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Dronescapes dataset

As introduced in our ICCV 2023 workshop paper: link

Logo

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.

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.

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)

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

Collage

The simplest way to explore the data is to use the provided notebook. 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/
Expected output 
[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}

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]
Script explanation 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.
Reproducing paper results for Mask2Former 
python scripts/evaluate_semantic_segmentation.py \
  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \
  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

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 216M 53.97 63.37 60.55 37.98
NGC(LR) 32M 40.75 46.51 45.59 30.17
CShift[^1] n/a 39.67 46.27 43.67 29.09
NGC[^1] 32M 35.32 44.34 38.99 22.63
SafeUAV[^1] 1.1M 32.79 n/a n/a n/a

[^1]: reported in the Dronescapes paper.

F1 Score

method #paramters average barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full
Mask2Former 216M 65.01 75.33 73.77 45.93
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