Merge branch 'main' of hf.co:datasets/Meehai/dronescapes

README.md

@@ -1,162 +1,36 @@
+---
+tags:
+- videos
+- video
+- uav
+- drones
+- multitask
+- multimodal
+---
 # 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)
 
 ![Logo](logo.png)
 
-# 1. Downloading the data
+Note: We are extending this dataset in another repository. GT data for benchmarking is the same, but we are generating 
+modalities as inputs: [dronescapes-2024](https://huggingface.co/datasets/Meehai/dronescapes-2024).
 
-## Option 1. Download the pre-processed dataset from HuggingFace repository
+# 1. Downloading the data
 
 ```
 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.
-
-<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.
-
-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
+Note: the dataset has about 200GB, so it may take a while to clone it.
 
-```
-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
-```
+## 1.2 Low level data for the dataset (GPS, camera rotation matrices)
 
-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>
+<summary> Click to expand </summary>
 
-### 1.2.7 Convert Camera Normals to World Normals
+### 1.2.1 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
@@ -172,7 +46,7 @@ def convert_camera_to_world(normals: np.ndarray, rotation_matrix: np.ndarray) ->
   return np.clip(camera_normals, 0.0, 1.0)
 ```
 
-### 1.2.8 Raw camera location
+### 1.2.2 Raw camera location
 
 The GPS location (lat/long/height) is in the `raw_data/raw_camera_info.tar.gz`. Each file over there is an archive over the
 original videos, so the train/val/test splits files from `raw_data/txt_files` must be used to map to proper indices. Each
@@ -186,20 +60,16 @@ Note: there is no GPS for the norway scene, the log seems to have been lost.
 
 As per the split from the paper:
 
-<details>
 <summary> Split </summary>
-<img src="split.png">
-</details>
+<img src="split.png" width="500px">
 
-The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface).
+The data is in the `data*` directory with 1 sub-directory for each split above (and a few more variants).
 
-## 2.1 Using the provided viewer
+The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
+it, you should get a collage with all the default tasks, like this:
 
 ![Collage](collage.png)
 
-The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). 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:
 
 ```
@@ -282,7 +152,9 @@ python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --c
 <summary> Script explanation </summary>
 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.)
+- `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset
+  - y_dir/1.npz, ..., y_dir/N.npz
+  - gt_dir/1.npz, ..., gt_dir.npz
 - `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
@@ -298,7 +170,7 @@ outputs one csv file with predictions for each npz file, the scenes are used for
 
 ```
 python scripts/evaluate_semantic_segmentation.py \
-  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # change this with your predictions dir
+  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # Mask2Former example, use yours here!
   data/test_set_annotated_only/semantic_segprop8/ \
   -o results.csv \
   --classes land forest residential road little-objects water sky hill \
@@ -343,5 +215,4 @@ all    60.456  73.261
 
 | method | #paramters |  average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full |  norway_210821_DJI_0015_full |
 |:-|:-|:-|:-|:-|:-|
-| [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 65.01 | 75.33 | 73.77 | 45.93 |
-
+| [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 65.01 | 75.33 | 73.77 | 45.93 |

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