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Dronescapes dataset

Logo

As introduced in our ICCV 2023 workshop paper: link

1. Downloading the data

Option 1. Download the pre-processed dataset from HuggingFace repository

TODO: recommended

Option 2. Generating the dataset from raw videos and basic labels .

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.

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 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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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 cfg.yaml --batch_size 3 --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

TODO

1.2.5 Check counts for consistency

Run: bash 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

./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
./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
./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
./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
./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
./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
./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
./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

2. Using the data

As per the split from the paper: Split

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:

./dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories
Expected output 
[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])}

TODOs

  • Fix remaining bad npz files
/scratch/sdc/datasets/dronescapes/data/semisupervised_set/depth_dpt/part0/herculane_DJI_0021_full_3565.npz
/scratch/sdc/datasets/dronescapes/data/semisupervised_set/depth_dpt/part0/herculane_DJI_0021_full_3570.npz
/scratch/sdc/datasets/dronescapes/data/semisupervised_set/depth_dpt/part0/herculane_DJI_0021_full_3582.npz
/scratch/sdc/datasets/dronescapes/data/semisupervised_set/depth_dpt/part0/herculane_DJI_0021_full_3592.npz
  • convert camera normals to world normals
  • mask2former convert
  • add raw script for reading data
    • add semantics for each representation in a DronescapesReader
  • add notebook for visualisation