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H2O/raw/script/README.md

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+# H2O Dataset information
+
+## News
+
+**24/Sep./2022**: We are pleased to announce that our total prize will be up to 4000 CHF (4000 USD) for our two challenges ([hand](https://codalab.lisn.upsaclay.fr/competitions/4822), [action](https://codalab.lisn.upsaclay.fr/competitions/4820)). <br>
+**15/Aug./2022**: Our challenges are open here ([hand](https://codalab.lisn.upsaclay.fr/competitions/4822), [action](https://codalab.lisn.upsaclay.fr/competitions/4820)). Our 1st phase of the challenges will be closed at October 1st, 2022. <br>
+**9/May/2022**: The download script (downlaod_script.py) is uploaded. <br>
+**8/May/2022**: Labels for hand pose and object pose are released. Please check https://h2odataset.ethz.ch <br>
+**4/May/2022**: The H2O dataset will be part of the workshop, Human Body, Hands, and Activities from Egocentric and Multi-view Cameras@ECCV2022. Please check our workshop site (https://sites.google.com/view/egocentric-hand-body-activity) and our challenges. <br>
+**12/Mar./2022**: Visualization code is published. https://github.com/taeinkwon/h2oplayer <br>
+**11/Mar./2022**: Updated new labels to the existing files. <br>
+**8/Dec./2021**: Please Download new labels "manolabel_v1.1.tar.gz" in https://h2odataset.ethz.ch for MANO parameters. We will also update existing files soon. <br>
+
+# Visualization code (H2OPlayer)
+![Alt text](h2o_player.png?raw=true "H2OPlayer")
+Please check the visualization code in the following link: https://github.com/taeinkwon/h2oplayer.
+
+# Download script
+
+## Dependencies
+- python3
+- tqdm
+- pathlib
+- requests
+- argparse
+
+
+Once you receive the username and password from the download page (https://h2odataset.ethz.ch), you can either download directly from the download page (https://h2odataset.ethz.ch) or use download_script.py.
+```
+python download_script.py --username "username" --password "password" --mode "type of view" --dest "dest folder path"
+```
+We provide three different modes (views):
+- all: download the entire data, including five views (four fixed views, one egocentric view).
+- ego: download only egocentric-view data.
+- pose: download only pose (hand, object, egocentric view) without RGB-D images.
+
+# Dataset Structure 
+
+<pre>
+.
+├── h1
+│   ├── 0
+│   │   │── cam0
+│   │   │   ├── rgb
+│   │   │   ├── depth
+│   │   │   ├── cam_pose
+│   │   │   ├── hand_pose
+│   │   │   ├── hand_pose_MANO
+│   │   │   ├── obj_pose
+│   │   │   ├── obj_pose_RT
+│   │   │   ├── action_label (only in cam4)
+│   │   │   ├── rgb256 (only in cam4)
+│   │   │   ├── verb_label
+│   │   │   └── cam_intrinsics.txt
+│   │   ├── cam1
+│   │   ├── cam2
+│   │   ├── cam3
+│   │   └── cam4
+│   ├── 1
+│   ├── 2
+│   ├── 3
+│   └── ...
+├── h2
+├── k1
+├── k2
+└── ...
+</pre>
+
+cam0 ~ cam3 are fixed cameras. cam4 is an head-mounted camera (egocentric view). <br>
+train_sequences = ['subject1/h1', 'subject1/h2', 'subject1/k1', 'subject1/k2', 'subject1/o1', 'subject1/o2', 'subject2/h1', 'subject2/h2', 'subject2/k1', 
+'subject2/k2', 'subject2/o1', 'subject2/o2', 'subject3/h1', 'subject3/h2', 'subject3/k1'] (subject 1,2,3) <br>
+val_sequences = ['subject3/k2', 'subject3/o1', 'subject3/o2'] (subject 3)<br>
+test_sequences  = ['subject4/h1', 'subject4/h2', 'subject4/k1', 'subject4/k2', 'subject4/o1', 'subject4/o2'] (subject4)<br>
+
+
+### rgb
+1280 * 720 resolution rgb images
+
+### rgb256
+455 * 256 resolution resized rgb images
+
+### depth
+1280 * 720 resolution depth images
+
+### cam_pose
+cam_to_world rotion matrix <br>
+
+16 numbers, 4x4 camera matrix
+### hand_pose
+cam_to_hand <br>
+1 (whether annotate or not, 0: not annotate 1: annotate) + 21 * 3 (x, y, z in order) + 1 + 21 * 3 (right hand) <br>
+First 64 numbers belong to the left hand. Next 64 numbers belong to the right hand 
+### hand_pose_MANO
+1 (whether annotate or not, 0: not annotate 1: annotate) + 3 translation values + 48 pose values + 10 shape values + 1 + 3 + 48 + 10 (right hand) <br>
+First 59 numbers belong to the left hand. Next 59 numbers belong to the right hand 
+### obj_pose
+cam_to_obj <br>
+1 (object class) + 21 * 3 (x, y, z in order) <br>
+21 numbers : 1 center, 8 corners, 12 mid edge point. <br>
+0 background (no object) <br>
+1 book <br>
+2 espresso <br>
+3 lotion <br>
+4 spray <br>
+5 milk <br>
+6 cocoa <br>
+7 chips <br>
+8 capuccino <br>
+
+<img id="image_canv" src="obj_point_order.jpg"/>
+
+### object_pose_RT
+1 (object class) + 16 numbers, 4x4 camera matrix
+
+### verb_label
+0 background (no verb) <br>
+1 grab <br>
+2 place <br>
+3 open <br>
+4 close <br>
+5 pour <br>
+6 take out <br>
+7 put in <br>
+8 apply <br>
+9 read <br>
+10 spray <br>
+11 squeeze <br>
+
+### action_label
+Combination of a noun (object class) and a verb (verb label)
+
+0 background <br>
+1 grab book <br>
+2 grab espresso <br>
+3 grab lotion <br>
+4 grab spray <br>
+5 grab milk <br>
+6 grab cocoa <br>
+7 grab chips <br>
+8 grab cappuccino <br>
+9 place book <br>
+10 place espresso <br>
+11 place lotion <br>
+12 place spray <br>
+13 place milk <br>
+14 place cocoa <br>
+15 place chips <br>
+16 place cappuccino <br>
+17 open lotion <br>
+18 open milk <br>
+19 open chips <br>
+20 close lotion <br>
+21 close milk <br>
+22 close chips <br>
+23 pour milk <br>
+24 take out espresso <br>
+25 take out cocoa <br>
+26 take out chips <br>
+27 take out cappuccino <br>
+28 put in espresso <br>
+29 put in cocoa <br>
+30 put in cappuccino <br>
+31 apply lotion <br>
+32 apply spray <br>
+33 read book <br>
+34 read espresso <br>
+35 spray spray <br>
+36 squeeze lotion <br>
+
+## For Actions
+[Train set file](action_labels/action_train.txt) <br>
+[Validation set file](action_labels/action_val.txt) <br>
+[Test set file](action_labels/action_test.txt) <br>
+
+## For Poses
+[Train set file](pose_lists/pose_test.txt) <br>
+[Validation set file](pose_lists/pose_train.txt) <br>
+[Test set file](pose_lists/pose_test.txt) <br>
+
+### cam_instrinsics.txt
+six numbers : fx, fy, cx, cy, width, height
+
+# Citations
+If you find any usefulness in this dataset, please consider citing:
+```
+@InProceedings{Kwon_2021_ICCV,
+    author    = {Kwon, Taein and Tekin, Bugra and St\"uhmer, Jan and Bogo, Federica and Pollefeys, Marc},
+    title     = {H2O: Two Hands Manipulating Objects for First Person Interaction Recognition},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    month     = {October},
+    year      = {2021},
+    pages     = {10138-10148}
+}
+```

H2O/raw/script/action_labels/action_test.txt

@@ -0,0 +1,243 @@
+id path start_act end_act start_frame end_frame
+1 subject4/h1/0 0 80 0 500
+2 subject4/h1/0 109 167 0 500
+3 subject4/h1/0 211 295 0 500
+4 subject4/h1/0 366 442 0 500
+5 subject4/h1/1 0 82 0 1003
+6 subject4/h1/1 120 323 0 1003
+7 subject4/h1/1 489 623 0 1003
+8 subject4/h1/1 658 719 0 1003
+9 subject4/h1/1 739 947 0 1003
+10 subject4/h1/2 12 103 0 563
+11 subject4/h1/2 135 207 0 563
+12 subject4/h1/2 237 524 0 563
+13 subject4/h1/3 0 66 0 786
+14 subject4/h1/3 95 142 0 786
+15 subject4/h1/3 146 230 0 786
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+17 subject4/h1/3 344 393 0 786
+18 subject4/h1/3 394 510 0 786
+19 subject4/h1/3 540 675 0 786
+20 subject4/h1/3 714 778 0 786
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+22 subject4/h1/4 77 165 0 712
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+39 subject4/h1/7 393 439 0 583
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+82 subject4/k1/0 0 84 0 547
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+85 subject4/k1/0 397 492 0 547
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H2O/raw/script/action_labels/action_train.txt

@@ -0,0 +1,570 @@
+id path action_label start_act end_act start_frame end_frame
+1 subject1/h1/0 16 0 61 0 449
+2 subject1/h1/0 8 64 127 0 449
+3 subject1/h1/0 27 133 255 0 449
+4 subject1/h1/0 30 317 417 0 449
+5 subject1/h1/1 13 0 48 0 642
+6 subject1/h1/1 5 55 108 0 642
+7 subject1/h1/1 18 109 268 0 642
+8 subject1/h1/1 23 304 442 0 642
+9 subject1/h1/1 21 496 625 0 642
+10 subject1/h1/2 9 11 72 0 506
+11 subject1/h1/2 1 79 169 0 506
+12 subject1/h1/2 33 184 491 0 506
+13 subject1/h1/3 11 0 49 0 928
+14 subject1/h1/3 3 75 150 0 928
+15 subject1/h1/3 17 156 241 0 928
+16 subject1/h1/3 36 255 424 0 928
+17 subject1/h1/3 11 425 485 0 928
+18 subject1/h1/3 31 491 780 0 928
+19 subject1/h1/3 3 789 833 0 928
+20 subject1/h1/3 20 834 928 0 928
+21 subject1/h1/4 15 0 48 0 586
+22 subject1/h1/4 7 59 118 0 586
+23 subject1/h1/4 19 122 210 0 586
+24 subject1/h1/4 26 249 335 0 586
+25 subject1/h1/4 22 418 586 0 586
+26 subject1/h1/5 12 0 52 0 490
+27 subject1/h1/5 4 67 111 0 490
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+29 subject1/h1/5 12 225 283 0 490
+30 subject1/h1/5 32 290 427 0 490
+31 subject1/h1/5 4 442 484 0 490
+32 subject1/h1/6 14 0 21 0 485
+33 subject1/h1/6 6 35 107 0 485
+34 subject1/h1/6 25 117 251 0 485
+35 subject1/h1/6 29 348 459 0 485
+36 subject1/h1/7 10 0 59 0 797
+37 subject1/h1/7 2 79 129 0 797
+38 subject1/h1/7 34 213 439 0 797
+39 subject1/h1/7 24 461 542 0 797
+40 subject1/h1/7 28 642 797 0 797
+41 subject1/h2/0 16 0 58 0 399
+42 subject1/h2/0 8 65 113 0 399
+43 subject1/h2/0 27 114 186 0 399
+44 subject1/h2/0 30 277 345 0 399
+45 subject1/h2/1 13 0 41 0 684
+46 subject1/h2/1 5 54 111 0 684
+47 subject1/h2/1 18 121 237 0 684
+48 subject1/h2/1 23 340 448 0 684
+49 subject1/h2/1 21 519 672 0 684
+50 subject1/h2/2 9 1 108 0 532
+51 subject1/h2/2 1 132 225 0 532
+52 subject1/h2/2 33 263 520 0 532
+53 subject1/h2/3 11 0 52 0 893
+54 subject1/h2/3 3 85 131 0 893
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+56 subject1/h2/3 36 234 353 0 893
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+58 subject1/h2/3 31 438 741 0 893
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+61 subject1/h2/4 15 0 54 0 544
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+75 subject1/h2/6 29 418 530 0 536
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+78 subject1/h2/7 34 129 291 0 717
+79 subject1/h2/7 24 339 402 0 717
+80 subject1/h2/7 28 486 714 0 717
+81 subject1/k1/0 16 16 67 0 350
+82 subject1/k1/0 8 68 115 0 350
+83 subject1/k1/0 27 116 208 0 350
+84 subject1/k1/0 30 256 320 0 350
+85 subject1/k1/1 13 0 62 0 753
+86 subject1/k1/1 5 63 105 0 753
+87 subject1/k1/1 18 106 280 0 753
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H2O/raw/script/action_labels/action_val.txt

@@ -0,0 +1,123 @@
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+120 subject3/o2/7 34 179 298 0 543
+121 subject3/o2/7 24 337 433 0 543
+122 subject3/o2/7 28 476 543 0 543

H2O/raw/script/download_script.py

@@ -0,0 +1,69 @@
+import tqdm
+import argparse
+import requests
+import os
+from pathlib import Path
+
+def downloader(url, dest, user, password, resume_byte_pos=None):
+    local_filename = url.split('/')[-1]
+    # Header information
+    r = requests.head(url, stream=True, auth=(user, password))
+    resume_header = ({'Range': f'bytes={resume_byte_pos}-'}
+                     if resume_byte_pos else None)
+    total_size_in_bytes = int(r.headers.get('content-length', 0))
+
+    # File
+    r = requests.get(url, stream=True, auth=(user, password),headers=resume_header)
+    block_size = 8192
+    initial_pos = resume_byte_pos if resume_byte_pos else 0
+    mode = 'ab' if resume_byte_pos else 'wb'
+
+    progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True, #unit_divisor=block_size,
+                  desc=local_filename, initial=initial_pos, ascii=True, miniters=1)
+    with open(os.path.join(dest, local_filename), mode) as f:
+        for chunk in r.iter_content(chunk_size=block_size): 
+            progress_bar.update(len(chunk))
+            f.write(chunk)
+    progress_bar.close()
+
+
+def download_file(url, dest, user, password):
+    local_filename = url.split('/')[-1]
+    r = requests.get(url, stream=True, auth=(user, password))
+    r.raise_for_status()   
+    total_size_in_bytes = int(r.headers.get('content-length', 0))
+    file_path = os.path.join(dest,local_filename)
+    if os.path.exists(file_path):
+        file_size_offline = Path(file_path).stat().st_size
+        if total_size_in_bytes != file_size_offline:
+            print(f'File {local_filename} is incomplete. Resume download.')
+            downloader(url, dest, user, password, file_size_offline)
+        else:
+            print(f'File {local_filename} is complete. Skip download.')
+    else:
+        downloader(url, dest, user, password)
+    return local_filename
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Download H2O")
+    parser.add_argument('--mode', type=str, default='ego', choices=['all', 'ego', 'pose'],
+                        help = 'Download all views, an egocentric view, or only poses')
+    parser.add_argument('--dest', type=str,help = 'Destination path to download files.', default='downloads')
+    parser.add_argument('--username', type=str,help = 'id from https://h2odataset.ethz.ch/', required=True)
+    parser.add_argument('--password', type=str,help = 'password from https://h2odataset.ethz.ch/',required=True)
+
+    args = parser.parse_args()
+    url = 'https://h2odataset.ethz.ch/data/dataset/'
+    if args.mode == 'all':
+        file_name_list = ['object.zip','label_split.zip','subject1_v1_1.tar.gz','subject2_v1_1.tar.gz','subject3_v1_1.tar.gz','subject4_v1_1.tar.gz']
+    elif args.mode == 'ego':
+        file_name_list = ['object.zip','label_split.zip','subject1_ego_v1_1.tar.gz','subject2_ego_v1_1.tar.gz','subject3_ego_v1_1.tar.gz','subject4_ego_v1_1.tar.gz']
+    elif args.mode == 'pose':
+        file_name_list = ['object.zip','label_split.zip','subject1_pose_v1_1.tar.gz','subject2_pose_v1_1.tar.gz','subject3_pose_v1_1.tar.gz','subject4_pose_v1_1.tar.gz']
+    file_list =  [url + s for s in file_name_list]
+
+    if not os.path.exists(args.dest):
+        os.makedirs(args.dest)
+
+    for file_url in tqdm.tqdm(file_list):
+        download_file(file_url, args.dest, args.username, args.password)