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https://git-lfs.github.com/spec/v1 +oid sha256:94e259f3f87626de3a8511117c3063ae9bce3dcce8d2c347a4211054d4cbbdb0 +size 193873 diff --git a/RHD/shared-v1.0/doc/validation/evidence.json b/RHD/shared-v1.0/doc/validation/evidence.json new file mode 100644 index 0000000000000000000000000000000000000000..85f911722d8d45100d512c1ea73f98232253918c --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/evidence.json @@ -0,0 +1,81 @@ +{ + "human_review": "approved", + "full_processing": { + "automated": "passed", + "counts": { + "training": 41258, + "evaluation": 2728 + }, + "shards": 87, + "full_random_review": "approved", + "report_ref": "full_random/REPORT.md" + }, + "sampling_seed": 20260918, + "reports": [ + { + "split": "training", + "selected_ids": [ + 7689, + 29103, + 41256, + 41257, + 20189, + 10098, + 40491, + 3306, + 39758, + 28259, + 23073, + 41179 + ], + "rgb_candidate_sizes": { + "ffv1": 825990, + "h264_rgb_lossless": 1498960 + }, + "selected_rgb_profile": "mkv/ffv1_rgb/v1", + "max_projection_difference_pixels": 0.6817232966423035, + "depth_float32_bits_exact": true, + "media_roundtrip_exact": true + }, + { + "split": "evaluation", + "selected_ids": [ + 2677, + 919, + 2725, + 2726, + 2248, + 2415, + 1513, + 511, + 400, + 891, + 779, + 190 + ], + "rgb_candidate_sizes": { + "ffv1": 798517, + "h264_rgb_lossless": 1541938 + }, + "selected_rgb_profile": "mkv/ffv1_rgb/v1", + "max_projection_difference_pixels": 0.39323511719703674, + "depth_float32_bits_exact": true, + "media_roundtrip_exact": true + } + ], + "metadata_schema_check": "passed", + "converter_sha256": "74c4d384d9b9370c5163a8a1fcb05dcbeafb750598f196f0a395d1de2d8726e4", + "human_review_confirmation": { + "reviewer": "user", + "statement": "没问题。", + "confirmed_at_local": "2026-09-18T17:20:06", + "scope": "24 pilot samples; approval for full preprocessing only" + }, + "publication": "approved", + "publication_confirmation": { + "reviewer": "user", + "statement": "RHD没问题，可以上传了，上传顺便测一下速度吧！", + "confirmed_at_local": "2026-09-18T18:25:54+0800", + "scope": "24 full-output random samples; publication of raw and share approved" + } +} \ No newline at end of file diff --git a/RHD/shared-v1.0/doc/validation/full_random/REPORT.md b/RHD/shared-v1.0/doc/validation/full_random/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..a4ec7ae7c7b753a0b302e139f4449a6787228780 --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/REPORT.md @@ -0,0 +1,7 @@ +# RHD 全量随机抽查 + +[离线预览](index.html) · [抽样清单](samples.json) + +全量 43,986 张，训练与评估各随机选 12 张；分层等额、固定种子20260918，无放回抽样。直接读取已完成的分片，不复用试点。所选 RGB、mask 和 float32 深度逐值/逐比特与 raw 对应数据匹配。 + +**人工确认已通过，用户已批准发布 raw 与 share。** 检查左右手身份、分割轮廓、UV/XYZ投影及深度；本报告不替代全量数值验证。 diff --git a/RHD/shared-v1.0/doc/validation/full_random/evaluation_00190.png b/RHD/shared-v1.0/doc/validation/full_random/evaluation_00190.png new file mode 100644 index 0000000000000000000000000000000000000000..815f60179ab5832cc6690b1740d0eb5a5534b5b9 --- 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b/RHD/shared-v1.0/doc/validation/full_random/evaluation_00511.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f00a9033ac6dd18325799f1d5c8bf3bfea18bcb55a9523c6a7355919f20487bb +size 141612 diff --git a/RHD/shared-v1.0/doc/validation/full_random/evaluation_00746.png b/RHD/shared-v1.0/doc/validation/full_random/evaluation_00746.png new file mode 100644 index 0000000000000000000000000000000000000000..5b636bc8a21c82e04160be1126b96b09e69e7738 --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/evaluation_00746.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4cf19936d6f93429c128b67d92ef21f320708ff03cfef058ec2cea39bed696f5 +size 202758 diff --git a/RHD/shared-v1.0/doc/validation/full_random/evaluation_00779.png b/RHD/shared-v1.0/doc/validation/full_random/evaluation_00779.png new file mode 100644 index 0000000000000000000000000000000000000000..b3ab3e5f8caaf2df299244d5e4d8eb5d9c41f794 --- /dev/null +++ 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b/RHD/shared-v1.0/doc/validation/full_random/evaluation_01513.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6542ce8710ac44b36e6e279f105e5103eb495316f3b351a10acdb1227faed824 +size 178644 diff --git a/RHD/shared-v1.0/doc/validation/full_random/evaluation_02248.png b/RHD/shared-v1.0/doc/validation/full_random/evaluation_02248.png new file mode 100644 index 0000000000000000000000000000000000000000..f0698053338dbd38785950e4266c18aaa274220f --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/evaluation_02248.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7d1c381e2514c36c7322f93c5e741ce3c59a3751c0cfb0cc290069863bab040 +size 178186 diff --git a/RHD/shared-v1.0/doc/validation/full_random/evaluation_02415.png b/RHD/shared-v1.0/doc/validation/full_random/evaluation_02415.png new file mode 100644 index 0000000000000000000000000000000000000000..dc133ac8e2601e4236cff1e7e074be8bc8cb6acb --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/evaluation_02415.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5af9c7d6a333c2d54ac50f98e0eb31944ef94fa4d1d2559f976116b877c27575 +size 157265 diff --git a/RHD/shared-v1.0/doc/validation/full_random/evaluation_02470.png b/RHD/shared-v1.0/doc/validation/full_random/evaluation_02470.png new file mode 100644 index 0000000000000000000000000000000000000000..545f934448939eddc5a332cbd167f4d5bcc37cfb --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/evaluation_02470.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f7f7acd25df3215f0663193c7b391e088ab8bd9093367a8a22f19b867bb51aa +size 178730 diff --git a/RHD/shared-v1.0/doc/validation/full_random/index.html b/RHD/shared-v1.0/doc/validation/full_random/index.html new file mode 100644 index 0000000000000000000000000000000000000000..4b52a8469ca9cd689602824e98ca2d6d154d52d9 --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/index.html @@ -0,0 +1 @@ +RHD 全量随机抽查

RHD 全量产物随机抽查：24 张

直接从全量产物读取，训练与评估各随机12张；种子20260918。分层等额抽样，不能据此估计总体错误率。RGB、mask及深度重新与 raw 对比通过，人工确认已通过，用户已批准发布。

依次为 RGB、左右手分割、原生 UV（空心=不可见）、XYZ 投影、逐图拉伸的 z 深度。颜色：红左手、蓝右手。

evaluation/00190

Source visible joints: 21/42. Shard data/evaluation_00000000/camera, frame 190. Depth display range: 0.252–5.000 m.

evaluation/00400

Source visible joints: 20/42. Shard data/evaluation_00000000/camera, frame 400. Depth display range: 0.373–5.000 m.

evaluation/00511

Source visible joints: 34/42. Shard data/evaluation_00000000/camera, frame 511. Depth display range: 0.577–5.000 m.

evaluation/00746

Source visible joints: 42/42. Shard data/evaluation_00000512/camera, frame 234. Depth display range: 0.633–5.000 m.

evaluation/00779

Source visible joints: 21/42. Shard data/evaluation_00000512/camera, frame 267. Depth display range: 0.528–5.000 m.

evaluation/00891

Source visible joints: 41/42. Shard data/evaluation_00000512/camera, frame 379. Depth display range: 0.611–5.000 m.

evaluation/01185

Source visible joints: 20/42. Shard data/evaluation_00001024/camera, frame 161. Depth display range: 0.456–5.000 m.

evaluation/01377

Source visible joints: 42/42. Shard data/evaluation_00001024/camera, frame 353. Depth display range: 0.480–5.000 m.

evaluation/01513

Source visible joints: 36/42. Shard data/evaluation_00001024/camera, frame 489. Depth display range: 0.412–5.000 m.

evaluation/02248

Source visible joints: 24/42. Shard data/evaluation_00002048/camera, frame 200. Depth display range: 0.499–5.000 m.

evaluation/02415

Source visible joints: 29/42. Shard data/evaluation_00002048/camera, frame 367. Depth display range: 0.444–5.000 m.

evaluation/02470

Source visible joints: 20/42. Shard data/evaluation_00002048/camera, frame 422. Depth display range: 0.522–5.000 m.

training/03306

Source visible joints: 40/42. Shard data/training_00003072/camera, frame 234. Depth display range: 0.336–5.000 m.

training/07180

Source visible joints: 21/42. Shard data/training_00007168/camera, frame 12. Depth display range: 0.416–5.000 m.

training/10098

Source visible joints: 42/42. Shard data/training_00009728/camera, frame 370. Depth display range: 0.357–5.000 m.

training/10271

Source visible joints: 42/42. Shard data/training_00010240/camera, frame 31. Depth display range: 0.632–5.000 m.

training/20189

Source visible joints: 31/42. Shard data/training_00019968/camera, frame 221. Depth display range: 0.471–5.000 m.

training/21319

Source visible joints: 21/42. Shard data/training_00020992/camera, frame 327. Depth display range: 0.446–5.000 m.

training/23073

Source visible joints: 42/42. Shard data/training_00023040/camera, frame 33. Depth display range: 0.549–5.000 m.

training/23469

Source visible joints: 21/42. Shard data/training_00023040/camera, frame 429. Depth display range: 0.553–5.000 m.

training/28259

Source visible joints: 24/42. Shard data/training_00028160/camera, frame 99. Depth display range: 0.521–5.000 m.

training/39758

Source visible joints: 21/42. Shard data/training_00039424/camera, frame 334. Depth display range: 0.335–5.000 m.

training/40491

Source visible joints: 21/42. Shard data/training_00040448/camera, frame 43. Depth display range: 0.642–5.000 m.

training/41179

Source visible joints: 31/42. Shard data/training_00040960/camera, frame 219. Depth display range: 0.464–5.000 m.

\ No newline at end of file diff --git a/RHD/shared-v1.0/doc/validation/full_random/samples.json b/RHD/shared-v1.0/doc/validation/full_random/samples.json new file mode 100644 index 0000000000000000000000000000000000000000..75607e6a6a6bce0f2703c44a9a10d0ea62294467 --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/full_random/samples.json @@ -0,0 +1,329 @@ +{ + "seed": 20260918, + "sampling": "12 samples without replacement per official split from all 43986 output frames; sorted populations, Python random.Random", + "counts": { + "training": 41258, + "evaluation": 2728 + }, + "human_review": "approved", + "samples": [ + { + "sample_id": "RHD/evaluation/00190", + "view": "data/evaluation_00000000/camera", + "frame_index": 190, + "source_locator": { + "archive": "raw/data/RHD_v1-1.zip", + "annotation": "RHD_published_v2/evaluation/anno_evaluation.pickle", + "source_frame_id": "190", + "annotation_index": 190, + "member": "RHD_published_v2/evaluation/color/00190.png" + }, + 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RHD：24 张 raw→share 转换核验

训练与评估各12张，包含可见性极端和随机样本。独立图片不是视频序列。用户已确认试点通过，即将进行全量处理；全量随机抽查另行确认后才上传。

从左至右：RGB、左右手分割、原生2D关键点、3D点经相机投影、米制z深度可视化。红=左手，蓝=右手；2D空心点表示源标注不可见，仍保留坐标。深度图逐图拉伸对比度，颜色不能跨图直接比较米数。

检查双手身份、关键点顺序与UV/XYZ对应、分割边缘和深度轮廓。MANO、物体等源未提供的组件缺省。

training/07689

Source visible joints: 1/42. Depth display range: 0.034–5.000 m.

training/29103

Source visible joints: 1/42. Depth display range: 0.040–5.000 m.

training/41256

Source visible joints: 42/42. Depth display range: 0.566–5.000 m.

training/41257

Source visible joints: 42/42. Depth display range: 0.570–5.000 m.

training/20189

Source visible joints: 31/42. Depth display range: 0.471–5.000 m.

training/10098

Source visible joints: 42/42. Depth display range: 0.357–5.000 m.

training/40491

Source visible joints: 21/42. Depth display range: 0.642–5.000 m.

training/03306

Source visible joints: 40/42. Depth display range: 0.336–5.000 m.

training/39758

Source visible joints: 21/42. Depth display range: 0.335–5.000 m.

training/28259

Source visible joints: 24/42. Depth display range: 0.521–5.000 m.

training/23073

Source visible joints: 42/42. Depth display range: 0.549–5.000 m.

training/41179

Source visible joints: 31/42. Depth display range: 0.464–5.000 m.

evaluation/02677

Source visible joints: 5/42. Depth display range: 0.075–5.000 m.

evaluation/00919

Source visible joints: 8/42. Depth display range: 0.204–5.000 m.

evaluation/02725

Source visible joints: 42/42. Depth display range: 0.513–5.000 m.

evaluation/02726

Source visible joints: 42/42. Depth display range: 0.462–5.000 m.

evaluation/02248

Source visible joints: 24/42. Depth display range: 0.499–5.000 m.

evaluation/02415

Source visible joints: 29/42. Depth display range: 0.444–5.000 m.

evaluation/01513

Source visible joints: 36/42. Depth display range: 0.412–5.000 m.

evaluation/00511

Source visible joints: 34/42. Depth display range: 0.577–5.000 m.

evaluation/00400

Source visible joints: 20/42. Depth display range: 0.373–5.000 m.

evaluation/00891

Source visible joints: 41/42. Depth display range: 0.611–5.000 m.

evaluation/00779

Source visible joints: 21/42. Depth display range: 0.528–5.000 m.

evaluation/00190

Source visible joints: 21/42. Depth display range: 0.252–5.000 m.

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a/RHD/shared-v1.0/doc/validation/training_41257.png b/RHD/shared-v1.0/doc/validation/training_41257.png new file mode 100644 index 0000000000000000000000000000000000000000..d611e9c88e25ef53681f535ffaa5c678048450b7 --- /dev/null +++ b/RHD/shared-v1.0/doc/validation/training_41257.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:625bbb7bdf8d26ab2ead5552658cafc32f2c8a77deb50e3e5ce18e2298345e5d +size 166977 diff --git a/RHD/shared-v1.0/script/convert_freihand_lossless.py b/RHD/shared-v1.0/script/convert_freihand_lossless.py new file mode 100644 index 0000000000000000000000000000000000000000..a5c1a583179d841f4826e0c3a8503d0161d6b8bc --- /dev/null +++ b/RHD/shared-v1.0/script/convert_freihand_lossless.py @@ -0,0 +1,123 @@ +"""FreiHAND complete ZIP -> lossless-video v1.0; never deletes raw. +--output is the hand-datasets root. --max-batches is for converter validation only. +""" +from sequence_pilot_images import * +import sequence_pilot_common as common +import hashlib + +BaseView=View +class View(BaseView): + def finish(self): + lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m) + save(self.root.parent/'tmp/production/checks'/f'{self.m["sequence_id"]}__{self.m["camera_id"]}.checks.json',{'checks':self.checks,'frames':self.T,'input':self.source}) + print('Converted',self.m['dataset_id'],self.m['sequence_id'],self.m['camera_id'],self.T,flush=True) + +def ff(args,data=None): + r=subprocess.run([FF,'-v','error','-y',*args],input=data,stdout=subprocess.PIPE,stderr=subprocess.PIPE) + if r.returncode: raise RuntimeError(r.stderr.decode(errors='replace')) + return r.stdout + +def storage(profile,codec,pix,size,lossy,params): + return dict(profile=profile,codec=codec,pixel_format=pix,encoded_size=size,source_lossy=lossy, + decoder=ff(['-version']).decode().splitlines()[0],encoder_parameters=params) + +def main(): + ap=argparse.ArgumentParser();ap.add_argument('--output',type=Path,required=True) + ap.add_argument('--source',type=Path,default=ROOT/'FreiHAND/raw/data') + ap.add_argument('--batch-size',type=int,default=4096);ap.add_argument('--max-batches',type=int);ap.add_argument('--split',choices=['all','training','evaluation'],default='all');ap.add_argument('--sample-indices',help='Comma-separated source RGB indices for local human review');ap.add_argument('--human-reviewed',action='store_true',help='Use only after explicit user approval of local review') + args=ap.parse_args();assert args.batch_size>0 + if not args.sample_indices and not args.max_batches and not args.human_reviewed:ap.error('Full conversion requires explicit human review approval first (--human-reviewed)') + common.ROOT=args.output;common.RELEASE='shared-v1.0' + # Do not mix a new sharding layout with existing production intermediates. + if not args.sample_indices: + existing=args.output/'FreiHAND'/common.RELEASE/'data' + for directory in existing.glob('*'): + if not directory.is_dir():continue + mp=directory/'camera/metainfo.json' + if not mp.exists():raise RuntimeError(f'Incomplete existing shard must be handled before rerun: {directory}') + previous=load(mp);start_index=int(directory.name.rsplit('_',1)[1]) + total=130240 if directory.name.startswith('training_') else 3960 + expected=min(args.batch_size,total-start_index) + if start_index%args.batch_size or previous['num_frames']!=expected: + raise RuntimeError(f'Existing shard layout differs from requested {args.batch_size}; archive/rebuild old intermediates before rerun: {directory}') + torch.set_num_threads(4) + model=Mano('right');count=0;summary=[] + for split,filename in [('training','FreiHAND_pub_v2.zip'),('evaluation','FreiHAND_pub_v2_eval.zip')]: + if args.split!='all' and args.split!=split:continue + with zipfile.ZipFile(args.source/filename) as z: + ks=np.asarray(json.loads(z.read(split+'_K.json')));xyz=np.asarray(json.loads(z.read(split+'_xyz.json'))) + theta=np.asarray(json.loads(z.read(split+'_mano.json'))).reshape(-1,61) + n=len(ks);assert len(xyz)==len(theta)==n + rgbnames=sorted(s for s in z.namelist() if s.startswith(split+'/rgb/') and s.endswith('.jpg')) + assert len(rgbnames)==n*(4 if split=='training' else 1) + if args.sample_indices: + chosen={int(x) for x in args.sample_indices.split(',')};rgbnames=[s for s in rgbnames if int(Path(s).stem) in chosen] + assert rgbnames,'No selected source images' + for start in range(0,len(rgbnames),args.batch_size): + names=rgbnames[start:start+args.batch_size];ids=[int(Path(s).stem) for s in names];ix=np.asarray(ids)%n;t=len(ids) + seq=f'{split}_{start:08d}';source={'archive':'raw/data/'+filename} + v=View('FreiHAND',seq,'camera',ids,[224,224],source,kind='image',split='train' if split=='training' else 'test') + v.m['split_scheme']='FreiHAND_official_train_evaluation' + v.m['split_source_locator']={'archive':'raw/data/'+filename,'member_prefix':split+'/'} + v.m['schema_version']='hand-warehouse/1.0';v.m['storage_revision']='lossless-video-2026-09-18' + v.m['sampling']['type']='independent' if t>1 else 'single' + v.m['sampling']['shard_max_images']=args.batch_size + v.m['sampling']['selection']='all released RGB images, sorted by source RGB index; all background variants retained' + for f,i,base,name in zip(v.frames,ids,ix,names): + f.update(sample_id=f'FreiHAND/{split}/{i:08d}',image_variant={'base_sample_id':int(base),'variant_index':int(i//n)}) + f['source_locator'].update(member=name,annotation_index=int(base)) + jpg=b''.join(z.read(s) for s in names);path=v.p/'observation/rgb.mkv';path.parent.mkdir(parents=True,exist_ok=True) + ff(['-f','image2pipe','-framerate','1','-c:v','mjpeg','-i','pipe:0','-c:v','copy',str(path)],jpg) + assert ff(['-i',str(path),'-c:v','copy','-f','image2pipe','pipe:1'])==jpg + decoded=ff(['-f','image2pipe','-c:v','mjpeg','-i','pipe:0','-f','rawvideo','-pix_fmt','rgb24','pipe:1'],jpg) + assert len(decoded)==t*224*224*3 + assert ff(['-i',str(path),'-f','rawvideo','-pix_fmt','rgb24','pipe:1'])==decoded + # FFmpeg reports the actual source pixel format without printing image data. + info=subprocess.run([FF,'-hide_banner','-i',str(path)],capture_output=True).stderr.decode(errors='replace') + import re + pix=re.search(r'Video: mjpeg[^\n]*?\b(yuvj?\d+p)\b',info) + if not pix: raise RuntimeError('Unrecognized JPEG pixel format: '+info) + v.add('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(t,bool), + storage=storage('mkv/mjpeg_copy/v1','mjpeg',pix.group(1),[224,224],True,{'mode':'stream_copy'}),storage_fps=1,timebase_role='index_only') + v.checks.append({'test':'all_rgb_packets_and_decoded_pixels_exact','frames':t}) + v.intrinsic(ks[ix]);v.m['components']['intrinsic']['source_locator'].update(member=split+'_K.json',index_ref='camera/frames.jsonl:source_locator.annotation_index') + kp=np.full((t,2,21,3),np.nan);kp[:,1]=xyz[ix];valid=np.zeros((t,2,21),bool);valid[:,1]=True + v.array('keypoints_xyz','annotation/keypoints/xyz.pt','keypoints/xyz/v1',kp,valid,['T','hand','joint','coordinate'],unit='m',coordinate_frame='camera',keypoint_schema='mano21/v1') + v.m['components']['keypoints_xyz']['source_locator'].update(member=split+'_xyz.json',index_ref='camera/frames.jsonl:source_locator.annotation_index') + a=theta[ix];pose=a[:,:48].copy();pose[:,3:]+=model.data['hands_mean'];beta=a[:,48:58] + verts,joints,_=model.geometry(pose,beta) + focal=(ks[ix,0,0]+ks[ix,1,1])/2 + root=np.concatenate([a[:,58:60]-ks[ix,:2,2],focal[:,None]],axis=1)/a[:,60,None] + translation=root-joints[:,9];motion=np.full((t,2,61),np.nan);motion[:,1]=np.concatenate([pose,beta,translation],axis=1) + valid=np.zeros((t,2),bool);valid[:,1]=True + v.array('mano','annotation/mano/motion.pt','mano/axis_angle61/v1',motion,valid,['T','hand','parameter'],unit='m',coordinate_frame='camera',angle_unit='rad',model_profile={'version':'MANO-v1.2','flat_hand_mean':True,'left_shapedirs_fix':False},method='Add MANO nonflat mean; recover source camera root from uv/scale; subtract posed middle MCP. No new fitting.') + v.m['components']['mano']['source_locator'].update(member=split+'_mano.json',index_ref='camera/frames.jsonl:source_locator.annotation_index') + _,restored,_=model.geometry(motion[:,1,:48],motion[:,1,48:58],motion[:,1,58:]) + assert np.max(np.abs(restored[:,9]-root))<2e-6 + masks=[];records=[] + for frame,(i,base) in enumerate(zip(ids,ix)): + member=f'training/mask/{base:08d}.jpg' if split=='training' else f'evaluation/segmap/{i:08d}.png' + arr=np.asarray(Image.open(io.BytesIO(z.read(member)))) + if split=='training': + assert arr.shape==(224,224,3) and np.array_equal(arr[:,:,0],arr[:,:,1]) and np.array_equal(arr[:,:,0],arr[:,:,2]) + arr=arr[:,:,0].astype(np.uint16) + else: + assert arr.shape==(224,224) + arr=arr.astype(np.uint16);arr[arr==0]=65534 + records.append({'frame_index':frame,'labels':[dict(mask_value=int(c),category_id='background' if c==65534 else 'hand',entity_id=None if c==65534 else 'hand_right',part_id=None,source_instance_id=0 if c==65534 else int(c)) for c in np.unique(arr)]}) + masks.append(arr) + pix='gray' if split=='training' else 'gray16le';raw=np.asarray(masks,dtype=np.uint8 if pix=='gray' else '=args.max_batches:return + save(v.root.parent/'tmp/production/conversion_progress.json',{'complete':args.split=='all' and not args.sample_indices,'split_selection':args.split,'batches':summary}) +if __name__=='__main__':main() diff --git a/RHD/shared-v1.0/script/convert_rhd_full.py b/RHD/shared-v1.0/script/convert_rhd_full.py new file mode 100644 index 0000000000000000000000000000000000000000..b959d1efba175704f7567c4a3b297a915b5bb9fc --- /dev/null +++ b/RHD/shared-v1.0/script/convert_rhd_full.py @@ -0,0 +1,67 @@ +"""RHD bounded raw-to-contract pilot, with lossless media and local human review.""" +from convert_freihand_lossless import View,ff,storage +from sequence_pilot_common import * +import sequence_pilot_common as common +import random,html,hashlib +BASE=Path('E:/hand-datasets/RHD');RELEASE='shared-v1.0' + +def media(v,name,path,raw,pix,size,profile,codec='ffv1',options=None): + dst=v.p/path;dst.parent.mkdir(parents=True,exist_ok=True);options=options or ['-level','3'] + ff(['-f','rawvideo','-pixel_format',pix,'-video_size',f'{size[0]}x{size[1]}','-framerate','1','-i','pipe:0','-c:v',codec,*options,str(dst)],raw) + restored=ff(['-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1']);assert restored==raw,name + v.checks.append({'test':'all_media_bytes_exact','component':name,'frames':v.T}) + actual_pix='bgr0' if codec=='ffv1' and pix=='rgb24' else ('gbrp' if codec=='libx264rgb' else pix) + return storage(profile,codec,actual_pix,size,False,options),restored + +def main(): + common.ROOT=Path('E:/hand-datasets');common.RELEASE=RELEASE;torch.set_num_threads(4) + root=BASE/RELEASE;out=root/'doc/validation';review=load(out/'evidence.json') + assert review['human_review']=='approved','Explicit pilot approval required' + assert not any((root/'data').glob('*')),'Existing data must be archived before full conversion' + order=load(WORK/'distribution/hand-warehouse-1.0/schema.json')['keypoints']['rhd21/v1']['source_index_map_per_hand'];reports=[] + with zipfile.ZipFile(BASE/'raw/data/RHD_v1-1.zip') as z: + for split,total in [('training',41258),('evaluation',2728)]: + member=next(n for n in z.namelist() if n.endswith(f'{split}/anno_{split}.pickle'));prefix=member.rsplit('/',1)[0] + anno=Restricted(io.BytesIO(z.read(member)),encoding='latin1').load();all_ids=sorted(anno);assert len(all_ids)==total + for offset in range(0,total,512): + ids=all_ids[offset:offset+512];t=len(ids) + v=View('RHD',f'{split}_{offset:08d}','camera',ids,[320,320],{'archive':'raw/data/RHD_v1-1.zip','annotation':member},kind='image',split='train' if split=='training' else 'test') + v.m.update(schema_version='hand-warehouse/1.0',storage_revision='lossless-video-2026-09-18',split_scheme='RHD_official_training_evaluation',split_source_locator={'archive':'raw/data/RHD_v1-1.zip','member_prefix':prefix},sample_type='image') + v.m['sampling'].update(type='independent',selection='all source independent images sorted by source ID',shard_max_images=512) + for row,i in zip(v.frames,ids):row.update(sample_id=f'RHD/{split}/{i:05d}');row['source_locator'].update(annotation_index=i,member=f'{prefix}/color/{i:05d}.png') + rgb=np.stack([np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/color/{i:05d}.png'))).convert('RGB')) for i in ids]) + rgbstore,_=media(v,'rgb','observation/rgb.mkv',rgb.tobytes(),'rgb24',[320,320],'mkv/ffv1_rgb/v1') + other=v.p/'observation/rgb_candidate.mkv';candidate,_=media(v,'rgb_candidate','observation/rgb_candidate.mkv',rgb.tobytes(),'rgb24',[320,320],'mkv/h264_rgb_lossless/v1','libx264rgb',['-crf','0','-preset','medium','-g','32']) + candidate['pixel_format']='gbrp' + sizes={'ffv1':(v.p/'observation/rgb.mkv').stat().st_size,'h264_rgb_lossless':other.stat().st_size} + if other.stat().st_size<(v.p/'observation/rgb.mkv').stat().st_size:other.replace(v.p/'observation/rgb.mkv');rgbstore=candidate + else:other.unlink() + v.add('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(t,bool),storage=rgbstore,storage_fps=1,timebase_role='index_only') + v.intrinsic(np.stack([anno[i]['K'] for i in ids])) + for key,stem,n,unit in [('xyz','xyz',3,'m'),('uv_vis','uv',2,'pixel')]: + a=np.stack([np.asarray(anno[i][key])[...,:n].reshape(2,21,n)[:,order] for i in ids]);v.array('keypoints_'+stem,'annotation/keypoints/'+stem+'.pt','keypoints/'+stem+'/v1',a,np.ones((t,2,21),bool),['T','hand','joint','coordinate'],unit=unit,keypoint_schema='rhd21/v1',coordinate_frame='camera' if n==3 else 'rgb_image',method='Keep both supplied hands including invisible points; reorder tip-to-palm to registered proximal-to-tip order',source_index_map=order) + vis=np.stack([np.asarray(anno[i]['uv_vis'])[:,2].reshape(2,21)[:,order].astype(bool) for i in ids]);tensor(v.p/'annotation/keypoints/visibility.pt',vis) + attribute={'data_ref':'annotation/keypoints/visibility.pt','axes':['T','hand','joint'],'dtype':'bool','visibility_type':'bool','meaning':'source per-keypoint visibility','valid':True,'provenance':{'kind':'format_conversion','method':'native uv_vis visibility reordered with coordinates'},'source_locator':v.source} + for k in ['keypoints_xyz','keypoints_uv']:v.m['components'][k]['attributes']={'visibility':attribute} + deps=[];masks=[];mapping=[] + for fi,i in enumerate(ids): + a=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/depth/{i:05d}.png'))),dtype=np.uint16);code=a[:,:,0]*256+a[:,:,1];deps.append(code.astype(np.float32)/65535*5) + mask=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/mask/{i:05d}.png')))).astype(np.uint16);assert mask.max()<=33;mask[mask==0]=65534;masks.append(mask) + mapping.append({'frame_index':fi,'labels':[{'mask_value':int(c),'category_id':'background' if c==65534 else ('person' if c==1 else 'hand'),'entity_id':None if c in [65534,1] else ('hand_left' if c<=17 else 'hand_right'),'part_id':None,'source_instance_id':0 if c==65534 else int(c),'source_attributes':{'native_region_code':0 if c==65534 else int(c)}} for c in np.unique(mask)]}) + depth=np.stack(deps).astype('>16).astype('=3] + if polygons:cv2.fillPoly(mask,polygons,int(value)) + entities.append({'mask_value':value,'instance_id':entity['id'],'track_id':None,'source':{k:v for k,v in entity.items() if k!='segments'}}) + if mask.shape!=rgb.shape[:2]:mask=cv2.resize(mask,(rgb.shape[1],rgb.shape[0]),interpolation=cv2.INTER_NEAREST) + masks.append(mask);records.append({'frame_index':i,'source_frame_id':frame['image']['name'],'subsequence':frame['image']['subsequence'],'annotation_valid':True,'instances':entities}) + assert set(np.unique(mask)) <= {0,*[x['mask_value'] for x in entities]} + m['rgb']=b.video(p/'observation/rgb.mkv',rgbs);m['segmentation']={**b.video(p/'annotation/segmentation/mask.mkv',masks),'provenance':'native_sparse_polygon_rasterized','background_value':0,'background_semantics':'unlabeled/background; source is not globally exhaustive','ignore_value':None,'polygon_canvas':[1920,1080],'overlap_policy':'source annotation order; last polygon wins','track_id':'unknown; no heuristic tracking'} + lines(p/'annotation/segmentation/instances.jsonl',records) + m['missing_modalities']={'mano':'not supplied','depth':'not supplied','camera':'not supplied','object_pose_mesh':'not supplied'} + im=rgbs[2].copy();mask=masks[2];colors=np.array([[0,0,0],[240,70,70],[70,240,90],[70,100,240],[230,190,60],[150,80,200]],np.uint8) + used=mask>0;im[used]=(im[used]*0.55+colors[mask[used]%len(colors)]*0.45).astype(np.uint8) + Image.fromarray(im).save(b.out/'doc/segmentation_overlay.png') + b.checks.append({'test':'official_zip_crc_all_members','passed':True,'members':len(zipfile.ZipFile(zp).namelist())}) + b.checks.append({'test':'all_mask_values_resolve_to_frame_instances','passed':True}) + return b.finish([]) + +def rhd(): + b=Build('RHD');src=OLD/'RHD/RHD_published_v2/evaluation';ann=read_pickle(b.copy(src/'anno_evaluation.pickle')) + for i in range(N): + p,m=b.view(f'evaluation_{i:05d}','camera',[i],split='evaluation',time_semantics='independent_image') + rgb=np.asarray(Image.open(b.copy(src/'color'/f'{i:05d}.png')).convert('RGB')) + depth=np.asarray(Image.open(b.copy(src/'depth'/f'{i:05d}.png')).convert('RGB'),dtype=np.uint16) + code=depth[:,:,0]*256+depth[:,:,1] + m['rgb']=b.video(p/'observation/rgb.mkv',[rgb]);m['depth']={**b.video(p/'observation/depth.mkv',[code]),'encoding':'source R*256+G','metric_unit':None,'metric_conversion_status':'not_tested; preserved integer code only'} + tensor(p/'camera/intrinsic.pt',ann[i]['K']);tensor(p/'annotation/keypoints/xyz.pt',ann[i]['xyz'].reshape(1,2,21,3));tensor(p/'annotation/keypoints/uv.pt',ann[i]['uv_vis'][:,:2].reshape(1,2,21,2));tensor(p/'annotation/keypoints/visible.pt',(ann[i]['uv_vis'][:,2]==1).reshape(1,2,21)) + m['keypoints']={'unit':'m','layout':'source 42-joint order reshaped as 2x21','hand_order':['left','right'],'joint_order':'official RHD: wrist then thumb/index/middle/ring/pinky, fingers tip-to-palm','definition_reference':'raw/doc/dataset_README.txt','visibility':'per joint; invisible does not mean unannotated'} + m['missing_modalities']={'mano':'not supplied by source','segmentation':'omitted by historical downloader','extrinsic':'not supplied in selected annotations'} + return b.finish(['Depth metric conversion pending; segmentation absent from historical input.']) + +def freihand(): + b=Build('FreiHAND');src=OLD/'FreiHAND';K=load(b.copy(src/'training_K.json'));mano=load(b.copy(src/'training_mano.json'));scale=load(b.copy(src/'training_scale.json')) + for base in range(4): + for variant in range(4): + i=base+variant*32560;p,m=b.view(f'training_{base:08d}',f'variant_{variant}',[i],split='training',time_semantics='independent_image',base_sample_id=str(base),variant_id=variant) + m['camera_group_semantics']='image_variant_not_physical_camera';m['physical_camera_id']=None + rgb=np.asarray(Image.open(b.copy(src/'training/rgb'/f'{i:08d}.jpg')).convert('RGB'));m['rgb']=b.video(p/'observation/rgb.mkv',[rgb]) + tensor(p/'camera/intrinsic.pt',np.asarray(K[base],np.float32));tensor(p/'annotation/mano/source_theta.pt',np.asarray(mano[base],np.float32));tensor(p/'annotation/mano/source_scale.pt',np.asarray(scale[base],np.float32)) + m['mano']={'source_theta_layout':['pose48','shape10','uv_root2','scale1'],'motion_status':'not_generated','reason':'To recover MANO translation, official code subtracts posed joint 9 from backprojected root. Requires correct MANO model; last three source values are not translation.'} + m['missing_modalities']={'depth':'not supplied','segmentation':'not present in historical input','extrinsic':'not supplied','normalized_mano':'model dependency unresolved'} + return b.finish(['Preserved source MANO theta; no misleading motion.pt produced. Correct model needed for normalized translation.']) + +def hoi4d(): + b=Build('HOI4D');rel=Path('ZY20210800001/H1/C20/N18/S286/s01/T1');src=OLD/'HOI4D/Hand_pose/handpose_left_hand'/rel + files=sorted(src.glob('*.pickle'),key=lambda p:int(p.stem))[:N];ids=[x.stem for x in files] + p,m=b.view('H1_C20_N18_S286_s01_T1','ZY20210800001',ids,view_type='ego',time_semantics='annotation_frame_ids_only') + a=np.full((len(ids),2,61),np.nan,np.float32);v=np.zeros((len(ids),2),bool);uv=np.full((len(ids),2,21,2),np.nan,np.float32) + for i,frame in enumerate(ids): + for side,folder in enumerate(['handpose_left_hand','handpose_right_hand']): + f=OLD/'HOI4D/Hand_pose'/folder/rel/(frame+'.pickle') + if not f.exists():continue + x=read_pickle(b.copy(f));a[i,side]=np.concatenate([x['poseCoeff'].reshape(-1),x['beta'].reshape(-1),x['trans'].reshape(-1)]);v[i,side]=True;uv[i,side]=x['kps2D'] + tensor(p/'annotation/mano/motion.pt',a);tensor(p/'annotation/mano/valid_flag.pt',v);tensor(p/'annotation/keypoints/uv.pt',uv) + tensor(p/'camera/intrinsic.pt',np.load(b.copy(OLD/'HOI4D/camera_params/ZY20210800001/intrin.npy'),allow_pickle=False)) + m['mano']={'layout':['pose_axis_angle48','betas10','translation3'],'slots':['left','right'],'translation_unit':'m','coordinate_frame':'camera','flat_hand_mean':True,'valid_flag':'source file present and parsed; not visibility','geometry_validation':'not_tested'} + m['missing_modalities']={'rgb':'not downloaded','depth':'not downloaded','segmentation':'not downloaded','extrinsic':'not available in selected subset','object':'not available in selected subset'} + return b.finish(['Annotation-only mini; no matching RGB/depth/segmentation downloaded. MANO geometry not rendered.']) + +def reinterhand(): + b=Build('ReInterHand');src=next(p for p in sorted((OLD/'ReInterHand').glob('m--*')) if (p/'mano_fits/params').exists());files=sorted((src/'mano_fits/params').glob('*_left.json'))[:N];ids=[x.stem.rsplit('_',1)[0] for x in files] + # Capture-level poses have no established camera transform in current local subset. + p,m=b.view(src.name,'source_unresolved',ids,time_semantics='capture_annotations_only; not a camera observation') + rows=[] + for frame in ids: + row={'source_frame_id':frame,'hands':{}} + for side in ['left','right']: + f=src/'mano_fits/params'/f'{frame}_{side}.json' + if f.exists():row['hands'][side]=load(b.copy(f)) + rows.append(row) + lines(p/'annotation/mano/source_parameters.jsonl',rows) + m['missing_modalities']={'rgb':'not extracted from incomplete archive set','camera':'not available','normalized_mano':'source-to-camera mapping unresolved'} + m['not_a_real_camera']=True + return b.finish(['Source-only annotation sample. source_unresolved is an explicit staging group, not an invented camera.']) + +def main(): + parser=argparse.ArgumentParser();parser.add_argument('--dataset',nargs='*',default=['VISOR','H2O','RHD','FreiHAND','HOI4D','ReInterHand']);args=parser.parse_args() + funcs={'VISOR':visor,'H2O':h2o,'RHD':rhd,'FreiHAND':freihand,'HOI4D':hoi4d,'ReInterHand':reinterhand} + for name in args.dataset: + try:funcs[name]() + except Exception as e: + save(ROOT/name/'pilot-v0/doc/build_error.json',{'error':str(e)});raise +if __name__=='__main__':main() diff --git a/RHD/shared-v1.0/script/pilot_fetch.py b/RHD/shared-v1.0/script/pilot_fetch.py new file mode 100644 index 0000000000000000000000000000000000000000..7671f83ac7a97438b95528fe9c13c00768eb7861 --- /dev/null +++ b/RHD/shared-v1.0/script/pilot_fetch.py @@ -0,0 +1,63 @@ +"""Download bounded official pilot inputs; keep archives intact. No upstream code execution.""" +from pathlib import Path +import concurrent.futures, hashlib, json, requests + +ROOT = Path(__file__).resolve().parents[1] / 'hand-datasets' +REFS = { + 'Dex-YCB': ('NVlabs/dex-ycb-toolkit', ['README.md','dex_ycb_toolkit/dex_ycb.py','dex_ycb_toolkit/sequence_loader.py']), + 'VISOR': ('epic-kitchens/VISOR-VIS', ['README.md','vis.py']), + 'H2O': ('taeinkwon/h2odataset', ['README.md','download_script.py']), + 'HOI4D': ('leolyliu/HOI4D-Instructions', ['README.md','utils/parse_2Dmask_img.py','utils/color_map.py']), + 'ARCTIC': ('zc-alexfan/arctic', ['docs/data/README.md','docs/data/data_doc.md']), + 'FreiHAND': ('lmb-freiburg/freihand', ['README.md','utils/model.py','utils/fh_utils.py','view_samples.py']), + 'RHD': ('lmb-freiburg/hand3d', ['README.md','create_binary_db.py']), + 'HOT3D': ('facebookresearch/hot3d', ['README.md','hot3d/data_loaders/loader_hand_poses.py']), + 'OakInk2': ('oakink/OakInk2', ['README.md']), + 'ReInterHand': ('facebookresearch/InterWild', ['tool/ReInterHand/download/download_checksum_framelist.py','tool/ReInterHand/download/download_ego_per_frame.py','tool/ReInterHand/download/download_mano_fits.py']), +} +BASE='https://data.bris.ac.uk/datasets/2v6cgv1x04ol22qp9rm9x2j6a7/' + +def sha(p): + h=hashlib.sha256() + with p.open('rb') as f: + for b in iter(lambda:f.read(1024*1024),b''):h.update(b) + return h.hexdigest() + +def fetch(dataset, url, relative, limit=30_000_000): + p=ROOT/dataset/'raw'/relative;p.parent.mkdir(parents=True,exist_ok=True) + receipt=p.with_name(p.name+'.receipt.json') + if p.exists() and receipt.exists(): + info=json.loads(receipt.read_text()); assert sha(p)==info['sha256'];return info + part=p.with_name(p.name+'.part') + with requests.get(url,stream=True,headers={'Accept-Encoding':'identity'},timeout=(15,40)) as r: + r.raise_for_status(); expected=int(r.headers.get('Content-Length',0)) + if expected>limit:raise ValueError(f'File exceeds pilot limit: {expected}') + size=0 + with part.open('wb') as f: + for chunk in r.iter_content(1024*1024): + size+=len(chunk) + if size>limit:raise ValueError('Stream exceeds pilot limit') + f.write(chunk) + if expected and not r.headers.get('Content-Encoding') and size!=expected:raise ValueError('Incomplete transfer') + part.replace(p) + info={'url':url,'path':str(p.relative_to(ROOT/dataset)), 'bytes':size,'sha256':sha(p),'official_checksum':None,'verification':'HTTP success; content length where supplied; locally computed SHA256'} + receipt.write_text(json.dumps(info,indent=2),encoding='utf-8');return info + +def reference(item): + name,(repo,paths)=item;results=[] + for section in ['raw/doc','raw/script','raw/data','pilot-v0/doc','pilot-v0/script','pilot-v0/data']:(ROOT/name/section).mkdir(parents=True,exist_ok=True) + for path in paths: + try:results.append(fetch(name,f'https://raw.githubusercontent.com/{repo}/HEAD/{path}',('script/' if path.endswith('.py') else 'doc/')+path)) + except Exception as e:results.append({'path':path,'error':str(e)}) + return name,results + +def main(): + results=dict(concurrent.futures.ThreadPoolExecutor(6).map(reference,REFS.items())) + for path in ['GroundTruth-SparseAnnotations/annotations/train/P01_03.json','GroundTruth-SparseAnnotations/rgb_frames/train/P01/P01_03.zip','EPIC_100_noun_classes_v2.csv','frame_mapping.json']: + try:results['VISOR'].append(fetch('VISOR',BASE+path,'data/'+path)) + except Exception as e:results['VISOR'].append({'path':path,'error':str(e)}) + for name,rows in results.items(): + (ROOT/name/'raw/doc/download_manifest.json').write_text(json.dumps(rows,indent=2),encoding='utf-8') + print(name,[(r.get('path'),r.get('bytes',r.get('error'))) for r in rows],flush=True) + +if __name__=='__main__':main() diff --git a/RHD/shared-v1.0/script/pilot_mano.py b/RHD/shared-v1.0/script/pilot_mano.py new file mode 100644 index 0000000000000000000000000000000000000000..d4596f225a22b61f642a2ef0193588172a5b581e --- /dev/null +++ b/RHD/shared-v1.0/script/pilot_mano.py @@ -0,0 +1,87 @@ +"""Local licensed MANO model dependency; never copies models into shared outputs.""" +from pilot_build import * +from types import SimpleNamespace +import smplx + +class StoredState: + def __setstate__(self,state):self.__dict__.update(state) +class ModelReader(Restricted): + def find_class(self,module,name): + if (module,name) in [('chumpy.ch','Ch'),('chumpy.reordering','Select'),('scipy.sparse.csc','csc_matrix')]:return StoredState + if (module,name)==('__builtin__','set'):return set + return super().find_class(module,name) + +def model_data(side): + p=WORK/'tmp/pilot-models'/f'MANO_{side.upper()}.pkl' + with p.open('rb') as f:d=ModelReader(f,encoding='latin1').load() + # Materialize only the two known serialized structures; execute no Chumpy code. + s=d['shapedirs'] + if isinstance(s,StoredState):d['shapedirs']=s.a.x.reshape(-1)[s.idxs].reshape(s.preferred_shape) + sparse=d['J_regressor'];dense=np.zeros(sparse._shape) + for col in range(sparse._shape[1]): + a,z=sparse.indptr[col:col+2];dense[sparse.indices[a:z],col]=sparse.data[a:z] + d['J_regressor']=dense + assert d['shapedirs'].shape==(778,3,10) and dense.shape==(16,778) + return d + +class Mano: + def __init__(self,side): + self.side=side + self.data=model_data(side) + self.layer=smplx.MANO(model_path=str(WORK/'tmp/pilot-models'),data_struct=SimpleNamespace(**self.data),is_rhand=side=='right',use_pca=False,flat_hand_mean=True) + def geometry(self,pose,betas,translation=None,keypoint_convention='freihand'): + pose=torch.as_tensor(np.asarray(pose).copy(),dtype=torch.float32).reshape(-1,48);betas=torch.as_tensor(np.asarray(betas).copy(),dtype=torch.float32).reshape(-1,10) + with torch.no_grad():o=self.layer(global_orient=pose[:,:3],hand_pose=pose[:,3:],betas=betas) + v=o.vertices.numpy();j=o.joints.numpy()[:,:16] + if translation is not None: + t=np.asarray(translation).reshape(-1,1,3);v=v+t;j=j+t + # Standard wrist + thumb,index,middle,ring,pinky proximal-to-distal order. + coords=[] + for k in [0,13,14,15,1,2,3,4,5,6,10,11,12,7,8,9]:coords.append(j[:,k]) + out=np.stack([j[:,0],j[:,13],j[:,14],j[:,15],v[:,744],j[:,1],j[:,2],j[:,3],v[:,320],j[:,4],j[:,5],j[:,6],v[:,443],j[:,10],j[:,11],j[:,12],v[:,555],j[:,7],j[:,8],j[:,9],v[:,672]],axis=1) + if keypoint_convention=='manopth':out[:,[4,8,12,16,20]]=v[:,[745,317,444 if self.side=='right' else 445,556,673]] + return v,out,j + +def freihand_normalize(): + base=ROOT/'FreiHAND/pilot-v0';model=Mano('right');reports=[] + for meta_file in sorted((base/'data').rglob('metainfo.json')): + p=meta_file.parent;m=load(meta_file);theta=torch.load(p/'annotation/mano/source_theta.pt',weights_only=True).numpy().reshape(1,61) + K=torch.load(p/'camera/intrinsic.pt',weights_only=True).numpy();focal=(K[0,0]+K[1,1])/2 + root=np.r_[(theta[0,58:60]-K[:2,2]),focal]/theta[0,60] + pose=theta[:,:48].copy();pose[:,3:]+=model.data['hands_mean'] + _,_,j=model.geometry(pose,theta[:,48:58]);translation=root-j[0,4] + motion=np.full((1,2,61),np.nan,np.float32);motion[0,1]=np.r_[pose[0],theta[0,48:58],translation] + tensor(p/'annotation/mano/motion.pt',motion);tensor(p/'annotation/mano/valid_flag.pt',[[False,True]]) + _,keypoints,_=model.geometry(pose,theta[:,48:58],translation) + assert np.allclose(keypoints[0,9],root,atol=1e-6) + m['mano']={'layout':['pose_axis_angle48','betas10','translation3'],'slots':['left','right'],'coordinate_frame':'camera','translation_unit':'m','flat_hand_mean':True,'model_version':'MANO v1.2','conversion':'source non-flat mean added to finger pose; official orthographic root recovery minus posed MANO joint 4 (21-joint index9)','valid_flag':'right-hand source annotation available; left not supplied'} + m['missing_modalities'].pop('normalized_mano',None);save(meta_file,m) + reports.append({'path':str(p.relative_to(base)),'root_recovery_error_m':float(np.linalg.norm(keypoints[0,9]-root))}) + save(base/'doc/mano_validation.json',{'status':'passed_algebraic_root_check','model_sha256':sha(WORK/'tmp/pilot-models/MANO_RIGHT.pkl'),'samples':reports,'limit':'No independent 3D ground truth currently in input; algebraic check does not prove fit accuracy.'}) + print('FreiHAND normalized',len(reports)) + +def validate_mano(name): + base=ROOT/name/'pilot-v0';models=[Mano('left'),Mano('right')];summaries=[] + for mp in (base/'data').rglob('metainfo.json'): + p=mp.parent;motionp=p/'annotation/mano/motion.pt' + if not motionp.exists():continue + a=torch.load(motionp,weights_only=True).numpy();valid=torch.load(p/'annotation/mano/valid_flag.pt',weights_only=True).numpy();K=torch.load(p/'camera/intrinsic.pt',weights_only=True).numpy();pred=np.full((len(a),2,21,3),np.nan,np.float32) + for side,model in enumerate(models): + ix=np.flatnonzero(valid[:,side]) + if len(ix):_,pred[ix,side],_=model.geometry(a[ix,side,:48],a[ix,side,48:58],a[ix,side,58:],keypoint_convention='manopth') + tensor(p/'annotation/mano/derived_keypoints.pt',pred) + uv=pred@K.T;uv=uv[...,:2]/uv[...,2:] + row={'sequence':str(p.relative_to(base)),'keypoint_convention':'official manopth, including handedness-specific fingertip vertices','finite_valid_geometry':bool(np.isfinite(pred[valid]).all())} + target=p/('annotation/keypoints/xyz.pt' if name=='H2O' else 'annotation/keypoints/uv.pt') + if target.exists(): + gt=torch.load(target,weights_only=True).numpy();err=np.linalg.norm((pred if name=='H2O' else uv)-gt,axis=-1);good=np.isfinite(err)&valid[:,:,None] + row['mean_error']=float(err[good].mean());row['max_error']=float(err[good].max());row['unit']='m' if name=='H2O' else 'pixels' + summaries.append(row) + meta=load(p/'metainfo.json');meta['mano']['geometry_validation']='measured against source keypoints; see doc/mano_validation.json';meta['mano']['model_version']='MANO v1.2' + meta['mano']['derived_keypoints_convention']='official manopth 21 joints, including side-specific fingertip vertices' + save(p/'metainfo.json',meta) + save(base/'doc/mano_validation.json',{'status':'measured_not_auto_accepted','samples':summaries,'model_sha256':{s:sha(WORK/'tmp/pilot-models'/f'MANO_{s.upper()}.pkl') for s in ['left','right']}}) + print(name,summaries) + +if __name__=='__main__': + freihand_normalize();validate_mano('H2O');validate_mano('HOI4D') diff --git a/RHD/shared-v1.0/script/pilot_rhd_lossless.py b/RHD/shared-v1.0/script/pilot_rhd_lossless.py new file mode 100644 index 0000000000000000000000000000000000000000..ff684982fcd8383353235f9dbfb760f030141eac --- /dev/null +++ b/RHD/shared-v1.0/script/pilot_rhd_lossless.py @@ -0,0 +1,79 @@ +"""RHD bounded raw-to-contract pilot, with lossless media and local human review.""" +from convert_freihand_lossless import View,ff,storage +from sequence_pilot_common import * +import sequence_pilot_common as common +import random,html,hashlib +BASE=Path('E:/hand-datasets/RHD');RELEASE='shared-v1.0' + +def media(v,name,path,raw,pix,size,profile,codec='ffv1',options=None): + dst=v.p/path;dst.parent.mkdir(parents=True,exist_ok=True);options=options or ['-level','3'] + ff(['-f','rawvideo','-pixel_format',pix,'-video_size',f'{size[0]}x{size[1]}','-framerate','1','-i','pipe:0','-c:v',codec,*options,str(dst)],raw) + restored=ff(['-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1']);assert restored==raw,name + v.checks.append({'test':'all_media_bytes_exact','component':name,'frames':v.T}) + actual_pix='bgr0' if codec=='ffv1' and pix=='rgb24' else ('gbrp' if codec=='libx264rgb' else pix) + return storage(profile,codec,actual_pix,size,False,options),restored + +def main(): + common.ROOT=Path('E:/hand-datasets');common.RELEASE=RELEASE + contract=load(WORK/'distribution/hand-warehouse-1.0/schema.json');order=contract['keypoints']['rhd21/v1']['source_index_map_per_hand'] + root=BASE/RELEASE;out=root/'doc/validation';out.mkdir(parents=True,exist_ok=True);cards=[];reports=[];rng=random.Random(20260918) + with zipfile.ZipFile(BASE/'raw/data/RHD_v1-1.zip') as z: + for split in ['training','evaluation']: + member=next(n for n in z.namelist() if n.endswith(f'{split}/anno_{split}.pickle'));prefix=member.rsplit('/',1)[0] + anno=Restricted(io.BytesIO(z.read(member)),encoding='latin1').load();rank=sorted(anno,key=lambda i:np.asarray(anno[i]['uv_vis'])[:,2].sum()) + ids=list(dict.fromkeys(rank[:2]+rank[-2:]+rng.sample(sorted(anno),12)))[:12];t=len(ids) + v=View('RHD',split+'_review','camera',ids,[320,320],{'archive':'raw/data/RHD_v1-1.zip','annotation':member},kind='image',split='train' if split=='training' else 'test') + v.m.update(schema_version='hand-warehouse/1.0',storage_revision='lossless-video-2026-09-18',split_scheme='RHD_official_training_evaluation',split_source_locator={'archive':'raw/data/RHD_v1-1.zip','member_prefix':prefix},sample_type='image') + v.m['sampling'].update(type='independent',selection='2 lowest and 2 highest source visibility counts plus seeded random unique images, total 12 per split',seed=20260918) + for row,i in zip(v.frames,ids):row.update(sample_id=f'RHD/{split}/{i:05d}');row['source_locator'].update(annotation_index=i,member=f'{prefix}/color/{i:05d}.png') + rgb=np.stack([np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/color/{i:05d}.png'))).convert('RGB')) for i in ids]) + rgbstore,_=media(v,'rgb','observation/rgb.mkv',rgb.tobytes(),'rgb24',[320,320],'mkv/ffv1_rgb/v1') + other=v.p/'observation/rgb_candidate.mkv';candidate,_=media(v,'rgb_candidate','observation/rgb_candidate.mkv',rgb.tobytes(),'rgb24',[320,320],'mkv/h264_rgb_lossless/v1','libx264rgb',['-crf','0','-preset','medium','-g','32']) + candidate['pixel_format']='gbrp' + sizes={'ffv1':(v.p/'observation/rgb.mkv').stat().st_size,'h264_rgb_lossless':other.stat().st_size} + if other.stat().st_size<(v.p/'observation/rgb.mkv').stat().st_size:other.replace(v.p/'observation/rgb.mkv');rgbstore=candidate + else:other.unlink() + v.add('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(t,bool),storage=rgbstore,storage_fps=1,timebase_role='index_only') + v.intrinsic(np.stack([anno[i]['K'] for i in ids])) + for key,stem,n,unit in [('xyz','xyz',3,'m'),('uv_vis','uv',2,'pixel')]: + a=np.stack([np.asarray(anno[i][key])[...,:n].reshape(2,21,n)[:,order] for i in ids]);v.array('keypoints_'+stem,'annotation/keypoints/'+stem+'.pt','keypoints/'+stem+'/v1',a,np.ones((t,2,21),bool),['T','hand','joint','coordinate'],unit=unit,keypoint_schema='rhd21/v1',coordinate_frame='camera' if n==3 else 'rgb_image',method='Keep both supplied hands including invisible points; reorder tip-to-palm to registered proximal-to-tip order',source_index_map=order) + vis=np.stack([np.asarray(anno[i]['uv_vis'])[:,2].reshape(2,21)[:,order].astype(bool) for i in ids]);tensor(v.p/'annotation/keypoints/visibility.pt',vis) + attribute={'data_ref':'annotation/keypoints/visibility.pt','axes':['T','hand','joint'],'dtype':'bool','visibility_type':'bool','meaning':'source per-keypoint visibility','valid':True,'provenance':{'kind':'format_conversion','method':'native uv_vis visibility reordered with coordinates'},'source_locator':v.source} + for k in ['keypoints_xyz','keypoints_uv']:v.m['components'][k]['attributes']={'visibility':attribute} + deps=[];masks=[];mapping=[] + for fi,i in enumerate(ids): + a=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/depth/{i:05d}.png'))),dtype=np.uint16);code=a[:,:,0]*256+a[:,:,1];deps.append(code.astype(np.float32)/65535*5) + mask=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/mask/{i:05d}.png')))).astype(np.uint16);assert mask.max()<=33;mask[mask==0]=65534;masks.append(mask) + mapping.append({'frame_index':fi,'labels':[{'mask_value':int(c),'category_id':'background' if c==65534 else ('person' if c==1 else 'hand'),'entity_id':None if c in [65534,1] else ('hand_left' if c<=17 else 'hand_right'),'part_id':None,'source_instance_id':0 if c==65534 else int(c),'source_attributes':{'native_region_code':0 if c==65534 else int(c)}} for c in np.unique(mask)]}) + depth=np.stack(deps).astype('>16).astype('=2)&(masks[j]<=17)]=[255,90,70];colored[(masks[j]>=18)&(masks[j]<=33)]=[30,180,255];active=colored.any(-1);seg[active]=(0.5*seg[active]+0.5*colored[active]).astype(np.uint8) + kpic=original.copy();ppic=original.copy() + for hand,color in [(0,(255,90,70)),(1,(30,180,255))]: + for joint in range(21): + x,y=uv[j,hand,joint];px,py=projected[j,hand,joint] + if abs(x)<10000 and abs(y)<10000:cv2.circle(kpic,(round(float(x)),round(float(y))),3,color,-1 if vis[j,hand,joint] else 1) + if abs(px)<10000 and abs(py)<10000:cv2.circle(ppic,(round(float(px)),round(float(py))),3,color,1) + dep=back[j];lo,hi=np.percentile(dep,[2,98]);display=np.clip((dep-lo)/max(hi-lo,1e-6),0,1);depthpic=cv2.cvtColor(cv2.applyColorMap((display*255).astype(np.uint8),cv2.COLORMAP_VIRIDIS),cv2.COLOR_BGR2RGB) + canvas=Image.new('RGB',(1600,346),'white');draw=ImageDraw.Draw(canvas) + for c,(a,label) in enumerate(zip([original,seg,kpic,ppic,depthpic],['RGB','Segmentation: L red / R blue','Native UV: hollow=invisible','XYZ projected with K','Z depth: per-image contrast'])):canvas.paste(Image.fromarray(a),(c*320,26));draw.text((c*320+4,6),label,fill='black') + name=f'{split}_{i:05d}.png';canvas.save(out/name);cards.append(f'

{split}/{i:05d}

Source visible joints: {int(vis[j].sum())}/42. Depth display range: {lo:.3f}–{hi:.3f} m.

') + save(out/'evidence.json',{'human_review':'pending','full_processing':'not_started','sampling_seed':20260918,'reports':reports}) + page='RHD 小规模人工核验

RHD：24 张 raw→share 转换核验

训练与评估各12张，包含可见性极端和随机样本。独立图片不是视频序列。尚待人工确认，不进行全量处理或上传。

从左至右：RGB、左右手分割、原生2D关键点、3D点经相机投影、米制z深度可视化。红=左手，蓝=右手；2D空心点表示源标注不可见，仍保留坐标。深度图逐图拉伸对比度，颜色不能跨图直接比较米数。

检查双手身份、关键点顺序与UV/XYZ对应、分割边缘和深度轮廓。MANO、物体等源未提供的组件缺省。

'+''.join(cards) + (out/'index.html').write_text(page,encoding='utf8') + for name in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/name,root/'doc'/name) + (root/'script').mkdir(exist_ok=True);shutil.copyfile(__file__,root/'script'/Path(__file__).name) + print(out/'index.html',flush=True) +if __name__=='__main__':main() diff --git a/RHD/shared-v1.0/script/review_rhd_full_random.py b/RHD/shared-v1.0/script/review_rhd_full_random.py new file mode 100644 index 0000000000000000000000000000000000000000..4499756f8534464f233882f68ec87563d0409d53 --- /dev/null +++ b/RHD/shared-v1.0/script/review_rhd_full_random.py @@ -0,0 +1,75 @@ +"""Independent schema check and random review of completed RHD outputs.""" +from sequence_pilot_common import * +from convert_freihand_lossless import ff +import random,html,hashlib +BASE=Path('E:/hand-datasets/RHD');root=BASE/'shared-v1.0' + +def main(): + registry=load(WORK/'distribution/hand-warehouse-1.0/schema.json');pools={'training':[],'evaluation':[]};frames_by_view={};counts={'training':0,'evaluation':0};seen=set();cards=[];records=[] + for mp in sorted((root/'data').glob('*/*/metainfo.json')): + m=load(mp);p=mp.parent;view=p.relative_to(root).as_posix();split='training' if m['split']=='train' else 'evaluation';t=m['num_frames'] + rows=[json.loads(l) for l in (p/'camera/frames.jsonl').read_text().splitlines()];assert len(rows)==t;frames_by_view[view]=rows + for j,row in enumerate(rows): + assert row['frame_index']==j and row['sample_id'] not in seen;seen.add(row['sample_id']);pools[split].append((view,j));counts[split]+=1 + for name,c in m['components'].items(): + spec=registry['components'][c['schema_id']] + for k in ['available','data_ref','valid_ref','axes','valid_axes','provenance','source_locator',*spec.get('required_metadata',[])]:assert k in c,(name,k) + for k in ['data_ref','valid_ref']:assert (p/c[k]).is_file() + if c['data_ref'].endswith('.pt'): + a=torch.load(p/c['data_ref'],weights_only=True).numpy();v=torch.load(p/c['valid_ref'],weights_only=True).numpy();assert a.dtype==np.float32 and v.dtype==bool + assert list(a.shape)==[t if x=='T' else x for x in spec['shape']] + assert list(v.shape)==[t if x=='T' else x for x in spec['valid_shape']] + assert np.isfinite(a[v]).all() + else: + assert c['storage']['profile'] in spec['allowed_storage_profiles'] + for key in registry['video_metadata']['required']:assert key in c['storage'] + for att in c.get('attributes',{}).values():assert (p/att['data_ref']).is_file() + assert counts=={'training':41258,'evaluation':2728} + seed=20260918;rng=random.Random(seed);chosen={} + for split in ['training','evaluation']: + for view,i in rng.sample(pools[split],12):chosen.setdefault(view,[]).append(i) + out=root/'doc/validation/full_random';out.mkdir(parents=True,exist_ok=True) + with zipfile.ZipFile(BASE/'raw/data/RHD_v1-1.zip') as z: + for view in sorted(chosen): + p=root/view;m=load(p/'metainfo.json');split='training' if m['split']=='train' else 'evaluation';indices=sorted(chosen[view]);t=len(indices);rows=[frames_by_view[view][i] for i in indices];ids=[int(f['source_frame_id']) for f in rows] + print('Random review',view,indices,flush=True) + expression='+'.join('eq(n\\,'+str(i)+')' for i in indices) + def decode(path,pix,dtype,width=320): + b=ff(['-i',str(path),'-vf','select='+expression,'-vsync','0','-frames:v',str(t),'-f','rawvideo','-pix_fmt',pix,'pipe:1']) + return np.frombuffer(b,dtype).reshape((t,320,width,3) if pix=='rgb24' else (t,320,width)) + rgb=decode(p/'observation/rgb.mkv','rgb24',np.uint8) + masks=decode(p/'annotation/segment/mask.mkv','gray16le','=2)&(masks[j]<=17)]=[255,90,70];colored[(masks[j]>=18)&(masks[j]<=33)]=[30,180,255];active=colored.any(-1);seg[active]=(0.5*seg[active]+0.5*colored[active]).astype(np.uint8) + kpic=original.copy();ppic=original.copy() + for hand,color in [(0,(255,90,70)),(1,(30,180,255))]: + for joint in range(21): + x,y=uv[j,hand,joint];px,py=projected[j,hand,joint] + if abs(x)<10000 and abs(y)<10000:cv2.circle(kpic,(round(float(x)),round(float(y))),3,color,-1 if vis[j,hand,joint] else 1) + if abs(px)<10000 and abs(py)<10000:cv2.circle(ppic,(round(float(px)),round(float(py))),3,color,1) + dep=back[j];lo,hi=np.percentile(dep,[2,98]);display=np.clip((dep-lo)/max(hi-lo,1e-6),0,1);depthpic=cv2.cvtColor(cv2.applyColorMap((display*255).astype(np.uint8),cv2.COLORMAP_VIRIDIS),cv2.COLOR_BGR2RGB) + canvas=Image.new('RGB',(1600,346),'white');draw=ImageDraw.Draw(canvas) + for c,(a,label) in enumerate(zip([original,seg,kpic,ppic,depthpic],['RGB','Segmentation: L red / R blue','Native UV: hollow=invisible','XYZ projected with K','Z depth: per-image contrast'])):canvas.paste(Image.fromarray(a),(c*320,26));draw.text((c*320+4,6),label,fill='black') + name=f'{split}_{i:05d}.png';canvas.save(out/name);cards.append(f'

{split}/{i:05d}

Source visible joints: {int(vis[j].sum())}/42. Shard {html.escape(view)}, frame {indices[j]}. Depth display range: {lo:.3f}–{hi:.3f} m.

') + + save(out/'samples.json',{'seed':seed,'sampling':'12 samples without replacement per official split from all 43986 output frames; sorted populations, Python random.Random','counts':counts,'human_review':'pending','samples':records}) + page='RHD 全量随机抽查

RHD 全量产物随机抽查：24 张

直接从全量产物读取，训练与评估各随机12张；种子20260918。分层等额抽样，不能据此估计总体错误率。RGB、mask及深度重新与 raw 对比通过，人工确认待完成。

依次为 RGB、左右手分割、原生 UV（空心=不可见）、XYZ 投影、逐图拉伸的 z 深度。颜色：红左手、蓝右手。

'+''.join(cards) + (out/'index.html').write_text(page,encoding='utf8') + (out/'REPORT.md').write_text('# RHD 全量随机抽查\n\n[离线预览](index.html) · [抽样清单](samples.json)\n\n全量 43,986 张，训练与评估各随机选 12 张；分层等额、固定种子20260918，无放回抽样。直接读取已完成的分片，不复用试点。所选 RGB、mask 和 float32 深度逐值/逐比特与 raw 对应数据匹配。\n\n**人工确认待完成，未上传。** 检查左右手身份、分割轮廓、UV/XYZ投影及深度；本报告不替代全量数值验证。\n',encoding='utf8') + checks=load(BASE/'tmp/production/full_checks.json');assert checks['passed'] + e=load(root/'doc/validation/evidence.json');e['full_processing']={'automated':'passed','counts':counts,'shards':len(frames_by_view),'full_random_review':'pending','report_ref':'full_random/REPORT.md'};save(root/'doc/validation/evidence.json',e) + with (root/'doc/validation/REPORT.md').open('a',encoding='utf8') as f:f.write('\n\n全量自动检查已通过：43,986 张。额外人工核验请看 [全量随机抽查](full_random/REPORT.md)，当前待确认。\n') + for name in ['convert_rhd_full.py','review_rhd_full_random.py']:shutil.copyfile(WORK/'scripts'/name,root/'script'/name) + state=load(WORK/'tmp/dataset-work-state.json');state['active']['RHD']['stage']='awaiting_full_random_review';save(WORK/'tmp/dataset-work-state.json',state) + print(out/'index.html',flush=True) +if __name__=='__main__':main() diff --git a/RHD/shared-v1.0/script/sequence_pilot_common.py b/RHD/shared-v1.0/script/sequence_pilot_common.py new file mode 100644 index 0000000000000000000000000000000000000000..169c4868c9fbba40790ee94c2600fb35e418acef --- /dev/null +++ b/RHD/shared-v1.0/script/sequence_pilot_common.py @@ -0,0 +1,53 @@ +"""Whole-selection conversion utilities. Never reads previous pilot outputs.""" +from pilot_build import * +import hashlib + +RELEASE='shared-sequence-v1.1' + +class View: + def __init__(self,dataset,seq,cam,ids,size,source,kind='video',sampling='dense',split='unknown'): + self.root=ROOT/dataset/RELEASE;self.p=self.root/'data'/seq/cam + self.p.mkdir(parents=True,exist_ok=True);self.T=len(ids);self.source=source;self.checks=[] + self.m={'schema_version':'hand-warehouse/1.1','dataset_id':dataset,'release_id':RELEASE,'sequence_id':seq,'camera_id':cam,'sample_type':kind,'num_frames':self.T,'hand_slots':['left','right'],'sampling':{'type':'single' if kind=='image' else sampling,'source_fps':None,'frames_ref':'camera/frames.jsonl'},'camera':{'model':'unknown','schema_id':None,'image_size':size},'components':{},'split':split} + self.frames=[{'frame_index':i,'source_frame_id':str(fid),'timestamp_ns':None,'clock_domain':None,'source_locator':{**source,'source_frame_id':str(fid)}} for i,fid in enumerate(ids)] + def add(self,name,path,schema,axes,valid,method='source values retained',**kw): + vp=Path(path).with_name(Path(path).stem+'_valid.pt').as_posix() + tensor(self.p/vp,valid) + self.m['components'][name]={'available':True,'schema_id':schema,'data_ref':path,'axes':axes,'valid_ref':vp,'valid_axes':axes[:valid.ndim],'provenance':{'kind':'format_conversion','method':method},'source_locator':self.source,**kw} + def array(self,name,path,schema,a,valid,axes,**kw): + a=np.asarray(a,np.float32).copy();valid=np.asarray(valid,bool) + assert a.shape[:valid.ndim]==valid.shape and a.shape[0]==self.T + assert np.isfinite(a[valid]).all(),name + a[~valid]=np.nan;tensor(self.p/path,a);self.add(name,path,schema,axes,valid,**kw) + restored=torch.load(self.p/path,map_location='cpu',weights_only=True).numpy() + for index in range(self.T):assert np.array_equal(restored[index],a[index],equal_nan=True) + self.checks.append({'test':'tensor_serialization_exact','file':path,'shape':list(a.shape),'valid_count':int(valid.sum())}) + def video(self,name,path,iterator,mask=False,**kw): + w,h=self.m['camera']['image_size'];pix='gray16le' if mask else 'rgb24';dtype='.001:v.m['semantic_review_required']=['MANO reconstruction discrepancy exceeds 1mm; inspect source model compatibility before accepting MANO component'] + raw=np.asarray(Image.open(io.BytesIO(z.read(f'evaluation/segmap/{i:08d}.png'))));assert raw.ndim==2;mask=raw.astype(np.uint16);mask[raw==0]=65534 + mappings=[{'mask_value':int(c),'category_id':'background' if c==65534 else f'freihand_label_{int(c)}','entity_id':None,'part_id':None,'source_instance_id':0 if c==65534 else int(c)} for c in np.unique(mask)] + v.m.setdefault('semantic_review_required',[]).append('segmap foreground codes preserved exactly; source part/category names require verification before cross-dataset semantic training') + frame_segment(v,mask,mappings);v.finish() + snapshot('FreiHAND',['sequence_pilot_images.py','pilot_mano.py']) + +def rhd(): + path=ROOT/'RHD/raw/data/RHD_v1-1.zip' + with zipfile.ZipFile(path) as z: + name=next(n for n in z.namelist() if n.endswith('evaluation/anno_evaluation.pickle'));prefix=name.rsplit('/',1)[0];a=Restricted(io.BytesIO(z.read(name)),encoding='latin1').load() + # Inspect official depth example, do not assume ray distance. + example=next(n for n in z.namelist() if n.endswith('view_samples.py'));text=z.read(example).decode();save(ROOT/'RHD'/RELEASE/'doc/depth_source.json',{'member':example,'source':text}) + assert '2**16 - 1' in text and '5.0' in text + for i in range(16): + rgb=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/color/{i:05d}.png'))).convert('RGB'));v=View('RHD',f'evaluation_{i:05d}','camera',[i],[320,320],{'archive':str(path.relative_to(WORK)),'annotation':name,'sample_index':i},kind='image',split='test');v.video('rgb','observation/rgb.mkv',[rgb]);v.intrinsic(a[i]['K']) + order=load(WORK/'schemas/hand-warehouse-1.1.json')['keypoints']['rhd21/v1']['source_index_map_per_hand'] + for key,stem,n,unit in [('xyz','xyz',3,'m'),('uv_vis','uv',2,'pixel')]: + arr=np.asarray(a[i][key])[...,:n].reshape(1,2,21,n)[:,:,order];v.array('keypoints_'+stem,'annotation/keypoints/'+stem+'.pt','keypoints/'+stem+'/v1',arr,np.ones((1,2,21),bool),['T','hand','joint','coordinate'],unit=unit,keypoint_schema='rhd21/v1',coordinate_frame='camera' if n==3 else 'rgb_image',method='source tip-to-palm reordered to registered palm-to-tip order',source_index_map=order) + tensor(v.p/'annotation/keypoints/visibility.pt',a[i]['uv_vis'][:,2].reshape(1,2,21)[:,:,order].astype(bool));v.add('keypoints_visibility','annotation/keypoints/visibility.pt','visibility/bool/v1',['T','hand','joint'],np.ones((1,2,21),bool)) + dep=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/depth/{i:05d}.png'))),dtype=np.uint16);code=dep[:,:,0]*256+dep[:,:,1];depth=code.astype(np.float32)/65535*5 + v.array('depth','observation/depth.pt','depth/z_float32/v1',depth[None],np.ones((1,320,320),bool),['T','height','width'],unit='m',depth_type='z',depth_origin='synthetic',method='official example: (R*256+G)/65535*5 metres') + raw=np.asarray(Image.open(io.BytesIO(z.read(f'{prefix}/mask/{i:05d}.png'))));mask=raw.astype(np.uint16);mask[raw==0]=65534 + mappings=[{'mask_value':int(c),'category_id':'background' if c==65534 else ('person' if c==1 else 'hand'),'entity_id':None if c in [65534,1] else ('hand_left' if c<18 else 'hand_right'),'part_id':None if c in [65534,1] else f'rhd_{int(c)}','source_instance_id':0 if c==65534 else int(c)} for c in np.unique(mask)];frame_segment(v,mask,mappings) + p=np.asarray(a[i]['xyz'])@np.asarray(a[i]['K']).T;uv=p[:,:2]/p[:,2:];err=np.linalg.norm(uv-a[i]['uv_vis'][:,:2],axis=-1);v.checks.append({'test':'native_xyz_K_vs_uv','max_pixel':float(err.max()),'mean_pixel':float(err.mean())});v.finish() + snapshot('RHD',['sequence_pilot_images.py','pilot_mano.py']) + +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('dataset',choices=['RHD','FreiHAND']);a=p.parse_args();{'RHD':rhd,'FreiHAND':freihand}[a.dataset]()