| # Dronescapes dataset | |
| As introduced in our ICCV 2023 workshop paper: [link](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | |
|  | |
| # 1. Downloading the data | |
| ## Option 1. Download the pre-processed dataset from HuggingFace repository | |
| ``` | |
| git lfs install # Make sure you have git-lfs installed (https://git-lfs.com) | |
| git clone https://huggingface.co/datasets/Meehai/dronescapes | |
| ``` | |
| Note: the dataset has about 500GB, so it may take a while to clone it. | |
| <details> | |
| <summary> <b> Option 2. Generating the dataset from raw videos and basic labels </b>.</summary> | |
| Recommended if you intend on understanding how the dataset was created or add new videos or representations. | |
| ### 1.2.1 Raw videos | |
| Follow the commands in each directory under `raw_data/videos/*/commands.txt` if you want to start from the 4K videos. | |
| If you only want the 540p videos as used in the paper, they are already provided in the `raw_data/videos/*` directories. | |
| ### 1.2.2 Semantic segmentation labels (human annotated) | |
| These were human annotated and then propagated using [segprop](https://github.com/vlicaret/segprop). | |
| ```bash | |
| cd raw_data/ | |
| tar -xzvf segprop_npz_540.tar.gz | |
| ``` | |
| ### 1.2.3 Generate the rest of the representations | |
| We use the [video-representations-extractor](https://gitlab.com/meehai/video-representations-extractor) to generate | |
| the rest of the labels using pre-traing networks or algoritms. | |
| Install it via `pip install video-representations-extractor` (or follow the README over there for docker or local env) | |
| ``` | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=0 vre raw_data/videos/atanasie_DJI_0652_full/atanasie_DJI_0652_full_540p.mp4 -o raw_data/npz_540p/atanasie_DJI_0652_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=1 vre raw_data/videos/barsana_DJI_0500_0501_combined_sliced_2700_14700/barsana_DJI_0500_0501_combined_sliced_2700_14700_540p.mp4 -o raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=2 vre raw_data/videos/comana_DJI_0881_full/comana_DJI_0881_full_540p.mp4 -o raw_data/npz_540p/comana_DJI_0881_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=3 vre raw_data/videos/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110_540p.mp4 -o raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=4 vre raw_data/videos/herculane_DJI_0021_full/herculane_DJI_0021_full_540p.mp4 -o raw_data/npz_540p/herculane_DJI_0021_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=5 vre raw_data/videos/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715_540p.mp4 -o raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=6 vre raw_data/videos/norway_210821_DJI_0015_full/norway_210821_DJI_0015_full_540p.mp4 -o raw_data/npz_540p/norway_210821_DJI_0015_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=7 vre raw_data/videos/olanesti_DJI_0416_full/olanesti_DJI_0416_full_540p.mp4 -o raw_data/npz_540p/olanesti_DJI_0416_full/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=0 vre raw_data/videos/petrova_DJI_0525_0526_combined_sliced_2850_11850/petrova_DJI_0525_0526_combined_sliced_2850_11850_540p.mp4 -o raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=1 vre raw_data/videos/slanic_DJI_0956_0957_combined_sliced_780_9780/slanic_DJI_0956_0957_combined_sliced_780_9780_540p.mp4 -o raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/ --cfg_path scripts/cfg.yaml --batch_size 3 --n_threads_data_storer 4 --output_dir_exist_mode overwrite | |
| ``` | |
| Note: `depth_sfm`, `normals_sfm` and `depth_ufo` are not available in VRE. Contact us for more info about them. | |
| Note: Add `--representations "rgb" "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" "softseg_gb"` to control if you only want a subset of the representations. | |
| Note: Some batch sizes are overwritten in the config itself. | |
| ### 1.2.4 Convert Mask2Former from Mapillary classes to segprop8 classes | |
| Since we are using pre-trained Mask2Former which has either mapillary or COCO panoptic classes, we need to convert them to dronescapes-compatible (8) classes. | |
| To do this, we use the `scripts/convert_m2f_to_dronescapes.py` script: | |
| ``` | |
| python scripts/convert_m2f_to_dronescapes.py in_dir out_dir mapillary/coco [--overwrite] | |
| ``` | |
| ``` | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/atanasie_DJI_0652_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/atanasie_DJI_0652_full/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/semantic_mask2former_swin_mapillary raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/comana_DJI_0881_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/comana_DJI_0881_full/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/semantic_mask2former_swin_mapillary raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/herculane_DJI_0021_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/herculane_DJI_0021_full/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/semantic_mask2former_swin_mapillary raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/norway_210821_DJI_0015_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/norway_210821_DJI_0015_full/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/olanesti_DJI_0416_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/olanesti_DJI_0416_full/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/semantic_mask2former_swin_mapillary raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/semantic_mask2former_swin_mapillary_converted mapillary | |
| python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/semantic_mask2former_swin_mapillary raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/semantic_mask2former_swin_mapillary_converted mapillary | |
| ``` | |
| ### 1.2.5 Check counts for consistency | |
| Run: `bash scripts/count_npz.sh raw_data/npz_540p`. At this point it should return: | |
| | scene | rgb | depth_dpt | depth_sfm_manual20.. | edges_dexined | normals_sfm_manual.. | opticalflow_rife | semantic_mask2form.. | semantic_segprop8 | | |
| |:----------|------:|------------:|-----------------------:|----------------:|-----------------------:|-------------------:|-----------------------:|--------------------:| | |
| | atanasie | 9021 | 9021 | 9020 | 9021 | 9020 | 9021 | 9021 | 9001 | | |
| | barsana | 12001 | 12001 | 12001 | 12001 | 12001 | 12000 | 12001 | 1573 | | |
| | comana | 9022 | 9022 | 0 | 9022 | 0 | 9022 | 9022 | 1210 | | |
| | gradistei | 9601 | 9601 | 9600 | 9601 | 9600 | 9600 | 9601 | 1210 | | |
| | herculane | 9022 | 9022 | 9021 | 9022 | 9021 | 9022 | 9022 | 1210 | | |
| | jupiter | 11066 | 11066 | 11065 | 11066 | 11065 | 11066 | 11066 | 1452 | | |
| | norway | 2983 | 2983 | 0 | 2983 | 0 | 2983 | 2983 | 2941 | | |
| | olanesti | 9022 | 9022 | 9021 | 9022 | 9021 | 9022 | 9022 | 1210 | | |
| | petrova | 9001 | 9001 | 9001 | 9001 | 9001 | 9000 | 9001 | 1210 | | |
| | slanic | 9001 | 9001 | 9001 | 9001 | 9001 | 9000 | 9001 | 9001 | | |
| ### 1.2.6. Split intro train, validation, semisupervised and train | |
| We include 8 splits: 4 using only GT annotated semantic data and 4 using all available data (i.e. segproped between | |
| annotated data). The indexes are taken from `txt_files/*`, i.e. `txt_files/manually_adnotated_files/test_files_116.txt` | |
| refers to the fact that the (unseen at train time) test set (norway + petrova + barsana) contains 116 manually | |
| annotated semantic files. We include all representations from above, not just semantic for all possible splits. | |
| Adding new representations is as simple as running VRE on the 540p mp4 file | |
| ``` | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/train_files_11664.txt -o data/train_set --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/val_files_605.txt -o data/validation_set --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/semisup_files_11299.txt -o data/semisupervised_set --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/annotated_and_segprop/test_files_5603.txt -o data/test_set --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/train_files_218.txt -o data/train_set_annotated_only --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/val_files_15.txt -o data/validation_set_annotated_only --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/semisup_files_207.txt -o data/semisupervised_set_annotated_nly --overwrite | |
| python scripts/symlinks_from_txt_list.py raw_data/npz_540p/ --txt_file txt_files/manually_annotated_files/test_files_116.txt -o data/test_set_annotated_only --overwrite | |
| ``` | |
| Note: `add --copy_files` if you want to make copies instead of using symlinks. | |
| Upon calling this, you should be able to see something like this: | |
| ``` | |
| user> ls data/* | |
| data/semisupervised_set: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/semisupervised_set_annotated_nly: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/test_set: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/test_set_annotated_nly: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/train_set: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/train_set_annotated_only: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/validation_set: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| data/validation_set_annotated_only: | |
| depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted | |
| depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 | |
| ``` | |
| ### 1.2.7 Convert Camera Normals to World Normals | |
| This is an optional step, but for some use cases, it may be better to use world normals instead of camera normals, which | |
| are provided by default in `normals_sfm_manual202204`. To convert, we provide camera rotation matrices in | |
| `raw_data/camera_matrics.tar.gz` for all 8 scenes that also have SfM. | |
| In order to convert, use this function (for each npz file): | |
| ``` | |
| def convert_camera_to_world(normals: np.ndarray, rotation_matrix: np.ndarray) -> np.ndarray: | |
| normals = (normals.copy() - 0.5) * 2 # [-1:1] -> [0:1] | |
| camera_normals = camera_normals @ np.linalg.inv(rotation_matrix) | |
| camera_normals = (camera_normals / 2) + 0.5 # [0:1] => [-1:1] | |
| return np.clip(camera_normals, 0.0, 1.0) | |
| ``` | |
| </details> | |
| ## 2. Using the data | |
| As per the split from the paper: | |
| <details> | |
| <summary> Split </summary> | |
| <img src="split.png"> | |
| </details> | |
| The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface). | |
| ## 2.1 Using the provided viewer | |
|  | |
| The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running | |
| it, you should get a collage with all the default tasks, like the picture at the top. | |
| For a CLI-only method, you can use the provided reader as well: | |
| ``` | |
| python scripts/dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories in data/ | |
| ``` | |
| <details> | |
| <summary> Expected output </summary> | |
| ``` | |
| [MultiTaskDataset] | |
| - Path: '/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/data/test_set_annotated_only' | |
| - Tasks (11): [DepthRepresentation(depth_dpt), DepthRepresentation(depth_sfm_manual202204), DepthRepresentation(depth_ufo), ColorRepresentation(edges_dexined), EdgesRepresentation(edges_gb), NpzRepresentation(normals_sfm_manual202204), OpticalFlowRepresentation(opticalflow_rife), ColorRepresentation(rgb), SemanticRepresentation(semantic_mask2former_swin_mapillary_converted), SemanticRepresentation(semantic_segprop8), ColorRepresentation(softseg_gb)] | |
| - Length: 116 | |
| - Handle missing data mode: 'fill_none' | |
| == Shapes == | |
| {'depth_dpt': torch.Size([540, 960]), | |
| 'depth_sfm_manual202204': torch.Size([540, 960]), | |
| 'depth_ufo': torch.Size([540, 960, 1]), | |
| 'edges_dexined': torch.Size([540, 960]), | |
| 'edges_gb': torch.Size([540, 960, 1]), | |
| 'normals_sfm_manual202204': torch.Size([540, 960, 3]), | |
| 'opticalflow_rife': torch.Size([540, 960, 2]), | |
| 'rgb': torch.Size([540, 960, 3]), | |
| 'semantic_mask2former_swin_mapillary_converted': torch.Size([540, 960, 8]), | |
| 'semantic_segprop8': torch.Size([540, 960, 8]), | |
| 'softseg_gb': torch.Size([540, 960, 3])} | |
| == Random loaded item == | |
| {'depth_dpt': tensor[540, 960] n=518400 (2.0Mb) x∈[0.043, 1.000] μ=0.341 σ=0.418, | |
| 'depth_sfm_manual202204': None, | |
| 'depth_ufo': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0.115, 0.588] μ=0.297 σ=0.138, | |
| 'edges_dexined': tensor[540, 960] n=518400 (2.0Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001, | |
| 'edges_gb': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0., 1.000] μ=0.063 σ=0.100, | |
| 'normals_sfm_manual202204': None, | |
| 'opticalflow_rife': tensor[540, 960, 2] n=1036800 (4.0Mb) x∈[-0.004, 0.005] μ=0.000 σ=0.000, | |
| 'rgb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 1.000] μ=0.392 σ=0.238, | |
| 'semantic_mask2former_swin_mapillary_converted': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331, | |
| 'semantic_segprop8': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331, | |
| 'softseg_gb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 0.004] μ=0.002 σ=0.001} | |
| == Random loaded batch == | |
| {'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.043, 1.000] μ=0.340 σ=0.417, | |
| 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) NaN!, | |
| 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.115, 0.588] μ=0.296 σ=0.137, | |
| 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001, | |
| 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.063 σ=0.102, | |
| 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) NaN!, | |
| 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.004, 0.006] μ=0.000 σ=0.000, | |
| 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.393 σ=0.238, | |
| 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, | |
| 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, | |
| 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001} | |
| == Random loaded batch using torch DataLoader == | |
| {'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.025, 1.000] μ=0.216 σ=0.343, | |
| 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.562 σ=0.335 NaN!, | |
| 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.100, 0.580] μ=0.290 σ=0.128, | |
| 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001, | |
| 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.079 σ=0.116, | |
| 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0.000, 1.000] μ=0.552 σ=0.253 NaN!, | |
| 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.013, 0.016] μ=0.000 σ=0.004, | |
| 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.338 σ=0.237, | |
| 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, | |
| 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, | |
| 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001} | |
| ``` | |
| </details> | |
| ## 3. Evaluation for semantic segmentation | |
| We evaluate in the paper on the 3 test scenes (unsees at train) as well as the semi-supervised scenes (seen, but | |
| different split) against the human annotated frames. The general evaluation script is in | |
| `scripts/evaluate_semantic_segmentation.py`. | |
| General usage is: | |
| ``` | |
| python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --classes C1 C2 .. Cn | |
| [--class_weights W1 W2 ... Wn] [--scenes s1 s2 ... sm] | |
| ``` | |
| <details> | |
| <summary> Script explanation </summary> | |
| The script is a bit convoluted, so let's break it into parts: | |
| - `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset (y_dir/1.npz, gt_dir/55.npz etc.) | |
| - `classes` A list of classes in the order that they appear in the predictions and gt files | |
| - `class_weights` (optional, but used in paper) How much to weigh each class. In the paper we compute these weights as | |
| the number of pixels in all the dataset (train/val/semisup/test) for each of the 8 classes resulting in the numbers | |
| below. | |
| - `scenes` if the `y_dir` and `gt_dir` contains multiple scenes that you want to evaluate separately, the script allows | |
| you to pass the prefix of all the scenes. For example, in `data/test_set_annotated_only/semantic_segprop8/` there are | |
| actually 3 scenes in the npz files and in the paper, we evaluate each scene independently. Even though the script | |
| outputs one csv file with predictions for each npz file, the scenes are used for proper aggregation at scene level. | |
| </details> | |
| <details> | |
| <summary> Reproducing paper results for Mask2Former </summary> | |
| ``` | |
| python scripts/evaluate_semantic_segmentation.py \ | |
| data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # change this with your predictions dir | |
| data/test_set_annotated_only/semantic_segprop8/ \ | |
| -o results.csv \ | |
| --classes land forest residential road little-objects water sky hill \ | |
| --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 \ | |
| --scenes barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full | |
| ``` | |
| Should output: | |
| ``` | |
| scene iou f1 | |
| barsana_DJI_0500_0501_combined_sliced_2700_14700 63.371 75.338 | |
| comana_DJI_0881_full 60.559 73.779 | |
| norway_210821_DJI_0015_full 37.986 45.939 | |
| mean 53.972 65.019 | |
| ``` | |
| Not providing `--scenes` will make an average across all 3 scenes (not average after each metric individually): | |
| ``` | |
| iou f1 | |
| scene | |
| all 60.456 73.261 | |
| ``` | |
| </details> | |
| ### 3.1 Official benchmark | |
| #### IoU | |
| | method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full | | |
| |:-|:-|:-|:-|:-|:-| | |
| | [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 53.97 | 63.37 | 60.55 | 37.98 | | |
| | [NGC(LR)](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | 32M | 40.75 | 46.51 | 45.59 | 30.17 | | |
| | [CShift](https://www.bmvc2021-virtualconference.com/assets/papers/0455.pdf)[^1] | n/a | 39.67 | 46.27 | 43.67 | 29.09 | | |
| | [NGC](https://cdn.aaai.org/ojs/16283/16283-13-19777-1-2-20210518.pdf)[^1] | 32M | 35.32 | 44.34 | 38.99 | 22.63 | | |
| | [SafeUAV](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11130/Marcu_SafeUAV_Learning_to_estimate_depth_and_safe_landing_areas_for_ECCVW_2018_paper.pdf)[^1] | 1.1M | 32.79 | n/a | n/a | n/a | | |
| [^1]: reported in the [Dronescapes paper](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf). | |
| #### F1 Score | |
| | method | #paramters | average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full | norway_210821_DJI_0015_full | | |
| |:-|:-|:-|:-|:-|:-| | |
| | [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 65.01 | 75.33 | 73.77 | 45.93 | | |