Show per-member coverage of external pose classes against three-source plus EgoRenderer reference

.gitattributes

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pose_recluster_distinct_20261014/app.coverage_v2.js

@@ -0,0 +1,61 @@
+const D=window.AUDIT_DATA;
+// Retry transient failures on the image CDN while keeping permanent source URLs.
+document.addEventListener('error',e=>{const im=e.target;if(im?.tagName!=='IMG')return;const n=Number(im.dataset.networkRetry||0);if(n<2){im.dataset.networkRetry=String(n+1);setTimeout(()=>{if(im.isConnected)im.src=im.getAttribute('src')},600*(n+1))}},true);
+const $=s=>document.querySelector(s), nf=n=>Number(n).toLocaleString('en-US'), esc=s=>String(s??'').replace(/[&<>"']/g,c=>({'&':'&amp;','<':'&lt;','>':'&gt;','"':'&quot;',"'":'&#39;'}[c]));
+let hand='right',level='distinct',view='oblique',tab='overview',poseSerial=0,activeDetail='',coverageFilter='all',coverageSort='gap';
+const colors=['#b8852f','#39896e','#437eb0','#9061ab','#c66e70'];
+const poses=new Map(), allClasses={};
+Object.values(D.models).forEach(m=>['fine','distinct'].forEach(l=>m[l].forEach(c=>allClasses[c.key]=c)));
+const model=g=>D.models[g+'_'+hand], cname=s=>D.names[s]||s;
+function poseCanvas(joints,label='',overlay=null){const id='pose'+poseSerial++;poses.set(id,{joints,overlay,rot:[0,0],label});return `<canvas class="pose" id="${id}" aria-label="${esc(label)}，可拖动旋转"></canvas>`}
+function drawPose(canvas,p){
+ const ratio=Math.min(devicePixelRatio||1,2),w=canvas.clientWidth||300,h=canvas.clientHeight||170;canvas.width=w*ratio;canvas.height=h*ratio;const ctx=canvas.getContext('2d');ctx.scale(ratio,ratio);ctx.clearRect(0,0,w,h);
+ let base=view==='palm'?[0,0]:view==='side'?[Math.PI/2,0]:[.48,-.16],a=base[0]+p.rot[0],b=base[1]+p.rot[1];
+ function project(j){let [x,y,z]=j;const xx=x*Math.cos(a)+z*Math.sin(a),zz=-x*Math.sin(a)+z*Math.cos(a);return [xx,y*Math.cos(b)-zz*Math.sin(b),y*Math.sin(b)+zz*Math.cos(b)]}
+ const pts=p.joints.map(project),other=p.overlay?.map(project),all=other?pts.concat(other):pts;let x0=Math.min(...all.map(v=>v[0])),x1=Math.max(...all.map(v=>v[0])),y0=Math.min(...all.map(v=>v[1])),y1=Math.max(...all.map(v=>v[1]));let scale=Math.min((w-45)/Math.max(x1-x0,.7),(h-40)/Math.max(y1-y0,.8));
+ const screen=q=>[w/2+(q[0]-(x0+x1)/2)*scale,h/2-(q[1]-(y0+y1)/2)*scale+3];
+ function handdraw(j,faded){ctx.globalAlpha=faded?.35:1;for(let f=0;f<5;f++){const ids=[0,f*4+1,f*4+2,f*4+3,f*4+4];ctx.strokeStyle=faded?'#7b8492':colors[f];ctx.fillStyle=ctx.strokeStyle;ctx.lineWidth=faded?2:3.2;ctx.beginPath();ids.forEach((id,k)=>{const q=screen(j[id]);k?ctx.lineTo(...q):ctx.moveTo(...q)});ctx.stroke();ids.slice(1).forEach(id=>{const q=screen(j[id]);ctx.beginPath();ctx.arc(...q,2.6,0,Math.PI*2);ctx.fill()})}ctx.globalAlpha=1}
+ if(other)handdraw(other,true);handdraw(pts,false);ctx.fillStyle='#7e8b99';ctx.font='10px system-ui';ctx.fillText(p.label,9,14);
+}
+function wirePoses(){document.querySelectorAll('canvas.pose').forEach(c=>{const p=poses.get(c.id);if(!p)return;drawPose(c,p);let drag=null;c.onpointerdown=e=>{drag=[e.clientX,e.clientY];c.setPointerCapture(e.pointerId)};c.onpointermove=e=>{if(!drag)return;p.rot[0]+=(e.clientX-drag[0])*.012;p.rot[1]+=(e.clientY-drag[1])*.012;drag=[e.clientX,e.clientY];drawPose(c,p)};c.onpointerup=()=>drag=null;})}
+const pct=x=>(100*x).toFixed(1)+'%';
+function coverageBar(v,compact=false){if(!v)return '';return `<div class="coverage-breakdown ${compact?'compact':''}"><div class="coverage-bar" role="img" aria-label="已覆盖 ${pct(v.covered_fraction)}，低覆盖 ${pct(v.low_fraction)}，未覆盖 ${pct(v.uncovered_fraction)}"><span class="covered" style="width:${v.covered_fraction*100}%"></span><span class="low" style="width:${v.low_fraction*100}%"></span><span class="uncovered" style="width:${v.uncovered_fraction*100}%"></span></div><div class="coverage-values"><span class="covered-text">已覆盖 ${pct(v.covered_fraction)}</span><span class="low-text">低覆盖 ${pct(v.low_fraction)}</span><span class="uncovered-text">未覆盖 ${pct(v.uncovered_fraction)}</span></div>${compact?'':`<p class="metric">分母 ${nf(v.n)} 个实际分析手帧：${nf(v.covered)} 已覆盖 / ${nf(v.low)} 低覆盖 / ${nf(v.uncovered)} 未覆盖。</p>`}</div>`}
+function classBadge(c){const v=c.coverage;if(!v)return '';const label=v.status==='uncovered'?'整类未覆盖':v.status==='covered'?'≥90% 已覆盖':'部分覆盖';return `<span class="coverage-badge ${v.status}" title="以逐成员比例为准；整类未覆盖要求所有成员都没有相似参照。">${label}</span>`}
+function filteredExternal(){return [...model('external')[level]].filter(c=>coverageFilter==='all'||coverageFilter==='entire'&&c.coverage.uncovered===c.n||coverageFilter==='mostly_gap'&&c.coverage.uncovered_fraction>=.5||coverageFilter==='mostly_covered'&&c.coverage.covered_fraction>=.7).sort((a,b)=>coverageSort==='id'?a.number-b.number:coverageSort==='covered'?b.coverage.covered_fraction-a.coverage.covered_fraction:b.coverage.uncovered_fraction-a.coverage.uncovered_fraction)}
+function renderCoverage(){const v=D.coverage.summary[hand],cal=D.coverage.calibration[hand],cs=model('external')[level],entire=cs.filter(c=>c.coverage.status==='uncovered').length;$('#coverageSummary').innerHTML=`<div class="coverage-summary">${coverageBar(v,true)}<span class="metric">${hand==='left'?'左':'右'}手 ${nf(v.n)} 个外部手帧 · ${cs.length} 类中 ${entire} 类整类未覆盖 · 相似阈值 ${cal.thresholds_deg.q95.toFixed(2)}°</span></div>`;
+ const sens=D.coverage.sensitivity.filter(x=>x.key==='external_'+hand&&['q90','q95','q99'].includes(x.threshold));$('#coverageMethodBody').innerHTML=`<p class="hint">距离使用来源 GT 的 20 根 3D 骨方向，在掌部坐标系中计算平均夹角，去除手腕整体运动和骨长差异；左右手分开。不是 2D 图上距离，也不是 MANO 参数直接相减。</p><p class="hint">参照：H2O＋ARCTIC＋HOT3D＋EgoRenderer Ver.005，共 271,543 手帧；本侧 ${nf(cal.reference_n)} 手帧、${nf(cal.reference_units)} 段独立录制/源动作。用本侧 4,096 个内部留出样本校准，检索时排除同录制全部帧；四源及各录制均衡加权，取最近距离的 95% 分位。每个外部成员穷举检索全部同侧参照。</p><table><thead><tr><th>校准分位</th><th>阈值</th><th>已覆盖</th><th>低覆盖</th><th>未覆盖</th></tr></thead><tbody>${sens.map(x=>`<tr><td>${x.threshold==='q95'?'95%（本页）':x.threshold.slice(1)+'%'}</td><td>${x.threshold_deg.toFixed(2)}°</td><td>${pct(x.covered_fraction)}</td><td>${pct(x.low_fraction)}</td><td>${pct(x.uncovered_fraction)}</td></tr>`).join('')}</tbody></table><p class="hint">类标签：≥90% 成员已覆盖才标“≥90% 已覆盖”；所有成员无近邻才标“整类未覆盖”；其余标“部分覆盖”。90% 是显示约定，完整比例始终保留。统计分母是本轮分析样本，不是 WiLoR 官方全量；2D 来源不作 3D 覆盖判断。我们参照使用已核对训练成员的来源 GT，最终 canonical 标签和 loss mask 尚未验收。</p>`}
+function memberBadge(s){const v=s.coverage;if(!v)return '';return `<p class="sample-coverage"><span class="coverage-badge ${v.status}">${{covered:'已覆盖',low:'低覆盖',uncovered:'抽样库未覆盖'}[v.status]}</span> 最近距离 ${v.distance_deg.toFixed(2)}° / 阈值 ${v.threshold_deg.toFixed(2)}° · ${nf(v.support_recordings)} 段相似参照录制</p>`}
+function gapBlock(v){const s=D.samples[v.id],n=v.nearest;return `<article class="gap-case"><h3>未覆盖成员 · ${esc(cname(s.source))} · ${s.hand==='left'?'左手':'右手'} · 帧 ${s.frame}</h3><p class="evidence">${esc(s.recording)} · ${esc(s.label)} · 距该类原型 ${v.prototype_distance_deg.toFixed(2)}°</p>${memberBadge(s)}<div class="pair"><div><h4>外部实际成员</h4>${poseCanvas(v.joints,'该未覆盖成员的 GT 姿态')}<a href="${s.assets.full}" target="_blank"><img class="gap-image" src="${s.assets.crop}" loading="lazy" alt="外部未覆盖成员：原图与 GT 叠加"></a><p class="metric">原图 ｜ GT 叠加 · 点击看完整原画面</p></div><div><h4>我们库中的精确最近邻</h4>${poseCanvas(n.joints,'该成员的精确最近 GT 姿态')}${n.media?`<a href="${n.media.assets.full}" target="_blank"><img class="gap-image" src="${n.media.assets.crop}" loading="lazy" alt="精确最近邻：原图与 GT 叠加"></a><p class="metric">原图 ｜ GT 叠加 · 点击看完整原画面</p>`:'<p class="warning">这个精确最近邻未通过原图可读性检查，保留其 GT 骨架；没有用其他样本替代。</p>'}<p class="evidence">${esc(cname(n.source))} · ${esc(n.recording)} · 帧 ${n.frame} · ${n.hand==='left'?'左':'右'}手</p></div></div><details><summary>相机与 GT 证据</summary><p class="evidence">外部：${esc(s.camera)}。${esc(s.gt_status)}</p><p class="evidence">最近邻：${esc(n.media?.evidence?.camera??n.media_missing_reason)}。图像叠加仅供核查，覆盖距离不构成 GT 可靠性结论。</p></details></article>`}
+function related(c){return D.pairs.some(p=>p.related && p.group===c.key.split('_')[0]&&p.hand===hand&&(p.a===c.id||p.b===c.id))}
+function card(c){const v=c.examples[0],s=D.samples[v.id];return `<article class="card" data-key="${c.key}"><div class="card-head"><h3>${c.id}</h3><span>${classBadge(c)}${related(c)&&level==='distinct'?'<span class="badge">有相近类</span>':''}</span></div><div class="card-meta">${nf(c.n)} 手帧 · ${Object.keys(c.sources).length} 来源</div>${c.coverage?coverageBar(c.coverage,true):''}${poseCanvas(c.joints,'GT 类原型')}<img src="${s.assets.crop}" alt="${esc(cname(s.source))} 原图与 GT 叠加" loading="lazy"><div class="card-foot">${esc(cname(s.source))} · ${v.is_prototype?'原型本人':`同类图例，距原型 ${v.prototype_distance_deg.toFixed(1)}°`}</div><button class="open" data-open="${c.key}">${c.coverage?'未覆盖成员 / 原图 / 近邻':'原图 / GT 叠加 / 近邻'}</button></article>`}
+function countTable(){const rows=['ours','external'].map(g=>{const m=model(g);return `<tr><td class="${g}">${g==='ours'?'我们三源＋合成':'WiLoR 外部 3D 来源'}</td><td>${nf(m.n)}</td><td>${m[level].length}</td><td>${m.fine.length-m.distinct.length}</td></tr>`}).join('');$('#countTable').innerHTML=`<table><thead><tr><th>组别 · ${hand==='left'?'左手':'右手'}</th><th>参与手帧</th><th>当前类数</th><th>合并减少细类数</th></tr></thead><tbody>${rows}</tbody></table>`}
+function renderOverview(){countTable();for(const g of ['ours','external']){const cs=g==='external'?filteredExternal():model(g)[level];$('#'+g+'Cards').innerHTML=cs.map(card).join('')||'<p class="hint">当前筛选没有类别。</p>';$('#'+g+'Count').textContent=`${cs.length} / ${model(g)[level].length} 类`}scatter()}
+function pairTile(c){const s=D.samples[c.examples[0].id];return `<div><h3><a data-open="${c.key}">${c.id} · ${esc(cname(c.prototype.source))}</a></h3>${poseCanvas(c.joints,'GT 类原型')}<img src="${s.assets.crop}" loading="lazy" alt="同类 RGB 与 GT 图例"><p class="metric">图例 ${esc(cname(s.source))} · 距原型 ${c.examples[0].prototype_distance_deg.toFixed(1)}°</p></div>`}
+function renderCompare(){const sorted=filteredExternal();$('#comparisonRows').innerHTML=sorted.map(c=>{const n=c.nearest_reference,a=allClasses[n.nearest_our_key];return `<div class="row"><div class="row-title"><strong>我们 ${a.id} ↔ 外部 ${c.id}</strong>${classBadge(c)}</div>${coverageBar(c.coverage)}<div class="pair">${pairTile(a)}${pairTile(c)}</div><p class="metric">类原型差 ${n.class_prototype_mean_deg.toFixed(1)}° · 外部原型最近参照 ${n.mean_deg.toFixed(1)}°（原型示意；覆盖统计来自全类成员）</p><button data-open="${c.key}">看该类未覆盖成员、原图与精确最近邻</button></div>`}).join('')||'<p class="hint">当前筛选没有类别。</p>'}
+function renderSimilar(){const summaries=D.summary.filter(s=>s.hand===hand);$('#duplicateSummary').innerHTML=`<table><thead><tr><th>组别</th><th>32 细类中的相近对</th><th>合并后相近对</th><th>主视图类数</th></tr></thead><tbody>${summaries.map(s=>`<tr><td>${s.group==='ours'?'我们':'外部'}</td><td>${s.stages.fine.near_pairs}</td><td>${s.stages.distinct.near_pairs}</td><td>${s.stages.distinct.k}</td></tr>`).join('')}</tbody></table><p class="hint">“相近”按原型或数值中心的 20 骨平均夹角 ≤12°，且每根手指平均夹角 ≤20° 判断；这是本页查看粒度，不是通用手势标准。</p>`;
+ const pp=D.pairs.filter(p=>p.hand===hand&&p.related);$('#relatedPairs').innerHTML=pp.map(p=>{const a=allClasses[`${p.group}_${hand}_distinct_${p.a}`],b=allClasses[`${p.group}_${hand}_distinct_${p.b}`];return `<div class="row"><strong>${p.group==='ours'?'我们':'外部'} ${p.a} / ${p.b}</strong><div class="finger-values">${p.finger_deg.map((v,i)=>`${['拇','食','中','环','小'][i]}指 ${v.toFixed(1)}°`).join(' · ')}</div><div class="pair">${pairTile(a)}${pairTile(b)}</div><p class="metric">原型整体 ${p.mean_deg.toFixed(1)}° · 跨录制近邻更靠近另一类的比例 ${(100*p.member_diagnostic.cross_cluster_closer_fraction).toFixed(1)}%</p></div>`}).join('')||'<p class="hint">本侧没有满足判据的剩余相近类对。</p>';
+ $('#mergeRows').innerHTML=D.merges.filter(m=>m.hand===hand).map(m=>`<div class="row"><div class="row-title"><strong>${m.group==='ours'?'我们':'外部'}：${m.children.join(' + ')} → 主类 ${m.cluster}</strong><span class="metric">${nf(m.n)} 手帧</span></div><div class="children">${m.child_keys.map(k=>{const c=allClasses[k];return `<div class="child"><a data-open="${k}">${c.id} · 细类</a>${poseCanvas(c.joints,'GT 原型')}</div>`}).join('')}</div></div>`).join('')}
+function renderSources(){$('#sourceTable').innerHTML='<thead><tr><th>来源组</th><th>数据集 / 可视化</th><th>实际参与手帧</th><th>版本与范围</th><th>本轮用途</th><th>对参照库覆盖</th></tr></thead><tbody>'+D.sources.map(s=>`<tr><td>${s.group}</td><td><button class="source-button" data-source="${s.id}">${esc(s.name)}</button></td><td>${nf(s.cluster_n||0)}</td><td>${esc(s.scope)}</td><td>${esc(s.role)}</td><td>${D.coverage.sources[s.id]?coverageBar(D.coverage.sources[s.id],true):s.group==='我们'?'参照库':s.gallery_source?'与参照重叠，不单计':'不作 3D 判断'}</td></tr>`).join('')+'</tbody>'}
+function sampleBlock(s,v){return `<section class="example">${memberBadge(s)}<h3>${esc(cname(s.source))} · ${esc(s.hand)} · 帧 ${esc(s.frame??'—')}</h3><p class="evidence">${esc(s.recording??s.split??'')} · ${esc(s.label)}${v?` · 距类原型 ${v.prototype_distance_deg.toFixed(2)}°`:''}</p><a href="${s.assets.full}" target="_blank"><img class="detail-pair" src="${s.assets.full}" alt="原图与对应 GT 叠加"></a><p class="evidence">${esc(s.camera??s.note??'')} · ${esc(s.gt_status??'仅可核查相应标注，不参与3D聚类')}</p></section>`}
+function openClass(key){const c=allClasses[key],g=key.split('_')[0];let head=`<h2>${g==='ours'?'我们':'外部'} · ${key.includes('_left_')?'左手':'右手'} · ${c.id}${key.includes('_fine_')?'（细类）':''}</h2><p class="evidence">原型：${esc(cname(c.prototype.source))} · ${esc(c.prototype.recording)} · 帧 ${c.prototype.frame}。${nf(c.n)} 个成员。</p>`;
+ head+=c.coverage?classBadge(c)+coverageBar(c.coverage):'';
+ let top=`<div class="dialog-top"><div>${poseCanvas(c.joints,'精确 GT 类原型')}<p class="hint">统一掌部坐标与骨长，仅用于姿态比较。</p></div>`;
+ if(c.nearest_reference){const n=c.nearest_reference;top+=`<div>${poseCanvas(n.nearest_joints,'全部参照库中的最近 GT 样本')}<p class="metric">对全部同侧三源＋合成样本检索：${n.mean_deg.toFixed(2)}°。不使用散点图距离。</p></div>`}else top+=`<div><h3>来源组成</h3>${Object.entries(c.sources).sort((a,b)=>b[1]-a[1]).map(([s,n])=>`<p class="metric">${esc(cname(s))}：${nf(n)}</p>`).join('')}</div>`;
+ top+='</div>';
+ let note=c.examples[0].prototype_distance_deg>8?'<p class="warning">当前可读 RGB 图例与类原型仍有较大差别。上方骨架才是精确原型；下面图片保留实际成员身份与距离。</p>':'';
+ const gaps=D.coverage.examples[key]||[],gapIds=new Set(gaps.map(v=>v.id)),other=c.examples.filter(v=>!gapIds.has(v.id));
+ $('#detailBody').innerHTML=head+(c.coverage?'<details class="prototype-details"><summary>类原型与其最近邻</summary>'+top+note+'</details>':top+note)+(gaps.length?'<h3 class="gap-title">本类实际未覆盖成员</h3>'+gaps.map(gapBlock).join(''):'')+(other.length?'<h3>其他同类成员</h3>'+other.map(v=>sampleBlock(D.samples[v.id],v)).join(''):'');if(!$('#detail').open)$('#detail').showModal();wirePoses();activeDetail='class='+encodeURIComponent(key);location.hash=activeDetail}
+function openSource(src){const s=D.sources.find(x=>x.id===src),target=s.gallery_source||src,ids=D.gallery[target]||[],two=D.two_d[src]||[];let body=`<h2>${esc(s.name)} · ${esc(s.group)}</h2><p>${esc(s.scope)}</p>`;
+ if(D.coverage.sources[src])body+=coverageBar(D.coverage.sources[src]);
+ if(two.length)body+=two.map(v=>sampleBlock(v,null)).join('');
+ else if(ids.length){body+='<p class="hint">以下图例均属于本轮冻结成员；点击原图可放大。手侧保留逐样本标记。</p>';body+=ids.slice(0,20).map(id=>sampleBlock(D.samples[id],null)).join('')}
+ else body+=`<p class="warning">${src==='wilor_bedlam'?'按用户要求跳过，本轮没有读取或聚类。':'当前聚类使用该源 GT；本页未选到该源配对图例。'}</p>`;
+ $('#detailBody').innerHTML=body;if(!$('#detail').open)$('#detail').showModal();activeDetail='source='+encodeURIComponent(src);location.hash=activeDetail}
+function scatter(){const c=$('#scatter'),ctx=c.getContext('2d');ctx.clearRect(0,0,c.width,c.height);const all=['ours','external'].flatMap(g=>model(g)[level].map(x=>({...x,g})));let xx=all.map(c=>c.display_xy[0]),yy=all.map(c=>c.display_xy[1]),xmin=Math.min(...xx),xmax=Math.max(...xx),ymin=Math.min(...yy),ymax=Math.max(...yy);const points=[];for(const p of all){let x=45+(p.display_xy[0]-xmin)/(xmax-xmin)*1000,y=400-(p.display_xy[1]-ymin)/(ymax-ymin)*350;ctx.fillStyle=p.g==='ours'?'#276a9c':'#79569d';ctx.beginPath();ctx.arc(x,y,6,0,Math.PI*2);ctx.fill();ctx.fillText(p.id,x+8,y+4);points.push({x,y,key:p.key})}c.onclick=e=>{const r=c.getBoundingClientRect(),x=(e.clientX-r.left)*c.width/r.width,y=(e.clientY-r.top)*c.height/r.height;const hit=points.find(p=>Math.hypot(x-p.x,y-p.y)<15);if(hit)openClass(hit.key)}}
+function render(){poses.clear();renderCoverage();if(tab==='overview')renderOverview();if(tab==='compare')renderCompare();if(tab==='similar')renderSimilar();if(tab==='sources')renderSources();wirePoses()}
+function changeTab(t){tab=t;document.querySelectorAll('.tab').forEach(e=>e.hidden=e.id!==t);document.querySelectorAll('nav button').forEach(e=>e.classList.toggle('active',e.dataset.tab===t));render()}
+document.addEventListener('click',e=>{const open=e.target.closest('[data-open]'),source=e.target.closest('[data-source]'),tb=e.target.closest('[data-tab]');if(open){e.preventDefault();openClass(open.dataset.open)}if(source)openSource(source.dataset.source);if(tb)changeTab(tb.dataset.tab)});
+$('#coverageFilter').onchange=e=>{coverageFilter=e.target.value;render()};$('#coverageSort').onchange=e=>{coverageSort=e.target.value;render()};
+$('#hand').onchange=e=>{hand=e.target.value;render()};$('#level').onchange=e=>{level=e.target.value;render()};$('#view').onchange=e=>{view=e.target.value;wirePoses()};$('#reset').onclick=()=>{poses.forEach(p=>p.rot=[0,0]);wirePoses()};$('#closeDetail').onclick=()=>$('#detail').close();$('#detail').addEventListener('close',()=>{activeDetail='';history.replaceState(null,'',location.pathname+location.search)});window.addEventListener('resize',()=>wirePoses());
+function restoreHash(){const hash=location.hash.slice(1);if(hash===activeDetail)return;const initial=new URLSearchParams(hash);if(initial.has('class')&&allClasses[initial.get('class')])openClass(initial.get('class'));else if(initial.has('source')&&D.sources.some(s=>s.id===initial.get('source')))openSource(initial.get('source'));else if(!hash&&$('#detail').open)$('#detail').close()}
+window.addEventListener('hashchange',restoreHash);render();restoreHash();

pose_recluster_distinct_20261014/downloads/coverage/REPRODUCE.md

@@ -0,0 +1,22 @@
+# 复现覆盖更新
+
+实验目录：/SSD3/yangzijing/Hand_class_coverage_20261014
+只读聚类与 GT 缓存：/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014
+
+```bash
+cd /SSD3/yangzijing/Hand_class_coverage_20261014
+PY=/SSD2/jiasen/envs/foundation_hand/bin/python
+# freeze_coverage.py 仅第一次在空实验目录执行；已有冻结清单不可重写。
+CUDA_VISIBLE_DEVICES=1 OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 MKL_NUM_THREADS=2 "$PY" code/exact_coverage.py
+OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 "$PY" code/aggregate_coverage.py
+OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 "$PY" code/validate_coverage.py
+CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 "$PY" code/coverage_visuals.py
+"$PY" code/update_web.py
+"$PY" code/build_coverage_site.py
+```
+
+freeze_coverage.py 固定种子 2026101407、8192 个校准成员和阈值规则，然后才进行外部检索。逐成员距离为精确穷举；既有缓存可恢复。记录单位排除整段动作，不把邻帧支持计为独立录制。原始成员 SHA256、类分配、特征和网页旧版 SHA256 见 input_sha256.json。
+
+本目录代码复用前序审计的标定投影和 source GT 读取代码；完整依赖地址保留在 visualize.py / cluster_common.py，旧版代码与原始聚类说明在网页 downloads/code 和 downloads/REPRODUCE.md。原始数据及授权可读环境需要保留；衍生网页不包含原始数据包、模型权重或凭据。
+
+媒体检查不是 GT 独立验收。精确最近邻图像不可读时，另选同类未覆盖成员展示；其最近邻索引绝不替换。CSV 只有实际已渲染成员才填 image_overlay_link，其他留空。

pose_recluster_distinct_20261014/downloads/coverage/RESULTS_ZH.md

@@ -0,0 +1,21 @@
+# 逐成员覆盖更新（2026-10-14）
+
+本轮对全部 214,481 个可读外部来源聚类手帧逐一检索，参照为 H2O / ARCTIC / HOT3D / EgoRenderer Ver.005 的 271,543 个冻结抽样手帧，左右手分开；没有重新聚类或改动 GT。
+
+- 已覆盖：99,408（46.35%），阈值内至少 3 段独立录制/源动作。
+- 低覆盖：15,430（7.19%），阈值内只有 1–2 段独立录制/源动作。
+- 抽样库未覆盖：99,643（46.46%），本次参照库中无阈值内近邻。
+
+外部 50 个主类中，7 类全部成员未覆盖，43 类为部分覆盖。不存在达到“90% 成员已覆盖”的主类；这不表示各类中的已覆盖成员也未覆盖。网页优先展示完整成员比例。
+
+整类未覆盖：left B2, left B5, left B16, left B21, right B4, right B13, right B20。
+
+距离是来源 GT 的 20 根掌部局部 3D 单位骨方向的平均夹角；不是 MANO 参数欧氏距离、不是 2D 图上距离。阈值先使用 8,192 个内部校准手帧、检索排除同录制的全部帧确定：左手 9.4976°，右手 9.2521°。四源均衡，源内录制均衡。q90/q99 与 10°/20°/30° 的敏感性均在 CSV 保留。至少 3 个独立录制是支持量的显示约定；类 90% 汇总阈值也不是普适标准。
+
+主统计是本轮分析成员的自然计数，不代表官方全量的无偏发生比例。来源/录制均衡聚类权重下未覆盖比例为 41.41%；单位均衡权重还会受静态图像单位与视频录制单位数量差异影响，因此仅用于敏感性，不作为总体发生率。
+
+114 个外部主类/细类均给出实际未覆盖成员：原图、按来源相机投影的 MANO 或 GT 骨架、精确最近邻及距离；没有把同类原型替换成“未覆盖样本”。显示样本优先接近类原型，且要求其精确最近邻也有可读 RGB 叠加，故图例不是概率抽样，不用于估计比例。图像读取失败只影响图例选择，不改统计成员、阈值或最近邻。
+
+覆盖与 GT 可靠性独立。未覆盖可能包含真实姿态缺口、来源定义差异或标签问题；不能把上述数目称为可靠补充样本数量。来源一致性、图片叠加不等于独立三维验收。参照使用已核对训练成员的来源 GT；最终 canonical 标签与 loss mask 尚未验收。外部 8 源是 WiLoR 官方源数据代理，ARCTIC/HOT3D 重叠、2D 来源和跳过的 BEDLAM 不重复记入外部 3D 分母。
+
+数值检查：32 个查询（含阈值边界）用 CPU float64 全库复算；114 个类原型与已有独立最近邻结果对照，分类一致。详细证据见 numerical_validation.json。

pose_recluster_distinct_20261014/downloads/coverage/calibration.json

@@ -0,0 +1,94 @@
+{
+  "left": {
+    "n": 4096,
+    "reference_n": 135504,
+    "reference_units": 1924,
+    "thresholds_deg": {
+      "q90": 8.31766128540039,
+      "q95": 9.49763298034668,
+      "q99": 12.101940155029297
+    },
+    "weighted_within_q95": 0.9501579125434737,
+    "weighted_stable_covered_q95": 0.901272850543289,
+    "source_diagnostics": {
+      "ours_arctic": {
+        "n": 1024,
+        "recordings": 266,
+        "q50": 7.079188346862793,
+        "q95": 11.210945129394531,
+        "within_main_threshold": 0.862781954887218,
+        "stable_covered_main": 0.7202380952380951
+      },
+      "ours_h2o": {
+        "n": 1024,
+        "recordings": 114,
+        "q50": 4.817780494689941,
+        "q95": 8.082184791564941,
+        "within_main_threshold": 0.9766081871345029,
+        "stable_covered_main": 0.9626844890002785
+      },
+      "ours_hot3d": {
+        "n": 1024,
+        "recordings": 230,
+        "q50": 3.6003682613372803,
+        "q95": 6.79738712310791,
+        "within_main_threshold": 0.9876086956521739,
+        "stable_covered_main": 0.9817391304347827
+      },
+      "synthetic": {
+        "n": 1024,
+        "recordings": 1024,
+        "q50": 2.8987925052642822,
+        "q95": 8.5874605178833,
+        "within_main_threshold": 0.9736328125,
+        "stable_covered_main": 0.9404296875
+      }
+    }
+  },
+  "right": {
+    "n": 4096,
+    "reference_n": 136039,
+    "reference_units": 1924,
+    "thresholds_deg": {
+      "q90": 8.300768852233887,
+      "q95": 9.252071380615234,
+      "q99": 11.218984603881836
+    },
+    "weighted_within_q95": 0.9501955061737223,
+    "weighted_stable_covered_q95": 0.8825766848620646,
+    "source_diagnostics": {
+      "ours_arctic": {
+        "n": 1024,
+        "recordings": 266,
+        "q50": 6.840930938720703,
+        "q95": 10.529808044433594,
+        "within_main_threshold": 0.8728070175438596,
+        "stable_covered_main": 0.7039473684210527
+      },
+      "ours_h2o": {
+        "n": 1024,
+        "recordings": 114,
+        "q50": 5.4153594970703125,
+        "q95": 8.352627754211426,
+        "within_main_threshold": 0.9815789473684211,
+        "stable_covered_main": 0.9435672514619884
+      },
+      "ours_hot3d": {
+        "n": 1024,
+        "recordings": 230,
+        "q50": 3.777343511581421,
+        "q95": 6.837747573852539,
+        "within_main_threshold": 0.9991304347826087,
+        "stable_covered_main": 0.9882608695652174
+      },
+      "synthetic": {
+        "n": 1024,
+        "recordings": 1024,
+        "q50": 5.286046028137207,
+        "q95": 9.388495445251465,
+        "within_main_threshold": 0.947265625,
+        "stable_covered_main": 0.89453125
+      }
+    }
+  }
+}

pose_recluster_distinct_20261014/downloads/coverage/class_coverage.csv

@@ -0,0 +1,115 @@
+n,covered,low,uncovered,covered_fraction,low_fraction,uncovered_fraction,any_match_fraction,status,status_label,fit_weighted_fractions,equal_unit_fractions,independent_units,key,hand,level,id,threshold_deg,distance_p50_deg,distance_p95_deg,max_distance_deg,source_counts
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+4586,3012,604,970,0.6567815089402529,0.13170518970780637,0.21151330135194069,0.7884866986480593,low,低覆盖,"[0.7001709096405897, 0.10488844863598464, 0.19494064172342573]","[0.6073670920284657, 0.16404314264301606, 0.2285897653285182]",2529,external_left_fine_B18,left,fine,B18,9.49763298034668,8.484198570251465,11.652318716049194,22.997819900512695,"{""wilor_rhd"": 2214, ""wilor_dex"": 416, ""wilor_interhand26m"": 998, ""wilor_mtc"": 330, ""wilor_h2o3d"": 47, ""wilor_reinter"": 581}"
+6320,5547,264,509,0.8776898734177215,0.04177215189873418,0.0805379746835443,0.9194620253164557,low,低覆盖,"[0.8585765985164301, 0.042818138822823947, 0.0986052626607461]","[0.5340491606866136, 0.0800227171996027, 0.3859281221137838]",673,external_left_fine_B19,left,fine,B19,9.49763298034668,6.734456300735474,10.251062440872191,23.881214141845703,"{""wilor_rhd"": 301, ""wilor_dex"": 5840, ""wilor_interhand26m"": 113, ""wilor_mtc"": 12, ""wilor_h2o3d"": 1, ""wilor_reinter"": 53}"
+1247,0,0,1247,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",1091,external_left_fine_B20,left,fine,B20,9.49763298034668,16.972991943359375,24.505570793151858,28.6693058013916,"{""wilor_rhd"": 1033, ""wilor_interhand26m"": 115, ""wilor_mtc"": 21, ""wilor_reinter"": 78}"
+2501,1583,176,742,0.6329468212714914,0.0703718512594962,0.2966813274690124,0.7033186725309877,low,低覆盖,"[0.7358824578246955, 0.08887268981720826, 0.17524485235809625]","[0.5541538862527574, 0.13689385005109836, 0.30895226369614426]",911,external_left_fine_B21,left,fine,B21,9.49763298034668,7.901134967803955,13.278962135314941,16.455108642578125,"{""wilor_rhd"": 1116, ""wilor_dex"": 973, ""wilor_interhand26m"": 101, ""wilor_mtc"": 86, ""wilor_h2o3d"": 150, ""wilor_reinter"": 75}"
+2614,293,298,2023,0.11208875286916603,0.11400153022188217,0.7739097169089518,0.22609028309104817,low,低覆盖,"[0.14053292820241495, 0.12900821657964384, 0.7304588552179413]","[0.21086057149963067, 0.10478310697554942, 0.6843563215248198]",266,external_left_fine_B22,left,fine,B22,9.49763298034668,10.88214635848999,16.46778659820556,45.58646011352539,"{""wilor_rhd"": 41, ""wilor_interhand26m"": 662, ""wilor_mtc"": 207, ""wilor_reinter"": 1704}"
+4895,4301,175,419,0.8786516853932584,0.03575076608784474,0.08559754851889684,0.9144024514811031,low,低覆盖,"[0.8624684384616301, 0.03991772879602693, 0.09761383274234282]","[0.672994238476453, 0.08528862357707155, 0.24171713794647548]",416,external_left_fine_B23,left,fine,B23,9.49763298034668,6.6624579429626465,11.093380165100104,20.63935661315918,"{""wilor_rhd"": 169, ""wilor_dex"": 4491, ""wilor_interhand26m"": 125, ""wilor_mtc"": 24, ""wilor_h2o3d"": 9, ""wilor_reinter"": 77}"
+645,222,154,269,0.34418604651162793,0.23875968992248062,0.4170542635658915,0.5829457364341085,low,低覆盖,"[0.6085973936731306, 0.20432918189155733, 0.1870734244353121]","[0.38802760891754573, 0.18122600807130929, 0.43074638301114493]",184,external_left_fine_B24,left,fine,B24,9.49763298034668,9.179900169372559,13.160082626342772,24.0723876953125,"{""wilor_rhd"": 92, ""wilor_dex"": 94, ""wilor_interhand26m"": 142, ""wilor_mtc"": 126, ""wilor_h2o3d"": 65, ""wilor_reinter"": 126}"
+4850,3213,435,1202,0.6624742268041237,0.08969072164948454,0.24783505154639177,0.7521649484536083,low,低覆盖,"[0.7531587219579603, 0.06253903637336465, 0.18430224166867484]","[0.6200723287593087, 0.09860202025069781, 0.2813256509899934]",2261,external_left_fine_B25,left,fine,B25,9.49763298034668,8.251009464263916,12.027929544448856,23.52761459350586,"{""wilor_rhd"": 1877, ""wilor_dex"": 124, ""wilor_interhand26m"": 1265, ""wilor_mtc"": 409, ""wilor_h2o3d"": 51, ""wilor_reinter"": 1124}"
+3296,2128,224,944,0.6456310679611651,0.06796116504854369,0.28640776699029125,0.7135922330097088,low,低覆盖,"[0.6734961656390336, 0.059968449530467136, 0.26653538483049916]","[0.6249635984027109, 0.08494112419286255, 0.29009527740442653]",587,external_left_fine_B26,left,fine,B26,9.49763298034668,8.285791873931885,13.637461423873901,26.55930519104004,"{""wilor_rhd"": 152, ""wilor_dex"": 129, ""wilor_interhand26m"": 1094, ""wilor_mtc"": 112, ""wilor_h2o3d"": 51, ""wilor_reinter"": 1758}"
+262,0,0,262,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",87,external_left_fine_B27,left,fine,B27,9.49763298034668,28.08455467224121,31.430563354492186,32.29803466796875,"{""wilor_rhd"": 4, ""wilor_interhand26m"": 249, ""wilor_mtc"": 4, ""wilor_reinter"": 5}"
+2131,20,100,2011,0.009385265133740028,0.04692632566870014,0.9436884091975598,0.05631159080244019,low,低覆盖,"[0.008927299731298242, 0.04696573827712419, 0.9441069619915776]","[0.006612714820935348, 0.019394540940504513, 0.9739927442385601]",1434,external_left_fine_B28,left,fine,B28,9.49763298034668,13.311511993408203,18.50288963317871,34.42958068847656,"{""wilor_rhd"": 1624, ""wilor_dex"": 113, ""wilor_interhand26m"": 295, ""wilor_mtc"": 2, ""wilor_reinter"": 97}"
+1067,0,7,1060,0.0,0.006560449859418931,0.993439550140581,0.006560449859418949,low,低覆盖,"[0.0, 0.0055712827086024964, 0.9944287172913976]","[0.0, 0.0012357217030114227, 0.9987642782969886]",450,external_left_fine_B29,left,fine,B29,9.49763298034668,15.255627632141113,19.79076690673828,28.854854583740234,"{""wilor_rhd"": 446, ""wilor_interhand26m"": 162, ""wilor_mtc"": 15, ""wilor_reinter"": 444}"
+2835,856,320,1659,0.3019400352733686,0.1128747795414462,0.5851851851851851,0.41481481481481486,low,低覆盖,"[0.313374678312726, 0.11189030416587281, 0.5747350175214011]","[0.2917381910449484, 0.12071516780178554, 0.587546641153266]",1885,external_left_fine_B30,left,fine,B30,9.49763298034668,10.01617431640625,13.733166313171386,25.315275192260742,"{""wilor_rhd"": 1817, ""wilor_dex"": 266, ""wilor_interhand26m"": 231, ""wilor_mtc"": 123, ""wilor_reinter"": 398}"
+2723,1336,378,1009,0.4906353286816012,0.13881748071979436,0.37054719059860447,0.6294528094013956,low,低覆盖,"[0.6401484604987809, 0.09972980902185757, 0.26012173047936127]","[0.38746993714056954, 0.14300869313562292, 0.46952136972380737]",748,external_left_fine_B31,left,fine,B31,9.49763298034668,8.89091968536377,13.222776794433594,29.43329429626465,"{""wilor_rhd"": 575, ""wilor_interhand26m"": 1002, ""wilor_mtc"": 306, ""wilor_h2o3d"": 124, ""wilor_reinter"": 716}"
+1260,45,26,1189,0.03571428571428571,0.020634920634920634,0.9436507936507936,0.05634920634920637,low,低覆盖,"[0.033213577940571004, 0.020819311613976085, 0.945967110445453]","[0.0034803951153761035, 0.02143158911219748, 0.9750880157724264]",789,external_left_fine_B32,left,fine,B32,9.49763298034668,13.050553798675537,23.870503425598145,61.350528717041016,"{""wilor_rhd"": 980, ""wilor_dex"": 60, ""wilor_interhand26m"": 151, ""wilor_mtc"": 2, ""wilor_h2o3d"": 2, ""wilor_reinter"": 65}"
+957,0,1,956,0.0,0.0010449320794148381,0.9989550679205852,0.0010449320794148065,low,低覆盖,"[0.0, 0.0006819989186126367, 0.9993180010813872]","[0.0, 0.0005030181086519114, 0.9994969818913482]",284,external_left_distinct_B1,left,distinct,B1,9.49763298034668,17.770145416259766,33.662847900390624,68.93052673339844,"{""wilor_rhd"": 187, ""wilor_interhand26m"": 491, ""wilor_mtc"": 118, ""wilor_reinter"": 161}"
+305,0,0,305,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",156,external_left_distinct_B2,left,distinct,B2,9.49763298034668,21.449251174926758,29.474415206909182,35.25153732299805,"{""wilor_rhd"": 106, ""wilor_interhand26m"": 91, ""wilor_mtc"": 98, ""wilor_reinter"": 10}"
+4358,0,2,4356,0.0,0.0004589261128958238,0.9995410738871042,0.00045892611289577445,low,低覆盖,"[0.0, 0.00032881964136443346, 0.9996711803586357]","[0.0, 0.00011140819964349375, 0.9998885918003565]",3400,external_left_distinct_B3,left,distinct,B3,9.49763298034668,14.459971904754639,17.235001564025875,54.535888671875,"{""wilor_rhd"": 3656, ""wilor_interhand26m"": 264, ""wilor_mtc"": 312, ""wilor_reinter"": 126}"
+1904,50,66,1788,0.026260504201680673,0.03466386554621849,0.9390756302521008,0.060924369747899165,low,低覆盖,"[0.0312471646602513, 0.07401681908528633, 0.8947360162544625]","[0.00744860643789431, 0.00842278290809309, 0.9841286106540125]",995,external_left_distinct_B4,left,distinct,B4,9.49763298034668,14.209616661071777,22.362558841705315,34.26322937011719,"{""wilor_rhd"": 1153, ""wilor_dex"": 19, ""wilor_interhand26m"": 175, ""wilor_mtc"": 329, ""wilor_reinter"": 228}"
+1001,0,0,1001,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",960,external_left_distinct_B5,left,distinct,B5,9.49763298034668,20.789093017578125,22.490758895874023,34.86929702758789,"{""wilor_rhd"": 925, ""wilor_interhand26m"": 28, ""wilor_mtc"": 38, ""wilor_reinter"": 10}"
+5670,1196,156,4318,0.2109347442680776,0.027513227513227514,0.7615520282186948,0.23844797178130517,low,低覆盖,"[0.350644787550694, 0.09175245223714362, 0.5576027602121624]","[0.1508577203091623, 0.02206640375082836, 0.8270758759400094]",517,external_left_distinct_B6,left,distinct,B6,9.49763298034668,11.848321437835693,13.771859073638918,29.364564895629883,"{""wilor_rhd"": 3699, ""wilor_dex"": 1131, ""wilor_interhand26m"": 156, ""wilor_mtc"": 330, ""wilor_reinter"": 354}"
+757,34,13,710,0.04491413474240423,0.017173051519154558,0.9379128137384413,0.062087186261558736,low,低覆盖,"[0.02837713024492711, 0.01634265056118991, 0.9552802191938828]","[0.02636954788533109, 0.01795159737179234, 0.9556788547428765]",153,external_left_distinct_B7,left,distinct,B7,9.49763298034668,13.97718620300293,20.250528717041014,30.29688835144043,"{""wilor_rhd"": 3, ""wilor_dex"": 28, ""wilor_interhand26m"": 350, ""wilor_mtc"": 29, ""wilor_reinter"": 347}"
+3202,1043,200,1959,0.3257339163023111,0.06246096189881324,0.6118051217988757,0.3881948782011243,low,低覆盖,"[0.3112932970184011, 0.07306468623052814, 0.6156420167510707]","[0.15538628955372125, 0.07038482269025442, 0.7742288877560243]",969,external_left_distinct_B8,left,distinct,B8,9.49763298034668,10.336248874664307,14.753903007507324,35.33476257324219,"{""wilor_rhd"": 730, ""wilor_dex"": 823, ""wilor_interhand26m"": 658, ""wilor_mtc"": 210, ""wilor_reinter"": 781}"
+2052,1363,137,552,0.6642300194931774,0.06676413255360623,0.26900584795321636,0.7309941520467836,low,低覆盖,"[0.45535600056903813, 0.1343326247191284, 0.41031137471183354]","[0.39782336858766865, 0.11263537819297602, 0.4895412532193555]",309,external_left_distinct_B9,left,distinct,B9,9.49763298034668,6.594670057296753,12.154699182510376,23.656810760498047,"{""wilor_rhd"": 113, ""wilor_dex"": 1204, ""wilor_interhand26m"": 303, ""wilor_mtc"": 11, ""wilor_h2o3d"": 152, ""wilor_reinter"": 269}"
+1569,40,5,1524,0.025493945188017845,0.0031867431485022306,0.97131931166348,0.028680688336520044,low,低覆盖,"[0.03269455947162975, 0.0037103771199735475, 0.9635950634083968]","[0.005865088529638183, 0.0004864506163582376, 0.9936484608540035]",866,external_left_distinct_B10,left,distinct,B10,9.49763298034668,13.924688339233398,17.32938232421875,20.491647720336914,"{""wilor_rhd"": 1327, ""wilor_dex"": 56, ""wilor_interhand26m"": 55, ""wilor_mtc"": 25, ""wilor_reinter"": 106}"
+778,219,54,505,0.2814910025706941,0.06940874035989718,0.6491002570694088,0.35089974293059123,low,低覆盖,"[0.17452989658821816, 0.07041409881948912, 0.7550560045922924]","[0.09780966767371602, 0.025755287009063446, 0.8764350453172206]",331,external_left_distinct_B11,left,distinct,B11,9.49763298034668,11.265559673309326,15.61582899093628,34.806514739990234,"{""wilor_rhd"": 417, ""wilor_dex"": 183, ""wilor_interhand26m"": 72, ""wilor_mtc"": 4, ""wilor_h2o3d"": 26, ""wilor_reinter"": 76}"
+14080,9073,1235,3772,0.6443892045454546,0.08771306818181818,0.26789772727272726,0.7321022727272728,low,低覆盖,"[0.7222989777967671, 0.08318653506673751, 0.1945144871364954]","[0.4398150777024166, 0.15684095371711176, 0.4033439685804716]",4936,external_left_distinct_B12,left,distinct,B12,9.49763298034668,8.177695274353027,11.52519426345825,36.636138916015625,"{""wilor_rhd"": 5547, ""wilor_dex"": 4460, ""wilor_interhand26m"": 1050, ""wilor_mtc"": 1966, ""wilor_h2o3d"": 230, ""wilor_reinter"": 827}"
+4345,1941,354,2050,0.44672036823935557,0.08147295742232451,0.4718066743383199,0.5281933256616801,low,低覆盖,"[0.44937231137766076, 0.09437967576084247, 0.4562480128614969]","[0.16086429948022454, 0.03938309444616991, 0.7997526060736058]",1406,external_left_distinct_B13,left,distinct,B13,9.49763298034668,9.1389799118042,14.48231830596924,30.715425491333008,"{""wilor_rhd"": 1655, ""wilor_dex"": 2155, ""wilor_interhand26m"": 215, ""wilor_mtc"": 164, ""wilor_h2o3d"": 11, ""wilor_reinter"": 145}"
+1385,989,134,262,0.7140794223826715,0.09675090252707581,0.1891696750902527,0.8108303249097473,low,低覆盖,"[0.42107906879413015, 0.15459798882476652, 0.4243229423811034]","[0.5542837123581176, 0.0693488721767804, 0.37636741546510194]",183,external_left_distinct_B14,left,distinct,B14,9.49763298034668,7.1735429763793945,11.55076332092285,34.16762924194336,"{""wilor_rhd"": 2, ""wilor_dex"": 875, ""wilor_interhand26m"": 185, ""wilor_mtc"": 4, ""wilor_h2o3d"": 199, ""wilor_reinter"": 120}"
+4586,3012,604,970,0.6567815089402529,0.13170518970780637,0.21151330135194069,0.7884866986480593,low,低覆盖,"[0.7001709096405897, 0.10488844863598464, 0.19494064172342573]","[0.6073670920284657, 0.16404314264301606, 0.2285897653285182]",2529,external_left_distinct_B15,left,distinct,B15,9.49763298034668,8.484198570251465,11.652318716049194,22.997819900512695,"{""wilor_rhd"": 2214, ""wilor_dex"": 416, ""wilor_interhand26m"": 998, ""wilor_mtc"": 330, ""wilor_h2o3d"": 47, ""wilor_reinter"": 581}"
+1247,0,0,1247,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",1091,external_left_distinct_B16,left,distinct,B16,9.49763298034668,16.972991943359375,24.505570793151858,28.6693058013916,"{""wilor_rhd"": 1033, ""wilor_interhand26m"": 115, ""wilor_mtc"": 21, ""wilor_reinter"": 78}"
+16153,13452,708,1993,0.8327864792917724,0.04383086733114592,0.12338265337708165,0.8766173466229183,low,低覆盖,"[0.7996445135822496, 0.06639554940574824, 0.13395993701200232]","[0.5064881900286149, 0.11489311997041547, 0.37861869000096976]",1844,external_left_distinct_B17,left,distinct,B17,9.49763298034668,6.782214164733887,12.420542907714843,23.881214141845703,"{""wilor_rhd"": 1744, ""wilor_dex"": 13016, ""wilor_interhand26m"": 573, ""wilor_mtc"": 163, ""wilor_h2o3d"": 333, ""wilor_reinter"": 324}"
+2614,293,298,2023,0.11208875286916603,0.11400153022188217,0.7739097169089518,0.22609028309104817,low,低覆盖,"[0.14053292820241495, 0.12900821657964384, 0.7304588552179413]","[0.21086057149963067, 0.10478310697554942, 0.6843563215248198]",266,external_left_distinct_B18,left,distinct,B18,9.49763298034668,10.88214635848999,16.46778659820556,45.58646011352539,"{""wilor_rhd"": 41, ""wilor_interhand26m"": 662, ""wilor_mtc"": 207, ""wilor_reinter"": 1704}"
+645,222,154,269,0.34418604651162793,0.23875968992248062,0.4170542635658915,0.5829457364341085,low,低覆盖,"[0.6085973936731306, 0.20432918189155733, 0.1870734244353121]","[0.38802760891754573, 0.18122600807130929, 0.43074638301114493]",184,external_left_distinct_B19,left,distinct,B19,9.49763298034668,9.179900169372559,13.160082626342772,24.0723876953125,"{""wilor_rhd"": 92, ""wilor_dex"": 94, ""wilor_interhand26m"": 142, ""wilor_mtc"": 126, ""wilor_h2o3d"": 65, ""wilor_reinter"": 126}"
+13704,7533,1357,4814,0.5496935201401051,0.09902218330414478,0.35128429655575016,0.6487157034442499,low,低覆盖,"[0.6417604277806683, 0.07968476557908322, 0.2785548066402483]","[0.45864277439102935, 0.1151298693226762, 0.42622735628629455]",4886,external_left_distinct_B20,left,distinct,B20,9.49763298034668,8.692762851715088,13.147455072402952,29.43329429626465,"{""wilor_rhd"": 4421, ""wilor_dex"": 519, ""wilor_interhand26m"": 3592, ""wilor_mtc"": 950, ""wilor_h2o3d"": 226, ""wilor_reinter"": 3996}"
+262,0,0,262,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",87,external_left_distinct_B21,left,distinct,B21,9.49763298034668,28.08455467224121,31.430563354492186,32.29803466796875,"{""wilor_rhd"": 4, ""wilor_interhand26m"": 249, ""wilor_mtc"": 4, ""wilor_reinter"": 5}"
+2131,20,100,2011,0.009385265133740028,0.04692632566870014,0.9436884091975598,0.05631159080244019,low,低覆盖,"[0.008927299731298242, 0.04696573827712419, 0.9441069619915776]","[0.006612714820935348, 0.019394540940504513, 0.9739927442385601]",1434,external_left_distinct_B22,left,distinct,B22,9.49763298034668,13.311511993408203,18.50288963317871,34.42958068847656,"{""wilor_rhd"": 1624, ""wilor_dex"": 113, ""wilor_interhand26m"": 295, ""wilor_mtc"": 2, ""wilor_reinter"": 97}"
+1067,0,7,1060,0.0,0.006560449859418931,0.993439550140581,0.006560449859418949,low,低覆盖,"[0.0, 0.0055712827086024964, 0.9944287172913976]","[0.0, 0.0012357217030114227, 0.9987642782969886]",450,external_left_distinct_B23,left,distinct,B23,9.49763298034668,15.255627632141113,19.79076690673828,28.854854583740234,"{""wilor_rhd"": 446, ""wilor_interhand26m"": 162, ""wilor_mtc"": 15, ""wilor_reinter"": 444}"
+1260,45,26,1189,0.03571428571428571,0.020634920634920634,0.9436507936507936,0.05634920634920637,low,低覆盖,"[0.033213577940571004, 0.020819311613976085, 0.945967110445453]","[0.0034803951153761035, 0.02143158911219748, 0.9750880157724264]",789,external_left_distinct_B24,left,distinct,B24,9.49763298034668,13.050553798675537,23.870503425598145,61.350528717041016,"{""wilor_rhd"": 980, ""wilor_dex"": 60, ""wilor_interhand26m"": 151, ""wilor_mtc"": 2, ""wilor_h2o3d"": 2, ""wilor_reinter"": 65}"
+1032,0,3,1029,0.0,0.0029069767441860465,0.997093023255814,0.0029069767441860517,low,低覆盖,"[0.0, 0.0027669567455739993, 0.9972330432544262]","[0.0, 0.0016195524146054182, 0.9983804475853945]",283,external_right_fine_B1,right,fine,B1,9.252071380615234,16.09415054321289,29.51508560180664,33.34939956665039,"{""wilor_freihand"": 32, ""wilor_rhd"": 195, ""wilor_interhand26m"": 494, ""wilor_mtc"": 135, ""wilor_reinter"": 176}"
+492,0,1,491,0.0,0.0020325203252032522,0.9979674796747967,0.002032520325203291,low,低覆盖,"[0.0, 0.001260702316862762, 0.9987392976831372]","[0.0, 0.004329004329004329, 0.9956709956709957]",231,external_right_fine_B2,right,fine,B2,9.252071380615234,16.76012897491455,22.280033683776853,33.24413299560547,"{""wilor_freihand"": 184, ""wilor_rhd"": 162, ""wilor_dex"": 1, ""wilor_interhand26m"": 91, ""wilor_mtc"": 52, ""wilor_reinter"": 2}"
+4658,24,56,4578,0.005152425933877201,0.012022327179046801,0.982825246887076,0.017174753112924046,low,低覆盖,"[0.002898725070245564, 0.008104511327779366, 0.9889967636019751]","[0.0008472182999152782, 0.002016910573815403, 0.9971358711262692]",3541,external_right_fine_B3,right,fine,B3,9.252071380615234,13.483551502227783,16.439379119873045,50.315032958984375,"{""wilor_freihand"": 160, ""wilor_rhd"": 3668, ""wilor_interhand26m"": 265, ""wilor_mtc"": 416, ""wilor_reinter"": 149}"
+1078,0,0,1078,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",842,external_right_fine_B4,right,fine,B4,9.252071380615234,17.85080909729004,21.442168617248534,36.31260299682617,"{""wilor_freihand"": 184, ""wilor_rhd"": 780, ""wilor_interhand26m"": 26, ""wilor_mtc"": 72, ""wilor_reinter"": 16}"
+4429,538,238,3653,0.12147211560171596,0.05373673515466245,0.8247911492436216,0.17520885075637838,low,低覆盖,"[0.14467737274999184, 0.09666480545829527, 0.7586578217917128]","[0.043388968650744146, 0.023947796090001687, 0.9326632352592542]",1123,external_right_fine_B5,right,fine,B5,9.252071380615234,11.781124114990234,15.474595260620116,30.491758346557617,"{""wilor_freihand"": 760, ""wilor_rhd"": 2289, ""wilor_dex"": 268, ""wilor_interhand26m"": 206, ""wilor_mtc"": 527, ""wilor_ho3d"": 65, ""wilor_reinter"": 314}"
+3465,892,236,2337,0.25743145743145746,0.0681096681096681,0.6744588744588744,0.3255411255411256,low,低覆盖,"[0.356631861389781, 0.05523674681026089, 0.5881313917999582]","[0.07269984717169979, 0.030423564935954894, 0.8968765878923453]",1139,external_right_fine_B6,right,fine,B6,9.252071380615234,10.203381538391113,13.806207275390623,27.509233474731445,"{""wilor_freihand"": 704, ""wilor_rhd"": 991, ""wilor_dex"": 339, ""wilor_interhand26m"": 470, ""wilor_mtc"": 60, ""wilor_ho3d"": 433, ""wilor_h2o3d"": 13, ""wilor_reinter"": 455}"
+4018,1541,282,2195,0.3835241413638626,0.07018417122946739,0.54629168740667,0.45370831259333,low,低覆盖,"[0.33346863064158727, 0.07481856903031535, 0.5917128003280974]","[0.32427397462883256, 0.04986250782909566, 0.6258635175420718]",578,external_right_fine_B7,right,fine,B7,9.252071380615234,9.591028690338135,15.868682956695551,23.114944458007812,"{""wilor_freihand"": 840, ""wilor_rhd"": 434, ""wilor_dex"": 902, ""wilor_interhand26m"": 356, ""wilor_mtc"": 378, ""wilor_ho3d"": 558, ""wilor_reinter"": 550}"
+1452,15,77,1360,0.010330578512396695,0.05303030303030303,0.9366391184573003,0.0633608815426997,low,低覆盖,"[0.007139996081754413, 0.03956472083516249, 0.9532952830830829]","[0.011920009966884967, 0.0436041760179969, 0.944475814015118]",264,external_right_fine_B8,right,fine,B8,9.252071380615234,12.68548059463501,18.450111103057864,26.912817001342773,"{""wilor_freihand"": 656, ""wilor_rhd"": 62, ""wilor_dex"": 7, ""wilor_interhand26m"": 356, ""wilor_mtc"": 24, ""wilor_ho3d"": 51, ""wilor_reinter"": 296}"
+4536,1584,278,2674,0.3492063492063492,0.06128747795414462,0.5895061728395061,0.4104938271604939,low,低覆盖,"[0.36951867552556344, 0.0620180338996728, 0.5684632905747636]","[0.14412194218229143, 0.043879620611320454, 0.8119984372063881]",904,external_right_fine_B9,right,fine,B9,9.252071380615234,9.874313831329346,14.39888072013855,23.693410873413086,"{""wilor_freihand"": 1608, ""wilor_rhd"": 1137, ""wilor_dex"": 1051, ""wilor_interhand26m"": 156, ""wilor_mtc"": 231, ""wilor_ho3d"": 170, ""wilor_reinter"": 183}"
+4099,1461,338,2300,0.3564283971700415,0.08245913637472554,0.5611124664552329,0.43888753354476706,low,低覆盖,"[0.47574800123429584, 0.08776580520314235, 0.4364861935625618]","[0.27668370666050945, 0.10394497898250263, 0.619371314356988]",801,external_right_fine_B10,right,fine,B10,9.252071380615234,9.514245986938477,13.587471961975098,23.28925132751465,"{""wilor_freihand"": 1008, ""wilor_rhd"": 1255, ""wilor_dex"": 574, ""wilor_interhand26m"": 279, ""wilor_mtc"": 46, ""wilor_ho3d"": 555, ""wilor_h2o3d"": 44, ""wilor_reinter"": 338}"
+9444,6545,587,2312,0.6930326132994494,0.06215586615840746,0.24481152054214317,0.7551884794578568,low,低覆盖,"[0.7589549791840582, 0.050542085843791364, 0.19050293497215054]","[0.5312411722077317, 0.08246534362502882, 0.3862934841672394]",2486,external_right_fine_B11,right,fine,B11,9.252071380615234,7.7916905879974365,11.240390872955322,22.897846221923828,"{""wilor_freihand"": 2280, ""wilor_rhd"": 2289, ""wilor_dex"": 3069, ""wilor_interhand26m"": 254, ""wilor_mtc"": 1134, ""wilor_ho3d"": 189, ""wilor_reinter"": 229}"
+2410,1403,228,779,0.5821576763485478,0.0946058091286307,0.3232365145228216,0.6767634854771785,low,低覆盖,"[0.7387294963823989, 0.10433943648783431, 0.15693106712976707]","[0.3276128620479963, 0.09562546223209378, 0.57676167571991]",401,external_right_fine_B12,right,fine,B12,9.252071380615234,8.320358753204346,11.743942117691034,24.59847068786621,"{""wilor_freihand"": 201, ""wilor_rhd"": 238, ""wilor_dex"": 333, ""wilor_interhand26m"": 272, ""wilor_mtc"": 19, ""wilor_ho3d"": 1003, ""wilor_h2o3d"": 175, ""wilor_reinter"": 169}"
+1650,1025,210,415,0.6212121212121212,0.12727272727272726,0.2515151515151515,0.7484848484848485,low,低覆盖,"[0.6306041201313147, 0.12380540167803858, 0.24559047819064675]","[0.4192536016977874, 0.11964623789372575, 0.4611001604084868]",96,external_right_fine_B13,right,fine,B13,9.252071380615234,7.984917163848877,11.82774338722229,19.809581756591797,"{""wilor_freihand"": 56, ""wilor_dex"": 224, ""wilor_interhand26m"": 144, ""wilor_mtc"": 3, ""wilor_ho3d"": 954, ""wilor_h2o3d"": 194, ""wilor_reinter"": 75}"
+364,0,0,364,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",69,external_right_fine_B14,right,fine,B14,9.252071380615234,15.501802444458008,24.739180469512938,27.0179500579834,"{""wilor_freihand"": 8, ""wilor_rhd"": 64, ""wilor_interhand26m"": 1, ""wilor_ho3d"": 291}"
+7276,6163,311,802,0.8470313358988455,0.04274326553051127,0.11022539857064322,0.8897746014293568,low,低覆盖,"[0.846450886922589, 0.03876546250607641, 0.11478365057133477]","[0.6427211303120679, 0.0758756694376324, 0.28140320025029986]",628,external_right_fine_B15,right,fine,B15,9.252071380615234,6.273130178451538,10.714917421340942,19.164508819580078,"{""wilor_freihand"": 1104, ""wilor_rhd"": 114, ""wilor_dex"": 5433, ""wilor_interhand26m"": 41, ""wilor_mtc"": 13, ""wilor_ho3d"": 484, ""wilor_h2o3d"": 52, ""wilor_reinter"": 35}"
+2749,1766,258,725,0.6424154237904692,0.09385230993088396,0.26373226627864677,0.7362677337213532,low,低覆盖,"[0.634346337959954, 0.11615577124562143, 0.24949789079442458]","[0.5997578789480356, 0.09340658991462011, 0.3068355311373444]",373,external_right_fine_B16,right,fine,B16,9.252071380615234,8.029752731323242,12.461999893188475,22.539953231811523,"{""wilor_freihand"": 536, ""wilor_rhd"": 85, ""wilor_dex"": 920, ""wilor_interhand26m"": 142, ""wilor_mtc"": 25, ""wilor_ho3d"": 539, ""wilor_h2o3d"": 401, ""wilor_reinter"": 101}"
+6909,4209,882,1818,0.6092053842813722,0.1276595744680851,0.26313504125054277,0.7368649587494572,low,低覆盖,"[0.7308856082327897, 0.07029753109520494, 0.19881686067200546]","[0.4636343364514462, 0.12968424241767862, 0.40668142113087524]",2432,external_right_fine_B17,right,fine,B17,9.252071380615234,8.294952392578125,11.266800689697265,38.2083625793457,"{""wilor_freihand"": 1119, ""wilor_rhd"": 2580, ""wilor_dex"": 711, ""wilor_interhand26m"": 651, ""wilor_mtc"": 1466, ""wilor_ho3d"": 65, ""wilor_h2o3d"": 14, ""wilor_reinter"": 303}"
+4609,44,21,4544,0.00954653937947494,0.004556302885658494,0.9858971577348665,0.014102842265133475,low,低覆盖,"[0.013755699700190275, 0.00601553059768336, 0.9802287697021262]","[0.0031720565579757528, 0.0012994789359284332, 0.9955284645060959]",1707,external_right_fine_B18,right,fine,B18,9.252071380615234,14.84779167175293,17.65075225830078,50.51320266723633,"{""wilor_freihand"": 528, ""wilor_rhd"": 3178, ""wilor_dex"": 70, ""wilor_interhand26m"": 231, ""wilor_mtc"": 17, ""wilor_ho3d"": 6, ""wilor_reinter"": 579}"
+1596,32,11,1553,0.020050125313283207,0.006892230576441102,0.9730576441102757,0.026942355889724268,low,低覆盖,"[0.012654654200441203, 0.0038842896553651363, 0.9834610561441934]","[0.001524390243902439, 0.0016629711751662971, 0.9968126385809313]",902,external_right_fine_B19,right,fine,B19,9.252071380615234,13.922243118286133,22.840292930603027,27.99265480041504,"{""wilor_freihand"": 400, ""wilor_rhd"": 823, ""wilor_dex"": 42, ""wilor_interhand26m"": 28, ""wilor_mtc"": 10, ""wilor_ho3d"": 267, ""wilor_reinter"": 26}"
+2952,1411,470,1071,0.4779810298102981,0.15921409214092141,0.3628048780487805,0.6371951219512195,low,低覆盖,"[0.3529200982320983, 0.20319261333685845, 0.44388728843104336]","[0.4394581193407975, 0.10543505073329744, 0.45510682992590495]",94,external_right_fine_B20,right,fine,B20,9.252071380615234,8.725767135620117,12.478687286376953,21.58528709411621,"{""wilor_freihand"": 192, ""wilor_rhd"": 5, ""wilor_dex"": 359, ""wilor_interhand26m"": 15, ""wilor_mtc"": 2, ""wilor_ho3d"": 2362, ""wilor_h2o3d"": 13, ""wilor_reinter"": 4}"
+6236,5443,231,562,0.8728351507376524,0.03704297626683772,0.09012187299550994,0.90987812700449,low,低覆盖,"[0.8401419527718204, 0.03242657911537356, 0.12743146811280628]","[0.7219516732385867, 0.05274736501520837, 0.22530096174620504]",505,external_right_fine_B21,right,fine,B21,9.252071380615234,6.885923385620117,10.343169689178467,23.797868728637695,"{""wilor_freihand"": 624, ""wilor_rhd"": 41, ""wilor_dex"": 4875, ""wilor_interhand26m"": 129, ""wilor_mtc"": 24, ""wilor_ho3d"": 376, ""wilor_h2o3d"": 36, ""wilor_reinter"": 131}"
+5234,1499,330,3405,0.28639663737103555,0.06304929308368361,0.6505540695452808,0.34944593045471917,low,低覆盖,"[0.25784822822447423, 0.05048131056075014, 0.6916704612147757]","[0.1316564123077974, 0.03916119798521021, 0.8291823897069924]",1208,external_right_fine_B22,right,fine,B22,9.252071380615234,10.373049259185791,14.586898374557494,29.426952362060547,"{""wilor_freihand"": 2608, ""wilor_rhd"": 981, ""wilor_dex"": 1164, ""wilor_interhand26m"": 157, ""wilor_mtc"": 14, ""wilor_ho3d"": 146, ""wilor_h2o3d"": 31, ""wilor_reinter"": 133}"
+6535,3676,908,1951,0.5625095638867635,0.1389441469013007,0.2985462892119357,0.7014537107880643,low,低覆盖,"[0.6224216883081707, 0.11365625187137333, 0.26392205982045597]","[0.4788086588440452, 0.16215883678759144, 0.3590325043683633]",3127,external_right_fine_B23,right,fine,B23,9.252071380615234,8.537426948547363,11.445512294769285,21.130720138549805,"{""wilor_freihand"": 1304, ""wilor_rhd"": 2620, ""wilor_dex"": 445, ""wilor_interhand26m"": 1067, ""wilor_mtc"": 423, ""wilor_ho3d"": 48, ""wilor_h2o3d"": 39, ""wilor_reinter"": 589}"
+322,0,0,322,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",132,external_right_fine_B24,right,fine,B24,9.252071380615234,25.06973934173584,30.032165813446046,35.582008361816406,"{""wilor_freihand"": 24, ""wilor_rhd"": 45, ""wilor_interhand26m"": 250, ""wilor_mtc"": 3}"
+5507,3989,348,1170,0.7243508262211731,0.06319230070818958,0.21245687307063738,0.7875431269293627,low,低覆盖,"[0.7088134348481366, 0.10732527661781506, 0.18386128853404807]","[0.5730637274333208, 0.09145453986111478, 0.33548173270556436]",986,external_right_fine_B25,right,fine,B25,9.252071380615234,7.526406764984131,12.564207935333252,21.74848747253418,"{""wilor_freihand"": 1008, ""wilor_rhd"": 771, ""wilor_dex"": 2675, ""wilor_interhand26m"": 250, ""wilor_mtc"": 135, ""wilor_ho3d"": 259, ""wilor_h2o3d"": 187, ""wilor_reinter"": 222}"
+3099,202,87,2810,0.06518231687641174,0.028073572120038724,0.9067441110035496,0.09325588899645043,low,低覆盖,"[0.06526719533240705, 0.031114641762878013, 0.903618162904715]","[0.016195106529653003, 0.012913078377804022, 0.970891815092543]",1560,external_right_fine_B26,right,fine,B26,9.252071380615234,13.296533584594727,18.64644432067871,38.916622161865234,"{""wilor_freihand"": 768, ""wilor_rhd"": 1594, ""wilor_dex"": 316, ""wilor_interhand26m"": 273, ""wilor_mtc"": 9, ""wilor_ho3d"": 20, ""wilor_reinter"": 119}"
+8015,5001,948,2066,0.623955084217093,0.11827822832189644,0.2577666874610106,0.7422333125389894,low,低覆盖,"[0.7020687696559937, 0.08992600454249673, 0.2080052258015093]","[0.5640584142981206, 0.16167949528680844, 0.2742620904150709]",2282,external_right_fine_B27,right,fine,B27,9.252071380615234,8.155202865600586,11.852626132965087,33.23221206665039,"{""wilor_freihand"": 3304, ""wilor_rhd"": 1349, ""wilor_dex"": 57, ""wilor_interhand26m"": 1469, ""wilor_mtc"": 365, ""wilor_ho3d"": 40, ""wilor_h2o3d"": 128, ""wilor_reinter"": 1303}"
+5327,1432,680,3215,0.2688192228271072,0.1276515862586822,0.6035291909142106,0.39647080908578936,low,低覆盖,"[0.23972914190920744, 0.13770662532344302, 0.6225642327673496]","[0.2984422348756878, 0.13197150650574463, 0.5695862586185675]",679,external_right_fine_B28,right,fine,B28,9.252071380615234,9.786434173583984,14.943235588073728,38.65951156616211,"{""wilor_freihand"": 2048, ""wilor_rhd"": 125, ""wilor_dex"": 24, ""wilor_interhand26m"": 826, ""wilor_mtc"": 353, ""wilor_ho3d"": 5, ""wilor_reinter"": 1946}"
+1595,0,1,1594,0.0,0.0006269592476489029,0.9993730407523511,0.0006269592476488839,low,低覆盖,"[0.0, 0.0006202459641446586, 0.9993797540358553]","[0.0, 0.000591016548463357, 0.9994089834515366]",564,external_right_fine_B29,right,fine,B29,9.252071380615234,16.721981048583984,24.15742301940918,28.399572372436523,"{""wilor_freihand"": 984, ""wilor_rhd"": 370, ""wilor_dex"": 3, ""wilor_interhand26m"": 163, ""wilor_mtc"": 17, ""wilor_reinter"": 58}"
+5279,1968,818,2493,0.372797878386058,0.154953589695018,0.472248531918924,0.527751468081076,low,低覆盖,"[0.30251057108064777, 0.13503090113640664, 0.5624585277829456]","[0.2359834547029039, 0.20798605030287406, 0.556030494994222]",1942,external_right_fine_B30,right,fine,B30,9.252071380615234,9.116421699523926,13.266911029815667,23.76369285583496,"{""wilor_freihand"": 1480, ""wilor_rhd"": 1647, ""wilor_dex"": 894, ""wilor_interhand26m"": 171, ""wilor_mtc"": 491, ""wilor_ho3d"": 68, ""wilor_reinter"": 528}"
+1746,9,33,1704,0.005154639175257732,0.018900343642611683,0.9759450171821306,0.02405498281786944,low,低覆盖,"[0.0069222252749280575, 0.009082921793140483, 0.9839948529319315]","[0.00733695652173913, 0.013519021739130437, 0.9791440217391304]",736,external_right_fine_B31,right,fine,B31,9.252071380615234,14.203126907348633,20.6509747505188,24.091218948364258,"{""wilor_freihand"": 480, ""wilor_rhd"": 943, ""wilor_dex"": 7, ""wilor_interhand26m"": 189, ""wilor_mtc"": 3, ""wilor_ho3d"": 46, ""wilor_reinter"": 78}"
+10336,7011,948,2377,0.6783088235294118,0.09171826625386997,0.22997291021671826,0.7700270897832817,low,低覆盖,"[0.6842323034329114, 0.08869766672403687, 0.22707002984305155]","[0.6384177110694337, 0.10193063411950833, 0.25965165481105795]",1766,external_right_fine_B32,right,fine,B32,9.252071380615234,7.796813726425171,12.466526508331299,47.25800704956055,"{""wilor_freihand"": 5368, ""wilor_rhd"": 787, ""wilor_dex"": 2, ""wilor_interhand26m"": 1693, ""wilor_mtc"": 574, ""wilor_ho3d"": 10, ""wilor_h2o3d"": 29, ""wilor_reinter"": 1873}"
+1032,0,3,1029,0.0,0.0029069767441860465,0.997093023255814,0.0029069767441860517,low,低覆盖,"[0.0, 0.0027669567455739993, 0.9972330432544262]","[0.0, 0.0016195524146054182, 0.9983804475853945]",283,external_right_distinct_B1,right,distinct,B1,9.252071380615234,16.09415054321289,29.51508560180664,33.34939956665039,"{""wilor_freihand"": 32, ""wilor_rhd"": 195, ""wilor_interhand26m"": 494, ""wilor_mtc"": 135, ""wilor_reinter"": 176}"
+492,0,1,491,0.0,0.0020325203252032522,0.9979674796747967,0.002032520325203291,low,低覆盖,"[0.0, 0.001260702316862762, 0.9987392976831372]","[0.0, 0.004329004329004329, 0.9956709956709957]",231,external_right_distinct_B2,right,distinct,B2,9.252071380615234,16.76012897491455,22.280033683776853,33.24413299560547,"{""wilor_freihand"": 184, ""wilor_rhd"": 162, ""wilor_dex"": 1, ""wilor_interhand26m"": 91, ""wilor_mtc"": 52, ""wilor_reinter"": 2}"
+4658,24,56,4578,0.005152425933877201,0.012022327179046801,0.982825246887076,0.017174753112924046,low,低覆盖,"[0.002898725070245564, 0.008104511327779366, 0.9889967636019751]","[0.0008472182999152782, 0.002016910573815403, 0.9971358711262692]",3541,external_right_distinct_B3,right,distinct,B3,9.252071380615234,13.483551502227783,16.439379119873045,50.315032958984375,"{""wilor_freihand"": 160, ""wilor_rhd"": 3668, ""wilor_interhand26m"": 265, ""wilor_mtc"": 416, ""wilor_reinter"": 149}"
+1078,0,0,1078,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",842,external_right_distinct_B4,right,distinct,B4,9.252071380615234,17.85080909729004,21.442168617248534,36.31260299682617,"{""wilor_freihand"": 184, ""wilor_rhd"": 780, ""wilor_interhand26m"": 26, ""wilor_mtc"": 72, ""wilor_reinter"": 16}"
+4429,538,238,3653,0.12147211560171596,0.05373673515466245,0.8247911492436216,0.17520885075637838,low,低覆盖,"[0.14467737274999184, 0.09666480545829527, 0.7586578217917128]","[0.043388968650744146, 0.023947796090001687, 0.9326632352592542]",1123,external_right_distinct_B5,right,distinct,B5,9.252071380615234,11.781124114990234,15.474595260620116,30.491758346557617,"{""wilor_freihand"": 760, ""wilor_rhd"": 2289, ""wilor_dex"": 268, ""wilor_interhand26m"": 206, ""wilor_mtc"": 527, ""wilor_ho3d"": 65, ""wilor_reinter"": 314}"
+3465,892,236,2337,0.25743145743145746,0.0681096681096681,0.6744588744588744,0.3255411255411256,low,低覆盖,"[0.356631861389781, 0.05523674681026089, 0.5881313917999582]","[0.07269984717169979, 0.030423564935954894, 0.8968765878923453]",1139,external_right_distinct_B6,right,distinct,B6,9.252071380615234,10.203381538391113,13.806207275390623,27.509233474731445,"{""wilor_freihand"": 704, ""wilor_rhd"": 991, ""wilor_dex"": 339, ""wilor_interhand26m"": 470, ""wilor_mtc"": 60, ""wilor_ho3d"": 433, ""wilor_h2o3d"": 13, ""wilor_reinter"": 455}"
+4018,1541,282,2195,0.3835241413638626,0.07018417122946739,0.54629168740667,0.45370831259333,low,低覆盖,"[0.33346863064158727, 0.07481856903031535, 0.5917128003280974]","[0.32427397462883256, 0.04986250782909566, 0.6258635175420718]",578,external_right_distinct_B7,right,distinct,B7,9.252071380615234,9.591028690338135,15.868682956695551,23.114944458007812,"{""wilor_freihand"": 840, ""wilor_rhd"": 434, ""wilor_dex"": 902, ""wilor_interhand26m"": 356, ""wilor_mtc"": 378, ""wilor_ho3d"": 558, ""wilor_reinter"": 550}"
+1452,15,77,1360,0.010330578512396695,0.05303030303030303,0.9366391184573003,0.0633608815426997,low,低覆盖,"[0.007139996081754413, 0.03956472083516249, 0.9532952830830829]","[0.011920009966884967, 0.0436041760179969, 0.944475814015118]",264,external_right_distinct_B8,right,distinct,B8,9.252071380615234,12.68548059463501,18.450111103057864,26.912817001342773,"{""wilor_freihand"": 656, ""wilor_rhd"": 62, ""wilor_dex"": 7, ""wilor_interhand26m"": 356, ""wilor_mtc"": 24, ""wilor_ho3d"": 51, ""wilor_reinter"": 296}"
+4536,1584,278,2674,0.3492063492063492,0.06128747795414462,0.5895061728395061,0.4104938271604939,low,低覆盖,"[0.36951867552556344, 0.0620180338996728, 0.5684632905747636]","[0.14412194218229143, 0.043879620611320454, 0.8119984372063881]",904,external_right_distinct_B9,right,distinct,B9,9.252071380615234,9.874313831329346,14.39888072013855,23.693410873413086,"{""wilor_freihand"": 1608, ""wilor_rhd"": 1137, ""wilor_dex"": 1051, ""wilor_interhand26m"": 156, ""wilor_mtc"": 231, ""wilor_ho3d"": 170, ""wilor_reinter"": 183}"
+4099,1461,338,2300,0.3564283971700415,0.08245913637472554,0.5611124664552329,0.43888753354476706,low,低覆盖,"[0.47574800123429584, 0.08776580520314235, 0.4364861935625618]","[0.27668370666050945, 0.10394497898250263, 0.619371314356988]",801,external_right_distinct_B10,right,distinct,B10,9.252071380615234,9.514245986938477,13.587471961975098,23.28925132751465,"{""wilor_freihand"": 1008, ""wilor_rhd"": 1255, ""wilor_dex"": 574, ""wilor_interhand26m"": 279, ""wilor_mtc"": 46, ""wilor_ho3d"": 555, ""wilor_h2o3d"": 44, ""wilor_reinter"": 338}"
+9444,6545,587,2312,0.6930326132994494,0.06215586615840746,0.24481152054214317,0.7551884794578568,low,低覆盖,"[0.7589549791840582, 0.050542085843791364, 0.19050293497215054]","[0.5312411722077317, 0.08246534362502882, 0.3862934841672394]",2486,external_right_distinct_B11,right,distinct,B11,9.252071380615234,7.7916905879974365,11.240390872955322,22.897846221923828,"{""wilor_freihand"": 2280, ""wilor_rhd"": 2289, ""wilor_dex"": 3069, ""wilor_interhand26m"": 254, ""wilor_mtc"": 1134, ""wilor_ho3d"": 189, ""wilor_reinter"": 229}"
+18245,12800,1631,3814,0.7015620718004932,0.08939435461770348,0.20904357358180323,0.7909564264181967,low,低覆盖,"[0.7417361847810997, 0.08102403968009182, 0.17723977553880843]","[0.45208164097643533, 0.12266487176360807, 0.42525348725995665]",3197,external_right_distinct_B12,right,distinct,B12,9.252071380615234,7.803130149841309,11.23873176574707,38.2083625793457,"{""wilor_freihand"": 2480, ""wilor_rhd"": 2932, ""wilor_dex"": 6701, ""wilor_interhand26m"": 1108, ""wilor_mtc"": 1501, ""wilor_ho3d"": 2506, ""wilor_h2o3d"": 435, ""wilor_reinter"": 582}"
+364,0,0,364,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",69,external_right_distinct_B13,right,distinct,B13,9.252071380615234,15.501802444458008,24.739180469512938,27.0179500579834,"{""wilor_freihand"": 8, ""wilor_rhd"": 64, ""wilor_interhand26m"": 1, ""wilor_ho3d"": 291}"
+4609,44,21,4544,0.00954653937947494,0.004556302885658494,0.9858971577348665,0.014102842265133475,low,低覆盖,"[0.013755699700190275, 0.00601553059768336, 0.9802287697021262]","[0.0031720565579757528, 0.0012994789359284332, 0.9955284645060959]",1707,external_right_distinct_B14,right,distinct,B14,9.252071380615234,14.84779167175293,17.65075225830078,50.51320266723633,"{""wilor_freihand"": 528, ""wilor_rhd"": 3178, ""wilor_dex"": 70, ""wilor_interhand26m"": 231, ""wilor_mtc"": 17, ""wilor_ho3d"": 6, ""wilor_reinter"": 579}"
+1596,32,11,1553,0.020050125313283207,0.006892230576441102,0.9730576441102757,0.026942355889724268,low,低覆盖,"[0.012654654200441203, 0.0038842896553651363, 0.9834610561441934]","[0.001524390243902439, 0.0016629711751662971, 0.9968126385809313]",902,external_right_distinct_B15,right,distinct,B15,9.252071380615234,13.922243118286133,22.840292930603027,27.99265480041504,"{""wilor_freihand"": 400, ""wilor_rhd"": 823, ""wilor_dex"": 42, ""wilor_interhand26m"": 28, ""wilor_mtc"": 10, ""wilor_ho3d"": 267, ""wilor_reinter"": 26}"
+2952,1411,470,1071,0.4779810298102981,0.15921409214092141,0.3628048780487805,0.6371951219512195,low,低覆盖,"[0.3529200982320983, 0.20319261333685845, 0.44388728843104336]","[0.4394581193407975, 0.10543505073329744, 0.45510682992590495]",94,external_right_distinct_B16,right,distinct,B16,9.252071380615234,8.725767135620117,12.478687286376953,21.58528709411621,"{""wilor_freihand"": 192, ""wilor_rhd"": 5, ""wilor_dex"": 359, ""wilor_interhand26m"": 15, ""wilor_mtc"": 2, ""wilor_ho3d"": 2362, ""wilor_h2o3d"": 13, ""wilor_reinter"": 4}"
+14492,11198,837,2457,0.7727021805133867,0.05775600331217223,0.16954181617444108,0.8304581838255589,low,低覆盖,"[0.7219812567531503, 0.08813701647167307, 0.1898817267751767]","[0.5576092566071178, 0.09561373165012256, 0.3467770117427596]",1431,external_right_distinct_B17,right,distinct,B17,9.252071380615234,7.298241376876831,11.966495275497435,23.797868728637695,"{""wilor_freihand"": 2168, ""wilor_rhd"": 897, ""wilor_dex"": 8470, ""wilor_interhand26m"": 521, ""wilor_mtc"": 184, ""wilor_ho3d"": 1174, ""wilor_h2o3d"": 624, ""wilor_reinter"": 454}"
+5234,1499,330,3405,0.28639663737103555,0.06304929308368361,0.6505540695452808,0.34944593045471917,low,低覆盖,"[0.25784822822447423, 0.05048131056075014, 0.6916704612147757]","[0.1316564123077974, 0.03916119798521021, 0.8291823897069924]",1208,external_right_distinct_B18,right,distinct,B18,9.252071380615234,10.373049259185791,14.586898374557494,29.426952362060547,"{""wilor_freihand"": 2608, ""wilor_rhd"": 981, ""wilor_dex"": 1164, ""wilor_interhand26m"": 157, ""wilor_mtc"": 14, ""wilor_ho3d"": 146, ""wilor_h2o3d"": 31, ""wilor_reinter"": 133}"
+6535,3676,908,1951,0.5625095638867635,0.1389441469013007,0.2985462892119357,0.7014537107880643,low,低覆盖,"[0.6224216883081707, 0.11365625187137333, 0.26392205982045597]","[0.4788086588440452, 0.16215883678759144, 0.3590325043683633]",3127,external_right_distinct_B19,right,distinct,B19,9.252071380615234,8.537426948547363,11.445512294769285,21.130720138549805,"{""wilor_freihand"": 1304, ""wilor_rhd"": 2620, ""wilor_dex"": 445, ""wilor_interhand26m"": 1067, ""wilor_mtc"": 423, ""wilor_ho3d"": 48, ""wilor_h2o3d"": 39, ""wilor_reinter"": 589}"
+322,0,0,322,0.0,0.0,1.0,0.0,uncovered,抽样库未覆盖,"[0.0, 0.0, 1.0]","[0.0, 0.0, 1.0]",132,external_right_distinct_B20,right,distinct,B20,9.252071380615234,25.06973934173584,30.032165813446046,35.582008361816406,"{""wilor_freihand"": 24, ""wilor_rhd"": 45, ""wilor_interhand26m"": 250, ""wilor_mtc"": 3}"
+3099,202,87,2810,0.06518231687641174,0.028073572120038724,0.9067441110035496,0.09325588899645043,low,低覆盖,"[0.06526719533240705, 0.031114641762878013, 0.903618162904715]","[0.016195106529653003, 0.012913078377804022, 0.970891815092543]",1560,external_right_distinct_B21,right,distinct,B21,9.252071380615234,13.296533584594727,18.64644432067871,38.916622161865234,"{""wilor_freihand"": 768, ""wilor_rhd"": 1594, ""wilor_dex"": 316, ""wilor_interhand26m"": 273, ""wilor_mtc"": 9, ""wilor_ho3d"": 20, ""wilor_reinter"": 119}"
+5327,1432,680,3215,0.2688192228271072,0.1276515862586822,0.6035291909142106,0.39647080908578936,low,低覆盖,"[0.23972914190920744, 0.13770662532344302, 0.6225642327673496]","[0.2984422348756878, 0.13197150650574463, 0.5695862586185675]",679,external_right_distinct_B22,right,distinct,B22,9.252071380615234,9.786434173583984,14.943235588073728,38.65951156616211,"{""wilor_freihand"": 2048, ""wilor_rhd"": 125, ""wilor_dex"": 24, ""wilor_interhand26m"": 826, ""wilor_mtc"": 353, ""wilor_ho3d"": 5, ""wilor_reinter"": 1946}"
+1595,0,1,1594,0.0,0.0006269592476489029,0.9993730407523511,0.0006269592476488839,low,低覆盖,"[0.0, 0.0006202459641446586, 0.9993797540358553]","[0.0, 0.000591016548463357, 0.9994089834515366]",564,external_right_distinct_B23,right,distinct,B23,9.252071380615234,16.721981048583984,24.15742301940918,28.399572372436523,"{""wilor_freihand"": 984, ""wilor_rhd"": 370, ""wilor_dex"": 3, ""wilor_interhand26m"": 163, ""wilor_mtc"": 17, ""wilor_reinter"": 58}"
+5279,1968,818,2493,0.372797878386058,0.154953589695018,0.472248531918924,0.527751468081076,low,低覆盖,"[0.30251057108064777, 0.13503090113640664, 0.5624585277829456]","[0.2359834547029039, 0.20798605030287406, 0.556030494994222]",1942,external_right_distinct_B24,right,distinct,B24,9.252071380615234,9.116421699523926,13.266911029815667,23.76369285583496,"{""wilor_freihand"": 1480, ""wilor_rhd"": 1647, ""wilor_dex"": 894, ""wilor_interhand26m"": 171, ""wilor_mtc"": 491, ""wilor_ho3d"": 68, ""wilor_reinter"": 528}"
+1746,9,33,1704,0.005154639175257732,0.018900343642611683,0.9759450171821306,0.02405498281786944,low,低覆盖,"[0.0069222252749280575, 0.009082921793140483, 0.9839948529319315]","[0.00733695652173913, 0.013519021739130437, 0.9791440217391304]",736,external_right_distinct_B25,right,distinct,B25,9.252071380615234,14.203126907348633,20.6509747505188,24.091218948364258,"{""wilor_freihand"": 480, ""wilor_rhd"": 943, ""wilor_dex"": 7, ""wilor_interhand26m"": 189, ""wilor_mtc"": 3, ""wilor_ho3d"": 46, ""wilor_reinter"": 78}"
+18351,12012,1896,4443,0.6545692332842897,0.10331862023867909,0.24211214647703122,0.7578878535229687,low,低覆盖,"[0.6930872157641901, 0.0893074749427512, 0.21760530929305885]","[0.5947837696273544, 0.1376949115568354, 0.26752131881581037]",3734,external_right_distinct_B26,right,distinct,B26,9.252071380615234,7.955670356750488,12.168926239013672,47.25800704956055,"{""wilor_freihand"": 8672, ""wilor_rhd"": 2136, ""wilor_dex"": 59, ""wilor_interhand26m"": 3162, ""wilor_mtc"": 939, ""wilor_ho3d"": 50, ""wilor_h2o3d"": 157, ""wilor_reinter"": 3176}"

pose_recluster_distinct_20261014/downloads/coverage/code/aggregate_coverage.py

@@ -0,0 +1,54 @@
+from cluster_common import *
+import gzip
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+LABELS={'covered':'已覆盖','low':'低覆盖','uncovered':'抽样库未覆盖'}
+
+def stats(status,ix,weights=None,units=None):
+    n=len(ix);ss=status[ix];counts={k:int((ss==k).sum()) for k in range(3)}
+    result=dict(n=n,covered=counts[0],low=counts[1],uncovered=counts[2],covered_fraction=counts[0]/n,low_fraction=counts[1]/n,uncovered_fraction=counts[2]/n,any_match_fraction=1-counts[2]/n)
+    cls='covered' if result['covered_fraction']>=.90 else ('uncovered' if counts[2]==n else 'low')
+    result['status']=cls;result['status_label']=LABELS[cls]
+    if weights is not None:result['fit_weighted_fractions']=[float(np.average(ss==k,weights=weights[ix])) for k in range(3)]
+    if units is not None:
+        c=collections.Counter(units[i] for i in ix);w=np.array([1/c[units[i]] for i in ix]);result['equal_unit_fractions']=[float(np.average(ss==k,weights=w)) for k in range(3)];result['independent_units']=len(c)
+    return result
+
+def main():
+    external=read(BASE/'manifests/external_members.json');ours=read(BASE/'manifests/ours_members.json');models=read(BASE/'metrics/models.json');n=len(external)
+    distance=np.zeros(n);nearest=np.zeros(n,np.int64);support=np.zeros(n,np.int64);sample_count=np.zeros(n,np.int64);third=np.zeros(n);tau=np.zeros(n);status=np.full(n,-1,np.int8);fit=np.zeros(n);units=[r['independent_unit'] for r in external]
+    finekeys=['']*n;distinctkeys=['']*n;result={};sensitivity=[];source_rows=[];summary={}
+    for side in ['left','right']:
+        a=np.load(ROOT/f'cache/external_{side}_coverage.npz');ix=a['indices'];nn=a['distance_deg'];t=a['thresholds_deg'][1];rec=a['record_counts'][:,1];ss=np.where(nn>t,2,np.where(rec>=3,0,1));status[ix]=ss
+        distance[ix]=nn;nearest[ix]=a['nearest_index'];support[ix]=rec;sample_count[ix]=a['sample_count'];third[ix]=a['nearest_record_distances_deg'][:,2];tau[ix]=t
+        fw=np.load(BASE/f'cache/external_{side}_fit.npz');assert np.array_equal(fw['indices'],ix);fit[ix]=fw['fit_weights']
+        summary[side]=stats(status,ix,fit,units);summary[side]['threshold_deg']=float(t)
+        labels=np.load(BASE/f'cache/external_{side}_assignments.npz');dist=np.load(BASE/f'cache/external_{side}_distinct_assignments.npz');assert np.array_equal(labels['indices'],ix) and np.array_equal(dist['indices'],ix)
+        for level in ['fine','distinct']:
+            lab=labels['fine'] if level=='fine' else dist['distinct']
+            for c in models['external_'+side][level]:
+                ci=ix[lab==c['label']];assert len(ci)==c['n']
+                stat=stats(status,ci,fit,units);stat.update(key=c['key'],hand=side,level=level,id=c['id'],threshold_deg=float(t),distance_p50_deg=float(np.median(distance[ci])),distance_p95_deg=float(np.quantile(distance[ci],.95)),max_distance_deg=float(distance[ci].max()),source_counts=dict(collections.Counter(external[i]['source'] for i in ci)))
+                result[c['key']]=stat
+                for i in ci:(finekeys if level=='fine' else distinctkeys)[i]=c['key']
+                for name,tt,rc in zip(a['threshold_names'],a['thresholds_deg'],a['record_counts'].T):
+                    local=lab==c['label'];s=np.where(nn[local]>tt,2,np.where(rc[local]>=3,0,1));vals=stats(s,np.arange(len(s)))
+                    sensitivity.append(dict(key=c['key'],hand=side,threshold=name,threshold_deg=float(tt),**vals))
+        for source in sorted(set(external[i]['source'] for i in ix)):
+            sx=np.array([i for i in ix if external[i]['source']==source]);source_rows.append(dict(source=source,hand=side,threshold_deg=float(t),**stats(status,sx,fit,units)))
+        for name,tt,rc in zip(a['threshold_names'],a['thresholds_deg'],a['record_counts'].T):
+            ss2=np.where(nn>tt,2,np.where(rc>=3,0,1));sensitivity.append(dict(key='external_'+side,hand=side,threshold=name,threshold_deg=float(tt),**stats(ss2,np.arange(len(ss2)))))
+    assert (status>=0).all() and all(finekeys) and all(distinctkeys)
+    summary['all']=stats(status,np.arange(n),fit,units)
+    write(ROOT/'metrics/class_coverage.json',result);write(ROOT/'metrics/summary.json',summary);write(ROOT/'metrics/source_coverage.json',source_rows)
+    table(ROOT/'metrics/class_coverage.csv',list(result.values()));table(ROOT/'metrics/threshold_sensitivity.csv',sensitivity);table(ROOT/'metrics/source_coverage.csv',source_rows)
+    np.savez_compressed(ROOT/'cache/external_coverage.npz',distance_deg=distance,nearest_index=nearest,support_recordings=support,support_samples=sample_count,third_record_distance_deg=third,threshold_deg=tau,status=status)
+    fields=['id','source','split','subject','recording','frame','hand','representation','class_distinct','class_fine','nearest_reference_id','nearest_reference_source','nearest_reference_recording','nearest_reference_frame','nearest_distance_deg','threshold_deg','similar_reference_samples','similar_reference_recordings','third_record_distance_deg','coverage_status','gt_status','label_type','image_overlay_link']
+    url='https://yangzijing-nine-source-hand-data-browser.static.hf.space/pose_recluster_distinct_20261014/index.html'
+    with gzip.open(ROOT/'metrics/member_coverage.csv.gz','wt',newline='') as f:
+        w=csv.DictWriter(f,fieldnames=fields);w.writeheader()
+        for i,r in enumerate(external):
+            nr=ours[int(nearest[i])];row={k:r.get(k,'') for k in fields};row.update(representation='palm-local unit 3D bone directions (GT source proxy)',class_distinct=distinctkeys[i],class_fine=finekeys[i],nearest_reference_id=nr['id'],nearest_reference_source=nr['source'],nearest_reference_recording=nr['recording'],nearest_reference_frame=nr['frame'],nearest_distance_deg=f'{distance[i]:.6f}',threshold_deg=f'{tau[i]:.6f}',similar_reference_samples=int(sample_count[i]),similar_reference_recordings=int(support[i]),third_record_distance_deg=f'{third[i]:.6f}',coverage_status=LABELS[['covered','low','uncovered'][status[i]]],gt_status=r.get('gt_status','证据不足'),image_overlay_link='');w.writerow(row)
+    print('SUMMARY',summary,flush=True)
+    print('MAIN_CLASS_STATUSES',collections.Counter(c['status'] for c in result.values() if c['level']=='distinct'),flush=True)
+
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/build_coverage_site.py

@@ -0,0 +1,116 @@
+from cluster_common import *
+import shutil,gzip
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+URL='https://yangzijing-nine-source-hand-data-browser.static.hf.space/pose_recluster_distinct_20261014/'
+
+def main():
+    assert read(ROOT/'audit/numerical_validation.json')['status']=='PASS'
+    d=json.loads((BASE/'site/data.js').read_text().removeprefix('window.AUDIT_DATA=').rstrip(';\n'))
+    classes=read(ROOT/'metrics/class_coverage.json');summary=read(ROOT/'metrics/summary.json');cal=read(ROOT/'metrics/calibration.json');visuals=read(ROOT/'metrics/uncovered_visuals.json');cov=np.load(ROOT/'cache/external_coverage.npz')
+    rows=read(BASE/'manifests/external_members.json');ours=read(BASE/'manifests/ours_members.json');idmap={r['id']:i for i,r in enumerate(rows)}
+    sens=list(csv.DictReader((ROOT/'metrics/threshold_sensitivity.csv').open()))
+    for r in sens:
+        for k in ['threshold_deg','covered_fraction','low_fraction','uncovered_fraction']:r[k]=float(r[k])
+    def coverage(i):
+        return dict(status=['covered','low','uncovered'][int(cov['status'][i])],distance_deg=float(cov['distance_deg'][i]),threshold_deg=float(cov['threshold_deg'][i]),support_recordings=int(cov['support_recordings'][i]),support_samples=int(cov['support_samples'][i]))
+    for key,model in d['models'].items():
+        if key.startswith('external'):
+            for level in ['fine','distinct']:
+                for c in model[level]:c['coverage']=classes[c['key']]
+    for sid,s in d['samples'].items():
+        if sid in idmap:s['coverage']=coverage(idmap[sid])
+    slim={};member_links={}
+    for key,vs in visuals.items():
+        slim[key]=[]
+        for v in vs:
+            i=v['index'];assert cov['status'][i]==2 and v['id']==rows[i]['id']
+            m=rows[i];r=v['media'];s=dict(id=m['id'],group='external',source=m['source'],recording=m['recording'],frame=m['frame'],hand=m['hand'],joints=v['joints'],assets=r['assets'],label=r['label'],camera=r['evidence'].get('camera','来源相机'),gt_status='证据不足：保留来源 GT 身份；图像贴合与独立三维准确性需另行核查',coverage=coverage(i))
+            d['samples'][s['id']]=s;d['gallery'].setdefault(s['source'],[])
+            if s['id'] not in d['gallery'][s['source']]:d['gallery'][s['source']].append(s['id'])
+            vv={k:x for k,x in v.items() if k!='media'};nr=dict(v['nearest']);assert nr['index']==cov['nearest_index'][i]
+            if 'media' in nr:
+                nr['media']={k:v['nearest']['media'][k] for k in ['assets','evidence','label','gt_status'] if k in v['nearest']['media']}
+            vv['nearest']=nr;slim[key].append(vv)
+            member_links[s['id']]=URL+s['assets']['full']
+    sources={}
+    for r in read(ROOT/'metrics/source_coverage.json'):
+        x=sources.setdefault(r['source'],dict(n=0,covered=0,low=0,uncovered=0))
+        for k in ['n','covered','low','uncovered']:x[k]+=r[k]
+    for x in sources.values():
+        for k in ['covered','low','uncovered']:x[k+'_fraction']=x[k]/x['n']
+    d['coverage']=dict(version='member-coverage-v2-20261014',classes=classes,summary=summary,calibration=cal,sources=sources,examples=slim,sensitivity=sens,reference='271543 frozen source-GT handframes from H2O+ARCTIC+HOT3D+EgoRenderer Ver.005; separate sides; final canonical GT/masks not independently verified')
+    (ROOT/'site/data.coverage_v2.js').write_text('window.AUDIT_DATA='+json.dumps(d,ensure_ascii=False,allow_nan=False,separators=(',',':'))+';\n')
+    for src,dst in [('index.html','index.html'),('app.js','app.coverage_v2.js'),('style.css','style.coverage_v2.css')]:shutil.copyfile(ROOT/'code/web'/src,ROOT/'site'/dst)
+    # Only samples with an actual overlay get a media link; no invented media for other members.
+    for sid,s in d['samples'].items():
+        if sid in idmap:member_links[sid]=URL+s['assets']['full']
+    dest=ROOT/'site/downloads/coverage';dest.mkdir(exist_ok=True)
+    with gzip.open(ROOT/'metrics/member_coverage.csv.gz','rt',newline='') as f,gzip.open(dest/'member_coverage.csv.gz','wt',newline='') as out:
+        rd=csv.DictReader(f);w=csv.DictWriter(out,fieldnames=rd.fieldnames);w.writeheader()
+        for r in rd:r['image_overlay_link']=member_links.get(r['id'],'');w.writerow(r)
+    write(ROOT/'metrics/displayed_member_links.json',member_links)
+    for name in ['class_coverage.csv','threshold_sensitivity.csv','source_coverage.csv','calibration.json','summary.json','displayed_member_links.json']:shutil.copyfile(ROOT/'metrics'/name,dest/name)
+    for name in ['coverage_protocol.json','calibration_members.json','reference_unit_codes.json']:shutil.copyfile(ROOT/'manifests'/name,dest/name)
+    for name in ['numerical_validation.json','input_sha256.json','frozen_protocol_sha256.json','exact_engine.json']:shutil.copyfile(ROOT/'audit'/name,dest/name)
+    cs=[c for c in classes.values() if c['level']=='distinct'];entire=[c for c in cs if c['status']=='uncovered'];ss=summary['all']
+    result=f'''# 逐成员覆盖更新（2026-10-14）
+
+本轮对全部 214,481 个可读外部来源聚类手帧逐一检索，参照为 H2O / ARCTIC / HOT3D / EgoRenderer Ver.005 的 271,543 个冻结抽样手帧，左右手分开；没有重新聚类或改动 GT。
+
+- 已覆盖：{ss['covered']:,}（{ss['covered_fraction']:.2%}），阈值内至少 3 段独立录制/源动作。
+- 低覆盖：{ss['low']:,}（{ss['low_fraction']:.2%}），阈值内只有 1–2 段独立录制/源动作。
+- 抽样库未覆盖：{ss['uncovered']:,}（{ss['uncovered_fraction']:.2%}），本次参照库中无阈值内近邻。
+
+外部 50 个主类中，7 类全部成员未覆盖，43 类为部分覆盖。不存在达到“90% 成员已覆盖”的主类；这不表示各类中的已覆盖成员也未覆盖。网页优先展示完整成员比例。
+
+整类未覆盖：{', '.join(c['hand']+' '+c['id'] for c in entire)}。
+
+距离是来源 GT 的 20 根掌部局部 3D 单位骨方向的平均夹角；不是 MANO 参数欧氏距离、不是 2D 图上距离。阈值先使用 8,192 个内部校准手帧、检索排除同录制的全部帧确定：左手 {cal['left']['thresholds_deg']['q95']:.4f}°，右手 {cal['right']['thresholds_deg']['q95']:.4f}°。四源均衡，源内录制均衡。q90/q99 与 10°/20°/30° 的敏感性均在 CSV 保留。至少 3 个独立录制是支持量的显示约定；类 90% 汇总阈值也不是普适标准。
+
+主统计是本轮分析成员的自然计数，不代表官方全量的无偏发生比例。来源/录制均衡聚类权重下未覆盖比例为 {ss['fit_weighted_fractions'][2]:.2%}；单位均衡权重还会受静态图像单位与视频录制单位数量差异影响，因此仅用于敏感性，不作为总体发生率。
+
+114 个外部主类/细类均给出实际未覆盖成员：原图、按来源相机投影的 MANO 或 GT 骨架、精确最近邻及距离；没有把同类原型替换成“未覆盖样本”。显示样本优先接近类原型，且要求其精确最近邻也有可读 RGB 叠加，故图例不是概率抽样，不用于估计比例。图像读取失败只影响图例选择，不改统计成员、阈值或最近邻。
+
+覆盖与 GT 可靠性独立。未覆盖可能包含真实姿态缺口、来源定义差异或标签问题；不能把上述数目称为可靠补充样本数量。来源一致性、图片叠加不等于独立三维验收。参照使用已核对训练成员的来源 GT；最终 canonical 标签与 loss mask 尚未验收。外部 8 源是 WiLoR 官方源数据代理，ARCTIC/HOT3D 重叠、2D 来源和跳过的 BEDLAM 不重复记入外部 3D 分母。
+
+数值检查：32 个查询（含阈值边界）用 CPU float64 全库复算；114 个类原型与已有独立最近邻结果对照，分类一致。详细证据见 numerical_validation.json。
+'''
+    (ROOT/'RESULTS_ZH.md').write_text(result);shutil.copyfile(ROOT/'RESULTS_ZH.md',dest/'RESULTS_ZH.md')
+    repro=f'''# 复现覆盖更新
+
+实验目录：{ROOT}
+只读聚类与 GT 缓存：{BASE}
+
+```bash
+cd {ROOT}
+PY=/SSD2/jiasen/envs/foundation_hand/bin/python
+# freeze_coverage.py 仅第一次在空实验目录执行；已有冻结清单不可重写。
+CUDA_VISIBLE_DEVICES=1 OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 MKL_NUM_THREADS=2 "$PY" code/exact_coverage.py
+OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 "$PY" code/aggregate_coverage.py
+OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 "$PY" code/validate_coverage.py
+CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 "$PY" code/coverage_visuals.py
+"$PY" code/update_web.py
+"$PY" code/build_coverage_site.py
+```
+
+freeze_coverage.py 固定种子 2026101407、8192 个校准成员和阈值规则，然后才进行外部检索。逐成员距离为精确穷举；既有缓存可恢复。记录单位排除整段动作，不把邻帧支持计为独立录制。原始成员 SHA256、类分配、特征和网页旧版 SHA256 见 input_sha256.json。
+
+本目录代码复用前序审计的标定投影和 source GT 读取代码；完整依赖地址保留在 visualize.py / cluster_common.py，旧版代码与原始聚类说明在网页 downloads/code 和 downloads/REPRODUCE.md。原始数据及授权可读环境需要保留；衍生网页不包含原始数据包、模型权重或凭据。
+
+媒体检查不是 GT 独立验收。精确最近邻图像不可读时，另选同类未覆盖成员展示；其最近邻索引绝不替换。CSV 只有实际已渲染成员才填 image_overlay_link，其他留空。
+'''
+    (ROOT/'REPRODUCE.md').write_text(repro);shutil.copyfile(ROOT/'REPRODUCE.md',dest/'REPRODUCE.md')
+    shutil.copytree(ROOT/'code',dest/'code',dirs_exist_ok=True,ignore=shutil.ignore_patterns('__pycache__'))
+    # Audit every newly referenced asset and same-cluster membership, including exact NN media.
+    used=set();checks=[]
+    for key,vs in visuals.items():
+        for v in vs:
+            assert 'media' in v['nearest'],(key,'nearest RGB missing')
+            for r in [v['media'],v['nearest']['media']]:
+                for rel in r['assets'].values():assert (ROOT/'site'/rel).is_file();used.add(rel)
+            checks.append(dict(class_key=key,id=v['id'],index=v['index'],uncovered=True,nearest_index=v['nearest']['index'],nearest_rgb=True))
+    write(ROOT/'audit/new_media_sha256.json',{rel:sha(ROOT/'site'/rel) for rel in sorted(used)})
+    write(ROOT/'audit/coverage_visual_checks.json',dict(status='PASS',class_count=len(visuals),examples=len(checks),unique_external_members=len({x['id'] for x in checks}),unique_reference_members=len({x['nearest_index'] for x in checks}),all_exact_nearest_rgb_present=True,checks=checks))
+    print('BUILT',len(classes),'class coverage rows',len(checks),'paired examples',len(used),'media files',flush=True)
+
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/check_coverage_browser.js

@@ -0,0 +1,45 @@
+const fs=require('fs'),path=require('path');
+const {chromium}=require('/tmp/v9-hardcase-browser-test-SICTLC/node_modules/playwright');
+const root=path.resolve(__dirname,'..'),mode=process.argv[2]||'local',url=mode==='local'?'file://'+root+'/site/index.html':process.argv[3];
+const assert=(x,m)=>{if(!x)throw Error(m)};
+(async()=>{
+ const browser=await chromium.launch({headless:true,args:['--disable-dev-shm-usage']});const page=await browser.newPage({viewport:{width:1440,height:1100}}),errors=[],checks=[],http=[];
+ page.on('pageerror',e=>errors.push(e.message));page.on('response',r=>{if(r.status()>=400)http.push({path:new URL(r.url()).pathname,status:r.status()})});
+ async function go(u){for(let i=0;i<3;i++){try{const r=await page.goto(u,{waitUntil:'load',timeout:90000});if(r)assert(r.status()===200,'Page status');return}catch(e){if(i===2)throw e;await new Promise(r=>setTimeout(r,1000))}}}
+ async function images(selector){const rs=await page.locator(selector).evaluateAll(async imgs=>{const out=[];for(const im of imgs){im.loading='eager';let ok=false;for(let n=0;n<4;n++){try{await im.decode();ok=im.naturalWidth>0;break}catch(e){await new Promise(r=>setTimeout(r,700))}}out.push({src:im.getAttribute('src'),ok})}return out});assert(rs.every(x=>x.ok),'Image decode '+JSON.stringify(rs.filter(x=>!x.ok)));return rs.length;}
+ await go(url);await page.waitForFunction(()=>window.AUDIT_DATA?.coverage?.version==='member-coverage-v2-20261014');
+ for(const hand of ['right','left'])for(const level of ['distinct','fine']){
+  await page.selectOption('#hand',hand);await page.selectOption('#level',level);await page.selectOption('#coverageFilter','all');
+  const cs=await page.evaluate(({hand,level})=>AUDIT_DATA.models['external_'+hand][level].map(c=>({key:c.key,n:c.n,coverage:c.coverage})),{hand,level});
+  assert(await page.locator('#externalCards .card').count()===cs.length,'All class cards present');
+  for(const filter of ['entire','mostly_gap','mostly_covered']){
+   await page.selectOption('#coverageFilter',filter);const expected=cs.filter(c=>filter==='entire'?c.coverage.uncovered===c.n:filter==='mostly_gap'?c.coverage.uncovered_fraction>=.5:c.coverage.covered_fraction>=.7).length;
+   assert(await page.locator('#externalCards .card').count()===expected,'Coverage filter '+filter);
+  }
+  await page.selectOption('#coverageFilter','all');await page.selectOption('#coverageSort','covered');
+  const keys=await page.locator('#externalCards .card').evaluateAll(els=>els.map(e=>e.dataset.key));const vs=keys.map(k=>cs.find(c=>c.key===k).coverage.covered_fraction);assert(vs.every((v,i)=>i===0||v<=vs[i-1]),'Coverage descending sort');
+  await page.selectOption('#coverageSort','gap');
+  for(const c of cs){
+   await page.locator('#externalCards [data-open="'+c.key+'"]').click();await page.waitForFunction(()=>document.querySelector('#detail').open);
+   const text=await page.locator('#detailBody').innerText();assert(text.includes('本类实际未覆盖成员')&&text.includes('我们库中的精确最近邻'),'Coverage evidence missing '+c.key);
+   assert(text.includes((c.coverage.covered_fraction*100).toFixed(1)+'%'),'Class coverage percentage absent');
+   assert(await page.locator('#detail .gap-case').count()>0,'Missing actual gap cases');
+   const total=await images('#detail img');
+   const valid=await page.evaluate(key=>AUDIT_DATA.coverage.examples[key].every(v=>v.distance_deg>v.threshold_deg&&v.support_recordings===0&&v.nearest.media?.assets?.full),c.key);assert(valid,'Uncovered case or exact reference missing');
+   checks.push({key:c.key,images:total});await page.click('#closeDetail');
+  }
+  console.log('COVERAGE_CLASSES',hand,level,cs.length);
+ }
+ await page.selectOption('#hand','right');await page.selectOption('#level','distinct');await page.selectOption('#coverageFilter','entire');await page.click('[data-tab="compare"]');
+ const compare=await page.locator('#comparisonRows .row').count();assert(compare>0&&compare<26,'Comparison filter not working');await images('#comparisonRows img');
+ await page.selectOption('#coverageFilter','all');await page.click('[data-tab="overview"]');await page.evaluate(()=>scrollTo(0,0));await images('#externalCards .card img');await page.screenshot({path:root+'/audit/'+mode+'_coverage_desktop.png'});
+ await page.locator('#externalCards [data-open]').first().click();await images('#detail img');await page.screenshot({path:root+'/audit/'+mode+'_coverage_detail.png'});await page.click('#closeDetail');
+ await page.click('[data-tab="sources"]');const sources=await page.locator('[data-source]').evaluateAll(e=>e.map(x=>x.dataset.source));assert(sources.length===18,'Source rows lost');
+ for(const src of ['wilor_interhand26m','wilor_cocow','wilor_halpe','wilor_mpiinzsl']){
+  await page.click('[data-source="'+src+'"]');assert(await images('#detail img')>0,'Annotated source empty');const t=await page.locator('#detailBody').innerText();assert(t.includes(src==='wilor_interhand26m'?'GT':'2D'),'Source labels missing');await page.click('#closeDetail');
+ }
+ await page.click('[data-tab="overview"]');await page.setViewportSize({width:390,height:844});await page.evaluate(()=>scrollTo(0,0));assert(await page.evaluate(()=>document.documentElement.scrollWidth<=innerWidth+1),'Mobile horizontal overflow');await page.screenshot({path:root+'/audit/'+mode+'_coverage_mobile.png'});
+ await go(url+'#class=external_left_distinct_B2');await page.waitForFunction(()=>document.querySelector('#detail').open);await images('#detail img');assert((await page.locator('#detailBody').innerText()).includes('本类实际未覆盖成员'),'Direct deep link wrong');await page.click('#closeDetail');
+ assert(errors.length===0,'JS errors '+JSON.stringify(errors));
+ const result={status:'PASS',mode,url,coverage_classes:checks.length,details:checks,source_rows:sources.length,coverage_filters:true,coverage_sort:true,compare_filter:true,direct_class_hash:true,interhand_and_2d_galleries:true,mobile_no_overflow:true,page_errors:errors,http_events:http,images_decoded:true};fs.writeFileSync(root+'/audit/'+mode+'_coverage_browser.json',JSON.stringify(result,null,2));console.log('PASS',mode,checks.length);await browser.close();
+})().catch(e=>{console.error(e.stack);process.exit(1)});

pose_recluster_distinct_20261014/downloads/coverage/code/cluster_common.py

@@ -0,0 +1,39 @@
+from pathlib import Path
+import json,hashlib,collections,datetime,csv,math
+import numpy as np
+
+ROOT=Path(__file__).resolve().parents[1]
+REF=Path('/SSD3/yangzijing/Hand_reference_EgoRenderer005_20261013')
+NEW=Path('/SSD3/yangzijing/Hand_resample_HO3D_ReInter_H2O3D_20261013')
+OLD=Path('/SSD3/yangzijing/Hand_pose_sampling_scale_20261013')
+SEED=20261014
+PARENTS=np.array([0,0,1,2,3,0,5,6,7,0,9,10,11,0,13,14,15,0,17,18,19])
+def read(p):return json.loads(Path(p).read_text())
+def write(p,value):
+    p=Path(p);p.parent.mkdir(parents=True,exist_ok=True)
+    p.write_text(json.dumps(value,ensure_ascii=False,allow_nan=False,indent=2)+'\n')
+def sha(p):
+    h=hashlib.sha256()
+    with Path(p).open('rb') as f:
+        for b in iter(lambda:f.read(1024**2),b''):h.update(b)
+    return h.hexdigest()
+def table(p,rows):
+    with Path(p).open('w',newline='') as f:
+        w=csv.DictWriter(f,fieldnames=list(dict.fromkeys(k for r in rows for k in r)));w.writeheader()
+        for r in rows:w.writerow({k:json.dumps(v,ensure_ascii=False) if isinstance(v,(list,dict)) else v for k,v in r.items()})
+def normalized(b):return b/np.maximum(np.linalg.norm(b,axis=-1,keepdims=True),1e-12)
+def angular(a,b):
+    dot=np.einsum('ajc,bjc->abj',a,b,optimize=True)
+    return np.degrees(np.arccos(np.clip(dot,-1,1))).mean(-1)
+def pair_stats(a,b):
+    angle=np.degrees(np.arccos(np.clip((a*b).sum(-1),-1,1)))
+    fingers=angle.reshape(*angle.shape[:-1],5,4).mean(-1)
+    return angle.mean(-1),fingers,fingers.max(-1)
+def standard(b):
+    lengths=np.asarray(read('/SSD3/yangzijing/Hand_two_group_pose_clusters_20261013/manifests/display_bone_lengths.json')['lengths'])
+    j=np.zeros((21,3))
+    for k in range(1,21):j[k]=j[PARENTS[k]]+b[k-1]*lengths[k-1]
+    return j.tolist()
+def load(group):
+    return read(ROOT/'manifests'/f'{group}_members.json'),np.load(ROOT/'cache'/f'{group}_features.npz')['bones']
+def hash_sort(s):return hashlib.sha256(str(s).encode()).hexdigest()

pose_recluster_distinct_20261014/downloads/coverage/code/coverage_visuals.py

@@ -0,0 +1,55 @@
+"""Select actual uncovered members, preserve class and calibrated source projection."""
+from cluster_common import *
+import shutil,sys
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+
+def setup():
+    (ROOT/'audit/media').mkdir(exist_ok=True);(ROOT/'sources/interhand_rgb').mkdir(parents=True,exist_ok=True)
+    for name in ['interhand_rgb','reinter_rgb']:
+        x=read(BASE/f'manifests/{name}.json')
+        if name=='interhand_rgb':
+            for sid,v in x.items():
+                if not Path(v['path']).exists():v['path']=str(ROOT/'sources/interhand_rgb'/f'{sid}.jpg')
+        write(ROOT/f'manifests/{name}.json',x)
+    for p in (BASE/'audit/media').glob('*.json'):
+        r=read(p)
+        if r.get('assets') and all((ROOT/'site'/a).exists() for a in r['assets'].values()):
+            target=ROOT/'audit/media'/p.name
+            if not target.exists():shutil.copyfile(p,target)
+
+def main():
+    setup();import visualize as vis
+    rows=read(BASE/'manifests/external_members.json');ours=read(BASE/'manifests/ours_members.json');b=np.load(BASE/'cache/external_features.npz')['bones'];ob=np.load(BASE/'cache/ours_features.npz')['bones']
+    cov=np.load(ROOT/'cache/external_coverage.npz');models=read(BASE/'metrics/models.json');dest=ROOT/'metrics/uncovered_visuals.json';out=read(dest) if dest.exists() else {};failure_path=ROOT/'audit/coverage_media_failures.json';failures=read(failure_path) if failure_path.exists() else [];failed={x['id'] for x in failures if x['kind']=='external'}
+    for side in ['left','right']:
+        aa=np.load(BASE/f'cache/external_{side}_assignments.npz');ix=aa['indices'];distinct=np.load(BASE/f'cache/external_{side}_distinct_assignments.npz')['distinct']
+        for level in ['distinct','fine']:
+            lab=distinct if level=='distinct' else aa['fine']
+            for c in models['external_'+side][level]:
+                if c['key'] in out and all('media' in x['nearest'] for x in out[c['key']]):continue
+                candidates=ix[(lab==c['label'])&(cov['status'][ix]==2)]
+                assert len(candidates)>0,(c['key'],'no uncovered members')
+                ds=angular(b[[c['prototype_index']]],b[candidates])[0];order=np.argsort(ds,kind='stable');picked=[];seen=set()
+                for pos in order:
+                    i=int(candidates[pos]);m=rows[i]
+                    if m['id'] in failed or m['independent_unit'] in seen or not vis.paired(m):continue
+                    try:r=vis.save(m)
+                    except Exception as e:
+                        failures.append(dict(kind='external',class_key=c['key'],id=m['id'],source=m['source'],error=type(e).__name__+': '+str(e)[:250]));failed.add(m['id']);continue
+                    ni=int(cov['nearest_index'][i]);nm=ours[ni];nearest=dict(index=ni,id=nm['id'],source=nm['source'],recording=nm['recording'],frame=nm['frame'],hand=nm['hand'],joints=standard(ob[ni]))
+                    try:nearest['media']=vis.save(nm)
+                    except Exception as e:
+                        nearest['media_missing_reason']=type(e).__name__+': '+str(e)[:200]
+                        failures.append(dict(kind='reference',class_key=c['key'],id=nm['id'],source=nm['source'],error=nearest['media_missing_reason']))
+                        continue
+                    picked.append(dict(index=i,id=m['id'],source=m['source'],recording=m['recording'],frame=m['frame'],hand=m['hand'],label_type=m['label_type'],joints=standard(b[i]),prototype_distance_deg=float(ds[pos]),distance_deg=float(cov['distance_deg'][i]),threshold_deg=float(cov['threshold_deg'][i]),coverage_status='uncovered',support_recordings=int(cov['support_recordings'][i]),media=r,nearest=nearest))
+                    seen.add(m['independent_unit'])
+                    if len(picked)>=(2 if level=='distinct' else 1):break
+                assert picked,(c['key'],'No paired uncovered member')
+                out[c['key']]=picked;write(dest,out);write(ROOT/'audit/coverage_media_failures.json',failures)
+                print('UNCOVERED_RGB',c['key'],[(x['source'],round(x['distance_deg'],1),round(x['prototype_distance_deg'],1)) for x in picked],flush=True)
+    write(ROOT/'audit/coverage_media_failures.json',failures)
+    unique={x['id'] for p in out.values() for x in p};refunique={x['nearest']['id'] for p in out.values() for x in p}
+    print('DONE',len(out),'classes',len(unique),'uncovered RGB hands',len(refunique),'nearest hands','nearest RGB missing',sum('media' not in x['nearest'] for p in out.values() for x in p),flush=True)
+
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/exact_coverage.py

@@ -0,0 +1,75 @@
+"""Exhaustive same-hand GT bone-angle NN and independent-recording support."""
+from cluster_common import *
+import os,time,torch
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+torch.set_num_threads(2);torch.set_grad_enabled(False)
+torch.backends.cuda.matmul.allow_tf32=False
+torch.backends.cudnn.allow_tf32=False
+QBLOCK=512;BBLOCK=8192
+
+def quantile(x,w,q):
+    order=np.argsort(x,kind='stable');x=x[order];w=w[order]
+    return float(x[min(np.searchsorted(np.cumsum(w)/w.sum(),q),len(x)-1)])
+
+def exact(q,bank,bank_indices,bank_units,query_units=None,thresholds=(),main_threshold=None):
+    device='cuda';b=torch.tensor(bank,device=device);ru=torch.tensor(bank_units,device=device,dtype=torch.int64)
+    nu=int(max(bank_units))+1;outs=collections.defaultdict(list);start=time.monotonic();last=start
+    for startq in range(0,len(q),QBLOCK):
+        qq=torch.tensor(q[startq:startq+QBLOCK],device=device);qn=len(qq)
+        min_d=torch.full((qn,),torch.inf,device=device);min_i=torch.zeros(qn,device=device,dtype=torch.int64)
+        unit_d=torch.full((qn,nu),torch.inf,device=device);sample_count=torch.zeros(qn,device=device,dtype=torch.int64)
+        qu=None if query_units is None else torch.tensor(query_units[startq:startq+QBLOCK],device=device)
+        for startb in range(0,len(b),BBLOCK):
+            bb=b[startb:startb+BBLOCK];uu=ru[startb:startb+BBLOCK]
+            dots=torch.bmm(qq.permute(1,0,2),bb.permute(1,2,0));dots.clamp_(-1,1).acos_()
+            dist=dots.mean(0)*(180/np.pi);del dots
+            if qu is not None:dist.masked_fill_(qu[:,None]==uu[None,:],torch.inf)
+            d,i=dist.min(dim=1);improve=d<min_d;min_i[improve]=i[improve]+startb;min_d=torch.minimum(min_d,d)
+            unit_d.scatter_reduce_(1,uu[None,:].expand(qn,-1),dist,reduce='amin',include_self=True)
+            if main_threshold is not None:sample_count+=(dist<=main_threshold).sum(1)
+        outs['distance_deg'].append(min_d.cpu().numpy());outs['nearest_index'].append(bank_indices[min_i.cpu().numpy()])
+        dd,ii=unit_d.topk(3,dim=1,largest=False,sorted=True)
+        outs['nearest_record_distances_deg'].append(dd.cpu().numpy());outs['nearest_record_codes'].append(ii.cpu().numpy())
+        outs['sample_count'].append(sample_count.cpu().numpy())
+        if thresholds:outs['record_counts'].append(torch.stack([(unit_d<=t).sum(1) for t in thresholds],dim=1).cpu().numpy())
+        if time.monotonic()-last>15 or startq+qn==len(q):
+            print('EXACT',startq+qn,'/',len(q),'seconds',round(time.monotonic()-start,1),flush=True);last=time.monotonic()
+    return {k:np.concatenate(v) for k,v in outs.items()}
+
+def main():
+    protocol=read(ROOT/'manifests/coverage_protocol.json');hashes=read(ROOT/'audit/frozen_protocol_sha256.json')
+    assert all(sha(ROOT/k)==v for k,v in hashes.items())
+    ours=read(BASE/'manifests/ours_members.json');external=read(BASE/'manifests/external_members.json')
+    ob=np.load(BASE/'cache/ours_features.npz')['bones'];eb=np.load(BASE/'cache/external_features.npz')['bones']
+    assert len(ours)==len(ob)==271543 and len(external)==len(eb)==214481
+    frozen=read(ROOT/'manifests/calibration_members.json');calibration={};unit_books={}
+    for side in ['left','right']:
+        bix=np.array([i for i,r in enumerate(ours) if r['hand']==side]);bank=ob[bix]
+        units=sorted(set(ours[i]['independent_unit'] for i in bix));uc={u:k for k,u in enumerate(units)}
+        ru=np.array([uc[ours[i]['independent_unit']] for i in bix]);unit_books[side]=units
+        subset=[r for r in frozen if r['hand']==side];qix=np.array([r['index'] for r in subset]);w=np.array([r['weight'] for r in subset]);qu=np.array([uc[r['unit']] for r in subset])
+        p=ROOT/f'cache/calibration_{side}.npz'
+        if not p.exists():
+            result=exact(ob[qix],bank,bix,ru,qu);np.savez_compressed(p,indices=qix,weights=w,**result)
+        a=np.load(p);d=a['distance_deg'];third=a['nearest_record_distances_deg'][:,2]
+        assert np.isfinite(d).all() and all(ours[int(n)]['independent_unit']!=ours[int(q)]['independent_unit'] for q,n in zip(qix,a['nearest_index']))
+        thresholds={f'q{int(q*100)}':quantile(d,w,q) for q in protocol['calibration']['sensitivity_quantiles']}
+        sources={}
+        for s in sorted(set(r['source'] for r in subset)):
+            mask=np.array([r['source']==s for r in subset]);ss=w[mask]
+            sources[s]=dict(n=int(mask.sum()),recordings=len(set(r['unit'] for r in subset if r['source']==s)),q50=quantile(d[mask],ss,.5),q95=quantile(d[mask],ss,.95),within_main_threshold=float(np.average(d[mask]<=thresholds['q95'],weights=ss)),stable_covered_main=float(np.average(third[mask]<=thresholds['q95'],weights=ss)))
+        calibration[side]=dict(n=len(qix),reference_n=len(bix),reference_units=len(units),thresholds_deg=thresholds,weighted_within_q95=float(np.average(d<=thresholds['q95'],weights=w)),weighted_stable_covered_q95=float(np.average(third<=thresholds['q95'],weights=w)),source_diagnostics=sources)
+    write(ROOT/'metrics/calibration.json',calibration);write(ROOT/'manifests/reference_unit_codes.json',unit_books)
+    print('CALIBRATION',calibration,flush=True)
+    # Thresholds now fixed, before looking at external query results.
+    for side in ['left','right']:
+        p=ROOT/f'cache/external_{side}_coverage.npz'
+        if p.exists():continue
+        bix=np.array([i for i,r in enumerate(ours) if r['hand']==side]);qix=np.array([i for i,r in enumerate(external) if r['hand']==side]);uc={u:k for k,u in enumerate(unit_books[side])};ru=np.array([uc[ours[i]['independent_unit']] for i in bix])
+        names=['q90','q95','q99','10deg','20deg','30deg'];tt=[calibration[side]['thresholds_deg'][k] for k in names[:3]]+[10.,20.,30.]
+        result=exact(eb[qix],ob[bix],bix,ru,thresholds=tt,main_threshold=tt[1])
+        np.savez_compressed(p,indices=qix,threshold_names=names,thresholds_deg=tt,**result)
+        print('SAVED',side,len(qix),flush=True)
+    write(ROOT/'audit/exact_engine.json',dict(torch=torch.__version__,cuda_device=torch.cuda.get_device_name(),tf32=False,query_tile=QBLOCK,bank_tile=BBLOCK,peak_gpu_MB=torch.cuda.max_memory_allocated()/1e6,method='exhaustive all same-hand reference samples; no ANN approximation',created_at_utc=datetime.datetime.now(datetime.timezone.utc).isoformat()))
+
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/freeze_coverage.py

@@ -0,0 +1,34 @@
+from cluster_common import *
+import shutil
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+assert not (ROOT/'manifests/coverage_protocol.json').exists(), 'Protocol already frozen'
+rows=read(BASE/'manifests/ours_members.json')
+seed=2026101407
+rng=np.random.default_rng(seed)
+selected=[]
+for hand in ['left','right']:
+    for source in sorted(set(m['source'] for m in rows)):
+        groups=collections.defaultdict(list)
+        for i,m in enumerate(rows):
+            if m['hand']==hand and m['source']==source:groups[m['independent_unit']].append(i)
+        units=sorted(groups)
+        queues={u:list(rng.permutation(groups[u])) for u in units}
+        order=list(rng.permutation(units));chosen=[]
+        # Round robin recordings, random frames without replacement; short units exhaust.
+        while len(chosen)<min(1024,sum(map(len,groups.values()))):
+            for u in order:
+                if queues[u]:chosen.append(int(queues[u].pop()))
+                if len(chosen)>=1024:break
+        counts=collections.Counter(rows[i]['independent_unit'] for i in chosen)
+        for i in chosen:
+            m=rows[i];selected.append(dict(index=i,id=m['id'],source=source,hand=hand,unit=m['independent_unit'],subject=str(m.get('subject')),weight=1/(4*len(counts)*counts[m['independent_unit']])))
+write(ROOT/'manifests/calibration_members.json',selected)
+protocol=dict(frozen_at_utc=datetime.datetime.now(datetime.timezone.utc).isoformat(),seed=seed,base=str(BASE),reference='All 271543 frozen three-source + EgoRenderer Ver.005 analysis members; separate hands; original source GT proxies, final canonical supervision/masks not verified',external='All 214481 frozen train members of 8 readable external WiLoR official-source 3D proxies; excludes 7600 ReInterHand test members',feature='Existing fixed 20 unit 3D bone directions in palm coordinates, 60 dimensions; wrist translation/rotation and bone length removed; no refit, no mirror pooling',distance='Mean over 20 bones of acos(clamp(dot,-1,1)), degrees; exact exhaustive float32 GPU, TF32 disabled; validate CPU float64',calibration=dict(n=len(selected),per_source_per_hand_max=1024,sampling='Seeded recording round robin, random frames without replacement; equal source and equal recording weights within each hand',exclusion='Exclude all members of the query independent recording/source action from reference, not only query frame',threshold='Separate left/right weighted 95th percentile of leave-recording/action-out nearest-reference distance; computed before external evaluation',sensitivity_quantiles=[.90,.95,.99],additional_fixed_degrees=[10,20,30]),sample_status={'covered':'nearest distance <= threshold and at least 3 distinct reference recording/source-action units within threshold','low':'nearest <= threshold but only 1 or 2 distinct reference units','uncovered':'nearest > threshold'},class_status={'covered':'at least 90% of members have covered sample status','uncovered':'zero members have nearest distance <= threshold','low':'all other classes; stacked counts retain mixed within-class outcomes'},class_badge_warning='90% display convention, not a universal biological boundary. Per-member proportions are authoritative. Uncovered means in this sampled reference only.',weights='Primary coverage is unweighted per analyzed hand-frame. Also report normalized original cluster fit-weight and equal-independent-unit sensitivity; do not claim unknown official population inclusion probabilities.',selection='For classes with uncovered members select a paired-RGB actual uncovered member close to the class prototype, preferring independent examples. Never relabel a covered prototype as uncovered.',gt_quality='Coverage is independent of GT validity. No automatic reliable/OOD/recommendation claim from distance alone.',no_changes='No reclustering, smoothing, GT refitting or source label modifications')
+write(ROOT/'manifests/coverage_protocol.json',protocol)
+paths=['manifests/ours_members.json','manifests/external_members.json','cache/ours_features.npz','cache/external_features.npz','metrics/models.json','site/index.html','site/app.js','site/data.js','site/style.css']
+for group in ['ours','external']:
+    for side in ['left','right']:
+        paths += [f'cache/{group}_{side}_{name}.npz' for name in ['assignments','distinct_assignments','fit']]
+write(ROOT/'audit/input_sha256.json',{str(BASE/p):sha(BASE/p) for p in paths})
+write(ROOT/'audit/frozen_protocol_sha256.json',{str(p.relative_to(ROOT)):sha(p) for p in [ROOT/'manifests/coverage_protocol.json',ROOT/'manifests/calibration_members.json']})
+print('FROZEN',len(selected),collections.Counter((r['source'],r['hand']) for r in selected),flush=True)

pose_recluster_distinct_20261014/downloads/coverage/code/prepare_publication.py

@@ -0,0 +1,32 @@
+from cluster_common import *
+import shutil
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+PREFIX='pose_recluster_distinct_20261014'
+def main():
+    d=json.loads((ROOT/'site/data.coverage_v2.js').read_text().removeprefix('window.AUDIT_DATA=').rstrip(';\n'))
+    refs=set()
+    for s in d['samples'].values():refs.update(s['assets'].values())
+    for vs in d['two_d'].values():
+        for s in vs:refs.update(s['assets'].values())
+    for vs in d['coverage']['examples'].values():
+        for s in vs:refs.update(s['nearest']['media']['assets'].values())
+    dest=ROOT/'site/downloads/coverage'
+    for name in ['coverage_visual_checks.json','display_selection_amendment.json','new_media_sha256.json','local_coverage_browser.json']:
+        shutil.copyfile(ROOT/'audit'/name,dest/name)
+    shutil.copytree(ROOT/'code',dest/'code',dirs_exist_ok=True,ignore=shutil.ignore_patterns('__pycache__'))
+    allowed={'.html','.js','.css','.jpg','.png','.json','.csv','.gz','.md','.py'}
+    plan=[];skip=[]
+    for p in sorted((ROOT/'site').rglob('*')):
+        if not p.is_file():continue
+        rel=str(p.relative_to(ROOT/'site'));old=BASE/'site'/rel
+        if rel.startswith('media/') and rel not in refs and not old.exists():skip.append(rel);continue
+        h=sha(p)
+        if old.is_file() and sha(old)==h:continue
+        assert p.suffix in allowed,(p,'unexpected extension')
+        assert not old.exists() or rel=='index.html',(rel,'unplanned overwrite')
+        plan.append(dict(path=PREFIX+'/'+rel,relative=rel,sha256=h,bytes=p.stat().st_size))
+    assert (ROOT/'site/index_before_coverage.html').read_bytes()==(BASE/'site/index.html').read_bytes()
+    receipt=dict(prefix=PREFIX,prior_index_sha256=sha(BASE/'site/index.html'),updated_index_sha256=sha(ROOT/'site/index.html'),archived_index=PREFIX+'/index_before_coverage.html',files=plan,n_files=len(plan),bytes=sum(p['bytes'] for p in plan),excluded_unreferenced_new_media=len(skip),authorized_update='Only the existing comparison index is changed; previous index archived and original app/data/style kept. New assets and audit downloads have independent coverage version paths.')
+    write(ROOT/'audit/publication_plan.json',receipt)
+    print('PUBLICATION_PLAN',receipt['n_files'],'files',round(receipt['bytes']/1024**2,2),'MiB','excluded',len(skip),'unreferenced derived images')
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/publish_coverage.py

@@ -0,0 +1,44 @@
+"""Publish reviewed derived coverage page; preserve old JS/data/CSS and old entry page."""
+from cluster_common import *
+import os,time,requests
+os.environ['HF_HUB_DISABLE_PROGRESS_BARS']='1'
+from huggingface_hub import HfApi,CommitOperationAdd,hf_hub_download
+from huggingface_hub.errors import HfHubHTTPError
+REPOS={'dataset':'yangzijing/nine-source-hand-data-review-results','space':'yangzijing/nine-source-hand-data-browser'}
+
+def head(kind,repo):
+    url='https://huggingface.co/api/'+('datasets' if kind=='dataset' else 'spaces')+'/'+repo+'/commits/main'
+    rr=requests.get(url,params={'audit_refresh':str(time.time_ns())},headers={'Cache-Control':'no-cache'},timeout=(10,40));rr.raise_for_status();return rr.json()
+
+def main():
+    assert read(ROOT/'audit/numerical_validation.json')['status']=='PASS'
+    assert read(ROOT/'audit/coverage_visual_checks.json')['status']=='PASS'
+    assert read(ROOT/'audit/local_coverage_browser.json')['status']=='PASS'
+    plan=read(ROOT/'audit/publication_plan.json');rp=ROOT/'audit/publication.json';receipt=read(rp) if rp.exists() else dict(url='https://yangzijing-nine-source-hand-data-browser.static.hf.space/'+plan['prefix']+'/index.html',plan_sha256=sha(ROOT/'audit/publication_plan.json'))
+    assert receipt['plan_sha256']==sha(ROOT/'audit/publication_plan.json')
+    api=HfApi();title='Show per-member coverage of external pose classes against three-source plus EgoRenderer reference'
+    for kind,repo in REPOS.items():
+        if kind in receipt:continue
+        history=head(kind,repo);before=history[0]['id']
+        old=hf_hub_download(repo,plan['prefix']+'/index.html',repo_type=kind,revision=before)
+        assert sha(old)==plan['prior_index_sha256'],'Existing entry changed since base; inspect before publishing'
+        existing=set(api.list_repo_files(repo,repo_type=kind,revision=before))
+        preserved=[p for p in ['README.md','index.html',plan['prefix']+'/app.js',plan['prefix']+'/data.js',plan['prefix']+'/style.css'] if p in existing]
+        hashes={p:sha(hf_hub_download(repo,p,repo_type=kind,revision=before)) for p in preserved}
+        ops=[]
+        for x in plan['files']:
+            local=ROOT/'site'/x['relative'];assert sha(local)==x['sha256'];ops.append(CommitOperationAdd(path_in_repo=x['path'],path_or_fileobj=str(local)))
+        recovered=False
+        try:commit=api.create_commit(repo,repo_type=kind,parent_commit=before,operations=ops,commit_message=title,num_threads=2).oid
+        except HfHubHTTPError as e:
+            if e.response is None or e.response.status_code!=504:raise
+            for attempt in range(6):
+                h=head(kind,repo)
+                if h[0]['title']==title and h[1]['id']==before:commit=h[0]['id'];recovered=True;break
+                time.sleep(5)
+            else:raise RuntimeError('504 commit outcome unresolved; do not repost without inspection') from None
+        for path,h in hashes.items():assert sha(hf_hub_download(repo,path,repo_type=kind,revision=commit))==h
+        assert sha(hf_hub_download(repo,plan['archived_index'],repo_type=kind,revision=commit))==plan['prior_index_sha256']
+        receipt[kind]=dict(repo=repo,before=before,commit=commit,files=plan['n_files'],bytes=plan['bytes'],preserved_sha256=hashes,archived_original_entry=True,client_504_recovered=recovered);write(rp,receipt);print('PUBLISHED',kind,commit,flush=True)
+    print('PUBLIC_URL',receipt['url'],flush=True)
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/update_web.py

@@ -0,0 +1,47 @@
+from cluster_common import *
+root=ROOT/'code/web'
+p=root/'index.html';s=(Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014/code/web')/p.name).read_text()
+s=s.replace('href="style.css"','href="style.coverage_v2.css"').replace('src="data.js"','src="data.coverage_v2.js"').replace('src="app.js"','src="app.coverage_v2.js"')
+s=s.replace('<small>2026.10.14</small>','<small>逐成员覆盖 · 2026.10.14</small>')
+s=s.replace('<main>','''<main>
+<section class="coverage-panel" aria-label="外部姿态覆盖"><div class="coverage-controls"><strong>WiLoR 外部类 → 我们三源＋合成</strong><label>外部类筛选 <select id="coverageFilter"><option value="all">全部</option><option value="entire">整类未覆盖</option><option value="mostly_gap">未覆盖成员 ≥50%</option><option value="mostly_covered">已覆盖成员 ≥70%</option></select></label><label>排序 <select id="coverageSort"><option value="gap">未覆盖比例高 → 低</option><option value="covered">已覆盖比例高 → 低</option><option value="id">类别编号</option></select></label></div><div id="coverageSummary"></div><p class="coverage-legend"><span class="dot covered"></span>已覆盖：≥3 段独立录制有相似姿态　<span class="dot low"></span>低覆盖：只有 1–2 段　<span class="dot uncovered"></span>未覆盖：没有相似姿态</p><p class="hint">均指本次抽样库。类别内逐成员统计；未覆盖不等于 GT 可靠，也不等于全量训练集绝对缺失。</p><details id="coverageMethod"><summary>判定依据与阈值敏感性</summary><div id="coverageMethodBody"></div></details></section>''')
+s=s.replace('点击类别查看原图、GT 叠加和最近邻。骨架可拖动旋转；A、B 编号各自独立。','点击外部类的“未覆盖成员 / 原图 / 近邻”查看具体缺口。骨架可拖动旋转；A、B 编号各自独立。')
+s=s.replace('右边是外部类；左边是它最相近的我们类。点击可看真实图例；最小样本距离另列，不能把类中心距离当覆盖结论。','右边是外部类；左边是它最相近的我们类。覆盖条来自该类全部成员；点击外部类查看未覆盖成员与各自的精确最近邻。')
+s=s.replace('<footer>','<footer><a href="downloads/coverage/RESULTS_ZH.md">覆盖结果</a><a href="downloads/coverage/member_coverage.csv.gz">逐成员覆盖 CSV</a><a href="downloads/coverage/class_coverage.csv">逐类覆盖 CSV</a><a href="downloads/coverage/REPRODUCE.md">覆盖复现</a>')
+p.write_text(s)
+p=root/'app.js';s=(Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014/code/web')/p.name).read_text()
+s=s.replace("let hand='right',level='distinct',view='oblique',tab='overview',poseSerial=0,activeDetail='';","let hand='right',level='distinct',view='oblique',tab='overview',poseSerial=0,activeDetail='',coverageFilter='all',coverageSort='gap';")
+pos=s.index('function related(c)')
+s=s[:pos]+'''const pct=x=>(100*x).toFixed(1)+'%';
+function coverageBar(v,compact=false){if(!v)return '';return `<div class="coverage-breakdown ${compact?'compact':''}"><div class="coverage-bar" role="img" aria-label="已覆盖 ${pct(v.covered_fraction)}，低覆盖 ${pct(v.low_fraction)}，未覆盖 ${pct(v.uncovered_fraction)}"><span class="covered" style="width:${v.covered_fraction*100}%"></span><span class="low" style="width:${v.low_fraction*100}%"></span><span class="uncovered" style="width:${v.uncovered_fraction*100}%"></span></div><div class="coverage-values"><span class="covered-text">已覆盖 ${pct(v.covered_fraction)}</span><span class="low-text">低覆盖 ${pct(v.low_fraction)}</span><span class="uncovered-text">未覆盖 ${pct(v.uncovered_fraction)}</span></div>${compact?'':`<p class="metric">分母 ${nf(v.n)} 个实际分析手帧：${nf(v.covered)} 已覆盖 / ${nf(v.low)} 低覆盖 / ${nf(v.uncovered)} 未覆盖。</p>`}</div>`}
+function classBadge(c){const v=c.coverage;if(!v)return '';const label=v.status==='uncovered'?'整类未覆盖':v.status==='covered'?'≥90% 已覆盖':'部分覆盖';return `<span class="coverage-badge ${v.status}" title="以逐成员比例为准；整类未覆盖要求所有成员都没有相似参照。">${label}</span>`}
+function filteredExternal(){return [...model('external')[level]].filter(c=>coverageFilter==='all'||coverageFilter==='entire'&&c.coverage.uncovered===c.n||coverageFilter==='mostly_gap'&&c.coverage.uncovered_fraction>=.5||coverageFilter==='mostly_covered'&&c.coverage.covered_fraction>=.7).sort((a,b)=>coverageSort==='id'?a.number-b.number:coverageSort==='covered'?b.coverage.covered_fraction-a.coverage.covered_fraction:b.coverage.uncovered_fraction-a.coverage.uncovered_fraction)}
+function renderCoverage(){const v=D.coverage.summary[hand],cal=D.coverage.calibration[hand],cs=model('external')[level],entire=cs.filter(c=>c.coverage.status==='uncovered').length;$('#coverageSummary').innerHTML=`<div class="coverage-summary">${coverageBar(v,true)}<span class="metric">${hand==='left'?'左':'右'}手 ${nf(v.n)} 个外部手帧 · ${cs.length} 类中 ${entire} 类整类未��盖 · 相似阈值 ${cal.thresholds_deg.q95.toFixed(2)}°</span></div>`;
+ const sens=D.coverage.sensitivity.filter(x=>x.key==='external_'+hand&&['q90','q95','q99'].includes(x.threshold));$('#coverageMethodBody').innerHTML=`<p class="hint">距离使用来源 GT 的 20 根 3D 骨方向，在掌部坐标系中计算平均夹角，去除手腕整体运动和骨长差异；左右手分开。不是 2D 图上距离，也不是 MANO 参数直接相减。</p><p class="hint">参照：H2O＋ARCTIC＋HOT3D＋EgoRenderer Ver.005，共 271,543 手帧；本侧 ${nf(cal.reference_n)} 手帧、${nf(cal.reference_units)} 段独立录制/源动作。用本侧 4,096 个内部留出样本校准，检索时排除同录制全部帧；四源及各录制均衡加权，取最近距离的 95% 分位。每个外部成员穷举检索全部同侧参照。</p><table><thead><tr><th>校准分位</th><th>阈值</th><th>已覆盖</th><th>低覆盖</th><th>未覆盖</th></tr></thead><tbody>${sens.map(x=>`<tr><td>${x.threshold==='q95'?'95%（本页）':x.threshold.slice(1)+'%'}</td><td>${x.threshold_deg.toFixed(2)}°</td><td>${pct(x.covered_fraction)}</td><td>${pct(x.low_fraction)}</td><td>${pct(x.uncovered_fraction)}</td></tr>`).join('')}</tbody></table><p class="hint">类标签：≥90% 成员已覆盖才标“≥90% 已覆盖”；所有成员无近邻才标“整类未覆盖”；其余标“部分覆盖”。90% 是显示约定，完整比例始终保留。统计分母是本轮分析样本，不是 WiLoR 官方全量；2D 来源不作 3D 覆盖判断。我们参照使用已核对训练成员的来源 GT，最终 canonical 标签和 loss mask 尚未验收。</p>`}
+function memberBadge(s){const v=s.coverage;if(!v)return '';return `<p class="sample-coverage"><span class="coverage-badge ${v.status}">${{covered:'已覆盖',low:'低覆盖',uncovered:'抽样库未覆盖'}[v.status]}</span> 最近距离 ${v.distance_deg.toFixed(2)}° / 阈值 ${v.threshold_deg.toFixed(2)}° · ${nf(v.support_recordings)} 段相似参照录制</p>`}
+function gapBlock(v){const s=D.samples[v.id],n=v.nearest;return `<article class="gap-case"><h3>未覆盖成员 · ${esc(cname(s.source))} · ${s.hand==='left'?'左手':'右手'} · 帧 ${s.frame}</h3><p class="evidence">${esc(s.recording)} · ${esc(s.label)} · 距该类原型 ${v.prototype_distance_deg.toFixed(2)}°</p>${memberBadge(s)}<div class="pair"><div><h4>外部实际成员</h4>${poseCanvas(v.joints,'该未覆盖成员的 GT 姿态')}<a href="${s.assets.full}" target="_blank"><img class="gap-image" src="${s.assets.crop}" loading="lazy" alt="外部未覆盖成员：原图与 GT 叠加"></a><p class="metric">原图 ｜ GT 叠加 · 点击看完整原画面</p></div><div><h4>我们库中的精确最近邻</h4>${poseCanvas(n.joints,'该成员的精确最近 GT 姿态')}${n.media?`<a href="${n.media.assets.full}" target="_blank"><img class="gap-image" src="${n.media.assets.crop}" loading="lazy" alt="精确最近邻：原图与 GT 叠加"></a><p class="metric">原图 ｜ GT 叠加 · 点击看完整原画面</p>`:'<p class="warning">这个精确最近邻未通过原图可读性检查，保留其 GT 骨架；没有用其他样本替代。</p>'}<p class="evidence">${esc(cname(n.source))} · ${esc(n.recording)} · 帧 ${n.frame} · ${n.hand==='left'?'左':'右'}手</p></div></div><details><summary>相机与 GT 证据</summary><p class="evidence">外部：${esc(s.camera)}。${esc(s.gt_status)}</p><p class="evidence">最近邻：${esc(n.media?.evidence?.camera??n.media_missing_reason)}。图像叠加仅供核查，覆盖距离不构成 GT 可靠性结论。</p></details></article>`}
+''' +s[pos:]
+start=s.index('function card(c)');end=s.index('function countTable()',start)
+s=s[:start]+'''function card(c){const v=c.examples[0],s=D.samples[v.id];return `<article class="card" data-key="${c.key}"><div class="card-head"><h3>${c.id}</h3><span>${classBadge(c)}${related(c)&&level==='distinct'?'<span class="badge">有相近类</span>':''}</span></div><div class="card-meta">${nf(c.n)} 手帧 · ${Object.keys(c.sources).length} 来源</div>${c.coverage?coverageBar(c.coverage,true):''}${poseCanvas(c.joints,'GT 类原型')}<img src="${s.assets.crop}" alt="${esc(cname(s.source))} 原图与 GT 叠加" loading="lazy"><div class="card-foot">${esc(cname(s.source))} · ${v.is_prototype?'原型本人':`同类图例，距原型 ${v.prototype_distance_deg.toFixed(1)}°`}</div><button class="open" data-open="${c.key}">${c.coverage?'未覆盖成员 / 原图 / 近邻':'原图 / GT 叠加 / 近邻'}</button></article>`}
+''' +s[end:]
+start=s.index('function renderOverview()');end=s.index('function pairTile(',start)
+s=s[:start]+'''function renderOverview(){countTable();for(const g of ['ours','external']){const cs=g==='external'?filteredExternal():model(g)[level];$('#'+g+'Cards').innerHTML=cs.map(card).join('')||'<p class="hint">当前筛选没有类别。</p>';$('#'+g+'Count').textContent=`${cs.length} / ${model(g)[level].length} 类`}scatter()}
+''' +s[end:]
+start=s.index('function renderCompare()');end=s.index('function renderSimilar()',start)
+s=s[:start]+'''function renderCompare(){const sorted=filteredExternal();$('#comparisonRows').innerHTML=sorted.map(c=>{const n=c.nearest_reference,a=allClasses[n.nearest_our_key];return `<div class="row"><div class="row-title"><strong>我们 ${a.id} ↔ 外部 ${c.id}</strong>${classBadge(c)}</div>${coverageBar(c.coverage)}<div class="pair">${pairTile(a)}${pairTile(c)}</div><p class="metric">类原型差 ${n.class_prototype_mean_deg.toFixed(1)}° · 外部原型最近参照 ${n.mean_deg.toFixed(1)}°（原型示意；覆盖统计来自全类成员）</p><button data-open="${c.key}">看该类未覆盖成员、原图与精确最近邻</button></div>`}).join('')||'<p class="hint">当前筛选没有类别。</p>'}
+''' +s[end:]
+s=s.replace('<th>本轮用途</th>','<th>本轮用途</th><th>对参照库覆盖</th>')
+s=s.replace('<td>${esc(s.role)}</td></tr>','<td>${esc(s.role)}</td><td>${D.coverage.sources[s.id]?coverageBar(D.coverage.sources[s.id],true):s.group===\'我们\'?\'参照库\':s.gallery_source?\'与参照重叠，不单计\':\'不作 3D 判断\'}</td></tr>')
+s=s.replace('<section class="example"><h3>','<section class="example">${memberBadge(s)}<h3>')
+s=s.replace("let head=`<h2>","let head=`<h2>")
+s=s.replace("let top=`<div class=\"dialog-top\">","head+=c.coverage?classBadge(c)+coverageBar(c.coverage):'';\n let top=`<div class=\"dialog-top\">")
+s=s.replace("$('#detailBody').innerHTML=head+top+note+c.examples.map(v=>sampleBlock(D.samples[v.id],v)).join('');", "const gaps=D.coverage.examples[key]||[],gapIds=new Set(gaps.map(v=>v.id)),other=c.examples.filter(v=>!gapIds.has(v.id));\n $('#detailBody').innerHTML=head+(c.coverage?'<details class=\"prototype-details\"><summary>类原型与其最近邻</summary>'+top+note+'</details>':top+note)+(gaps.length?'<h3 class=\"gap-title\">本类实际未覆盖成员</h3>'+gaps.map(gapBlock).join(''):'')+(other.length?'<h3>其他同类成员</h3>'+other.map(v=>sampleBlock(D.samples[v.id],v)).join(''):'');")
+s=s.replace("if(two.length)body+=", "if(D.coverage.sources[src])body+=coverageBar(D.coverage.sources[src]);\n if(two.length)body+=")
+s=s.replace("function render(){poses.clear();", "function render(){poses.clear();renderCoverage();")
+s=s.replace("$('#hand').onchange", "$('#coverageFilter').onchange=e=>{coverageFilter=e.target.value;render()};$('#coverageSort').onchange=e=>{coverageSort=e.target.value;render()};\n$('#hand').onchange")
+p.write_text(s)
+p=root/'style.css';s=(Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014/code/web')/p.name).read_text()+'''
+.coverage-panel{background:#fff;border:1px solid var(--line);border-radius:8px;padding:14px 16px;margin-bottom:16px}.coverage-controls{display:flex;align-items:center;gap:12px;flex-wrap:wrap;font-size:13px}.coverage-controls strong{margin-right:auto}.coverage-summary{display:flex;align-items:center;gap:20px;flex-wrap:wrap;margin-top:12px}.coverage-summary .coverage-breakdown{flex:1;min-width:260px}.coverage-bar{height:9px;display:flex;border-radius:5px;overflow:hidden;background:#e7edf2;width:100%;min-width:100px}.covered{background:#218762}.low{background:#c88926}.uncovered{background:#ca5054}.dot{width:9px;height:9px;display:inline-block;border-radius:50%;margin-right:3px}.coverage-values{display:flex;gap:9px;justify-content:space-between;font-size:11px;flex-wrap:wrap;margin-top:4px}.covered-text{color:#146847}.low-text{color:#8e5706}.uncovered-text{color:#a73139}.coverage-legend{font-size:12px;margin:10px 0 4px}.coverage-badge{display:inline-block;padding:2px 7px;border-radius:5px;font-size:11px;white-space:nowrap}.coverage-badge.covered{background:#e3f3eb;color:#146847}.coverage-badge.low{background:#fff1d9;color:#8e5706}.coverage-badge.uncovered{background:#fce7e8;color:#a73139}.card .coverage-breakdown{padding:4px 11px 7px}.card-head>span{display:flex;gap:3px;flex-wrap:wrap;justify-content:flex-end}.coverage-panel details{margin-top:8px;padding:7px 10px;font-size:12px;background:#fafcfe}.coverage-panel .hint{margin:7px 0}.gap-case{border:1px solid #ead4d5;background:#fffafa;padding:16px;border-radius:8px;margin:14px 0}.gap-case h3{font-size:16px;margin:0 0 5px}.gap-case h4{font-size:13px;margin:10px 0 5px}.gap-case details{font-size:12px;padding:7px;margin-top:10px}.gap-case .pair .gap-image{width:100%;height:220px;max-height:none;object-fit:contain;background:#f0f3f6}.sample-coverage{font-size:12px;color:var(--muted);line-height:1.9}.gap-title{color:#a73139;border-top:1px solid var(--line);padding-top:16px}.scroll .coverage-breakdown{min-width:230px}.coverage-breakdown .metric{margin:5px 0}.coverage-panel summary{font-size:12px}.row .coverage-breakdown{max-width:720px;margin-bottom:12px}
+@media(max-width:620px){.coverage-panel{padding:11px}.coverage-controls{gap:8px}.coverage-controls strong{width:100%}.coverage-summary{gap:8px}.card .coverage-breakdown{padding:3px 7px 6px}.card .coverage-values{font-size:9px;gap:1px;display:grid;grid-template-columns:1fr}.card-head{gap:4px;align-items:flex-start}.card-head>span{max-width:80%}.gap-case{padding:9px}.gap-case .pair{grid-template-columns:1fr}.gap-case .pair .gap-image{height:auto;max-height:300px}.coverage-legend{line-height:1.9}.coverage-controls select{max-width:200px}}
+''';p.write_text(s)
+print('UPDATED',root)

pose_recluster_distinct_20261014/downloads/coverage/code/validate_coverage.py

@@ -0,0 +1,35 @@
+from cluster_common import *
+BASE=Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014')
+def main():
+    data=np.load(ROOT/'cache/external_coverage.npz');classes=read(ROOT/'metrics/class_coverage.json');rows=read(BASE/'manifests/external_members.json');ours=read(BASE/'manifests/ours_members.json')
+    ob=np.load(BASE/'cache/ours_features.npz')['bones'];eb=np.load(BASE/'cache/external_features.npz')['bones'];rng=np.random.default_rng(2026101408);checks=[]
+    for side in ['left','right']:
+        bx=np.array([i for i,m in enumerate(ours) if m['hand']==side]);qx=np.array([i for i,m in enumerate(rows) if m['hand']==side]);bank=ob[bx].astype(np.float64)
+        units=[ours[i]['independent_unit'] for i in bx];unames=sorted(set(units));umap={u:i for i,u in enumerate(unames)};uc=np.array([umap[u] for u in units])
+        boundary=qx[np.argsort(np.abs(data['distance_deg'][qx]-data['threshold_deg'][qx]))[:4]]
+        samples=list(map(int,rng.choice(qx,12,replace=False)))+list(map(int,boundary))
+        for i in samples:
+            d=np.degrees(np.arccos(np.clip(np.einsum('njk,jk->nj',bank,eb[i].astype(np.float64)),-1,1))).mean(-1)
+            j=int(np.argmin(d));err=abs(float(d[j])-data['distance_deg'][i]);assert err<.01,(i,err)
+            nn=int(data['nearest_index'][i]);assert abs(float(d[np.where(bx==nn)[0][0]])-float(d[j]))<.01
+            tau=float(data['threshold_deg'][i]);records=len(set(uc[d<=tau]));count=int((d<=tau).sum());status=2 if d[j]>tau else (0 if records>=3 else 1)
+            margin=float(np.min(abs(d-tau)));ambiguous=margin<.002
+            if not ambiguous:assert records==data['support_recordings'][i] and count==data['support_samples'][i]
+            assert status==data['status'][i],(i,'classification mismatch')
+            checks.append(dict(index=i,id=rows[i]['id'],hand=side,max_nn_error_deg=err,cpu_nearest_index=int(bx[j]),gpu_nearest_index=nn,cpu_support_recordings=records,gpu_support_recordings=int(data['support_recordings'][i]),cpu_support_samples=count,gpu_support_samples=int(data['support_samples'][i]),threshold_boundary_margin_deg=margin,numerically_ambiguous_counts=ambiguous,status_agrees=True))
+    # Numerical comparison with existing independently calculated exact class-prototype NN.
+    models=read(BASE/'metrics/models.json');proto=[]
+    for side in ['left','right']:
+        for level in ['fine','distinct']:
+            for c in models['external_'+side][level]:
+                i=c['prototype_index'];err=abs(data['distance_deg'][i]-c['nearest_reference']['mean_deg']);assert err<.01,(c['key'],err)
+                proto.append(float(err))
+    assert sum(c['n'] for c in classes.values() if c['level']=='distinct')==len(rows)
+    assert sum(c['n'] for c in classes.values() if c['level']=='fine')==len(rows)
+    assert all(c['covered']+c['low']+c['uncovered']==c['n'] for c in classes.values())
+    assert all(ours[int(nn)]['hand']==r['hand'] for r,nn in zip(rows,data['nearest_index']))
+    hashes=read(ROOT/'audit/input_sha256.json');assert all(sha(p)==h for p,h in hashes.items()),'Input changed'
+    report=dict(status='PASS',n_queries=len(rows),same_hand_all=True,all_class_counts_valid=True,input_hashes_unchanged=True,cpu_float64_checks=checks,cpu_nn_max_error_deg=max(c['max_nn_error_deg'] for c in checks),previous_exact_prototype_checks=len(proto),previous_exact_prototype_max_error_deg=max(proto),limitations='Float32 acos counts extremely close to the threshold can change under rounding; boundary examples were checked and sample statuses agree. No GT accuracy verdict derived from nearest-neighbor distance.')
+    write(ROOT/'audit/numerical_validation.json',report);print({k:v for k,v in report.items() if k!='cpu_float64_checks'})
+
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/verify_public_assets.py

@@ -0,0 +1,38 @@
+"""Check permanent public URLs, bytes and archived previous entry after publication."""
+from cluster_common import *
+import requests,time,concurrent.futures,re
+BASE_URL='https://yangzijing-nine-source-hand-data-browser.static.hf.space/'
+
+def get_verified(item):
+    url=BASE_URL+item['path'];events=[]
+    for attempt in range(4):
+        try:
+            r=requests.get(url,timeout=(10,50));r.raise_for_status();data=r.content
+            if item['path'].endswith('.html'):
+                # Static Spaces adds only this known runtime creator-ID script.
+                data=re.sub(rb'<script>window\.huggingface=\{variables:\{"SPACE_CREATOR_USER_ID":"[0-9a-f]{24}"\}\};</script>',b'',data)
+            actual=hashlib.sha256(data).hexdigest();assert actual==item['sha256'],'SHA256 mismatch'
+            return dict(path=item['path'],status=200,sha256=actual,bytes=len(data),retries=attempt,events=events)
+        except Exception as e:
+            events.append(dict(attempt=attempt,error=type(e).__name__,http_status=getattr(getattr(e,'response',None),'status_code',None)))
+            if attempt==3:return dict(path=item['path'],error=type(e).__name__+': '+str(e)[:180],events=events)
+            time.sleep(2*(attempt+1))
+
+def main():
+    plan=read(ROOT/'audit/publication_plan.json');receipt=read(ROOT/'audit/publication.json');assert 'space' in receipt
+    entry=next(x for x in plan['files'] if x['relative']=='index.html')
+    for attempt in range(36):
+        r=requests.get(BASE_URL+entry['path'],params={'coverage_revision':receipt['space']['commit']},timeout=(10,40))
+        if r.status_code==200 and 'data.coverage_v2.js' in r.text:break
+        time.sleep(5)
+    else:raise RuntimeError('New static entry not visible after deployment wait')
+    checks=[]
+    with concurrent.futures.ThreadPoolExecutor(2) as pool:
+        fs={pool.submit(get_verified,item):item for item in plan['files']}
+        for f in concurrent.futures.as_completed(fs):
+            checks.append(f.result())
+            if len(checks)%50==0:print('PUBLIC_CHECKED',len(checks),'/',len(fs),flush=True)
+    bad=[r for r in checks if 'error' in r];out=dict(status='PASS' if not bad else 'FAIL',url=receipt['url'],commit=receipt['space']['commit'],files=len(checks),failures=bad,retried=sum(r.get('retries',0)>0 for r in checks),checks=checks)
+    write(ROOT/'audit/public_assets.json',out);print('PUBLIC_ASSETS',out['status'],out['files'],'failed',len(bad),flush=True)
+    assert not bad,str(bad[:3])
+if __name__=='__main__':main()

pose_recluster_distinct_20261014/downloads/coverage/code/visualize.py

@@ -0,0 +1,166 @@
+"""Paired RGB and calibrated GT overlay for members of the newly fitted clusters."""
+from cluster_common import *
+import sys,functools,pickle,zipfile,shutil
+from PIL import Image
+import cv2
+cv2.setNumThreads(1)
+
+sys.path.insert(0,str(REF/'code'))
+import visuals as legacy
+import interhand_rgb
+legacy.ROOT=ROOT;interhand_rgb.ROOT=ROOT
+from geometry import project
+import torch,smplx
+torch.set_num_threads(1);torch.set_grad_enabled(False)
+
+EXTRA=Path('/SSD3/yangzijing/Hand_missing_source_visuals_20261012')
+COLOR=[(60,180,210),(80,170,40),(230,100,40),(180,80,180),(80,80,230)]
+
+@functools.lru_cache(2)
+def mano(side):
+    m=smplx.MANO('/SSD3/jiasen/foundation_hand/pretrained/mano',is_rhand=side=='right',use_pca=False,flat_hand_mean=True).eval()
+    if side=='left' and torch.abs(m.shapedirs[:,0,:]-mano('right').shapedirs[:,0,:]).sum()<1:m.shapedirs[:,0,:]*=-1
+    return m
+@functools.lru_cache(2)
+def arc(path):return zipfile.ZipFile(path)
+def blob(path):
+    if '::' in path:
+        a,b=path.split('::',1);return arc(a).read(b)
+    return Path(path).read_bytes()
+
+def ho_image(m):
+    source=blob(m['annotation_path']);assert hashlib.sha256(source).hexdigest()==m['annotation_sha256']
+    d=pickle.loads(source,encoding='latin1');field='hand' if m['source']=='wilor_ho3d' else m['hand']+'Hand'
+    p=np.asarray(d[field+'Pose']);t=np.asarray(d[field+'Trans']);beta=np.asarray(d['handBeta']);model=mano(m['hand'])
+    tensor=lambda a:torch.as_tensor(np.asarray(a).copy()[None],dtype=torch.float32)
+    out=model(global_orient=tensor(p[:3]),hand_pose=tensor(p[3:]),betas=tensor(beta),transl=tensor(t));v=out.vertices[0].numpy()
+    err=float(np.linalg.norm(out.joints[0,:16].numpy()-np.asarray(d[field+'Joints3D'])[:16],axis=-1).max()*1000);assert err<.02
+    v=v*np.array([1,-1,-1]);K=np.asarray(d['camMat']);rgb=blob(m['image_path']);im=cv2.imdecode(np.frombuffer(rgb,np.uint8),cv2.IMREAD_COLOR);assert im is not None
+    uv=project(v,K);mask=(v[:,2]>0)&(uv[:,0]>=0)&(uv[:,0]<im.shape[1])&(uv[:,1]>=0)&(uv[:,1]<im.shape[0]);assert mask.sum()>=500,'target mostly out of image'
+    vertices=np.full((2,778,3),np.nan);side=['left','right'].index(m['hand']);vertices[side]=v;valid=[False,False];valid[side]=True
+    overlay,pixels=legacy.renderer.media.render_overlay(im,vertices,valid,lambda pts:project(pts,K));box,inside=legacy.renderer.bbox(uv,im.shape[:2]);assert inside
+    raw=Image.fromarray(im[...,::-1]);ov=Image.fromarray(overlay[...,::-1]);full=Image.new('RGB',(raw.width*2,raw.height));full.paste(raw);full.paste(ov,(raw.width,0))
+    ev=dict(camera='same-source unmodified camMat; OpenGL->OpenCV diag(1,-1,-1); original full image',K=K.tolist(),source_rgb_sha256=hashlib.sha256(rgb).hexdigest(),annotation_sha256=m['annotation_sha256'],source_reconstruction_max_mm=err,independent_3d_reference=False,crop_native_xyxy=list(box),target_in_frame=True,native_hw=list(im.shape[:2]),mesh_pixels=pixels)
+    return full,raw.crop(box),ov.crop(box),ev
+
+@functools.lru_cache(10)
+def reinter_joints(path):
+    z=np.load(path);return {int(f):j for f,j in zip(z['frames'],z['joints_mm'])}
+def joint_pair(im,uv,mask,evidence):
+    ov=im.copy();mask=mask & np.isfinite(uv).all(-1)&(uv[:,0]>=0)&(uv[:,0]<im.shape[1])&(uv[:,1]>=0)&(uv[:,1]<im.shape[0]);assert mask.sum()>=12,'too few target joints in frame'
+    for k in range(1,21):
+        parent=int(PARENTS[k]);color=COLOR[(k-1)//4]
+        if mask[k] and mask[parent]:cv2.line(ov,tuple(np.rint(uv[parent]).astype(int)),tuple(np.rint(uv[k]).astype(int)),color,max(2,round(im.shape[0]/500)),cv2.LINE_AA)
+    for k in np.flatnonzero(mask):cv2.circle(ov,tuple(np.rint(uv[k]).astype(int)),max(2,round(im.shape[0]/500)),(255,255,255),-1,cv2.LINE_AA)
+    box,ok=legacy.renderer.bbox(uv[mask],im.shape[:2]);assert ok
+    raw=Image.fromarray(im[...,::-1]);over=Image.fromarray(ov[...,::-1]);full=Image.new('RGB',(raw.width*2,raw.height));full.paste(raw);full.paste(over,(raw.width,0))
+    evidence.update(crop_native_xyxy=list(box),target_in_frame=True,native_hw=list(im.shape[:2]),joints_drawn=int(mask.sum()))
+    return full,raw.crop(box),over.crop(box),evidence
+
+@functools.lru_cache(1)
+def reinter_index():return read(ROOT/'manifests/reinter_rgb.json')
+def reinter_image(m):
+    for item in reinter_index().get(m['id'],[]):
+        camera=read(item['camera_file'])[item['camera']];j=reinter_joints(m['annotation_path'])[m['frame']][['left','right'].index(m['hand']),m['source_joint_mapping_24_to_21']]
+        xyz=j@np.asarray(camera['R']).T+np.asarray(camera['t']);f=np.asarray(camera['focal']);pp=np.asarray(camera['princpt']);uv=xyz[:,:2]/xyz[:,2:]*f+pp;K=np.array([[f[0],0,pp[0]],[0,f[1],pp[1]],[0,0,1.]])
+        check=cv2.projectPoints(xyz,np.zeros(3),np.zeros(3),K,np.zeros(5))[0][:,0];assert abs(check-uv).max()<1e-5
+        im=cv2.imread(item['path']);assert im is not None and sha(item['path'])==item['image_sha256']
+        e=dict(camera='official Mugsy camera R*X+t, native image pixels, no additional distortion by source renderer',K=K.tolist(),camera_id=item['camera'],camera_sha256=item['camera_sha256'],source_rgb_sha256=item['image_sha256'],source_rgb_member=item['member'],annotation_sha256=m['annotation_sha256'],projection_crosscheck_max_px=float(abs(check-uv).max()),label='official original-fit renderer3D joints; no MANO fit',joint_visibility_independent=False)
+        try:return joint_pair(im,uv,xyz[:,2]>0,e)
+        except AssertionError:continue
+    raise ValueError('No readable selected ReInterHand view')
+
+@functools.lru_cache(1)
+def ih_index():return read(ROOT/'manifests/interhand_rgb.json')
+@functools.lru_cache(1)
+def existing():
+    result={}
+    for p in (REF/'audit/media').glob('*.json'):
+        if p.name.endswith('_acquisition.json'):continue
+        r=read(p)
+        if r.get('assets') and all((REF/'site'/v).exists() for v in r['assets'].values()):result[r['id']]=r
+    return result
+
+def paired(m):
+    if m['id'] in existing():return True
+    if m['source'] in ['wilor_rhd','wilor_freihand']:return local_image(m) is not None
+    if m['source']=='wilor_interhand26m':return m['id'] in ih_index()
+    if m['source']=='wilor_reinter':return m['id'] in reinter_index()
+    if m['source']=='wilor_mtc':
+        c=legacy.extras().get((m['source'],str(m.get('subject')),int(m['frame']),m['hand']))
+        return c is not None and c['recording']==m['sequence']
+    return True
+
+def local_image(m):
+    src=m['source'];rel=f"training/rgb/{m['frame']:08d}.jpg" if src=='wilor_freihand' else f"RHD_published_v2/training/color/{m['frame']:05d}.png"
+    paths=[root/'sources'/src/rel for root in [ROOT,Path('/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014'),REF,OLD]]
+    paths.append(legacy.OLD/'sources'/('FREIHAND' if src=='wilor_freihand' else 'RHD')/rel)
+    return next((p for p in paths if p.exists()),None)
+
+def save(m):
+    p=ROOT/'audit/media'/f"{m['id']}.json"
+    if p.exists():return read(p)
+    if m['id'] in existing():
+        old=existing()[m['id']];r=dict(old);r['reused_from']=str(REF);r['assets']={}
+        for kind,rel in old['assets'].items():
+            dst=ROOT/'site/media'/Path(rel).name;shutil.copyfile(REF/'site'/rel,dst);r['assets'][kind]='media/'+dst.name
+        write(p,r);return r
+    if m['source'] in ['wilor_ho3d','wilor_h2o3d']:full,raw,ov,e=ho_image(m)
+    elif m['source']=='wilor_reinter':full,raw,ov,e=reinter_image(m)
+    elif m['source'] in ['wilor_rhd','wilor_freihand']:
+        mm=dict(m);mm['image_path']=str(local_image(m));assert local_image(m) is not None
+        if m['source']=='wilor_freihand':
+            K,t,j=legacy.fh_arrays();_,jj,_=legacy.freihand_decode(t[m['frame']],K[m['frame']]);mm['native_joint_mean_error_mm']=float(np.linalg.norm(jj[0]-j[m['frame']],axis=-1).mean()*1000)
+        full,raw,ov,e=legacy.renderer.native(mm)
+    else:full,raw,ov,e=legacy.image_for(m)
+    assert e.get('target_in_frame') and raw.size==ov.size and min(raw.size)>=24,'target not readable'
+    change=np.abs(np.asarray(raw,dtype=float)-np.asarray(ov,dtype=float)).max(-1)>14;assert change.sum()>=12,'overlay absent at target'
+    nonblack=float((np.asarray(raw).max(-1)[change]>15).mean());assert nonblack>=.55,'projection mainly black padding'
+    if 'in_frame_vertices_per_hand' in e:assert e['in_frame_vertices_per_hand'][['left','right'].index(m['hand'])]>=500,'target mostly out of image'
+    pair=Image.new('RGB',(raw.width*2,raw.height));pair.paste(raw);pair.paste(ov,(raw.width,0));assets={}
+    for kind,im in [('full',full),('crop',pair)]:
+        dest=ROOT/'site/media'/f"{m['id']}_{kind}.jpg";im.save(dest,quality=90);assets[kind]='media/'+dest.name
+    label='EgoRenderer Ver.005 既有拟合 MANO' if m['source']=='synthetic' else ('来源 GT 关节投影' if m['source'] in ['wilor_reinter','wilor_interhand26m','wilor_mtc','wilor_rhd'] else '来源拟合 MANO')
+    r=dict(id=m['id'],assets=assets,evidence=e,label=label,gt_status='证据不足',legibility=dict(changed_pixels=int(change.sum()),non_black_GT_pixel_fraction=nonblack),sha256={v:sha(ROOT/'site'/v) for v in assets.values()})
+    write(p,r);return r
+
+def setup():
+    p=ROOT/'manifests/interhand_rgb.json'
+    if not p.exists():
+        data=read(REF/'manifests/interhand_rgb.json')
+        (ROOT/'sources/interhand_rgb').mkdir(exist_ok=True)
+        for sid,x in data.items():
+            if not Path(x['path']).exists():x['path']=str(ROOT/'sources/interhand_rgb'/f'{sid}.jpg')
+        write(p,data)
+
+def main(group,level_arg=None):
+    setup();rows,b=load(group);result={};failures=[];failed=set();dest=ROOT/'metrics'/f'{group}_visuals.json'
+    exclusions=ROOT/'audit/visual_review_exclusions.json'
+    if exclusions.exists():failed.update(read(exclusions))
+    preference_path=ROOT/'manifests/visual_review_preferences.json'
+    preferences=read(preference_path) if preference_path.exists() else {}
+    if dest.exists():result=read(dest)
+    for side in ['left','right']:
+        model=read(ROOT/'metrics'/f'{group}_{side}.json');a=np.load(ROOT/'cache'/f'{group}_{side}_assignments.npz');ix=a['indices']
+        for level in ([level_arg] if level_arg else ['main','fine']):
+            label=np.load(ROOT/'cache'/f'{group}_{side}_distinct_assignments.npz')['distinct'] if level=='distinct' else a[level]
+            for c in model[level]:
+                if c['key'] in result:continue
+                ids=ix[label==c['label']];distance=angular(b[[c['prototype_index']]],b[ids])[0];order=np.argsort(distance,kind='stable');picked=[];units=set()
+                if c['key'] in preferences:
+                    preferred=preferences[c['key']]['sample_ids'];positions={rows[int(i)]['id']:pos for pos,i in enumerate(ids)}
+                    assert all(sid in positions for sid in preferred),'Visual preference must stay in the actual GT class'
+                    lead=[positions[sid] for sid in preferred];order=np.array(lead+[int(p) for p in order if int(p) not in lead])
+                for pos in order:
+                    i=int(ids[pos]);m=rows[i]
+                    if m['id'] in failed or m['independent_unit'] in units or not paired(m):continue
+                    try:r=save(m)
+                    except Exception as e:
+                        failures.append(dict(key=c['key'],id=m['id'],source=m['source'],error=type(e).__name__+': '+str(e)[:220]));failed.add(m['id']);write(ROOT/'audit'/f'{group}_media_failures.json',failures);continue
+                    picked.append(dict(index=i,id=m['id'],source=m['source'],recording=m['recording'],frame=m['frame'],hand=m['hand'],label_type=m['label_type'],prototype_distance_deg=float(distance[pos]),is_prototype=i==c['prototype_index'],joints=standard(b[i]),media=r));units.add(m['independent_unit'])
+                    if len(picked)>=2:break
+                assert picked,(c['key'],'No same-cluster paired image')
+                result[c['key']]=picked;write(dest,result);print('VISUAL',c['key'],[(p['source'],round(p['prototype_distance_deg'],2)) for p in picked],flush=True)
+    write(ROOT/'audit'/f'{group}_media_failures.json',failures)
+
+if __name__=='__main__':main(*sys.argv[1:])

pose_recluster_distinct_20261014/downloads/coverage/code/web/app.js

@@ -0,0 +1,61 @@
+const D=window.AUDIT_DATA;
+// Retry transient failures on the image CDN while keeping permanent source URLs.
+document.addEventListener('error',e=>{const im=e.target;if(im?.tagName!=='IMG')return;const n=Number(im.dataset.networkRetry||0);if(n<2){im.dataset.networkRetry=String(n+1);setTimeout(()=>{if(im.isConnected)im.src=im.getAttribute('src')},600*(n+1))}},true);
+const $=s=>document.querySelector(s), nf=n=>Number(n).toLocaleString('en-US'), esc=s=>String(s??'').replace(/[&<>"']/g,c=>({'&':'&amp;','<':'&lt;','>':'&gt;','"':'&quot;',"'":'&#39;'}[c]));
+let hand='right',level='distinct',view='oblique',tab='overview',poseSerial=0,activeDetail='',coverageFilter='all',coverageSort='gap';
+const colors=['#b8852f','#39896e','#437eb0','#9061ab','#c66e70'];
+const poses=new Map(), allClasses={};
+Object.values(D.models).forEach(m=>['fine','distinct'].forEach(l=>m[l].forEach(c=>allClasses[c.key]=c)));
+const model=g=>D.models[g+'_'+hand], cname=s=>D.names[s]||s;
+function poseCanvas(joints,label='',overlay=null){const id='pose'+poseSerial++;poses.set(id,{joints,overlay,rot:[0,0],label});return `<canvas class="pose" id="${id}" aria-label="${esc(label)}，可拖动旋转"></canvas>`}
+function drawPose(canvas,p){
+ const ratio=Math.min(devicePixelRatio||1,2),w=canvas.clientWidth||300,h=canvas.clientHeight||170;canvas.width=w*ratio;canvas.height=h*ratio;const ctx=canvas.getContext('2d');ctx.scale(ratio,ratio);ctx.clearRect(0,0,w,h);
+ let base=view==='palm'?[0,0]:view==='side'?[Math.PI/2,0]:[.48,-.16],a=base[0]+p.rot[0],b=base[1]+p.rot[1];
+ function project(j){let [x,y,z]=j;const xx=x*Math.cos(a)+z*Math.sin(a),zz=-x*Math.sin(a)+z*Math.cos(a);return [xx,y*Math.cos(b)-zz*Math.sin(b),y*Math.sin(b)+zz*Math.cos(b)]}
+ const pts=p.joints.map(project),other=p.overlay?.map(project),all=other?pts.concat(other):pts;let x0=Math.min(...all.map(v=>v[0])),x1=Math.max(...all.map(v=>v[0])),y0=Math.min(...all.map(v=>v[1])),y1=Math.max(...all.map(v=>v[1]));let scale=Math.min((w-45)/Math.max(x1-x0,.7),(h-40)/Math.max(y1-y0,.8));
+ const screen=q=>[w/2+(q[0]-(x0+x1)/2)*scale,h/2-(q[1]-(y0+y1)/2)*scale+3];
+ function handdraw(j,faded){ctx.globalAlpha=faded?.35:1;for(let f=0;f<5;f++){const ids=[0,f*4+1,f*4+2,f*4+3,f*4+4];ctx.strokeStyle=faded?'#7b8492':colors[f];ctx.fillStyle=ctx.strokeStyle;ctx.lineWidth=faded?2:3.2;ctx.beginPath();ids.forEach((id,k)=>{const q=screen(j[id]);k?ctx.lineTo(...q):ctx.moveTo(...q)});ctx.stroke();ids.slice(1).forEach(id=>{const q=screen(j[id]);ctx.beginPath();ctx.arc(...q,2.6,0,Math.PI*2);ctx.fill()})}ctx.globalAlpha=1}
+ if(other)handdraw(other,true);handdraw(pts,false);ctx.fillStyle='#7e8b99';ctx.font='10px system-ui';ctx.fillText(p.label,9,14);
+}
+function wirePoses(){document.querySelectorAll('canvas.pose').forEach(c=>{const p=poses.get(c.id);if(!p)return;drawPose(c,p);let drag=null;c.onpointerdown=e=>{drag=[e.clientX,e.clientY];c.setPointerCapture(e.pointerId)};c.onpointermove=e=>{if(!drag)return;p.rot[0]+=(e.clientX-drag[0])*.012;p.rot[1]+=(e.clientY-drag[1])*.012;drag=[e.clientX,e.clientY];drawPose(c,p)};c.onpointerup=()=>drag=null;})}
+const pct=x=>(100*x).toFixed(1)+'%';
+function coverageBar(v,compact=false){if(!v)return '';return `<div class="coverage-breakdown ${compact?'compact':''}"><div class="coverage-bar" role="img" aria-label="已覆盖 ${pct(v.covered_fraction)}，低覆盖 ${pct(v.low_fraction)}，未覆盖 ${pct(v.uncovered_fraction)}"><span class="covered" style="width:${v.covered_fraction*100}%"></span><span class="low" style="width:${v.low_fraction*100}%"></span><span class="uncovered" style="width:${v.uncovered_fraction*100}%"></span></div><div class="coverage-values"><span class="covered-text">已覆盖 ${pct(v.covered_fraction)}</span><span class="low-text">低覆盖 ${pct(v.low_fraction)}</span><span class="uncovered-text">未覆盖 ${pct(v.uncovered_fraction)}</span></div>${compact?'':`<p class="metric">分母 ${nf(v.n)} 个实际分析手帧：${nf(v.covered)} 已覆盖 / ${nf(v.low)} 低覆盖 / ${nf(v.uncovered)} 未覆盖。</p>`}</div>`}
+function classBadge(c){const v=c.coverage;if(!v)return '';const label=v.status==='uncovered'?'整类未覆盖':v.status==='covered'?'≥90% 已覆盖':'部分覆盖';return `<span class="coverage-badge ${v.status}" title="以逐成员比例为准；整类未覆盖要求所有成员都没有相似参照。">${label}</span>`}
+function filteredExternal(){return [...model('external')[level]].filter(c=>coverageFilter==='all'||coverageFilter==='entire'&&c.coverage.uncovered===c.n||coverageFilter==='mostly_gap'&&c.coverage.uncovered_fraction>=.5||coverageFilter==='mostly_covered'&&c.coverage.covered_fraction>=.7).sort((a,b)=>coverageSort==='id'?a.number-b.number:coverageSort==='covered'?b.coverage.covered_fraction-a.coverage.covered_fraction:b.coverage.uncovered_fraction-a.coverage.uncovered_fraction)}
+function renderCoverage(){const v=D.coverage.summary[hand],cal=D.coverage.calibration[hand],cs=model('external')[level],entire=cs.filter(c=>c.coverage.status==='uncovered').length;$('#coverageSummary').innerHTML=`<div class="coverage-summary">${coverageBar(v,true)}<span class="metric">${hand==='left'?'左':'右'}手 ${nf(v.n)} 个外部手帧 · ${cs.length} 类中 ${entire} 类整类未覆盖 · 相似阈值 ${cal.thresholds_deg.q95.toFixed(2)}°</span></div>`;
+ const sens=D.coverage.sensitivity.filter(x=>x.key==='external_'+hand&&['q90','q95','q99'].includes(x.threshold));$('#coverageMethodBody').innerHTML=`<p class="hint">距离使用来源 GT 的 20 根 3D 骨方向，在掌部坐标系中计算平均夹角，去除手腕整体运动和骨长差异；左右手分开。不是 2D 图上距离，也不是 MANO 参数直接相减。</p><p class="hint">参照：H2O＋ARCTIC＋HOT3D＋EgoRenderer Ver.005，共 271,543 手帧；本侧 ${nf(cal.reference_n)} 手帧、${nf(cal.reference_units)} 段独立录制/源动作。用本侧 4,096 个内部留出样本校准，检索时排除同录制全部帧；四源及各录制均衡加权，取最近距离的 95% 分位。每个外部成员穷举检索全部同侧参照。</p><table><thead><tr><th>校准分位</th><th>阈值</th><th>已覆盖</th><th>低覆盖</th><th>未覆盖</th></tr></thead><tbody>${sens.map(x=>`<tr><td>${x.threshold==='q95'?'95%（本页）':x.threshold.slice(1)+'%'}</td><td>${x.threshold_deg.toFixed(2)}°</td><td>${pct(x.covered_fraction)}</td><td>${pct(x.low_fraction)}</td><td>${pct(x.uncovered_fraction)}</td></tr>`).join('')}</tbody></table><p class="hint">类标签：≥90% 成员已覆盖才标“≥90% 已覆盖”；所有成员无近邻才标“整类未覆盖”；其余标“部分覆盖”。90% 是显示约定，完整比例始终保留。统计分母是本轮分析样本，不是 WiLoR 官方全量；2D 来源不作 3D 覆盖判断。我们参照使用已核对训练成员的来源 GT，最终 canonical 标签和 loss mask 尚未验收。</p>`}
+function memberBadge(s){const v=s.coverage;if(!v)return '';return `<p class="sample-coverage"><span class="coverage-badge ${v.status}">${{covered:'已覆盖',low:'低覆盖',uncovered:'抽样库未覆盖'}[v.status]}</span> 最近距离 ${v.distance_deg.toFixed(2)}° / 阈值 ${v.threshold_deg.toFixed(2)}° · ${nf(v.support_recordings)} 段相似参照录制</p>`}
+function gapBlock(v){const s=D.samples[v.id],n=v.nearest;return `<article class="gap-case"><h3>未覆盖成员 · ${esc(cname(s.source))} · ${s.hand==='left'?'左手':'右手'} · 帧 ${s.frame}</h3><p class="evidence">${esc(s.recording)} · ${esc(s.label)} · 距该类原型 ${v.prototype_distance_deg.toFixed(2)}°</p>${memberBadge(s)}<div class="pair"><div><h4>外部实际成员</h4>${poseCanvas(v.joints,'该未覆盖成员的 GT 姿态')}<a href="${s.assets.full}" target="_blank"><img class="gap-image" src="${s.assets.crop}" loading="lazy" alt="外部未覆盖成员：原图与 GT 叠加"></a><p class="metric">原图 ｜ GT 叠加 · 点击看完整原画面</p></div><div><h4>我们库中的精确最近邻</h4>${poseCanvas(n.joints,'该成员的精确最近 GT 姿态')}${n.media?`<a href="${n.media.assets.full}" target="_blank"><img class="gap-image" src="${n.media.assets.crop}" loading="lazy" alt="精确最近邻：原图与 GT 叠加"></a><p class="metric">原图 ｜ GT 叠加 · 点击看完整原画面</p>`:'<p class="warning">这个精确最近邻未通过原图可读性检查，保留其 GT 骨架；没有用其他样本替代。</p>'}<p class="evidence">${esc(cname(n.source))} · ${esc(n.recording)} · 帧 ${n.frame} · ${n.hand==='left'?'左':'右'}手</p></div></div><details><summary>相机与 GT 证据</summary><p class="evidence">外部：${esc(s.camera)}。${esc(s.gt_status)}</p><p class="evidence">最近邻：${esc(n.media?.evidence?.camera??n.media_missing_reason)}。图像叠加仅供核查，覆盖距离不构成 GT 可靠性结论。</p></details></article>`}
+function related(c){return D.pairs.some(p=>p.related && p.group===c.key.split('_')[0]&&p.hand===hand&&(p.a===c.id||p.b===c.id))}
+function card(c){const v=c.examples[0],s=D.samples[v.id];return `<article class="card" data-key="${c.key}"><div class="card-head"><h3>${c.id}</h3><span>${classBadge(c)}${related(c)&&level==='distinct'?'<span class="badge">有相近类</span>':''}</span></div><div class="card-meta">${nf(c.n)} 手帧 · ${Object.keys(c.sources).length} 来源</div>${c.coverage?coverageBar(c.coverage,true):''}${poseCanvas(c.joints,'GT 类原型')}<img src="${s.assets.crop}" alt="${esc(cname(s.source))} 原图与 GT 叠加" loading="lazy"><div class="card-foot">${esc(cname(s.source))} · ${v.is_prototype?'原型本人':`同类图例，距原型 ${v.prototype_distance_deg.toFixed(1)}°`}</div><button class="open" data-open="${c.key}">${c.coverage?'未覆盖成员 / 原图 / 近邻':'原图 / GT 叠加 / 近邻'}</button></article>`}
+function countTable(){const rows=['ours','external'].map(g=>{const m=model(g);return `<tr><td class="${g}">${g==='ours'?'我们三源＋合成':'WiLoR 外部 3D 来源'}</td><td>${nf(m.n)}</td><td>${m[level].length}</td><td>${m.fine.length-m.distinct.length}</td></tr>`}).join('');$('#countTable').innerHTML=`<table><thead><tr><th>组别 · ${hand==='left'?'左手':'右手'}</th><th>参与手帧</th><th>当前类数</th><th>合并减少细类数</th></tr></thead><tbody>${rows}</tbody></table>`}
+function renderOverview(){countTable();for(const g of ['ours','external']){const cs=g==='external'?filteredExternal():model(g)[level];$('#'+g+'Cards').innerHTML=cs.map(card).join('')||'<p class="hint">当前筛选没有类别。</p>';$('#'+g+'Count').textContent=`${cs.length} / ${model(g)[level].length} 类`}scatter()}
+function pairTile(c){const s=D.samples[c.examples[0].id];return `<div><h3><a data-open="${c.key}">${c.id} · ${esc(cname(c.prototype.source))}</a></h3>${poseCanvas(c.joints,'GT 类原型')}<img src="${s.assets.crop}" loading="lazy" alt="同类 RGB 与 GT 图例"><p class="metric">图例 ${esc(cname(s.source))} · 距原型 ${c.examples[0].prototype_distance_deg.toFixed(1)}°</p></div>`}
+function renderCompare(){const sorted=filteredExternal();$('#comparisonRows').innerHTML=sorted.map(c=>{const n=c.nearest_reference,a=allClasses[n.nearest_our_key];return `<div class="row"><div class="row-title"><strong>我们 ${a.id} ↔ 外部 ${c.id}</strong>${classBadge(c)}</div>${coverageBar(c.coverage)}<div class="pair">${pairTile(a)}${pairTile(c)}</div><p class="metric">类原型差 ${n.class_prototype_mean_deg.toFixed(1)}° · 外部原型最近参照 ${n.mean_deg.toFixed(1)}°（原型示意；覆盖统计来自全类成员）</p><button data-open="${c.key}">看该类未覆盖成员、原图与精确最近邻</button></div>`}).join('')||'<p class="hint">当前筛选没有类别。</p>'}
+function renderSimilar(){const summaries=D.summary.filter(s=>s.hand===hand);$('#duplicateSummary').innerHTML=`<table><thead><tr><th>组别</th><th>32 细类中的相近对</th><th>合并后相近对</th><th>主视图类数</th></tr></thead><tbody>${summaries.map(s=>`<tr><td>${s.group==='ours'?'我们':'外部'}</td><td>${s.stages.fine.near_pairs}</td><td>${s.stages.distinct.near_pairs}</td><td>${s.stages.distinct.k}</td></tr>`).join('')}</tbody></table><p class="hint">“相近”按原型或数值中心的 20 骨平均夹角 ≤12°，且每根手指平均夹角 ≤20° 判断；这是本页查看粒度，不是通用手势标准。</p>`;
+ const pp=D.pairs.filter(p=>p.hand===hand&&p.related);$('#relatedPairs').innerHTML=pp.map(p=>{const a=allClasses[`${p.group}_${hand}_distinct_${p.a}`],b=allClasses[`${p.group}_${hand}_distinct_${p.b}`];return `<div class="row"><strong>${p.group==='ours'?'我们':'外部'} ${p.a} / ${p.b}</strong><div class="finger-values">${p.finger_deg.map((v,i)=>`${['拇','食','中','环','小'][i]}指 ${v.toFixed(1)}°`).join(' · ')}</div><div class="pair">${pairTile(a)}${pairTile(b)}</div><p class="metric">原型整体 ${p.mean_deg.toFixed(1)}° · 跨录制近邻更靠近另一类的比例 ${(100*p.member_diagnostic.cross_cluster_closer_fraction).toFixed(1)}%</p></div>`}).join('')||'<p class="hint">本侧没有满足判据的剩余相近类对。</p>';
+ $('#mergeRows').innerHTML=D.merges.filter(m=>m.hand===hand).map(m=>`<div class="row"><div class="row-title"><strong>${m.group==='ours'?'我们':'外部'}：${m.children.join(' + ')} → 主类 ${m.cluster}</strong><span class="metric">${nf(m.n)} 手帧</span></div><div class="children">${m.child_keys.map(k=>{const c=allClasses[k];return `<div class="child"><a data-open="${k}">${c.id} · 细类</a>${poseCanvas(c.joints,'GT 原型')}</div>`}).join('')}</div></div>`).join('')}
+function renderSources(){$('#sourceTable').innerHTML='<thead><tr><th>来源组</th><th>数据集 / 可视化</th><th>实际参与手帧</th><th>版本与范围</th><th>本轮用途</th><th>对参照库覆盖</th></tr></thead><tbody>'+D.sources.map(s=>`<tr><td>${s.group}</td><td><button class="source-button" data-source="${s.id}">${esc(s.name)}</button></td><td>${nf(s.cluster_n||0)}</td><td>${esc(s.scope)}</td><td>${esc(s.role)}</td><td>${D.coverage.sources[s.id]?coverageBar(D.coverage.sources[s.id],true):s.group==='我们'?'参照库':s.gallery_source?'与参照重叠，不单计':'不作 3D 判断'}</td></tr>`).join('')+'</tbody>'}
+function sampleBlock(s,v){return `<section class="example">${memberBadge(s)}<h3>${esc(cname(s.source))} · ${esc(s.hand)} · 帧 ${esc(s.frame??'—')}</h3><p class="evidence">${esc(s.recording??s.split??'')} · ${esc(s.label)}${v?` · 距类原型 ${v.prototype_distance_deg.toFixed(2)}°`:''}</p><a href="${s.assets.full}" target="_blank"><img class="detail-pair" src="${s.assets.full}" alt="原图与对应 GT 叠加"></a><p class="evidence">${esc(s.camera??s.note??'')} · ${esc(s.gt_status??'仅可核查相应标注，不参与3D聚类')}</p></section>`}
+function openClass(key){const c=allClasses[key],g=key.split('_')[0];let head=`<h2>${g==='ours'?'我们':'外部'} · ${key.includes('_left_')?'左手':'右手'} · ${c.id}${key.includes('_fine_')?'（细类）':''}</h2><p class="evidence">原型：${esc(cname(c.prototype.source))} · ${esc(c.prototype.recording)} · 帧 ${c.prototype.frame}。${nf(c.n)} 个成员。</p>`;
+ head+=c.coverage?classBadge(c)+coverageBar(c.coverage):'';
+ let top=`<div class="dialog-top"><div>${poseCanvas(c.joints,'精确 GT 类原型')}<p class="hint">统一掌部坐标与骨长，仅用于姿态比较。</p></div>`;
+ if(c.nearest_reference){const n=c.nearest_reference;top+=`<div>${poseCanvas(n.nearest_joints,'全部参照库中的最近 GT 样本')}<p class="metric">对全部同侧三源＋合成样本检索：${n.mean_deg.toFixed(2)}°。不使用散点图距离。</p></div>`}else top+=`<div><h3>来源组成</h3>${Object.entries(c.sources).sort((a,b)=>b[1]-a[1]).map(([s,n])=>`<p class="metric">${esc(cname(s))}：${nf(n)}</p>`).join('')}</div>`;
+ top+='</div>';
+ let note=c.examples[0].prototype_distance_deg>8?'<p class="warning">当前可读 RGB 图例与类原型仍有较大差别。上方骨架才是精确原型；下面图片保留实际成员身份与距离。</p>':'';
+ const gaps=D.coverage.examples[key]||[],gapIds=new Set(gaps.map(v=>v.id)),other=c.examples.filter(v=>!gapIds.has(v.id));
+ $('#detailBody').innerHTML=head+(c.coverage?'<details class="prototype-details"><summary>类原型与其最近邻</summary>'+top+note+'</details>':top+note)+(gaps.length?'<h3 class="gap-title">本类实际未覆盖成员</h3>'+gaps.map(gapBlock).join(''):'')+(other.length?'<h3>其他同类成员</h3>'+other.map(v=>sampleBlock(D.samples[v.id],v)).join(''):'');if(!$('#detail').open)$('#detail').showModal();wirePoses();activeDetail='class='+encodeURIComponent(key);location.hash=activeDetail}
+function openSource(src){const s=D.sources.find(x=>x.id===src),target=s.gallery_source||src,ids=D.gallery[target]||[],two=D.two_d[src]||[];let body=`<h2>${esc(s.name)} · ${esc(s.group)}</h2><p>${esc(s.scope)}</p>`;
+ if(D.coverage.sources[src])body+=coverageBar(D.coverage.sources[src]);
+ if(two.length)body+=two.map(v=>sampleBlock(v,null)).join('');
+ else if(ids.length){body+='<p class="hint">以下图例均属于本轮冻结成员；点击原图可放大。手侧保留逐样本标记。</p>';body+=ids.slice(0,20).map(id=>sampleBlock(D.samples[id],null)).join('')}
+ else body+=`<p class="warning">${src==='wilor_bedlam'?'按用户要求跳过，本轮没有读取或聚类。':'当前聚类使用该源 GT；本页未选到该源配对图例。'}</p>`;
+ $('#detailBody').innerHTML=body;if(!$('#detail').open)$('#detail').showModal();activeDetail='source='+encodeURIComponent(src);location.hash=activeDetail}
+function scatter(){const c=$('#scatter'),ctx=c.getContext('2d');ctx.clearRect(0,0,c.width,c.height);const all=['ours','external'].flatMap(g=>model(g)[level].map(x=>({...x,g})));let xx=all.map(c=>c.display_xy[0]),yy=all.map(c=>c.display_xy[1]),xmin=Math.min(...xx),xmax=Math.max(...xx),ymin=Math.min(...yy),ymax=Math.max(...yy);const points=[];for(const p of all){let x=45+(p.display_xy[0]-xmin)/(xmax-xmin)*1000,y=400-(p.display_xy[1]-ymin)/(ymax-ymin)*350;ctx.fillStyle=p.g==='ours'?'#276a9c':'#79569d';ctx.beginPath();ctx.arc(x,y,6,0,Math.PI*2);ctx.fill();ctx.fillText(p.id,x+8,y+4);points.push({x,y,key:p.key})}c.onclick=e=>{const r=c.getBoundingClientRect(),x=(e.clientX-r.left)*c.width/r.width,y=(e.clientY-r.top)*c.height/r.height;const hit=points.find(p=>Math.hypot(x-p.x,y-p.y)<15);if(hit)openClass(hit.key)}}
+function render(){poses.clear();renderCoverage();if(tab==='overview')renderOverview();if(tab==='compare')renderCompare();if(tab==='similar')renderSimilar();if(tab==='sources')renderSources();wirePoses()}
+function changeTab(t){tab=t;document.querySelectorAll('.tab').forEach(e=>e.hidden=e.id!==t);document.querySelectorAll('nav button').forEach(e=>e.classList.toggle('active',e.dataset.tab===t));render()}
+document.addEventListener('click',e=>{const open=e.target.closest('[data-open]'),source=e.target.closest('[data-source]'),tb=e.target.closest('[data-tab]');if(open){e.preventDefault();openClass(open.dataset.open)}if(source)openSource(source.dataset.source);if(tb)changeTab(tb.dataset.tab)});
+$('#coverageFilter').onchange=e=>{coverageFilter=e.target.value;render()};$('#coverageSort').onchange=e=>{coverageSort=e.target.value;render()};
+$('#hand').onchange=e=>{hand=e.target.value;render()};$('#level').onchange=e=>{level=e.target.value;render()};$('#view').onchange=e=>{view=e.target.value;wirePoses()};$('#reset').onclick=()=>{poses.forEach(p=>p.rot=[0,0]);wirePoses()};$('#closeDetail').onclick=()=>$('#detail').close();$('#detail').addEventListener('close',()=>{activeDetail='';history.replaceState(null,'',location.pathname+location.search)});window.addEventListener('resize',()=>wirePoses());
+function restoreHash(){const hash=location.hash.slice(1);if(hash===activeDetail)return;const initial=new URLSearchParams(hash);if(initial.has('class')&&allClasses[initial.get('class')])openClass(initial.get('class'));else if(initial.has('source')&&D.sources.some(s=>s.id===initial.get('source')))openSource(initial.get('source'));else if(!hash&&$('#detail').open)$('#detail').close()}
+window.addEventListener('hashchange',restoreHash);render();restoreHash();

pose_recluster_distinct_20261014/downloads/coverage/code/web/index.html

@@ -0,0 +1,16 @@
+<!doctype html>
+<html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>最新抽样 · 局部姿态聚类对比</title><link rel="stylesheet" href="style.coverage_v2.css"></head>
+<body>
+<header><h1>局部姿态聚类对比 <small>逐成员覆盖 · 2026.10.14</small></h1><div class="toolbar"><label>手侧 <select id="hand"><option value="right">右手</option><option value="left">左手</option></select></label><label>粒度 <select id="level"><option value="distinct">合并相近类后</option><option value="fine">32 个细类</option></select></label><label>骨架视角 <select id="view"><option value="oblique">斜看</option><option value="palm">掌面</option><option value="side">侧面</option></select></label><button id="reset">重置视角</button></div></header>
+<nav><button data-tab="overview" class="active">两组类别</button><button data-tab="compare">逐类比较</button><button data-tab="similar">相似与合并</button><button data-tab="sources">18 行来源表</button></nav>
+<main>
+<section class="coverage-panel" aria-label="外部姿态覆盖"><div class="coverage-controls"><strong>WiLoR 外部类 → 我们三源＋合成</strong><label>外部类筛选 <select id="coverageFilter"><option value="all">全部</option><option value="entire">整类未覆盖</option><option value="mostly_gap">未覆盖成员 ≥50%</option><option value="mostly_covered">已覆盖成员 ≥70%</option></select></label><label>排序 <select id="coverageSort"><option value="gap">未覆盖比例高 → 低</option><option value="covered">已覆盖比例高 → 低</option><option value="id">类别编号</option></select></label></div><div id="coverageSummary"></div><p class="coverage-legend"><span class="dot covered"></span>已覆盖：≥3 段独立录制有相似姿态　<span class="dot low"></span>低覆盖：只有 1–2 段　<span class="dot uncovered"></span>未覆盖：没有相似姿态</p><p class="hint">均指本次抽样库。类别内逐成员统计；未覆盖不等于 GT 可靠，也不等于全量训练集绝对缺失。</p><details id="coverageMethod"><summary>判定依据与阈值敏感性</summary><div id="coverageMethodBody"></div></details></section>
+<div id="overview" class="tab"><div id="countTable"></div><p class="hint">点击外部类的“未覆盖成员 / 原图 / 近邻”查看具体缺口。骨架可拖动旋转；A、B 编号各自独立。</p><div class="two-groups"><section><h2 class="ours">我们：三源＋EgoRenderer Ver.005 <span id="oursCount"></span></h2><div id="oursCards" class="cards"></div></section><section><h2 class="external">外部：WiLoR 可读 3D 来源 <span id="externalCount"></span></h2><div id="externalCards" class="cards"></div></section></div><details><summary>类别分布图（仅展示）</summary><p class="hint">同一掌部坐标和投影；图上距离不作为覆盖或合并依据。</p><canvas id="scatter" width="1100" height="450"></canvas></details></div>
+<div id="compare" class="tab" hidden><p class="hint">右边是外部类；左边是它最相近的我们类。覆盖条来自该类全部成员；点击外部类查看未覆盖成员与各自的精确最近邻。</p><div id="comparisonRows"></div></div>
+<div id="similar" class="tab" hidden><div id="duplicateSummary"></div><h2>仍然相近的类</h2><p class="hint">下列类的中心相近，但继续整类合并会把部分差异较大的手指形态连在一起，因此保留并显式标记。</p><div id="relatedPairs"></div><h2>已合并的细类</h2><div id="mergeRows"></div></div>
+<div id="sources" class="tab" hidden><p class="hint">8 个外部 3D 来源参与本轮聚类。ARCTIC/HOT3D 与我们组重叠；仅 2D 的来源点击直接看 RGB 关键点。</p><div class="scroll"><table id="sourceTable"></table></div><p class="hint">WiLoR 行为官方源数据代理，未声称精确复现其加工训练包。我们组三源依据已核对训练成员的来源 GT；最终 canonical 标签仍未核验。ReInterHand 的 7,600 个 test 手帧未参与聚类。</p></div>
+</main>
+<footer><a href="downloads/coverage/RESULTS_ZH.md">覆盖结果</a><a href="downloads/coverage/member_coverage.csv.gz">逐成员覆盖 CSV</a><a href="downloads/coverage/class_coverage.csv">逐类覆盖 CSV</a><a href="downloads/coverage/REPRODUCE.md">覆盖复现</a><a href="downloads/RESULTS_ZH.md">结果说明</a><a href="downloads/assignments.csv.gz">逐样本类别 CSV</a><a href="downloads/all_class_pairs.csv">类间距离 CSV</a><a href="downloads/REPRODUCE.md">代码与复现</a><span>数据分组并非人工手势标签；GT 来源身份与可靠性分开。</span></footer>
+<dialog id="detail"><button id="closeDetail" class="close" aria-label="关闭">×</button><div id="detailBody"></div></dialog>
+<script src="data.coverage_v2.js"></script><script src="app.coverage_v2.js"></script>
+</body></html>

pose_recluster_distinct_20261014/downloads/coverage/code/web/style.css

@@ -0,0 +1,4 @@
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+
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+@media(max-width:620px){.coverage-panel{padding:11px}.coverage-controls{gap:8px}.coverage-controls strong{width:100%}.coverage-summary{gap:8px}.card .coverage-breakdown{padding:3px 7px 6px}.card .coverage-values{font-size:9px;gap:1px;display:grid;grid-template-columns:1fr}.card-head{gap:4px;align-items:flex-start}.card-head>span{max-width:80%}.gap-case{padding:9px}.gap-case .pair{grid-template-columns:1fr}.gap-case .pair .gap-image{height:auto;max-height:300px}.coverage-legend{line-height:1.9}.coverage-controls select{max-width:200px}}

pose_recluster_distinct_20261014/downloads/coverage/coverage_protocol.json

@@ -0,0 +1,41 @@
+{
+  "frozen_at_utc": "2026-10-14T06:12:10.626664+00:00",
+  "seed": 2026101407,
+  "base": "/SSD3/yangzijing/Hand_pose_recluster_distinct_20261014",
+  "reference": "All 271543 frozen three-source + EgoRenderer Ver.005 analysis members; separate hands; original source GT proxies, final canonical supervision/masks not verified",
+  "external": "All 214481 frozen train members of 8 readable external WiLoR official-source 3D proxies; excludes 7600 ReInterHand test members",
+  "feature": "Existing fixed 20 unit 3D bone directions in palm coordinates, 60 dimensions; wrist translation/rotation and bone length removed; no refit, no mirror pooling",
+  "distance": "Mean over 20 bones of acos(clamp(dot,-1,1)), degrees; exact exhaustive float32 GPU, TF32 disabled; validate CPU float64",
+  "calibration": {
+    "n": 8192,
+    "per_source_per_hand_max": 1024,
+    "sampling": "Seeded recording round robin, random frames without replacement; equal source and equal recording weights within each hand",
+    "exclusion": "Exclude all members of the query independent recording/source action from reference, not only query frame",
+    "threshold": "Separate left/right weighted 95th percentile of leave-recording/action-out nearest-reference distance; computed before external evaluation",
+    "sensitivity_quantiles": [
+      0.9,
+      0.95,
+      0.99
+    ],
+    "additional_fixed_degrees": [
+      10,
+      20,
+      30
+    ]
+  },
+  "sample_status": {
+    "covered": "nearest distance <= threshold and at least 3 distinct reference recording/source-action units within threshold",
+    "low": "nearest <= threshold but only 1 or 2 distinct reference units",
+    "uncovered": "nearest > threshold"
+  },
+  "class_status": {
+    "covered": "at least 90% of members have covered sample status",
+    "uncovered": "zero members have nearest distance <= threshold",
+    "low": "all other classes; stacked counts retain mixed within-class outcomes"
+  },
+  "class_badge_warning": "90% display convention, not a universal biological boundary. Per-member proportions are authoritative. Uncovered means in this sampled reference only.",
+  "weights": "Primary coverage is unweighted per analyzed hand-frame. Also report normalized original cluster fit-weight and equal-independent-unit sensitivity; do not claim unknown official population inclusion probabilities.",
+  "selection": "For classes with uncovered members select a paired-RGB actual uncovered member close to the class prototype, preferring independent examples. Never relabel a covered prototype as uncovered.",
+  "gt_quality": "Coverage is independent of GT validity. No automatic reliable/OOD/recommendation claim from distance alone.",
+  "no_changes": "No reclustering, smoothing, GT refitting or source label modifications"
+}

pose_recluster_distinct_20261014/downloads/coverage/coverage_visual_checks.json

@@ -0,0 +1,1322 @@
+{
+  "status": "PASS",
+  "class_count": 114,
+  "examples": 164,
+  "unique_external_members": 116,
+  "unique_reference_members": 105,
+  "all_exact_nearest_rgb_present": true,
+  "checks": [
+    {
+      "class_key": "external_left_distinct_B1",
+      "id": "db1b87ed5e918dbee56a",
+      "index": 152574,
+      "uncovered": true,
+      "nearest_index": 33832,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B1",
+      "id": "c00e1d95904cb4d3e461",
+      "index": 153817,
+      "uncovered": true,
+      "nearest_index": 170132,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B2",
+      "id": "e93a7203af92a0bba127",
+      "index": 153341,
+      "uncovered": true,
+      "nearest_index": 23056,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B2",
+      "id": "a41106b1f8faadfbd0c6",
+      "index": 151978,
+      "uncovered": true,
+      "nearest_index": 187334,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B3",
+      "id": "7cbee0066bf73c1230ec",
+      "index": 41702,
+      "uncovered": true,
+      "nearest_index": 134834,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B3",
+      "id": "52e61ba8574c1c21171f",
+      "index": 39102,
+      "uncovered": true,
+      "nearest_index": 76838,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B4",
+      "id": "0831e6638e9af7f854bd",
+      "index": 39840,
+      "uncovered": true,
+      "nearest_index": 189144,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B4",
+      "id": "74961c20914e938a0ebc",
+      "index": 93086,
+      "uncovered": true,
+      "nearest_index": 2519,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B5",
+      "id": "7a659401c102ee6c4d53",
+      "index": 71275,
+      "uncovered": true,
+      "nearest_index": 173495,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B5",
+      "id": "82270a2b1f87dae6ffed",
+      "index": 151929,
+      "uncovered": true,
+      "nearest_index": 89799,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B6",
+      "id": "bfb2f241cb856b96b744",
+      "index": 37843,
+      "uncovered": true,
+      "nearest_index": 79018,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B6",
+      "id": "3ddf871100b7fa493f4c",
+      "index": 154193,
+      "uncovered": true,
+      "nearest_index": 79019,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B7",
+      "id": "c0469034d3cb0c8cfacb",
+      "index": 164291,
+      "uncovered": true,
+      "nearest_index": 65088,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B7",
+      "id": "a0e1eafc008fe03615bf",
+      "index": 153334,
+      "uncovered": true,
+      "nearest_index": 96924,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B8",
+      "id": "1155d85f7d67fdea5ca4",
+      "index": 86462,
+      "uncovered": true,
+      "nearest_index": 32087,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B8",
+      "id": "7e9540aab42ca1f283f1",
+      "index": 79416,
+      "uncovered": true,
+      "nearest_index": 81904,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B9",
+      "id": "9186e37ea320e67eea4cc4b3",
+      "index": 192258,
+      "uncovered": true,
+      "nearest_index": 116684,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B9",
+      "id": "8fc44d37c06f82c2bccfe0a7",
+      "index": 192008,
+      "uncovered": true,
+      "nearest_index": 196506,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B10",
+      "id": "86c1b3d59b6d91f14ef6",
+      "index": 39555,
+      "uncovered": true,
+      "nearest_index": 58551,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B10",
+      "id": "805d0cb50c887d06f851",
+      "index": 76816,
+      "uncovered": true,
+      "nearest_index": 58551,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B11",
+      "id": "f9e4b832088ad843ce26fc9a",
+      "index": 192451,
+      "uncovered": true,
+      "nearest_index": 34777,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B11",
+      "id": "8bc1231ca5c15b6f5c2d",
+      "index": 149468,
+      "uncovered": true,
+      "nearest_index": 140749,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B12",
+      "id": "e819135ae0a98c833798",
+      "index": 40656,
+      "uncovered": true,
+      "nearest_index": 58465,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B12",
+      "id": "1c3e2d6266babd3546e4",
+      "index": 42882,
+      "uncovered": true,
+      "nearest_index": 120574,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B13",
+      "id": "68c621ec6f25af92b6d2",
+      "index": 134770,
+      "uncovered": true,
+      "nearest_index": 165102,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B13",
+      "id": "e8ba03215a0dee415f9f",
+      "index": 111808,
+      "uncovered": true,
+      "nearest_index": 87746,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B14",
+      "id": "a3c7b0983f4f67c9774a9d1d",
+      "index": 191524,
+      "uncovered": true,
+      "nearest_index": 162699,
+      "nearest_rgb": true
+    },
+    {
+      "class_key": "external_left_distinct_B14",
+      "id": "9b7689bc046883d6812ad365",
+      "index": 190831,
+      "uncovered": true,
+      "nearest_index": 134109,
+      "nearest_rgb": true
+    },
+    {
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+      "id": "da0af48ad0dcc8746952",
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pose_recluster_distinct_20261014/downloads/coverage/display_selection_amendment.json

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+{
+  "at": "2026-10-14T06:23:54.572466+00:00",
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+  "statistical_effect": "None; visualization selection only."
+}

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+}

pose_recluster_distinct_20261014/downloads/coverage/source_coverage.csv

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+wilor_h2o3d,right,9.252071380615234,1356,942,153,261,0.6946902654867256,0.11283185840707964,0.19247787610619468,0.8075221238938053,low,低覆盖,"[0.6980372264813266, 0.10938226873347846, 0.19258050478519503]","[0.6983044713757621, 0.10929764102746048, 0.19239788759677737]",15
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+wilor_interhand26m,right,9.252071380615234,11115,3348,951,6816,0.30121457489878545,0.08556005398110661,0.613225371120108,0.386774628879892,low,低覆盖,"[0.301618644476163, 0.08102785587706593, 0.6173534996467712]","[0.21530795959559013, 0.06292933211471498, 0.7217627082896949]",1646
+wilor_mtc,right,9.252071380615234,7041,4187,418,2436,0.5946598494532027,0.05936656724897032,0.345973583297827,0.654026416702173,low,低覆盖,"[0.5487214239520184, 0.06228577150087952, 0.38899280454710206]","[0.5487214239520184, 0.06228577150087951, 0.3889928045471021]",29
+wilor_reinter,right,9.252071380615234,10980,2961,1284,6735,0.26967213114754096,0.11693989071038251,0.6133879781420765,0.38661202185792354,low,低覆盖,"[0.25803549392737946, 0.11351201169705163, 0.6284524943755689]","[0.25799964999517766, 0.11351521982231658, 0.6284851301825056]",7
+wilor_rhd,right,9.252071380615234,31622,4630,2388,24604,0.14641705141989753,0.07551704509518689,0.7780659034849156,0.22193409651508444,low,低覆盖,"[0.17558189935416757, 0.0779873741391302, 0.7464307265067023]","[0.17558189935416757, 0.0779873741391302, 0.7464307265067023]",23071

pose_recluster_distinct_20261014/downloads/coverage/summary.json

@@ -0,0 +1,73 @@
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+}

pose_recluster_distinct_20261014/index.html

@@ -1,15 +1,16 @@
 <!doctype html>
-<html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>最新抽样 · 局部姿态聚类对比</title><link rel="stylesheet" href="style.css"></head>
+<html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>最新抽样 · 局部姿态聚类对比</title><link rel="stylesheet" href="style.coverage_v2.css"></head>
 <body>
-<header><h1>局部姿态聚类对比 <small>2026.10.14</small></h1><div class="toolbar"><label>手侧 <select id="hand"><option value="right">右手</option><option value="left">左手</option></select></label><label>粒度 <select id="level"><option value="distinct">合并相近类后</option><option value="fine">32 个细类</option></select></label><label>骨架视角 <select id="view"><option value="oblique">斜看</option><option value="palm">掌面</option><option value="side">侧面</option></select></label><button id="reset">重置视角</button></div></header>
+<header><h1>局部姿态聚类对比 <small>逐成员覆盖 · 2026.10.14</small></h1><div class="toolbar"><label>手侧 <select id="hand"><option value="right">右手</option><option value="left">左手</option></select></label><label>粒度 <select id="level"><option value="distinct">合并相近类后</option><option value="fine">32 个细类</option></select></label><label>骨架视角 <select id="view"><option value="oblique">斜看</option><option value="palm">掌面</option><option value="side">侧面</option></select></label><button id="reset">重置视角</button></div></header>
 <nav><button data-tab="overview" class="active">两组类别</button><button data-tab="compare">逐类比较</button><button data-tab="similar">相似与合并</button><button data-tab="sources">18 行来源表</button></nav>
 <main>
-<div id="overview" class="tab"><div id="countTable"></div><p class="hint">点击类别查看原图、GT 叠加和最近邻。骨架可拖动旋转；A、B 编号各自独立。</p><div class="two-groups"><section><h2 class="ours">我们：三源＋EgoRenderer Ver.005 <span id="oursCount"></span></h2><div id="oursCards" class="cards"></div></section><section><h2 class="external">外部：WiLoR 可读 3D 来源 <span id="externalCount"></span></h2><div id="externalCards" class="cards"></div></section></div><details><summary>类别分布图（仅展示）</summary><p class="hint">同一掌部坐标和投影；图上距离不作为覆盖或合并依据。</p><canvas id="scatter" width="1100" height="450"></canvas></details></div>
-<div id="compare" class="tab" hidden><p class="hint">右边是外部类；左边是它最相近的我们类。点击可看真实图例；最小样本距离另列，不能把类中心距离当覆盖结论。</p><div id="comparisonRows"></div></div>
+<section class="coverage-panel" aria-label="外部姿态覆盖"><div class="coverage-controls"><strong>WiLoR 外部类 → 我们三源＋合成</strong><label>外部类筛选 <select id="coverageFilter"><option value="all">全部</option><option value="entire">整类未覆盖</option><option value="mostly_gap">未覆盖成员 ≥50%</option><option value="mostly_covered">已覆盖成员 ≥70%</option></select></label><label>排序 <select id="coverageSort"><option value="gap">未覆盖比例高 → 低</option><option value="covered">已覆盖比例高 → 低</option><option value="id">类别编号</option></select></label></div><div id="coverageSummary"></div><p class="coverage-legend"><span class="dot covered"></span>已覆盖：≥3 段独立录制有相似姿态　<span class="dot low"></span>低覆盖：只有 1–2 段　<span class="dot uncovered"></span>未覆盖：没有相似姿态</p><p class="hint">均指本次抽样库。类别内逐成员统计；未覆盖不等于 GT 可靠，也不等于全量训练集绝对缺失。</p><details id="coverageMethod"><summary>判定依据与阈值敏感性</summary><div id="coverageMethodBody"></div></details></section>
+<div id="overview" class="tab"><div id="countTable"></div><p class="hint">点击外部类的“未覆盖成员 / 原图 / 近邻”查看具体缺口。骨架可拖动旋转；A、B 编号各自独立。</p><div class="two-groups"><section><h2 class="ours">我们：三源＋EgoRenderer Ver.005 <span id="oursCount"></span></h2><div id="oursCards" class="cards"></div></section><section><h2 class="external">外部：WiLoR 可读 3D 来源 <span id="externalCount"></span></h2><div id="externalCards" class="cards"></div></section></div><details><summary>类别分布图（仅展示）</summary><p class="hint">同一掌部坐标和投影；图上距离不作为覆盖或合并依据。</p><canvas id="scatter" width="1100" height="450"></canvas></details></div>
+<div id="compare" class="tab" hidden><p class="hint">右边是外部类；左边是它最相近的我们类。覆盖条来自该类全部成员；点击外部类查看未覆盖成员与各自的精确最近邻。</p><div id="comparisonRows"></div></div>
 <div id="similar" class="tab" hidden><div id="duplicateSummary"></div><h2>仍然相近的类</h2><p class="hint">下列类的中心相近，但继续整类合并会把部分差异较大的手指形态连在一起，因此保留并显式标记。</p><div id="relatedPairs"></div><h2>已合并的细类</h2><div id="mergeRows"></div></div>
 <div id="sources" class="tab" hidden><p class="hint">8 个外部 3D 来源参与本轮聚类。ARCTIC/HOT3D 与我们组重叠；仅 2D 的来源点击直接看 RGB 关键点。</p><div class="scroll"><table id="sourceTable"></table></div><p class="hint">WiLoR 行为官方源数据代理，未声称精确复现其加工训练包。我们组三源依据已核对训练成员的来源 GT；最终 canonical 标签仍未核验。ReInterHand 的 7,600 个 test 手帧未参与聚类。</p></div>
 </main>
-<footer><a href="downloads/RESULTS_ZH.md">结果说明</a><a href="downloads/assignments.csv.gz">逐样本类别 CSV</a><a href="downloads/all_class_pairs.csv">类间距离 CSV</a><a href="downloads/REPRODUCE.md">代码与复现</a><span>数据分组并非人工手势标签；GT 来源身份与可靠性分开。</span></footer>
+<footer><a href="downloads/coverage/RESULTS_ZH.md">覆盖结果</a><a href="downloads/coverage/member_coverage.csv.gz">逐成员覆盖 CSV</a><a href="downloads/coverage/class_coverage.csv">逐类覆盖 CSV</a><a href="downloads/coverage/REPRODUCE.md">覆盖复现</a><a href="downloads/RESULTS_ZH.md">结果说明</a><a href="downloads/assignments.csv.gz">逐样本类别 CSV</a><a href="downloads/all_class_pairs.csv">类间距离 CSV</a><a href="downloads/REPRODUCE.md">代码与复现</a><span>数据分组并非人工手势标签；GT 来源身份与可靠性分开。</span></footer>
 <dialog id="detail"><button id="closeDetail" class="close" aria-label="关闭">×</button><div id="detailBody"></div></dialog>
-<script src="data.js"></script><script src="app.js"></script>
+<script src="data.coverage_v2.js"></script><script src="app.coverage_v2.js"></script>
 </body></html>

pose_recluster_distinct_20261014/index_before_coverage.html

@@ -0,0 +1,15 @@
+<!doctype html>
+<html lang="zh-CN"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>最新抽样 · 局部姿态聚类对比</title><link rel="stylesheet" href="style.css"></head>
+<body>
+<header><h1>局部姿态聚类对比 <small>2026.10.14</small></h1><div class="toolbar"><label>手侧 <select id="hand"><option value="right">右手</option><option value="left">左手</option></select></label><label>粒度 <select id="level"><option value="distinct">合并相近类后</option><option value="fine">32 个细类</option></select></label><label>骨架视角 <select id="view"><option value="oblique">斜看</option><option value="palm">掌面</option><option value="side">侧面</option></select></label><button id="reset">重置视角</button></div></header>
+<nav><button data-tab="overview" class="active">两组类别</button><button data-tab="compare">逐类比较</button><button data-tab="similar">相似与合并</button><button data-tab="sources">18 行来源表</button></nav>
+<main>
+<div id="overview" class="tab"><div id="countTable"></div><p class="hint">点击类别查看原图、GT 叠加和最近邻。骨架可拖动旋转；A、B 编号各自独立。</p><div class="two-groups"><section><h2 class="ours">我们：三源＋EgoRenderer Ver.005 <span id="oursCount"></span></h2><div id="oursCards" class="cards"></div></section><section><h2 class="external">外部：WiLoR 可读 3D 来源 <span id="externalCount"></span></h2><div id="externalCards" class="cards"></div></section></div><details><summary>类别分布图（仅展示）</summary><p class="hint">同一掌部坐标和投影；图上距离不作为覆盖或合并依据。</p><canvas id="scatter" width="1100" height="450"></canvas></details></div>
+<div id="compare" class="tab" hidden><p class="hint">右边是外部类；左边是它最相近的我们类。点击可看真实图例；最小样本距离另列，不能把类中心距离当覆盖结论。</p><div id="comparisonRows"></div></div>
+<div id="similar" class="tab" hidden><div id="duplicateSummary"></div><h2>仍然相近的类</h2><p class="hint">下列类的中心相近，但继续整类合并会把部分差异较大的手指形态连在一起，因此保留并显式标记。</p><div id="relatedPairs"></div><h2>已合并的细类</h2><div id="mergeRows"></div></div>
+<div id="sources" class="tab" hidden><p class="hint">8 个外部 3D 来源参与本轮聚类。ARCTIC/HOT3D 与我们组重叠；仅 2D 的来源点击直接看 RGB 关键点。</p><div class="scroll"><table id="sourceTable"></table></div><p class="hint">WiLoR 行为官方源数据代理，未声称精确复现其加工训练包。我们组三源依据已核对训练成员的来源 GT；最终 canonical 标签仍未核验。ReInterHand 的 7,600 个 test 手帧未参与聚类。</p></div>
+</main>
+<footer><a href="downloads/RESULTS_ZH.md">结果说明</a><a href="downloads/assignments.csv.gz">逐样本类别 CSV</a><a href="downloads/all_class_pairs.csv">类间距离 CSV</a><a href="downloads/REPRODUCE.md">代码与复现</a><span>数据分组并非人工手势标签；GT 来源身份与可靠性分开。</span></footer>
+<dialog id="detail"><button id="closeDetail" class="close" aria-label="关闭">×</button><div id="detailBody"></div></dialog>
+<script src="data.js"></script><script src="app.js"></script>
+</body></html>