wut

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.gitignore

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-raw_data/representations/
-*.mp4
-*.MP4
-voting_learn_data/
-*.npz
-list.txt
-*.tar.gz
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-error.txt
-sanity_check.py
-commands.txt
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-here.csv
-*.ttf
-scripts/results/
-scripts/collage*
-vre_dronescapes/
-scripts/dev.ipynb
-

README.md

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 ---
-tags:
-- videos
-- video
-- uav
-- drones
-- multitask
-- multimodal
+license: wtfpl
 ---
-# 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)
-
-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).
-
-# 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 200GB, so it may take a while to clone it.
-
-## 1.2 Low level data for the dataset (GPS, camera rotation matrices)
-
-<details>
-<summary> Click to expand </summary>
-
-### 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
-`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)
-```
-
-### 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
-archive contains a bunch of raw data, however the key `lat_long_height` should provide the GPS location.
-
-Note: there is no GPS for the norway scene, the log seems to have been lost.
-
-</details>
-
-## 2. Using the data
-
-As per the split from the paper:
-
-<summary> Split </summary>
-<img src="split.png" width="500px">
-
-The data is in the `data*` directory with 1 sub-directory for each split above (and a few more variants).
-
-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)
-
-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/
-```
-
-<details>
-<summary> Expected output </summary>
-
-```
-[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}
-```
-</details>
-
-## 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]
-```
-
-<details>
-<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, ..., 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
-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.
-</details>
-
-<details>
-<summary> Reproducing paper results for Mask2Former </summary>
-
-```
-python scripts/evaluate_semantic_segmentation.py \
-  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 \
-  --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
-```
-</details>
-
-### 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](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 53.97 | 63.37 | 60.55 | 37.98 |
-| [NGC(LR)](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | 32M | 40.75  | 46.51 | 45.59 | 30.17 |
-| [CShift](https://www.bmvc2021-virtualconference.com/assets/papers/0455.pdf)[^1] | n/a | 39.67 | 46.27 | 43.67 | 29.09 |
-| [NGC](https://cdn.aaai.org/ojs/16283/16283-13-19777-1-2-20210518.pdf)[^1] | 32M | 35.32 | 44.34 | 38.99 | 22.63 |
-| [SafeUAV](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11130/Marcu_SafeUAV_Learning_to_estimate_depth_and_safe_landing_areas_for_ECCVW_2018_paper.pdf)[^1] | 1.1M | 32.79 | n/a | n/a | n/a |
-
-[^1]: reported in the [Dronescapes paper](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf).
-
-#### F1 Score
-
-| 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 |

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