{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mask2former import Mask2Former\n", "import torch as tr\n", "import os\n", "from datetime import datetime\n", "import numpy as np\n", "from PIL import Image\n", "from vre.utils import (FFmpegVideo, collage_fn, semantic_mapper, FakeVideo,\n", " colorize_semantic_segmentation, image_resize, image_write)\n", "from pathlib import Path\n", "import pandas as pd\n", "from torchmetrics.functional.classification import multiclass_stat_scores\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapi_mapping = {\n", " \"land\": [\"Terrain\", \"Sand\", \"Snow\"],\n", " \"forest\": [\"Vegetation\"],\n", " \"residential\": [\"Building\", \"Utility Pole\", \"Pole\", \"Fence\", \"Wall\", \"Manhole\", \"Street Light\", \"Curb\",\n", " \"Guard Rail\", \"Caravan\", \"Junction Box\", \"Traffic Sign (Front)\", \"Billboard\", \"Banner\",\n", " \"Mailbox\", \"Traffic Sign (Back)\", \"Bench\", \"Fire Hydrant\", \"Trash Can\", \"CCTV Camera\",\n", " \"Traffic Light\", \"Barrier\", \"Rail Track\", \"Phone Booth\", \"Curb Cut\", \"Traffic Sign Frame\",\n", " \"Bike Rack\"],\n", " \"road\": [\"Road\", \"Lane Marking - General\", \"Sidewalk\", \"Bridge\", \"Other Vehicle\", \"Motorcyclist\", \"Pothole\",\n", " \"Catch Basin\", \"Car Mount\", \"Tunnel\", \"Parking\", \"Service Lane\", \"Lane Marking - Crosswalk\",\n", " \"Pedestrian Area\", \"On Rails\", \"Bike Lane\", \"Crosswalk - Plain\"],\n", " \"little-objects\": [\"Car\", \"Person\", \"Truck\", \"Boat\", \"Wheeled Slow\", \"Trailer\", \"Ground Animal\", \"Bicycle\",\n", " \"Motorcycle\", \"Bird\", \"Bus\", \"Ego Vehicle\", \"Bicyclist\", \"Other Rider\"],\n", " \"water\": [\"Water\"],\n", " \"sky\": [\"Sky\"],\n", " \"hill\": [\"Mountain\"]\n", "}\n", "\n", "coco_mapping = {\n", " \"land\": [\"grass-merged\", \"dirt-merged\", \"sand\", \"gravel\", \"flower\", \"playingfield\", \"snow\", \"platform\"],\n", " \"forest\": [\"tree-merged\"],\n", " \"residential\": [\"building-other-merged\", \"house\", \"roof\", \"fence-merged\", \"wall-other-merged\", \"wall-brick\",\n", " \"rock-merged\", \"tent\", \"bridge\", \"bench\", \"window-other\", \"fire hydrant\", \"traffic light\",\n", " \"umbrella\", \"wall-stone\", \"clock\", \"chair\", \"sports ball\", \"floor-other-merged\",\n", " \"floor-wood\", \"stop sign\", \"door-stuff\", \"banner\", \"light\", \"net\", \"surfboard\", \"frisbee\",\n", " \"rug-merged\", \"potted plant\", \"parking meter\", \"tennis racket\", \"sink\", \"hair drier\",\n", " \"food-other-merged\", \"curtain\", \"mirror-stuff\", \"baseball glove\", \"baseball bat\", \"zebra\",\n", " \"spoon\", \"towel\", \"donut\", \"apple\", \"handbag\", \"couch\", \"orange\", \"wall-wood\",\n", " \"window-blind\", \"pizza\", \"cabinet-merged\", \"skateboard\", \"remote\", \"bottle\", \"bed\",\n", " \"table-merged\", \"backpack\", \"bear\", \"wall-tile\", \"cup\", \"scissors\", \"ceiling-merged\",\n", " \"oven\", \"cell phone\", \"microwave\", \"toaster\", \"carrot\", \"fork\", \"giraffe\", \"paper-merged\",\n", " \"cat\", \"book\", \"sandwich\", \"wine glass\", \"pillow\", \"blanket\", \"tie\", \"bowl\", \"snowboard\",\n", " \"vase\", \"toothbrush\", \"toilet\", \"dining table\", \"laptop\", \"tv\", \"cardboard\", \"keyboard\",\n", " \"hot dog\", \"cake\", \"knife\", \"suitcase\", \"refrigerator\", \"fruit\", \"shelf\", \"counter\", \"skis\",\n", " \"banana\", \"teddy bear\", \"broccoli\", \"mouse\"],\n", " \"road\": [\"road\", \"railroad\", \"pavement-merged\", \"stairs\"],\n", " \"little-objects\": [\"truck\", \"car\", \"boat\", \"horse\", \"person\", \"train\", \"elephant\", \"bus\", \"bird\", \"sheep\",\n", " \"cow\", \"motorcycle\", \"dog\", \"bicycle\", \"airplane\", \"kite\"],\n", " \"water\": [\"river\", \"water-other\", \"sea\"],\n", " \"sky\": [\"sky-other-merged\"],\n", " \"hill\": [\"mountain-merged\"]\n", "}\n", "\n", "color_map = [[0, 255, 0], [0, 127, 0], [255, 255, 0], [255, 255, 255],\n", " [255, 0, 0], [0, 0, 255], [0, 255, 255], [127, 127, 63]]\n", "\n", "def eval(y: np.ndarray, gt: np.ndarray) -> float:\n", " tp, fp, _, fn = multiclass_stat_scores(tr.from_numpy(y), tr.from_numpy(gt), num_classes=8, average=None)[:, 0:4].T\n", " iou = (tp / (tp + fp + fn)).nan_to_num(0, 0, 0)\n", " weights = tr.FloatTensor([0.28172092, 0.30589653, 0.13341699, 0.05937348,\n", " 0.00474491, 0.05987466, 0.08660721, 0.06836531])\n", " iou_avg = (iou * weights).sum().item()\n", " return iou_avg\n", "\n", "def collage_fn2(images: list[np.ndarray], size: tuple[int, int], **kwargs):\n", " images_rsz = [image_resize(image, *size) for image in images]\n", " return collage_fn(images_rsz, **kwargs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "video = FFmpegVideo((\"/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/raw_data/videos\"\n", " \"/norway_210821_DJI_0015_full/DJI_0015.MP4\"))\n", "gt_dir = (\"/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/data/\"\n", " \"test_set_annotated_only/semantic_segprop8/norway_210821_DJI_0015_full_\")\n", "print(video)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_id = \"49189528_0\" # \"49189528_1\" (r50/mapillary), \"47429163_0\" (swin/coco), \"49189528_0\" (swin/mapillary)\n", "os.environ[\"VRE_DEVICE\"] = device = \"cuda\" #\"cpu\"\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"7\"\n", "\n", "m2f_1 = Mask2Former(model_id, disk_data_argmax=False, name=\"m2f\", dependencies=[])\n", "m2f_2 = Mask2Former(\"47429163_0\", disk_data_argmax=False, name=\"m2f\", dependencies=[])\n", "m2f_3 = Mask2Former(\"49189528_1\", disk_data_argmax=False, name=\"m2f\", dependencies=[])\n", "\n", "m2f_1.device = \"cuda\" if tr.cuda.is_available() else \"cpu\"\n", "m2f_2.device = \"cuda\" if tr.cuda.is_available() else \"cpu\"\n", "m2f_3.device = \"cuda\" if tr.cuda.is_available() else \"cpu\"\n", "\n", "metrics = {}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "frame_ix = 900\n", "def load_gt(ix: int) -> np.ndarray:\n", " gt_path = f\"{gt_dir}{ix}.npz\"\n", " assert Path(gt_path).exists(), gt_path\n", " gt_data = np.load(gt_path)[\"arr_0\"]\n", " return gt_data\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def m2f_do_one(m2f: Mask2Former, frame: np.ndarray, gt_data_shape, mapping: dict) -> tuple[np.ndarray, np.ndarray]:\n", " m2f_1.vre_free() if m2f_1.setup_called and id(m2f) != id(m2f_1) else None\n", " m2f_2.vre_free() if m2f_2.setup_called and id(m2f) != id(m2f_1) else None\n", " m2f_3.vre_free() if m2f_3.setup_called and id(m2f) != id(m2f_1) else None\n", " m2f.vre_setup() if not m2f.setup_called else None\n", "\n", " now = datetime.now()\n", " m2f.data = None\n", " m2f.compute(FakeVideo(frame[None], fps=1), [0])\n", " print(f\"Pred took: {datetime.now() - now}\"); now = datetime.now()\n", " m2f_mapped = semantic_mapper(m2f.data.output.argmax(-1)[0], mapping, m2f.classes)\n", " m2f_mapped = image_resize(m2f_mapped, *gt_data_shape, interpolation=\"nearest\")\n", " print(f\"semantic_mapper took: {datetime.now() - now}\"); now = datetime.now()\n", " m2f_colorized = colorize_semantic_segmentation(m2f_mapped[None], list(mapping), color_map, rgb=rgb_rsz[None])[0]\n", " print(f\"colorize took: {datetime.now() - now}\"); now = datetime.now()\n", " return m2f_mapped, m2f_colorized\n", "\n", "def eval_and_store(frame, frame_ix, res_all: list[tuple[np.ndarray], np.ndarray], gt_color: np.ndarray,\n", " columns: list[str]):\n", " collage_data = []\n", " for item in res_all:\n", " collage_data.extend([frame, item[1], gt_color])\n", " clg = collage_fn2(collage_data, size=gt_color.shape[0:2], rows_cols=(-1, 3))\n", " image_write(clg, f\"collage_{frame_ix}.png\")\n", " display(Image.fromarray(clg))\n", " evals = [eval(item[0], gt_data) for item in res_all]\n", "\n", " try:\n", " metrics = pd.read_csv(\"metrics.csv\", index_col=0)\n", " except Exception as e:\n", " metrics = pd.DataFrame(None, columns=columns)\n", "\n", " metrics.loc[frame_ix] = evals\n", " display(metrics.sort_index())\n", " metrics.to_csv(\"metrics.csv\")\n", "\n", "for frame_ix in [60, 120, 300, 600, 900, 1200, 1500]:\n", " frame, gt_data = video[frame_ix], load_gt(frame_ix)\n", " rgb_rsz = image_resize(frame, *gt_data.shape)\n", " gt_color = colorize_semantic_segmentation(gt_data[None], classes=list(mapi_mapping), color_map=color_map,\n", " rgb=rgb_rsz[None])[0]\n", " mapped1, colorized1 = m2f_do_one(m2f_1, frame, gt_data.shape, mapi_mapping)\n", " mapped2, colorized2 = m2f_do_one(m2f_2, frame, gt_data.shape, coco_mapping)\n", " mapped3, colorized3 = m2f_do_one(m2f_3, frame, gt_data.shape, mapi_mapping)\n", "\n", " mapped1_rsz, colorized1_rsz = m2f_do_one(m2f_1, rgb_rsz, gt_data.shape, mapi_mapping)\n", " mapped2_rsz, colorized2_rsz = m2f_do_one(m2f_2, rgb_rsz, gt_data.shape, coco_mapping)\n", " mapped3_rsz, colorized3_rsz = m2f_do_one(m2f_3, rgb_rsz, gt_data.shape, mapi_mapping)\n", "\n", " all_res = [\n", " (mapped1, colorized1), (mapped2, colorized2), (mapped3, colorized3),\n", " (mapped1_rsz, colorized1_rsz), (mapped2_rsz, colorized2_rsz), (mapped3_rsz, colorized3_rsz),\n", " ]\n", " columns = [\"swin_mapillary\", \"swin_coco\", \"r50_mapillary\",\n", " \"swin_mapillary_rsz\", \"swin_coco_rsz\", \"r50_mapillary_rsz\"]\n", "\n", " eval_and_store(frame, frame_ix, all_res, gt_color, columns)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "ngc", "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 2 }