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+[打开离线核验页面](index.html) · [核验范围与文件摘要](evidence.json) + +## 当前结论 + +- 小样本转换自动检查通过；15 张预览的 mask 已与 raw 解码数值逐像素核对。 +- **小样本人工确认：用户于 2026-09-18T16:00:21 确认通过。全量自动验收及额外随机抽查已通过，详见下文；不代表对每张图都做过人工检查。** +- 用户已确认当前 15 个样本没有问题，允许继续全量转换；全量完成后补充本地验收结果，再决定发布。 + +## 范围和检查方法 + +12 张训练图覆盖三个基础样本的全部四种背景版本，另含三张评估图。选择是有目的的抽查，不是随机统计质量估计。FreiHAND 为独立图片，这里不验证视频时序。 + +自动检查包括 RGB 原 JPEG 码流及固定解码路径的像素一致性、分割值一致性、规范数组序列化和 MANO 根位置恢复。页面依次展示 RGB、分割叠加、源 XYZ 投影与转换 MANO 投影；可单独打开原 JPEG。人工检查关注图片/标注对应、左右手、方向、尺度、投影与分割边缘。 + +## 已知问题与边界 + +预览曾错误地让 FFmpeg 将 gray8 扩展为 gray16，强度被放大 257 倍，造成荧光绿色叠加。已改为按存储位深解码，再在数组中拓宽类型；15 个预览重新核对通过。该问题在预览读取端，保存的 mask 未因此改变。 + +MANO 拟合与原生 XYZ 允许存在源标注残差，不能要求完全重合。训练 JPEG mask 保留灰度，不二值化；评估区域图仅确认手/背景类别，不猜测细分手指名称。源 JSON 到规范 float32 的舍入与视频无损编码是两件事。 + +本报告只绑定 evidence.json 中的样本产物和脚本摘要；后续全量产物需另行验收，不能直接继承“通过”。HTML 和配图均为相对链接，下载整个 validation 目录即可离线查看，无需原始包、Python 或模型权重。配图仅为核验展示，不替代 observation 中的视频数据。 + +## 样本清单 + +| 样本 ID | 原标注索引 | 背景版本 | +|---|---:|---:| +| FreiHAND/evaluation/00000000 | 0 | 0 | +| FreiHAND/evaluation/00001000 | 1000 | 0 | +| FreiHAND/evaluation/00003959 | 3959 | 0 | +| FreiHAND/training/00000000 | 0 | 0 | +| FreiHAND/training/00001000 | 1000 | 0 | +| FreiHAND/training/00030000 | 30000 | 0 | +| FreiHAND/training/00032560 | 0 | 1 | +| FreiHAND/training/00033560 | 1000 | 1 | +| FreiHAND/training/00062560 | 30000 | 1 | +| FreiHAND/training/00065120 | 0 | 2 | +| FreiHAND/training/00066120 | 1000 | 2 | +| FreiHAND/training/00095120 | 30000 | 2 | +| FreiHAND/training/00097680 | 0 | 3 | +| FreiHAND/training/00098680 | 1000 | 3 | +| FreiHAND/training/00127680 | 30000 | 3 | + + +## 全量自动验收 + +4096 张上限的 33 个分片已完成转换及本地自动验收：130,240 张训练图片、3,960 张评估图片。逐批 RGB 码流/像素及 mask 数值核对通过，数组与规范登记、帧索引和样本唯一性检查通过。 + +全量追加随机抽查已获用户确认；小样本报告与全量随机报告分开保留。运行日志仍只放 tmp。 + + +## 全量随机抽查 + +[查看独立随机抽查报告](full_random/REPORT.md) · [打开 30 张随机样本预览](full_random/index.html)。全量随机抽查已获用户确认。 + + +发布确认：用户于 2026-09-18T17:01:03 确认全量随机抽查没有问题，允许发布。本报告仅描述实际完成的检查范围，不保证未抽中样本零缺陷。 diff --git a/FreiHAND/shared-v1.0/doc/validation/evidence.json b/FreiHAND/shared-v1.0/doc/validation/evidence.json new file mode 100644 index 0000000000000000000000000000000000000000..22487dc8c1947be6ca8c4d4d1e1bee2c6b03f1ec --- /dev/null +++ b/FreiHAND/shared-v1.0/doc/validation/evidence.json @@ -0,0 +1,90 @@ +{ + "dataset": "FreiHAND", + "release": "shared-v1.0", + "schema_version": "hand-warehouse/1.0", + "scope": "15 selected images, not full release", + "human_review": "approved", + "full_release_validation": { + "automated": "passed", + "views": 33, + "counts": { + "training": 130240, + "evaluation": 3960 + }, + "completed_at_local": "2026-09-18T16:47:34", + "full_release_spot_review": "approved", + "random_review_ref": "full_random/samples.json", + "publication_approved_at_local": "2026-09-18T17:01:03" + }, + "automated_checks_observed": [ + "MANO_middle_MCP_root_recovery", + "all_mask_values_exact", + "all_rgb_packets_and_decoded_pixels_exact", + "tensor_serialization_exact" + ], + 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sha256:ea8095e29cba6526185a301e32a7eebd5faf19d51b982ec1291bd2c12df3f9b5 +size 22033 diff --git a/FreiHAND/shared-v1.0/doc/validation/full_random/REPORT.md b/FreiHAND/shared-v1.0/doc/validation/full_random/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..95ccd71476fcd3273fc69fa29f3fe729475d231d --- /dev/null +++ b/FreiHAND/shared-v1.0/doc/validation/full_random/REPORT.md @@ -0,0 +1,11 @@ +# FreiHAND 全量产物随机抽查报告 + +[打开离线预览](index.html) · [抽样清单及可复现参数](samples.json) + +全量共 134,200 张；按训练背景版本 0–3 与评估集分成五层，各随机抽取 6 张，共 30 张，种子为 20260918。属于分层等额随机抽样，不是总体均匀抽样，不据此估计全量错误率。样本来自已完成的 4096 张分片产物，直接读取其视频、相机及标注数组，未重新转换试点。 + +每个选中 mask 均再次与 raw 对应帧逐值比较，通过后生成图。关键点和 MANO 使用全量产物数组投影；MANO 拟合不要求与源关键点无残差。自动检查通过不代表人工确认通过。 + +**人工确认：用户于 2026-09-18T17:01:03 确认通过，允许发布。** + +检查图片与标注对应、背景版本、分割边缘、关键点顺序及 MANO 投影；反馈样本 ID 即可。HTML 及配图使用相对链接，可下载后离线查看。 diff --git a/FreiHAND/shared-v1.0/doc/validation/full_random/index.html b/FreiHAND/shared-v1.0/doc/validation/full_random/index.html new file mode 100644 index 0000000000000000000000000000000000000000..8b114fc36952273c0719d5644cd4c2993a451111 --- /dev/null +++ b/FreiHAND/shared-v1.0/doc/validation/full_random/index.html @@ -0,0 +1 @@ +FreiHAND 全量随机抽查

FreiHAND：全量产物随机抽查

从完整 134,200 张产物中分层随机选取 30 张：四种训练背景版本及评估集各 6 张。随机种子 20260918；分层等额抽样，不按全体样本占比加权，不能估计总体错误率。用户已于 2026-09-18T17:01:03 确认通过。每行依次为 RGB、分割叠加、源 XYZ 投影、转换 MANO 投影。原始 JPEG 可单独打开。

FreiHAND/evaluation/00000206

Source annotation index: 206; variant: 0. Original JPEG

FreiHAND/evaluation/00000631

Source annotation index: 631; variant: 0. Original JPEG

FreiHAND/evaluation/00001261

Source annotation index: 1261; variant: 0. Original JPEG

FreiHAND/evaluation/00002530

Source annotation index: 2530; variant: 0. Original JPEG

FreiHAND/evaluation/00003556

Source annotation index: 3556; variant: 0. Original JPEG

FreiHAND/evaluation/00003606

Source annotation index: 3606; variant: 0. Original JPEG

FreiHAND/training/00011536

Source annotation index: 11536; variant: 0. Original JPEG

FreiHAND/training/00014129

Source annotation index: 14129; variant: 0. Original JPEG

FreiHAND/training/00019879

Source annotation index: 19879; variant: 0. Original JPEG

FreiHAND/training/00026329

Source annotation index: 26329; variant: 0. Original JPEG

FreiHAND/training/00026902

Source annotation index: 26902; variant: 0. Original JPEG

FreiHAND/training/00030008

Source annotation index: 30008; variant: 0. Original JPEG

FreiHAND/training/00037695

Source annotation index: 5135; variant: 1. Original JPEG

FreiHAND/training/00043219

Source annotation index: 10659; variant: 1. Original JPEG

FreiHAND/training/00044294

Source annotation index: 11734; variant: 1. Original JPEG

FreiHAND/training/00053149

Source annotation index: 20589; variant: 1. Original JPEG

FreiHAND/training/00054162

Source annotation index: 21602; variant: 1. Original JPEG

FreiHAND/training/00059674

Source annotation index: 27114; variant: 1. Original JPEG

FreiHAND/training/00068710

Source annotation index: 3590; variant: 2. Original JPEG

FreiHAND/training/00069210

Source annotation index: 4090; variant: 2. Original JPEG

FreiHAND/training/00077227

Source annotation index: 12107; variant: 2. Original JPEG

FreiHAND/training/00083104

Source annotation index: 17984; variant: 2. Original JPEG

FreiHAND/training/00084447

Source annotation index: 19327; variant: 2. Original JPEG

FreiHAND/training/00089313

Source annotation index: 24193; variant: 2. Original JPEG

FreiHAND/training/00099203

Source annotation index: 1523; variant: 3. Original JPEG

FreiHAND/training/00100886

Source annotation index: 3206; variant: 3. Original JPEG

FreiHAND/training/00103916

Source annotation index: 6236; variant: 3. Original JPEG

FreiHAND/training/00104813

Source annotation index: 7133; variant: 3. Original JPEG

FreiHAND/training/00108700

Source annotation index: 11020; variant: 3. Original JPEG

FreiHAND/training/00125881

Source annotation index: 28201; variant: 3. Original JPEG

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"FreiHAND/training/00054162", + "view": "data/training_00053248/camera", + "frame_index": 914, + "annotation_index": 21602, + "variant_index": 1, + "preview": "FreiHAND_training_00054162.png", + "rgb_decoded_sha256": "1bcef8ce23cd089879c626358f662019544e2f45454036dad1c00304f9d8408e", + "mask_uint16_sha256": "0eaba0265d85a9b2bf19a3a3629115f35b49a62aba468101b489ddee596fa096", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00059674", + "view": "data/training_00057344/camera", + "frame_index": 2330, + "annotation_index": 27114, + "variant_index": 1, + "preview": "FreiHAND_training_00059674.png", + "rgb_decoded_sha256": "282766294013e163869c66836895efacb935a1f6c7e3d9086f3275e7484b63ab", + "mask_uint16_sha256": "9e79c1d8a589920154de9f09aa3cbe1a2cd91b8149ddebe5476ee8e781a36d80", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00068710", + "view": "data/training_00065536/camera", + "frame_index": 3174, + "annotation_index": 3590, + "variant_index": 2, + "preview": "FreiHAND_training_00068710.png", + "rgb_decoded_sha256": "030c885956afec5c9099d08c240f70fc8f7b7a640cd1b3d56bf9f605457cf96a", + "mask_uint16_sha256": "b728c6813ef6a901fcfdc52b3ea7a1beb2710eddb2b83a16b2a43667330da78b", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00069210", + "view": "data/training_00065536/camera", + "frame_index": 3674, + "annotation_index": 4090, + "variant_index": 2, + "preview": "FreiHAND_training_00069210.png", + "rgb_decoded_sha256": "087bfa3e8d3744b6b01c4c40417d2eb29c61e2487f306460458510dfa7b8c357", + "mask_uint16_sha256": "8aac184624c4d6f54d361b369b21af6ed19d4c3d989d965487b65a99051cb1e1", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00077227", + "view": "data/training_00073728/camera", + "frame_index": 3499, + "annotation_index": 12107, + "variant_index": 2, + "preview": "FreiHAND_training_00077227.png", + "rgb_decoded_sha256": "82c77f45982bbeb4bbf995ee0441e72b6f235ed1209648d65f818c50683771b4", + "mask_uint16_sha256": "4d5f4e311c457f38e3f250e1fd42477206677b847bf7e7638c8533bda7783ec6", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00083104", + "view": "data/training_00081920/camera", + "frame_index": 1184, + "annotation_index": 17984, + "variant_index": 2, + "preview": "FreiHAND_training_00083104.png", + "rgb_decoded_sha256": "9c98778beafdbf09e802ff67f7cd6da1ab9e28805c32ab6cd888d6123552cf74", + "mask_uint16_sha256": "9ddba5d4b08289640e4ce1f44421496fd380801b512c6cbff15f9661a8ab4511", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00084447", + "view": "data/training_00081920/camera", + "frame_index": 2527, + "annotation_index": 19327, + "variant_index": 2, + "preview": "FreiHAND_training_00084447.png", + "rgb_decoded_sha256": "fc8e5e4ec49cd705fc6871279148ff783aef1f923f7162ad3b337ea90cf085ef", + "mask_uint16_sha256": "7e1884231e4977b33e8c58367448304772270013050875e8d2b12505792e5e6a", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00089313", + "view": "data/training_00086016/camera", + "frame_index": 3297, + "annotation_index": 24193, + "variant_index": 2, + "preview": "FreiHAND_training_00089313.png", + "rgb_decoded_sha256": "e2b5555a92963356de96e611a4f17ab84cb7443e4928e4ac529d2ecc56831135", + "mask_uint16_sha256": "7f457f2d267f6fc2c3dc5a90b1a548b2f9dc8737c40cc9ad275b86173ea0c47e", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00099203", + "view": "data/training_00098304/camera", + "frame_index": 899, + "annotation_index": 1523, + "variant_index": 3, + "preview": "FreiHAND_training_00099203.png", + "rgb_decoded_sha256": "930117e359092cef12444402b5015334df672b1608e07c68ddde2ad89601b2eb", + "mask_uint16_sha256": "018e593aaa8e037491e2f9f4152a5bf6e856e2de0bb01f728e58bdfbeb065e76", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00100886", + "view": "data/training_00098304/camera", + "frame_index": 2582, + "annotation_index": 3206, + "variant_index": 3, + "preview": "FreiHAND_training_00100886.png", + "rgb_decoded_sha256": "ce75bdb97b0482a36b36c6a6677558a324ed36c8501b7c07ad5ba681155fd8ef", + "mask_uint16_sha256": "ce07f3a5151a4e2bfc1a2d68070ee048f1c35c01c40fc5e0b89f83f8935b3725", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00103916", + "view": "data/training_00102400/camera", + "frame_index": 1516, + "annotation_index": 6236, + "variant_index": 3, + "preview": "FreiHAND_training_00103916.png", + "rgb_decoded_sha256": "d4560b8a9c8e7ba97de2ea044c568e60f9275c38967f7a02c5519060bfa54cf2", + "mask_uint16_sha256": "fca913c882327f38ac1a9e8f75daf09d30293c2377431e8c9891d6ac3047596d", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00104813", + "view": "data/training_00102400/camera", + "frame_index": 2413, + "annotation_index": 7133, + "variant_index": 3, + "preview": "FreiHAND_training_00104813.png", + "rgb_decoded_sha256": "449088ce0c878ff9144fcb869b08249fa0666578249e47a2f37654a944dcee91", + "mask_uint16_sha256": "cd619f2a9d86696b8adf8152d83536d8f65b77a9136e028e0578ccf47cca8bca", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00108700", + "view": "data/training_00106496/camera", + "frame_index": 2204, + "annotation_index": 11020, + "variant_index": 3, + "preview": "FreiHAND_training_00108700.png", + "rgb_decoded_sha256": "a87cfec33e7c070b43e72df31faea6b7ccfe1b98cdce0c401b62eff17c983f4d", + "mask_uint16_sha256": "4ce19cdf99b1506dc199493609a6c8dc4f092bd2a5b57cb423757d596647bf32", + "raw_mask_comparison": "passed" + }, + { + "sample_id": "FreiHAND/training/00125881", + "view": "data/training_00122880/camera", + "frame_index": 3001, + "annotation_index": 28201, + "variant_index": 3, + "preview": "FreiHAND_training_00125881.png", + "rgb_decoded_sha256": "83be1d4f9296420b3fcd38d594e906db4ee1dfec1d5344572e079b70e315d8e2", + "mask_uint16_sha256": "c665a81f0da51295d02351df3b0594c86dbf4af08da02ea4fa6b19bc0a70a841", + "raw_mask_comparison": "passed" + } + ], + "human_review_confirmation": { + "reviewer": "user", + "confirmed_at_local": "2026-09-18T17:01:03", + "statement": "没问题了", + "scope": "30 full-output stratified random samples; proceed with publication" + } +} \ No newline at end of file diff --git a/FreiHAND/shared-v1.0/doc/validation/index.html b/FreiHAND/shared-v1.0/doc/validation/index.html new file mode 100644 index 0000000000000000000000000000000000000000..bb2c9fbbf4483205d3e2dc3bcc31a7cb8c10e515 --- /dev/null +++ b/FreiHAND/shared-v1.0/doc/validation/index.html @@ -0,0 +1 @@ +FreiHAND 本地人工核验

FreiHAND：预处理前本地人工核验

15 个实际转换样本；用户已确认小样本通过；全量验收未完成。每行依次为 RGB、分割叠加、源 XYZ 投影、转换 MANO 投影。原始 JPEG 可单独打开。

FreiHAND/evaluation/00000000

Source annotation index: 0; variant: 0. Original JPEG

FreiHAND/evaluation/00001000

Source annotation index: 1000; variant: 0. Original JPEG

FreiHAND/evaluation/00003959

Source annotation index: 3959; variant: 0. Original JPEG

FreiHAND/training/00000000

Source annotation index: 0; variant: 0. Original JPEG

FreiHAND/training/00001000

Source annotation index: 1000; variant: 0. Original JPEG

FreiHAND/training/00030000

Source annotation index: 30000; variant: 0. Original JPEG

FreiHAND/training/00032560

Source annotation index: 0; variant: 1. Original JPEG

FreiHAND/training/00033560

Source annotation index: 1000; variant: 1. Original JPEG

FreiHAND/training/00062560

Source annotation index: 30000; variant: 1. Original JPEG

FreiHAND/training/00065120

Source annotation index: 0; variant: 2. Original JPEG

FreiHAND/training/00066120

Source annotation index: 1000; variant: 2. Original JPEG

FreiHAND/training/00095120

Source annotation index: 30000; variant: 2. Original JPEG

FreiHAND/training/00097680

Source annotation index: 0; variant: 3. Original JPEG

FreiHAND/training/00098680

Source annotation index: 1000; variant: 3. Original JPEG

FreiHAND/training/00127680

Source annotation index: 30000; variant: 3. Original JPEG

\ No newline at end of file diff --git a/FreiHAND/shared-v1.0/script/archive_freihand_review.py b/FreiHAND/shared-v1.0/script/archive_freihand_review.py new file mode 100644 index 0000000000000000000000000000000000000000..77441aa0197091ac7afca0ca7d4ca43053e6120a --- /dev/null +++ b/FreiHAND/shared-v1.0/script/archive_freihand_review.py @@ -0,0 +1,59 @@ +"""Archive a distributable human review report, not console/progress logs.""" +from pathlib import Path +import json,hashlib,shutil,re +WORK=Path(__file__).resolve().parents[1] +OUT=Path('E:/hand-datasets/FreiHAND/shared-v1.0/doc/validation') + +def sha(p):return hashlib.sha256(p.read_bytes()).hexdigest() +def main(): + OUT.mkdir(parents=True,exist_ok=True) + source=OUT if (OUT/'index.html').exists() else WORK/'tmp/manual-review/preview' + for p in source.iterdir(): + if (p.name=='index.html' or p.suffix.lower() in ['.png','.jpg']) and p.resolve()!=(OUT/p.name).resolve():shutil.copyfile(p,OUT/p.name) + rows=[];snapshots=[];tests=set() + root=WORK/'tmp/manual-review/FreiHAND' + for mp in sorted((root/'shared-v1.0/data').glob('*/*/metainfo.json')): + m=json.loads(mp.read_text());frames=[json.loads(l) for l in (mp.parent/'camera/frames.jsonl').read_text().splitlines()] + checks=json.loads((root/'tmp/production/checks'/f'{m["sequence_id"]}__{m["camera_id"]}.checks.json').read_text()) + tests.update(c['test'] for c in checks['checks']) + for f in frames:rows.append((f['sample_id'],f['source_locator']['annotation_index'],f['image_variant']['variant_index'])) + snapshots.append({'sample_ids':[f['sample_id'] for f in frames],'converted_files_sha256':{p.relative_to(mp.parent).as_posix():sha(p) for p in sorted(mp.parent.rglob('*')) if p.is_file()}}) + assert len(rows)==15 + evidence={'dataset':'FreiHAND','release':'shared-v1.0','schema_version':'hand-warehouse/1.0','scope':'15 selected images, not full release','human_review':'pending','full_release_validation':'not_completed','automated_checks_observed':sorted(tests),'preview_mask_vs_raw':'15 selected frames passed exact decoded-value comparison after native-bit-depth fix','scripts_sha256':{n:sha(WORK/'scripts'/n) for n in ['convert_freihand_lossless.py','render_freihand_review.py']},'reviewed_sample_snapshots':snapshots} + (OUT/'evidence.json').write_text(json.dumps(evidence,ensure_ascii=False,indent=2),encoding='utf8') + report='''# FreiHAND 核验报告（待人工确认） + +[打开离线核验页面](index.html) · [核验范围与文件摘要](evidence.json) + +## 当前结论 + +- 小样本转换自动检查通过；15 张预览的 mask 已与 raw 解码数值逐像素核对。 +- **人工确认：待确认。全量验收：未完成。此报告不代表整套数据已通过验收。** +- 在人工确认前，全量转换及上传保持暂停。确认后应记录核验人、日期、结论和问题处理结果；全量完成后补充全量验收范围与结果。 + +## 范围和检查方法 + +12 张训练图覆盖三个基础样本的全部四种背景版本，另含三张评估图。选择是有目的的抽查，不是随机统计质量估计。FreiHAND 为独立图片，这里不验证视频时序。 + +自动检查包括 RGB 原 JPEG 码流及固定解码路径的像素一致性、分割值一致性、规范数组序列化和 MANO 根位置恢复。页面依次展示 RGB、分割叠加、源 XYZ 投影与转换 MANO 投影；可单独打开原 JPEG。人工检查关注图片/标注对应、左右手、方向、尺度、投影与分割边缘。 + +## 已知问题与边界 + +预览曾错误地让 FFmpeg 将 gray8 扩展为 gray16，强度被放大 257 倍，造成荧光绿色叠加。已改为按存储位深解码，再在数组中拓宽类型；15 个预览重新核对通过。该问题在预览读取端，保存的 mask 未因此改变。 + +MANO 拟合与原生 XYZ 允许存在源标注残差，不能要求完全重合。训练 JPEG mask 保留灰度，不二值化；评估区域图仅确认手/背景类别，不猜测细分手指名称。源 JSON 到规范 float32 的舍入与视频无损编码是两件事。 + +本报告只绑定 evidence.json 中的样本产物和脚本摘要；后续全量产物需另行验收，不能直接继承“通过”。HTML 和配图均为相对链接，下载整个 validation 目录即可离线查看，无需原始包、Python 或模型权重。配图仅为核验展示，不替代 observation 中的视频数据。 + +## 样本清单 + +| 样本 ID | 原标注索引 | 背景版本 | +|---|---:|---:| +''' + for sid,idx,var in rows:report+=f'| {sid} | {idx} | {var} |\n' + (OUT/'REPORT.md').write_text(report,encoding='utf8') + # Check every local dependency of the offline HTML. + text=(OUT/'index.html').read_text(encoding='utf8') + for ref in re.findall(r'(?:src|href)="([^"]+)"',text):assert (OUT/ref).is_file(),ref + print('Archived report with 15 samples; all HTML links resolve:',OUT) +if __name__=='__main__':main() diff --git a/FreiHAND/shared-v1.0/script/convert_freihand_lossless.py b/FreiHAND/shared-v1.0/script/convert_freihand_lossless.py new file mode 100644 index 0000000000000000000000000000000000000000..a5c1a583179d841f4826e0c3a8503d0161d6b8bc --- /dev/null +++ b/FreiHAND/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/FreiHAND/shared-v1.0/script/freihand_production_preflight.py b/FreiHAND/shared-v1.0/script/freihand_production_preflight.py new file mode 100644 index 0000000000000000000000000000000000000000..be035c97c85fcc73cbd2aa0a242f56bb03b6779f --- /dev/null +++ b/FreiHAND/shared-v1.0/script/freihand_production_preflight.py @@ -0,0 +1,42 @@ +"""Verify complete FreiHAND source archives before production conversion. +Reads archives in place; does not extract or delete source files. +""" +from pathlib import Path +import hashlib, json, zipfile, time +ROOT=Path(__file__).resolve().parents[1] +SOURCE=ROOT/'hand-datasets/FreiHAND/raw/data' +OUT=ROOT/'tmp/freihand-production/freihand-production-preflight.json' + +def main(): + OUT.parent.mkdir(parents=True,exist_ok=True) + report={'status':'checking','archives':[], 'target':'E:/hand-datasets/FreiHAND', + 'release':'shared-v1.0','storage_revision':'lossless-video-2026-09-18'} + for filename in ['FreiHAND_pub_v2.zip','FreiHAND_pub_v2_eval.zip']: + p=SOURCE/filename + h=hashlib.sha256() + with p.open('rb') as f: + for block in iter(lambda:f.read(8*1024*1024),b''): h.update(block) + with zipfile.ZipFile(p) as z: + bad=z.testzip() + if bad: raise RuntimeError('ZIP CRC failure: '+bad) + names=z.namelist() + counts={prefix:sum(n.startswith(prefix) and not n.endswith('/') for n in names) + for prefix in ['training/rgb/','training/mask/','evaluation/rgb/','evaluation/segmap/','evaluation/anno/']} + arrays={n:len(json.loads(z.read(n))) for n in names if n in [ + 'training_K.json','training_mano.json','training_xyz.json', + 'evaluation_K.json','evaluation_mano.json','evaluation_xyz.json']} + receipt=p.with_name(p.name+'.receipt.json') + known=json.loads(receipt.read_text(encoding='utf-8')) if receipt.exists() else {} + expected=known.get('sha256') + if expected and expected!=h.hexdigest(): raise RuntimeError('SHA256 changed: '+filename) + item={'file':filename,'bytes':p.stat().st_size,'sha256':h.hexdigest(), + 'zip_crc':'passed_all_members','member_counts':counts,'array_lengths':arrays, + 'local_receipt_sha256_matches':bool(expected), + 'note':'Local receipt consistency is not an independent upstream checksum.'} + report['archives'].append(item) + OUT.write_text(json.dumps(report,indent=2),encoding='utf-8') + print(json.dumps(item),flush=True) + report['status']='raw_integrity_passed' + report['completed_at']=time.strftime('%Y-%m-%dT%H:%M:%S%z') + OUT.write_text(json.dumps(report,indent=2),encoding='utf-8') +if __name__=='__main__':main() diff --git a/FreiHAND/shared-v1.0/script/pilot_build.py b/FreiHAND/shared-v1.0/script/pilot_build.py new file mode 100644 index 0000000000000000000000000000000000000000..2881d0878bd6d3456c2bf84edbf151cb3851a3ac --- /dev/null +++ b/FreiHAND/shared-v1.0/script/pilot_build.py @@ -0,0 +1,209 @@ +"""Small, source-traceable dataset pilot. Originals are copied, never edited. + +Run with Python + numpy + torch + Pillow + opencv and imageio-ffmpeg. +Optional runtime installed at tmp/pilot-runtime. Outputs: hand-datasets/. +""" +from pathlib import Path +import argparse, codecs, collections, csv, io, json, pickle, shutil, subprocess, sys, zipfile +sys.path.insert(0,str(Path(__file__).resolve().parents[1]/'tmp/pilot-runtime')) +import numpy as np +import torch +from PIL import Image, ImageDraw +import cv2 +from pilot_fetch import ROOT, sha + +WORK=ROOT.parent +OLD=WORK/'datasets' +sys.path.insert(0,str(WORK/'tmp/pilot-runtime')) +import imageio_ffmpeg +FF=imageio_ffmpeg.get_ffmpeg_exe() +N=16 + +def save(p,value): + p.parent.mkdir(parents=True,exist_ok=True) + p.write_text(json.dumps(value,ensure_ascii=False,indent=2,allow_nan=False),encoding='utf-8') +def lines(p,rows): + p.parent.mkdir(parents=True,exist_ok=True) + p.write_text(''.join(json.dumps(x,ensure_ascii=False,allow_nan=False)+'\n' for x in rows),encoding='utf-8') +def tensor(p,a): + p.parent.mkdir(parents=True,exist_ok=True);torch.save(torch.as_tensor(np.asarray(a).copy()),p) +def load(p):return json.loads(p.read_text(encoding='utf-8')) + +class Restricted(pickle.Unpickler): + def find_class(self,module,name): + allowed={('numpy.core.multiarray','_reconstruct'):np.core.multiarray._reconstruct, + ('numpy._core.multiarray','_reconstruct'):np.core.multiarray._reconstruct, + ('numpy','ndarray'):np.ndarray,('numpy','dtype'):np.dtype, + ('numpy.core.numeric','_frombuffer'):np.core.numeric._frombuffer, + ('numpy._core.numeric','_frombuffer'):np.core.numeric._frombuffer, + ('_codecs','encode'):codecs.encode, + ('collections','OrderedDict'):collections.OrderedDict, + ('torch._utils','_rebuild_tensor_v2'):torch._utils._rebuild_tensor_v2, + ('torch.storage','_load_from_bytes'):lambda b:torch.load(io.BytesIO(b),map_location='cpu',weights_only=True)} + if (module,name) not in allowed:raise pickle.UnpicklingError(f'Unsupported class: {module}.{name}') + return allowed[(module,name)] +def read_pickle(p): + with p.open('rb') as f:return Restricted(f,encoding='latin1').load() + +class Build: + def __init__(self,name): + self.name=name;self.base=ROOT/name;self.out=self.base/'pilot-v0';self.refs=[];self.views=[];self.checks=[] + for p in ['raw/doc','raw/script','raw/data','pilot-v0/doc','pilot-v0/script','pilot-v0/data']:(self.base/p).mkdir(parents=True,exist_ok=True) + def copy(self,src): + rel=src.relative_to(OLD/self.name);dst=self.base/'raw/data/local_subset'/rel + dst.parent.mkdir(parents=True,exist_ok=True) + if not dst.exists():shutil.copyfile(src,dst) + source_hash=sha(src);assert sha(dst)==source_hash + self.refs.append({'source':str(src),'stored':str(dst.relative_to(self.base)), 'sha256':source_hash,'bytes':src.stat().st_size,'kind':'historical_extracted_file_copy','official_archive_verified':False}) + return dst + def view(self,seq,cam,ids,**meta): + p=self.out/'data'/seq/cam;p.mkdir(parents=True,exist_ok=True) + m={'schema_version':'pilot-v0','dataset':self.name,'sequence_id':seq,'camera_id':cam,'num_frames':len(ids),'fps':None,'split':None,'view_type':None,'time_semantics':'unknown','missing_modalities':{},**meta} + self.views.append((p,m)) + lines(p/'frames.jsonl',[{'frame_index':i,'source_frame_id':str(s),'timestamp_ns':None,'clock_domain':None} for i,s in enumerate(ids)]) + return p,m + def video(self,p,arrays): + a=np.stack(arrays);h,w=a.shape[1:3] + pixel='rgb24' if a.ndim==4 else ('gray16le' if a.dtype==np.uint16 else 'gray') + if pixel=='gray16le':a=a.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/FreiHAND/shared-v1.0/script/pilot_fetch.py b/FreiHAND/shared-v1.0/script/pilot_fetch.py new file mode 100644 index 0000000000000000000000000000000000000000..7671f83ac7a97438b95528fe9c13c00768eb7861 --- /dev/null +++ b/FreiHAND/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/FreiHAND/shared-v1.0/script/pilot_mano.py b/FreiHAND/shared-v1.0/script/pilot_mano.py new file mode 100644 index 0000000000000000000000000000000000000000..d4596f225a22b61f642a2ef0193588172a5b581e --- /dev/null +++ b/FreiHAND/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/FreiHAND/shared-v1.0/script/render_freihand_full_random_review.py b/FreiHAND/shared-v1.0/script/render_freihand_full_random_review.py new file mode 100644 index 0000000000000000000000000000000000000000..74a0afa3becbb19b79cbb9c20043e2956708fcf0 --- /dev/null +++ b/FreiHAND/shared-v1.0/script/render_freihand_full_random_review.py @@ -0,0 +1,71 @@ +"""Render local human review from actual converted tensors/videos; never uploads.""" +from sequence_pilot_images import * +import html, random, hashlib +from collections import defaultdict + +def main(): + root=Path('E:/hand-datasets/FreiHAND/shared-v1.0');out=root/'doc/validation/full_random';out.mkdir(parents=True,exist_ok=True) + pools=defaultdict(list);selection=defaultdict(list);sample_records=[] + for mp in sorted((root/'data').glob('*/*/metainfo.json')): + for f in [json.loads(l) for l in (mp.parent/'camera/frames.jsonl').read_text().splitlines()]: + stratum='evaluation' if f['sample_id'].split('/')[1]=='evaluation' else 'training_variant_'+str(f['image_variant']['variant_index']) + pools[stratum].append((mp.parent.relative_to(root).as_posix(),f['frame_index'])) + seed=20260918;rng=random.Random(seed) + for stratum in sorted(pools): + for view,idx in rng.sample(pools[stratum],6):selection[view].append(idx) + torch.set_num_threads(4) + model=Mano('right');cards=[];edges=[(0,1),(1,2),(2,3),(3,4),(0,5),(5,6),(6,7),(7,8),(0,9),(9,10),(10,11),(11,12),(0,13),(13,14),(14,15),(15,16),(0,17),(17,18),(18,19),(19,20)] + for mp in sorted((root/'data').glob('*/*/metainfo.json')): + m=load(mp);p=mp.parent;view=p.relative_to(root).as_posix() + if view not in selection:continue + indices=sorted(selection[view]);t=len(indices) + all_rows=[json.loads(l) for l in (p/'camera/frames.jsonl').read_text().splitlines()];rows=[all_rows[i] for i in indices] + expression='+'.join('eq(n\\,'+str(i)+')' for i in indices) + print('Reading full output',view,indices,flush=True) + def decode(path,pix,dtype,shape): + b=subprocess.check_output([FF,'-v','error','-i',str(path),'-vf','select='+expression,'-vsync','0','-frames:v',str(t),'-f','rawvideo','-pix_fmt',pix,'pipe:1']);return np.frombuffer(b,dtype=dtype).reshape(shape) + images=decode(p/'observation/rgb.mkv','rgb24',np.uint8,(t,224,224,3)) + # FFmpeg gray8 -> gray16 rescales intensities by 257; decode native storage + # first and widen numerically in NumPy to preserve labels/intensities. + mask_pix=m['components']['segment']['storage']['pixel_format'] + assert mask_pix in ['gray','gray16le'] + masks=decode(p/'annotation/segment/mask.mkv',mask_pix,np.uint8 if mask_pix=='gray' else '

{html.escape(frame["sample_id"])}

Source annotation index: {frame["source_locator"]["annotation_index"]}; variant: {frame["image_variant"]["variant_index"]}. Original JPEG

') + text='''FreiHAND 全量随机抽查

FreiHAND：全量产物随机抽查

从完整 134,200 张产物中分层随机选取 30 张：四种训练背景版本及评估集各 6 张。随机种子 20260918；分层等额抽样，不按全体样本占比加权，不能估计总体错误率。尚待人工确认。每行依次为 RGB、分割叠加、源 XYZ 投影、转换 MANO 投影。原始 JPEG 可单独打开。

  • 检查图片与标注是否同一只手、同一姿态；左右手、镜像、坐标尺度与相机方向是否合理。
  • 检查分割边缘、前景范围和训练背景变体的对应关系；训练灰度保留原强度，未阈值化。
  • 检查关键点顺序与投影；MANO 是拟合标注，不能要求与源 XYZ 完全重合。明显整体偏移、缩放或旋转须反馈。
  • 检查原图细节是否一致。此页面用于视觉检查，编码数值一致性由转换器逐值验证。
  • 请确认这份全量随机抽查结果，或指出样本 ID 和问题；确认前不上传。
'''+''.join(cards) + (out/'index.html').write_text(text,encoding='utf8') + save(out/'samples.json',{'seed':seed,'method':'Python random.Random; sample without replacement separately in each sorted stratum; 6 per stratum','population_sizes':{k:len(v) for k,v in pools.items()},'human_review':'pending','source':'actual completed shared-v1.0 data, not pilot outputs','samples':sample_records}) + assert len(sample_records)==30 and len({x['sample_id'] for x in sample_records})==30 + report='# FreiHAND 全量产物随机抽查报告\n\n[打开离线预览](index.html) · [抽样清单及可复现参数](samples.json)\n\n全量共 134,200 张；按训练背景版本 0–3 与评估集分成五层，各随机抽取 6 张，共 30 张，种子为 20260918。属于分层等额随机抽样，不是总体均匀抽样，不据此估计全量错误率。样本来自已完成的 4096 张分片产物，直接读取其视频、相机及标注数组，未重新转换试点。\n\n每个选中 mask 均再次与 raw 对应帧逐值比较，通过后生成图。关键点和 MANO 使用全量产物数组投影；MANO 拟合不要求与源关键点无残差。自动检查通过不代表人工确认通过。\n\n**人工确认：待确认。尚未上传。**\n\n检查图片与标注对应、背景版本、分割边缘、关键点顺序及 MANO 投影；反馈样本 ID 即可。HTML 及配图使用相对链接，可下载后离线查看。\n' + (out/'REPORT.md').write_text(report,encoding='utf8') + parent_report=root/'doc/validation/REPORT.md';parent_text=parent_report.read_text(encoding='utf8') + if 'full_random/REPORT.md' not in parent_text:parent_report.write_text(parent_text+'\n\n## 全量随机抽查\n\n[查看独立随机抽查报告](full_random/REPORT.md) · [打开 30 张随机样本预览](full_random/index.html)。本次从全量产物抽取，人工确认待完成。\n',encoding='utf8') + evidence=root/'doc/validation/evidence.json';d=load(evidence);d['full_release_validation']['random_review_ref']='full_random/samples.json';save(evidence,d) + print(out/'index.html') +if __name__=='__main__':main() diff --git a/FreiHAND/shared-v1.0/script/render_freihand_review.py b/FreiHAND/shared-v1.0/script/render_freihand_review.py new file mode 100644 index 0000000000000000000000000000000000000000..c8443bd1cbb4dcc20b28769e586414aeca416fd8 --- /dev/null +++ b/FreiHAND/shared-v1.0/script/render_freihand_review.py @@ -0,0 +1,47 @@ +"""Render local human review from actual converted tensors/videos; never uploads.""" +from sequence_pilot_images import * +import html + +def main(): + root=WORK/'tmp/manual-review/FreiHAND/shared-v1.0';out=Path('E:/hand-datasets/FreiHAND/shared-v1.0/doc/validation');out.mkdir(parents=True,exist_ok=True) + model=Mano('right');cards=[];edges=[(0,1),(1,2),(2,3),(3,4),(0,5),(5,6),(6,7),(7,8),(0,9),(9,10),(10,11),(11,12),(0,13),(13,14),(14,15),(15,16),(0,17),(17,18),(18,19),(19,20)] + for mp in sorted((root/'data').glob('*/*/metainfo.json')): + m=load(mp);p=mp.parent;t=m['num_frames'];rows=[json.loads(l) for l in (p/'camera/frames.jsonl').read_text().splitlines()] + def decode(path,pix,dtype,shape): + b=subprocess.check_output([FF,'-v','error','-i',str(path),'-f','rawvideo','-pix_fmt',pix,'pipe:1']);return np.frombuffer(b,dtype=dtype).reshape(shape) + images=decode(p/'observation/rgb.mkv','rgb24',np.uint8,(t,224,224,3)) + # FFmpeg gray8 -> gray16 rescales intensities by 257; decode native storage + # first and widen numerically in NumPy to preserve labels/intensities. + mask_pix=m['components']['segment']['storage']['pixel_format'] + assert mask_pix in ['gray','gray16le'] + masks=decode(p/'annotation/segment/mask.mkv',mask_pix,np.uint8 if mask_pix=='gray' else '

{html.escape(frame["sample_id"])}

Source annotation index: {frame["source_locator"]["annotation_index"]}; variant: {frame["image_variant"]["variant_index"]}. Original JPEG

') + text='''FreiHAND 本地人工核验

FreiHAND：预处理前本地人工核验

15 个实际转换样本；尚未人工验收，不代表全量质量通过。每行依次为 RGB、分割叠加、源 XYZ 投影、转换 MANO 投影。原始 JPEG 可单独打开。

  • 检查图片与标注是否同一只手、同一姿态；左右手、镜像、坐标尺度与相机方向是否合理。
  • 检查分割边缘、前景范围和训练背景变体的对应关系；训练灰度保留原强度，未阈值化。
  • 检查关键点顺序与投影；MANO 是拟合标注，不能要求与源 XYZ 完全重合。明显整体偏移、缩放或旋转须反馈。
  • 检查原图细节是否一致。此页面用于视觉检查，编码数值一致性由转换器逐值验证。
  • 请在对话中明确通过或指出样本 ID 和问题；通过前不恢复全量转换，不上传。
'''+''.join(cards) + (out/'index.html').write_text(text,encoding='utf8');print(out/'index.html') +if __name__=='__main__':main() diff --git a/FreiHAND/shared-v1.0/script/run_freihand_local.py b/FreiHAND/shared-v1.0/script/run_freihand_local.py new file mode 100644 index 0000000000000000000000000000000000000000..1e428651db5b2e1410bb3e3b2c8fb609d823b3de --- /dev/null +++ b/FreiHAND/shared-v1.0/script/run_freihand_local.py @@ -0,0 +1,23 @@ +"""Approved full FreiHAND conversion followed by local acceptance; no upload or raw deletion.""" +from pathlib import Path +import subprocess,sys,json,shutil,datetime +WORK=Path(__file__).resolve().parents[1] +BASE=Path('E:/hand-datasets/FreiHAND');RELEASE=BASE/'shared-v1.0' + +def main(): + evidence=RELEASE/'doc/validation/evidence.json' + assert json.loads(evidence.read_text(encoding='utf8'))['human_review']=='approved' + subprocess.run([sys.executable,str(WORK/'scripts/convert_freihand_lossless.py'),'--output','E:/hand-datasets','--source',str(BASE/'raw/data'),'--batch-size','4096','--human-reviewed'],check=True,cwd=WORK) + subprocess.run([sys.executable,str(WORK/'scripts/validate_freihand_release.py'),str(RELEASE)],check=True,cwd=WORK) + result=json.loads((BASE/'tmp/production/acceptance.json').read_text()) + assert result['passed'] and result['full_release'] and result['views']==33 + d=json.loads(evidence.read_text(encoding='utf8'));d['full_release_validation']={'automated':'passed','views':result['views'],'counts':result['counts'],'completed_at_local':datetime.datetime.now().isoformat(timespec='seconds'),'full_release_spot_review':'pending','upload':'not_started'} + evidence.write_text(json.dumps(d,ensure_ascii=False,indent=2),encoding='utf8') + with (RELEASE/'doc/validation/REPORT.md').open('a',encoding='utf8') as f: + f.write('\n\n## 全量自动验收\n\n4096 张上限的 33 个分片已完成转换及本地自动验收：130,240 张训练图片、3,960 张评估图片。逐批 RGB 码流/像素及 mask 数值核对通过，数组与规范登记、帧索引和样本唯一性检查通过。\n\n全量追加人工抽查尚未完成，尚未上传。小样本人工通过不替代此步骤。运行日志仍只放 tmp。\n') + target=RELEASE/'script';target.mkdir(exist_ok=True) + for name in ['convert_freihand_lossless.py','validate_freihand_release.py','render_freihand_review.py','archive_freihand_review.py','run_freihand_local.py','render_freihand_full_random_review.py','sequence_pilot_images.py','sequence_pilot_common.py','pilot_build.py','pilot_fetch.py','pilot_mano.py']:shutil.copyfile(WORK/'scripts'/name,target/name) + for name in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/name,RELEASE/'doc'/name) + subprocess.run([sys.executable,str(WORK/'scripts/render_freihand_full_random_review.py')],check=True,cwd=WORK) + print('LOCAL_FULL_ACCEPTANCE_PASSED: 33 shards; upload not started; raw retained.',flush=True) +if __name__=='__main__':main() diff --git a/FreiHAND/shared-v1.0/script/sequence_pilot_common.py b/FreiHAND/shared-v1.0/script/sequence_pilot_common.py new file mode 100644 index 0000000000000000000000000000000000000000..169c4868c9fbba40790ee94c2600fb35e418acef --- /dev/null +++ b/FreiHAND/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]() diff --git a/FreiHAND/shared-v1.0/script/validate_freihand_release.py b/FreiHAND/shared-v1.0/script/validate_freihand_release.py new file mode 100644 index 0000000000000000000000000000000000000000..850af370fdad843e845899793f2b361d0a7a44a4 --- /dev/null +++ b/FreiHAND/shared-v1.0/script/validate_freihand_release.py @@ -0,0 +1,45 @@ +"""Check the exact v1.0 registry and FreiHAND release counts; hash all payload files.""" +from sequence_pilot_common import * + +def main(): + p=argparse.ArgumentParser();p.add_argument('release',type=Path);p.add_argument('--partial',action='store_true');a=p.parse_args() + registry=load(WORK/'distribution/hand-warehouse-1.0/schema.json');counts={'training':0,'evaluation':0};seen=set();views=0 + for path in sorted((a.release/'data').glob('*/*/metainfo.json')): + m=load(path);base=path.parent;t=m['num_frames'];views+=1 + for key in ['schema_version','split_scheme','split_source_locator','sampling','camera','components']:assert key in m,(path,key) + assert m['schema_version']=='hand-warehouse/1.0' + frames=[json.loads(l) for l in (base/'camera/frames.jsonl').read_text().splitlines()] + assert len(frames)==t + for i,frame in enumerate(frames): + assert frame['frame_index']==i and frame['sample_id'] not in seen + seen.add(frame['sample_id']);counts[frame['sample_id'].split('/')[1]]+=1 + assert frame['source_locator']['annotation_index']==int(frame['source_frame_id'])%(32560 if frame['sample_id'].split('/')[1]=='training' else 3960) + for name,c in m['components'].items(): + definition=registry['components'][c['schema_id']] + for key in ['available','data_ref','axes','valid_ref','valid_axes','provenance','source_locator',*definition.get('required_metadata',[])]:assert key in c,(path,name,key) + assert (base/c['data_ref']).is_file() + valid=torch.load(base/c['valid_ref'],weights_only=True).numpy();assert valid.dtype==bool + shape=[t if x=='T' else x for x in definition['valid_shape']];assert list(valid.shape)==shape + if c['data_ref'].endswith('.pt'): + arr=torch.load(base/c['data_ref'],weights_only=True).numpy() + assert arr.dtype==np.float32 + assert list(arr.shape)==[t if x=='T' else x for x in definition['shape']] + assert np.isfinite(arr[valid]).all() and np.isnan(arr[~valid]).all() + else: + assert c['storage']['profile'] in definition['allowed_storage_profiles'] + for key in registry['video_metadata']['required']:assert key in c['storage'],(name,key) + checks=load((a.release.parent/'tmp/production/checks')/f'{m["sequence_id"]}__{m["camera_id"]}.checks.json')['checks'] + assert any(c['test']=='all_rgb_packets_and_decoded_pixels_exact' for c in checks) + assert any(c['test']=='all_mask_values_exact' for c in checks) + if not a.partial:assert counts=={'training':130240,'evaluation':3960},counts + results={'passed':True,'full_release':not a.partial,'views':views,'counts':counts,'schema_version':'hand-warehouse/1.0'} + report=a.release.parent/'tmp/production';report.mkdir(parents=True,exist_ok=True) + save(report/'acceptance.json',results) + manifest=[] + for path in sorted(a.release.rglob('*')): + if not path.is_file() or path.name=='files.sha256.json' or '.cache' in path.parts:continue + b=path.read_bytes();manifest.append({'path':path.relative_to(a.release).as_posix(),'size':len(b),'sha256':hashlib.sha256(b).hexdigest(),'git_blob_sha1':hashlib.sha1(b'blob '+str(len(b)).encode()+b'\0'+b).hexdigest()}) + save(report/'files.sha256.json',{'files':manifest,'bytes':sum(x['size'] for x in manifest)}) + print(json.dumps(results),flush=True) +if __name__=='__main__':main() +