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# Dronescapes dataset |
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![Logo](logo.png) |
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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) |
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# 1. Downloading the data |
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## Option 1. Download the pre-processed dataset from HuggingFace repository |
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``` |
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git lfs install # Make sure you have git-lfs installed (https://git-lfs.com) |
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git clone https://huggingface.co/datasets/Meehai/dronescapes |
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``` |
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Note: the dataset has about 300GB, so it may take a while to clone it. |
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<details> |
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<summary> <b> Option 2. Generating the dataset from raw videos and basic labels </b>.</summary> |
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Recommended if you intend on understanding how the dataset was created or add new videos or representations. |
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### 1.2.1 Raw videos |
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Follow the commands in each directory under `raw_data/videos/*/commands.txt` if you want to start from the 4K videos. |
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If you only want the 540p videos as used in the paper, they are already provided in the `raw_data/videos/*` directories. |
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### 1.2.2 Semantic segmentation labels (human annotated) |
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These were human annotated and then propagated using [segprop](https://github.com/vlicaret/segprop). |
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```bash |
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cd raw_data/ |
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tar -xzvf segprop_npz_540.tar.gz |
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``` |
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### 1.2.3 Generate the rest of the representations |
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We use the [video-representations-extractor](https://gitlab.com/meehai/video-representations-extractor) to generate |
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the rest of the labels using pre-traing networks or algoritms. |
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``` |
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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" |
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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" |
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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" |
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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" |
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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" |
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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" |
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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" |
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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" |
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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" |
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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" |
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``` |
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### 1.2.4 Convert Mask2Former from Mapillary classes to segprop8 classes |
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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. |
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To do this, we use the `scripts/convert_m2f_to_dronescapes.py` script: |
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``` |
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python scripts/convert_m2f_to_dronescapes.py in_dir out_dir mapillary/coco [--overwrite] |
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``` |
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``` |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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``` |
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### 1.2.5 Check counts for consistency |
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Run: `bash scripts/count_npz.sh raw_data/npz_540p`. At this point it should return: |
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| scene | rgb | depth_dpt | depth_sfm_manual20.. | edges_dexined | normals_sfm_manual.. | opticalflow_rife | semantic_mask2form.. | semantic_segprop8 | |
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|:----------|------:|------------:|-----------------------:|----------------:|-----------------------:|-------------------:|-----------------------:|--------------------:| |
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| atanasie | 9021 | 9021 | 9020 | 9021 | 9020 | 9021 | 9021 | 9001 | |
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| barsana | 12001 | 12001 | 12001 | 12001 | 12001 | 12000 | 12001 | 1573 | |
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| comana | 9022 | 9022 | 0 | 9022 | 0 | 9022 | 9022 | 1210 | |
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| gradistei | 9601 | 9601 | 9600 | 9601 | 9600 | 9600 | 9601 | 1210 | |
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| herculane | 9022 | 9022 | 9021 | 9022 | 9021 | 9022 | 9022 | 1210 | |
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| jupiter | 11066 | 11066 | 11065 | 11066 | 11065 | 11066 | 11066 | 1452 | |
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| norway | 2983 | 2983 | 0 | 2983 | 0 | 2983 | 2983 | 2941 | |
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| olanesti | 9022 | 9022 | 9021 | 9022 | 9021 | 9022 | 9022 | 1210 | |
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| petrova | 9001 | 9001 | 9001 | 9001 | 9001 | 9000 | 9001 | 1210 | |
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| slanic | 9001 | 9001 | 9001 | 9001 | 9001 | 9000 | 9001 | 9001 | |
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### 1.2.6. Split intro train, validation, semisupervised and train |
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We include 8 splits: 4 using only GT annotated semantic data and 4 using all available data (i.e. segproped between |
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annotated data). The indexes are taken from `txt_files/*`, i.e. `txt_files/manually_adnotated_files/test_files_116.txt` |
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refers to the fact that the (unseen at train time) test set (norway + petrova + barsana) contains 116 manually |
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annotated semantic files. We include all representations from above, not just semantic for all possible splits. |
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Adding new representations is as simple as running VRE on the 540p mp4 file |
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``` |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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``` |
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Note: `add --copy_files` if you want to make copies instead of using symlinks. |
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Upon calling this, you should be able to see something like this: |
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``` |
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user> ls data/* |
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data/semisupervised_set: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/semisupervised_set_annotated_nly: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/test_set: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/test_set_annotated_nly: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/train_set: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/train_set_annotated_only: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/validation_set: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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data/validation_set_annotated_only: |
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depth_dpt edges_dexined opticalflow_rife semantic_mask2former_swin_mapillary_converted |
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depth_sfm_manual202204 normals_sfm_manual202204 rgb semantic_segprop8 |
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``` |
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### 1.2.7 Convert Camera Normals to World Normals |
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This is an optional step, but for some use cases, it may be better to use world normals instead of camera normals, which |
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are provided by default in `normals_sfm_manual202204`. To convert, we provide camera rotation matrices in |
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`raw_data/camera_matrics.tar.gz` for all 8 scenes that also have SfM. |
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In order to convert, use this function (for each npz file): |
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``` |
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def convert_camera_to_world(normals: np.ndarray, rotation_matrix: np.ndarray) -> np.ndarray: |
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normals = (normals.copy() - 0.5) * 2 # [-1:1] -> [0:1] |
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camera_normals = camera_normals @ np.linalg.inv(rotation_matrix) |
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camera_normals = (camera_normals / 2) + 0.5 # [0:1] => [-1:1] |
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return np.clip(camera_normals, 0.0, 1.0) |
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``` |
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</details> |
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## 2. Using the data |
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As per the split from the paper: |
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![Split](split.png) |
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The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface). |
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## 2.1 Using the provided viewer |
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Basic usage: |
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``` |
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python scripts/dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories in data/ |
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``` |
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<details> |
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<summary> Expected output </summary> |
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``` |
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[MultiTaskDataset] |
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- Path: '/scratch/sdc/datasets/dronescapes/data/test_set_annotated_only' |
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- Only full data: False |
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- 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)] |
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- Length: 116 |
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== Shapes == |
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{'depth_dpt': torch.Size([540, 960]), |
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'depth_sfm_manual202204': torch.Size([540, 960]), |
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'edges_dexined': torch.Size([540, 960]), |
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'normals_sfm_manual202204': torch.Size([540, 960, 3]), |
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'opticalflow_rife': torch.Size([540, 960, 2]), |
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'rgb': torch.Size([540, 960, 3]), |
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'semantic_mask2former_swin_mapillary_converted': torch.Size([540, 960]), |
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'semantic_segprop8': torch.Size([540, 960])} |
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== Random loaded item == |
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/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 |
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arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where) |
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{'depth_dpt': tensor[540, 960] x∈[0.031, 1.000] μ=0.060 σ=0.038, |
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'depth_sfm_manual202204': tensor[540, 960] f16 x∈[0., 1.195e+03] μ=360.250 σ=inf, |
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'edges_dexined': tensor[540, 960] x∈[0.131, 1.000] μ=0.848 σ=0.188, |
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'normals_sfm_manual202204': tensor[540, 960, 3] f16 x∈[0.000, 1.000] μ=0.525 σ=inf, |
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'opticalflow_rife': tensor[540, 960, 2] f16 x∈[-0.000, 0.007] μ=0.002 σ=0.002, |
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'rgb': tensor[540, 960, 3] u8 x∈[0, 255] μ=68.154 σ=33.902, |
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'semantic_mask2former_swin_mapillary_converted': tensor[540, 960] u8 x∈[0, 7] μ=3.591 σ=3.058, |
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'semantic_segprop8': tensor[540, 960] u8 x∈[0, 6] μ=1.057 σ=0.916} |
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== Random loaded batch == |
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{'depth_dpt': torch.Size([5, 540, 960]), |
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'depth_sfm_manual202204': torch.Size([5, 540, 960]), |
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'edges_dexined': torch.Size([5, 540, 960]), |
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'normals_sfm_manual202204': torch.Size([5, 540, 960, 3]), |
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'opticalflow_rife': torch.Size([5, 540, 960, 2]), |
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'rgb': torch.Size([5, 540, 960, 3]), |
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'semantic_mask2former_swin_mapillary_converted': torch.Size([5, 540, 960]), |
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'semantic_segprop8': torch.Size([5, 540, 960])} |
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== Random loaded batch using torch DataLoader == |
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{'depth_dpt': torch.Size([5, 540, 960]), |
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'depth_sfm_manual202204': torch.Size([5, 540, 960]), |
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'edges_dexined': torch.Size([5, 540, 960]), |
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'normals_sfm_manual202204': torch.Size([5, 540, 960, 3]), |
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'opticalflow_rife': torch.Size([5, 540, 960, 2]), |
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'rgb': torch.Size([5, 540, 960, 3]), |
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'semantic_mask2former_swin_mapillary_converted': torch.Size([5, 540, 960]), |
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'semantic_segprop8': torch.Size([5, 540, 960])} |
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``` |
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</details> |
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## 3. Evaluation for semantic segmentation |
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We evaluate in the paper on the 3 test scenes (unsees at train) as well as the semi-supervised scenes (seen, but |
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different split) against the human annotated frames. The general evaluation script is in |
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`scripts/evaluate_semantic_segmentation.py`. |
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General usage is: |
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``` |
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python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --classes C1 C2 .. Cn |
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[--class_weights W1 W2 ... Wn] [--scenes s1 s2 ... sm] |
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``` |
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<details> |
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<summary> Script explanation </summary> |
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The script is a bit convoluted, so let's break it into parts: |
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- `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.) |
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- `classes` A list of classes in the order that they appear in the predictions and gt files |
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- `class_weights` (optional, but used in paper) How much to weigh each class. In the paper we compute these weights as |
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the number of pixels in all the dataset (train/val/semisup/test) for each of the 8 classes resulting in the numbers |
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below. |
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- `scenes` if the `y_dir` and `gt_dir` contains multiple scenes that you want to evaluate separately, the script allows |
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you to pass the prefix of all the scenes. For example, in `data/test_set_annotated_only/semantic_segprop8/` there are |
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actually 3 scenes in the npz files and in the paper, we evaluate each scene independently. Even though the script |
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outputs one csv file with predictions for each npz file, the scenes are used for proper aggregation at scene level. |
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</details> |
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|
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<details> |
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<summary> Reproducing paper results for Mask2Former </summary> |
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|
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``` |
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python scripts/evaluate_semantic_segmentation.py \ |
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data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # change this with your predictions dir |
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data/test_set_annotated_only/semantic_segprop8/ \ |
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-o results.csv \ |
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--classes land forest residential road little-objects water sky hill \ |
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--class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 \ |
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--scenes barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full |
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``` |
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|
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Should output: |
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``` |
|
scene iou f1 |
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barsana_DJI_0500_0501_combined_sliced_2700_14700 63.367 75.327 |
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comana_DJI_0881_full 60.554 73.757 |
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norway_210821_DJI_0015_full 37.998 45.928 |
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overall avg 53.973 65.004 |
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``` |
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|
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Not providing `--scenes` will make an average across all 3 scenes (not average after each metric individually): |
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|
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``` |
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iou f1 |
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scene |
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all 60.456 73.261 |
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``` |
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</details> |
|
|