Organize HOT3D and ARCTIC shared scripts

ARCTIC/shared-v1.0/script/internal/arctic_object_review.py

@@ -60,8 +60,8 @@ def prepare(item):
 
 def check_geometry(item,p):
     seq,sid,oid,seqid,doc,tmp=context(item);m,frames,structure=validate_view(p);cam=m['camera_id'];ref=load(doc/'reference.json')
-    with np.load(tmp/'reference.npz') as loaded:a={k:loaded[k] for k in loaded.files}
-    ix=np.array([int(f['source_frame_id'])-ref['ioi_offset'] for f in frames]);known=(ix>=0)&(ix<len(a['object_world']))
+    with np.load(tmp/'reference.npz') as loaded:a={k:loaded[k] for k in loaded.files}
+    ix=np.array([int(f['source_frame_id'])-ref['ioi_offset'] for f in frames]);known=(ix>=0)&(ix<len(a['object_world']))
     assert [f['source_annotation_index'] for f in frames]==[int(j) if ok else None for j,ok in zip(ix,known)]
     E=arr(p/'camera/extrinsic.pt');K=arr(p/'camera/intrinsic.pt');op=p/'annotation/object'/oid;meta=load(op/'metainfo.json')
     vertices=np.array([[float(x) for x in row.split()[1:4]] for row in (p/meta['mesh_ref']).read_text().splitlines() if row.startswith('v ')])
@@ -162,59 +162,58 @@ def object_page(item):
 
 STYLE='body{font:16px/1.6 system-ui;max-width:1550px;margin:24px auto;padding:0 20px;background:#f2f5f9;color:#162137}section,header{background:white;padding:20px;margin:18px 0;border-radius:10px}video,img{max-width:100%}table{border-collapse:collapse;width:100%}td,th{border:1px solid #ccd3de;padding:8px;text-align:left}.warn{color:#9b4414}a{color:#1265a7}'
 
-def write_object_html(r):
-    doc=DOC/r['object'];status=r['agent_visual_review'];cards=''
-    review_text=html.escape(status.get('summary',status['status']))
-    if status.get('notes'):review_text+='<ul>'+''.join('<li>'+html.escape(n)+'</li>' for n in status['notes'])+'</ul>'
+def write_object_html(r):
+    doc=DOC/r['object'];status=r['agent_visual_review'];cards=''
+    review_text=html.escape(status.get('summary',status['status']))
+    if status.get('notes'):review_text+='<ul>'+''.join('<li>'+html.escape(n)+'</li>' for n in status['notes'])+'</ul>'
     for v in r['views']:
         vv=v['video'];cards+=f'<section><h2>Camera {v["camera_id"]} · {vv["frames"]}帧</h2><video controls preload="metadata" src="{vv["video_ref"]}"></video><p>几何数值检查通过；暗帧候选索引：{r["dark_candidates"][v["camera_id"]]}。有效MANO左右：{vv["mano_valid_counts"]}。</p></section>'
     page=f'<!doctype html><html lang="zh-CN"><meta charset="utf-8"><title>ARCTIC {r["object"]} 完整序列验收</title><style>{STYLE}</style><header><a href="../index.html">全部物体</a><h1>{r["object"]} · {r["sequence"]}</h1><p>固定相机6、{r["camera_frames"]}个相机帧，全部已渲染。最大几何数值差{r["maximum_geometry_error_m"]:.3g}米。<a href="report.json">完整结果</a> · <a href="reference.json">官方对照及反例测试</a></p><p class="warn">代理视觉检查：{html.escape(json.dumps(status,ensure_ascii=False))}；用户人工确认待完成，不自动全量或上传。</p><p>左：shared原图；中：源参数诊断投影；右：shared当前valid监督。顶盖紫、底座绿，左手青、右手橙。透视线框显示遮挡面，不是分割。播放30fps仅用于预览，源采集FPS未知。</p><p>暗帧指标仅作候选；除已有laptop曝光规则外，未添加按帧号的人工屏蔽。该序列通过不代表该物体全部序列通过。</p></header>'+cards+'<section><h2>资产及纹理</h2><video controls src="'+r['asset_preview']['video_ref']+'"></video></section><section><h2>视觉抽查对照（不代替完整视频）</h2>'+''.join(f'<a href="{f}"><img src="{f}"></a>' for f in r['contact_sheets'])+'</section><script>document.querySelectorAll("video").forEach(v=>v.addEventListener("play",()=>document.querySelectorAll("video").forEach(o=>{if(o!==v)o.pause()})));</script></html>'
-    page=page.replace(html.escape(json.dumps(status,ensure_ascii=False)),review_text)
-    page=page.replace('<p class="warn">代理视觉检查：','<div class="warn">代理视觉检查：').replace('用户人工确认待完成，不自动全量或上传。</p>','用户人工确认待完成，不自动全量或上传。</div>')
-    (doc/'index.html').write_text(page,encoding='utf-8')
+    page=page.replace(html.escape(json.dumps(status,ensure_ascii=False)),review_text)
+    page=page.replace('<p class="warn">代理视觉检查：','<div class="warn">代理视觉检查：').replace('用户人工确认待完成，不自动全量或上传。</p>','用户人工确认待完成，不自动全量或上传。</div>')
+    (doc/'index.html').write_text(page,encoding='utf-8')
 
 def overview(selection):
     rows=[];reports=[]
     for item in selection['objects']:
         doc=DOC/item['object'];p=doc/'report.json'
         if p.exists():
-            r=load(p);reports.append(r);state='数值通过；'+('视觉已抽查（见问题记录）' if r['agent_visual_review']['status']=='reviewed_with_findings' else '视觉待检查');count=r['camera_frames'];link=f'<a href="{item["object"]}/index.html">{item["object"]}</a>'
+            r=load(p);reports.append(r);state='数值通过；'+('视觉已抽查（见问题记录）' if r['agent_visual_review']['status']=='reviewed_with_findings' else '视觉待检查');count=r['camera_frames'];link=f'<a href="{item["object"]}/index.html">{item["object"]}</a>'
         else:state='制作中';count='—';link=item['object']
         rows.append(f'<tr><td>{link}</td><td>{item["sequence"]}</td><td>{count}</td><td>{state}</td></tr>')
     DOC.mkdir(parents=True,exist_ok=True)
-    player=''
-    if reports:
-        options=''.join(f'<option value="{r["object"]}">{r["object"]} · {r["camera_frames"]}帧</option>' for r in reports)
-        player='<section><h2>固定相机6，切换物体</h2><label>物体 <select id="object-picker">'+options+'</select></label> · <a id="object-detail">该物体检查记录与原图对照</a><p id="object-result"></p><video id="object-video" controls preload="metadata" style="width:100%"></video></section>'
-        data={r['object']:{'video':r['object']+'/'+r['views'][0]['video']['video_ref'],'summary':r['agent_visual_review'].get('summary','数值通过；视觉检查待完成')} for r in reports}
-        player+='<script>const objects='+json.dumps(data,ensure_ascii=False)+';const picker=document.getElementById("object-picker"),video=document.getElementById("object-video");function choose(){const k=picker.value;video.pause();video.src=objects[k].video;document.getElementById("object-detail").href=k+"/index.html";document.getElementById("object-result").textContent=objects[k].summary;video.load();}picker.addEventListener("change",choose);choose();</script>'
-    (DOC/'index.html').write_text(f'<!doctype html><html lang="zh-CN"><meta charset="utf-8"><title>ARCTIC 11种物体完整序列验收</title><style>{STYLE}</style><header><h1>ARCTIC · 11种物体逐项验收</h1><p>新增组统一采用s06的固定相机6，11种物体各一段完整序列；相机内外参保持一致。<a href="../index.html">原s09 laptop九路相机预览</a>保留。按明显开合幅度选择较短完整序列，不裁切源帧。</p><p>已完成{len(reports)}/11种物体。<a href="selection.json">选择依据</a> · <a href="REPORT.md">验收结论</a> · <a href="report.json">总报告</a></p><p class="warn">自动数值通过≠图像对齐无误≠用户人工通过。未开始全量处理或上传。</p></header>'+player+'<section><table><tr><th>物体/完整视频</th><th>序列</th><th>相机帧数</th><th>检查状态</th></tr>'+''.join(rows)+'</table></section></html>',encoding='utf-8')
+    player=''
+    if reports:
+        options=''.join(f'<option value="{r["object"]}">{r["object"]} · {r["camera_frames"]}帧</option>' for r in reports)
+        player='<section><h2>固定相机6，切换物体</h2><label>物体 <select id="object-picker">'+options+'</select></label> · <a id="object-detail">该物体检查记录与原图对照</a><p id="object-result"></p><video id="object-video" controls preload="metadata" style="width:100%"></video></section>'
+        data={r['object']:{'video':r['object']+'/'+r['views'][0]['video']['video_ref'],'summary':r['agent_visual_review'].get('summary','数值通过；视觉检查待完成')} for r in reports}
+        player+='<script>const objects='+json.dumps(data,ensure_ascii=False)+';const picker=document.getElementById("object-picker"),video=document.getElementById("object-video");function choose(){const k=picker.value;video.pause();video.src=objects[k].video;document.getElementById("object-detail").href=k+"/index.html";document.getElementById("object-result").textContent=objects[k].summary;video.load();}picker.addEventListener("change",choose);choose();</script>'
+    (DOC/'index.html').write_text(f'<!doctype html><html lang="zh-CN"><meta charset="utf-8"><title>ARCTIC 11种物体完整序列验收</title><style>{STYLE}</style><header><h1>ARCTIC · 11种物体逐项验收</h1><p>新增组统一采用s06的固定相机6，11种物体各一段完整序列；相机内外参保持一致。<a href="../index.html">原s09 laptop九路相机预览</a>保留。按明显开合幅度选择较短完整序列，不裁切源帧。</p><p>已完成{len(reports)}/11种物体。<a href="selection.json">选择依据</a> · <a href="REPORT.md">验收结论</a> · <a href="report.json">总报告</a></p><p class="warn">自动数值通过≠图像对齐无误≠用户人工通过。未开始全量处理或上传。</p></header>'+player+'<section><table><tr><th>物体/完整视频</th><th>序列</th><th>相机帧数</th><th>检查状态</th></tr>'+''.join(rows)+'</table></section></html>',encoding='utf-8')
     save(DOC/'selection.json',selection);save(DOC/'report.json',{'scope':'11 whole sequences, fixed subject s06 and fixed static camera 6; original laptop 9-camera group retained separately','completed_objects':len(reports),'expected_objects':11,'objects':[{'object':r['object'],'sequence':r['sequence'],'camera_frames':r['camera_frames'],'maximum_geometry_error_m':r['maximum_geometry_error_m'],'agent_visual_review':r['agent_visual_review']} for r in reports],'human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False})
 
-def run():
+def run():
     selection=load(SELECTION);TMP.mkdir(parents=True,exist_ok=True);DOC.mkdir(parents=True,exist_ok=True)
     state=load(WORK/'tmp/dataset-work-state.json');state['active']['ARCTIC'].update(stage='pilot_all_objects_rendering',object_review_scope=selection['objects']);save(WORK/'tmp/dataset-work-state.json',state)
     setup('ARCTIC','eleven_object_review');overview(selection)
-    def launch(item):
-        seq,sid,oid,seqid,doc,tmp=context(item)
-        for cam in selection['cameras']:
-            log=tmp/f'worker-{cam}.log'
-            with log.open('ab') as f:
-                proc=subprocess.run([sys.executable,'-u',str(Path(__file__).resolve()),'worker','--object',oid,'--camera',cam],stdout=f,stderr=subprocess.STDOUT)
-            if proc.returncode:raise RuntimeError(f'{oid} camera {cam} failed, see {log}')
-        return item
-    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as pool:
-        futures=[]
-        for item in selection['objects']:
-            prepare(item)
-            if (context(item)[4]/'report.json').exists():print('RESUME_OBJECT',item['object'],flush=True);continue
-            futures.append(pool.submit(launch,item))
-        for future in concurrent.futures.as_completed(futures):
-            item=future.result();object_page(item);overview(selection)
-            save(TMP/'status.json',{'object':item['object'],'stage':'converting_rendering','completed_views_total':len(list(DOC.glob('*/checks/*-complete.json')))})
-    for name in ['arctic_object_review.py','convert_fresh_sequence_pilots.py','render_full_sequence_review.py','diagnose_arctic_review.py','fresh_sequence_common.py','review_fresh_sequence_pilots.py']:
-        shutil.copyfile(WORK/'scripts'/name,OUT/'script'/name)
-    save(TMP/'status.json',{'stage':'all_objects_rendered_pending_visual_inspection','completed_views_total':len(selection['objects'])*len(selection['cameras'])})
+    def launch(item):
+        seq,sid,oid,seqid,doc,tmp=context(item)
+        for cam in selection['cameras']:
+            log=tmp/f'worker-{cam}.log'
+            with log.open('ab') as f:
+                proc=subprocess.run([sys.executable,'-u',str(Path(__file__).resolve()),'worker','--object',oid,'--camera',cam],stdout=f,stderr=subprocess.STDOUT)
+            if proc.returncode:raise RuntimeError(f'{oid} camera {cam} failed, see {log}')
+        return item
+    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as pool:
+        futures=[]
+        for item in selection['objects']:
+            prepare(item)
+            if (context(item)[4]/'report.json').exists():print('RESUME_OBJECT',item['object'],flush=True);continue
+            futures.append(pool.submit(launch,item))
+        for future in concurrent.futures.as_completed(futures):
+            item=future.result();object_page(item);overview(selection)
+            save(TMP/'status.json',{'object':item['object'],'stage':'converting_rendering','completed_views_total':len(list(DOC.glob('*/checks/*-complete.json')))})
+    # Do not repopulate the organized script root while rendering review artifacts.
+    save(TMP/'status.json',{'stage':'all_objects_rendered_pending_visual_inspection','completed_views_total':len(selection['objects'])*len(selection['cameras'])})
     print('ALL_OBJECTS_RENDERED',flush=True)
 
 if __name__=='__main__':

ARCTIC/shared-v1.0/script/internal/finalize_arctic_objects_review.py

@@ -1,144 +1,142 @@
-"""Assemble evidence after the bounded eleven-object review; never authorizes production."""
-from arctic_object_review import *
-from html.parser import HTMLParser
-from urllib.parse import unquote, urlsplit
-
-NOTES=WORK/'tmp/arctic-object-visual-review.json'
-
-def asset_contacts():
-    selection=load(SELECTION);outputs=[]
-    for item in selection['objects']:
-        _,_,oid,seqid,doc,tmp=context(item)
-        video=doc/'assets'/f'{oid}.mp4'
-        if not video.exists():continue
-        asset=asset_preview(OUT/'data'/seqid/'6'/'annotation/object'/oid,video)
-        r=load(doc/'report.json');r['asset_preview']=asset;save(doc/'report.json',r)
-        target=tmp/'asset-angles.jpg'
-        command([FF,'-v','error','-y','-threads','2','-i',video,'-vf',
-            'select=eq(n\\,12)+eq(n\\,36)+eq(n\\,60),scale=480:-1,tile=3x1',
-            '-frames:v','1','-threads','2',target])
-        outputs.append((oid,target))
-    for group,start in enumerate(range(0,len(outputs),6),1):
-        chosen=outputs[start:start+6];images=[Image.open(p).convert('RGB') for _,p in chosen]
-        height=max(im.height for im in images)+32
-        sheet=Image.new('RGB',(1440,height*len(images)),(20,20,20));draw=ImageDraw.Draw(sheet)
-        for i,((oid,_),im) in enumerate(zip(chosen,images)):
-            draw.text((10,i*height+6),f'{oid}: turntable frames 12 / 36 / 60',fill='white')
-            sheet.paste(im,(0,i*height+32))
-        target=DOC/f'assets-contact-{group}.jpg';sheet.save(target,quality=94);print(target,flush=True)
-
-class Links(HTMLParser):
-    def __init__(self):super().__init__();self.links=[]
-    def handle_starttag(self,tag,attrs):
-        for k,v in attrs:
-            if k in ('href','src','poster') and v:self.links.append(v)
-
-def finalize():
-    selection=load(SELECTION);notes=json.loads(NOTES.read_text(encoding='utf-8-sig'))
-    assert set(notes)=={x['object'] for x in selection['objects']}
-    assert load(TMP/'status.json')['stage'] in ('all_objects_rendered_pending_visual_inspection','review_complete_pending_user')
-    reports=[];cameras=[];extra=[];old=load(DOC.parent/'report.json')
-    for item in selection['objects']:
-        seq,sid,oid,seqid,doc,tmp=context(item);r=load(doc/'report.json');p=OUT/'data'/seqid/'6'
-        assert r['reference']['ioi_offset']==1 and r['camera_frames']==r['reference']['annotation_frames']+1
-        assert notes[oid]['status']=='reviewed_with_findings' and notes[oid].get('asset_visual_review')
-        r['agent_visual_review']=notes[oid]
-        r['agent_visual_review']['inspected_frame_indices']=r['views'][0]['visual_sample_indices']
-        r['agent_visual_review']['entire_video_watched']=False
-        r['agent_visual_review']['sample_method']='beginning/end, uniform temporal samples, joint-angle extremes; exact indices recorded'
-        K=arr(p/'camera/intrinsic.pt');E=arr(p/'camera/extrinsic.pt')
-        assert np.array_equal(K,np.broadcast_to(K[0],K.shape)) and np.array_equal(E,np.broadcast_to(E[0],E.shape))
-        cameras.append((K[0],E[0]))
-        m=load(p/'metainfo.json');assert not m['components']['mano'].get('valid_policy',{}).get('local_excluded_frame_indices')
-        m['sampling']['selection']='Every released image of this selected camera in the complete official sequence ZIP; boundary images without annotations retained'
-        save(p/'metainfo.json',m)
-        view=r['views'][0];v=doc/view['video']['video_ref'];assert digest(v)==view['video']['video_sha256']
-        decoded=framehash(['-i',str(v)]);assert len(decoded)==r['camera_frames']
-        asset=doc/r['asset_preview']['video_ref'];assert len(framehash(['-i',str(asset)]))==72
-        texture_checks=[]
-        with zipfile.ZipFile(BASE/'raw/data/meta.zip') as z:
-            for f in (p/f'annotation/object/{oid}/assets').rglob('*'):
-                if f.suffix.lower() in ('.mtl','.jpg','.jpeg','.png'):
-                    source=z.read(f'meta/object_vtemplates/{oid}/'+f.name)
-                    assert f.read_bytes()==source
-                    texture_checks.append({'file':f.name,'sha256':digest(f),'raw_bytes_equal':True})
-        r['delivery_checks']={'preview_fully_decoded_frames':len(decoded),'preview_sha256':digest(v),
-            'asset_preview_frames':72,'asset_video_sha256':digest(asset),'texture_and_material_files':texture_checks}
-        save(doc/'report.json',r);write_object_html(r);reports.append(r)
-        extra.append({'object':oid,'preview_frames':len(decoded),'asset_frames':72,'texture_files_checked':len(texture_checks)})
-        print('FINAL_CHECK',oid,len(decoded),flush=True)
-    assert all(np.array_equal(k,cameras[0][0]) and np.array_equal(e,cameras[0][1]) for k,e in cameras)
-    oldviews=old['full_video_views'];assert len(oldviews)==9
-    for v in oldviews:assert digest(DOC.parent/v['video_ref'])==v['video_sha256']
-    # The same canonical laptop asset is shared by both review groups; fix its
-    # original report's texture preview too, without touching its nine frame videos.
-    laptop=next(r for r in reports if r['object']=='laptop')
-    shutil.copyfile(DOC/'laptop'/laptop['asset_preview']['video_ref'],DOC.parent/'assets/laptop.mp4')
-    old['asset_previews']=[laptop['asset_preview']]
-    overview(selection)
-    total=sum(r['camera_frames'] for r in reports);n=sum(len(r['views'][0]['visual_sample_indices']) for r in reports)
-    maximum=max(r['maximum_geometry_error_m'] for r in reports)
-    rows='\n'.join(f'| [{r["object"]}]({r["object"]}/index.html) | {r["sequence"]} | {r["camera_frames"]} | {r["maximum_geometry_error_m"]:.3g} | {r["agent_visual_review"]["summary"]} |' for r in reports)
-    text=f'''# ARCTIC 11种物体验收结果
-
-[固定相机切换物体](index.html) · [原laptop九视角](../index.html) · [方法](README.md)
-
-新增组：s06、静态相机6，11个完整序列，共{total}帧；内外参逐帧恒定且在11个物体间完全一致。原s09/laptop_use_03九视角视频摘要未变。
-
-**转换数值检查通过；源图像和标注质量仍有已知问题，用户确认待完成。** 不据此启动全量处理或上传。
-
-| 物体 | 完整序列 | 帧数 | 最大几何差/m | 视觉抽查结果 |
-|---|---|---:|---:|---|
-{rows}
-
-## 检查边界
-
-- 数值：全部有标注帧、全部物体顶点/关键点和双手MANO几何，对照raw及固定官方实现；最大差{maximum:.6g}m，容差2e-5m。交换部件、反转关节角、误用毫米的反例在11个物体上均被检出。
-- 媒体：全部原JPEG包和统一解码RGB逐帧一致；完整预览视频再次全部解码，无缺帧/解码错误。MP4仅用于doc展示，不替代原码流MKV。
-- 视觉：实际查看{n}个原图/投影对照时刻（每段的起止、均匀样本及开合极值），检查资产旋转预览的3个方位。完整视频已全部渲染；代理未逐帧人工观看全部视频。
-- 模态：MANO、物体姿态/铰接/32个模板关键点、内外参、同步索引、RGB、资产纹理、valid均有检查/展示。未提供的深度、像素分割、接触、源visibility没有伪造。
-- 开头暗帧与局部手指贴合误差如实记录；本轮没有修改按帧号的排除mask。末帧无源动捕标注是源范围边界，RGB保留、几何valid=false。
-- 数值忠实不证明原标注准确；本次检查11种物体各一段，不能推导其它所有序列质量通过。
-
-[资产预览抽查1](assets-contact-1.jpg) · [资产预览抽查2](assets-contact-2.jpg)
-'''
-    (DOC/'REPORT.md').write_text(text,encoding='utf-8')
-    summary=load(DOC/'report.json');summary.update(camera_parameter_equality='exact_across_all_11_views_and_all_frames',
-        total_frames=total,visually_inspected_frame_count=n,maximum_geometry_error_m=maximum,
-        original_nine_camera_videos_unchanged=True,delivery_checks=extra,known_issues=['Source dark opening frames','Residual hand-to-image fit differences','Source annotation absent on final image'],
-        interpretation='All-frame faithful conversion passed; sampled visual review has findings; source quality and human acceptance not asserted')
-    save(DOC/'report.json',summary)
-    # Add navigation to the original page without rebuilding or replacing its nine videos.
-    home=DOC.parent/'index.html';body=home.read_text(encoding='utf-8');marker='id="eleven-object-review"'
-    if marker not in body:
-        body=body.replace('</header>','<p id="eleven-object-review"><strong>新增：</strong><a href="objects/index.html">固定相机6，切换11种物体的完整序列预览</a> · <a href="objects/REPORT.md">逐物体验收结果</a></p></header>',1)
-        home.write_text(body,encoding='utf-8')
-    mapping=OUT/'doc/RAW_TO_SHARED.md';body=mapping.read_text(encoding='utf-8')
-    if '## 新增固定相机物体组' not in body:
-        body+='\n## 新增固定相机物体组\n\n在原s09/laptop_use_03九视角之外，新增s06相机6的11物体完整序列。字段规则相同，具体选择、来源、帧数和校验见[逐物体报告](validation/objects/REPORT.md)及[来源摘要](validation/objects/source-manifest.json)。本组ioi_offset=1，图像编号减1对应标注索引；末图超出标注范围，保留RGB且几何valid=false。本组前两帧暗图只记录，没有套用旧s09曝光排除。仍非全量验收。\n'
-        mapping.write_text(body,encoding='utf-8')
-    for name in ['arctic_object_review.py','finalize_arctic_objects_review.py','convert_fresh_sequence_pilots.py','render_full_sequence_review.py','diagnose_arctic_review.py','fresh_sequence_common.py','review_fresh_sequence_pilots.py']:
-        shutil.copyfile(WORK/'scripts'/name,OUT/'script'/name)
-    manifest=[{'path':p.relative_to(OUT).as_posix(),'sha256':digest(p)} for p in sorted((OUT/'script').glob('*.py'))]
-    save(DOC/'script-manifest.json',manifest)
-    old.setdefault('previous_script_manifest_before_object_review',old['script_manifest']);old['script_manifest']=manifest
-    old['additional_fixed_camera_group']={'report_ref':'objects/report.json','sequence_count':11,'camera_id':'6','subject_id':'s06','human_review':'pending'}
-    save(DOC.parent/'report.json',old)
-    missing=[]
-    for page in [home,*DOC.rglob('*.html')]:
-        parser=Links();parser.feed(page.read_text(encoding='utf-8'))
-        for url in parser.links:
-            parts=urlsplit(url)
-            if parts.scheme or not parts.path:continue
-            if not (page.parent/unquote(parts.path)).exists():missing.append([str(page),url])
-    assert not missing,missing
-    artifacts=[{'path':p.relative_to(DOC).as_posix(),'size':p.stat().st_size,'sha256':digest(p)} for p in sorted(DOC.rglob('*')) if p.is_file() and p.name!='artifact-manifest.json']
-    save(DOC/'artifact-manifest.json',{'files':artifacts,'offline_html_links':'passed','log_files_in_report':False})
-    status={'stage':'review_complete_pending_user','completed_views_total':11,'human_review':'pending','report':str(DOC/'index.html'),'full_preprocessing_authorized':False,'upload_authorized':False}
-    save(TMP/'status.json',status);save(DOC.parent/'status.json',status)
-    state=load(WORK/'tmp/dataset-work-state.json');state['active']['ARCTIC'].update(stage='pilot_two_group_review_awaiting_human_review',object_review_report=str(DOC/'index.html'),object_review_completed=11);save(WORK/'tmp/dataset-work-state.json',state)
-    print('FINALIZED',total,n,maximum,flush=True)
-
-if __name__=='__main__':
-    parser=argparse.ArgumentParser();parser.add_argument('mode',choices=['assets','finalize']);args=parser.parse_args()
-    asset_contacts() if args.mode=='assets' else finalize()
+"""Assemble evidence after the bounded eleven-object review; never authorizes production."""
+from arctic_object_review import *
+from html.parser import HTMLParser
+from urllib.parse import unquote, urlsplit
+
+NOTES=WORK/'tmp/arctic-object-visual-review.json'
+
+def asset_contacts():
+    selection=load(SELECTION);outputs=[]
+    for item in selection['objects']:
+        _,_,oid,seqid,doc,tmp=context(item)
+        video=doc/'assets'/f'{oid}.mp4'
+        if not video.exists():continue
+        asset=asset_preview(OUT/'data'/seqid/'6'/'annotation/object'/oid,video)
+        r=load(doc/'report.json');r['asset_preview']=asset;save(doc/'report.json',r)
+        target=tmp/'asset-angles.jpg'
+        command([FF,'-v','error','-y','-threads','2','-i',video,'-vf',
+            'select=eq(n\\,12)+eq(n\\,36)+eq(n\\,60),scale=480:-1,tile=3x1',
+            '-frames:v','1','-threads','2',target])
+        outputs.append((oid,target))
+    for group,start in enumerate(range(0,len(outputs),6),1):
+        chosen=outputs[start:start+6];images=[Image.open(p).convert('RGB') for _,p in chosen]
+        height=max(im.height for im in images)+32
+        sheet=Image.new('RGB',(1440,height*len(images)),(20,20,20));draw=ImageDraw.Draw(sheet)
+        for i,((oid,_),im) in enumerate(zip(chosen,images)):
+            draw.text((10,i*height+6),f'{oid}: turntable frames 12 / 36 / 60',fill='white')
+            sheet.paste(im,(0,i*height+32))
+        target=DOC/f'assets-contact-{group}.jpg';sheet.save(target,quality=94);print(target,flush=True)
+
+class Links(HTMLParser):
+    def __init__(self):super().__init__();self.links=[]
+    def handle_starttag(self,tag,attrs):
+        for k,v in attrs:
+            if k in ('href','src','poster') and v:self.links.append(v)
+
+def finalize():
+    selection=load(SELECTION);notes=json.loads(NOTES.read_text(encoding='utf-8-sig'))
+    assert set(notes)=={x['object'] for x in selection['objects']}
+    assert load(TMP/'status.json')['stage'] in ('all_objects_rendered_pending_visual_inspection','review_complete_pending_user')
+    reports=[];cameras=[];extra=[];old=load(DOC.parent/'report.json')
+    for item in selection['objects']:
+        seq,sid,oid,seqid,doc,tmp=context(item);r=load(doc/'report.json');p=OUT/'data'/seqid/'6'
+        assert r['reference']['ioi_offset']==1 and r['camera_frames']==r['reference']['annotation_frames']+1
+        assert notes[oid]['status']=='reviewed_with_findings' and notes[oid].get('asset_visual_review')
+        r['agent_visual_review']=notes[oid]
+        r['agent_visual_review']['inspected_frame_indices']=r['views'][0]['visual_sample_indices']
+        r['agent_visual_review']['entire_video_watched']=False
+        r['agent_visual_review']['sample_method']='beginning/end, uniform temporal samples, joint-angle extremes; exact indices recorded'
+        K=arr(p/'camera/intrinsic.pt');E=arr(p/'camera/extrinsic.pt')
+        assert np.array_equal(K,np.broadcast_to(K[0],K.shape)) and np.array_equal(E,np.broadcast_to(E[0],E.shape))
+        cameras.append((K[0],E[0]))
+        m=load(p/'metainfo.json');assert not m['components']['mano'].get('valid_policy',{}).get('local_excluded_frame_indices')
+        m['sampling']['selection']='Every released image of this selected camera in the complete official sequence ZIP; boundary images without annotations retained'
+        save(p/'metainfo.json',m)
+        view=r['views'][0];v=doc/view['video']['video_ref'];assert digest(v)==view['video']['video_sha256']
+        decoded=framehash(['-i',str(v)]);assert len(decoded)==r['camera_frames']
+        asset=doc/r['asset_preview']['video_ref'];assert len(framehash(['-i',str(asset)]))==72
+        texture_checks=[]
+        with zipfile.ZipFile(BASE/'raw/data/meta.zip') as z:
+            for f in (p/f'annotation/object/{oid}/assets').rglob('*'):
+                if f.suffix.lower() in ('.mtl','.jpg','.jpeg','.png'):
+                    source=z.read(f'meta/object_vtemplates/{oid}/'+f.name)
+                    assert f.read_bytes()==source
+                    texture_checks.append({'file':f.name,'sha256':digest(f),'raw_bytes_equal':True})
+        r['delivery_checks']={'preview_fully_decoded_frames':len(decoded),'preview_sha256':digest(v),
+            'asset_preview_frames':72,'asset_video_sha256':digest(asset),'texture_and_material_files':texture_checks}
+        save(doc/'report.json',r);write_object_html(r);reports.append(r)
+        extra.append({'object':oid,'preview_frames':len(decoded),'asset_frames':72,'texture_files_checked':len(texture_checks)})
+        print('FINAL_CHECK',oid,len(decoded),flush=True)
+    assert all(np.array_equal(k,cameras[0][0]) and np.array_equal(e,cameras[0][1]) for k,e in cameras)
+    oldviews=old['full_video_views'];assert len(oldviews)==9
+    for v in oldviews:assert digest(DOC.parent/v['video_ref'])==v['video_sha256']
+    # The same canonical laptop asset is shared by both review groups; fix its
+    # original report's texture preview too, without touching its nine frame videos.
+    laptop=next(r for r in reports if r['object']=='laptop')
+    shutil.copyfile(DOC/'laptop'/laptop['asset_preview']['video_ref'],DOC.parent/'assets/laptop.mp4')
+    old['asset_previews']=[laptop['asset_preview']]
+    overview(selection)
+    total=sum(r['camera_frames'] for r in reports);n=sum(len(r['views'][0]['visual_sample_indices']) for r in reports)
+    maximum=max(r['maximum_geometry_error_m'] for r in reports)
+    rows='\n'.join(f'| [{r["object"]}]({r["object"]}/index.html) | {r["sequence"]} | {r["camera_frames"]} | {r["maximum_geometry_error_m"]:.3g} | {r["agent_visual_review"]["summary"]} |' for r in reports)
+    text=f'''# ARCTIC 11种物体验收结果
+
+[固定相机切换物体](index.html) · [原laptop九视角](../index.html) · [方法](README.md)
+
+新增组：s06、静态相机6，11个完整序列，共{total}帧；内外参逐帧恒定且在11个物体间完全一致。原s09/laptop_use_03九视角视频摘要未变。
+
+**转换数值检查通过；源图像和标注质量仍有已知问题，用户确认待完成。** 不据此启动全量处理或上传。
+
+| 物体 | 完整序列 | 帧数 | 最大几何差/m | 视觉抽查结果 |
+|---|---|---:|---:|---|
+{rows}
+
+## 检查边界
+
+- 数值：全部有标注帧、全部物体顶点/关键点和双手MANO几何，对照raw及固定官方实现；最大差{maximum:.6g}m，容差2e-5m。交换部件、反转关节角、误用毫米的反例在11个物体上均被检出。
+- 媒体：全部原JPEG包和统一解码RGB逐帧一致；完整预览视频再次全部解码，无缺帧/解码错误。MP4仅用于doc展示，不替代原码流MKV。
+- 视觉：实际查看{n}个原图/投影对照时刻（每段的起止、均匀样本及开合极值），检查资产旋转预览的3个方位。完整视频已全部渲染；代理未逐帧人工观看全部视频。
+- 模态：MANO、物体姿态/铰接/32个模板关键点、内外参、同步索引、RGB、资产纹理、valid均有检查/展示。未提供的深度、像素分割、接触、源visibility没有伪造。
+- 开头暗帧与局部手指贴合误差如实记录；本轮没有修改按帧号的排除mask。末帧无源动捕标注是源范围边界，RGB保留、几何valid=false。
+- 数值忠实不证明原标注准确；本次检查11种物体各一段，不能推导其它所有序列质量通过。
+
+[资产预览抽查1](assets-contact-1.jpg) · [资产预览抽查2](assets-contact-2.jpg)
+'''
+    (DOC/'REPORT.md').write_text(text,encoding='utf-8')
+    summary=load(DOC/'report.json');summary.update(camera_parameter_equality='exact_across_all_11_views_and_all_frames',
+        total_frames=total,visually_inspected_frame_count=n,maximum_geometry_error_m=maximum,
+        original_nine_camera_videos_unchanged=True,delivery_checks=extra,known_issues=['Source dark opening frames','Residual hand-to-image fit differences','Source annotation absent on final image'],
+        interpretation='All-frame faithful conversion passed; sampled visual review has findings; source quality and human acceptance not asserted')
+    save(DOC/'report.json',summary)
+    # Add navigation to the original page without rebuilding or replacing its nine videos.
+    home=DOC.parent/'index.html';body=home.read_text(encoding='utf-8');marker='id="eleven-object-review"'
+    if marker not in body:
+        body=body.replace('</header>','<p id="eleven-object-review"><strong>新增：</strong><a href="objects/index.html">固定相机6，切换11种物体的完整序列预览</a> · <a href="objects/REPORT.md">逐物体验收结果</a></p></header>',1)
+        home.write_text(body,encoding='utf-8')
+    mapping=OUT/'doc/RAW_TO_SHARED.md';body=mapping.read_text(encoding='utf-8')
+    if '## 新增固定相机物体组' not in body:
+        body+='\n## 新增固定相机物体组\n\n在原s09/laptop_use_03九视角之外，新增s06相机6的11物体完整序列。字段规则相同，具体选择、来源、帧数和校验见[逐物体报告](validation/objects/REPORT.md)及[来源摘要](validation/objects/source-manifest.json)。本组ioi_offset=1，图像编号减1对应标注索引；末图超出标注范围，保留RGB且几何valid=false。本组前两帧暗图只记录，没有套用旧s09曝光排除。仍非全量验收。\n'
+        mapping.write_text(body,encoding='utf-8')
+    manifest=[{'path':p.relative_to(OUT).as_posix(),'sha256':digest(p)} for p in sorted((OUT/'script').rglob('*.py'))]
+    save(DOC/'script-manifest.json',manifest)
+    old.setdefault('previous_script_manifest_before_object_review',old['script_manifest']);old['script_manifest']=manifest
+    old['additional_fixed_camera_group']={'report_ref':'objects/report.json','sequence_count':11,'camera_id':'6','subject_id':'s06','human_review':'pending'}
+    save(DOC.parent/'report.json',old)
+    missing=[]
+    for page in [home,*DOC.rglob('*.html')]:
+        parser=Links();parser.feed(page.read_text(encoding='utf-8'))
+        for url in parser.links:
+            parts=urlsplit(url)
+            if parts.scheme or not parts.path:continue
+            if not (page.parent/unquote(parts.path)).exists():missing.append([str(page),url])
+    assert not missing,missing
+    artifacts=[{'path':p.relative_to(DOC).as_posix(),'size':p.stat().st_size,'sha256':digest(p)} for p in sorted(DOC.rglob('*')) if p.is_file() and p.name!='artifact-manifest.json']
+    save(DOC/'artifact-manifest.json',{'files':artifacts,'offline_html_links':'passed','log_files_in_report':False})
+    status={'stage':'review_complete_pending_user','completed_views_total':11,'human_review':'pending','report':str(DOC/'index.html'),'full_preprocessing_authorized':False,'upload_authorized':False}
+    save(TMP/'status.json',status);save(DOC.parent/'status.json',status)
+    state=load(WORK/'tmp/dataset-work-state.json');state['active']['ARCTIC'].update(stage='pilot_two_group_review_awaiting_human_review',object_review_report=str(DOC/'index.html'),object_review_completed=11);save(WORK/'tmp/dataset-work-state.json',state)
+    print('FINALIZED',total,n,maximum,flush=True)
+
+if __name__=='__main__':
+    parser=argparse.ArgumentParser();parser.add_argument('mode',choices=['assets','finalize']);args=parser.parse_args()
+    asset_contacts() if args.mode=='assets' else finalize()

ARCTIC/shared-v1.0/script/internal/fresh_sequence_common.py

@@ -1,212 +1,209 @@
-"""Helpers for the fresh ARCTIC/HOT3D whole-sequence v1.0 pilots.
-
-Original archives are read only. Temporary extraction is outside raw.
-Licensed MANO weights stay in the local model directory and are never published.
-"""
-from sequence_pilot_world import camera_mano, Mano
-from pilot_build import *
-from scipy.spatial.transform import Rotation
-import hashlib
-import re
-import time
-import trimesh
-from concurrent.futures import ThreadPoolExecutor,wait,FIRST_COMPLETED
-from contextlib import contextmanager
-
-DATA_ROOT = Path('E:/hand-datasets')
-RELEASE = 'shared-v1.0'
-RUN = 'fresh-sequence-pilot-20260920'
-VRS = WORK/'scripts/tools/vrs_env/Library/bin/vrs.exe'
-MEDIA_POOL=None
-MEDIA_PENDING=[]
-MEDIA_WORKERS=1
-MEDIA_POLICY='legacy_full_pixels'
-OUTPUT_OVERRIDE=None
-TMP_OVERRIDE=None
-
-@contextmanager
-def media_pipeline(workers=1,policy='jpeg-packets-all-rgb-sample16/v1'):
-    global MEDIA_POOL,MEDIA_PENDING,MEDIA_WORKERS,MEDIA_POLICY
-    assert MEDIA_POOL is None and not MEDIA_PENDING
-    MEDIA_WORKERS=workers;MEDIA_POLICY=policy
-    MEDIA_POOL=ThreadPoolExecutor(max_workers=workers)
-    try:
-        yield
-        for future in MEDIA_PENDING:future.result()
-    finally:
-        MEDIA_POOL.shutdown(wait=True,cancel_futures=True)
-        MEDIA_POOL=None;MEDIA_PENDING=[];MEDIA_POLICY='legacy_full_pixels'
-
-def submit_media(view,members_factory):
-    global MEDIA_PENDING
-    def execute():
-        jpeg_video(view,members_factory());view.m['pilot_complete']=True;view.finish()
-    if MEDIA_POOL is None:execute();return
-    # Bound pending views and source/video spools, not only running FFmpeg tasks.
-    while len(MEDIA_PENDING)>=MEDIA_WORKERS:
-        finished,_=wait(MEDIA_PENDING,return_when=FIRST_COMPLETED)
-        for future in finished:future.result()
-        MEDIA_PENDING=[f for f in MEDIA_PENDING if f not in finished]
-    MEDIA_PENDING.append(MEDIA_POOL.submit(execute))
-
-def digest(p):
-    h=hashlib.sha256()
-    with Path(p).open('rb') as f:
-        for b in iter(lambda:f.read(4*1024*1024),b''): h.update(b)
-    return h.hexdigest()
-
-def native_npy_bytes(data):
-    f=io.BytesIO(data);version=np.lib.format.read_magic(f)
-    shape,order,dtype=np.lib.format._read_array_header(f,version)
-    if dtype.hasobject:return Restricted(f,encoding='latin1').load().item()
-    return np.frombuffer(f.read(),dtype=dtype).reshape(shape,order='F' if order else 'C').copy()
-
-def command(args):
-    r=subprocess.run([str(x) for x in args],capture_output=True)
-    if r.returncode:raise RuntimeError(r.stderr.decode(errors='replace')[-3000:])
-    return r.stdout
-
-def framehash(args,copy=False):
-    output=command([FF,'-v','error','-threads','2','-noautorotate',*args,'-map','0:v:0',
-        *(['-c:v','copy'] if copy else ['-pix_fmt','rgb24','-threads','2']),
-        '-f','framehash','-hash','sha256','-fps_mode','passthrough','pipe:1']).decode()
-    return [(int(r.split(',')[-2]),r.split(',')[-1].strip()) for r in output.splitlines() if r and not r.startswith('#')]
-
-def jpeg_video(view, members):
-    """members is a fresh iterator of (locator, JPEG bytes). Verify every packet/frame."""
-    if MEDIA_POLICY!='legacy_full_pixels':
-        from accelerated_media import write_video
-        return write_video(view,members)
-    temporary=view.tmp/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.mjpeg'
-    temporary.parent.mkdir(parents=True,exist_ok=True)
-    hashes=[]
-    with temporary.open('wb') as out:
-        for locator,b in members:
-            assert b[:2]==b'\xff\xd8',locator
-            hashes.append((len(b),hashlib.sha256(b).hexdigest()));out.write(b)
-    assert len(hashes)==view.T
-    dst=view.p/'observation/rgb.mkv';dst.parent.mkdir(parents=True,exist_ok=True)
-    source=['-f','image2pipe','-framerate','1','-c:v','mjpeg','-i',str(temporary)]
-    command([FF,'-v','error','-y',*source,'-map','0:v:0','-c:v','copy',dst])
-    packet=framehash(['-i',str(dst)],True)
-    assert packet==hashes,'source JPEG packets changed in MKV'
-    before=framehash(source);after=framehash(['-i',str(dst)])
-    assert before==after and len(before)==view.T,'RGB decoding differs'
-    w,h=view.m['camera']['image_size'];assert all(n==w*h*3 for n,_ in after)
-    info=subprocess.run([FF,'-hide_banner','-i',str(dst)],capture_output=True).stderr.decode(errors='replace')
-    line=next(s.strip() for s in info.splitlines() if 'Video: mjpeg' in s)
-    pixel=re.search(r'Video: mjpeg[^,]*,\s*(\w+)',line).group(1)
-    version=command([FF,'-version']).decode().splitlines()[0]
-    view.component('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(view.T,bool),
-        source=view.source,method='Original JPEG packets copied from raw into MKV; no recompression or rotation',
-        storage={'profile':'mkv/mjpeg_copy/v1','codec':'mjpeg','pixel_format':pixel,'encoded_size':[w,h],
-          'source_lossy':True,'decoder':{'name':'ffmpeg','version':version,'output_pixel_format':'rgb24','autorotate':False,'source_color_declaration':'unknown','actual_stream_description':line},
-          'encoder_parameters':{'mode':'stream_copy','version':version}},storage_fps=1,timebase_role='index_only')
-    view.checks.append({'test':'all_source_JPEG_packets_and_RGB_pixels_exact','passed':True,'frames':view.T,
-        'packet_manifest_sha256':hashlib.sha256(json.dumps(hashes).encode()).hexdigest(),
-        'pixel_manifest_sha256':hashlib.sha256(json.dumps(after).encode()).hexdigest(),'output_bytes':dst.stat().st_size})
-    save(view.tmp/'media_checks'/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.json',{'packets':hashes,'pixels':after})
-    # This precise file was created by this call within the current run's temporary directory.
-    assert temporary.resolve().is_relative_to(view.tmp.resolve());temporary.unlink()
-
-class FreshView:
-    def __init__(self,dataset,seq,cam,ids,size,source):
-        self.root=(OUTPUT_OVERRIDE/dataset/RELEASE if OUTPUT_OVERRIDE else DATA_ROOT/dataset/RELEASE);self.p=self.root/'data'/seq/cam
-        self.tmp=(TMP_OVERRIDE/dataset/RUN if TMP_OVERRIDE else DATA_ROOT/dataset/'tmp'/RUN);self.p.mkdir(parents=True,exist_ok=True)
-        self.T=len(ids);self.source=source;self.checks=[]
-        self.m={'schema_version':'hand-warehouse/1.0','storage_revision':'lossless-video-2026-09-18',
-            'dataset_id':dataset,'release_id':RELEASE,'sequence_id':seq,'camera_id':cam,
-            'sample_type':'video','num_frames':self.T,'hand_slots':['left','right'],
-            'sampling':{'type':'dense','source_fps':None,'frames_ref':'camera/frames.jsonl','selection':'all released images in selected complete source sequence'},
-            'camera':{'model':'unknown','schema_id':None,'image_size':size},'split':'unknown','components':{},'pilot':True}
-        self.frames=[{'frame_index':i,'source_frame_id':str(fid),'timestamp_ns':None,'clock_domain':None,
-            'source_locator':dict(source,source_frame_id=str(fid))} for i,fid in enumerate(ids)]
-    def component(self,name,path,schema,axes,valid,source=None,method='source values retained',kind='format_conversion',**kw):
-        valid=np.asarray(valid,bool);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':kind,'method':method},
-            'source_locator':source or self.source,**kw}
-    def array(self,name,path,schema,data,valid,axes,**kw):
-        a=np.asarray(data,np.float32).copy();valid=np.asarray(valid,bool)
-        assert a.shape[0]==self.T and a.shape[:valid.ndim]==valid.shape
-        assert np.isfinite(a[valid]).all(),name
-        a[~valid]=np.nan;tensor(self.p/path,a)
-        self.component(name,path,schema,axes,valid,**kw)
-        restored=torch.load(self.p/path,map_location='cpu',weights_only=True).numpy()
-        assert np.array_equal(a,restored,equal_nan=True)
-        self.checks.append({'test':'tensor_exact_roundtrip','file':path,'shape':list(a.shape),'valid_count':int(valid.sum()),'passed':True})
-    def attribute(self,parent,path,data,axes,source,meaning):
-        data=np.asarray(data,np.float32);valid=np.isfinite(data)
-        assert ((data[valid]>=0)&(data[valid]<=1)).all()
-        vp=Path(path).with_name(Path(path).stem+'_valid.pt').as_posix();tensor(self.p/path,data);tensor(self.p/vp,valid)
-        self.m['components'][parent].setdefault('attributes',{})['visibility']={'data_ref':path,'valid_ref':vp,
-            'axes':axes,'valid_axes':axes,'dtype':'float32','visibility_type':'ratio','meaning':meaning,
-            'provenance':{'kind':'source','method':'native HOT3D visibility ratio retained in [0,1]; misleading [%] header is not a percentage scale'},'source_locator':source}
-    def mesh(self,oid,path,source):
-        self.component('object_'+oid+'_mesh',path,'mesh/obj/v1',['vertex','coordinate'],np.array(True),source=source,
-            method='Object vertices in metres; topology, material and texture dependencies retained',static=True,unit='m',coordinate_frame='object')
-    def finish(self):
-        lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
-        save(self.tmp/'checks'/f'{self.m["sequence_id"]}__{self.m["camera_id"]}.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 assert_se3(a):
-    a=np.asarray(a);assert a.shape[-2:]==(4,4)
-    # A component may exist while QA leaves no usable poses in this view.
-    if a.size==0:return
-    assert np.isfinite(a).all() and np.allclose(a[...,3,:],[0,0,0,1],atol=1e-6)
-    r=a[...,:3,:3];assert np.max(np.abs(r@np.swapaxes(r,-1,-2)-np.eye(3)))<2e-5
-    assert np.max(np.abs(np.linalg.det(r)-1))<2e-5
-
-def setup(dataset,seq):
-    base=DATA_ROOT/dataset;out=base/RELEASE
-    for folder in ['doc','script','doc/validation']: (out/folder).mkdir(parents=True,exist_ok=True)
-    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,out/'doc'/f)
-    save(out/'doc/validation/status.json',{'stage':'building_sequence_pilot','human_review':'pending','sequence_id':seq,'full_preprocessing_authorized':False,'upload_authorized':False})
-    sources=['fresh_sequence_common.py','convert_fresh_sequence_pilots.py','pilot_build.py','pilot_fetch.py','pilot_mano.py',
-        'sequence_pilot_common.py','sequence_pilot_world.py','pilot_hot_normalize.py','audit_source_fields.py']
-    for name in sources:
-        if (WORK/'scripts'/name).exists():shutil.copyfile(WORK/'scripts'/name,out/'script'/name)
-    (base/'tmp'/RUN).mkdir(parents=True,exist_ok=True)
-
-def object_textured_assets(z,prefix,target):
-    target.mkdir(parents=True,exist_ok=True)
-    for name in z.namelist():
-        if not name.startswith(prefix) or name.endswith('/'):continue
-        rel=Path(name[len(prefix):]);dest=target/rel
-        assert dest.resolve().is_relative_to(target.resolve());dest.parent.mkdir(parents=True,exist_ok=True)
-        if dest.suffix.lower() in ('.obj','.mtl','.jpg','.jpeg','.png'):
-            b=z.read(name)
-            if dest.suffix=='.obj':
-                rows=b.decode().splitlines()
-                for i,row in enumerate(rows):
-                    if row.startswith('v '):
-                        v=row.split();v[1:4]=[format(float(x)/1000,'.17g') for x in v[1:4]];rows[i]=' '.join(v)
-                b=('\n'.join(rows)+'\n').encode()
-            dest.write_bytes(b)
-    repair_arctic_material_refs(target)
-
-def repair_arctic_material_refs(target):
-    """Repair a source filename mismatch only when material identifiers agree."""
-    material=target/'material.mtl'
-    if not material.exists():return
-    ids={x.split(maxsplit=1)[1] for x in material.read_text().splitlines() if x.startswith('newmtl ')}
-    for obj in target.glob('*.obj'):
-        text=obj.read_text();rows=text.splitlines();used={x.split(maxsplit=1)[1] for x in rows if x.startswith('usemtl ')}
-        if 'mtllib material_0.mtl' in rows and not (target/'material_0.mtl').exists():
-            assert used and used.issubset(ids),(obj,used,ids)
-            obj.write_text(text.replace('mtllib material_0.mtl','mtllib material.mtl'))
-
-def project_points(xyz,camera,params):
-    xyz=np.asarray(xyz,float);uv=np.full((len(xyz),2),np.nan);good=np.isfinite(xyz).all(1)&(xyz[:,2]>1e-6)
-    if camera=='fisheye624':
-        p=np.asarray(params);xy=xyz[good,:2]/xyz[good,2,None];r=np.linalg.norm(xy,axis=1);th=np.arctan(r)
-        radial=th*(1+sum(p[3+i]*th**(2*i+2) for i in range(6)))
-        xy=xy*np.divide(radial,r,out=np.ones_like(r),where=r>1e-12)[:,None]
-        x,y=xy.T;rr=x*x+y*y
-        xd=x+(2*x*x+rr)*p[9]+2*x*y*p[10]+p[11]*rr+p[12]*rr*rr
-        yd=y+(2*y*y+rr)*p[10]+2*x*y*p[9]+p[13]*rr+p[14]*rr*rr
-        uv[good]=p[0]*np.stack([xd,yd],1)+p[1:3]
-    else:
-        K,D=params;uv[good]=cv2.projectPoints(xyz[good],np.zeros(3),np.zeros(3),K,D)[0].reshape(-1,2)
-    return uv
+"""Helpers for the fresh ARCTIC/HOT3D whole-sequence v1.0 pilots.
+
+Original archives are read only. Temporary extraction is outside raw.
+Licensed MANO weights stay in the local model directory and are never published.
+"""
+from sequence_pilot_world import camera_mano, Mano
+from pilot_build import *
+from scipy.spatial.transform import Rotation
+import hashlib
+import re
+import time
+import trimesh
+from concurrent.futures import ThreadPoolExecutor,wait,FIRST_COMPLETED
+from contextlib import contextmanager
+
+DATA_ROOT = Path('E:/hand-datasets')
+RELEASE = 'shared-v1.0'
+RUN = 'fresh-sequence-pilot-20260920'
+VRS = WORK/'scripts/tools/vrs_env/Library/bin/vrs.exe'
+MEDIA_POOL=None
+MEDIA_PENDING=[]
+MEDIA_WORKERS=1
+MEDIA_POLICY='legacy_full_pixels'
+OUTPUT_OVERRIDE=None
+TMP_OVERRIDE=None
+
+@contextmanager
+def media_pipeline(workers=1,policy='jpeg-packets-all-rgb-sample16/v1'):
+    global MEDIA_POOL,MEDIA_PENDING,MEDIA_WORKERS,MEDIA_POLICY
+    assert MEDIA_POOL is None and not MEDIA_PENDING
+    MEDIA_WORKERS=workers;MEDIA_POLICY=policy
+    MEDIA_POOL=ThreadPoolExecutor(max_workers=workers)
+    try:
+        yield
+        for future in MEDIA_PENDING:future.result()
+    finally:
+        MEDIA_POOL.shutdown(wait=True,cancel_futures=True)
+        MEDIA_POOL=None;MEDIA_PENDING=[];MEDIA_POLICY='legacy_full_pixels'
+
+def submit_media(view,members_factory):
+    global MEDIA_PENDING
+    def execute():
+        jpeg_video(view,members_factory());view.m['pilot_complete']=True;view.finish()
+    if MEDIA_POOL is None:execute();return
+    # Bound pending views and source/video spools, not only running FFmpeg tasks.
+    while len(MEDIA_PENDING)>=MEDIA_WORKERS:
+        finished,_=wait(MEDIA_PENDING,return_when=FIRST_COMPLETED)
+        for future in finished:future.result()
+        MEDIA_PENDING=[f for f in MEDIA_PENDING if f not in finished]
+    MEDIA_PENDING.append(MEDIA_POOL.submit(execute))
+
+def digest(p):
+    h=hashlib.sha256()
+    with Path(p).open('rb') as f:
+        for b in iter(lambda:f.read(4*1024*1024),b''): h.update(b)
+    return h.hexdigest()
+
+def native_npy_bytes(data):
+    f=io.BytesIO(data);version=np.lib.format.read_magic(f)
+    shape,order,dtype=np.lib.format._read_array_header(f,version)
+    if dtype.hasobject:return Restricted(f,encoding='latin1').load().item()
+    return np.frombuffer(f.read(),dtype=dtype).reshape(shape,order='F' if order else 'C').copy()
+
+def command(args):
+    r=subprocess.run([str(x) for x in args],capture_output=True)
+    if r.returncode:raise RuntimeError(r.stderr.decode(errors='replace')[-3000:])
+    return r.stdout
+
+def framehash(args,copy=False):
+    output=command([FF,'-v','error','-threads','2','-noautorotate',*args,'-map','0:v:0',
+        *(['-c:v','copy'] if copy else ['-pix_fmt','rgb24','-threads','2']),
+        '-f','framehash','-hash','sha256','-fps_mode','passthrough','pipe:1']).decode()
+    return [(int(r.split(',')[-2]),r.split(',')[-1].strip()) for r in output.splitlines() if r and not r.startswith('#')]
+
+def jpeg_video(view, members):
+    """members is a fresh iterator of (locator, JPEG bytes). Verify every packet/frame."""
+    if MEDIA_POLICY!='legacy_full_pixels':
+        from accelerated_media import write_video
+        return write_video(view,members)
+    temporary=view.tmp/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.mjpeg'
+    temporary.parent.mkdir(parents=True,exist_ok=True)
+    hashes=[]
+    with temporary.open('wb') as out:
+        for locator,b in members:
+            assert b[:2]==b'\xff\xd8',locator
+            hashes.append((len(b),hashlib.sha256(b).hexdigest()));out.write(b)
+    assert len(hashes)==view.T
+    dst=view.p/'observation/rgb.mkv';dst.parent.mkdir(parents=True,exist_ok=True)
+    source=['-f','image2pipe','-framerate','1','-c:v','mjpeg','-i',str(temporary)]
+    command([FF,'-v','error','-y',*source,'-map','0:v:0','-c:v','copy',dst])
+    packet=framehash(['-i',str(dst)],True)
+    assert packet==hashes,'source JPEG packets changed in MKV'
+    before=framehash(source);after=framehash(['-i',str(dst)])
+    assert before==after and len(before)==view.T,'RGB decoding differs'
+    w,h=view.m['camera']['image_size'];assert all(n==w*h*3 for n,_ in after)
+    info=subprocess.run([FF,'-hide_banner','-i',str(dst)],capture_output=True).stderr.decode(errors='replace')
+    line=next(s.strip() for s in info.splitlines() if 'Video: mjpeg' in s)
+    pixel=re.search(r'Video: mjpeg[^,]*,\s*(\w+)',line).group(1)
+    version=command([FF,'-version']).decode().splitlines()[0]
+    view.component('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(view.T,bool),
+        source=view.source,method='Original JPEG packets copied from raw into MKV; no recompression or rotation',
+        storage={'profile':'mkv/mjpeg_copy/v1','codec':'mjpeg','pixel_format':pixel,'encoded_size':[w,h],
+          'source_lossy':True,'decoder':{'name':'ffmpeg','version':version,'output_pixel_format':'rgb24','autorotate':False,'source_color_declaration':'unknown','actual_stream_description':line},
+          'encoder_parameters':{'mode':'stream_copy','version':version}},storage_fps=1,timebase_role='index_only')
+    view.checks.append({'test':'all_source_JPEG_packets_and_RGB_pixels_exact','passed':True,'frames':view.T,
+        'packet_manifest_sha256':hashlib.sha256(json.dumps(hashes).encode()).hexdigest(),
+        'pixel_manifest_sha256':hashlib.sha256(json.dumps(after).encode()).hexdigest(),'output_bytes':dst.stat().st_size})
+    save(view.tmp/'media_checks'/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.json',{'packets':hashes,'pixels':after})
+    # This precise file was created by this call within the current run's temporary directory.
+    assert temporary.resolve().is_relative_to(view.tmp.resolve());temporary.unlink()
+
+class FreshView:
+    def __init__(self,dataset,seq,cam,ids,size,source):
+        self.root=(OUTPUT_OVERRIDE/dataset/RELEASE if OUTPUT_OVERRIDE else DATA_ROOT/dataset/RELEASE);self.p=self.root/'data'/seq/cam
+        self.tmp=(TMP_OVERRIDE/dataset/RUN if TMP_OVERRIDE else DATA_ROOT/dataset/'tmp'/RUN);self.p.mkdir(parents=True,exist_ok=True)
+        self.T=len(ids);self.source=source;self.checks=[]
+        self.m={'schema_version':'hand-warehouse/1.0','storage_revision':'lossless-video-2026-09-18',
+            'dataset_id':dataset,'release_id':RELEASE,'sequence_id':seq,'camera_id':cam,
+            'sample_type':'video','num_frames':self.T,'hand_slots':['left','right'],
+            'sampling':{'type':'dense','source_fps':None,'frames_ref':'camera/frames.jsonl','selection':'all released images in selected complete source sequence'},
+            'camera':{'model':'unknown','schema_id':None,'image_size':size},'split':'unknown','components':{},'pilot':True}
+        self.frames=[{'frame_index':i,'source_frame_id':str(fid),'timestamp_ns':None,'clock_domain':None,
+            'source_locator':dict(source,source_frame_id=str(fid))} for i,fid in enumerate(ids)]
+    def component(self,name,path,schema,axes,valid,source=None,method='source values retained',kind='format_conversion',**kw):
+        valid=np.asarray(valid,bool);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':kind,'method':method},
+            'source_locator':source or self.source,**kw}
+    def array(self,name,path,schema,data,valid,axes,**kw):
+        a=np.asarray(data,np.float32).copy();valid=np.asarray(valid,bool)
+        assert a.shape[0]==self.T and a.shape[:valid.ndim]==valid.shape
+        assert np.isfinite(a[valid]).all(),name
+        a[~valid]=np.nan;tensor(self.p/path,a)
+        self.component(name,path,schema,axes,valid,**kw)
+        restored=torch.load(self.p/path,map_location='cpu',weights_only=True).numpy()
+        assert np.array_equal(a,restored,equal_nan=True)
+        self.checks.append({'test':'tensor_exact_roundtrip','file':path,'shape':list(a.shape),'valid_count':int(valid.sum()),'passed':True})
+    def attribute(self,parent,path,data,axes,source,meaning):
+        data=np.asarray(data,np.float32);valid=np.isfinite(data)
+        assert ((data[valid]>=0)&(data[valid]<=1)).all()
+        vp=Path(path).with_name(Path(path).stem+'_valid.pt').as_posix();tensor(self.p/path,data);tensor(self.p/vp,valid)
+        self.m['components'][parent].setdefault('attributes',{})['visibility']={'data_ref':path,'valid_ref':vp,
+            'axes':axes,'valid_axes':axes,'dtype':'float32','visibility_type':'ratio','meaning':meaning,
+            'provenance':{'kind':'source','method':'native HOT3D visibility ratio retained in [0,1]; misleading [%] header is not a percentage scale'},'source_locator':source}
+    def mesh(self,oid,path,source):
+        self.component('object_'+oid+'_mesh',path,'mesh/obj/v1',['vertex','coordinate'],np.array(True),source=source,
+            method='Object vertices in metres; topology, material and texture dependencies retained',static=True,unit='m',coordinate_frame='object')
+    def finish(self):
+        lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
+        save(self.tmp/'checks'/f'{self.m["sequence_id"]}__{self.m["camera_id"]}.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 assert_se3(a):
+    a=np.asarray(a);assert a.shape[-2:]==(4,4)
+    # A component may exist while QA leaves no usable poses in this view.
+    if a.size==0:return
+    assert np.isfinite(a).all() and np.allclose(a[...,3,:],[0,0,0,1],atol=1e-6)
+    r=a[...,:3,:3];assert np.max(np.abs(r@np.swapaxes(r,-1,-2)-np.eye(3)))<2e-5
+    assert np.max(np.abs(np.linalg.det(r)-1))<2e-5
+
+def setup(dataset,seq):
+    base=DATA_ROOT/dataset;out=base/RELEASE
+    for folder in ['doc','script','doc/validation']: (out/folder).mkdir(parents=True,exist_ok=True)
+    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,out/'doc'/f)
+    save(out/'doc/validation/status.json',{'stage':'building_sequence_pilot','human_review':'pending','sequence_id':seq,'full_preprocessing_authorized':False,'upload_authorized':False})
+    # The organized script bundle is already self-contained; runtime work must not repopulate its root.
+    (base/'tmp'/RUN).mkdir(parents=True,exist_ok=True)
+
+def object_textured_assets(z,prefix,target):
+    target.mkdir(parents=True,exist_ok=True)
+    for name in z.namelist():
+        if not name.startswith(prefix) or name.endswith('/'):continue
+        rel=Path(name[len(prefix):]);dest=target/rel
+        assert dest.resolve().is_relative_to(target.resolve());dest.parent.mkdir(parents=True,exist_ok=True)
+        if dest.suffix.lower() in ('.obj','.mtl','.jpg','.jpeg','.png'):
+            b=z.read(name)
+            if dest.suffix=='.obj':
+                rows=b.decode().splitlines()
+                for i,row in enumerate(rows):
+                    if row.startswith('v '):
+                        v=row.split();v[1:4]=[format(float(x)/1000,'.17g') for x in v[1:4]];rows[i]=' '.join(v)
+                b=('\n'.join(rows)+'\n').encode()
+            dest.write_bytes(b)
+    repair_arctic_material_refs(target)
+
+def repair_arctic_material_refs(target):
+    """Repair a source filename mismatch only when material identifiers agree."""
+    material=target/'material.mtl'
+    if not material.exists():return
+    ids={x.split(maxsplit=1)[1] for x in material.read_text().splitlines() if x.startswith('newmtl ')}
+    for obj in target.glob('*.obj'):
+        text=obj.read_text();rows=text.splitlines();used={x.split(maxsplit=1)[1] for x in rows if x.startswith('usemtl ')}
+        if 'mtllib material_0.mtl' in rows and not (target/'material_0.mtl').exists():
+            assert used and used.issubset(ids),(obj,used,ids)
+            obj.write_text(text.replace('mtllib material_0.mtl','mtllib material.mtl'))
+
+def project_points(xyz,camera,params):
+    xyz=np.asarray(xyz,float);uv=np.full((len(xyz),2),np.nan);good=np.isfinite(xyz).all(1)&(xyz[:,2]>1e-6)
+    if camera=='fisheye624':
+        p=np.asarray(params);xy=xyz[good,:2]/xyz[good,2,None];r=np.linalg.norm(xy,axis=1);th=np.arctan(r)
+        radial=th*(1+sum(p[3+i]*th**(2*i+2) for i in range(6)))
+        xy=xy*np.divide(radial,r,out=np.ones_like(r),where=r>1e-12)[:,None]
+        x,y=xy.T;rr=x*x+y*y
+        xd=x+(2*x*x+rr)*p[9]+2*x*y*p[10]+p[11]*rr+p[12]*rr*rr
+        yd=y+(2*y*y+rr)*p[10]+2*x*y*p[9]+p[13]*rr+p[14]*rr*rr
+        uv[good]=p[0]*np.stack([xd,yd],1)+p[1:3]
+    else:
+        K,D=params;uv[good]=cv2.projectPoints(xyz[good],np.zeros(3),np.zeros(3),K,D)[0].reshape(-1,2)
+    return uv

ARCTIC/shared-v1.0/script/internal/pilot_build.py

@@ -1,209 +1,207 @@
-"""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('<u2')
-  a=np.ascontiguousarray(a);p.parent.mkdir(parents=True,exist_ok=True)
-  command=[FF,'-hide_banner','-loglevel','error','-y','-f','rawvideo','-pixel_format',pixel,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-an','-c:v','ffv1','-level','3','-g','1',str(p)]
-  subprocess.run(command,input=a.tobytes(),check=True,capture_output=True)
-  decoded=subprocess.run([FF,'-v','error','-i',str(p),'-f','rawvideo','-pix_fmt',pixel,'pipe:1'],check=True,capture_output=True).stdout
-  assert decoded==a.tobytes(),f'Lossless roundtrip failed: {p}'
-  self.checks.append({'file':str(p.relative_to(self.out)),'test':'decode_all_frames_pixel_exact','passed':True,'frames':len(a),'dtype':str(a.dtype),'shape':list(a.shape)})
-  return {'codec':'FFV1','pixel_format':pixel,'container_fps':1,'container_time_is_capture_time':False}
- def finish(self,notes):
-  for p,m in self.views:save(p/'metainfo.json',m)
-  save(self.base/'raw/doc/local_subset_manifest.json',self.refs)
-  report={'dataset':self.name,'views':len(self.views),'observations':sum(m['num_frames'] for _,m in self.views),'checks':self.checks,'notes':notes,'status':'partial' if notes else 'passed'}
-  save(self.out/'doc/report.json',report)
-  for script in ['pilot_build.py','pilot_fetch.py']:
-   shutil.copyfile(WORK/'scripts'/script,self.out/'script'/script)
-  (self.out/'script/README.md').write_text('执行入口：工作区 scripts/pilot_build.py --dataset '+self.name+'。此目录为脚本快照���脚本当前依赖工作区的 datasets 与 tmp/pilot-runtime，尚不是独立发布包。\n',encoding='utf-8')
-  files=[{'path':str(p.relative_to(self.out)), 'bytes':p.stat().st_size,'sha256':sha(p)} for p in sorted((self.out/'data').rglob('*')) if p.is_file()]
-  save(self.out/'doc/output_manifest.json',files)
-  print(json.dumps(report,ensure_ascii=False),flush=True)
-  return report
-
-def h2o():
- b=Build('H2O');src=OLD/'H2O/subject4_ego/h1/0/cam4';ids=list(range(N))
- p,m=b.view('subject4_h1_0','cam4',ids,split='test',view_type='ego',time_semantics='ordered_source_frames; acquisition FPS not yet verified')
- for folder in ['rgb','depth','hand_pose','hand_pose_mano','cam_pose']:
-  for i in ids:b.copy(src/folder/f'{i:06d}.{ "png" if folder in ["rgb","depth"] else "txt"}')
- intr=np.loadtxt(b.copy(src/'cam_intrinsics.txt'));fx,fy,cx,cy,w,h=intr
- tensor(p/'camera/intrinsic.pt',np.array([[fx,0,cx],[0,fy,cy],[0,0,1]],np.float32))
- ext=np.stack([np.loadtxt(src/'cam_pose'/f'{i:06d}.txt').reshape(4,4) for i in ids])
- tensor(p/'camera/extrinsic.pt',np.linalg.inv(ext).astype(np.float32))
- m['camera']={'model':'pinhole','width':int(w),'height':int(h),'extrinsic':'T_camera_from_world','source_extrinsic':'T_world_from_camera','unit':'m','distortion':'not supplied in selected intrinsics'}
- a=np.stack([np.loadtxt(src/'hand_pose_mano'/f'{i:06d}.txt').reshape(2,62) for i in ids])
- valid=a[:,:,0]==1;motion=np.concatenate([a[:,:,4:52],a[:,:,52:62],a[:,:,1:4]],axis=-1).astype(np.float32)
- motion[~valid]=np.nan;tensor(p/'annotation/mano/motion.pt',motion);tensor(p/'annotation/mano/valid_flag.pt',valid)
- m['mano']={'layout':['pose_axis_angle:48','betas:10','translation:3'],'shape':[N,2,61],'slots':['left','right'],'coordinate_frame':'camera','translation_unit':'m','flat_hand_mean':True,'model_version':'not supplied; MANO model not bundled','valid_flag':'source annotation flag, not visibility','geometry_validation':'not_tested; model unavailable'}
- joints=np.stack([np.loadtxt(src/'hand_pose'/f'{i:06d}.txt').reshape(2,64) for i in ids]);tensor(p/'annotation/keypoints/xyz.pt',joints[:,:,1:].reshape(N,2,21,3).astype(np.float32));tensor(p/'annotation/keypoints/valid_flag.pt',joints[:,:,0]==1)
- rgb=[np.asarray(Image.open(src/'rgb'/f'{i:06d}.png').convert('RGB')) for i in ids]
- depth=[np.asarray(Image.open(src/'depth'/f'{i:06d}.png'),dtype=np.uint16) for i in ids]
- m['rgb']=b.video(p/'observation/rgb.mkv',rgb);m['depth']={**b.video(p/'observation/depth.mkv',depth),'unit':'mm','scale_to_meters':0.001,'invalid_value':0,'rgb_registration':'not_tested'}
- m['missing_modalities']={'object':'not present in selected historical subset','segmentation':'no native mask in selected subset'}
- # Independent check: 3D keypoint projections must be finite where the source flags valid.
- xyz=joints[:,:,1:].reshape(N,2,21,3);projection=xyz@np.array([[fx,0,cx],[0,fy,cy],[0,0,1]]).T
- uv=projection[...,:2]/projection[...,2:];assert np.isfinite(uv[joints[:,:,0]==1]).all()
- image=Image.fromarray(rgb[0]);draw=ImageDraw.Draw(image)
- for side in range(2):
-  for u,v in uv[0,side]:draw.ellipse((u-3,v-3,u+3,v+3),fill=('red' if side==0 else 'cyan'))
- image.save(b.out/'doc/keypoints_overlay.png')
- return b.finish(['MANO geometry not rendered; model asset unavailable. RGB-depth registration not tested. No native segmentation or object input in subset.'])
-
-def visor():
- b=Build('VISOR');raw=b.base/'raw/data/GroundTruth-SparseAnnotations';jp=raw/'annotations/train/P01_03.json';zp=raw/'rgb_frames/train/P01/P01_03.zip'
- source=load(jp);annotations=source['video_annotations'][:N]
- ids=[x['image']['name'] for x in annotations];p,m=b.view('P01_03','ego',ids,split='train',view_type='ego',time_semantics='sparse_annotations; use source frame IDs; MKV timestamps are storage indices')
- rgbs=[];masks=[];records=[]
- with zipfile.ZipFile(zp) as z:
-  assert z.testzip() is None
-  for i,frame in enumerate(annotations):
-   rgb=np.asarray(Image.open(io.BytesIO(z.read(frame['image']['name']))).convert('RGB'));rgbs.append(rgb)
-   mask=np.zeros((1080,1920),np.uint16);entities=[]
-   for value,entity in enumerate(frame['annotations'],1):
-    # Same polygon rasterization primitive and integer conversion as official VISOR-VIS.
-    polygons=[np.asarray(poly,dtype=np.int32) for poly in entity['segments'] if len(poly)>=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()
+"""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('<u2')
+  a=np.ascontiguousarray(a);p.parent.mkdir(parents=True,exist_ok=True)
+  command=[FF,'-hide_banner','-loglevel','error','-y','-f','rawvideo','-pixel_format',pixel,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-an','-c:v','ffv1','-level','3','-g','1',str(p)]
+  subprocess.run(command,input=a.tobytes(),check=True,capture_output=True)
+  decoded=subprocess.run([FF,'-v','error','-i',str(p),'-f','rawvideo','-pix_fmt',pixel,'pipe:1'],check=True,capture_output=True).stdout
+  assert decoded==a.tobytes(),f'Lossless roundtrip failed: {p}'
+  self.checks.append({'file':str(p.relative_to(self.out)),'test':'decode_all_frames_pixel_exact','passed':True,'frames':len(a),'dtype':str(a.dtype),'shape':list(a.shape)})
+  return {'codec':'FFV1','pixel_format':pixel,'container_fps':1,'container_time_is_capture_time':False}
+ def finish(self,notes):
+  for p,m in self.views:save(p/'metainfo.json',m)
+  save(self.base/'raw/doc/local_subset_manifest.json',self.refs)
+  report={'dataset':self.name,'views':len(self.views),'observations':sum(m['num_frames'] for _,m in self.views),'checks':self.checks,'notes':notes,'status':'partial' if notes else 'passed'}
+  save(self.out/'doc/report.json',report)
+  # Script provenance is supplied by the organized bundle and validation manifests.
+  files=[{'path':str(p.relative_to(self.out)), 'bytes':p.stat().st_size,'sha256':sha(p)} for p in sorted((self.out/'data').rglob('*')) if p.is_file()]
+  save(self.out/'doc/output_manifest.json',files)
+  print(json.dumps(report,ensure_ascii=False),flush=True)
+  return report
+
+def h2o():
+ b=Build('H2O');src=OLD/'H2O/subject4_ego/h1/0/cam4';ids=list(range(N))
+ p,m=b.view('subject4_h1_0','cam4',ids,split='test',view_type='ego',time_semantics='ordered_source_frames; acquisition FPS not yet verified')
+ for folder in ['rgb','depth','hand_pose','hand_pose_mano','cam_pose']:
+  for i in ids:b.copy(src/folder/f'{i:06d}.{ "png" if folder in ["rgb","depth"] else "txt"}')
+ intr=np.loadtxt(b.copy(src/'cam_intrinsics.txt'));fx,fy,cx,cy,w,h=intr
+ tensor(p/'camera/intrinsic.pt',np.array([[fx,0,cx],[0,fy,cy],[0,0,1]],np.float32))
+ ext=np.stack([np.loadtxt(src/'cam_pose'/f'{i:06d}.txt').reshape(4,4) for i in ids])
+ tensor(p/'camera/extrinsic.pt',np.linalg.inv(ext).astype(np.float32))
+ m['camera']={'model':'pinhole','width':int(w),'height':int(h),'extrinsic':'T_camera_from_world','source_extrinsic':'T_world_from_camera','unit':'m','distortion':'not supplied in selected intrinsics'}
+ a=np.stack([np.loadtxt(src/'hand_pose_mano'/f'{i:06d}.txt').reshape(2,62) for i in ids])
+ valid=a[:,:,0]==1;motion=np.concatenate([a[:,:,4:52],a[:,:,52:62],a[:,:,1:4]],axis=-1).astype(np.float32)
+ motion[~valid]=np.nan;tensor(p/'annotation/mano/motion.pt',motion);tensor(p/'annotation/mano/valid_flag.pt',valid)
+ m['mano']={'layout':['pose_axis_angle:48','betas:10','translation:3'],'shape':[N,2,61],'slots':['left','right'],'coordinate_frame':'camera','translation_unit':'m','flat_hand_mean':True,'model_version':'not supplied; MANO model not bundled','valid_flag':'source annotation flag, not visibility','geometry_validation':'not_tested; model unavailable'}
+ joints=np.stack([np.loadtxt(src/'hand_pose'/f'{i:06d}.txt').reshape(2,64) for i in ids]);tensor(p/'annotation/keypoints/xyz.pt',joints[:,:,1:].reshape(N,2,21,3).astype(np.float32));tensor(p/'annotation/keypoints/valid_flag.pt',joints[:,:,0]==1)
+ rgb=[np.asarray(Image.open(src/'rgb'/f'{i:06d}.png').convert('RGB')) for i in ids]
+ depth=[np.asarray(Image.open(src/'depth'/f'{i:06d}.png'),dtype=np.uint16) for i in ids]
+ m['rgb']=b.video(p/'observation/rgb.mkv',rgb);m['depth']={**b.video(p/'observation/depth.mkv',depth),'unit':'mm','scale_to_meters':0.001,'invalid_value':0,'rgb_registration':'not_tested'}
+ m['missing_modalities']={'object':'not present in selected historical subset','segmentation':'no native mask in selected subset'}
+ # Independent check: 3D keypoint projections must be finite where the source flags valid.
+ xyz=joints[:,:,1:].reshape(N,2,21,3);projection=xyz@np.array([[fx,0,cx],[0,fy,cy],[0,0,1]]).T
+ uv=projection[...,:2]/projection[...,2:];assert np.isfinite(uv[joints[:,:,0]==1]).all()
+ image=Image.fromarray(rgb[0]);draw=ImageDraw.Draw(image)
+ for side in range(2):
+  for u,v in uv[0,side]:draw.ellipse((u-3,v-3,u+3,v+3),fill=('red' if side==0 else 'cyan'))
+ image.save(b.out/'doc/keypoints_overlay.png')
+ return b.finish(['MANO geometry not rendered; model asset unavailable. RGB-depth registration not tested. No native segmentation or object input in subset.'])
+
+def visor():
+ b=Build('VISOR');raw=b.base/'raw/data/GroundTruth-SparseAnnotations';jp=raw/'annotations/train/P01_03.json';zp=raw/'rgb_frames/train/P01/P01_03.zip'
+ source=load(jp);annotations=source['video_annotations'][:N]
+ ids=[x['image']['name'] for x in annotations];p,m=b.view('P01_03','ego',ids,split='train',view_type='ego',time_semantics='sparse_annotations; use source frame IDs; MKV timestamps are storage indices')
+ rgbs=[];masks=[];records=[]
+ with zipfile.ZipFile(zp) as z:
+  assert z.testzip() is None
+  for i,frame in enumerate(annotations):
+   rgb=np.asarray(Image.open(io.BytesIO(z.read(frame['image']['name']))).convert('RGB'));rgbs.append(rgb)
+   mask=np.zeros((1080,1920),np.uint16);entities=[]
+   for value,entity in enumerate(frame['annotations'],1):
+    # Same polygon rasterization primitive and integer conversion as official VISOR-VIS.
+    polygons=[np.asarray(poly,dtype=np.int32) for poly in entity['segments'] if len(poly)>=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()

ARCTIC/shared-v1.0/script/internal/preprocess_fresh_full.py

@@ -28,10 +28,10 @@ def atomic(p,value):
 
 def temporary(ds):return DATA_ROOT/ds/'tmp'/JOB
 
-PRODUCTION_SCRIPTS=['preprocess_fresh_full.py','accelerated_media.py','convert_fresh_sequence_pilots.py','apply_hot3d_qa.py','review_fresh_sequence_pilots.py','render_full_sequence_review.py','fresh_sequence_common.py','diagnose_arctic_review.py','pilot_build.py','pilot_fetch.py','pilot_mano.py','sequence_pilot_common.py','sequence_pilot_world.py','pilot_hot_normalize.py','audit_source_fields.py']
+PRODUCTION_SCRIPTS=sorted(p.name for p in SCRIPT.parent.glob('*.py'))
 
 def code_manifest():
-    return [{'path':'script/'+f,'sha256':digest(WORK/'scripts'/f)} for f in PRODUCTION_SCRIPTS]
+    return [{'path':'script/internal/'+f,'sha256':digest(SCRIPT.parent/f)} for f in PRODUCTION_SCRIPTS]
 
 def verify_completed(ds,row,source):
     root=DATA_ROOT/ds/RELEASE;record=temporary(ds)/'completed'/f'{row["sequence_id"]}.json';done=load(record)
@@ -75,9 +75,11 @@ def migrate_completed(ds):
 
 def snapshot_scripts(ds):
     root=DATA_ROOT/ds/RELEASE
-    for f in PRODUCTION_SCRIPTS:shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
-    shutil.copyfile(CONTROL/'acceleration-config.json',root/'script/acceleration-config.json')
-    atomic(root/'doc/validation/full/script-manifest.json',{'created_at':now(),'scope':'Current production converter and validation scripts; earlier pilot manifests describe their historical run','files':code_manifest()})
+    target=root/'script/internal';target.mkdir(parents=True,exist_ok=True)
+    for f in PRODUCTION_SCRIPTS:
+        src=SCRIPT.parent/f;dst=target/f
+        if src.resolve()!=dst.resolve():shutil.copyfile(src,dst)
+    atomic(root/'doc/validation/full/script-manifest.json',{'created_at':now(),'scope':'Current organized production implementation under script/internal; earlier manifests retain historical paths and hashes','files':code_manifest()})
 
 def inputs(ds):
     base=DATA_ROOT/ds
@@ -178,8 +180,7 @@ def approve():
         docprod=doc/'full';docprod.mkdir(parents=True,exist_ok=True)
         atomic(docprod/'plan.json',p['datasets'][ds])
         (docprod/'README.md').write_text('# 全量预处理\n\n试点已获用户确认。逐序列转换、逐帧码流/像素和几何校验，完成后另建随机抽查报告。原始包保留，不自动上传。\n\n每个序列的正式自动检查在本目录sequences/；运行日志和进度只在数据集tmp。断点以输入官方摘要及全部输出文件摘要核验。\n\nHOT3D测试集未发布手物姿态真值：保留RGB、相机和物体资产，姿态及可见性缺省。ARCTIC仅保留已确认s09/laptop的曝光排除，其它序列不新增帧号屏蔽。\n',encoding='utf-8')
-        for f in ['preprocess_fresh_full.py','convert_fresh_sequence_pilots.py','apply_hot3d_qa.py','review_fresh_sequence_pilots.py','render_full_sequence_review.py','fresh_sequence_common.py','diagnose_arctic_review.py']:
-            shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
+        snapshot_scripts(ds)
     atomic(WORK/'tmp/dataset-work-state.json',state)
 
 def official_arctic_check(row,views):

ARCTIC/shared-v1.0/script/internal/render_full_sequence_review.py

@@ -1,256 +1,256 @@
-"""Full-frame, all-camera diagnostic videos read from actual shared tensors/media.
-
-Preview MP4 is explicitly display-only; warehouse MKV packets remain untouched.
-"""
-from fresh_sequence_common import *
-from review_fresh_sequence_pilots import arr, validate_view, project_for_view
-from datetime import datetime
-import html
-
-VIDEO_RUN='full-video-review-v2'
-CELL=512
-WIDTH=3*CELL
-HEIGHT=800
-COLORS=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
-
-def text(im,txt,xy,color=(235,235,235),size=.5):cv2.putText(im,txt,xy,cv2.FONT_HERSHEY_SIMPLEX,size,color,1,cv2.LINE_AA)
-
-def fast_edges(im,uv,edges,color):
-    xy=uv[edges];good=np.isfinite(xy).all((1,2)) & (np.max(np.nan_to_num(np.abs(xy),nan=CELL*20),axis=(1,2))<CELL*12)
-    xy=xy[good];xy=xy[np.linalg.norm(xy[:,0]-xy[:,1],axis=1)<CELL*.7]
-    if len(xy):cv2.polylines(im,np.rint(xy).astype(np.int32),False,color,1,cv2.LINE_AA)
-
-def dots(im,uv,color,radius=2):
-    good=np.isfinite(uv).all(1)&(uv[:,0]>=0)&(uv[:,0]<im.shape[1])&(uv[:,1]>=0)&(uv[:,1]<im.shape[0])
-    xy=np.rint(uv[good]).astype(int);xy[:,0]=np.clip(xy[:,0],0,im.shape[1]-1);xy[:,1]=np.clip(xy[:,1],0,im.shape[0]-1)
-    for d in range(max(1,radius)):
-        im[np.clip(xy[:,1]+d,0,im.shape[0]-1),xy[:,0]]=color
-        im[xy[:,1],np.clip(xy[:,0]+d,0,im.shape[1]-1)]=color
-
-def model_vertices(motion,valid,side,fix):
-    model=Mano(['left','right'][side])
-    if side==0 and fix:
-        model.data['shapedirs'][:,0]*=-1
-        with torch.no_grad():model.layer.shapedirs[:,0]*=-1
-    verts=np.full((len(motion),778,3),np.nan,np.float32);joints=np.full((len(motion),16,3),np.nan,np.float32)
-    ids=np.flatnonzero(valid[:,side])
-    for start in range(0,len(ids),64):
-        ix=ids[start:start+64];a=motion[ix,side];verts[ix],_,joints[ix]=model.geometry(a[:,:48],a[:,48:58],a[:,58:])
-    faces=np.asarray(model.layer.faces);edges=np.unique(np.sort(np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]),axis=1),axis=0)
-    return verts,joints,edges
-
-def encoder(path,fps,log,size=(WIDTH,HEIGHT)):
-    path.parent.mkdir(parents=True,exist_ok=True)
-    return subprocess.Popen([str(FF),'-v','error','-y','-f','rawvideo','-pix_fmt','rgb24','-s',f'{size[0]}x{size[1]}','-r',str(fps),'-i','pipe:0',
-        '-an','-c:v','libx264','-preset','veryfast','-crf','19','-pix_fmt','yuv420p','-threads','2','-bf','0','-g','60','-movflags','+faststart',str(path)],stdin=subprocess.PIPE,stderr=log)
-
-def read_exact(pipe,n):
-    chunks=[];count=0
-    while count<n:
-        b=pipe.read(n-count)
-        if not b:break
-        chunks.append(b);count+=len(b)
-    return b''.join(chunks)
-
-def trajectory_plot(E,width=265,height=145):
-    good=np.isfinite(E).all((1,2));xyz=np.full((len(E),3),np.nan)
-    xyz[good]=-(np.swapaxes(E[good,:3,:3],1,2)@E[good,:3,3,None])[:,:,0]
-    base=np.full((height,width,3),24,np.uint8);xy=xyz[:,[0,2]];valid=np.isfinite(xy).all(1)
-    if not valid.any():return base,np.zeros((len(E),2),int),xyz
-    lo=np.nanmin(xy,0);hi=np.nanmax(xy,0);span=np.maximum(hi-lo,.05);scale=min((width-24)/span[0],(height-35)/span[1]);pix=(xy-lo)*scale+12
-    pix[:,1]=height-12-pix[:,1];pixels=np.nan_to_num(pix).astype(int)
-    for i in range(1,len(E)):
-        if valid[i-1] and valid[i]:cv2.line(base,tuple(pixels[i-1]),tuple(pixels[i]),(110,110,110),1)
-    text(base,'camera world trajectory X/Z (m)',(7,15),size=.38)
-    return base,pixels,xyz
-
-def object_specs(p,source):
-    result=[]
-    for k,op in enumerate(sorted((p/'annotation/object').glob('*'))):
-        if not (op/'pose.pt').exists():continue
-        om=load(op/'metainfo.json');rel=op.relative_to(p);mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
-        rawpose=arr(source/rel/'pose.pt');rawvalid=arr(source/rel/'pose_valid.pt');valid=arr(op/'pose_valid.pt')
-        spec={'id':om['object_id'],'name':om.get('category_name',om['object_id']),'color':COLORS[k%len(COLORS)],'P':rawpose,'rawvalid':rawvalid,'valid':valid,'op':op}
-        if 'vertex_part_ids_ref' in om:
-            xyz=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
-            part=arr(p/om['vertex_part_ids_ref']);idx=np.arange(0,len(xyz),max(1,len(xyz)//4500))
-            spec.update(vertices=xyz[idx],parts=part[idx],top=arr(source/rel/'parts/top/pose.pt'),edges=None,
-                keypoints=arr(source/rel/'keypoints/xyz.pt'),joint=arr(source/rel/'joint_state.pt')[:,0])
-        else:
-            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//1100)]
-            unique,inv=np.unique(edges,return_inverse=True);spec.update(vertices=np.asarray(mesh.vertices)[unique],edges=inv.reshape(-1,2),parts=None)
-        spec['visibility']=arr(op/'visibility.pt') if (op/'visibility.pt').exists() else None
-        spec['raw_visibility']=arr(source/rel/'visibility.pt') if (source/rel/'visibility.pt').exists() else None
-        result.append(spec)
-    return result
-
-def render_view(p,dataset,qa_data,doc=None,tmp=None,source=None,snapshot_indices=None):
-    m,frames,structural=validate_view(p);cam=m['camera_id'];T=m['num_frames'];root=DATA_ROOT/dataset/RELEASE
-    doc=doc or root/'doc/validation';tmp=tmp or DATA_ROOT/dataset/'tmp'/VIDEO_RUN;tmp.mkdir(parents=True,exist_ok=True)
-    if source is None:
-        source=p
-        for candidate in [DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/m['sequence_id']/cam,DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/cam]:
-            if (candidate/'snapshot.json').exists() and Path(load(candidate/'snapshot.json')['source_view']).resolve()==p.resolve():source=candidate;break
-    qa=np.array(qa_data[cam]['qa_pass'],bool) if dataset=='HOT3D' and qa_data and qa_data[cam].get('qa_pass') is not None else None
-    if 'mano' in m['components']:
-        motion=arr(source/'annotation/mano/motion.pt');present=arr(source/'annotation/mano/motion_valid.pt');valid=arr(p/'annotation/mano/motion_valid.pt')
-    else:motion=np.full((T,2,61),np.nan);present=np.zeros((T,2),bool);valid=present.copy()
-    visibility=arr(p/'annotation/mano/visibility.pt') if (p/'annotation/mano/visibility.pt').exists() else None
-    rawvis=arr(source/'annotation/mano/visibility.pt') if (source/'annotation/mano/visibility.pt').exists() else None
-    hands=[model_vertices(motion,present,s,m['components'].get('mano',{}).get('model_profile',{}).get('left_shapedirs_fix',False)) for s in range(2)]
-    objs=object_specs(p,source);K=arr(p/'camera/intrinsic.pt') if (p/'camera/intrinsic.pt').exists() else np.full((T,3,3),np.nan);D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None;E=arr(p/'camera/extrinsic.pt') if (p/'camera/extrinsic.pt').exists() else np.full((T,4,4),np.nan)
-    W,H=m['camera']['image_size'];scale0=min(CELL/W,CELL/H);w=int(W*scale0)//2*2;h=int(H*scale0)//2*2;scale=np.array([w/W,h/H])
-    fps=m['sampling']['source_fps'] or 30;path=doc/'videos'/f'{cam}-full.mp4';logpath=tmp/f'render-{cam}.log';log=logpath.open('wb')
-    dec=subprocess.Popen([str(FF),'-v','error','-threads','2','-noautorotate','-i',str(p/'observation/rgb.mkv'),'-vf',f'scale={w}:{h}','-pix_fmt','rgb24','-fps_mode','passthrough','-f','rawvideo','pipe:1'],stdout=subprocess.PIPE,stderr=log)
-    enc=encoder(path,fps,log);traj,tp,world=trajectory_plot(E);rendered=0;exposure=[]
-    snapshots=sorted(set(snapshot_indices or [0,1,26,27,179,180,T//4,T//2,3*T//4,T-1]))
-    timeline=np.zeros((12,WIDTH,3),np.uint8)
-    for x in range(WIDTH):timeline[:,x]=(40,135,65) if qa is not None and qa[min(T-1,x*T//WIDTH)] else ((170,42,50) if qa is not None else (75,80,95))
-    for i in range(T):
-        b=read_exact(dec.stdout,w*h*3);assert len(b)==w*h*3,(cam,i,'decoder ended early')
-        original=np.frombuffer(b,np.uint8).reshape(h,w,3).copy();raw=original.copy();usable=original.copy()
-        gray=cv2.cvtColor(cv2.resize(original,(96,96)),cv2.COLOR_RGB2GRAY)
-        exposure.append({'frame_index':i,'p95_luma':float(np.percentile(gray,95)),'median_luma':float(np.median(gray))})
-        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
-        for side,(verts,joints,edges) in enumerate(hands):
-            if not present[i,side]:continue
-            uv=project_for_view(verts[i],m,params,scale);color=(0,235,255) if side==0 else (255,135,20)
-            fast_edges(raw,uv,edges,color)
-            if valid[i,side]:fast_edges(usable,uv,edges,color)
-            # White MANO joints are a diagnostic derivation, not a new keypoint component.
-            dots(raw,project_for_view(joints[i],m,params,scale),(255,255,255),2)
-            if valid[i,side]:dots(usable,project_for_view(joints[i],m,params,scale),(255,255,255),2)
-        for obj in objs:
-            if not obj['rawvalid'][i]:continue
-            P=obj['P'][i];xyz=obj['vertices']@P[:3,:3].T+P[:3,3]
-            if obj['parts'] is not None:
-                top=obj['top'][i];sel=obj['parts']==1;xyz[sel]=obj['vertices'][sel]@top[:3,:3].T+top[:3,3]
-            uv=project_for_view(xyz,m,params,scale)
-            for image,enabled in [(raw,True),(usable,obj['valid'][i])]:
-                if not enabled:continue
-                if obj['edges'] is not None:fast_edges(image,uv,obj['edges'],obj['color'])
-                else:
-                    dots(image,uv[obj['parts']==0],(100,240,90));dots(image,uv[obj['parts']==1],(230,80,230))
-                    dots(image,project_for_view(obj['keypoints'][i],m,params,scale),(255,255,255),3)
-                # Local object axes in metres make pose orientation visible.
-                axes=np.vstack([np.zeros(3),np.eye(3)*.045])@P[:3,:3].T+P[:3,3];auv=project_for_view(axes,m,params,scale)
-                for j,c in enumerate([(255,60,60),(60,255,60),(60,120,255)]):fast_edges(image,auv,np.array([[0,j+1]]),c)
-        canvas=np.full((HEIGHT,WIDTH,3),20,np.uint8)
-        for col,(im,label) in enumerate([(original,'SOURCE RGB'),(raw,'NATIVE LABEL DIAGNOSTIC'),(usable,'SHARED USABLE SUPERVISION')]):
-            canvas[40+(CELL-h)//2:40+(CELL-h)//2+h,col*CELL+(CELL-w)//2:col*CELL+(CELL-w)//2+w]=im
-            text(canvas,label,(col*CELL+12,25),size=.58)
-        status='NO OFFICIAL QA PROVIDED' if qa is None else ('OFFICIAL QA PASS' if qa[i] else 'OFFICIAL QA FAIL - NOT SUPERVISION')
-        if dataset=='ARCTIC' and i in m['components'].get('mano',{}).get('valid_policy',{}).get('local_excluded_frame_indices',[]):status='LOCAL REVIEW REJECT - NOT HAND SUPERVISION'
-        color=(235,195,65) if qa is None else ((80,240,110) if qa[i] else (255,90,90))
-        text(canvas,f'{dataset} {cam}  index={i}/{T-1}  source={frames[i]["source_frame_id"]}   {status}',(12,578),color,.6)
-        text(canvas,f'MANO source L/R={int(present[i,0])}/{int(present[i,1])}  shared valid L/R={int(valid[i,0])}/{int(valid[i,1])}  (m/rad)',(12,603),size=.49)
-        def vals(a):return 'n/a' if a is None else ','.join('unknown' if not np.isfinite(x) else f'{100*x:.1f}%' for x in np.atleast_1d(a))
-        text(canvas,f'visibility native L/R: {vals(rawvis[i] if rawvis is not None else None)}  usable: {vals(visibility[i] if visibility is not None else None)}',(12,626),size=.48)
-        text(canvas,f'camera={m["camera"]["model"]}; intrinsic '+np.array2string(K[i].ravel()[:5],precision=2),(12,649),size=.45)
-        text(canvas,'camera world position (m): '+np.array2string(world[i],precision=3),(12,672),size=.45)
-        clock=frames[i]['timestamp_ns'];text(canvas,f'timestamp_ns={clock}  clock={frames[i]["clock_domain"]}',(12,695),size=.44)
-        text(canvas,f'preview FPS={fps}; '+('capture FPS unknown, playback only' if m['sampling']['source_fps'] is None else 'source capture time is stored separately'),(12,718),size=.44)
-        text(canvas,'cyan=left; orange=right; white=derived MANO joints / source object points',(12,741),size=.41)
-        text(canvas,'Projection shows hidden surfaces; it is NOT native segmentation.',(12,762),(180,180,180),.43)
-        for j,obj in enumerate(objs):
-            a=obj['raw_visibility'];v=obj['visibility'];s=f'{obj["name"]}: valid={int(obj["valid"][i])}'
-            if a is not None:s+=f' vis={vals(v[i])} (raw {vals(a[i])})'
-            if 'joint' in obj:s+=f' hinge={obj["joint"][i]:.3f}rad  keypoints=32'
-            text(canvas,s,(660,603+j*24),obj['color'],.44)
-        tr=traj.copy()
-        if np.isfinite(world[i]).all():cv2.circle(tr,tuple(tp[i]),4,(255,220,70),-1)
-        canvas[610:755,WIDTH-277:WIDTH-12]=tr
-        canvas[-12:]=timeline;cursor=min(WIDTH-1,int(i*WIDTH/T));canvas[-12:,cursor:cursor+2]=255
-        enc.stdin.write(canvas.tobytes());rendered+=1
-        if i in snapshots:
-            snap=doc/'video_snapshots'/cam;snap.mkdir(parents=True,exist_ok=True);Image.fromarray(canvas).save(snap/f'{i:06d}.jpg',quality=92)
-        if i%200==0:print('VIDEO_FRAME',dataset,cam,i,T,flush=True)
-    assert not dec.stdout.read(1),'extra source frames';dec.stdout.close();assert dec.wait()==0
-    enc.stdin.close();assert enc.wait()==0;log.close();assert rendered==T
-    packets=framehash(['-i',str(path)],copy=True);assert len(packets)==T
-    result={'sequence_id':m['sequence_id'],'camera_id':cam,'frames':T,'rendered_frames':rendered,'video_ref':path.relative_to(doc).as_posix(),'video_sha256':digest(path),'preview_fps':fps,
-        'preview_is_training_data':False,'preview_encoding':'H264 CRF19, scaled, display only; shared original MKV unchanged',
-        'source_labels_from':'raw-derived diagnostic cache' if source!=p else 'shared unfiltered source-compatible labels',
-        'all_source_and_shared_valid_masks_used':True,'structure_checks':structural,'mano_valid_counts':valid.sum(0).tolist(),
-        'official_qa_pass_frames':int(qa.sum()) if qa is not None else None,'official_qa_fail_frames':int((~qa).sum()) if qa is not None else None,
-        'exposure_diagnostics':{'method':'scaled shared decoded RGB to 96x96 gray; p95<35 flags a candidate only, does not modify valid','suspect_dark_frame_indices':[x['frame_index'] for x in exposure if x['p95_luma']<35]}}
-    save(tmp/f'{cam}-exposure.json',exposure)
-    save(tmp/f'{cam}-video-result.json',result);print('VIDEO_COMPLETE',dataset,cam,T,flush=True)
-    return result
-
-def asset_preview(op,out):
-    """Textured point-splat turntable plus the source texture; diagnostic, not photoreal rendering."""
-    p=op.parents[2];m=load(op/'metainfo.json');meshpath=p/m['mesh_ref']
-    if (op/'assets/mesh_tex.obj').exists():meshpath=op/'assets/mesh_tex.obj'
-    mesh=trimesh.load(meshpath,process=False,force='mesh');xyz=np.asarray(mesh.vertices);idx=np.arange(0,len(xyz),max(1,len(xyz)//35000));xyz=xyz[idx]
-    texture=getattr(mesh.visual.material,'image',None) if hasattr(mesh.visual,'material') else None
-    texture_source=None
-    if meshpath.name=='mesh_tex.obj' and op.relative_to(DATA_ROOT).parts[0]=='ARCTIC':
-        # The loader can silently return a 2x2 default material. Resolve the
-        # official single-texture MTL explicitly instead of accepting that placeholder.
-        mtl=[r.split(maxsplit=1)[1] for r in meshpath.read_text().splitlines() if r.startswith('mtllib ')]
-        diffuse=[]
-        for name in mtl:
-            material=meshpath.parent/name
-            diffuse.extend(material.parent/r.split(maxsplit=1)[1] for r in material.read_text().splitlines() if r.startswith('map_Kd '))
-        diffuse=list(set(diffuse));assert len(diffuse)==1,(meshpath,diffuse)
-        assert diffuse[0].resolve().is_relative_to(meshpath.parent.resolve())
-        texture=Image.open(diffuse[0]).convert('RGB');assert min(texture.size)>2
-        texture_source={'file':diffuse[0].relative_to(p).as_posix(),'sha256':digest(diffuse[0]),'size':list(texture.size),'resolution_method':'explicit official MTL map_Kd; reject loader placeholder'}
-    if texture is not None and getattr(mesh.visual,'uv',None) is not None:
-        tex=np.asarray(texture.convert('RGB'));uv=np.asarray(mesh.visual.uv)[idx]
-        rgb=tex[np.clip(np.rint((1-uv[:,1])*(len(tex)-1)).astype(int),0,len(tex)-1),np.clip(np.rint(uv[:,0]*(tex.shape[1]-1)).astype(int),0,tex.shape[1]-1)]
-    else:rgb=np.broadcast_to([150,210,245],xyz.shape).astype(np.uint8)
-    extent=np.ptp(xyz,axis=0);xyz=xyz-(xyz.min(0)+xyz.max(0))/2;scale=300/max(np.linalg.norm(extent),.001)
-    logpath=DATA_ROOT/op.relative_to(DATA_ROOT).parts[0]/'tmp'/VIDEO_RUN/'assets.log'
-    logpath.parent.mkdir(parents=True,exist_ok=True)
-    out.parent.mkdir(parents=True,exist_ok=True)
-    with logpath.open('ab') as log:
-        enc=encoder(out,12,log,size=(800,480))
-        for i in range(72):
-            image=np.full((480,800,3),24,np.uint8);R=Rotation.from_euler('xy',[.35,i*2*np.pi/72]).as_matrix();v=xyz@R.T
-            order=np.argsort(v[:,2]);xy=np.rint(v[:,:2]*scale+[220,220]).astype(int)
-            good=(xy[:,0]>=0)&(xy[:,0]<430)&(xy[:,1]>=35)&(xy[:,1]<400);order=order[good[order]]
-            for dx,dy in [(0,0),(1,0),(0,1)]:image[np.clip(xy[order,1]+dy,0,479),np.clip(xy[order,0]+dx,0,429)]=rgb[order]
-            if texture is not None:
-                thumb=texture.convert('RGB');thumb.thumbnail((350,345));image[40:40+thumb.height,440:440+thumb.width]=np.asarray(thumb)
-            text(image,m.get('category_name',m['object_id']),(12,25),size=.65)
-            text(image,'TEXTURED 3D POINT PREVIEW',(12,420),size=.55);text(image,'SOURCE MATERIAL TEXTURE',(445,420),size=.5)
-            text(image,'OBJ extent (m): '+np.array2string(extent,precision=4),(12,450),size=.5);enc.stdin.write(image.tobytes())
-        enc.stdin.close();assert enc.wait()==0
-    assert len(framehash(['-i',str(out)],copy=True))==72
-    return {'object_id':m['object_id'],'video_ref':'assets/'+out.name,'frames':72,'texture_present':texture is not None,'texture_source':texture_source,'extent_m':extent.tolist(),'method':'diagnostic textured point turntable, not photoreal material rendering'}
-
-def publish_review(dataset,results,assets):
-    root=DATA_ROOT/dataset/RELEASE;doc=root/'doc/validation'
-    scope=[('RGB','全部帧原图与逐帧码流/像素校验'),('相机内参/畸变','投影使用声明模型和全部参数；HUD显示相机模型'),('相机外参','全帧姿态、米制相机位置与世界XZ轨迹'),('MANO','逐帧网格线框、派生关节点、左右手valid'),('物体姿态/几何','逐帧轮廓、局部XYZ轴、物体ID'),('材质/纹理','每个物体的原纹理与完整旋转预览')]
-    scope+=([('部件/关节','底座/顶盖分别着色，逐帧关节角'),('物体关键点','源32个物体点逐帧叠加'),('visibility','原数据未提供，明确缺省')] if dataset=='ARCTIC' else [('visibility','原值0–1已修正，原生/可用比例同时显示'),('官方QA','完整红绿时间带；QA失败帧保留诊断、停用监督'),('部件/关节、独立物体关键点','本序列无该源模态，不虚构')])
-    scope+=[('深度/像素分割/接触','本次raw范围未提供，不以投影或帧筛选CSV冒充')]
-    table=''.join(f'<tr><td>{a}</td><td>{b}</td></tr>' for a,b in scope)
-    cards=''.join(f'<section><h2>Camera {r["camera_id"]} · {r["frames"]} 帧 · 有效MANO左右 {r["mano_valid_counts"]}</h2><video controls preload="metadata" src="{r["video_ref"]}"></video><p>全部{r["rendered_frames"]}帧已渲染，无码帧跳过。官方QA通过/未通过：{r["official_qa_pass_frames"]}/{r["official_qa_fail_frames"]}（null表示来源无此QA）。</p></section>' for r in results)
-    gallery=''.join(f'<section><h3>物体 {a["object_id"]}</h3><video controls loop preload="metadata" src="{a["video_ref"]}"></video><p>纹理点云旋转预览及原始材质图；此预览不证明完整PBR渲染等价。</p></section>' for a in assets)
-    known=('已修正转换器颠倒顶盖/底座编码的错误：原parts.json的0是顶盖、1是底座，共享层重新映射为0底座/1顶盖。9路相机前两帧均为源欠曝图，已排除对应几何监督；camera 6 index 270的手部偏移作为已知问题记录，按用户要求撤销单帧屏蔽，恢复原标注和valid。整套试点仍待验收。中栏显示源参数诊断，右栏按当前valid绘制。详见 <a href="ARCTIC_FINDINGS.md">问题与修复证据</a>、<a href="diagnosis/camera6-270-before-after.jpg">修复前后对照</a>��预览30fps仅用于播放，不代表真实采集帧率；没有源准确性QA。' if dataset=='ARCTIC' else '原始开头帧存在明显错位，官方QA为False；源头戴位姿开头27帧保持不变，帧号/时间/包顺序均未人为移动。中栏保留原始诊断投影，右栏只绘制当前valid监督。已修正visibility误除100。QA按相机整帧保守筛选，不能断言具体哪个实体错。')
-    page=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 完整视频审核 v2</title><style>body{{font:16px/1.7 system-ui;background:#edf1f7;color:#172136;max-width:1550px;margin:25px auto;padding:0 20px}}header,section{{background:white;padding:22px;border-radius:12px;margin-bottom:20px}}video{{width:100%;background:#151515}}h1{{margin:0}}.warn{{color:#a71d2b;font-weight:bold}}table{{border-collapse:collapse;width:100%}}td,th{{border:1px solid #ccd4df;padding:8px;text-align:left}}.assets{{display:grid;grid-template-columns:repeat(auto-fit,minmax(500px,1fr));gap:18px}}a{{color:#1567a3}}</style><header><h1>{dataset} · 完整序列审核 v2</h1><p class="warn">人工确认待完成。旧“抽帧投影通过”结论撤回，本页替代旧报告；尚未全量或发布。</p><p>视频三栏：原图 ｜ 原始标注诊断 ｜ 共享层有效监督。所有相机、所有帧都已渲染；青=左手，橙=右手，其他色=物体/部件，白点=MANO派生关节点或源物体关键点。投影不做遮挡剔除，也不是分割真值。</p><p>{known}</p><p>标注 valid=实际标注存在且数值合法，并通过适用官方QA及已明确登记的本地排除规则；可见性不代替valid，剩余帧不因此自动认定人工通过。诊断中被排除的参数不进入有效监督，raw保留。MP4经过缩放与展示编码，仅为doc预览，训练输入仍是无新增损失的shared MKV。</p><p><a href="REPORT.md">正式说明</a> · <a href="report.json">逐相机检查结果</a> · <a href="../RAW_TO_SHARED.md">字段映射</a> · <a href="../CONTRACT.md">更新后的valid定义</a></p><h2>逐模态检查覆盖</h2><table><tr><th>模态</th><th>检查/可视化方式</th></tr>{table}</table></header>{cards}<h2>物体资产检查</h2><div class="assets">{gallery}</div><script>document.querySelectorAll('video').forEach(v=>v.addEventListener('play',()=>document.querySelectorAll('video').forEach(o=>{{if(o!==v)o.pause()}})));</script></html>'''
-    (doc/'index.html').write_text(page,encoding='utf-8')
-    old=load(doc/'report.json');old.update(stage='full_video_review_awaiting_human',human_review='pending',review_revision='full-video-v2',
-        old_sample_projection_conclusion='withdrawn',full_video_views=results,asset_previews=assets,modality_coverage=[{'modality':a,'check':b} for a,b in scope],known_issues=known,
-        agent_visual_inspection={'status':'pending_new_full_video_review','not_equivalent_to_user_approval':True},created_at=datetime.now().astimezone().isoformat())
-    old.pop('views',None);old.pop('offline_artifact_check',None);save(doc/'report.json',old)
-    save(doc/'status.json',{'stage':'full_video_review_awaiting_human','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False})
-    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | {r["rendered_frames"]} | {r["mano_valid_counts"]} | {r["official_qa_pass_frames"]} |' for r in results)
-    (doc/'REPORT.md').write_text(f'# {dataset} 完整序列审核 v2\n\n[离线完整视频](index.html) · [机器结果](report.json)\n\n人工确认待完成；旧抽帧投影通过结论撤回。{known}\n\n|相机|源帧数|渲染帧数|左右手有效标注帧数|官方QA通过帧数|\n|---|---:|---:|---|---:|\n{rows}\n\n## 逐模态覆盖\n\n'+''.join(f'- {a}：{b}。\n' for a,b in scope)+'\n完整视频表示每帧已渲染，并不等于每帧人工审核通过。被官方QA排除的源标注仅在诊断栏显示；不挪帧、不隐藏原始异常。所有有效性筛选、可见性与图像编码验收分开记录。源标注质量问题无法通过格式转换修好；通过valid屏蔽，并保留raw溯源。\n',encoding='utf-8')
-    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,root/'doc'/f)
-    for f in ['render_full_sequence_review.py','apply_hot3d_qa.py','fresh_sequence_common.py','review_fresh_sequence_pilots.py','convert_fresh_sequence_pilots.py']:shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
-    print('FULL_VIDEO_REVIEW_READY',dataset,flush=True)
-
-if __name__=='__main__':
-    ap=argparse.ArgumentParser();ap.add_argument('dataset',choices=['HOT3D','ARCTIC']);a=ap.parse_args();torch.set_num_threads(3)
-    root=DATA_ROOT/a.dataset/RELEASE;qa=load(root/'doc/validation/source_qa.json') if a.dataset=='HOT3D' else None
-    # This entry point owns the original single-sequence report. Other review
-    # sequences have their own output directories in arctic_object_review.py.
-    sequence={'ARCTIC':'s09_laptop_use_03','HOT3D':'P0015_179e1b84'}[a.dataset]
-    views=sorted(p.parent for p in (root/'data'/sequence).glob('*/metainfo.json'));results=[]
-    for p in views:results.append(render_view(p,a.dataset,qa))
-    assets=[]
-    for op in sorted((views[0]/'annotation/object').iterdir()):assets.append(asset_preview(op,root/'doc/validation/assets'/f'{op.name}.mp4'))
-    publish_review(a.dataset,results,assets)
+"""Full-frame, all-camera diagnostic videos read from actual shared tensors/media.
+
+Preview MP4 is explicitly display-only; warehouse MKV packets remain untouched.
+"""
+from fresh_sequence_common import *
+from review_fresh_sequence_pilots import arr, validate_view, project_for_view
+from datetime import datetime
+import html
+
+VIDEO_RUN='full-video-review-v2'
+CELL=512
+WIDTH=3*CELL
+HEIGHT=800
+COLORS=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
+
+def text(im,txt,xy,color=(235,235,235),size=.5):cv2.putText(im,txt,xy,cv2.FONT_HERSHEY_SIMPLEX,size,color,1,cv2.LINE_AA)
+
+def fast_edges(im,uv,edges,color):
+    xy=uv[edges];good=np.isfinite(xy).all((1,2)) & (np.max(np.nan_to_num(np.abs(xy),nan=CELL*20),axis=(1,2))<CELL*12)
+    xy=xy[good];xy=xy[np.linalg.norm(xy[:,0]-xy[:,1],axis=1)<CELL*.7]
+    if len(xy):cv2.polylines(im,np.rint(xy).astype(np.int32),False,color,1,cv2.LINE_AA)
+
+def dots(im,uv,color,radius=2):
+    good=np.isfinite(uv).all(1)&(uv[:,0]>=0)&(uv[:,0]<im.shape[1])&(uv[:,1]>=0)&(uv[:,1]<im.shape[0])
+    xy=np.rint(uv[good]).astype(int);xy[:,0]=np.clip(xy[:,0],0,im.shape[1]-1);xy[:,1]=np.clip(xy[:,1],0,im.shape[0]-1)
+    for d in range(max(1,radius)):
+        im[np.clip(xy[:,1]+d,0,im.shape[0]-1),xy[:,0]]=color
+        im[xy[:,1],np.clip(xy[:,0]+d,0,im.shape[1]-1)]=color
+
+def model_vertices(motion,valid,side,fix):
+    model=Mano(['left','right'][side])
+    if side==0 and fix:
+        model.data['shapedirs'][:,0]*=-1
+        with torch.no_grad():model.layer.shapedirs[:,0]*=-1
+    verts=np.full((len(motion),778,3),np.nan,np.float32);joints=np.full((len(motion),16,3),np.nan,np.float32)
+    ids=np.flatnonzero(valid[:,side])
+    for start in range(0,len(ids),64):
+        ix=ids[start:start+64];a=motion[ix,side];verts[ix],_,joints[ix]=model.geometry(a[:,:48],a[:,48:58],a[:,58:])
+    faces=np.asarray(model.layer.faces);edges=np.unique(np.sort(np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]),axis=1),axis=0)
+    return verts,joints,edges
+
+def encoder(path,fps,log,size=(WIDTH,HEIGHT)):
+    path.parent.mkdir(parents=True,exist_ok=True)
+    return subprocess.Popen([str(FF),'-v','error','-y','-f','rawvideo','-pix_fmt','rgb24','-s',f'{size[0]}x{size[1]}','-r',str(fps),'-i','pipe:0',
+        '-an','-c:v','libx264','-preset','veryfast','-crf','19','-pix_fmt','yuv420p','-threads','2','-bf','0','-g','60','-movflags','+faststart',str(path)],stdin=subprocess.PIPE,stderr=log)
+
+def read_exact(pipe,n):
+    chunks=[];count=0
+    while count<n:
+        b=pipe.read(n-count)
+        if not b:break
+        chunks.append(b);count+=len(b)
+    return b''.join(chunks)
+
+def trajectory_plot(E,width=265,height=145):
+    good=np.isfinite(E).all((1,2));xyz=np.full((len(E),3),np.nan)
+    xyz[good]=-(np.swapaxes(E[good,:3,:3],1,2)@E[good,:3,3,None])[:,:,0]
+    base=np.full((height,width,3),24,np.uint8);xy=xyz[:,[0,2]];valid=np.isfinite(xy).all(1)
+    if not valid.any():return base,np.zeros((len(E),2),int),xyz
+    lo=np.nanmin(xy,0);hi=np.nanmax(xy,0);span=np.maximum(hi-lo,.05);scale=min((width-24)/span[0],(height-35)/span[1]);pix=(xy-lo)*scale+12
+    pix[:,1]=height-12-pix[:,1];pixels=np.nan_to_num(pix).astype(int)
+    for i in range(1,len(E)):
+        if valid[i-1] and valid[i]:cv2.line(base,tuple(pixels[i-1]),tuple(pixels[i]),(110,110,110),1)
+    text(base,'camera world trajectory X/Z (m)',(7,15),size=.38)
+    return base,pixels,xyz
+
+def object_specs(p,source):
+    result=[]
+    for k,op in enumerate(sorted((p/'annotation/object').glob('*'))):
+        if not (op/'pose.pt').exists():continue
+        om=load(op/'metainfo.json');rel=op.relative_to(p);mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
+        rawpose=arr(source/rel/'pose.pt');rawvalid=arr(source/rel/'pose_valid.pt');valid=arr(op/'pose_valid.pt')
+        spec={'id':om['object_id'],'name':om.get('category_name',om['object_id']),'color':COLORS[k%len(COLORS)],'P':rawpose,'rawvalid':rawvalid,'valid':valid,'op':op}
+        if 'vertex_part_ids_ref' in om:
+            xyz=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
+            part=arr(p/om['vertex_part_ids_ref']);idx=np.arange(0,len(xyz),max(1,len(xyz)//4500))
+            spec.update(vertices=xyz[idx],parts=part[idx],top=arr(source/rel/'parts/top/pose.pt'),edges=None,
+                keypoints=arr(source/rel/'keypoints/xyz.pt'),joint=arr(source/rel/'joint_state.pt')[:,0])
+        else:
+            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//1100)]
+            unique,inv=np.unique(edges,return_inverse=True);spec.update(vertices=np.asarray(mesh.vertices)[unique],edges=inv.reshape(-1,2),parts=None)
+        spec['visibility']=arr(op/'visibility.pt') if (op/'visibility.pt').exists() else None
+        spec['raw_visibility']=arr(source/rel/'visibility.pt') if (source/rel/'visibility.pt').exists() else None
+        result.append(spec)
+    return result
+
+def render_view(p,dataset,qa_data,doc=None,tmp=None,source=None,snapshot_indices=None):
+    m,frames,structural=validate_view(p);cam=m['camera_id'];T=m['num_frames'];root=DATA_ROOT/dataset/RELEASE
+    doc=doc or root/'doc/validation';tmp=tmp or DATA_ROOT/dataset/'tmp'/VIDEO_RUN;tmp.mkdir(parents=True,exist_ok=True)
+    if source is None:
+        source=p
+        for candidate in [DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/m['sequence_id']/cam,DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/cam]:
+            if (candidate/'snapshot.json').exists() and Path(load(candidate/'snapshot.json')['source_view']).resolve()==p.resolve():source=candidate;break
+    qa=np.array(qa_data[cam]['qa_pass'],bool) if dataset=='HOT3D' and qa_data and qa_data[cam].get('qa_pass') is not None else None
+    if 'mano' in m['components']:
+        motion=arr(source/'annotation/mano/motion.pt');present=arr(source/'annotation/mano/motion_valid.pt');valid=arr(p/'annotation/mano/motion_valid.pt')
+    else:motion=np.full((T,2,61),np.nan);present=np.zeros((T,2),bool);valid=present.copy()
+    visibility=arr(p/'annotation/mano/visibility.pt') if (p/'annotation/mano/visibility.pt').exists() else None
+    rawvis=arr(source/'annotation/mano/visibility.pt') if (source/'annotation/mano/visibility.pt').exists() else None
+    hands=[model_vertices(motion,present,s,m['components'].get('mano',{}).get('model_profile',{}).get('left_shapedirs_fix',False)) for s in range(2)]
+    objs=object_specs(p,source);K=arr(p/'camera/intrinsic.pt') if (p/'camera/intrinsic.pt').exists() else np.full((T,3,3),np.nan);D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None;E=arr(p/'camera/extrinsic.pt') if (p/'camera/extrinsic.pt').exists() else np.full((T,4,4),np.nan)
+    W,H=m['camera']['image_size'];scale0=min(CELL/W,CELL/H);w=int(W*scale0)//2*2;h=int(H*scale0)//2*2;scale=np.array([w/W,h/H])
+    fps=m['sampling']['source_fps'] or 30;path=doc/'videos'/f'{cam}-full.mp4';logpath=tmp/f'render-{cam}.log';log=logpath.open('wb')
+    dec=subprocess.Popen([str(FF),'-v','error','-threads','2','-noautorotate','-i',str(p/'observation/rgb.mkv'),'-vf',f'scale={w}:{h}','-pix_fmt','rgb24','-fps_mode','passthrough','-f','rawvideo','pipe:1'],stdout=subprocess.PIPE,stderr=log)
+    enc=encoder(path,fps,log);traj,tp,world=trajectory_plot(E);rendered=0;exposure=[]
+    snapshots=sorted(set(snapshot_indices or [0,1,26,27,179,180,T//4,T//2,3*T//4,T-1]))
+    timeline=np.zeros((12,WIDTH,3),np.uint8)
+    for x in range(WIDTH):timeline[:,x]=(40,135,65) if qa is not None and qa[min(T-1,x*T//WIDTH)] else ((170,42,50) if qa is not None else (75,80,95))
+    for i in range(T):
+        b=read_exact(dec.stdout,w*h*3);assert len(b)==w*h*3,(cam,i,'decoder ended early')
+        original=np.frombuffer(b,np.uint8).reshape(h,w,3).copy();raw=original.copy();usable=original.copy()
+        gray=cv2.cvtColor(cv2.resize(original,(96,96)),cv2.COLOR_RGB2GRAY)
+        exposure.append({'frame_index':i,'p95_luma':float(np.percentile(gray,95)),'median_luma':float(np.median(gray))})
+        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
+        for side,(verts,joints,edges) in enumerate(hands):
+            if not present[i,side]:continue
+            uv=project_for_view(verts[i],m,params,scale);color=(0,235,255) if side==0 else (255,135,20)
+            fast_edges(raw,uv,edges,color)
+            if valid[i,side]:fast_edges(usable,uv,edges,color)
+            # White MANO joints are a diagnostic derivation, not a new keypoint component.
+            dots(raw,project_for_view(joints[i],m,params,scale),(255,255,255),2)
+            if valid[i,side]:dots(usable,project_for_view(joints[i],m,params,scale),(255,255,255),2)
+        for obj in objs:
+            if not obj['rawvalid'][i]:continue
+            P=obj['P'][i];xyz=obj['vertices']@P[:3,:3].T+P[:3,3]
+            if obj['parts'] is not None:
+                top=obj['top'][i];sel=obj['parts']==1;xyz[sel]=obj['vertices'][sel]@top[:3,:3].T+top[:3,3]
+            uv=project_for_view(xyz,m,params,scale)
+            for image,enabled in [(raw,True),(usable,obj['valid'][i])]:
+                if not enabled:continue
+                if obj['edges'] is not None:fast_edges(image,uv,obj['edges'],obj['color'])
+                else:
+                    dots(image,uv[obj['parts']==0],(100,240,90));dots(image,uv[obj['parts']==1],(230,80,230))
+                    dots(image,project_for_view(obj['keypoints'][i],m,params,scale),(255,255,255),3)
+                # Local object axes in metres make pose orientation visible.
+                axes=np.vstack([np.zeros(3),np.eye(3)*.045])@P[:3,:3].T+P[:3,3];auv=project_for_view(axes,m,params,scale)
+                for j,c in enumerate([(255,60,60),(60,255,60),(60,120,255)]):fast_edges(image,auv,np.array([[0,j+1]]),c)
+        canvas=np.full((HEIGHT,WIDTH,3),20,np.uint8)
+        for col,(im,label) in enumerate([(original,'SOURCE RGB'),(raw,'NATIVE LABEL DIAGNOSTIC'),(usable,'SHARED USABLE SUPERVISION')]):
+            canvas[40+(CELL-h)//2:40+(CELL-h)//2+h,col*CELL+(CELL-w)//2:col*CELL+(CELL-w)//2+w]=im
+            text(canvas,label,(col*CELL+12,25),size=.58)
+        status='NO OFFICIAL QA PROVIDED' if qa is None else ('OFFICIAL QA PASS' if qa[i] else 'OFFICIAL QA FAIL - NOT SUPERVISION')
+        if dataset=='ARCTIC' and i in m['components'].get('mano',{}).get('valid_policy',{}).get('local_excluded_frame_indices',[]):status='LOCAL REVIEW REJECT - NOT HAND SUPERVISION'
+        color=(235,195,65) if qa is None else ((80,240,110) if qa[i] else (255,90,90))
+        text(canvas,f'{dataset} {cam}  index={i}/{T-1}  source={frames[i]["source_frame_id"]}   {status}',(12,578),color,.6)
+        text(canvas,f'MANO source L/R={int(present[i,0])}/{int(present[i,1])}  shared valid L/R={int(valid[i,0])}/{int(valid[i,1])}  (m/rad)',(12,603),size=.49)
+        def vals(a):return 'n/a' if a is None else ','.join('unknown' if not np.isfinite(x) else f'{100*x:.1f}%' for x in np.atleast_1d(a))
+        text(canvas,f'visibility native L/R: {vals(rawvis[i] if rawvis is not None else None)}  usable: {vals(visibility[i] if visibility is not None else None)}',(12,626),size=.48)
+        text(canvas,f'camera={m["camera"]["model"]}; intrinsic '+np.array2string(K[i].ravel()[:5],precision=2),(12,649),size=.45)
+        text(canvas,'camera world position (m): '+np.array2string(world[i],precision=3),(12,672),size=.45)
+        clock=frames[i]['timestamp_ns'];text(canvas,f'timestamp_ns={clock}  clock={frames[i]["clock_domain"]}',(12,695),size=.44)
+        text(canvas,f'preview FPS={fps}; '+('capture FPS unknown, playback only' if m['sampling']['source_fps'] is None else 'source capture time is stored separately'),(12,718),size=.44)
+        text(canvas,'cyan=left; orange=right; white=derived MANO joints / source object points',(12,741),size=.41)
+        text(canvas,'Projection shows hidden surfaces; it is NOT native segmentation.',(12,762),(180,180,180),.43)
+        for j,obj in enumerate(objs):
+            a=obj['raw_visibility'];v=obj['visibility'];s=f'{obj["name"]}: valid={int(obj["valid"][i])}'
+            if a is not None:s+=f' vis={vals(v[i])} (raw {vals(a[i])})'
+            if 'joint' in obj:s+=f' hinge={obj["joint"][i]:.3f}rad  keypoints=32'
+            text(canvas,s,(660,603+j*24),obj['color'],.44)
+        tr=traj.copy()
+        if np.isfinite(world[i]).all():cv2.circle(tr,tuple(tp[i]),4,(255,220,70),-1)
+        canvas[610:755,WIDTH-277:WIDTH-12]=tr
+        canvas[-12:]=timeline;cursor=min(WIDTH-1,int(i*WIDTH/T));canvas[-12:,cursor:cursor+2]=255
+        enc.stdin.write(canvas.tobytes());rendered+=1
+        if i in snapshots:
+            snap=doc/'video_snapshots'/cam;snap.mkdir(parents=True,exist_ok=True);Image.fromarray(canvas).save(snap/f'{i:06d}.jpg',quality=92)
+        if i%200==0:print('VIDEO_FRAME',dataset,cam,i,T,flush=True)
+    assert not dec.stdout.read(1),'extra source frames';dec.stdout.close();assert dec.wait()==0
+    enc.stdin.close();assert enc.wait()==0;log.close();assert rendered==T
+    packets=framehash(['-i',str(path)],copy=True);assert len(packets)==T
+    result={'sequence_id':m['sequence_id'],'camera_id':cam,'frames':T,'rendered_frames':rendered,'video_ref':path.relative_to(doc).as_posix(),'video_sha256':digest(path),'preview_fps':fps,
+        'preview_is_training_data':False,'preview_encoding':'H264 CRF19, scaled, display only; shared original MKV unchanged',
+        'source_labels_from':'raw-derived diagnostic cache' if source!=p else 'shared unfiltered source-compatible labels',
+        'all_source_and_shared_valid_masks_used':True,'structure_checks':structural,'mano_valid_counts':valid.sum(0).tolist(),
+        'official_qa_pass_frames':int(qa.sum()) if qa is not None else None,'official_qa_fail_frames':int((~qa).sum()) if qa is not None else None,
+        'exposure_diagnostics':{'method':'scaled shared decoded RGB to 96x96 gray; p95<35 flags a candidate only, does not modify valid','suspect_dark_frame_indices':[x['frame_index'] for x in exposure if x['p95_luma']<35]}}
+    save(tmp/f'{cam}-exposure.json',exposure)
+    save(tmp/f'{cam}-video-result.json',result);print('VIDEO_COMPLETE',dataset,cam,T,flush=True)
+    return result
+
+def asset_preview(op,out):
+    """Textured point-splat turntable plus the source texture; diagnostic, not photoreal rendering."""
+    p=op.parents[2];m=load(op/'metainfo.json');meshpath=p/m['mesh_ref']
+    if (op/'assets/mesh_tex.obj').exists():meshpath=op/'assets/mesh_tex.obj'
+    mesh=trimesh.load(meshpath,process=False,force='mesh');xyz=np.asarray(mesh.vertices);idx=np.arange(0,len(xyz),max(1,len(xyz)//35000));xyz=xyz[idx]
+    texture=getattr(mesh.visual.material,'image',None) if hasattr(mesh.visual,'material') else None
+    texture_source=None
+    if meshpath.name=='mesh_tex.obj' and op.relative_to(DATA_ROOT).parts[0]=='ARCTIC':
+        # The loader can silently return a 2x2 default material. Resolve the
+        # official single-texture MTL explicitly instead of accepting that placeholder.
+        mtl=[r.split(maxsplit=1)[1] for r in meshpath.read_text().splitlines() if r.startswith('mtllib ')]
+        diffuse=[]
+        for name in mtl:
+            material=meshpath.parent/name
+            diffuse.extend(material.parent/r.split(maxsplit=1)[1] for r in material.read_text().splitlines() if r.startswith('map_Kd '))
+        diffuse=list(set(diffuse));assert len(diffuse)==1,(meshpath,diffuse)
+        assert diffuse[0].resolve().is_relative_to(meshpath.parent.resolve())
+        texture=Image.open(diffuse[0]).convert('RGB');assert min(texture.size)>2
+        texture_source={'file':diffuse[0].relative_to(p).as_posix(),'sha256':digest(diffuse[0]),'size':list(texture.size),'resolution_method':'explicit official MTL map_Kd; reject loader placeholder'}
+    if texture is not None and getattr(mesh.visual,'uv',None) is not None:
+        tex=np.asarray(texture.convert('RGB'));uv=np.asarray(mesh.visual.uv)[idx]
+        rgb=tex[np.clip(np.rint((1-uv[:,1])*(len(tex)-1)).astype(int),0,len(tex)-1),np.clip(np.rint(uv[:,0]*(tex.shape[1]-1)).astype(int),0,tex.shape[1]-1)]
+    else:rgb=np.broadcast_to([150,210,245],xyz.shape).astype(np.uint8)
+    extent=np.ptp(xyz,axis=0);xyz=xyz-(xyz.min(0)+xyz.max(0))/2;scale=300/max(np.linalg.norm(extent),.001)
+    logpath=DATA_ROOT/op.relative_to(DATA_ROOT).parts[0]/'tmp'/VIDEO_RUN/'assets.log'
+    logpath.parent.mkdir(parents=True,exist_ok=True)
+    out.parent.mkdir(parents=True,exist_ok=True)
+    with logpath.open('ab') as log:
+        enc=encoder(out,12,log,size=(800,480))
+        for i in range(72):
+            image=np.full((480,800,3),24,np.uint8);R=Rotation.from_euler('xy',[.35,i*2*np.pi/72]).as_matrix();v=xyz@R.T
+            order=np.argsort(v[:,2]);xy=np.rint(v[:,:2]*scale+[220,220]).astype(int)
+            good=(xy[:,0]>=0)&(xy[:,0]<430)&(xy[:,1]>=35)&(xy[:,1]<400);order=order[good[order]]
+            for dx,dy in [(0,0),(1,0),(0,1)]:image[np.clip(xy[order,1]+dy,0,479),np.clip(xy[order,0]+dx,0,429)]=rgb[order]
+            if texture is not None:
+                thumb=texture.convert('RGB');thumb.thumbnail((350,345));image[40:40+thumb.height,440:440+thumb.width]=np.asarray(thumb)
+            text(image,m.get('category_name',m['object_id']),(12,25),size=.65)
+            text(image,'TEXTURED 3D POINT PREVIEW',(12,420),size=.55);text(image,'SOURCE MATERIAL TEXTURE',(445,420),size=.5)
+            text(image,'OBJ extent (m): '+np.array2string(extent,precision=4),(12,450),size=.5);enc.stdin.write(image.tobytes())
+        enc.stdin.close();assert enc.wait()==0
+    assert len(framehash(['-i',str(out)],copy=True))==72
+    return {'object_id':m['object_id'],'video_ref':'assets/'+out.name,'frames':72,'texture_present':texture is not None,'texture_source':texture_source,'extent_m':extent.tolist(),'method':'diagnostic textured point turntable, not photoreal material rendering'}
+
+def publish_review(dataset,results,assets):
+    root=DATA_ROOT/dataset/RELEASE;doc=root/'doc/validation'
+    scope=[('RGB','全部帧原图与逐帧码流/像素校验'),('相机内参/畸变','投影使用声明模型和全部参数；HUD显示相机模型'),('相机外参','全帧姿态、米制相机位置与世界XZ轨迹'),('MANO','逐帧网格线框、派生关节点、左右手valid'),('物体姿态/几何','逐帧轮廓、局部XYZ轴、物体ID'),('材质/纹理','每个物体的原纹理与完整旋转预览')]
+    scope+=([('部件/关节','底座/顶盖分别着色，逐帧关节角'),('物体关键点','源32个物体点逐帧叠加'),('visibility','原数据未提供，明确缺省')] if dataset=='ARCTIC' else [('visibility','原值0–1已修正，原生/可用比例同时显示'),('官方QA','完整红绿时间带；QA失败帧保留诊断、停用监督'),('部件/关节、独立物体关键点','本序列无该源模态，不虚构')])
+    scope+=[('深度/像素分割/接触','本次raw范围未提供，不以投影或帧筛选CSV冒充')]
+    table=''.join(f'<tr><td>{a}</td><td>{b}</td></tr>' for a,b in scope)
+    cards=''.join(f'<section><h2>Camera {r["camera_id"]} · {r["frames"]} 帧 · 有效MANO左右 {r["mano_valid_counts"]}</h2><video controls preload="metadata" src="{r["video_ref"]}"></video><p>全部{r["rendered_frames"]}帧已渲染，无码帧跳过。官方QA通过/未通过：{r["official_qa_pass_frames"]}/{r["official_qa_fail_frames"]}（null表示来源无此QA）。</p></section>' for r in results)
+    gallery=''.join(f'<section><h3>物体 {a["object_id"]}</h3><video controls loop preload="metadata" src="{a["video_ref"]}"></video><p>纹理点云旋转预览及原始材质图；此预览不证明完整PBR渲染等价。</p></section>' for a in assets)
+    known=('已修正转换器颠倒顶盖/底座编码的错误：原parts.json的0是顶盖、1是底座，共享层重新映射为0底座/1顶盖。9路相机前两帧均为源欠曝图，已排除对应几何监督；camera 6 index 270的手部偏移作为已知问题记录，按用户要求撤销单帧屏蔽，恢复原标注和valid。整套试点仍待验收。中栏显示源参数诊断，右栏按当前valid绘制。详见 <a href="ARCTIC_FINDINGS.md">问题与修复证据</a>、<a href="diagnosis/camera6-270-before-after.jpg">修复前后对照</a>。预览30fps仅用于播放，不代表真实采集帧率；没有源准确性QA。' if dataset=='ARCTIC' else '原始开头帧存在明显错位，官方QA为False；源头戴位姿开头27帧保持不变，帧号/时间/包顺序均未人为移动。中栏保留原始诊断投影，右栏只绘制当前valid监督。已修正visibility误除100。QA按相机整帧保守筛选，不能断言具体哪个实体错。')
+    page=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 完整视频审核 v2</title><style>body{{font:16px/1.7 system-ui;background:#edf1f7;color:#172136;max-width:1550px;margin:25px auto;padding:0 20px}}header,section{{background:white;padding:22px;border-radius:12px;margin-bottom:20px}}video{{width:100%;background:#151515}}h1{{margin:0}}.warn{{color:#a71d2b;font-weight:bold}}table{{border-collapse:collapse;width:100%}}td,th{{border:1px solid #ccd4df;padding:8px;text-align:left}}.assets{{display:grid;grid-template-columns:repeat(auto-fit,minmax(500px,1fr));gap:18px}}a{{color:#1567a3}}</style><header><h1>{dataset} · 完整序列审核 v2</h1><p class="warn">人工确认待完成。旧“抽帧投影通过”结论撤回，本页替代旧报告；尚未全量或发布。</p><p>视频三栏：原图 ｜ 原始标注诊断 ｜ 共享层有效监督。所有相机、所有帧都已渲染；青=左手，橙=右手，其他色=物体/部件，白点=MANO派生关节点或源物体关键点。投影不做遮挡剔除，也不是分割真值。</p><p>{known}</p><p>标注 valid=实际标注存在且数值合法，并通过适用官方QA及已明确登记的本地排除规则；可见性不代替valid，剩余帧不因此自动认定人工通过。诊断中被排除的参数不进入有效监督，raw保留。MP4经过缩放与展示编码，仅为doc预览，训练输入仍是无新增损失的shared MKV。</p><p><a href="REPORT.md">正式说明</a> · <a href="report.json">逐相机检查结果</a> · <a href="../RAW_TO_SHARED.md">字段映射</a> · <a href="../CONTRACT.md">更新后的valid定义</a></p><h2>逐模态检查覆盖</h2><table><tr><th>模态</th><th>检查/可视化方式</th></tr>{table}</table></header>{cards}<h2>物体资产检查</h2><div class="assets">{gallery}</div><script>document.querySelectorAll('video').forEach(v=>v.addEventListener('play',()=>document.querySelectorAll('video').forEach(o=>{{if(o!==v)o.pause()}})));</script></html>'''
+    (doc/'index.html').write_text(page,encoding='utf-8')
+    old=load(doc/'report.json');old.update(stage='full_video_review_awaiting_human',human_review='pending',review_revision='full-video-v2',
+        old_sample_projection_conclusion='withdrawn',full_video_views=results,asset_previews=assets,modality_coverage=[{'modality':a,'check':b} for a,b in scope],known_issues=known,
+        agent_visual_inspection={'status':'pending_new_full_video_review','not_equivalent_to_user_approval':True},created_at=datetime.now().astimezone().isoformat())
+    old.pop('views',None);old.pop('offline_artifact_check',None);save(doc/'report.json',old)
+    save(doc/'status.json',{'stage':'full_video_review_awaiting_human','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False})
+    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | {r["rendered_frames"]} | {r["mano_valid_counts"]} | {r["official_qa_pass_frames"]} |' for r in results)
+    (doc/'REPORT.md').write_text(f'# {dataset} 完整序列审核 v2\n\n[离线完整视频](index.html) · [机器结果](report.json)\n\n人工确认待完成；旧抽帧投影通过结论撤回。{known}\n\n|相机|源帧数|渲染帧数|左右手有效标注帧数|官方QA通过帧数|\n|---|---:|---:|---|---:|\n{rows}\n\n## 逐模态覆盖\n\n'+''.join(f'- {a}：{b}。\n' for a,b in scope)+'\n完整视频表示每帧已渲染，并不等于每帧人工审核通过。被官方QA排除的源标注仅在诊断栏显示；不挪帧、不隐藏原始异常。所有有效性筛选、可见性与图像编码验收分开记录。源标注质量问题无法通过格式转换修好；通过valid屏蔽，并保留raw溯源。\n',encoding='utf-8')
+    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,root/'doc'/f)
+    # Keep the published script bundle immutable while regenerating review artifacts.
+    print('FULL_VIDEO_REVIEW_READY',dataset,flush=True)
+
+if __name__=='__main__':
+    ap=argparse.ArgumentParser();ap.add_argument('dataset',choices=['HOT3D','ARCTIC']);a=ap.parse_args();torch.set_num_threads(3)
+    root=DATA_ROOT/a.dataset/RELEASE;qa=load(root/'doc/validation/source_qa.json') if a.dataset=='HOT3D' else None
+    # This entry point owns the original single-sequence report. Other review
+    # sequences have their own output directories in arctic_object_review.py.
+    sequence={'ARCTIC':'s09_laptop_use_03','HOT3D':'P0015_179e1b84'}[a.dataset]
+    views=sorted(p.parent for p in (root/'data'/sequence).glob('*/metainfo.json'));results=[]
+    for p in views:results.append(render_view(p,a.dataset,qa))
+    assets=[]
+    for op in sorted((views[0]/'annotation/object').iterdir()):assets.append(asset_preview(op,root/'doc/validation/assets'/f'{op.name}.mp4'))
+    publish_review(a.dataset,results,assets)

ARCTIC/shared-v1.0/script/internal/review_fresh_sequence_pilots.py

@@ -1,225 +1,223 @@
-"""Validate actual shared outputs and create portable, offline pilot reviews.
-
-Run after convert_fresh_sequence_pilots.py. Never authorizes full processing.
-"""
-from fresh_sequence_common import *
-import html
-from datetime import datetime
-
-def arr(p):
-    value=torch.load(p,map_location='cpu',weights_only=True)
-    assert isinstance(value,torch.Tensor) and value.device.type=='cpu'
-    return value.numpy()
-
-def safe_ref(root,ref):
-    path=(root/ref).resolve()
-    assert path.is_relative_to(root.resolve()) and path.is_file(),ref
-    return path
-
-def validate_view(p):
-    m=load(p/'metainfo.json');T=m['num_frames'];registry=load(WORK/'distribution/hand-warehouse-1.0/schema.json')
-    assert m['schema_version']==registry['schema_version'] and m['pilot_complete']
-    frames=[json.loads(line) for line in (p/'camera/frames.jsonl').read_text().splitlines()]
-    assert len(frames)==T and [f['frame_index'] for f in frames]==list(range(T))
-    assert len({f['source_frame_id'] for f in frames})==T
-    for frame in frames:
-        for match in frame.get('annotation_matches',{}).values():
-            if 'available' in match:match['source_record_present']=match.pop('available')
-    lines(p/'camera/frames.jsonl',frames)
-    summary=[]
-    for key,c in m['components'].items():
-        assert c['schema_id'] in registry['components'],(key,c['schema_id'])
-        assert c['available'] and c['source_locator'] and c['provenance']['kind'] in ['source','derived','format_conversion']
-        data=safe_ref(p,c['data_ref']);valid=arr(safe_ref(p,c['valid_ref']))
-        assert valid.dtype==bool and valid.ndim==len(c['valid_axes'])
-        if data.suffix=='.pt':
-            a=arr(data);assert a.dtype==np.float32 and a.shape[0]==T
-            assert a.shape[:valid.ndim]==valid.shape and len(c['axes'])==a.ndim
-            assert np.isfinite(a[valid]).all() and np.isnan(a[~valid]).all()
-            if c['schema_id'] in ['object/rigid/v1','transform/se3/v1']:assert_se3(a[valid])
-            if c['schema_id']=='mano/axis_angle61/v1':assert a.shape==(T,2,61)
-        elif data.suffix=='.mkv':
-            assert c['storage']['profile'] in registry['storage_profiles']
-            assert valid.shape==(T,) and valid.all()
-        for attr in c.get('attributes',{}).values():
-            a=arr(safe_ref(p,attr['data_ref']));b=arr(safe_ref(p,attr['valid_ref']))
-            assert b.dtype==bool and a.shape==b.shape
-            assert np.isfinite(a[b]).all() and ((a[b]>=0)&(a[b]<=1)).all() and np.isnan(a[~b]).all()
-        if 'distortion_ref' in c:
-            d=arr(safe_ref(p,c['distortion_ref']));assert d.shape==(T,8) and np.isfinite(d).all()
-        summary.append({'component':key,'schema':c['schema_id'],'valid':int(valid.sum()),'total':int(valid.size)})
-    for op in sorted((p/'annotation/object').glob('*')):
-        om=load(op/'metainfo.json');mesh=safe_ref(p,om['mesh_ref'])
-        if 'vertex_part_ids_ref' in om:
-            ids=arr(safe_ref(p,om['vertex_part_ids_ref']));assert ids.dtype==np.int64
-            assert len(ids)==sum(line.startswith('v ') for line in mesh.read_text().splitlines())
-            assert om['topology_id']=='sha256:'+digest(mesh)
-            if not (op/'pose.pt').exists():
-                assert m.get('omitted_components',{}).get('object_pose')
-            else:
-                root=arr(op/'pose.pt');top=arr(op/'parts/top/pose.pt');joint=arr(op/'joint_state.pt')[:,0]
-                ok=arr(op/'pose_valid.pt');H=np.broadcast_to(np.eye(4),(ok.sum(),4,4)).copy()
-                H[:,:3,:3]=Rotation.from_rotvec(np.c_[np.zeros((ok.sum(),2)),-joint[ok]]).as_matrix()
-                if ok.any():assert np.max(np.abs(root[ok]@H-top[ok]))<2e-6
-        for obj in (op/'assets').glob('*.obj'):
-            for row in obj.read_text().splitlines():
-                if row.startswith('mtllib '):
-                    material=safe_ref(obj.parent,row.split(maxsplit=1)[1])
-                    for r in material.read_text().splitlines():
-                        if r.lower().startswith('map_kd '):safe_ref(material.parent,r.split(maxsplit=1)[1])
-    records=load(DATA_ROOT/m['dataset_id']/'tmp'/RUN/'checks'/f'{m["sequence_id"]}__{m["camera_id"]}.json')['checks']
-    assert records and all(x.get('passed',False) for x in records)
-    media=next(r for r in records if r['test'] in ['all_source_JPEG_packets_and_RGB_pixels_exact','all_source_JPEG_packets_exact']);assert media['frames']==T
-    if media['test']=='all_source_JPEG_packets_exact':
-        pixels=next(r for r in records if r['test']=='source_RGB_pixels_exact')
-        assert pixels['scope']=='deterministic_sample' and pixels['sampled_frames']==len(pixels['frame_indices']) and pixels['sampled_frames']==min(T,16)
-        assert pixels['decoded_from_container'] and pixels['frame_indices']==sorted(set(pixels['frame_indices']))
-    if 'mano' not in m['components']:
-        assert m.get('omitted_components',{}).get('mano')
-        assert not (p/'annotation/mano/motion.pt').exists()
-        return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed','mano':'source modality absent, omitted'}
-    actual=arr(p/'annotation/mano/motion_valid.pt')
-    if m['dataset_id']=='HOT3D':
-        src=m['components']['mano']['source_locator']
-        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/src['archive']) as z:
-            native={r['timestamp_ns']:r['hand_poses'] for r in map(json.loads,z.read(src['member']).splitlines())}
-        expected=np.array([[str(side) in native.get(f['annotation_matches']['mano']['source_timestamp_ns'],{}) for side in range(2)] for f in frames],bool)
-    else:expected=np.array([[f['source_annotation_index'] is not None]*2 for f in frames],bool)
-    if m['dataset_id']=='HOT3D' and m['components']['mano'].get('valid_policy'):
-        source=m['components']['mano']['valid_policy']['source_locator']
-        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/source['archive']) as z:
-            lookup={int(r['timestamp[ns]']):r['mask']=='True' for r in csv.DictReader(io.StringIO(z.read(source['member']).decode())) if r['stream_id']==m['camera_id']}
-        qa=[lookup[f['timecode_ns']] for f in frames]
-        expected &= np.array(qa,bool)[:,None]
-    if m['dataset_id']=='ARCTIC':
-        rejected=m['components']['mano'].get('valid_policy',{}).get('local_excluded_frame_indices',[])
-        expected[rejected]=False
-    assert np.array_equal(expected,actual),'annotation validity disagrees with declared source and QA/local policy'
-    records.append({'test':'all_frame_source_annotation_and_QA_policy','passed':True,'left_right_valid_counts':actual.sum(0).tolist(),'frames_without_usable_hand':int((~actual.any(1)).sum())})
-    return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed'}
-
-def select_images(p,indices,dest):
-    dest.mkdir(parents=True,exist_ok=True)
-    selection='+'.join(f'eq(n,{i})' for i in indices)
-    command([FF,'-v','error','-y','-threads','2','-noautorotate','-i',p/'observation/rgb.mkv',
-        '-vf',f"select='{selection}',scale=900:900:force_original_aspect_ratio=decrease",'-fps_mode','vfr',dest/'decoded-%03d.png'])
-    output=sorted(dest.glob('decoded-*.png'));assert len(output)==len(indices)
-    return dict(zip(indices,output))
-
-def project_for_view(points,m,params,scale):
-    uv=project_points(points,m['camera']['model'],params)
-    if m['camera']['model']=='fisheye624':
-        angle=np.arctan2(np.linalg.norm(points[:,:2],axis=1),points[:,2])
-        uv[angle>m['camera']['max_solid_angle']]=np.nan
-    return uv*scale
-
-def draw_edges(canvas,uv,edges,color):
-    H,W=canvas.shape[:2]
-    for a,b in edges:
-        xy=uv[[a,b]]
-        if not np.isfinite(xy).all():continue
-        if np.max(np.abs(xy))>max(W,H)*20:continue
-        xy=np.rint(xy).astype(int)
-        if np.linalg.norm(xy[0]-xy[1])>max(W,H)*.35:continue
-        ok,start,end=cv2.clipLine((0,0,W,H),tuple(xy[0]),tuple(xy[1]))
-        if ok:cv2.line(canvas,start,end,color,1,cv2.LINE_AA)
-
-def review_view(p,m,frames,dest):
-    T=m['num_frames'];indices=sorted(set([0,1,T//4,T//2,3*T//4,T-1]));imgs=select_images(p,indices,dest)
-    K=arr(p/'camera/intrinsic.pt');D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None
-    motion=arr(p/'annotation/mano/motion.pt');valid=arr(p/'annotation/mano/motion_valid.pt');models=[Mano('left'),Mano('right')]
-    if m['components']['mano']['model_profile']['left_shapedirs_fix']:
-        models[0].data['shapedirs'][:,0]*=-1
-        with torch.no_grad():models[0].layer.shapedirs[:,0]*=-1
-    objects=[]
-    for op in sorted((p/'annotation/object').iterdir()):
-        om=load(op/'metainfo.json');mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
-        if 'vertex_part_ids_ref' in om:
-            vertices=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
-            # trimesh may split OBJ vertices along UV seams; render exact source-ordered vertices as points.
-            objects.append((op,om,vertices,None,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),arr(p/om['vertex_part_ids_ref']),arr(op/'parts/top/pose.pt')))
-        else:
-            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//2500)]
-            objects.append((op,om,np.asarray(mesh.vertices),edges,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),None,None))
-    records=[]
-    for i,source in imgs.items():
-        original=np.asarray(Image.open(source).convert('RGB'));overlay=original.copy()
-        scale=np.array([original.shape[1]/m['camera']['image_size'][0],original.shape[0]/m['camera']['image_size'][1]])
-        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
-        for side,model in enumerate(models):
-            if not valid[i,side]:continue
-            h=motion[i,side];vertices,_,_=model.geometry(h[:48],h[48:58],h[58:]);uv=project_for_view(vertices[0],m,params,scale)
-            faces=np.asarray(model.layer.faces);edges=np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]);draw_edges(overlay,uv,edges,(0,235,255) if side==0 else (255,135,20))
-        palette=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
-        for c,(op,om,vertices,edges,P,ok,parts,top) in enumerate(objects):
-            if not ok[i]:continue
-            xyz=vertices@P[i,:3,:3].T+P[i,:3,3]
-            if parts is not None:xyz[parts==1]=vertices[parts==1]@top[i,:3,:3].T+top[i,:3,3]
-            uv=project_for_view(xyz,m,params,scale)
-            if edges is not None:draw_edges(overlay,uv,edges,palette[c%len(palette)])
-            else:
-                for xy in uv[::max(1,len(uv)//6000)]:
-                    if np.isfinite(xy).all() and 0<=xy[0]<overlay.shape[1] and 0<=xy[1]<overlay.shape[0]:cv2.circle(overlay,tuple(np.rint(xy).astype(int)),1,palette[c%len(palette)],-1)
-        blended=cv2.addWeighted(overlay,.8,original,.2,0);output=dest/f'frame-{i:06d}-overlay.png';raw=dest/f'frame-{i:06d}-rgb.png'
-        Image.fromarray(blended).save(output);source.replace(raw)
-        records.append({'frame_index':i,'source_frame_id':frames[i]['source_frame_id'],'rgb':raw.name,'overlay':output.name,'mano_valid':valid[i].tolist()})
-    return records
-
-def write_reports(dataset,views):
-    base=DATA_ROOT/dataset;root=base/RELEASE;doc=root/'doc';review=doc/'validation';reports=[];cards=[];thumbs=[]
-    rawpaths=set()
-    for p in views:
-        if dataset=='ARCTIC':
-            for assets in (p/'annotation/object').glob('*/assets'):repair_arctic_material_refs(assets)
-        m,frames,summary=validate_view(p);cam=m['camera_id'];samples=review_view(p,m,frames,review/'images'/cam)
-        summary['visual_samples']=samples;reports.append(summary)
-        # All actually consumed source files; camera/mano/object locators name source archives.
-        rawpaths.add(m['components']['rgb']['source_locator'].get('archive',m['components']['rgb']['source_locator'].get('file')))
-        for comp in m['components'].values():
-            src=comp['source_locator']
-            if 'archive' in src:rawpaths.add(src['archive'])
-        if dataset=='ARCTIC':rawpaths.add('raw/data/splits_json.zip')
-        for op in (p/'annotation/object').iterdir():
-            glb=load(op/'metainfo.json').get('original_glb_ref')
-            if glb:rawpaths.add(load(op/'metainfo.json')['original_glb_source_locator']['file'])
-        opts=''.join(f'<option value="{i}"'+(' selected' if s['frame_index']==m['num_frames']//2 else '')+f'>index {s["frame_index"]} · source {s["source_frame_id"]} · valid {s["mano_valid"]}</option>' for i,s in enumerate(samples))
-        samples_js=json.dumps([{k:(f'images/{cam}/'+s[k] if k in ('rgb','overlay') else s[k]) for k in s} for s in samples])
-        cards.append(f'<section class="card" data-samples=\'{html.escape(samples_js,quote=True)}\'><h2>Camera {cam} · {m["camera"]["model"]} · {m["num_frames"]} 帧</h2><select onchange="show(this.closest(\'.card\'))">{opts}</select><label><input type="checkbox" checked onchange="show(this.closest(\'.card\'))"> 显示投影</label><br><a class="zoom" target="_blank"><img alt="共享产物实际帧与投影"></a><p class="caption"></p></section>')
-        middle=samples[len(samples)//2];thumb=Image.open(review/'images'/cam/middle['overlay']).convert('RGB');thumb.thumbnail((400,320));tile=Image.new('RGB',(420,355),'#18202b');tile.paste(thumb,((420-thumb.width)//2,28));ImageDraw.Draw(tile).text((10,8),f'{dataset} camera {cam} / frame {middle["frame_index"]}',fill='white');thumbs.append(tile)
-        print('REVIEWED',dataset,cam,flush=True)
-    cols=3;grid=Image.new('RGB',(cols*420,((len(thumbs)+cols-1)//cols)*355),'#18202b')
-    for i,t in enumerate(thumbs):grid.paste(t,((i%cols)*420,(i//cols)*355))
-    grid.save(review/'overview.png')
-    source_manifest=[]
-    ledger=load(base/'raw/doc'/('archive_checksums.json' if dataset=='ARCTIC' else 'file_checksums.json'))
-    for rel in sorted(x for x in rawpaths if x):
-        file=base/rel;sha=digest(file);record=ledger[rel.removeprefix('raw/data/')]
-        assert record['sha256']==sha and record['size']==file.stat().st_size
-        assert record.get('official_sha256_match',record.get('official_digest_match')) is True
-        if file.suffix=='.zip':assert record['zip_crc']=='passed_all_members'
-        if dataset=='HOT3D':assert record['fresh_download_run']=='2026-09-18-aria-fresh'
-        source_manifest.append({'path':rel,'bytes':file.stat().st_size,'sha256':sha,'matches_verified_official_download':True,'zip_crc_at_download':record.get('zip_crc')})
-    save(doc/'pilot-source-manifest.json',source_manifest)
-    script_manifest=[]
-    for name in ['review_fresh_sequence_pilots.py','fresh_sequence_common.py','convert_fresh_sequence_pilots.py']:
-        shutil.copyfile(WORK/'scripts'/name,root/'script'/name)
-    for p in sorted((root/'script').glob('*.py')):script_manifest.append({'path':p.relative_to(root).as_posix(),'sha256':digest(p)})
-    full={'dataset':dataset,'stage':'pilot_awaiting_human_review','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False,
-        'scope':'One complete selected sequence, every released camera frame; not full-dataset acceptance','views':reports,
-        'sources_manifest':'../pilot-source-manifest.json','script_manifest':script_manifest,'preview_selection':'Deterministic boundary and quartile samples per camera; not random or full-dataset review',
-        'preview_rendering':'Derived MANO/object projections from shared tensors, not segmentation ground truth; no depth occlusion test, hidden surfaces may be drawn',
-        'created_at':datetime.now().astimezone().isoformat()}
-    save(review/'report.json',full);save(review/'status.json',{k:full[k] for k in ['dataset','stage','human_review','full_preprocessing_authorized','upload_authorized']})
-    notes=('首帧 RGB 存在，动捕标注无对应项，valid=false；已保留。相机 0 使用 rational8 畸变，外部八相机使用官方针孔投影。关节绕父坐标系 -Z 轴旋转，部件点/网格保持米制。' if dataset=='ARCTIC' else 'Aria 三路相机均保留；RGB 与两路灰度均由原 JPEG 直接封装。device time 经官方映射对齐 timecode；原始 visibility 按百分比转 [0,1]，独立于标注 valid。三路按时间匹配，不假定相同行号自动同步。')
-    if dataset=='HOT3D':notes+=' 每相机双手各806帧有MANO、208帧原始手参数为空；画面中有手但无投影的末尾帧属于源标注缺失，已验证并保留为valid=false。'
-    if dataset=='ARCTIC':notes+=' 原始部件OBJ的材质文件名引用错误已在共享层修正为实际存在的material.mtl，材质标识与纹理不变。'
-    missing=('本次官方 raw 中无独立深度、像素分割、接触标签、实测手关键点数组。保留 MANO，供任务层按需生成关键点；未把预览投影写成新增标注。' if dataset=='ARCTIC' else '本范围无深度流或像素分割；masks/*.csv 是帧筛选条件，不能当作像素 mask。UmeTrack 与 MPS 替代估计保留在 raw；shared 选择 MANO 和官方 GT 相机。没有额外生成关键点或接触标签。')
-    text=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 序列试点审核</title><style>body{{font:16px/1.65 system-ui;background:#f3f5f8;color:#18202b;max-width:1450px;margin:24px auto;padding:0 20px}}header,.card{{background:white;padding:22px;border-radius:14px;margin-bottom:18px}}.status{{color:#9a5800;font-weight:bold}}.grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(360px,1fr));gap:18px}}.card img{{max-width:100%;max-height:670px;margin-top:12px}}select{{max-width:100%;padding:8px}}a{{color:#1766ab}}label{{display:inline-block;margin:10px}}h1{{margin:0}}.caption{{font-size:14px}}</style><header><h1>{dataset} · 完整序列试点</h1><p class="status">自动校验已完成；人工审核待确认。尚未开始全量预处理或上传。</p><p>共 {len(views)} 路相机、{sum(r['frames'] for r in reports):,} 个相机帧。全部源 JPEG 码流、全部解码 RGB 帧已逐一比较一致；数值转换、有效性和引用检查通过。</p><p>{notes}</p><p>{missing}</p><p>下方是从 shared 实际产物读取的边界及四分位样本。青色=左手，橙色=右手，其余颜色=物体。投影仅用于几何核验，<b>不是源分割，也没有做遮挡剔除</b>。点击图像可放大；取消勾选可查看原图。</p><p><a href="REPORT.md">验收说明</a> · <a href="report.json">机器可读结果</a> · <a href="../RAW_TO_SHARED.md">raw → share 对照</a> · <a href="../CONTRACT.md">约定</a> · <a href="overview.png">全部相机总览</a></p></header><main class="grid">{''.join(cards)}</main><script>function show(card){{let s=JSON.parse(card.dataset.samples)[Number(card.querySelector('select').value)];let path=card.querySelector('input').checked?s.overlay:s.rgb;card.querySelector('img').src=path;card.querySelector('.zoom').href=path;card.querySelector('.caption').textContent='frame_index='+s.frame_index+' / 原帧 '+s.source_frame_id+' / 左右手 valid='+s.mano_valid;}}document.querySelectorAll('.card').forEach(show)</script></html>'''
-    (review/'index.html').write_text(text,encoding='utf-8')
-    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | '+', '.join(str(x['frame_index']) for x in r['visual_samples'])+' | 通过 |' for r in reports)
-    report=f'''# {dataset} 完整序列试点验收\n\n状态：**自动检查通过，人工确认待完成**。本报告不是全量验收或发布许可。\n\n[离线预览](index.html) · [机器结果](report.json) · [原始包摘要](../pilot-source-manifest.json)\n\n| 相机 | 全序列帧数 | 可视化帧索引 | 自动校验 |\n|---|---:|---|---|\n{rows}\n\n## 已验证\n\n- 完整所选序列的每一张源 JPEG 与 MKV 取回的压缩包 SHA256 相同；全部帧在同一 FFmpeg/noautorotate/rgb24 配置下解码字节相同，顺序和数量一致。不是有损重编码。\n- ��部有效 MANO 帧的相机坐标变换与直接变换几何一致；独立源模型配置抽样校验非平手均值/PCA 展开。张量 float32 保存读取一致，invalid 为 NaN，valid 为 bool。\n- SE(3) 正交性、单位与资产引用检查；ARCTIC 部件关节运动与源旋转公式逐帧对照，HOT3D 物体 OBJ 尺度、三角形及纹理像素往返一致。\n- 所有实际组件使用现有注册 schema；没有为缺省模态伪造值。\n\n## 帧对应和缺省范围\n\n{notes}\n\n{missing}\n\n## 人工核验边界\n\n每相机选序列起点、紧邻帧、四分位、中点、四分之三及终点，不是随机抽查。预览只用于核对几何对齐；投影线条/点不是分割真值，遮挡表面可能被画出。数值自洽不等于图像投影正确，请检查所有相机和多个时刻。此处一序列通过不能覆盖全数据集、其他物体或异常序列。全量处理后仍需独立随机抽查和用户发布确认。\n'''
-    (review/'REPORT.md').write_text(report,encoding='utf-8')
-    return full
-
-if __name__=='__main__':
-    raise SystemExit('Static-sample review is retired. Use render_full_sequence_review.py DATASET and finalize_fresh_sequence_review.py; validate_view remains a reusable numeric helper.')
+"""Validate actual shared outputs and create portable, offline pilot reviews.
+
+Run after convert_fresh_sequence_pilots.py. Never authorizes full processing.
+"""
+from fresh_sequence_common import *
+import html
+from datetime import datetime
+
+def arr(p):
+    value=torch.load(p,map_location='cpu',weights_only=True)
+    assert isinstance(value,torch.Tensor) and value.device.type=='cpu'
+    return value.numpy()
+
+def safe_ref(root,ref):
+    path=(root/ref).resolve()
+    assert path.is_relative_to(root.resolve()) and path.is_file(),ref
+    return path
+
+def validate_view(p):
+    m=load(p/'metainfo.json');T=m['num_frames'];registry=load(WORK/'distribution/hand-warehouse-1.0/schema.json')
+    assert m['schema_version']==registry['schema_version'] and m['pilot_complete']
+    frames=[json.loads(line) for line in (p/'camera/frames.jsonl').read_text().splitlines()]
+    assert len(frames)==T and [f['frame_index'] for f in frames]==list(range(T))
+    assert len({f['source_frame_id'] for f in frames})==T
+    for frame in frames:
+        for match in frame.get('annotation_matches',{}).values():
+            if 'available' in match:match['source_record_present']=match.pop('available')
+    lines(p/'camera/frames.jsonl',frames)
+    summary=[]
+    for key,c in m['components'].items():
+        assert c['schema_id'] in registry['components'],(key,c['schema_id'])
+        assert c['available'] and c['source_locator'] and c['provenance']['kind'] in ['source','derived','format_conversion']
+        data=safe_ref(p,c['data_ref']);valid=arr(safe_ref(p,c['valid_ref']))
+        assert valid.dtype==bool and valid.ndim==len(c['valid_axes'])
+        if data.suffix=='.pt':
+            a=arr(data);assert a.dtype==np.float32 and a.shape[0]==T
+            assert a.shape[:valid.ndim]==valid.shape and len(c['axes'])==a.ndim
+            assert np.isfinite(a[valid]).all() and np.isnan(a[~valid]).all()
+            if c['schema_id'] in ['object/rigid/v1','transform/se3/v1']:assert_se3(a[valid])
+            if c['schema_id']=='mano/axis_angle61/v1':assert a.shape==(T,2,61)
+        elif data.suffix=='.mkv':
+            assert c['storage']['profile'] in registry['storage_profiles']
+            assert valid.shape==(T,) and valid.all()
+        for attr in c.get('attributes',{}).values():
+            a=arr(safe_ref(p,attr['data_ref']));b=arr(safe_ref(p,attr['valid_ref']))
+            assert b.dtype==bool and a.shape==b.shape
+            assert np.isfinite(a[b]).all() and ((a[b]>=0)&(a[b]<=1)).all() and np.isnan(a[~b]).all()
+        if 'distortion_ref' in c:
+            d=arr(safe_ref(p,c['distortion_ref']));assert d.shape==(T,8) and np.isfinite(d).all()
+        summary.append({'component':key,'schema':c['schema_id'],'valid':int(valid.sum()),'total':int(valid.size)})
+    for op in sorted((p/'annotation/object').glob('*')):
+        om=load(op/'metainfo.json');mesh=safe_ref(p,om['mesh_ref'])
+        if 'vertex_part_ids_ref' in om:
+            ids=arr(safe_ref(p,om['vertex_part_ids_ref']));assert ids.dtype==np.int64
+            assert len(ids)==sum(line.startswith('v ') for line in mesh.read_text().splitlines())
+            assert om['topology_id']=='sha256:'+digest(mesh)
+            if not (op/'pose.pt').exists():
+                assert m.get('omitted_components',{}).get('object_pose')
+            else:
+                root=arr(op/'pose.pt');top=arr(op/'parts/top/pose.pt');joint=arr(op/'joint_state.pt')[:,0]
+                ok=arr(op/'pose_valid.pt');H=np.broadcast_to(np.eye(4),(ok.sum(),4,4)).copy()
+                H[:,:3,:3]=Rotation.from_rotvec(np.c_[np.zeros((ok.sum(),2)),-joint[ok]]).as_matrix()
+                if ok.any():assert np.max(np.abs(root[ok]@H-top[ok]))<2e-6
+        for obj in (op/'assets').glob('*.obj'):
+            for row in obj.read_text().splitlines():
+                if row.startswith('mtllib '):
+                    material=safe_ref(obj.parent,row.split(maxsplit=1)[1])
+                    for r in material.read_text().splitlines():
+                        if r.lower().startswith('map_kd '):safe_ref(material.parent,r.split(maxsplit=1)[1])
+    records=load(DATA_ROOT/m['dataset_id']/'tmp'/RUN/'checks'/f'{m["sequence_id"]}__{m["camera_id"]}.json')['checks']
+    assert records and all(x.get('passed',False) for x in records)
+    media=next(r for r in records if r['test'] in ['all_source_JPEG_packets_and_RGB_pixels_exact','all_source_JPEG_packets_exact']);assert media['frames']==T
+    if media['test']=='all_source_JPEG_packets_exact':
+        pixels=next(r for r in records if r['test']=='source_RGB_pixels_exact')
+        assert pixels['scope']=='deterministic_sample' and pixels['sampled_frames']==len(pixels['frame_indices']) and pixels['sampled_frames']==min(T,16)
+        assert pixels['decoded_from_container'] and pixels['frame_indices']==sorted(set(pixels['frame_indices']))
+    if 'mano' not in m['components']:
+        assert m.get('omitted_components',{}).get('mano')
+        assert not (p/'annotation/mano/motion.pt').exists()
+        return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed','mano':'source modality absent, omitted'}
+    actual=arr(p/'annotation/mano/motion_valid.pt')
+    if m['dataset_id']=='HOT3D':
+        src=m['components']['mano']['source_locator']
+        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/src['archive']) as z:
+            native={r['timestamp_ns']:r['hand_poses'] for r in map(json.loads,z.read(src['member']).splitlines())}
+        expected=np.array([[str(side) in native.get(f['annotation_matches']['mano']['source_timestamp_ns'],{}) for side in range(2)] for f in frames],bool)
+    else:expected=np.array([[f['source_annotation_index'] is not None]*2 for f in frames],bool)
+    if m['dataset_id']=='HOT3D' and m['components']['mano'].get('valid_policy'):
+        source=m['components']['mano']['valid_policy']['source_locator']
+        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/source['archive']) as z:
+            lookup={int(r['timestamp[ns]']):r['mask']=='True' for r in csv.DictReader(io.StringIO(z.read(source['member']).decode())) if r['stream_id']==m['camera_id']}
+        qa=[lookup[f['timecode_ns']] for f in frames]
+        expected &= np.array(qa,bool)[:,None]
+    if m['dataset_id']=='ARCTIC':
+        rejected=m['components']['mano'].get('valid_policy',{}).get('local_excluded_frame_indices',[])
+        expected[rejected]=False
+    assert np.array_equal(expected,actual),'annotation validity disagrees with declared source and QA/local policy'
+    records.append({'test':'all_frame_source_annotation_and_QA_policy','passed':True,'left_right_valid_counts':actual.sum(0).tolist(),'frames_without_usable_hand':int((~actual.any(1)).sum())})
+    return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed'}
+
+def select_images(p,indices,dest):
+    dest.mkdir(parents=True,exist_ok=True)
+    selection='+'.join(f'eq(n,{i})' for i in indices)
+    command([FF,'-v','error','-y','-threads','2','-noautorotate','-i',p/'observation/rgb.mkv',
+        '-vf',f"select='{selection}',scale=900:900:force_original_aspect_ratio=decrease",'-fps_mode','vfr',dest/'decoded-%03d.png'])
+    output=sorted(dest.glob('decoded-*.png'));assert len(output)==len(indices)
+    return dict(zip(indices,output))
+
+def project_for_view(points,m,params,scale):
+    uv=project_points(points,m['camera']['model'],params)
+    if m['camera']['model']=='fisheye624':
+        angle=np.arctan2(np.linalg.norm(points[:,:2],axis=1),points[:,2])
+        uv[angle>m['camera']['max_solid_angle']]=np.nan
+    return uv*scale
+
+def draw_edges(canvas,uv,edges,color):
+    H,W=canvas.shape[:2]
+    for a,b in edges:
+        xy=uv[[a,b]]
+        if not np.isfinite(xy).all():continue
+        if np.max(np.abs(xy))>max(W,H)*20:continue
+        xy=np.rint(xy).astype(int)
+        if np.linalg.norm(xy[0]-xy[1])>max(W,H)*.35:continue
+        ok,start,end=cv2.clipLine((0,0,W,H),tuple(xy[0]),tuple(xy[1]))
+        if ok:cv2.line(canvas,start,end,color,1,cv2.LINE_AA)
+
+def review_view(p,m,frames,dest):
+    T=m['num_frames'];indices=sorted(set([0,1,T//4,T//2,3*T//4,T-1]));imgs=select_images(p,indices,dest)
+    K=arr(p/'camera/intrinsic.pt');D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None
+    motion=arr(p/'annotation/mano/motion.pt');valid=arr(p/'annotation/mano/motion_valid.pt');models=[Mano('left'),Mano('right')]
+    if m['components']['mano']['model_profile']['left_shapedirs_fix']:
+        models[0].data['shapedirs'][:,0]*=-1
+        with torch.no_grad():models[0].layer.shapedirs[:,0]*=-1
+    objects=[]
+    for op in sorted((p/'annotation/object').iterdir()):
+        om=load(op/'metainfo.json');mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
+        if 'vertex_part_ids_ref' in om:
+            vertices=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
+            # trimesh may split OBJ vertices along UV seams; render exact source-ordered vertices as points.
+            objects.append((op,om,vertices,None,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),arr(p/om['vertex_part_ids_ref']),arr(op/'parts/top/pose.pt')))
+        else:
+            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//2500)]
+            objects.append((op,om,np.asarray(mesh.vertices),edges,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),None,None))
+    records=[]
+    for i,source in imgs.items():
+        original=np.asarray(Image.open(source).convert('RGB'));overlay=original.copy()
+        scale=np.array([original.shape[1]/m['camera']['image_size'][0],original.shape[0]/m['camera']['image_size'][1]])
+        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
+        for side,model in enumerate(models):
+            if not valid[i,side]:continue
+            h=motion[i,side];vertices,_,_=model.geometry(h[:48],h[48:58],h[58:]);uv=project_for_view(vertices[0],m,params,scale)
+            faces=np.asarray(model.layer.faces);edges=np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]);draw_edges(overlay,uv,edges,(0,235,255) if side==0 else (255,135,20))
+        palette=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
+        for c,(op,om,vertices,edges,P,ok,parts,top) in enumerate(objects):
+            if not ok[i]:continue
+            xyz=vertices@P[i,:3,:3].T+P[i,:3,3]
+            if parts is not None:xyz[parts==1]=vertices[parts==1]@top[i,:3,:3].T+top[i,:3,3]
+            uv=project_for_view(xyz,m,params,scale)
+            if edges is not None:draw_edges(overlay,uv,edges,palette[c%len(palette)])
+            else:
+                for xy in uv[::max(1,len(uv)//6000)]:
+                    if np.isfinite(xy).all() and 0<=xy[0]<overlay.shape[1] and 0<=xy[1]<overlay.shape[0]:cv2.circle(overlay,tuple(np.rint(xy).astype(int)),1,palette[c%len(palette)],-1)
+        blended=cv2.addWeighted(overlay,.8,original,.2,0);output=dest/f'frame-{i:06d}-overlay.png';raw=dest/f'frame-{i:06d}-rgb.png'
+        Image.fromarray(blended).save(output);source.replace(raw)
+        records.append({'frame_index':i,'source_frame_id':frames[i]['source_frame_id'],'rgb':raw.name,'overlay':output.name,'mano_valid':valid[i].tolist()})
+    return records
+
+def write_reports(dataset,views):
+    base=DATA_ROOT/dataset;root=base/RELEASE;doc=root/'doc';review=doc/'validation';reports=[];cards=[];thumbs=[]
+    rawpaths=set()
+    for p in views:
+        if dataset=='ARCTIC':
+            for assets in (p/'annotation/object').glob('*/assets'):repair_arctic_material_refs(assets)
+        m,frames,summary=validate_view(p);cam=m['camera_id'];samples=review_view(p,m,frames,review/'images'/cam)
+        summary['visual_samples']=samples;reports.append(summary)
+        # All actually consumed source files; camera/mano/object locators name source archives.
+        rawpaths.add(m['components']['rgb']['source_locator'].get('archive',m['components']['rgb']['source_locator'].get('file')))
+        for comp in m['components'].values():
+            src=comp['source_locator']
+            if 'archive' in src:rawpaths.add(src['archive'])
+        if dataset=='ARCTIC':rawpaths.add('raw/data/splits_json.zip')
+        for op in (p/'annotation/object').iterdir():
+            glb=load(op/'metainfo.json').get('original_glb_ref')
+            if glb:rawpaths.add(load(op/'metainfo.json')['original_glb_source_locator']['file'])
+        opts=''.join(f'<option value="{i}"'+(' selected' if s['frame_index']==m['num_frames']//2 else '')+f'>index {s["frame_index"]} · source {s["source_frame_id"]} · valid {s["mano_valid"]}</option>' for i,s in enumerate(samples))
+        samples_js=json.dumps([{k:(f'images/{cam}/'+s[k] if k in ('rgb','overlay') else s[k]) for k in s} for s in samples])
+        cards.append(f'<section class="card" data-samples=\'{html.escape(samples_js,quote=True)}\'><h2>Camera {cam} · {m["camera"]["model"]} · {m["num_frames"]} 帧</h2><select onchange="show(this.closest(\'.card\'))">{opts}</select><label><input type="checkbox" checked onchange="show(this.closest(\'.card\'))"> 显示投影</label><br><a class="zoom" target="_blank"><img alt="共享产物实际帧与投影"></a><p class="caption"></p></section>')
+        middle=samples[len(samples)//2];thumb=Image.open(review/'images'/cam/middle['overlay']).convert('RGB');thumb.thumbnail((400,320));tile=Image.new('RGB',(420,355),'#18202b');tile.paste(thumb,((420-thumb.width)//2,28));ImageDraw.Draw(tile).text((10,8),f'{dataset} camera {cam} / frame {middle["frame_index"]}',fill='white');thumbs.append(tile)
+        print('REVIEWED',dataset,cam,flush=True)
+    cols=3;grid=Image.new('RGB',(cols*420,((len(thumbs)+cols-1)//cols)*355),'#18202b')
+    for i,t in enumerate(thumbs):grid.paste(t,((i%cols)*420,(i//cols)*355))
+    grid.save(review/'overview.png')
+    source_manifest=[]
+    ledger=load(base/'raw/doc'/('archive_checksums.json' if dataset=='ARCTIC' else 'file_checksums.json'))
+    for rel in sorted(x for x in rawpaths if x):
+        file=base/rel;sha=digest(file);record=ledger[rel.removeprefix('raw/data/')]
+        assert record['sha256']==sha and record['size']==file.stat().st_size
+        assert record.get('official_sha256_match',record.get('official_digest_match')) is True
+        if file.suffix=='.zip':assert record['zip_crc']=='passed_all_members'
+        if dataset=='HOT3D':assert record['fresh_download_run']=='2026-09-18-aria-fresh'
+        source_manifest.append({'path':rel,'bytes':file.stat().st_size,'sha256':sha,'matches_verified_official_download':True,'zip_crc_at_download':record.get('zip_crc')})
+    save(doc/'pilot-source-manifest.json',source_manifest)
+    script_manifest=[]
+    for p in sorted((root/'script').rglob('*.py')):script_manifest.append({'path':p.relative_to(root).as_posix(),'sha256':digest(p)})
+    full={'dataset':dataset,'stage':'pilot_awaiting_human_review','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False,
+        'scope':'One complete selected sequence, every released camera frame; not full-dataset acceptance','views':reports,
+        'sources_manifest':'../pilot-source-manifest.json','script_manifest':script_manifest,'preview_selection':'Deterministic boundary and quartile samples per camera; not random or full-dataset review',
+        'preview_rendering':'Derived MANO/object projections from shared tensors, not segmentation ground truth; no depth occlusion test, hidden surfaces may be drawn',
+        'created_at':datetime.now().astimezone().isoformat()}
+    save(review/'report.json',full);save(review/'status.json',{k:full[k] for k in ['dataset','stage','human_review','full_preprocessing_authorized','upload_authorized']})
+    notes=('首帧 RGB 存在，动捕标注无对应项，valid=false；已保留。相机 0 使用 rational8 畸变，外部八相机使用官方针孔投影。关节绕父坐标系 -Z 轴旋转，部件点/网格保持米制。' if dataset=='ARCTIC' else 'Aria 三路相机均保留；RGB 与两路灰度均由原 JPEG 直接封装。device time 经官方映射对齐 timecode；原始 visibility 按百分比转 [0,1]，独立于标注 valid。三路按时间匹配，不假定相同行号自动同步。')
+    if dataset=='HOT3D':notes+=' 每相机双手各806帧有MANO、208帧原始手参数为空；画面中有手但无投影的末尾帧属于源标注缺失，已验证并保留为valid=false。'
+    if dataset=='ARCTIC':notes+=' 原始部件OBJ的材质文件名引用错误已在共享层修正为实际存在的material.mtl，材质标识与纹理不变。'
+    missing=('本次官方 raw 中无独立深度、像素分割、接触标签、实测手关键点数组。保留 MANO，供任务层按需生成关键点；未把预览投影写成新增标注。' if dataset=='ARCTIC' else '本范围无深度流或像素分割；masks/*.csv 是帧筛选条件，不能当作像素 mask。UmeTrack 与 MPS 替代估计保留在 raw；shared 选择 MANO 和官方 GT 相机。没有额外生成关键点或接触标签。')
+    text=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 序列试点审核</title><style>body{{font:16px/1.65 system-ui;background:#f3f5f8;color:#18202b;max-width:1450px;margin:24px auto;padding:0 20px}}header,.card{{background:white;padding:22px;border-radius:14px;margin-bottom:18px}}.status{{color:#9a5800;font-weight:bold}}.grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(360px,1fr));gap:18px}}.card img{{max-width:100%;max-height:670px;margin-top:12px}}select{{max-width:100%;padding:8px}}a{{color:#1766ab}}label{{display:inline-block;margin:10px}}h1{{margin:0}}.caption{{font-size:14px}}</style><header><h1>{dataset} · 完整序列试点</h1><p class="status">自动校验已完成；人工审核待确认。尚未开始全量预处理或上传。</p><p>共 {len(views)} 路相机、{sum(r['frames'] for r in reports):,} 个相机帧。全部源 JPEG 码流、全部解码 RGB 帧已逐一比较一致；数值转换、有效性和引用检查通过。</p><p>{notes}</p><p>{missing}</p><p>下方是从 shared 实际产物读取的边界及四分位样本。青色=左手，橙色=右手，其余颜色=物体。投影仅用于几何核验，<b>不是源分割，也没有做遮挡剔除</b>。点击图像可放大；取消勾选可查看原图。</p><p><a href="REPORT.md">验收说明</a> · <a href="report.json">机器可读结果</a> · <a href="../RAW_TO_SHARED.md">raw → share 对照</a> · <a href="../CONTRACT.md">约定</a> · <a href="overview.png">全部相机总览</a></p></header><main class="grid">{''.join(cards)}</main><script>function show(card){{let s=JSON.parse(card.dataset.samples)[Number(card.querySelector('select').value)];let path=card.querySelector('input').checked?s.overlay:s.rgb;card.querySelector('img').src=path;card.querySelector('.zoom').href=path;card.querySelector('.caption').textContent='frame_index='+s.frame_index+' / 原帧 '+s.source_frame_id+' / 左右手 valid='+s.mano_valid;}}document.querySelectorAll('.card').forEach(show)</script></html>'''
+    (review/'index.html').write_text(text,encoding='utf-8')
+    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | '+', '.join(str(x['frame_index']) for x in r['visual_samples'])+' | 通过 |' for r in reports)
+    report=f'''# {dataset} 完整序列试点验收\n\n状态：**自动检查通过，人工确认待完成**。本报告不是全量验收或发布许可。\n\n[离线预览](index.html) · [机器结果](report.json) · [原始包摘要](../pilot-source-manifest.json)\n\n| 相机 | 全序列帧数 | 可视化帧索引 | 自动校验 |\n|---|---:|---|---|\n{rows}\n\n## 已验证\n\n- 完整所选序列的每一张源 JPEG 与 MKV 取回的压缩包 SHA256 相同；全部帧在同一 FFmpeg/noautorotate/rgb24 配置下解码字节相同，顺序和数量一致。不是有损重编码。\n- 全部有效 MANO 帧的相机坐标变换与直接变换几何一致；独立源模型配置抽样校验非平手均值/PCA 展开。张量 float32 保存读取一致，invalid 为 NaN，valid 为 bool。\n- SE(3) 正交性、单位与资产引用检查；ARCTIC 部件关节运动与源旋转公式逐帧对照，HOT3D 物体 OBJ 尺度、三角形及纹理像素往返一致。\n- 所有实际组件使用现有注册 schema；没有为缺省模态伪造值。\n\n## 帧对应和缺省范围\n\n{notes}\n\n{missing}\n\n## 人工核验边界\n\n每相机选序列起点、紧邻帧、四分位、中点、四分之三及终点，不是随机抽查。预览只用于核对几何对齐；投影线条/点不是分割真值，遮挡表面可能被画出。数值自洽不等于图像投影正确，请检查所有相机和多个时刻。此处一序列通过不能覆盖全数据集、其他物体或异常序列。全量处理后仍需独立随机抽查和用户发布确认。\n'''
+    (review/'REPORT.md').write_text(report,encoding='utf-8')
+    return full
+
+if __name__=='__main__':
+    raise SystemExit('Static-sample review is retired. Use render_full_sequence_review.py DATASET and finalize_fresh_sequence_review.py; validate_view remains a reusable numeric helper.')

ARCTIC/shared-v1.0/script/internal/sequence_pilot_common.py

@@ -1,53 +1,51 @@
-"""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='<u2' if mask else 'uint8'
-  dst=self.p/path;dst.parent.mkdir(parents=True,exist_ok=True);hashes=[]
-  proc=subprocess.Popen([FF,'-v','error','-y','-f','rawvideo','-pixel_format',pix,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-c:v','ffv1','-level','3','-g','1',str(dst)],stdin=subprocess.PIPE)
-  try:
-   for frame in iterator:
-    a=np.asarray(frame,dtype=dtype);assert a.shape==((h,w) if mask else (h,w,3));b=a.tobytes();hashes.append(hashlib.sha256(b).hexdigest());proc.stdin.write(b)
-  finally:proc.stdin.close()
-  assert proc.wait()==0 and len(hashes)==self.T
-  proc=subprocess.Popen([FF,'-v','error','-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1'],stdout=subprocess.PIPE)
-  nbytes=w*h*(2 if mask else 3)
-  for digest in hashes:
-   b=proc.stdout.read(nbytes);assert len(b)==nbytes and hashlib.sha256(b).hexdigest()==digest,'video roundtrip'
-  assert not proc.stdout.read(1);assert proc.wait()==0
-  self.checks.append({'test':'all_frames_pixel_exact_roundtrip','file':path,'frames':self.T})
-  self.add(name,path,'segment/uint16_labels/v1' if mask else 'rgb/uint8/v1',['T','height','width'] if mask else ['T','height','width','channel'],np.ones(self.T,bool),storage_fps=1,timebase_role='index_only',**kw)
- def intrinsic(self,K):
-  self.m['camera'].update(model='pinhole',schema_id='camera/pinhole/v1')
-  self.array('intrinsic','camera/intrinsic.pt','camera/pinhole/v1',np.broadcast_to(K,(self.T,3,3)),np.ones(self.T,bool),['T','row','column'])
- def finish(self):
-  lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
-  save(self.root/'doc'/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 snapshot(dataset,scripts):
- out=ROOT/dataset/RELEASE/'script';out.mkdir(parents=True,exist_ok=True)
- names={'sequence_pilot_common.py','pilot_build.py','pilot_fetch.py','pilot_mano.py','pilot_hot_normalize.py','audit_source_fields.py',*scripts}
- names.update(p.name for p in (WORK/'scripts').glob('sequence_pilot_*.py'))
- for name in names:shutil.copyfile(WORK/'scripts'/name,out/name)
+"""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='<u2' if mask else 'uint8'
+  dst=self.p/path;dst.parent.mkdir(parents=True,exist_ok=True);hashes=[]
+  proc=subprocess.Popen([FF,'-v','error','-y','-f','rawvideo','-pixel_format',pix,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-c:v','ffv1','-level','3','-g','1',str(dst)],stdin=subprocess.PIPE)
+  try:
+   for frame in iterator:
+    a=np.asarray(frame,dtype=dtype);assert a.shape==((h,w) if mask else (h,w,3));b=a.tobytes();hashes.append(hashlib.sha256(b).hexdigest());proc.stdin.write(b)
+  finally:proc.stdin.close()
+  assert proc.wait()==0 and len(hashes)==self.T
+  proc=subprocess.Popen([FF,'-v','error','-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1'],stdout=subprocess.PIPE)
+  nbytes=w*h*(2 if mask else 3)
+  for digest in hashes:
+   b=proc.stdout.read(nbytes);assert len(b)==nbytes and hashlib.sha256(b).hexdigest()==digest,'video roundtrip'
+  assert not proc.stdout.read(1);assert proc.wait()==0
+  self.checks.append({'test':'all_frames_pixel_exact_roundtrip','file':path,'frames':self.T})
+  self.add(name,path,'segment/uint16_labels/v1' if mask else 'rgb/uint8/v1',['T','height','width'] if mask else ['T','height','width','channel'],np.ones(self.T,bool),storage_fps=1,timebase_role='index_only',**kw)
+ def intrinsic(self,K):
+  self.m['camera'].update(model='pinhole',schema_id='camera/pinhole/v1')
+  self.array('intrinsic','camera/intrinsic.pt','camera/pinhole/v1',np.broadcast_to(K,(self.T,3,3)),np.ones(self.T,bool),['T','row','column'])
+ def finish(self):
+  lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
+  save(self.root/'doc'/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 snapshot(dataset,scripts):
+ # Retained for old callers. The organized bundle must not be repopulated at runtime.
+ (ROOT/dataset/RELEASE/'script').mkdir(parents=True,exist_ok=True)

HOT3D/shared-v1.0/script/internal/apply_hot3d_qa.py

@@ -1,65 +1,65 @@
-"""Apply native HOT3D manual QA to supervision validity, preserving raw diagnostics."""
-from fresh_sequence_common import *
-
-VIDEO_RUN='full-video-review-v2'
-SEQ='P0015_179e1b84'
-
-def csvrows(z,name):return list(csv.DictReader(io.StringIO(z.read(name).decode())))
-
-def apply_qa(sequence=None,publish=True):
-    seq=sequence or SEQ
-    base=DATA_ROOT/'HOT3D';root=base/RELEASE;src=base/'raw/data/Aria'/seq
-    gtfile=next(src.glob('*ground_truth.zip'));qa_report={}
-    with zipfile.ZipFile(gtfile) as z:
-        masks=csvrows(z,'masks/mask_qa_pass.csv');hands=csvrows(z,'box2d_hands.csv');objects=csvrows(z,'box2d_objects.csv')
-    for meta in sorted((root/'data'/seq).glob('*/metainfo.json')):
-        p=meta.parent;m=load(meta);cam=m['camera_id'];T=m['num_frames'];frames=[json.loads(x) for x in (p/'camera/frames.jsonl').read_text().splitlines()]
-        cache=base/'tmp'/VIDEO_RUN/'diagnostic'/(cam if seq==SEQ else seq+'/'+cam)
-        if (cache/'snapshot.json').exists():assert Path(load(cache/'snapshot.json')['source_view']).resolve()==p.resolve()
-        if not (cache/'snapshot.json').exists():
-            cache.mkdir(parents=True,exist_ok=True)
-            for file in p.rglob('*.pt'):
-                target=cache/file.relative_to(p);target.parent.mkdir(parents=True,exist_ok=True);shutil.copyfile(file,target)
-            save(cache/'metainfo.json',m);save(cache/'snapshot.json',{'purpose':'pre-QA native parameter diagnostics only; not shared usable labels','source_view':str(p),'frames':T})
-        lookup={int(r['timestamp[ns]']):r['mask']=='True' for r in masks if r['stream_id']==cam}
-        qa=np.array([lookup[f['timecode_ns']] for f in frames],bool)
-        source={'archive':gtfile.relative_to(base).as_posix(),'member':'masks/mask_qa_pass.csv','stream_id':cam,'time_index_ref':'camera/frames.jsonl:timecode_ns'}
-        policy={'semantics':'usable_annotation','formula':'source_annotation_present AND source_numerically_valid AND official_manual_qa_pass',
-            'qa_scope':'all visible hand/object poses in this camera frame; conservatively exclude all hand/object supervision if false',
-            'source_locator':source,'no_qa_source_rule':'use the component source-validity rules; do not invent QA'}
-        for key,c in m['components'].items():
-            # Camera calibration and RGB are not labels covered by this manual hand/object QA flag.
-            if key=='mano' or c['schema_id'] in ['object/rigid/v1','object/joint_state/v1','object_keypoints/camera/v1']:
-                values=torch.load(cache/c['data_ref'],weights_only=True).numpy();present=torch.load(cache/c['valid_ref'],weights_only=True).numpy()
-                valid=present & qa.reshape((T,)+(1,)*(present.ndim-1));values=values.copy();values[~valid]=np.nan
-                tensor(p/c['data_ref'],values);tensor(p/c['valid_ref'],valid);c['valid_policy']=policy
-            for attrname,a in c.get('attributes',{}).items():
-                if attrname!='visibility':continue
-                ishand=key=='mano';oid=c['source_locator'].get('object_uid');ids=[0,1] if ishand else [oid]
-                rows=hands if ishand else objects;identity='hand_index' if ishand else 'object_uid'
-                native={(int(r['timestamp[ns]']),str(r[identity])):float(r['visibility_ratio[%]']) for r in rows if r['stream_id']==cam and r['visibility_ratio[%]']!=''}
-                values=np.array([[native.get((f['timecode_ns'],str(k)),np.nan) for k in ids] for f in frames],np.float32)
-                if not ishand:values=values[:,0]
-                present=np.isfinite(values);assert ((values[present]>=0)&(values[present]<=1)).all()
-                # Correct the diagnostic copy as well: these values are fractions, despite the source header.
-                tensor(cache/a['data_ref'],values);tensor(cache/a['valid_ref'],present)
-                valid=present & qa.reshape((T,)+(1,)*(present.ndim-1));filtered=values.copy();filtered[~valid]=np.nan
-                tensor(p/a['data_ref'],filtered);tensor(p/a['valid_ref'],valid)
-                a['provenance']={'kind':'source','method':'native [0,1] ratio retained; official loader does not divide misleading [%] header by 100'}
-                a['valid_policy']={'semantics':'usable_visibility_annotation','formula':'source_ratio_present AND official_manual_qa_pass','source_locator':source}
-                a['source_locator']['match_method']='exact'
-        m['validity_revision']='usable-annotation-2026-09-20'
-        m['split']='train';m['split_scheme']='HOT3D_official_participant_split';m.pop('split_reason',None)
-        m['split_source_locator']={'url':'https://github.com/facebookresearch/hot3d/blob/146b34afef8c1a32adeef7e981c070109f225c87/README.md#testtrain-split','participant_id':seq.split('_')[0]}
-        assert seq.split('_')[0] not in {'P0004','P0005','P0006','P0008','P0016','P0020'}
-        save(meta,m)
-        qa_report[cam]={'frames':T,'qa_pass':qa.tolist(),'passed_count':int(qa.sum()),'source_locator':source,
-            'interpretation':'manual QA of visible hand/object poses; not RGB validity or individual error localization'}
-        print('QA_APPLIED',cam,'pass',int(qa.sum()),'fail',int((~qa).sum()),flush=True)
-    save(root/'doc/validation/source_qa.json' if publish else base/'tmp'/RUN/'qa'/f'{seq}.json',qa_report)
-    if publish:
-        for f in ['apply_hot3d_qa.py','fresh_sequence_common.py','convert_fresh_sequence_pilots.py']:
-            shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
-    return qa_report
-
-if __name__=='__main__':apply_qa()
+"""Apply native HOT3D manual QA to supervision validity, preserving raw diagnostics."""
+from fresh_sequence_common import *
+
+VIDEO_RUN='full-video-review-v2'
+SEQ='P0015_179e1b84'
+
+def csvrows(z,name):return list(csv.DictReader(io.StringIO(z.read(name).decode())))
+
+def apply_qa(sequence=None,publish=True):
+    seq=sequence or SEQ
+    base=DATA_ROOT/'HOT3D';root=base/RELEASE;src=base/'raw/data/Aria'/seq
+    gtfile=next(src.glob('*ground_truth.zip'));qa_report={}
+    with zipfile.ZipFile(gtfile) as z:
+        masks=csvrows(z,'masks/mask_qa_pass.csv');hands=csvrows(z,'box2d_hands.csv');objects=csvrows(z,'box2d_objects.csv')
+    for meta in sorted((root/'data'/seq).glob('*/metainfo.json')):
+        p=meta.parent;m=load(meta);cam=m['camera_id'];T=m['num_frames'];frames=[json.loads(x) for x in (p/'camera/frames.jsonl').read_text().splitlines()]
+        cache=base/'tmp'/VIDEO_RUN/'diagnostic'/(cam if seq==SEQ else seq+'/'+cam)
+        if (cache/'snapshot.json').exists():assert Path(load(cache/'snapshot.json')['source_view']).resolve()==p.resolve()
+        if not (cache/'snapshot.json').exists():
+            cache.mkdir(parents=True,exist_ok=True)
+            for file in p.rglob('*.pt'):
+                target=cache/file.relative_to(p);target.parent.mkdir(parents=True,exist_ok=True);shutil.copyfile(file,target)
+            save(cache/'metainfo.json',m);save(cache/'snapshot.json',{'purpose':'pre-QA native parameter diagnostics only; not shared usable labels','source_view':str(p),'frames':T})
+        lookup={int(r['timestamp[ns]']):r['mask']=='True' for r in masks if r['stream_id']==cam}
+        qa=np.array([lookup[f['timecode_ns']] for f in frames],bool)
+        source={'archive':gtfile.relative_to(base).as_posix(),'member':'masks/mask_qa_pass.csv','stream_id':cam,'time_index_ref':'camera/frames.jsonl:timecode_ns'}
+        policy={'semantics':'usable_annotation','formula':'source_annotation_present AND source_numerically_valid AND official_manual_qa_pass',
+            'qa_scope':'all visible hand/object poses in this camera frame; conservatively exclude all hand/object supervision if false',
+            'source_locator':source,'no_qa_source_rule':'use the component source-validity rules; do not invent QA'}
+        for key,c in m['components'].items():
+            # Camera calibration and RGB are not labels covered by this manual hand/object QA flag.
+            if key=='mano' or c['schema_id'] in ['object/rigid/v1','object/joint_state/v1','object_keypoints/camera/v1']:
+                values=torch.load(cache/c['data_ref'],weights_only=True).numpy();present=torch.load(cache/c['valid_ref'],weights_only=True).numpy()
+                valid=present & qa.reshape((T,)+(1,)*(present.ndim-1));values=values.copy();values[~valid]=np.nan
+                tensor(p/c['data_ref'],values);tensor(p/c['valid_ref'],valid);c['valid_policy']=policy
+            for attrname,a in c.get('attributes',{}).items():
+                if attrname!='visibility':continue
+                ishand=key=='mano';oid=c['source_locator'].get('object_uid');ids=[0,1] if ishand else [oid]
+                rows=hands if ishand else objects;identity='hand_index' if ishand else 'object_uid'
+                native={(int(r['timestamp[ns]']),str(r[identity])):float(r['visibility_ratio[%]']) for r in rows if r['stream_id']==cam and r['visibility_ratio[%]']!=''}
+                values=np.array([[native.get((f['timecode_ns'],str(k)),np.nan) for k in ids] for f in frames],np.float32)
+                if not ishand:values=values[:,0]
+                present=np.isfinite(values);assert ((values[present]>=0)&(values[present]<=1)).all()
+                # Correct the diagnostic copy as well: these values are fractions, despite the source header.
+                tensor(cache/a['data_ref'],values);tensor(cache/a['valid_ref'],present)
+                valid=present & qa.reshape((T,)+(1,)*(present.ndim-1));filtered=values.copy();filtered[~valid]=np.nan
+                tensor(p/a['data_ref'],filtered);tensor(p/a['valid_ref'],valid)
+                a['provenance']={'kind':'source','method':'native [0,1] ratio retained; official loader does not divide misleading [%] header by 100'}
+                a['valid_policy']={'semantics':'usable_visibility_annotation','formula':'source_ratio_present AND official_manual_qa_pass','source_locator':source}
+                a['source_locator']['match_method']='exact'
+        m['validity_revision']='usable-annotation-2026-09-20'
+        m['split']='train';m['split_scheme']='HOT3D_official_participant_split';m.pop('split_reason',None)
+        m['split_source_locator']={'url':'https://github.com/facebookresearch/hot3d/blob/146b34afef8c1a32adeef7e981c070109f225c87/README.md#testtrain-split','participant_id':seq.split('_')[0]}
+        assert seq.split('_')[0] not in {'P0004','P0005','P0006','P0008','P0016','P0020'}
+        save(meta,m)
+        qa_report[cam]={'frames':T,'qa_pass':qa.tolist(),'passed_count':int(qa.sum()),'source_locator':source,
+            'interpretation':'manual QA of visible hand/object poses; not RGB validity or individual error localization'}
+        print('QA_APPLIED',cam,'pass',int(qa.sum()),'fail',int((~qa).sum()),flush=True)
+    save(root/'doc/validation/source_qa.json' if publish else base/'tmp'/RUN/'qa'/f'{seq}.json',qa_report)
+    if publish:
+        # QA publication updates data/report artifacts only; the organized script bundle stays immutable.
+        pass
+    return qa_report
+
+if __name__=='__main__':apply_qa()

HOT3D/shared-v1.0/script/internal/fresh_sequence_common.py

@@ -1,212 +1,209 @@
-"""Helpers for the fresh ARCTIC/HOT3D whole-sequence v1.0 pilots.
-
-Original archives are read only. Temporary extraction is outside raw.
-Licensed MANO weights stay in the local model directory and are never published.
-"""
-from sequence_pilot_world import camera_mano, Mano
-from pilot_build import *
-from scipy.spatial.transform import Rotation
-import hashlib
-import re
-import time
-import trimesh
-from concurrent.futures import ThreadPoolExecutor,wait,FIRST_COMPLETED
-from contextlib import contextmanager
-
-DATA_ROOT = Path('E:/hand-datasets')
-RELEASE = 'shared-v1.0'
-RUN = 'fresh-sequence-pilot-20260920'
-VRS = WORK/'scripts/tools/vrs_env/Library/bin/vrs.exe'
-MEDIA_POOL=None
-MEDIA_PENDING=[]
-MEDIA_WORKERS=1
-MEDIA_POLICY='legacy_full_pixels'
-OUTPUT_OVERRIDE=None
-TMP_OVERRIDE=None
-
-@contextmanager
-def media_pipeline(workers=1,policy='jpeg-packets-all-rgb-sample16/v1'):
-    global MEDIA_POOL,MEDIA_PENDING,MEDIA_WORKERS,MEDIA_POLICY
-    assert MEDIA_POOL is None and not MEDIA_PENDING
-    MEDIA_WORKERS=workers;MEDIA_POLICY=policy
-    MEDIA_POOL=ThreadPoolExecutor(max_workers=workers)
-    try:
-        yield
-        for future in MEDIA_PENDING:future.result()
-    finally:
-        MEDIA_POOL.shutdown(wait=True,cancel_futures=True)
-        MEDIA_POOL=None;MEDIA_PENDING=[];MEDIA_POLICY='legacy_full_pixels'
-
-def submit_media(view,members_factory):
-    global MEDIA_PENDING
-    def execute():
-        jpeg_video(view,members_factory());view.m['pilot_complete']=True;view.finish()
-    if MEDIA_POOL is None:execute();return
-    # Bound pending views and source/video spools, not only running FFmpeg tasks.
-    while len(MEDIA_PENDING)>=MEDIA_WORKERS:
-        finished,_=wait(MEDIA_PENDING,return_when=FIRST_COMPLETED)
-        for future in finished:future.result()
-        MEDIA_PENDING=[f for f in MEDIA_PENDING if f not in finished]
-    MEDIA_PENDING.append(MEDIA_POOL.submit(execute))
-
-def digest(p):
-    h=hashlib.sha256()
-    with Path(p).open('rb') as f:
-        for b in iter(lambda:f.read(4*1024*1024),b''): h.update(b)
-    return h.hexdigest()
-
-def native_npy_bytes(data):
-    f=io.BytesIO(data);version=np.lib.format.read_magic(f)
-    shape,order,dtype=np.lib.format._read_array_header(f,version)
-    if dtype.hasobject:return Restricted(f,encoding='latin1').load().item()
-    return np.frombuffer(f.read(),dtype=dtype).reshape(shape,order='F' if order else 'C').copy()
-
-def command(args):
-    r=subprocess.run([str(x) for x in args],capture_output=True)
-    if r.returncode:raise RuntimeError(r.stderr.decode(errors='replace')[-3000:])
-    return r.stdout
-
-def framehash(args,copy=False):
-    output=command([FF,'-v','error','-threads','2','-noautorotate',*args,'-map','0:v:0',
-        *(['-c:v','copy'] if copy else ['-pix_fmt','rgb24','-threads','2']),
-        '-f','framehash','-hash','sha256','-fps_mode','passthrough','pipe:1']).decode()
-    return [(int(r.split(',')[-2]),r.split(',')[-1].strip()) for r in output.splitlines() if r and not r.startswith('#')]
-
-def jpeg_video(view, members):
-    """members is a fresh iterator of (locator, JPEG bytes). Verify every packet/frame."""
-    if MEDIA_POLICY!='legacy_full_pixels':
-        from accelerated_media import write_video
-        return write_video(view,members)
-    temporary=view.tmp/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.mjpeg'
-    temporary.parent.mkdir(parents=True,exist_ok=True)
-    hashes=[]
-    with temporary.open('wb') as out:
-        for locator,b in members:
-            assert b[:2]==b'\xff\xd8',locator
-            hashes.append((len(b),hashlib.sha256(b).hexdigest()));out.write(b)
-    assert len(hashes)==view.T
-    dst=view.p/'observation/rgb.mkv';dst.parent.mkdir(parents=True,exist_ok=True)
-    source=['-f','image2pipe','-framerate','1','-c:v','mjpeg','-i',str(temporary)]
-    command([FF,'-v','error','-y',*source,'-map','0:v:0','-c:v','copy',dst])
-    packet=framehash(['-i',str(dst)],True)
-    assert packet==hashes,'source JPEG packets changed in MKV'
-    before=framehash(source);after=framehash(['-i',str(dst)])
-    assert before==after and len(before)==view.T,'RGB decoding differs'
-    w,h=view.m['camera']['image_size'];assert all(n==w*h*3 for n,_ in after)
-    info=subprocess.run([FF,'-hide_banner','-i',str(dst)],capture_output=True).stderr.decode(errors='replace')
-    line=next(s.strip() for s in info.splitlines() if 'Video: mjpeg' in s)
-    pixel=re.search(r'Video: mjpeg[^,]*,\s*(\w+)',line).group(1)
-    version=command([FF,'-version']).decode().splitlines()[0]
-    view.component('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(view.T,bool),
-        source=view.source,method='Original JPEG packets copied from raw into MKV; no recompression or rotation',
-        storage={'profile':'mkv/mjpeg_copy/v1','codec':'mjpeg','pixel_format':pixel,'encoded_size':[w,h],
-          'source_lossy':True,'decoder':{'name':'ffmpeg','version':version,'output_pixel_format':'rgb24','autorotate':False,'source_color_declaration':'unknown','actual_stream_description':line},
-          'encoder_parameters':{'mode':'stream_copy','version':version}},storage_fps=1,timebase_role='index_only')
-    view.checks.append({'test':'all_source_JPEG_packets_and_RGB_pixels_exact','passed':True,'frames':view.T,
-        'packet_manifest_sha256':hashlib.sha256(json.dumps(hashes).encode()).hexdigest(),
-        'pixel_manifest_sha256':hashlib.sha256(json.dumps(after).encode()).hexdigest(),'output_bytes':dst.stat().st_size})
-    save(view.tmp/'media_checks'/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.json',{'packets':hashes,'pixels':after})
-    # This precise file was created by this call within the current run's temporary directory.
-    assert temporary.resolve().is_relative_to(view.tmp.resolve());temporary.unlink()
-
-class FreshView:
-    def __init__(self,dataset,seq,cam,ids,size,source):
-        self.root=(OUTPUT_OVERRIDE/dataset/RELEASE if OUTPUT_OVERRIDE else DATA_ROOT/dataset/RELEASE);self.p=self.root/'data'/seq/cam
-        self.tmp=(TMP_OVERRIDE/dataset/RUN if TMP_OVERRIDE else DATA_ROOT/dataset/'tmp'/RUN);self.p.mkdir(parents=True,exist_ok=True)
-        self.T=len(ids);self.source=source;self.checks=[]
-        self.m={'schema_version':'hand-warehouse/1.0','storage_revision':'lossless-video-2026-09-18',
-            'dataset_id':dataset,'release_id':RELEASE,'sequence_id':seq,'camera_id':cam,
-            'sample_type':'video','num_frames':self.T,'hand_slots':['left','right'],
-            'sampling':{'type':'dense','source_fps':None,'frames_ref':'camera/frames.jsonl','selection':'all released images in selected complete source sequence'},
-            'camera':{'model':'unknown','schema_id':None,'image_size':size},'split':'unknown','components':{},'pilot':True}
-        self.frames=[{'frame_index':i,'source_frame_id':str(fid),'timestamp_ns':None,'clock_domain':None,
-            'source_locator':dict(source,source_frame_id=str(fid))} for i,fid in enumerate(ids)]
-    def component(self,name,path,schema,axes,valid,source=None,method='source values retained',kind='format_conversion',**kw):
-        valid=np.asarray(valid,bool);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':kind,'method':method},
-            'source_locator':source or self.source,**kw}
-    def array(self,name,path,schema,data,valid,axes,**kw):
-        a=np.asarray(data,np.float32).copy();valid=np.asarray(valid,bool)
-        assert a.shape[0]==self.T and a.shape[:valid.ndim]==valid.shape
-        assert np.isfinite(a[valid]).all(),name
-        a[~valid]=np.nan;tensor(self.p/path,a)
-        self.component(name,path,schema,axes,valid,**kw)
-        restored=torch.load(self.p/path,map_location='cpu',weights_only=True).numpy()
-        assert np.array_equal(a,restored,equal_nan=True)
-        self.checks.append({'test':'tensor_exact_roundtrip','file':path,'shape':list(a.shape),'valid_count':int(valid.sum()),'passed':True})
-    def attribute(self,parent,path,data,axes,source,meaning):
-        data=np.asarray(data,np.float32);valid=np.isfinite(data)
-        assert ((data[valid]>=0)&(data[valid]<=1)).all()
-        vp=Path(path).with_name(Path(path).stem+'_valid.pt').as_posix();tensor(self.p/path,data);tensor(self.p/vp,valid)
-        self.m['components'][parent].setdefault('attributes',{})['visibility']={'data_ref':path,'valid_ref':vp,
-            'axes':axes,'valid_axes':axes,'dtype':'float32','visibility_type':'ratio','meaning':meaning,
-            'provenance':{'kind':'source','method':'native HOT3D visibility ratio retained in [0,1]; misleading [%] header is not a percentage scale'},'source_locator':source}
-    def mesh(self,oid,path,source):
-        self.component('object_'+oid+'_mesh',path,'mesh/obj/v1',['vertex','coordinate'],np.array(True),source=source,
-            method='Object vertices in metres; topology, material and texture dependencies retained',static=True,unit='m',coordinate_frame='object')
-    def finish(self):
-        lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
-        save(self.tmp/'checks'/f'{self.m["sequence_id"]}__{self.m["camera_id"]}.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 assert_se3(a):
-    a=np.asarray(a);assert a.shape[-2:]==(4,4)
-    # A component may exist while QA leaves no usable poses in this view.
-    if a.size==0:return
-    assert np.isfinite(a).all() and np.allclose(a[...,3,:],[0,0,0,1],atol=1e-6)
-    r=a[...,:3,:3];assert np.max(np.abs(r@np.swapaxes(r,-1,-2)-np.eye(3)))<2e-5
-    assert np.max(np.abs(np.linalg.det(r)-1))<2e-5
-
-def setup(dataset,seq):
-    base=DATA_ROOT/dataset;out=base/RELEASE
-    for folder in ['doc','script','doc/validation']: (out/folder).mkdir(parents=True,exist_ok=True)
-    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,out/'doc'/f)
-    save(out/'doc/validation/status.json',{'stage':'building_sequence_pilot','human_review':'pending','sequence_id':seq,'full_preprocessing_authorized':False,'upload_authorized':False})
-    sources=['fresh_sequence_common.py','convert_fresh_sequence_pilots.py','pilot_build.py','pilot_fetch.py','pilot_mano.py',
-        'sequence_pilot_common.py','sequence_pilot_world.py','pilot_hot_normalize.py','audit_source_fields.py']
-    for name in sources:
-        if (WORK/'scripts'/name).exists():shutil.copyfile(WORK/'scripts'/name,out/'script'/name)
-    (base/'tmp'/RUN).mkdir(parents=True,exist_ok=True)
-
-def object_textured_assets(z,prefix,target):
-    target.mkdir(parents=True,exist_ok=True)
-    for name in z.namelist():
-        if not name.startswith(prefix) or name.endswith('/'):continue
-        rel=Path(name[len(prefix):]);dest=target/rel
-        assert dest.resolve().is_relative_to(target.resolve());dest.parent.mkdir(parents=True,exist_ok=True)
-        if dest.suffix.lower() in ('.obj','.mtl','.jpg','.jpeg','.png'):
-            b=z.read(name)
-            if dest.suffix=='.obj':
-                rows=b.decode().splitlines()
-                for i,row in enumerate(rows):
-                    if row.startswith('v '):
-                        v=row.split();v[1:4]=[format(float(x)/1000,'.17g') for x in v[1:4]];rows[i]=' '.join(v)
-                b=('\n'.join(rows)+'\n').encode()
-            dest.write_bytes(b)
-    repair_arctic_material_refs(target)
-
-def repair_arctic_material_refs(target):
-    """Repair a source filename mismatch only when material identifiers agree."""
-    material=target/'material.mtl'
-    if not material.exists():return
-    ids={x.split(maxsplit=1)[1] for x in material.read_text().splitlines() if x.startswith('newmtl ')}
-    for obj in target.glob('*.obj'):
-        text=obj.read_text();rows=text.splitlines();used={x.split(maxsplit=1)[1] for x in rows if x.startswith('usemtl ')}
-        if 'mtllib material_0.mtl' in rows and not (target/'material_0.mtl').exists():
-            assert used and used.issubset(ids),(obj,used,ids)
-            obj.write_text(text.replace('mtllib material_0.mtl','mtllib material.mtl'))
-
-def project_points(xyz,camera,params):
-    xyz=np.asarray(xyz,float);uv=np.full((len(xyz),2),np.nan);good=np.isfinite(xyz).all(1)&(xyz[:,2]>1e-6)
-    if camera=='fisheye624':
-        p=np.asarray(params);xy=xyz[good,:2]/xyz[good,2,None];r=np.linalg.norm(xy,axis=1);th=np.arctan(r)
-        radial=th*(1+sum(p[3+i]*th**(2*i+2) for i in range(6)))
-        xy=xy*np.divide(radial,r,out=np.ones_like(r),where=r>1e-12)[:,None]
-        x,y=xy.T;rr=x*x+y*y
-        xd=x+(2*x*x+rr)*p[9]+2*x*y*p[10]+p[11]*rr+p[12]*rr*rr
-        yd=y+(2*y*y+rr)*p[10]+2*x*y*p[9]+p[13]*rr+p[14]*rr*rr
-        uv[good]=p[0]*np.stack([xd,yd],1)+p[1:3]
-    else:
-        K,D=params;uv[good]=cv2.projectPoints(xyz[good],np.zeros(3),np.zeros(3),K,D)[0].reshape(-1,2)
-    return uv
+"""Helpers for the fresh ARCTIC/HOT3D whole-sequence v1.0 pilots.
+
+Original archives are read only. Temporary extraction is outside raw.
+Licensed MANO weights stay in the local model directory and are never published.
+"""
+from sequence_pilot_world import camera_mano, Mano
+from pilot_build import *
+from scipy.spatial.transform import Rotation
+import hashlib
+import re
+import time
+import trimesh
+from concurrent.futures import ThreadPoolExecutor,wait,FIRST_COMPLETED
+from contextlib import contextmanager
+
+DATA_ROOT = Path('E:/hand-datasets')
+RELEASE = 'shared-v1.0'
+RUN = 'fresh-sequence-pilot-20260920'
+VRS = WORK/'scripts/tools/vrs_env/Library/bin/vrs.exe'
+MEDIA_POOL=None
+MEDIA_PENDING=[]
+MEDIA_WORKERS=1
+MEDIA_POLICY='legacy_full_pixels'
+OUTPUT_OVERRIDE=None
+TMP_OVERRIDE=None
+
+@contextmanager
+def media_pipeline(workers=1,policy='jpeg-packets-all-rgb-sample16/v1'):
+    global MEDIA_POOL,MEDIA_PENDING,MEDIA_WORKERS,MEDIA_POLICY
+    assert MEDIA_POOL is None and not MEDIA_PENDING
+    MEDIA_WORKERS=workers;MEDIA_POLICY=policy
+    MEDIA_POOL=ThreadPoolExecutor(max_workers=workers)
+    try:
+        yield
+        for future in MEDIA_PENDING:future.result()
+    finally:
+        MEDIA_POOL.shutdown(wait=True,cancel_futures=True)
+        MEDIA_POOL=None;MEDIA_PENDING=[];MEDIA_POLICY='legacy_full_pixels'
+
+def submit_media(view,members_factory):
+    global MEDIA_PENDING
+    def execute():
+        jpeg_video(view,members_factory());view.m['pilot_complete']=True;view.finish()
+    if MEDIA_POOL is None:execute();return
+    # Bound pending views and source/video spools, not only running FFmpeg tasks.
+    while len(MEDIA_PENDING)>=MEDIA_WORKERS:
+        finished,_=wait(MEDIA_PENDING,return_when=FIRST_COMPLETED)
+        for future in finished:future.result()
+        MEDIA_PENDING=[f for f in MEDIA_PENDING if f not in finished]
+    MEDIA_PENDING.append(MEDIA_POOL.submit(execute))
+
+def digest(p):
+    h=hashlib.sha256()
+    with Path(p).open('rb') as f:
+        for b in iter(lambda:f.read(4*1024*1024),b''): h.update(b)
+    return h.hexdigest()
+
+def native_npy_bytes(data):
+    f=io.BytesIO(data);version=np.lib.format.read_magic(f)
+    shape,order,dtype=np.lib.format._read_array_header(f,version)
+    if dtype.hasobject:return Restricted(f,encoding='latin1').load().item()
+    return np.frombuffer(f.read(),dtype=dtype).reshape(shape,order='F' if order else 'C').copy()
+
+def command(args):
+    r=subprocess.run([str(x) for x in args],capture_output=True)
+    if r.returncode:raise RuntimeError(r.stderr.decode(errors='replace')[-3000:])
+    return r.stdout
+
+def framehash(args,copy=False):
+    output=command([FF,'-v','error','-threads','2','-noautorotate',*args,'-map','0:v:0',
+        *(['-c:v','copy'] if copy else ['-pix_fmt','rgb24','-threads','2']),
+        '-f','framehash','-hash','sha256','-fps_mode','passthrough','pipe:1']).decode()
+    return [(int(r.split(',')[-2]),r.split(',')[-1].strip()) for r in output.splitlines() if r and not r.startswith('#')]
+
+def jpeg_video(view, members):
+    """members is a fresh iterator of (locator, JPEG bytes). Verify every packet/frame."""
+    if MEDIA_POLICY!='legacy_full_pixels':
+        from accelerated_media import write_video
+        return write_video(view,members)
+    temporary=view.tmp/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.mjpeg'
+    temporary.parent.mkdir(parents=True,exist_ok=True)
+    hashes=[]
+    with temporary.open('wb') as out:
+        for locator,b in members:
+            assert b[:2]==b'\xff\xd8',locator
+            hashes.append((len(b),hashlib.sha256(b).hexdigest()));out.write(b)
+    assert len(hashes)==view.T
+    dst=view.p/'observation/rgb.mkv';dst.parent.mkdir(parents=True,exist_ok=True)
+    source=['-f','image2pipe','-framerate','1','-c:v','mjpeg','-i',str(temporary)]
+    command([FF,'-v','error','-y',*source,'-map','0:v:0','-c:v','copy',dst])
+    packet=framehash(['-i',str(dst)],True)
+    assert packet==hashes,'source JPEG packets changed in MKV'
+    before=framehash(source);after=framehash(['-i',str(dst)])
+    assert before==after and len(before)==view.T,'RGB decoding differs'
+    w,h=view.m['camera']['image_size'];assert all(n==w*h*3 for n,_ in after)
+    info=subprocess.run([FF,'-hide_banner','-i',str(dst)],capture_output=True).stderr.decode(errors='replace')
+    line=next(s.strip() for s in info.splitlines() if 'Video: mjpeg' in s)
+    pixel=re.search(r'Video: mjpeg[^,]*,\s*(\w+)',line).group(1)
+    version=command([FF,'-version']).decode().splitlines()[0]
+    view.component('rgb','observation/rgb.mkv','rgb/uint8/v1',['T','height','width','channel'],np.ones(view.T,bool),
+        source=view.source,method='Original JPEG packets copied from raw into MKV; no recompression or rotation',
+        storage={'profile':'mkv/mjpeg_copy/v1','codec':'mjpeg','pixel_format':pixel,'encoded_size':[w,h],
+          'source_lossy':True,'decoder':{'name':'ffmpeg','version':version,'output_pixel_format':'rgb24','autorotate':False,'source_color_declaration':'unknown','actual_stream_description':line},
+          'encoder_parameters':{'mode':'stream_copy','version':version}},storage_fps=1,timebase_role='index_only')
+    view.checks.append({'test':'all_source_JPEG_packets_and_RGB_pixels_exact','passed':True,'frames':view.T,
+        'packet_manifest_sha256':hashlib.sha256(json.dumps(hashes).encode()).hexdigest(),
+        'pixel_manifest_sha256':hashlib.sha256(json.dumps(after).encode()).hexdigest(),'output_bytes':dst.stat().st_size})
+    save(view.tmp/'media_checks'/f'{view.m["sequence_id"]}__{view.m["camera_id"]}.json',{'packets':hashes,'pixels':after})
+    # This precise file was created by this call within the current run's temporary directory.
+    assert temporary.resolve().is_relative_to(view.tmp.resolve());temporary.unlink()
+
+class FreshView:
+    def __init__(self,dataset,seq,cam,ids,size,source):
+        self.root=(OUTPUT_OVERRIDE/dataset/RELEASE if OUTPUT_OVERRIDE else DATA_ROOT/dataset/RELEASE);self.p=self.root/'data'/seq/cam
+        self.tmp=(TMP_OVERRIDE/dataset/RUN if TMP_OVERRIDE else DATA_ROOT/dataset/'tmp'/RUN);self.p.mkdir(parents=True,exist_ok=True)
+        self.T=len(ids);self.source=source;self.checks=[]
+        self.m={'schema_version':'hand-warehouse/1.0','storage_revision':'lossless-video-2026-09-18',
+            'dataset_id':dataset,'release_id':RELEASE,'sequence_id':seq,'camera_id':cam,
+            'sample_type':'video','num_frames':self.T,'hand_slots':['left','right'],
+            'sampling':{'type':'dense','source_fps':None,'frames_ref':'camera/frames.jsonl','selection':'all released images in selected complete source sequence'},
+            'camera':{'model':'unknown','schema_id':None,'image_size':size},'split':'unknown','components':{},'pilot':True}
+        self.frames=[{'frame_index':i,'source_frame_id':str(fid),'timestamp_ns':None,'clock_domain':None,
+            'source_locator':dict(source,source_frame_id=str(fid))} for i,fid in enumerate(ids)]
+    def component(self,name,path,schema,axes,valid,source=None,method='source values retained',kind='format_conversion',**kw):
+        valid=np.asarray(valid,bool);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':kind,'method':method},
+            'source_locator':source or self.source,**kw}
+    def array(self,name,path,schema,data,valid,axes,**kw):
+        a=np.asarray(data,np.float32).copy();valid=np.asarray(valid,bool)
+        assert a.shape[0]==self.T and a.shape[:valid.ndim]==valid.shape
+        assert np.isfinite(a[valid]).all(),name
+        a[~valid]=np.nan;tensor(self.p/path,a)
+        self.component(name,path,schema,axes,valid,**kw)
+        restored=torch.load(self.p/path,map_location='cpu',weights_only=True).numpy()
+        assert np.array_equal(a,restored,equal_nan=True)
+        self.checks.append({'test':'tensor_exact_roundtrip','file':path,'shape':list(a.shape),'valid_count':int(valid.sum()),'passed':True})
+    def attribute(self,parent,path,data,axes,source,meaning):
+        data=np.asarray(data,np.float32);valid=np.isfinite(data)
+        assert ((data[valid]>=0)&(data[valid]<=1)).all()
+        vp=Path(path).with_name(Path(path).stem+'_valid.pt').as_posix();tensor(self.p/path,data);tensor(self.p/vp,valid)
+        self.m['components'][parent].setdefault('attributes',{})['visibility']={'data_ref':path,'valid_ref':vp,
+            'axes':axes,'valid_axes':axes,'dtype':'float32','visibility_type':'ratio','meaning':meaning,
+            'provenance':{'kind':'source','method':'native HOT3D visibility ratio retained in [0,1]; misleading [%] header is not a percentage scale'},'source_locator':source}
+    def mesh(self,oid,path,source):
+        self.component('object_'+oid+'_mesh',path,'mesh/obj/v1',['vertex','coordinate'],np.array(True),source=source,
+            method='Object vertices in metres; topology, material and texture dependencies retained',static=True,unit='m',coordinate_frame='object')
+    def finish(self):
+        lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
+        save(self.tmp/'checks'/f'{self.m["sequence_id"]}__{self.m["camera_id"]}.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 assert_se3(a):
+    a=np.asarray(a);assert a.shape[-2:]==(4,4)
+    # A component may exist while QA leaves no usable poses in this view.
+    if a.size==0:return
+    assert np.isfinite(a).all() and np.allclose(a[...,3,:],[0,0,0,1],atol=1e-6)
+    r=a[...,:3,:3];assert np.max(np.abs(r@np.swapaxes(r,-1,-2)-np.eye(3)))<2e-5
+    assert np.max(np.abs(np.linalg.det(r)-1))<2e-5
+
+def setup(dataset,seq):
+    base=DATA_ROOT/dataset;out=base/RELEASE
+    for folder in ['doc','script','doc/validation']: (out/folder).mkdir(parents=True,exist_ok=True)
+    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,out/'doc'/f)
+    save(out/'doc/validation/status.json',{'stage':'building_sequence_pilot','human_review':'pending','sequence_id':seq,'full_preprocessing_authorized':False,'upload_authorized':False})
+    # The organized script bundle is already self-contained; runtime work must not repopulate its root.
+    (base/'tmp'/RUN).mkdir(parents=True,exist_ok=True)
+
+def object_textured_assets(z,prefix,target):
+    target.mkdir(parents=True,exist_ok=True)
+    for name in z.namelist():
+        if not name.startswith(prefix) or name.endswith('/'):continue
+        rel=Path(name[len(prefix):]);dest=target/rel
+        assert dest.resolve().is_relative_to(target.resolve());dest.parent.mkdir(parents=True,exist_ok=True)
+        if dest.suffix.lower() in ('.obj','.mtl','.jpg','.jpeg','.png'):
+            b=z.read(name)
+            if dest.suffix=='.obj':
+                rows=b.decode().splitlines()
+                for i,row in enumerate(rows):
+                    if row.startswith('v '):
+                        v=row.split();v[1:4]=[format(float(x)/1000,'.17g') for x in v[1:4]];rows[i]=' '.join(v)
+                b=('\n'.join(rows)+'\n').encode()
+            dest.write_bytes(b)
+    repair_arctic_material_refs(target)
+
+def repair_arctic_material_refs(target):
+    """Repair a source filename mismatch only when material identifiers agree."""
+    material=target/'material.mtl'
+    if not material.exists():return
+    ids={x.split(maxsplit=1)[1] for x in material.read_text().splitlines() if x.startswith('newmtl ')}
+    for obj in target.glob('*.obj'):
+        text=obj.read_text();rows=text.splitlines();used={x.split(maxsplit=1)[1] for x in rows if x.startswith('usemtl ')}
+        if 'mtllib material_0.mtl' in rows and not (target/'material_0.mtl').exists():
+            assert used and used.issubset(ids),(obj,used,ids)
+            obj.write_text(text.replace('mtllib material_0.mtl','mtllib material.mtl'))
+
+def project_points(xyz,camera,params):
+    xyz=np.asarray(xyz,float);uv=np.full((len(xyz),2),np.nan);good=np.isfinite(xyz).all(1)&(xyz[:,2]>1e-6)
+    if camera=='fisheye624':
+        p=np.asarray(params);xy=xyz[good,:2]/xyz[good,2,None];r=np.linalg.norm(xy,axis=1);th=np.arctan(r)
+        radial=th*(1+sum(p[3+i]*th**(2*i+2) for i in range(6)))
+        xy=xy*np.divide(radial,r,out=np.ones_like(r),where=r>1e-12)[:,None]
+        x,y=xy.T;rr=x*x+y*y
+        xd=x+(2*x*x+rr)*p[9]+2*x*y*p[10]+p[11]*rr+p[12]*rr*rr
+        yd=y+(2*y*y+rr)*p[10]+2*x*y*p[9]+p[13]*rr+p[14]*rr*rr
+        uv[good]=p[0]*np.stack([xd,yd],1)+p[1:3]
+    else:
+        K,D=params;uv[good]=cv2.projectPoints(xyz[good],np.zeros(3),np.zeros(3),K,D)[0].reshape(-1,2)
+    return uv

HOT3D/shared-v1.0/script/internal/pilot_build.py

@@ -1,209 +1,207 @@
-"""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('<u2')
-  a=np.ascontiguousarray(a);p.parent.mkdir(parents=True,exist_ok=True)
-  command=[FF,'-hide_banner','-loglevel','error','-y','-f','rawvideo','-pixel_format',pixel,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-an','-c:v','ffv1','-level','3','-g','1',str(p)]
-  subprocess.run(command,input=a.tobytes(),check=True,capture_output=True)
-  decoded=subprocess.run([FF,'-v','error','-i',str(p),'-f','rawvideo','-pix_fmt',pixel,'pipe:1'],check=True,capture_output=True).stdout
-  assert decoded==a.tobytes(),f'Lossless roundtrip failed: {p}'
-  self.checks.append({'file':str(p.relative_to(self.out)),'test':'decode_all_frames_pixel_exact','passed':True,'frames':len(a),'dtype':str(a.dtype),'shape':list(a.shape)})
-  return {'codec':'FFV1','pixel_format':pixel,'container_fps':1,'container_time_is_capture_time':False}
- def finish(self,notes):
-  for p,m in self.views:save(p/'metainfo.json',m)
-  save(self.base/'raw/doc/local_subset_manifest.json',self.refs)
-  report={'dataset':self.name,'views':len(self.views),'observations':sum(m['num_frames'] for _,m in self.views),'checks':self.checks,'notes':notes,'status':'partial' if notes else 'passed'}
-  save(self.out/'doc/report.json',report)
-  for script in ['pilot_build.py','pilot_fetch.py']:
-   shutil.copyfile(WORK/'scripts'/script,self.out/'script'/script)
-  (self.out/'script/README.md').write_text('执行入口：工作区 scripts/pilot_build.py --dataset '+self.name+'。此目录为脚本快照���脚本当前依赖工作区的 datasets 与 tmp/pilot-runtime，尚不是独立发布包。\n',encoding='utf-8')
-  files=[{'path':str(p.relative_to(self.out)), 'bytes':p.stat().st_size,'sha256':sha(p)} for p in sorted((self.out/'data').rglob('*')) if p.is_file()]
-  save(self.out/'doc/output_manifest.json',files)
-  print(json.dumps(report,ensure_ascii=False),flush=True)
-  return report
-
-def h2o():
- b=Build('H2O');src=OLD/'H2O/subject4_ego/h1/0/cam4';ids=list(range(N))
- p,m=b.view('subject4_h1_0','cam4',ids,split='test',view_type='ego',time_semantics='ordered_source_frames; acquisition FPS not yet verified')
- for folder in ['rgb','depth','hand_pose','hand_pose_mano','cam_pose']:
-  for i in ids:b.copy(src/folder/f'{i:06d}.{ "png" if folder in ["rgb","depth"] else "txt"}')
- intr=np.loadtxt(b.copy(src/'cam_intrinsics.txt'));fx,fy,cx,cy,w,h=intr
- tensor(p/'camera/intrinsic.pt',np.array([[fx,0,cx],[0,fy,cy],[0,0,1]],np.float32))
- ext=np.stack([np.loadtxt(src/'cam_pose'/f'{i:06d}.txt').reshape(4,4) for i in ids])
- tensor(p/'camera/extrinsic.pt',np.linalg.inv(ext).astype(np.float32))
- m['camera']={'model':'pinhole','width':int(w),'height':int(h),'extrinsic':'T_camera_from_world','source_extrinsic':'T_world_from_camera','unit':'m','distortion':'not supplied in selected intrinsics'}
- a=np.stack([np.loadtxt(src/'hand_pose_mano'/f'{i:06d}.txt').reshape(2,62) for i in ids])
- valid=a[:,:,0]==1;motion=np.concatenate([a[:,:,4:52],a[:,:,52:62],a[:,:,1:4]],axis=-1).astype(np.float32)
- motion[~valid]=np.nan;tensor(p/'annotation/mano/motion.pt',motion);tensor(p/'annotation/mano/valid_flag.pt',valid)
- m['mano']={'layout':['pose_axis_angle:48','betas:10','translation:3'],'shape':[N,2,61],'slots':['left','right'],'coordinate_frame':'camera','translation_unit':'m','flat_hand_mean':True,'model_version':'not supplied; MANO model not bundled','valid_flag':'source annotation flag, not visibility','geometry_validation':'not_tested; model unavailable'}
- joints=np.stack([np.loadtxt(src/'hand_pose'/f'{i:06d}.txt').reshape(2,64) for i in ids]);tensor(p/'annotation/keypoints/xyz.pt',joints[:,:,1:].reshape(N,2,21,3).astype(np.float32));tensor(p/'annotation/keypoints/valid_flag.pt',joints[:,:,0]==1)
- rgb=[np.asarray(Image.open(src/'rgb'/f'{i:06d}.png').convert('RGB')) for i in ids]
- depth=[np.asarray(Image.open(src/'depth'/f'{i:06d}.png'),dtype=np.uint16) for i in ids]
- m['rgb']=b.video(p/'observation/rgb.mkv',rgb);m['depth']={**b.video(p/'observation/depth.mkv',depth),'unit':'mm','scale_to_meters':0.001,'invalid_value':0,'rgb_registration':'not_tested'}
- m['missing_modalities']={'object':'not present in selected historical subset','segmentation':'no native mask in selected subset'}
- # Independent check: 3D keypoint projections must be finite where the source flags valid.
- xyz=joints[:,:,1:].reshape(N,2,21,3);projection=xyz@np.array([[fx,0,cx],[0,fy,cy],[0,0,1]]).T
- uv=projection[...,:2]/projection[...,2:];assert np.isfinite(uv[joints[:,:,0]==1]).all()
- image=Image.fromarray(rgb[0]);draw=ImageDraw.Draw(image)
- for side in range(2):
-  for u,v in uv[0,side]:draw.ellipse((u-3,v-3,u+3,v+3),fill=('red' if side==0 else 'cyan'))
- image.save(b.out/'doc/keypoints_overlay.png')
- return b.finish(['MANO geometry not rendered; model asset unavailable. RGB-depth registration not tested. No native segmentation or object input in subset.'])
-
-def visor():
- b=Build('VISOR');raw=b.base/'raw/data/GroundTruth-SparseAnnotations';jp=raw/'annotations/train/P01_03.json';zp=raw/'rgb_frames/train/P01/P01_03.zip'
- source=load(jp);annotations=source['video_annotations'][:N]
- ids=[x['image']['name'] for x in annotations];p,m=b.view('P01_03','ego',ids,split='train',view_type='ego',time_semantics='sparse_annotations; use source frame IDs; MKV timestamps are storage indices')
- rgbs=[];masks=[];records=[]
- with zipfile.ZipFile(zp) as z:
-  assert z.testzip() is None
-  for i,frame in enumerate(annotations):
-   rgb=np.asarray(Image.open(io.BytesIO(z.read(frame['image']['name']))).convert('RGB'));rgbs.append(rgb)
-   mask=np.zeros((1080,1920),np.uint16);entities=[]
-   for value,entity in enumerate(frame['annotations'],1):
-    # Same polygon rasterization primitive and integer conversion as official VISOR-VIS.
-    polygons=[np.asarray(poly,dtype=np.int32) for poly in entity['segments'] if len(poly)>=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()
+"""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('<u2')
+  a=np.ascontiguousarray(a);p.parent.mkdir(parents=True,exist_ok=True)
+  command=[FF,'-hide_banner','-loglevel','error','-y','-f','rawvideo','-pixel_format',pixel,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-an','-c:v','ffv1','-level','3','-g','1',str(p)]
+  subprocess.run(command,input=a.tobytes(),check=True,capture_output=True)
+  decoded=subprocess.run([FF,'-v','error','-i',str(p),'-f','rawvideo','-pix_fmt',pixel,'pipe:1'],check=True,capture_output=True).stdout
+  assert decoded==a.tobytes(),f'Lossless roundtrip failed: {p}'
+  self.checks.append({'file':str(p.relative_to(self.out)),'test':'decode_all_frames_pixel_exact','passed':True,'frames':len(a),'dtype':str(a.dtype),'shape':list(a.shape)})
+  return {'codec':'FFV1','pixel_format':pixel,'container_fps':1,'container_time_is_capture_time':False}
+ def finish(self,notes):
+  for p,m in self.views:save(p/'metainfo.json',m)
+  save(self.base/'raw/doc/local_subset_manifest.json',self.refs)
+  report={'dataset':self.name,'views':len(self.views),'observations':sum(m['num_frames'] for _,m in self.views),'checks':self.checks,'notes':notes,'status':'partial' if notes else 'passed'}
+  save(self.out/'doc/report.json',report)
+  # Script provenance is supplied by the organized bundle and validation manifests.
+  files=[{'path':str(p.relative_to(self.out)), 'bytes':p.stat().st_size,'sha256':sha(p)} for p in sorted((self.out/'data').rglob('*')) if p.is_file()]
+  save(self.out/'doc/output_manifest.json',files)
+  print(json.dumps(report,ensure_ascii=False),flush=True)
+  return report
+
+def h2o():
+ b=Build('H2O');src=OLD/'H2O/subject4_ego/h1/0/cam4';ids=list(range(N))
+ p,m=b.view('subject4_h1_0','cam4',ids,split='test',view_type='ego',time_semantics='ordered_source_frames; acquisition FPS not yet verified')
+ for folder in ['rgb','depth','hand_pose','hand_pose_mano','cam_pose']:
+  for i in ids:b.copy(src/folder/f'{i:06d}.{ "png" if folder in ["rgb","depth"] else "txt"}')
+ intr=np.loadtxt(b.copy(src/'cam_intrinsics.txt'));fx,fy,cx,cy,w,h=intr
+ tensor(p/'camera/intrinsic.pt',np.array([[fx,0,cx],[0,fy,cy],[0,0,1]],np.float32))
+ ext=np.stack([np.loadtxt(src/'cam_pose'/f'{i:06d}.txt').reshape(4,4) for i in ids])
+ tensor(p/'camera/extrinsic.pt',np.linalg.inv(ext).astype(np.float32))
+ m['camera']={'model':'pinhole','width':int(w),'height':int(h),'extrinsic':'T_camera_from_world','source_extrinsic':'T_world_from_camera','unit':'m','distortion':'not supplied in selected intrinsics'}
+ a=np.stack([np.loadtxt(src/'hand_pose_mano'/f'{i:06d}.txt').reshape(2,62) for i in ids])
+ valid=a[:,:,0]==1;motion=np.concatenate([a[:,:,4:52],a[:,:,52:62],a[:,:,1:4]],axis=-1).astype(np.float32)
+ motion[~valid]=np.nan;tensor(p/'annotation/mano/motion.pt',motion);tensor(p/'annotation/mano/valid_flag.pt',valid)
+ m['mano']={'layout':['pose_axis_angle:48','betas:10','translation:3'],'shape':[N,2,61],'slots':['left','right'],'coordinate_frame':'camera','translation_unit':'m','flat_hand_mean':True,'model_version':'not supplied; MANO model not bundled','valid_flag':'source annotation flag, not visibility','geometry_validation':'not_tested; model unavailable'}
+ joints=np.stack([np.loadtxt(src/'hand_pose'/f'{i:06d}.txt').reshape(2,64) for i in ids]);tensor(p/'annotation/keypoints/xyz.pt',joints[:,:,1:].reshape(N,2,21,3).astype(np.float32));tensor(p/'annotation/keypoints/valid_flag.pt',joints[:,:,0]==1)
+ rgb=[np.asarray(Image.open(src/'rgb'/f'{i:06d}.png').convert('RGB')) for i in ids]
+ depth=[np.asarray(Image.open(src/'depth'/f'{i:06d}.png'),dtype=np.uint16) for i in ids]
+ m['rgb']=b.video(p/'observation/rgb.mkv',rgb);m['depth']={**b.video(p/'observation/depth.mkv',depth),'unit':'mm','scale_to_meters':0.001,'invalid_value':0,'rgb_registration':'not_tested'}
+ m['missing_modalities']={'object':'not present in selected historical subset','segmentation':'no native mask in selected subset'}
+ # Independent check: 3D keypoint projections must be finite where the source flags valid.
+ xyz=joints[:,:,1:].reshape(N,2,21,3);projection=xyz@np.array([[fx,0,cx],[0,fy,cy],[0,0,1]]).T
+ uv=projection[...,:2]/projection[...,2:];assert np.isfinite(uv[joints[:,:,0]==1]).all()
+ image=Image.fromarray(rgb[0]);draw=ImageDraw.Draw(image)
+ for side in range(2):
+  for u,v in uv[0,side]:draw.ellipse((u-3,v-3,u+3,v+3),fill=('red' if side==0 else 'cyan'))
+ image.save(b.out/'doc/keypoints_overlay.png')
+ return b.finish(['MANO geometry not rendered; model asset unavailable. RGB-depth registration not tested. No native segmentation or object input in subset.'])
+
+def visor():
+ b=Build('VISOR');raw=b.base/'raw/data/GroundTruth-SparseAnnotations';jp=raw/'annotations/train/P01_03.json';zp=raw/'rgb_frames/train/P01/P01_03.zip'
+ source=load(jp);annotations=source['video_annotations'][:N]
+ ids=[x['image']['name'] for x in annotations];p,m=b.view('P01_03','ego',ids,split='train',view_type='ego',time_semantics='sparse_annotations; use source frame IDs; MKV timestamps are storage indices')
+ rgbs=[];masks=[];records=[]
+ with zipfile.ZipFile(zp) as z:
+  assert z.testzip() is None
+  for i,frame in enumerate(annotations):
+   rgb=np.asarray(Image.open(io.BytesIO(z.read(frame['image']['name']))).convert('RGB'));rgbs.append(rgb)
+   mask=np.zeros((1080,1920),np.uint16);entities=[]
+   for value,entity in enumerate(frame['annotations'],1):
+    # Same polygon rasterization primitive and integer conversion as official VISOR-VIS.
+    polygons=[np.asarray(poly,dtype=np.int32) for poly in entity['segments'] if len(poly)>=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()

HOT3D/shared-v1.0/script/internal/preprocess_fresh_full.py

@@ -28,10 +28,10 @@ def atomic(p,value):
 
 def temporary(ds):return DATA_ROOT/ds/'tmp'/JOB
 
-PRODUCTION_SCRIPTS=['preprocess_fresh_full.py','accelerated_media.py','convert_fresh_sequence_pilots.py','apply_hot3d_qa.py','review_fresh_sequence_pilots.py','render_full_sequence_review.py','fresh_sequence_common.py','diagnose_arctic_review.py','pilot_build.py','pilot_fetch.py','pilot_mano.py','sequence_pilot_common.py','sequence_pilot_world.py','pilot_hot_normalize.py','audit_source_fields.py']
+PRODUCTION_SCRIPTS=sorted(p.name for p in SCRIPT.parent.glob('*.py'))
 
 def code_manifest():
-    return [{'path':'script/'+f,'sha256':digest(WORK/'scripts'/f)} for f in [*PRODUCTION_SCRIPTS,'hot3d_storage.py']]
+    return [{'path':'script/internal/'+f,'sha256':digest(SCRIPT.parent/f)} for f in PRODUCTION_SCRIPTS]
 
 def verify_completed(ds,row,source):
     root=DATA_ROOT/ds/RELEASE;record=temporary(ds)/'completed'/f'{row["sequence_id"]}.json';done=load(record)
@@ -80,9 +80,11 @@ def migrate_completed(ds):
 
 def snapshot_scripts(ds):
     root=DATA_ROOT/ds/RELEASE
-    for f in [*PRODUCTION_SCRIPTS,'hot3d_storage.py']:shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
-    shutil.copyfile(CONTROL/'acceleration-config.json',root/'script/acceleration-config.json')
-    atomic(root/'doc/validation/full/script-manifest.json',{'created_at':now(),'scope':'Current production converter and validation scripts; earlier pilot manifests describe their historical run','files':code_manifest()})
+    target=root/'script/internal';target.mkdir(parents=True,exist_ok=True)
+    for f in PRODUCTION_SCRIPTS:
+        src=SCRIPT.parent/f;dst=target/f
+        if src.resolve()!=dst.resolve():shutil.copyfile(src,dst)
+    atomic(root/'doc/validation/full/script-manifest.json',{'created_at':now(),'scope':'Current organized production implementation under script/internal; earlier manifests retain historical paths and hashes','files':code_manifest()})
 
 def inputs(ds):
     base=DATA_ROOT/ds
@@ -183,8 +185,7 @@ def approve():
         docprod=doc/'full';docprod.mkdir(parents=True,exist_ok=True)
         atomic(docprod/'plan.json',p['datasets'][ds])
         (docprod/'README.md').write_text('# 全量预处理\n\n试点已获用户确认。逐序列转换、逐帧码流/像素和几何校验，完成后另建随机抽查报告。原始包保留，不自动上传。\n\n每个序列的正式自动检查在本目录sequences/；运行日志和进度只在数据集tmp。断点以输入官方摘要及全部输出文件摘要核验。\n\nHOT3D测试集未发布手物姿态真值：保留RGB、相机和物体资产，姿态及可见性缺省。ARCTIC仅保留已确认s09/laptop的曝光排除，其它序列不新增帧号屏蔽。\n',encoding='utf-8')
-        for f in ['preprocess_fresh_full.py','convert_fresh_sequence_pilots.py','apply_hot3d_qa.py','review_fresh_sequence_pilots.py','render_full_sequence_review.py','fresh_sequence_common.py','diagnose_arctic_review.py']:
-            shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
+        snapshot_scripts(ds)
     atomic(WORK/'tmp/dataset-work-state.json',state)
 
 def official_arctic_check(row,views):

HOT3D/shared-v1.0/script/internal/render_full_sequence_review.py

@@ -1,256 +1,256 @@
-"""Full-frame, all-camera diagnostic videos read from actual shared tensors/media.
-
-Preview MP4 is explicitly display-only; warehouse MKV packets remain untouched.
-"""
-from fresh_sequence_common import *
-from review_fresh_sequence_pilots import arr, validate_view, project_for_view
-from datetime import datetime
-import html
-
-VIDEO_RUN='full-video-review-v2'
-CELL=512
-WIDTH=3*CELL
-HEIGHT=800
-COLORS=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
-
-def text(im,txt,xy,color=(235,235,235),size=.5):cv2.putText(im,txt,xy,cv2.FONT_HERSHEY_SIMPLEX,size,color,1,cv2.LINE_AA)
-
-def fast_edges(im,uv,edges,color):
-    xy=uv[edges];good=np.isfinite(xy).all((1,2)) & (np.max(np.nan_to_num(np.abs(xy),nan=CELL*20),axis=(1,2))<CELL*12)
-    xy=xy[good];xy=xy[np.linalg.norm(xy[:,0]-xy[:,1],axis=1)<CELL*.7]
-    if len(xy):cv2.polylines(im,np.rint(xy).astype(np.int32),False,color,1,cv2.LINE_AA)
-
-def dots(im,uv,color,radius=2):
-    good=np.isfinite(uv).all(1)&(uv[:,0]>=0)&(uv[:,0]<im.shape[1])&(uv[:,1]>=0)&(uv[:,1]<im.shape[0])
-    xy=np.rint(uv[good]).astype(int);xy[:,0]=np.clip(xy[:,0],0,im.shape[1]-1);xy[:,1]=np.clip(xy[:,1],0,im.shape[0]-1)
-    for d in range(max(1,radius)):
-        im[np.clip(xy[:,1]+d,0,im.shape[0]-1),xy[:,0]]=color
-        im[xy[:,1],np.clip(xy[:,0]+d,0,im.shape[1]-1)]=color
-
-def model_vertices(motion,valid,side,fix):
-    model=Mano(['left','right'][side])
-    if side==0 and fix:
-        model.data['shapedirs'][:,0]*=-1
-        with torch.no_grad():model.layer.shapedirs[:,0]*=-1
-    verts=np.full((len(motion),778,3),np.nan,np.float32);joints=np.full((len(motion),16,3),np.nan,np.float32)
-    ids=np.flatnonzero(valid[:,side])
-    for start in range(0,len(ids),64):
-        ix=ids[start:start+64];a=motion[ix,side];verts[ix],_,joints[ix]=model.geometry(a[:,:48],a[:,48:58],a[:,58:])
-    faces=np.asarray(model.layer.faces);edges=np.unique(np.sort(np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]),axis=1),axis=0)
-    return verts,joints,edges
-
-def encoder(path,fps,log,size=(WIDTH,HEIGHT)):
-    path.parent.mkdir(parents=True,exist_ok=True)
-    return subprocess.Popen([str(FF),'-v','error','-y','-f','rawvideo','-pix_fmt','rgb24','-s',f'{size[0]}x{size[1]}','-r',str(fps),'-i','pipe:0',
-        '-an','-c:v','libx264','-preset','veryfast','-crf','19','-pix_fmt','yuv420p','-threads','2','-bf','0','-g','60','-movflags','+faststart',str(path)],stdin=subprocess.PIPE,stderr=log)
-
-def read_exact(pipe,n):
-    chunks=[];count=0
-    while count<n:
-        b=pipe.read(n-count)
-        if not b:break
-        chunks.append(b);count+=len(b)
-    return b''.join(chunks)
-
-def trajectory_plot(E,width=265,height=145):
-    good=np.isfinite(E).all((1,2));xyz=np.full((len(E),3),np.nan)
-    xyz[good]=-(np.swapaxes(E[good,:3,:3],1,2)@E[good,:3,3,None])[:,:,0]
-    base=np.full((height,width,3),24,np.uint8);xy=xyz[:,[0,2]];valid=np.isfinite(xy).all(1)
-    if not valid.any():return base,np.zeros((len(E),2),int),xyz
-    lo=np.nanmin(xy,0);hi=np.nanmax(xy,0);span=np.maximum(hi-lo,.05);scale=min((width-24)/span[0],(height-35)/span[1]);pix=(xy-lo)*scale+12
-    pix[:,1]=height-12-pix[:,1];pixels=np.nan_to_num(pix).astype(int)
-    for i in range(1,len(E)):
-        if valid[i-1] and valid[i]:cv2.line(base,tuple(pixels[i-1]),tuple(pixels[i]),(110,110,110),1)
-    text(base,'camera world trajectory X/Z (m)',(7,15),size=.38)
-    return base,pixels,xyz
-
-def object_specs(p,source):
-    result=[]
-    for k,op in enumerate(sorted((p/'annotation/object').glob('*'))):
-        if not (op/'pose.pt').exists():continue
-        om=load(op/'metainfo.json');rel=op.relative_to(p);mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
-        rawpose=arr(source/rel/'pose.pt');rawvalid=arr(source/rel/'pose_valid.pt');valid=arr(op/'pose_valid.pt')
-        spec={'id':om['object_id'],'name':om.get('category_name',om['object_id']),'color':COLORS[k%len(COLORS)],'P':rawpose,'rawvalid':rawvalid,'valid':valid,'op':op}
-        if 'vertex_part_ids_ref' in om:
-            xyz=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
-            part=arr(p/om['vertex_part_ids_ref']);idx=np.arange(0,len(xyz),max(1,len(xyz)//4500))
-            spec.update(vertices=xyz[idx],parts=part[idx],top=arr(source/rel/'parts/top/pose.pt'),edges=None,
-                keypoints=arr(source/rel/'keypoints/xyz.pt'),joint=arr(source/rel/'joint_state.pt')[:,0])
-        else:
-            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//1100)]
-            unique,inv=np.unique(edges,return_inverse=True);spec.update(vertices=np.asarray(mesh.vertices)[unique],edges=inv.reshape(-1,2),parts=None)
-        spec['visibility']=arr(op/'visibility.pt') if (op/'visibility.pt').exists() else None
-        spec['raw_visibility']=arr(source/rel/'visibility.pt') if (source/rel/'visibility.pt').exists() else None
-        result.append(spec)
-    return result
-
-def render_view(p,dataset,qa_data,doc=None,tmp=None,source=None,snapshot_indices=None):
-    m,frames,structural=validate_view(p);cam=m['camera_id'];T=m['num_frames'];root=DATA_ROOT/dataset/RELEASE
-    doc=doc or root/'doc/validation';tmp=tmp or DATA_ROOT/dataset/'tmp'/VIDEO_RUN;tmp.mkdir(parents=True,exist_ok=True)
-    if source is None:
-        source=p
-        for candidate in [DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/m['sequence_id']/cam,DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/cam]:
-            if (candidate/'snapshot.json').exists() and Path(load(candidate/'snapshot.json')['source_view']).resolve()==p.resolve():source=candidate;break
-    qa=np.array(qa_data[cam]['qa_pass'],bool) if dataset=='HOT3D' and qa_data and qa_data[cam].get('qa_pass') is not None else None
-    if 'mano' in m['components']:
-        motion=arr(source/'annotation/mano/motion.pt');present=arr(source/'annotation/mano/motion_valid.pt');valid=arr(p/'annotation/mano/motion_valid.pt')
-    else:motion=np.full((T,2,61),np.nan);present=np.zeros((T,2),bool);valid=present.copy()
-    visibility=arr(p/'annotation/mano/visibility.pt') if (p/'annotation/mano/visibility.pt').exists() else None
-    rawvis=arr(source/'annotation/mano/visibility.pt') if (source/'annotation/mano/visibility.pt').exists() else None
-    hands=[model_vertices(motion,present,s,m['components'].get('mano',{}).get('model_profile',{}).get('left_shapedirs_fix',False)) for s in range(2)]
-    objs=object_specs(p,source);K=arr(p/'camera/intrinsic.pt') if (p/'camera/intrinsic.pt').exists() else np.full((T,3,3),np.nan);D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None;E=arr(p/'camera/extrinsic.pt') if (p/'camera/extrinsic.pt').exists() else np.full((T,4,4),np.nan)
-    W,H=m['camera']['image_size'];scale0=min(CELL/W,CELL/H);w=int(W*scale0)//2*2;h=int(H*scale0)//2*2;scale=np.array([w/W,h/H])
-    fps=m['sampling']['source_fps'] or 30;path=doc/'videos'/f'{cam}-full.mp4';logpath=tmp/f'render-{cam}.log';log=logpath.open('wb')
-    dec=subprocess.Popen([str(FF),'-v','error','-threads','2','-noautorotate','-i',str(p/'observation/rgb.mkv'),'-vf',f'scale={w}:{h}','-pix_fmt','rgb24','-fps_mode','passthrough','-f','rawvideo','pipe:1'],stdout=subprocess.PIPE,stderr=log)
-    enc=encoder(path,fps,log);traj,tp,world=trajectory_plot(E);rendered=0;exposure=[]
-    snapshots=sorted(set(snapshot_indices or [0,1,26,27,179,180,T//4,T//2,3*T//4,T-1]))
-    timeline=np.zeros((12,WIDTH,3),np.uint8)
-    for x in range(WIDTH):timeline[:,x]=(40,135,65) if qa is not None and qa[min(T-1,x*T//WIDTH)] else ((170,42,50) if qa is not None else (75,80,95))
-    for i in range(T):
-        b=read_exact(dec.stdout,w*h*3);assert len(b)==w*h*3,(cam,i,'decoder ended early')
-        original=np.frombuffer(b,np.uint8).reshape(h,w,3).copy();raw=original.copy();usable=original.copy()
-        gray=cv2.cvtColor(cv2.resize(original,(96,96)),cv2.COLOR_RGB2GRAY)
-        exposure.append({'frame_index':i,'p95_luma':float(np.percentile(gray,95)),'median_luma':float(np.median(gray))})
-        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
-        for side,(verts,joints,edges) in enumerate(hands):
-            if not present[i,side]:continue
-            uv=project_for_view(verts[i],m,params,scale);color=(0,235,255) if side==0 else (255,135,20)
-            fast_edges(raw,uv,edges,color)
-            if valid[i,side]:fast_edges(usable,uv,edges,color)
-            # White MANO joints are a diagnostic derivation, not a new keypoint component.
-            dots(raw,project_for_view(joints[i],m,params,scale),(255,255,255),2)
-            if valid[i,side]:dots(usable,project_for_view(joints[i],m,params,scale),(255,255,255),2)
-        for obj in objs:
-            if not obj['rawvalid'][i]:continue
-            P=obj['P'][i];xyz=obj['vertices']@P[:3,:3].T+P[:3,3]
-            if obj['parts'] is not None:
-                top=obj['top'][i];sel=obj['parts']==1;xyz[sel]=obj['vertices'][sel]@top[:3,:3].T+top[:3,3]
-            uv=project_for_view(xyz,m,params,scale)
-            for image,enabled in [(raw,True),(usable,obj['valid'][i])]:
-                if not enabled:continue
-                if obj['edges'] is not None:fast_edges(image,uv,obj['edges'],obj['color'])
-                else:
-                    dots(image,uv[obj['parts']==0],(100,240,90));dots(image,uv[obj['parts']==1],(230,80,230))
-                    dots(image,project_for_view(obj['keypoints'][i],m,params,scale),(255,255,255),3)
-                # Local object axes in metres make pose orientation visible.
-                axes=np.vstack([np.zeros(3),np.eye(3)*.045])@P[:3,:3].T+P[:3,3];auv=project_for_view(axes,m,params,scale)
-                for j,c in enumerate([(255,60,60),(60,255,60),(60,120,255)]):fast_edges(image,auv,np.array([[0,j+1]]),c)
-        canvas=np.full((HEIGHT,WIDTH,3),20,np.uint8)
-        for col,(im,label) in enumerate([(original,'SOURCE RGB'),(raw,'NATIVE LABEL DIAGNOSTIC'),(usable,'SHARED USABLE SUPERVISION')]):
-            canvas[40+(CELL-h)//2:40+(CELL-h)//2+h,col*CELL+(CELL-w)//2:col*CELL+(CELL-w)//2+w]=im
-            text(canvas,label,(col*CELL+12,25),size=.58)
-        status='NO OFFICIAL QA PROVIDED' if qa is None else ('OFFICIAL QA PASS' if qa[i] else 'OFFICIAL QA FAIL - NOT SUPERVISION')
-        if dataset=='ARCTIC' and i in m['components'].get('mano',{}).get('valid_policy',{}).get('local_excluded_frame_indices',[]):status='LOCAL REVIEW REJECT - NOT HAND SUPERVISION'
-        color=(235,195,65) if qa is None else ((80,240,110) if qa[i] else (255,90,90))
-        text(canvas,f'{dataset} {cam}  index={i}/{T-1}  source={frames[i]["source_frame_id"]}   {status}',(12,578),color,.6)
-        text(canvas,f'MANO source L/R={int(present[i,0])}/{int(present[i,1])}  shared valid L/R={int(valid[i,0])}/{int(valid[i,1])}  (m/rad)',(12,603),size=.49)
-        def vals(a):return 'n/a' if a is None else ','.join('unknown' if not np.isfinite(x) else f'{100*x:.1f}%' for x in np.atleast_1d(a))
-        text(canvas,f'visibility native L/R: {vals(rawvis[i] if rawvis is not None else None)}  usable: {vals(visibility[i] if visibility is not None else None)}',(12,626),size=.48)
-        text(canvas,f'camera={m["camera"]["model"]}; intrinsic '+np.array2string(K[i].ravel()[:5],precision=2),(12,649),size=.45)
-        text(canvas,'camera world position (m): '+np.array2string(world[i],precision=3),(12,672),size=.45)
-        clock=frames[i]['timestamp_ns'];text(canvas,f'timestamp_ns={clock}  clock={frames[i]["clock_domain"]}',(12,695),size=.44)
-        text(canvas,f'preview FPS={fps}; '+('capture FPS unknown, playback only' if m['sampling']['source_fps'] is None else 'source capture time is stored separately'),(12,718),size=.44)
-        text(canvas,'cyan=left; orange=right; white=derived MANO joints / source object points',(12,741),size=.41)
-        text(canvas,'Projection shows hidden surfaces; it is NOT native segmentation.',(12,762),(180,180,180),.43)
-        for j,obj in enumerate(objs):
-            a=obj['raw_visibility'];v=obj['visibility'];s=f'{obj["name"]}: valid={int(obj["valid"][i])}'
-            if a is not None:s+=f' vis={vals(v[i])} (raw {vals(a[i])})'
-            if 'joint' in obj:s+=f' hinge={obj["joint"][i]:.3f}rad  keypoints=32'
-            text(canvas,s,(660,603+j*24),obj['color'],.44)
-        tr=traj.copy()
-        if np.isfinite(world[i]).all():cv2.circle(tr,tuple(tp[i]),4,(255,220,70),-1)
-        canvas[610:755,WIDTH-277:WIDTH-12]=tr
-        canvas[-12:]=timeline;cursor=min(WIDTH-1,int(i*WIDTH/T));canvas[-12:,cursor:cursor+2]=255
-        enc.stdin.write(canvas.tobytes());rendered+=1
-        if i in snapshots:
-            snap=doc/'video_snapshots'/cam;snap.mkdir(parents=True,exist_ok=True);Image.fromarray(canvas).save(snap/f'{i:06d}.jpg',quality=92)
-        if i%200==0:print('VIDEO_FRAME',dataset,cam,i,T,flush=True)
-    assert not dec.stdout.read(1),'extra source frames';dec.stdout.close();assert dec.wait()==0
-    enc.stdin.close();assert enc.wait()==0;log.close();assert rendered==T
-    packets=framehash(['-i',str(path)],copy=True);assert len(packets)==T
-    result={'sequence_id':m['sequence_id'],'camera_id':cam,'frames':T,'rendered_frames':rendered,'video_ref':path.relative_to(doc).as_posix(),'video_sha256':digest(path),'preview_fps':fps,
-        'preview_is_training_data':False,'preview_encoding':'H264 CRF19, scaled, display only; shared original MKV unchanged',
-        'source_labels_from':'raw-derived diagnostic cache' if source!=p else 'shared unfiltered source-compatible labels',
-        'all_source_and_shared_valid_masks_used':True,'structure_checks':structural,'mano_valid_counts':valid.sum(0).tolist(),
-        'official_qa_pass_frames':int(qa.sum()) if qa is not None else None,'official_qa_fail_frames':int((~qa).sum()) if qa is not None else None,
-        'exposure_diagnostics':{'method':'scaled shared decoded RGB to 96x96 gray; p95<35 flags a candidate only, does not modify valid','suspect_dark_frame_indices':[x['frame_index'] for x in exposure if x['p95_luma']<35]}}
-    save(tmp/f'{cam}-exposure.json',exposure)
-    save(tmp/f'{cam}-video-result.json',result);print('VIDEO_COMPLETE',dataset,cam,T,flush=True)
-    return result
-
-def asset_preview(op,out):
-    """Textured point-splat turntable plus the source texture; diagnostic, not photoreal rendering."""
-    p=op.parents[2];m=load(op/'metainfo.json');meshpath=p/m['mesh_ref']
-    if (op/'assets/mesh_tex.obj').exists():meshpath=op/'assets/mesh_tex.obj'
-    mesh=trimesh.load(meshpath,process=False,force='mesh');xyz=np.asarray(mesh.vertices);idx=np.arange(0,len(xyz),max(1,len(xyz)//35000));xyz=xyz[idx]
-    texture=getattr(mesh.visual.material,'image',None) if hasattr(mesh.visual,'material') else None
-    texture_source=None
-    if meshpath.name=='mesh_tex.obj' and op.relative_to(DATA_ROOT).parts[0]=='ARCTIC':
-        # The loader can silently return a 2x2 default material. Resolve the
-        # official single-texture MTL explicitly instead of accepting that placeholder.
-        mtl=[r.split(maxsplit=1)[1] for r in meshpath.read_text().splitlines() if r.startswith('mtllib ')]
-        diffuse=[]
-        for name in mtl:
-            material=meshpath.parent/name
-            diffuse.extend(material.parent/r.split(maxsplit=1)[1] for r in material.read_text().splitlines() if r.startswith('map_Kd '))
-        diffuse=list(set(diffuse));assert len(diffuse)==1,(meshpath,diffuse)
-        assert diffuse[0].resolve().is_relative_to(meshpath.parent.resolve())
-        texture=Image.open(diffuse[0]).convert('RGB');assert min(texture.size)>2
-        texture_source={'file':diffuse[0].relative_to(p).as_posix(),'sha256':digest(diffuse[0]),'size':list(texture.size),'resolution_method':'explicit official MTL map_Kd; reject loader placeholder'}
-    if texture is not None and getattr(mesh.visual,'uv',None) is not None:
-        tex=np.asarray(texture.convert('RGB'));uv=np.asarray(mesh.visual.uv)[idx]
-        rgb=tex[np.clip(np.rint((1-uv[:,1])*(len(tex)-1)).astype(int),0,len(tex)-1),np.clip(np.rint(uv[:,0]*(tex.shape[1]-1)).astype(int),0,tex.shape[1]-1)]
-    else:rgb=np.broadcast_to([150,210,245],xyz.shape).astype(np.uint8)
-    extent=np.ptp(xyz,axis=0);xyz=xyz-(xyz.min(0)+xyz.max(0))/2;scale=300/max(np.linalg.norm(extent),.001)
-    logpath=DATA_ROOT/op.relative_to(DATA_ROOT).parts[0]/'tmp'/VIDEO_RUN/'assets.log'
-    logpath.parent.mkdir(parents=True,exist_ok=True)
-    out.parent.mkdir(parents=True,exist_ok=True)
-    with logpath.open('ab') as log:
-        enc=encoder(out,12,log,size=(800,480))
-        for i in range(72):
-            image=np.full((480,800,3),24,np.uint8);R=Rotation.from_euler('xy',[.35,i*2*np.pi/72]).as_matrix();v=xyz@R.T
-            order=np.argsort(v[:,2]);xy=np.rint(v[:,:2]*scale+[220,220]).astype(int)
-            good=(xy[:,0]>=0)&(xy[:,0]<430)&(xy[:,1]>=35)&(xy[:,1]<400);order=order[good[order]]
-            for dx,dy in [(0,0),(1,0),(0,1)]:image[np.clip(xy[order,1]+dy,0,479),np.clip(xy[order,0]+dx,0,429)]=rgb[order]
-            if texture is not None:
-                thumb=texture.convert('RGB');thumb.thumbnail((350,345));image[40:40+thumb.height,440:440+thumb.width]=np.asarray(thumb)
-            text(image,m.get('category_name',m['object_id']),(12,25),size=.65)
-            text(image,'TEXTURED 3D POINT PREVIEW',(12,420),size=.55);text(image,'SOURCE MATERIAL TEXTURE',(445,420),size=.5)
-            text(image,'OBJ extent (m): '+np.array2string(extent,precision=4),(12,450),size=.5);enc.stdin.write(image.tobytes())
-        enc.stdin.close();assert enc.wait()==0
-    assert len(framehash(['-i',str(out)],copy=True))==72
-    return {'object_id':m['object_id'],'video_ref':'assets/'+out.name,'frames':72,'texture_present':texture is not None,'texture_source':texture_source,'extent_m':extent.tolist(),'method':'diagnostic textured point turntable, not photoreal material rendering'}
-
-def publish_review(dataset,results,assets):
-    root=DATA_ROOT/dataset/RELEASE;doc=root/'doc/validation'
-    scope=[('RGB','全部帧原图与逐帧码流/像素校验'),('相机内参/畸变','投影使用声明模型和全部参数；HUD显示相机模型'),('相机外参','全帧姿态、米制相机位置与世界XZ轨迹'),('MANO','逐帧网格线框、派生关节点、左右手valid'),('物体姿态/几何','逐帧轮廓、局部XYZ轴、物体ID'),('材质/纹理','每个物体的原纹理与完整旋转预览')]
-    scope+=([('部件/关节','底座/顶盖分别着色，逐帧关节角'),('物体关键点','源32个物体点逐帧叠加'),('visibility','原数据未提供，明确缺省')] if dataset=='ARCTIC' else [('visibility','原值0–1已修正，原生/可用比例同时显示'),('官方QA','完整红绿时间带；QA失败帧保留诊断、停用监督'),('部件/关节、独立物体关键点','本序列无该源模态，不虚构')])
-    scope+=[('深度/像素分割/接触','本次raw范围未提供，不以投影或帧筛选CSV冒充')]
-    table=''.join(f'<tr><td>{a}</td><td>{b}</td></tr>' for a,b in scope)
-    cards=''.join(f'<section><h2>Camera {r["camera_id"]} · {r["frames"]} 帧 · 有效MANO左右 {r["mano_valid_counts"]}</h2><video controls preload="metadata" src="{r["video_ref"]}"></video><p>全部{r["rendered_frames"]}帧已渲染，无码帧跳过。官方QA通过/未通过：{r["official_qa_pass_frames"]}/{r["official_qa_fail_frames"]}（null表示来源无此QA）。</p></section>' for r in results)
-    gallery=''.join(f'<section><h3>物体 {a["object_id"]}</h3><video controls loop preload="metadata" src="{a["video_ref"]}"></video><p>纹理点云旋转预览及原始材质图；此预览不证明完整PBR渲染等价。</p></section>' for a in assets)
-    known=('已修正转换器颠倒顶盖/底座编码的错误：原parts.json的0是顶盖、1是底座，共享层重新映射为0底座/1顶盖。9路相机前两帧均为源欠曝图，已排除对应几何监督；camera 6 index 270的手部偏移作为已知问题记录，按用户要求撤销单帧屏蔽，恢复原标注和valid。整套试点仍待验收。中栏显示源参数诊断，右栏按当前valid绘制。详见 <a href="ARCTIC_FINDINGS.md">问题与修复证据</a>、<a href="diagnosis/camera6-270-before-after.jpg">修复前后对照</a>��预览30fps仅用于播放，不代表真实采集帧率；没有源准确性QA。' if dataset=='ARCTIC' else '原始开头帧存在明显错位，官方QA为False；源头戴位姿开头27帧保持不变，帧号/时间/包顺序均未人为移动。中栏保留原始诊断投影，右栏只绘制当前valid监督。已修正visibility误除100。QA按相机整帧保守筛选，不能断言具体哪个实体错。')
-    page=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 完整视频审核 v2</title><style>body{{font:16px/1.7 system-ui;background:#edf1f7;color:#172136;max-width:1550px;margin:25px auto;padding:0 20px}}header,section{{background:white;padding:22px;border-radius:12px;margin-bottom:20px}}video{{width:100%;background:#151515}}h1{{margin:0}}.warn{{color:#a71d2b;font-weight:bold}}table{{border-collapse:collapse;width:100%}}td,th{{border:1px solid #ccd4df;padding:8px;text-align:left}}.assets{{display:grid;grid-template-columns:repeat(auto-fit,minmax(500px,1fr));gap:18px}}a{{color:#1567a3}}</style><header><h1>{dataset} · 完整序列审核 v2</h1><p class="warn">人工确认待完成。旧“抽帧投影通过”结论撤回，本页替代旧报告；尚未全量或发布。</p><p>视频三栏：原图 ｜ 原始标注诊断 ｜ 共享层有效监督。所有相机、所有帧都已渲染；青=左手，橙=右手，其他色=物体/部件，白点=MANO派生关节点或源物体关键点。投影不做遮挡剔除，也不是分割真值。</p><p>{known}</p><p>标注 valid=实际标注存在且数值合法，并通过适用官方QA及已明确登记的本地排除规则；可见性不代替valid，剩余帧不因此自动认定人工通过。诊断中被排除的参数不进入有效监督，raw保留。MP4经过缩放与展示编码，仅为doc预览，训练输入仍是无新增损失的shared MKV。</p><p><a href="REPORT.md">正式说明</a> · <a href="report.json">逐相机检查结果</a> · <a href="../RAW_TO_SHARED.md">字段映射</a> · <a href="../CONTRACT.md">更新后的valid定义</a></p><h2>逐模态检查覆盖</h2><table><tr><th>模态</th><th>检查/可视化方式</th></tr>{table}</table></header>{cards}<h2>物体资产检查</h2><div class="assets">{gallery}</div><script>document.querySelectorAll('video').forEach(v=>v.addEventListener('play',()=>document.querySelectorAll('video').forEach(o=>{{if(o!==v)o.pause()}})));</script></html>'''
-    (doc/'index.html').write_text(page,encoding='utf-8')
-    old=load(doc/'report.json');old.update(stage='full_video_review_awaiting_human',human_review='pending',review_revision='full-video-v2',
-        old_sample_projection_conclusion='withdrawn',full_video_views=results,asset_previews=assets,modality_coverage=[{'modality':a,'check':b} for a,b in scope],known_issues=known,
-        agent_visual_inspection={'status':'pending_new_full_video_review','not_equivalent_to_user_approval':True},created_at=datetime.now().astimezone().isoformat())
-    old.pop('views',None);old.pop('offline_artifact_check',None);save(doc/'report.json',old)
-    save(doc/'status.json',{'stage':'full_video_review_awaiting_human','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False})
-    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | {r["rendered_frames"]} | {r["mano_valid_counts"]} | {r["official_qa_pass_frames"]} |' for r in results)
-    (doc/'REPORT.md').write_text(f'# {dataset} 完整序列审核 v2\n\n[离线完整视频](index.html) · [机器结果](report.json)\n\n人工确认待完成；旧抽帧投影通过结论撤回。{known}\n\n|相机|源帧数|渲染帧数|左右手有效标注帧数|官方QA通过帧数|\n|---|---:|---:|---|---:|\n{rows}\n\n## 逐模态覆盖\n\n'+''.join(f'- {a}：{b}。\n' for a,b in scope)+'\n完整视频表示每帧已渲染，并不等于每帧人工审核通过。被官方QA排除的源标注仅在诊断栏显示；不挪帧、不隐藏原始异常。所有有效性筛选、可见性与图像编码验收分开记录。源标注质量问题无法通过格式转换修好；通过valid屏蔽，并保留raw溯源。\n',encoding='utf-8')
-    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,root/'doc'/f)
-    for f in ['render_full_sequence_review.py','apply_hot3d_qa.py','fresh_sequence_common.py','review_fresh_sequence_pilots.py','convert_fresh_sequence_pilots.py']:shutil.copyfile(WORK/'scripts'/f,root/'script'/f)
-    print('FULL_VIDEO_REVIEW_READY',dataset,flush=True)
-
-if __name__=='__main__':
-    ap=argparse.ArgumentParser();ap.add_argument('dataset',choices=['HOT3D','ARCTIC']);a=ap.parse_args();torch.set_num_threads(3)
-    root=DATA_ROOT/a.dataset/RELEASE;qa=load(root/'doc/validation/source_qa.json') if a.dataset=='HOT3D' else None
-    # This entry point owns the original single-sequence report. Other review
-    # sequences have their own output directories in arctic_object_review.py.
-    sequence={'ARCTIC':'s09_laptop_use_03','HOT3D':'P0015_179e1b84'}[a.dataset]
-    views=sorted(p.parent for p in (root/'data'/sequence).glob('*/metainfo.json'));results=[]
-    for p in views:results.append(render_view(p,a.dataset,qa))
-    assets=[]
-    for op in sorted((views[0]/'annotation/object').iterdir()):assets.append(asset_preview(op,root/'doc/validation/assets'/f'{op.name}.mp4'))
-    publish_review(a.dataset,results,assets)
+"""Full-frame, all-camera diagnostic videos read from actual shared tensors/media.
+
+Preview MP4 is explicitly display-only; warehouse MKV packets remain untouched.
+"""
+from fresh_sequence_common import *
+from review_fresh_sequence_pilots import arr, validate_view, project_for_view
+from datetime import datetime
+import html
+
+VIDEO_RUN='full-video-review-v2'
+CELL=512
+WIDTH=3*CELL
+HEIGHT=800
+COLORS=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
+
+def text(im,txt,xy,color=(235,235,235),size=.5):cv2.putText(im,txt,xy,cv2.FONT_HERSHEY_SIMPLEX,size,color,1,cv2.LINE_AA)
+
+def fast_edges(im,uv,edges,color):
+    xy=uv[edges];good=np.isfinite(xy).all((1,2)) & (np.max(np.nan_to_num(np.abs(xy),nan=CELL*20),axis=(1,2))<CELL*12)
+    xy=xy[good];xy=xy[np.linalg.norm(xy[:,0]-xy[:,1],axis=1)<CELL*.7]
+    if len(xy):cv2.polylines(im,np.rint(xy).astype(np.int32),False,color,1,cv2.LINE_AA)
+
+def dots(im,uv,color,radius=2):
+    good=np.isfinite(uv).all(1)&(uv[:,0]>=0)&(uv[:,0]<im.shape[1])&(uv[:,1]>=0)&(uv[:,1]<im.shape[0])
+    xy=np.rint(uv[good]).astype(int);xy[:,0]=np.clip(xy[:,0],0,im.shape[1]-1);xy[:,1]=np.clip(xy[:,1],0,im.shape[0]-1)
+    for d in range(max(1,radius)):
+        im[np.clip(xy[:,1]+d,0,im.shape[0]-1),xy[:,0]]=color
+        im[xy[:,1],np.clip(xy[:,0]+d,0,im.shape[1]-1)]=color
+
+def model_vertices(motion,valid,side,fix):
+    model=Mano(['left','right'][side])
+    if side==0 and fix:
+        model.data['shapedirs'][:,0]*=-1
+        with torch.no_grad():model.layer.shapedirs[:,0]*=-1
+    verts=np.full((len(motion),778,3),np.nan,np.float32);joints=np.full((len(motion),16,3),np.nan,np.float32)
+    ids=np.flatnonzero(valid[:,side])
+    for start in range(0,len(ids),64):
+        ix=ids[start:start+64];a=motion[ix,side];verts[ix],_,joints[ix]=model.geometry(a[:,:48],a[:,48:58],a[:,58:])
+    faces=np.asarray(model.layer.faces);edges=np.unique(np.sort(np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]),axis=1),axis=0)
+    return verts,joints,edges
+
+def encoder(path,fps,log,size=(WIDTH,HEIGHT)):
+    path.parent.mkdir(parents=True,exist_ok=True)
+    return subprocess.Popen([str(FF),'-v','error','-y','-f','rawvideo','-pix_fmt','rgb24','-s',f'{size[0]}x{size[1]}','-r',str(fps),'-i','pipe:0',
+        '-an','-c:v','libx264','-preset','veryfast','-crf','19','-pix_fmt','yuv420p','-threads','2','-bf','0','-g','60','-movflags','+faststart',str(path)],stdin=subprocess.PIPE,stderr=log)
+
+def read_exact(pipe,n):
+    chunks=[];count=0
+    while count<n:
+        b=pipe.read(n-count)
+        if not b:break
+        chunks.append(b);count+=len(b)
+    return b''.join(chunks)
+
+def trajectory_plot(E,width=265,height=145):
+    good=np.isfinite(E).all((1,2));xyz=np.full((len(E),3),np.nan)
+    xyz[good]=-(np.swapaxes(E[good,:3,:3],1,2)@E[good,:3,3,None])[:,:,0]
+    base=np.full((height,width,3),24,np.uint8);xy=xyz[:,[0,2]];valid=np.isfinite(xy).all(1)
+    if not valid.any():return base,np.zeros((len(E),2),int),xyz
+    lo=np.nanmin(xy,0);hi=np.nanmax(xy,0);span=np.maximum(hi-lo,.05);scale=min((width-24)/span[0],(height-35)/span[1]);pix=(xy-lo)*scale+12
+    pix[:,1]=height-12-pix[:,1];pixels=np.nan_to_num(pix).astype(int)
+    for i in range(1,len(E)):
+        if valid[i-1] and valid[i]:cv2.line(base,tuple(pixels[i-1]),tuple(pixels[i]),(110,110,110),1)
+    text(base,'camera world trajectory X/Z (m)',(7,15),size=.38)
+    return base,pixels,xyz
+
+def object_specs(p,source):
+    result=[]
+    for k,op in enumerate(sorted((p/'annotation/object').glob('*'))):
+        if not (op/'pose.pt').exists():continue
+        om=load(op/'metainfo.json');rel=op.relative_to(p);mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
+        rawpose=arr(source/rel/'pose.pt');rawvalid=arr(source/rel/'pose_valid.pt');valid=arr(op/'pose_valid.pt')
+        spec={'id':om['object_id'],'name':om.get('category_name',om['object_id']),'color':COLORS[k%len(COLORS)],'P':rawpose,'rawvalid':rawvalid,'valid':valid,'op':op}
+        if 'vertex_part_ids_ref' in om:
+            xyz=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
+            part=arr(p/om['vertex_part_ids_ref']);idx=np.arange(0,len(xyz),max(1,len(xyz)//4500))
+            spec.update(vertices=xyz[idx],parts=part[idx],top=arr(source/rel/'parts/top/pose.pt'),edges=None,
+                keypoints=arr(source/rel/'keypoints/xyz.pt'),joint=arr(source/rel/'joint_state.pt')[:,0])
+        else:
+            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//1100)]
+            unique,inv=np.unique(edges,return_inverse=True);spec.update(vertices=np.asarray(mesh.vertices)[unique],edges=inv.reshape(-1,2),parts=None)
+        spec['visibility']=arr(op/'visibility.pt') if (op/'visibility.pt').exists() else None
+        spec['raw_visibility']=arr(source/rel/'visibility.pt') if (source/rel/'visibility.pt').exists() else None
+        result.append(spec)
+    return result
+
+def render_view(p,dataset,qa_data,doc=None,tmp=None,source=None,snapshot_indices=None):
+    m,frames,structural=validate_view(p);cam=m['camera_id'];T=m['num_frames'];root=DATA_ROOT/dataset/RELEASE
+    doc=doc or root/'doc/validation';tmp=tmp or DATA_ROOT/dataset/'tmp'/VIDEO_RUN;tmp.mkdir(parents=True,exist_ok=True)
+    if source is None:
+        source=p
+        for candidate in [DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/m['sequence_id']/cam,DATA_ROOT/dataset/'tmp'/VIDEO_RUN/'diagnostic'/cam]:
+            if (candidate/'snapshot.json').exists() and Path(load(candidate/'snapshot.json')['source_view']).resolve()==p.resolve():source=candidate;break
+    qa=np.array(qa_data[cam]['qa_pass'],bool) if dataset=='HOT3D' and qa_data and qa_data[cam].get('qa_pass') is not None else None
+    if 'mano' in m['components']:
+        motion=arr(source/'annotation/mano/motion.pt');present=arr(source/'annotation/mano/motion_valid.pt');valid=arr(p/'annotation/mano/motion_valid.pt')
+    else:motion=np.full((T,2,61),np.nan);present=np.zeros((T,2),bool);valid=present.copy()
+    visibility=arr(p/'annotation/mano/visibility.pt') if (p/'annotation/mano/visibility.pt').exists() else None
+    rawvis=arr(source/'annotation/mano/visibility.pt') if (source/'annotation/mano/visibility.pt').exists() else None
+    hands=[model_vertices(motion,present,s,m['components'].get('mano',{}).get('model_profile',{}).get('left_shapedirs_fix',False)) for s in range(2)]
+    objs=object_specs(p,source);K=arr(p/'camera/intrinsic.pt') if (p/'camera/intrinsic.pt').exists() else np.full((T,3,3),np.nan);D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None;E=arr(p/'camera/extrinsic.pt') if (p/'camera/extrinsic.pt').exists() else np.full((T,4,4),np.nan)
+    W,H=m['camera']['image_size'];scale0=min(CELL/W,CELL/H);w=int(W*scale0)//2*2;h=int(H*scale0)//2*2;scale=np.array([w/W,h/H])
+    fps=m['sampling']['source_fps'] or 30;path=doc/'videos'/f'{cam}-full.mp4';logpath=tmp/f'render-{cam}.log';log=logpath.open('wb')
+    dec=subprocess.Popen([str(FF),'-v','error','-threads','2','-noautorotate','-i',str(p/'observation/rgb.mkv'),'-vf',f'scale={w}:{h}','-pix_fmt','rgb24','-fps_mode','passthrough','-f','rawvideo','pipe:1'],stdout=subprocess.PIPE,stderr=log)
+    enc=encoder(path,fps,log);traj,tp,world=trajectory_plot(E);rendered=0;exposure=[]
+    snapshots=sorted(set(snapshot_indices or [0,1,26,27,179,180,T//4,T//2,3*T//4,T-1]))
+    timeline=np.zeros((12,WIDTH,3),np.uint8)
+    for x in range(WIDTH):timeline[:,x]=(40,135,65) if qa is not None and qa[min(T-1,x*T//WIDTH)] else ((170,42,50) if qa is not None else (75,80,95))
+    for i in range(T):
+        b=read_exact(dec.stdout,w*h*3);assert len(b)==w*h*3,(cam,i,'decoder ended early')
+        original=np.frombuffer(b,np.uint8).reshape(h,w,3).copy();raw=original.copy();usable=original.copy()
+        gray=cv2.cvtColor(cv2.resize(original,(96,96)),cv2.COLOR_RGB2GRAY)
+        exposure.append({'frame_index':i,'p95_luma':float(np.percentile(gray,95)),'median_luma':float(np.median(gray))})
+        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
+        for side,(verts,joints,edges) in enumerate(hands):
+            if not present[i,side]:continue
+            uv=project_for_view(verts[i],m,params,scale);color=(0,235,255) if side==0 else (255,135,20)
+            fast_edges(raw,uv,edges,color)
+            if valid[i,side]:fast_edges(usable,uv,edges,color)
+            # White MANO joints are a diagnostic derivation, not a new keypoint component.
+            dots(raw,project_for_view(joints[i],m,params,scale),(255,255,255),2)
+            if valid[i,side]:dots(usable,project_for_view(joints[i],m,params,scale),(255,255,255),2)
+        for obj in objs:
+            if not obj['rawvalid'][i]:continue
+            P=obj['P'][i];xyz=obj['vertices']@P[:3,:3].T+P[:3,3]
+            if obj['parts'] is not None:
+                top=obj['top'][i];sel=obj['parts']==1;xyz[sel]=obj['vertices'][sel]@top[:3,:3].T+top[:3,3]
+            uv=project_for_view(xyz,m,params,scale)
+            for image,enabled in [(raw,True),(usable,obj['valid'][i])]:
+                if not enabled:continue
+                if obj['edges'] is not None:fast_edges(image,uv,obj['edges'],obj['color'])
+                else:
+                    dots(image,uv[obj['parts']==0],(100,240,90));dots(image,uv[obj['parts']==1],(230,80,230))
+                    dots(image,project_for_view(obj['keypoints'][i],m,params,scale),(255,255,255),3)
+                # Local object axes in metres make pose orientation visible.
+                axes=np.vstack([np.zeros(3),np.eye(3)*.045])@P[:3,:3].T+P[:3,3];auv=project_for_view(axes,m,params,scale)
+                for j,c in enumerate([(255,60,60),(60,255,60),(60,120,255)]):fast_edges(image,auv,np.array([[0,j+1]]),c)
+        canvas=np.full((HEIGHT,WIDTH,3),20,np.uint8)
+        for col,(im,label) in enumerate([(original,'SOURCE RGB'),(raw,'NATIVE LABEL DIAGNOSTIC'),(usable,'SHARED USABLE SUPERVISION')]):
+            canvas[40+(CELL-h)//2:40+(CELL-h)//2+h,col*CELL+(CELL-w)//2:col*CELL+(CELL-w)//2+w]=im
+            text(canvas,label,(col*CELL+12,25),size=.58)
+        status='NO OFFICIAL QA PROVIDED' if qa is None else ('OFFICIAL QA PASS' if qa[i] else 'OFFICIAL QA FAIL - NOT SUPERVISION')
+        if dataset=='ARCTIC' and i in m['components'].get('mano',{}).get('valid_policy',{}).get('local_excluded_frame_indices',[]):status='LOCAL REVIEW REJECT - NOT HAND SUPERVISION'
+        color=(235,195,65) if qa is None else ((80,240,110) if qa[i] else (255,90,90))
+        text(canvas,f'{dataset} {cam}  index={i}/{T-1}  source={frames[i]["source_frame_id"]}   {status}',(12,578),color,.6)
+        text(canvas,f'MANO source L/R={int(present[i,0])}/{int(present[i,1])}  shared valid L/R={int(valid[i,0])}/{int(valid[i,1])}  (m/rad)',(12,603),size=.49)
+        def vals(a):return 'n/a' if a is None else ','.join('unknown' if not np.isfinite(x) else f'{100*x:.1f}%' for x in np.atleast_1d(a))
+        text(canvas,f'visibility native L/R: {vals(rawvis[i] if rawvis is not None else None)}  usable: {vals(visibility[i] if visibility is not None else None)}',(12,626),size=.48)
+        text(canvas,f'camera={m["camera"]["model"]}; intrinsic '+np.array2string(K[i].ravel()[:5],precision=2),(12,649),size=.45)
+        text(canvas,'camera world position (m): '+np.array2string(world[i],precision=3),(12,672),size=.45)
+        clock=frames[i]['timestamp_ns'];text(canvas,f'timestamp_ns={clock}  clock={frames[i]["clock_domain"]}',(12,695),size=.44)
+        text(canvas,f'preview FPS={fps}; '+('capture FPS unknown, playback only' if m['sampling']['source_fps'] is None else 'source capture time is stored separately'),(12,718),size=.44)
+        text(canvas,'cyan=left; orange=right; white=derived MANO joints / source object points',(12,741),size=.41)
+        text(canvas,'Projection shows hidden surfaces; it is NOT native segmentation.',(12,762),(180,180,180),.43)
+        for j,obj in enumerate(objs):
+            a=obj['raw_visibility'];v=obj['visibility'];s=f'{obj["name"]}: valid={int(obj["valid"][i])}'
+            if a is not None:s+=f' vis={vals(v[i])} (raw {vals(a[i])})'
+            if 'joint' in obj:s+=f' hinge={obj["joint"][i]:.3f}rad  keypoints=32'
+            text(canvas,s,(660,603+j*24),obj['color'],.44)
+        tr=traj.copy()
+        if np.isfinite(world[i]).all():cv2.circle(tr,tuple(tp[i]),4,(255,220,70),-1)
+        canvas[610:755,WIDTH-277:WIDTH-12]=tr
+        canvas[-12:]=timeline;cursor=min(WIDTH-1,int(i*WIDTH/T));canvas[-12:,cursor:cursor+2]=255
+        enc.stdin.write(canvas.tobytes());rendered+=1
+        if i in snapshots:
+            snap=doc/'video_snapshots'/cam;snap.mkdir(parents=True,exist_ok=True);Image.fromarray(canvas).save(snap/f'{i:06d}.jpg',quality=92)
+        if i%200==0:print('VIDEO_FRAME',dataset,cam,i,T,flush=True)
+    assert not dec.stdout.read(1),'extra source frames';dec.stdout.close();assert dec.wait()==0
+    enc.stdin.close();assert enc.wait()==0;log.close();assert rendered==T
+    packets=framehash(['-i',str(path)],copy=True);assert len(packets)==T
+    result={'sequence_id':m['sequence_id'],'camera_id':cam,'frames':T,'rendered_frames':rendered,'video_ref':path.relative_to(doc).as_posix(),'video_sha256':digest(path),'preview_fps':fps,
+        'preview_is_training_data':False,'preview_encoding':'H264 CRF19, scaled, display only; shared original MKV unchanged',
+        'source_labels_from':'raw-derived diagnostic cache' if source!=p else 'shared unfiltered source-compatible labels',
+        'all_source_and_shared_valid_masks_used':True,'structure_checks':structural,'mano_valid_counts':valid.sum(0).tolist(),
+        'official_qa_pass_frames':int(qa.sum()) if qa is not None else None,'official_qa_fail_frames':int((~qa).sum()) if qa is not None else None,
+        'exposure_diagnostics':{'method':'scaled shared decoded RGB to 96x96 gray; p95<35 flags a candidate only, does not modify valid','suspect_dark_frame_indices':[x['frame_index'] for x in exposure if x['p95_luma']<35]}}
+    save(tmp/f'{cam}-exposure.json',exposure)
+    save(tmp/f'{cam}-video-result.json',result);print('VIDEO_COMPLETE',dataset,cam,T,flush=True)
+    return result
+
+def asset_preview(op,out):
+    """Textured point-splat turntable plus the source texture; diagnostic, not photoreal rendering."""
+    p=op.parents[2];m=load(op/'metainfo.json');meshpath=p/m['mesh_ref']
+    if (op/'assets/mesh_tex.obj').exists():meshpath=op/'assets/mesh_tex.obj'
+    mesh=trimesh.load(meshpath,process=False,force='mesh');xyz=np.asarray(mesh.vertices);idx=np.arange(0,len(xyz),max(1,len(xyz)//35000));xyz=xyz[idx]
+    texture=getattr(mesh.visual.material,'image',None) if hasattr(mesh.visual,'material') else None
+    texture_source=None
+    if meshpath.name=='mesh_tex.obj' and op.relative_to(DATA_ROOT).parts[0]=='ARCTIC':
+        # The loader can silently return a 2x2 default material. Resolve the
+        # official single-texture MTL explicitly instead of accepting that placeholder.
+        mtl=[r.split(maxsplit=1)[1] for r in meshpath.read_text().splitlines() if r.startswith('mtllib ')]
+        diffuse=[]
+        for name in mtl:
+            material=meshpath.parent/name
+            diffuse.extend(material.parent/r.split(maxsplit=1)[1] for r in material.read_text().splitlines() if r.startswith('map_Kd '))
+        diffuse=list(set(diffuse));assert len(diffuse)==1,(meshpath,diffuse)
+        assert diffuse[0].resolve().is_relative_to(meshpath.parent.resolve())
+        texture=Image.open(diffuse[0]).convert('RGB');assert min(texture.size)>2
+        texture_source={'file':diffuse[0].relative_to(p).as_posix(),'sha256':digest(diffuse[0]),'size':list(texture.size),'resolution_method':'explicit official MTL map_Kd; reject loader placeholder'}
+    if texture is not None and getattr(mesh.visual,'uv',None) is not None:
+        tex=np.asarray(texture.convert('RGB'));uv=np.asarray(mesh.visual.uv)[idx]
+        rgb=tex[np.clip(np.rint((1-uv[:,1])*(len(tex)-1)).astype(int),0,len(tex)-1),np.clip(np.rint(uv[:,0]*(tex.shape[1]-1)).astype(int),0,tex.shape[1]-1)]
+    else:rgb=np.broadcast_to([150,210,245],xyz.shape).astype(np.uint8)
+    extent=np.ptp(xyz,axis=0);xyz=xyz-(xyz.min(0)+xyz.max(0))/2;scale=300/max(np.linalg.norm(extent),.001)
+    logpath=DATA_ROOT/op.relative_to(DATA_ROOT).parts[0]/'tmp'/VIDEO_RUN/'assets.log'
+    logpath.parent.mkdir(parents=True,exist_ok=True)
+    out.parent.mkdir(parents=True,exist_ok=True)
+    with logpath.open('ab') as log:
+        enc=encoder(out,12,log,size=(800,480))
+        for i in range(72):
+            image=np.full((480,800,3),24,np.uint8);R=Rotation.from_euler('xy',[.35,i*2*np.pi/72]).as_matrix();v=xyz@R.T
+            order=np.argsort(v[:,2]);xy=np.rint(v[:,:2]*scale+[220,220]).astype(int)
+            good=(xy[:,0]>=0)&(xy[:,0]<430)&(xy[:,1]>=35)&(xy[:,1]<400);order=order[good[order]]
+            for dx,dy in [(0,0),(1,0),(0,1)]:image[np.clip(xy[order,1]+dy,0,479),np.clip(xy[order,0]+dx,0,429)]=rgb[order]
+            if texture is not None:
+                thumb=texture.convert('RGB');thumb.thumbnail((350,345));image[40:40+thumb.height,440:440+thumb.width]=np.asarray(thumb)
+            text(image,m.get('category_name',m['object_id']),(12,25),size=.65)
+            text(image,'TEXTURED 3D POINT PREVIEW',(12,420),size=.55);text(image,'SOURCE MATERIAL TEXTURE',(445,420),size=.5)
+            text(image,'OBJ extent (m): '+np.array2string(extent,precision=4),(12,450),size=.5);enc.stdin.write(image.tobytes())
+        enc.stdin.close();assert enc.wait()==0
+    assert len(framehash(['-i',str(out)],copy=True))==72
+    return {'object_id':m['object_id'],'video_ref':'assets/'+out.name,'frames':72,'texture_present':texture is not None,'texture_source':texture_source,'extent_m':extent.tolist(),'method':'diagnostic textured point turntable, not photoreal material rendering'}
+
+def publish_review(dataset,results,assets):
+    root=DATA_ROOT/dataset/RELEASE;doc=root/'doc/validation'
+    scope=[('RGB','全部帧原图与逐帧码流/像素校验'),('相机内参/畸变','投影使用声明模型和全部参数；HUD显示相机模型'),('相机外参','全帧姿态、米制相机位置与世界XZ轨迹'),('MANO','逐帧网格线框、派生关节点、左右手valid'),('物体姿态/几何','逐帧轮廓、局部XYZ轴、物体ID'),('材质/纹理','每个物体的原纹理与完整旋转预览')]
+    scope+=([('部件/关节','底座/顶盖分别着色，逐帧关节角'),('物体关键点','源32个物体点逐帧叠加'),('visibility','原数据未提供，明确缺省')] if dataset=='ARCTIC' else [('visibility','原值0–1已修正，原生/可用比例同时显示'),('官方QA','完整红绿时间带；QA失败帧保留诊断、停用监督'),('部件/关节、独立物体关键点','本序列无该源模态，不虚构')])
+    scope+=[('深度/像素分割/接触','本次raw范围未提供，不以投影或帧筛选CSV冒充')]
+    table=''.join(f'<tr><td>{a}</td><td>{b}</td></tr>' for a,b in scope)
+    cards=''.join(f'<section><h2>Camera {r["camera_id"]} · {r["frames"]} 帧 · 有效MANO左右 {r["mano_valid_counts"]}</h2><video controls preload="metadata" src="{r["video_ref"]}"></video><p>全部{r["rendered_frames"]}帧已渲染，无码帧跳过。官方QA通过/未通过：{r["official_qa_pass_frames"]}/{r["official_qa_fail_frames"]}（null表示来源无此QA）。</p></section>' for r in results)
+    gallery=''.join(f'<section><h3>物体 {a["object_id"]}</h3><video controls loop preload="metadata" src="{a["video_ref"]}"></video><p>纹理点云旋转预览及原始材质图；此预览不证明完整PBR渲染等价。</p></section>' for a in assets)
+    known=('已修正转换器颠倒顶盖/底座编码的错误：原parts.json的0是顶盖、1是底座，共享层重新映射为0底座/1顶盖。9路相机前两帧均为源欠曝图，已排除对应几何监督；camera 6 index 270的手部偏移作为已知问题记录，按用户要求撤销单帧屏蔽，恢复原标注和valid。整套试点仍待验收。中栏显示源参数诊断，右栏按当前valid绘制。详见 <a href="ARCTIC_FINDINGS.md">问题与修复证据</a>、<a href="diagnosis/camera6-270-before-after.jpg">修复前后对照</a>。预览30fps仅用于播放，不代表真实采集帧率；没有源准确性QA。' if dataset=='ARCTIC' else '原始开头帧存在明显错位，官方QA为False；源头戴位姿开头27帧保持不变，帧号/时间/包顺序均未人为移动。中栏保留原始诊断投影，右栏只绘制当前valid监督。已修正visibility误除100。QA按相机整帧保守筛选，不能断言具体哪个实体错。')
+    page=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 完整视频审核 v2</title><style>body{{font:16px/1.7 system-ui;background:#edf1f7;color:#172136;max-width:1550px;margin:25px auto;padding:0 20px}}header,section{{background:white;padding:22px;border-radius:12px;margin-bottom:20px}}video{{width:100%;background:#151515}}h1{{margin:0}}.warn{{color:#a71d2b;font-weight:bold}}table{{border-collapse:collapse;width:100%}}td,th{{border:1px solid #ccd4df;padding:8px;text-align:left}}.assets{{display:grid;grid-template-columns:repeat(auto-fit,minmax(500px,1fr));gap:18px}}a{{color:#1567a3}}</style><header><h1>{dataset} · 完整序列审核 v2</h1><p class="warn">人工确认待完成。旧“抽帧投影通过”结论撤回，本页替代旧报告；尚未全量或发布。</p><p>视频三栏：原图 ｜ 原始标注诊断 ｜ 共享层有效监督。所有相机、所有帧都已渲染；青=左手，橙=右手，其他色=物体/部件，白点=MANO派生关节点或源物体关键点。投影不做遮挡剔除，也不是分割真值。</p><p>{known}</p><p>标注 valid=实际标注存在且数值合法，并通过适用官方QA及已明确登记的本地排除规则；可见性不代替valid，剩余帧不因此自动认定人工通过。诊断中被排除的参数不进入有效监督，raw保留。MP4经过缩放与展示编码，仅为doc预览，训练输入仍是无新增损失的shared MKV。</p><p><a href="REPORT.md">正式说明</a> · <a href="report.json">逐相机检查结果</a> · <a href="../RAW_TO_SHARED.md">字段映射</a> · <a href="../CONTRACT.md">更新后的valid定义</a></p><h2>逐模态检查覆盖</h2><table><tr><th>模态</th><th>检查/可视化方式</th></tr>{table}</table></header>{cards}<h2>物体资产检查</h2><div class="assets">{gallery}</div><script>document.querySelectorAll('video').forEach(v=>v.addEventListener('play',()=>document.querySelectorAll('video').forEach(o=>{{if(o!==v)o.pause()}})));</script></html>'''
+    (doc/'index.html').write_text(page,encoding='utf-8')
+    old=load(doc/'report.json');old.update(stage='full_video_review_awaiting_human',human_review='pending',review_revision='full-video-v2',
+        old_sample_projection_conclusion='withdrawn',full_video_views=results,asset_previews=assets,modality_coverage=[{'modality':a,'check':b} for a,b in scope],known_issues=known,
+        agent_visual_inspection={'status':'pending_new_full_video_review','not_equivalent_to_user_approval':True},created_at=datetime.now().astimezone().isoformat())
+    old.pop('views',None);old.pop('offline_artifact_check',None);save(doc/'report.json',old)
+    save(doc/'status.json',{'stage':'full_video_review_awaiting_human','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False})
+    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | {r["rendered_frames"]} | {r["mano_valid_counts"]} | {r["official_qa_pass_frames"]} |' for r in results)
+    (doc/'REPORT.md').write_text(f'# {dataset} 完整序列审核 v2\n\n[离线完整视频](index.html) · [机器结果](report.json)\n\n人工确认待完成；旧抽帧投影通过结论撤回。{known}\n\n|相机|源帧数|渲染帧数|左右手有效标注帧数|官方QA通过帧数|\n|---|---:|---:|---|---:|\n{rows}\n\n## 逐模态覆盖\n\n'+''.join(f'- {a}：{b}。\n' for a,b in scope)+'\n完整视频表示每帧已渲染，并不等于每帧人工审核通过。被官方QA排除的源标注仅在诊断栏显示；不挪帧、不隐藏原始异常。所有有效性筛选、可见性与图像编码验收分开记录。源标注质量问题无法通过格式转换修好；通过valid屏蔽，并保留raw溯源。\n',encoding='utf-8')
+    for f in ['CONTRACT.md','schema.json']:shutil.copyfile(WORK/'distribution/hand-warehouse-1.0'/f,root/'doc'/f)
+    # Keep the published script bundle immutable while regenerating review artifacts.
+    print('FULL_VIDEO_REVIEW_READY',dataset,flush=True)
+
+if __name__=='__main__':
+    ap=argparse.ArgumentParser();ap.add_argument('dataset',choices=['HOT3D','ARCTIC']);a=ap.parse_args();torch.set_num_threads(3)
+    root=DATA_ROOT/a.dataset/RELEASE;qa=load(root/'doc/validation/source_qa.json') if a.dataset=='HOT3D' else None
+    # This entry point owns the original single-sequence report. Other review
+    # sequences have their own output directories in arctic_object_review.py.
+    sequence={'ARCTIC':'s09_laptop_use_03','HOT3D':'P0015_179e1b84'}[a.dataset]
+    views=sorted(p.parent for p in (root/'data'/sequence).glob('*/metainfo.json'));results=[]
+    for p in views:results.append(render_view(p,a.dataset,qa))
+    assets=[]
+    for op in sorted((views[0]/'annotation/object').iterdir()):assets.append(asset_preview(op,root/'doc/validation/assets'/f'{op.name}.mp4'))
+    publish_review(a.dataset,results,assets)

HOT3D/shared-v1.0/script/internal/review_fresh_sequence_pilots.py

@@ -1,225 +1,223 @@
-"""Validate actual shared outputs and create portable, offline pilot reviews.
-
-Run after convert_fresh_sequence_pilots.py. Never authorizes full processing.
-"""
-from fresh_sequence_common import *
-import html
-from datetime import datetime
-
-def arr(p):
-    value=torch.load(p,map_location='cpu',weights_only=True)
-    assert isinstance(value,torch.Tensor) and value.device.type=='cpu'
-    return value.numpy()
-
-def safe_ref(root,ref):
-    path=(root/ref).resolve()
-    assert path.is_relative_to(root.resolve()) and path.is_file(),ref
-    return path
-
-def validate_view(p):
-    m=load(p/'metainfo.json');T=m['num_frames'];registry=load(WORK/'distribution/hand-warehouse-1.0/schema.json')
-    assert m['schema_version']==registry['schema_version'] and m['pilot_complete']
-    frames=[json.loads(line) for line in (p/'camera/frames.jsonl').read_text().splitlines()]
-    assert len(frames)==T and [f['frame_index'] for f in frames]==list(range(T))
-    assert len({f['source_frame_id'] for f in frames})==T
-    for frame in frames:
-        for match in frame.get('annotation_matches',{}).values():
-            if 'available' in match:match['source_record_present']=match.pop('available')
-    lines(p/'camera/frames.jsonl',frames)
-    summary=[]
-    for key,c in m['components'].items():
-        assert c['schema_id'] in registry['components'],(key,c['schema_id'])
-        assert c['available'] and c['source_locator'] and c['provenance']['kind'] in ['source','derived','format_conversion']
-        data=safe_ref(p,c['data_ref']);valid=arr(safe_ref(p,c['valid_ref']))
-        assert valid.dtype==bool and valid.ndim==len(c['valid_axes'])
-        if data.suffix=='.pt':
-            a=arr(data);assert a.dtype==np.float32 and a.shape[0]==T
-            assert a.shape[:valid.ndim]==valid.shape and len(c['axes'])==a.ndim
-            assert np.isfinite(a[valid]).all() and np.isnan(a[~valid]).all()
-            if c['schema_id'] in ['object/rigid/v1','transform/se3/v1']:assert_se3(a[valid])
-            if c['schema_id']=='mano/axis_angle61/v1':assert a.shape==(T,2,61)
-        elif data.suffix=='.mkv':
-            assert c['storage']['profile'] in registry['storage_profiles']
-            assert valid.shape==(T,) and valid.all()
-        for attr in c.get('attributes',{}).values():
-            a=arr(safe_ref(p,attr['data_ref']));b=arr(safe_ref(p,attr['valid_ref']))
-            assert b.dtype==bool and a.shape==b.shape
-            assert np.isfinite(a[b]).all() and ((a[b]>=0)&(a[b]<=1)).all() and np.isnan(a[~b]).all()
-        if 'distortion_ref' in c:
-            d=arr(safe_ref(p,c['distortion_ref']));assert d.shape==(T,8) and np.isfinite(d).all()
-        summary.append({'component':key,'schema':c['schema_id'],'valid':int(valid.sum()),'total':int(valid.size)})
-    for op in sorted((p/'annotation/object').glob('*')):
-        om=load(op/'metainfo.json');mesh=safe_ref(p,om['mesh_ref'])
-        if 'vertex_part_ids_ref' in om:
-            ids=arr(safe_ref(p,om['vertex_part_ids_ref']));assert ids.dtype==np.int64
-            assert len(ids)==sum(line.startswith('v ') for line in mesh.read_text().splitlines())
-            assert om['topology_id']=='sha256:'+digest(mesh)
-            if not (op/'pose.pt').exists():
-                assert m.get('omitted_components',{}).get('object_pose')
-            else:
-                root=arr(op/'pose.pt');top=arr(op/'parts/top/pose.pt');joint=arr(op/'joint_state.pt')[:,0]
-                ok=arr(op/'pose_valid.pt');H=np.broadcast_to(np.eye(4),(ok.sum(),4,4)).copy()
-                H[:,:3,:3]=Rotation.from_rotvec(np.c_[np.zeros((ok.sum(),2)),-joint[ok]]).as_matrix()
-                if ok.any():assert np.max(np.abs(root[ok]@H-top[ok]))<2e-6
-        for obj in (op/'assets').glob('*.obj'):
-            for row in obj.read_text().splitlines():
-                if row.startswith('mtllib '):
-                    material=safe_ref(obj.parent,row.split(maxsplit=1)[1])
-                    for r in material.read_text().splitlines():
-                        if r.lower().startswith('map_kd '):safe_ref(material.parent,r.split(maxsplit=1)[1])
-    records=load(DATA_ROOT/m['dataset_id']/'tmp'/RUN/'checks'/f'{m["sequence_id"]}__{m["camera_id"]}.json')['checks']
-    assert records and all(x.get('passed',False) for x in records)
-    media=next(r for r in records if r['test'] in ['all_source_JPEG_packets_and_RGB_pixels_exact','all_source_JPEG_packets_exact']);assert media['frames']==T
-    if media['test']=='all_source_JPEG_packets_exact':
-        pixels=next(r for r in records if r['test']=='source_RGB_pixels_exact')
-        assert pixels['scope']=='deterministic_sample' and pixels['sampled_frames']==len(pixels['frame_indices']) and pixels['sampled_frames']==min(T,16)
-        assert pixels['decoded_from_container'] and pixels['frame_indices']==sorted(set(pixels['frame_indices']))
-    if 'mano' not in m['components']:
-        assert m.get('omitted_components',{}).get('mano')
-        assert not (p/'annotation/mano/motion.pt').exists()
-        return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed','mano':'source modality absent, omitted'}
-    actual=arr(p/'annotation/mano/motion_valid.pt')
-    if m['dataset_id']=='HOT3D':
-        src=m['components']['mano']['source_locator']
-        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/src['archive']) as z:
-            native={r['timestamp_ns']:r['hand_poses'] for r in map(json.loads,z.read(src['member']).splitlines())}
-        expected=np.array([[str(side) in native.get(f['annotation_matches']['mano']['source_timestamp_ns'],{}) for side in range(2)] for f in frames],bool)
-    else:expected=np.array([[f['source_annotation_index'] is not None]*2 for f in frames],bool)
-    if m['dataset_id']=='HOT3D' and m['components']['mano'].get('valid_policy'):
-        source=m['components']['mano']['valid_policy']['source_locator']
-        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/source['archive']) as z:
-            lookup={int(r['timestamp[ns]']):r['mask']=='True' for r in csv.DictReader(io.StringIO(z.read(source['member']).decode())) if r['stream_id']==m['camera_id']}
-        qa=[lookup[f['timecode_ns']] for f in frames]
-        expected &= np.array(qa,bool)[:,None]
-    if m['dataset_id']=='ARCTIC':
-        rejected=m['components']['mano'].get('valid_policy',{}).get('local_excluded_frame_indices',[])
-        expected[rejected]=False
-    assert np.array_equal(expected,actual),'annotation validity disagrees with declared source and QA/local policy'
-    records.append({'test':'all_frame_source_annotation_and_QA_policy','passed':True,'left_right_valid_counts':actual.sum(0).tolist(),'frames_without_usable_hand':int((~actual.any(1)).sum())})
-    return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed'}
-
-def select_images(p,indices,dest):
-    dest.mkdir(parents=True,exist_ok=True)
-    selection='+'.join(f'eq(n,{i})' for i in indices)
-    command([FF,'-v','error','-y','-threads','2','-noautorotate','-i',p/'observation/rgb.mkv',
-        '-vf',f"select='{selection}',scale=900:900:force_original_aspect_ratio=decrease",'-fps_mode','vfr',dest/'decoded-%03d.png'])
-    output=sorted(dest.glob('decoded-*.png'));assert len(output)==len(indices)
-    return dict(zip(indices,output))
-
-def project_for_view(points,m,params,scale):
-    uv=project_points(points,m['camera']['model'],params)
-    if m['camera']['model']=='fisheye624':
-        angle=np.arctan2(np.linalg.norm(points[:,:2],axis=1),points[:,2])
-        uv[angle>m['camera']['max_solid_angle']]=np.nan
-    return uv*scale
-
-def draw_edges(canvas,uv,edges,color):
-    H,W=canvas.shape[:2]
-    for a,b in edges:
-        xy=uv[[a,b]]
-        if not np.isfinite(xy).all():continue
-        if np.max(np.abs(xy))>max(W,H)*20:continue
-        xy=np.rint(xy).astype(int)
-        if np.linalg.norm(xy[0]-xy[1])>max(W,H)*.35:continue
-        ok,start,end=cv2.clipLine((0,0,W,H),tuple(xy[0]),tuple(xy[1]))
-        if ok:cv2.line(canvas,start,end,color,1,cv2.LINE_AA)
-
-def review_view(p,m,frames,dest):
-    T=m['num_frames'];indices=sorted(set([0,1,T//4,T//2,3*T//4,T-1]));imgs=select_images(p,indices,dest)
-    K=arr(p/'camera/intrinsic.pt');D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None
-    motion=arr(p/'annotation/mano/motion.pt');valid=arr(p/'annotation/mano/motion_valid.pt');models=[Mano('left'),Mano('right')]
-    if m['components']['mano']['model_profile']['left_shapedirs_fix']:
-        models[0].data['shapedirs'][:,0]*=-1
-        with torch.no_grad():models[0].layer.shapedirs[:,0]*=-1
-    objects=[]
-    for op in sorted((p/'annotation/object').iterdir()):
-        om=load(op/'metainfo.json');mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
-        if 'vertex_part_ids_ref' in om:
-            vertices=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
-            # trimesh may split OBJ vertices along UV seams; render exact source-ordered vertices as points.
-            objects.append((op,om,vertices,None,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),arr(p/om['vertex_part_ids_ref']),arr(op/'parts/top/pose.pt')))
-        else:
-            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//2500)]
-            objects.append((op,om,np.asarray(mesh.vertices),edges,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),None,None))
-    records=[]
-    for i,source in imgs.items():
-        original=np.asarray(Image.open(source).convert('RGB'));overlay=original.copy()
-        scale=np.array([original.shape[1]/m['camera']['image_size'][0],original.shape[0]/m['camera']['image_size'][1]])
-        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
-        for side,model in enumerate(models):
-            if not valid[i,side]:continue
-            h=motion[i,side];vertices,_,_=model.geometry(h[:48],h[48:58],h[58:]);uv=project_for_view(vertices[0],m,params,scale)
-            faces=np.asarray(model.layer.faces);edges=np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]);draw_edges(overlay,uv,edges,(0,235,255) if side==0 else (255,135,20))
-        palette=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
-        for c,(op,om,vertices,edges,P,ok,parts,top) in enumerate(objects):
-            if not ok[i]:continue
-            xyz=vertices@P[i,:3,:3].T+P[i,:3,3]
-            if parts is not None:xyz[parts==1]=vertices[parts==1]@top[i,:3,:3].T+top[i,:3,3]
-            uv=project_for_view(xyz,m,params,scale)
-            if edges is not None:draw_edges(overlay,uv,edges,palette[c%len(palette)])
-            else:
-                for xy in uv[::max(1,len(uv)//6000)]:
-                    if np.isfinite(xy).all() and 0<=xy[0]<overlay.shape[1] and 0<=xy[1]<overlay.shape[0]:cv2.circle(overlay,tuple(np.rint(xy).astype(int)),1,palette[c%len(palette)],-1)
-        blended=cv2.addWeighted(overlay,.8,original,.2,0);output=dest/f'frame-{i:06d}-overlay.png';raw=dest/f'frame-{i:06d}-rgb.png'
-        Image.fromarray(blended).save(output);source.replace(raw)
-        records.append({'frame_index':i,'source_frame_id':frames[i]['source_frame_id'],'rgb':raw.name,'overlay':output.name,'mano_valid':valid[i].tolist()})
-    return records
-
-def write_reports(dataset,views):
-    base=DATA_ROOT/dataset;root=base/RELEASE;doc=root/'doc';review=doc/'validation';reports=[];cards=[];thumbs=[]
-    rawpaths=set()
-    for p in views:
-        if dataset=='ARCTIC':
-            for assets in (p/'annotation/object').glob('*/assets'):repair_arctic_material_refs(assets)
-        m,frames,summary=validate_view(p);cam=m['camera_id'];samples=review_view(p,m,frames,review/'images'/cam)
-        summary['visual_samples']=samples;reports.append(summary)
-        # All actually consumed source files; camera/mano/object locators name source archives.
-        rawpaths.add(m['components']['rgb']['source_locator'].get('archive',m['components']['rgb']['source_locator'].get('file')))
-        for comp in m['components'].values():
-            src=comp['source_locator']
-            if 'archive' in src:rawpaths.add(src['archive'])
-        if dataset=='ARCTIC':rawpaths.add('raw/data/splits_json.zip')
-        for op in (p/'annotation/object').iterdir():
-            glb=load(op/'metainfo.json').get('original_glb_ref')
-            if glb:rawpaths.add(load(op/'metainfo.json')['original_glb_source_locator']['file'])
-        opts=''.join(f'<option value="{i}"'+(' selected' if s['frame_index']==m['num_frames']//2 else '')+f'>index {s["frame_index"]} · source {s["source_frame_id"]} · valid {s["mano_valid"]}</option>' for i,s in enumerate(samples))
-        samples_js=json.dumps([{k:(f'images/{cam}/'+s[k] if k in ('rgb','overlay') else s[k]) for k in s} for s in samples])
-        cards.append(f'<section class="card" data-samples=\'{html.escape(samples_js,quote=True)}\'><h2>Camera {cam} · {m["camera"]["model"]} · {m["num_frames"]} 帧</h2><select onchange="show(this.closest(\'.card\'))">{opts}</select><label><input type="checkbox" checked onchange="show(this.closest(\'.card\'))"> 显示投影</label><br><a class="zoom" target="_blank"><img alt="共享产物实际帧与投影"></a><p class="caption"></p></section>')
-        middle=samples[len(samples)//2];thumb=Image.open(review/'images'/cam/middle['overlay']).convert('RGB');thumb.thumbnail((400,320));tile=Image.new('RGB',(420,355),'#18202b');tile.paste(thumb,((420-thumb.width)//2,28));ImageDraw.Draw(tile).text((10,8),f'{dataset} camera {cam} / frame {middle["frame_index"]}',fill='white');thumbs.append(tile)
-        print('REVIEWED',dataset,cam,flush=True)
-    cols=3;grid=Image.new('RGB',(cols*420,((len(thumbs)+cols-1)//cols)*355),'#18202b')
-    for i,t in enumerate(thumbs):grid.paste(t,((i%cols)*420,(i//cols)*355))
-    grid.save(review/'overview.png')
-    source_manifest=[]
-    ledger=load(base/'raw/doc'/('archive_checksums.json' if dataset=='ARCTIC' else 'file_checksums.json'))
-    for rel in sorted(x for x in rawpaths if x):
-        file=base/rel;sha=digest(file);record=ledger[rel.removeprefix('raw/data/')]
-        assert record['sha256']==sha and record['size']==file.stat().st_size
-        assert record.get('official_sha256_match',record.get('official_digest_match')) is True
-        if file.suffix=='.zip':assert record['zip_crc']=='passed_all_members'
-        if dataset=='HOT3D':assert record['fresh_download_run']=='2026-09-18-aria-fresh'
-        source_manifest.append({'path':rel,'bytes':file.stat().st_size,'sha256':sha,'matches_verified_official_download':True,'zip_crc_at_download':record.get('zip_crc')})
-    save(doc/'pilot-source-manifest.json',source_manifest)
-    script_manifest=[]
-    for name in ['review_fresh_sequence_pilots.py','fresh_sequence_common.py','convert_fresh_sequence_pilots.py']:
-        shutil.copyfile(WORK/'scripts'/name,root/'script'/name)
-    for p in sorted((root/'script').glob('*.py')):script_manifest.append({'path':p.relative_to(root).as_posix(),'sha256':digest(p)})
-    full={'dataset':dataset,'stage':'pilot_awaiting_human_review','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False,
-        'scope':'One complete selected sequence, every released camera frame; not full-dataset acceptance','views':reports,
-        'sources_manifest':'../pilot-source-manifest.json','script_manifest':script_manifest,'preview_selection':'Deterministic boundary and quartile samples per camera; not random or full-dataset review',
-        'preview_rendering':'Derived MANO/object projections from shared tensors, not segmentation ground truth; no depth occlusion test, hidden surfaces may be drawn',
-        'created_at':datetime.now().astimezone().isoformat()}
-    save(review/'report.json',full);save(review/'status.json',{k:full[k] for k in ['dataset','stage','human_review','full_preprocessing_authorized','upload_authorized']})
-    notes=('首帧 RGB 存在，动捕标注无对应项，valid=false；已保留。相机 0 使用 rational8 畸变，外部八相机使用官方针孔投影。关节绕父坐标系 -Z 轴旋转，部件点/网格保持米制。' if dataset=='ARCTIC' else 'Aria 三路相机均保留；RGB 与两路灰度均由原 JPEG 直接封装。device time 经官方映射对齐 timecode；原始 visibility 按百分比转 [0,1]，独立于标注 valid。三路按时间匹配，不假定相同行号自动同步。')
-    if dataset=='HOT3D':notes+=' 每相机双手各806帧有MANO、208帧原始手参数为空；画面中有手但无投影的末尾帧属于源标注缺失，已验证并保留为valid=false。'
-    if dataset=='ARCTIC':notes+=' 原始部件OBJ的材质文件名引用错误已在共享层修正为实际存在的material.mtl，材质标识与纹理不变。'
-    missing=('本次官方 raw 中无独立深度、像素分割、接触标签、实测手关键点数组。保留 MANO，供任务层按需生成关键点；未把预览投影写成新增标注。' if dataset=='ARCTIC' else '本范围无深度流或像素分割；masks/*.csv 是帧筛选条件，不能当作像素 mask。UmeTrack 与 MPS 替代估计保留在 raw；shared 选择 MANO 和官方 GT 相机。没有额外生成关键点或接触标签。')
-    text=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 序列试点审核</title><style>body{{font:16px/1.65 system-ui;background:#f3f5f8;color:#18202b;max-width:1450px;margin:24px auto;padding:0 20px}}header,.card{{background:white;padding:22px;border-radius:14px;margin-bottom:18px}}.status{{color:#9a5800;font-weight:bold}}.grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(360px,1fr));gap:18px}}.card img{{max-width:100%;max-height:670px;margin-top:12px}}select{{max-width:100%;padding:8px}}a{{color:#1766ab}}label{{display:inline-block;margin:10px}}h1{{margin:0}}.caption{{font-size:14px}}</style><header><h1>{dataset} · 完整序列试点</h1><p class="status">自动校验已完成；人工审核待确认。尚未开始全量预处理或上传。</p><p>共 {len(views)} 路相机、{sum(r['frames'] for r in reports):,} 个相机帧。全部源 JPEG 码流、全部解码 RGB 帧已逐一比较一致；数值转换、有效性和引用检查通过。</p><p>{notes}</p><p>{missing}</p><p>下方是从 shared 实际产物读取的边界及四分位样本。青色=左手，橙色=右手，其余颜色=物体。投影仅用于几何核验，<b>不是源分割，也没有做遮挡剔除</b>。点击图像可放大；取消勾选可查看原图。</p><p><a href="REPORT.md">验收说明</a> · <a href="report.json">机器可读结果</a> · <a href="../RAW_TO_SHARED.md">raw → share 对照</a> · <a href="../CONTRACT.md">约定</a> · <a href="overview.png">全部相机总览</a></p></header><main class="grid">{''.join(cards)}</main><script>function show(card){{let s=JSON.parse(card.dataset.samples)[Number(card.querySelector('select').value)];let path=card.querySelector('input').checked?s.overlay:s.rgb;card.querySelector('img').src=path;card.querySelector('.zoom').href=path;card.querySelector('.caption').textContent='frame_index='+s.frame_index+' / 原帧 '+s.source_frame_id+' / 左右手 valid='+s.mano_valid;}}document.querySelectorAll('.card').forEach(show)</script></html>'''
-    (review/'index.html').write_text(text,encoding='utf-8')
-    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | '+', '.join(str(x['frame_index']) for x in r['visual_samples'])+' | 通过 |' for r in reports)
-    report=f'''# {dataset} 完整序列试点验收\n\n状态：**自动检查通过，人工确认待完成**。本报告不是全量验收或发布许可。\n\n[离线预览](index.html) · [机器结果](report.json) · [原始包摘要](../pilot-source-manifest.json)\n\n| 相机 | 全序列帧数 | 可视化帧索引 | 自动校验 |\n|---|---:|---|---|\n{rows}\n\n## 已验证\n\n- 完整所选序列的每一张源 JPEG 与 MKV 取回的压缩包 SHA256 相同；全部帧在同一 FFmpeg/noautorotate/rgb24 配置下解码字节相同，顺序和数量一致。不是有损重编码。\n- ��部有效 MANO 帧的相机坐标变换与直接变换几何一致；独立源模型配置抽样校验非平手均值/PCA 展开。张量 float32 保存读取一致，invalid 为 NaN，valid 为 bool。\n- SE(3) 正交性、单位与资产引用检查；ARCTIC 部件关节运动与源旋转公式逐帧对照，HOT3D 物体 OBJ 尺度、三角形及纹理像素往返一致。\n- 所有实际组件使用现有注册 schema；没有为缺省模态伪造值。\n\n## 帧对应和缺省范围\n\n{notes}\n\n{missing}\n\n## 人工核验边界\n\n每相机选序列起点、紧邻帧、四分位、中点、四分之三及终点，不是随机抽查。预览只用于核对几何对齐；投影线条/点不是分割真值，遮挡表面可能被画出。数值自洽不等于图像投影正确，请检查所有相机和多个时刻。此处一序列通过不能覆盖全数据集、其他物体或异常序列。全量处理后仍需独立随机抽查和用户发布确认。\n'''
-    (review/'REPORT.md').write_text(report,encoding='utf-8')
-    return full
-
-if __name__=='__main__':
-    raise SystemExit('Static-sample review is retired. Use render_full_sequence_review.py DATASET and finalize_fresh_sequence_review.py; validate_view remains a reusable numeric helper.')
+"""Validate actual shared outputs and create portable, offline pilot reviews.
+
+Run after convert_fresh_sequence_pilots.py. Never authorizes full processing.
+"""
+from fresh_sequence_common import *
+import html
+from datetime import datetime
+
+def arr(p):
+    value=torch.load(p,map_location='cpu',weights_only=True)
+    assert isinstance(value,torch.Tensor) and value.device.type=='cpu'
+    return value.numpy()
+
+def safe_ref(root,ref):
+    path=(root/ref).resolve()
+    assert path.is_relative_to(root.resolve()) and path.is_file(),ref
+    return path
+
+def validate_view(p):
+    m=load(p/'metainfo.json');T=m['num_frames'];registry=load(WORK/'distribution/hand-warehouse-1.0/schema.json')
+    assert m['schema_version']==registry['schema_version'] and m['pilot_complete']
+    frames=[json.loads(line) for line in (p/'camera/frames.jsonl').read_text().splitlines()]
+    assert len(frames)==T and [f['frame_index'] for f in frames]==list(range(T))
+    assert len({f['source_frame_id'] for f in frames})==T
+    for frame in frames:
+        for match in frame.get('annotation_matches',{}).values():
+            if 'available' in match:match['source_record_present']=match.pop('available')
+    lines(p/'camera/frames.jsonl',frames)
+    summary=[]
+    for key,c in m['components'].items():
+        assert c['schema_id'] in registry['components'],(key,c['schema_id'])
+        assert c['available'] and c['source_locator'] and c['provenance']['kind'] in ['source','derived','format_conversion']
+        data=safe_ref(p,c['data_ref']);valid=arr(safe_ref(p,c['valid_ref']))
+        assert valid.dtype==bool and valid.ndim==len(c['valid_axes'])
+        if data.suffix=='.pt':
+            a=arr(data);assert a.dtype==np.float32 and a.shape[0]==T
+            assert a.shape[:valid.ndim]==valid.shape and len(c['axes'])==a.ndim
+            assert np.isfinite(a[valid]).all() and np.isnan(a[~valid]).all()
+            if c['schema_id'] in ['object/rigid/v1','transform/se3/v1']:assert_se3(a[valid])
+            if c['schema_id']=='mano/axis_angle61/v1':assert a.shape==(T,2,61)
+        elif data.suffix=='.mkv':
+            assert c['storage']['profile'] in registry['storage_profiles']
+            assert valid.shape==(T,) and valid.all()
+        for attr in c.get('attributes',{}).values():
+            a=arr(safe_ref(p,attr['data_ref']));b=arr(safe_ref(p,attr['valid_ref']))
+            assert b.dtype==bool and a.shape==b.shape
+            assert np.isfinite(a[b]).all() and ((a[b]>=0)&(a[b]<=1)).all() and np.isnan(a[~b]).all()
+        if 'distortion_ref' in c:
+            d=arr(safe_ref(p,c['distortion_ref']));assert d.shape==(T,8) and np.isfinite(d).all()
+        summary.append({'component':key,'schema':c['schema_id'],'valid':int(valid.sum()),'total':int(valid.size)})
+    for op in sorted((p/'annotation/object').glob('*')):
+        om=load(op/'metainfo.json');mesh=safe_ref(p,om['mesh_ref'])
+        if 'vertex_part_ids_ref' in om:
+            ids=arr(safe_ref(p,om['vertex_part_ids_ref']));assert ids.dtype==np.int64
+            assert len(ids)==sum(line.startswith('v ') for line in mesh.read_text().splitlines())
+            assert om['topology_id']=='sha256:'+digest(mesh)
+            if not (op/'pose.pt').exists():
+                assert m.get('omitted_components',{}).get('object_pose')
+            else:
+                root=arr(op/'pose.pt');top=arr(op/'parts/top/pose.pt');joint=arr(op/'joint_state.pt')[:,0]
+                ok=arr(op/'pose_valid.pt');H=np.broadcast_to(np.eye(4),(ok.sum(),4,4)).copy()
+                H[:,:3,:3]=Rotation.from_rotvec(np.c_[np.zeros((ok.sum(),2)),-joint[ok]]).as_matrix()
+                if ok.any():assert np.max(np.abs(root[ok]@H-top[ok]))<2e-6
+        for obj in (op/'assets').glob('*.obj'):
+            for row in obj.read_text().splitlines():
+                if row.startswith('mtllib '):
+                    material=safe_ref(obj.parent,row.split(maxsplit=1)[1])
+                    for r in material.read_text().splitlines():
+                        if r.lower().startswith('map_kd '):safe_ref(material.parent,r.split(maxsplit=1)[1])
+    records=load(DATA_ROOT/m['dataset_id']/'tmp'/RUN/'checks'/f'{m["sequence_id"]}__{m["camera_id"]}.json')['checks']
+    assert records and all(x.get('passed',False) for x in records)
+    media=next(r for r in records if r['test'] in ['all_source_JPEG_packets_and_RGB_pixels_exact','all_source_JPEG_packets_exact']);assert media['frames']==T
+    if media['test']=='all_source_JPEG_packets_exact':
+        pixels=next(r for r in records if r['test']=='source_RGB_pixels_exact')
+        assert pixels['scope']=='deterministic_sample' and pixels['sampled_frames']==len(pixels['frame_indices']) and pixels['sampled_frames']==min(T,16)
+        assert pixels['decoded_from_container'] and pixels['frame_indices']==sorted(set(pixels['frame_indices']))
+    if 'mano' not in m['components']:
+        assert m.get('omitted_components',{}).get('mano')
+        assert not (p/'annotation/mano/motion.pt').exists()
+        return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed','mano':'source modality absent, omitted'}
+    actual=arr(p/'annotation/mano/motion_valid.pt')
+    if m['dataset_id']=='HOT3D':
+        src=m['components']['mano']['source_locator']
+        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/src['archive']) as z:
+            native={r['timestamp_ns']:r['hand_poses'] for r in map(json.loads,z.read(src['member']).splitlines())}
+        expected=np.array([[str(side) in native.get(f['annotation_matches']['mano']['source_timestamp_ns'],{}) for side in range(2)] for f in frames],bool)
+    else:expected=np.array([[f['source_annotation_index'] is not None]*2 for f in frames],bool)
+    if m['dataset_id']=='HOT3D' and m['components']['mano'].get('valid_policy'):
+        source=m['components']['mano']['valid_policy']['source_locator']
+        with zipfile.ZipFile(DATA_ROOT/'HOT3D'/source['archive']) as z:
+            lookup={int(r['timestamp[ns]']):r['mask']=='True' for r in csv.DictReader(io.StringIO(z.read(source['member']).decode())) if r['stream_id']==m['camera_id']}
+        qa=[lookup[f['timecode_ns']] for f in frames]
+        expected &= np.array(qa,bool)[:,None]
+    if m['dataset_id']=='ARCTIC':
+        rejected=m['components']['mano'].get('valid_policy',{}).get('local_excluded_frame_indices',[])
+        expected[rejected]=False
+    assert np.array_equal(expected,actual),'annotation validity disagrees with declared source and QA/local policy'
+    records.append({'test':'all_frame_source_annotation_and_QA_policy','passed':True,'left_right_valid_counts':actual.sum(0).tolist(),'frames_without_usable_hand':int((~actual.any(1)).sum())})
+    return m,frames,{'camera_id':m['camera_id'],'frames':T,'components':summary,'conversion_checks':records,'structure_and_references':'passed'}
+
+def select_images(p,indices,dest):
+    dest.mkdir(parents=True,exist_ok=True)
+    selection='+'.join(f'eq(n,{i})' for i in indices)
+    command([FF,'-v','error','-y','-threads','2','-noautorotate','-i',p/'observation/rgb.mkv',
+        '-vf',f"select='{selection}',scale=900:900:force_original_aspect_ratio=decrease",'-fps_mode','vfr',dest/'decoded-%03d.png'])
+    output=sorted(dest.glob('decoded-*.png'));assert len(output)==len(indices)
+    return dict(zip(indices,output))
+
+def project_for_view(points,m,params,scale):
+    uv=project_points(points,m['camera']['model'],params)
+    if m['camera']['model']=='fisheye624':
+        angle=np.arctan2(np.linalg.norm(points[:,:2],axis=1),points[:,2])
+        uv[angle>m['camera']['max_solid_angle']]=np.nan
+    return uv*scale
+
+def draw_edges(canvas,uv,edges,color):
+    H,W=canvas.shape[:2]
+    for a,b in edges:
+        xy=uv[[a,b]]
+        if not np.isfinite(xy).all():continue
+        if np.max(np.abs(xy))>max(W,H)*20:continue
+        xy=np.rint(xy).astype(int)
+        if np.linalg.norm(xy[0]-xy[1])>max(W,H)*.35:continue
+        ok,start,end=cv2.clipLine((0,0,W,H),tuple(xy[0]),tuple(xy[1]))
+        if ok:cv2.line(canvas,start,end,color,1,cv2.LINE_AA)
+
+def review_view(p,m,frames,dest):
+    T=m['num_frames'];indices=sorted(set([0,1,T//4,T//2,3*T//4,T-1]));imgs=select_images(p,indices,dest)
+    K=arr(p/'camera/intrinsic.pt');D=arr(p/'camera/distortion.pt') if (p/'camera/distortion.pt').exists() else None
+    motion=arr(p/'annotation/mano/motion.pt');valid=arr(p/'annotation/mano/motion_valid.pt');models=[Mano('left'),Mano('right')]
+    if m['components']['mano']['model_profile']['left_shapedirs_fix']:
+        models[0].data['shapedirs'][:,0]*=-1
+        with torch.no_grad():models[0].layer.shapedirs[:,0]*=-1
+    objects=[]
+    for op in sorted((p/'annotation/object').iterdir()):
+        om=load(op/'metainfo.json');mesh=trimesh.load(p/om['mesh_ref'],process=False,force='mesh')
+        if 'vertex_part_ids_ref' in om:
+            vertices=np.array([[float(x) for x in r.split()[1:4]] for r in (p/om['mesh_ref']).read_text().splitlines() if r.startswith('v ')])
+            # trimesh may split OBJ vertices along UV seams; render exact source-ordered vertices as points.
+            objects.append((op,om,vertices,None,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),arr(p/om['vertex_part_ids_ref']),arr(op/'parts/top/pose.pt')))
+        else:
+            edges=np.asarray(mesh.edges_unique);edges=edges[::max(1,len(edges)//2500)]
+            objects.append((op,om,np.asarray(mesh.vertices),edges,arr(op/'pose.pt'),arr(op/'pose_valid.pt'),None,None))
+    records=[]
+    for i,source in imgs.items():
+        original=np.asarray(Image.open(source).convert('RGB'));overlay=original.copy()
+        scale=np.array([original.shape[1]/m['camera']['image_size'][0],original.shape[0]/m['camera']['image_size'][1]])
+        params=K[i] if m['camera']['model']=='fisheye624' else (K[i],D[i] if D is not None else None)
+        for side,model in enumerate(models):
+            if not valid[i,side]:continue
+            h=motion[i,side];vertices,_,_=model.geometry(h[:48],h[48:58],h[58:]);uv=project_for_view(vertices[0],m,params,scale)
+            faces=np.asarray(model.layer.faces);edges=np.concatenate([faces[:,[0,1]],faces[:,[1,2]],faces[:,[2,0]]]);draw_edges(overlay,uv,edges,(0,235,255) if side==0 else (255,135,20))
+        palette=[(110,255,100),(255,80,210),(240,220,60),(90,180,255),(190,160,255),(255,120,140)]
+        for c,(op,om,vertices,edges,P,ok,parts,top) in enumerate(objects):
+            if not ok[i]:continue
+            xyz=vertices@P[i,:3,:3].T+P[i,:3,3]
+            if parts is not None:xyz[parts==1]=vertices[parts==1]@top[i,:3,:3].T+top[i,:3,3]
+            uv=project_for_view(xyz,m,params,scale)
+            if edges is not None:draw_edges(overlay,uv,edges,palette[c%len(palette)])
+            else:
+                for xy in uv[::max(1,len(uv)//6000)]:
+                    if np.isfinite(xy).all() and 0<=xy[0]<overlay.shape[1] and 0<=xy[1]<overlay.shape[0]:cv2.circle(overlay,tuple(np.rint(xy).astype(int)),1,palette[c%len(palette)],-1)
+        blended=cv2.addWeighted(overlay,.8,original,.2,0);output=dest/f'frame-{i:06d}-overlay.png';raw=dest/f'frame-{i:06d}-rgb.png'
+        Image.fromarray(blended).save(output);source.replace(raw)
+        records.append({'frame_index':i,'source_frame_id':frames[i]['source_frame_id'],'rgb':raw.name,'overlay':output.name,'mano_valid':valid[i].tolist()})
+    return records
+
+def write_reports(dataset,views):
+    base=DATA_ROOT/dataset;root=base/RELEASE;doc=root/'doc';review=doc/'validation';reports=[];cards=[];thumbs=[]
+    rawpaths=set()
+    for p in views:
+        if dataset=='ARCTIC':
+            for assets in (p/'annotation/object').glob('*/assets'):repair_arctic_material_refs(assets)
+        m,frames,summary=validate_view(p);cam=m['camera_id'];samples=review_view(p,m,frames,review/'images'/cam)
+        summary['visual_samples']=samples;reports.append(summary)
+        # All actually consumed source files; camera/mano/object locators name source archives.
+        rawpaths.add(m['components']['rgb']['source_locator'].get('archive',m['components']['rgb']['source_locator'].get('file')))
+        for comp in m['components'].values():
+            src=comp['source_locator']
+            if 'archive' in src:rawpaths.add(src['archive'])
+        if dataset=='ARCTIC':rawpaths.add('raw/data/splits_json.zip')
+        for op in (p/'annotation/object').iterdir():
+            glb=load(op/'metainfo.json').get('original_glb_ref')
+            if glb:rawpaths.add(load(op/'metainfo.json')['original_glb_source_locator']['file'])
+        opts=''.join(f'<option value="{i}"'+(' selected' if s['frame_index']==m['num_frames']//2 else '')+f'>index {s["frame_index"]} · source {s["source_frame_id"]} · valid {s["mano_valid"]}</option>' for i,s in enumerate(samples))
+        samples_js=json.dumps([{k:(f'images/{cam}/'+s[k] if k in ('rgb','overlay') else s[k]) for k in s} for s in samples])
+        cards.append(f'<section class="card" data-samples=\'{html.escape(samples_js,quote=True)}\'><h2>Camera {cam} · {m["camera"]["model"]} · {m["num_frames"]} 帧</h2><select onchange="show(this.closest(\'.card\'))">{opts}</select><label><input type="checkbox" checked onchange="show(this.closest(\'.card\'))"> 显示投影</label><br><a class="zoom" target="_blank"><img alt="共享产物实际帧与投影"></a><p class="caption"></p></section>')
+        middle=samples[len(samples)//2];thumb=Image.open(review/'images'/cam/middle['overlay']).convert('RGB');thumb.thumbnail((400,320));tile=Image.new('RGB',(420,355),'#18202b');tile.paste(thumb,((420-thumb.width)//2,28));ImageDraw.Draw(tile).text((10,8),f'{dataset} camera {cam} / frame {middle["frame_index"]}',fill='white');thumbs.append(tile)
+        print('REVIEWED',dataset,cam,flush=True)
+    cols=3;grid=Image.new('RGB',(cols*420,((len(thumbs)+cols-1)//cols)*355),'#18202b')
+    for i,t in enumerate(thumbs):grid.paste(t,((i%cols)*420,(i//cols)*355))
+    grid.save(review/'overview.png')
+    source_manifest=[]
+    ledger=load(base/'raw/doc'/('archive_checksums.json' if dataset=='ARCTIC' else 'file_checksums.json'))
+    for rel in sorted(x for x in rawpaths if x):
+        file=base/rel;sha=digest(file);record=ledger[rel.removeprefix('raw/data/')]
+        assert record['sha256']==sha and record['size']==file.stat().st_size
+        assert record.get('official_sha256_match',record.get('official_digest_match')) is True
+        if file.suffix=='.zip':assert record['zip_crc']=='passed_all_members'
+        if dataset=='HOT3D':assert record['fresh_download_run']=='2026-09-18-aria-fresh'
+        source_manifest.append({'path':rel,'bytes':file.stat().st_size,'sha256':sha,'matches_verified_official_download':True,'zip_crc_at_download':record.get('zip_crc')})
+    save(doc/'pilot-source-manifest.json',source_manifest)
+    script_manifest=[]
+    for p in sorted((root/'script').rglob('*.py')):script_manifest.append({'path':p.relative_to(root).as_posix(),'sha256':digest(p)})
+    full={'dataset':dataset,'stage':'pilot_awaiting_human_review','human_review':'pending','full_preprocessing_authorized':False,'upload_authorized':False,
+        'scope':'One complete selected sequence, every released camera frame; not full-dataset acceptance','views':reports,
+        'sources_manifest':'../pilot-source-manifest.json','script_manifest':script_manifest,'preview_selection':'Deterministic boundary and quartile samples per camera; not random or full-dataset review',
+        'preview_rendering':'Derived MANO/object projections from shared tensors, not segmentation ground truth; no depth occlusion test, hidden surfaces may be drawn',
+        'created_at':datetime.now().astimezone().isoformat()}
+    save(review/'report.json',full);save(review/'status.json',{k:full[k] for k in ['dataset','stage','human_review','full_preprocessing_authorized','upload_authorized']})
+    notes=('首帧 RGB 存在，动捕标注无对应项，valid=false；已保留。相机 0 使用 rational8 畸变，外部八相机使用官方针孔投影。关节绕父坐标系 -Z 轴旋转，部件点/网格保持米制。' if dataset=='ARCTIC' else 'Aria 三路相机均保留；RGB 与两路灰度均由原 JPEG 直接封装。device time 经官方映射对齐 timecode；原始 visibility 按百分比转 [0,1]，独立于标注 valid。三路按时间匹配，不假定相同行号自动同步。')
+    if dataset=='HOT3D':notes+=' 每相机双手各806帧有MANO、208帧原始手参数为空；画面中有手但无投影的末尾帧属于源标注缺失，已验证并保留为valid=false。'
+    if dataset=='ARCTIC':notes+=' 原始部件OBJ的材质文件名引用错误已在共享层修正为实际存在的material.mtl，材质标识与纹理不变。'
+    missing=('本次官方 raw 中无独立深度、像素分割、接触标签、实测手关键点数组。保留 MANO，供任务层按需生成关键点；未把预览投影写成新增标注。' if dataset=='ARCTIC' else '本范围无深度流或像素分割；masks/*.csv 是帧筛选条件，不能当作像素 mask。UmeTrack 与 MPS 替代估计保留在 raw；shared 选择 MANO 和官方 GT 相机。没有额外生成关键点或接触标签。')
+    text=f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>{dataset} 序列试点审核</title><style>body{{font:16px/1.65 system-ui;background:#f3f5f8;color:#18202b;max-width:1450px;margin:24px auto;padding:0 20px}}header,.card{{background:white;padding:22px;border-radius:14px;margin-bottom:18px}}.status{{color:#9a5800;font-weight:bold}}.grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(360px,1fr));gap:18px}}.card img{{max-width:100%;max-height:670px;margin-top:12px}}select{{max-width:100%;padding:8px}}a{{color:#1766ab}}label{{display:inline-block;margin:10px}}h1{{margin:0}}.caption{{font-size:14px}}</style><header><h1>{dataset} · 完整序列试点</h1><p class="status">自动校验已完成；人工审核待确认。尚未开始全量预处理或上传。</p><p>共 {len(views)} 路相机、{sum(r['frames'] for r in reports):,} 个相机帧。全部源 JPEG 码流、全部解码 RGB 帧已逐一比较一致；数值转换、有效性和引用检查通过。</p><p>{notes}</p><p>{missing}</p><p>下方是从 shared 实际产物读取的边界及四分位样本。青色=左手，橙色=右手，其余颜色=物体。投影仅用于几何核验，<b>不是源分割，也没有做遮挡剔除</b>。点击图像可放大；取消勾选可查看原图。</p><p><a href="REPORT.md">验收说明</a> · <a href="report.json">机器可读结果</a> · <a href="../RAW_TO_SHARED.md">raw → share 对照</a> · <a href="../CONTRACT.md">约定</a> · <a href="overview.png">全部相机总览</a></p></header><main class="grid">{''.join(cards)}</main><script>function show(card){{let s=JSON.parse(card.dataset.samples)[Number(card.querySelector('select').value)];let path=card.querySelector('input').checked?s.overlay:s.rgb;card.querySelector('img').src=path;card.querySelector('.zoom').href=path;card.querySelector('.caption').textContent='frame_index='+s.frame_index+' / 原帧 '+s.source_frame_id+' / 左右手 valid='+s.mano_valid;}}document.querySelectorAll('.card').forEach(show)</script></html>'''
+    (review/'index.html').write_text(text,encoding='utf-8')
+    rows='\n'.join(f'| {r["camera_id"]} | {r["frames"]} | '+', '.join(str(x['frame_index']) for x in r['visual_samples'])+' | 通过 |' for r in reports)
+    report=f'''# {dataset} 完整序列试点验收\n\n状态：**自动检查通过，人工确认待完成**。本报告不是全量验收或发布许可。\n\n[离线预览](index.html) · [机器结果](report.json) · [原始包摘要](../pilot-source-manifest.json)\n\n| 相机 | 全序列帧数 | 可视化帧索引 | 自动校验 |\n|---|---:|---|---|\n{rows}\n\n## 已验证\n\n- 完整所选序列的每一张源 JPEG 与 MKV 取回的压缩包 SHA256 相同；全部帧在同一 FFmpeg/noautorotate/rgb24 配置下解码字节相同，顺序和数量一致。不是有损重编码。\n- 全部有效 MANO 帧的相机坐标变换与直接变换几何一致；独立源模型配置抽样校验非平手均值/PCA 展开。张量 float32 保存读取一致，invalid 为 NaN，valid 为 bool。\n- SE(3) 正交性、单位与资产引用检查；ARCTIC 部件关节运动与源旋转公式逐帧对照，HOT3D 物体 OBJ 尺度、三角形及纹理像素往返一致。\n- 所有实际组件使用现有注册 schema；没有为缺省模态伪造值。\n\n## 帧对应和缺省范围\n\n{notes}\n\n{missing}\n\n## 人工核验边界\n\n每相机选序列起点、紧邻帧、四分位、中点、四分之三及终点，不是随机抽查。预览只用于核对几何对齐；投影线条/点不是分割真值，遮挡表面可能被画出。数值自洽不等于图像投影正确，请检查所有相机和多个时刻。此处一序列通过不能覆盖全数据集、其他物体或异常序列。全量处理后仍需独立随机抽查和用户发布确认。\n'''
+    (review/'REPORT.md').write_text(report,encoding='utf-8')
+    return full
+
+if __name__=='__main__':
+    raise SystemExit('Static-sample review is retired. Use render_full_sequence_review.py DATASET and finalize_fresh_sequence_review.py; validate_view remains a reusable numeric helper.')

HOT3D/shared-v1.0/script/internal/sequence_pilot_common.py

@@ -1,53 +1,51 @@
-"""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='<u2' if mask else 'uint8'
-  dst=self.p/path;dst.parent.mkdir(parents=True,exist_ok=True);hashes=[]
-  proc=subprocess.Popen([FF,'-v','error','-y','-f','rawvideo','-pixel_format',pix,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-c:v','ffv1','-level','3','-g','1',str(dst)],stdin=subprocess.PIPE)
-  try:
-   for frame in iterator:
-    a=np.asarray(frame,dtype=dtype);assert a.shape==((h,w) if mask else (h,w,3));b=a.tobytes();hashes.append(hashlib.sha256(b).hexdigest());proc.stdin.write(b)
-  finally:proc.stdin.close()
-  assert proc.wait()==0 and len(hashes)==self.T
-  proc=subprocess.Popen([FF,'-v','error','-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1'],stdout=subprocess.PIPE)
-  nbytes=w*h*(2 if mask else 3)
-  for digest in hashes:
-   b=proc.stdout.read(nbytes);assert len(b)==nbytes and hashlib.sha256(b).hexdigest()==digest,'video roundtrip'
-  assert not proc.stdout.read(1);assert proc.wait()==0
-  self.checks.append({'test':'all_frames_pixel_exact_roundtrip','file':path,'frames':self.T})
-  self.add(name,path,'segment/uint16_labels/v1' if mask else 'rgb/uint8/v1',['T','height','width'] if mask else ['T','height','width','channel'],np.ones(self.T,bool),storage_fps=1,timebase_role='index_only',**kw)
- def intrinsic(self,K):
-  self.m['camera'].update(model='pinhole',schema_id='camera/pinhole/v1')
-  self.array('intrinsic','camera/intrinsic.pt','camera/pinhole/v1',np.broadcast_to(K,(self.T,3,3)),np.ones(self.T,bool),['T','row','column'])
- def finish(self):
-  lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
-  save(self.root/'doc'/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 snapshot(dataset,scripts):
- out=ROOT/dataset/RELEASE/'script';out.mkdir(parents=True,exist_ok=True)
- names={'sequence_pilot_common.py','pilot_build.py','pilot_fetch.py','pilot_mano.py','pilot_hot_normalize.py','audit_source_fields.py',*scripts}
- names.update(p.name for p in (WORK/'scripts').glob('sequence_pilot_*.py'))
- for name in names:shutil.copyfile(WORK/'scripts'/name,out/name)
+"""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='<u2' if mask else 'uint8'
+  dst=self.p/path;dst.parent.mkdir(parents=True,exist_ok=True);hashes=[]
+  proc=subprocess.Popen([FF,'-v','error','-y','-f','rawvideo','-pixel_format',pix,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-c:v','ffv1','-level','3','-g','1',str(dst)],stdin=subprocess.PIPE)
+  try:
+   for frame in iterator:
+    a=np.asarray(frame,dtype=dtype);assert a.shape==((h,w) if mask else (h,w,3));b=a.tobytes();hashes.append(hashlib.sha256(b).hexdigest());proc.stdin.write(b)
+  finally:proc.stdin.close()
+  assert proc.wait()==0 and len(hashes)==self.T
+  proc=subprocess.Popen([FF,'-v','error','-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1'],stdout=subprocess.PIPE)
+  nbytes=w*h*(2 if mask else 3)
+  for digest in hashes:
+   b=proc.stdout.read(nbytes);assert len(b)==nbytes and hashlib.sha256(b).hexdigest()==digest,'video roundtrip'
+  assert not proc.stdout.read(1);assert proc.wait()==0
+  self.checks.append({'test':'all_frames_pixel_exact_roundtrip','file':path,'frames':self.T})
+  self.add(name,path,'segment/uint16_labels/v1' if mask else 'rgb/uint8/v1',['T','height','width'] if mask else ['T','height','width','channel'],np.ones(self.T,bool),storage_fps=1,timebase_role='index_only',**kw)
+ def intrinsic(self,K):
+  self.m['camera'].update(model='pinhole',schema_id='camera/pinhole/v1')
+  self.array('intrinsic','camera/intrinsic.pt','camera/pinhole/v1',np.broadcast_to(K,(self.T,3,3)),np.ones(self.T,bool),['T','row','column'])
+ def finish(self):
+  lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m)
+  save(self.root/'doc'/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 snapshot(dataset,scripts):
+ # Retained for old callers. The organized bundle must not be repopulated at runtime.
+ (ROOT/dataset/RELEASE/'script').mkdir(parents=True,exist_ok=True)