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dronescapes

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dronescapes
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# Dronescapes dataset

![Logo](logo.png)

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 Download the 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, then download them from: [link](https://drive.google.com/file/d/1AH-9K1RjxCJr8atRU8p3CqCOGKmQtLbU).

```bash
cd raw_data/
tar -xzvf videos_540p.tar.gz
```

### 1.2.2 Download the GT semantic segmentation labels

These were human annotated and then propagated using [segprop](https://github.com/vlicaret/segprop).
Direct link: [link](https://drive.google.com/file/d/1ElBDox_UUqngqZeuJpibYX2wSU8YdQt7/view?usp=sharing).

or:

```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
```

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:
![Split](split.png)

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
```

Should output:
```
```

## TODOs
- mask2former convert
- add raw script for reading data
  - add semantics for each representation in a DronescapesReader
- add notebook for visualisation
- push to huggingface dataset