hm

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
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-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
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-*.lfs.* filter=lfs diff=lfs merge=lfs -text
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-*.model filter=lfs diff=lfs merge=lfs -text
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-*.rar filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
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.gitignore

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+raw_data/representations/
+*.mp4
+*.MP4
+voting_learn_data/
+*.npz
+list.txt
+*.tar.gz
+copyfiles3.txt
+raw_data/npz_540p
+exception.txt
+__pycache__
+

README.md

@@ -1,3 +1,158 @@
----
-license: wtfpl
----
+# 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

cfg.yaml

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+vre:
+  start_frame: ${oc.env:VRE_START_IX}
+  end_frame: ${oc.env:VRE_END_IX,null}
+  export_npy: True
+  export_png: True
+  exception_mode: skip_representation
+
+representations:
+  rgb:
+    type: default
+    name: rgb
+    dependencies: []
+    parameters: {}
+
+  hsv:
+    type: default
+    name: hsv
+    dependencies: []
+    parameters: {}
+
+  halftone1:
+    type: soft-segmentation
+    name: python-halftone
+    dependencies: []
+    parameters:
+      sample: 3
+      scale: 1
+      percentage: 91
+      angles: [0, 15, 30, 45]
+      antialias: False
+      resolution: [240, 426]
+
+  edges_canny:
+    type: edges
+    name: canny
+    dependencies: []
+    parameters:
+      threshold1: 100
+      threshold2: 200
+      aperture_size: 3
+      l2_gradient: True
+
+  softseg_kmeans:
+    type: soft-segmentation
+    name: kmeans
+    dependencies: []
+    parameters:
+      n_clusters: 6
+      epsilon: 2
+      max_iterations: 10
+      attempts: 3
+
+  softseg_gb:
+    type: soft-segmentation
+    name: generalized_boundaries
+    dependencies: []
+    parameters:
+      use_median_filtering: True
+      adjust_to_rgb: True
+      max_channels: 3
+
+  edges_dexined:
+    type: edges
+    name: dexined
+    dependencies: []
+    parameters: {} 
+    batch_size: 15
+    vre_parameters:
+      device: ${oc.env:VRE_DEVICE,cpu}
+
+  fastsam(s):
+    type: semantic_segmentation
+    name: fastsam
+    dependencies: []
+    parameters:
+      variant: fastsam-s
+      iou: 0.9
+      conf: 0.4
+    vre_parameters:
+      device: ${oc.env:VRE_DEVICE,cpu}
+
+  depth_dpt:
+    type: depth
+    name: dpt
+    dependencies: []
+    parameters: {}
+    batch_size: 10
+    vre_parameters:
+      device: ${oc.env:VRE_DEVICE,cpu}
+
+  semantic_mask2former_swin_coco:
+    type: semantic_segmentation
+    name: mask2former
+    dependencies: []
+    batch_size: 1
+    parameters:
+      model_id: "47429163_0"
+      semantic_argmax_only: True
+    vre_parameters:
+      device: ${oc.env:VRE_DEVICE,cpu}
+
+  semantic_mask2former_swin_mapilary:
+    type: semantic_segmentation
+    name: mask2former
+    dependencies: []
+    batch_size: 1
+    parameters:
+      model_id: "49189528_0"
+      semantic_argmax_only: True
+    vre_parameters:
+      device: ${oc.env:VRE_DEVICE,cpu}
+
+  # only works if fps is also set (for images) via --frame_rate in cli. For videos, it works just fine.
+  opticalflow_rife:
+    type: optical-flow
+    name: rife
+    dependencies: []
+    batch_size: 15
+    parameters:
+      uhd: False
+      compute_backward_flow: False
+    vre_parameters:
+      device: ${oc.env:VRE_DEVICE,cpu}

commands.txt

@@ -0,0 +1,63 @@
+---- vre calls ----
+
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco;
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=6 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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=7 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 --output_dir_exist_mode overwrite --representations rgb opticalflow_rife depth_dpt edges_dexined semantic_mask2former_swin_mapilary semantic_mask2former_swin_coco
+ 
+ ---- copyfiles from cetal ----
+
+/Date3/hpc/datasets/dronescapes/all_scenes/atanasie_DJI_0652_full/depthSfmManual202204 atanasie_DJI_0652_full/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/atanasie_DJI_0652_full/normalsSfmManual202204 atanasie_DJI_0652_full/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/atanasie_DJI_0652_full/segprop8 atanasie_DJI_0652_full/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/atanasie_DJI_0652_full atanasie_DJI_0652_full/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/barsana_combined_sliced_2700_14700/depthSfmManual202204 barsana_combined_sliced_2700_14700/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/barsana_combined_sliced_2700_14700/normalsSfmManual202204 barsana_combined_sliced_2700_14700/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/barsana_combined_sliced_2700_14700/segprop8 barsana_combined_sliced_2700_14700/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/barsana_combined_sliced_2700_14700 barsana_combined_sliced_2700_14700/mapillary_sseg
+/Date8/hpc/vre/comana_DJI_0881_full/segprop8 comana_DJI_0881_full/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/comana_DJI_0881_full comana_DJI_0881_full/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/gradistei_combined_sliced_3510_13110/depthSfmManual202204 gradistei_combined_sliced_3510_13110/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/gradistei_combined_sliced_3510_13110/normalsSfmManual202204 gradistei_combined_sliced_3510_13110/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/gradistei_combined_sliced_3510_13110/segprop8 gradistei_combined_sliced_3510_13110/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/gradistei_combined_sliced_3510_13110 gradistei_combined_sliced_3510_13110/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/herculane_DJI_0021_full/depthSfmManual202204 herculane_DJI_0021_full/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/herculane_DJI_0021_full/normalsSfmManual202204 herculane_DJI_0021_full/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/herculane_DJI_0021_full/segprop8 herculane_DJI_0021_full/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/herculane_DJI_0021_full herculane_DJI_0021_full/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/jupiter_combined_sliced_10650_21715/depthSfmManual202204 jupiter_combined_sliced_10650_21715/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/jupiter_combined_sliced_10650_21715/normalsSfmManual202204 jupiter_combined_sliced_10650_21715/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/jupiter_combined_sliced_10650_21715/segprop8 jupiter_combined_sliced_10650_21715/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/jupiter_combined_sliced_10650_21715 jupiter_combined_sliced_10650_21715/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/norce_got_210821_dji_0015/segprop8 norce_got_210821_dji_0015/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/norce_got_210821_dji_0015 norce_got_210821_dji_0015/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/olanesti_DJI_0416_full/depthSfmManual202204 olanesti_DJI_0416_full/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/olanesti_DJI_0416_full/normalsSfmManual202204 olanesti_DJI_0416_full/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/olanesti_DJI_0416_full/segprop8 olanesti_DJI_0416_full/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/olanesti_DJI_0416_full olanesti_DJI_0416_full/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/petrova_combined_sliced_2850_11850/depthSfmManual202204 petrova_combined_sliced_2850_11850/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/petrova_combined_sliced_2850_11850/normalsSfmManual202204 petrova_combined_sliced_2850_11850/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/petrova_combined_sliced_2850_11850/segprop8 petrova_combined_sliced_2850_11850/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/petrova_combined_sliced_2850_11850 petrova_combined_sliced_2850_11850/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/all_scenes/slanic_combined_sliced_780_9780/depthSfmManual202204 slanic_combined_sliced_780_9780/depthSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/slanic_combined_sliced_780_9780/normalsSfmManual202204 slanic_combined_sliced_780_9780/normalsSfmManual202204
+/Date3/hpc/datasets/dronescapes/all_scenes/slanic_combined_sliced_780_9780/segprop8 slanic_combined_sliced_780_9780/segprop8
+/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg/slanic_combined_sliced_780_9780 slanic_combined_sliced_780_9780/mapillary_sseg
+/Date3/hpc/datasets/dronescapes/velocities velocities
+
+---- link files from raw data to train/val/semisup/test data ----
+./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_only --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_only --overwrite

count_npz.sh

@@ -0,0 +1,18 @@
+# Usage: bash count_npz npz_540p
+(
+  echo "scene,repr,counts"
+  ls $1 | while read line; do
+    scene_name=$(echo $line | cut -d "_" -f1);
+    ls $1/$line | while read line2; do
+      n_files=$(find $1/$line/"$line2" -name "*.npz" | wc -l);
+      echo "$scene_name","$line2","$n_files";
+    done;
+  done ) | python -c '''
+import sys, pandas as pd;
+df = pd.read_csv(sys.stdin);
+df2 = df.groupby("scene").apply(lambda x: dict(zip(x["repr"], x["counts"])), include_groups=False).reset_index();
+df3 = pd.json_normalize(df2[0]).set_index(df2["scene"]).fillna(0).astype(int);
+df4 = df3.reindex(columns=["rgb", *sorted(x for x in df3.columns if x != "rgb")])
+df4.columns = [f"{x[0:18]}.." if len(x) > 20 else x for x in df4.columns]
+print(df4.to_markdown())
+'''

dronescapes_reader/__init__.py

@@ -0,0 +1,2 @@
+"""init file"""
+from .multitask_dataset import MultiTaskDataset

dronescapes_reader/multitask_dataset.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""MultiTask Dataset module compatible with torch.utils.data.Dataset & DataLoader."""
+from __future__ import annotations
+from pathlib import Path
+from argparse import Namespace, ArgumentParser
+from pprint import pprint
+from natsort import natsorted
+from loguru import logger
+import torch as tr
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+from lovely_tensors import monkey_patch
+
+monkey_patch()
+BuildDatasetTuple = tuple[dict[str, list[Path]], list[str]]
+MultiTaskItem = tuple[dict[str, tr.Tensor], str, list[str]]
+
+class NpzRepresentation:
+    """Generic Task with data read from/saved to npz files. Tries to read data as-is from disk and store it as well"""
+    def __init__(self, name: str):
+        self.name = name
+
+    def load_from_disk(self, path: Path) -> tr.Tensor:
+        """Reads the npz data from the disk and transforms it properly"""
+        data = np.load(path, allow_pickle=False)
+        data = data if isinstance(data, np.ndarray) else data["arr_0"] # in case on npz, we need this as well
+        return tr.from_numpy(data)
+
+    def save_to_disk(self, data: tr.Tensor, path: Path):
+        """stores this item to the disk which can then be loaded via `load_from_disk`"""
+        np.save(path, data.cpu().numpy(), allow_pickle=False)
+
+    def plot_fn(self, x: tr.Tensor) -> np.ndarray:
+        """very basic implementation of converting this representation to a viewable image. You should overwrite this"""
+        assert isinstance(x, tr.Tensor), type(x)
+        if len(x.shape) == 2: x = x.unsqueeze(-1)
+        assert len(x.shape) == 3, x.shape # guaranteed to be (H, W, C) at this point
+        if x.shape[-1] != 3: x = x[..., 0:1]
+        if x.shape[-1] == 1: x = x.repeat(1, 1, 3)
+        x = x.nan_to_num(0).cpu().numpy() # guaranteed to be (H, W, 3) at this point hopefully
+        if x.dtype != np.uint8: x = np.nan_to_num((x - x.min() / (x.max() - x.min())) * 255, 0).astype(np.uint8)
+        return x
+
+    def __repr__(self):
+        return str(self)
+
+    def __str__(self):
+        return f"{str(type(self)).split('.')[-1][0:-2]}({self.name})"
+
+class MultiTaskDataset(Dataset):
+    """
+    MultiTaskDataset implementation. Reads data from npz files and returns them as a dict.
+
+    Parameters:
+    - path: Path to the directory containing the npz files.
+    - task_names: List of tasks that are present in the dataset. If set to None, will infer from the files on disk.
+    - handle_missing_data: Modes to handle missing data. Valid options are:
+      - drop: Drop the data point if any of the representations is missing.
+      - fill_none: Fill the missing data with Nones.
+
+    Expected directory structure:
+    path/
+    - task_1/0.npz, ..., N.npz
+    - ...
+    - task_n/0.npz, ..., N.npz
+
+    Names can be in a different format (i.e. 2022-01-01.npz), but must be consistent and equal across all tasks.
+    """
+
+    def __init__(self, path: Path, task_names: list[str] | None = None, handle_missing_data: str = "fill_none",
+                 files_suffix: str = "npz"):
+        assert path.exists(), f"Provided path '{path}' doesn't exist!"
+        assert handle_missing_data in ("drop", "fill_none"), f"Invalid handle_missing_data mode: {handle_missing_data}"
+        assert files_suffix == "npz", "Only npz supported right now (though trivial to update)"
+        self.path = Path(path).absolute()
+        self.handle_missing_data = handle_missing_data
+        self.suffix = files_suffix
+        self.files_per_repr, self.file_names = self._build_dataset()
+
+        if task_names is None:
+            task_names = list(self.files_per_repr.keys())
+            logger.debug(f"No explicit tasks provided. Using all of them as read from the paths ({len(task_names)}).")
+        assert all(isinstance(x, str) for x in task_names), tuple(zip(task_names, (type(x) for x in task_names)))
+        self.task_names = sorted(task_names)
+        self._data_shape: tuple[int, ...] | None = None
+        self._tasks: list[NpzRepresentation] | None = None
+        self.name_to_task = {task.name: task for task in self.tasks}
+        logger.info(f"Tasks used in this dataset: {self.task_names}")
+
+    # Public methods and properties
+
+    @property
+    def data_shape(self) -> dict[str, tuple[int, ...]]:
+        """Returns a {task: shape_tuple} for all representations. At least one npz file must exist for each."""
+        first_npz = {task: [_v for _v in files if _v is not None][0] for task, files in self.files_per_repr.items()}
+        data_shape = {task: self.name_to_task[task].load_from_disk(first_npz[task]).shape for task in self.task_names}
+        return data_shape
+
+    @property
+    def tasks(self) -> list[NpzRepresentation]:
+        """
+        Returns a list of instantiated tasks in the same order as self.task_names. Overwrite this to add
+        new tasks and semantics (i.e. plot_fn or doing some preprocessing after loading from disk in some tasks.
+        """
+        if self._tasks is not None:
+            return self._tasks
+        self._tasks = [NpzRepresentation(task_name) for task_name in self.task_names]
+        return self._tasks
+
+    def collate_fn(self, items: list[MultiTaskItem]) -> MultiTaskItem:
+        """
+        given a list of items (i.e. from a reader[n:n+k] call), return the item batched on 1st dimension.
+        Nones (missing data points) are turned into zeros as per the data shape of that dim.
+        """
+        assert all(item[2] == self.task_names for item in items), ((item[2] for item in items), self.task_names)
+        items_name = [item[1] for item in items]
+        res = {k: tr.zeros(len(items), *self.data_shape[k]) for k in self.task_names}
+        for i in range(len(items)):
+            for k in self.task_names:
+                res[k][i] = items[i][0][k] if items[i][0][k] is not None else 0
+        return res, items_name, self.task_names
+
+    # Private methods
+
+    def _get_all_npz_files(self) -> dict[str, list[Path]]:
+        """returns a dict of form: {"rgb": ["0.npz", "1.npz", ..., "N.npz"]}"""
+        in_files = {}
+        all_repr_dirs: list[str] = [x.name for x in self.path.iterdir() if x.is_dir()]
+        for repr_dir_name in all_repr_dirs:
+            dir_name = self.path / repr_dir_name
+            items = natsorted(dir_name.glob(f"*.{self.suffix}"), key=lambda x: x.name) # important: use natsorted() here
+            in_files[repr_dir_name] = items
+        assert not any(len(x) == 0 for x in in_files.values()), f"{ [k for k, v in in_files.items() if len(v) == 0] }"
+        return in_files
+
+    def _build_dataset_drop(self) -> BuildDatasetTuple:
+        in_files = self._get_all_npz_files()
+        common = set(x.name for x in next(iter(in_files.values())))
+        nodes = in_files.keys()
+        for node in nodes:
+            common = common.intersection([f.name for f in in_files[node]])
+            assert len(common) > 0, f"Node '{node}' made the intersection null"
+        common = natsorted(list(common))
+        logger.info(f"Found {len(common)} data points for each node ({len(nodes)} nodes).")
+        files_per_repr = {node: [self.path / node / x for x in common] for node in nodes}
+        return files_per_repr, common
+
+    def _build_dataset_fill_none(self) -> BuildDatasetTuple:
+        in_files = self._get_all_npz_files()
+        all_files = set(x.name for x in next(iter(in_files.values())))
+        nodes = in_files.keys()
+        for node in nodes:
+            all_files = all_files.union([f.name for f in in_files[node]])
+        all_files = natsorted(list(all_files))
+        logger.info(f"Found {len(all_files)} data points as union of all nodes' data ({len(nodes)} nodes).")
+
+        files_per_repr = {node: [] for node in nodes}
+        in_file_names = {node: [f.name for f in in_files[node]] for node in nodes}
+        for node in nodes:
+            for file_name in all_files:
+                file_path = self.path / node / file_name if file_name in in_file_names[node] else None
+                files_per_repr[node].append(file_path)
+        return files_per_repr, all_files
+
+    def _build_dataset(self) -> BuildDatasetTuple:
+        logger.debug(f"Building dataset from: '{self.path}'")
+        if self.handle_missing_data == "drop":
+            return self._build_dataset_drop()
+        else:
+            return self._build_dataset_fill_none()
+
+    # Python magic methods (pretty printing the reader object, reader[0], len(reader) etc.)
+
+    def __getitem__(self, index: int | slice | list[int] | tuple) -> MultiTaskItem:
+        """Read the data all the desired nodes"""
+        assert isinstance(index, (int, slice, list, tuple)), type(index)
+        if isinstance(index, slice):
+            assert index.start is not None and index.stop is not None and index.step is None, "Only reader[l:r] allowed"
+            index = list(range(index.stop)[index])
+        if isinstance(index, (list, tuple)):
+            return self.collate_fn([self.__getitem__(ix) for ix in index])
+        res = {}
+        item_name = self.file_names[index]
+
+        for _repr in self.tasks:
+            file_path = self.files_per_repr[_repr.name][index]
+            file_path = file_path.resolve() if file_path is not None else None
+            assert self.handle_missing_data == "fill_none" or (file_path is not None and file_path.exists()), item_name
+            item = _repr.load_from_disk(file_path) if file_path is not None and file_path.exists() else None
+            res[_repr.name] = item
+        return (res, item_name, self.task_names)
+
+    def __len__(self) -> int:
+        return len(self.files_per_repr[self.task_names[0]]) # all of them have the same number (filled with None or not)
+
+    def __str__(self):
+        f_str = f"[{str(type(self)).rsplit('.', maxsplit=1)[-1][0:-2]}]"
+        f_str += f"\n - Path: '{self.path}'"
+        f_str += f"\n - Only full data: {self.handle_missing_data == 'drop'}"
+        f_str += f"\n - Representations ({len(self.tasks)}): {self.tasks}"
+        f_str += f"\n - Length: {len(self)}"
+        return f_str
+
+    def __repr__(self):
+        return str(self)
+
+def main():
+    """main fn"""
+    parser = ArgumentParser()
+    parser.add_argument("dataset_path", type=Path)
+    parser.add_argument("--handle_missing_data", choices=("drop", "fill_none"), default="fill_none")
+    args = parser.parse_args()
+
+    reader = MultiTaskDataset(args.dataset_path, task_names=None, handle_missing_data=args.handle_missing_data)
+    print(reader)
+    print(f"Shape: {reader.data_shape}")
+
+    rand_ix = np.random.randint(len(reader))
+    data, name, repr_names = reader[rand_ix] # get a random single data point
+    print(f"Name: {name}. Nodes: {repr_names}")
+    pprint({k: (v.shape if v is not None else None) for k, v in data.items()})
+
+    data, name, repr_names = reader[rand_ix: min(len(reader), rand_ix + 5)] # get a random batch
+    print(f"Name: {name}. Nodes: {repr_names}")
+    pprint({k: v.shape for k, v in data.items()}) # Nones are converted to 0s automagically
+
+    loader = DataLoader(reader, collate_fn=reader.collate_fn, batch_size=5, shuffle=True)
+    data, name, repr_names = next(iter(loader)) # get a random batch using torch DataLoader
+    print(f"Name: {name}. Nodes: {repr_names}")
+    pprint({k: v.shape for k, v in data.items()}) # Nones are converted to 0s automagically
+
+if __name__ == "__main__":
+    main()

dronescapes_viewer.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+import sys
+from pathlib import Path
+from dronescapes_reader import MultiTaskDataset
+from pprint import pprint
+from torch.utils.data import DataLoader
+import random
+
+def main():
+    reader = MultiTaskDataset(Path(sys.argv[1]), handle_missing_data="fill_none")
+    print(reader)
+
+    print("== Shapes ==")
+    pprint(reader.data_shape)
+
+    print("== Random loaded item ==")
+    rand_ix = random.randint(0, len(reader))
+    data, name, repr_names = reader[rand_ix] # get a random item
+    pprint({k: v for k, v in data.items()})
+
+    print("== Random loaded batch ==")
+    batch_data, name, repr_names = reader[rand_ix: min(len(reader), rand_ix + 5)] # get a random batch
+    pprint({k: v.shape for k, v in batch_data.items()}) # Nones are converted to 0s automagically
+
+    print("== Random loaded batch using torch DataLoader ==")
+    loader = DataLoader(reader, collate_fn=reader.collate_fn, batch_size=5, shuffle=True)
+    batch_data, name, repr_names = reader[rand_ix: min(len(reader), rand_ix + 5)] # get a random batch
+    pprint({k: v.shape for k, v in batch_data.items()}) # Nones are converted to 0s automagically
+
+if __name__ == "__main__":
+    main()

raw_data/videos/atanasie_DJI_0652_full/commands.txt

@@ -0,0 +1,4 @@
+gdown 1qz32oi-6bOKBL6A84lglOUtHK0OQyo4Y # DJI_0652.MP4
+ffmpeg -i DJI_0652.MP4 -vf "scale=960:540" -c:v libx264 atanasie_DJI_0652_full_540p.mp4
+ffmpeg -i DJI_0652.MP4 -vf "scale=1280:720" -c:v libx264 atanasie_DJI_0652_full_720p.mp4
+

raw_data/videos/barsana_DJI_0500_0501_combined_sliced_2700_14700/commands.txt

@@ -0,0 +1,7 @@
+gdown 1GzBnD8peVafDP7ZQAJOu7j4TYfLfdHd3 # DJI_0500.MP4
+gdown 1Mud2unSluYPbLuLvXTo-10TFmyABXSQO # DJI_0501.MP4
+echo -e "file DJI_0500.MP4\nfile DJI_0501.MP4\n" > list.txt
+ffmpeg -f concat -i list.txt -c copy output_tmp.mp4
+ffmpeg -ss 90.09 -i output_tmp.mp4 -vf "scale=960:540" -c:v libx264 -frames:v 12001 barsana_DJI_0500_0501_combined_sliced_2700_14700_540p.mp4 # 2700 / 29.97 = 90.09
+ffmpeg -ss 90.09 -i output_tmp.mp4 -vf "scale=1280:720" -c:v libx264 -frames:v 12001 barsana_DJI_0500_0501_combined_sliced_2700_14700_720p.mp4
+rm list.txt output_tmp.mp4

raw_data/videos/comana_DJI_0881_full/commands.txt

@@ -0,0 +1,4 @@
+gdown 1Sp1CwGVHzOVwE0U3o2teLs7FoSI72nVX # DJI_0881.MP4
+ffmpeg -i DJI_0881.MP4 -vf "scale=960:540" -c:v libx264 comana_DJI_0881_full_540p.mp4
+ffmpeg -i DJI_0881.MP4 -vf "scale=1280:720" -c:v libx264 comana_DJI_0881_full_720p.mp4
+

raw_data/videos/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/commands.txt

@@ -0,0 +1,8 @@
+gdown 1_CmYdwN6TXrowUVtwosHUFCJ0yYVP6s5 # DJI_0787.MP4
+gdown 1-S0EmEbV5X4O-V7bOWSSTOeaINSlwiev # DJI_0788.MP4
+gdown 1VL2afMtjS5zObTL49_gwl3Z7bncMDwFw # DJI_0789.MP4
+echo -e "file DJI_0787.MP4\nfile DJI_0788.MP4\nfile DJI_0789.MP4" > list.txt
+ffmpeg -f concat -i list.txt -c copy output_tmp.mp4
+ffmpeg -ss 117.117 -i output_tmp.mp4 -vf "scale=960:540" -c:v libx264 -frames:v 12001 gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110_540p.mp4 # 3510 / 29.97 = 117.117
+ffmpeg -ss 117.117 -i output_tmp.mp4 -vf "scale=1280:720" -c:v libx264 -frames:v 12001 gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110_720p.mp4 # 3510 / 29.97 = 117.117
+rm list.txt output_tmp.mp4

raw_data/videos/herculane_DJI_0021_full/commands.txt

@@ -0,0 +1,4 @@
+gdown 18-5eFwJQkFtM_1jwMK9i9qFLAEENQv3o # DJI_0021.MP4
+ffmpeg -i DJI_0021.MP4 -vf "scale=960:540" -c:v libx264 herculane_DJI_0021_full_540p.mp4
+ffmpeg -i DJI_0021.MP4 -vf "scale=1280:720" -c:v libx264 herculane_DJI_0021_full_720p.mp4
+

raw_data/videos/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/commands.txt

@@ -0,0 +1,8 @@
+gdown 1I2wHteOfHJ8585abjWRXCJw-4HlEvoD- # DJI_0703.MP4
+gdown 1Qk9ViYMJHh3a3AKHW_EGhFT4mw32_4O5 # DJI_0704.MP4
+gdown 1CBYiWOnkXvYBL8l3EgCEp-RGyV84kPte # DJI_0705.MP4
+echo -e "file DJI_0703.MP4\nfile DJI_0704.MP4\nfile DJI_0705.MP4" > list.txt
+ffmpeg -f concat -i list.txt -c copy output_tmp.mp4
+ffmpeg -ss 355.355 -i output_tmp.mp4 -vf "scale=960:540" -c:v libx264 -frames:v 12001 jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715_540p.mp4 # 10650 / 29.97 = 355.355
+ffmpeg -ss 355.355 -i output_tmp.mp4 -vf "scale=1280:720" -c:v libx264 -frames:v 12001 jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715_720p.mp4
+rm list.txt output_tmp.mp4

raw_data/videos/norway_210821_DJI_0015_full/commands.txt

@@ -0,0 +1,3 @@
+gdown 1idHZBqiS4ZlLgtnNOwJG08ecPSWcNo-Q # DJI_0015.MP4
+ffmpeg -i DJI_0015.MP4 -vf "scale=960:540" -c:v libx264 norway_210821_DJI_0015_full_540p.mp4
+ffmpeg -i DJI_0015.MP4 -vf "scale=1280:720" -c:v libx264 norway_210821_DJI_0015_full_720p.mp4

raw_data/videos/olanesti_DJI_0416_full/commands.txt

@@ -0,0 +1,3 @@
+gdown 1V8uIVi1jZgmlDMymUVoblU-GVmPKTvzV # DJI_0416.MP4
+ffmpeg -i DJI_0416.MP4 -vf "scale=960:540" -c:v libx264 olanesti_DJI_0416_full_540p.mp4
+ffmpeg -i DJI_0416.MP4 -vf "scale=1280:720" -c:v libx264 olanesti_DJI_0416_full_720p.mp4

raw_data/videos/petrova_DJI_0525_0526_combined_sliced_2850_11850/commands.txt

@@ -0,0 +1,7 @@
+gdown 1WusHf8gYSFCTs4ncXf5sgr-w8ELZ1UNA # DJI_0525_good.MP4
+gdown 1QNy2Yi_VBL362zF68lXJ7Tw6xSV6P4Vo # DJI_0526_good.MP4
+echo -e "file DJI_0525_good.MP4\nfile DJI_0526_good.MP4\n" > list.txt
+ffmpeg -f concat -i list.txt -c copy output_tmp.mp4
+ffmpeg -ss 95.095 -i output_tmp.mp4 -vf "scale=960:540" -c:v libx264 -frames:v 12001 petrova_DJI_0525_0526_combined_sliced_2850_11850_540p.mp4 # 2850 / 29.97 = 95.095
+ffmpeg -ss 95.095 -i output_tmp.mp4 -vf "scale=1280:720" -c:v libx264 -frames:v 12001 petrova_DJI_0525_0526_combined_sliced_2850_11850_720p.mp4
+rm list.txt output_tmp.mp4

raw_data/videos/slanic_DJI_0956_0957_combined_sliced_780_9780/commands.txt

@@ -0,0 +1,7 @@
+gdown 1dZgxsBjJjyuCJWeBgkIm1hc_A3Nm5PsA # DJI_0956.MP4
+gdown 11NnTUchvWXmJmwQxXUYe5-v7nCuWd4D4 # DJI_0957.MP4
+echo -e "file DJI_0956.MP4\nfile DJI_0957.MP4\n" > list.txt
+ffmpeg -f concat -i list.txt -c copy output_tmp.mp4
+ffmpeg -ss 26.02 -i output_tmp.mp4 -vf "scale=960:540" -c:v libx264 -frames:v 9001 slanic_DJI_0956_0957_combined_sliced_780_9780_540p.mp4
+ffmpeg -ss 26.02 -i output_tmp.mp4 -vf "scale=1280:720" -c:v libx264 -frames:v 9001 slanic_DJI_0956_0957_combined_sliced_780_9780_720p.mp4
+rm list.txt output_tmp.mp4

symlinks_from_txt_list.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+import shutil
+import os
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+from loguru import logger
+from tqdm import tqdm
+
+def _check_and_find(dataset_dir: Path, output_path: str, scene: str, _repr: str,
+                    stem: str, suffix: str) -> tuple[Path, Path] | None:
+    """handle all weirdnesses on how we store data. VRE uses npy/x.npy, but others have different format too."""
+    out_file = Path(f"{output_path}/{_repr}/{scene}_{stem}{suffix}") # e:g. rgb/slanic_1.npz
+    if (in_path := (dataset_dir / scene / _repr / f"{stem}{suffix}")).exists(): # e.g.: slanic/rgb/1.npz
+        return in_path, out_file
+    if (in_path := (dataset_dir / scene / _repr / "npy" / f"{stem}{suffix}")).exists(): # e.g.: slanic/rgb/npy/1.npz
+        return in_path, out_file
+
+    try:
+        int_stem = int(stem)
+    except ValueError:
+        return None
+
+    if (in_path := (dataset_dir / scene / _repr / f"{int_stem:06d}{suffix}")).exists(): # e.g.: slanic/rgb/000001.npz
+        return in_path, out_file
+    # e.g.: slanic/rgb/npy/000001.npz
+    if (in_path := (dataset_dir / scene / _repr / "npy" / f"{int_stem:06d}{suffix}")).exists():
+        return in_path, out_file
+    return None
+
+def check_and_gather_all_files(dataset_dir: Path, txt_data: list[tuple[str, str]],
+                               output_path: Path, suffix: str) -> dict[str, str]:
+    """returns a {in_dir/scene/repr/stem.suffix: out_dir/repr/scene/stem.suffix} dict based on dataset_dir"""
+    assert f"{suffix}".startswith("."), suffix
+    scene_reprs = {}
+    symlinks_to_do = {}
+    for scene, stem in tqdm(txt_data, desc="Gather data"):
+        assert (dataset_dir / scene).exists(), f"Scene '{scene}' does not exist in '{dataset_dir}'"
+        if scene not in scene_reprs:
+            scene_reprs[scene] = [x.name for x in (dataset_dir / scene).iterdir() if x.is_dir()]
+        n_found = 0
+        for _repr in scene_reprs[scene]:
+            if (res := _check_and_find(dataset_dir, output_path, scene, _repr, stem, suffix)) is not None:
+                in_file, out_file = res
+                n_found += 1
+                symlinks_to_do[in_file] = out_file
+        assert n_found > 0, f"Stem '{stem}' not found in any repr ({scene_reprs[scene]}) of scene '{scene}'"
+    assert len(symlinks_to_do) > 0
+    logger.info(f"Gathered {len(symlinks_to_do)} symlinks to create")
+    return symlinks_to_do
+
+def read_txt_data(txt_file: Path) -> list[tuple[str, str]]:
+    """reads the data from the txt file with format scene/stem"""
+    f = open(txt_file, "r")
+    res = []
+    for row in f.readlines():
+        assert len(split_row := row.strip().split("/")) == 2, row
+        res.append(split_row)
+    logger.info(f"Read {len(res)} paths.")
+    return res
+
+def get_args() -> Namespace:
+    """cli args"""
+    parser = ArgumentParser()
+    parser.add_argument("dataset_dir", type=lambda p: Path(p).absolute())
+    parser.add_argument("--txt_file", type=Path)
+    parser.add_argument("--output_path", "-o", type=lambda p: Path(p).absolute())
+    parser.add_argument("--overwrite", action="store_true")
+    parser.add_argument("--copy_files", action="store_true")
+    args = parser.parse_args()
+    assert not args.output_path.exists() or args.overwrite, f"'{args.output_path}' exists. Use --overwrite."
+    if args.output_path.exists():
+        shutil.rmtree(args.output_path)
+    assert args.dataset_dir.exists() and args.dataset_dir.is_dir(), f"'{args.dataset_dir}' doesn't exist."
+    assert args.txt_file.exists(), f"'{args.txt_file}' doesn't exist."
+    return args
+
+def main(args: Namespace):
+    """main fn"""
+    logger.info(f"\n- In dir: {args.dataset_dir}\n- Out dir: {args.output_path} \n- Symlinks: {not args.copy_files}")
+    args.output_path.mkdir(exist_ok=False, parents=True)
+    txt_data = read_txt_data(args.txt_file)
+    symlinks_to_do = check_and_gather_all_files(args.dataset_dir, txt_data, args.output_path, suffix=".npz")
+    for in_file, out_file in tqdm(symlinks_to_do.items(), desc="symlinks"):
+        Path(out_file).parent.mkdir(exist_ok=True, parents=True)
+        if args.copy_files:
+            shutil.copyfile(in_file, out_file, follow_symlinks=True)
+        else:
+            rel_path = f"{os.path.relpath(in_file.parent, out_file.parent)}/{in_file.name}"
+            assert (pth := Path(f"{out_file.parent}/{rel_path}")).exists(), pth
+            os.symlink(rel_path, out_file)
+
+if __name__ == "__main__":
+    main(get_args())

txt_files/annotated_and_segprop/val_files_605.txt

@@ -0,0 +1,605 @@
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txt_files/manually_annotated_files/test_files_116.txt

@@ -0,0 +1,116 @@
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txt_files/manually_annotated_files/train_files_218.txt

@@ -0,0 +1,218 @@
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txt_files/manually_annotated_files/val_files_15.txt

@@ -0,0 +1,15 @@
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+gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/8940
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+herculane_DJI_0021_full/8880
+herculane_DJI_0021_full/8940
+herculane_DJI_0021_full/9000
+jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/10680
+jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/10740
+jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/10800
+olanesti_DJI_0416_full/8880
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+petrova_DJI_0525_0526_combined_sliced_2850_11850/8880
+petrova_DJI_0525_0526_combined_sliced_2850_11850/8940
+petrova_DJI_0525_0526_combined_sliced_2850_11850/9000