDDp1pmwHmiigBR16Uv4UUUDQm2kyRx0oopiHdetN6fSiikMXBpD6UUUCQ3HbvQOvFFFAMdg9aaV3UUUAMKEd6Oe9FFIBwOc0jcUUUhn/2Q==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image \n",
"\n",
"results_dir = os.path.join(os.environ[\"LOCAL_PROJECT_DIR\"], \"local/training/tao/detectnet_v2/resnet18_palletjack/test_loco/images_annotated\")\n",
"# pil_img = Image(filename=os.path.join(os.getenv(\"LOCAL_PROJECT_DIR\"), 'detecnet_v2/july_resnet18_trials/new_pellet_distractors_10k/test_loco/images_annotated/1564562568.298206.jpg'))\n",
" \n",
"image_names = [\"1564562568.298206.jpg\", \"1564562628.517229.jpg\", \"1564562843.0618184.jpg\", \"593768,3659.jpg\", \"516447400,977.jpg\"] \n",
" \n",
"images = [Image(filename = os.path.join(results_dir, image_name)) for image_name in image_names]\n",
"\n",
"display(*images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next Steps <a class=\"anchor\" id=\"head-8\"></a>\n",
"\n",
"#### Generating Synthetic Data for your use case:\n",
"\n",
"* Make changes in the Domain Randomization under the Synthetic Data Generation script (`palletjack_sdg/standalone_palletjack_sdg.py`\n",
"* Add additional objects of interest in the scene (similar to how `palletjacks` are added, you can add `forklifts`, `ladders` etc.) to generate data\n",
"* Use [different](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#downloading-the-models) models for training with TAO (for object detection, you can use `YOLO`, `SSD`, `EfficientDet`) \n",
"* Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html). These can be used for training a model of your own framework and choice\n",
"* Exploring the option of using `Synthetic + Real` data for training a network. Can be particularly useful for generating more data around particular corner cases\n",
"\n",
"\n",
"#### Deploying Trained Models:\n",
"\n",
"* After obtaining satisfactory results with the training process, you can further optimize your model for deployment with the help of Pruning and QAT.\n",
"* TAO models can directly be deployed on Jetson with Isaac ROS or Deepstream which ensures your end-to-end pipeline being optimized (data acquisition -> model inference -> results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "f23a2831654361cfd8b219e05b5055fdda3e37fe5c0b020e6226f740844c300a"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/README.md | # Requirements
- Install [Isaac Sim](https://docs.omniverse.nvidia.com/isaacsim/latest/install_workstation.html)
- Training via TAO Toolkit Docker container (TAO setup instructions in `local_train` notebook)
## Synthetic Data Generation
- Provide the path of your Isaac Sim installation folder in the `generate_data.sh` script
- Make the script an executable after adding the Isaac Sim Path (`chmod +x generate_data.sh`)
- Run the script (`./generate_data.sh`)
- We will generate data for the `palletjack` class of objects with annotations in KITTI format
- This generated data can be used to train your own model (framework and architecture of your choice)
## Training with TAO Toolkit
- The data generated in the previus step can be directly fed to TAO for training
- The `local_train` notebook provides a walkthrough of the steps:
- Setting up TAO docker container
- Downloading pre-trained model, we will use the `DetectNet_v2` model with a `resnet_18` backbone
- Running TAO training with `spec` files provided
- Visualizing model performance on real world data
- Visualize model metric with Tensorboard
<img src="../images/tensorboard/tensorboard_resized_palletjack.png"/>
## Next steps
### Generating Synthetic Data for your use case
- Make changes in the Domain Randomization under the Synthetic Data Generation [script](../palletjack_sdg/standalone_palletjack_sdg.py)
- Add additional objects of interest in the scene (similar to how palletjacks are added, you can add forklifts, ladders etc.) to generate dataUse different models for training with TAO (for object detection, you can use YOLO, SSD, EfficientDet)
- Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html) These can be used for training a model of your own framework and choice)
- Exploring the option of using Synthetic + Real data for training a network. Can be particularly useful for generating more data around particular corner cases
### Deploying Trained Models
- The trained model can be pruned and optimized for inference with TAO
- This can then be deployed on a robot with NVIDIA Jetson |
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/generate_data.sh | #!/bin/bash
# This is the path where Isaac Sim is installed which contains the python.sh script
ISAAC_SIM_PATH="<ENTER_FULL_PATH_TO_ISAAC_SIM_HERE>"
## Go to location of the SDG script
cd ../palletjack_sdg
SCRIPT_PATH="${PWD}/standalone_palletjack_sdg.py"
OUTPUT_WAREHOUSE="${PWD}/palletjack_data/distractors_warehouse"
OUTPUT_ADDITIONAL="${PWD}/palletjack_data/distractors_additional"
OUTPUT_NO_DISTRACTORS="${PWD}/palletjack_data/no_distractors"
## Go to Isaac Sim location for running with ./python.sh
cd $ISAAC_SIM_PATH
echo "Starting Data Generation"
./python.sh $SCRIPT_PATH --height 544 --width 960 --num_frames 2000 --distractors warehouse --data_dir $OUTPUT_WAREHOUSE
./python.sh $SCRIPT_PATH --height 544 --width 960 --num_frames 2000 --distractors additional --data_dir $OUTPUT_ADDITIONAL
./python.sh $SCRIPT_PATH --height 544 --width 960 --num_frames 1000 --distractors None --data_dir $OUTPUT_NO_DISTRACTORS
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/training/tao/specs/training/resnet18_distractors.txt | random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tao-experiments/local/training/tao/tfrecords/distractors_warehouse/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/local/training/tao/tfrecords/distractors_additional/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/local/training/tao/tfrecords/no_distractors/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
}
image_extension: "png"
target_class_mapping {
key: "palletjack"
value: "palletjack"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 960
output_image_height: 544
min_bbox_width: 20.0
min_bbox_height: 20.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 0.5
zoom_max: 1.5
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "palletjack"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15000000596
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 20
}
}
}
}
model_config {
pretrained_model_file: "/workspace/tao-experiments/local/training/tao/pretrained_model/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
arch: "resnet"
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 5
minimum_detection_ground_truth_overlap {
key: "palletjack"
value: 0.5
}
evaluation_box_config {
key: "palletjack"
value {
minimum_height: 25
maximum_height: 9999
minimum_width: 25
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "palletjack"
class_weight: 1.0
coverage_foreground_weight: 0.0500000007451
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 1.0
}
}
enable_autoweighting: true
max_objective_weight: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 32
num_epochs: 100
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-06
max_learning_rate: 5e-04
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
visualizer{
enabled: true
num_images: 10
scalar_logging_frequency: 10
infrequent_logging_frequency: 5
target_class_config {
key: "palletjack"
value: {
coverage_threshold: 0.005
}
}
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "palletjack"
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.400000154972
}
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/training/tao/specs/inference/new_inference_specs.txt | inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "palletjack"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 32
gpu_index: 0
# model handler config
tlt_config{
model: "/workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"palletjack"
value: {
confidence_model: "aggregate_cov"
output_map: "palletjack"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.005
clustering_algorithm: DBSCAN
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
dbscan_confidence_threshold: 0.9
minimum_bounding_box_height: 20
}
}
}
classwise_bbox_handler_config{
key:"default"
value: {
confidence_model: "aggregate_cov"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 20
}
}
}
}
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/training/tao/specs/tfrecords/distractors_warehouse.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/training/tao/specs/tfrecords/no_distractors.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/local/training/tao/specs/tfrecords/distractors_additional.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/palletjack_sdg/palletjack_datagen.sh | #!/bin/bash
# This is the path where Isaac Sim is installed which contains the python.sh script
ISAAC_SIM_PATH='/isaac-sim'
echo "Starting Data Generation"
cd $ISAAC_SIM_PATH
echo $PWD
./python.sh /isaac-sim/palletjack_sdg/standalone_palletjack_sdg.py --headless True --height 544 --width 960 --num_frames 2000 --distractors warehouse --data_dir /isaac-sim/palletjack_sdg/palletjack_data/distractors_warehouse
./python.sh /isaac-sim/palletjack_sdg/standalone_palletjack_sdg.py --headless True --height 544 --width 960 --num_frames 2000 --distractors additional --data_dir /isaac-sim/palletjack_sdg/palletjack_data/distractors_additional
./python.sh /isaac-sim/palletjack_sdg/standalone_palletjack_sdg.py --headless True --height 544 --width 960 --num_frames 1000 --distractors None --data_dir /isaac-sim/palletjack_sdg/palletjack_data/no_distractors
|
NVIDIA-AI-IOT/synthetic_data_generation_training_workflow/palletjack_sdg/standalone_palletjack_sdg.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from omni.isaac.kit import SimulationApp
import os
import argparse
parser = argparse.ArgumentParser("Dataset generator")
parser.add_argument("--headless", type=bool, default=False, help="Launch script headless, default is False")
parser.add_argument("--height", type=int, default=544, help="Height of image")
parser.add_argument("--width", type=int, default=960, help="Width of image")
parser.add_argument("--num_frames", type=int, default=1000, help="Number of frames to record")
parser.add_argument("--distractors", type=str, default="warehouse",
help="Options are 'warehouse' (default), 'additional' or None")
parser.add_argument("--data_dir", type=str, default=os.getcwd() + "/_palletjack_data",
help="Location where data will be output")
args, unknown_args = parser.parse_known_args()
# This is the config used to launch simulation.
CONFIG = {"renderer": "RayTracedLighting", "headless": args.headless,
"width": args.width, "height": args.height, "num_frames": args.num_frames}
simulation_app = SimulationApp(launch_config=CONFIG)
## This is the path which has the background scene in which objects will be added.
ENV_URL = "/Isaac/Environments/Simple_Warehouse/warehouse.usd"
import carb
import omni
import omni.usd
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import get_current_stage, open_stage
from pxr import Semantics
import omni.replicator.core as rep
from omni.isaac.core.utils.semantics import get_semantics
# Increase subframes if shadows/ghosting appears of moving objects
# See known issues: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html#known-issues
rep.settings.carb_settings("/omni/replicator/RTSubframes", 4)
# This is the location of the palletjacks in the simready asset library
PALLETJACKS = ["http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Scale_A/PalletTruckScale_A01_PR_NVD_01.usd",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Heavy_Duty_A/HeavyDutyPalletTruck_A01_PR_NVD_01.usd",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Low_Profile_A/LowProfilePalletTruck_A01_PR_NVD_01.usd"]
# The warehouse distractors which will be added to the scene and randomized
DISTRACTORS_WAREHOUSE = 2 * ["/Isaac/Environments/Simple_Warehouse/Props/S_TrafficCone.usd",
"/Isaac/Environments/Simple_Warehouse/Props/S_WetFloorSign.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_C_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticB_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticD_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticE_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BucketPlastic_B.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1262.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1268.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1482.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1683.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_291.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1454.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1513.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_A_04.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_05.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_C_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_E_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_PushcartA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_04.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_03.usd"]
## Additional distractors which can be added to the scene
DISTRACTORS_ADDITIONAL = ["/Isaac/Environments/Hospital/Props/Pharmacy_Low.usd",
"/Isaac/Environments/Hospital/Props/SM_BedSideTable_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_BooksSet_26.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleB.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleA.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleC.usd",
"/Isaac/Environments/Hospital/Props/SM_Cart_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Chair_02a.usd",
"/Isaac/Environments/Hospital/Props/SM_Chair_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Computer_02b.usd",
"/Isaac/Environments/Hospital/Props/SM_Desk_04a.usd",
"/Isaac/Environments/Hospital/Props/SM_DisposalStand_02.usd",
"/Isaac/Environments/Hospital/Props/SM_FirstAidKit_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_GasCart_01c.usd",
"/Isaac/Environments/Hospital/Props/SM_Gurney_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_HospitalBed_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_MedicalBag_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Mirror.usd",
"/Isaac/Environments/Hospital/Props/SM_MopSet_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_SideTable_02a.usd",
"/Isaac/Environments/Hospital/Props/SM_SupplyCabinet_01c.usd",
"/Isaac/Environments/Hospital/Props/SM_SupplyCart_01e.usd",
"/Isaac/Environments/Hospital/Props/SM_TrashCan.usd",
"/Isaac/Environments/Hospital/Props/SM_Washbasin.usd",
"/Isaac/Environments/Hospital/Props/SM_WheelChair_01a.usd",
"/Isaac/Environments/Office/Props/SM_WaterCooler.usd",
"/Isaac/Environments/Office/Props/SM_TV.usd",
"/Isaac/Environments/Office/Props/SM_TableC.usd",
"/Isaac/Environments/Office/Props/SM_Recliner.usd",
"/Isaac/Environments/Office/Props/SM_Personenleitsystem_Red1m.usd",
"/Isaac/Environments/Office/Props/SM_Lamp02_162.usd",
"/Isaac/Environments/Office/Props/SM_Lamp02.usd",
"/Isaac/Environments/Office/Props/SM_HandDryer.usd",
"/Isaac/Environments/Office/Props/SM_Extinguisher.usd"]
# The textures which will be randomized for the wall and floor
TEXTURES = ["/Isaac/Materials/Textures/Patterns/nv_asphalt_yellow_weathered.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_green_white.jpg",
"/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg",
"/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_square_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_marble.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg",
"/Isaac/Materials/Textures/Patterns/nv_concrete_aged_with_lines.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stone_painted_grey.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_various.jpg",
"/Isaac/Materials/Textures/Patterns/nv_carpet_abstract_pattern.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_siding_weathered_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_animalfur_pattern_greys.jpg",
"/Isaac/Materials/Textures/Patterns/nv_artificialgrass_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_bamboo_desktop.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_red_stacked.jpg",
"/Isaac/Materials/Textures/Patterns/nv_fireplace_wall.jpg",
"/Isaac/Materials/Textures/Patterns/nv_fabric_square_grid.jpg",
"/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg",
"/Isaac/Materials/Textures/Patterns/nv_marble.jpg",
"/Isaac/Materials/Textures/Patterns/nv_gravel_grey_leaves.jpg",
"/Isaac/Materials/Textures/Patterns/nv_plastic_blue.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stone_red_hatch.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stucco_red_painted.jpg",
"/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stucco_smooth_blue.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg"]
def update_semantics(stage, keep_semantics=[]):
""" Remove semantics from the stage except for keep_semantic classes"""
for prim in stage.Traverse():
if prim.HasAPI(Semantics.SemanticsAPI):
processed_instances = set()
for property in prim.GetProperties():
is_semantic = Semantics.SemanticsAPI.IsSemanticsAPIPath(property.GetPath())
if is_semantic:
instance_name = property.SplitName()[1]
if instance_name in processed_instances:
# Skip repeated instance, instances are iterated twice due to their two semantic properties (class, data)
continue
processed_instances.add(instance_name)
sem = Semantics.SemanticsAPI.Get(prim, instance_name)
type_attr = sem.GetSemanticTypeAttr()
data_attr = sem.GetSemanticDataAttr()
for semantic_class in keep_semantics:
# Check for our data classes needed for the model
if data_attr.Get() == semantic_class:
continue
else:
# remove semantics of all other prims
prim.RemoveProperty(type_attr.GetName())
prim.RemoveProperty(data_attr.GetName())
prim.RemoveAPI(Semantics.SemanticsAPI, instance_name)
# needed for loading textures correctly
def prefix_with_isaac_asset_server(relative_path):
assets_root_path = get_assets_root_path()
if assets_root_path is None:
raise Exception("Nucleus server not found, could not access Isaac Sim assets folder")
return assets_root_path + relative_path
def full_distractors_list(distractor_type="warehouse"):
"""Distractor type allowed are warehouse, additional or None. They load corresponding objects and add
them to the scene for DR"""
full_dist_list = []
if distractor_type == "warehouse":
for distractor in DISTRACTORS_WAREHOUSE:
full_dist_list.append(prefix_with_isaac_asset_server(distractor))
elif distractor_type == "additional":
for distractor in DISTRACTORS_ADDITIONAL:
full_dist_list.append(prefix_with_isaac_asset_server(distractor))
else:
print("No Distractors being added to the current scene for SDG")
return full_dist_list
def full_textures_list():
full_tex_list = []
for texture in TEXTURES:
full_tex_list.append(prefix_with_isaac_asset_server(texture))
return full_tex_list
def add_palletjacks():
rep_obj_list = [rep.create.from_usd(palletjack_path, semantics=[("class", "palletjack")], count=2) for palletjack_path in PALLETJACKS]
rep_palletjack_group = rep.create.group(rep_obj_list)
return rep_palletjack_group
def add_distractors(distractor_type="warehouse"):
full_distractors = full_distractors_list(distractor_type)
distractors = [rep.create.from_usd(distractor_path, count=1) for distractor_path in full_distractors]
distractor_group = rep.create.group(distractors)
return distractor_group
# This will handle replicator
def run_orchestrator():
rep.orchestrator.run()
# Wait until started
while not rep.orchestrator.get_is_started():
simulation_app.update()
# Wait until stopped
while rep.orchestrator.get_is_started():
simulation_app.update()
rep.BackendDispatch.wait_until_done()
rep.orchestrator.stop()
def main():
# Open the environment in a new stage
print(f"Loading Stage {ENV_URL}")
open_stage(prefix_with_isaac_asset_server(ENV_URL))
stage = get_current_stage()
# Run some app updates to make sure things are properly loaded
for i in range(100):
if i % 10 == 0:
print(f"App uppdate {i}..")
simulation_app.update()
textures = full_textures_list()
rep_palletjack_group = add_palletjacks()
rep_distractor_group = add_distractors(distractor_type=args.distractors)
# We only need labels for the palletjack objects
update_semantics(stage=stage, keep_semantics=["palletjack"])
# Create camera with Replicator API for gathering data
cam = rep.create.camera(clipping_range=(0.1, 1000000))
# trigger replicator pipeline
with rep.trigger.on_frame(num_frames=CONFIG["num_frames"]):
# Move the camera around in the scene, focus on the center of warehouse
with cam:
rep.modify.pose(position=rep.distribution.uniform((-9.2, -11.8, 0.4), (7.2, 15.8, 4)),
look_at=(0, 0, 0))
# Get the Palletjack body mesh and modify its color
with rep.get.prims(path_pattern="SteerAxles"):
rep.randomizer.color(colors=rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
# Randomize the pose of all the added palletjacks
with rep_palletjack_group:
rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)),
rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)),
scale=rep.distribution.uniform((0.01, 0.01, 0.01), (0.01, 0.01, 0.01)))
# Modify the pose of all the distractors in the scene
with rep_distractor_group:
rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)),
rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)),
scale=rep.distribution.uniform(1, 1.5))
# Randomize the lighting of the scene
with rep.get.prims(path_pattern="RectLight"):
rep.modify.attribute("color", rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
rep.modify.attribute("intensity", rep.distribution.normal(100000.0, 600000.0))
rep.modify.visibility(rep.distribution.choice([True, False, False, False, False, False, False]))
# select floor material
random_mat_floor = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures),
roughness=rep.distribution.uniform(0, 1),
metallic=rep.distribution.choice([0, 1]),
emissive_texture=rep.distribution.choice(textures),
emissive_intensity=rep.distribution.uniform(0, 1000),)
with rep.get.prims(path_pattern="SM_Floor"):
rep.randomizer.materials(random_mat_floor)
# select random wall material
random_mat_wall = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures),
roughness=rep.distribution.uniform(0, 1),
metallic=rep.distribution.choice([0, 1]),
emissive_texture=rep.distribution.choice(textures),
emissive_intensity=rep.distribution.uniform(0, 1000),)
with rep.get.prims(path_pattern="SM_Wall"):
rep.randomizer.materials(random_mat_wall)
# Set up the writer
writer = rep.WriterRegistry.get("KittiWriter")
# output directory of writer
output_directory = args.data_dir
print("Outputting data to ", output_directory)
# use writer for bounding boxes, rgb and segmentation
writer.initialize(output_dir=output_directory,
omit_semantic_type=True,)
# attach camera render products to wrieter so that data is outputted
RESOLUTION = (CONFIG["width"], CONFIG["height"])
render_product = rep.create.render_product(cam, RESOLUTION)
writer.attach(render_product)
# run rep pipeline
run_orchestrator()
simulation_app.update()
if __name__ == "__main__":
try:
main()
except Exception as e:
carb.log_error(f"Exception: {e}")
import traceback
traceback.print_exc()
finally:
simulation_app.close()
|
abizovnuralem/go2_omniverse/terrain_cfg.py | # Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from terrain_generator_cfg import TerrainGeneratorCfg
import omni.isaac.orbit.terrains as terrain_gen
ROUGH_TERRAINS_CFG = TerrainGeneratorCfg(
size=(8.0, 8.0),
border_width=0.0,
num_rows=1,
num_cols=2,
horizontal_scale=0.1,
vertical_scale=0.005,
slope_threshold=0.75,
use_cache=False,
sub_terrains={
"pyramid_stairs": terrain_gen.MeshPyramidStairsTerrainCfg(
proportion=0.2,
step_height_range=(0.05, 0.23),
step_width=0.3,
platform_width=3.0,
border_width=1.0,
holes=False,
),
"pyramid_stairs_inv": terrain_gen.MeshInvertedPyramidStairsTerrainCfg(
proportion=0.2,
step_height_range=(0.05, 0.23),
step_width=0.3,
platform_width=3.0,
border_width=1.0,
holes=False,
),
},
) |
abizovnuralem/go2_omniverse/agent_cfg.py | # Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
unitree_go2_agent_cfg = {
'seed': 42,
'device': 'cuda',
'num_steps_per_env': 24,
'max_iterations': 15000,
'empirical_normalization': False,
'policy': {
'class_name': 'ActorCritic',
'init_noise_std': 1.0,
'actor_hidden_dims': [512, 256, 128],
'critic_hidden_dims': [512, 256, 128],
'activation': 'elu'
},
'algorithm': {
'class_name': 'PPO',
'value_loss_coef': 1.0,
'use_clipped_value_loss': True,
'clip_param': 0.2,
'entropy_coef': 0.01,
'num_learning_epochs': 5,
'num_mini_batches': 4,
'learning_rate': 0.001,
'schedule': 'adaptive',
'gamma': 0.99,
'lam': 0.95,
'desired_kl': 0.01,
'max_grad_norm': 1.0
},
'save_interval': 50,
'experiment_name': 'unitree_go2_rough',
'run_name': '',
'logger': 'tensorboard',
'neptune_project': 'orbit',
'wandb_project': 'orbit',
'resume': False,
'load_run': '.*',
'load_checkpoint': 'model_.*.pt'
} |
abizovnuralem/go2_omniverse/terrain_generator_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
Configuration classes defining the different terrains available. Each configuration class must
inherit from ``omni.isaac.orbit.terrains.terrains_cfg.TerrainConfig`` and define the following attributes:
- ``name``: Name of the terrain. This is used for the prim name in the USD stage.
- ``function``: Function to generate the terrain. This function must take as input the terrain difficulty
and the configuration parameters and return a `tuple with the `trimesh`` mesh object and terrain origin.
"""
from __future__ import annotations
import numpy as np
import trimesh
from collections.abc import Callable
from dataclasses import MISSING
from typing import Literal
from omni.isaac.orbit.utils import configclass
@configclass
class FlatPatchSamplingCfg:
"""Configuration for sampling flat patches on the sub-terrain.
For a given sub-terrain, this configuration specifies how to sample flat patches on the terrain.
The sampled flat patches can be used for spawning robots, targets, etc.
Please check the function :meth:`~omni.isaac.orbit.terrains.utils.find_flat_patches` for more details.
"""
num_patches: int = MISSING
"""Number of patches to sample."""
patch_radius: float | list[float] = MISSING
"""Radius of the patches.
A list of radii can be provided to check for patches of different sizes. This is useful to deal with
cases where the terrain may have holes or obstacles in some areas.
"""
x_range: tuple[float, float] = (-1e6, 1e6)
"""The range of x-coordinates to sample from. Defaults to (-1e6, 1e6).
This range is internally clamped to the size of the terrain mesh.
"""
y_range: tuple[float, float] = (-1e6, 1e6)
"""The range of y-coordinates to sample from. Defaults to (-1e6, 1e6).
This range is internally clamped to the size of the terrain mesh.
"""
z_range: tuple[float, float] = (-1e6, 1e6)
"""Allowed range of z-coordinates for the sampled patch. Defaults to (-1e6, 1e6)."""
max_height_diff: float = MISSING
"""Maximum allowed height difference between the highest and lowest points on the patch."""
@configclass
class SubTerrainBaseCfg:
"""Base class for terrain configurations.
All the sub-terrain configurations must inherit from this class.
The :attr:`size` attribute is the size of the generated sub-terrain. Based on this, the terrain must
extend from :math:`(0, 0)` to :math:`(size[0], size[1])`.
"""
function: Callable[[float, SubTerrainBaseCfg], tuple[list[trimesh.Trimesh], np.ndarray]] = MISSING
"""Function to generate the terrain.
This function must take as input the terrain difficulty and the configuration parameters and
return a tuple with a list of ``trimesh`` mesh objects and the terrain origin.
"""
proportion: float = 1.0
"""Proportion of the terrain to generate. Defaults to 1.0.
This is used to generate a mix of terrains. The proportion corresponds to the probability of sampling
the particular terrain. For example, if there are two terrains, A and B, with proportions 0.3 and 0.7,
respectively, then the probability of sampling terrain A is 0.3 and the probability of sampling terrain B
is 0.7.
"""
size: tuple[float, float] = MISSING
"""The width (along x) and length (along y) of the terrain (in m)."""
flat_patch_sampling: dict[str, FlatPatchSamplingCfg] | None = None
"""Dictionary of configurations for sampling flat patches on the sub-terrain. Defaults to None,
in which case no flat patch sampling is performed.
The keys correspond to the name of the flat patch sampling configuration and the values are the
corresponding configurations.
"""
@configclass
class TerrainGeneratorCfg:
"""Configuration for the terrain generator."""
seed: int | None = None
"""The seed for the random number generator. Defaults to None,
in which case the seed is not set."""
curriculum: bool = False
"""Whether to use the curriculum mode. Defaults to False.
If True, the terrains are generated based on their difficulty parameter. Otherwise,
they are randomly generated.
"""
size: tuple[float, float] = MISSING
"""The width (along x) and length (along y) of each sub-terrain (in m).
Note:
This value is passed on to all the sub-terrain configurations.
"""
border_width: float = 0.0
"""The width of the border around the terrain (in m). Defaults to 0.0."""
num_rows: int = 1
"""Number of rows of sub-terrains to generate. Defaults to 1."""
num_cols: int = 1
"""Number of columns of sub-terrains to generate. Defaults to 1."""
color_scheme: Literal["height", "random", "none"] = "none"
"""Color scheme to use for the terrain. Defaults to "none".
The available color schemes are:
- "height": Color based on the height of the terrain.
- "random": Random color scheme.
- "none": No color scheme.
"""
horizontal_scale: float = 0.1
"""The discretization of the terrain along the x and y axes (in m). Defaults to 0.1.
This value is passed on to all the height field sub-terrain configurations.
"""
vertical_scale: float = 0.005
"""The discretization of the terrain along the z axis (in m). Defaults to 0.005.
This value is passed on to all the height field sub-terrain configurations.
"""
slope_threshold: float | None = 0.75
"""The slope threshold above which surfaces are made vertical. Defaults to 0.75.
If None no correction is applied.
This value is passed on to all the height field sub-terrain configurations.
"""
sub_terrains: dict[str, SubTerrainBaseCfg] = MISSING
"""Dictionary of sub-terrain configurations.
The keys correspond to the name of the sub-terrain configuration and the values are the corresponding
configurations.
"""
difficulty_range: tuple[float, float] = (0.0, 1.0)
"""The range of difficulty values for the sub-terrains. Defaults to (0.0, 1.0).
If curriculum is enabled, the terrains will be generated based on this range in ascending order
of difficulty. Otherwise, the terrains will be generated based on this range in a random order.
"""
use_cache: bool = False
"""Whether to load the terrain from cache if it exists. Defaults to True."""
cache_dir: str = "/tmp/orbit/terrains"
"""The directory where the terrain cache is stored. Defaults to "/tmp/orbit/terrains"."""
|
abizovnuralem/go2_omniverse/main.py | # Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Script to play a checkpoint if an RL agent from RSL-RL."""
from __future__ import annotations
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# local imports
import cli_args # isort: skip
# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
parser.add_argument("--cpu", action="store_true", default=False, help="Use CPU pipeline.")
parser.add_argument(
"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
)
parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default="Isaac-Velocity-Rough-Unitree-Go2-v0", help="Name of the task.")
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
# append RSL-RL cli arguments
cli_args.add_rsl_rl_args(parser)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import omni
ext_manager = omni.kit.app.get_app().get_extension_manager()
ext_manager.set_extension_enabled_immediate("omni.isaac.ros2_bridge", True)
"""Rest everything follows."""
import os
import math
import gymnasium as gym
import torch
import carb
import usdrt.Sdf
from omni.isaac.orbit_tasks.utils import get_checkpoint_path
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlVecEnvWrapper
)
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_assets.unitree import UNITREE_GO2_CFG
from omni.isaac.orbit.envs import RLTaskEnvCfg
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg
from omni.isaac.orbit.managers import CurriculumTermCfg as CurrTerm
from omni.isaac.orbit.managers import EventTermCfg as EventTerm
from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup
from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm
from omni.isaac.orbit.managers import RewardTermCfg as RewTerm
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm
from omni.isaac.orbit.scene import InteractiveSceneCfg
from omni.isaac.orbit.sensors import ContactSensorCfg, RayCasterCfg, patterns, CameraCfg
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit.utils.noise import AdditiveUniformNoiseCfg as Unoise
import omni.isaac.orbit_tasks.locomotion.velocity.mdp as mdp
import omni.appwindow # Contains handle to keyboard
from rsl_rl.runners import OnPolicyRunner
from typing import Literal
from dataclasses import MISSING
from omnigraph import create_front_cam_omnigraph
from agent_cfg import unitree_go2_agent_cfg
from terrain_cfg import ROUGH_TERRAINS_CFG
base_command = [0, 0, 0]
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Configuration for the terrain scene with a legged robot."""
# ground terrain
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="generator",
terrain_generator=ROUGH_TERRAINS_CFG,
max_init_terrain_level=5,
collision_group=-1,
physics_material=sim_utils.RigidBodyMaterialCfg(
friction_combine_mode="multiply",
restitution_combine_mode="multiply",
static_friction=1.0,
dynamic_friction=1.0,
),
visual_material=sim_utils.MdlFileCfg(
mdl_path="{NVIDIA_NUCLEUS_DIR}/Materials/Base/Architecture/Shingles_01.mdl",
project_uvw=True,
),
debug_vis=False,
)
# robots
robot: ArticulationCfg = MISSING
# sensors
camera = CameraCfg(
prim_path="{ENV_REGEX_NS}/Robot/base/front_cam",
update_period=0.1,
height=480,
width=640,
data_types=["rgb", "distance_to_image_plane"],
spawn=sim_utils.PinholeCameraCfg(
focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5)
),
offset=CameraCfg.OffsetCfg(pos=(0.510, 0.0, 0.015), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"),
)
height_scanner = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
attach_yaw_only=True,
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
debug_vis=False,
mesh_prim_paths=["/World/ground"],
)
contact_forces = ContactSensorCfg(prim_path="{ENV_REGEX_NS}/Robot/.*", history_length=3, track_air_time=True)
# lights
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
)
sky_light = AssetBaseCfg(
prim_path="/World/skyLight",
spawn=sim_utils.DomeLightCfg(color=(0.13, 0.13, 0.13), intensity=1000.0),
)
def constant_commands(env: RLTaskEnvCfg) -> torch.Tensor:
global base_command
"""The generated command from the command generator."""
return torch.tensor([base_command], device=env.device).repeat(env.num_envs, 1)
@configclass
class ObservationsCfg:
"""Observation specifications for the MDP."""
@configclass
class PolicyCfg(ObsGroup):
"""Observations for policy group."""
# observation terms (order preserved)
base_lin_vel = ObsTerm(func=mdp.base_lin_vel)
base_ang_vel = ObsTerm(func=mdp.base_ang_vel)
projected_gravity = ObsTerm(
func=mdp.projected_gravity,
noise=Unoise(n_min=-0.05, n_max=0.05),
)
velocity_commands = ObsTerm(func=constant_commands)
joint_pos = ObsTerm(func=mdp.joint_pos_rel)
joint_vel = ObsTerm(func=mdp.joint_vel_rel)
actions = ObsTerm(func=mdp.last_action)
height_scan = ObsTerm(
func=mdp.height_scan,
params={"sensor_cfg": SceneEntityCfg("height_scanner")},
clip=(-1.0, 1.0),
)
def __post_init__(self):
self.enable_corruption = True
self.concatenate_terms = True
# observation groups
policy: PolicyCfg = PolicyCfg()
@configclass
class ActionsCfg:
"""Action specifications for the MDP."""
joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True)
@configclass
class CommandsCfg:
"""Command specifications for the MDP."""
base_velocity = mdp.UniformVelocityCommandCfg(
asset_name="robot",
resampling_time_range=(0.0, 0.0),
rel_standing_envs=0.02,
rel_heading_envs=1.0,
heading_command=True,
heading_control_stiffness=0.5,
debug_vis=True,
ranges=mdp.UniformVelocityCommandCfg.Ranges(
lin_vel_x=(0.0, 0.0), lin_vel_y=(0.0, 0.0), ang_vel_z=(0.0, 0.0), heading=(0, 0)
),
)
@configclass
class RewardsCfg:
"""Reward terms for the MDP."""
# -- task
track_lin_vel_xy_exp = RewTerm(
func=mdp.track_lin_vel_xy_exp, weight=1.0, params={"command_name": "base_velocity", "std": math.sqrt(0.25)}
)
track_ang_vel_z_exp = RewTerm(
func=mdp.track_ang_vel_z_exp, weight=0.5, params={"command_name": "base_velocity", "std": math.sqrt(0.25)}
)
# -- penalties
lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=-2.0)
ang_vel_xy_l2 = RewTerm(func=mdp.ang_vel_xy_l2, weight=-0.05)
dof_torques_l2 = RewTerm(func=mdp.joint_torques_l2, weight=-1.0e-5)
dof_acc_l2 = RewTerm(func=mdp.joint_acc_l2, weight=-2.5e-7)
action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01)
feet_air_time = RewTerm(
func=mdp.feet_air_time,
weight=0.125,
params={
"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*FOOT"),
"command_name": "base_velocity",
"threshold": 0.5,
},
)
undesired_contacts = RewTerm(
func=mdp.undesired_contacts,
weight=-1.0,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*THIGH"), "threshold": 1.0},
)
# -- optional penalties
flat_orientation_l2 = RewTerm(func=mdp.flat_orientation_l2, weight=0.0)
dof_pos_limits = RewTerm(func=mdp.joint_pos_limits, weight=0.0)
@configclass
class TerminationsCfg:
"""Termination terms for the MDP."""
time_out = DoneTerm(func=mdp.time_out, time_out=True)
base_contact = DoneTerm(
func=mdp.illegal_contact,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names="base"), "threshold": 1.0},
)
@configclass
class EventCfg:
"""Configuration for events."""
# startup
physics_material = EventTerm(
func=mdp.randomize_rigid_body_material,
mode="startup",
params={
"asset_cfg": SceneEntityCfg("robot", body_names=".*"),
"static_friction_range": (0.8, 0.8),
"dynamic_friction_range": (0.6, 0.6),
"restitution_range": (0.0, 0.0),
"num_buckets": 64,
},
)
@configclass
class CurriculumCfg:
"""Curriculum terms for the MDP."""
terrain_levels = CurrTerm(func=mdp.terrain_levels_vel)
@configclass
class ViewerCfg:
"""Configuration of the scene viewport camera."""
eye: tuple[float, float, float] = (7.5, 7.5, 7.5)
lookat: tuple[float, float, float] = (0.0, 0.0, 0.0)
cam_prim_path: str = "/OmniverseKit_Persp"
resolution: tuple[int, int] = (1920, 1080)
origin_type: Literal["world", "env", "asset_root"] = "world"
env_index: int = 0
asset_name: str | None = None
@configclass
class LocomotionVelocityRoughEnvCfg(RLTaskEnvCfg):
"""Configuration for the locomotion velocity-tracking environment."""
# Scene settings
scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=2.5)
viewer: ViewerCfg = ViewerCfg()
# Basic settings
observations: ObservationsCfg = ObservationsCfg()
actions: ActionsCfg = ActionsCfg()
commands: CommandsCfg = CommandsCfg()
# MDP settings
rewards: RewardsCfg = RewardsCfg()
terminations: TerminationsCfg = TerminationsCfg()
events: EventCfg = EventCfg()
curriculum: CurriculumCfg = CurriculumCfg()
def __post_init__(self):
"""Post initialization."""
# general settings
self.decimation = 4
self.episode_length_s = 20.0
# simulation settings
self.sim.dt = 0.005
self.sim.disable_contact_processing = True
self.sim.physics_material = self.scene.terrain.physics_material
# update sensor update periods
# we tick all the sensors based on the smallest update period (physics update period)
if self.scene.height_scanner is not None:
self.scene.height_scanner.update_period = self.decimation * self.sim.dt
if self.scene.contact_forces is not None:
self.scene.contact_forces.update_period = self.sim.dt
# check if terrain levels curriculum is enabled - if so, enable curriculum for terrain generator
# this generates terrains with increasing difficulty and is useful for training
if getattr(self.curriculum, "terrain_levels", None) is not None:
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.curriculum = True
else:
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.curriculum = False
@configclass
class UnitreeGo2RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
self.scene.robot = UNITREE_GO2_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/base"
# reduce action scale
self.actions.joint_pos.scale = 0.25
# rewards
self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot"
self.rewards.feet_air_time.weight = 0.01
self.rewards.undesired_contacts = None
self.rewards.dof_torques_l2.weight = -0.0002
self.rewards.track_lin_vel_xy_exp.weight = 1.5
self.rewards.track_ang_vel_z_exp.weight = 0.75
self.rewards.dof_acc_l2.weight = -2.5e-7
# terminations
self.terminations.base_contact.params["sensor_cfg"].body_names = "base"
#create ros2 camera stream omnigraph
create_front_cam_omnigraph()
def sub_keyboard_event(event, *args, **kwargs) -> bool:
global base_command
if event.type == carb.input.KeyboardEventType.KEY_PRESS:
if event.input.name == 'W':
base_command = [1, 0, 0]
if event.input.name == 'S':
base_command = [-1, 0, 0]
if event.input.name == 'A':
base_command = [0, 1, 0]
if event.input.name == 'D':
base_command = [0, -1, 0]
if event.input.name == 'Q':
base_command = [0, 0, 1]
if event.input.name == 'E':
base_command = [0, 0, -1]
elif event.type == carb.input.KeyboardEventType.KEY_RELEASE:
base_command = [0, 0, 0]
return True
def main():
# acquire input interface
_input = carb.input.acquire_input_interface()
_appwindow = omni.appwindow.get_default_app_window()
_keyboard = _appwindow.get_keyboard()
_sub_keyboard = _input.subscribe_to_keyboard_events(_keyboard, sub_keyboard_event)
"""Play with RSL-RL agent."""
# parse configuration
env_cfg = UnitreeGo2RoughEnvCfg()
env_cfg.scene.num_envs = 1
agent_cfg: RslRlOnPolicyRunnerCfg = unitree_go2_agent_cfg
# create isaac environment
env = gym.make(args_cli.task, cfg=env_cfg)
# wrap around environment for rsl-rl
env = RslRlVecEnvWrapper(env)
# specify directory for logging experiments
log_root_path = os.path.join("logs", "rsl_rl", agent_cfg["experiment_name"])
log_root_path = os.path.abspath(log_root_path)
print(f"[INFO] Loading experiment from directory: {log_root_path}")
resume_path = get_checkpoint_path(log_root_path, agent_cfg["load_run"], agent_cfg["load_checkpoint"])
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# load previously trained model
ppo_runner = OnPolicyRunner(env, agent_cfg, log_dir=None, device=agent_cfg["device"])
ppo_runner.load(resume_path)
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# obtain the trained policy for inference
policy = ppo_runner.get_inference_policy(device=env.unwrapped.device)
# reset environment
obs, _ = env.get_observations()
# simulate environment
while simulation_app.is_running():
# run everything in inference mode
with torch.inference_mode():
# agent stepping
actions = policy(obs)
# env stepping
obs, _, _, _ = env.step(actions)
# close the simulator
env.close()
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close() |
abizovnuralem/go2_omniverse/omnigraph.py | # Copyright (c) 2024, RoboVerse community
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import omni
import omni.graph.core as og
def create_front_cam_omnigraph():
"""Define the OmniGraph for the Isaac Sim environment."""
keys = og.Controller.Keys
graph_path = "/ROS_" + "front_cam"
(camera_graph, _, _, _) = og.Controller.edit(
{
"graph_path": graph_path,
"evaluator_name": "execution",
"pipeline_stage": og.GraphPipelineStage.GRAPH_PIPELINE_STAGE_SIMULATION,
},
{
keys.CREATE_NODES: [
("OnPlaybackTick", "omni.graph.action.OnPlaybackTick"),
("IsaacCreateRenderProduct", "omni.isaac.core_nodes.IsaacCreateRenderProduct"),
("ROS2CameraHelper", "omni.isaac.ros2_bridge.ROS2CameraHelper"),
],
keys.SET_VALUES: [
("IsaacCreateRenderProduct.inputs:cameraPrim", "/World/envs/env_0/Robot/base/front_cam"),
("IsaacCreateRenderProduct.inputs:enabled", True),
("ROS2CameraHelper.inputs:type", "rgb"),
("ROS2CameraHelper.inputs:topicName", "unitree_go2/front_cam/rgb"),
("ROS2CameraHelper.inputs:frameId", "unitree_go2"),
],
keys.CONNECT: [
("OnPlaybackTick.outputs:tick", "IsaacCreateRenderProduct.inputs:execIn"),
("IsaacCreateRenderProduct.outputs:execOut", "ROS2CameraHelper.inputs:execIn"),
("IsaacCreateRenderProduct.outputs:renderProductPath", "ROS2CameraHelper.inputs:renderProductPath"),
],
},
) |
abizovnuralem/go2_omniverse/cli_args.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import argparse
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import RslRlOnPolicyRunnerCfg
def add_rsl_rl_args(parser: argparse.ArgumentParser):
"""Add RSL-RL arguments to the parser.
Args:
parser: The parser to add the arguments to.
"""
# create a new argument group
arg_group = parser.add_argument_group("rsl_rl", description="Arguments for RSL-RL agent.")
# -- experiment arguments
arg_group.add_argument(
"--experiment_name", type=str, default=None, help="Name of the experiment folder where logs will be stored."
)
arg_group.add_argument("--run_name", type=str, default=None, help="Run name suffix to the log directory.")
# -- load arguments
arg_group.add_argument("--resume", type=bool, default=None, help="Whether to resume from a checkpoint.")
arg_group.add_argument("--load_run", type=str, default=None, help="Name of the run folder to resume from.")
arg_group.add_argument("--checkpoint", type=str, default=None, help="Checkpoint file to resume from.")
# -- logger arguments
arg_group.add_argument(
"--logger", type=str, default=None, choices={"wandb", "tensorboard", "neptune"}, help="Logger module to use."
)
arg_group.add_argument(
"--log_project_name", type=str, default=None, help="Name of the logging project when using wandb or neptune."
)
def parse_rsl_rl_cfg(task_name: str, args_cli: argparse.Namespace) -> RslRlOnPolicyRunnerCfg:
"""Parse configuration for RSL-RL agent based on inputs.
Args:
task_name: The name of the environment.
args_cli: The command line arguments.
Returns:
The parsed configuration for RSL-RL agent based on inputs.
"""
from omni.isaac.orbit_tasks.utils.parse_cfg import load_cfg_from_registry
# load the default configuration
rslrl_cfg: RslRlOnPolicyRunnerCfg = load_cfg_from_registry(task_name, "rsl_rl_cfg_entry_point")
# override the default configuration with CLI arguments
if args_cli.seed is not None:
rslrl_cfg.seed = args_cli.seed
if args_cli.resume is not None:
rslrl_cfg.resume = args_cli.resume
if args_cli.load_run is not None:
rslrl_cfg.load_run = args_cli.load_run
if args_cli.checkpoint is not None:
rslrl_cfg.load_checkpoint = args_cli.checkpoint
if args_cli.run_name is not None:
rslrl_cfg.run_name = args_cli.run_name
if args_cli.logger is not None:
rslrl_cfg.logger = args_cli.logger
# set the project name for wandb and neptune
if rslrl_cfg.logger in {"wandb", "neptune"} and args_cli.log_project_name:
rslrl_cfg.wandb_project = args_cli.log_project_name
rslrl_cfg.neptune_project = args_cli.log_project_name
return rslrl_cfg
|
abizovnuralem/go2_omniverse/README.md | # Welcome to the Unitree Go2 Omniverse Project!
I am thrilled to announce that the Unitree Go2 robot has now been integrated with the Nvidia Isaac Sim (Orbit), marking a major step forward in robotics research and development. The combination of these two cutting-edge technologies opens up a world of possibilities for creating and testing algorithms in a variety of simulated environments.
Get ready to take your research to the next level with this powerful new resource at your fingertips!
Real time Go2 Balancing:
<p align="center">
<img width="1280" height="600" src="https://github.com/abizovnuralem/go2_omniverse/assets/33475993/60c2233a-7586-49b6-a134-a7bddc4dd9ae" alt='Go2'>
</p>
Go2 Ros2 Camera stream:
<p align="center">
<img width="1200" height="440" src="https://github.com/abizovnuralem/go2_omniverse/assets/33475993/c740147b-ce00-4d7c-94de-0140be135e3e" alt='Go2'>
</p>
## Project RoadMap:
1. PPO balancing algorithm :white_check_mark:
2. Keyboard real time control :white_check_mark:
3. Camera stream to ROS2 :white_check_mark:
4. Lidar stream to ROS2
5. IMU data stream to ROS2
6. URDF real-time joints sync
## Your feedback and support mean the world to us.
If you're as enthusiastic about this project as we are, please consider giving it a :star: star on our GitHub repository.
Your encouragement fuels our passion and helps us develop our RoadMap further. We welcome any help or suggestions you can offer!
Together, let's push the boundaries of what's possible with the Unitree Go2 and ROS2!
## System requirements
You need to install Ubuntu 20.04 with Nvidia Isaac Sim and Nvidia Orbit.
The full instruction:
```
https://isaac-orbit.github.io/orbit/source/setup/installation.html
```
Also, you need to install ROS2 on your system and configure it:
```
https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_ros.html#isaac-sim-app-install-ros
```
## Usage
Go inside the repo folder, then
```
conda activate orbit
python main.py
```
## Development
To contribute or modify the project, refer to these resources for implementing additional features or improving the existing codebase. PRs are welcome!
## License
This project is licensed under the BSD 2-clause License - see the [LICENSE](https://github.com/abizovnuralem/go2_omniverse/blob/master/LICENSE) file for details.
|
abizovnuralem/go2_omniverse/logs/rsl_rl/unitree_go2_rough/2024-04-06_02-37-07/params/env.yaml | viewer:
eye: !!python/tuple
- 7.5
- 7.5
- 7.5
lookat: !!python/tuple
- 0.0
- 0.0
- 0.0
cam_prim_path: /OmniverseKit_Persp
resolution: !!python/tuple
- 1280
- 720
origin_type: world
env_index: 0
asset_name: null
sim:
physics_prim_path: /physicsScene
dt: 0.005
substeps: 1
gravity: !!python/tuple
- 0.0
- 0.0
- -9.81
enable_scene_query_support: false
use_fabric: true
disable_contact_processing: true
use_gpu_pipeline: true
device: cuda:0
physx:
use_gpu: true
solver_type: 1
min_position_iteration_count: 1
max_position_iteration_count: 255
min_velocity_iteration_count: 0
max_velocity_iteration_count: 255
enable_ccd: false
enable_stabilization: true
enable_enhanced_determinism: false
bounce_threshold_velocity: 0.5
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
gpu_max_rigid_contact_count: 8388608
gpu_max_rigid_patch_count: 163840
gpu_found_lost_pairs_capacity: 2097152
gpu_found_lost_aggregate_pairs_capacity: 33554432
gpu_total_aggregate_pairs_capacity: 2097152
gpu_collision_stack_size: 67108864
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
physics_material:
func: omni.isaac.orbit.sim.spawners.materials.physics_materials:spawn_rigid_body_material
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
improve_patch_friction: true
friction_combine_mode: multiply
restitution_combine_mode: multiply
compliant_contact_stiffness: 0.0
compliant_contact_damping: 0.0
ui_window_class_type: omni.isaac.orbit.envs.ui.rl_task_env_window:RLTaskEnvWindow
decimation: 4
scene:
num_envs: 4096
env_spacing: 2.5
lazy_sensor_update: true
replicate_physics: true
robot:
class_type: omni.isaac.orbit.assets.articulation.articulation:Articulation
prim_path: /World/envs/env_.*/Robot
spawn:
func: omni.isaac.orbit.sim.spawners.from_files.from_files:spawn_from_usd
visible: true
semantic_tags: null
copy_from_source: true
mass_props: null
rigid_props:
rigid_body_enabled: null
kinematic_enabled: null
disable_gravity: false
linear_damping: 0.0
angular_damping: 0.0
max_linear_velocity: 1000.0
max_angular_velocity: 1000.0
max_depenetration_velocity: 1.0
max_contact_impulse: null
enable_gyroscopic_forces: null
retain_accelerations: false
solver_position_iteration_count: null
solver_velocity_iteration_count: null
sleep_threshold: null
stabilization_threshold: null
collision_props: null
activate_contact_sensors: true
scale: null
articulation_props:
articulation_enabled: null
enabled_self_collisions: false
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: null
stabilization_threshold: null
fixed_tendons_props: null
joint_drive_props: null
visual_material_path: material
visual_material: null
usd_path: http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/2023.1.1/Isaac/Samples/Orbit/Robots/Unitree/Go2/go2.usd
init_state:
pos: !!python/tuple
- 0.0
- 0.0
- 0.4
rot: &id007 !!python/tuple
- 1.0
- 0.0
- 0.0
- 0.0
lin_vel: &id001 !!python/tuple
- 0.0
- 0.0
- 0.0
ang_vel: *id001
joint_pos:
.*L_hip_joint: 0.1
.*R_hip_joint: -0.1
F[L,R]_thigh_joint: 0.8
R[L,R]_thigh_joint: 1.0
.*_calf_joint: -1.5
joint_vel:
.*: 0.0
collision_group: 0
debug_vis: false
soft_joint_pos_limit_factor: 0.9
actuators:
base_legs:
class_type: omni.isaac.orbit.actuators.actuator_pd:DCMotor
joint_names_expr:
- .*_hip_joint
- .*_thigh_joint
- .*_calf_joint
effort_limit: 23.5
velocity_limit: 30.0
stiffness: 25.0
damping: 0.5
armature: null
friction: 0.0
saturation_effort: 23.5
terrain:
class_type: omni.isaac.orbit.terrains.terrain_importer:TerrainImporter
collision_group: -1
prim_path: /World/ground
num_envs: 4096
terrain_type: generator
terrain_generator:
seed: null
curriculum: true
size: &id002 !!python/tuple
- 8.0
- 8.0
border_width: 20.0
num_rows: 10
num_cols: 20
color_scheme: none
horizontal_scale: 0.1
vertical_scale: 0.005
slope_threshold: 0.75
sub_terrains:
pyramid_stairs:
function: omni.isaac.orbit.terrains.trimesh.mesh_terrains:pyramid_stairs_terrain
proportion: 0.2
size: *id002
flat_patch_sampling: null
border_width: 1.0
step_height_range: &id003 !!python/tuple
- 0.05
- 0.23
step_width: 0.3
platform_width: 3.0
holes: false
difficulty: 0.968515003601978
seed: null
pyramid_stairs_inv:
function: omni.isaac.orbit.terrains.trimesh.mesh_terrains:inverted_pyramid_stairs_terrain
proportion: 0.2
size: *id002
flat_patch_sampling: null
border_width: 1.0
step_height_range: *id003
step_width: 0.3
platform_width: 3.0
holes: false
difficulty: 0.994603458291093
seed: null
boxes:
function: omni.isaac.orbit.terrains.trimesh.mesh_terrains:random_grid_terrain
proportion: 0.2
size: *id002
flat_patch_sampling: null
grid_width: 0.45
grid_height_range: !!python/tuple
- 0.025
- 0.1
platform_width: 2.0
holes: false
difficulty: 0.944897318205577
seed: null
random_rough:
function: omni.isaac.orbit.terrains.height_field.hf_terrains:random_uniform_terrain
proportion: 0.2
size: *id002
flat_patch_sampling: null
border_width: 0.25
horizontal_scale: 0.1
vertical_scale: 0.005
slope_threshold: 0.75
noise_range: !!python/tuple
- 0.01
- 0.06
noise_step: 0.01
downsampled_scale: 0.1
difficulty: 0.9722392846049497
seed: null
hf_pyramid_slope:
function: omni.isaac.orbit.terrains.height_field.hf_terrains:pyramid_sloped_terrain
proportion: 0.1
size: *id002
flat_patch_sampling: null
border_width: 0.25
horizontal_scale: 0.1
vertical_scale: 0.005
slope_threshold: 0.75
slope_range: &id004 !!python/tuple
- 0.0
- 0.4
platform_width: 2.0
inverted: false
difficulty: 0.9325158713374326
seed: null
hf_pyramid_slope_inv:
function: omni.isaac.orbit.terrains.height_field.hf_terrains:pyramid_sloped_terrain
proportion: 0.1
size: *id002
flat_patch_sampling: null
border_width: 0.25
horizontal_scale: 0.1
vertical_scale: 0.005
slope_threshold: 0.75
slope_range: *id004
platform_width: 2.0
inverted: true
difficulty: 0.9193030378580339
seed: null
difficulty_range: !!python/tuple
- 0.0
- 1.0
use_cache: false
cache_dir: /tmp/orbit/terrains
usd_path: null
env_spacing: 2.5
visual_material:
func: omni.isaac.orbit.sim.spawners.materials.visual_materials:spawn_from_mdl_file
mdl_path: '{NVIDIA_NUCLEUS_DIR}/Materials/Base/Architecture/Shingles_01.mdl'
project_uvw: true
albedo_brightness: null
texture_scale: null
physics_material:
func: omni.isaac.orbit.sim.spawners.materials.physics_materials:spawn_rigid_body_material
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
improve_patch_friction: true
friction_combine_mode: multiply
restitution_combine_mode: multiply
compliant_contact_stiffness: 0.0
compliant_contact_damping: 0.0
max_init_terrain_level: 5
debug_vis: false
height_scanner:
class_type: omni.isaac.orbit.sensors.ray_caster.ray_caster:RayCaster
prim_path: /World/envs/env_.*/Robot/base
update_period: 0.02
history_length: 0
debug_vis: false
mesh_prim_paths:
- /World/ground
offset:
pos: !!python/tuple
- 0.0
- 0.0
- 20.0
rot: !!python/tuple
- 1.0
- 0.0
- 0.0
- 0.0
attach_yaw_only: true
pattern_cfg:
func: omni.isaac.orbit.sensors.ray_caster.patterns.patterns:grid_pattern
resolution: 0.1
size:
- 1.6
- 1.0
direction: !!python/tuple
- 0.0
- 0.0
- -1.0
max_distance: 1000000.0
drift_range: !!python/tuple
- 0.0
- 0.0
visualizer_cfg:
prim_path: /Visuals/RayCaster
markers:
hit:
func: omni.isaac.orbit.sim.spawners.shapes.shapes:spawn_sphere
visible: true
semantic_tags: null
copy_from_source: true
mass_props: null
rigid_props: null
collision_props: null
activate_contact_sensors: false
visual_material_path: material
visual_material:
func: omni.isaac.orbit.sim.spawners.materials.visual_materials:spawn_preview_surface
diffuse_color: &id005 !!python/tuple
- 1.0
- 0.0
- 0.0
emissive_color: &id006 !!python/tuple
- 0.0
- 0.0
- 0.0
roughness: 0.5
metallic: 0.0
opacity: 1.0
physics_material_path: material
physics_material: null
radius: 0.02
contact_forces:
class_type: omni.isaac.orbit.sensors.contact_sensor.contact_sensor:ContactSensor
prim_path: /World/envs/env_.*/Robot/.*
update_period: 0.005
history_length: 3
debug_vis: false
track_pose: false
track_air_time: true
force_threshold: 1.0
filter_prim_paths_expr: []
visualizer_cfg:
prim_path: /Visuals/ContactSensor
markers:
contact:
func: omni.isaac.orbit.sim.spawners.shapes.shapes:spawn_sphere
visible: true
semantic_tags: null
copy_from_source: true
mass_props: null
rigid_props: null
collision_props: null
activate_contact_sensors: false
visual_material_path: material
visual_material:
func: omni.isaac.orbit.sim.spawners.materials.visual_materials:spawn_preview_surface
diffuse_color: *id005
emissive_color: *id006
roughness: 0.5
metallic: 0.0
opacity: 1.0
physics_material_path: material
physics_material: null
radius: 0.02
no_contact:
func: omni.isaac.orbit.sim.spawners.shapes.shapes:spawn_sphere
visible: false
semantic_tags: null
copy_from_source: true
mass_props: null
rigid_props: null
collision_props: null
activate_contact_sensors: false
visual_material_path: material
visual_material:
func: omni.isaac.orbit.sim.spawners.materials.visual_materials:spawn_preview_surface
diffuse_color: !!python/tuple
- 0.0
- 1.0
- 0.0
emissive_color: *id006
roughness: 0.5
metallic: 0.0
opacity: 1.0
physics_material_path: material
physics_material: null
radius: 0.02
light:
class_type: {}
prim_path: /World/light
spawn:
func: omni.isaac.orbit.sim.spawners.lights.lights:spawn_light
visible: true
semantic_tags: null
copy_from_source: true
prim_type: DistantLight
color: !!python/tuple
- 0.75
- 0.75
- 0.75
enable_color_temperature: false
color_temperature: 6500.0
normalize: false
exposure: 0.0
intensity: 3000.0
angle: 0.53
init_state:
pos: &id008 !!python/tuple
- 0.0
- 0.0
- 0.0
rot: *id007
collision_group: 0
debug_vis: false
sky_light:
class_type: {}
prim_path: /World/skyLight
spawn:
func: omni.isaac.orbit.sim.spawners.lights.lights:spawn_light
visible: true
semantic_tags: null
copy_from_source: true
prim_type: DomeLight
color: !!python/tuple
- 0.13
- 0.13
- 0.13
enable_color_temperature: false
color_temperature: 6500.0
normalize: false
exposure: 0.0
intensity: 1000.0
texture_file: null
texture_format: automatic
init_state:
pos: *id008
rot: *id007
collision_group: 0
debug_vis: false
observations:
policy:
concatenate_terms: true
enable_corruption: true
base_lin_vel:
func: omni.isaac.orbit.envs.mdp.observations:base_lin_vel
params: {}
noise:
func: omni.isaac.orbit.utils.noise.noise_model:additive_uniform_noise
n_min: -0.1
n_max: 0.1
clip: null
scale: null
base_ang_vel:
func: omni.isaac.orbit.envs.mdp.observations:base_ang_vel
params: {}
noise:
func: omni.isaac.orbit.utils.noise.noise_model:additive_uniform_noise
n_min: -0.2
n_max: 0.2
clip: null
scale: null
projected_gravity:
func: omni.isaac.orbit.envs.mdp.observations:projected_gravity
params: {}
noise:
func: omni.isaac.orbit.utils.noise.noise_model:additive_uniform_noise
n_min: -0.05
n_max: 0.05
clip: null
scale: null
velocity_commands:
func: omni.isaac.orbit.envs.mdp.observations:generated_commands
params:
command_name: base_velocity
noise: null
clip: null
scale: null
joint_pos:
func: omni.isaac.orbit.envs.mdp.observations:joint_pos_rel
params: {}
noise:
func: omni.isaac.orbit.utils.noise.noise_model:additive_uniform_noise
n_min: -0.01
n_max: 0.01
clip: null
scale: null
joint_vel:
func: omni.isaac.orbit.envs.mdp.observations:joint_vel_rel
params: {}
noise:
func: omni.isaac.orbit.utils.noise.noise_model:additive_uniform_noise
n_min: -1.5
n_max: 1.5
clip: null
scale: null
actions:
func: omni.isaac.orbit.envs.mdp.observations:last_action
params: {}
noise: null
clip: null
scale: null
height_scan:
func: omni.isaac.orbit.envs.mdp.observations:height_scan
params:
sensor_cfg:
name: height_scanner
joint_names: null
joint_ids: !!python/object/apply:builtins.slice
- null
- null
- null
body_names: null
body_ids: !!python/object/apply:builtins.slice
- null
- null
- null
noise:
func: omni.isaac.orbit.utils.noise.noise_model:additive_uniform_noise
n_min: -0.1
n_max: 0.1
clip: &id012 !!python/tuple
- -1.0
- 1.0
scale: null
actions:
joint_pos:
class_type: omni.isaac.orbit.envs.mdp.actions.joint_actions:JointPositionAction
asset_name: robot
joint_names:
- .*
scale: 0.25
offset: 0.0
use_default_offset: true
events:
physics_material:
func: omni.isaac.orbit.envs.mdp.events:randomize_rigid_body_material
params:
asset_cfg:
name: robot
joint_names: null
joint_ids: !!python/object/apply:builtins.slice
- null
- null
- null
body_names: .*
body_ids: !!python/object/apply:builtins.slice
- null
- null
- null
static_friction_range: !!python/tuple
- 0.8
- 0.8
dynamic_friction_range: !!python/tuple
- 0.6
- 0.6
restitution_range: &id009 !!python/tuple
- 0.0
- 0.0
num_buckets: 64
mode: startup
interval_range_s: null
add_base_mass:
func: omni.isaac.orbit.envs.mdp.events:add_body_mass
params:
asset_cfg:
name: robot
joint_names: null
joint_ids: !!python/object/apply:builtins.slice
- null
- null
- null
body_names: base
body_ids: !!python/object/apply:builtins.slice
- null
- null
- null
mass_range: !!python/tuple
- -1.0
- 3.0
mode: startup
interval_range_s: null
base_external_force_torque:
func: omni.isaac.orbit.envs.mdp.events:apply_external_force_torque
params:
asset_cfg:
name: robot
joint_names: null
joint_ids: !!python/object/apply:builtins.slice
- null
- null
- null
body_names: base
body_ids: !!python/object/apply:builtins.slice
- null
- null
- null
force_range: *id009
torque_range: !!python/tuple
- -0.0
- 0.0
mode: reset
interval_range_s: null
reset_base:
func: omni.isaac.orbit.envs.mdp.events:reset_root_state_uniform
params:
pose_range:
x: &id010 !!python/tuple
- -0.5
- 0.5
y: *id010
yaw: !!python/tuple
- -3.14
- 3.14
velocity_range:
x: &id011 !!python/tuple
- 0.0
- 0.0
y: *id011
z: *id011
roll: *id011
pitch: *id011
yaw: *id011
mode: reset
interval_range_s: null
reset_robot_joints:
func: omni.isaac.orbit.envs.mdp.events:reset_joints_by_scale
params:
position_range: !!python/tuple
- 1.0
- 1.0
velocity_range: *id009
mode: reset
interval_range_s: null
push_robot: null
randomization: null
is_finite_horizon: false
episode_length_s: 20.0
rewards:
track_lin_vel_xy_exp:
func: omni.isaac.orbit.envs.mdp.rewards:track_lin_vel_xy_exp
params:
command_name: base_velocity
std: 0.5
weight: 1.5
track_ang_vel_z_exp:
func: omni.isaac.orbit.envs.mdp.rewards:track_ang_vel_z_exp
params:
command_name: base_velocity
std: 0.5
weight: 0.75
lin_vel_z_l2:
func: omni.isaac.orbit.envs.mdp.rewards:lin_vel_z_l2
params: {}
weight: -2.0
ang_vel_xy_l2:
func: omni.isaac.orbit.envs.mdp.rewards:ang_vel_xy_l2
params: {}
weight: -0.05
dof_torques_l2:
func: omni.isaac.orbit.envs.mdp.rewards:joint_torques_l2
params: {}
weight: -0.0002
dof_acc_l2:
func: omni.isaac.orbit.envs.mdp.rewards:joint_acc_l2
params: {}
weight: -2.5e-07
action_rate_l2:
func: omni.isaac.orbit.envs.mdp.rewards:action_rate_l2
params: {}
weight: -0.01
feet_air_time:
func: omni.isaac.orbit_tasks.locomotion.velocity.mdp.rewards:feet_air_time
params:
sensor_cfg:
name: contact_forces
joint_names: null
joint_ids: !!python/object/apply:builtins.slice
- null
- null
- null
body_names: .*_foot
body_ids: !!python/object/apply:builtins.slice
- null
- null
- null
command_name: base_velocity
threshold: 0.5
weight: 0.01
undesired_contacts: null
flat_orientation_l2:
func: omni.isaac.orbit.envs.mdp.rewards:flat_orientation_l2
params: {}
weight: 0.0
dof_pos_limits:
func: omni.isaac.orbit.envs.mdp.rewards:joint_pos_limits
params: {}
weight: 0.0
terminations:
time_out:
func: omni.isaac.orbit.envs.mdp.terminations:time_out
params: {}
time_out: true
base_contact:
func: omni.isaac.orbit.envs.mdp.terminations:illegal_contact
params:
sensor_cfg:
name: contact_forces
joint_names: null
joint_ids: !!python/object/apply:builtins.slice
- null
- null
- null
body_names: base
body_ids: !!python/object/apply:builtins.slice
- null
- null
- null
threshold: 1.0
time_out: false
curriculum:
terrain_levels:
func: omni.isaac.orbit_tasks.locomotion.velocity.mdp.curriculums:terrain_levels_vel
params: {}
commands:
base_velocity:
class_type: omni.isaac.orbit.envs.mdp.commands.velocity_command:UniformVelocityCommand
resampling_time_range: !!python/tuple
- 10.0
- 10.0
debug_vis: true
asset_name: robot
heading_command: true
heading_control_stiffness: 0.5
rel_standing_envs: 0.02
rel_heading_envs: 1.0
ranges:
lin_vel_x: *id012
lin_vel_y: *id012
ang_vel_z: *id012
heading: !!python/tuple
- -3.141592653589793
- 3.141592653589793
|
abizovnuralem/go2_omniverse/logs/rsl_rl/unitree_go2_rough/2024-04-06_02-37-07/params/agent.yaml | seed: 42
device: cuda
num_steps_per_env: 24
max_iterations: 15000
empirical_normalization: false
policy:
class_name: ActorCritic
init_noise_std: 1.0
actor_hidden_dims:
- 512
- 256
- 128
critic_hidden_dims:
- 512
- 256
- 128
activation: elu
algorithm:
class_name: PPO
value_loss_coef: 1.0
use_clipped_value_loss: true
clip_param: 0.2
entropy_coef: 0.01
num_learning_epochs: 5
num_mini_batches: 4
learning_rate: 0.001
schedule: adaptive
gamma: 0.99
lam: 0.95
desired_kl: 0.01
max_grad_norm: 1.0
save_interval: 50
experiment_name: unitree_go2_rough
run_name: ''
logger: tensorboard
neptune_project: orbit
wandb_project: orbit
resume: false
load_run: .*
load_checkpoint: model_.*.pt
|
PatrickPalmer/Omniverse-Connect-cmake/CMakeLists.txt |
cmake_minimum_required(VERSION 3.21)
project(OmniverseConnectCmake VERSION 0.5.0)
if (CMAKE_SOURCE_DIR STREQUAL CMAKE_BINARY_DIR)
message(FATAL_ERROR "Do not build in-source, use a build directory.")
endif()
###############
### MODULES ###
###############
### Standard CMake modules
#include(CMakePackageConfigHelpers)
#include(GNUInstallDirs)
#include(InstallRequiredSystemLibraries)
# add in our CMake module scripts
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/CMake"
"${CMAKE_CURRENT_LIST_DIR}/CMake/Modules")
###############
### OPTIONS ###
###############
# copy connect libraries locally
option(COPY_CONNECT_LOCALLY "Copy Connect Libraries Locally" OFF)
################
### SETTINGS ###
################
# C++17 standard
set(CMAKE_CXX_STANDARD 17)
if (MSVC)
add_compile_options(/EHsc)
endif()
# global use_folders property so that all generic targets go into CMakePredefinedTargets folder
set_property(GLOBAL PROPERTY USE_FOLDERS ON)
if(MSVC)
add_compile_definitions(WIN32)
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
add_compile_definitions(_WINSOCK_DEPRECATED_NO_WARNINGS)
add_compile_definitions(NOMINMAX)
add_compile_definitions(WIN32_LEAN_AND_MEAN)
add_compile_definitions(_WINSOCKAPI_)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP")
SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /MTd")
SET(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /MT")
SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /MT")
endif(MSVC)
add_compile_definitions(BOOST_ALL_DYN_LINK)
add_compile_definitions(TBB_USE_DEBUG=$<IF:$<CONFIG:Debug>,1,0>)
#################################
### Omniverse Connect Library ###
#################################
FIND_PACKAGE(OmniverseConnectSample)
IF (NOT OmniverseConnectSample_FOUND)
MESSAGE("Missing Omniverse Connect Sample directory. Skipping.")
RETURN()
ENDIF()
# Copy over dependencies into project
if (COPY_CONNECT_LOCALLY)
if (NOT EXISTS "${CMAKE_BINARY_DIR}/deps")
file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/deps")
endif()
if (NOT EXISTS "${CMAKE_BINARY_DIR}/deps/usd")
if (WIN32)
execute_process(COMMAND robocopy /S "${OmniverseConnectSample_DIR}/_build/target-deps/usd" "${CMAKE_BINARY_DIR}/deps/usd" /NFL /NDL /NJH /NJS /nc /ns /np)
endif()
endif()
if (NOT EXISTS "${CMAKE_BINARY_DIR}/deps/omni_client_library")
if (WIN32)
execute_process(COMMAND robocopy /S "${OmniverseConnectSample_DIR}/_build/target-deps/omni_client_library" "${CMAKE_BINARY_DIR}/deps/omni_client_library" /NFL /NDL /NJH /NJS /nc /ns /np)
endif()
endif()
SET(OmniverseClient_ROOT ${CMAKE_BINARY_DIR}/deps/omni_client_library)
SET(NVidiaUSD_ROOT ${CMAKE_BINARY_DIR}/deps/usd)
else()
# use libraries in place
SET(OmniverseClient_ROOT ${OmniverseConnectSample_DIR}/_build/target-deps/omni_client_library)
SET(NVidiaUSD_ROOT ${OmniverseConnectSample_DIR}/_build/target-deps/usd)
endif()
###############
### PROJECT ###
###############
add_subdirectory(SimpleApp)
|
PatrickPalmer/Omniverse-Connect-cmake/README.md | # Omniverse Connector Sample using CMake Build generator
NVidia had provided [instructions](https://forums.developer.nvidia.com/t/creating-an-omniverse-usd-app-from-the-connect-sample/189557) to hand wire in the Omniverse Connector Sample into a Visual Studio project. For more structured C++ projects, cmake is common. This repo codifies the steps in the NVidia document into a cmake project. This should be considered a lightweight simple integration though and not the level you'd expect if NVidia USD was packaged for distribution. Proper USD Cmake module should use modern CMake with optional loading of USD components and using target properties. But this is enough to get started. Currently hardwired to Connect Sample v 200.0.0.
## Setup
* Windows 10.
* Visual Studio 2019.
* cmake v3.21 or greater.
* NVidia Omniverse with Connector Sample installed locally.
* Hardwired to version 200.0.0.
* Installed in the default local users home directory in %LOCALAPPDATA%/ov/pkg.
* Run build.bat in the Connector Sample directory to download the required header and library files for OmniVerse Client and USD.
## Build
```
mkdir build
cd build
cmake -G "Visual Studio 16 2019" -A x64 ..
```
NVidia suggests copying the NVidia USD and Omniverse Client libraries locally. By default, this isn't done. To do it, add the option COPY_CONNECT_LOCALLY to cmake to copy the libraries into the build deps directory.
```
cmake -G "Visual Studio 16 2019" -A x64 -DCOPY_CONNECT_LOCALLY=ON ..
```
If the Omniverse Client libraries are not installed in the default location of %LOCALAPPDATA%\ov\pkg, set the OmniverseConnectSample_ROOT variable.
```
cmake -G "Visual Studio 16 2019" -A x64 -DOmniverseConnectSample_ROOT=D:/Omniverse/Library/connectsample-200.0.0 ..
```
## Reference
* https://forums.developer.nvidia.com/t/creating-an-omniverse-usd-app-from-the-connect-sample/189557
|
PatrickPalmer/Omniverse-Connect-cmake/CMake/Modules/FindNVidiaUSD.cmake | # Locate NVidia USD
# lightweight find module
# This module defines
# NVidiaUSD_FOUND, if false, do not use
# NVidiaUSD_INCLUDE_DIR, where to find the headers
# NVidiaUSD_LIBRARY_DIR, where to find the library
# NVidiaUSD_LIBRARIES
#
# the length of the Release and Debug lists need to match (same number of libraries)
SET(NVidiaUSD_LIB_NAMES_SHARED ar arch gf js kind pcp plug sdf tf trace usd usdGeom vt work usdShade usdLux usdSkel python37.lib)
SET(NVidiaUSD_LIB_NAMES_RELEASE ${NVidiaUSD_LIB_NAMES_SHARED} tbb boost_python37-vc141-mt-x64-1_68.lib)
SET(NVidiaUSD_LIB_NAMES_DEBUG ${NVidiaUSD_LIB_NAMES_SHARED} tbb_debug boost_python37-vc141-mt-gd-x64-1_68.lib)
FIND_PATH(NVidiaUSD_INCLUDE_DIR_RELEASE
NAMES "pxr/pxr.h"
PATHS
${NVidiaUSD_ROOT}/release/include
${NVidiaUSD_ROOT}
/usr/local/include
/usr/include
NO_DEFAULT_PATH
)
FIND_PATH(NVidiaUSD_INCLUDE_DIR_DEBUG
NAMES "pxr/pxr.h"
PATHS
${NVidiaUSD_ROOT}/debug/include
${NVidiaUSD_ROOT}
/usr/local/include
/usr/include
NO_DEFAULT_PATH
)
# NVidiaUSD does not have a common include directory but separated by build config
# as such, use a generator expression for the include dir variable
SET(NVidiaUSD_INCLUDE_DIR $<IF:$<CONFIG:Debug>,${NVidiaUSD_INCLUDE_DIR_DEBUG},${NVidiaUSD_INCLUDE_DIR_RELEASE}>)
mark_as_advanced(NVidiaUSD_INCLUDE_DIR_RELEASE NVidiaUSD_INCLUDE_DIR_DEBUG)
# pull the USD version
if (EXISTS ${NVidiaUSD_INCLUDE_DIR_RELEASE})
foreach(_usd_comp MAJOR MINOR PATCH)
file(STRINGS
"${NVidiaUSD_INCLUDE_DIR_RELEASE}/pxr/pxr.h"
_usd_tmp
REGEX "#define PXR_${_usd_comp}_VERSION .*$")
string(REGEX MATCHALL "[0-9]+" USD_${_usd_comp}_VERSION ${_usd_tmp})
endforeach()
set(USD_VERSION ${USD_MAJOR_VERSION}.${USD_MINOR_VERSION}.${USD_PATCH_VERSION})
math(EXPR PXR_VERSION "${USD_MAJOR_VERSION} * 10000 + ${USD_MINOR_VERSION} * 100 + ${USD_PATCH_VERSION}")
endif()
FIND_PATH(NVidiaUSD_LIBRARY_DIR_RELEASE
NAMES "usd.lib"
PATHS
${NVidiaUSD_ROOT}/release/lib
${NVidiaUSD_ROOT}
/usr/local/include
/usr/include
NO_DEFAULT_PATH
)
FIND_PATH(NVidiaUSD_LIBRARY_DIR_DEBUG
NAMES "usd.lib"
PATHS
${NVidiaUSD_ROOT}/debug/lib
${NVidiaUSD_ROOT}
/usr/local/include
/usr/include
NO_DEFAULT_PATH
)
# set as an generator expression so it can have different uses
SET(NVidiaUSD_LIBRARY_DIR $<IF:$<CONFIG:Debug>,${NVidiaUSD_LIBRARY_DIR_DEBUG},${NVidiaUSD_LIBRARY_DIR_RELEASE}>)
mark_as_advanced(NVidiaUSD_LIBRARY_DIR_RELEASE NVidiaUSD_LIBRARY_DIR_DEBUG)
foreach(dl rl IN ZIP_LISTS NVidiaUSD_LIB_NAMES_DEBUG NVidiaUSD_LIB_NAMES_RELEASE)
unset(_nvusd_dl_name CACHE)
unset(_nvusd_rl_name CACHE)
FIND_LIBRARY(_nvusd_dl_name
NAMES ${dl}
PATHS
${NVidiaUSD_LIBRARY_DIR_DEBUG}
)
FIND_LIBRARY(_nvusd_rl_name
NAMES ${rl}
PATHS
${NVidiaUSD_LIBRARY_DIR_RELEASE}
)
list(APPEND NVidiaUSD_LIBRARIES debug ${_nvusd_dl_name} optimized ${_nvusd_rl_name})
unset(_nvusd_dl_name CACHE)
unset(_nvusd_rl_name CACHE)
endforeach()
unset(NVidiaUSD_LIB_NAMES_SHARED CACHE)
unset(NVidiaUSD_LIB_NAMES_RELEASE CACHE)
unset(NVidiaUSD_LIB_NAMES_DEBUG CACHE)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(
NVidiaUSD
REQUIRED_VARS
NVidiaUSD_INCLUDE_DIR
NVidiaUSD_LIBRARY_DIR
NVidiaUSD_LIBRARIES
USD_VERSION
PXR_VERSION
VERSION_VAR
USD_VERSION
)
|
PatrickPalmer/Omniverse-Connect-cmake/CMake/Modules/FindOmniverseConnectSample.cmake | # Locate Omniverse Connect Sample Directory
# Users may adjust the behaviors of this module by modifying these variables.
# OmniverseConnectSample_ROOT - install location
# This module defines
# OmniverseConnectSample_FOUND - if false, do not use
# OmniverseConnectSample_DIR - where to find the headers
#
SET(OmniverseConnectSample_VERSION "200.0.0")
FIND_PATH(OmniverseConnectSample_DIR
NAMES "run_omniSimpleSensor.bat"
PATHS
${OmniverseConnectSample_ROOT}
$ENV{LOCALAPPDATA}/ov/pkg
PATH_SUFFIXES
connectsample-${OmniverseConnectSample_VERSION}
NO_DEFAULT_PATH
)
include(FindPackageHandleStandardArgs)
FIND_PACKAGE_HANDLE_STANDARD_ARGS(
OmniverseConnectSample
REQUIRED_VARS
OmniverseConnectSample_DIR
VERSION_VAR
OmniverseConnectSample_VERSION
)
|
PatrickPalmer/Omniverse-Connect-cmake/CMake/Modules/FindOmniverseClient.cmake | # Locate Omniverse Client
# lightweight find module
# This module defines
# OmniverseClient_FOUND, if false, do not use
# OmniverseClient_INCLUDE_DIR, where to find the headers
# OmniverseClient_LIBRARY_DIR
# OmniverseClient_LIBRARIES
#
# include directory is shared for both debug and release
FIND_PATH(OmniverseClient_INCLUDE_DIR
NAMES "OmniClient.h"
PATHS
${OmniverseClient_ROOT}/include
NO_DEFAULT_PATH
)
FIND_PATH(OmniverseClient_LIBRARY_DIR_RELEASE
NAMES "OmniClient.lib"
PATHS
${OmniverseClient_ROOT}/release
NO_DEFAULT_PATH
)
FIND_PATH(OmniverseClient_LIBRARY_DIR_DEBUG
NAMES "OmniClient.lib"
PATHS
${OmniverseClient_ROOT}/debug
NO_DEFAULT_PATH
)
# Created as a generator expressed so it can more useful
SET(OmniverseClient_LIBRARY_DIR $<IF:$<CONFIG:Debug>,${OmniverseClient_LIBRARY_DIR_DEBUG},${OmniverseClient_LIBRARY_DIR_RELEASE}>)
mark_as_advanced(
OmniverseClient_LIBRARY_DIR_RELEASE
OmniverseClient_LIBRARY_DIR_DEBUG
)
FIND_LIBRARY(OmniverseClient_LIBRARIES_RELEASE
NAMES "OmniClient.lib"
PATHS
${OmniverseClient_LIBRARY_DIR_RELEASE}
)
FIND_LIBRARY(OmniverseClient_LIBRARIES_DEBUG
NAMES "OmniClient.lib"
PATHS
${OmniverseClient_LIBRARY_DIR_DEBUG}
)
SET(OmniverseClient_LIBRARIES debug "${OmniverseClient_LIBRARIES_DEBUG}" optimized "${OmniverseClient_LIBRARIES_RELEASE}")
mark_as_advanced(
OmniverseClient_LIBRARIES_RELEASE
OmniverseClient_LIBRARIES_DEBUG
)
include(FindPackageHandleStandardArgs)
FIND_PACKAGE_HANDLE_STANDARD_ARGS(
OmniverseClient
REQUIRED_VARS
OmniverseClient_INCLUDE_DIR
OmniverseClient_LIBRARY_DIR
OmniverseClient_LIBRARIES
)
|
PatrickPalmer/Omniverse-Connect-cmake/SimpleApp/CMakeLists.txt |
cmake_minimum_required(VERSION 3.21)
project(SimpleApp)
FIND_PACKAGE(OmniverseClient REQUIRED)
include_directories(${OmniverseClient_INCLUDE_DIR})
FIND_PACKAGE(NVidiaUSD REQUIRED)
include_directories(${NVidiaUSD_INCLUDE_DIR})
add_executable(${PROJECT_NAME} Main.cpp)
target_link_libraries(${PROJECT_NAME}
${OmniverseClient_LIBRARIES}
${NVidiaUSD_LIBRARIES})
if(WIN32)
target_link_libraries(${PROJECT_NAME} wsock32 ws2_32)
endif(WIN32)
if(WIN32)
# robocopy returns positive numbers for success as well as errors
add_custom_command(
TARGET ${PROJECT_NAME} POST_BUILD
COMMAND cmd /c robocopy /s ${OmniverseClient_LIBRARY_DIR} $<TARGET_FILE_DIR:${PROJECT_NAME}> /NFL /NDL /NJH /NJS /nc /ns /np ^& IF %ERRORLEVEL% LEQ 8 exit 0
)
add_custom_command(
TARGET ${PROJECT_NAME} POST_BUILD
COMMAND cmd /c robocopy /s ${NVidiaUSD_LIBRARY_DIR} $<TARGET_FILE_DIR:${PROJECT_NAME}> /NFL /NDL /NJH /NJS /nc /ns /np ^& IF %ERRORLEVEL% LEQ 8 exit 0
)
endif(WIN32)
|
PatrickPalmer/Omniverse-Connect-cmake/SimpleApp/Main.cpp |
#include <string>
#include <vector>
#include <iostream>
#include <iomanip>
#include "OmniClient.h"
#include "pxr/usd/usd/stage.h"
#include "pxr/usd/usd/prim.h"
#include "pxr/usd/usd/primRange.h"
#include "pxr/usd/usdGeom/metrics.h"
using namespace pxr;
static void OmniClientConnectionStatusCallbackImpl(void* userData, const char* url, OmniClientConnectionStatus status) noexcept
{
std::cout << "Connection Status: " << omniClientGetConnectionStatusString(status) << " [" << url << "]" << std::endl;
if (status == eOmniClientConnectionStatus_ConnectError)
{
// We shouldn't just exit here - we should clean up a bit, but we're going to do it anyway
std::cout << "[ERROR] Failed connection, exiting." << std::endl;
exit(-1);
}
}
// Startup Omniverse
static bool startOmniverse()
{
// Register a function to be called whenever the library wants to print something to a log
omniClientSetLogCallback(
[](char const* threadName, char const* component, OmniClientLogLevel level, char const* message)
{
std::cout << "[" << omniClientGetLogLevelString(level) << "] " << message << std::endl;
});
// The default log level is "Info", set it to "Debug" to see all messages
omniClientSetLogLevel(eOmniClientLogLevel_Info);
// Initialize the library and pass it the version constant defined in OmniClient.h
// This allows the library to verify it was built with a compatible version. It will
// return false if there is a version mismatch.
if (!omniClientInitialize(kOmniClientVersion))
{
return false;
}
omniClientRegisterConnectionStatusCallback(nullptr, OmniClientConnectionStatusCallbackImpl);
return true;
}
int main(int argc, char* argv[])
{
if (argc != 2)
{
std::cout << "Please provide an Omniverse stage URL to read." << std::endl;
return -1;
}
startOmniverse();
UsdStageRefPtr stage = UsdStage::Open(argv[1]);
if (!stage)
{
std::cout << "Failure to open stage. Exiting." << std::endl;
return -2;
}
// Print the up-axis
std::cout << "Stage up-axis: " << UsdGeomGetStageUpAxis(stage) << std::endl;
// Print the stage's linear units, or "meters per unit"
std::cout << "Meters per unit: " << std::setprecision(5) << UsdGeomGetStageMetersPerUnit(stage) << std::endl;
auto range = stage->Traverse();
for (const auto& node : range)
{
std::cout << "Node: " << node.GetPath() << std::endl;
}
// The stage is a sophisticated object that needs to be destroyed properly.
// Since stage is a smart pointer we can just reset it
stage.Reset();
omniClientShutdown();
}
|
An-u-rag/synthetic-visual-dataset-generation/main.py | import numpy as np
import os
import json
import os
# class_name_to_id_mapping = {"Cow": 0,
# "Chicken": 1,
# "Sheep": 2,
# "Goat": 3,
# "Pig": 4}
class_name_to_id_mapping = {"cow_1": 0,
"cow_2": 1,
"cow_3": 2,
"cow_4": 3,
"cow_5": 4,
"pig_clean": 5,
"pig_dirty": 6
}
# Convert the info dict to the required yolo format and write it to disk
def convert_to_yolov5(info_dict, image_file, name_file):
print_buffer = []
print(info_dict)
data = np.load(info_dict)
# image_file = Image.open(image_file)
image_w, image_h = image_file.size
class_id = {}
with open(name_file, 'r') as info_name:
data_name = json.load(info_name)
# print(data_name)
for k, v in data_name.items():
class_id[k] = class_name_to_id_mapping[v["class"]]
# for values in data_name.values():
# # print(values)
# class_id[values["class"]] = class_name_to_id_mapping[values["class"]]
# class_id[class_name_to_id_mapping[values["class"]]] = values["class"]
# class_id.append(class_name_to_id_mapping[values["class"]])
# class_id = class_name_to_id_mapping[values["name"]]
# print(class_id)
# counter = 0
# For each bounding box
for b in data:
# Transform the bbox co-ordinates as per the format required by YOLO v5
b_center_x = (b[1] + b[3]) / 2
b_center_y = (b[2] + b[4]) / 2
b_width = (b[3] - b[1])
b_height = (b[4] - b[2])
# Normalise the co-ordinates by the dimensions of the image
b_center_x /= image_w
b_center_y /= image_h
b_width /= image_w
b_height /= image_h
# print(counter)
print(class_id)
# Write the bbox details to the file
print(class_id.get(str(b[0])))
print_buffer.append(
"{} {:.3f} {:.3f} {:.3f} {:.3f}".format(class_id.get(str(b[0])), b_center_x, b_center_y, b_width, b_height))
# counter += 1
# print(print_buffer)
# Name of the file which we have to save
path_pic = "C:/Users/xyche/Downloads/dataset"
save_file_name = os.path.join(path_pic, info_dict.replace("bounding_box_2d_tight_", "rgb_").replace("npy", "txt"))
# Save the annotation to disk
print("\n".join(print_buffer), file=open(save_file_name, "w"))
import os
from PIL import Image
# Convert and save the annotations
# path_label = "/content/RenderProduct_Replicator/bounding_box_2d_loose"
path_pic = "C:/Users/xyche/Downloads/dataset"
datanames = os.listdir(path_pic)
for i in datanames:
if os.path.splitext(i)[1] == '.npy':
# np.load("../"+info_dict)
# info_dict = open(os.path.join(path_pic,i), "rb")
info_dict = os.path.join(path_pic, i)
image_file = i.replace("bounding_box_2d_tight_", "rgb_").replace("npy", "png")
# os.listdir(path_pic)
image_file = Image.open(os.path.join(path_pic, image_file))
info_name = i.replace("bounding_box_2d_tight_", "bounding_box_2d_tight_labels_").replace("npy", "json")
name_file = os.path.join(path_pic, info_name)
convert_to_yolov5(info_dict, image_file, name_file)
# print(os.listdir(path_pic))
annotations = [os.path.join(path_pic, x) for x in os.listdir(path_pic) if x[-3:] == "txt" and x != 'metadata.txt']
# print(len(annotations))
from sklearn.model_selection import train_test_split
# Read images and annotations
images = [os.path.join(path_pic, x) for x in os.listdir(path_pic) if x[-3:] == "png"]
# print(len(images))
# datanames = os.listdir(path_pic)
annotations = [os.path.join(path_pic, x) for x in os.listdir(path_pic) if x[-3:] == "txt" and x != 'metadata.txt']
# print(len(annotations))
images.sort()
annotations.sort()
# for i in annotations:
# update_annotations = i.replace("bounding_box_2d_loose_", "rgb_").replace("txt", "png")
# if update_annotations not in images:
# print(update_annotations)
# Split the dataset into train-valid-test splits
train_images, val_images, train_annotations, val_annotations = train_test_split(images, annotations, test_size=0.2,
random_state=1)
val_images, test_images, val_annotations, test_annotations = train_test_split(val_images, val_annotations,
test_size=0.5, random_state=1)
path1 = 'C:/Users/xyche/Downloads/dataset'
os.mkdir(path1 + '/images')
os.mkdir(path1 + '/labels')
file_name = ['/train', '/val', '/test']
path2 = 'C:/Users/xyche/Downloads/dataset/images'
for name in file_name:
os.mkdir(path2 + name)
path3 = 'C:/Users/xyche/Downloads/dataset/labels'
for name in file_name:
os.mkdir(path3 + name)
import shutil
# Utility function to move images
def move_files_to_folder(list_of_files, destination_folder):
for f in list_of_files:
shutil.copy(f, destination_folder)
# Move the splits into their folders
move_files_to_folder(train_images, 'C:/Users/xyche/Downloads/dataset/images/train/')
move_files_to_folder(val_images, 'C:/Users/xyche/Downloads/dataset/images/val/')
move_files_to_folder(test_images, 'C:/Users/xyche/Downloads/dataset/images/test/')
move_files_to_folder(train_annotations, 'C:/Users/xyche/Downloads/dataset/labels/train/')
move_files_to_folder(val_annotations, 'C:/Users/xyche/Downloads/dataset/labels/val/')
move_files_to_folder(test_annotations, 'C:/Users/xyche/Downloads/dataset/labels/test/')
import yaml
desired_caps = {
'train': 'C:/Users/xyche/Downloads/dataset/images/train/',
'val': 'C:/Users/xyche/Downloads/dataset/images/val/',
'test': 'C:/Users/xyche/Downloads/dataset/images/test/',
# number of classes
'nc': 7,
# class names
#'names': ['Sam', 'Lucy', 'Ross', 'Mary', 'Elon', 'Alex', 'Max']
'names': ['0', '1', '2', '3', '4', '5', '6']
}
curpath = 'C:/Users/xyche/Downloads/dataset'
yamlpath = os.path.join(curpath, "./dataset.yaml")
with open(yamlpath, "w", encoding="utf-8") as f:
yaml.dump(desired_caps, f) |
An-u-rag/synthetic-visual-dataset-generation/fiftyone.py | import fiftyone as fo
name = "my-dataset"
dataset_dir = "C:/Users/xyche/Downloads/dataset"
# Create the dataset
dataset = fo.Dataset.from_dir(
dataset_dir=dataset_dir,
dataset_type=fo.types.YOLOv5Dataset,
name=name,
)
# View summary info about the dataset
print(dataset)
# Print the first few samples in the dataset
print(dataset.head())
session = fo.launch_app(dataset) |
An-u-rag/synthetic-visual-dataset-generation/lfs-files.txt | Yolov5TrainingOutputs/yolov5m_100ep_syntheticOnly/weights/best.pt
Yolov5TrainingOutputs/yolov5m_100ep_syntheticOnly/weights/last.pt
Yolov5TrainingOutputs/yolov5m_35ep_syntheticAndReal/weights/best.pt
Yolov5TrainingOutputs/yolov5m_35ep_syntheticAndReal/weights/last.pt
Yolov5TrainingOutputs/yolov5m_calibrateddataset_60ep_syntheticOnly/weights/best.pt
Yolov5TrainingOutputs/yolov5m_calibrateddataset_60ep_syntheticOnly/weights/last.pt
|
An-u-rag/synthetic-visual-dataset-generation/README.md | # synthetic-visual-dataset-generation
Generation of synthetic visual datasets using NVIDIA Omniverse for training Deep Learning Models.
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_35ep_syntheticAndReal/opt.yaml | weights: yolov5m.pt
cfg: models/yolov5m.yaml
data: data/cocow_less.yaml
hyp:
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
epochs: 100
batch_size: 16
imgsz: 640
rect: false
resume: false
nosave: false
noval: false
noautoanchor: false
noplots: false
evolve: null
bucket: ''
cache: disk
image_weights: false
device: ''
multi_scale: false
single_cls: false
optimizer: SGD
sync_bn: false
workers: 8
project: runs\train
name: realaugmented
exist_ok: false
quad: false
cos_lr: false
label_smoothing: 0.0
patience: 100
freeze:
- 0
save_period: 5
seed: 0
local_rank: -1
entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs\train\realaugmented
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_35ep_syntheticAndReal/results.csv | epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.060179, 0.042501, 0.047399, 0.38924, 0.58961, 0.48335, 0.31683, 0.038864, 0.021892, 0.041219, 0.070062, 0.0033265, 0.0033265
1, 0.045851, 0.030008, 0.02648, 0.84678, 0.89435, 0.88778, 0.55861, 0.030752, 0.017828, 0.0097327, 0.039996, 0.0065939, 0.0065939
2, 0.040427, 0.025773, 0.00924, 0.88862, 0.9312, 0.96263, 0.65276, 0.029045, 0.016602, 0.0049679, 0.0098639, 0.0097953, 0.0097953
3, 0.033084, 0.023386, 0.0058665, 0.96658, 0.97895, 0.98833, 0.81208, 0.018265, 0.013582, 0.0035737, 0.009703, 0.009703, 0.009703
4, 0.028449, 0.02152, 0.0045342, 0.99715, 0.95724, 0.97818, 0.81673, 0.016513, 0.01289, 0.003272, 0.009703, 0.009703, 0.009703
5, 0.025808, 0.020455, 0.0040525, 0.97977, 0.97979, 0.99223, 0.8316, 0.017152, 0.012812, 0.0027164, 0.009604, 0.009604, 0.009604
6, 0.024069, 0.019483, 0.003736, 0.98733, 0.96324, 0.98814, 0.83119, 0.015148, 0.011801, 0.0026119, 0.009505, 0.009505, 0.009505
7, 0.022668, 0.018624, 0.0033666, 0.98269, 0.98, 0.99199, 0.87309, 0.013042, 0.010928, 0.0023726, 0.009406, 0.009406, 0.009406
8, 0.021808, 0.018091, 0.0031848, 0.9865, 0.98408, 0.99171, 0.87604, 0.012709, 0.010442, 0.0023114, 0.009307, 0.009307, 0.009307
9, 0.020751, 0.017611, 0.0030756, 0.99408, 0.96869, 0.98919, 0.88347, 0.012, 0.010555, 0.0023487, 0.009208, 0.009208, 0.009208
10, 0.019956, 0.017083, 0.0029383, 0.99071, 0.98764, 0.99358, 0.8975, 0.0119, 0.0099307, 0.0021842, 0.009109, 0.009109, 0.009109
11, 0.019526, 0.016697, 0.002809, 0.99476, 0.9939, 0.99455, 0.90896, 0.010995, 0.0098474, 0.0021046, 0.00901, 0.00901, 0.00901
12, 0.018668, 0.016344, 0.0026424, 0.99294, 0.98829, 0.99396, 0.91621, 0.010793, 0.009251, 0.0019554, 0.008911, 0.008911, 0.008911
13, 0.018254, 0.016011, 0.0026178, 0.99156, 0.97781, 0.99436, 0.91446, 0.010353, 0.010057, 0.0019347, 0.008812, 0.008812, 0.008812
14, 0.017736, 0.015725, 0.002538, 0.98594, 0.99119, 0.99391, 0.91523, 0.0099938, 0.0091978, 0.0019249, 0.008713, 0.008713, 0.008713
15, 0.017273, 0.015367, 0.0024644, 0.98999, 0.98619, 0.99365, 0.91431, 0.010048, 0.0093011, 0.0018645, 0.008614, 0.008614, 0.008614
16, 0.016718, 0.014907, 0.0023995, 0.99021, 0.97447, 0.99393, 0.92152, 0.009701, 0.0091074, 0.0018957, 0.008515, 0.008515, 0.008515
17, 0.01656, 0.01485, 0.0023341, 0.99011, 0.98631, 0.99376, 0.92276, 0.0092555, 0.0089365, 0.0017182, 0.008416, 0.008416, 0.008416
18, 0.016142, 0.014425, 0.0022984, 0.98509, 0.9865, 0.99368, 0.92635, 0.0096592, 0.00844, 0.0017572, 0.008317, 0.008317, 0.008317
19, 0.015829, 0.014284, 0.0022234, 0.988, 0.99097, 0.99396, 0.92942, 0.0088655, 0.0080538, 0.0017466, 0.008218, 0.008218, 0.008218
20, 0.015527, 0.014098, 0.0022255, 0.99375, 0.99316, 0.99415, 0.93728, 0.0089408, 0.0081174, 0.0017531, 0.008119, 0.008119, 0.008119
21, 0.014933, 0.013685, 0.0021141, 0.9929, 0.99389, 0.99417, 0.93966, 0.0086126, 0.0079557, 0.0017107, 0.00802, 0.00802, 0.00802
22, 0.014726, 0.013503, 0.0021003, 0.98886, 0.99446, 0.99449, 0.94076, 0.0086606, 0.0082272, 0.0016251, 0.007921, 0.007921, 0.007921
23, 0.0149, 0.013721, 0.0020671, 0.9945, 0.99513, 0.99416, 0.94212, 0.0083909, 0.0082583, 0.0015583, 0.007822, 0.007822, 0.007822
24, 0.014716, 0.013467, 0.002043, 0.99502, 0.99395, 0.99447, 0.94638, 0.0081723, 0.0076178, 0.0015954, 0.007723, 0.007723, 0.007723
25, 0.014264, 0.013156, 0.002026, 0.99506, 0.99564, 0.99426, 0.94239, 0.0082105, 0.007427, 0.0016404, 0.007624, 0.007624, 0.007624
26, 0.014162, 0.013073, 0.0019942, 0.99423, 0.99521, 0.99452, 0.94939, 0.0080693, 0.0075285, 0.0016187, 0.007525, 0.007525, 0.007525
27, 0.013821, 0.012836, 0.0019212, 0.98903, 0.99089, 0.99451, 0.94248, 0.0079473, 0.0076233, 0.0015469, 0.007426, 0.007426, 0.007426
28, 0.013842, 0.0128, 0.0019677, 0.99452, 0.99698, 0.9946, 0.95438, 0.0076927, 0.0074841, 0.0014706, 0.007327, 0.007327, 0.007327
29, 0.013677, 0.012499, 0.0019183, 0.99376, 0.99536, 0.99467, 0.94731, 0.0076623, 0.007266, 0.0014881, 0.007228, 0.007228, 0.007228
30, 0.01327, 0.012368, 0.0018926, 0.98719, 0.99135, 0.994, 0.95829, 0.007451, 0.0068985, 0.0015266, 0.007129, 0.007129, 0.007129
31, 0.013294, 0.012339, 0.0018751, 0.99307, 0.99345, 0.99448, 0.95462, 0.0073822, 0.0069229, 0.0014756, 0.00703, 0.00703, 0.00703
32, 0.013183, 0.012274, 0.0018667, 0.99522, 0.99445, 0.99444, 0.95766, 0.0074064, 0.0068268, 0.0014567, 0.006931, 0.006931, 0.006931
33, 0.012935, 0.011993, 0.0018597, 0.98975, 0.99406, 0.99382, 0.95339, 0.0074483, 0.0066758, 0.0014333, 0.006832, 0.006832, 0.006832
34, 0.012791, 0.012048, 0.0018015, 0.99404, 0.9955, 0.99439, 0.95637, 0.0071088, 0.0065162, 0.0014506, 0.006733, 0.006733, 0.006733
35, 0.012767, 0.01201, 0.0018019, 0.99139, 0.99445, 0.99443, 0.95678, 0.0071472, 0.0066286, 0.0014406, 0.006634, 0.006634, 0.006634
36, 0.01253, 0.011927, 0.0018265, 0.98938, 0.99588, 0.99432, 0.95861, 0.0070048, 0.0066891, 0.0014507, 0.006535, 0.006535, 0.006535
37, 0.012214, 0.011523, 0.0017486, 0.99475, 0.99433, 0.9943, 0.95977, 0.0070086, 0.0064074, 0.0014152, 0.006436, 0.006436, 0.006436
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_35ep_syntheticAndReal/hyp.yaml | lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_calibrateddataset_60ep_syntheticOnly/opt.yaml | weights: yolov5m.pt
cfg: models/yolov5m.yaml
data: C:\Users\anura\Desktop\project_training\yolov5-master\data\cocow_less.yaml
hyp:
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
epochs: 100
batch_size: 16
imgsz: 640
rect: false
resume: false
nosave: false
noval: false
noautoanchor: false
noplots: false
evolve: null
bucket: ''
cache: disk
image_weights: false
device: ''
multi_scale: false
single_cls: false
optimizer: SGD
sync_bn: false
workers: 8
project: runs\train
name: main_less_100e
exist_ok: false
quad: false
cos_lr: false
label_smoothing: 0.0
patience: 100
freeze:
- 0
save_period: 5
seed: 0
local_rank: -1
entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs\train\main_less_100e
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_calibrateddataset_60ep_syntheticOnly/results.csv | epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.06036, 0.042522, 0.047339, 0.43193, 0.57683, 0.50897, 0.29284, 0.037561, 0.021421, 0.040685, 0.070062, 0.0033265, 0.0033265
1, 0.045826, 0.029832, 0.025899, 0.79915, 0.87939, 0.87473, 0.54704, 0.030453, 0.017562, 0.0094032, 0.039996, 0.0065939, 0.0065939
2, 0.040571, 0.025681, 0.0089956, 0.91205, 0.93545, 0.95254, 0.67118, 0.028203, 0.016307, 0.0050178, 0.0098639, 0.0097953, 0.0097953
3, 0.033393, 0.023388, 0.005679, 0.95455, 0.95867, 0.98463, 0.77179, 0.020015, 0.013782, 0.0037655, 0.009703, 0.009703, 0.009703
4, 0.028392, 0.02143, 0.004597, 0.9973, 0.96404, 0.9912, 0.80805, 0.018956, 0.013767, 0.0032529, 0.009703, 0.009703, 0.009703
5, 0.025768, 0.020211, 0.0039779, 0.98685, 0.99066, 0.99278, 0.84008, 0.01532, 0.012183, 0.0028106, 0.009604, 0.009604, 0.009604
6, 0.023929, 0.01948, 0.0035725, 0.99245, 0.96972, 0.99252, 0.86321, 0.014135, 0.011416, 0.0026064, 0.009505, 0.009505, 0.009505
7, 0.022555, 0.01851, 0.0033424, 0.99805, 0.99088, 0.99412, 0.87773, 0.012932, 0.010801, 0.0024772, 0.009406, 0.009406, 0.009406
8, 0.021614, 0.018145, 0.0031845, 0.99694, 0.98772, 0.99418, 0.8712, 0.013115, 0.011065, 0.0023238, 0.009307, 0.009307, 0.009307
9, 0.020801, 0.017407, 0.0030313, 0.98752, 0.99463, 0.99409, 0.89299, 0.011547, 0.01016, 0.0021781, 0.009208, 0.009208, 0.009208
10, 0.020119, 0.017075, 0.002845, 0.98968, 0.99375, 0.99375, 0.90403, 0.01174, 0.0097189, 0.0021253, 0.009109, 0.009109, 0.009109
11, 0.019486, 0.016625, 0.0027621, 0.98435, 0.98112, 0.99293, 0.89644, 0.011111, 0.0097137, 0.0019732, 0.00901, 0.00901, 0.00901
12, 0.018561, 0.016132, 0.0026911, 0.99488, 0.99231, 0.99443, 0.91311, 0.010754, 0.0099099, 0.0020187, 0.008911, 0.008911, 0.008911
13, 0.018289, 0.016036, 0.0025881, 0.9958, 0.97826, 0.99372, 0.91157, 0.010188, 0.0096808, 0.0019328, 0.008812, 0.008812, 0.008812
14, 0.017733, 0.015797, 0.0025334, 0.9958, 0.99337, 0.99457, 0.90558, 0.010342, 0.0091216, 0.0018223, 0.008713, 0.008713, 0.008713
15, 0.017407, 0.015218, 0.0024321, 0.99519, 0.99526, 0.99465, 0.92172, 0.0099861, 0.0084875, 0.0019184, 0.008614, 0.008614, 0.008614
16, 0.016727, 0.014891, 0.002368, 0.99325, 0.97374, 0.99377, 0.91629, 0.0098618, 0.0084582, 0.0017659, 0.008515, 0.008515, 0.008515
17, 0.016323, 0.014651, 0.0023237, 0.99385, 0.98955, 0.99426, 0.93251, 0.0094019, 0.009003, 0.0018288, 0.008416, 0.008416, 0.008416
18, 0.015833, 0.014261, 0.0022907, 0.99821, 0.99451, 0.99481, 0.93042, 0.0091559, 0.0082033, 0.0016856, 0.008317, 0.008317, 0.008317
19, 0.015685, 0.014107, 0.0021862, 0.99307, 0.99395, 0.99475, 0.93629, 0.0090504, 0.0081659, 0.0016275, 0.008218, 0.008218, 0.008218
20, 0.015607, 0.01404, 0.0022534, 0.99896, 0.99377, 0.99467, 0.93647, 0.0089169, 0.0081356, 0.0016453, 0.008119, 0.008119, 0.008119
21, 0.015242, 0.013822, 0.0021715, 0.99795, 0.99594, 0.99479, 0.93556, 0.0086989, 0.0080034, 0.001641, 0.00802, 0.00802, 0.00802
22, 0.014771, 0.013508, 0.0021106, 0.99053, 0.98376, 0.99424, 0.93896, 0.0084821, 0.0077857, 0.0015874, 0.007921, 0.007921, 0.007921
23, 0.01482, 0.01342, 0.0020465, 0.99777, 0.99474, 0.99467, 0.94501, 0.008435, 0.0077849, 0.0015689, 0.007822, 0.007822, 0.007822
24, 0.014648, 0.013443, 0.0020316, 0.99766, 0.99447, 0.9947, 0.94683, 0.0081288, 0.0077714, 0.0015725, 0.007723, 0.007723, 0.007723
25, 0.014352, 0.013285, 0.002045, 0.99757, 0.99463, 0.99467, 0.94678, 0.0082016, 0.0076461, 0.0015988, 0.007624, 0.007624, 0.007624
26, 0.014043, 0.013041, 0.0019901, 0.99765, 0.99354, 0.99469, 0.94276, 0.0080048, 0.0080778, 0.0015639, 0.007525, 0.007525, 0.007525
27, 0.013853, 0.012921, 0.0019673, 0.98964, 0.99268, 0.99433, 0.94964, 0.0079928, 0.0078901, 0.0015146, 0.007426, 0.007426, 0.007426
28, 0.013591, 0.012683, 0.0019622, 0.99707, 0.9733, 0.99437, 0.94426, 0.0077802, 0.0078065, 0.0015033, 0.007327, 0.007327, 0.007327
29, 0.013546, 0.012629, 0.0018869, 0.99399, 0.99324, 0.9946, 0.9454, 0.0077216, 0.0073548, 0.0014648, 0.007228, 0.007228, 0.007228
30, 0.013396, 0.012396, 0.0018777, 0.9947, 0.98624, 0.99427, 0.9551, 0.0075515, 0.0075262, 0.001424, 0.007129, 0.007129, 0.007129
31, 0.013184, 0.012317, 0.0018621, 0.99609, 0.97427, 0.99445, 0.95507, 0.0073972, 0.0071544, 0.0014313, 0.00703, 0.00703, 0.00703
32, 0.01302, 0.012232, 0.0018348, 0.99748, 0.99543, 0.99462, 0.95773, 0.007316, 0.0070902, 0.0014177, 0.006931, 0.006931, 0.006931
33, 0.012658, 0.01176, 0.001841, 0.9975, 0.99444, 0.99484, 0.96213, 0.0071074, 0.0065696, 0.0014346, 0.006832, 0.006832, 0.006832
34, 0.012903, 0.011961, 0.0017873, 0.996, 0.9956, 0.99453, 0.95779, 0.0071908, 0.0066222, 0.001386, 0.006733, 0.006733, 0.006733
35, 0.012679, 0.011875, 0.0017975, 0.99661, 0.99235, 0.99466, 0.96159, 0.0071131, 0.0062099, 0.0014056, 0.006634, 0.006634, 0.006634
36, 0.012548, 0.011875, 0.0017872, 0.99669, 0.99476, 0.99463, 0.96107, 0.0071703, 0.0067759, 0.0013848, 0.006535, 0.006535, 0.006535
37, 0.012383, 0.011594, 0.0017304, 0.99524, 0.99386, 0.99433, 0.96197, 0.0067959, 0.006299, 0.0013741, 0.006436, 0.006436, 0.006436
38, 0.012033, 0.011302, 0.0016816, 0.99604, 0.99527, 0.99464, 0.96327, 0.0068942, 0.0061426, 0.0013715, 0.006337, 0.006337, 0.006337
39, 0.012107, 0.011484, 0.0017168, 0.99749, 0.9939, 0.99453, 0.96391, 0.0068864, 0.0063842, 0.0013392, 0.006238, 0.006238, 0.006238
40, 0.012044, 0.011451, 0.0017354, 0.9974, 0.99242, 0.99452, 0.9636, 0.0067647, 0.0062284, 0.0013249, 0.006139, 0.006139, 0.006139
41, 0.011867, 0.011247, 0.0016914, 0.99796, 0.99645, 0.99468, 0.96295, 0.0066323, 0.0061993, 0.0013248, 0.00604, 0.00604, 0.00604
42, 0.011757, 0.011208, 0.0017036, 0.99483, 0.98957, 0.99471, 0.9627, 0.0065679, 0.0060496, 0.001303, 0.005941, 0.005941, 0.005941
43, 0.011799, 0.011131, 0.0016881, 0.9978, 0.99471, 0.99478, 0.96055, 0.0065614, 0.0063466, 0.001292, 0.005842, 0.005842, 0.005842
44, 0.01165, 0.011001, 0.0016854, 0.99638, 0.99353, 0.99474, 0.96608, 0.0066315, 0.0065575, 0.0012776, 0.005743, 0.005743, 0.005743
45, 0.011293, 0.010738, 0.0016411, 0.99715, 0.99579, 0.99476, 0.96692, 0.0064578, 0.0064124, 0.0012827, 0.005644, 0.005644, 0.005644
46, 0.011464, 0.010901, 0.0016774, 0.99843, 0.99317, 0.99469, 0.96639, 0.0063927, 0.0063397, 0.0012654, 0.005545, 0.005545, 0.005545
47, 0.011451, 0.010874, 0.0016478, 0.99834, 0.99568, 0.99474, 0.96609, 0.0064438, 0.0062474, 0.0012572, 0.005446, 0.005446, 0.005446
48, 0.011113, 0.010614, 0.0016245, 0.99333, 0.99526, 0.99474, 0.96536, 0.0063187, 0.0060366, 0.0012694, 0.005347, 0.005347, 0.005347
49, 0.010864, 0.010309, 0.0015959, 0.997, 0.98009, 0.99459, 0.96601, 0.0062244, 0.0059283, 0.0012538, 0.005248, 0.005248, 0.005248
50, 0.010863, 0.010352, 0.0015885, 0.99715, 0.99739, 0.99474, 0.96867, 0.0062025, 0.0056641, 0.0012219, 0.005149, 0.005149, 0.005149
51, 0.010723, 0.010268, 0.0015846, 0.99747, 0.98557, 0.99459, 0.96763, 0.0061429, 0.0060346, 0.0012092, 0.00505, 0.00505, 0.00505
52, 0.010805, 0.010181, 0.0015806, 0.99653, 0.98943, 0.99464, 0.9684, 0.0060786, 0.0057757, 0.0012346, 0.004951, 0.004951, 0.004951
53, 0.01072, 0.010196, 0.0015567, 0.99727, 0.99187, 0.99464, 0.96902, 0.0061362, 0.0058278, 0.0012183, 0.004852, 0.004852, 0.004852
54, 0.010332, 0.010039, 0.0015308, 0.9977, 0.99787, 0.99472, 0.97158, 0.006058, 0.0057146, 0.0011911, 0.004753, 0.004753, 0.004753
55, 0.010384, 0.0099824, 0.0015599, 0.99709, 0.99439, 0.99468, 0.96925, 0.0059206, 0.0057064, 0.0011789, 0.004654, 0.004654, 0.004654
56, 0.010443, 0.0099996, 0.0015322, 0.99745, 0.98431, 0.99453, 0.97073, 0.0059617, 0.0054204, 0.0011688, 0.004555, 0.004555, 0.004555
57, 0.010291, 0.0098232, 0.0015227, 0.99853, 0.97825, 0.99413, 0.96922, 0.0058381, 0.0052677, 0.0011719, 0.004456, 0.004456, 0.004456
58, 0.010212, 0.0099103, 0.0015426, 0.99813, 0.97986, 0.99462, 0.97059, 0.0058662, 0.0056099, 0.0011627, 0.004357, 0.004357, 0.004357
59, 0.010029, 0.0095723, 0.001481, 0.99836, 0.9775, 0.99462, 0.97104, 0.0057745, 0.0056545, 0.0011559, 0.004258, 0.004258, 0.004258
60, 0.0097918, 0.0094386, 0.0014769, 0.9975, 0.98776, 0.99468, 0.97134, 0.0057568, 0.0055459, 0.0011348, 0.004159, 0.004159, 0.004159
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_calibrateddataset_60ep_syntheticOnly/hyp.yaml | lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_100ep_syntheticOnly/opt.yaml | weights: yolov5m.pt
cfg: ''
data: C:\Users\anura\Desktop\project_training\yolov5-master\data\cocow.yaml
hyp:
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
epochs: 100
batch_size: 16
imgsz: 640
rect: false
resume: false
nosave: false
noval: false
noautoanchor: false
noplots: false
evolve: null
bucket: ''
cache: disk
image_weights: false
device: ''
multi_scale: false
single_cls: false
optimizer: SGD
sync_bn: false
workers: 8
project: runs\train
name: exp
exist_ok: false
quad: false
cos_lr: false
label_smoothing: 0.0
patience: 100
freeze:
- 0
save_period: -1
seed: 0
local_rank: -1
entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs\train\exp4
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_100ep_syntheticOnly/results.csv | epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.053992, 0.043881, 0.042489, 0.80915, 0.53564, 0.62308, 0.37816, 0.03692, 0.019704, 0.024569, 0.070038, 0.0033291, 0.0033291
1, 0.041013, 0.030541, 0.013805, 0.77323, 0.90354, 0.91275, 0.6445, 0.024961, 0.015431, 0.0069777, 0.039972, 0.0065965, 0.0065965
2, 0.034824, 0.026796, 0.0065317, 0.99289, 0.95388, 0.98029, 0.73448, 0.019706, 0.013803, 0.0040424, 0.0098402, 0.0097978, 0.0097978
3, 0.0286, 0.024296, 0.0047052, 0.99645, 0.96278, 0.98469, 0.81874, 0.015262, 0.012031, 0.002947, 0.009703, 0.009703, 0.009703
4, 0.024491, 0.021988, 0.0038549, 0.99342, 0.97388, 0.99392, 0.85883, 0.013596, 0.011177, 0.002958, 0.009703, 0.009703, 0.009703
5, 0.022322, 0.020845, 0.0034538, 0.98043, 0.98639, 0.99294, 0.88503, 0.01239, 0.010614, 0.0023886, 0.009604, 0.009604, 0.009604
6, 0.020819, 0.019651, 0.0031572, 0.98936, 0.99187, 0.99457, 0.89202, 0.011855, 0.010083, 0.0021047, 0.009505, 0.009505, 0.009505
7, 0.019755, 0.019044, 0.0029975, 0.99487, 0.96931, 0.9925, 0.90579, 0.011184, 0.0095185, 0.0021285, 0.009406, 0.009406, 0.009406
8, 0.018675, 0.018323, 0.0028243, 0.99684, 0.97324, 0.99343, 0.90901, 0.010004, 0.0086223, 0.0019519, 0.009307, 0.009307, 0.009307
9, 0.017978, 0.017717, 0.0026391, 0.99209, 0.98135, 0.99359, 0.91294, 0.0099189, 0.0087705, 0.0019111, 0.009208, 0.009208, 0.009208
10, 0.017052, 0.016924, 0.0025099, 0.99733, 0.97318, 0.99421, 0.92398, 0.0095667, 0.0086577, 0.0017517, 0.009109, 0.009109, 0.009109
11, 0.016492, 0.016508, 0.0024441, 0.98633, 0.99551, 0.99444, 0.93245, 0.0090813, 0.0081271, 0.0017412, 0.00901, 0.00901, 0.00901
12, 0.015925, 0.015973, 0.002392, 0.99527, 0.98446, 0.99443, 0.94081, 0.008827, 0.0081322, 0.0017193, 0.008911, 0.008911, 0.008911
13, 0.015317, 0.015597, 0.0022919, 0.99303, 0.99351, 0.99459, 0.93457, 0.008684, 0.0076958, 0.0015863, 0.008812, 0.008812, 0.008812
14, 0.015275, 0.015449, 0.0022428, 0.99833, 0.99615, 0.99473, 0.9377, 0.0082429, 0.0073264, 0.0015869, 0.008713, 0.008713, 0.008713
15, 0.014917, 0.015227, 0.0022078, 0.99778, 0.99661, 0.99475, 0.93715, 0.0081567, 0.0077125, 0.001574, 0.008614, 0.008614, 0.008614
16, 0.014697, 0.014991, 0.0021662, 0.99639, 0.99722, 0.99471, 0.94213, 0.00814, 0.0073724, 0.0015369, 0.008515, 0.008515, 0.008515
17, 0.014245, 0.014666, 0.0021245, 0.99827, 0.99495, 0.99477, 0.94664, 0.0079245, 0.0074133, 0.0015044, 0.008416, 0.008416, 0.008416
18, 0.014151, 0.014381, 0.002103, 0.9875, 0.99144, 0.99353, 0.9516, 0.0076035, 0.0067676, 0.0014876, 0.008317, 0.008317, 0.008317
19, 0.013788, 0.014153, 0.0020145, 0.99542, 0.98667, 0.99459, 0.9499, 0.0074192, 0.006648, 0.0014307, 0.008218, 0.008218, 0.008218
20, 0.013525, 0.013879, 0.0020332, 0.99374, 0.98956, 0.99461, 0.95101, 0.0074504, 0.006845, 0.0014223, 0.008119, 0.008119, 0.008119
21, 0.013336, 0.013762, 0.0019992, 0.99768, 0.99657, 0.99475, 0.94978, 0.0073046, 0.0063414, 0.0014073, 0.00802, 0.00802, 0.00802
22, 0.013308, 0.013869, 0.0019656, 0.99691, 0.99483, 0.99459, 0.95232, 0.00723, 0.006183, 0.0014209, 0.007921, 0.007921, 0.007921
23, 0.013052, 0.013467, 0.0019421, 0.99809, 0.99213, 0.99463, 0.95068, 0.0070617, 0.0061643, 0.0013937, 0.007822, 0.007822, 0.007822
24, 0.012968, 0.013566, 0.0019183, 0.98851, 0.99379, 0.99418, 0.9576, 0.0070222, 0.0066221, 0.0014043, 0.007723, 0.007723, 0.007723
25, 0.012683, 0.013212, 0.0019014, 0.99172, 0.99593, 0.99469, 0.95811, 0.0068197, 0.005894, 0.0013725, 0.007624, 0.007624, 0.007624
26, 0.012677, 0.013209, 0.001895, 0.99098, 0.99631, 0.9946, 0.95915, 0.0067775, 0.0064261, 0.0013523, 0.007525, 0.007525, 0.007525
27, 0.012488, 0.012923, 0.0018977, 0.99762, 0.99643, 0.99472, 0.96084, 0.0066267, 0.0060853, 0.0013421, 0.007426, 0.007426, 0.007426
28, 0.012261, 0.012823, 0.0018607, 0.99633, 0.99059, 0.99463, 0.96122, 0.0064941, 0.006194, 0.0013099, 0.007327, 0.007327, 0.007327
29, 0.012312, 0.012814, 0.0018499, 0.99819, 0.97984, 0.99476, 0.96016, 0.0064562, 0.0058697, 0.0012896, 0.007228, 0.007228, 0.007228
30, 0.011994, 0.012406, 0.0018205, 0.99874, 0.97481, 0.99467, 0.95663, 0.0064797, 0.0059383, 0.0013218, 0.007129, 0.007129, 0.007129
31, 0.011994, 0.012542, 0.0018374, 0.99788, 0.97503, 0.99465, 0.96131, 0.0063512, 0.0058845, 0.0012763, 0.00703, 0.00703, 0.00703
32, 0.011771, 0.012253, 0.0017875, 0.99651, 0.98672, 0.99453, 0.95916, 0.0063035, 0.0062422, 0.0012582, 0.006931, 0.006931, 0.006931
33, 0.011791, 0.012417, 0.0018011, 0.99347, 0.99194, 0.99437, 0.96346, 0.0061872, 0.0055724, 0.001257, 0.006832, 0.006832, 0.006832
34, 0.011675, 0.01216, 0.0017483, 0.99635, 0.98249, 0.99467, 0.96166, 0.006092, 0.0054784, 0.0012443, 0.006733, 0.006733, 0.006733
35, 0.011585, 0.012104, 0.0017636, 0.99474, 0.99253, 0.99468, 0.96798, 0.0061511, 0.0054464, 0.0012497, 0.006634, 0.006634, 0.006634
36, 0.01133, 0.011868, 0.0017604, 0.9971, 0.99047, 0.9947, 0.96573, 0.0059862, 0.0054573, 0.0012375, 0.006535, 0.006535, 0.006535
37, 0.011271, 0.011769, 0.0016996, 0.99398, 0.99076, 0.99468, 0.96532, 0.0059818, 0.0053213, 0.0012393, 0.006436, 0.006436, 0.006436
38, 0.011234, 0.011808, 0.0017401, 0.9946, 0.99423, 0.99461, 0.96806, 0.0059143, 0.0053788, 0.0012181, 0.006337, 0.006337, 0.006337
39, 0.011292, 0.011758, 0.0017098, 0.99622, 0.97761, 0.99372, 0.96381, 0.0058671, 0.0054468, 0.0011896, 0.006238, 0.006238, 0.006238
40, 0.011034, 0.011434, 0.0016856, 0.99801, 0.97302, 0.99344, 0.96249, 0.0058074, 0.0051224, 0.0012005, 0.006139, 0.006139, 0.006139
41, 0.010928, 0.011397, 0.0017113, 0.98786, 0.99288, 0.99395, 0.96732, 0.0057401, 0.0049549, 0.0011882, 0.00604, 0.00604, 0.00604
42, 0.010795, 0.011262, 0.0016451, 0.99434, 0.98655, 0.99459, 0.96755, 0.0057887, 0.0051588, 0.0011834, 0.005941, 0.005941, 0.005941
43, 0.010831, 0.011303, 0.0016869, 0.99453, 0.98417, 0.99452, 0.96686, 0.0057753, 0.0050893, 0.0011726, 0.005842, 0.005842, 0.005842
44, 0.01056, 0.011057, 0.0016837, 0.99801, 0.97813, 0.99461, 0.96927, 0.0056266, 0.0047512, 0.0011716, 0.005743, 0.005743, 0.005743
45, 0.010689, 0.011219, 0.0016507, 0.99302, 0.99383, 0.99463, 0.96799, 0.0056299, 0.004791, 0.0011675, 0.005644, 0.005644, 0.005644
46, 0.010557, 0.010989, 0.0016426, 0.99457, 0.99455, 0.99467, 0.96857, 0.0055653, 0.0047597, 0.0011638, 0.005545, 0.005545, 0.005545
47, 0.010409, 0.0109, 0.00159, 0.99705, 0.99182, 0.99478, 0.9684, 0.0055317, 0.0047575, 0.0011425, 0.005446, 0.005446, 0.005446
48, 0.010438, 0.010944, 0.0016192, 0.99605, 0.98553, 0.99469, 0.96795, 0.005547, 0.0047806, 0.0011413, 0.005347, 0.005347, 0.005347
49, 0.01036, 0.010887, 0.0016174, 0.99263, 0.99308, 0.99447, 0.96776, 0.0055246, 0.0046542, 0.0011295, 0.005248, 0.005248, 0.005248
50, 0.010159, 0.010688, 0.0015995, 0.9909, 0.99025, 0.99423, 0.96987, 0.0054631, 0.0046698, 0.0011178, 0.005149, 0.005149, 0.005149
51, 0.010167, 0.010606, 0.0016181, 0.99059, 0.9871, 0.9943, 0.97104, 0.0054264, 0.0047213, 0.001115, 0.00505, 0.00505, 0.00505
52, 0.010026, 0.010464, 0.0015992, 0.99013, 0.98468, 0.99408, 0.97025, 0.005382, 0.0046813, 0.0011111, 0.004951, 0.004951, 0.004951
53, 0.010005, 0.010444, 0.0015927, 0.99109, 0.99285, 0.99411, 0.97025, 0.0053544, 0.0046419, 0.0011078, 0.004852, 0.004852, 0.004852
54, 0.0098413, 0.010218, 0.0015808, 0.99285, 0.99416, 0.99467, 0.9709, 0.0053416, 0.0045208, 0.0011029, 0.004753, 0.004753, 0.004753
55, 0.0098253, 0.010183, 0.0015565, 0.99468, 0.98911, 0.99471, 0.9707, 0.0052938, 0.0045025, 0.0010998, 0.004654, 0.004654, 0.004654
56, 0.0096983, 0.010051, 0.0015634, 0.99656, 0.99674, 0.99483, 0.97151, 0.0052691, 0.0044604, 0.001093, 0.004555, 0.004555, 0.004555
57, 0.0096215, 0.010016, 0.0015319, 0.99601, 0.99344, 0.99479, 0.9717, 0.0052511, 0.0043612, 0.0010861, 0.004456, 0.004456, 0.004456
58, 0.0094835, 0.0098895, 0.0015372, 0.99522, 0.98238, 0.99467, 0.97193, 0.0052241, 0.00436, 0.0010772, 0.004357, 0.004357, 0.004357
59, 0.0095116, 0.0099899, 0.0015378, 0.99356, 0.98954, 0.99469, 0.97191, 0.0051944, 0.0044131, 0.0010707, 0.004258, 0.004258, 0.004258
60, 0.0093184, 0.0096889, 0.0015185, 0.99552, 0.99361, 0.99471, 0.97184, 0.0051687, 0.0044351, 0.0010673, 0.004159, 0.004159, 0.004159
61, 0.009209, 0.0096735, 0.0015009, 0.99598, 0.99566, 0.99476, 0.97193, 0.0051386, 0.0044436, 0.0010581, 0.00406, 0.00406, 0.00406
62, 0.0091865, 0.0095144, 0.0014903, 0.99594, 0.99445, 0.99477, 0.97193, 0.00512, 0.0044761, 0.001048, 0.003961, 0.003961, 0.003961
63, 0.009112, 0.0094079, 0.0015019, 0.99591, 0.99425, 0.99475, 0.9725, 0.0051146, 0.0045, 0.0010401, 0.003862, 0.003862, 0.003862
64, 0.0090833, 0.0094307, 0.0014957, 0.99623, 0.99462, 0.99476, 0.97205, 0.0050969, 0.0044698, 0.0010349, 0.003763, 0.003763, 0.003763
65, 0.0089284, 0.0091275, 0.0014478, 0.99688, 0.99395, 0.99478, 0.97242, 0.0050802, 0.0044144, 0.0010253, 0.003664, 0.003664, 0.003664
66, 0.0088557, 0.0092427, 0.0014616, 0.99685, 0.99286, 0.99479, 0.97194, 0.005064, 0.0043157, 0.0010201, 0.003565, 0.003565, 0.003565
67, 0.0087932, 0.0091385, 0.0014637, 0.99646, 0.99282, 0.9948, 0.97291, 0.0050451, 0.0042883, 0.0010145, 0.003466, 0.003466, 0.003466
68, 0.0087475, 0.0090739, 0.0014493, 0.99696, 0.99395, 0.9948, 0.97251, 0.0050389, 0.0042524, 0.001007, 0.003367, 0.003367, 0.003367
69, 0.0086352, 0.0089867, 0.0014343, 0.99641, 0.9931, 0.99482, 0.97235, 0.0050328, 0.0042336, 0.00099831, 0.003268, 0.003268, 0.003268
70, 0.0084897, 0.0088415, 0.0014166, 0.99637, 0.99206, 0.99481, 0.97245, 0.0050203, 0.0042108, 0.00099072, 0.003169, 0.003169, 0.003169
71, 0.0084783, 0.0087955, 0.0014366, 0.99626, 0.99267, 0.99481, 0.97245, 0.0050025, 0.0042041, 0.00098386, 0.00307, 0.00307, 0.00307
72, 0.0083285, 0.0086172, 0.0014173, 0.99584, 0.99277, 0.99482, 0.97208, 0.0049866, 0.004201, 0.00097591, 0.002971, 0.002971, 0.002971
73, 0.0083312, 0.0085593, 0.0013961, 0.99631, 0.99257, 0.99482, 0.97265, 0.0049783, 0.0041851, 0.00096843, 0.002872, 0.002872, 0.002872
74, 0.0082518, 0.008499, 0.0014003, 0.99684, 0.99323, 0.99483, 0.97266, 0.0049655, 0.0041626, 0.00096167, 0.002773, 0.002773, 0.002773
75, 0.0080546, 0.008221, 0.0013787, 0.99688, 0.99273, 0.99483, 0.973, 0.0049557, 0.0041532, 0.00095526, 0.002674, 0.002674, 0.002674
76, 0.0080255, 0.0081992, 0.0013771, 0.99701, 0.99444, 0.99484, 0.97319, 0.0049389, 0.0041419, 0.00094924, 0.002575, 0.002575, 0.002575
77, 0.0080186, 0.0081498, 0.0013811, 0.99681, 0.99449, 0.99484, 0.97352, 0.0049259, 0.004125, 0.00094319, 0.002476, 0.002476, 0.002476
78, 0.0077765, 0.0079398, 0.0013374, 0.99725, 0.99449, 0.99484, 0.97415, 0.0049132, 0.0041096, 0.00093813, 0.002377, 0.002377, 0.002377
79, 0.0077427, 0.0079202, 0.0013485, 0.99733, 0.99449, 0.99484, 0.97403, 0.0049055, 0.0040818, 0.00093243, 0.002278, 0.002278, 0.002278
80, 0.0076722, 0.0079143, 0.0013201, 0.99734, 0.99449, 0.99484, 0.97412, 0.0048931, 0.004066, 0.00092718, 0.002179, 0.002179, 0.002179
81, 0.00752, 0.0076574, 0.001304, 0.99779, 0.99366, 0.99483, 0.97445, 0.0048855, 0.0040564, 0.00092194, 0.00208, 0.00208, 0.00208
82, 0.0075028, 0.0075824, 0.0013176, 0.9978, 0.99415, 0.99483, 0.97389, 0.0048788, 0.0040542, 0.00091638, 0.001981, 0.001981, 0.001981
83, 0.0074239, 0.0074832, 0.0013193, 0.99781, 0.99267, 0.99483, 0.97442, 0.0048735, 0.0040427, 0.00091174, 0.001882, 0.001882, 0.001882
84, 0.0073179, 0.0074265, 0.0012882, 0.99784, 0.99244, 0.99483, 0.97416, 0.0048645, 0.0040304, 0.00090672, 0.001783, 0.001783, 0.001783
85, 0.0072502, 0.0073046, 0.0012928, 0.99769, 0.99184, 0.99482, 0.97451, 0.0048588, 0.0040194, 0.00090222, 0.001684, 0.001684, 0.001684
86, 0.0070948, 0.0070816, 0.0012633, 0.99784, 0.99126, 0.99482, 0.9742, 0.0048498, 0.0040095, 0.00089805, 0.001585, 0.001585, 0.001585
87, 0.0070968, 0.0070016, 0.0012718, 0.99782, 0.99032, 0.99483, 0.97438, 0.0048421, 0.0040006, 0.00089406, 0.001486, 0.001486, 0.001486
88, 0.0069572, 0.0068926, 0.0012635, 0.99783, 0.9901, 0.99483, 0.97466, 0.0048343, 0.0039934, 0.00089022, 0.001387, 0.001387, 0.001387
89, 0.0068929, 0.0067657, 0.0012573, 0.99786, 0.98976, 0.99482, 0.97457, 0.0048266, 0.0039832, 0.0008862, 0.001288, 0.001288, 0.001288
90, 0.0068226, 0.0066651, 0.0012433, 0.99784, 0.98823, 0.99483, 0.97474, 0.0048199, 0.0039665, 0.00088202, 0.001189, 0.001189, 0.001189
91, 0.0067551, 0.0066722, 0.0012306, 0.99785, 0.98785, 0.99483, 0.97458, 0.0048127, 0.0039585, 0.00087784, 0.00109, 0.00109, 0.00109
92, 0.0066082, 0.0064306, 0.0012021, 0.99788, 0.98759, 0.99482, 0.97462, 0.0048067, 0.0039513, 0.0008739, 0.000991, 0.000991, 0.000991
93, 0.0065665, 0.0063193, 0.0011967, 0.99791, 0.98678, 0.99482, 0.97466, 0.0048018, 0.0039469, 0.00087033, 0.000892, 0.000892, 0.000892
94, 0.0064373, 0.0062334, 0.0011686, 0.99796, 0.98658, 0.99482, 0.9747, 0.0047982, 0.0039393, 0.0008667, 0.000793, 0.000793, 0.000793
95, 0.0064655, 0.0061475, 0.0011966, 0.99799, 0.98639, 0.99482, 0.97494, 0.0047955, 0.0039318, 0.00086296, 0.000694, 0.000694, 0.000694
96, 0.0063656, 0.0060382, 0.0011719, 0.99802, 0.98597, 0.99482, 0.97464, 0.0047918, 0.0039261, 0.0008594, 0.000595, 0.000595, 0.000595
97, 0.0063953, 0.0060144, 0.0012074, 0.99803, 0.98561, 0.99482, 0.97478, 0.0047899, 0.0039211, 0.00085555, 0.000496, 0.000496, 0.000496
98, 0.0062851, 0.0058722, 0.0011716, 0.99803, 0.98533, 0.99481, 0.97511, 0.0047867, 0.0039175, 0.00085191, 0.000397, 0.000397, 0.000397
99, 0.0062306, 0.0057919, 0.0011629, 0.99804, 0.98513, 0.99482, 0.97456, 0.0047861, 0.0039176, 0.00084868, 0.000298, 0.000298, 0.000298
|
An-u-rag/synthetic-visual-dataset-generation/Yolov5TrainingOutputs/yolov5m_100ep_syntheticOnly/hyp.yaml | lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/MAT_gt_grass0.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MAT_gt_grass0(
uniform texture_2d WeightmapRocks = texture_2d("./Textures/WeightMapNullTexture.png",::tex::gamma_linear)
[[
anno::hidden(),
sampler_masks()
]],
uniform texture_2d WeightmapGrass = texture_2d("./Textures/WeightMapNullTexture.png",::tex::gamma_linear)
[[
anno::hidden(),
sampler_masks()
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float2 CustomizedUV1_mdl = float2(state::texture_coordinate(math::min(1,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(1,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(WeightmapRocks,float2(CustomizedUV1_mdl.x,1.0-CustomizedUV1_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local1 = math::dot(Local0, float4(1.0,0.0,0.0,0.0));
float4 Local3 = tex::lookup_float4(WeightmapGrass,float2(CustomizedUV1_mdl.x,1.0-CustomizedUV1_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local4 = math::dot(Local3, float4(1.0,0.0,0.0,0.0));
float4 Local7 = tex::lookup_float4(texture_2d("./Textures/T_AlpinePatch001_D_alt_R.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local8 = (float3(Local7.x,Local7.y,Local7.z) * Local1);
float3 Local9 = (0.0 + Local8);
float4 Local10 = tex::lookup_float4(texture_2d("./Textures/T_GDC_Grass01_D_NoisyAlpha.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local11 = (float3(Local10.x,Local10.y,Local10.z) * Local4);
float3 Local12 = (Local9 + Local11);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local12;
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
float4 Local13 = tex::lookup_float4(WeightmapGrass,float2(CustomizedUV1_mdl.x,1.0-CustomizedUV1_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local14 = math::dot(Local13, float4(1.0,0.0,0.0,0.0));
float GetGrassWeight0_mdl = Local14;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/MI_Brick_Facade_vizcegj_2K.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_Brick_Facade_vizcegj_2K(
float4 Tiling_Offset = float4(1.0,1.0,0.0,0.0)
[[
anno::display_name("Tiling/Offset"),
anno::ui_order(2),
anno::in_group("00 - Global")
]],
float RotationAngle = 0.0
[[
anno::display_name("Rotation Angle"),
anno::ui_order(3),
anno::in_group("00 - Global")
]],
uniform texture_2d Normal = texture_2d("./Textures/Placeholder_Normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(3),
anno::in_group("07 - Texture Maps"),
sampler_normal()
]],
float NormalStrength = 1.0
[[
anno::display_name("Normal Strength"),
anno::ui_order(32),
anno::in_group("05 - Normal")
]],
uniform texture_2d Albedo = texture_2d("./Textures/DefaultDiffuse.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float4 AlbedoControls = float4(1.0,1.0,1.0,0.0)
[[
anno::display_name("Albedo Controls"),
anno::ui_order(32),
anno::in_group("01 - Albedo")
]],
float4 AlbedoTint = float4(1.0,1.0,1.0,1.0)
[[
anno::display_name("Albedo Tint"),
anno::ui_order(1),
anno::in_group("01 - Albedo")
]],
float4 MetallicControls = float4(1.0,0.0,1.0,1.0)
[[
anno::display_name("Metallic Controls"),
anno::ui_order(32),
anno::in_group("02 - Metallic")
]],
uniform texture_2d Metalness = texture_2d("./Textures/BlackPlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("Metalness"),
anno::ui_order(1),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float BaseSpecular = 0.5
[[
anno::display_name("Base Specular"),
anno::ui_order(1),
anno::in_group("03 - Specular")
]],
float4 Specular_Desaturation = float4(0.5,0.5,0.5,1.0)
[[
anno::display_name("Specular - Desaturation"),
anno::ui_order(2),
anno::in_group("03 - Specular")
]],
float SpecularFromAlbedoOverride = 0.0
[[
anno::display_name("Specular From Albedo Override"),
anno::ui_order(32),
anno::in_group("03 - Specular")
]],
float MinRoughness = 0.0
[[
anno::display_name("Min Roughness"),
anno::ui_order(32),
anno::in_group("04 - Roughness")
]],
float MaxRoughness = 1.0
[[
anno::display_name("Max Roughness"),
anno::ui_order(1),
anno::in_group("04 - Roughness")
]],
uniform texture_2d ARD = texture_2d("./Textures/WhitePlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("ARD"),
anno::description("AO/R/D"),
anno::ui_order(2),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float Local0 = (0.0 * -1.0);
float2 Local1 = (float2(Tiling_Offset.x,Tiling_Offset.y) / 2.0);
float2 Local2 = (Local1 + float2(Tiling_Offset.z,Tiling_Offset.w));
float2 Local3 = (Local2 * -1.0);
float2 Local4 = (CustomizedUV0_mdl * float2(Tiling_Offset.x,Tiling_Offset.y));
float2 Local5 = (Local4 + float2(Tiling_Offset.z,Tiling_Offset.w));
float2 Local6 = (Local3 + Local5);
float Local7 = (RotationAngle * 6.283185);
float Local8 = math::cos(Local7);
float Local9 = math::sin(Local7);
float Local10 = (Local9 * -1.0);
float Local11 = math::dot(Local6, float2(Local8,Local10));
float Local12 = math::dot(Local6, float2(Local9,Local8));
float2 Local13 = (Local2 + float2(Local11,Local12));
float4 Local14 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local15 = (float2(float3(Local14.x,Local14.y,Local14.z).x,float3(Local14.x,Local14.y,Local14.z).y) * NormalStrength);
float2 Local16 = (Local0 + Local15);
float Local17 = (RotationAngle * -1.0);
float Local18 = (Local17 * 6.283185);
float Local19 = math::cos(Local18);
float Local20 = math::sin(Local18);
float Local21 = (Local20 * -1.0);
float Local22 = math::dot(Local16, float2(Local19,Local21));
float Local23 = math::dot(Local16, float2(Local20,Local19));
float2 Local24 = (0.0 + float2(Local22,Local23));
float3 Normal_mdl = float3(Local24.x,Local24.y,float3(Local14.x,Local14.y,Local14.z).z);
float4 Local25 = tex::lookup_float4(Albedo,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat);
float Local26 = math::dot(float3(Local25.x,Local25.y,Local25.z), float3(0.3,0.59,0.11));
float Local27 = (1.0 - AlbedoControls.x);
float3 Local28 = math::lerp(float3(Local25.x,Local25.y,Local25.z),float3(Local26,Local26,Local26),Local27);
float3 Local29 = (Local28 * AlbedoControls.y);
float3 Local30 = (Local29 * float3(AlbedoTint.x,AlbedoTint.y,AlbedoTint.z));
float3 Local31 = math::pow(math::max(Local30,float3(0.000001)),float3(AlbedoControls.z,AlbedoControls.z,AlbedoControls.z));
float4 Local32 = tex::lookup_float4(Metalness,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat);
float Local33 = (float3(Local32.x,Local32.y,Local32.z).x * MetallicControls.z);
float Local34 = math::round(MetallicControls.x);
float Local35 = math::lerp(MetallicControls.y,Local33,Local34);
float Local36 = math::dot(float3(Local25.x,Local25.y,Local25.z), float3(Specular_Desaturation.x,Specular_Desaturation.y,Specular_Desaturation.z));
float Local37 = math::saturate(Local36);
float Local38 = (Local37 * 0.5);
float Local39 = math::lerp(BaseSpecular,Local38,SpecularFromAlbedoOverride);
float4 Local40 = tex::lookup_float4(ARD,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat);
float Local41 = math::lerp(MinRoughness,MaxRoughness,Local40.y);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local31;
float Metallic_mdl = Local35;
float Specular_mdl = Local39;
float Roughness_mdl = Local41;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/DefaultMaterial.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material DefaultMaterial(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float2 Local0 = (CustomizedUV0_mdl / 2.0);
float2 Local1 = (Local0 / 0.05);
float4 Local2 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_N.png",::tex::gamma_linear),float2(Local1.x,1.0-Local1.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Local3 = (float3(Local2.x,Local2.y,Local2.z) * float3(0.3,0.3,1.0));
float3 Normal_mdl = Local3;
float2 Local4 = (CustomizedUV0_mdl * 20.0);
float4 Local5 = tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_M.png",::tex::gamma_linear),float2(Local4.x,1.0-Local4.y),tex::wrap_repeat,tex::wrap_repeat);
float Local6 = math::lerp(0.4,1.0,Local5.x);
float Local7 = (1.0 - Local6);
float2 Local8 = (Local0 / 0.1);
float4 Local9 = tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_M.png",::tex::gamma_linear),float2(Local8.x,1.0-Local8.y),tex::wrap_repeat,tex::wrap_repeat);
float Local10 = math::lerp(Local9.y,1.0,0.0);
float Local11 = math::lerp(Local6,Local7,Local10);
float4 Local12 = tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_M.png",::tex::gamma_linear),float2(Local0.x,1.0-Local0.y),tex::wrap_repeat,tex::wrap_repeat);
float Local13 = math::lerp(Local9.y,0.0,0.0);
float Local14 = (Local12.y + Local13);
float Local15 = math::lerp(Local14,0.5,0.5);
float Local16 = math::lerp(0.295,0.66,Local15);
float Local17 = (Local16 * 0.5);
float Local18 = (Local11 * Local17);
float Local19 = math::lerp(0.0,0.5,Local12.y);
float Local20 = math::lerp(0.7,1.0,Local9.y);
float Local21 = math::lerp(Local20,1.0,0.0);
float Local22 = (Local21 * 1.0);
float Local23 = (Local19 + Local22);
float Local24 = math::min(math::max(Local23,0.0),1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local18,Local18,Local18);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local24;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_Japanese_Roof_Tiles_vfgjddcew_2K_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Japanese_Roof_Tiles_vfgjddcew_2K_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Japanese_Roof_Tiles_vfgjddcew_2K_N.exr",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float4 Local1 = tex::lookup_float4(texture_2d("./Textures/T_Japanese_Roof_Tiles_vfgjddcew_2K_D.exr",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local1.x,Local1.y,Local1.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_Pig_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Pig_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Pig_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_Cow2_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Cow2_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Cow2_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/MI_Mossy_Forest_Boulder_wjwtbb3_2K.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Subsurface import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_Mossy_Forest_Boulder_wjwtbb3_2K(
float4 Tiling_Offset = float4(1.0,1.0,0.0,0.0)
[[
anno::display_name("Tiling/Offset"),
anno::ui_order(32),
anno::in_group("00 - Global")
]],
float RotationAngle = 0.0
[[
anno::display_name("Rotation Angle"),
anno::ui_order(1),
anno::in_group("00 - Global")
]],
uniform texture_2d Normal = texture_2d("./Textures/Placeholder_Normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(3),
anno::in_group("07 - Texture Maps"),
sampler_normal()
]],
float NormalStrength = 1.0
[[
anno::display_name("Normal Strength"),
anno::ui_order(32),
anno::in_group("05 - Normal")
]],
uniform texture_2d Albedo = texture_2d("./Textures/DefaultDiffuse.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float4 AlbedoControls = float4(1.0,1.0,1.0,0.0)
[[
anno::display_name("Albedo Controls"),
anno::ui_order(32),
anno::in_group("01 - Albedo")
]],
float4 AlbedoTint = float4(1.0,1.0,1.0,1.0)
[[
anno::display_name("Albedo Tint"),
anno::ui_order(1),
anno::in_group("01 - Albedo")
]],
float4 MetallicControls = float4(1.0,0.0,1.0,1.0)
[[
anno::display_name("Metallic Controls"),
anno::ui_order(32),
anno::in_group("02 - Metallic")
]],
uniform texture_2d Metalness = texture_2d("./Textures/BlackPlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("Metalness"),
anno::ui_order(1),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float BaseSpecular = 0.5
[[
anno::display_name("Base Specular"),
anno::ui_order(1),
anno::in_group("03 - Specular")
]],
float4 Specular_Desaturation = float4(0.5,0.5,0.5,1.0)
[[
anno::display_name("Specular - Desaturation"),
anno::ui_order(2),
anno::in_group("03 - Specular")
]],
float SpecularFromAlbedoOverride = 0.0
[[
anno::display_name("Specular From Albedo Override"),
anno::ui_order(32),
anno::in_group("03 - Specular")
]],
float MinRoughness = 0.0
[[
anno::display_name("Min Roughness"),
anno::ui_order(32),
anno::in_group("04 - Roughness")
]],
float MaxRoughness = 1.0
[[
anno::display_name("Max Roughness"),
anno::ui_order(1),
anno::in_group("04 - Roughness")
]],
uniform texture_2d DRF = texture_2d("./Textures/WhitePlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("DRF"),
anno::description("DRF"),
anno::ui_order(2),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float FuzzyRoughness = 1.0
[[
anno::display_name("Fuzzy Roughness"),
anno::ui_order(5),
anno::in_group("06 - Fuzz")
]],
float4 SSSControls = float4(0.0,1.0,1.0,1.0)
[[
anno::display_name("SSS Controls"),
anno::ui_order(2),
anno::in_group("06 - SSS")
]],
uniform texture_2d Transmission = texture_2d("./Textures/Black.png",::tex::gamma_srgb)
[[
anno::display_name("Transmission"),
anno::ui_order(32),
anno::in_group("06 - SSS"),
sampler_color()
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
[[
distill_off()
]]
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float Local0 = (0.0 * -1.0);
float2 Local1 = (float2(Tiling_Offset.x,Tiling_Offset.y) / 2.0);
float2 Local2 = (Local1 + float2(Tiling_Offset.z,Tiling_Offset.w));
float2 Local3 = (Local2 * -1.0);
float2 Local4 = (CustomizedUV0_mdl * float2(Tiling_Offset.x,Tiling_Offset.y));
float2 Local5 = (Local4 + float2(Tiling_Offset.z,Tiling_Offset.w));
float2 Local6 = (Local3 + Local5);
float Local7 = (RotationAngle * 6.283185);
float Local8 = math::cos(Local7);
float Local9 = math::sin(Local7);
float Local10 = (Local9 * -1.0);
float Local11 = math::dot(Local6, float2(Local8,Local10));
float Local12 = math::dot(Local6, float2(Local9,Local8));
float2 Local13 = (Local2 + float2(Local11,Local12));
float4 Local14 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local15 = (float2(float3(Local14.x,Local14.y,Local14.z).x,float3(Local14.x,Local14.y,Local14.z).y) * NormalStrength);
float2 Local16 = (Local0 + Local15);
float Local17 = (RotationAngle * -1.0);
float Local18 = (Local17 * 6.283185);
float Local19 = math::cos(Local18);
float Local20 = math::sin(Local18);
float Local21 = (Local20 * -1.0);
float Local22 = math::dot(Local16, float2(Local19,Local21));
float Local23 = math::dot(Local16, float2(Local20,Local19));
float2 Local24 = (0.0 + float2(Local22,Local23));
float3 Normal_mdl = float3(Local24.x,Local24.y,float3(Local14.x,Local14.y,Local14.z).z);
float4 Local25 = tex::lookup_float4(Albedo,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat);
float Local26 = math::dot(float3(Local25.x,Local25.y,Local25.z), float3(0.3,0.59,0.11));
float Local27 = (1.0 - AlbedoControls.x);
float3 Local28 = math::lerp(float3(Local25.x,Local25.y,Local25.z),float3(Local26,Local26,Local26),Local27);
float3 Local29 = (Local28 * AlbedoControls.y);
float3 Local30 = (Local29 * float3(AlbedoTint.x,AlbedoTint.y,AlbedoTint.z));
float3 Local31 = math::pow(math::max(Local30,float3(0.000001)),float3(AlbedoControls.z,AlbedoControls.z,AlbedoControls.z));
float4 Local32 = tex::lookup_float4(Metalness,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat);
float Local33 = (Local32.x * MetallicControls.z);
float Local34 = math::round(MetallicControls.x);
float Local35 = math::lerp(MetallicControls.y,Local33,Local34);
float Local36 = math::dot(float3(Local25.x,Local25.y,Local25.z), float3(Specular_Desaturation.x,Specular_Desaturation.y,Specular_Desaturation.z));
float Local37 = math::saturate(Local36);
float Local38 = (Local37 * 0.5);
float Local39 = math::lerp(BaseSpecular,Local38,SpecularFromAlbedoOverride);
float4 Local40 = tex::lookup_float4(DRF,float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat);
float Local41 = math::lerp(MinRoughness,MaxRoughness,Local40.y);
float Local42 = math::lerp(0.0,FuzzyRoughness,Local41);
float Local43 = math::lerp(Local41,Local42,Local40.z);
float4 Local44 = tex::lookup_float4(Transmission,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local45 = math::lerp(SSSControls.x,SSSControls.y,Local44.z);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float Opacity_mdl = Local45;
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local31;
float Metallic_mdl = Local35;
float Specular_mdl = Local39;
float Roughness_mdl = Local43;
float3 SubsurfaceColor_mdl = 0;
} in
::OmniUe4Subsurface(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: Opacity_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
opacity_mask: OpacityMask_mdl,
subsurface_color: SubsurfaceColor_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/MI_Old_Wooden_Trough_ugrxdbsfa_2K.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_Old_Wooden_Trough_ugrxdbsfa_2K(
uniform texture_2d Normal = texture_2d("./Textures/Placeholder_Normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(3),
anno::in_group("07 - Texture Maps"),
sampler_normal()
]],
float NormalStrength = 1.0
[[
anno::display_name("Normal Strength"),
anno::ui_order(32),
anno::in_group("05 - Normal")
]],
uniform texture_2d Albedo = texture_2d("./Textures/DefaultDiffuse.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float4 AlbedoControls = float4(1.0,1.0,1.0,0.0)
[[
anno::display_name("Albedo Controls"),
anno::ui_order(32),
anno::in_group("01 - Albedo")
]],
float4 AlbedoTint = float4(1.0,1.0,1.0,1.0)
[[
anno::display_name("Albedo Tint"),
anno::ui_order(1),
anno::in_group("01 - Albedo")
]],
float4 MetallicControls = float4(1.0,0.0,1.0,1.0)
[[
anno::display_name("Metallic Controls"),
anno::ui_order(32),
anno::in_group("02 - Metallic")
]],
uniform texture_2d Metalness = texture_2d("./Textures/BlackPlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("Metalness"),
anno::ui_order(1),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float BaseSpecular = 0.5
[[
anno::display_name("Base Specular"),
anno::ui_order(1),
anno::in_group("03 - Specular")
]],
float4 Specular_Desaturation = float4(0.5,0.5,0.5,1.0)
[[
anno::display_name("Specular - Desaturation"),
anno::ui_order(2),
anno::in_group("03 - Specular")
]],
float SpecularFromAlbedoOverride = 0.0
[[
anno::display_name("Specular From Albedo Override"),
anno::ui_order(32),
anno::in_group("03 - Specular")
]],
float MinRoughness = 0.0
[[
anno::display_name("Min Roughness"),
anno::ui_order(32),
anno::in_group("04 - Roughness")
]],
float MaxRoughness = 1.0
[[
anno::display_name("Max Roughness"),
anno::ui_order(1),
anno::in_group("04 - Roughness")
]],
uniform texture_2d DR = texture_2d("./Textures/WhitePlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("DR"),
anno::description("DR"),
anno::ui_order(32),
sampler_color()
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local1 = (float2(float3(Local0.x,Local0.y,Local0.z).x,float3(Local0.x,Local0.y,Local0.z).y) * NormalStrength);
float3 Normal_mdl = float3(Local1.x,Local1.y,float3(Local0.x,Local0.y,Local0.z).z);
float4 Local2 = tex::lookup_float4(Albedo,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local3 = math::dot(float3(Local2.x,Local2.y,Local2.z), float3(0.3,0.59,0.11));
float Local4 = (1.0 - AlbedoControls.x);
float3 Local5 = math::lerp(float3(Local2.x,Local2.y,Local2.z),float3(Local3,Local3,Local3),Local4);
float3 Local6 = (Local5 * AlbedoControls.y);
float3 Local7 = (Local6 * float3(AlbedoTint.x,AlbedoTint.y,AlbedoTint.z));
float3 Local8 = math::pow(math::max(Local7,float3(0.000001)),float3(AlbedoControls.z,AlbedoControls.z,AlbedoControls.z));
float4 Local9 = tex::lookup_float4(Metalness,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local10 = (Local9.x * MetallicControls.z);
float Local11 = math::round(MetallicControls.x);
float Local12 = math::lerp(MetallicControls.y,Local10,Local11);
float Local13 = math::dot(float3(Local2.x,Local2.y,Local2.z), float3(Specular_Desaturation.x,Specular_Desaturation.y,Specular_Desaturation.z));
float Local14 = math::saturate(Local13);
float Local15 = (Local14 * 0.5);
float Local16 = math::lerp(BaseSpecular,Local15,SpecularFromAlbedoOverride);
float4 Local17 = tex::lookup_float4(DR,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local18 = math::lerp(MinRoughness,MaxRoughness,Local17.y);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local8;
float Metallic_mdl = Local12;
float Specular_mdl = Local16;
float Roughness_mdl = Local18;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/OmniUe4Subsurface.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.0 - first version
//* 1.0.1 - fix reflection and transmission with subsurface color
//* 1.0.2 - Fix EDF in the back side: the EDF contained in surface is only used for the front side and not for the back side
//* 1.0.3 - using absolute import paths when importing standard modules
mdl 1.3;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
export annotation distill_off();
float emissive_multiplier()
[[
anno::description("the multiplier to convert UE4 emissive to raw data"),
anno::noinline()
]]
{
return 20.0f * 128.0f;
}
float get_subsurface_weight()
[[
anno::noinline()
]]
{
return 0.5f;
}
color get_subsurface_color(color subsurface_color)
[[
anno::noinline()
]]
{
return subsurface_color;
}
float get_subsurface_opacity(float subsurface_opacity)
[[
anno::noinline()
]]
{
return subsurface_opacity;
}
float3 tangent_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in tangent space"),
anno::noinline()
]]
{
return math::normalize(
tangent_u * normal.x - /* flip_tangent_v */
tangent_v * normal.y +
state::normal() * (normal.z));
}
float3 world_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in world space"),
anno::noinline()
]]
{
return tangent_space_normal(
math::normalize(
normal.x * float3(tangent_u.x, tangent_v.x, state::normal().x) -
normal.y * float3(tangent_u.y, tangent_v.y, state::normal().y) +
normal.z * float3(tangent_u.z, tangent_v.z, state::normal().z)),
tangent_u,
tangent_v
);
}
export material OmniUe4Subsurface(
float3 base_color = float3(0.0, 0.0, 0.0),
float metallic = 0.0,
float roughness = 0.5,
float specular = 0.5,
float3 normal = float3(0.0,0.0,1.0),
uniform bool enable_opacity = true,
float opacity = 1.0,
float opacity_mask = 1.0,
float3 emissive_color = float3(0.0, 0.0, 0.0),
float3 subsurface_color = float3(1.0, 1.0, 1.0),
float3 displacement = float3(0.0),
uniform bool is_tangent_space_normal = true,
uniform bool two_sided = false
)
[[
anno::display_name("Omni UE4 Subsurface"),
anno::description("Omni UE4 Subsurface, supports UE4 Subsurface shading model"),
anno::version( 1, 0, 0),
anno::author("NVIDIA CORPORATION"),
anno::key_words(string[]("omni", "UE4", "omniverse", "subsurface")),
distill_off()
]]
= let {
color final_base_color = math::saturate(base_color);
float final_metallic = math::saturate(metallic);
float final_roughness = math::saturate(roughness);
float final_specular = math::saturate(specular);
color final_emissive_color = math::max(emissive_color, 0.0f) * emissive_multiplier(); /*factor for converting ue4 emissive to raw value*/
float3 final_normal = math::normalize(normal);
color final_subsurface_color = math::saturate(subsurface_color);
float final_opacity = math::saturate(opacity);
// - compute final roughness by squaring the "roughness" parameter
float alpha = final_roughness * final_roughness;
// reduce the reflectivity at grazing angles to avoid "dark edges" for high roughness due to the layering
float grazing_refl = math::max((1.0 - final_roughness), 0.0);
bsdf reflection_component = df::diffuse_reflection_bsdf(tint: final_base_color);
bsdf subsurface_reflection_component = df::diffuse_reflection_bsdf(tint: get_subsurface_color(subsurface_color: final_subsurface_color));
bsdf transmit_component = df::diffuse_transmission_bsdf(tint: get_subsurface_color(subsurface_color: final_subsurface_color));
// for the dielectric component we layer the glossy component on top of the diffuse one,
// the glossy layer has no color tint
bsdf dielectric_component = df::custom_curve_layer(
weight: final_specular,
normal_reflectivity: 0.08,
grazing_reflectivity: grazing_refl,
layer: df::microfacet_ggx_smith_bsdf(roughness_u: alpha),
base: df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: reflection_component,
weight: 1.0f - get_subsurface_weight()),
df::bsdf_component(
component: subsurface_reflection_component,
weight: get_subsurface_opacity(subsurface_opacity: final_opacity) * get_subsurface_weight()),
df::bsdf_component(
component: transmit_component,
weight: (1.0 - get_subsurface_opacity(subsurface_opacity: final_opacity)) * get_subsurface_weight())
)
)
);
// the metallic component doesn't have a diffuse component, it's only glossy
// base_color is applied to tint it
bsdf metallic_component = df::microfacet_ggx_smith_bsdf(tint: final_base_color, roughness_u: alpha);
// final BSDF is a linear blend between dielectric and metallic component
bsdf dielectric_metal_mix =
df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: metallic_component,
weight: final_metallic),
df::bsdf_component(
component: dielectric_component,
weight: 1.0-final_metallic)
)
);
float3 the_normal = is_tangent_space_normal ?
tangent_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
);
bsdf surface = dielectric_metal_mix;
}
in material(
thin_walled: two_sided, // Graphene?
surface: material_surface(
scattering: surface,
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
backface: material_surface(
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
geometry: material_geometry(
displacement: displacement,
normal: the_normal,
cutout_opacity: enable_opacity ? opacity_mask : 1.0
)
);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/MI_ProcGrid.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_ProcGrid(
float4 LineColour = float4(0.338542,0.338542,0.338542,0.0)
[[
anno::display_name("Line Colour"),
anno::ui_order(32),
anno::in_group("Checker")
]],
float4 CheckerColour1 = float4(0.291667,0.291667,0.291667,0.0)
[[
anno::display_name("Checker Colour 1"),
anno::ui_order(32),
anno::in_group("Checker")
]],
float4 CheckerColour2 = float4(0.239583,0.239583,0.239583,0.0)
[[
anno::display_name("Checker Colour 2"),
anno::ui_order(32),
anno::in_group("Checker")
]],
float TileScale = 100.0
[[
anno::display_name("Tile Scale"),
anno::ui_order(32),
anno::in_group("Grid Properties")
]],
float CheckerRough1 = 0.5
[[
anno::display_name("Checker Rough 1"),
anno::ui_order(32),
anno::in_group("Checker")
]],
float CheckerRough2 = 0.65
[[
anno::display_name("Checker Rough 2"),
anno::ui_order(32),
anno::in_group("Checker")
]])
=
let {
float3 VertexInterpolator0_mdl = ::vertex_normal_world_space(true);
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float3 Local0 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / TileScale);
float3 Local1 = (Local0 * 0.5);
float4 Local2 = tex::lookup_float4(texture_2d("./Textures/T_GridChecker_A.png",::tex::gamma_srgb),float2(float2(Local1.x,Local1.z).x,1.0-float2(Local1.x,Local1.z).y),tex::wrap_repeat,tex::wrap_repeat);
float4 Local3 = tex::lookup_float4(texture_2d("./Textures/T_GridChecker_A.png",::tex::gamma_srgb),float2(float2(Local1.y,Local1.z).x,1.0-float2(Local1.y,Local1.z).y),tex::wrap_repeat,tex::wrap_repeat);
float Local4 = (0.0 - 1.0);
float Local5 = (1.0 + 1.0);
float Local6 = math::abs(::vertex_normal_world_space(true).x);
float Local7 = math::lerp(Local4,Local5,Local6);
float Local8 = math::min(math::max(Local7,0.0),1.0);
float Local9 = math::lerp(Local2.y,Local3.y,Local8);
float4 Local10 = tex::lookup_float4(texture_2d("./Textures/T_GridChecker_A.png",::tex::gamma_srgb),float2(float2(Local1.x,Local1.y).x,1.0-float2(Local1.x,Local1.y).y),tex::wrap_repeat,tex::wrap_repeat);
float Local11 = math::abs(::vertex_normal_world_space(true).z);
float Local12 = math::lerp(Local4,Local5,Local11);
float Local13 = math::min(math::max(Local12,0.0),1.0);
float Local14 = math::lerp(Local9,Local10.y,Local13);
float Local15 = (Local14 * 1.0);
float3 Local16 = math::lerp(float3(CheckerColour1.x,CheckerColour1.y,CheckerColour1.z),float3(CheckerColour2.x,CheckerColour2.y,CheckerColour2.z),Local15);
float4 Local17 = tex::lookup_float4(texture_2d("./Textures/T_GridChecker_A.png",::tex::gamma_srgb),float2(float2(Local0.x,Local0.z).x,1.0-float2(Local0.x,Local0.z).y),tex::wrap_repeat,tex::wrap_repeat);
float4 Local18 = tex::lookup_float4(texture_2d("./Textures/T_GridChecker_A.png",::tex::gamma_srgb),float2(float2(Local0.y,Local0.z).x,1.0-float2(Local0.y,Local0.z).y),tex::wrap_repeat,tex::wrap_repeat);
float Local19 = math::lerp(Local17.x,Local18.x,Local8);
float4 Local20 = tex::lookup_float4(texture_2d("./Textures/T_GridChecker_A.png",::tex::gamma_srgb),float2(float2(Local0.x,Local0.y).x,1.0-float2(Local0.x,Local0.y).y),tex::wrap_repeat,tex::wrap_repeat);
float Local21 = math::lerp(Local19,Local20.x,Local13);
float Local22 = (1.0 - Local21);
float3 Local23 = math::lerp(float3(LineColour.x,LineColour.y,LineColour.z),Local16,Local22);
float Local24 = (1.0 - Local22);
float Local25 = math::lerp(CheckerRough1,CheckerRough2,Local14);
float Local26 = math::lerp(0.3,Local25,Local22);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local23;
float Metallic_mdl = Local24;
float Specular_mdl = 0.5;
float Roughness_mdl = Local26;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_PigDirty_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_PigDirty_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_PigDirty_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/OmniUe4Base.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.0 - first version
//* 1.0.1 - merge unlit template
//* 1.0.2 - Fix EDF in the back side: the EDF contained in surface is only used for the front side and not for the back side
//* 1.0.3 - UE4 normal mapping: Geometry normal shouldn't be changed
//* 1.0.4 - using absolute import paths when importing standard modules
mdl 1.3;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
float emissive_multiplier()
[[
anno::description("the multiplier to convert UE4 emissive to raw data"),
anno::noinline()
]]
{
return 20.0f * 128.0f;
}
float3 tangent_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in tangent space"),
anno::noinline()
]]
{
return math::normalize(
tangent_u * normal.x - /* flip_tangent_v */
tangent_v * normal.y +
state::normal() * (normal.z));
}
float3 world_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in world space"),
anno::noinline()
]]
{
return tangent_space_normal(
math::normalize(
normal.x * float3(tangent_u.x, tangent_v.x, state::normal().x) -
normal.y * float3(tangent_u.y, tangent_v.y, state::normal().y) +
normal.z * float3(tangent_u.z, tangent_v.z, state::normal().z)),
tangent_u,
tangent_v
);
}
export material OmniUe4Base(
float3 base_color = float3(0.0, 0.0, 0.0),
float metallic = 0.0,
float roughness = 0.5,
float specular = 0.5,
float3 normal = float3(0.0,0.0,1.0),
float clearcoat_weight = 0.0,
float clearcoat_roughness = 0.0,
float3 clearcoat_normal = float3(0.0,0.0,1.0),
uniform bool enable_opacity = true,
float opacity = 1.0,
float3 emissive_color = float3(0.0, 0.0, 0.0),
float3 displacement = float3(0.0),
uniform bool is_tangent_space_normal = true,
uniform bool two_sided = false,
uniform bool is_unlit = false
)
[[
anno::display_name("Omni UE4 Base"),
anno::description("Omni UE4 Base, supports UE4 default lit and clearcoat shading model"),
anno::version( 1, 0, 0),
anno::author("NVIDIA CORPORATION"),
anno::key_words(string[]("omni", "UE4", "omniverse", "lit", "clearcoat", "generic"))
]]
= let {
color final_base_color = math::saturate(base_color);
float final_metallic = math::saturate(metallic);
float final_roughness = math::saturate(roughness);
float final_specular = math::saturate(specular);
color final_emissive_color = math::max(emissive_color, 0.0f) * emissive_multiplier(); /*factor for converting ue4 emissive to raw value*/
float final_clearcoat_weight = math::saturate(clearcoat_weight);
float final_clearcoat_roughness = math::saturate(clearcoat_roughness);
float3 final_normal = math::normalize(normal);
float3 final_clearcoat_normal = math::normalize(clearcoat_normal);
// - compute final roughness by squaring the "roughness" parameter
float alpha = final_roughness * final_roughness;
// reduce the reflectivity at grazing angles to avoid "dark edges" for high roughness due to the layering
float grazing_refl = math::max((1.0 - final_roughness), 0.0);
float3 the_normal = is_unlit ? state::normal() :
(is_tangent_space_normal ?
tangent_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
));
// for the dielectric component we layer the glossy component on top of the diffuse one,
// the glossy layer has no color tint
bsdf dielectric_component = df::custom_curve_layer(
weight: final_specular,
normal_reflectivity: 0.08,
grazing_reflectivity: grazing_refl,
layer: df::microfacet_ggx_smith_bsdf(roughness_u: alpha),
base: df::diffuse_reflection_bsdf(tint: final_base_color),
normal: the_normal);
// the metallic component doesn't have a diffuse component, it's only glossy
// base_color is applied to tint it
bsdf metallic_component = df::microfacet_ggx_smith_bsdf(tint: final_base_color, roughness_u: alpha);
// final BSDF is a linear blend between dielectric and metallic component
bsdf dielectric_metal_mix =
df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: metallic_component,
weight: final_metallic),
df::bsdf_component(
component: dielectric_component,
weight: 1.0-final_metallic)
)
);
// clearcoat layer
float clearcoat_grazing_refl = math::max((1.0 - final_clearcoat_roughness), 0.0);
float clearcoat_alpha = final_clearcoat_roughness * final_clearcoat_roughness;
float3 the_clearcoat_normal = is_tangent_space_normal ? tangent_space_normal(
normal: final_clearcoat_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_clearcoat_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
);
bsdf clearcoat =
df::custom_curve_layer(
base: df::weighted_layer(
layer: dielectric_metal_mix,
weight: 1.0,
normal: final_clearcoat_weight == 0.0 ? state::normal() : the_normal
),
layer: df::microfacet_ggx_smith_bsdf(
roughness_u: clearcoat_alpha,
tint: color(1.0)
),
normal_reflectivity: 0.04,
grazing_reflectivity: clearcoat_grazing_refl,
normal: the_clearcoat_normal,
weight: final_clearcoat_weight
);
bsdf surface = is_unlit ? bsdf() : clearcoat;
}
in material(
thin_walled: two_sided, // Graphene?
surface: material_surface(
scattering: surface,
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
backface: material_surface(
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
geometry: material_geometry(
displacement: displacement,
normal: final_clearcoat_weight == 0.0 ? the_normal : state::normal(),
cutout_opacity: enable_opacity ? opacity : 1.0
)
);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/OmniUe4Function.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.1 - using absolute import paths when importing standard modules
mdl 1.6;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
export float3 convert_to_left_hand(float3 vec3, uniform bool up_z = true, uniform bool is_position = true)
[[
anno::description("convert from RH to LH"),
anno::noinline()
]]
{
float4x4 ZupConversion = float4x4(
1.0f, 0.0f, 0.0f, 0.0f,
0.0f, -1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 0.0f, 0.0f, 1.0f
);
float4x4 YupConversion = float4x4(
1.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 1.0f
);
float4 vec4 = float4(vec3.x, vec3.y, vec3.z, is_position ? 1.0f : 0.0f);
vec4 = vec4 * (up_z ? ZupConversion : YupConversion);
return float3(vec4.x, vec4.y, vec4.z);
}
export float3 transform_vector_from_tangent_to_world(float3 vector,
uniform bool up_z = true,
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0))
[[
anno::description("Transform vector from tangent space to world space"),
anno::noinline()
]]
{
/* flip_tangent_v */
return convert_to_left_hand(
tangent_u * vector.x - tangent_v * vector.y + state::normal() * vector.z,
up_z, false);
}
export float3 transform_vector_from_world_to_tangent(float3 vector,
uniform bool up_z = true,
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0))
[[
anno::description("Transform vector from world space to tangent space"),
anno::noinline()
]]
{
float3 vecRH = convert_to_left_hand(vector, up_z, false);
/* flip_tangent_v */
return vecRH.x * float3(tangent_u.x, -tangent_v.x, state::normal().x) +
vecRH.y * float3(tangent_u.y, -tangent_v.y, state::normal().y) +
vecRH.z * float3(tangent_u.z, -tangent_v.z, state::normal().z);
}
export float4 unpack_normal_map(
float4 texture_sample = float4(0.0, 0.0, 1.0, 1.0)
)
[[
anno::description("Unpack a normal stored in a normal map"),
anno::noinline()
]]
{
float2 normal_xy = float2(texture_sample.x, texture_sample.y);
normal_xy = normal_xy * float2(2.0,2.0) - float2(1.0,1.0);
float normal_z = math::sqrt( math::saturate( 1.0 - math::dot( normal_xy, normal_xy ) ) );
return float4( normal_xy.x, normal_xy.y, normal_z, 1.0 );
}
// for get color value from normal.
export float4 pack_normal_map(
float4 texture_sample = float4(0.0, 0.0, 1.0, 1.0)
)
[[
anno::description("Pack to color from a normal")
]]
{
float2 return_xy = float2(texture_sample.x, texture_sample.y);
return_xy = (return_xy + float2(1.0,1.0)) / float2(2.0,2.0);
return float4( return_xy.x, return_xy.y, 0.0, 1.0 );
}
export float4 greyscale_texture_lookup(
float4 texture_sample = float4(0.0, 0.0, 0.0, 1.0)
)
[[
anno::description("Sampling a greyscale texture"),
anno::noinline()
]]
{
return float4(texture_sample.x, texture_sample.x, texture_sample.x, texture_sample.x);
}
export float3 pixel_normal_world_space(uniform bool up_z = true)
[[
anno::description("Pixel normal in world space"),
anno::noinline()
]]
{
return convert_to_left_hand(state::transform_normal(state::coordinate_internal,state::coordinate_world,state::normal()), up_z, false);
}
export float3 vertex_normal_world_space(uniform bool up_z = true)
[[
anno::description("Vertex normal in world space"),
anno::noinline()
]]
{
return convert_to_left_hand(state::transform_normal(state::coordinate_internal,state::coordinate_world,state::normal()), up_z, false);
}
export float3 landscape_normal_world_space(uniform bool up_z = true)
[[
anno::description("Landscape normal in world space")
]]
{
float3 normalFromNormalmap = math::floor((::vertex_normal_world_space(up_z) * 0.5 + 0.5) * 255.0) / 255.0 * 2.0 - 1.0;
float2 normalXY = float2(normalFromNormalmap.x, normalFromNormalmap.y);
return float3(normalXY.x, normalXY.y, math::sqrt(math::saturate(1.0 - math::dot(normalXY, normalXY))));
}
// Different implementation specific between mdl and hlsl for smoothstep
export float smoothstep(float a, float b, float l)
{
if (a < b)
{
return math::smoothstep(a, b, l);
}
else if (a > b)
{
return 1.0 - math::smoothstep(b, a, l);
}
else
{
return l <= a ? 0.0 : 1.0;
}
}
export float2 smoothstep(float2 a, float2 b, float2 l)
{
return float2(smoothstep(a.x, b.x, l.x), smoothstep(a.y, b.y, l.y));
}
export float3 smoothstep(float3 a, float3 b, float3 l)
{
return float3(smoothstep(a.x, b.x, l.x), smoothstep(a.y, b.y, l.y), smoothstep(a.z, b.z, l.z));
}
export float4 smoothstep(float4 a, float4 b, float4 l)
{
return float4(smoothstep(a.x, b.x, l.x), smoothstep(a.y, b.y, l.y), smoothstep(a.z, b.z, l.z), smoothstep(a.w, b.w, l.w));
}
export float2 smoothstep(float2 a, float2 b, float l)
{
return float2(smoothstep(a.x, b.x, l), smoothstep(a.y, b.y, l));
}
export float3 smoothstep(float3 a, float3 b, float l)
{
return float3(smoothstep(a.x, b.x, l), smoothstep(a.y, b.y, l), smoothstep(a.z, b.z, l));
}
export float4 smoothstep(float4 a, float4 b, float l)
{
return float4(smoothstep(a.x, b.x, l), smoothstep(a.y, b.y, l), smoothstep(a.z, b.z, l), smoothstep(a.w, b.w, l));
}
export float2 smoothstep(float a, float b, float2 l)
{
return float2(smoothstep(a, b, l.x), smoothstep(a, b, l.y));
}
export float3 smoothstep(float a, float b, float3 l)
{
return float3(smoothstep(a, b, l.x), smoothstep(a, b, l.y), smoothstep(a, b, l.z));
}
export float4 smoothstep(float a, float b, float4 l)
{
return float4(smoothstep(a, b, l.x), smoothstep(a, b, l.y), smoothstep(a, b, l.z), smoothstep(a, b, l.w));
}
//------------------ Random from UE4 -----------------------
float length2(float3 v)
{
return math::dot(v, v);
}
float3 GetPerlinNoiseGradientTextureAt(uniform texture_2d PerlinNoiseGradientTexture, float3 v)
{
const float2 ZShear = float2(17.0f, 89.0f);
float2 OffsetA = v.z * ZShear;
float2 TexA = (float2(v.x, v.y) + OffsetA + 0.5f) / 128.0f;
float4 PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA.x,1.0-TexA.y),tex::wrap_repeat,tex::wrap_repeat);
return float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z) * 2.0 - 1.0;
}
float3 SkewSimplex(float3 In)
{
return In + math::dot(In, float3(1.0 / 3.0f) );
}
float3 UnSkewSimplex(float3 In)
{
return In - math::dot(In, float3(1.0 / 6.0f) );
}
// 3D random number generator inspired by PCGs (permuted congruential generator)
// Using a **simple** Feistel cipher in place of the usual xor shift permutation step
// @param v = 3D integer coordinate
// @return three elements w/ 16 random bits each (0-0xffff).
// ~8 ALU operations for result.x (7 mad, 1 >>)
// ~10 ALU operations for result.xy (8 mad, 2 >>)
// ~12 ALU operations for result.xyz (9 mad, 3 >>)
//TODO: uint3
int3 Rand3DPCG16(int3 p)
{
// taking a signed int then reinterpreting as unsigned gives good behavior for negatives
//TODO: uint3
int3 v = int3(p);
// Linear congruential step. These LCG constants are from Numerical Recipies
// For additional #'s, PCG would do multiple LCG steps and scramble each on output
// So v here is the RNG state
v = v * 1664525 + 1013904223;
// PCG uses xorshift for the final shuffle, but it is expensive (and cheap
// versions of xorshift have visible artifacts). Instead, use simple MAD Feistel steps
//
// Feistel ciphers divide the state into separate parts (usually by bits)
// then apply a series of permutation steps one part at a time. The permutations
// use a reversible operation (usually ^) to part being updated with the result of
// a permutation function on the other parts and the key.
//
// In this case, I'm using v.x, v.y and v.z as the parts, using + instead of ^ for
// the combination function, and just multiplying the other two parts (no key) for
// the permutation function.
//
// That gives a simple mad per round.
v.x += v.y*v.z;
v.y += v.z*v.x;
v.z += v.x*v.y;
v.x += v.y*v.z;
v.y += v.z*v.x;
v.z += v.x*v.y;
// only top 16 bits are well shuffled
return v >> 16;
}
// Wraps noise for tiling texture creation
// @param v = unwrapped texture parameter
// @param bTiling = true to tile, false to not tile
// @param RepeatSize = number of units before repeating
// @return either original or wrapped coord
float3 NoiseTileWrap(float3 v, bool bTiling, float RepeatSize)
{
return bTiling ? (math::frac(v / RepeatSize) * RepeatSize) : v;
}
// Evaluate polynomial to get smooth transitions for Perlin noise
// only needed by Perlin functions in this file
// scalar(per component): 2 add, 5 mul
float4 PerlinRamp(float4 t)
{
return t * t * t * (t * (t * 6 - 15) + 10);
}
// Blum-Blum-Shub-inspired pseudo random number generator
// http://www.umbc.edu/~olano/papers/mNoise.pdf
// real BBS uses ((s*s) mod M) with bignums and M as the product of two huge Blum primes
// instead, we use a single prime M just small enough not to overflow
// note that the above paper used 61, which fits in a half, but is unusably bad
// @param Integer valued floating point seed
// @return random number in range [0,1)
// ~8 ALU operations (5 *, 3 frac)
float RandBBSfloat(float seed)
{
float BBS_PRIME24 = 4093.0;
float s = math::frac(seed / BBS_PRIME24);
s = math::frac(s * s * BBS_PRIME24);
s = math::frac(s * s * BBS_PRIME24);
return s;
}
// Modified noise gradient term
// @param seed - random seed for integer lattice position
// @param offset - [-1,1] offset of evaluation point from lattice point
// @return gradient direction (xyz) and contribution (w) from this lattice point
float4 MGradient(int seed, float3 offset)
{
//TODO uint
int rand = Rand3DPCG16(int3(seed,0,0)).x;
int3 MGradientMask = int3(0x8000, 0x4000, 0x2000);
float3 MGradientScale = float3(1.0 / 0x4000, 1.0 / 0x2000, 1.0 / 0x1000);
float3 direction = float3(int3(rand, rand, rand) & MGradientMask) * MGradientScale - 1;
return float4(direction.x, direction.y, direction.z, math::dot(direction, offset));
}
// compute Perlin and related noise corner seed values
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = true to return seed values for a repeating noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @param seed000-seed111 = hash function seeds for the eight corners
// @return fractional part of v
struct SeedValue
{
float3 fv = float3(0);
float seed000 = 0;
float seed001 = 0;
float seed010 = 0;
float seed011 = 0;
float seed100 = 0;
float seed101 = 0;
float seed110 = 0;
float seed111 = 0;
};
SeedValue NoiseSeeds(float3 v, bool bTiling, float RepeatSize)
{
SeedValue seeds;
seeds.fv = math::frac(v);
float3 iv = math::floor(v);
const float3 primes = float3(19, 47, 101);
if (bTiling)
{ // can't algebraically combine with primes
seeds.seed000 = math::dot(primes, NoiseTileWrap(iv, true, RepeatSize));
seeds.seed100 = math::dot(primes, NoiseTileWrap(iv + float3(1, 0, 0), true, RepeatSize));
seeds.seed010 = math::dot(primes, NoiseTileWrap(iv + float3(0, 1, 0), true, RepeatSize));
seeds.seed110 = math::dot(primes, NoiseTileWrap(iv + float3(1, 1, 0), true, RepeatSize));
seeds.seed001 = math::dot(primes, NoiseTileWrap(iv + float3(0, 0, 1), true, RepeatSize));
seeds.seed101 = math::dot(primes, NoiseTileWrap(iv + float3(1, 0, 1), true, RepeatSize));
seeds.seed011 = math::dot(primes, NoiseTileWrap(iv + float3(0, 1, 1), true, RepeatSize));
seeds.seed111 = math::dot(primes, NoiseTileWrap(iv + float3(1, 1, 1), true, RepeatSize));
}
else
{ // get to combine offsets with multiplication by primes in this case
seeds.seed000 = math::dot(iv, primes);
seeds.seed100 = seeds.seed000 + primes.x;
seeds.seed010 = seeds.seed000 + primes.y;
seeds.seed110 = seeds.seed100 + primes.y;
seeds.seed001 = seeds.seed000 + primes.z;
seeds.seed101 = seeds.seed100 + primes.z;
seeds.seed011 = seeds.seed010 + primes.z;
seeds.seed111 = seeds.seed110 + primes.z;
}
return seeds;
}
struct SimplexWeights
{
float4 Result = float4(0);
float3 PosA = float3(0);
float3 PosB = float3(0);
float3 PosC = float3(0);
float3 PosD = float3(0);
};
// Computed weights and sample positions for simplex interpolation
// @return float4(a,b,c, d) Barycentric coordinate defined as Filtered = Tex(PosA) * a + Tex(PosB) * b + Tex(PosC) * c + Tex(PosD) * d
SimplexWeights ComputeSimplexWeights3D(float3 OrthogonalPos)
{
SimplexWeights weights;
float3 OrthogonalPosFloor = math::floor(OrthogonalPos);
weights.PosA = OrthogonalPosFloor;
weights.PosB = weights.PosA + float3(1, 1, 1);
OrthogonalPos -= OrthogonalPosFloor;
float Largest = math::max(OrthogonalPos.x, math::max(OrthogonalPos.y, OrthogonalPos.z));
float Smallest = math::min(OrthogonalPos.x, math::min(OrthogonalPos.y, OrthogonalPos.z));
weights.PosC = weights.PosA + float3(Largest == OrthogonalPos.x, Largest == OrthogonalPos.y, Largest == OrthogonalPos.z);
weights.PosD = weights.PosA + float3(Smallest != OrthogonalPos.x, Smallest != OrthogonalPos.y, Smallest != OrthogonalPos.z);
float RG = OrthogonalPos.x - OrthogonalPos.y;
float RB = OrthogonalPos.x - OrthogonalPos.z;
float GB = OrthogonalPos.y - OrthogonalPos.z;
weights.Result.z =
math::min(math::max(0, RG), math::max(0, RB)) // X
+ math::min(math::max(0, -RG), math::max(0, GB)) // Y
+ math::min(math::max(0, -RB), math::max(0, -GB)); // Z
weights.Result.w =
math::min(math::max(0, -RG), math::max(0, -RB)) // X
+ math::min(math::max(0, RG), math::max(0, -GB)) // Y
+ math::min(math::max(0, RB), math::max(0, GB)); // Z
weights.Result.y = Smallest;
weights.Result.x = 1.0f - weights.Result.y - weights.Result.z - weights.Result.w;
return weights;
}
// filtered 3D gradient simple noise (few texture lookups, high quality)
// @param v >0
// @return random number in the range -1 .. 1
float SimplexNoise3D_TEX(uniform texture_2d PerlinNoiseGradientTexture, float3 EvalPos)
{
float3 OrthogonalPos = SkewSimplex(EvalPos);
SimplexWeights Weights = ComputeSimplexWeights3D(OrthogonalPos);
// can be optimized to 1 or 2 texture lookups (4 or 8 channel encoded in 32 bit)
float3 A = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosA);
float3 B = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosB);
float3 C = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosC);
float3 D = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosD);
Weights.PosA = UnSkewSimplex(Weights.PosA);
Weights.PosB = UnSkewSimplex(Weights.PosB);
Weights.PosC = UnSkewSimplex(Weights.PosC);
Weights.PosD = UnSkewSimplex(Weights.PosD);
float DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosA)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float a = math::dot(A, EvalPos - Weights.PosA) * DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosB)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float b = math::dot(B, EvalPos - Weights.PosB) * DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosC)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float c = math::dot(C, EvalPos - Weights.PosC) * DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosD)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float d = math::dot(D, EvalPos - Weights.PosD) * DistanceWeight;
return 32 * (a + b + c + d);
}
// filtered 3D noise, can be optimized
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @return random number in the range -1 .. 1
float GradientNoise3D_TEX(uniform texture_2d PerlinNoiseGradientTexture, float3 v, bool bTiling, float RepeatSize)
{
bTiling = true;
float3 fv = math::frac(v);
float3 iv0 = NoiseTileWrap(math::floor(v), bTiling, RepeatSize);
float3 iv1 = NoiseTileWrap(iv0 + 1, bTiling, RepeatSize);
const int2 ZShear = int2(17, 89);
float2 OffsetA = iv0.z * ZShear;
float2 OffsetB = OffsetA + ZShear; // non-tiling, use relative offset
if (bTiling) // tiling, have to compute from wrapped coordinates
{
OffsetB = iv1.z * ZShear;
}
// Texture size scale factor
float ts = 1 / 128.0f;
// texture coordinates for iv0.xy, as offset for both z slices
float2 TexA0 = (float2(iv0.x, iv0.y) + OffsetA + 0.5f) * ts;
float2 TexB0 = (float2(iv0.x, iv0.y) + OffsetB + 0.5f) * ts;
// texture coordinates for iv1.xy, as offset for both z slices
float2 TexA1 = TexA0 + ts; // for non-tiling, can compute relative to existing coordinates
float2 TexB1 = TexB0 + ts;
if (bTiling) // for tiling, need to compute from wrapped coordinates
{
TexA1 = (float2(iv1.x, iv1.y) + OffsetA + 0.5f) * ts;
TexB1 = (float2(iv1.x, iv1.y) + OffsetB + 0.5f) * ts;
}
// can be optimized to 1 or 2 texture lookups (4 or 8 channel encoded in 8, 16 or 32 bit)
float4 PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA0.x,1.0-TexA0.y),tex::wrap_repeat,tex::wrap_repeat);
float3 PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 A = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA1.x,1.0-TexA0.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 B = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA0.x,1.0-TexA1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 C = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA1.x,1.0-TexA1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 D = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB0.x,1.0-TexB0.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 E = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB1.x,1.0-TexB0.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 F = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB0.x,1.0-TexB1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 G = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB1.x,1.0-TexB1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 H = PerlinNoiseColor * 2 - 1;
float a = math::dot(A, fv - float3(0, 0, 0));
float b = math::dot(B, fv - float3(1, 0, 0));
float c = math::dot(C, fv - float3(0, 1, 0));
float d = math::dot(D, fv - float3(1, 1, 0));
float e = math::dot(E, fv - float3(0, 0, 1));
float f = math::dot(F, fv - float3(1, 0, 1));
float g = math::dot(G, fv - float3(0, 1, 1));
float h = math::dot(H, fv - float3(1, 1, 1));
float4 Weights = PerlinRamp(math::frac(float4(fv.x, fv.y, fv.z, 0)));
float i = math::lerp(math::lerp(a, b, Weights.x), math::lerp(c, d, Weights.x), Weights.y);
float j = math::lerp(math::lerp(e, f, Weights.x), math::lerp(g, h, Weights.x), Weights.y);
return math::lerp(i, j, Weights.z);
}
// @return random number in the range -1 .. 1
// scalar: 6 frac, 31 mul/mad, 15 add,
float FastGradientPerlinNoise3D_TEX(uniform texture_3d PerlinNoise3DTexture, float3 xyz)
{
// needs to be the same value when creating the PerlinNoise3D texture
float Extent = 16;
// last texel replicated and needed for filtering
// scalar: 3 frac, 6 mul
xyz = math::frac(xyz / (Extent - 1)) * (Extent - 1);
// scalar: 3 frac
float3 uvw = math::frac(xyz);
// = floor(xyz);
// scalar: 3 add
float3 p0 = xyz - uvw;
// float3 f = math::pow(uvw, 2) * 3.0f - math::pow(uvw, 3) * 2.0f; // original perlin hermite (ok when used without bump mapping)
// scalar: 2*3 add 5*3 mul
float4 pr = PerlinRamp(float4(uvw.x, uvw.y, uvw.z, 0));
float3 f = float3(pr.x, pr.y, pr.z); // new, better with continues second derivative for bump mapping
// scalar: 3 add
float3 p = p0 + f;
// scalar: 3 mad
// TODO: need reverse???
float4 NoiseSample = tex::lookup_float4(PerlinNoise3DTexture, p / Extent + 0.5f / Extent); // +0.5f to get rid of bilinear offset
// reconstruct from 8bit (using mad with 2 constants and dot4 was same instruction count)
// scalar: 4 mad, 3 mul, 3 add
float3 n = float3(NoiseSample.x, NoiseSample.y, NoiseSample.z) * 255.0f / 127.0f - 1.0f;
float d = NoiseSample.w * 255.f - 127;
return math::dot(xyz, n) - d;
}
// Perlin-style "Modified Noise"
// http://www.umbc.edu/~olano/papers/index.html#mNoise
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @return random number in the range -1 .. 1
float GradientNoise3D_ALU(float3 v, bool bTiling, float RepeatSize)
{
SeedValue seeds = NoiseSeeds(v, bTiling, RepeatSize);
float rand000 = MGradient(int(seeds.seed000), seeds.fv - float3(0, 0, 0)).w;
float rand100 = MGradient(int(seeds.seed100), seeds.fv - float3(1, 0, 0)).w;
float rand010 = MGradient(int(seeds.seed010), seeds.fv - float3(0, 1, 0)).w;
float rand110 = MGradient(int(seeds.seed110), seeds.fv - float3(1, 1, 0)).w;
float rand001 = MGradient(int(seeds.seed001), seeds.fv - float3(0, 0, 1)).w;
float rand101 = MGradient(int(seeds.seed101), seeds.fv - float3(1, 0, 1)).w;
float rand011 = MGradient(int(seeds.seed011), seeds.fv - float3(0, 1, 1)).w;
float rand111 = MGradient(int(seeds.seed111), seeds.fv - float3(1, 1, 1)).w;
float4 Weights = PerlinRamp(float4(seeds.fv.x, seeds.fv.y, seeds.fv.z, 0));
float i = math::lerp(math::lerp(rand000, rand100, Weights.x), math::lerp(rand010, rand110, Weights.x), Weights.y);
float j = math::lerp(math::lerp(rand001, rand101, Weights.x), math::lerp(rand011, rand111, Weights.x), Weights.y);
return math::lerp(i, j, Weights.z);
}
// 3D value noise - used to be incorrectly called Perlin noise
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @return random number in the range -1 .. 1
float ValueNoise3D_ALU(float3 v, bool bTiling, float RepeatSize)
{
SeedValue seeds = NoiseSeeds(v, bTiling, RepeatSize);
float rand000 = RandBBSfloat(seeds.seed000) * 2 - 1;
float rand100 = RandBBSfloat(seeds.seed100) * 2 - 1;
float rand010 = RandBBSfloat(seeds.seed010) * 2 - 1;
float rand110 = RandBBSfloat(seeds.seed110) * 2 - 1;
float rand001 = RandBBSfloat(seeds.seed001) * 2 - 1;
float rand101 = RandBBSfloat(seeds.seed101) * 2 - 1;
float rand011 = RandBBSfloat(seeds.seed011) * 2 - 1;
float rand111 = RandBBSfloat(seeds.seed111) * 2 - 1;
float4 Weights = PerlinRamp(float4(seeds.fv.x, seeds.fv.y, seeds.fv.z, 0));
float i = math::lerp(math::lerp(rand000, rand100, Weights.x), math::lerp(rand010, rand110, Weights.x), Weights.y);
float j = math::lerp(math::lerp(rand001, rand101, Weights.x), math::lerp(rand011, rand111, Weights.x), Weights.y);
return math::lerp(i, j, Weights.z);
}
// 3D jitter offset within a voronoi noise cell
// @param pos - integer lattice corner
// @return random offsets vector
float3 VoronoiCornerSample(float3 pos, int Quality)
{
// random values in [-0.5, 0.5]
float3 noise = float3(Rand3DPCG16(int3(pos))) / 0xffff - 0.5;
// quality level 1 or 2: searches a 2x2x2 neighborhood with points distributed on a sphere
// scale factor to guarantee jittered points will be found within a 2x2x2 search
if (Quality <= 2)
{
return math::normalize(noise) * 0.2588;
}
// quality level 3: searches a 3x3x3 neighborhood with points distributed on a sphere
// scale factor to guarantee jittered points will be found within a 3x3x3 search
if (Quality == 3)
{
return math::normalize(noise) * 0.3090;
}
// quality level 4: jitter to anywhere in the cell, needs 4x4x4 search
return noise;
}
// compare previous best with a new candidate
// not producing point locations makes it easier for compiler to eliminate calculations when they're not needed
// @param minval = location and distance of best candidate seed point before the new one
// @param candidate = candidate seed point
// @param offset = 3D offset to new candidate seed point
// @param bDistanceOnly = if true, only set maxval.w with distance, otherwise maxval.w is distance and maxval.xyz is position
// @return position (if bDistanceOnly is false) and distance to closest seed point so far
float4 VoronoiCompare(float4 minval, float3 candidate, float3 offset, bool bDistanceOnly)
{
if (bDistanceOnly)
{
return float4(0, 0, 0, math::min(minval.w, math::dot(offset, offset)));
}
else
{
float newdist = math::dot(offset, offset);
return newdist > minval.w ? minval : float4(candidate.x, candidate.y, candidate.z, newdist);
}
}
// 220 instruction Worley noise
float4 VoronoiNoise3D_ALU(float3 v, int Quality, bool bTiling, float RepeatSize, bool bDistanceOnly)
{
float3 fv = math::frac(v), fv2 = math::frac(v + 0.5);
float3 iv = math::floor(v), iv2 = math::floor(v + 0.5);
// with initial minimum distance = infinity (or at least bigger than 4), first min is optimized away
float4 mindist = float4(0,0,0,100);
float3 p, offset;
// quality level 3: do a 3x3x3 search
if (Quality == 3)
{
int offset_x;
int offset_y;
int offset_z;
for (offset_x = -1; offset_x <= 1; ++offset_x)
{
for (offset_y = -1; offset_y <= 1; ++offset_y)
{
for (offset_z = -1; offset_z <= 1; ++offset_z)
{
offset = float3(offset_x, offset_y, offset_z);
p = offset + VoronoiCornerSample(NoiseTileWrap(iv2 + offset, bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv2 + p, fv2 - p, bDistanceOnly);
}
}
}
}
// everybody else searches a base 2x2x2 neighborhood
else
{
int offset_x;
int offset_y;
int offset_z;
for (offset_x = 0; offset_x <= 1; ++offset_x)
{
for (offset_y = 0; offset_y <= 1; ++offset_y)
{
for (offset_z = 0; offset_z <= 1; ++offset_z)
{
offset = float3(offset_x, offset_y, offset_z);
p = offset + VoronoiCornerSample(NoiseTileWrap(iv + offset, bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
// quality level 2, do extra set of points, offset by half a cell
if (Quality == 2)
{
// 467 is just an offset to a different area in the random number field to avoid similar neighbor artifacts
p = offset + VoronoiCornerSample(NoiseTileWrap(iv2 + offset, bTiling, RepeatSize) + 467, Quality);
mindist = VoronoiCompare(mindist, iv2 + p, fv2 - p, bDistanceOnly);
}
}
}
}
}
// quality level 4: add extra sets of four cells in each direction
if (Quality >= 4)
{
int offset_x;
int offset_y;
int offset_z;
for (offset_x = -1; offset_x <= 2; offset_x += 3)
{
for (offset_y = 0; offset_y <= 1; ++offset_y)
{
for (offset_z = 0; offset_z <= 1; ++offset_z)
{
offset = float3(offset_x, offset_y, offset_z);
// along x axis
p = offset + VoronoiCornerSample(NoiseTileWrap(iv + offset, bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
// along y axis
p = float3(offset.y, offset.z, offset.x) + VoronoiCornerSample(NoiseTileWrap(iv + float3(offset.y, offset.z, offset.x), bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
// along z axis
p = float3(offset.z, offset.x, offset.y) + VoronoiCornerSample(NoiseTileWrap(iv + float3(offset.z, offset.x, offset.y), bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
}
}
}
}
// transform squared distance to real distance
return float4(mindist.x, mindist.y, mindist.z, math::sqrt(mindist.w));
}
// Coordinates for corners of a Simplex tetrahedron
// Based on McEwan et al., Efficient computation of noise in GLSL, JGT 2011
// @param v = 3D noise argument
// @return 4 corner locations
float4x3 SimplexCorners(float3 v)
{
// find base corner by skewing to tetrahedral space and back
float3 tet = math::floor(v + v.x/3 + v.y/3 + v.z/3);
float3 base = tet - tet.x/6 - tet.y/6 - tet.z/6;
float3 f = v - base;
// Find offsets to other corners (McEwan did this in tetrahedral space,
// but since skew is along x=y=z axis, this works in Euclidean space too.)
float3 g = math::step(float3(f.y,f.z,f.x), float3(f.x,f.y,f.z)), h = 1 - float3(g.z, g.x, g.y);
float3 a1 = math::min(g, h) - 1.0 / 6.0, a2 = math::max(g, h) - 1.0 / 3.0;
// four corners
return float4x3(base, base + a1, base + a2, base + 0.5);
}
// Improved smoothing function for simplex noise
// @param f = fractional distance to four tetrahedral corners
// @return weight for each corner
float4 SimplexSmooth(float4x3 f)
{
const float scale = 1024. / 375.; // scale factor to make noise -1..1
float4 d = float4(math::dot(f[0], f[0]), math::dot(f[1], f[1]), math::dot(f[2], f[2]), math::dot(f[3], f[3]));
float4 s = math::saturate(2 * d);
return (1 * scale + s*(-3 * scale + s*(3 * scale - s*scale)));
}
// Derivative of simplex noise smoothing function
// @param f = fractional distanc eto four tetrahedral corners
// @return derivative of smoothing function for each corner by x, y and z
float3x4 SimplexDSmooth(float4x3 f)
{
const float scale = 1024. / 375.; // scale factor to make noise -1..1
float4 d = float4(math::dot(f[0], f[0]), math::dot(f[1], f[1]), math::dot(f[2], f[2]), math::dot(f[3], f[3]));
float4 s = math::saturate(2 * d);
s = -12 * scale + s*(24 * scale - s * 12 * scale);
return float3x4(
s * float4(f[0][0], f[1][0], f[2][0], f[3][0]),
s * float4(f[0][1], f[1][1], f[2][1], f[3][1]),
s * float4(f[0][2], f[1][2], f[2][2], f[3][2]));
}
// Simplex noise and its Jacobian derivative
// @param v = 3D noise argument
// @param bTiling = whether to repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension, must be a multiple of 3
// @return float3x3 Jacobian in J[*].xyz, vector noise in J[*].w
// J[0].w, J[1].w, J[2].w is a Perlin-style simplex noise with vector output, e.g. (Nx, Ny, Nz)
// J[i].x is X derivative of the i'th component of the noise so J[2].x is dNz/dx
// You can use this to compute the noise, gradient, curl, or divergence:
// float3x4 J = JacobianSimplex_ALU(...);
// float3 VNoise = float3(J[0].w, J[1].w, J[2].w); // 3D noise
// float3 Grad = J[0].xyz; // gradient of J[0].w
// float3 Curl = float3(J[1][2]-J[2][1], J[2][0]-J[0][2], J[0][1]-J[1][2]);
// float Div = J[0][0]+J[1][1]+J[2][2];
// All of these are confirmed to compile out all unneeded terms.
// So Grad of X doesn't compute Y or Z components, and VNoise doesn't do any of the derivative computation.
float3x4 JacobianSimplex_ALU(float3 v, bool bTiling, float RepeatSize)
{
int3 MGradientMask = int3(0x8000, 0x4000, 0x2000);
float3 MGradientScale = float3(1. / 0x4000, 1. / 0x2000, 1. / 0x1000);
// corners of tetrahedron
float4x3 T = SimplexCorners(v);
// TODO: uint3
int3 rand = int3(0);
float4x3 gvec0 = float4x3(1.0);
float4x3 gvec1 = float4x3(1.0);
float4x3 gvec2 = float4x3(1.0);
float4x3 fv = float4x3(1.0);
float3x4 grad = float3x4(1.0);
// processing of tetrahedral vertices, unrolled
// to compute gradient at each corner
fv[0] = v - T[0];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[0] + 0.5, bTiling, RepeatSize))));
gvec0[0] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[0] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec2[0] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][0] = math::dot(gvec0[0], fv[0]);
grad[1][0] = math::dot(gvec1[0], fv[0]);
grad[2][0] = math::dot(gvec2[0], fv[0]);
fv[1] = v - T[1];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[1] + 0.5, bTiling, RepeatSize))));
gvec0[1] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[1] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec1[1] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][1] = math::dot(gvec0[1], fv[1]);
grad[1][1] = math::dot(gvec1[1], fv[1]);
grad[2][1] = math::dot(gvec2[1], fv[1]);
fv[2] = v - T[2];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[2] + 0.5, bTiling, RepeatSize))));
gvec0[2] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[2] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec2[2] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][2] = math::dot(gvec0[2], fv[2]);
grad[1][2] = math::dot(gvec1[2], fv[2]);
grad[2][2] = math::dot(gvec2[2], fv[2]);
fv[3] = v - T[3];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[3] + 0.5, bTiling, RepeatSize))));
gvec0[3] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[3] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec2[3] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][3] = math::dot(gvec0[3], fv[3]);
grad[1][3] = math::dot(gvec1[3], fv[3]);
grad[2][3] = math::dot(gvec2[3], fv[3]);
// blend gradients
float4 sv = SimplexSmooth(fv);
float3x4 ds = SimplexDSmooth(fv);
float3x4 jacobian = float3x4(1.0);
float3 vec0 = gvec0*sv + grad[0]*ds; // NOTE: mdl is column major, convert from UE4 (row major)
jacobian[0] = float4(vec0.x, vec0.y, vec0.z, math::dot(sv, grad[0]));
float3 vec1 = gvec1*sv + grad[1]*ds;
jacobian[1] = float4(vec1.x, vec1.y, vec1.z, math::dot(sv, grad[1]));
float3 vec2 = gvec2*sv + grad[2]*ds;
jacobian[2] = float4(vec2.x, vec2.y, vec2.z, math::dot(sv, grad[2]));
return jacobian;
}
// While RepeatSize is a float here, the expectation is that it would be largely integer values coming in from the UI. The downstream logic assumes
// floats for all called functions (NoiseTileWrap) and this prevents any float-to-int conversion errors from automatic type conversion.
float Noise3D_Multiplexer(uniform texture_2d PerlinNoiseGradientTexture, uniform texture_3d PerlinNoise3DTexture, int Function, float3 Position, int Quality, bool bTiling, float RepeatSize)
{
// verified, HLSL compiled out the switch if Function is a constant
switch(Function)
{
case 0:
return SimplexNoise3D_TEX(PerlinNoiseGradientTexture, Position);
case 1:
return GradientNoise3D_TEX(PerlinNoiseGradientTexture, Position, bTiling, RepeatSize);
case 2:
return FastGradientPerlinNoise3D_TEX(PerlinNoise3DTexture, Position);
case 3:
return GradientNoise3D_ALU(Position, bTiling, RepeatSize);
case 4:
return ValueNoise3D_ALU(Position, bTiling, RepeatSize);
case 5:
return VoronoiNoise3D_ALU(Position, Quality, bTiling, RepeatSize, true).w * 2.0 - 1.0;
}
return 0;
}
//----------------------------------------------------------
export float noise(uniform texture_2d PerlinNoiseGradientTexture, uniform texture_3d PerlinNoise3DTexture, float3 Position, float Scale, float Quality, float Function, float Turbulence, float Levels, float OutputMin, float OutputMax, float LevelScale, float FilterWidth, float Tiling, float RepeatSize)
[[
anno::description("Noise"),
anno::noinline()
]]
{
Position *= Scale;
FilterWidth *= Scale;
float Out = 0.0f;
float OutScale = 1.0f;
float InvLevelScale = 1.0f / LevelScale;
int iFunction(Function);
int iQuality(Quality);
int iLevels(Levels);
bool bTurbulence(Turbulence);
bool bTiling(Tiling);
for(int i = 0; i < iLevels; ++i)
{
// fade out noise level that are too high frequent (not done through dynamic branching as it usually requires gradient instructions)
OutScale *= math::saturate(1.0 - FilterWidth);
if(bTurbulence)
{
Out += math::abs(Noise3D_Multiplexer(PerlinNoiseGradientTexture, PerlinNoise3DTexture, iFunction, Position, iQuality, bTiling, RepeatSize)) * OutScale;
}
else
{
Out += Noise3D_Multiplexer(PerlinNoiseGradientTexture, PerlinNoise3DTexture, iFunction, Position, iQuality, bTiling, RepeatSize) * OutScale;
}
Position *= LevelScale;
RepeatSize *= LevelScale;
OutScale *= InvLevelScale;
FilterWidth *= LevelScale;
}
if(!bTurbulence)
{
// bring -1..1 to 0..1 range
Out = Out * 0.5f + 0.5f;
}
// Out is in 0..1 range
return math::lerp(OutputMin, OutputMax, Out);
}
// Material node for noise functions returning a vector value
// @param LevelScale usually 2 but higher values allow efficient use of few levels
// @return in user defined range (OutputMin..OutputMax)
export float4 vector4_noise(float3 Position, float Quality, float Function, float Tiling, float TileSize)
[[
anno::description("Vector Noise"),
anno::noinline()
]]
{
float4 result = float4(0,0,0,1);
float3 ret = float3(0);
int iQuality = int(Quality);
int iFunction = int(Function);
bool bTiling = Tiling > 0.0;
float3x4 Jacobian = JacobianSimplex_ALU(Position, bTiling, TileSize); // compiled out if not used
// verified, HLSL compiled out the switch if Function is a constant
switch (iFunction)
{
case 0: // Cellnoise
ret = float3(Rand3DPCG16(int3(math::floor(NoiseTileWrap(Position, bTiling, TileSize))))) / 0xffff;
result = float4(ret.x, ret.y, ret.z, 1);
break;
case 1: // Color noise
ret = float3(Jacobian[0].w, Jacobian[1].w, Jacobian[2].w);
result = float4(ret.x, ret.y, ret.z, 1);
break;
case 2: // Gradient
result = Jacobian[0];
break;
case 3: // Curl
ret = float3(Jacobian[2][1] - Jacobian[1][2], Jacobian[0][2] - Jacobian[2][0], Jacobian[1][0] - Jacobian[0][1]);
result = float4(ret.x, ret.y, ret.z, 1);
break;
case 4: // Voronoi
result = VoronoiNoise3D_ALU(Position, iQuality, bTiling, TileSize, false);
break;
}
return result;
}
export float3 vector3_noise(float3 Position, float Quality, float Function, float Tiling, float TileSize)
[[
anno::description("Vector Noise float3 version"),
anno::noinline()
]]
{
float4 noise = vector4_noise(Position, Quality, Function, Tiling, TileSize);
return float3(noise.x, noise.y, noise.z);
}
// workaround for ue4 fresnel (without supporting for camera vector) : replacing it with 0.0, means facing to the view
export float fresnel(float exponent [[anno::unused()]], float base_reflect_fraction [[anno::unused()]], float3 normal [[anno::unused()]])
[[
anno::description("Fresnel"),
anno::noinline()
]]
{
return 0.0;
}
export float fresnel_function(float3 normal_vector [[anno::unused()]], float3 camera_vector [[anno::unused()]],
bool invert_fresnel [[anno::unused()]], float power [[anno::unused()]],
bool use_cheap_contrast [[anno::unused()]], float cheap_contrast_dark [[anno::unused()]], float cheap_contrast_bright [[anno::unused()]],
bool clamp_fresnel_dot_product [[anno::unused()]])
[[
anno::description("Fresnel Function"),
anno::noinline()
]]
{
return 0.0;
}
export float3 camera_vector(uniform bool up_z = true)
[[
anno::description("Camera Vector"),
anno::noinline()
]]
{
// assume camera postion is 0,0,0
return math::normalize(float3(0) - convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), up_z));
}
export float pixel_depth()
[[
anno::description("Pixel Depth"),
anno::noinline()
]]
{
return 256.0f;
}
export float scene_depth()
[[
anno::description("Scene Depth")
]]
{
return 65500.0f;
}
export float3 scene_color()
[[
anno::description("Scene Color")
]]
{
return float3(1.0f);
}
export float4 vertex_color()
[[
anno::description("Vertex Color"),
anno::noinline()
]]
{
return float4(1.0f);
}
export float4 vertex_color_from_coordinate(int VertexColorCoordinateIndex)
[[
anno::description("Vertex Color for float2 PrimVar"),
anno::noinline()
]]
{
// Kit only supports 4 uv sets, 2 uvs are available to vertex color. if vertex color index is invalid, output the constant WHITE color intead
return (VertexColorCoordinateIndex > 2) ? float4(1.0f) : float4(state::texture_coordinate(VertexColorCoordinateIndex).x, state::texture_coordinate(VertexColorCoordinateIndex).y, state::texture_coordinate(VertexColorCoordinateIndex+1).x, state::texture_coordinate(VertexColorCoordinateIndex+1).y);
}
export float3 camera_position()
[[
anno::description("Camera Position"),
anno::noinline()
]]
{
return float3(1000.0f, 0, 0);
}
export float3 rotate_about_axis(float4 NormalizedRotationAxisAndAngle, float3 PositionOnAxis, float3 Position)
[[
anno::description("Rotates Position about the given axis by the given angle")
]]
{
// Project Position onto the rotation axis and find the closest point on the axis to Position
float3 NormalizedRotationAxis = float3(NormalizedRotationAxisAndAngle.x,NormalizedRotationAxisAndAngle.y,NormalizedRotationAxisAndAngle.z);
float3 ClosestPointOnAxis = PositionOnAxis + NormalizedRotationAxis * math::dot(NormalizedRotationAxis, Position - PositionOnAxis);
// Construct orthogonal axes in the plane of the rotation
float3 UAxis = Position - ClosestPointOnAxis;
float3 VAxis = math::cross(NormalizedRotationAxis, UAxis);
float[2] SinCosAngle = math::sincos(NormalizedRotationAxisAndAngle.w);
// Rotate using the orthogonal axes
float3 R = UAxis * SinCosAngle[1] + VAxis * SinCosAngle[0];
// Reconstruct the rotated world space position
float3 RotatedPosition = ClosestPointOnAxis + R;
// Convert from position to a position offset
return RotatedPosition - Position;
}
export float2 rotate_scale_offset_texcoords(float2 InTexCoords, float4 InRotationScale, float2 InOffset)
[[
anno::description("Returns a float2 texture coordinate after 2x2 transform and offset applied")
]]
{
return float2(math::dot(InTexCoords, float2(InRotationScale.x, InRotationScale.y)), math::dot(InTexCoords, float2(InRotationScale.z, InRotationScale.w))) + InOffset;
}
export float3 reflection_custom_world_normal(float3 WorldNormal, bool bNormalizeInputNormal, uniform bool up_z = true)
[[
anno::description("Reflection vector about the specified world space normal")
]]
{
if (bNormalizeInputNormal)
{
WorldNormal = math::normalize(WorldNormal);
}
return -camera_vector(up_z) + WorldNormal * math::dot(WorldNormal, camera_vector(up_z)) * 2.0;
}
export float3 reflection_vector(uniform bool up_z = true)
[[
anno::description("Reflection Vector"),
anno::noinline()
]]
{
float3 normal = convert_to_left_hand(state::transform_normal(state::coordinate_internal,state::coordinate_world,state::normal()), up_z, false);
return reflection_custom_world_normal(normal, false, up_z);
}
export float dither_temporalAA(float AlphaThreshold = 0.5f, float Random = 1.0f [[anno::unused()]])
[[
anno::description("Dither TemporalAA"),
anno::noinline()
]]
{
return AlphaThreshold;
}
export float3 black_body( float Temp )
[[
anno::description("Black Body"),
anno::noinline()
]]
{
float u = ( 0.860117757f + 1.54118254e-4f * Temp + 1.28641212e-7f * Temp*Temp ) / ( 1.0f + 8.42420235e-4f * Temp + 7.08145163e-7f * Temp*Temp );
float v = ( 0.317398726f + 4.22806245e-5f * Temp + 4.20481691e-8f * Temp*Temp ) / ( 1.0f - 2.89741816e-5f * Temp + 1.61456053e-7f * Temp*Temp );
float x = 3*u / ( 2*u - 8*v + 4 );
float y = 2*v / ( 2*u - 8*v + 4 );
float z = 1 - x - y;
float Y = 1;
float X = Y/y * x;
float Z = Y/y * z;
float3x3 XYZtoRGB = float3x3(
float3(3.2404542, -1.5371385, -0.4985314),
float3(-0.9692660, 1.8760108, 0.0415560),
float3(0.0556434, -0.2040259, 1.0572252)
);
return XYZtoRGB * float3( X, Y, Z ) * math::pow( 0.0004 * Temp, 4 );
}
export float per_instance_random(uniform texture_2d PerlinNoiseGradientTexture, int NumberInstances)
[[
anno::description("Per Instance Random"),
anno::noinline()
]]
{
float weight = state::object_id() / float(NumberInstances);
return NumberInstances == 0 ? 0.0 : tex::lookup_float4(PerlinNoiseGradientTexture, float2(weight, 1.0 - weight), tex::wrap_repeat, tex::wrap_repeat).x;
}
//------------------ Hair from UE4 -----------------------
float3 hair_absorption_to_color(float3 A)
{
const float B = 0.3f;
float b2 = B * B;
float b3 = B * b2;
float b4 = b2 * b2;
float b5 = B * b4;
float D = (5.969f - 0.215f * B + 2.532f * b2 - 10.73f * b3 + 5.574f * b4 + 0.245f * b5);
return math::exp(-math::sqrt(A) * D);
}
float3 hair_color_to_absorption(float3 C)
{
const float B = 0.3f;
float b2 = B * B;
float b3 = B * b2;
float b4 = b2 * b2;
float b5 = B * b4;
float D = (5.969f - 0.215f * B + 2.532f * b2 - 10.73f * b3 + 5.574f * b4 + 0.245f * b5);
return math::pow(math::log(C) / D, 2.0f);
}
export float3 get_hair_color_from_melanin(float InMelanin, float InRedness, float3 InDyeColor)
[[
anno::description("Hair Color")
]]
{
InMelanin = math::saturate(InMelanin);
InRedness = math::saturate(InRedness);
float Melanin = -math::log(math::max(1 - InMelanin, 0.0001f));
float Eumelanin = Melanin * (1 - InRedness);
float Pheomelanin = Melanin * InRedness;
float3 DyeAbsorption = hair_color_to_absorption(math::saturate(InDyeColor));
float3 Absorption = Eumelanin * float3(0.506f, 0.841f, 1.653f) + Pheomelanin * float3(0.343f, 0.733f, 1.924f);
return hair_absorption_to_color(Absorption + DyeAbsorption);
}
export float3 local_object_bounds_min()
[[
anno::description("Local Object Bounds Min"),
anno::noinline()
]]
{
return float3(0.0);
}
export float3 local_object_bounds_max()
[[
anno::description("Local Object Bounds Max"),
anno::noinline()
]]
{
return float3(100.0);
}
export float3 object_bounds()
[[
anno::description("Object Bounds"),
anno::noinline()
]]
{
return float3(100.0);
}
export float object_radius()
[[
anno::description("Object Radius"),
anno::noinline()
]]
{
return 100.0f;
}
export float3 object_world_position(uniform bool up_z = true)
[[
anno::description("Object World Position"),
anno::noinline()
]]
{
return convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), up_z)*state::meters_per_scene_unit()*100.0;
}
export float3 object_orientation()
[[
anno::description("Object Orientation"),
anno::noinline()
]]
{
return float3(0);
}
export float rcp(float x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export float2 rcp(float2 x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export float3 rcp(float3 x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export float4 rcp(float4 x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export int BitFieldExtractI32(int Data, int Size, int Offset)
[[
anno::description("BitFieldExtractI32 int"),
anno::noinline()
]]
{
Size &= 3;
Offset &= 3;
if (Size == 0)
return 0;
else if (Offset + Size < 32)
return (Data << (32 - Size - Offset)) >> (32 - Size);
else
return Data >> Offset;
}
export int BitFieldExtractI32(float Data, float Size, float Offset)
[[
anno::description("BitFieldExtractI32 float"),
anno::noinline()
]]
{
return BitFieldExtractI32(int(Data), int(Size), int(Offset));
}
export int BitFieldExtractU32(float Data, float Size, float Offset)
[[
anno::description("BitFieldExtractU32 float"),
anno::noinline()
]]
{
return BitFieldExtractI32(Data, Size, Offset);
}
export int BitFieldExtractU32(int Data, int Size, int Offset)
[[
anno::description("BitFieldExtractU32 int"),
anno::noinline()
]]
{
return BitFieldExtractI32(Data, Size, Offset);
}
export float3 EyeAdaptationInverseLookup(float3 LightValue, float Alpha)
[[
anno::description("EyeAdaptationInverseLookup"),
anno::noinline()
]]
{
float Adaptation = 1.0f;
// When Alpha=0.0, we want to multiply by 1.0. when Alpha = 1.0, we want to multiply by 1/Adaptation.
// So the lerped value is:
// LerpLogScale = Lerp(log(1),log(1/Adaptaiton),T)
// Which is simplified as:
// LerpLogScale = Lerp(0,-log(Adaptation),T)
// LerpLogScale = -T * logAdaptation;
float LerpLogScale = -Alpha * math::log(Adaptation);
float Scale = math::exp(LerpLogScale);
return LightValue * Scale;
}
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/MI_European_Spindle_wk1ncbxja_2K.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Subsurface import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_European_Spindle_wk1ncbxja_2K(
uniform texture_2d Normal = texture_2d("./Textures/Placeholder_Normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(2),
anno::in_group("01- Input Textures"),
sampler_normal()
]],
float NormalIntensity = 1.0
[[
anno::display_name("Normal Intensity"),
anno::ui_order(32),
anno::in_group("05 - Normal")
]],
float ColorVariation = 0.0
[[
anno::display_name("Color Variation"),
anno::ui_order(2),
anno::in_group("02 - Albedo")
]],
int NumberInstances = 0
[[
anno::hidden()
]],
uniform texture_2d Albedo = texture_2d("./Textures/DefaultDiffuse.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::in_group("01- Input Textures"),
sampler_color()
]],
float4 ColorOverlay = float4(0.5,0.5,0.5,1.0)
[[
anno::display_name("Color Overlay"),
anno::ui_order(32),
anno::in_group("02 - Albedo")
]],
float OverlayIntensity = 1.0
[[
anno::display_name("Overlay Intensity"),
anno::ui_order(1),
anno::in_group("02 - Albedo"),
anno::soft_range(0.0, 1.5)
]],
uniform texture_2d ORT = texture_2d("./Textures/WhitePlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("ORT"),
anno::ui_order(3),
anno::in_group("01- Input Textures"),
sampler_color()
]],
float RoughnessIntensity = 1.0
[[
anno::display_name("Roughness Intensity"),
anno::ui_order(32),
anno::in_group("03- Roughness")
]],
float OpacityIntensity = 1.0
[[
anno::display_name("Opacity Intensity"),
anno::ui_order(32),
anno::in_group("04 - Opacity")
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
[[
dither_masked_off(),
distill_off()
]]
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float Local1 = (1.0 - NormalIntensity);
float3 Local2 = math::lerp(float3(Local0.x,Local0.y,Local0.z),float3(0.0,0.0,1.0),Local1);
float3 Normal_mdl = Local2;
float3 Local3 = (float3(100.0,10.0,1.0) * ::per_instance_random(texture_2d("./Textures/PerlinNoiseGradientTexture.png",tex::gamma_linear), NumberInstances));
float3 Local4 = (::object_world_position(true) * 0.01);
float3 Local5 = (Local3 + Local4);
float3 Local6 = math::frac(Local5);
float Local7 = math::dot(float2(Local6.x,Local6.y), float2(Local6.y,Local6.z));
float Local8 = (-0.5 + Local7);
float Local9 = (Local8 * 2.0);
float Local10 = (ColorVariation * Local9);
float3 Local11 = math::normalize(Local6);
float3 Local12 = (Local10 * Local11);
float4 Local13 = tex::lookup_float4(Albedo,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local14 = (1.0 - float3(Local13.x,Local13.y,Local13.z));
float3 Local15 = (Local14 * 2.0);
float3 Local16 = (1.0 - float3(ColorOverlay.x,ColorOverlay.y,ColorOverlay.z));
float3 Local17 = (Local15 * Local16);
float3 Local18 = (1.0 - Local17);
float3 Local19 = (float3(Local13.x,Local13.y,Local13.z) * 2.0);
float3 Local20 = (Local19 * float3(ColorOverlay.x,ColorOverlay.y,ColorOverlay.z));
float Local21 = ((float3(Local13.x,Local13.y,Local13.z).x >= 0.5) ? Local18.x : Local20.x);
float Local22 = ((float3(Local13.x,Local13.y,Local13.z).y >= 0.5) ? Local18.y : Local20.y);
float Local23 = ((float3(Local13.x,Local13.y,Local13.z).z >= 0.5) ? Local18.z : Local20.z);
float3 Local24 = math::lerp(float3(Local13.x,Local13.y,Local13.z),float3(float2(Local21,Local22).x,float2(Local21,Local22).y,Local23),OverlayIntensity);
float3 Local25 = (Local12 + Local24);
float4 Local26 = tex::lookup_float4(ORT,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local27 = (Local26.y * RoughnessIntensity);
float Local28 = (Local26.x * OpacityIntensity);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float Opacity_mdl = 1.0;
float OpacityMask_mdl = (Local28 - 0.3333) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = Local25;
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local27;
float3 SubsurfaceColor_mdl = 0;
} in
::OmniUe4Subsurface(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: Opacity_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
opacity_mask: OpacityMask_mdl,
subsurface_color: SubsurfaceColor_mdl,
two_sided: true);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/M_Basic_Wall.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Basic_Wall(
float4 Color = float4(0.810345,0.787878,0.654683,1.0)
[[
anno::display_name("Color"),
anno::ui_order(32),
anno::in_group("Wall")
]],
float Roughness = 0.640708
[[
anno::display_name("Roughness"),
anno::ui_order(32),
anno::in_group("Wall")
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Color.x,Color.y,Color.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Roughness;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_Red_Wooden_Planks_td2ledlg_2K_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Red_Wooden_Planks_td2ledlg_2K_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Red_Wooden_Planks_td2ledlg_2K_N.exr",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float4 Local1 = tex::lookup_float4(texture_2d("./Textures/T_Red_Wooden_Planks_td2ledlg_2K_D.exr",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local1.x,Local1.y,Local1.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/M_FieldGrass_Inst_01.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Subsurface import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_FieldGrass_Inst_01(
uniform texture_2d Diffuse = texture_2d("./Textures/T_FieldGrass_01_D.png",::tex::gamma_srgb)
[[
anno::display_name("Diffuse"),
anno::ui_order(32),
anno::in_group("Diffuse"),
sampler_color()
]],
float4 GrassTipColor = float4(3.0,2.915346,2.825072,1.0)
[[
anno::display_name("GrassTipColor"),
anno::ui_order(32)
]],
float4 GrassDeadColor = float4(4.0,3.243198,2.62,1.0)
[[
anno::display_name("GrassDeadColor"),
anno::ui_order(32)
]],
float SpecularAmount = 0.05
[[
anno::display_name("SpecularAmount"),
anno::ui_order(32),
anno::in_group("Specular")
]],
float Roughness = 0.4
[[
anno::display_name("Roughness"),
anno::ui_order(32),
anno::in_group("Specular")
]],
float4 WPOWaveAmounttXYZ = float4(0.9,0.9,-0.5,1.0)
[[
anno::display_name("WPOWaveAmounttXYZ"),
anno::ui_order(32),
anno::in_group("WPO")
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
[[
world_space_normal(),
dither_masked_off(),
distill_off()
]]
=
let {
float2 Local43 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) / 600.0);
float Local48 = (state::animation_time() * 0.14);
float Local49 = (Local48 * 1.0);
float Local50 = math::frac(Local49);
float Local51 = (Local48 * 0.0);
float Local52 = math::frac(Local51);
float2 Local53 = (float2(Local50,Local52) + Local43);
float4 Local54 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Water_N.png",::tex::gamma_linear),float2(Local53.x,1.0-Local53.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local55 = (float2(Local52,Local50) + Local43);
float4 Local56 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Water_N.png",::tex::gamma_linear),float2(Local55.x,1.0-Local55.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Local57 = (float3(Local54.x,Local54.y,Local54.z) + float3(Local56.x,Local56.y,Local56.z));
float3 Local58 = math::normalize(Local57);
float3 Local59 = (Local58 * 6.283185);
float3 Local60 = ::rotate_about_axis(float4(::vertex_normal_world_space(true).x,::vertex_normal_world_space(true).y,::vertex_normal_world_space(true).z,Local59.x),float3(0.0,0.0,-50.0),float3(0.0,0.0,0.0));
float3 Local61 = (Local60 * float3(WPOWaveAmounttXYZ.x,WPOWaveAmounttXYZ.y,WPOWaveAmounttXYZ.z));
float3 Local62 = math::lerp(float3(0.0,0.0,0.0),Local61,float4(scene::data_lookup_float3("displayColor").x, scene::data_lookup_float3("displayColor").y, scene::data_lookup_float3("displayColor").z, scene::data_lookup_float("displayOpacity")).y);
float3 Local63 = (float3(0.0,0.0,0.0) + Local62);
float3 Local67 = ::camera_position();
float3 Local68 = (Local67 - (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float Local69 = math::length(Local68);
float Local70 = (Local69 / 1600.0);
float Local71 = math::min(math::max(Local70,0.0),1.0);
float3 Local72 = ::convert_to_left_hand(state::transform_vector(state::coordinate_object, state::coordinate_world, ::convert_to_left_hand(float3(float3(0.0,0.0,1.0).x,float3(0.0,0.0,1.0).y,float3(0.0,0.0,1.0).z), true, false)), true, false);
float3 WorldPositionOffset_mdl = Local63;
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float2 CustomizedUV1_mdl = float2(Local71,Local71);
float2 CustomizedUV2_mdl = float2(Local72.x,Local72.y);
float2 CustomizedUV3_mdl = float2(Local72.z,Local72.z);
float3 Local0 = math::normalize(float3(CustomizedUV2_mdl.x,CustomizedUV2_mdl.y,CustomizedUV3_mdl.x));
float3 Local1 = math::normalize(float3(0.0001,0.0,1.0));
float3 Local2 = math::cross(Local0,Local1);
float3 Local3 = math::cross(Local2,Local0);
float Local4 = math::dot(Local3, Local3);
float3 Local5 = math::normalize(Local3);
float4 Local6 = ((math::abs(Local4 - 0.000001) > 0.00001) ? (Local4 >= 0.000001 ? float4(Local5.x,Local5.y,Local5.z,0.0) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0)) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0));
float Local7 = (state::animation_time() * 0.14);
float Local8 = (Local7 * 1.0);
float Local9 = math::frac(Local8);
float Local10 = (Local7 * 0.0);
float Local11 = math::frac(Local10);
float2 Local12 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) / 600.0);
float2 Local13 = (float2(Local9,Local11) + Local12);
float4 Local14 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Water_N.png",::tex::gamma_linear),float2(Local13.x,1.0-Local13.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local15 = (float2(Local11,Local9) + Local12);
float4 Local16 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Water_N.png",::tex::gamma_linear),float2(Local15.x,1.0-Local15.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Local17 = (float3(Local14.x,Local14.y,Local14.z) + float3(Local16.x,Local16.y,Local16.z));
float3 Local18 = (Local17 * float3(10.0,10.0,1.0));
float3 Local19 = math::lerp(float3(0.0,0.0,1.0),Local18,float4(scene::data_lookup_float3("displayColor").x, scene::data_lookup_float3("displayColor").y, scene::data_lookup_float3("displayColor").z, scene::data_lookup_float("displayOpacity")).y);
float3 Local20 = math::lerp(Local19,float3(0.0,0.0,1.0),CustomizedUV1_mdl.x);
float3 Local21 = (float3(0.0,0.0,0.5) + Local20);
float3 Local22 = (float3(Local6.x,Local6.y,Local6.z) * Local21.x);
float Local23 = math::dot(Local2, Local2);
float3 Local24 = math::normalize(Local2);
float4 Local25 = ((math::abs(Local23 - 0.000001) > 0.00001) ? (Local23 >= 0.000001 ? float4(Local24.x,Local24.y,Local24.z,0.0) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0)) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0));
float3 Local26 = (float3(Local25.x,Local25.y,Local25.z) * Local21.y);
float3 Local27 = (Local22 + Local26);
float3 Local28 = (Local0 * Local21.z);
float3 Local29 = (Local28 + float3(0.0,0.0,0.0));
float3 Local30 = (Local27 + Local29);
float3 Normal_mdl = Local30;
float4 Local31 = tex::lookup_float4(Diffuse,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local32 = (float3(GrassTipColor.x,GrassTipColor.y,GrassTipColor.z) * float3(Local31.x,Local31.y,Local31.z));
float4 Local33 = tex::lookup_float4(texture_2d("./Textures/T_FieldGrass_02_M.png",::tex::gamma_linear),float2(Local12.x,1.0-Local12.y),tex::wrap_repeat,tex::wrap_repeat);
float Local34 = (float4(scene::data_lookup_float3("displayColor").x, scene::data_lookup_float3("displayColor").y, scene::data_lookup_float3("displayColor").z, scene::data_lookup_float("displayOpacity")).y * Local33.x);
float Local35 = math::max(Local34,0.0);
float3 Local36 = math::lerp(float3(Local31.x,Local31.y,Local31.z),Local32,Local35);
float3 Local37 = (float3(GrassDeadColor.x,GrassDeadColor.y,GrassDeadColor.z) * Local31.y);
float Local38 = (float4(scene::data_lookup_float3("displayColor").x, scene::data_lookup_float3("displayColor").y, scene::data_lookup_float3("displayColor").z, scene::data_lookup_float("displayOpacity")).y * Local33.y);
float Local39 = math::max(Local38,0.0);
float3 Local40 = math::lerp(Local36,Local37,Local39);
float Local41 = math::lerp(0.0,SpecularAmount,float4(scene::data_lookup_float3("displayColor").x, scene::data_lookup_float3("displayColor").y, scene::data_lookup_float3("displayColor").z, scene::data_lookup_float("displayOpacity")).y);
float Local42 = (float4(scene::data_lookup_float3("displayColor").x, scene::data_lookup_float3("displayColor").y, scene::data_lookup_float3("displayColor").z, scene::data_lookup_float("displayOpacity")).y * Local41);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float Opacity_mdl = 0.3;
float OpacityMask_mdl = (Local31.w - 0.3333) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = Local40;
float Metallic_mdl = 0.0;
float Specular_mdl = Local42;
float Roughness_mdl = Roughness;
float3 SubsurfaceColor_mdl = 0;
} in
::OmniUe4Subsurface(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: Opacity_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
opacity_mask: OpacityMask_mdl,
subsurface_color: SubsurfaceColor_mdl,
two_sided: true,
is_tangent_space_normal: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_Cow3_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Cow3_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Cow3_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/SanjanaStage/OutdoorRanch/Materials/T_Cow1_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Cow1_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Cow1_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/ReplicatorScripts/Replicator_RandomizeCows_Basic.py | import io
import json
import time
import asyncio
from typing import List
import omni.kit
import omni.usd
import omni.replicator.core as rep
import numpy as np
from omni.replicator.core import AnnotatorRegistry, BackendDispatch, Writer, WriterRegistry, orchestrator
from omni.syntheticdata.scripts.SyntheticData import SyntheticData
camera_positions = [(1720, -1220, 200), (3300, -1220, 200),
(3300, -3500, 200), (1720, -3500, 200)]
# Attach Render Product
with rep.new_layer():
camera = rep.create.camera()
render_product = rep.create.render_product(camera, (1280, 1280))
# Randomizer Function
def randomize_cows():
cows = rep.get.prims(semantics=[('class', 'cow')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows)
# Trigger to call randomizer
with rep.trigger.on_frame(num_frames=10):
with camera:
rep.modify.pose(position=rep.distribution.choice(
camera_positions), look_at=(2500, -2300, 0))
rep.randomizer.randomize_cows()
# Initialize and attach Writer to store result
writer = rep.WriterRegistry.get('CowWriter')
writer.initialize(output_dir='C:/Users/anura/Desktop/IndoorRanch_ReplicatorOutputs/Run4',
rgb=True, bounding_box_2d_tight=True)
writer.attach([render_product])
|
An-u-rag/synthetic-visual-dataset-generation/ReplicatorScripts/Replicator_RandomizeSimple.py | import io
import json
import time
import asyncio
from typing import List
import omni.kit
import omni.usd
import omni.replicator.core as rep
import csv
import numpy as np
from omni.replicator.core import AnnotatorRegistry, BackendDispatch, Writer, WriterRegistry, orchestrator
from omni.syntheticdata.scripts.SyntheticData import SyntheticData
class CowWriter(Writer):
def __init__(
self,
output_dir: str,
semantic_types: List[str] = None,
rgb: bool = True,
bounding_box_2d_loose: bool = False,
image_output_format: str = "png",
):
self._output_dir = output_dir
self._backend = BackendDispatch({"paths": {"out_dir": output_dir}})
self._frame_id = 0
self._sequence_id = 0
self._image_output_format = image_output_format
self._output_data_format = {}
self.annotators = []
if semantic_types is None:
semantic_types = ["class"]
# RGB
if rgb:
self.annotators.append(AnnotatorRegistry.get_annotator("rgb"))
if bounding_box_2d_loose:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_2d_loose", init_params={
"semanticTypes": semantic_types})
)
def write(self, data: dict):
sequence_id = ""
for trigger_name, call_count in data["trigger_outputs"].items():
if "on_time" in trigger_name:
sequence_id = f"{call_count}_{sequence_id}"
if sequence_id != self._sequence_id:
self._frame_id = 0
self._sequence_id = sequence_id
for annotator in data.keys():
annotator_split = annotator.split("-")
render_product_path = ""
multi_render_prod = 0
if len(annotator_split) > 1:
multi_render_prod = 1
render_product_name = annotator_split[-1]
render_product_path = f"{render_product_name}/"
if annotator.startswith("rgb"):
if multi_render_prod:
render_product_path += "rgb/"
self._write_rgb(data, render_product_path, annotator)
if annotator.startswith("bounding_box_2d_loose"):
if multi_render_prod:
render_product_path += "bounding_box_2d_loose/"
self._write_bounding_box_data(
data, "2d_loose", render_product_path, annotator)
self._frame_id += 1
def _write_rgb(self, data: dict, render_product_path: str, annotator: str):
file_path = f"{render_product_path}rgb_{self._sequence_id}{self._frame_id:0}.{self._image_output_format}"
self._backend.write_image(file_path, data[annotator])
def _write_bounding_box_data(self, data: dict, bbox_type: str, render_product_path: str, annotator: str):
bbox_data_all = data[annotator]["data"]
id_to_labels = data[annotator]["info"]["idToLabels"]
prim_paths = data[annotator]["info"]["primPaths"]
target_coco_bbox_data = []
count = 0
print(bbox_data_all)
labels_file_path = f"{render_product_path}rgb_{self._sequence_id}{self._frame_id:0}.txt"
for bbox_data in bbox_data_all:
target_bbox_data = {'x_min': bbox_data['x_min'], 'y_min': bbox_data['y_min'],
'x_max': bbox_data['x_max'], 'y_max': bbox_data['y_max']}
width = int(
abs(target_bbox_data["x_max"] - target_bbox_data["x_min"]))
height = int(
abs(target_bbox_data["y_max"] - target_bbox_data["y_min"]))
semantic_label = data[annotator]["info"]["idToLabels"].get(bbox_data["semanticId"])
coco_bbox_data = []
coco_bbox_data.append(semantic_label)
coco_bbox_data.append(str((float(target_bbox_data["x_min"]) + float(target_bbox_data["x_max"]))/2))
coco_bbox_data.append(str((float(target_bbox_data["y_min"]) + float(target_bbox_data["y_max"]))/2))
coco_bbox_data.append(str(width))
coco_bbox_data.append(str(height))
target_coco_bbox_data.append(coco_bbox_data)
count += 1
buf = io.StringIO()
writer = csv.writer(buf, delimiter = " ")
writer.writerows(target_coco_bbox_data)
self._backend.write_blob(labels_file_path, bytes(buf.getvalue(), "utf-8"))
rep.WriterRegistry.register(CowWriter)
camera_positions = [(-1100, 1480, -900), (-1100, 3355, -900),
(2815, 3410, -800), (2815, 1380, -800)]
# Attach Render Product
with rep.new_layer():
camera = rep.create.camera()
render_product = rep.create.render_product(camera, (1280, 1280))
# Randomizer Function
def randomize_pigsdirty():
pigs = rep.get.prims(semantics=[('class', 'pig_dirty')])
with pigs:
rep.modify.visibility(rep.distribution.choice([True, False]))
return pigs.node
rep.randomizer.register(randomize_pigsdirty)
def randomize_pigsclean():
pigs = rep.get.prims(semantics=[('class', 'pig_clean')])
with pigs:
rep.modify.visibility(rep.distribution.choice([True, False]))
return pigs.node
rep.randomizer.register(randomize_pigsclean)
def randomize_cows2():
cows = rep.get.prims(semantics=[('class', 'cow_2')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows2)
def randomize_cows3():
cows = rep.get.prims(semantics=[('class', 'cow_3')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows3)
def randomize_cows4():
cows = rep.get.prims(semantics=[('class', 'cow_4')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows4)
def randomize_environment():
envs = ["omniverse://localhost/NVIDIA/Assets/Skies/Clear/noon_grass_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Night/moonlit_golf_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Storm/approaching_storm_4k.hdr"]
lights = rep.create.light(
light_type = "Dome",
position = (2500, -2300, 0),
intensity = rep.distribution.choice([1., 10., 100., 1000.]),
texture = rep.distribution.choice(envs)
)
return lights.node
rep.randomizer.register(randomize_environment)
# Trigger to call randomizer
with rep.trigger.on_frame(num_frames=3000):
with camera:
rep.modify.pose(position=rep.distribution.choice(
camera_positions), look_at=(790, 2475, -1178))
rep.randomizer.randomize_environment()
rep.randomizer.randomize_pigsdirty()
rep.randomizer.randomize_pigsclean()
rep.randomizer.randomize_cows2()
rep.randomizer.randomize_cows3()
rep.randomizer.randomize_cows4()
writer = rep.WriterRegistry.get('BasicWriter')
writer.initialize(output_dir='C:/Users/anura/Desktop/IndoorRanch_ReplicatorOutputs/SanjRun1',
rgb=True, bounding_box_2d_tight=True, bounding_box_2d_loose=True, semantic_segmentation=True, bounding_box_3d=True)
writer.attach([render_product])
|
An-u-rag/synthetic-visual-dataset-generation/ReplicatorScripts/Replicator_RandomizeCows.py | import io
import json
import time
import asyncio
from typing import List
import omni.kit
import omni.usd
import omni.replicator.core as rep
import numpy as np
from omni.replicator.core import AnnotatorRegistry, BackendDispatch, Writer, WriterRegistry, orchestrator
from omni.syntheticdata.scripts.SyntheticData import SyntheticData
class CowWriter(Writer):
def __init__(
self,
output_dir: str,
semantic_types: List[str] = None,
rgb: bool = True,
bounding_box_2d_tight: bool = False,
bounding_box_2d_loose: bool = False,
semantic_segmentation: bool = False,
instance_id_segmentation: bool = False,
instance_segmentation: bool = False,
distance_to_camera: bool = False,
bounding_box_3d: bool = False,
image_output_format: str = "png",
):
self._output_dir = output_dir
self._backend = BackendDispatch({"paths": {"out_dir": output_dir}})
self._frame_id = 0
self._sequence_id = 0
self._image_output_format = image_output_format
self._output_data_format = {}
self.annotators = []
if semantic_types is None:
semantic_types = ["class"]
# RGB
if rgb:
self.annotators.append(AnnotatorRegistry.get_annotator("rgb"))
# Bounding Box 2D
if bounding_box_2d_tight:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_2d_tight", init_params={
"semanticTypes": semantic_types})
)
if bounding_box_2d_loose:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_2d_loose", init_params={
"semanticTypes": semantic_types})
)
# Semantic Segmentation
if semantic_segmentation:
self.annotators.append(
AnnotatorRegistry.get_annotator(
"semantic_segmentation",
init_params={"semanticTypes": semantic_types},
)
)
# Instance Segmentation
if instance_id_segmentation:
self.annotators.append(
AnnotatorRegistry.get_annotator(
"instance_id_segmentation", init_params={}
)
)
# Instance Segmentation
if instance_segmentation:
self.annotators.append(
AnnotatorRegistry.get_annotator(
"instance_segmentation",
init_params={"semanticTypes": semantic_types},
)
)
# Depth
if distance_to_camera:
self.annotators.append(
AnnotatorRegistry.get_annotator("distance_to_camera"))
# Bounding Box 3D
if bounding_box_3d:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_3d", init_params={
"semanticTypes": semantic_types})
)
def write(self, data: dict):
sequence_id = ""
for trigger_name, call_count in data["trigger_outputs"].items():
if "on_time" in trigger_name:
sequence_id = f"{call_count}_{sequence_id}"
if sequence_id != self._sequence_id:
self._frame_id = 0
self._sequence_id = sequence_id
for annotator in data.keys():
annotator_split = annotator.split("-")
render_product_path = ""
multi_render_prod = 0
if len(annotator_split) > 1:
multi_render_prod = 1
render_product_name = annotator_split[-1]
render_product_path = f"{render_product_name}/"
if annotator.startswith("rgb"):
if multi_render_prod:
render_product_path += "rgb/"
self._write_rgb(data, render_product_path, annotator)
if annotator.startswith("distance_to_camera"):
if multi_render_prod:
render_product_path += "distance_to_camera/"
self._write_distance_to_camera(
data, render_product_path, annotator)
if annotator.startswith("semantic_segmentation"):
if multi_render_prod:
render_product_path += "semantic_segmentation/"
self._write_semantic_segmentation(
data, render_product_path, annotator)
if annotator.startswith("instance_id_segmentation"):
if multi_render_prod:
render_product_path += "instance_id_segmentation/"
self._write_instance_id_segmentation(
data, render_product_path, annotator)
if annotator.startswith("instance_segmentation"):
if multi_render_prod:
render_product_path += "instance_segmentation/"
self._write_instance_segmentation(
data, render_product_path, annotator)
if annotator.startswith("bounding_box_3d"):
if multi_render_prod:
render_product_path += "bounding_box_3d/"
self._write_bounding_box_data(
data, "3d", render_product_path, annotator)
if annotator.startswith("bounding_box_2d_loose"):
if multi_render_prod:
render_product_path += "bounding_box_2d_loose/"
self._write_bounding_box_data(
data, "2d_loose", render_product_path, annotator)
if annotator.startswith("bounding_box_2d_tight"):
if multi_render_prod:
render_product_path += "bounding_box_2d_tight/"
self._write_bounding_box_data(
data, "2d_tight", render_product_path, annotator)
self._frame_id += 1
def _write_rgb(self, data: dict, render_product_path: str, annotator: str):
file_path = f"{render_product_path}rgb_{self._sequence_id}{self._frame_id:0}.{self._image_output_format}"
self._backend.write_image(file_path, data[annotator])
def _write_distance_to_camera(self, data: dict, render_product_path: str, annotator: str):
dist_to_cam_data = data[annotator]
file_path = (
f"{render_product_path}distance_to_camera_{self._sequence_id}{self._frame_id:0}.npy"
)
buf = io.BytesIO()
np.save(buf, dist_to_cam_data)
self._backend.write_blob(file_path, buf.getvalue())
def _write_semantic_segmentation(self, data: dict, render_product_path: str, annotator: str):
semantic_seg_data = data[annotator]["data"]
height, width = semantic_seg_data.shape[:2]
file_path = (
f"{render_product_path}semantic_segmentation_{self._sequence_id}{self._frame_id:0}.png"
)
if self.colorize_semantic_segmentation:
semantic_seg_data = semantic_seg_data.view(
np.uint8).reshape(height, width, -1)
self._backend.write_image(file_path, semantic_seg_data)
else:
semantic_seg_data = semantic_seg_data.view(
np.uint32).reshape(height, width)
self._backend.write_image(file_path, semantic_seg_data)
id_to_labels = data[annotator]["info"]["idToLabels"]
file_path = f"{render_product_path}semantic_segmentation_labels_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_labels.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
def _write_instance_id_segmentation(self, data: dict, render_product_path: str, annotator: str):
instance_seg_data = data[annotator]["data"]
height, width = instance_seg_data.shape[:2]
file_path = f"{render_product_path}instance_id_segmentation_{self._sequence_id}{self._frame_id:0}.png"
if self.colorize_instance_id_segmentation:
instance_seg_data = instance_seg_data.view(
np.uint8).reshape(height, width, -1)
self._backend.write_image(file_path, instance_seg_data)
else:
instance_seg_data = instance_seg_data.view(
np.uint32).reshape(height, width)
self._backend.write_image(file_path, instance_seg_data)
id_to_labels = data[annotator]["info"]["idToLabels"]
file_path = f"{render_product_path}instance_id_segmentation_mapping_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_labels.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
def _write_instance_segmentation(self, data: dict, render_product_path: str, annotator: str):
instance_seg_data = data[annotator]["data"]
height, width = instance_seg_data.shape[:2]
file_path = (
f"{render_product_path}instance_segmentation_{self._sequence_id}{self._frame_id:0}.png"
)
if self.colorize_instance_segmentation:
instance_seg_data = instance_seg_data.view(
np.uint8).reshape(height, width, -1)
self._backend.write_image(file_path, instance_seg_data)
else:
instance_seg_data = instance_seg_data.view(
np.uint32).reshape(height, width)
self._backend.write_image(file_path, instance_seg_data)
id_to_labels = data[annotator]["info"]["idToLabels"]
file_path = f"{render_product_path}instance_segmentation_mapping_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_labels.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
id_to_semantics = data[annotator]["info"]["idToSemantics"]
file_path = f"{render_product_path}instance_segmentation_semantics_mapping_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_semantics.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
def _write_bounding_box_data(self, data: dict, bbox_type: str, render_product_path: str, annotator: str):
bbox_data_all = data[annotator]["data"]
print(bbox_data_all)
id_to_labels = data[annotator]["info"]["idToLabels"]
prim_paths = data[annotator]["info"]["primPaths"]
file_path = f"{render_product_path}bounding_box_{bbox_type}_{self._sequence_id}{self._frame_id:0}.npy"
buf = io.BytesIO()
np.save(buf, bbox_data_all)
self._backend.write_blob(file_path, buf.getvalue())
labels_file_path = f"{render_product_path}bounding_box_{bbox_type}_labels_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(id_to_labels).encode())
self._backend.write_blob(labels_file_path, buf.getvalue())
labels_file_path = f"{render_product_path}bounding_box_{bbox_type}_prim_paths_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(prim_paths).encode())
self._backend.write_blob(labels_file_path, buf.getvalue())
target_coco_bbox_data = []
count = 0
for bbox_data in bbox_data_all:
target_bbox_data = {'x_min': bbox_data['x_min'], 'y_min': bbox_data['y_min'],
'x_max': bbox_data['x_max'], 'y_max': bbox_data['y_max']}
width = int(
abs(target_bbox_data["x_max"] - target_bbox_data["x_min"]))
height = int(
abs(target_bbox_data["y_max"] - target_bbox_data["y_min"]))
if width != 2147483647 and height != 2147483647:
# filepath = f"rgb_{self._frame_id}.{self._image_output_format}"
# self._backend.write_image(filepath, data["rgb"])
bbox_filepath = f"bbox_{self._frame_id}.txt"
coco_bbox_data = {
"name": prim_paths[count],
"x_min": int(target_bbox_data["x_min"]),
"y_min": int(target_bbox_data["y_min"]),
"x_max": int(target_bbox_data["x_max"]),
"y_max": int(target_bbox_data["y_max"]),
"width": width,
"height": height}
target_coco_bbox_data.append(coco_bbox_data)
count += 1
buf = io.BytesIO()
buf.write(json.dumps(target_coco_bbox_data).encode())
self._backend.write_blob(bbox_filepath, buf.getvalue())
rep.WriterRegistry.register(CowWriter)
camera_positions = [(1720, -1220, 200), (3300, -1220, 200),
(3300, -3500, 200), (1720, -3500, 200)]
# Attach Render Product
with rep.new_layer():
camera = rep.create.camera()
render_product = rep.create.render_product(camera, (1280, 1280))
# Randomizer Function
def randomize_cows1():
cows = rep.get.prims(semantics=[('class', 'cow_1')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows1)
def randomize_cows2():
cows = rep.get.prims(semantics=[('class', 'cow_2')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows2)
def randomize_cows3():
cows = rep.get.prims(semantics=[('class', 'cow_3')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows3)
def randomize_cows4():
cows = rep.get.prims(semantics=[('class', 'cow_4')])
with cows:
rep.modify.visibility(rep.distribution.choice([True, False]))
return cows.node
rep.randomizer.register(randomize_cows4)
def randomize_environment():
envs = ["omniverse://localhost/NVIDIA/Assets/Skies/Clear/noon_grass_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Night/moonlit_golf_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Storm/approaching_storm_4k.hdr"]
lights = rep.create.light(
light_type = "Dome",
position = (2500, -2300, 0),
intensity = rep.distribution.choice([1., 10., 100., 1000.]),
texture = rep.distribution.choice(envs)
)
return lights.node
rep.randomizer.register(randomize_environment)
# Trigger to call randomizer
with rep.trigger.on_frame(num_frames=10):
with camera:
rep.modify.pose(position=rep.distribution.choice(
camera_positions), look_at=(2500, -2300, 0))
rep.randomizer.randomize_environment()
rep.randomizer.randomize_cows1()
rep.randomizer.randomize_cows2()
rep.randomizer.randomize_cows3()
rep.randomizer.randomize_cows4()
writer = rep.WriterRegistry.get('BasicWriter')
writer.initialize(output_dir='C:/Users/anura/Desktop/IndoorRanch_ReplicatorOutputs/NewRun1',
rgb=True, bounding_box_2d_tight=True)
writer.attach([render_product])
|
An-u-rag/synthetic-visual-dataset-generation/ReplicatorScripts/CowWriter_COCO.py | import io
import json
import time
import asyncio
from typing import List
import omni.kit
import omni.usd
import omni.replicator.core as rep
import numpy as np
from omni.replicator.core import AnnotatorRegistry, BackendDispatch, Writer, WriterRegistry, orchestrator
from omni.syntheticdata.scripts.SyntheticData import SyntheticData
class CowWriter(Writer):
def __init__(
self,
output_dir: str,
semantic_types: List[str] = None,
rgb: bool = True,
bounding_box_2d_tight: bool = False,
bounding_box_2d_loose: bool = False,
semantic_segmentation: bool = False,
instance_id_segmentation: bool = False,
instance_segmentation: bool = False,
distance_to_camera: bool = False,
bounding_box_3d: bool = False,
image_output_format: str = "png",
):
self._output_dir = output_dir
self._backend = BackendDispatch({"paths": {"out_dir": output_dir}})
self._frame_id = 0
self._sequence_id = 0
self._image_output_format = image_output_format
self._output_data_format = {}
self.annotators = []
if semantic_types is None:
semantic_types = ["class"]
# RGB
if rgb:
self.annotators.append(AnnotatorRegistry.get_annotator("rgb"))
# Bounding Box 2D
if bounding_box_2d_tight:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_2d_tight", init_params={
"semanticTypes": semantic_types})
)
if bounding_box_2d_loose:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_2d_loose", init_params={
"semanticTypes": semantic_types})
)
# Semantic Segmentation
if semantic_segmentation:
self.annotators.append(
AnnotatorRegistry.get_annotator(
"semantic_segmentation",
init_params={"semanticTypes": semantic_types},
)
)
# Instance Segmentation
if instance_id_segmentation:
self.annotators.append(
AnnotatorRegistry.get_annotator(
"instance_id_segmentation", init_params={}
)
)
# Instance Segmentation
if instance_segmentation:
self.annotators.append(
AnnotatorRegistry.get_annotator(
"instance_segmentation",
init_params={"semanticTypes": semantic_types},
)
)
# Depth
if distance_to_camera:
self.annotators.append(
AnnotatorRegistry.get_annotator("distance_to_camera"))
# Bounding Box 3D
if bounding_box_3d:
self.annotators.append(
AnnotatorRegistry.get_annotator("bounding_box_3d", init_params={
"semanticTypes": semantic_types})
)
def write(self, data: dict):
sequence_id = ""
for trigger_name, call_count in data["trigger_outputs"].items():
if "on_time" in trigger_name:
sequence_id = f"{call_count}_{sequence_id}"
if sequence_id != self._sequence_id:
self._frame_id = 0
self._sequence_id = sequence_id
for annotator in data.keys():
annotator_split = annotator.split("-")
render_product_path = ""
multi_render_prod = 0
if len(annotator_split) > 1:
multi_render_prod = 1
render_product_name = annotator_split[-1]
render_product_path = f"{render_product_name}/"
if annotator.startswith("rgb"):
if multi_render_prod:
render_product_path += "rgb/"
self._write_rgb(data, render_product_path, annotator)
if annotator.startswith("distance_to_camera"):
if multi_render_prod:
render_product_path += "distance_to_camera/"
self._write_distance_to_camera(
data, render_product_path, annotator)
if annotator.startswith("semantic_segmentation"):
if multi_render_prod:
render_product_path += "semantic_segmentation/"
self._write_semantic_segmentation(
data, render_product_path, annotator)
if annotator.startswith("instance_id_segmentation"):
if multi_render_prod:
render_product_path += "instance_id_segmentation/"
self._write_instance_id_segmentation(
data, render_product_path, annotator)
if annotator.startswith("instance_segmentation"):
if multi_render_prod:
render_product_path += "instance_segmentation/"
self._write_instance_segmentation(
data, render_product_path, annotator)
if annotator.startswith("bounding_box_3d"):
if multi_render_prod:
render_product_path += "bounding_box_3d/"
self._write_bounding_box_data(
data, "3d", render_product_path, annotator)
if annotator.startswith("bounding_box_2d_loose"):
if multi_render_prod:
render_product_path += "bounding_box_2d_loose/"
self._write_bounding_box_data(
data, "2d_loose", render_product_path, annotator)
if annotator.startswith("bounding_box_2d_tight"):
if multi_render_prod:
render_product_path += "bounding_box_2d_tight/"
self._write_bounding_box_data(
data, "2d_tight", render_product_path, annotator)
self._frame_id += 1
def _write_rgb(self, data: dict, render_product_path: str, annotator: str):
file_path = f"{render_product_path}rgb_{self._sequence_id}{self._frame_id:0}.{self._image_output_format}"
self._backend.write_image(file_path, data[annotator])
def _write_distance_to_camera(self, data: dict, render_product_path: str, annotator: str):
dist_to_cam_data = data[annotator]
file_path = (
f"{render_product_path}distance_to_camera_{self._sequence_id}{self._frame_id:0}.npy"
)
buf = io.BytesIO()
np.save(buf, dist_to_cam_data)
self._backend.write_blob(file_path, buf.getvalue())
def _write_semantic_segmentation(self, data: dict, render_product_path: str, annotator: str):
semantic_seg_data = data[annotator]["data"]
height, width = semantic_seg_data.shape[:2]
file_path = (
f"{render_product_path}semantic_segmentation_{self._sequence_id}{self._frame_id:0}.png"
)
if self.colorize_semantic_segmentation:
semantic_seg_data = semantic_seg_data.view(
np.uint8).reshape(height, width, -1)
self._backend.write_image(file_path, semantic_seg_data)
else:
semantic_seg_data = semantic_seg_data.view(
np.uint32).reshape(height, width)
self._backend.write_image(file_path, semantic_seg_data)
id_to_labels = data[annotator]["info"]["idToLabels"]
file_path = f"{render_product_path}semantic_segmentation_labels_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_labels.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
def _write_instance_id_segmentation(self, data: dict, render_product_path: str, annotator: str):
instance_seg_data = data[annotator]["data"]
height, width = instance_seg_data.shape[:2]
file_path = f"{render_product_path}instance_id_segmentation_{self._sequence_id}{self._frame_id:0}.png"
if self.colorize_instance_id_segmentation:
instance_seg_data = instance_seg_data.view(
np.uint8).reshape(height, width, -1)
self._backend.write_image(file_path, instance_seg_data)
else:
instance_seg_data = instance_seg_data.view(
np.uint32).reshape(height, width)
self._backend.write_image(file_path, instance_seg_data)
id_to_labels = data[annotator]["info"]["idToLabels"]
file_path = f"{render_product_path}instance_id_segmentation_mapping_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_labels.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
def _write_instance_segmentation(self, data: dict, render_product_path: str, annotator: str):
instance_seg_data = data[annotator]["data"]
height, width = instance_seg_data.shape[:2]
file_path = (
f"{render_product_path}instance_segmentation_{self._sequence_id}{self._frame_id:0}.png"
)
if self.colorize_instance_segmentation:
instance_seg_data = instance_seg_data.view(
np.uint8).reshape(height, width, -1)
self._backend.write_image(file_path, instance_seg_data)
else:
instance_seg_data = instance_seg_data.view(
np.uint32).reshape(height, width)
self._backend.write_image(file_path, instance_seg_data)
id_to_labels = data[annotator]["info"]["idToLabels"]
file_path = f"{render_product_path}instance_segmentation_mapping_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_labels.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
id_to_semantics = data[annotator]["info"]["idToSemantics"]
file_path = f"{render_product_path}instance_segmentation_semantics_mapping_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(
{str(k): v for k, v in id_to_semantics.items()}).encode())
self._backend.write_blob(file_path, buf.getvalue())
def _write_bounding_box_data(self, data: dict, bbox_type: str, render_product_path: str, annotator: str):
bbox_data_all = data[annotator]["data"]
print(bbox_data_all)
id_to_labels = data[annotator]["info"]["idToLabels"]
prim_paths = data[annotator]["info"]["primPaths"]
file_path = f"{render_product_path}bounding_box_{bbox_type}_{self._sequence_id}{self._frame_id:0}.npy"
buf = io.BytesIO()
np.save(buf, bbox_data_all)
self._backend.write_blob(file_path, buf.getvalue())
labels_file_path = f"{render_product_path}bounding_box_{bbox_type}_labels_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(id_to_labels).encode())
self._backend.write_blob(labels_file_path, buf.getvalue())
labels_file_path = f"{render_product_path}bounding_box_{bbox_type}_prim_paths_{self._sequence_id}{self._frame_id:0}.json"
buf = io.BytesIO()
buf.write(json.dumps(prim_paths).encode())
self._backend.write_blob(labels_file_path, buf.getvalue())
target_coco_bbox_data = []
count = 0
for bbox_data in bbox_data_all:
target_bbox_data = {'x_min': bbox_data['x_min'], 'y_min': bbox_data['y_min'],
'x_max': bbox_data['x_max'], 'y_max': bbox_data['y_max']}
width = int(
abs(target_bbox_data["x_max"] - target_bbox_data["x_min"]))
height = int(
abs(target_bbox_data["y_max"] - target_bbox_data["y_min"]))
if width != 2147483647 and height != 2147483647:
bbox_filepath = f"bbox_{self._frame_id}.json"
coco_bbox_data = {
"name": prim_paths[count],
"x_min": int(target_bbox_data["x_min"]),
"y_min": int(target_bbox_data["y_min"]),
"x_max": int(target_bbox_data["x_max"]),
"y_max": int(target_bbox_data["y_max"]),
"width": width,
"height": height}
target_coco_bbox_data.append(coco_bbox_data)
count += 1
buf = io.BytesIO()
buf.write(json.dumps(target_coco_bbox_data).encode())
self._backend.write_blob(bbox_filepath, buf.getvalue())
rep.WriterRegistry.register(CowWriter)
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/MaterialOverrides.usda | #usda 1.0
over "Root"
{
over "WaterBodyCustom5"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
}
}
over "WaterBodyCustom2"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
}
}
over "Cylinder13" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder13/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder13/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder40" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder40/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder40/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame9" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame9/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame9/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cylinder18" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder18/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder18/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cylinder25" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder25/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder25/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder38" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder38/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder38/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "WaterBodyCustom"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
}
}
over "Cylinder16" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder16/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder16/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow11"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder15" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder15/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder15/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow5"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_DoorFrame2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_DoorFrame2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Chrome.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_DoorFrame2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Chrome.M_Metal_Chrome@
token outputs:out
}
}
}
over "Cylinder14" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder14/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder14/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder30" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder30/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder30/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cow_Y5"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD3/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD3/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD3/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD3/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD4"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD4/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD4/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD4/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD4/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
}
over "Cylinder29" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder29/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder29/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder9" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder9/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder9/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder7" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder7/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder7/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder45" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder45/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder45/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame8" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame8/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame8/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "cow_ground" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/cow_ground/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Ground_Gravel.usda@
)
{
token outputs:unreal:surface.connect = </Root/cow_ground/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Ground_Gravel.M_Ground_Gravel@
token outputs:out
}
}
}
over "SM_PillarFrame2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Wall_Door_400x301" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Wall_Door_400x301/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Basic_Wall.usda@
)
{
token outputs:unreal:surface.connect = </Root/Wall_Door_400x301/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Basic_Wall.M_Basic_Wall@
token outputs:out
}
}
}
over "SK_Cow7"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame11" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame11/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame11/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cow_F4"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SK_Cow13"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cow_Y2"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD3/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD3/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD3/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD3/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD4"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD4/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD4/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD4/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD4/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
}
over "SM_PillarFrame10" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame10/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame10/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "SK_Cow"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder24" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder24/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder24/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder44" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder44/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder44/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder12" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder12/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder12/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "WaterBodyCustom6"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
}
}
over "SM_DoorFrame" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_DoorFrame/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Chrome.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_DoorFrame/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Chrome.M_Metal_Chrome@
token outputs:out
}
}
}
over "Cylinder32" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder32/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder32/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow3"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cow_F6"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder37" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder37/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder37/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Shape_Cube3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Shape_Cube3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Wood_Walnut.usda@
)
{
token outputs:unreal:surface.connect = </Root/Shape_Cube3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Wood_Walnut.M_Wood_Walnut@
token outputs:out
}
}
}
over "Cylinder10" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder10/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder10/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder21" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder21/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder21/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "WaterBodyCustom7"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
}
}
over "Cylinder43" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder43/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder43/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder33" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder33/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder33/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder34" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder34/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder34/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder28" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder28/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder28/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cow_F5"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SK_Cow10"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder31" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder31/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder31/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow8"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Shape_Cube2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Shape_Cube2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Wood_Walnut.usda@
)
{
token outputs:unreal:surface.connect = </Root/Shape_Cube2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Wood_Walnut.M_Wood_Walnut@
token outputs:out
}
}
}
over "Cylinder6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow9"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder23" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder23/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder23/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow12"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "WaterBodyCustom4"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
}
}
over "Cylinder46" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder46/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder46/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame7" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame7/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame7/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cow_Y4"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD3/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD3/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD3/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD3/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD4"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD4/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD4/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD4/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD4/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
}
over "Cylinder8" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder8/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder8/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder26" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder26/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder26/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Wall_Door_400x300" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Wall_Door_400x300/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Basic_Wall.usda@
)
{
token outputs:unreal:surface.connect = </Root/Wall_Door_400x300/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Basic_Wall.M_Basic_Wall@
token outputs:out
}
}
}
over "Cylinder41" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder41/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder41/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder39" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder39/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder39/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder19" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder19/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder19/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder35" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder35/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder35/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder17" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder17/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder17/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder11" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder11/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder11/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder48" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder48/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder48/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow4"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SK_Cow2"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "SK_Cow6"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "WaterBodyCustom3"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
}
}
over "Cylinder42" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder42/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder42/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder47" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder47/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder47/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder36" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder36/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder36/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow14"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder27" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder27/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder27/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder22" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder22/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder22/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Shape_Plane" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Shape_Plane/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Basic_Floor.usda@
)
{
token outputs:unreal:surface.connect = </Root/Shape_Plane/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Basic_Floor.M_Basic_Floor@
token outputs:out
}
}
}
over "Cylinder20" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder20/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder20/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
}
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/IndoorRanch.usda | #usda 1.0
(
defaultPrim = "Root"
metersPerUnit = 0.009999999776482582
subLayers = [
@MaterialOverrides.usda@
]
upAxis = "Z"
)
def Xform "Root"
{
def Xform "WaterBodyCustom5"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.8942598739177813e-9, -0.23785899999999996, 0, 0), (0.9684019999999999, 1.1783481181799838e-8, 0, 0), (0, 0, 1, 0), (2653.6490236561244, -3375.609794935123, 3.410605131648481e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod50" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1415.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod80" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1450.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf6" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (1725.2796744175348, -1033.405977, 113.030142, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod30" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320527583947, -2701.6814383211854, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "WaterBodyCustom2"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.237859, -0, 0, 0), (0, 0.968402, -0, 0), (0, 0, 1, 0), (2342.2327969938447, -1702.943951009665, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Mesh "Cylinder13" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.1, -1.224646794682525e-17, 1.2167963906789763e-9, 0), (-1.2167963906789763e-9, 1.745329236690907e-9, -0.09999999999999999, 0), (-6.967742326146334e-16, -7.749999999999999, -1.3526301584354528e-7, 0), (1712, -3123, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod46" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.7146777583948, -2369.5656693211854, -42.34565000000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod128" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2391.7296039397283, -1921.965423818353, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder40" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2797, -3136.021574, 4.000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow6" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (3486.326755, -3000.2259646797925, -37.122805, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod51" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -2102.487463175721, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder4" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.749999999999999, 1.3526301584354528e-7, 0), (3314, -2349, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder2" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.749999999999999, 1.3526301584354528e-7, 0), (3314, -1578, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod100" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787884441667, -1492.179084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod103" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3293.262899444167, -1346.995492, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod17" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.7146883733167, -3021.747318, -34.544246012370564, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod73" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1335.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod107" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.7563184441674, -1303.764881, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod29" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320517583948, -2862.9058723211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush3" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2871.784637, -554.1046373965738, -37.713193, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod166" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3083.157772109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod1" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2677.273224608386, -1419.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod103" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2472.7162128084883, -1931.9654218714786, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod67" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -2257.8420661757214, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod179" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2751.930240109376, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod6" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1394.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod44" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.7146777583948, -2908.8451613211855, -42.34565000000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod144" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2388.592447958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod230" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3050.890200810139, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod118" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2542.7162123217695, -1911.9654201679637, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod129" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -1535.924557472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod27" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320517583948, -2815.4729133211854, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod24" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320517583948, -2904.367296321185, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod94" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2447.7162123217704, -1911.965422479877, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame9" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592850604335e-8, 0, 0), (2.433592850604335e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (1599.9999990265633, -2333.4102038915125, -49.99999987642485, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush8" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2199.9788118742936, -668.258127, -37.71319234857811, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder18" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.999999999999999, 1.3962633893527254e-7, 0), (2490.126882232145, -2935.373245603948, -31.00000025881377, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod13" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.3726968059568, -3103.21894, -34.54424600295556, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod104" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2572.716212808488, -1931.9654194378854, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod8" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1449.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod8" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320630730756, -3248.4025320889127, -34.5442460000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod14" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1459.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame3" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (3480, -1660, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder25" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2197, -2536.021574391874, 4.0000011762016925, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod168" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -3031.348652109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def DistantLight "DirectionalLight"
{
float inputs:angle = 0.5357
color3f inputs:color = (1, 1, 1)
float inputs:colorTemperature = 6500
bool inputs:enableColorTemperature = 0
float inputs:exposure = 0
float inputs:intensity = 10
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.0502426179288482, 0.9287109829739543, 2.0699000829966425, 0), (-0.9075516014157601, 2.2624627755283173, -0.5546280556520662, 0), (-2.0792644214369767, -0.5184188455797625, 1.2875951872751386, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod65" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2367.0469420265676, -1295.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod83" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2527.7162129301682, -1936.9654205330025, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod193" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -2741.3823240739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod201" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.861413, -2615.9637440739552, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod93" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2437.71621232177, -1911.9654227232359, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder38" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2197, -2936.021574, 4.000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod85" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1515.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod60" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1435.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod38" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1479.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod26" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1309.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod79" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1460.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "WaterBodyCustom"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.23785875737667084, -0, 0, 0), (0, 0.9684022068977356, -0, 0), (0, 0, 1, 0), (2684.7597252997607, -1702.943946910829, -4.547473508864641e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Mesh "Cylinder16" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.1, -1.224646786410719e-17, 1.7453292519943295e-9, 0), (-1.7453292519943295e-9, 1.7453292255886767e-9, -0.09999999999999998, 0), (-1.4572219034593626e-15, -8, -1.3962633893527254e-7, 0), (2199.0000000555488, -2935.24525643426, -31.00002899999977, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod101" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791444167, -1390.94245, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow11" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.216796297054401e-8, 1, 0, 0), (-1, 1.216796297054401e-8, 0, 0), (0, 0, 1, 0), (1942, -2827, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod55" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2337.0469420265676, -1470.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod84" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.609583, -1303.7648812738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf7" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (0.9999999999999996, -2.9621257002787615e-8, 0, 0), (0, -0, 1, 0), (2142.5993884276086, -3638.0228470917605, 113.0301419624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod25" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1304.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder15" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.999999999999999, 1.3962633893527254e-7, 0), (2802.000000083737, -2934.695263256634, -30.999999999997385, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod122" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2447.7162123217704, -1911.965422479877, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod9" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1419.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow5" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (3107, -3317, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_DoorFrame2" (
prepend references = @Assets/Game/StarterContent/Props/SM_DoorFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621256558698406e-8, -0.9999999999999996, 0, 0), (-5.679789999999997, -1.682425167895296e-7, 0, 0), (0, 0, 1.332465, 0), (2538.783501427609, -3653.2519800917603, -38.567142037588596, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod127" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3309.95556, -1579.155168472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod80" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2462.7162128084888, -1931.9654221148376, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod29" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1314.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod9" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320659927864, -3147.1658978728465, -34.54424599999999, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod6" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1474.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod27" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1294.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod215" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -3350.373314810139, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod67" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1335.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def DomeLight "SkyLight"
{
color3f inputs:color = (1, 1, 1)
float inputs:exposure = 0
float inputs:intensity = 1
asset inputs:texture:file
token visibility = "inherited"
matrix4d xformOp:transform = ( (0, -1, 2.220446049250313e-16, 0), (2.220446049250313e-16, 2.220446049250313e-16, 1, 0), (-1, 0, 2.220446049250313e-16, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod155" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.861413, -1968.0801449589828, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod63" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.714678, -1791.3904741757217, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod53" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232053, -2050.6783431757212, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod14" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1409.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod123" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -1724.338760472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod26" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320517583948, -2790.7902793211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod75" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -1686.0361732738083, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod48" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1390.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder14" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964413833943e-9, -0.09999999999999999, 1.1102230246251566e-17, 0), (1.745329203384216e-9, 3.33066907387547e-17, 0.09999999999999998, 0), (-4.249999999999999, -5.1713848522871864e-8, 7.417649114382918e-8, 0), (2512, -1969, 9, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod163" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3180.587828109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod49" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1400.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod145" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -2227.368013958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod121" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2477.71621232177, -1911.9654217497991, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod70" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2362.0469420265676, -1370.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod69" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1718.5625741757215, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod82" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.372687, -1346.9954922738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod60" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1445.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder30" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2492.917771214866, -2535.5808600796327, 0.9351394588689979, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod44" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1445.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod7" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320630037434, -3281.035186799211, -34.54424599999999, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod33" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2702.273224608386, -1329.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod55" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232056, -1916.8090541757213, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod77" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2427.71621232177, -1911.9654229665953, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "Cow_Y5" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Meshes/Cow_Y.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964191789338e-8, 0.9999999999999998, 0, 0), (-0.9999999999999998, 1.2167964191789338e-8, 0, 0), (0, 0, 1, 0), (2275, -1744, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod25" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1414.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod37" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1450.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod32" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1359.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder29" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2502.9177715340934, -3334.8340012805493, -0.44723278099108654, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod204" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3021.9022280739555, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod68" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1335.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod5" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1474.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "SkyAtmosphere"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod69" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1315.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod79" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232056, -1404.4684582738087, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod57" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1470.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod165" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3145.140127109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod13" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1494.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod180" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2845.631138109375, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod39" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1489.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod51" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1455.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod19" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2677.273224608386, -1464.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod81" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1659.808825, -1346.9954922738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod187" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2964.360454073955, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod157" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2041.6504999589831, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod54" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1470.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod214" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -3383.005969810139, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod127" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2397.71621232177, -1911.9654236966735, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder9" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.1, -1.224646786410719e-17, 1.216796393779304e-9, 0), (-1.216796393779304e-9, 1.7453293255087488e-9, -0.09999999999999999, 0), (-6.967743287743796e-16, -7.749999999999999, -1.3526302444777372e-7, 0), (2303.999999999998, -1578, 8.999971186260353, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow4" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (3486.326755, -1669.012701866217, -37.12280541645734, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod58" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1460.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder7" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, 0.09999999999999998, 5.551115123125783e-18, 0), (-1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2418, -3507, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod82" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2482.716212930168, -1936.9654216281192, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod106" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422444167, -1303.764881, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod99" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2407.7162128084883, -1931.965423453314, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod183" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2993.823430109375, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod115" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.8614134441673, -1192.69597, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod148" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -2107.0247329589833, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow8" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (1587.6097136892786, -1669.012702, -37.122805, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder45" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (1709.522299657568, -1186.121356, -1.537979, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod199" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -2654.2047550739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame8" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592850604335e-8, 0, 0), (2.433592850604335e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (1600.0000316367077, -2996.262429463009, -49.99999902716388, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod90" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2532.2881658981596, -1921.9654204113226, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod158" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2374.018628958983, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow3" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418395996094, 0, 0), (0, 0, 1.8349626064300537, 0), (3486.326755190476, -2340.2698859149336, -37.12280652855546, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod58" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1659.808825, -1872.8620961757213, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod7" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1414.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod52" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -2211.902777175721, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod207" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2543.1358440739555, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod105" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2537.7162128084888, -1931.9654202896436, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod97" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787874441673, -1576.620859, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod159" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2434.531736958983, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "cow_ground" (
prepend references = @Assets/Game/StarterContent/Shapes/Shape_Plane.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (6, -0, 0, 0), (0, 8, -0, 0), (0, 0, 1, 0), (2498.2608658054764, -2936.7318105066574, -28.729074522147073, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod124" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2422.7162123217695, -1911.965423088275, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame2" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (3480, -1020, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod118" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -1846.080256472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod174" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -2853.5324051093753, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod98" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787874441673, -1686.036173, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Wall_Door_400x301" (
prepend references = @Assets/Game/StarterContent/Architecture/Wall_Door_400x300.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (4.715909, 5.775322861920195e-16, 0, 0), (1.2246467991473532e-16, -1, 0, 0), (0, -0, 1.292164, 0), (1594.7554394276071, -3648.02282209176, -36.45310903758859, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod12" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1659.8088354250408, -3103.21894, -34.544246008926564, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf4" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (3373.0259434374525, -1033.405977, 113.030142, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod205" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3082.415336073955, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod102" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2442.7162128084888, -1931.9654226015555, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod40" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1480.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod112" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2592.7162128084883, -1931.9654189511668, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod164" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3120.4574931093753, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod119" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -1870.7628904725948, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod146" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -2194.735358958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod93" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2486.7296040146516, -1921.9654215064395, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod85" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2507.716212930168, -1936.9654210197211, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod94" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787874441673, -1674.050915, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush6" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2871.784637, -908.2657873581747, -37.713193, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod141" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2316.476854958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod102" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2597.716213295207, -1951.9654188294876, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod77" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232053, -1492.1790842738083, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod42" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.7146777583948, -2515.9639243211855, -42.34565000000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod66" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -2197.3289581757213, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod167" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3192.573086109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush11" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2199.9788118742936, -325.28145, -37.71319234857811, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod47" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1380.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod89" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2552.716212565129, -1921.965419924604, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod38" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.6095827583947, -2480.6345803211852, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod143" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2279.177133958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow7" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2051, -1946.1535981385955, -35.0000078043405, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod52" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1475.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod99" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787884441667, -1524.811739, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame11" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592850604335e-8, 0, 0), (2.433592850604335e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (1599.9999347797116, -1025.0000214156196, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod150" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3309.95556, -2049.551766958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod116" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -1959.657273472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod20" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714688, -3427.6858023693694, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush10" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2199.9788118742936, -443.299404, -37.71319234857811, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "Cow_F4" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Meshes/Cow_F.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964191789338e-8, -0.9999999999999998, 0, 0), (0.9999999999999998, 1.2167964191789338e-8, 0, 0), (0, 0, 1, 0), (2772.116431782072, -1777.9821718244452, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow13" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, 0.9999999999999999, 0, 0), (-0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (2311, -2837, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod12" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1384.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod202" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2595.8332010739555, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod106" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2552.7162128084883, -1931.9654199246047, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "Cow_Y2" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Meshes/Cow_Y.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.7071067682804721, -0.7071067940926227, 0, 0), (0.7071067940926227, -0.7071067682804721, 0, 0), (0, 0, 1, 0), (2631.752279706987, -3041.338576288301, -29.866197223789527, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod23" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714688, -2948.919418424772, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod17" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1429.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod112" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.8614134441673, -1671.462354, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod15" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.6095931462303, -3059.988329, -34.544246002573686, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod88" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2512.716212565129, -1921.965420898041, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod63" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1355.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush12" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2199.9788118742936, -908.265787, -37.71319234857811, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod72" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1320.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod62" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1380.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod10" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1419.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SphereLight "PointLight5"
{
color3f inputs:color = (1, 1, 1)
float inputs:colorTemperature = 6500
bool inputs:enableColorTemperature = 0
float inputs:exposure = 0
float inputs:intensity = 1600000
float inputs:radius = 0
bool treatAsPoint = 1
token visibility = "inherited"
matrix4d xformOp:transform = ( (0, -1, 2.220446049250313e-16, 0), (2.220446049250313e-16, 2.220446049250313e-16, 1, 0), (-1, 0, 2.220446049250313e-16, 0), (1760.5419805930135, -2310.227945, 386.179305, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod36" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1364.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod18" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1464.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var4" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2337.0469420265676, -1400.000000009696, -80, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod27" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1324.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod132" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.861413, -1497.683546472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod221" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -3161.9591118101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod203" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2689.5340990739555, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame10" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592850604335e-8, 0, 0), (2.433592850604335e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (1599.9999664164193, -1676.298416754414, -50.000000804355125, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod120" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2502.7162123217704, -1911.9654211414006, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod76" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2427.7162124434494, -1916.965422966595, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod56" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1470.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod107" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2556.7296042580115, -1931.9654198029248, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod86" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2492.7162129301682, -1936.9654213847598, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod181" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3177.9992671093755, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592895013256e-8, 0, 0), (2.433592895013256e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (2511, -2077, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod101" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2432.7162128084883, -1931.965422844915, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush5" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2871.784637, -325.2814500076494, -37.713193, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod117" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -1906.2105914725948, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod124" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -1623.102126472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod19" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714688, -3095.3176729642373, -42.345649698772135, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod95" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787874441673, -1613.92058, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod66" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1340.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod90" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1731.9754622738087, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod1" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088300704956, -0, 0, 0), (0, 1.2328583002090454, -0, 0), (0, 0, 3.2545266151428223, 0), (1664.2320624241856, -3483.7210448829755, -34.544246102811485, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod80" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1643.116164, -1346.9954922738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod70" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -1727.4975972738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod216" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -3249.1366808101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod28" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320517583948, -2753.4905583211853, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod182" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3238.5123751093747, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow12" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (-4.760413999999998, 1.410094465336682e-7, -0, 0), (0, -0, 1, 0), (2777.8697124276086, -3647.7840200917603, 550.4042079624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf12" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (0.9999999999999996, -2.9621257002787615e-8, 0, 0), (0, -0, 1, 0), (1725.2796744276072, -3638.0228470917605, 113.0301419624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod36" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.3726867583948, -2523.8651913211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod24" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1329.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod63" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1330.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod48" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1380.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod41" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1480.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow11" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (-11.191591999999995, 3.3150902290234185e-7, -0, 0), (0, -0, 1, 0), (3099.3913604276086, -3647.7840200917603, 350.5478259624113, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod71" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -1674.0509152738086, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod108" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2581.729604258011, -1931.965419194526, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod22" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714688, -3243.5099650538104, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod43" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1509.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf10" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (0.9999999999999996, -2.9621257002787615e-8, 0, 0), (0, -0, 1, 0), (3373.0259434276086, -3638.0228470917605, 113.0301419624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder24" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-2.4424906541753446e-16, -0.09999999999999998, 1.7453291983721213e-9, 0), (-0.09999999999999996, 2.997602166487923e-16, 2.9621257569341495e-9, 0), (-2.962125756934154e-8, -1.7453291983721133e-8, -0.9999999999999996, 0), (2202, -3334, 4, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod47" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -2253.364201175721, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod107" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2537.716212565129, -1921.965420289643, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder44" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2304.4867165710725, -1186.121356, -1.537979, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod38" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1470.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod5" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1374.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder12" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.1, -1.224646794682525e-17, 1.2167963906789763e-9, 0), (-1.2167963906789763e-9, 1.745329236690907e-9, -0.09999999999999999, 0), (-6.967742326146334e-16, -7.749999999999999, -1.3526301584354528e-7, 0), (1712, -2349, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod31" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1304.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod37" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.3726867583948, -2480.6345803211852, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod17" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1454.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod53" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1460.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod184" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2699.232883109376, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod96" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2412.7162125651294, -1921.9654233316344, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "WaterBodyCustom6"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.0035789766616002596, -0.2378320727047883, 0, 0), (0.9682923701498047, -0.014571187792124808, 0, 0), (0, 0, 1, 0), (2347.473634, -2498.47206396336, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod30" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2707.273224608386, -1304.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod189" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2927.0607330739554, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_DoorFrame" (
prepend references = @Assets/Game/StarterContent/Props/SM_DoorFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964191789338e-8, 0.9999999999999998, 0, 0), (-5.679790496826171, 6.911148738224623e-8, 0, 0), (0, 0, 1.3324648141860962, 0), (2538.783500626253, -1018.176843623808, -38.567141648980424, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod40" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.7146777583948, -2442.3935693211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod212" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3434.815089810139, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod128" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -1579.155168472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod55" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2342.0469420265676, -1500.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod15" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1449.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod91" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2527.288165898159, -1921.9654205330019, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod2" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1464.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod120" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -1808.7805354725951, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod83" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1505.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod102" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791444167, -1404.468458, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod61" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1420.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod89" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1671.4623542738086, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod105" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2572.716213173527, -1946.9654194378854, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod142" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2341.159488958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod46" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2352.0469420265676, -1425.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod110" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.8614134441673, -1245.393327, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod177" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -2772.060783109376, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod220" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -3205.1897228101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod30" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1329.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod109" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.8614134441673, -1265.52387, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod6" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320621087722, -3442.2596213628767, -34.544245999999816, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder32" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.999999999999999, 1.3962633893527254e-7, 0), (2491.1056941471134, -2934.3158304120084, 49.992744755158824, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod28" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1294.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow3" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.25881905489697976, -0.9659258236646509, 0, 0), (0.9659258236646509, 0.25881905489697976, 0, 0), (0, 0, 1, 0), (2684.2339161598143, -2634.503980305214, -35.000000073930494, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame5" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (3480, -3000, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod34" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1399.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod61" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1390.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod100" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2442.7162128084888, -1931.9654226015555, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod83" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.372687, -1303.7648812738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod225" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3103.587557810139, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod194" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -2754.9083320739555, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow10" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (1587.6097136892786, -3000.225965, -37.122805, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod78" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232056, -1390.9424502738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod74" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2437.7162124434503, -1916.9654227232363, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod92" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1192.6959702738086, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "Cow_F6" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Meshes/Cow_F.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964191789338e-8, 0.9999999999999998, 0, 0), (-0.9999999999999998, 1.2167964191789338e-8, 0, 0), (0, 0, 1, 0), (2261.0000022875774, -1409.9999995376174, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod1" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1444.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod87" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2482.716212565129, -1921.9654216281194, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder37" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2197, -2736.021574, 4.000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod66" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2367.0469420265676, -1310.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod34" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2702.273224608386, -1364.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod111" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2597.7162128084883, -1931.9654188294876, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod43" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.7146777583948, -2848.3320533211854, -42.34565000000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod126" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3293.262899, -1579.155168472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "GroupActor1"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2684.759765625, -1702.9439086914062, 250, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod122" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -1756.9714154725948, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod135" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1903.6220304725948, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Shape_Cube3" (
prepend references = @Assets/Game/StarterContent/Shapes/Shape_Cube.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (9.959292143521042, 2.423686004756266e-7, 5.749999999999998, 0), (8.517574214168777e-7, -34.999999999999986, 3.71201327839164e-15, 0), (0.24999999999999986, 6.0839815356248715e-9, -0.4330127018922193, 0), (2040, -2310, 640, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod60" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.372687, -1829.6314851757215, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod5" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320620904143, -3332.8443065104207, -34.54424599999999, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder10" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.1, -1.224646794682525e-17, 1.2167963906789763e-9, 0), (-1.2167963906789763e-9, 1.745329236690907e-9, -0.09999999999999999, 0), (-6.967742326146334e-16, -7.749999999999999, -1.3526301584354528e-7, 0), (1712, -1578, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod96" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2462.7162123217704, -1911.965422114838, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod147" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -2093.498724958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder21" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2497, -2536.021574391874, 49.00000117620169, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod23" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1439.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod185" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3077.9374710739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod11" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1643.1161742242314, -3103.218939580428, -34.544246000000044, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush2" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2871.784637, -668.2581266666577, -37.713193, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod88" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1339.0942252738087, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "WaterBodyCustom7"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (4.151422669100624e-9, -0.23785899999999996, 0, 0), (0.9684019999999999, 1.6901803234699474e-8, 0, 0), (0, 0, 1, 0), (2653.649024, -2496.9490029633594, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Mesh "Cylinder43" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2721.5887407125415, -1186.1213560683916, -1.5379788092038789, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod70" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1300.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod26" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1374.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod88" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2447.7162128084883, -1931.9654224798767, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf2" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (2936.5674694509876, -1033.405977, 113.030142, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod76" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1385.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod23" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1334.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder33" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2497.000005802394, -3135.7763032195776, 49.000000000000824, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder34" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2497.000006, -2935.776303, 49, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "GroupActor"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2540, -2310, 437.9810104370117, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod195" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3293.262899, -2697.4353660739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod50" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1420.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder28" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2492.9177715340934, -3334.8340012805493, -0.44723278099108654, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod156" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1947.9496019589828, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "Cow_F5" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Meshes/Cow_F.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.49999998594964157, -0.8660254118964166, 0, 0), (0.8660254118964166, -0.49999998594964157, 0, 0), (0, 0, 1, 0), (2043, -3295, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod106" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2537.716213173528, -1946.9654202896434, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod18" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.2062542961067844, 0.2547340206723992, 0), (-0, -0.6724527464613778, 3.1842979285088178, 0), (1673.7146881047584, -3001.6167752703745, -42.34564852407355, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf5" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (1931.7508259754636, -1033.405977, 113.030142, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod19" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1364.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod137" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1719.446193472595, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow10" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.216796297054401e-8, 1, 0, 0), (-1, 1.216796297054401e-8, 0, 0), (0, 0, 1, 0), (2311.2346507694524, -2644.872135950068, -35.00000070494673, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod58" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1475.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod29" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2707.273224608386, -1289.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod78" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1430.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod79" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2427.7162128084883, -1931.9654229665948, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod222" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -3161.9591118101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod14" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.3726968139736, -3059.988328789811, -34.5442460000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod173" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3309.95556, -2853.5324051093753, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod42" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1509.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod116" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2572.7162123217704, -1911.9654194378859, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod97" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2407.716212565129, -1921.9654234533139, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow2" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (-4.760414123535155, -8.308489948562226e-8, -0, 0), (0, 0, 1, 0), (2777.8697118122163, -1023.6448039999925, 550.4042075968896, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod13" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1394.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod3" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232062083943, -3370.1440280315305, -34.54424599999834, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod152" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -2006.321155958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod218" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3293.262899, -3205.1897228101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod20" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1464.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod95" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2462.7162123217704, -1911.965422114838, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod32" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320557583948, -2567.8121493211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod191" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -2875.2516130739555, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf8" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (0.9999999999999996, -2.9621257002787615e-8, 0, 0), (0, -0, 1, 0), (2936.5674694276086, -3638.0228470917605, 113.0301419624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod72" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1370.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod8" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1439.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod129" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2392.7162128084883, -1931.9654238183527, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod68" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -2013.1531211757213, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder31" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2502.917771214866, -2535.5808600796327, 0.9351394588689979, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SphereLight "PointLight2"
{
color3f inputs:color = (1, 1, 1)
float inputs:colorTemperature = 6500
bool inputs:enableColorTemperature = 0
float inputs:exposure = 0
float inputs:intensity = 1600000
float inputs:radius = 0
bool treatAsPoint = 1
token visibility = "inherited"
matrix4d xformOp:transform = ( (0, -1, 2.220446049250313e-16, 0), (2.220446049250313e-16, 2.220446049250313e-16, 1, 0), (-1, 0, 2.220446049250313e-16, 0), (2541.925416, -1544.0657344155316, 803.80202042106, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod37" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1444.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod131" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -1497.683546472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod86" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1515.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow8" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.7071067854885724, -0.7071067768845226, 0, 0), (0.7071067768845226, 0.7071067854885724, 0, 0), (0, 0, 1, 0), (1834.2401595636475, -1600.2008888766159, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod186" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3024.4907890739555, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod57" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1465.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf3" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (3156.913469743765, -1033.405977, 113.03014193409216, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod22" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1319.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod161" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1895.2522449589828, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod172" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3293.262899, -2853.5324051093753, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod198" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -2654.2047550739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod59" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1465.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod43" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1480.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod97" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2487.71621232177, -1911.9654215064397, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod113" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.8614134441673, -1731.975462, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964524856245e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964524856245e-8, 0, 0), (0, 0, 1, 0), (2142.5993877638693, -1033.4059765448635, 113.03014221816875, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod35" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2677.273224608386, -1364.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod35" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1659.8088247583948, -2523.8651913211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod4" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1369.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod16" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1459.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod134" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1571.2539014725949, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod119" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2522.2881656548, -1911.9654206546822, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod11" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1494.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod7" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1474.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var10" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.015046631246243614, -0.9998867930361613, 0, 0), (0.9998867930361613, -0.015046631246243614, 0, 0), (0, 0, 1, 0), (2345.450923, -2496.2411789633593, -80, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod64" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1350.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod34" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1643.1161637583948, -2523.8651913211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod171" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -2911.0053711093756, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame4" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (3480, -2330, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Shape_Cube2" (
prepend references = @Assets/Game/StarterContent/Shapes/Shape_Cube.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-9.95929209687719, -1.2196615188214182e-15, 5.750000080789523, 0), (-2.1431318683961103e-15, 35, 3.7120133181521426e-15, 0), (-0.25000000351258794, 3.0616169548515865e-17, -0.43301269986422564, 0), (3040, -2310, 640, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod36" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1454.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod103" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2597.7162128084883, -1931.9654188294876, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod109" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2567.7162128084883, -1931.9654195595647, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod111" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.8614134441673, -1339.094225, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod151" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -2049.551766958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod33" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1379.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod229" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3345.4807478101393, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder6" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, 0.09999999999999998, 5.551115123125783e-18, 0), (-1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (3018, -3507, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod114" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2606.7296037712927, -1911.9654185861275, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod16" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1454.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var8" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.015046634692274852, -0.9998867929843044, 0, 0), (0.9998867929843044, -0.015046634692274852, 0, 0), (0, 0, 1, 0), (2345.45092322568, -3374.9019709050344, -80.00000031829498, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod98" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2527.71621232177, -1911.9654205330028, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod40" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1499.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod108" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2537.7162124434494, -1916.9654202896431, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod33" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320557583948, -2581.3381573211855, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod210" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3472.1148108101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod41" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.7146777583948, -2422.2630263211854, -42.34564900000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow9" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592895013256e-8, 0, 0), (2.433592895013256e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (2904, -2617, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow7" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (1587.6097136892786, -2340.269886, -37.122807, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod91" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2437.7162125651294, -1921.9654227232359, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow9" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (1587.6097136892786, -1029.020792, -37.122805, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod224" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.861413, -3123.7181008101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod153" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -2006.321155958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod138" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1424.8556464725948, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.2167965746101573e-9, 0.09999999999999999, 0), (-0, -7.749999999999999, 9.430173453228718e-8, 0), (2722, -1578, 9, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod2" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2677.273224608386, -1389.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod126" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2401.729603771292, -1911.9654235749936, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod176" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -2810.3017941093754, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod73" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2457.7162124434494, -1916.965422236517, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder23" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2497, -2536.021574391874, -30.999998823798308, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod71" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2337.0469420265676, -1370.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod50" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -2164.4698181757212, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod197" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -2697.4353660739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod68" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1310.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var5" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2507.716212565129, -1921.9654210197207, -80, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush9" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2199.9788118742936, -554.104637, -37.71319234857811, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod104" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3309.955560444167, -1346.995492, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod56" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232056, -1930.3350621757215, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod41" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1504.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod86" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.714678, -1265.5238702738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod113" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2607.7162128084883, -1931.9654185861282, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2871.7846369606764, -790.4039063000382, -37.71319284593372, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow12" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2834, -2022, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod45" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1425.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod81" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2482.716212808488, -1931.9654216281185, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "WaterBodyCustom4"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.0035789774812698036, -0.23783207269245366, 0, 0), (0.9682923700995864, -0.01457119112926835, 0, 0), (0, 0, 1, 0), (2347.47363391836, -3377.1328564205505, -3.1829497970647935e-7, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Mesh "SM_Bush7" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2199.9788118742936, -790.403906, -37.71319234857811, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var11" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.7453292366909068e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.7453292366909068e-8, 0, 0), (0, 0, 1, 0), (2648.611794, -2494.74880496336, -80, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod4" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1474.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod178" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.861413, -2772.060783109376, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Landscape" (
prepend references = @Assets/Game/IndoorRanch/Landscape_0/Landscape_0_asset.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (10, -0, 0, 0), (0, 10, -0, 0), (0, 0, 10, 0), (1, -1, 0.5, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod20" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1349.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod47" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1375.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder46" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (1709.5223, -3506.876310130215, -1.537979, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod149" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3293.262899, -2049.551766958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod45" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2337.0469420265676, -1395.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod65" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1325.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod74" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -1576.6208592738085, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod59" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1673.372687, -1872.8620961757213, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod78" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2426.729603892972, -1916.965422966595, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod64" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1295.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod45" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.7146777583948, -2664.1562163211856, -42.34565000000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame7" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9999999999999996, -2.433592850604335e-8, 0, 0), (2.433592850604335e-8, -0.9999999999999996, 0, 0), (0, 0, 1, 0), (1600.0000627866962, -3654.0000214156153, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod75" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2432.7162124434494, -1916.9654228449156, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod24" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1439.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod31" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320527583947, -2669.0487833211855, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "Cow_Y4" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Meshes/Cow_Y.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-0.9659258331125535, 0.25881901963692594, 0, 0), (-0.25881901963692594, -0.9659258331125535, 0, 0), (0, 0, 1, 0), (1954, -3224, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder8" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, 0.09999999999999998, 5.551115123125783e-18, 0), (-1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2016, -3507, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod169" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -2998.7159971093747, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod77" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1405.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod10" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1489.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var6" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2340.0325989938447, -1705.000000009665, -80, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod92" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2507.716212565129, -1921.9654210197207, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder26" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2797, -2536.021574391874, 4.0000011762016925, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Wall_Door_400x300" (
prepend references = @Assets/Game/StarterContent/Architecture/Wall_Door_400x300.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (4.715909481048584, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1.2921637296676636, 0), (1594.755439410047, -1023.4060015291457, -36.45310895168251, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod72" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -1613.9205802738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod196" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3309.95556, -2697.4353660739553, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod206" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2837.7263910739553, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod75" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1365.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod69" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2362.0469420265676, -1335.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod90" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2437.7162129301682, -1936.9654227232359, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod98" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2406.7296040614083, -1926.9654234533139, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod67" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1310.000000009696, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder41" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2797, -2936.021574, 4.000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod82" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1495.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder39" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2197, -3136.021574, 4.000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod136" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1964.135138472595, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod217" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -3262.662688810139, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SphereLight "PointLight4"
{
color3f inputs:color = (1, 1, 1)
float inputs:colorTemperature = 6500
bool inputs:enableColorTemperature = 0
float inputs:exposure = 0
float inputs:intensity = 1600000
float inputs:radius = 0
bool treatAsPoint = 1
token visibility = "inherited"
matrix4d xformOp:transform = ( (0, -1, 2.220446049250313e-16, 0), (2.220446049250313e-16, 2.220446049250313e-16, 1, 0), (-1, 0, 2.220446049250313e-16, 0), (3311.0486571263245, -2310.2279453434035, 386.1793051602203, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod3" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2672.273224608386, -1469.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Bush4" (
prepend references = @Assets/Game/StarterContent/Props/SM_Bush.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2871.784637, -443.29940398138086, -37.713193, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod121" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -1918.195849472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf11" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (0.9999999999999996, -2.9621257002787615e-8, 0, 0), (0, -0, 1, 0), (1931.750826427607, -3638.0228470917605, 113.0301419624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod99" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2577.7162123217695, -1911.9654193162064, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod223" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -3123.7181008101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var9" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964524856245e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964524856245e-8, 0, 0), (0, 0, 1, 0), (2648.6117944863163, -3373.4095969851583, -79.99999997443547, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod125" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -1636.6281344725949, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod44" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2337.0469420265676, -1425.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod94" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2452.716212565129, -1921.965422358197, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod2" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320621196195, -3430.274363169878, -34.544246000002886, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod162" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3234.034510109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder19" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.999999999999999, 1.3962633893527254e-7, 0), (2505.126882232145, -2935.373245603948, -31.00000025881377, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964524856245e-8, -0.9999999999999999, 0, 0), (-11.191592216491697, -1.3617889706692827e-7, -0, 0), (0, 0, 1, 0), (3099.391359627092, -1023.6448042678318, 350.54782578484867, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod96" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787874441673, -1638.603214, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod32" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2687.273224608386, -1329.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod226" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -3197.288455810139, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod100" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2612.71621232177, -1911.9654184644482, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod35" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1424.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder35" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2497.000006, -2735.776303, 49, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder17" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.999999999999999, 1.3962633893527254e-7, 0), (2502.7318092436826, -2934.3158304120084, 49.99274420958625, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod93" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.3787874441673, -1727.497597, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod71" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1300.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod64" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1771.259931175722, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod160" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -2189.842791958983, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod61" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.609583, -1829.6314851757215, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod133" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.861413, -1477.5530034725948, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod87" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1245.3933272738086, -42.345649, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_Shelf9" (
prepend references = @Assets/Game/StarterContent/Props/SM_Shelf.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (2.9621257002787615e-8, 0.9999999999999996, 0, 0), (0.9999999999999996, -2.9621257002787615e-8, 0, 0), (0, -0, 1, 0), (3156.9134704276084, -3638.0228470917605, 113.0301419624114, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder11" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-1.7453290368507624e-9, 0.09999999999999998, -7.771561172376097e-17, 0), (-6.88263385173471e-10, -8.881784197001253e-17, -0.09999999999999998, 0), (-5.999999999999998, -1.0471974187797883e-7, 4.129580410960898e-8, 0), (2007.999999999999, -1186, 38.99998559313083, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder48" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (3313.5837120065007, -1186.7499448223516, -1.5379790000001137, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod175" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422, -2810.3017941093754, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow4" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.216796297054401e-8, 1, 0, 0), (-1, 1.216796297054401e-8, 0, 0), (0, 0, 1, 0), (2311, -3217, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod21" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1344.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod39" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.6095827583947, -2442.3935693211856, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod76" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232053, -1524.8117392738086, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow2" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.8660254058124327, -0.4999999964874112, 0, 0), (0.4999999964874112, 0.8660254058124327, 0, 0), (0, 0, 1, 0), (2694.6868155501215, -2814.9755818514095, -34.99999999999994, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_PillarFrame6" (
prepend references = @Assets/Game/StarterContent/Props/SM_PillarFrame.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (3480, -3650, -50, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod140" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2376.607189958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod22" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1454.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod51" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1455.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod15" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1469.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod123" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2456.7296037712927, -1911.9654222365182, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod10" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232066193293, -3160.6919061609888, -34.54424599999999, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod46" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1355.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod200" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -2615.9637440739552, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var7" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2682.559527, -1704.999996, -80, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod62" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.609583, -1791.3904741757217, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod54" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1500.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod130" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -1535.924557472595, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod62" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1370.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod48" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -2199.9175191757213, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod188" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2989.0430880739555, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod104" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2516.729604258011, -1931.9654207763615, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SphereLight "PointLight3"
{
color3f inputs:color = (1, 1, 1)
float inputs:colorTemperature = 6500
bool inputs:enableColorTemperature = 0
float inputs:exposure = 0
float inputs:intensity = 1600000
float inputs:radius = 0
bool treatAsPoint = 1
token visibility = "inherited"
matrix4d xformOp:transform = ( (0, -1, 2.220446049250313e-16, 0), (2.220446049250313e-16, 2.220446049250313e-16, 1, 0), (-1, 0, 2.220446049250313e-16, 0), (2541.925416, -3129.9946398609054, 803.802005, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow6" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964524856245e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964524856245e-8, 0, 0), (0, 0, 1, 0), (2814, -1392, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod53" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1495.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod190" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3036.476047073955, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod211" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -3496.797444810139, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "WaterBodyCustom3"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.237859, -0, 0, 0), (0, 0.968402, -0, 0), (0, 0, 1, 0), (2684.759725, -1702.943947, -0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
def Xform "WaterSpline"
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "CustomMeshComponent" (
prepend references = @Assets/Water/Meshes/S_WaterPlane_256.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (0, -0, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod91" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1487.2865172738084, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder42" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2797, -2736.021574, 4.000001, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder47" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (3313.583711857934, -3506.87631, -1.537979, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod65" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714678, -1864.9608291757215, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod108" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.7563184441674, -1265.52387, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod4" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320620324372, -3394.8266617414574, -34.54424599999999, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder5" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (0.1, -0, 0, 0), (0, 1.745329236690907e-9, 0.09999999999999999, 0), (-0, -7.749999999999999, 1.3526301584354528e-7, 0), (3314, -3123, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod31" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2667.273224608386, -1344.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod39" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1475.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod42" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2332.0469420265676, -1480.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod154" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3310.756318, -1968.0801449589828, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder36" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2497.000006, -2535.776303, 49, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod105" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3323.519422444167, -1346.995492, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod73" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -1638.6032142738086, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod28" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1289.9999999949496, -29.999999999999318, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def SkelRoot "SK_Cow14" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Meshes/SK_Cow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (-4.17892187520863e-8, -0.9999999999999991, 0, 0), (0.9999999999999991, -4.17892187520863e-8, 0, 0), (0, 0, 1, 0), (3083.6898521648413, -1612.1914535637293, -34.936257332741405, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod16" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.609593132998, -3021.7473177156353, -34.54424599999999, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod9" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2677.273224608386, -1469.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod59" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2327.0469420265676, -1445.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod49" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232052, -2139.787184175721, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder27" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.1102230246251566e-17, -0.09999999999999999, 1.7453292519943295e-9, 0), (-0.09999999999999995, 3.33066907387547e-17, 3.490658485628281e-9, 0), (-3.490658485628281e-8, -1.745329251994329e-8, -0.9999999999999993, 0), (2802, -3334, 4, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod57" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1643.116164, -1872.8620961757213, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod84" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1510.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder22" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2502, -3334, -31, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod192" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378788, -2842.618958073955, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod11" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2692.273224608386, -1374.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod95" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2431.729603939729, -1921.9654228449158, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod139" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378787, -2430.053871958983, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod3" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254058124318, -0.4999999964874127, 0), (0, 0.4999999964874127, 0.8660254058124318, 0), (2677.273224608386, -1349.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod54" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.232053, -2018.0456881757214, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Shape_Plane" (
prepend references = @Assets/Game/StarterContent/Shapes/Shape_Plane.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (19.75, -0, 0, 0), (0, 26.534889221191406, -0, 0), (0, 0, 5.143391132354736, 0), (2536.0133909964266, -2356.7010702120265, -34.5442386393286, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod74" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1350.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod110" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2572.716212808488, -1931.9654194378854, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod117" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2556.7296037712927, -1911.9654198029248, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder20" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.745329236690907e-9, -0.09999999999999998, -5.551115123125783e-18, 0), (1.7453292311397917e-9, 2.2204460492503132e-17, 0.09999999999999998, 0), (-5.999999999999998, -1.047197538683875e-7, 1.0471975420145441e-7, 0), (2502, -3334, 49, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "SM_GlassWindow5" (
prepend references = @Assets/Game/StarterContent/Props/SM_GlassWindow.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (-0, -6.530418, 0, 0), (0, 0, 1.834963, 0), (3486.326755, -1029.0207916017387, -37.122805, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var3" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2682.559527017553, -1704.9999960834332, -80.00000000000034, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod49" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2337.0469420265676, -1400.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod170" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3314.378791, -2897.479363109375, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod85" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1660.609583, -1265.5238702738084, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Mesh "Cylinder3" (
prepend references = @Assets/Engine/BasicShapes/Cylinder.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167962970544012e-9, 0.1, 5.551115123125783e-18, 0), (-1.7453292200375615e-9, 1.1102230246251566e-17, 0.09999999999999999, 0), (5.999999999999998, -7.300777782326406e-8, 1.0471975286918678e-7, 0), (3018, -1186, 39, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod219" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (3309.95556, -3205.1897228101393, -34.544246, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod115" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2587.71621232177, -1911.9654190728468, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod84" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2517.7162129301682, -1936.965420776362, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod125" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167965302012362e-8, -1, 8.326672684688674e-17, 0), (0.866025403784439, 1.0537766992335662e-8, -0.4999999999999992, 0), (0.4999999999999991, 6.083982789784059e-9, 0.8660254037844392, 0), (2412.71621232177, -1911.9654233316344, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod56" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2347.0469420265676, -1500.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod18" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2687.273224608386, -1384.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod21" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2667.273224608386, -1464.9999999949496, 6.821210263296962e-13, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod114" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (3323.8614134441673, -1487.286517, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1" (
prepend references = @Assets/Game/Megascans/3D_Assets/Old_Wooden_Trough_ugrxdbsfa/S_Old_Wooden_Trough_ugrxdbsfa_lod3_Var1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2677.273224608386, -1394.9999999949496, -79.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod25" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.232858, -0, 0), (0, 0, 3.254527, 0), (1664.2320517583948, -2850.9206143211854, -34.54424600000017, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Grass_Clumps_qmBr2_Var1_lod21" (
prepend references = @Assets/Game/Megascans/3D_Plants/Grass_Clumps_qmBr2/S_Grass_Clumps_qmBr2_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.410088, -0, 0, 0), (0, 1.206254294115066, 0.25473403010387885, 0), (-0, -0.6724527713588154, 3.184297923251034, 0), (1673.714688, -3488.1989098564377, -42.34565, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod12" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2682.273224608386, -1494.9999999949496, -34.99999999999932, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod101" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2612.716213295207, -1951.965418464449, -30, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod89" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2447.716212930168, -1936.9654224798767, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod52" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 1, -0, 0), (0, 0, 1, 0), (2347.0469420265676, -1460.000000009696, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod87" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2451.729604258011, -1931.965422358197, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Buffelgrass_tmjhcayia_Var1_lod92" (
prepend references = @Assets/Game/Megascans/3D_Plants/Buffelgrass_tmjhcayia/S_Buffelgrass_tmjhcayia_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1.2167964857923153e-8, -0.9999999999999999, 0, 0), (0.9999999999999999, 1.2167964857923153e-8, 0, 0), (0, 0, 1, 0), (2437.7162124434503, -1916.9654227232363, 0, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
def Xform "S_Wheat_smdoejyr_Var1_lod81" (
prepend references = @Assets/Game/Megascans/3D_Plants/Wheat_smdoejyr/S_Wheat_smdoejyr_Var1_lod1.usda@
)
{
token visibility = "inherited"
matrix4d xformOp:transform = ( (1, -0, 0, 0), (0, 0.8660254037844387, -0.49999999999999994, 0), (0, 0.49999999999999994, 0.8660254037844387, 0), (2327.0469420265676, -1485.000000009696, -35, 1) )
uniform token[] xformOpOrder = ["xformOp:transform"]
}
}
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/MaterialOverrides.usda | #usda 1.0
over "Root"
{
over "WaterBodyCustom5"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom5/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom5/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_18@
token outputs:out
}
}
}
}
}
over "WaterBodyCustom2"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom2/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom2/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_12@
token outputs:out
}
}
}
}
}
over "Cylinder13" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder13/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder13/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder40" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder40/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder40/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame9" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame9/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame9/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cylinder18" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder18/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder18/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cylinder25" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder25/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder25/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder38" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder38/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder38/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "WaterBodyCustom"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_10@
token outputs:out
}
}
}
}
}
over "Cylinder16" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder16/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder16/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow11"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow11/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow11/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder15" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder15/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder15/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow5"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow5/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow5/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_DoorFrame2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_DoorFrame2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Chrome.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_DoorFrame2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Chrome.M_Metal_Chrome@
token outputs:out
}
}
}
over "Cylinder14" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder14/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder14/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder30" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder30/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder30/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cow_Y5"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD3/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD3/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD3/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD3/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD4"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD4/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD4/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y5/LOD4/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y5/LOD4/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
}
over "Cylinder29" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder29/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder29/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder9" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder9/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder9/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder7" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder7/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder7/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder45" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder45/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder45/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame8" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame8/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame8/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "cow_ground" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/cow_ground/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Ground_Gravel.usda@
)
{
token outputs:unreal:surface.connect = </Root/cow_ground/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Ground_Gravel.M_Ground_Gravel@
token outputs:out
}
}
}
over "SM_PillarFrame2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Wall_Door_400x301" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Wall_Door_400x301/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Basic_Wall.usda@
)
{
token outputs:unreal:surface.connect = </Root/Wall_Door_400x301/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Basic_Wall.M_Basic_Wall@
token outputs:out
}
}
}
over "SK_Cow7"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow7/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow7/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame11" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame11/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame11/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cow_F4"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F4/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F4/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SK_Cow13"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow13/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow13/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cow_Y2"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD3/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD3/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD3/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD3/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD4"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD4/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD4/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y2/LOD4/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y2/LOD4/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
}
over "SM_PillarFrame10" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame10/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame10/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "SK_Cow"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder24" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder24/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder24/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder44" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder44/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder44/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder12" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder12/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder12/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "WaterBodyCustom6"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom6/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom6/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_20@
token outputs:out
}
}
}
}
}
over "SM_DoorFrame" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_DoorFrame/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Chrome.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_DoorFrame/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Chrome.M_Metal_Chrome@
token outputs:out
}
}
}
over "Cylinder32" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder32/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder32/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow3"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow3/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow3/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cow_F6"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F6/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F6/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder37" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder37/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder37/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Shape_Cube3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Shape_Cube3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Wood_Walnut.usda@
)
{
token outputs:unreal:surface.connect = </Root/Shape_Cube3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Wood_Walnut.M_Wood_Walnut@
token outputs:out
}
}
}
over "Cylinder10" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder10/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder10/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder21" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder21/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder21/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "WaterBodyCustom7"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom7/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom7/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_22@
token outputs:out
}
}
}
}
}
over "Cylinder43" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder43/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder43/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder33" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder33/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder33/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder34" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder34/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder34/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder28" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder28/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder28/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cow_F5"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_APFur_BaseColor_Mat.Cow_F_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_F5/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_F5/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_F/Textures/Cow_F_BaseColor_Mat.Cow_F_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SK_Cow10"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow10/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow10/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder31" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder31/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder31/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow8"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow8/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow8/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Shape_Cube2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Shape_Cube2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Wood_Walnut.usda@
)
{
token outputs:unreal:surface.connect = </Root/Shape_Cube2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Wood_Walnut.M_Wood_Walnut@
token outputs:out
}
}
}
over "Cylinder6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow9"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow9/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow9/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder23" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder23/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder23/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow12"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow12/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow12/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "WaterBodyCustom4"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom4/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom4/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_16@
token outputs:out
}
}
}
}
}
over "Cylinder46" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder46/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder46/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SM_PillarFrame7" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame7/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame7/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "Cow_Y4"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD3/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD3/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD3/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD3/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD4"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD4/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD4/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_BaseColor_Mat.Cow_Y_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cow_Y4/LOD4/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cow_Y4/LOD4/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/ToSend/Cow_Y/Textures/Cow_Y_APFur_BaseColor_Mat_Auto1.Cow_Y_APFur_BaseColor_Mat@
token outputs:out
}
}
}
}
}
over "Cylinder8" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder8/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder8/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder26" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder26/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder26/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Wall_Door_400x300" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Wall_Door_400x300/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Basic_Wall.usda@
)
{
token outputs:unreal:surface.connect = </Root/Wall_Door_400x300/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Basic_Wall.M_Basic_Wall@
token outputs:out
}
}
}
over "Cylinder41" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder41/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder41/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder39" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder39/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder39/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder19" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder19/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder19/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder35" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder35/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder35/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder17" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder17/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder17/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder11" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder11/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder11/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder48" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder48/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder48/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow4"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow4/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow4/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SK_Cow2"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow2/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow2/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow2_BaseColor_Mat_Auto1.T_Cow2_BaseColor_Mat@
token outputs:out
}
}
}
}
over "SM_PillarFrame6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SM_PillarFrame6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Metal_Burnished_Steel.usda@
)
{
token outputs:unreal:surface.connect = </Root/SM_PillarFrame6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Metal_Burnished_Steel.M_Metal_Burnished_Steel@
token outputs:out
}
}
}
over "SK_Cow6"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow6/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow6/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow3_BaseColor_Mat_Auto1.T_Cow3_BaseColor_Mat@
token outputs:out
}
}
}
}
over "WaterBodyCustom3"
{
over "CustomMeshComponent"
{
over "LOD0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD2" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD2/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD2/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD4" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD4/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD4/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
def "LOD6" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/WaterBodyCustom3/CustomMeshComponent/LOD6/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/Transient.usda@
)
{
token outputs:unreal:surface.connect = </Root/WaterBodyCustom3/CustomMeshComponent/LOD6/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/Transient.WaterMID_14@
token outputs:out
}
}
}
}
}
over "Cylinder42" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder42/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder42/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder47" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder47/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder47/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder5" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder5/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder5/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder36" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder36/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder36/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "SK_Cow14"
{
over "LOD0"
{
over "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD0/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD0/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
over "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD0/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD0/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD1"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD1/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD1/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD1/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD1/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD2"
{
def "Section0" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD2/Section0/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD2/Section0/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
def "Section1" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD2/Section1/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD2/Section1/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
def "LOD3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/SK_Cow14/LOD3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.usda@
)
{
token outputs:unreal:surface.connect = </Root/SK_Cow14/LOD3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/Assets/FarmAnimalsPack/Cow/Textures/T_Cow1_BaseColor_Mat_Auto1.T_Cow1_BaseColor_Mat@
token outputs:out
}
}
}
}
over "Cylinder27" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder27/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder27/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder22" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder22/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder22/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Shape_Plane" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Shape_Plane/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Game/StarterContent/Materials/M_Basic_Floor.usda@
)
{
token outputs:unreal:surface.connect = </Root/Shape_Plane/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Game/StarterContent/Materials/M_Basic_Floor.M_Basic_Floor@
token outputs:out
}
}
}
over "Cylinder20" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder20/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder20/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
over "Cylinder3" (
prepend apiSchemas = ["MaterialBindingAPI"]
)
{
rel material:binding = </Root/Cylinder3/UnrealMaterial>
def Material "UnrealMaterial" (
prepend references = @Assets/Engine/BasicShapes/BasicShapeMaterial.usda@
)
{
token outputs:unreal:surface.connect = </Root/Cylinder3/UnrealMaterial/UnrealShader.outputs:out>
def Shader "UnrealShader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:unreal:sourceAsset = @/Engine/BasicShapes/BasicShapeMaterial.BasicShapeMaterial@
token outputs:out
}
}
}
}
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Wood_Walnut.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Wood_Walnut(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Wood_Walnut_N.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float2 Local1 = (CustomizedUV0_mdl * 0.2134);
float4 Local2 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local1.x,1.0-Local1.y),tex::wrap_repeat,tex::wrap_repeat);
float Local3 = (Local2.x + 0.5);
float2 Local4 = (CustomizedUV0_mdl * 0.05341);
float4 Local5 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local4.x,1.0-Local4.y),tex::wrap_repeat,tex::wrap_repeat);
float Local6 = (Local5.x + 0.5);
float2 Local7 = (CustomizedUV0_mdl * 0.002);
float4 Local8 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local7.x,1.0-Local7.y),tex::wrap_repeat,tex::wrap_repeat);
float Local9 = (Local8.x + 0.5);
float Local10 = (Local6 * Local9);
float Local11 = (Local3 * Local10);
float3 Local12 = math::lerp(float3(0.8,0.8,0.8),float3(1.0,1.0,1.0),Local11);
float4 Local13 = tex::lookup_float4(texture_2d("./Textures/T_Wood_Walnut_D.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local14 = (Local12 * float3(Local13.x,Local13.y,Local13.z));
float Local15 = (Local11 * Local13.w);
float Local16 = math::lerp(0.8,0.5,Local15);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local14;
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local16;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/Cow_F_APFur_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material Cow_F_APFur_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/Cow_F_APFur_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/DefaultMaterial.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material DefaultMaterial(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float2 Local0 = (CustomizedUV0_mdl / 2.0);
float2 Local1 = (Local0 / 0.05);
float4 Local2 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_N.png",::tex::gamma_linear),float2(Local1.x,1.0-Local1.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Local3 = (float3(Local2.x,Local2.y,Local2.z) * float3(0.3,0.3,1.0));
float3 Normal_mdl = Local3;
float2 Local4 = (CustomizedUV0_mdl * 20.0);
float4 Local5 = tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_M.png",::tex::gamma_linear),float2(Local4.x,1.0-Local4.y),tex::wrap_repeat,tex::wrap_repeat);
float Local6 = math::lerp(0.4,1.0,Local5.x);
float Local7 = (1.0 - Local6);
float2 Local8 = (Local0 / 0.1);
float4 Local9 = tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_M.png",::tex::gamma_linear),float2(Local8.x,1.0-Local8.y),tex::wrap_repeat,tex::wrap_repeat);
float Local10 = math::lerp(Local9.y,1.0,0.0);
float Local11 = math::lerp(Local6,Local7,Local10);
float4 Local12 = tex::lookup_float4(texture_2d("./Textures/T_Default_Material_Grid_M.png",::tex::gamma_linear),float2(Local0.x,1.0-Local0.y),tex::wrap_repeat,tex::wrap_repeat);
float Local13 = math::lerp(Local9.y,0.0,0.0);
float Local14 = (Local12.y + Local13);
float Local15 = math::lerp(Local14,0.5,0.5);
float Local16 = math::lerp(0.295,0.66,Local15);
float Local17 = (Local16 * 0.5);
float Local18 = (Local11 * Local17);
float Local19 = math::lerp(0.0,0.5,Local12.y);
float Local20 = math::lerp(0.7,1.0,Local9.y);
float Local21 = math::lerp(Local20,1.0,0.0);
float Local22 = (Local21 * 1.0);
float Local23 = (Local19 + Local22);
float Local24 = math::min(math::max(Local23,0.0),1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local18,Local18,Local18);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local24;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/WaterMID_10.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
float3 CustomExpression0(float WaterBodyIndex,float2 WorldPosition,float Time)
{
WaveOutput Waves;
Waves = GetAllGerstnerWavesNew(WaterBodyIndex, Time, WorldPosition);
return PackNormalAndWPONew(Waves);
}
float3 CustomExpression1(float3 inPackedWave)
{
float3 outnormal = math::frac(inPackedWave);
outnormal *= 2.02;
outnormal -= 1.01;
return outnormal;
}
float CustomExpression2(float WaterBodyIndex,float WaterDepth)
{
return ComputeWaveDepthAttenuationFactorNew(WaterBodyIndex, WaterDepth);
}
float3 CustomExpression3(float3 inPackedWave)
{
float3 outWPO;
outWPO = math::floor(inPackedWave) / 100;
return outWPO;
}
float CustomExpression4(float x)
{
return 1.0-math::exp(-x);;
}
float CustomExpression5(float X)
{
return math::log2(X);
}
float CustomExpression6(float x)
{
return 1-math::exp(-x);
}
float3 CustomExpression7(float3 Color,float x)
{
clip(x);
return Color;
}
export material WaterMID_10(
float WaterBodyIndex = 0.0
[[
anno::display_name("WaterBodyIndex"),
anno::ui_order(32)
]],
float Time = 0.0
[[
anno::display_name("Time")
]],
float ManualTime = 0.0
[[
anno::display_name("ManualTime")
]],
float FreezeTime = 0.0
[[
anno::display_name("FreezeTime")
]],
float FixedWaterDepth = 0.0
[[
anno::display_name("FixedWaterDepth"),
anno::ui_order(32)
]],
float DefaultDisantWaterSpeed = 0.75
[[
anno::display_name("Default Disant Water Speed"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultNearWaterScale = 512.0
[[
anno::display_name("Default Near Water Scale"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultDistantWaterScale = 3500.0
[[
anno::display_name("Default Distant Water Scale"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float WaterDistantNormalOffset = 5000.0
[[
anno::display_name("Water Distant Normal Offset"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float WaterDistantNormalLength = 10000.0
[[
anno::display_name("Water Distant Normal Length"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultNearNormalStrength = 0.25
[[
anno::display_name("Default Near Normal Strength"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultDistantNormalStrengthB = 0.25
[[
anno::display_name("Default Distant Normal StrengthB"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultDistantNormalStrength = 0.3
[[
anno::display_name("Default Distant Normal Strength"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float FarNormalFresnelPower = 9.0
[[
anno::display_name("Far Normal Fresnel Power"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float WaveoverRockSteepness = 0.0
[[
anno::display_name("Wave over Rock Steepness"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 SimLocation = float4(0.0,0.0,0.0,0.0)
[[
anno::display_name("SimLocation")
]],
float FluidSimSize = 2048.0
[[
anno::display_name("FluidSimSize")
]],
uniform texture_2d NormalAndHeight = texture_2d("./Textures/Black_1x1_EXR_Texture.exr",::tex::gamma_linear)
[[
anno::display_name("NormalAndHeight"),
anno::ui_order(32),
sampler_color()
]],
float FluidsimNormalStrength = 1.0
[[
anno::display_name("Fluidsim Normal Strength"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
uniform texture_2d Velocity = texture_2d("./Textures/Black_1x1_EXR_Texture.exr",::tex::gamma_linear)
[[
anno::display_name("Velocity"),
anno::ui_order(32),
sampler_color()
]],
float FluidsimVelocityEdgeFalloff = 0.2
[[
anno::display_name("Fluidsim Velocity EdgeFalloff"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FluidsimVelocityEdgeoffset = 0.02
[[
anno::display_name("Fluidsim Velocity Edgeoffset"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimVelocityNormalStrength = 40.0
[[
anno::display_name("Sim Velocity Normal Strength"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFoamNormal = 1.0
[[
anno::display_name("Sim Foam Normal"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FoamAttachment = 0.4
[[
anno::display_name("Foam Attachment"),
anno::ui_order(32),
anno::in_group("General Foam")
]],
float SimFoamScale = 350.0
[[
anno::display_name("Sim Foam Scale"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SImFlowmapSpeed = 1.5
[[
anno::display_name("SIm Flowmap Speed"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float WPOFlowmap = 6.0
[[
anno::display_name("WPO Flowmap"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FluidsimFlowDensity = 0.25
[[
anno::display_name("Fluidsim Flow Density"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimulationFlowmapSpeed = 0.75
[[
anno::display_name("Simulation Flowmap Speed"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFoamContrast = 0.0
[[
anno::display_name("SimFoam Contrast"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFlowmapDetection = 1.5
[[
anno::display_name("Sim Flowmap Detection"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFoamDpethMin = 8.0
[[
anno::display_name("Sim Foam Dpeth Min"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 FoamEmissive = float4(0.28744,1.0,0.1875,1.0)
[[
anno::display_name("Foam Emissive"),
anno::ui_order(32),
anno::in_group("General Foam")
]],
float VelocityDebug = 0.0
[[
anno::display_name("Velocity Debug"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 WaterAlbedo = float4(0.85,0.85,0.85,0.5)
[[
anno::display_name("Water Albedo"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float CausticsMaxIntensity = 1.0
[[
anno::display_name("CausticsMaxIntensity")
]],
float WaterOpacityMaskOffset = -24.0
[[
anno::display_name("Water Opacity Mask Offset"),
anno::ui_order(32),
anno::in_group("Opacity Mask")
]],
float FixedZHeight = 0.0
[[
anno::display_name("FixedZHeight"),
anno::ui_order(32)
]],
uniform texture_2d WaterVelocityAndHeight = texture_2d("./Textures/Black_1x1_EXR_Texture.exr",::tex::gamma_linear)
[[
anno::display_name("WaterVelocityAndHeight"),
anno::ui_order(32),
sampler_color()
]],
float4 WaterArea = float4(0.0,0.0,0.0,0.0)
[[
anno::display_name("WaterArea"),
anno::ui_order(32)
]],
float TerrainMaxZ = 10000.0
[[
anno::display_name("TerrainMaxZ")
]],
float TerrainMinZ = -10000.0
[[
anno::display_name("TerrainMinZ")
]],
float WaterSpecular = 0.225
[[
anno::display_name("Water Specular"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float CriticalAngleDot = 0.225
[[
anno::display_name("Critical Angle Dot"),
anno::ui_order(32),
anno::in_group("TwoSided Settings")
]],
float CriticalAngleWidth = 0.01
[[
anno::display_name("Critical Angle Width"),
anno::ui_order(32),
anno::in_group("TwoSided Settings")
]],
float TwoSidedSign = 1.0
[[
anno::hidden()
]],
float WaterRoughness = 0.02
[[
anno::display_name("Water Roughness"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float WaterFresnelRoughness = 0.1
[[
anno::display_name("Water Fresnel Roughness"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float FoamRoughness = 0.4
[[
anno::display_name("Foam Roughness"),
anno::ui_order(32),
anno::in_group("General Foam")
]],
float MaxFluidDisplacement = 1024.0
[[
anno::display_name("Max Fluid Displacement"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FluidDisplacementMultiplier = 1.0
[[
anno::display_name("Fluid Displacement Multiplier"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float ShowSim = 1.0
[[
anno::display_name("ShowSim"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float WaveDFMaskBias = -64.0
[[
anno::display_name("Wave DF Mask Bias"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float WaveOverObjectMaskDistance = 256.0
[[
anno::display_name("Wave Over Object Mask Distance"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 Scattering = float4(1.0,1.0,1.0,0.5)
[[
anno::display_name("Scattering"),
anno::ui_order(32)
]],
float4 Absorption = float4(10.0,150.0,350.0,8.0)
[[
anno::display_name("Absorption"),
anno::ui_order(32)
]],
float Anisotropy = 0.1
[[
anno::display_name("Anisotropy"),
anno::ui_order(32),
anno::in_group("Water Shading")
]])
[[
dither_masked_off()
]]
=
let {
float Local221 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local222 = CustomExpression0(WaterBodyIndex,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local221);
float3 Local223 = CustomExpression3(Local222);
float Local224 = math::max(FixedWaterDepth,0.0);
float Local225 = CustomExpression2(WaterBodyIndex,Local224);
float3 Local226 = (Local223 * Local225);
float Local227 = (Local221 * DefaultDisantWaterSpeed);
float Local228 = (Local227 * 0.1);
float Local229 = math::frac(Local228);
float Local230 = (::pixel_depth() - WaterDistantNormalOffset);
float Local231 = (Local230 / WaterDistantNormalLength);
float Local232 = math::saturate(Local231);
float Local233 = math::ceil(Local232);
float Local234 = math::saturate(Local233);
float Local235 = math::lerp(DefaultNearWaterScale,DefaultDistantWaterScale,Local234);
float3 Local236 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / Local235);
float2 Local237 = (float2(Local229,Local229) + float2(Local236.x,Local236.y));
float4 Local238 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local237.x,1.0-Local237.y),tex::wrap_repeat,tex::wrap_repeat);
float Local239 = (Local227 * -0.1);
float Local240 = math::frac(Local239);
float2 Local241 = (float2(Local236.x,Local236.y) + float2(0.4181,0.3548));
float2 Local242 = (Local241 / 1.618);
float2 Local243 = (float2(Local240,Local240) + Local242);
float4 Local244 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local243.x,1.0-Local243.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local245 = (float3(Local238.x,Local238.y,Local238.z) + float3(Local244.x,Local244.y,Local244.z));
float2 Local246 = (float2(Local236.x,Local236.y) + float2(0.864861,0.148384));
float2 Local247 = (Local246 / 1.236094);
float2 Local248 = (float2(Local240,Local229) + Local247);
float4 Local249 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local248.x,1.0-Local248.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local250 = (Local245 + float3(Local249.x,Local249.y,Local249.z));
float3 Local251 = (Local250 * 0.333333);
float Local252 = (Local251.z - 0.5);
float Local253 = (Local252 * Local232);
float Local254 = (Local253 * 0.0);
float3 Local255 = (Local254 * float3(0.0,0.0,1.0));
float3 Local256 = math::lerp(Local226,Local255,Local232);
float3 Local257 = (Local256 * 1.0);
float Local258 = (MaxFluidDisplacement * -1.0);
float3 Local259 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float3 Local260 = (Local259 * Local256);
float3 Local261 = (Local260 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local262 = (float2(Local261.x,Local261.y) - float2(SimLocation.x,SimLocation.y));
float2 Local263 = (Local262 / FluidSimSize);
float2 Local264 = (Local263 + 0.5);
float4 Local265 = tex::lookup_float4(NormalAndHeight,float2(Local264.x,1.0-Local264.y),tex::wrap_clamp,tex::wrap_clamp);
float Local266 = (FluidDisplacementMultiplier * ShowSim);
float Local267 = (Local265.z * Local266);
float Local268 = math::min(math::max(Local267,Local258),MaxFluidDisplacement);
float3 Local269 = (Local268 * float3(0.0,0.0,1.0));
float3 Local270 = (Local257 + Local269);
float3 Local271 = (Local257 * float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0));
float Local272 = math::max(Local257.z,Local269.z);
float3 Local273 = (Local272 * float3(0.0,0.0,1.0));
float3 Local274 = (Local271 + Local273);
float3 Local275 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) - float3(SimLocation.x,SimLocation.y,SimLocation.z));
float3 Local276 = (Local275 + Local256);
float3 Local277 = (Local276 / FluidSimSize);
float3 Local278 = (Local277 + 0.5);
float2 Local279 = math::saturate(float2(Local278.x,Local278.y));
float4 Local280 = tex::lookup_float4(texture_2d("./Textures/WaterDistanceField.png",::tex::gamma_linear),float2(Local279.x,1.0-Local279.y),tex::wrap_clamp,tex::wrap_clamp);
float Local281 = (Local280.x * FluidSimSize);
float Local282 = (Local281 + WaveDFMaskBias);
float Local283 = (Local282 / WaveOverObjectMaskDistance);
float Local284 = math::saturate(Local283);
float Local285 = (1.0 - Local284);
float Local286 = (Local285 * 0.0);
float3 Local287 = math::lerp(Local270,Local274,Local286);
float3 WorldPositionOffset_mdl = Local287;
float Local0 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local1 = CustomExpression0(WaterBodyIndex,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local0);
float3 Local2 = CustomExpression1(Local1);
float Local3 = math::max(FixedWaterDepth,0.0);
float Local4 = CustomExpression2(WaterBodyIndex,Local3);
float3 Local5 = math::lerp(float3(0.0,0.0,1.0),Local2,Local4);
float Local6 = (Local0 * DefaultDisantWaterSpeed);
float Local7 = (Local6 * 0.1);
float Local8 = math::frac(Local7);
float Local9 = (::pixel_depth() - WaterDistantNormalOffset);
float Local10 = (Local9 / WaterDistantNormalLength);
float Local11 = math::saturate(Local10);
float Local12 = math::ceil(Local11);
float Local13 = math::saturate(Local12);
float Local14 = math::lerp(DefaultNearWaterScale,DefaultDistantWaterScale,Local13);
float3 Local15 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / Local14);
float2 Local16 = (float2(Local8,Local8) + float2(Local15.x,Local15.y));
float4 Local17 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local16.x,1.0-Local16.y),tex::wrap_repeat,tex::wrap_repeat);
float Local18 = (Local6 * -0.1);
float Local19 = math::frac(Local18);
float2 Local20 = (float2(Local15.x,Local15.y) + float2(0.4181,0.3548));
float2 Local21 = (Local20 / 1.618);
float2 Local22 = (float2(Local19,Local19) + Local21);
float4 Local23 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local22.x,1.0-Local22.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local24 = (float3(Local17.x,Local17.y,Local17.z) + float3(Local23.x,Local23.y,Local23.z));
float2 Local25 = (float2(Local15.x,Local15.y) + float2(0.864861,0.148384));
float2 Local26 = (Local25 / 1.236094);
float2 Local27 = (float2(Local19,Local8) + Local26);
float4 Local28 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local27.x,1.0-Local27.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local29 = (Local24 + float3(Local28.x,Local28.y,Local28.z));
float3 Local30 = (Local29 * 0.333333);
float2 Local31 = (float2(Local30.x,Local30.y) - 0.5);
float Local32 = (::pixel_depth() / WaterDistantNormalOffset);
float Local33 = math::saturate(Local32);
float Local34 = math::lerp(DefaultNearNormalStrength,0.0,Local33);
float2 Local35 = (Local31 * Local34);
float3 Local36 = (Local5 + float3(Local35.x,Local35.y,0.0));
float Local37 = ::fresnel(FarNormalFresnelPower, 0.04, float3(0.0,0.0,1.0));
float Local38 = math::lerp(DefaultDistantNormalStrengthB,DefaultDistantNormalStrength,Local37);
float Local39 = (1.0 / Local38);
float3 Local40 = math::normalize(float3(Local31.x,Local31.y,Local39));
float3 Local41 = math::lerp(Local36,Local40,Local11);
float3 Local42 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float3 Local43 = CustomExpression3(Local1);
float3 Local44 = (Local43 * Local4);
float Local45 = (Local30.z - 0.5);
float Local46 = (Local45 * Local11);
float Local47 = (Local46 * 0.0);
float3 Local48 = (Local47 * float3(0.0,0.0,1.0));
float3 Local49 = math::lerp(Local44,Local48,Local11);
float3 Local50 = (Local42 * Local49);
float3 Local51 = (Local50 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local52 = (float2(Local51.x,Local51.y) - float2(SimLocation.x,SimLocation.y));
float2 Local53 = (Local52 / FluidSimSize);
float2 Local54 = (Local53 + 0.5);
float4 Local55 = tex::lookup_float4(NormalAndHeight,float2(Local54.x,1.0-Local54.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local56 = (float2(float3(Local55.x,Local55.y,Local55.z).x,float3(Local55.x,Local55.y,Local55.z).y) * FluidsimNormalStrength);
float3 Local57 = (Local41 + float3(Local56.x,Local56.y,0.0));
float2 Local58 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(SimLocation.x,SimLocation.y));
float2 Local59 = (Local58 / FluidSimSize);
float2 Local60 = (Local59 + 0.5);
float4 Local61 = tex::lookup_float4(Velocity,float2(Local60.x,1.0-Local60.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local62 = (Local60 - 0.5);
float2 Local63 = math::abs(Local62);
float Local64 = (FluidsimVelocityEdgeFalloff * 2.0);
float Local65 = (1.0 - Local64);
float Local66 = (Local65 * 0.5);
float2 Local67 = (Local63 - Local66);
float2 Local68 = math::max(Local67,float2(0.0,0.0));
float2 Local69 = (Local68 - 0.0);
float Local70 = math::length(Local69);
float Local71 = (Local70 / FluidsimVelocityEdgeFalloff);
float Local72 = math::min(math::max(Local71,0.0),1.0);
float Local73 = (1.0 - Local72);
float Local74 = (Local73 - FluidsimVelocityEdgeoffset);
float Local75 = (1.0 - FluidsimVelocityEdgeoffset);
float Local76 = (Local74 / Local75);
float Local77 = math::min(math::max(Local76,0.0),1.0);
float3 Local78 = (float3(Local61.x,Local61.y,Local61.z) * Local77);
float2 Local79 = (float2(Local78.x,Local78.y) - 0.0);
float Local80 = math::length(Local79);
float Local81 = (Local80 / SimVelocityNormalStrength);
float Local82 = (Local81 + SimFoamNormal);
float3 Local83 = math::lerp((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0),(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0),FoamAttachment);
float3 Local84 = (Local83 / SimFoamScale);
float Local85 = (Local0 * SImFlowmapSpeed);
float Local86 = (Local85 - 0.5);
float Local87 = math::frac(Local86);
float3 Local88 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) - float3(SimLocation.x,SimLocation.y,SimLocation.z));
float3 Local89 = (Local88 + Local49);
float3 Local90 = (Local89 / FluidSimSize);
float3 Local91 = (Local90 + 0.5);
float2 Local92 = math::saturate(float2(Local91.x,Local91.y));
float4 Local93 = tex::lookup_float4(texture_2d("./Textures/WaterDistanceField.png",::tex::gamma_linear),float2(Local92.x,1.0-Local92.y),tex::wrap_clamp,tex::wrap_clamp);
float Local94 = (Local93.x * 0.0);
float Local95 = math::saturate(Local94);
float2 Local96 = (Local95 * float2(Local49.x,Local49.y));
float2 Local97 = (Local96 * WPOFlowmap);
float2 Local98 = (Local97 / 1000.0);
float Local99 = math::dot(Local78, Local78);
float3 Local100 = math::normalize(Local78);
float4 Local101 = ((math::abs(Local99 - 0.000001) > 0.00001) ? (Local99 >= 0.000001 ? float4(Local100.x,Local100.y,Local100.z,0.0) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0)) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0));
float Local102 = (Local80 / 100.0);
float Local103 = (Local102 * FluidsimFlowDensity);
float Local104 = CustomExpression4(Local103);
float3 Local105 = (float3(Local101.x,Local101.y,Local101.z) * Local104);
float2 Local106 = (float2(Local105.x,Local105.y) * SimulationFlowmapSpeed);
float2 Local107 = (Local98 + Local106);
float2 Local108 = (Local107 * float2(1.0,-1.0));
float2 Local109 = (Local108 * float2(-1.0,1.0));
float2 Local110 = (Local87 * Local109);
float2 Local111 = (float2(Local84.x,Local84.y) + Local110);
float2 Local112 = math::frac(Local111);
float2 Local113 = (Local112 + float2(0.5,0.5));
float4 Local121 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled_N.png",::tex::gamma_linear),float2(Local113.x,1.0-Local113.y),tex::wrap_repeat,tex::wrap_repeat));
float Local122 = math::frac(Local85);
float2 Local123 = (Local109 * Local122);
float2 Local124 = (Local123 + float2(Local84.x,Local84.y));
float4 Local125 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled_N.png",::tex::gamma_linear),float2(Local124.x,1.0-Local124.y),tex::wrap_repeat,tex::wrap_repeat));
float Local126 = (Local122 / 1.0);
float Local127 = math::frac(Local126);
float Local128 = (Local127 * 2.0);
float Local129 = (1.0 - Local127);
float Local130 = (2.0 * Local129);
float Local131 = math::floor(Local128);
float Local132 = math::lerp(Local128,Local130,Local131);
float3 Local133 = math::lerp(float3(Local121.x,Local121.y,Local121.z),float3(Local125.x,Local125.y,Local125.z),Local132);
float3 Local134 = (float3(float2(Local82,Local82).x,float2(Local82,Local82).y,0.0) * Local133);
float Local135 = (0.0 - SimFoamContrast);
float Local136 = (SimFoamContrast + 1.0);
float4 Local137 = tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled.png",::tex::gamma_srgb),float2(Local113.x,1.0-Local113.y),tex::wrap_repeat,tex::wrap_repeat);
float4 Local138 = tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled.png",::tex::gamma_srgb),float2(Local124.x,1.0-Local124.y),tex::wrap_repeat,tex::wrap_repeat);
float4 Local139 = math::lerp(float4(float3(Local137.x,Local137.y,Local137.z).x,float3(Local137.x,Local137.y,Local137.z).y,float3(Local137.x,Local137.y,Local137.z).z,Local137.w),float4(float3(Local138.x,Local138.y,Local138.z).x,float3(Local138.x,Local138.y,Local138.z).y,float3(Local138.x,Local138.y,Local138.z).z,Local138.w),Local132);
float Local140 = (float3(Local139.x,Local139.y,Local139.z).x - 1.0);
float Local141 = (Local55.w / SimFlowmapDetection);
float Local142 = CustomExpression6(Local141);
float Local143 = math::saturate(Local142);
float Local144 = (Local143 * 2.0);
float Local145 = (Local140 + Local144);
float Local146 = math::min(math::max(Local145,0.0),1.0);
float Local147 = math::lerp(Local135,Local136,Local146);
float Local148 = math::min(math::max(Local147,0.0),1.0);
float Local149 = MaterialExpressionSceneDepthWithoutWater(ScreenAlignedPosition(GetScreenPosition(Parameters)), 1000000.0);
float Local150 = (Local149 - ::pixel_depth());
float Local151 = (Local150 / SimFoamDpethMin);
float Local152 = math::saturate(Local151);
float Local153 = (Local148 * Local152);
float Local154 = math::saturate(Local153);
float Local155 = (Local154 * 0.0);
float3 Local156 = (Local134 * Local155);
float3 Local157 = (Local57 + Local156);
float3 Normal_mdl = Local157;
float3 Local158 = (float3(FoamEmissive.x,FoamEmissive.y,FoamEmissive.z) * FoamEmissive.w);
float3 Local159 = float3(0);
float3 Local160 = (Local158 * Local159);
float3 Local161 = (Local160 * Local155);
float3 Local162 = (0.0 + Local161);
float3 Local163 = math::abs(Local78);
float3 Local164 = (Local163 * VelocityDebug);
float3 Local165 = (float3(0.0,0.0,0.0) + Local164);
float3 Local166 = (Local162 + Local165);
float3 Local167 = (float3(WaterAlbedo.x,WaterAlbedo.y,WaterAlbedo.z) / CausticsMaxIntensity);
float Local168 = (FixedWaterDepth - WaterOpacityMaskOffset);
float Local169 = math::saturate(Local168);
float Local170 = (FixedZHeight + Local55.z);
float2 Local171 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local172 = (Local171 / float2(WaterArea.z,WaterArea.w));
float2 Local173 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local174 = (Local173 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local175 = (Local172 * Local174);
float2 Local176 = math::saturate(Local175);
float2 Local177 = (Local176 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local178 = (Local177 - 0.5);
float2 Local179 = math::floor(Local178);
float2 Local180 = (Local179 + 0.5);
float2 Local181 = (Local180 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local182 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local181.x,1.0-Local181.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local183 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local184 = (Local181 + float2(Local183.x,0.0));
float4 Local185 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local184.x,1.0-Local184.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local186 = math::frac(Local178);
float4 Local187 = math::lerp(Local182,Local185,Local186.x);
float2 Local188 = (Local181 + float2(0.0,Local183.y));
float4 Local189 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local188.x,1.0-Local188.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local190 = (Local181 + Local183);
float4 Local191 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local190.x,1.0-Local190.y),tex::wrap_clamp,tex::wrap_clamp);
float4 Local192 = math::lerp(Local189,Local191,Local186.x);
float4 Local193 = math::lerp(Local187,Local192,Local186.y);
float Local194 = (TerrainMaxZ - TerrainMinZ);
float Local195 = (Local193.w * Local194);
float Local196 = (Local195 + TerrainMinZ);
float Local197 = (Local170 - Local196);
float Local198 = math::max(Local197,0.0);
float Local199 = math::saturate(Local198);
float Local200 = math::max(Local169,Local199);
float Local201 = math::lerp(-1.0,1.0,Local200);
float3 Local202 = CustomExpression7(Local167,Local201);
float Local203 = math::dot(::pixel_normal_world_space(true), ::camera_vector(true));
float Local204 = (CriticalAngleDot - Local203);
float Local205 = (Local204 / CriticalAngleWidth);
float Local206 = math::saturate(Local205);
float Local207 = math::saturate(TwoSidedSign);
float Local208 = math::lerp(Local206,0.0,Local207);
float Local209 = math::lerp(WaterSpecular,1.0,Local208);
float Local210 = math::saturate(Local201);
float Local211 = (Local209 * Local210);
float Local212 = ::fresnel(5.0, 0.0, ::pixel_normal_world_space(true));
float Local213 = math::lerp(WaterRoughness,WaterFresnelRoughness,Local212);
float Local214 = (FoamRoughness * Local155);
float Local215 = (Local213 + Local214);
float3 EmissiveColor_mdl = Local166;
float OpacityMask_mdl = (1.0 - 0.3333) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = Local202;
float Metallic_mdl = 0.0;
float Specular_mdl = Local211;
float Roughness_mdl = Local215;
float Local374 = (FixedWaterDepth - WaterOpacityMaskOffset);
float Local375 = math::saturate(Local374);
float3 Local376 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float Local377 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local378 = CustomExpression0(WaterBodyIndex,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local377);
float3 Local379 = CustomExpression3(Local378);
float Local380 = math::max(FixedWaterDepth,0.0);
float Local381 = CustomExpression2(WaterBodyIndex,Local380);
float3 Local382 = (Local379 * Local381);
float Local383 = (Local377 * DefaultDisantWaterSpeed);
float Local384 = (Local383 * 0.1);
float Local385 = math::frac(Local384);
float Local386 = (::pixel_depth() - WaterDistantNormalOffset);
float Local387 = (Local386 / WaterDistantNormalLength);
float Local388 = math::saturate(Local387);
float3 Local391 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / DefaultNearWaterScale);
float2 Local392 = (float2(Local385,Local385) + float2(Local391.x,Local391.y));
float4 Local393 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local392.x,1.0-Local392.y),tex::wrap_repeat,tex::wrap_repeat);
float Local394 = (Local383 * -0.1);
float Local395 = math::frac(Local394);
float2 Local396 = (float2(Local391.x,Local391.y) + float2(0.4181,0.3548));
float2 Local397 = (Local396 / 1.618);
float2 Local398 = (float2(Local395,Local395) + Local397);
float4 Local399 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local398.x,1.0-Local398.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local400 = (float3(Local393.x,Local393.y,Local393.z) + float3(Local399.x,Local399.y,Local399.z));
float2 Local401 = (float2(Local391.x,Local391.y) + float2(0.864861,0.148384));
float2 Local402 = (Local401 / 1.236094);
float2 Local403 = (float2(Local395,Local385) + Local402);
float4 Local404 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local403.x,1.0-Local403.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local405 = (Local400 + float3(Local404.x,Local404.y,Local404.z));
float3 Local406 = (Local405 * 0.333333);
float Local407 = (Local406.z - 0.5);
float Local408 = (Local407 * Local388);
float Local409 = (Local408 * 0.0);
float3 Local410 = (Local409 * float3(0.0,0.0,1.0));
float3 Local411 = math::lerp(Local382,Local410,Local388);
float3 Local412 = (Local376 * Local411);
float3 Local413 = (Local412 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local414 = (float2(Local413.x,Local413.y) - float2(SimLocation.x,SimLocation.y));
float2 Local415 = (Local414 / FluidSimSize);
float2 Local416 = (Local415 + 0.5);
float4 Local417 = tex::lookup_float4(NormalAndHeight,float2(Local416.x,1.0-Local416.y),tex::wrap_clamp,tex::wrap_clamp);
float Local418 = (FixedZHeight + Local417.z);
float2 Local419 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local420 = (Local419 / float2(WaterArea.z,WaterArea.w));
float2 Local421 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local422 = (Local421 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local423 = (Local420 * Local422);
float2 Local424 = math::saturate(Local423);
float2 Local425 = (Local424 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local426 = (Local425 - 0.5);
float2 Local427 = math::floor(Local426);
float2 Local428 = (Local427 + 0.5);
float2 Local429 = (Local428 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local430 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local429.x,1.0-Local429.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local431 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local432 = (Local429 + float2(Local431.x,0.0));
float4 Local433 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local432.x,1.0-Local432.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local434 = math::frac(Local426);
float4 Local435 = math::lerp(Local430,Local433,Local434.x);
float Local441 = (TerrainMaxZ - TerrainMinZ);
float Local442 = (Local435.w * Local441);
float Local443 = (Local442 + TerrainMinZ);
float Local444 = (Local418 - Local443);
float Local445 = math::max(Local444,0.0);
float Local446 = math::saturate(Local445);
float Local447 = math::max(Local375,Local446);
float Local448 = math::lerp(-1.0,1.0,Local447);
float Local449 = math::saturate(Local448);
float3 Local450 = (float3(Scattering.x,Scattering.y,Scattering.z) * Scattering.w);
float3 Local451 = (0.0 + Local450);
float3 Local452 = (Local451 / 1000.0);
float Local453 = math::saturate(TwoSidedSign);
float3 Local454 = (Local452 * Local453);
float3 Local455 = (Local449 * Local454);
float3 GetSingleLayerWaterMaterialOutput0_mdl = Local455;
float Local456 = (FixedWaterDepth - WaterOpacityMaskOffset);
float Local457 = math::saturate(Local456);
float3 Local458 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float Local459 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local460 = CustomExpression0(WaterBodyIndex,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local459);
float3 Local461 = CustomExpression3(Local460);
float Local462 = math::max(FixedWaterDepth,0.0);
float Local463 = CustomExpression2(WaterBodyIndex,Local462);
float3 Local464 = (Local461 * Local463);
float Local465 = (Local459 * DefaultDisantWaterSpeed);
float Local466 = (Local465 * 0.1);
float Local467 = math::frac(Local466);
float Local468 = (::pixel_depth() - WaterDistantNormalOffset);
float Local469 = (Local468 / WaterDistantNormalLength);
float Local470 = math::saturate(Local469);
float3 Local473 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / DefaultNearWaterScale);
float2 Local474 = (float2(Local467,Local467) + float2(Local473.x,Local473.y));
float4 Local475 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local474.x,1.0-Local474.y),tex::wrap_repeat,tex::wrap_repeat);
float Local476 = (Local465 * -0.1);
float Local477 = math::frac(Local476);
float2 Local478 = (float2(Local473.x,Local473.y) + float2(0.4181,0.3548));
float2 Local479 = (Local478 / 1.618);
float2 Local480 = (float2(Local477,Local477) + Local479);
float4 Local481 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local480.x,1.0-Local480.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local482 = (float3(Local475.x,Local475.y,Local475.z) + float3(Local481.x,Local481.y,Local481.z));
float2 Local483 = (float2(Local473.x,Local473.y) + float2(0.864861,0.148384));
float2 Local484 = (Local483 / 1.236094);
float2 Local485 = (float2(Local477,Local467) + Local484);
float4 Local486 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local485.x,1.0-Local485.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local487 = (Local482 + float3(Local486.x,Local486.y,Local486.z));
float3 Local488 = (Local487 * 0.333333);
float Local489 = (Local488.z - 0.5);
float Local490 = (Local489 * Local470);
float Local491 = (Local490 * 0.0);
float3 Local492 = (Local491 * float3(0.0,0.0,1.0));
float3 Local493 = math::lerp(Local464,Local492,Local470);
float3 Local494 = (Local458 * Local493);
float3 Local495 = (Local494 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local496 = (float2(Local495.x,Local495.y) - float2(SimLocation.x,SimLocation.y));
float2 Local497 = (Local496 / FluidSimSize);
float2 Local498 = (Local497 + 0.5);
float4 Local499 = tex::lookup_float4(NormalAndHeight,float2(Local498.x,1.0-Local498.y),tex::wrap_clamp,tex::wrap_clamp);
float Local500 = (FixedZHeight + Local499.z);
float2 Local501 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local502 = (Local501 / float2(WaterArea.z,WaterArea.w));
float2 Local503 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local504 = (Local503 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local505 = (Local502 * Local504);
float2 Local506 = math::saturate(Local505);
float2 Local507 = (Local506 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local508 = (Local507 - 0.5);
float2 Local509 = math::floor(Local508);
float2 Local510 = (Local509 + 0.5);
float2 Local511 = (Local510 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local512 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local511.x,1.0-Local511.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local513 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local514 = (Local511 + float2(Local513.x,0.0));
float4 Local515 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local514.x,1.0-Local514.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local516 = math::frac(Local508);
float4 Local517 = math::lerp(Local512,Local515,Local516.x);
float Local523 = (TerrainMaxZ - TerrainMinZ);
float Local524 = (Local517.w * Local523);
float Local525 = (Local524 + TerrainMinZ);
float Local526 = (Local500 - Local525);
float Local527 = math::max(Local526,0.0);
float Local528 = math::saturate(Local527);
float Local529 = math::max(Local457,Local528);
float Local530 = math::lerp(-1.0,1.0,Local529);
float Local531 = math::saturate(Local530);
float3 Local532 = (1.0 / float3(Absorption.x,Absorption.y,Absorption.z));
float3 Local533 = (Local532 / Absorption.w);
float Local534 = math::saturate(TwoSidedSign);
float3 Local535 = (Local533 * Local534);
float3 Local536 = (Local531 * Local535);
float3 GetSingleLayerWaterMaterialOutput1_mdl = Local536;
float GetSingleLayerWaterMaterialOutput2_mdl = Anisotropy;
float GetSingleLayerWaterMaterialOutput3_mdl = 1.0;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: true);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/T_Cow2_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Cow2_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Cow2_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/MI_Old_Wooden_Trough_ugrxdbsfa_2K.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_Old_Wooden_Trough_ugrxdbsfa_2K(
uniform texture_2d Normal = texture_2d("./Textures/Placeholder_Normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(3),
anno::in_group("07 - Texture Maps"),
sampler_normal()
]],
float NormalStrength = 1.0
[[
anno::display_name("Normal Strength"),
anno::ui_order(32),
anno::in_group("05 - Normal")
]],
uniform texture_2d Albedo = texture_2d("./Textures/DefaultDiffuse.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float4 AlbedoControls = float4(1.0,1.0,1.0,0.0)
[[
anno::display_name("Albedo Controls"),
anno::ui_order(32),
anno::in_group("01 - Albedo")
]],
float4 AlbedoTint = float4(1.0,1.0,1.0,1.0)
[[
anno::display_name("Albedo Tint"),
anno::ui_order(1),
anno::in_group("01 - Albedo")
]],
float4 MetallicControls = float4(1.0,0.0,1.0,1.0)
[[
anno::display_name("Metallic Controls"),
anno::ui_order(32),
anno::in_group("02 - Metallic")
]],
uniform texture_2d Metalness = texture_2d("./Textures/BlackPlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("Metalness"),
anno::ui_order(1),
anno::in_group("07 - Texture Maps"),
sampler_color()
]],
float BaseSpecular = 0.5
[[
anno::display_name("Base Specular"),
anno::ui_order(1),
anno::in_group("03 - Specular")
]],
float4 Specular_Desaturation = float4(0.5,0.5,0.5,1.0)
[[
anno::display_name("Specular - Desaturation"),
anno::ui_order(2),
anno::in_group("03 - Specular")
]],
float SpecularFromAlbedoOverride = 0.0
[[
anno::display_name("Specular From Albedo Override"),
anno::ui_order(32),
anno::in_group("03 - Specular")
]],
float MinRoughness = 0.0
[[
anno::display_name("Min Roughness"),
anno::ui_order(32),
anno::in_group("04 - Roughness")
]],
float MaxRoughness = 1.0
[[
anno::display_name("Max Roughness"),
anno::ui_order(1),
anno::in_group("04 - Roughness")
]],
uniform texture_2d DR = texture_2d("./Textures/WhitePlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("DR"),
anno::description("DR"),
anno::ui_order(32),
sampler_color()
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local1 = (float2(float3(Local0.x,Local0.y,Local0.z).x,float3(Local0.x,Local0.y,Local0.z).y) * NormalStrength);
float3 Normal_mdl = float3(Local1.x,Local1.y,float3(Local0.x,Local0.y,Local0.z).z);
float4 Local2 = tex::lookup_float4(Albedo,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local3 = math::dot(float3(Local2.x,Local2.y,Local2.z), float3(0.3,0.59,0.11));
float Local4 = (1.0 - AlbedoControls.x);
float3 Local5 = math::lerp(float3(Local2.x,Local2.y,Local2.z),float3(Local3,Local3,Local3),Local4);
float3 Local6 = (Local5 * AlbedoControls.y);
float3 Local7 = (Local6 * float3(AlbedoTint.x,AlbedoTint.y,AlbedoTint.z));
float3 Local8 = math::pow(math::max(Local7,float3(0.000001)),float3(AlbedoControls.z,AlbedoControls.z,AlbedoControls.z));
float4 Local9 = tex::lookup_float4(Metalness,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local10 = (Local9.x * MetallicControls.z);
float Local11 = math::round(MetallicControls.x);
float Local12 = math::lerp(MetallicControls.y,Local10,Local11);
float Local13 = math::dot(float3(Local2.x,Local2.y,Local2.z), float3(Specular_Desaturation.x,Specular_Desaturation.y,Specular_Desaturation.z));
float Local14 = math::saturate(Local13);
float Local15 = (Local14 * 0.5);
float Local16 = math::lerp(BaseSpecular,Local15,SpecularFromAlbedoOverride);
float4 Local17 = tex::lookup_float4(DR,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local18 = math::lerp(MinRoughness,MaxRoughness,Local17.y);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local8;
float Metallic_mdl = Local12;
float Specular_mdl = Local16;
float Roughness_mdl = Local18;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/OmniUe4Translucent.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.0 - first version
//* 1.0.1 - Emissive color affected by opacity
// - Support opacity mask
//* 1.0.2 - Unlit translucent
//* 1.0.3 - specular bsdf instead of microfacet ggx smith bsdf
//* 1.0.4 - using absolute import paths when importing standard modules
mdl 1.3;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
float emissive_multiplier()
[[
anno::description("the multiplier to convert UE4 emissive to raw data"),
anno::noinline()
]]
{
return 20.0f * 128.0f;
}
color get_translucent_tint(color base_color, float opacity)
[[
anno::description("base color of UE4 translucent"),
anno::noinline()
]]
{
return math::lerp(color(1.0), base_color, opacity);
}
// Just for UE4 distilling
float get_translucent_opacity(float opacity)
[[
anno::noinline()
]]
{
return opacity;
}
color get_emissive_intensity(color emissive, float opacity)
[[
anno::description("emissive color of UE4 translucent"),
anno::noinline()
]]
{
return emissive * opacity;
}
float3 tangent_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in tangent space"),
anno::noinline()
]]
{
return math::normalize(
tangent_u * normal.x -
tangent_v * normal.y + /* flip_tangent_v */
state::normal() * (normal.z));
}
float3 world_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in world space"),
anno::noinline()
]]
{
return tangent_space_normal(
math::normalize(
normal.x * float3(tangent_u.x, tangent_v.x, state::normal().x) -
normal.y * float3(tangent_u.y, tangent_v.y, state::normal().y) +
normal.z * float3(tangent_u.z, tangent_v.z, state::normal().z)),
tangent_u,
tangent_v
);
}
export material OmniUe4Translucent(
float3 base_color = float3(0.0, 0.0, 0.0),
float metallic = 0.0,
float roughness = 0.5,
float specular = 0.5,
float3 normal = float3(0.0,0.0,1.0),
uniform bool enable_opacity = true,
float opacity = 1.0,
float opacity_mask = 1.0,
float3 emissive_color = float3(0.0, 0.0, 0.0),
float3 displacement = float3(0.0),
uniform float refraction = 1.0,
uniform bool two_sided = false,
uniform bool is_tangent_space_normal = true,
uniform bool is_unlit = false
)
[[
anno::display_name("Omni UE4 Translucent"),
anno::description("Omni UE4 Translucent, supports UE4 Translucent shading model"),
anno::version( 1, 0, 0),
anno::author("NVIDIA CORPORATION"),
anno::key_words(string[]("omni", "UE4", "omniverse", "translucent"))
]]
= let {
color final_base_color = math::saturate(base_color);
float final_metallic = math::min(math::max(metallic, 0.0f), 0.99f);
float final_roughness = math::saturate(roughness);
float final_specular = math::saturate(specular);
color final_emissive_color = math::max(emissive_color, 0.0f) * emissive_multiplier(); /*factor for converting ue4 emissive to raw value*/
float final_opacity = math::saturate(opacity);
float3 final_normal = math::normalize(normal);
// - compute final roughness by squaring the "roughness" parameter
float alpha = final_roughness * final_roughness;
// reduce the reflectivity at grazing angles to avoid "dark edges" for high roughness due to the layering
float grazing_refl = math::max((1.0 - final_roughness), 0.0);
float3 the_normal = is_unlit ? state::normal() :
(is_tangent_space_normal ?
tangent_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
));
// for the dielectric component we layer the glossy component on top of the diffuse one,
// the glossy layer has no color tint
bsdf dielectric_component = df::custom_curve_layer(
weight: final_specular,
normal_reflectivity: 0.08,
grazing_reflectivity: grazing_refl,
layer: df::microfacet_ggx_smith_bsdf(roughness_u: alpha),
base: df::diffuse_reflection_bsdf(tint: final_base_color));
// the metallic component doesn't have a diffuse component, it's only glossy
// base_color is applied to tint it
bsdf metallic_component = df::microfacet_ggx_smith_bsdf(tint: final_base_color, roughness_u: alpha);
// final BSDF is a linear blend between dielectric and metallic component
bsdf dielectric_metal_mix =
df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: metallic_component,
weight: final_metallic),
df::bsdf_component(
component: dielectric_component,
weight: 1.0-final_metallic)
)
);
bsdf frosted_bsdf = df::specular_bsdf(
tint: color(1),
mode: df::scatter_reflect_transmit
);
bsdf final_mix_bsdf =
is_unlit ? df::specular_bsdf(
tint: get_translucent_tint(base_color: final_base_color, opacity: final_opacity),
mode: df::scatter_reflect_transmit
)
: df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: dielectric_metal_mix,
weight: get_translucent_opacity(final_opacity)),
df::bsdf_component(
component: frosted_bsdf,
weight: 1.0-get_translucent_opacity(final_opacity))
)
);
}
in material(
thin_walled: two_sided, // Graphene?
ior: color(refraction), //refraction
surface: material_surface(
scattering: final_mix_bsdf,
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: get_emissive_intensity(emissive: final_emissive_color, opacity: final_opacity)
)
),
geometry: material_geometry(
displacement: displacement,
normal: the_normal,
cutout_opacity: enable_opacity ? opacity_mask : 1.0
)
);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/Water_Material.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
float CustomExpression0()
{
return GetWaterWaveParamIndex(Parameters);
}
float3 CustomExpression1(float WaterBodyIndex,float2 WorldPosition,float Time)
{
WaveOutput Waves;
Waves = GetAllGerstnerWavesNew(WaterBodyIndex, Time, WorldPosition);
return PackNormalAndWPONew(Waves);
}
float3 CustomExpression2(float3 inPackedWave)
{
float3 outnormal = math::frac(inPackedWave);
outnormal *= 2.02;
outnormal -= 1.01;
return outnormal;
}
float CustomExpression3(float WaterBodyIndex,float WaterDepth)
{
return ComputeWaveDepthAttenuationFactorNew(WaterBodyIndex, WaterDepth);
}
float3 CustomExpression4(float3 inPackedWave)
{
float3 outWPO;
outWPO = math::floor(inPackedWave) / 100;
return outWPO;
}
float CustomExpression5(float x)
{
return 1.0-math::exp(-x);;
}
float CustomExpression6(float X)
{
return math::log2(X);
}
float CustomExpression7(float x)
{
return 1-math::exp(-x);
}
float3 CustomExpression8(float3 Color,float x)
{
clip(x);
return Color;
}
export material Water_Material(
float Time = 0.0
[[
anno::display_name("Time")
]],
float ManualTime = 0.0
[[
anno::display_name("ManualTime")
]],
float FreezeTime = 0.0
[[
anno::display_name("FreezeTime")
]],
uniform texture_2d WaterVelocityAndHeight = texture_2d("./Textures/Black_1x1_EXR_Texture.exr",::tex::gamma_linear)
[[
anno::display_name("WaterVelocityAndHeight"),
anno::ui_order(32),
sampler_color()
]],
float4 WaterArea = float4(0.0,0.0,0.0,0.0)
[[
anno::display_name("WaterArea"),
anno::ui_order(32)
]],
float TerrainMaxZ = 10000.0
[[
anno::display_name("TerrainMaxZ")
]],
float TerrainMinZ = -10000.0
[[
anno::display_name("TerrainMinZ")
]],
float DefaultDisantWaterSpeed = 0.75
[[
anno::display_name("Default Disant Water Speed"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultNearWaterScale = 512.0
[[
anno::display_name("Default Near Water Scale"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultDistantWaterScale = 3500.0
[[
anno::display_name("Default Distant Water Scale"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float WaterDistantNormalOffset = 5000.0
[[
anno::display_name("Water Distant Normal Offset"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float WaterDistantNormalLength = 10000.0
[[
anno::display_name("Water Distant Normal Length"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultNearNormalStrength = 0.25
[[
anno::display_name("Default Near Normal Strength"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultDistantNormalStrengthB = 0.25
[[
anno::display_name("Default Distant Normal StrengthB"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float DefaultDistantNormalStrength = 0.3
[[
anno::display_name("Default Distant Normal Strength"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float FarNormalFresnelPower = 9.0
[[
anno::display_name("Far Normal Fresnel Power"),
anno::ui_order(32),
anno::in_group("Detail Normal Settings")
]],
float WaveoverRockSteepness = 0.0
[[
anno::display_name("Wave over Rock Steepness"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 SimLocation = float4(0.0,0.0,0.0,0.0)
[[
anno::display_name("SimLocation")
]],
float FluidSimSize = 2048.0
[[
anno::display_name("FluidSimSize")
]],
uniform texture_2d NormalAndHeight = texture_2d("./Textures/Black_1x1_EXR_Texture.exr",::tex::gamma_linear)
[[
anno::display_name("NormalAndHeight"),
anno::ui_order(32),
sampler_color()
]],
float FluidsimNormalStrength = 1.0
[[
anno::display_name("Fluidsim Normal Strength"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
uniform texture_2d Velocity = texture_2d("./Textures/Black_1x1_EXR_Texture.exr",::tex::gamma_linear)
[[
anno::display_name("Velocity"),
anno::ui_order(32),
sampler_color()
]],
float FluidsimVelocityEdgeFalloff = 0.2
[[
anno::display_name("Fluidsim Velocity EdgeFalloff"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FluidsimVelocityEdgeoffset = 0.02
[[
anno::display_name("Fluidsim Velocity Edgeoffset"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimVelocityNormalStrength = 40.0
[[
anno::display_name("Sim Velocity Normal Strength"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFoamNormal = 1.0
[[
anno::display_name("Sim Foam Normal"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FoamAttachment = 0.4
[[
anno::display_name("Foam Attachment"),
anno::ui_order(32),
anno::in_group("General Foam")
]],
float SimFoamScale = 350.0
[[
anno::display_name("Sim Foam Scale"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SImFlowmapSpeed = 1.5
[[
anno::display_name("SIm Flowmap Speed"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float WPOFlowmap = 6.0
[[
anno::display_name("WPO Flowmap"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FluidsimFlowDensity = 0.25
[[
anno::display_name("Fluidsim Flow Density"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimulationFlowmapSpeed = 0.75
[[
anno::display_name("Simulation Flowmap Speed"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFoamContrast = 0.0
[[
anno::display_name("SimFoam Contrast"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFlowmapDetection = 1.5
[[
anno::display_name("Sim Flowmap Detection"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float SimFoamDpethMin = 8.0
[[
anno::display_name("Sim Foam Dpeth Min"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 FoamEmissive = float4(0.28744,1.0,0.1875,1.0)
[[
anno::display_name("Foam Emissive"),
anno::ui_order(32),
anno::in_group("General Foam")
]],
float VelocityDebug = 0.0
[[
anno::display_name("Velocity Debug"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 WaterAlbedo = float4(0.85,0.85,0.85,0.5)
[[
anno::display_name("Water Albedo"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float CausticsMaxIntensity = 1.0
[[
anno::display_name("CausticsMaxIntensity")
]],
float WaterOpacityMaskOffset = -24.0
[[
anno::display_name("Water Opacity Mask Offset"),
anno::ui_order(32),
anno::in_group("Opacity Mask")
]],
float WaterSpecular = 0.225
[[
anno::display_name("Water Specular"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float CriticalAngleDot = 0.225
[[
anno::display_name("Critical Angle Dot"),
anno::ui_order(32),
anno::in_group("TwoSided Settings")
]],
float CriticalAngleWidth = 0.01
[[
anno::display_name("Critical Angle Width"),
anno::ui_order(32),
anno::in_group("TwoSided Settings")
]],
float TwoSidedSign = 1.0
[[
anno::hidden()
]],
float WaterRoughness = 0.02
[[
anno::display_name("Water Roughness"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float WaterFresnelRoughness = 0.1
[[
anno::display_name("Water Fresnel Roughness"),
anno::ui_order(32),
anno::in_group("Water Shading")
]],
float FoamRoughness = 0.4
[[
anno::display_name("Foam Roughness"),
anno::ui_order(32),
anno::in_group("General Foam")
]],
float MaxFluidDisplacement = 1024.0
[[
anno::display_name("Max Fluid Displacement"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float FluidDisplacementMultiplier = 1.0
[[
anno::display_name("Fluid Displacement Multiplier"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float ShowSim = 1.0
[[
anno::display_name("ShowSim"),
anno::ui_order(32),
anno::in_group("FluidSimulation")
]],
float4 Scattering = float4(1.0,1.0,1.0,0.5)
[[
anno::display_name("Scattering"),
anno::ui_order(32)
]],
float4 Absorption = float4(10.0,150.0,350.0,8.0)
[[
anno::display_name("Absorption"),
anno::ui_order(32)
]],
float Anisotropy = 0.1
[[
anno::display_name("Anisotropy"),
anno::ui_order(32),
anno::in_group("Water Shading")
]])
[[
dither_masked_off()
]]
=
let {
float Local223 = CustomExpression0();
float Local224 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local225 = CustomExpression1(Local223,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local224);
float3 Local226 = CustomExpression4(Local225);
float2 Local227 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local228 = (Local227 / float2(WaterArea.z,WaterArea.w));
float2 Local229 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local230 = (Local229 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local231 = (Local228 * Local230);
float2 Local232 = math::saturate(Local231);
float2 Local233 = (Local232 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local234 = (Local233 - 0.5);
float2 Local235 = math::floor(Local234);
float2 Local236 = (Local235 + 0.5);
float2 Local237 = (Local236 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local238 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local237.x,1.0-Local237.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local239 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local240 = (Local237 + float2(Local239.x,0.0));
float4 Local241 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local240.x,1.0-Local240.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local242 = math::frac(Local234);
float4 Local243 = math::lerp(Local238,Local241,Local242.x);
float2 Local244 = (Local237 + float2(0.0,Local239.y));
float4 Local245 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local244.x,1.0-Local244.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local246 = (Local237 + Local239);
float4 Local247 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local246.x,1.0-Local246.y),tex::wrap_clamp,tex::wrap_clamp);
float4 Local248 = math::lerp(Local245,Local247,Local242.x);
float4 Local249 = math::lerp(Local243,Local248,Local242.y);
float Local250 = (TerrainMaxZ - TerrainMinZ);
float Local251 = (Local249.w * Local250);
float Local252 = (Local251 + TerrainMinZ);
float Local253 = (Local249.z - Local252);
float Local254 = math::max(Local253,0.0);
float Local255 = CustomExpression3(Local223,Local254);
float3 Local256 = (Local226 * Local255);
float Local257 = (Local224 * DefaultDisantWaterSpeed);
float Local258 = (Local257 * 0.1);
float Local259 = math::frac(Local258);
float Local260 = (::pixel_depth() - WaterDistantNormalOffset);
float Local261 = (Local260 / WaterDistantNormalLength);
float Local262 = math::saturate(Local261);
float Local263 = math::ceil(Local262);
float Local264 = math::saturate(Local263);
float Local265 = math::lerp(DefaultNearWaterScale,DefaultDistantWaterScale,Local264);
float3 Local266 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / Local265);
float2 Local267 = (float2(Local259,Local259) + float2(Local266.x,Local266.y));
float4 Local268 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local267.x,1.0-Local267.y),tex::wrap_repeat,tex::wrap_repeat);
float Local269 = (Local257 * -0.1);
float Local270 = math::frac(Local269);
float2 Local271 = (float2(Local266.x,Local266.y) + float2(0.4181,0.3548));
float2 Local272 = (Local271 / 1.618);
float2 Local273 = (float2(Local270,Local270) + Local272);
float4 Local274 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local273.x,1.0-Local273.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local275 = (float3(Local268.x,Local268.y,Local268.z) + float3(Local274.x,Local274.y,Local274.z));
float2 Local276 = (float2(Local266.x,Local266.y) + float2(0.864861,0.148384));
float2 Local277 = (Local276 / 1.236094);
float2 Local278 = (float2(Local270,Local259) + Local277);
float4 Local279 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local278.x,1.0-Local278.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local280 = (Local275 + float3(Local279.x,Local279.y,Local279.z));
float3 Local281 = (Local280 * 0.333333);
float Local282 = (Local281.z - 0.5);
float Local283 = (Local282 * Local262);
float Local284 = (Local283 * 0.0);
float3 Local285 = (Local284 * float3(0.0,0.0,1.0));
float3 Local286 = math::lerp(Local256,Local285,Local262);
float3 Local287 = (Local286 * 1.0);
float Local288 = (MaxFluidDisplacement * -1.0);
float3 Local289 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float3 Local290 = (Local289 * Local286);
float3 Local291 = (Local290 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local292 = (float2(Local291.x,Local291.y) - float2(SimLocation.x,SimLocation.y));
float2 Local293 = (Local292 / FluidSimSize);
float2 Local294 = (Local293 + 0.5);
float4 Local295 = tex::lookup_float4(NormalAndHeight,float2(Local294.x,1.0-Local294.y),tex::wrap_clamp,tex::wrap_clamp);
float Local296 = (FluidDisplacementMultiplier * ShowSim);
float Local297 = (Local295.z * Local296);
float Local298 = math::min(math::max(Local297,Local288),MaxFluidDisplacement);
float3 Local299 = (Local298 * float3(0.0,0.0,1.0));
float3 Local300 = (Local287 + Local299);
float Local317 = (Local243.z - (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).z);
float3 Local318 = (Local317 * float3(0.0,0.0,1.0));
float3 Local319 = (Local300 + Local318);
float3 WorldPositionOffset_mdl = Local319;
float Local0 = CustomExpression0();
float Local1 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local2 = CustomExpression1(Local0,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local1);
float3 Local3 = CustomExpression2(Local2);
float2 Local4 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local5 = (Local4 / float2(WaterArea.z,WaterArea.w));
float2 Local6 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local7 = (Local6 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local8 = (Local5 * Local7);
float2 Local9 = math::saturate(Local8);
float2 Local10 = (Local9 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local11 = (Local10 - 0.5);
float2 Local12 = math::floor(Local11);
float2 Local13 = (Local12 + 0.5);
float2 Local14 = (Local13 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local15 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local14.x,1.0-Local14.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local16 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local17 = (Local14 + float2(Local16.x,0.0));
float4 Local18 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local17.x,1.0-Local17.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local19 = math::frac(Local11);
float4 Local20 = math::lerp(Local15,Local18,Local19.x);
float2 Local21 = (Local14 + float2(0.0,Local16.y));
float4 Local22 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local21.x,1.0-Local21.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local23 = (Local14 + Local16);
float4 Local24 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local23.x,1.0-Local23.y),tex::wrap_clamp,tex::wrap_clamp);
float4 Local25 = math::lerp(Local22,Local24,Local19.x);
float4 Local26 = math::lerp(Local20,Local25,Local19.y);
float Local27 = (TerrainMaxZ - TerrainMinZ);
float Local28 = (Local26.w * Local27);
float Local29 = (Local28 + TerrainMinZ);
float Local30 = (Local26.z - Local29);
float Local31 = math::max(Local30,0.0);
float Local32 = CustomExpression3(Local0,Local31);
float3 Local33 = math::lerp(float3(0.0,0.0,1.0),Local3,Local32);
float Local34 = (Local1 * DefaultDisantWaterSpeed);
float Local35 = (Local34 * 0.1);
float Local36 = math::frac(Local35);
float Local37 = (::pixel_depth() - WaterDistantNormalOffset);
float Local38 = (Local37 / WaterDistantNormalLength);
float Local39 = math::saturate(Local38);
float Local40 = math::ceil(Local39);
float Local41 = math::saturate(Local40);
float Local42 = math::lerp(DefaultNearWaterScale,DefaultDistantWaterScale,Local41);
float3 Local43 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / Local42);
float2 Local44 = (float2(Local36,Local36) + float2(Local43.x,Local43.y));
float4 Local45 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local44.x,1.0-Local44.y),tex::wrap_repeat,tex::wrap_repeat);
float Local46 = (Local34 * -0.1);
float Local47 = math::frac(Local46);
float2 Local48 = (float2(Local43.x,Local43.y) + float2(0.4181,0.3548));
float2 Local49 = (Local48 / 1.618);
float2 Local50 = (float2(Local47,Local47) + Local49);
float4 Local51 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local50.x,1.0-Local50.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local52 = (float3(Local45.x,Local45.y,Local45.z) + float3(Local51.x,Local51.y,Local51.z));
float2 Local53 = (float2(Local43.x,Local43.y) + float2(0.864861,0.148384));
float2 Local54 = (Local53 / 1.236094);
float2 Local55 = (float2(Local47,Local36) + Local54);
float4 Local56 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local55.x,1.0-Local55.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local57 = (Local52 + float3(Local56.x,Local56.y,Local56.z));
float3 Local58 = (Local57 * 0.333333);
float2 Local59 = (float2(Local58.x,Local58.y) - 0.5);
float Local60 = (::pixel_depth() / WaterDistantNormalOffset);
float Local61 = math::saturate(Local60);
float Local62 = math::lerp(DefaultNearNormalStrength,0.0,Local61);
float2 Local63 = (Local59 * Local62);
float3 Local64 = (Local33 + float3(Local63.x,Local63.y,0.0));
float Local65 = ::fresnel(FarNormalFresnelPower, 0.04, float3(0.0,0.0,1.0));
float Local66 = math::lerp(DefaultDistantNormalStrengthB,DefaultDistantNormalStrength,Local65);
float Local67 = (1.0 / Local66);
float3 Local68 = math::normalize(float3(Local59.x,Local59.y,Local67));
float3 Local69 = math::lerp(Local64,Local68,Local39);
float3 Local70 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float3 Local71 = CustomExpression4(Local2);
float3 Local72 = (Local71 * Local32);
float Local73 = (Local58.z - 0.5);
float Local74 = (Local73 * Local39);
float Local75 = (Local74 * 0.0);
float3 Local76 = (Local75 * float3(0.0,0.0,1.0));
float3 Local77 = math::lerp(Local72,Local76,Local39);
float3 Local78 = (Local70 * Local77);
float3 Local79 = (Local78 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local80 = (float2(Local79.x,Local79.y) - float2(SimLocation.x,SimLocation.y));
float2 Local81 = (Local80 / FluidSimSize);
float2 Local82 = (Local81 + 0.5);
float4 Local83 = tex::lookup_float4(NormalAndHeight,float2(Local82.x,1.0-Local82.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local84 = (float2(float3(Local83.x,Local83.y,Local83.z).x,float3(Local83.x,Local83.y,Local83.z).y) * FluidsimNormalStrength);
float3 Local85 = (Local69 + float3(Local84.x,Local84.y,0.0));
float2 Local86 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(SimLocation.x,SimLocation.y));
float2 Local87 = (Local86 / FluidSimSize);
float2 Local88 = (Local87 + 0.5);
float4 Local89 = tex::lookup_float4(Velocity,float2(Local88.x,1.0-Local88.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local90 = (Local88 - 0.5);
float2 Local91 = math::abs(Local90);
float Local92 = (FluidsimVelocityEdgeFalloff * 2.0);
float Local93 = (1.0 - Local92);
float Local94 = (Local93 * 0.5);
float2 Local95 = (Local91 - Local94);
float2 Local96 = math::max(Local95,float2(0.0,0.0));
float2 Local97 = (Local96 - 0.0);
float Local98 = math::length(Local97);
float Local99 = (Local98 / FluidsimVelocityEdgeFalloff);
float Local100 = math::min(math::max(Local99,0.0),1.0);
float Local101 = (1.0 - Local100);
float Local102 = (Local101 - FluidsimVelocityEdgeoffset);
float Local103 = (1.0 - FluidsimVelocityEdgeoffset);
float Local104 = (Local102 / Local103);
float Local105 = math::min(math::max(Local104,0.0),1.0);
float3 Local106 = (float3(Local89.x,Local89.y,Local89.z) * Local105);
float2 Local107 = (float2(Local106.x,Local106.y) - 0.0);
float Local108 = math::length(Local107);
float Local109 = (Local108 / SimVelocityNormalStrength);
float Local110 = (Local109 + SimFoamNormal);
float3 Local111 = math::lerp((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0),(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0),FoamAttachment);
float3 Local112 = (Local111 / SimFoamScale);
float Local113 = (Local1 * SImFlowmapSpeed);
float Local114 = (Local113 - 0.5);
float Local115 = math::frac(Local114);
float3 Local116 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) - float3(SimLocation.x,SimLocation.y,SimLocation.z));
float3 Local117 = (Local116 + Local77);
float3 Local118 = (Local117 / FluidSimSize);
float3 Local119 = (Local118 + 0.5);
float2 Local120 = math::saturate(float2(Local119.x,Local119.y));
float4 Local121 = tex::lookup_float4(texture_2d("./Textures/WaterDistanceField.png",::tex::gamma_linear),float2(Local120.x,1.0-Local120.y),tex::wrap_clamp,tex::wrap_clamp);
float Local122 = (Local121.x * 0.0);
float Local123 = math::saturate(Local122);
float2 Local124 = (Local123 * float2(Local77.x,Local77.y));
float2 Local125 = (Local124 * WPOFlowmap);
float2 Local126 = (Local125 / 1000.0);
float Local127 = math::dot(Local106, Local106);
float3 Local128 = math::normalize(Local106);
float4 Local129 = ((math::abs(Local127 - 0.000001) > 0.00001) ? (Local127 >= 0.000001 ? float4(Local128.x,Local128.y,Local128.z,0.0) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0)) : float4(float3(0.0,0.0,0.0).x,float3(0.0,0.0,0.0).y,float3(0.0,0.0,0.0).z,1.0));
float Local130 = (Local108 / 100.0);
float Local131 = (Local130 * FluidsimFlowDensity);
float Local132 = CustomExpression5(Local131);
float3 Local133 = (float3(Local129.x,Local129.y,Local129.z) * Local132);
float2 Local134 = (float2(Local133.x,Local133.y) * SimulationFlowmapSpeed);
float2 Local135 = (Local126 + Local134);
float2 Local136 = (Local135 * float2(1.0,-1.0));
float2 Local137 = (Local136 * float2(-1.0,1.0));
float2 Local138 = (Local115 * Local137);
float2 Local139 = (float2(Local112.x,Local112.y) + Local138);
float2 Local140 = math::frac(Local139);
float2 Local141 = (Local140 + float2(0.5,0.5));
float4 Local149 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled_N.png",::tex::gamma_linear),float2(Local141.x,1.0-Local141.y),tex::wrap_repeat,tex::wrap_repeat));
float Local150 = math::frac(Local113);
float2 Local151 = (Local137 * Local150);
float2 Local152 = (Local151 + float2(Local112.x,Local112.y));
float4 Local153 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled_N.png",::tex::gamma_linear),float2(Local152.x,1.0-Local152.y),tex::wrap_repeat,tex::wrap_repeat));
float Local154 = (Local150 / 1.0);
float Local155 = math::frac(Local154);
float Local156 = (Local155 * 2.0);
float Local157 = (1.0 - Local155);
float Local158 = (2.0 * Local157);
float Local159 = math::floor(Local156);
float Local160 = math::lerp(Local156,Local158,Local159);
float3 Local161 = math::lerp(float3(Local149.x,Local149.y,Local149.z),float3(Local153.x,Local153.y,Local153.z),Local160);
float3 Local162 = (float3(float2(Local110,Local110).x,float2(Local110,Local110).y,0.0) * Local161);
float Local163 = (0.0 - SimFoamContrast);
float Local164 = (SimFoamContrast + 1.0);
float4 Local165 = tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled.png",::tex::gamma_srgb),float2(Local141.x,1.0-Local141.y),tex::wrap_repeat,tex::wrap_repeat);
float4 Local166 = tex::lookup_float4(texture_2d("./Textures/T_WaterFlow_01_Foam_Tiled.png",::tex::gamma_srgb),float2(Local152.x,1.0-Local152.y),tex::wrap_repeat,tex::wrap_repeat);
float4 Local167 = math::lerp(float4(float3(Local165.x,Local165.y,Local165.z).x,float3(Local165.x,Local165.y,Local165.z).y,float3(Local165.x,Local165.y,Local165.z).z,Local165.w),float4(float3(Local166.x,Local166.y,Local166.z).x,float3(Local166.x,Local166.y,Local166.z).y,float3(Local166.x,Local166.y,Local166.z).z,Local166.w),Local160);
float Local168 = (float3(Local167.x,Local167.y,Local167.z).x - 1.0);
float Local169 = (Local83.w / SimFlowmapDetection);
float Local170 = CustomExpression7(Local169);
float Local171 = math::saturate(Local170);
float Local172 = (Local171 * 2.0);
float Local173 = (Local168 + Local172);
float Local174 = math::min(math::max(Local173,0.0),1.0);
float Local175 = math::lerp(Local163,Local164,Local174);
float Local176 = math::min(math::max(Local175,0.0),1.0);
float Local177 = MaterialExpressionSceneDepthWithoutWater(ScreenAlignedPosition(GetScreenPosition(Parameters)), 1000000.0);
float Local178 = (Local177 - ::pixel_depth());
float Local179 = (Local178 / SimFoamDpethMin);
float Local180 = math::saturate(Local179);
float Local181 = (Local176 * Local180);
float Local182 = math::saturate(Local181);
float Local183 = (Local182 * 0.0);
float3 Local184 = (Local162 * Local183);
float3 Local185 = (Local85 + Local184);
float3 Normal_mdl = Local185;
float3 Local186 = (float3(FoamEmissive.x,FoamEmissive.y,FoamEmissive.z) * FoamEmissive.w);
float3 Local187 = float3(0);
float3 Local188 = (Local186 * Local187);
float3 Local189 = (Local188 * Local183);
float3 Local190 = (0.0 + Local189);
float3 Local191 = math::abs(Local106);
float3 Local192 = (Local191 * VelocityDebug);
float3 Local193 = (float3(0.0,0.0,0.0) + Local192);
float3 Local194 = (Local190 + Local193);
float3 Local195 = (float3(WaterAlbedo.x,WaterAlbedo.y,WaterAlbedo.z) / CausticsMaxIntensity);
float Local196 = (Local30 - WaterOpacityMaskOffset);
float Local197 = math::saturate(Local196);
float Local198 = (Local26.z + Local83.z);
float Local199 = (Local198 - Local29);
float Local200 = math::max(Local199,0.0);
float Local201 = math::saturate(Local200);
float Local202 = math::max(Local197,Local201);
float Local203 = math::lerp(-1.0,1.0,Local202);
float3 Local204 = CustomExpression8(Local195,Local203);
float Local205 = math::dot(::pixel_normal_world_space(true), ::camera_vector(true));
float Local206 = (CriticalAngleDot - Local205);
float Local207 = (Local206 / CriticalAngleWidth);
float Local208 = math::saturate(Local207);
float Local209 = math::saturate(TwoSidedSign);
float Local210 = math::lerp(Local208,0.0,Local209);
float Local211 = math::lerp(WaterSpecular,1.0,Local210);
float Local212 = math::saturate(Local203);
float Local213 = (Local211 * Local212);
float Local214 = ::fresnel(5.0, 0.0, ::pixel_normal_world_space(true));
float Local215 = math::lerp(WaterRoughness,WaterFresnelRoughness,Local214);
float Local216 = (FoamRoughness * Local183);
float Local217 = (Local215 + Local216);
float3 EmissiveColor_mdl = Local194;
float OpacityMask_mdl = (1.0 - 0.3333) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = Local204;
float Metallic_mdl = 0.0;
float Specular_mdl = Local213;
float Roughness_mdl = Local217;
float2 Local410 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local411 = (Local410 / float2(WaterArea.z,WaterArea.w));
float2 Local412 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local413 = (Local412 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local414 = (Local411 * Local413);
float2 Local415 = math::saturate(Local414);
float2 Local416 = (Local415 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local417 = (Local416 - 0.5);
float2 Local418 = math::floor(Local417);
float2 Local419 = (Local418 + 0.5);
float2 Local420 = (Local419 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local421 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local420.x,1.0-Local420.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local422 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local423 = (Local420 + float2(Local422.x,0.0));
float4 Local424 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local423.x,1.0-Local423.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local425 = math::frac(Local417);
float2 Local426 = (Local420 + float2(0.0,Local422.y));
float4 Local427 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local426.x,1.0-Local426.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local428 = (Local420 + Local422);
float4 Local429 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local428.x,1.0-Local428.y),tex::wrap_clamp,tex::wrap_clamp);
float4 Local430 = math::lerp(Local424,Local429,Local425.y);
float Local431 = (TerrainMaxZ - TerrainMinZ);
float Local432 = (Local430.w * Local431);
float Local433 = (Local432 + TerrainMinZ);
float Local434 = (Local430.z - Local433);
float Local435 = (Local434 - WaterOpacityMaskOffset);
float Local436 = math::saturate(Local435);
float4 Local437 = math::lerp(Local421,Local424,Local425.x);
float4 Local438 = math::lerp(Local427,Local429,Local425.x);
float4 Local439 = math::lerp(Local437,Local438,Local425.y);
float3 Local440 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float Local441 = CustomExpression0();
float Local442 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local443 = CustomExpression1(Local441,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local442);
float3 Local444 = CustomExpression4(Local443);
float4 Local445 = math::lerp(Local421,Local427,Local425.y);
float Local446 = (Local445.w * Local431);
float Local447 = (Local446 + TerrainMinZ);
float Local448 = (Local445.z - Local447);
float Local449 = math::max(Local448,0.0);
float Local450 = CustomExpression3(Local441,Local449);
float3 Local451 = (Local444 * Local450);
float Local452 = (Local442 * DefaultDisantWaterSpeed);
float Local453 = (Local452 * 0.1);
float Local454 = math::frac(Local453);
float Local455 = (::pixel_depth() - WaterDistantNormalOffset);
float Local456 = (Local455 / WaterDistantNormalLength);
float Local457 = math::saturate(Local456);
float Local458 = math::ceil(Local457);
float Local459 = math::saturate(Local458);
float Local460 = math::lerp(DefaultNearWaterScale,DefaultDistantWaterScale,Local459);
float3 Local461 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / Local460);
float2 Local462 = (float2(Local454,Local454) + float2(Local461.x,Local461.y));
float4 Local463 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local462.x,1.0-Local462.y),tex::wrap_repeat,tex::wrap_repeat);
float Local464 = (Local452 * -0.1);
float Local465 = math::frac(Local464);
float2 Local466 = (float2(Local461.x,Local461.y) + float2(0.4181,0.3548));
float2 Local467 = (Local466 / 1.618);
float2 Local468 = (float2(Local465,Local465) + Local467);
float4 Local469 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local468.x,1.0-Local468.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local470 = (float3(Local463.x,Local463.y,Local463.z) + float3(Local469.x,Local469.y,Local469.z));
float2 Local471 = (float2(Local461.x,Local461.y) + float2(0.864861,0.148384));
float2 Local472 = (Local471 / 1.236094);
float2 Local473 = (float2(Local465,Local454) + Local472);
float4 Local474 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local473.x,1.0-Local473.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local475 = (Local470 + float3(Local474.x,Local474.y,Local474.z));
float3 Local476 = (Local475 * 0.333333);
float Local477 = (Local476.z - 0.5);
float Local478 = (Local477 * Local457);
float Local479 = (Local478 * 0.0);
float3 Local480 = (Local479 * float3(0.0,0.0,1.0));
float3 Local481 = math::lerp(Local451,Local480,Local457);
float3 Local482 = (Local440 * Local481);
float3 Local483 = (Local482 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local484 = (float2(Local483.x,Local483.y) - float2(SimLocation.x,SimLocation.y));
float2 Local485 = (Local484 / FluidSimSize);
float2 Local486 = (Local485 + 0.5);
float4 Local487 = tex::lookup_float4(NormalAndHeight,float2(Local486.x,1.0-Local486.y),tex::wrap_clamp,tex::wrap_clamp);
float Local488 = (Local439.z + Local487.z);
float Local489 = (Local439.w * Local431);
float Local490 = (Local489 + TerrainMinZ);
float Local491 = (Local488 - Local490);
float Local492 = math::max(Local491,0.0);
float Local493 = math::saturate(Local492);
float Local494 = math::max(Local436,Local493);
float Local495 = math::lerp(-1.0,1.0,Local494);
float Local496 = math::saturate(Local495);
float3 Local497 = (float3(Scattering.x,Scattering.y,Scattering.z) * Scattering.w);
float3 Local498 = (0.0 + Local497);
float3 Local499 = (Local498 / 1000.0);
float Local500 = math::saturate(TwoSidedSign);
float3 Local501 = (Local499 * Local500);
float3 Local502 = (Local496 * Local501);
float3 GetSingleLayerWaterMaterialOutput0_mdl = Local502;
float2 Local503 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) - float2(float3(WaterArea.x,WaterArea.y,WaterArea.z).x,float3(WaterArea.x,WaterArea.y,WaterArea.z).y));
float2 Local504 = (Local503 / float2(WaterArea.z,WaterArea.w));
float2 Local505 = (float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)) - 1.0);
float2 Local506 = (Local505 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local507 = (Local504 * Local506);
float2 Local508 = math::saturate(Local507);
float2 Local509 = (Local508 * float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local510 = (Local509 - 0.5);
float2 Local511 = math::floor(Local510);
float2 Local512 = (Local511 + 0.5);
float2 Local513 = (Local512 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float4 Local514 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local513.x,1.0-Local513.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local515 = (1.0 / float2(tex::width(WaterVelocityAndHeight),tex::height(WaterVelocityAndHeight)));
float2 Local516 = (Local513 + float2(Local515.x,0.0));
float4 Local517 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local516.x,1.0-Local516.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local518 = math::frac(Local510);
float2 Local519 = (Local513 + float2(0.0,Local515.y));
float4 Local520 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local519.x,1.0-Local519.y),tex::wrap_clamp,tex::wrap_clamp);
float2 Local521 = (Local513 + Local515);
float4 Local522 = tex::lookup_float4(WaterVelocityAndHeight,float2(Local521.x,1.0-Local521.y),tex::wrap_clamp,tex::wrap_clamp);
float4 Local523 = math::lerp(Local517,Local522,Local518.y);
float Local524 = (TerrainMaxZ - TerrainMinZ);
float Local525 = (Local523.w * Local524);
float Local526 = (Local525 + TerrainMinZ);
float Local527 = (Local523.z - Local526);
float Local528 = (Local527 - WaterOpacityMaskOffset);
float Local529 = math::saturate(Local528);
float4 Local530 = math::lerp(Local514,Local517,Local518.x);
float4 Local531 = math::lerp(Local520,Local522,Local518.x);
float4 Local532 = math::lerp(Local530,Local531,Local518.y);
float3 Local533 = math::lerp(float3(1.0,1.0,1.0),float3(float2(WaveoverRockSteepness,WaveoverRockSteepness).x,float2(WaveoverRockSteepness,WaveoverRockSteepness).y,0.0),0.5);
float Local534 = CustomExpression0();
float Local535 = math::lerp(Time,ManualTime,FreezeTime);
float3 Local536 = CustomExpression1(Local534,float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y),Local535);
float3 Local537 = CustomExpression4(Local536);
float4 Local538 = math::lerp(Local514,Local520,Local518.y);
float Local539 = (Local538.w * Local524);
float Local540 = (Local539 + TerrainMinZ);
float Local541 = (Local538.z - Local540);
float Local542 = math::max(Local541,0.0);
float Local543 = CustomExpression3(Local534,Local542);
float3 Local544 = (Local537 * Local543);
float Local545 = (Local535 * DefaultDisantWaterSpeed);
float Local546 = (Local545 * 0.1);
float Local547 = math::frac(Local546);
float Local548 = (::pixel_depth() - WaterDistantNormalOffset);
float Local549 = (Local548 / WaterDistantNormalLength);
float Local550 = math::saturate(Local549);
float Local551 = math::ceil(Local550);
float Local552 = math::saturate(Local551);
float Local553 = math::lerp(DefaultNearWaterScale,DefaultDistantWaterScale,Local552);
float3 Local554 = ((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0) / Local553);
float2 Local555 = (float2(Local547,Local547) + float2(Local554.x,Local554.y));
float4 Local556 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local555.x,1.0-Local555.y),tex::wrap_repeat,tex::wrap_repeat);
float Local557 = (Local545 * -0.1);
float Local558 = math::frac(Local557);
float2 Local559 = (float2(Local554.x,Local554.y) + float2(0.4181,0.3548));
float2 Local560 = (Local559 / 1.618);
float2 Local561 = (float2(Local558,Local558) + Local560);
float4 Local562 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local561.x,1.0-Local561.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local563 = (float3(Local556.x,Local556.y,Local556.z) + float3(Local562.x,Local562.y,Local562.z));
float2 Local564 = (float2(Local554.x,Local554.y) + float2(0.864861,0.148384));
float2 Local565 = (Local564 / 1.236094);
float2 Local566 = (float2(Local558,Local547) + Local565);
float4 Local567 = tex::lookup_float4(texture_2d("./Textures/T_Water_TilingNormal_With_Height_02_Softened.png",::tex::gamma_linear),float2(Local566.x,1.0-Local566.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local568 = (Local563 + float3(Local567.x,Local567.y,Local567.z));
float3 Local569 = (Local568 * 0.333333);
float Local570 = (Local569.z - 0.5);
float Local571 = (Local570 * Local550);
float Local572 = (Local571 * 0.0);
float3 Local573 = (Local572 * float3(0.0,0.0,1.0));
float3 Local574 = math::lerp(Local544,Local573,Local550);
float3 Local575 = (Local533 * Local574);
float3 Local576 = (Local575 + (::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0));
float2 Local577 = (float2(Local576.x,Local576.y) - float2(SimLocation.x,SimLocation.y));
float2 Local578 = (Local577 / FluidSimSize);
float2 Local579 = (Local578 + 0.5);
float4 Local580 = tex::lookup_float4(NormalAndHeight,float2(Local579.x,1.0-Local579.y),tex::wrap_clamp,tex::wrap_clamp);
float Local581 = (Local532.z + Local580.z);
float Local582 = (Local532.w * Local524);
float Local583 = (Local582 + TerrainMinZ);
float Local584 = (Local581 - Local583);
float Local585 = math::max(Local584,0.0);
float Local586 = math::saturate(Local585);
float Local587 = math::max(Local529,Local586);
float Local588 = math::lerp(-1.0,1.0,Local587);
float Local589 = math::saturate(Local588);
float3 Local590 = (1.0 / float3(Absorption.x,Absorption.y,Absorption.z));
float3 Local591 = (Local590 / Absorption.w);
float Local592 = math::saturate(TwoSidedSign);
float3 Local593 = (Local591 * Local592);
float3 Local594 = (Local589 * Local593);
float3 GetSingleLayerWaterMaterialOutput1_mdl = Local594;
float GetSingleLayerWaterMaterialOutput2_mdl = Anisotropy;
float GetSingleLayerWaterMaterialOutput3_mdl = 1.0;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: true);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/OmniUe4Subsurface.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.0 - first version
//* 1.0.1 - fix reflection and transmission with subsurface color
//* 1.0.2 - Fix EDF in the back side: the EDF contained in surface is only used for the front side and not for the back side
//* 1.0.3 - using absolute import paths when importing standard modules
mdl 1.3;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
export annotation distill_off();
float emissive_multiplier()
[[
anno::description("the multiplier to convert UE4 emissive to raw data"),
anno::noinline()
]]
{
return 20.0f * 128.0f;
}
float get_subsurface_weight()
[[
anno::noinline()
]]
{
return 0.5f;
}
color get_subsurface_color(color subsurface_color)
[[
anno::noinline()
]]
{
return subsurface_color;
}
float get_subsurface_opacity(float subsurface_opacity)
[[
anno::noinline()
]]
{
return subsurface_opacity;
}
float3 tangent_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in tangent space"),
anno::noinline()
]]
{
return math::normalize(
tangent_u * normal.x - /* flip_tangent_v */
tangent_v * normal.y +
state::normal() * (normal.z));
}
float3 world_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in world space"),
anno::noinline()
]]
{
return tangent_space_normal(
math::normalize(
normal.x * float3(tangent_u.x, tangent_v.x, state::normal().x) -
normal.y * float3(tangent_u.y, tangent_v.y, state::normal().y) +
normal.z * float3(tangent_u.z, tangent_v.z, state::normal().z)),
tangent_u,
tangent_v
);
}
export material OmniUe4Subsurface(
float3 base_color = float3(0.0, 0.0, 0.0),
float metallic = 0.0,
float roughness = 0.5,
float specular = 0.5,
float3 normal = float3(0.0,0.0,1.0),
uniform bool enable_opacity = true,
float opacity = 1.0,
float opacity_mask = 1.0,
float3 emissive_color = float3(0.0, 0.0, 0.0),
float3 subsurface_color = float3(1.0, 1.0, 1.0),
float3 displacement = float3(0.0),
uniform bool is_tangent_space_normal = true,
uniform bool two_sided = false
)
[[
anno::display_name("Omni UE4 Subsurface"),
anno::description("Omni UE4 Subsurface, supports UE4 Subsurface shading model"),
anno::version( 1, 0, 0),
anno::author("NVIDIA CORPORATION"),
anno::key_words(string[]("omni", "UE4", "omniverse", "subsurface")),
distill_off()
]]
= let {
color final_base_color = math::saturate(base_color);
float final_metallic = math::saturate(metallic);
float final_roughness = math::saturate(roughness);
float final_specular = math::saturate(specular);
color final_emissive_color = math::max(emissive_color, 0.0f) * emissive_multiplier(); /*factor for converting ue4 emissive to raw value*/
float3 final_normal = math::normalize(normal);
color final_subsurface_color = math::saturate(subsurface_color);
float final_opacity = math::saturate(opacity);
// - compute final roughness by squaring the "roughness" parameter
float alpha = final_roughness * final_roughness;
// reduce the reflectivity at grazing angles to avoid "dark edges" for high roughness due to the layering
float grazing_refl = math::max((1.0 - final_roughness), 0.0);
bsdf reflection_component = df::diffuse_reflection_bsdf(tint: final_base_color);
bsdf subsurface_reflection_component = df::diffuse_reflection_bsdf(tint: get_subsurface_color(subsurface_color: final_subsurface_color));
bsdf transmit_component = df::diffuse_transmission_bsdf(tint: get_subsurface_color(subsurface_color: final_subsurface_color));
// for the dielectric component we layer the glossy component on top of the diffuse one,
// the glossy layer has no color tint
bsdf dielectric_component = df::custom_curve_layer(
weight: final_specular,
normal_reflectivity: 0.08,
grazing_reflectivity: grazing_refl,
layer: df::microfacet_ggx_smith_bsdf(roughness_u: alpha),
base: df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: reflection_component,
weight: 1.0f - get_subsurface_weight()),
df::bsdf_component(
component: subsurface_reflection_component,
weight: get_subsurface_opacity(subsurface_opacity: final_opacity) * get_subsurface_weight()),
df::bsdf_component(
component: transmit_component,
weight: (1.0 - get_subsurface_opacity(subsurface_opacity: final_opacity)) * get_subsurface_weight())
)
)
);
// the metallic component doesn't have a diffuse component, it's only glossy
// base_color is applied to tint it
bsdf metallic_component = df::microfacet_ggx_smith_bsdf(tint: final_base_color, roughness_u: alpha);
// final BSDF is a linear blend between dielectric and metallic component
bsdf dielectric_metal_mix =
df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: metallic_component,
weight: final_metallic),
df::bsdf_component(
component: dielectric_component,
weight: 1.0-final_metallic)
)
);
float3 the_normal = is_tangent_space_normal ?
tangent_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
);
bsdf surface = dielectric_metal_mix;
}
in material(
thin_walled: two_sided, // Graphene?
surface: material_surface(
scattering: surface,
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
backface: material_surface(
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
geometry: material_geometry(
displacement: displacement,
normal: the_normal,
cutout_opacity: enable_opacity ? opacity_mask : 1.0
)
);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/OmniUe4Base.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.0 - first version
//* 1.0.1 - merge unlit template
//* 1.0.2 - Fix EDF in the back side: the EDF contained in surface is only used for the front side and not for the back side
//* 1.0.3 - UE4 normal mapping: Geometry normal shouldn't be changed
//* 1.0.4 - using absolute import paths when importing standard modules
mdl 1.3;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
float emissive_multiplier()
[[
anno::description("the multiplier to convert UE4 emissive to raw data"),
anno::noinline()
]]
{
return 20.0f * 128.0f;
}
float3 tangent_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in tangent space"),
anno::noinline()
]]
{
return math::normalize(
tangent_u * normal.x - /* flip_tangent_v */
tangent_v * normal.y +
state::normal() * (normal.z));
}
float3 world_space_normal(
float3 normal = float3(0.0,0.0,1.0),
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0)
)
[[
anno::description("Interprets the vector in world space"),
anno::noinline()
]]
{
return tangent_space_normal(
math::normalize(
normal.x * float3(tangent_u.x, tangent_v.x, state::normal().x) -
normal.y * float3(tangent_u.y, tangent_v.y, state::normal().y) +
normal.z * float3(tangent_u.z, tangent_v.z, state::normal().z)),
tangent_u,
tangent_v
);
}
export material OmniUe4Base(
float3 base_color = float3(0.0, 0.0, 0.0),
float metallic = 0.0,
float roughness = 0.5,
float specular = 0.5,
float3 normal = float3(0.0,0.0,1.0),
float clearcoat_weight = 0.0,
float clearcoat_roughness = 0.0,
float3 clearcoat_normal = float3(0.0,0.0,1.0),
uniform bool enable_opacity = true,
float opacity = 1.0,
float3 emissive_color = float3(0.0, 0.0, 0.0),
float3 displacement = float3(0.0),
uniform bool is_tangent_space_normal = true,
uniform bool two_sided = false,
uniform bool is_unlit = false
)
[[
anno::display_name("Omni UE4 Base"),
anno::description("Omni UE4 Base, supports UE4 default lit and clearcoat shading model"),
anno::version( 1, 0, 0),
anno::author("NVIDIA CORPORATION"),
anno::key_words(string[]("omni", "UE4", "omniverse", "lit", "clearcoat", "generic"))
]]
= let {
color final_base_color = math::saturate(base_color);
float final_metallic = math::saturate(metallic);
float final_roughness = math::saturate(roughness);
float final_specular = math::saturate(specular);
color final_emissive_color = math::max(emissive_color, 0.0f) * emissive_multiplier(); /*factor for converting ue4 emissive to raw value*/
float final_clearcoat_weight = math::saturate(clearcoat_weight);
float final_clearcoat_roughness = math::saturate(clearcoat_roughness);
float3 final_normal = math::normalize(normal);
float3 final_clearcoat_normal = math::normalize(clearcoat_normal);
// - compute final roughness by squaring the "roughness" parameter
float alpha = final_roughness * final_roughness;
// reduce the reflectivity at grazing angles to avoid "dark edges" for high roughness due to the layering
float grazing_refl = math::max((1.0 - final_roughness), 0.0);
float3 the_normal = is_unlit ? state::normal() :
(is_tangent_space_normal ?
tangent_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
));
// for the dielectric component we layer the glossy component on top of the diffuse one,
// the glossy layer has no color tint
bsdf dielectric_component = df::custom_curve_layer(
weight: final_specular,
normal_reflectivity: 0.08,
grazing_reflectivity: grazing_refl,
layer: df::microfacet_ggx_smith_bsdf(roughness_u: alpha),
base: df::diffuse_reflection_bsdf(tint: final_base_color),
normal: the_normal);
// the metallic component doesn't have a diffuse component, it's only glossy
// base_color is applied to tint it
bsdf metallic_component = df::microfacet_ggx_smith_bsdf(tint: final_base_color, roughness_u: alpha);
// final BSDF is a linear blend between dielectric and metallic component
bsdf dielectric_metal_mix =
df::normalized_mix(
components:
df::bsdf_component[](
df::bsdf_component(
component: metallic_component,
weight: final_metallic),
df::bsdf_component(
component: dielectric_component,
weight: 1.0-final_metallic)
)
);
// clearcoat layer
float clearcoat_grazing_refl = math::max((1.0 - final_clearcoat_roughness), 0.0);
float clearcoat_alpha = final_clearcoat_roughness * final_clearcoat_roughness;
float3 the_clearcoat_normal = is_tangent_space_normal ? tangent_space_normal(
normal: final_clearcoat_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
) : world_space_normal(
normal: final_clearcoat_normal,
tangent_u: state::texture_tangent_u(0),
tangent_v: state::texture_tangent_v(0)
);
bsdf clearcoat =
df::custom_curve_layer(
base: df::weighted_layer(
layer: dielectric_metal_mix,
weight: 1.0,
normal: final_clearcoat_weight == 0.0 ? state::normal() : the_normal
),
layer: df::microfacet_ggx_smith_bsdf(
roughness_u: clearcoat_alpha,
tint: color(1.0)
),
normal_reflectivity: 0.04,
grazing_reflectivity: clearcoat_grazing_refl,
normal: the_clearcoat_normal,
weight: final_clearcoat_weight
);
bsdf surface = is_unlit ? bsdf() : clearcoat;
}
in material(
thin_walled: two_sided, // Graphene?
surface: material_surface(
scattering: surface,
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
backface: material_surface(
emission:
material_emission (
emission: df::diffuse_edf (),
intensity: final_emissive_color
)
),
geometry: material_geometry(
displacement: displacement,
normal: final_clearcoat_weight == 0.0 ? the_normal : state::normal(),
cutout_opacity: enable_opacity ? opacity : 1.0
)
);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/Cow_Y_APFur_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material Cow_Y_APFur_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/Cow_Y_APFur_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/OmniUe4Function.mdl | /***************************************************************************************************
* Copyright 2020 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************************/
//* 1.0.1 - using absolute import paths when importing standard modules
mdl 1.6;
import ::df::*;
import ::state::*;
import ::math::*;
import ::tex::*;
import ::anno::*;
export float3 convert_to_left_hand(float3 vec3, uniform bool up_z = true, uniform bool is_position = true)
[[
anno::description("convert from RH to LH"),
anno::noinline()
]]
{
float4x4 ZupConversion = float4x4(
1.0f, 0.0f, 0.0f, 0.0f,
0.0f, -1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 0.0f, 0.0f, 1.0f
);
float4x4 YupConversion = float4x4(
1.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 1.0f
);
float4 vec4 = float4(vec3.x, vec3.y, vec3.z, is_position ? 1.0f : 0.0f);
vec4 = vec4 * (up_z ? ZupConversion : YupConversion);
return float3(vec4.x, vec4.y, vec4.z);
}
export float3 transform_vector_from_tangent_to_world(float3 vector,
uniform bool up_z = true,
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0))
[[
anno::description("Transform vector from tangent space to world space"),
anno::noinline()
]]
{
/* flip_tangent_v */
return convert_to_left_hand(
tangent_u * vector.x - tangent_v * vector.y + state::normal() * vector.z,
up_z, false);
}
export float3 transform_vector_from_world_to_tangent(float3 vector,
uniform bool up_z = true,
float3 tangent_u = state::texture_tangent_u(0),
float3 tangent_v = state::texture_tangent_v(0))
[[
anno::description("Transform vector from world space to tangent space"),
anno::noinline()
]]
{
float3 vecRH = convert_to_left_hand(vector, up_z, false);
/* flip_tangent_v */
return vecRH.x * float3(tangent_u.x, -tangent_v.x, state::normal().x) +
vecRH.y * float3(tangent_u.y, -tangent_v.y, state::normal().y) +
vecRH.z * float3(tangent_u.z, -tangent_v.z, state::normal().z);
}
export float4 unpack_normal_map(
float4 texture_sample = float4(0.0, 0.0, 1.0, 1.0)
)
[[
anno::description("Unpack a normal stored in a normal map"),
anno::noinline()
]]
{
float2 normal_xy = float2(texture_sample.x, texture_sample.y);
normal_xy = normal_xy * float2(2.0,2.0) - float2(1.0,1.0);
float normal_z = math::sqrt( math::saturate( 1.0 - math::dot( normal_xy, normal_xy ) ) );
return float4( normal_xy.x, normal_xy.y, normal_z, 1.0 );
}
// for get color value from normal.
export float4 pack_normal_map(
float4 texture_sample = float4(0.0, 0.0, 1.0, 1.0)
)
[[
anno::description("Pack to color from a normal")
]]
{
float2 return_xy = float2(texture_sample.x, texture_sample.y);
return_xy = (return_xy + float2(1.0,1.0)) / float2(2.0,2.0);
return float4( return_xy.x, return_xy.y, 0.0, 1.0 );
}
export float4 greyscale_texture_lookup(
float4 texture_sample = float4(0.0, 0.0, 0.0, 1.0)
)
[[
anno::description("Sampling a greyscale texture"),
anno::noinline()
]]
{
return float4(texture_sample.x, texture_sample.x, texture_sample.x, texture_sample.x);
}
export float3 pixel_normal_world_space(uniform bool up_z = true)
[[
anno::description("Pixel normal in world space"),
anno::noinline()
]]
{
return convert_to_left_hand(state::transform_normal(state::coordinate_internal,state::coordinate_world,state::normal()), up_z, false);
}
export float3 vertex_normal_world_space(uniform bool up_z = true)
[[
anno::description("Vertex normal in world space"),
anno::noinline()
]]
{
return convert_to_left_hand(state::transform_normal(state::coordinate_internal,state::coordinate_world,state::normal()), up_z, false);
}
export float3 landscape_normal_world_space(uniform bool up_z = true)
[[
anno::description("Landscape normal in world space")
]]
{
float3 normalFromNormalmap = math::floor((::vertex_normal_world_space(up_z) * 0.5 + 0.5) * 255.0) / 255.0 * 2.0 - 1.0;
float2 normalXY = float2(normalFromNormalmap.x, normalFromNormalmap.y);
return float3(normalXY.x, normalXY.y, math::sqrt(math::saturate(1.0 - math::dot(normalXY, normalXY))));
}
// Different implementation specific between mdl and hlsl for smoothstep
export float smoothstep(float a, float b, float l)
{
if (a < b)
{
return math::smoothstep(a, b, l);
}
else if (a > b)
{
return 1.0 - math::smoothstep(b, a, l);
}
else
{
return l <= a ? 0.0 : 1.0;
}
}
export float2 smoothstep(float2 a, float2 b, float2 l)
{
return float2(smoothstep(a.x, b.x, l.x), smoothstep(a.y, b.y, l.y));
}
export float3 smoothstep(float3 a, float3 b, float3 l)
{
return float3(smoothstep(a.x, b.x, l.x), smoothstep(a.y, b.y, l.y), smoothstep(a.z, b.z, l.z));
}
export float4 smoothstep(float4 a, float4 b, float4 l)
{
return float4(smoothstep(a.x, b.x, l.x), smoothstep(a.y, b.y, l.y), smoothstep(a.z, b.z, l.z), smoothstep(a.w, b.w, l.w));
}
export float2 smoothstep(float2 a, float2 b, float l)
{
return float2(smoothstep(a.x, b.x, l), smoothstep(a.y, b.y, l));
}
export float3 smoothstep(float3 a, float3 b, float l)
{
return float3(smoothstep(a.x, b.x, l), smoothstep(a.y, b.y, l), smoothstep(a.z, b.z, l));
}
export float4 smoothstep(float4 a, float4 b, float l)
{
return float4(smoothstep(a.x, b.x, l), smoothstep(a.y, b.y, l), smoothstep(a.z, b.z, l), smoothstep(a.w, b.w, l));
}
export float2 smoothstep(float a, float b, float2 l)
{
return float2(smoothstep(a, b, l.x), smoothstep(a, b, l.y));
}
export float3 smoothstep(float a, float b, float3 l)
{
return float3(smoothstep(a, b, l.x), smoothstep(a, b, l.y), smoothstep(a, b, l.z));
}
export float4 smoothstep(float a, float b, float4 l)
{
return float4(smoothstep(a, b, l.x), smoothstep(a, b, l.y), smoothstep(a, b, l.z), smoothstep(a, b, l.w));
}
//------------------ Random from UE4 -----------------------
float length2(float3 v)
{
return math::dot(v, v);
}
float3 GetPerlinNoiseGradientTextureAt(uniform texture_2d PerlinNoiseGradientTexture, float3 v)
{
const float2 ZShear = float2(17.0f, 89.0f);
float2 OffsetA = v.z * ZShear;
float2 TexA = (float2(v.x, v.y) + OffsetA + 0.5f) / 128.0f;
float4 PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA.x,1.0-TexA.y),tex::wrap_repeat,tex::wrap_repeat);
return float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z) * 2.0 - 1.0;
}
float3 SkewSimplex(float3 In)
{
return In + math::dot(In, float3(1.0 / 3.0f) );
}
float3 UnSkewSimplex(float3 In)
{
return In - math::dot(In, float3(1.0 / 6.0f) );
}
// 3D random number generator inspired by PCGs (permuted congruential generator)
// Using a **simple** Feistel cipher in place of the usual xor shift permutation step
// @param v = 3D integer coordinate
// @return three elements w/ 16 random bits each (0-0xffff).
// ~8 ALU operations for result.x (7 mad, 1 >>)
// ~10 ALU operations for result.xy (8 mad, 2 >>)
// ~12 ALU operations for result.xyz (9 mad, 3 >>)
//TODO: uint3
int3 Rand3DPCG16(int3 p)
{
// taking a signed int then reinterpreting as unsigned gives good behavior for negatives
//TODO: uint3
int3 v = int3(p);
// Linear congruential step. These LCG constants are from Numerical Recipies
// For additional #'s, PCG would do multiple LCG steps and scramble each on output
// So v here is the RNG state
v = v * 1664525 + 1013904223;
// PCG uses xorshift for the final shuffle, but it is expensive (and cheap
// versions of xorshift have visible artifacts). Instead, use simple MAD Feistel steps
//
// Feistel ciphers divide the state into separate parts (usually by bits)
// then apply a series of permutation steps one part at a time. The permutations
// use a reversible operation (usually ^) to part being updated with the result of
// a permutation function on the other parts and the key.
//
// In this case, I'm using v.x, v.y and v.z as the parts, using + instead of ^ for
// the combination function, and just multiplying the other two parts (no key) for
// the permutation function.
//
// That gives a simple mad per round.
v.x += v.y*v.z;
v.y += v.z*v.x;
v.z += v.x*v.y;
v.x += v.y*v.z;
v.y += v.z*v.x;
v.z += v.x*v.y;
// only top 16 bits are well shuffled
return v >> 16;
}
// Wraps noise for tiling texture creation
// @param v = unwrapped texture parameter
// @param bTiling = true to tile, false to not tile
// @param RepeatSize = number of units before repeating
// @return either original or wrapped coord
float3 NoiseTileWrap(float3 v, bool bTiling, float RepeatSize)
{
return bTiling ? (math::frac(v / RepeatSize) * RepeatSize) : v;
}
// Evaluate polynomial to get smooth transitions for Perlin noise
// only needed by Perlin functions in this file
// scalar(per component): 2 add, 5 mul
float4 PerlinRamp(float4 t)
{
return t * t * t * (t * (t * 6 - 15) + 10);
}
// Blum-Blum-Shub-inspired pseudo random number generator
// http://www.umbc.edu/~olano/papers/mNoise.pdf
// real BBS uses ((s*s) mod M) with bignums and M as the product of two huge Blum primes
// instead, we use a single prime M just small enough not to overflow
// note that the above paper used 61, which fits in a half, but is unusably bad
// @param Integer valued floating point seed
// @return random number in range [0,1)
// ~8 ALU operations (5 *, 3 frac)
float RandBBSfloat(float seed)
{
float BBS_PRIME24 = 4093.0;
float s = math::frac(seed / BBS_PRIME24);
s = math::frac(s * s * BBS_PRIME24);
s = math::frac(s * s * BBS_PRIME24);
return s;
}
// Modified noise gradient term
// @param seed - random seed for integer lattice position
// @param offset - [-1,1] offset of evaluation point from lattice point
// @return gradient direction (xyz) and contribution (w) from this lattice point
float4 MGradient(int seed, float3 offset)
{
//TODO uint
int rand = Rand3DPCG16(int3(seed,0,0)).x;
int3 MGradientMask = int3(0x8000, 0x4000, 0x2000);
float3 MGradientScale = float3(1.0 / 0x4000, 1.0 / 0x2000, 1.0 / 0x1000);
float3 direction = float3(int3(rand, rand, rand) & MGradientMask) * MGradientScale - 1;
return float4(direction.x, direction.y, direction.z, math::dot(direction, offset));
}
// compute Perlin and related noise corner seed values
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = true to return seed values for a repeating noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @param seed000-seed111 = hash function seeds for the eight corners
// @return fractional part of v
struct SeedValue
{
float3 fv = float3(0);
float seed000 = 0;
float seed001 = 0;
float seed010 = 0;
float seed011 = 0;
float seed100 = 0;
float seed101 = 0;
float seed110 = 0;
float seed111 = 0;
};
SeedValue NoiseSeeds(float3 v, bool bTiling, float RepeatSize)
{
SeedValue seeds;
seeds.fv = math::frac(v);
float3 iv = math::floor(v);
const float3 primes = float3(19, 47, 101);
if (bTiling)
{ // can't algebraically combine with primes
seeds.seed000 = math::dot(primes, NoiseTileWrap(iv, true, RepeatSize));
seeds.seed100 = math::dot(primes, NoiseTileWrap(iv + float3(1, 0, 0), true, RepeatSize));
seeds.seed010 = math::dot(primes, NoiseTileWrap(iv + float3(0, 1, 0), true, RepeatSize));
seeds.seed110 = math::dot(primes, NoiseTileWrap(iv + float3(1, 1, 0), true, RepeatSize));
seeds.seed001 = math::dot(primes, NoiseTileWrap(iv + float3(0, 0, 1), true, RepeatSize));
seeds.seed101 = math::dot(primes, NoiseTileWrap(iv + float3(1, 0, 1), true, RepeatSize));
seeds.seed011 = math::dot(primes, NoiseTileWrap(iv + float3(0, 1, 1), true, RepeatSize));
seeds.seed111 = math::dot(primes, NoiseTileWrap(iv + float3(1, 1, 1), true, RepeatSize));
}
else
{ // get to combine offsets with multiplication by primes in this case
seeds.seed000 = math::dot(iv, primes);
seeds.seed100 = seeds.seed000 + primes.x;
seeds.seed010 = seeds.seed000 + primes.y;
seeds.seed110 = seeds.seed100 + primes.y;
seeds.seed001 = seeds.seed000 + primes.z;
seeds.seed101 = seeds.seed100 + primes.z;
seeds.seed011 = seeds.seed010 + primes.z;
seeds.seed111 = seeds.seed110 + primes.z;
}
return seeds;
}
struct SimplexWeights
{
float4 Result = float4(0);
float3 PosA = float3(0);
float3 PosB = float3(0);
float3 PosC = float3(0);
float3 PosD = float3(0);
};
// Computed weights and sample positions for simplex interpolation
// @return float4(a,b,c, d) Barycentric coordinate defined as Filtered = Tex(PosA) * a + Tex(PosB) * b + Tex(PosC) * c + Tex(PosD) * d
SimplexWeights ComputeSimplexWeights3D(float3 OrthogonalPos)
{
SimplexWeights weights;
float3 OrthogonalPosFloor = math::floor(OrthogonalPos);
weights.PosA = OrthogonalPosFloor;
weights.PosB = weights.PosA + float3(1, 1, 1);
OrthogonalPos -= OrthogonalPosFloor;
float Largest = math::max(OrthogonalPos.x, math::max(OrthogonalPos.y, OrthogonalPos.z));
float Smallest = math::min(OrthogonalPos.x, math::min(OrthogonalPos.y, OrthogonalPos.z));
weights.PosC = weights.PosA + float3(Largest == OrthogonalPos.x, Largest == OrthogonalPos.y, Largest == OrthogonalPos.z);
weights.PosD = weights.PosA + float3(Smallest != OrthogonalPos.x, Smallest != OrthogonalPos.y, Smallest != OrthogonalPos.z);
float RG = OrthogonalPos.x - OrthogonalPos.y;
float RB = OrthogonalPos.x - OrthogonalPos.z;
float GB = OrthogonalPos.y - OrthogonalPos.z;
weights.Result.z =
math::min(math::max(0, RG), math::max(0, RB)) // X
+ math::min(math::max(0, -RG), math::max(0, GB)) // Y
+ math::min(math::max(0, -RB), math::max(0, -GB)); // Z
weights.Result.w =
math::min(math::max(0, -RG), math::max(0, -RB)) // X
+ math::min(math::max(0, RG), math::max(0, -GB)) // Y
+ math::min(math::max(0, RB), math::max(0, GB)); // Z
weights.Result.y = Smallest;
weights.Result.x = 1.0f - weights.Result.y - weights.Result.z - weights.Result.w;
return weights;
}
// filtered 3D gradient simple noise (few texture lookups, high quality)
// @param v >0
// @return random number in the range -1 .. 1
float SimplexNoise3D_TEX(uniform texture_2d PerlinNoiseGradientTexture, float3 EvalPos)
{
float3 OrthogonalPos = SkewSimplex(EvalPos);
SimplexWeights Weights = ComputeSimplexWeights3D(OrthogonalPos);
// can be optimized to 1 or 2 texture lookups (4 or 8 channel encoded in 32 bit)
float3 A = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosA);
float3 B = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosB);
float3 C = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosC);
float3 D = GetPerlinNoiseGradientTextureAt(PerlinNoiseGradientTexture, Weights.PosD);
Weights.PosA = UnSkewSimplex(Weights.PosA);
Weights.PosB = UnSkewSimplex(Weights.PosB);
Weights.PosC = UnSkewSimplex(Weights.PosC);
Weights.PosD = UnSkewSimplex(Weights.PosD);
float DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosA)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float a = math::dot(A, EvalPos - Weights.PosA) * DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosB)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float b = math::dot(B, EvalPos - Weights.PosB) * DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosC)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float c = math::dot(C, EvalPos - Weights.PosC) * DistanceWeight;
DistanceWeight = math::saturate(0.6f - length2(EvalPos - Weights.PosD)); DistanceWeight *= DistanceWeight; DistanceWeight *= DistanceWeight;
float d = math::dot(D, EvalPos - Weights.PosD) * DistanceWeight;
return 32 * (a + b + c + d);
}
// filtered 3D noise, can be optimized
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @return random number in the range -1 .. 1
float GradientNoise3D_TEX(uniform texture_2d PerlinNoiseGradientTexture, float3 v, bool bTiling, float RepeatSize)
{
bTiling = true;
float3 fv = math::frac(v);
float3 iv0 = NoiseTileWrap(math::floor(v), bTiling, RepeatSize);
float3 iv1 = NoiseTileWrap(iv0 + 1, bTiling, RepeatSize);
const int2 ZShear = int2(17, 89);
float2 OffsetA = iv0.z * ZShear;
float2 OffsetB = OffsetA + ZShear; // non-tiling, use relative offset
if (bTiling) // tiling, have to compute from wrapped coordinates
{
OffsetB = iv1.z * ZShear;
}
// Texture size scale factor
float ts = 1 / 128.0f;
// texture coordinates for iv0.xy, as offset for both z slices
float2 TexA0 = (float2(iv0.x, iv0.y) + OffsetA + 0.5f) * ts;
float2 TexB0 = (float2(iv0.x, iv0.y) + OffsetB + 0.5f) * ts;
// texture coordinates for iv1.xy, as offset for both z slices
float2 TexA1 = TexA0 + ts; // for non-tiling, can compute relative to existing coordinates
float2 TexB1 = TexB0 + ts;
if (bTiling) // for tiling, need to compute from wrapped coordinates
{
TexA1 = (float2(iv1.x, iv1.y) + OffsetA + 0.5f) * ts;
TexB1 = (float2(iv1.x, iv1.y) + OffsetB + 0.5f) * ts;
}
// can be optimized to 1 or 2 texture lookups (4 or 8 channel encoded in 8, 16 or 32 bit)
float4 PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA0.x,1.0-TexA0.y),tex::wrap_repeat,tex::wrap_repeat);
float3 PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 A = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA1.x,1.0-TexA0.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 B = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA0.x,1.0-TexA1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 C = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexA1.x,1.0-TexA1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 D = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB0.x,1.0-TexB0.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 E = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB1.x,1.0-TexB0.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 F = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB0.x,1.0-TexB1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 G = PerlinNoiseColor * 2 - 1;
PerlinNoise = tex::lookup_float4(PerlinNoiseGradientTexture,float2(TexB1.x,1.0-TexB1.y),tex::wrap_repeat,tex::wrap_repeat);
PerlinNoiseColor = float3(PerlinNoise.x, PerlinNoise.y, PerlinNoise.z);
float3 H = PerlinNoiseColor * 2 - 1;
float a = math::dot(A, fv - float3(0, 0, 0));
float b = math::dot(B, fv - float3(1, 0, 0));
float c = math::dot(C, fv - float3(0, 1, 0));
float d = math::dot(D, fv - float3(1, 1, 0));
float e = math::dot(E, fv - float3(0, 0, 1));
float f = math::dot(F, fv - float3(1, 0, 1));
float g = math::dot(G, fv - float3(0, 1, 1));
float h = math::dot(H, fv - float3(1, 1, 1));
float4 Weights = PerlinRamp(math::frac(float4(fv.x, fv.y, fv.z, 0)));
float i = math::lerp(math::lerp(a, b, Weights.x), math::lerp(c, d, Weights.x), Weights.y);
float j = math::lerp(math::lerp(e, f, Weights.x), math::lerp(g, h, Weights.x), Weights.y);
return math::lerp(i, j, Weights.z);
}
// @return random number in the range -1 .. 1
// scalar: 6 frac, 31 mul/mad, 15 add,
float FastGradientPerlinNoise3D_TEX(uniform texture_3d PerlinNoise3DTexture, float3 xyz)
{
// needs to be the same value when creating the PerlinNoise3D texture
float Extent = 16;
// last texel replicated and needed for filtering
// scalar: 3 frac, 6 mul
xyz = math::frac(xyz / (Extent - 1)) * (Extent - 1);
// scalar: 3 frac
float3 uvw = math::frac(xyz);
// = floor(xyz);
// scalar: 3 add
float3 p0 = xyz - uvw;
// float3 f = math::pow(uvw, 2) * 3.0f - math::pow(uvw, 3) * 2.0f; // original perlin hermite (ok when used without bump mapping)
// scalar: 2*3 add 5*3 mul
float4 pr = PerlinRamp(float4(uvw.x, uvw.y, uvw.z, 0));
float3 f = float3(pr.x, pr.y, pr.z); // new, better with continues second derivative for bump mapping
// scalar: 3 add
float3 p = p0 + f;
// scalar: 3 mad
// TODO: need reverse???
float4 NoiseSample = tex::lookup_float4(PerlinNoise3DTexture, p / Extent + 0.5f / Extent); // +0.5f to get rid of bilinear offset
// reconstruct from 8bit (using mad with 2 constants and dot4 was same instruction count)
// scalar: 4 mad, 3 mul, 3 add
float3 n = float3(NoiseSample.x, NoiseSample.y, NoiseSample.z) * 255.0f / 127.0f - 1.0f;
float d = NoiseSample.w * 255.f - 127;
return math::dot(xyz, n) - d;
}
// Perlin-style "Modified Noise"
// http://www.umbc.edu/~olano/papers/index.html#mNoise
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @return random number in the range -1 .. 1
float GradientNoise3D_ALU(float3 v, bool bTiling, float RepeatSize)
{
SeedValue seeds = NoiseSeeds(v, bTiling, RepeatSize);
float rand000 = MGradient(int(seeds.seed000), seeds.fv - float3(0, 0, 0)).w;
float rand100 = MGradient(int(seeds.seed100), seeds.fv - float3(1, 0, 0)).w;
float rand010 = MGradient(int(seeds.seed010), seeds.fv - float3(0, 1, 0)).w;
float rand110 = MGradient(int(seeds.seed110), seeds.fv - float3(1, 1, 0)).w;
float rand001 = MGradient(int(seeds.seed001), seeds.fv - float3(0, 0, 1)).w;
float rand101 = MGradient(int(seeds.seed101), seeds.fv - float3(1, 0, 1)).w;
float rand011 = MGradient(int(seeds.seed011), seeds.fv - float3(0, 1, 1)).w;
float rand111 = MGradient(int(seeds.seed111), seeds.fv - float3(1, 1, 1)).w;
float4 Weights = PerlinRamp(float4(seeds.fv.x, seeds.fv.y, seeds.fv.z, 0));
float i = math::lerp(math::lerp(rand000, rand100, Weights.x), math::lerp(rand010, rand110, Weights.x), Weights.y);
float j = math::lerp(math::lerp(rand001, rand101, Weights.x), math::lerp(rand011, rand111, Weights.x), Weights.y);
return math::lerp(i, j, Weights.z);
}
// 3D value noise - used to be incorrectly called Perlin noise
// @param v = 3D noise argument, use float3(x,y,0) for 2D or float3(x,0,0) for 1D
// @param bTiling = repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension
// @return random number in the range -1 .. 1
float ValueNoise3D_ALU(float3 v, bool bTiling, float RepeatSize)
{
SeedValue seeds = NoiseSeeds(v, bTiling, RepeatSize);
float rand000 = RandBBSfloat(seeds.seed000) * 2 - 1;
float rand100 = RandBBSfloat(seeds.seed100) * 2 - 1;
float rand010 = RandBBSfloat(seeds.seed010) * 2 - 1;
float rand110 = RandBBSfloat(seeds.seed110) * 2 - 1;
float rand001 = RandBBSfloat(seeds.seed001) * 2 - 1;
float rand101 = RandBBSfloat(seeds.seed101) * 2 - 1;
float rand011 = RandBBSfloat(seeds.seed011) * 2 - 1;
float rand111 = RandBBSfloat(seeds.seed111) * 2 - 1;
float4 Weights = PerlinRamp(float4(seeds.fv.x, seeds.fv.y, seeds.fv.z, 0));
float i = math::lerp(math::lerp(rand000, rand100, Weights.x), math::lerp(rand010, rand110, Weights.x), Weights.y);
float j = math::lerp(math::lerp(rand001, rand101, Weights.x), math::lerp(rand011, rand111, Weights.x), Weights.y);
return math::lerp(i, j, Weights.z);
}
// 3D jitter offset within a voronoi noise cell
// @param pos - integer lattice corner
// @return random offsets vector
float3 VoronoiCornerSample(float3 pos, int Quality)
{
// random values in [-0.5, 0.5]
float3 noise = float3(Rand3DPCG16(int3(pos))) / 0xffff - 0.5;
// quality level 1 or 2: searches a 2x2x2 neighborhood with points distributed on a sphere
// scale factor to guarantee jittered points will be found within a 2x2x2 search
if (Quality <= 2)
{
return math::normalize(noise) * 0.2588;
}
// quality level 3: searches a 3x3x3 neighborhood with points distributed on a sphere
// scale factor to guarantee jittered points will be found within a 3x3x3 search
if (Quality == 3)
{
return math::normalize(noise) * 0.3090;
}
// quality level 4: jitter to anywhere in the cell, needs 4x4x4 search
return noise;
}
// compare previous best with a new candidate
// not producing point locations makes it easier for compiler to eliminate calculations when they're not needed
// @param minval = location and distance of best candidate seed point before the new one
// @param candidate = candidate seed point
// @param offset = 3D offset to new candidate seed point
// @param bDistanceOnly = if true, only set maxval.w with distance, otherwise maxval.w is distance and maxval.xyz is position
// @return position (if bDistanceOnly is false) and distance to closest seed point so far
float4 VoronoiCompare(float4 minval, float3 candidate, float3 offset, bool bDistanceOnly)
{
if (bDistanceOnly)
{
return float4(0, 0, 0, math::min(minval.w, math::dot(offset, offset)));
}
else
{
float newdist = math::dot(offset, offset);
return newdist > minval.w ? minval : float4(candidate.x, candidate.y, candidate.z, newdist);
}
}
// 220 instruction Worley noise
float4 VoronoiNoise3D_ALU(float3 v, int Quality, bool bTiling, float RepeatSize, bool bDistanceOnly)
{
float3 fv = math::frac(v), fv2 = math::frac(v + 0.5);
float3 iv = math::floor(v), iv2 = math::floor(v + 0.5);
// with initial minimum distance = infinity (or at least bigger than 4), first min is optimized away
float4 mindist = float4(0,0,0,100);
float3 p, offset;
// quality level 3: do a 3x3x3 search
if (Quality == 3)
{
int offset_x;
int offset_y;
int offset_z;
for (offset_x = -1; offset_x <= 1; ++offset_x)
{
for (offset_y = -1; offset_y <= 1; ++offset_y)
{
for (offset_z = -1; offset_z <= 1; ++offset_z)
{
offset = float3(offset_x, offset_y, offset_z);
p = offset + VoronoiCornerSample(NoiseTileWrap(iv2 + offset, bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv2 + p, fv2 - p, bDistanceOnly);
}
}
}
}
// everybody else searches a base 2x2x2 neighborhood
else
{
int offset_x;
int offset_y;
int offset_z;
for (offset_x = 0; offset_x <= 1; ++offset_x)
{
for (offset_y = 0; offset_y <= 1; ++offset_y)
{
for (offset_z = 0; offset_z <= 1; ++offset_z)
{
offset = float3(offset_x, offset_y, offset_z);
p = offset + VoronoiCornerSample(NoiseTileWrap(iv + offset, bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
// quality level 2, do extra set of points, offset by half a cell
if (Quality == 2)
{
// 467 is just an offset to a different area in the random number field to avoid similar neighbor artifacts
p = offset + VoronoiCornerSample(NoiseTileWrap(iv2 + offset, bTiling, RepeatSize) + 467, Quality);
mindist = VoronoiCompare(mindist, iv2 + p, fv2 - p, bDistanceOnly);
}
}
}
}
}
// quality level 4: add extra sets of four cells in each direction
if (Quality >= 4)
{
int offset_x;
int offset_y;
int offset_z;
for (offset_x = -1; offset_x <= 2; offset_x += 3)
{
for (offset_y = 0; offset_y <= 1; ++offset_y)
{
for (offset_z = 0; offset_z <= 1; ++offset_z)
{
offset = float3(offset_x, offset_y, offset_z);
// along x axis
p = offset + VoronoiCornerSample(NoiseTileWrap(iv + offset, bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
// along y axis
p = float3(offset.y, offset.z, offset.x) + VoronoiCornerSample(NoiseTileWrap(iv + float3(offset.y, offset.z, offset.x), bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
// along z axis
p = float3(offset.z, offset.x, offset.y) + VoronoiCornerSample(NoiseTileWrap(iv + float3(offset.z, offset.x, offset.y), bTiling, RepeatSize), Quality);
mindist = VoronoiCompare(mindist, iv + p, fv - p, bDistanceOnly);
}
}
}
}
// transform squared distance to real distance
return float4(mindist.x, mindist.y, mindist.z, math::sqrt(mindist.w));
}
// Coordinates for corners of a Simplex tetrahedron
// Based on McEwan et al., Efficient computation of noise in GLSL, JGT 2011
// @param v = 3D noise argument
// @return 4 corner locations
float4x3 SimplexCorners(float3 v)
{
// find base corner by skewing to tetrahedral space and back
float3 tet = math::floor(v + v.x/3 + v.y/3 + v.z/3);
float3 base = tet - tet.x/6 - tet.y/6 - tet.z/6;
float3 f = v - base;
// Find offsets to other corners (McEwan did this in tetrahedral space,
// but since skew is along x=y=z axis, this works in Euclidean space too.)
float3 g = math::step(float3(f.y,f.z,f.x), float3(f.x,f.y,f.z)), h = 1 - float3(g.z, g.x, g.y);
float3 a1 = math::min(g, h) - 1.0 / 6.0, a2 = math::max(g, h) - 1.0 / 3.0;
// four corners
return float4x3(base, base + a1, base + a2, base + 0.5);
}
// Improved smoothing function for simplex noise
// @param f = fractional distance to four tetrahedral corners
// @return weight for each corner
float4 SimplexSmooth(float4x3 f)
{
const float scale = 1024. / 375.; // scale factor to make noise -1..1
float4 d = float4(math::dot(f[0], f[0]), math::dot(f[1], f[1]), math::dot(f[2], f[2]), math::dot(f[3], f[3]));
float4 s = math::saturate(2 * d);
return (1 * scale + s*(-3 * scale + s*(3 * scale - s*scale)));
}
// Derivative of simplex noise smoothing function
// @param f = fractional distanc eto four tetrahedral corners
// @return derivative of smoothing function for each corner by x, y and z
float3x4 SimplexDSmooth(float4x3 f)
{
const float scale = 1024. / 375.; // scale factor to make noise -1..1
float4 d = float4(math::dot(f[0], f[0]), math::dot(f[1], f[1]), math::dot(f[2], f[2]), math::dot(f[3], f[3]));
float4 s = math::saturate(2 * d);
s = -12 * scale + s*(24 * scale - s * 12 * scale);
return float3x4(
s * float4(f[0][0], f[1][0], f[2][0], f[3][0]),
s * float4(f[0][1], f[1][1], f[2][1], f[3][1]),
s * float4(f[0][2], f[1][2], f[2][2], f[3][2]));
}
// Simplex noise and its Jacobian derivative
// @param v = 3D noise argument
// @param bTiling = whether to repeat noise pattern
// @param RepeatSize = integer units before tiling in each dimension, must be a multiple of 3
// @return float3x3 Jacobian in J[*].xyz, vector noise in J[*].w
// J[0].w, J[1].w, J[2].w is a Perlin-style simplex noise with vector output, e.g. (Nx, Ny, Nz)
// J[i].x is X derivative of the i'th component of the noise so J[2].x is dNz/dx
// You can use this to compute the noise, gradient, curl, or divergence:
// float3x4 J = JacobianSimplex_ALU(...);
// float3 VNoise = float3(J[0].w, J[1].w, J[2].w); // 3D noise
// float3 Grad = J[0].xyz; // gradient of J[0].w
// float3 Curl = float3(J[1][2]-J[2][1], J[2][0]-J[0][2], J[0][1]-J[1][2]);
// float Div = J[0][0]+J[1][1]+J[2][2];
// All of these are confirmed to compile out all unneeded terms.
// So Grad of X doesn't compute Y or Z components, and VNoise doesn't do any of the derivative computation.
float3x4 JacobianSimplex_ALU(float3 v, bool bTiling, float RepeatSize)
{
int3 MGradientMask = int3(0x8000, 0x4000, 0x2000);
float3 MGradientScale = float3(1. / 0x4000, 1. / 0x2000, 1. / 0x1000);
// corners of tetrahedron
float4x3 T = SimplexCorners(v);
// TODO: uint3
int3 rand = int3(0);
float4x3 gvec0 = float4x3(1.0);
float4x3 gvec1 = float4x3(1.0);
float4x3 gvec2 = float4x3(1.0);
float4x3 fv = float4x3(1.0);
float3x4 grad = float3x4(1.0);
// processing of tetrahedral vertices, unrolled
// to compute gradient at each corner
fv[0] = v - T[0];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[0] + 0.5, bTiling, RepeatSize))));
gvec0[0] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[0] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec2[0] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][0] = math::dot(gvec0[0], fv[0]);
grad[1][0] = math::dot(gvec1[0], fv[0]);
grad[2][0] = math::dot(gvec2[0], fv[0]);
fv[1] = v - T[1];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[1] + 0.5, bTiling, RepeatSize))));
gvec0[1] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[1] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec1[1] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][1] = math::dot(gvec0[1], fv[1]);
grad[1][1] = math::dot(gvec1[1], fv[1]);
grad[2][1] = math::dot(gvec2[1], fv[1]);
fv[2] = v - T[2];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[2] + 0.5, bTiling, RepeatSize))));
gvec0[2] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[2] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec2[2] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][2] = math::dot(gvec0[2], fv[2]);
grad[1][2] = math::dot(gvec1[2], fv[2]);
grad[2][2] = math::dot(gvec2[2], fv[2]);
fv[3] = v - T[3];
rand = Rand3DPCG16(int3(math::floor(NoiseTileWrap(6 * T[3] + 0.5, bTiling, RepeatSize))));
gvec0[3] = float3(int3(rand.x,rand.x,rand.x) & MGradientMask) * MGradientScale - 1;
gvec1[3] = float3(int3(rand.y,rand.y,rand.y) & MGradientMask) * MGradientScale - 1;
gvec2[3] = float3(int3(rand.z,rand.z,rand.z) & MGradientMask) * MGradientScale - 1;
grad[0][3] = math::dot(gvec0[3], fv[3]);
grad[1][3] = math::dot(gvec1[3], fv[3]);
grad[2][3] = math::dot(gvec2[3], fv[3]);
// blend gradients
float4 sv = SimplexSmooth(fv);
float3x4 ds = SimplexDSmooth(fv);
float3x4 jacobian = float3x4(1.0);
float3 vec0 = gvec0*sv + grad[0]*ds; // NOTE: mdl is column major, convert from UE4 (row major)
jacobian[0] = float4(vec0.x, vec0.y, vec0.z, math::dot(sv, grad[0]));
float3 vec1 = gvec1*sv + grad[1]*ds;
jacobian[1] = float4(vec1.x, vec1.y, vec1.z, math::dot(sv, grad[1]));
float3 vec2 = gvec2*sv + grad[2]*ds;
jacobian[2] = float4(vec2.x, vec2.y, vec2.z, math::dot(sv, grad[2]));
return jacobian;
}
// While RepeatSize is a float here, the expectation is that it would be largely integer values coming in from the UI. The downstream logic assumes
// floats for all called functions (NoiseTileWrap) and this prevents any float-to-int conversion errors from automatic type conversion.
float Noise3D_Multiplexer(uniform texture_2d PerlinNoiseGradientTexture, uniform texture_3d PerlinNoise3DTexture, int Function, float3 Position, int Quality, bool bTiling, float RepeatSize)
{
// verified, HLSL compiled out the switch if Function is a constant
switch(Function)
{
case 0:
return SimplexNoise3D_TEX(PerlinNoiseGradientTexture, Position);
case 1:
return GradientNoise3D_TEX(PerlinNoiseGradientTexture, Position, bTiling, RepeatSize);
case 2:
return FastGradientPerlinNoise3D_TEX(PerlinNoise3DTexture, Position);
case 3:
return GradientNoise3D_ALU(Position, bTiling, RepeatSize);
case 4:
return ValueNoise3D_ALU(Position, bTiling, RepeatSize);
case 5:
return VoronoiNoise3D_ALU(Position, Quality, bTiling, RepeatSize, true).w * 2.0 - 1.0;
}
return 0;
}
//----------------------------------------------------------
export float noise(uniform texture_2d PerlinNoiseGradientTexture, uniform texture_3d PerlinNoise3DTexture, float3 Position, float Scale, float Quality, float Function, float Turbulence, float Levels, float OutputMin, float OutputMax, float LevelScale, float FilterWidth, float Tiling, float RepeatSize)
[[
anno::description("Noise"),
anno::noinline()
]]
{
Position *= Scale;
FilterWidth *= Scale;
float Out = 0.0f;
float OutScale = 1.0f;
float InvLevelScale = 1.0f / LevelScale;
int iFunction(Function);
int iQuality(Quality);
int iLevels(Levels);
bool bTurbulence(Turbulence);
bool bTiling(Tiling);
for(int i = 0; i < iLevels; ++i)
{
// fade out noise level that are too high frequent (not done through dynamic branching as it usually requires gradient instructions)
OutScale *= math::saturate(1.0 - FilterWidth);
if(bTurbulence)
{
Out += math::abs(Noise3D_Multiplexer(PerlinNoiseGradientTexture, PerlinNoise3DTexture, iFunction, Position, iQuality, bTiling, RepeatSize)) * OutScale;
}
else
{
Out += Noise3D_Multiplexer(PerlinNoiseGradientTexture, PerlinNoise3DTexture, iFunction, Position, iQuality, bTiling, RepeatSize) * OutScale;
}
Position *= LevelScale;
RepeatSize *= LevelScale;
OutScale *= InvLevelScale;
FilterWidth *= LevelScale;
}
if(!bTurbulence)
{
// bring -1..1 to 0..1 range
Out = Out * 0.5f + 0.5f;
}
// Out is in 0..1 range
return math::lerp(OutputMin, OutputMax, Out);
}
// Material node for noise functions returning a vector value
// @param LevelScale usually 2 but higher values allow efficient use of few levels
// @return in user defined range (OutputMin..OutputMax)
export float4 vector4_noise(float3 Position, float Quality, float Function, float Tiling, float TileSize)
[[
anno::description("Vector Noise"),
anno::noinline()
]]
{
float4 result = float4(0,0,0,1);
float3 ret = float3(0);
int iQuality = int(Quality);
int iFunction = int(Function);
bool bTiling = Tiling > 0.0;
float3x4 Jacobian = JacobianSimplex_ALU(Position, bTiling, TileSize); // compiled out if not used
// verified, HLSL compiled out the switch if Function is a constant
switch (iFunction)
{
case 0: // Cellnoise
ret = float3(Rand3DPCG16(int3(math::floor(NoiseTileWrap(Position, bTiling, TileSize))))) / 0xffff;
result = float4(ret.x, ret.y, ret.z, 1);
break;
case 1: // Color noise
ret = float3(Jacobian[0].w, Jacobian[1].w, Jacobian[2].w);
result = float4(ret.x, ret.y, ret.z, 1);
break;
case 2: // Gradient
result = Jacobian[0];
break;
case 3: // Curl
ret = float3(Jacobian[2][1] - Jacobian[1][2], Jacobian[0][2] - Jacobian[2][0], Jacobian[1][0] - Jacobian[0][1]);
result = float4(ret.x, ret.y, ret.z, 1);
break;
case 4: // Voronoi
result = VoronoiNoise3D_ALU(Position, iQuality, bTiling, TileSize, false);
break;
}
return result;
}
export float3 vector3_noise(float3 Position, float Quality, float Function, float Tiling, float TileSize)
[[
anno::description("Vector Noise float3 version"),
anno::noinline()
]]
{
float4 noise = vector4_noise(Position, Quality, Function, Tiling, TileSize);
return float3(noise.x, noise.y, noise.z);
}
// workaround for ue4 fresnel (without supporting for camera vector) : replacing it with 0.0, means facing to the view
export float fresnel(float exponent [[anno::unused()]], float base_reflect_fraction [[anno::unused()]], float3 normal [[anno::unused()]])
[[
anno::description("Fresnel"),
anno::noinline()
]]
{
return 0.0;
}
export float fresnel_function(float3 normal_vector [[anno::unused()]], float3 camera_vector [[anno::unused()]],
bool invert_fresnel [[anno::unused()]], float power [[anno::unused()]],
bool use_cheap_contrast [[anno::unused()]], float cheap_contrast_dark [[anno::unused()]], float cheap_contrast_bright [[anno::unused()]],
bool clamp_fresnel_dot_product [[anno::unused()]])
[[
anno::description("Fresnel Function"),
anno::noinline()
]]
{
return 0.0;
}
export float3 camera_vector(uniform bool up_z = true)
[[
anno::description("Camera Vector"),
anno::noinline()
]]
{
// assume camera postion is 0,0,0
return math::normalize(float3(0) - convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), up_z));
}
export float pixel_depth()
[[
anno::description("Pixel Depth"),
anno::noinline()
]]
{
return 256.0f;
}
export float scene_depth()
[[
anno::description("Scene Depth")
]]
{
return 65500.0f;
}
export float3 scene_color()
[[
anno::description("Scene Color")
]]
{
return float3(1.0f);
}
export float4 vertex_color()
[[
anno::description("Vertex Color"),
anno::noinline()
]]
{
return float4(1.0f);
}
export float4 vertex_color_from_coordinate(int VertexColorCoordinateIndex)
[[
anno::description("Vertex Color for float2 PrimVar"),
anno::noinline()
]]
{
// Kit only supports 4 uv sets, 2 uvs are available to vertex color. if vertex color index is invalid, output the constant WHITE color intead
return (VertexColorCoordinateIndex > 2) ? float4(1.0f) : float4(state::texture_coordinate(VertexColorCoordinateIndex).x, state::texture_coordinate(VertexColorCoordinateIndex).y, state::texture_coordinate(VertexColorCoordinateIndex+1).x, state::texture_coordinate(VertexColorCoordinateIndex+1).y);
}
export float3 camera_position()
[[
anno::description("Camera Position"),
anno::noinline()
]]
{
return float3(1000.0f, 0, 0);
}
export float3 rotate_about_axis(float4 NormalizedRotationAxisAndAngle, float3 PositionOnAxis, float3 Position)
[[
anno::description("Rotates Position about the given axis by the given angle")
]]
{
// Project Position onto the rotation axis and find the closest point on the axis to Position
float3 NormalizedRotationAxis = float3(NormalizedRotationAxisAndAngle.x,NormalizedRotationAxisAndAngle.y,NormalizedRotationAxisAndAngle.z);
float3 ClosestPointOnAxis = PositionOnAxis + NormalizedRotationAxis * math::dot(NormalizedRotationAxis, Position - PositionOnAxis);
// Construct orthogonal axes in the plane of the rotation
float3 UAxis = Position - ClosestPointOnAxis;
float3 VAxis = math::cross(NormalizedRotationAxis, UAxis);
float[2] SinCosAngle = math::sincos(NormalizedRotationAxisAndAngle.w);
// Rotate using the orthogonal axes
float3 R = UAxis * SinCosAngle[1] + VAxis * SinCosAngle[0];
// Reconstruct the rotated world space position
float3 RotatedPosition = ClosestPointOnAxis + R;
// Convert from position to a position offset
return RotatedPosition - Position;
}
export float2 rotate_scale_offset_texcoords(float2 InTexCoords, float4 InRotationScale, float2 InOffset)
[[
anno::description("Returns a float2 texture coordinate after 2x2 transform and offset applied")
]]
{
return float2(math::dot(InTexCoords, float2(InRotationScale.x, InRotationScale.y)), math::dot(InTexCoords, float2(InRotationScale.z, InRotationScale.w))) + InOffset;
}
export float3 reflection_custom_world_normal(float3 WorldNormal, bool bNormalizeInputNormal, uniform bool up_z = true)
[[
anno::description("Reflection vector about the specified world space normal")
]]
{
if (bNormalizeInputNormal)
{
WorldNormal = math::normalize(WorldNormal);
}
return -camera_vector(up_z) + WorldNormal * math::dot(WorldNormal, camera_vector(up_z)) * 2.0;
}
export float3 reflection_vector(uniform bool up_z = true)
[[
anno::description("Reflection Vector"),
anno::noinline()
]]
{
float3 normal = convert_to_left_hand(state::transform_normal(state::coordinate_internal,state::coordinate_world,state::normal()), up_z, false);
return reflection_custom_world_normal(normal, false, up_z);
}
export float dither_temporalAA(float AlphaThreshold = 0.5f, float Random = 1.0f [[anno::unused()]])
[[
anno::description("Dither TemporalAA"),
anno::noinline()
]]
{
return AlphaThreshold;
}
export float3 black_body( float Temp )
[[
anno::description("Black Body"),
anno::noinline()
]]
{
float u = ( 0.860117757f + 1.54118254e-4f * Temp + 1.28641212e-7f * Temp*Temp ) / ( 1.0f + 8.42420235e-4f * Temp + 7.08145163e-7f * Temp*Temp );
float v = ( 0.317398726f + 4.22806245e-5f * Temp + 4.20481691e-8f * Temp*Temp ) / ( 1.0f - 2.89741816e-5f * Temp + 1.61456053e-7f * Temp*Temp );
float x = 3*u / ( 2*u - 8*v + 4 );
float y = 2*v / ( 2*u - 8*v + 4 );
float z = 1 - x - y;
float Y = 1;
float X = Y/y * x;
float Z = Y/y * z;
float3x3 XYZtoRGB = float3x3(
float3(3.2404542, -1.5371385, -0.4985314),
float3(-0.9692660, 1.8760108, 0.0415560),
float3(0.0556434, -0.2040259, 1.0572252)
);
return XYZtoRGB * float3( X, Y, Z ) * math::pow( 0.0004 * Temp, 4 );
}
export float per_instance_random(uniform texture_2d PerlinNoiseGradientTexture, int NumberInstances)
[[
anno::description("Per Instance Random"),
anno::noinline()
]]
{
float weight = state::object_id() / float(NumberInstances);
return NumberInstances == 0 ? 0.0 : tex::lookup_float4(PerlinNoiseGradientTexture, float2(weight, 1.0 - weight), tex::wrap_repeat, tex::wrap_repeat).x;
}
//------------------ Hair from UE4 -----------------------
float3 hair_absorption_to_color(float3 A)
{
const float B = 0.3f;
float b2 = B * B;
float b3 = B * b2;
float b4 = b2 * b2;
float b5 = B * b4;
float D = (5.969f - 0.215f * B + 2.532f * b2 - 10.73f * b3 + 5.574f * b4 + 0.245f * b5);
return math::exp(-math::sqrt(A) * D);
}
float3 hair_color_to_absorption(float3 C)
{
const float B = 0.3f;
float b2 = B * B;
float b3 = B * b2;
float b4 = b2 * b2;
float b5 = B * b4;
float D = (5.969f - 0.215f * B + 2.532f * b2 - 10.73f * b3 + 5.574f * b4 + 0.245f * b5);
return math::pow(math::log(C) / D, 2.0f);
}
export float3 get_hair_color_from_melanin(float InMelanin, float InRedness, float3 InDyeColor)
[[
anno::description("Hair Color")
]]
{
InMelanin = math::saturate(InMelanin);
InRedness = math::saturate(InRedness);
float Melanin = -math::log(math::max(1 - InMelanin, 0.0001f));
float Eumelanin = Melanin * (1 - InRedness);
float Pheomelanin = Melanin * InRedness;
float3 DyeAbsorption = hair_color_to_absorption(math::saturate(InDyeColor));
float3 Absorption = Eumelanin * float3(0.506f, 0.841f, 1.653f) + Pheomelanin * float3(0.343f, 0.733f, 1.924f);
return hair_absorption_to_color(Absorption + DyeAbsorption);
}
export float3 local_object_bounds_min()
[[
anno::description("Local Object Bounds Min"),
anno::noinline()
]]
{
return float3(0.0);
}
export float3 local_object_bounds_max()
[[
anno::description("Local Object Bounds Max"),
anno::noinline()
]]
{
return float3(100.0);
}
export float3 object_bounds()
[[
anno::description("Object Bounds"),
anno::noinline()
]]
{
return float3(100.0);
}
export float object_radius()
[[
anno::description("Object Radius"),
anno::noinline()
]]
{
return 100.0f;
}
export float3 object_world_position(uniform bool up_z = true)
[[
anno::description("Object World Position"),
anno::noinline()
]]
{
return convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), up_z)*state::meters_per_scene_unit()*100.0;
}
export float3 object_orientation()
[[
anno::description("Object Orientation"),
anno::noinline()
]]
{
return float3(0);
}
export float rcp(float x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export float2 rcp(float2 x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export float3 rcp(float3 x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export float4 rcp(float4 x)
[[
anno::description("hlsl rcp"),
anno::noinline()
]]
{
return 1.0f / x;
}
export int BitFieldExtractI32(int Data, int Size, int Offset)
[[
anno::description("BitFieldExtractI32 int"),
anno::noinline()
]]
{
Size &= 3;
Offset &= 3;
if (Size == 0)
return 0;
else if (Offset + Size < 32)
return (Data << (32 - Size - Offset)) >> (32 - Size);
else
return Data >> Offset;
}
export int BitFieldExtractI32(float Data, float Size, float Offset)
[[
anno::description("BitFieldExtractI32 float"),
anno::noinline()
]]
{
return BitFieldExtractI32(int(Data), int(Size), int(Offset));
}
export int BitFieldExtractU32(float Data, float Size, float Offset)
[[
anno::description("BitFieldExtractU32 float"),
anno::noinline()
]]
{
return BitFieldExtractI32(Data, Size, Offset);
}
export int BitFieldExtractU32(int Data, int Size, int Offset)
[[
anno::description("BitFieldExtractU32 int"),
anno::noinline()
]]
{
return BitFieldExtractI32(Data, Size, Offset);
}
export float3 EyeAdaptationInverseLookup(float3 LightValue, float Alpha)
[[
anno::description("EyeAdaptationInverseLookup"),
anno::noinline()
]]
{
float Adaptation = 1.0f;
// When Alpha=0.0, we want to multiply by 1.0. when Alpha = 1.0, we want to multiply by 1/Adaptation.
// So the lerped value is:
// LerpLogScale = Lerp(log(1),log(1/Adaptaiton),T)
// Which is simplified as:
// LerpLogScale = Lerp(0,-log(Adaptation),T)
// LerpLogScale = -T * logAdaptation;
float LerpLogScale = -Alpha * math::log(Adaptation);
float Scale = math::exp(LerpLogScale);
return LightValue * Scale;
}
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/Cow_F_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material Cow_F_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/Cow_F_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Basic_Floor.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Basic_Floor(
float4 Color = float4(0.259027,0.320382,0.383775,1.0)
[[
anno::display_name("Color"),
anno::ui_order(32),
anno::in_group("Wall")
]],
float Roughness = 0.5
[[
anno::display_name("Roughness"),
anno::ui_order(32),
anno::in_group("Wall")
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Color.x,Color.y,Color.z);
float Metallic_mdl = 0.5;
float Specular_mdl = 0.5;
float Roughness_mdl = Roughness;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/Cow_Y_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material Cow_Y_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/Cow_Y_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Shelf.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Shelf(
float4 ColorShelf = float4(0.974138,0.337885,0.034461,1.0)
[[
anno::display_name("ColorShelf"),
anno::ui_order(32)
]],
float4 ColorMetal = float4(0.94,0.94,0.94,1.0)
[[
anno::display_name("ColorMetal"),
anno::ui_order(32)
]],
float RoughnessShelf = 0.0
[[
anno::display_name("RoughnessShelf"),
anno::ui_order(32)
]],
float RoughnessMetal = 0.2
[[
anno::display_name("RoughnessMetal"),
anno::ui_order(32)
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Shelf_N.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float4 Local1 = tex::lookup_float4(texture_2d("./Textures/T_Shelf_M.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local2 = math::lerp(float3(ColorShelf.x,ColorShelf.y,ColorShelf.z),float3(ColorMetal.x,ColorMetal.y,ColorMetal.z),Local1.y);
float Local3 = math::lerp(0.0,1.0,Local1.y);
float Local4 = math::lerp(RoughnessShelf,RoughnessMetal,Local1.y);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local2;
float Metallic_mdl = Local3;
float Specular_mdl = 0.5;
float Roughness_mdl = Local4;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Bush.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Bush(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
[[
dither_masked_off()
]]
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Bush_N.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float4 Local1 = tex::lookup_float4(texture_2d("./Textures/T_Bush_D.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = (Local1.w - 0.3333) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = float3(Local1.x,Local1.y,Local1.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: true);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Basic_Wall.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Basic_Wall(
float4 Color = float4(0.810345,0.787878,0.654683,1.0)
[[
anno::display_name("Color"),
anno::ui_order(32),
anno::in_group("Wall")
]],
float Roughness = 0.640708
[[
anno::display_name("Roughness"),
anno::ui_order(32),
anno::in_group("Wall")
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Color.x,Color.y,Color.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Roughness;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/BasicShapeMaterial.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material BasicShapeMaterial(
float4 Color = float4(1.0,1.0,1.0,1.0)
[[
anno::display_name("Color"),
anno::ui_order(32)
]],
float Roughness = 0.6407
[[
anno::display_name("Roughness"),
anno::ui_order(32)
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Color.x,Color.y,Color.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Roughness;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Glass.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Translucent import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Glass(
float4 ColorGlass = float4(0.0,0.001202,0.003303,1.0)
[[
anno::display_name("ColorGlass"),
anno::ui_order(32),
anno::in_group("Glass")
]],
float Specular = 10.0
[[
anno::display_name("Specular"),
anno::ui_order(32),
anno::in_group("Glass")
]],
float Roughness = 0.0
[[
anno::display_name("Roughness"),
anno::ui_order(32),
anno::in_group("Glass")
]],
float Opacity = 0.35
[[
anno::display_name("Opacity"),
anno::ui_order(32),
anno::in_group("Glass")
]],
uniform float Refraction = 1.4
[[
anno::display_name("Refraction"),
anno::ui_order(32),
anno::in_group("Refraction")
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float Opacity_mdl = Opacity;
float OpacityMask_mdl = (math::saturate(Opacity) - 1.0f / 255.0f) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = float3(ColorGlass.x,ColorGlass.y,ColorGlass.z);
float Metallic_mdl = 0.0;
float Specular_mdl = Specular;
float Roughness_mdl = Roughness;
float2 Refraction_mdl = float2(Refraction,Refraction);
} in
::OmniUe4Translucent(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: Opacity_mdl,
opacity_mask: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
refraction: Refraction_mdl.x,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Frame.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Frame(
float4 Color = float4(0.487765,0.447871,0.383775,1.0)
[[
anno::display_name("Color"),
anno::ui_order(32),
anno::in_group("Frame")
]],
float Metallic = 1.0
[[
anno::display_name("Metallic"),
anno::ui_order(32),
anno::in_group("Frame")
]],
float RoughnessHigh = 1.0
[[
anno::display_name("RoughnessHigh"),
anno::ui_order(32),
anno::in_group("Frame")
]],
float RoughnessLOW = 0.5
[[
anno::display_name("RoughnessLOW"),
anno::ui_order(32),
anno::in_group("Frame")
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Frame_N.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float4 Local1 = tex::lookup_float4(texture_2d("./Textures/T_Frame_M.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local2 = (float3(Color.x,Color.y,Color.z) * Local1.x);
float Local3 = math::lerp(RoughnessHigh,RoughnessLOW,Local1.x);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local2;
float Metallic_mdl = Metallic;
float Specular_mdl = 0.5;
float Roughness_mdl = Local3;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/MI_Grass_I_LayerGround_Tes0.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_Grass_I_LayerGround_Tes0(
uniform texture_2d WeightmapbaseGround_I = texture_2d("./Textures/WeightMapNullTexture.png",::tex::gamma_linear)
[[
anno::hidden(),
sampler_masks()
]],
float Tiling_I = 0.005
[[
anno::display_name("Tiling_I"),
anno::ui_order(32),
anno::in_group("baseGround_I")
]],
uniform texture_2d Normal = texture_2d("./Textures/gl1_ground_I_normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(32),
sampler_normal()
]],
uniform texture_2d Albedo = texture_2d("./Textures/gl1_ground_I_albedo.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::ui_order(32),
sampler_color()
]],
float Brightness_I = 1.0
[[
anno::display_name("Brightness_I"),
anno::ui_order(32),
anno::in_group("baseGround_I")
]],
float Roughness = 10.0
[[
anno::display_name("Roughness"),
anno::ui_order(32)
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float2 CustomizedUV1_mdl = float2(state::texture_coordinate(math::min(1,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(1,MaxTexCoordIndex)).y);
float4 Local0 = tex::lookup_float4(WeightmapbaseGround_I,float2(CustomizedUV1_mdl.x,1.0-CustomizedUV1_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local1 = math::dot(Local0, float4(1.0,0.0,0.0,0.0));
float2 Local4 = (float2((::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).x,(::convert_to_left_hand(state::transform_point(state::coordinate_internal,state::coordinate_world,state::position()), true)*state::meters_per_scene_unit()*100.0).y) * Tiling_I);
float4 Local5 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(Local4.x,1.0-Local4.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Local6 = (float3(Local5.x,Local5.y,Local5.z) * Local1);
float3 Local7 = (0.0 + Local6);
float3 Normal_mdl = Local7;
float4 Local8 = tex::lookup_float4(Albedo,float2(Local4.x,1.0-Local4.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local9 = (float3(Local8.x,Local8.y,Local8.z) * Brightness_I);
float3 Local10 = (Local9 * Local1);
float3 Local11 = (0.0 + Local10);
float Local12 = (Roughness * Local1);
float Local13 = (0.0 + Local12);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local11;
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local13;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/MI_Wheat_smdoejyr_2K.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Subsurface import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material MI_Wheat_smdoejyr_2K(
uniform texture_2d Normal = texture_2d("./Textures/Placeholder_Normal.png",::tex::gamma_linear)
[[
anno::display_name("Normal"),
anno::ui_order(2),
anno::in_group("01- Input Textures"),
sampler_normal()
]],
float NormalIntensity = 1.0
[[
anno::display_name("Normal Intensity"),
anno::ui_order(32),
anno::in_group("05 - Normal")
]],
float ColorVariation = 0.0
[[
anno::display_name("Color Variation"),
anno::ui_order(2),
anno::in_group("02 - Albedo")
]],
int NumberInstances = 0
[[
anno::hidden()
]],
uniform texture_2d Albedo = texture_2d("./Textures/DefaultDiffuse.png",::tex::gamma_srgb)
[[
anno::display_name("Albedo"),
anno::in_group("01- Input Textures"),
sampler_color()
]],
float4 ColorOverlay = float4(0.5,0.5,0.5,1.0)
[[
anno::display_name("Color Overlay"),
anno::ui_order(32),
anno::in_group("02 - Albedo")
]],
float OverlayIntensity = 1.0
[[
anno::display_name("Overlay Intensity"),
anno::ui_order(1),
anno::in_group("02 - Albedo"),
anno::soft_range(0.0, 1.5)
]],
uniform texture_2d ORT = texture_2d("./Textures/WhitePlaceholder.png",::tex::gamma_linear)
[[
anno::display_name("ORT"),
anno::ui_order(3),
anno::in_group("01- Input Textures"),
sampler_color()
]],
float RoughnessIntensity = 1.0
[[
anno::display_name("Roughness Intensity"),
anno::ui_order(32),
anno::in_group("03- Roughness")
]],
float OpacityIntensity = 1.0
[[
anno::display_name("Opacity Intensity"),
anno::ui_order(32),
anno::in_group("04 - Opacity")
]],
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
[[
dither_masked_off(),
distill_off()
]]
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(Normal,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float Local1 = (1.0 - NormalIntensity);
float3 Local2 = math::lerp(float3(Local0.x,Local0.y,Local0.z),float3(0.0,0.0,1.0),Local1);
float3 Normal_mdl = Local2;
float3 Local3 = (float3(100.0,10.0,1.0) * ::per_instance_random(texture_2d("./Textures/PerlinNoiseGradientTexture.png",tex::gamma_linear), NumberInstances));
float3 Local4 = (::object_world_position(true) * 0.01);
float3 Local5 = (Local3 + Local4);
float3 Local6 = math::frac(Local5);
float Local7 = math::dot(float2(Local6.x,Local6.y), float2(Local6.y,Local6.z));
float Local8 = (-0.5 + Local7);
float Local9 = (Local8 * 2.0);
float Local10 = (ColorVariation * Local9);
float3 Local11 = math::normalize(Local6);
float3 Local12 = (Local10 * Local11);
float4 Local13 = tex::lookup_float4(Albedo,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local14 = (1.0 - float3(Local13.x,Local13.y,Local13.z));
float3 Local15 = (Local14 * 2.0);
float3 Local16 = (1.0 - float3(ColorOverlay.x,ColorOverlay.y,ColorOverlay.z));
float3 Local17 = (Local15 * Local16);
float3 Local18 = (1.0 - Local17);
float3 Local19 = (float3(Local13.x,Local13.y,Local13.z) * 2.0);
float3 Local20 = (Local19 * float3(ColorOverlay.x,ColorOverlay.y,ColorOverlay.z));
float Local21 = ((float3(Local13.x,Local13.y,Local13.z).x >= 0.5) ? Local18.x : Local20.x);
float Local22 = ((float3(Local13.x,Local13.y,Local13.z).y >= 0.5) ? Local18.y : Local20.y);
float Local23 = ((float3(Local13.x,Local13.y,Local13.z).z >= 0.5) ? Local18.z : Local20.z);
float3 Local24 = math::lerp(float3(Local13.x,Local13.y,Local13.z),float3(float2(Local21,Local22).x,float2(Local21,Local22).y,Local23),OverlayIntensity);
float3 Local25 = (Local12 + Local24);
float4 Local26 = tex::lookup_float4(ORT,float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float Local27 = (Local26.y * RoughnessIntensity);
float Local28 = (Local26.x * OpacityIntensity);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float Opacity_mdl = 1.0;
float OpacityMask_mdl = (Local28 - 0.3333) < 0.0f ? 0.0f : 1.0f;
float3 BaseColor_mdl = Local25;
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local27;
float3 SubsurfaceColor_mdl = 0;
} in
::OmniUe4Subsurface(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: Opacity_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
opacity_mask: OpacityMask_mdl,
subsurface_color: SubsurfaceColor_mdl,
two_sided: true);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Metal_Chrome.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Metal_Chrome(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float2 Local0 = (CustomizedUV0_mdl * 0.2134);
float4 Local1 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local0.x,1.0-Local0.y),tex::wrap_repeat,tex::wrap_repeat);
float Local2 = (Local1.x + 0.5);
float2 Local3 = (CustomizedUV0_mdl * 0.05341);
float4 Local4 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local3.x,1.0-Local3.y),tex::wrap_repeat,tex::wrap_repeat);
float Local5 = (Local4.x + 0.5);
float2 Local6 = (CustomizedUV0_mdl * 0.002);
float4 Local7 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local6.x,1.0-Local6.y),tex::wrap_repeat,tex::wrap_repeat);
float Local8 = (Local7.x + 0.5);
float Local9 = (Local5 * Local8);
float Local10 = (Local2 * Local9);
float Local11 = math::lerp(1.0,0.9,Local10);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local11,Local11,Local11);
float Metallic_mdl = 1.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.25;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/T_Cow3_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Cow3_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Cow3_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Metal_Burnished_Steel.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Metal_Burnished_Steel(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Metal_Gold_N.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float3 Normal_mdl = float3(Local0.x,Local0.y,Local0.z);
float2 Local1 = (CustomizedUV0_mdl * 0.2134);
float4 Local2 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local1.x,1.0-Local1.y),tex::wrap_repeat,tex::wrap_repeat);
float Local3 = (Local2.x + 0.5);
float2 Local4 = (CustomizedUV0_mdl * 0.05341);
float4 Local5 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local4.x,1.0-Local4.y),tex::wrap_repeat,tex::wrap_repeat);
float Local6 = (Local5.x + 0.5);
float2 Local7 = (CustomizedUV0_mdl * 0.002);
float4 Local8 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local7.x,1.0-Local7.y),tex::wrap_repeat,tex::wrap_repeat);
float Local9 = (Local8.x + 0.5);
float Local10 = (Local6 * Local9);
float Local11 = (Local3 * Local10);
float3 Local12 = math::lerp(float3(0.5,0.5,0.5),float3(1.0,1.0,1.0),Local11);
float4 Local13 = tex::lookup_float4(texture_2d("./Textures/T_Metal_Aluminum_D.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local14 = (Local12 * float3(Local13.x,Local13.y,Local13.z));
float Local15 = (Local11 * Local13.w);
float Local16 = math::lerp(0.3,0.1,Local15);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local14;
float Metallic_mdl = 1.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local16;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/M_Ground_Gravel.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material M_Ground_Gravel(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float4 Local0 = ::unpack_normal_map(tex::lookup_float4(texture_2d("./Textures/T_Ground_Gravel_N.png",::tex::gamma_linear),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat));
float2 Local1 = (CustomizedUV0_mdl * 0.05);
float4 Local2 = tex::lookup_float4(texture_2d("./Textures/T_Perlin_Noise_M.png",::tex::gamma_linear),float2(Local1.x,1.0-Local1.y),tex::wrap_repeat,tex::wrap_repeat);
float Local3 = math::lerp(-2000.0,2000.0,Local2.x);
float Local4 = (Local3 + ::pixel_depth());
float Local5 = (Local4 - 1000.0);
float Local6 = (Local5 / 2000.0);
float Local7 = math::min(math::max(Local6,0.0),1.0);
float3 Local8 = math::lerp(float3(Local0.x,Local0.y,Local0.z),float3(0.0,0.0,1.0),Local7);
float3 Normal_mdl = Local8;
float2 Local9 = (CustomizedUV0_mdl * 0.2134);
float4 Local10 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local9.x,1.0-Local9.y),tex::wrap_repeat,tex::wrap_repeat);
float Local11 = (Local10.x + 0.5);
float2 Local12 = (CustomizedUV0_mdl * 0.05341);
float4 Local13 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local12.x,1.0-Local12.y),tex::wrap_repeat,tex::wrap_repeat);
float Local14 = (Local13.x + 0.5);
float2 Local15 = (CustomizedUV0_mdl * 0.002);
float4 Local16 = tex::lookup_float4(texture_2d("./Textures/T_MacroVariation.png",::tex::gamma_srgb),float2(Local15.x,1.0-Local15.y),tex::wrap_repeat,tex::wrap_repeat);
float Local17 = (Local16.x + 0.5);
float Local18 = (Local14 * Local17);
float Local19 = (Local11 * Local18);
float4 Local20 = tex::lookup_float4(texture_2d("./Textures/T_Ground_Gravel_D.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 Local21 = (Local19 * float3(Local20.x,Local20.y,Local20.z));
float Local22 = (Local19 * Local20.w);
float Local23 = math::lerp(0.8,0.4,Local22);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = Local21;
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = Local23;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
An-u-rag/synthetic-visual-dataset-generation/AnuragStage/IndoorRanch/Materials/T_Cow1_BaseColor_Mat.mdl | mdl 1.6;
import ::math::*;
import ::state::*;
import ::tex::*;
import ::anno::*;
import ::scene::*;
using .::OmniUe4Function import *;
using .::OmniUe4Base import *;
export annotation sampler_color();
export annotation sampler_normal();
export annotation sampler_grayscale();
export annotation sampler_alpha();
export annotation sampler_masks();
export annotation sampler_distancefield();
export annotation dither_masked_off();
export annotation world_space_normal();
export material T_Cow1_BaseColor_Mat(
int MaxTexCoordIndex = 3
[[
anno::hidden()
]])
=
let {
float3 WorldPositionOffset_mdl = float3(0.0,0.0,0.0);
float2 CustomizedUV0_mdl = float2(state::texture_coordinate(math::min(0,MaxTexCoordIndex)).x,1.0-state::texture_coordinate(math::min(0,MaxTexCoordIndex)).y);
float3 Normal_mdl = float3(0.0,0.0,1.0);
float4 Local0 = tex::lookup_float4(texture_2d("./Textures/T_Cow1_BaseColor.png",::tex::gamma_srgb),float2(CustomizedUV0_mdl.x,1.0-CustomizedUV0_mdl.y),tex::wrap_repeat,tex::wrap_repeat);
float3 EmissiveColor_mdl = float3(0.0,0.0,0.0);
float OpacityMask_mdl = 1.0;
float3 BaseColor_mdl = float3(Local0.x,Local0.y,Local0.z);
float Metallic_mdl = 0.0;
float Specular_mdl = 0.5;
float Roughness_mdl = 0.5;
} in
::OmniUe4Base(
base_color: BaseColor_mdl,
metallic: Metallic_mdl,
roughness: Roughness_mdl,
specular: Specular_mdl,
normal: Normal_mdl,
opacity: OpacityMask_mdl,
emissive_color: EmissiveColor_mdl,
displacement: WorldPositionOffset_mdl,
two_sided: false);
|
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