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# 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
TODO: recommended
<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 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
```
</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:
```
./dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories
```
<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
- 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
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