| # 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 300GB, 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. | |
| ``` | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| 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 --representations rgb "opticalflow_rife" "depth_dpt" "edges_dexined" "semantic_mask2former_swin_mapillary" | |
| ``` | |
| ### 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_nly --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 | |
| ``` | |
| </details> | |
| ## 2. Using the data | |
| As per the split from the paper: | |
|  | |
| 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 | |
| Basic usage: | |
| ``` | |
| 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: '/scratch/sdc/datasets/dronescapes/data/test_set_annotated_only' | |
| - Only full data: False | |
| - Representations (8): [NpzRepresentation(depth_dpt), NpzRepresentation(depth_sfm_manual202204), NpzRepresentation(edges_dexined), NpzRepresentation(normals_sfm_manual202204), NpzRepresentation(opticalflow_rife), NpzRepresentation(rgb), NpzRepresentation(semantic_mask2former_swin_mapillary_converted), NpzRepresentation(semantic_segprop8)] | |
| - Length: 116 | |
| == Shapes == | |
| {'depth_dpt': torch.Size([540, 960]), | |
| 'depth_sfm_manual202204': torch.Size([540, 960]), | |
| 'edges_dexined': torch.Size([540, 960]), | |
| '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]), | |
| 'semantic_segprop8': torch.Size([540, 960])} | |
| == Random loaded item == | |
| /export/home/proiecte/aux/mihai_cristian.pirvu/.conda/envs/ngc/lib/python3.10/site-packages/numpy/core/_methods.py:215: RuntimeWarning: overflow encountered in reduce | |
| arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where) | |
| {'depth_dpt': tensor[540, 960] x∈[0.031, 1.000] μ=0.060 σ=0.038, | |
| 'depth_sfm_manual202204': tensor[540, 960] f16 x∈[0., 1.195e+03] μ=360.250 σ=inf, | |
| 'edges_dexined': tensor[540, 960] x∈[0.131, 1.000] μ=0.848 σ=0.188, | |
| 'normals_sfm_manual202204': tensor[540, 960, 3] f16 x∈[0.000, 1.000] μ=0.525 σ=inf, | |
| 'opticalflow_rife': tensor[540, 960, 2] f16 x∈[-0.000, 0.007] μ=0.002 σ=0.002, | |
| 'rgb': tensor[540, 960, 3] u8 x∈[0, 255] μ=68.154 σ=33.902, | |
| 'semantic_mask2former_swin_mapillary_converted': tensor[540, 960] u8 x∈[0, 7] μ=3.591 σ=3.058, | |
| 'semantic_segprop8': tensor[540, 960] u8 x∈[0, 6] μ=1.057 σ=0.916} | |
| == Random loaded batch == | |
| {'depth_dpt': torch.Size([5, 540, 960]), | |
| 'depth_sfm_manual202204': torch.Size([5, 540, 960]), | |
| 'edges_dexined': torch.Size([5, 540, 960]), | |
| 'normals_sfm_manual202204': torch.Size([5, 540, 960, 3]), | |
| 'opticalflow_rife': torch.Size([5, 540, 960, 2]), | |
| 'rgb': torch.Size([5, 540, 960, 3]), | |
| 'semantic_mask2former_swin_mapillary_converted': torch.Size([5, 540, 960]), | |
| 'semantic_segprop8': torch.Size([5, 540, 960])} | |
| == Random loaded batch using torch DataLoader == | |
| {'depth_dpt': torch.Size([5, 540, 960]), | |
| 'depth_sfm_manual202204': torch.Size([5, 540, 960]), | |
| 'edges_dexined': torch.Size([5, 540, 960]), | |
| 'normals_sfm_manual202204': torch.Size([5, 540, 960, 3]), | |
| 'opticalflow_rife': torch.Size([5, 540, 960, 2]), | |
| 'rgb': torch.Size([5, 540, 960, 3]), | |
| 'semantic_mask2former_swin_mapillary_converted': torch.Size([5, 540, 960]), | |
| 'semantic_segprop8': torch.Size([5, 540, 960])} | |
| ``` | |
| </details> | |
| ## TODOs | |
| - convert camera normals to world normals | |
| - add semantics for each representation in a DronescapesReader | |
| - evaluation script for sseg | |