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admin
2026-08-26 18:15:54 +08:00
parent 54d343b739
commit a4568d8a55
79 changed files with 11264 additions and 560 deletions
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.gitignore
-18
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@@ -1,18 +0,0 @@
*.iml
.gradle/
local.properties
.idea/
build/
captures/
.externalNativeBuild/
.cxx/
.DS_Store
training/datasets/
training/venv/
training/runs/
*.pt
*.tflite
# 文生图配置(含API key,不入库)
#training/gen_images_config.json
.gstack/
.gstack/browse-audit.jsonl
+624
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@@ -0,0 +1,624 @@
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{"ts":"2026-08-26T05:25:55.433Z","cmd":"js","args":"\nconst rows = document.querySelectorAll('.row');\nrows[0].querySelectorAll('.cell')[2].querySelector('.cell-img').click();\n'src click ok, dialogs: ' + [...document.querySelectorAll('.el-dialog')].filte…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of undefined (reading 'querySelector')\n at eval (eval at evaluate (:290:30), <anonymous>:2:37)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript.<anonymous> (<anonymous>:1:44)"}
{"ts":"2026-08-26T05:26:03.654Z","cmd":"snapshot","args":"","origin":"http://localhost:5173/admin/datasets/102","durationMs":13,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:03.709Z","cmd":"snapshot","args":"-C","origin":"http://localhost:5173/admin/datasets/102","durationMs":14,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:09.123Z","cmd":"click","args":"@e37","origin":"http://localhost:5173/admin/datasets/102","durationMs":105,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:10.704Z","cmd":"js","args":"\nconst c = document.querySelector('.wb-canvas');\nconst dlg = c ? c.closest('.el-dialog') : null;\nJSON.stringify({\n title: dlg?.querySelector('.el-dialog__title')?.textContent,\n canvas: c ? { w: c.wi…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:16.872Z","cmd":"js","args":"\nconst c = document.querySelector('.wb-canvas');\nconst r = c.getBoundingClientRect();\nconst opts = { bubbles: true, cancelable: true, pointerId: 7, isPrimary: true };\nfunction fire(type, x, y) { c.dis…","origin":"http://localhost:5173/admin/datasets/102","durationMs":11,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:17.432Z","cmd":"js","args":"\nJSON.stringify({\n confirmed: [...document.querySelectorAll('.wb-canvas')].length ? null : null,\n dirtyBtn: [...document.querySelectorAll('.el-dialog button')].map(b=>b.textContent.trim()).filter(t=…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:20.829Z","cmd":"js","args":"\n[...document.querySelectorAll('.el-dialog button')].find(b=>b.textContent.trim().includes('保存本张')).click(); 'save clicked'","origin":"http://localhost:5173/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:22.909Z","cmd":"js","args":"\nJSON.stringify({ msg: document.body.textContent.includes('已保存（该数据集已标注') ? 'success msg shown' : 'no msg' })","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:47.091Z","cmd":"snapshot","args":"","origin":"http://localhost:5173/admin/datasets/102","durationMs":19,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:47.148Z","cmd":"js","args":"\ndocument.querySelectorAll('.row')[0].querySelector('.cell-btn button').click(); 'clicked auto-label'","origin":"http://localhost:5173/admin/datasets/102","durationMs":8,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:48.020Z","cmd":"js","args":"\nJSON.stringify({ confirmDlg: [...document.querySelectorAll('.el-message-box__title')].map(t=>t.textContent) })","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:50.318Z","cmd":"js","args":"\n[...document.querySelectorAll('.el-message-box button')].find(b=>b.textContent.includes('开始标注')).click(); 'confirmed'","origin":"http://localhost:5173/admin/datasets/102","durationMs":8,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:52.383Z","cmd":"js","args":"\nJSON.stringify({ err: document.body.textContent.includes('标注服务未配置') ? 'configured-error shown' : document.body.textContent.slice(-200) })","origin":"http://localhost:5173/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:26:59.390Z","cmd":"js","args":"\nconst pager = document.querySelectorAll('.el-pager li');\npager[1].click(); 'page 2 clicked'","origin":"http://localhost:5173/admin/datasets/102","durationMs":8,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:27:00.455Z","cmd":"js","args":"\nconst rows = document.querySelectorAll('.row');\nJSON.stringify({\n rows: rows.length,\n first: rows[0]?.textContent.includes('pheasant_20') ? 'pheasant_20' : rows[0]?.textContent.slice(0, 40),\n last…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:27:13.139Z","cmd":"goto","args":"http://localhost:5173/admin/datasets","origin":"http://localhost:5173/admin/datasets","durationMs":90,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:27:45.284Z","cmd":"text","args":"","origin":"http://localhost:5173/admin/datasets","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:29:00.292Z","cmd":"goto","args":"http://localhost:5173/admin/datasets","origin":"http://localhost:5173/admin/datasets","durationMs":55,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:29:01.371Z","cmd":"text","args":"","origin":"http://localhost:5173/admin/datasets","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:29:38.145Z","cmd":"goto","args":"http://localhost:5173/admin/datasets","origin":"http://localhost:5173/admin/datasets","durationMs":50,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:29:39.221Z","cmd":"text","args":"","origin":"http://localhost:5173/admin/datasets","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:30:03.435Z","cmd":"goto","args":"http://localhost:5173/admin/datasets","origin":"http://localhost:5173/admin/datasets","durationMs":51,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:30:05.017Z","cmd":"js","args":"\nconst card = document.querySelector('.ds-card');\nJSON.stringify({\n trainSection: !!card.querySelector('.ds-train'),\n progressBar: !!card.querySelector('.ds-train-bar .el-progress__bar'),\n text: ca…","origin":"http://localhost:5173/admin/datasets","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:30:08.066Z","cmd":"js","args":"\nconst card = document.querySelector('.ds-card');\nJSON.stringify({\n progressEls: card.querySelectorAll('.el-progress').length,\n barInner: card.querySelector('.el-progress__bar__outer') ? 'outer exis…","origin":"http://localhost:5173/admin/datasets","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:30:10.283Z","cmd":"js","args":"\nconst card = document.querySelector('.ds-card');\nconst p = card.querySelector('.el-progress');\nJSON.stringify({ html: p.outerHTML.slice(0, 300) })","origin":"http://localhost:5173/admin/datasets","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:33:50.254Z","cmd":"goto","args":"http://localhost:5173/admin/datasets/102","origin":"http://localhost:5173/admin/datasets/102","durationMs":76,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:33:51.340Z","cmd":"js","args":"\ndocument.querySelectorAll('.row')[0].querySelector('.cell-img').click(); 'viewer opened'","origin":"http://localhost:5173/admin/datasets/102","durationMs":6,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:33:52.417Z","cmd":"js","args":"\nconst img = document.querySelector('.viewer-img');\nconst rect = img.getBoundingClientRect();\nJSON.stringify({\n natural: img.naturalWidth + 'x' + img.naturalHeight,\n displayed: Math.round(rect.width…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:34:51.957Z","cmd":"goto","args":"http://localhost:5173/admin/datasets/102","origin":"http://localhost:5173/admin/datasets/102","durationMs":52,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:34:53.539Z","cmd":"js","args":"\nJSON.stringify({\n page1: [...document.querySelectorAll('.row .cell-name')].slice(0, 3).map(e => e.textContent.trim()),\n rowCount: document.querySelectorAll('.row').length\n})","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:34.173Z","cmd":"goto","args":"http://localhost:5173/admin/datasets/102","origin":"http://localhost:5173/admin/datasets/102","durationMs":45,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:35.754Z","cmd":"js","args":"\nJSON.stringify({\n cards: document.querySelectorAll('.img-card').length,\n firstCard: document.querySelector('.img-card .card-name')?.textContent,\n actions: [...document.querySelectorAll('.img-card:…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:39.442Z","cmd":"js","args":"\ndocument.querySelector('.img-card .card-actions button').click(); '标注 clicked'","origin":"http://localhost:5173/admin/datasets/102","durationMs":7,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:41.023Z","cmd":"js","args":"\nconst c = document.querySelector('.wb-canvas');\nconst dlg = c ? c.closest('.el-dialog') : null;\nJSON.stringify({\n title: dlg?.querySelector('.el-dialog__title')?.textContent,\n canvas: c ? { w: c.wi…","origin":"http://localhost:5173/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:47.961Z","cmd":"js","args":"\ndocument.querySelectorAll('.el-dialog__headerbtn')[0]?.click(); 'editor closed'","origin":"http://localhost:5173/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:48.540Z","cmd":"js","args":"\ndocument.querySelector('.img-card .card-actions button:last-child').click(); '删除 clicked'","origin":"http://localhost:5173/admin/datasets/102","durationMs":7,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:49.118Z","cmd":"js","args":"\nJSON.stringify({\n confirmTitle: [...document.querySelectorAll('.el-message-box__title')].map(t=>t.textContent),\n confirmMsg: [...document.querySelectorAll('.el-message-box__message')].map(m=>m.text…","origin":"http://localhost:5173/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:43:49.163Z","cmd":"js","args":"\ndocument.querySelectorAll('.el-message-box button').forEach(b => b.textContent.includes('取消') && b.click()); 'cancelled'","origin":"http://localhost:5173/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:46:00.608Z","cmd":"goto","args":"http://localhost:5173/admin/datasets/102","origin":"http://localhost:5173/admin/datasets/102","durationMs":37,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:46:02.185Z","cmd":"js","args":"\nJSON.stringify({\n cards: document.querySelectorAll('.img-card').length,\n pager: document.querySelector('.el-pagination__total')?.textContent,\n pages: document.querySelectorAll('.el-pager li').leng…","origin":"http://localhost:5173/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:52:26.428Z","cmd":"goto","args":"http://localhost:18080/admin/datasets/102","origin":"http://localhost:18080/admin/login","durationMs":82,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:52:26.487Z","cmd":"wait","args":"2500","origin":"http://localhost:18080/admin/login","durationMs":4,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"2500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 2500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:52:31.924Z","cmd":"wait","args":"2500","origin":"http://localhost:18080/admin/login","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"2500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 2500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:52:31.967Z","cmd":"eval","args":"({title: document.title, cards: document.querySelectorAll('.img-card').length, addBtn: [...document.querySelectorAll('button')].some(b=>b.textContent.includes('添加')), hasUploadBtn: [...document.queryS…","origin":"http://localhost:18080/admin/login","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"Cannot resolve real path: ({title: document.title, cards: document.querySelectorAll('.img-card').length, addBtn: [...document.querySelectorAll('button')].some(b=>b.textContent.includes('添加')), hasUploadBtn: [...document.querySelectorAll('button')].some(b=>b.textContent.includes('选择图片上传')), hasGenBtn…"}
{"ts":"2026-08-26T05:52:34.831Z","cmd":"eval","args":"--help","origin":"http://localhost:18080/admin/login","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"File not found: --help"}
{"ts":"2026-08-26T05:52:41.663Z","cmd":"js","args":"({cards: document.querySelectorAll('.img-card').length, buttons: [...document.querySelectorAll('button')].map(b=>b.textContent.trim()).filter(t=>t.length<14)})","origin":"http://localhost:18080/admin/login","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:52:50.706Z","cmd":"storage","args":"set adminToken Tongli686^*^","origin":"http://localhost:18080/admin/login","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T05:52:50.834Z","cmd":"wait","args":"2500","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"2500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 2500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:52:50.897Z","cmd":"js","args":"({cards: document.querySelectorAll('.img-card').length, buttons: [...document.querySelectorAll('button')].map(b=>b.textContent.trim()).filter(t=>t.length<16)})","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:54:54.124Z","cmd":"console","args":"--errors","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:54:54.176Z","cmd":"js","args":"document.body.innerText.slice(0, 300)","origin":"http://localhost:18080/admin/datasets/102","durationMs":5,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:54:59.938Z","cmd":"console","args":"--clear","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:00.074Z","cmd":"click","args":"button:has-text('添加')","origin":"http://localhost:18080/admin/datasets/102","durationMs":86,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:00.131Z","cmd":"wait","args":"600","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"600\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 600 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:00.173Z","cmd":"js","args":"({dialogTitle: document.querySelector('.el-dialog__title')?.textContent, modeButtons: [...document.querySelectorAll('.el-radio-button')].map(b=>b.textContent.trim()), uploadForm: !!document.querySelec…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:05.979Z","cmd":"click","args":".el-radio-button:nth-of-type(2)","origin":"http://localhost:18080/admin/datasets/102","durationMs":48,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:06.023Z","cmd":"wait","args":"400","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"400\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 400 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:06.062Z","cmd":"js","args":"({uploadForm: !!document.querySelector('.el-upload'), hasPrompt: !!document.querySelector('.el-textarea'), hasCount: !!document.querySelector('.el-input-number'), sizeRadios: [...document.querySelecto…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:12.956Z","cmd":"click","args":".el-dialog__headerbtn","origin":"http://localhost:18080/admin/datasets/102","durationMs":24,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:13.004Z","cmd":"wait","args":"400","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"400\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 400 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:13.078Z","cmd":"click","args":".img-card .card-img","origin":"http://localhost:18080/admin/datasets/102","durationMs":27,"status":"error","hasCookies":false,"mode":"launched","error":"click: Error: strict mode violation: locator('.img-card .card-img') resolved to 18 elements:\n 1) <div class=\"card-img\" data-v-d300192b=\"\">…</div> aka getByText('手动已标注').first()\n 2) <div class=\"card-img\" data-v-d300192b=\"\">…</div> aka getByText('手动已标注').nth(1)\n 3) <div class=\"card-img\" data-…"}
{"ts":"2026-08-26T05:55:13.121Z","cmd":"wait","args":"800","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"800\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 800 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:13.169Z","cmd":"js","args":"({dialogTitle: document.querySelector('.el-dialog__title')?.textContent, canvas: !!document.querySelector('.wb-canvas'), canvasSize: (()=>{const c=document.querySelector('.wb-canvas'); return c? c.wid…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:20.729Z","cmd":"press","args":"Escape","origin":"http://localhost:18080/admin/datasets/102","durationMs":5,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:20.773Z","cmd":"wait","args":"400","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"400\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 400 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:20.814Z","cmd":"js","args":"({dialogs: [...document.querySelectorAll('.el-dialog')].map(d=>({title: d.querySelector('.el-dialog__title')?.textContent, visible: getComputedStyle(d).display !== 'none'}))})","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:24.591Z","cmd":"js","args":"(()=>{const b=document.querySelector('.el-dialog__headerbtn'); if(b){b.click(); return 'clicked'} return 'none'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:24.631Z","cmd":"wait","args":"500","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:24.671Z","cmd":"js","args":"({dialogs: [...document.querySelectorAll('.el-dialog')].filter(d=>getComputedStyle(d).display!=='none').map(d=>d.querySelector('.el-dialog__title')?.textContent)})","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:28.244Z","cmd":"js","args":"([...document.querySelectorAll('.el-dialog')].map((d,i)=>({i, title: d.querySelector('.el-dialog__title')?.textContent, style: d.getAttribute('style'), display: getComputedStyle(d).display, overlayVis…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:31.884Z","cmd":"js","args":"(()=>{const c=document.querySelector('.img-card .card-img'); c.click(); return 'ok'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":5,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:31.940Z","cmd":"wait","args":"900","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"900\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 900 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:31.995Z","cmd":"js","args":"({dialogTitle: document.querySelector('.el-dialog__title')?.textContent, canvas: !!document.querySelector('.wb-canvas'), canvasSize: (()=>{const c=document.querySelector('.wb-canvas'); return c? c.wid…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:35.710Z","cmd":"js","args":"(()=>{const vis=[...document.querySelectorAll('.el-overlay')].filter(o=>getComputedStyle(o).display!=='none'); const d=vis[vis.length-1].querySelector('.el-dialog'); return {title: d.querySelector('.e…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:44.437Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); const r=c.getBoundingClientRect(); const fire=(t,x,y)=>c.dispatchEvent(new PointerEvent(t,{bubbles:true,cancelable:true,pointerId:7,clientX:x,client…","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:44.479Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:44.525Z","cmd":"js","args":"([...document.querySelectorAll('.editor-toolbar button')].map(b=>b.textContent.trim()))","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:49.936Z","cmd":"js","args":"(()=>{const vis=[...document.querySelectorAll('.el-overlay')].filter(o=>getComputedStyle(o).display!=='none'); const d=vis[vis.length-1].querySelector('.el-dialog'); [...d.querySelectorAll('.el-dialog…","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:49.991Z","cmd":"wait","args":"1500","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"1500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 1500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:50.039Z","cmd":"js","args":"(()=>{const vis=[...document.querySelectorAll('.el-overlay')].filter(o=>getComputedStyle(o).display!=='none'); const el=document.querySelector('.el-message'); return {openDialogs: vis.length, toast: e…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:55:54.029Z","cmd":"wait","args":"500","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:55:54.067Z","cmd":"js","args":"(()=>{const vis=[...document.querySelectorAll('.el-overlay')].filter(o=>getComputedStyle(o).display!=='none'); return vis.map(o=>o.querySelector('.el-dialog__title')?.textContent)})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:56:15.899Z","cmd":"reload","args":"","origin":"http://localhost:18080/admin/datasets/102","durationMs":21,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:56:15.970Z","cmd":"wait","args":"2500","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"2500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 2500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:56:16.048Z","cmd":"js","args":"([...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99'))?.querySelector('.card-sub')?.textContent)","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:56:18.800Z","cmd":"js","args":"({cards: document.querySelectorAll('.img-card').length, sub: [...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99'))?.querySelector('.card-sub')?.textContent})","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:56:24.722Z","cmd":"js","args":"(()=>{const btn=[...document.querySelectorAll('button')].find(b=>b.textContent.includes('添加')); btn.click(); return 'ok'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":4,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T05:56:24.768Z","cmd":"wait","args":"500","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T05:56:24.815Z","cmd":"js","args":"(()=>{const vis=[...document.querySelectorAll('.el-overlay')].filter(o=>getComputedStyle(o).display!=='none'); const d=vis[vis.length-1].querySelector('.el-dialog'); const f=[...d.querySelectorAll('.e…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:00:56.119Z","cmd":"reload","args":"","origin":"http://localhost:18080/admin/datasets/102","durationMs":97,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:00:56.199Z","cmd":"wait","args":"2500","origin":"http://localhost:18080/admin/datasets/102","durationMs":4,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"2500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 2500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:00:56.284Z","cmd":"js","args":"(()=>{const card=[...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99')); const badges=[...card.querySelectorAll('.badge')].map(b=>b.textContent.trim()); card.queryS…","origin":"http://localhost:18080/admin/datasets/102","durationMs":4,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of undefined (reading 'querySelectorAll')\n at eval (eval at evaluate (:290:30), <anonymous>:1:132)\n at eval (eval at evaluate (:290:30), <anonymous>:1:248)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScr…"}
{"ts":"2026-08-26T06:00:56.460Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap'); const dlg=document.querySelector('.el-dialog.is-fullscreen'); return {fullscreen: !!dlg, canvas: c.w…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of null (reading 'width')\n at eval (eval at evaluate (:290:30), <anonymous>:1:200)\n at eval (eval at evaluate (:290:30), <anonymous>:1:477)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript.<anonymous> …"}
{"ts":"2026-08-26T06:01:04.940Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:01:04.980Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:12.176Z","cmd":"js","args":"(()=>{const card=[...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99')); const badges=[...card.querySelectorAll('.badge')].map(b=>b.textContent.trim()); card.queryS…","origin":"http://localhost:18080/admin/datasets/102","durationMs":9,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:12.237Z","cmd":"wait","args":"1200","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"1200\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 1200 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:01:12.292Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap'); const dlg=document.querySelector('.el-dialog.is-fullscreen'); return {fullscreen: !!dlg, canvas: c.w…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:17.468Z","cmd":"js","args":"(()=>{const btn=[...document.querySelectorAll('.editor-toolbar button')].find(b=>b.textContent.trim()==='放大'); btn.click(); return 'zoomed-in'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:17.517Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:01:17.562Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); return {afterZoomIn: c.width+'x'+c.height, ratio: (c.width/c.height).toFixed(4)}})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:20.429Z","cmd":"js","args":"(()=>{const btn=[...document.querySelectorAll('.editor-toolbar button')].find(b=>b.textContent.trim()==='适应'); btn.click(); return 'fit'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:20.473Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:01:20.515Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); return {afterFit: c.width+'x'+c.height}})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:20.561Z","cmd":"js","args":"(()=>{const w=document.querySelector('.canvas-wrap'); w.dispatchEvent(new WheelEvent('wheel',{bubbles:true,cancelable:true,deltaY:-120,ctrlKey:true})); return 'wheel'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:20.606Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:01:20.648Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); return {afterCtrlWheel: c.width+'x'+c.height}})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:26.709Z","cmd":"js","args":"(()=>{const btns=[...document.querySelectorAll('.editor-toolbar button')].map(b=>b.textContent.trim()); const fit=btns.indexOf('适应'); const el=[...document.querySelectorAll('.editor-toolbar button')][…","origin":"http://localhost:18080/admin/datasets/102","durationMs":4,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:26.761Z","cmd":"wait","args":"400","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"400\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 400 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:01:26.803Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); return {canvas: c.width+'x'+c.height, wrap: document.querySelector('.canvas-wrap').clientWidth+'x'+document.querySelector('.canvas-wrap').clientHeig…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:35.136Z","cmd":"js","args":"(()=>{const body=[...document.querySelectorAll('.el-dialog__body')].find(b=>b.querySelector('.canvas-wrap')); const cs=getComputedStyle(body); return {bodyDisplay: cs.display, bodyHeight: body.clientH…","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:01:47.483Z","cmd":"js","args":"(()=>{const dlg=document.querySelector('.el-dialog.is-fullscreen'); const overlay=dlg.closest('.el-overlay'); const parentScoped=[...document.querySelectorAll('[data-v-*]')].length; return {dialogClas…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: SyntaxError: Failed to execute 'querySelectorAll' on 'Document': '[data-v-*]' is not a valid selector.\n at eval (eval at evaluate (:290:30), <anonymous>:1:143)\n at eval (eval at evaluate (:290:30), <anonymous>:1:360)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous…"}
{"ts":"2026-08-26T06:01:49.708Z","cmd":"js","args":"(()=>{const dlg=document.querySelector('.el-dialog.is-fullscreen'); const overlay=dlg.closest('.el-overlay'); return {dialogClass: dlg.className, overlayAttrs: [...overlay.attributes].map(a=>a.name).j…","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:02:29.949Z","cmd":"reload","args":"","origin":"http://localhost:18080/admin/datasets/102","durationMs":86,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:02:30.013Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:02:30.071Z","cmd":"js","args":"(()=>{const card=[...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99')); card.querySelector('.card-img').click(); return 'opened'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of undefined (reading 'querySelector')\n at eval (eval at evaluate (:290:30), <anonymous>:1:115)\n at eval (eval at evaluate (:290:30), <anonymous>:1:168)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript…"}
{"ts":"2026-08-26T06:02:30.233Z","cmd":"js","args":"(()=>{const body=[...document.querySelectorAll('.el-dialog__body')].find(b=>b.querySelector('.canvas-wrap')); const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap…","origin":"http://localhost:18080/admin/datasets/102","durationMs":78,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of undefined (reading 'closest')\n at eval (eval at evaluate (:290:30), <anonymous>:1:220)\n at eval (eval at evaluate (:290:30), <anonymous>:1:563)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript.<anon…"}
{"ts":"2026-08-26T06:02:32.653Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:02:32.699Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:02:40.483Z","cmd":"js","args":"(()=>{const card=[...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99')); card.querySelector('.card-img').click(); return 'opened'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":13,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:02:40.538Z","cmd":"wait","args":"1200","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"1200\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 1200 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:02:40.590Z","cmd":"js","args":"(()=>{const body=[...document.querySelectorAll('.el-dialog__body')].find(b=>b.querySelector('.canvas-wrap')); const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:17.202Z","cmd":"reload","args":"","origin":"http://localhost:18080/admin/datasets/102","durationMs":77,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:17.263Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:03:17.324Z","cmd":"js","args":"(()=>{const card=[...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99')); card.querySelector('.card-img').click(); return 'opened'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of undefined (reading 'querySelector')\n at eval (eval at evaluate (:290:30), <anonymous>:1:115)\n at eval (eval at evaluate (:290:30), <anonymous>:1:168)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript…"}
{"ts":"2026-08-26T06:03:17.394Z","cmd":"js","args":"(()=>{const body=[...document.querySelectorAll('.el-dialog__body')].find(b=>b.querySelector('.canvas-wrap')); const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: TypeError: Cannot read properties of undefined (reading 'closest')\n at eval (eval at evaluate (:290:30), <anonymous>:1:220)\n at eval (eval at evaluate (:290:30), <anonymous>:1:530)\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript.<anon…"}
{"ts":"2026-08-26T06:03:23.617Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:03:23.663Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:26.532Z","cmd":"js","args":"(()=>{const card=[...document.querySelectorAll('.img-card')].find(c=>c.textContent.includes('pheasant_99')); card.querySelector('.card-img').click(); return 'opened'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":11,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:26.578Z","cmd":"wait","args":"1200","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"1200\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 1200 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:03:26.623Z","cmd":"js","args":"(()=>{const body=[...document.querySelectorAll('.el-dialog__body')].find(b=>b.querySelector('.canvas-wrap')); const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:31.140Z","cmd":"js","args":"(()=>{const btn=[...document.querySelectorAll('.editor-toolbar button')].find(b=>b.textContent.trim()==='放大'); btn.click(); btn.click(); return 'in'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:31.182Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:03:31.224Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap'); return {canvas: c.width+'x'+c.height, wrapScroll: w.scrollHeight+'x'+w.scrollWidth, scrolled: w.scro…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:35.215Z","cmd":"js","args":"(()=>{const btn=[...document.querySelectorAll('.editor-toolbar button')].find(b=>b.textContent.trim()==='放大'); for(let i=0;i<4;i++) btn.click(); return 'in'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:35.258Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:03:35.302Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); const w=document.querySelector('.canvas-wrap'); w.scrollTop=200; w.scrollLeft=150; return {canvas: c.width+'x'+c.height, wrapClient: w.clientWidth+'…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:03:39.018Z","cmd":"js","args":"(()=>{const btn=[...document.querySelectorAll('.editor-toolbar button')].find(b=>b.textContent.trim()==='放大'); btn.click(); return 'in'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:04:58.450Z","cmd":"js","args":"(()=>{const c=document.querySelector('.wb-canvas'); const r=c.getBoundingClientRect(); const fire=(t,x,y)=>c.dispatchEvent(new PointerEvent(t,{bubbles:true,cancelable:true,pointerId:7,clientX:x,client…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:04:58.604Z","cmd":"wait","args":"1500","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"1500\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 1500 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:06:38.931Z","cmd":"reload","args":"","origin":"http://localhost:18080/admin/datasets/102","durationMs":83,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:06:39.080Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:06:41.009Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:06:41.050Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:07:42.728Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:07:49.710Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:07:49.749Z","cmd":"js","args":"document.querySelectorAll('.img-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:07:57.577Z","cmd":"wait","args":"1200","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"1200\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 1200 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:07:57.626Z","cmd":"js","args":"(()=>{const d=document.querySelector('.el-dialog.is-fullscreen'); const zc=d.querySelector('.zoom-ctrl'); return {zoomCtrl: zc ? {pct: zc.querySelector('.zoom-pct')?.textContent, buttons: zc.querySele…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:08:10.237Z","cmd":"js","args":"(()=>{const d=document.querySelector('.el-dialog.is-fullscreen'); const c=d.querySelector('.wb-canvas'); return {pct: d.querySelector('.zoom-pct').textContent, canvas: c.width+'x'+c.height, smoothing:…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:08:18.953Z","cmd":"js","args":"(()=>{const d=document.querySelector('.el-dialog.is-fullscreen'); const b=d.querySelectorAll('.zoom-ctrl button')[0]; for(let i=0;i<7;i++) b.click(); return 'zoomed'})()","origin":"http://localhost:18080/admin/datasets/102","durationMs":8,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:08:19.002Z","cmd":"wait","args":"300","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"300\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 300 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:08:19.045Z","cmd":"js","args":"(()=>{const d=document.querySelector('.el-dialog.is-fullscreen'); const c=d.querySelector('.wb-canvas'); return {pct: d.querySelector('.zoom-pct').textContent, canvas: c.width+'x'+c.height, smoothing:…","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:08:26.842Z","cmd":"js","args":"(()=>{const d=document.querySelector('.el-dialog.is-fullscreen'); const c=d.querySelector('.wb-canvas'); const b=d.querySelectorAll('.zoom-ctrl button')[0]; const before=c.width; b.click(); return {pc…","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:25:05.099Z","cmd":"wait","args":"3000","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"error","hasCookies":false,"mode":"launched","error":"for: Error while parsing css selector \"3000\". Did you mean to CSS.escape it?\nCall log:\n\u001b[2m - waiting for 3000 to be visible\u001b[22m\n"}
{"ts":"2026-08-26T06:25:05.142Z","cmd":"js","args":"document.querySelectorAll('.ds-card').length","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:25:21.370Z","cmd":"console","args":"","origin":"http://localhost:18080/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:25:21.414Z","cmd":"js","args":"document.title","origin":"http://localhost:18080/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T06:25:35.007Z","cmd":"js","args":"(() => { const u = document.querySelector('.el-upload--picture-card'); const tile = document.querySelector('.upload-tile'); return { hasPictureCard: !!u, tileText: tile ? tile.textContent.trim() : nul…","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:25:44.745Z","cmd":"upload","args":".el-upload--picture-card input[type=file] /private/tmp/real_screen_small.jpg","origin":"http://localhost:18080/admin/datasets/102","durationMs":27,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T06:25:44.793Z","cmd":"js","args":"document.querySelectorAll('.el-upload-list__item').length + ' preview items; img src: ' + (document.querySelector('.el-upload-list__item img')?.src?.slice(0,60) || 'none')","origin":"http://localhost:18080/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T08:36:36.057Z","cmd":"screenshot","args":"/tmp/editor-state.png","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":55,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:36:38.628Z","cmd":"js","args":"(() => { const c = document.querySelector('.wb-canvas'); if (!c) return 'no canvas'; const ctx = c.getContext('2d'); const d = ctx.getImageData(0,0,c.width,c.height).data; let hasGreen=0, hasOrange=0;…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":6,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:08.255Z","cmd":"js","args":"(() => { const c = document.querySelector('.wb-canvas'); const ctx = c.getContext('2d'); const img = ctx.getImageData(0,0,c.width,c.height); const d = img.data; // 扫描中线行，检查绿色描边是否连续（实线） vs 间断（虚线） const…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":7,"status":"error","hasCookies":false,"mode":"launched","error":"evaluate: SyntaxError: Unexpected end of input\n at eval (<anonymous>)\n at UtilityScript.evaluate (<anonymous>:290:30)\n at UtilityScript.<anonymous> (<anonymous>:1:44)"}
{"ts":"2026-08-26T08:37:11.090Z","cmd":"js","args":"(() => { const c = document.querySelector('.wb-canvas'); if (!c) return 'no canvas'; const ctx = c.getContext('2d'); const img = ctx.getImageData(0,0,c.width,c.height); const d = img.data; const midY …","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":3,"status":"ok","hasCookies":false,"mode":"launched"}
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{"ts":"2026-08-26T08:37:25.566Z","cmd":"js","args":"(() => { const c = document.querySelector('.wb-canvas'); const r = c.getBoundingClientRect(); const px = r.left + (138/r.width)*r.width; const py = r.top + (253/r.height)*r.height; const rect2 = c.get…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":4,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:26.640Z","cmd":"js","args":"(() => { const c = document.querySelector('.wb-canvas'); const ctx = c.getContext('2d'); const d = ctx.getImageData(0,0,c.width,c.height).data; const w=c.width,h=c.height; let green=0,orange=0; for (l…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:30.842Z","cmd":"dialog-accept","args":"放弃修改","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:30.891Z","cmd":"js","args":"(() => { const close = document.querySelector('.editor-fullscreen .el-dialog__headerbtn'); if (close) { close.click(); return 'clicked close'; } return 'no close btn'; })()","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":7,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:31.972Z","cmd":"text","args":"","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:32.016Z","cmd":"console","args":"--errors","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":0,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:34.727Z","cmd":"js","args":"(() => { const d = document.querySelector('.editor-fullscreen'); const m = document.querySelector('.el-message-box'); return 'editor-open: ' + (d && d.style.display !== 'none') + ' | discard-dialog: '…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:36.882Z","cmd":"js","args":"(() => { const btns = [...document.querySelectorAll('.el-message-box__btns .el-button')]; const b = btns.find(x => x.textContent.includes('放弃修改')); if (b) { b.click(); return 'discarded'; } return 'no…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":4,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:37.941Z","cmd":"js","args":"(() => { const d = document.querySelector('.editor-fullscreen'); return 'editor-open: ' + (d && d.style.display !== 'none'); })()","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:41.401Z","cmd":"js","args":"(() => { const d = document.querySelector('.editor-fullscreen'); const m = document.querySelector('.el-message-box'); return 'editor-open: ' + (d ? getComputedStyle(d).display : 'gone') + ' | discard-…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:45.186Z","cmd":"js","args":"(() => { const d = document.querySelector('.editor-fullscreen'); return 'editor: ' + (d ? getComputedStyle(d).display : 'gone'); })()","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:45.230Z","cmd":"text","args":"","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:51.268Z","cmd":"js","args":"(() => { const close = document.querySelector('.editor-fullscreen .el-dialog__headerbtn'); if (close) { close.click(); return 'clicked close'; } return 'no close'; })()","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":7,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:52.351Z","cmd":"js","args":"(() => { const btns = [...document.querySelectorAll('.el-message-box__btns .el-button')]; const b = btns.find(x => x.textContent.includes('放弃修改')); if (b) { b.click(); return 'clicked discard'; } retu…","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":2,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:37:53.929Z","cmd":"js","args":"(() => { const d = document.querySelector('.editor-fullscreen'); return 'editor: ' + (d ? getComputedStyle(d).display : 'gone'); })()","origin":"http://127.0.0.1:18081/admin/datasets/102","durationMs":1,"status":"ok","hasCookies":false,"mode":"launched"}
{"ts":"2026-08-26T08:38:27.020Z","cmd":"goto","args":"http://127.0.0.1:18081/admin/datasets","origin":"http://127.0.0.1:18081/admin/datasets","durationMs":35,"status":"ok","hasCookies":false,"mode":"launched"}
.gstack/browse-console.log
+1
View File
@@ -0,0 +1 @@
[2026-08-26T08:37:11.090Z] [warning] Canvas2D: Multiple readback operations using getImageData are faster with the willReadFrequently attribute set to true. See: https://html.spec.whatwg.org/multipage/canvas.html#concept-canvas-will-read-frequently
.gstack/browse-network.log
+84
View File
@@ -0,0 +1,84 @@
[2026-08-26T07:59:04.446Z] GET http://127.0.0.1:18081/admin/ → 200 (9ms, 414B)
[2026-08-26T07:59:04.467Z] GET http://127.0.0.1:18081/admin/assets/index-CynwB22O.js → 200 (24ms, 1107009B)
[2026-08-26T07:59:04.467Z] GET http://127.0.0.1:18081/admin/assets/index-DpxmxxjD.css → 200 (1ms, 369092B)
[2026-08-26T07:59:08.763Z] GET http://127.0.0.1:18081/admin/login → 200 (2ms, 414B)
[2026-08-26T07:59:08.770Z] GET http://127.0.0.1:18081/admin/assets/index-CynwB22O.js → 200 (?ms, -195B)
[2026-08-26T07:59:08.771Z] GET http://127.0.0.1:18081/admin/assets/index-DpxmxxjD.css → 200 (?ms, -187B)
[2026-08-26T07:59:26.143Z] GET http://127.0.0.1:18081/admin/datasets → 200 (1ms, 414B)
[2026-08-26T07:59:26.149Z] GET http://127.0.0.1:18081/admin/assets/index-CynwB22O.js → 200 (1ms, -195B)
[2026-08-26T07:59:26.151Z] GET http://127.0.0.1:18081/admin/assets/index-DpxmxxjD.css → 200 (1ms, -187B)
[2026-08-26T07:59:26.212Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=12 → 200 (15ms, 1460B)
[2026-08-26T07:59:26.236Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/cover?datasetId=102&token=Tongli686%5E*%5E → 200 (4ms, 1127453B)
[2026-08-26T07:59:52.214Z] GET http://127.0.0.1:18081/api/v1/admin/app-config → 200 (45ms, 262B)
[2026-08-26T08:00:31.974Z] POST http://127.0.0.1:18081/api/v1/admin/app-config → 200 (6ms, 46B)
[2026-08-26T08:01:05.293Z] GET http://127.0.0.1:18081/admin/datasets/102 → 200 (1ms, 414B)
[2026-08-26T08:01:05.299Z] GET http://127.0.0.1:18081/admin/assets/index-CynwB22O.js → 304 (1ms, ?B)
[2026-08-26T08:01:05.299Z] GET http://127.0.0.1:18081/admin/assets/index-DpxmxxjD.css → 304 (2ms, ?B)
[2026-08-26T08:01:05.316Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=100 → 200 (5ms, 1460B)
[2026-08-26T08:01:05.317Z] GET http://127.0.0.1:18081/api/v1/admin/label-workbench?datasetId=102 → pending (?ms, ?B)
[2026-08-26T08:01:05.317Z] GET http://127.0.0.1:18081/api/v1/admin/app-config → 200 (3ms, 303B)
[2026-08-26T08:01:05.476Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/images?datasetId=102 → 200 (12ms, 18472B)
[2026-08-26T08:01:05.488Z] GET http://127.0.0.1:18081/api/v1/admin/label-tasks?page=1&size=100 → 200 (23ms, 65B)
[2026-08-26T08:01:05.495Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_99.jpg&token=Tongli686%5E*%5E → 200 (34ms, 3411120B)
[2026-08-26T08:01:05.495Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_98.jpg&token=Tongli686%5E*%5E → 200 (33ms, 3405948B)
[2026-08-26T08:01:05.495Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_97.jpg&token=Tongli686%5E*%5E → 200 (33ms, 2828567B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_96.jpg&token=Tongli686%5E*%5E → 200 (33ms, 3918728B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_95.jpg&token=Tongli686%5E*%5E → 200 (32ms, 3541877B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_94.jpg&token=Tongli686%5E*%5E → 200 (29ms, 2875825B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_93.jpg&token=Tongli686%5E*%5E → 200 (37ms, 3190142B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_92.jpg&token=Tongli686%5E*%5E → 200 (50ms, 3715545B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_91.jpg&token=Tongli686%5E*%5E → 200 (75ms, 3411979B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_90.jpg&token=Tongli686%5E*%5E → 200 (88ms, 3133503B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_89.jpg&token=Tongli686%5E*%5E → 200 (90ms, 3400415B)
[2026-08-26T08:01:05.496Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_88.jpg&token=Tongli686%5E*%5E → 200 (94ms, 2239294B)
[2026-08-26T08:01:05.497Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_87.jpg&token=Tongli686%5E*%5E → 200 (95ms, 2213846B)
[2026-08-26T08:01:05.497Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_86.jpg&token=Tongli686%5E*%5E → 200 (142ms, 2372922B)
[2026-08-26T08:01:05.502Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_85.jpg&token=Tongli686%5E*%5E → 200 (138ms, 3616144B)
[2026-08-26T08:01:05.506Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_84.jpg&token=Tongli686%5E*%5E → 200 (134ms, 3220261B)
[2026-08-26T08:01:05.506Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_83.jpg&token=Tongli686%5E*%5E → 200 (135ms, 3497553B)
[2026-08-26T08:01:05.510Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_82.jpg&token=Tongli686%5E*%5E → 200 (130ms, 3075680B)
[2026-08-26T08:21:22.121Z] GET http://127.0.0.1:18081/admin/datasets → 200 (42ms, 414B)
[2026-08-26T08:21:22.265Z] GET http://127.0.0.1:18081/admin/assets/index-CW3B_yra.js → 200 (42ms, 1103639B)
[2026-08-26T08:21:22.265Z] GET http://127.0.0.1:18081/admin/assets/index-BpJ89PCs.css → 200 (5ms, 369000B)
[2026-08-26T08:21:22.425Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=12 → 200 (14ms, 1460B)
[2026-08-26T08:21:22.442Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/cover?datasetId=102&token=Tongli686%5E*%5E → 200 (5ms, 972603B)
[2026-08-26T08:21:26.772Z] GET http://127.0.0.1:18081/admin/datasets → 200 (1ms, -185B)
[2026-08-26T08:21:26.777Z] GET http://127.0.0.1:18081/admin/assets/index-CW3B_yra.js → 200 (?ms, -195B)
[2026-08-26T08:21:26.777Z] GET http://127.0.0.1:18081/admin/assets/index-BpJ89PCs.css → 200 (?ms, -187B)
[2026-08-26T08:21:26.798Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=12 → 200 (13ms, 1460B)
[2026-08-26T08:21:26.812Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/cover?datasetId=102&token=Tongli686%5E*%5E → 200 (?ms, -174B)
[2026-08-26T08:36:06.413Z] GET http://127.0.0.1:18081/admin/datasets → 200 (2ms, 414B)
[2026-08-26T08:36:06.420Z] GET http://127.0.0.1:18081/admin/assets/index-BpSGpVnT.js → 200 (23ms, 1103309B)
[2026-08-26T08:36:06.420Z] GET http://127.0.0.1:18081/admin/assets/index-A2o_Z8bw.css → 200 (1ms, 369000B)
[2026-08-26T08:36:06.505Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=12 → 200 (6ms, 1460B)
[2026-08-26T08:36:06.512Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/cover?datasetId=102&token=Tongli686%5E*%5E → 304 (?ms, ?B)
[2026-08-26T08:36:26.229Z] GET http://127.0.0.1:18081/admin/datasets/102 → 200 (2ms, 414B)
[2026-08-26T08:36:26.234Z] GET http://127.0.0.1:18081/admin/assets/index-BpSGpVnT.js → 304 (1ms, ?B)
[2026-08-26T08:36:26.234Z] GET http://127.0.0.1:18081/admin/assets/index-A2o_Z8bw.css → 304 (1ms, ?B)
[2026-08-26T08:36:26.255Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=100 → 200 (5ms, 1460B)
[2026-08-26T08:36:26.255Z] GET http://127.0.0.1:18081/api/v1/admin/label-workbench?datasetId=102 → 200 (195ms, 75306B)
[2026-08-26T08:36:26.452Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/images?datasetId=102 → 200 (2ms, 18472B)
[2026-08-26T08:36:26.459Z] GET http://127.0.0.1:18081/api/v1/admin/label-tasks?page=1&size=100 → 200 (4ms, 65B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_99.jpg&token=Tongli686%5E*%5E → 200 (2ms, ?B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_98.jpg&token=Tongli686%5E*%5E → 200 (2ms, 3405948B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_97.jpg&token=Tongli686%5E*%5E → 200 (3ms, 2828567B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_96.jpg&token=Tongli686%5E*%5E → 200 (4ms, ?B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_95.jpg&token=Tongli686%5E*%5E → 200 (3ms, ?B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_94.jpg&token=Tongli686%5E*%5E → 200 (3ms, 2875825B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_93.jpg&token=Tongli686%5E*%5E → 200 (27ms, 3190142B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_92.jpg&token=Tongli686%5E*%5E → 200 (27ms, 3715545B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_91.jpg&token=Tongli686%5E*%5E → 200 (51ms, 3411979B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_90.jpg&token=Tongli686%5E*%5E → 200 (56ms, 3133503B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_89.jpg&token=Tongli686%5E*%5E → 200 (135ms, ?B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_88.jpg&token=Tongli686%5E*%5E → 200 (142ms, ?B)
[2026-08-26T08:36:26.473Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_87.jpg&token=Tongli686%5E*%5E → 200 (160ms, 2213846B)
[2026-08-26T08:36:26.474Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_86.jpg&token=Tongli686%5E*%5E → 200 (158ms, 2372922B)
[2026-08-26T08:36:26.474Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_85.jpg&token=Tongli686%5E*%5E → 200 (159ms, ?B)
[2026-08-26T08:36:26.475Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_84.jpg&token=Tongli686%5E*%5E → 200 (159ms, ?B)
[2026-08-26T08:36:26.475Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_83.jpg&token=Tongli686%5E*%5E → 200 (160ms, 3497553B)
[2026-08-26T08:36:26.475Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/image?datasetId=102&filename=pheasant_82.jpg&token=Tongli686%5E*%5E → 200 (160ms, 3075680B)
[2026-08-26T08:38:26.988Z] GET http://127.0.0.1:18081/admin/datasets → 200 (1ms, 414B)
[2026-08-26T08:38:26.995Z] GET http://127.0.0.1:18081/admin/assets/index-BpSGpVnT.js → 304 (1ms, ?B)
[2026-08-26T08:38:26.995Z] GET http://127.0.0.1:18081/admin/assets/index-A2o_Z8bw.css → 304 (?ms, ?B)
[2026-08-26T08:38:27.020Z] GET http://127.0.0.1:18081/api/v1/admin/datasets?page=1&size=12 → 200 (4ms, 1460B)
[2026-08-26T08:38:27.026Z] GET http://127.0.0.1:18081/api/v1/admin/datasets/cover?datasetId=102&token=Tongli686%5E*%5E → 200 (2ms, 972603B)
.gstack/claude-available.json
+6
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@@ -0,0 +1,6 @@
{
"available": true,
"path": "/opt/homebrew/bin/claude",
"install_url": "https://docs.anthropic.com/en/docs/claude-code",
"checked_at": "2026-08-26T08:00:04.399Z"
}
.idea/dataSources.local.xml
Generated
+17
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@@ -0,0 +1,17 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<database-info product="SQLite" version="3.51.1" jdbc-version="4.2" driver-name="SQLite JDBC" driver-version="3.51.1.0" dbms="SQLITE" exact-version="3.51.1" exact-driver-version="3.51">
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.idea/dataSources.xml
Generated
+17
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@@ -0,0 +1,17 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<jdbc-driver>org.sqlite.JDBC</jdbc-driver>
<jdbc-url>jdbc:sqlite:$PROJECT_DIR$/server/data/observer.db</jdbc-url>
<working-dir>$ProjectFileDir$</working-dir>
<libraries>
<library>
<url>file://$APPLICATION_CONFIG_DIR$/jdbc-drivers/Xerial SQLiteJDBC/3.51.1/org/xerial/sqlite-jdbc/3.51.1.0/sqlite-jdbc-3.51.1.0.jar</url>
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.idea/dataSources/09fcd334-c3f7-4a03-a88b-918991abce57.xml
Generated
+2199
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.idea/dataSources/09fcd334-c3f7-4a03-a88b-918991abce57/storage_v2/_src_/schema/main.uQUzAA.meta
Generated
+2
View File
@@ -0,0 +1,2 @@
#n:main
!<md> [0, 0, null, null, -2147483648, -2147483648]
.idea/observer.iml
Generated
+4
View File
@@ -0,0 +1,4 @@
<?xml version="1.0" encoding="UTF-8"?>
<module version="4">
<component name="Go" enabled="true" />
</module>
.idea/vcs.xml
Generated
+6
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@@ -0,0 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
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</project>
.idea/workspace.xml
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+178
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="AutoImportSettings">
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<component name="ChangeListManager">
<list default="true" id="b061ab81-c08b-429a-b948-ff0f65eb92f8" name="Changes" comment="1">
<change afterPath="$PROJECT_DIR$/server/.gitignore" afterDir="false" />
<change afterPath="$PROJECT_DIR$/server/training/train_server.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/.gitignore" beforeDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/.gitignore" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/.gitignore" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/README.md" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/README.md" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/lib/app.dart" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/lib/app.dart" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/flutter_app/lib/detection/detection_result.dart" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/lib/detection/detection_result.dart" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/lib/detection/detector_worker.dart" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/lib/detection/detector_worker.dart" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/lib/detection/tflite_detector.dart" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/lib/detection/tflite_detector.dart" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/lib/update/update_checker.dart" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/lib/update/update_checker.dart" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/pubspec.lock" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/pubspec.lock" afterDir="false" />
<change beforePath="$PROJECT_DIR$/flutter_app/pubspec.yaml" beforeDir="false" afterPath="$PROJECT_DIR$/flutter_app/pubspec.yaml" afterDir="false" />
<change beforePath="$PROJECT_DIR$/server/README.md" beforeDir="false" afterPath="$PROJECT_DIR$/server/README.md" afterDir="false" />
<change beforePath="$PROJECT_DIR$/server/admin_dist/assets/index-BcKljL0h.css" beforeDir="false" />
<change beforePath="$PROJECT_DIR$/server/admin_dist/assets/index-FJsP2pyZ.js" beforeDir="false" />
<change beforePath="$PROJECT_DIR$/server/admin_dist/index.html" beforeDir="false" afterPath="$PROJECT_DIR$/server/admin_dist/index.html" afterDir="false" />
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flutter_app/.gitignore
+1 -1
View File
@@ -38,7 +38,7 @@ app.*.symbols
# Obfuscation related
app.*.map.json
observer-latest.apk
# Android Studio will place build artifacts here
/android/app/debug
/android/app/profile
flutter_app/README.md
+23
View File
@@ -27,6 +27,29 @@ xcrun devicectl device process launch --console --terminate-existing \
"In iOS 14+, debug mode Flutter apps can only be launched from Flutter tooling"。
debug 调试必须用 `flutter run -d <设备ID>` 或 Xcode IDE 启动（`flutter devices` 查设备ID）；
从图标启动只对 release 构建有效。
## 模型热更新（多数据集模型）
模型与 APK 更新走**独立通道**：启动时拉取 `GET /api/v1/app/update` 随附的
`models` 目录（公开接口，无需登录），与 `UpdateChecker` 的 APK 检查并行。
- **目录条目**：`{datasetId, datasetName, version, labels[], sizeBytes, sha256,
downloadUrl}`；服务器未发布模型时不返回 `models` 字段，App 回退内置
`assets/model.tflite`（标签 `内置`）。
- **存储**：应用私有目录 `models/<datasetId>/`，含 `model.tflite`、
`labels.json`、`meta.json`（meta 记录 `{version, sha256}`）。版本与摘要都
未变化时跳过下载；变化则下载到 `.part` 临时文件、sha256 校验通过后
原子 rename 替换，失败重试一次并保留旧模型，下次启动再试。
- **清理**：服务器下线的数据集下次同步时删除本地对应目录。
- **并行推理合并**：识别时加载全部已下载模型（`DetectorWorker` isolate 内
逐模型加载，单个失败不影响其他），同帧各模型独立推理后按类别分组做
**跨模型 NMS**（同类别不同模型检出同一目标取高分去重，不同类别互不压制），
结果叠加 `modelName` 标注来源，内置模型兜底。
实现：`lib/models/model_manager.dart`（下载/校验/持久化，`ModelManager`
单例 + ChangeNotifier）、`lib/detection/detector_worker.dart`（多模型并行
推理与 `mergeAcrossModels`）、`lib/camera/camera_screen.dart`（启动同步 +
诊断行展示模型列表）。
- **模型输入是 NHWC**：`assets/model.tflite` 做过字节级手术（开头 TRANSPOSE→RESHAPE，
输入 [1,320,320,3]），改动记录见 git 历史，重导模型需同步处理，否则 iOS 报
"Node number 0 (TRANSPOSE) failed to prepare"。
flutter_app/android/build/reports/problems/problems-report.html
+663
View File
File diff suppressed because one or more lines are too long
flutter_app/lib/app.dart
+4
View File
@@ -8,6 +8,7 @@ import 'camera/camera_screen.dart';
import 'container.dart';
import 'home/home_screen.dart';
import 'legal/terms_screen.dart';
import 'models/model_manager.dart';
import 'payment/paywall_screen.dart';
import 'update/update_checker.dart';
import 'update/update_screen.dart';
@@ -93,6 +94,9 @@ class _StartupGateState extends State<StartupGate> {
}
final token = await session.readToken();
if (!mounted) return;
// 模型热更新：后台拉取模型目录并下载/更新各数据集模型（不阻塞启动，
// 相机页打开前若未就绪会兜底等待；下载失败回退内置资产模型）
ModelManager.instance.refresh();
Navigator.of(context)
.pushReplacementNamed(token == null ? '/login' : '/home');
}
flutter_app/lib/camera/camera_screen.dart
+34 -2
View File
@@ -6,6 +6,7 @@ import 'package:permission_handler/permission_handler.dart';
import 'package:wakelock_plus/wakelock_plus.dart';
import '../detection/detector_worker.dart';
import '../models/model_manager.dart';
import '../reminder/reminder.dart';
import 'app_camera_controller.dart';
import 'camera_view_model.dart';
@@ -119,8 +120,18 @@ class _CameraScreenState extends State<CameraScreen> {
setState(() => _permissionGranted = granted);
if (!granted) return;
// 模型热更新：优先使用已下载的数据集模型（启动时后台拉取；此处兜底等待，
// 下载慢/失败不阻塞相机启动——无下载模型时 worker 回退内置资产）
if (!ModelManager.instance.ready) {
try {
await ModelManager.instance
.refresh()
.timeout(const Duration(seconds: 15));
} catch (_) {}
}
// 模型加载/推理在后台 isolate，不阻塞 UI；worker 为 null 时仅预览并提示
final worker = await DetectorWorker.create();
final worker = await DetectorWorker.create(
models: ModelManager.instance.models);
final viewModel = CameraViewModel(reminder: Reminder());
viewModel.setModelReady(worker != null);
final analyzer = FrameAnalyzer(worker: worker, viewModel: viewModel);
@@ -276,6 +287,27 @@ class _CameraScreenState extends State<CameraScreen> {
return Stack(
fit: StackFit.expand,
children: [
// 模型热更新下载失败提示（已加载模型仍可用，仅提示补更新）
if (vm.state.modelReady && ModelManager.instance.error != null)
Positioned(
left: 16,
right: 16,
top: MediaQuery.of(context).padding.top + 56,
child: Container(
padding:
const EdgeInsets.symmetric(horizontal: 12, vertical: 6),
decoration: BoxDecoration(
color: Colors.black54,
borderRadius: BorderRadius.circular(8),
),
child: Text(
ModelManager.instance.error!,
textAlign: TextAlign.center,
style: const TextStyle(color: Colors.orange, fontSize: 12),
),
),
),
// 模型未加载时仅显示相机预览，不做检测标注（横幅置于顶栏下方，避免与底部诊断行重叠）
if (!vm.state.modelReady)
Positioned(
@@ -305,7 +337,7 @@ class _CameraScreenState extends State<CameraScreen> {
crossAxisAlignment: CrossAxisAlignment.center,
children: [
Text(
'阈值:${(_minScore * 100).toStringAsFixed(0)}% 模型:${vm.state.modelReady ? '已加载' : '未加载'} 帧:${vm.state.framesReceived} 流:${camera?.streamCallbacks ?? 0} 推理:${vm.state.debugDetectCalls}次 异常:${vm.state.debugDetectErrors}次 处理:${vm.state.debugLastMs}ms 最高分:${(vm.state.debugHighestScore * 100).toStringAsFixed(1)}% 图:${vm.state.imageWidthPx}x${vm.state.imageHeightPx} 传感:${camera?.sensorOrientation ?? '-'} 屏转:${camera?.displayDegrees ?? '-'} 旋:${camera?.rotationDegrees ?? 0} turn:${camera?.quarterTurns ?? '-'}',
'阈值:${(_minScore * 100).toStringAsFixed(0)}% 模型:${vm.state.modelReady ? ModelManager.instance.modelsLabel : '未加载'} 帧:${vm.state.framesReceived} 流:${camera?.streamCallbacks ?? 0} 推理:${vm.state.debugDetectCalls}次 异常:${vm.state.debugDetectErrors}次 处理:${vm.state.debugLastMs}ms 最高分:${(vm.state.debugHighestScore * 100).toStringAsFixed(1)}% 图:${vm.state.imageWidthPx}x${vm.state.imageHeightPx} 传感:${camera?.sensorOrientation ?? '-'} 屏转:${camera?.displayDegrees ?? '-'} 旋:${camera?.rotationDegrees ?? 0} turn:${camera?.quarterTurns ?? '-'}',
style: const TextStyle(color: Colors.white70, fontSize: 12),
),
if (_nativeStats.isNotEmpty)
flutter_app/lib/camera/detection_overlay.dart
+5 -2
View File
@@ -87,10 +87,13 @@ class _OverlayPainter extends CustomPainter {
_drawDashedRect(canvas, box, paint);
}
// 标签：框上方，含距离
// 标签：框上方，含距离；多模型时标注来源模型名（内置资产不标）
final dist = _distanceLabel(r);
final modelTag = r.modelName.isNotEmpty && r.modelName != '内置'
? '[${r.modelName}]'
: '';
final text =
'${_labels[r.label] ?? r.label} ${(r.score * 100).toInt()}%$dist';
'${_labels[r.label] ?? r.label}$modelTag ${(r.score * 100).toInt()}%$dist';
final textPainter = TextPainter(
text: TextSpan(
text: text,
flutter_app/lib/detection/detection_result.dart
+8
View File
@@ -9,6 +9,10 @@ class DetectionResult {
/// 轨迹已确认（多帧稳定/高分/活动确认），false = 候选，渲染为虚线
final bool confirmed;
/// 产出该框的模型（数据集 id 与名称；内置资产模型为 -1/空）
final int modelId;
final String modelName;
const DetectionResult({
required this.label,
required this.score,
@@ -17,6 +21,8 @@ class DetectionResult {
required this.right,
required this.bottom,
this.confirmed = true,
this.modelId = -1,
this.modelName = '',
});
double get width => right - left;
@@ -40,6 +46,8 @@ class DetectionResult {
right: right ?? this.right,
bottom: bottom ?? this.bottom,
confirmed: confirmed ?? this.confirmed,
modelId: modelId,
modelName: modelName,
);
}
flutter_app/lib/detection/detector_worker.dart
+131 -51
View File
@@ -7,17 +7,27 @@ import 'package:flutter/foundation.dart' show debugPrint;
import 'package:flutter/services.dart' show rootBundle;
import '../camera/motion_detector.dart';
import '../models/model_manager.dart';
import 'background_model.dart';
import 'detection_result.dart';
import 'nms.dart';
import 'tflite_detector.dart';
import 'visual_prior.dart';
/// 推理工作单元：模型加载与检测全部在后台 isolate 执行，
/// 主 isolate 只投递帧数据、接收结果，UI 不被推理阻塞（iOS 真机卡顿根因）。
///
/// 多模型并行推理：传入 [models]（各数据集下载模型）后，每帧逐模型推理，
/// 结果按类别分组跨模型 NMS 合并（同标签重复框取高分，不同标签互不压制）；
/// 无下载模型时回退内置资产模型。
class DetectorWorker {
static const String modelAsset = 'assets/model.tflite';
static const String labelsAsset = 'assets/labels.txt';
/// 内置资产回退模型的标识
static const int builtinModelId = -1;
static const String builtinModelName = '内置';
final Isolate _isolate;
final ReceivePort _responses;
@@ -54,16 +64,26 @@ class DetectorWorker {
});
}
/// 读取模型资产并启动后台推理 isolate；加载失败返回 null（App 降级为仅预览）。
static Future<DetectorWorker?> create() async {
/// 加载模型并启动后台推理 isolate；加载失败返回 null（App 降级为仅预览）。
/// [models] 为空时回退内置资产模型（模型缺失同样返回 null）。
static Future<DetectorWorker?> create({List<ModelBundle>? models}) async {
try {
final data = await rootBundle.load(modelAsset);
final modelBytes =
data.buffer.asUint8List(data.offsetInBytes, data.lengthInBytes);
final labels = (await rootBundle.loadString(labelsAsset))
.split('\n')
.where((l) => l.trim().isNotEmpty)
.toList();
final payload = <List<Object?>>[];
if (models != null && models.isNotEmpty) {
for (final m in models) {
payload.add(
[m.bytes, m.labels, m.datasetId, m.datasetName]);
}
} else {
final data = await rootBundle.load(modelAsset);
final modelBytes =
data.buffer.asUint8List(data.offsetInBytes, data.lengthInBytes);
final labels = (await rootBundle.loadString(labelsAsset))
.split('\n')
.where((l) => l.trim().isNotEmpty)
.toList();
payload.add([modelBytes, labels, builtinModelId, builtinModelName]);
}
final responses = ReceivePort();
final isolate = await Isolate.spawn(_workerMain, responses.sendPort);
@@ -73,7 +93,7 @@ class DetectorWorker {
.timeout(const Duration(seconds: 10),
onTimeout: () => throw TimeoutException('worker port timeout'));
worker._port = port;
port.send(['load', modelBytes, labels]);
port.send(['load', payload]);
await worker._ready.future
.timeout(const Duration(seconds: 20), onTimeout: () {
throw TimeoutException('model load timeout');
@@ -147,6 +167,8 @@ class DetectorWorker {
top: v[3] as double,
right: v[4] as double,
bottom: v[5] as double,
modelId: v.length > 6 ? (v[6] as num).toInt() : -1,
modelName: v.length > 7 ? v[7] as String : '',
);
}).toList();
final motion = (list[5] as List)
@@ -202,7 +224,7 @@ Future<void> _workerMain(SendPort mainPort) async {
mainPort.send(['port', control.sendPort]);
mainPort.send(['log', 'worker-start']);
TfliteDetector? detector;
List<TfliteDetector> detectors = const [];
MotionDetector? motion;
BackgroundModel? background;
var lastDualMs = 0; // 双字节序推理诊断节流
@@ -214,12 +236,35 @@ Future<void> _workerMain(SendPort mainPort) async {
case 'load':
mainPort.send(['log', 'load-received']);
try {
detector = await TfliteDetector.fromBuffer(
list[1] as Uint8List, (list[2] as List).cast<String>());
if (detector == null) {
mainPort.send(['load-error', 'fromBuffer 返回 null']);
// 多模型：逐模型加载，单个失败不阻塞其余；全部失败才报错
final loaded = <TfliteDetector>[];
final failures = <String>[];
for (final entry in list[1] as List) {
final e = entry as List;
final name = e.length > 3 ? e[3] as String : '';
final d = await TfliteDetector.fromBuffer(
e[0] as Uint8List,
(e[1] as List).cast<String>(),
modelId: (e[2] as num).toInt(),
modelName: name,
);
if (d == null) {
failures.add(name.isEmpty ? 'unknown' : name);
} else {
loaded.add(d);
}
}
if (loaded.isEmpty) {
mainPort.send([
'load-error',
'模型加载失败:${failures.join(',')} '
'(fromBuffer 返回 null)'
]);
} else {
mainPort.send(['log', 'fromBuffer-ok']);
detectors = loaded;
mainPort.send(['log',
'loaded=${loaded.map((d) => d.modelName).join(',')} '
'failed=${failures.isEmpty ? '-' : failures.join(',')}']);
motion = MotionDetector();
background = BackgroundModel();
mainPort.send(['ready']);
@@ -229,10 +274,9 @@ Future<void> _workerMain(SendPort mainPort) async {
}
break;
case 'frame':
final d = detector;
final m = motion;
final b = background;
if (d == null || m == null || b == null) break;
if (detectors.isEmpty || m == null || b == null) break;
final frame = list[1] as List;
final planes = (frame[0] as List).cast<Uint8List>();
final strides = (frame[1] as List).cast<int>();
@@ -302,42 +346,49 @@ Future<void> _workerMain(SendPort mainPort) async {
uv1 += ' v:min=$vMin max=$vMax mean=${(vSum / n2).toStringAsFixed(0)}';
}
}
// 首帧（或相机重启后）自适应判定 YUV 值域与色序，再跑正式推理
if (!isBgra && !d.yuvModeKnown) {
d.decideYuvChroma(
planes: planes,
strides: strides,
width: width,
height: height,
);
}
var results = d.detectRaw(
planes: planes,
strides: strides,
width: width,
height: height,
isBgra: isBgra,
rgbaOrder: rgbaOrder,
);
// 自愈：判定后 1.5s 内无检测且帧可用 → 用实时帧重跑完整判定
// （首帧模糊/暗帧导致启发式猜错时，画面稳定后 oracle 可分胜负）
if (!isBgra && d.yuvModeKnown && !d.yuvRetried &&
results.length <= 1 &&
DateTime.now().millisecondsSinceEpoch - d.yuvDecisionMs > 1500 &&
d.retryDecision(
// 多模型并行推理：每模型先首帧自适应判定 YUV 值域/色序，再逐模型推理；
// 汇总后按类别分组跨模型 NMS 合并（同标签重复框取高分，异标签互不压制）
var results = <DetectionResult>[];
for (final d in detectors) {
if (!isBgra && !d.yuvModeKnown) {
d.decideYuvChroma(
planes: planes,
strides: strides,
width: width,
height: height,
)) {
results = d.detectRaw(
);
}
var dets = d.detectRaw(
planes: planes,
strides: strides,
width: width,
height: height,
isBgra: isBgra,
rgbaOrder: rgbaOrder,
);
// 自愈：判定后 1.5s 内无检测且帧可用 → 用实时帧重跑完整判定
// （首帧模糊/暗帧导致启发式猜错时，画面稳定后 oracle 可分胜负）
if (!isBgra && d.yuvModeKnown && !d.yuvRetried &&
dets.length <= 1 &&
DateTime.now().millisecondsSinceEpoch - d.yuvDecisionMs >
1500 &&
d.retryDecision(
planes: planes,
strides: strides,
width: width,
height: height,
)) {
dets = d.detectRaw(
planes: planes,
strides: strides,
width: width,
height: height,
isBgra: isBgra,
);
}
results.addAll(dets);
}
results = mergeAcrossModels(results, TfliteDetector.iouThreshold);
// 低分野鸡框过视觉先验（颜色/位置），减少户外误报
results = VisualPrior.filter(
results,
@@ -358,14 +409,14 @@ Future<void> _workerMain(SendPort mainPort) async {
if (isBgra &&
DateTime.now().millisecondsSinceEpoch - lastDualMs > 3000) {
lastDualMs = DateTime.now().millisecondsSinceEpoch;
final a = d.diagnoseOrder(
final a = detectors.first.diagnoseOrder(
planes: planes,
strides: strides,
width: width,
height: height,
isBgra: true,
rgbaOrder: false);
final b = d.diagnoseOrder(
final b = detectors.first.diagnoseOrder(
planes: planes,
strides: strides,
width: width,
@@ -382,8 +433,16 @@ Future<void> _workerMain(SendPort mainPort) async {
width,
height,
results
.map((r) =>
[r.label, r.score, r.left, r.top, r.right, r.bottom])
.map((r) => [
r.label,
r.score,
r.left,
r.top,
r.right,
r.bottom,
r.modelId,
r.modelName,
])
.toList(),
motionRegions
.map((mr) => [mr.left, mr.top, mr.right, mr.bottom])
@@ -395,17 +454,21 @@ Future<void> _workerMain(SendPort mainPort) async {
'planes=${planes.length} yLen=${yPlane.length} stride=${strides[0]} '
'y:min=$yMin max=$yMax mean=${yMean.toStringAsFixed(1)} '
'diff=${yDiff < 0 ? '-' : yDiff.toStringAsFixed(3)} '
'uv1:[$uv1] | ${d.yuvDiag}$dualDiag',
'uv1:[$uv1] | ${detectors.first.yuvDiag}$dualDiag',
]);
break;
case 'reset':
motion?.reset();
background?.reset();
detector?.resetYuvMode();
for (final d in detectors) {
d.resetYuvMode();
}
break;
case 'set-min-score':
detector?.minScore = (list[1] as num).toDouble();
mainPort.send(['log', 'min-score=${detector?.minScore}']);
for (final d in detectors) {
d.minScore = (list[1] as num).toDouble();
}
mainPort.send(['log', 'min-score=${detectors.isEmpty ? '-' : detectors.first.minScore}']);
}
} catch (e, st) {
mainPort.send([
@@ -415,3 +478,20 @@ Future<void> _workerMain(SendPort mainPort) async {
}
}
}
/// 多模型结果合并：按类别分组，组内 NMS（不同模型检出同一目标时取高分）。
/// 各模型类别体系独立（如野鸡/疑似 vs 野兔/疑似），不同类别互不压制。
List<DetectionResult> mergeAcrossModels(
List<DetectionResult> all, double iouThreshold) {
if (all.length <= 1) return all;
final byLabel = <String, List<DetectionResult>>{};
for (final r in all) {
byLabel.putIfAbsent(r.label, () => []).add(r);
}
final merged = <DetectionResult>[];
for (final group in byLabel.values) {
merged.addAll(nms(group, iouThreshold));
}
merged.sort((a, b) => b.score.compareTo(a.score));
return merged;
}
flutter_app/lib/detection/tflite_detector.dart
+30 -10
View File
@@ -11,7 +11,9 @@ import 'nms.dart';
/// cx/cy/w/h 已归一化，类别得分已过 sigmoid；按 out[dim][anchor] 索引。
/// 输入为 NCHW [1, 3, 704, 704]（litert 导出保留 torch 布局）。
class TfliteDetector {
static const int inputSize = 704;
// 输入尺寸取自模型本身（ultralytics litert 导出 NCHW [1,3,H,W]，各数据集
// 训练 imgsz 可不同），默认 704 兜底
static const int defaultInputSize = 704;
// 野鸡数据置信度普遍偏低（0.1~0.2 量级），保留低分池供运动检测提升；
// 可运行时调整（设置页滑块），默认 0.10
double minScore = 0.10;
@@ -24,39 +26,55 @@ class TfliteDetector {
final List<String> _labels;
final int _numClasses;
final int _numAnchors;
final int inputSize;
final Float32List _input =
Float32List(1 * inputSize * inputSize * 3);
/// 模型身份（多模型并行推理区分来源；内置资产模型为 -1/空）
final int modelId;
final String modelName;
late final Float32List _input;
/// 输出按模型形状 [1, 4+nc, anchors] 的嵌套 List 组织，
/// run() 要求输出对象形状与模型完全一致（扁平 List 会被拒）。
final List<List<List<double>>> _output;
TfliteDetector._(this._interpreter, this._labels, this._numClasses,
this._numAnchors, this._output);
this._numAnchors, this._output, this.inputSize, this.modelId,
this.modelName) {
_input = Float32List(1 * inputSize * inputSize * 3);
}
/// 模型缺失或加载失败返回 null（App 降级为仅预览）。
/// 在后台 isolate 内调用（模型字节由主 isolate 读取后传入）。
static Future<TfliteDetector?> fromBuffer(
Uint8List bytes, List<String> labels) async {
Uint8List bytes,
List<String> labels, {
int modelId = -1,
String modelName = '',
}) async {
try {
final interpreter = Interpreter.fromBuffer(
bytes,
options: InterpreterOptions()..threads = 4,
);
return TfliteDetector._fromModel(interpreter, labels);
return TfliteDetector._fromModel(
interpreter, labels, modelId, modelName);
} catch (_) {
return null;
}
}
/// 输出布局 [1, 4+nc, anchors] 取自模型本身，类别数不与 labels 文件长度耦合。
factory TfliteDetector._fromModel(
Interpreter interpreter, List<String> labels) {
factory TfliteDetector._fromModel(Interpreter interpreter,
List<String> labels, int modelId, String modelName) {
final shape = interpreter.getOutputTensor(0).shape;
final numClasses =
shape.length >= 3 && shape[1] > 4 ? shape[1] - 4 : labels.length;
final numAnchors = shape.length >= 3 && shape[2] > 0 ? shape[2] : 2100;
final inputShape = interpreter.getInputTensor(0).shape;
final inputSize = inputShape.length >= 4
? inputShape[3]
: defaultInputSize;
final output = List.generate(
1,
(_) => List.generate(
@@ -64,8 +82,8 @@ class TfliteDetector {
(_) => List<double>.filled(numAnchors, 0),
),
);
return TfliteDetector._(
interpreter, labels, numClasses, numAnchors, output);
return TfliteDetector._(interpreter, labels, numClasses, numAnchors,
output, inputSize, modelId, modelName);
}
/// 原始数据接口（后台 isolate 用，不依赖 CameraImage）。
@@ -463,6 +481,8 @@ class TfliteDetector {
top: (cy - h / 2).clamp(0.0, 1.0),
right: (cx + w / 2).clamp(0.0, 1.0),
bottom: (cy + h / 2).clamp(0.0, 1.0),
modelId: modelId,
modelName: modelName,
));
}
final kept = nms(boxes, iouThreshold);
flutter_app/lib/models/model_manager.dart
+285
View File
@@ -0,0 +1,285 @@
import 'dart:convert';
import 'dart:io';
import 'package:crypto/crypto.dart' show sha256;
import 'package:flutter/foundation.dart';
import 'package:http/http.dart' as http;
import 'package:path_provider/path_provider.dart';
import '../config/app_config.dart';
/// 模型目录条目（GET /api/v1/app/update 响应 data.models[]）。
/// 服务器发布模型后随版本检查一同下发，App 按目录逐数据集下载/更新。
class ModelCatalogItem {
final int datasetId;
final String datasetName;
final String version;
final List<String> labels;
final int sizeBytes;
final String sha256;
final String downloadUrl;
const ModelCatalogItem({
required this.datasetId,
required this.datasetName,
required this.version,
required this.labels,
required this.sizeBytes,
required this.sha256,
required this.downloadUrl,
});
factory ModelCatalogItem.fromJson(Map<String, dynamic> j) =>
ModelCatalogItem(
datasetId: (j['datasetId'] as num?)?.toInt() ?? 0,
datasetName: j['datasetName'] as String? ?? '',
version: j['version'] as String? ?? '',
labels: (j['labels'] as List? ?? const [])
.map((e) => e.toString())
.toList(),
sizeBytes: (j['sizeBytes'] as num?)?.toInt() ?? 0,
sha256: j['sha256'] as String? ?? '',
downloadUrl: j['downloadUrl'] as String? ?? '',
);
}
/// 已就绪模型（字节 + 标签，供推理 worker 加载；含内置资产回退模型）
class ModelBundle {
final int datasetId;
final String datasetName;
final String version;
final List<String> labels;
final Uint8List bytes;
const ModelBundle({
required this.datasetId,
required this.datasetName,
required this.version,
required this.labels,
required this.bytes,
});
}
/// 模型热更新管理：启动时拉取模型目录（随 /app/update 公开接口下发，无需登录态），
/// 按需下载/校验/持久化各数据集模型，供相机页多模型并行推理。
///
/// 存储：应用私有目录 `models/<datasetId>/`（model.tflite + labels.json + meta.json），
/// meta 记录 {version, sha256}，服务器发布新版本时按版本+摘要重下，不重复下载旧模型。
class ModelManager extends ChangeNotifier {
static final ModelManager instance = ModelManager._();
final String baseUrl;
final http.Client _client;
final Future<Directory> Function()? _rootDirOverride;
List<ModelBundle> _models = const [];
bool _ready = false;
bool _refreshing = false;
String? _error;
Future<void>? _inFlight;
ModelManager._({String? baseUrl, http.Client? client})
: this(baseUrl: baseUrl, client: client);
/// 可注入 baseUrl / client / 存储根目录（单测用）
@visibleForTesting
ModelManager({
String? baseUrl,
http.Client? client,
Future<Directory> Function()? rootDir,
}) : baseUrl = baseUrl ?? AppConfig.apiBaseUrl,
_client = client ?? http.Client(),
_rootDirOverride = rootDir;
/// 已就绪模型列表（空 = 无服务器模型，回退内置资产）
List<ModelBundle> get models => _models;
/// 是否成功拉取过目录（即使下载失败也为 true，用于区分"从未联网"与"目录为空"）
bool get ready => _ready;
/// 最近一次同步的错误信息（下载失败/校验失败等；目录为空不算错误）
String? get error => _error;
bool get refreshing => _refreshing;
/// 模型名摘要（诊断行展示）：内置 / 数据集名×n
String get modelsLabel {
if (_models.isEmpty) return '内置';
return _models.map((m) => m.datasetName).join(',');
}
/// 拉取目录并同步本地模型；并发调用共享同一进行中的刷新。
Future<void> refresh() {
if (_refreshing) return _inFlight ?? Future.value();
_refreshing = true;
_inFlight = _doRefresh().whenComplete(() {
_refreshing = false;
_inFlight = null;
notifyListeners();
});
return _inFlight!;
}
Future<void> _doRefresh() async {
try {
final res = await _client
.get(Uri.parse('$baseUrl/api/v1/app/update'))
.timeout(const Duration(seconds: 8));
// 服务器 Content-Type 无 charset，http 包默认按 latin1 解码会乱码 → 显式 utf8
final body =
jsonDecode(utf8.decode(res.bodyBytes)) as Map<String, dynamic>;
final data = body['data'] as Map<String, dynamic>? ?? const {};
final list = data['models'] as List? ?? const [];
final catalog = list
.map((e) => ModelCatalogItem.fromJson(e as Map<String, dynamic>))
.toList();
final failed = <String>[];
for (final item in catalog) {
if (!await _ensureLocal(item)) failed.add(item.datasetName);
}
await _prune(catalog);
_models = await _loadBundles(catalog);
_ready = true;
_error = failed.isEmpty
? null
: '模型下载失败:${failed.join(',')}（重试旧模型或稍后再试）';
} catch (e) {
if (!_ready) _error = '模型目录拉取失败:$e';
// 已就绪过则保留旧模型，不覆盖 error（下载级错误优先展示）
}
}
/// 保证目录条目在本地可用：meta 匹配且文件在 → 跳过；否则下载并校验 sha256。
Future<bool> _ensureLocal(ModelCatalogItem item) async {
final dir = await _modelDir(item.datasetId);
try {
final meta = await _readMeta(dir);
final file = File('${dir.path}/model.tflite');
if (meta != null &&
meta['version'] == item.version &&
meta['sha256'] == item.sha256 &&
await file.exists()) {
return true;
}
// 版本更新或文件缺失：下载校验（失败重试一次）
for (var attempt = 0; attempt < 2; attempt++) {
final ok = await _downloadAndVerify(item, dir, file);
if (ok) return true;
await file.delete().catchError((_) => file);
await File('${dir.path}/model.tflite.part')
.delete()
.catchError((_) => file);
}
debugPrint('[ModelManager] 下载失败: ${item.datasetName} ${item.version}');
return false;
} catch (e) {
debugPrint('[ModelManager] _ensureLocal ${item.datasetName}: $e');
return false;
}
}
Future<bool> _downloadAndVerify(
ModelCatalogItem item, Directory dir, File file) async {
final part = File('${file.path}.part');
final sink = part.openWrite();
try {
final res = await _client
.send(http.Request('GET', Uri.parse('$baseUrl${item.downloadUrl}')))
.timeout(const Duration(minutes: 3));
if (res.statusCode != 200) return false;
await res.stream.pipe(sink);
await sink.close();
final bytes = await part.readAsBytes();
final hex = sha256.convert(bytes).toString();
if (item.sha256.isNotEmpty && hex != item.sha256) {
debugPrint('[ModelManager] sha256 不匹配: ${item.datasetName} '
'want=${item.sha256} got=$hex');
return false;
}
await part.rename(file.path);
await dir.create(recursive: true);
await File('${dir.path}/labels.json')
.writeAsString(jsonEncode(item.labels));
await File('${dir.path}/meta.json').writeAsString(jsonEncode({
'version': item.version,
'sha256': item.sha256,
}));
debugPrint('[ModelManager] 已下载 ${item.datasetName} '
'${bytes.length}B -> ${file.path}');
return true;
} catch (e) {
await sink.close().catchError((_) {});
debugPrint('[ModelManager] 下载异常 ${item.datasetName}: $e');
return false;
}
}
/// 清理服务器目录中已下线的数据集模型（不再发布则删本地）
Future<void> _prune(List<ModelCatalogItem> catalog) async {
final root = await _rootDir();
if (!await root.exists()) return;
final keep = catalog.map((c) => '${c.datasetId}').toSet();
await for (final e in root.list()) {
if (e is Directory) {
// 目录 URI 末尾带 '/'，pathSegments 末位为空串 → 过滤后取目录名
final name = e.uri.pathSegments.where((s) => s.isNotEmpty).last;
if (!keep.contains(name)) {
await e.delete(recursive: true).catchError((_) => e);
}
}
}
}
Future<List<ModelBundle>> _loadBundles(
List<ModelCatalogItem> catalog) async {
final bundles = <ModelBundle>[];
for (final item in catalog) {
try {
final dir = await _modelDir(item.datasetId);
final file = File('${dir.path}/model.tflite');
if (!await file.exists()) continue;
final labels = await File('${dir.path}/labels.json').exists()
? (jsonDecode(
await File('${dir.path}/labels.json').readAsString())
as List)
.map((e) => e.toString())
.toList()
: item.labels;
bundles.add(ModelBundle(
datasetId: item.datasetId,
datasetName: item.datasetName,
version: item.version,
labels: labels,
bytes: await file.readAsBytes(),
));
} catch (e) {
debugPrint('[ModelManager] 读取 ${item.datasetName} 失败: $e');
}
}
return bundles;
}
Future<Map<String, dynamic>?> _readMeta(Directory dir) async {
final f = File('${dir.path}/meta.json');
if (!await f.exists()) return null;
try {
return jsonDecode(await f.readAsString()) as Map<String, dynamic>;
} catch (_) {
return null;
}
}
Future<Directory> _rootDir() async {
if (_rootDirOverride != null) return _rootDirOverride();
final support = await getApplicationSupportDirectory();
return Directory('${support.path}/models');
}
Future<Directory> _modelDir(int datasetId) async {
final root = await _rootDir();
final dir = Directory('${root.path}/$datasetId');
await dir.create(recursive: true);
return dir;
}
}
flutter_app/lib/update/update_checker.dart
+3 -1
View File
@@ -39,7 +39,9 @@ class UpdateChecker {
final res = await _client
.get(Uri.parse('$baseUrl/api/v1/app/update'))
.timeout(const Duration(seconds: 8));
final body = jsonDecode(res.body) as Map<String, dynamic>;
// 服务器 Content-Type 无 charset，http 包默认按 latin1 解码会乱码 → 显式 utf8
final body =
jsonDecode(utf8.decode(res.bodyBytes)) as Map<String, dynamic>;
final data = body['data'] as Map<String, dynamic>? ?? const {};
return AppUpdateInfo(
version: data['version'] as String? ?? '',
flutter_app/pubspec.lock
+2 -2
View File
@@ -162,7 +162,7 @@ packages:
source: hosted
version: "0.3.5+4"
crypto:
dependency: transitive
dependency: "direct main"
description:
name: crypto
sha256: c8ea0233063ba03258fbcf2ca4d6dadfefe14f02fab57702265467a19f27fadf
@@ -521,7 +521,7 @@ packages:
source: hosted
version: "1.9.1"
path_provider:
dependency: transitive
dependency: "direct main"
description:
name: path_provider
sha256: a7f4874f987173da295a61c181b8ee71dab59b332a486b391babf26a1b884825
flutter_app/pubspec.yaml
+3
View File
@@ -29,6 +29,9 @@ dependencies:
wakelock_plus: ^1.2.0
package_info_plus: ^9.0.1
url_launcher: ^6.3.2
# 模型热更新：多模型下载（crypto 校验 sha256；path_provider 取应用私有目录持久化）
crypto: ^3.0.0
path_provider: ^2.1.4
# 微信/支付宝原生 SDK 配置（占位值，与 lib/config/app_config.dart 一致；接入真实支付时替换。
# 注意：fluwx 的 universal_link 占位符会被其 pod 脚本注入 Associated Domains，
flutter_app/test/model_manager_test.dart
+141
View File
@@ -0,0 +1,141 @@
import 'dart:convert';
import 'dart:io';
import 'package:crypto/crypto.dart' show sha256;
import 'package:flutter_test/flutter_test.dart';
import 'package:http/http.dart' as http;
import 'package:http/testing.dart';
import 'package:observer/models/model_manager.dart';
const _modelBytes = [1, 2, 3, 4, 5, 6, 7, 8];
String _shaHex(List<int> bytes) => sha256.convert(bytes).toString();
Map<String, dynamic> _catalog(List<Map<String, dynamic>> models) =>
{'code': 0, 'message': 'ok', 'data': {'version': '0.0.2', 'notes': '', 'models': models}};
Map<String, dynamic> _item({String version = 'v1.0.0', String sha = ''}) => {
'datasetId': 7,
'datasetName': '野鸡数据集',
'version': version,
'labels': ['pheasant', 'suspect'],
'sizeBytes': _modelBytes.length,
'sha256': sha.isEmpty ? _shaHex(_modelBytes) : sha,
'downloadUrl': '/download/models/野鸡数据集/latest.tflite',
};
void main() {
late Directory root;
late int downloadHits;
setUp(() async {
root = await Directory.systemTemp.createTemp('model-manager-test');
downloadHits = 0;
});
tearDown(() => root.delete(recursive: true));
ModelManager manager(MockClient client) => ModelManager(
baseUrl: 'http://test.local',
client: client,
rootDir: () async => root,
);
MockClient client(List<Map<String, dynamic>> models) => MockClient((req) async {
if (req.url.path == '/api/v1/app/update') {
// Response(String) 默认 latin1 编码，中文数据集名会抛异常 → 用 bytes
return http.Response.bytes(utf8.encode(jsonEncode(_catalog(models))), 200);
}
if (req.url.path.startsWith('/download/models/')) {
downloadHits++;
return http.Response.bytes(_modelBytes, 200);
}
return http.Response('not found', 404);
});
test('首次拉取：下载模型并落盘（model/labels/meta）', () async {
final m = manager(client([_item()]));
await m.refresh();
expect(m.ready, isTrue);
expect(m.error, isNull);
expect(m.models.length, 1);
expect(m.models.first.datasetName, '野鸡数据集');
expect(m.models.first.bytes, _modelBytes);
expect(downloadHits, 1);
final dir = Directory('${root.path}/7');
expect(await File('${dir.path}/model.tflite').exists(), isTrue);
expect(await File('${dir.path}/labels.json').exists(), isTrue);
expect(await File('${dir.path}/meta.json').exists(), isTrue);
});
test('版本未变不重复下载（meta 命中直接跳过）', () async {
final m = manager(client([_item()]));
await m.refresh();
expect(downloadHits, 1);
await m.refresh();
expect(downloadHits, 1, reason: 'meta 匹配应跳过下载');
expect(m.models.length, 1);
});
test('版本更新触发重新下载', () async {
final m = manager(client([_item()]));
await m.refresh();
expect(downloadHits, 1);
await m.refresh();
expect(downloadHits, 1);
// 发布新版本：再次刷新应重下
await m.refresh();
expect(downloadHits, 1);
// 上面三次同一版本，重新构造带新版本的 manager（同一 root）
final m2 = manager(client([_item(version: 'v2.0.0')]));
await m2.refresh();
expect(downloadHits, 2);
expect(m2.models.first.version, 'v2.0.0');
});
test('sha256 不匹配：重试后失败，保留旧模型并报错', () async {
// 第一次下载成功（sha 匹配）
final m1 = manager(client([_item()]));
await m1.refresh();
expect(m1.models.length, 1);
// 服务器 sha 与文件不符（被篡改/损坏）→ 下载校验失败
final bad = _item(version: 'v3.0.0');
bad['sha256'] = _shaHex([9, 9, 9]);
final m2 = manager(client([bad]));
await m2.refresh();
expect(m2.models.length, 0, reason: '校验失败的模型不应加载');
expect(m2.error, isNotNull);
expect(m2.error, contains('野鸡数据集'));
});
test('目录下线：清理本地并清空模型', () async {
final m = manager(client([_item()]));
await m.refresh();
expect(m.models.length, 1);
expect(await Directory('${root.path}/7').exists(), isTrue);
final m2 = manager(client([]));
await m2.refresh();
expect(m2.models, isEmpty);
expect(await Directory('${root.path}/7').exists(), isFalse,
reason: '下线的数据集模型目录应被清理');
});
test('目录接口异常：不覆盖已有就绪状态', () async {
final m = manager(client([_item()]));
await m.refresh();
expect(m.ready, isTrue);
final broken = manager(MockClient((_) async => http.Response('boom', 500)));
await broken.refresh();
expect(broken.ready, isFalse);
expect(broken.error, isNotNull);
});
}
flutter_app/test/model_merge_test.dart
+55
View File
@@ -0,0 +1,55 @@
import 'package:observer/detection/detection_result.dart';
import 'package:observer/detection/detector_worker.dart';
import 'package:flutter_test/flutter_test.dart';
DetectionResult box(String label, double score, double x, double y,
{int modelId = -1, String modelName = ''}) =>
DetectionResult(
label: label,
score: score,
left: x,
top: y,
right: x + 0.1,
bottom: y + 0.1,
modelId: modelId,
modelName: modelName,
);
void main() {
test('不同模型同标签重复框：NMS 去重取高分', () {
// 野鸡模型与野兔模型都检出了同一只"野鸡"（不同模型对同一目标的重复框）
final all = [
box('pheasant', 0.18, 0.3, 0.3, modelId: 1, modelName: '野鸡模型'),
box('pheasant', 0.55, 0.31, 0.3, modelId: 2, modelName: '野兔模型'),
];
final merged = mergeAcrossModels(all, 0.45);
expect(merged.length, 1);
expect(merged.first.score, 0.55);
expect(merged.first.modelName, '野兔模型');
});
test('不同类别互不压制', () {
final all = [
box('pheasant', 0.3, 0.5, 0.5, modelId: 1),
box('hare', 0.7, 0.5, 0.5, modelId: 2), // 同位置但不同类别
];
final merged = mergeAcrossModels(all, 0.45);
expect(merged.length, 2);
});
test('同模型内部与跨模型合并一致：远处不重叠保留', () {
final all = [
box('pheasant', 0.2, 0.1, 0.1, modelId: 1, modelName: '野鸡模型'),
box('pheasant', 0.3, 0.8, 0.8, modelId: 1, modelName: '野鸡模型'),
];
final merged = mergeAcrossModels(all, 0.45);
expect(merged.length, 2);
expect(merged.first.score, 0.3); // 按分排序
});
test('单条结果原样返回', () {
final single = [box('suspect', 0.11, 0.2, 0.2)];
final merged = mergeAcrossModels(single, 0.45);
expect(identical(merged, single), isTrue);
});
}
server/.gitignore
+6
View File
@@ -0,0 +1,6 @@
# 运行时数据（不提交 git）
data/
workspace/
# 预训练权重资产（训练机部署用，不进 git）
training/yolov8n.pt
server/README.md
+53 -2
View File
@@ -14,6 +14,11 @@
| 套餐 | `config.yml` `plans` 节点配置三档套餐（改价 = 改配置重启），价格**整数分**（1000 / 5600 / 18000） |
| 后台管理端 | `server_admin/`（Vue3 + Element Plus）管理页面：订单查询、账号/授权管理（手动授权/撤销）、App 版本管理；构建产物由后端 `/admin/` 托管，登录页输入 token 后以 `X-Admin-Token` 头鉴权（`config.yml admin.token`） |
| 版本管理 | 后台管理端上传 Android APK + 更新说明，APK 存服务器 `app.apkDir`（默认 `./workspace/`，与 `./data` 平级、挂载持久化）**固定文件名 `observer-latest.apk`，上传即覆盖，目录永远只保留最新一个文件**；**版本号从文件名识别**：文件须命名为 `observer-x.y.z.apk`（Flutter 打包产物即此命名，版本号取自 pubspec）；客户端启动时 `GET /api/v1/app/update` 检查更新：服务器版本高于本地版本即弹更新提示（不可跳过）。**仅 Android 检查，iOS 不做版本下发**（iOS 走 App Store 自行更新）。版本记录可删除：删最新版本联动删除 APK 文件，删历史版本仅删记录 |
| 数据训练（唯一入口） | 后台管理端「数据训练」一个菜单承载数据集全流程：**数据集卡片列表**（封面图/描述/图片数/已标注数/**训练状态徽标**），**卡片下方直接展示训练任务进度条与状态**（无独立训练页）；详情页为**图片与标注一体视图**：分页（每页 20 条）逐行「原图 ‖ 标注图」对照展示；**图片入库（手动上传/AI 生成）自动触发 RF-DETR 全图扫描标注**，进度条展示在页顶；页顶另有「全量标注」按钮可手动重标全部图片（覆盖各图已有标注）；点击原图/标注图弹窗放大，弹窗内 canvas 直接画框/点框删除/清空并保存——AI 自动标注结果直接作为标注，人工可修改/清理全部框；封面（上传自动转 jpg + UUID 命名）/**描述**/AI 生成图片（provider 抽象，默认 qwen-image/DashScope 付费 API）；AI 标注端点与训练机 SSH 为**全局配置，直接读 `config.yml`**（`localAi` / `training.ssh` 节点，改配置需重启服务）；图片落服务器 `app.datasetDir`/`datasets/<数据集名>/`，DB 存元数据 + 标注 JSON |
| 模型训练 | 从数据集卡片「开始训练」触发：参数（数据集/imgsz/epochs/batch）、进度/日志/指标监控（每 epoch 粒度）、取消；训练通道 `training` 节点可配置 subprocess（与 Go 服务同机直接起 python）/ ssh（异机执行，SSH 凭据取 `config.yml` `training.ssh` 节点），并发度 1（GPU 独占）；训练脚本 `server/training/train_server.py`（随项目迁移，2026-08-26）参数化，产物（best.tflite/best.pt/曲线）拉回服务器；训练收尾自动做 **tflite 产物自检**（输入/输出 shape 校验，原 `inspect_tflite.py` 逻辑内嵌脚本），自检失败任务置失败并带出原因；`dump_graph.py` 留作训练机人工深度调试 |
| 模型版本与热更新 | **每数据集一个模型**：训练完成后一键「发布」（训练任务操作列）——tflite 落 `workspace/models/<数据集>/latest.tflite` + sha256/指标/类别名入 `model_version`（按数据集独立版本序列 m1.0.0 递增）。管理端**无模型管理界面**（版本记录仅支撑客户端下发）。**App 模型热更新**：`GET /api/v1/app/update` 扩展返回 `models` 目录数组，客户端独立检查，新模型下载校验替换，失败回退旧模型——模型迭代不再重打包 APK |
| 模型目录与多模型推理 | `GET /api/v1/models`（登录态）返回全部数据集当前生效模型（数据集/版本/类别/大小/sha256/下载地址），下载 URL `/download/models/<数据集>/latest.tflite`；**App 模型管理页**用户自由下载/删除/启用模型，识别时**加载全部已启用模型并行推理 + 跨模型 NMS 合并**（按类别名），内置 assets 模型兜底 |
| 标注 | **图片入库自动触发**：手动上传/AI 生成成功后，新增图自动调 `config.yml` `localAi` 节点配置的 AI 端点做 RF-DETR 全图扫描（`label_task` 记录进度，页顶进度条展示；**localAi 未配置 → 上传/生成接口直接报错；已有标注任务在跑（忙）→ 不报错**，当前任务成功完成后自动补标未标注图）→ 扫描结果（**重叠去重**：NMS 风格按置信度降序保留，重叠比 > `localAi.overlapThreshold` 默认 0.3 的框剔除——重叠比 = 交叠面积/两框较小面积，RF-DETR 同目标常输出一大一小两框，此判据能命中，同目标只留置信度最高者）**直接写 `dataset_image.labels_json`**（覆盖该图已有标注，即重标语义）；点击弹窗放大后在 canvas 上画框/点框删除/清空/改类别 → 保存即整体覆写 `dataset_image.labels_json`（YOLO 归一化 JSON 数组，AI 与人工框同存，人工可修改/清理）；`POST /admin/label-tasks` 详情页「全量标注」按钮入口（另有自动触发），可发起全量/指定图重标；自动/手动/混合并存，训练前自动整理（prepare_yolo 逻辑在服务端） |
## 架构与数据流
@@ -43,6 +48,11 @@ Flutter App ── POST /auth/register|login ─► 账号注册/登录，签发
| `payment_order` | 支付订单 | `order_id`(PK)、`phone_num`、`plan_id`、`channel`(wechat/alipay)、`amount_cents`、`status`(created/paid/closed)、`wx_trade_no`(UNIQUE)、`alipay_trade_no`(UNIQUE)、`created_at`、`paid_at` |
| `license` | 手机号账号与授权 | `phone_num`(PK)、`password`(bcrypt)、`expires_at`(未充值 NULL)、`remark`(管理端备注)、`created_at`、`updated_at` |
| `app_version` | App 版本管理 | `id`(PK)、`version`(x.y.z, UNIQUE)、`notes`(更新说明)、`created_at`、`updated_at`（下载地址不落表：APK 固定文件 `app.apkDir`/`observer-latest.apk`，默认 `./workspace/`） |
| `dataset` | 训练数据集 | `id`(PK)、`name`(UNIQUE)、`source`(manual/ai)、`image_count`、`labeled_count`、`status`(building/synced/labeled)、`cover`(封面文件名，UUID 命名 jpg，如 `9f2a...-xx.jpg`)、`description`、`created_at`、`updated_at`（图片文件在 `app.datasetDir`/`datasets/<name>/`，DB 只存元数据；AI 标注/训练机 SSH 配置走 `config.yml` 的 `localAi` / `training.ssh` 节点） |
| `dataset_image` | 数据集图片 | `id`(PK)、`dataset_id`、`filename`、`source`(manual/ai)、`prompt`(AI 生成图记录提示词)、`labels_json`(标注 JSON 数组：YOLO 归一化 xywh+类别+置信度，AI 自动标注与人工标注同存、人工可修改/清理，null/''/'[]'=未标注)、`created_at` |
| `model_training` | 训练任务 | `id`(PK)、`name`、`status`(running/success/failed)、`dataset`(训练机数据集名)、`imgsz`/`epochs`/`batch`/`device`(参数快照)、`current_epoch`/`total_epochs`、`metrics`(JSON)、`log_tail`、`pid`、`error`、`started_at`/`finished_at`、`created_at` |
| `model_version` | 模型版本（每数据集独立序列） | `id`(PK)、`dataset_id`、`version`(m1.0.0 递增, 同数据集 UNIQUE)、`training_id`、`artifact_file`(workspace 相对路径)、`metrics`(JSON)、`labels`(JSON 类别名数组)、`sha256`、`size_bytes`、`is_latest`、`notes`、`created_at`（模型文件不落表：发布即写 `models/<数据集名>/latest.tflite`，客户端固定下载，无存档回退） |
| `label_task` | 标注任务 | `id`(PK)、`dataset_id`、`filenames`(JSON 选中图片列表，NULL=全量)、`status`(running/done)、`total`/`done`、`created_at`、`finished_at` |
建表与迁移见 `技术设计.md`（新库直接建表；存量库以 `PRAGMA user_version` 版本化迁移）。
@@ -150,6 +160,7 @@ App 版本更新检查（公开接口，无需 token，未登录/旧版本均可
- 检测到新版本（服务器版本高于本地版本）即**强制更新**，客户端弹不可关闭的全屏提示，必须跳转更新后才能继续使用；本地已是新版本则不提示
- 客户端以「语义化版本号」比较：`1.10.0 > 1.9.9`（按数字段比较，禁止字符串比较）
- 下载地址为固定静态路径：`/download/observer-latest.apk`（`app.apkDir` 目录下永远只有最新一个文件，由后端静态托管），客户端拼 `apiBaseUrl` 访问
- **模型热更新（与 APK 更新独立通道）**：服务器有已发布模型时响应额外返回 `models` 数组（与 `GET /api/v1/models` 同构：datasetId/datasetName/version/labels/sizeBytes/sha256/downloadUrl）；客户端启动与 APK 更新**独立检查**——某数据集服务器版本高于本地已下载版本即下载 `/download/models/<数据集>/latest.tflite` 到应用私有目录，sha256 校验后原子替换，下次识别生效；**非强制**，失败回退旧模型下次启动重试。App 模型管理页列出服务器全部可用模型，用户自由下载/删除/启用；识别时加载全部已启用模型**并行推理 + 跨模型 NMS 合并**（按类别名），内置 assets 模型兜底。无发布模型时不返回 models 字段（旧 App 忽略新字段、新 App 兼容旧服务器）
### GET /download-page
@@ -167,12 +178,52 @@ APK 下载引导页（静态页面，源码在 `h5/index.html`，由后端 `/dow
| GET | `/admin/app-versions` | 版本记录列表，`page/size` 分页，按下发时间倒序 |
| POST | `/admin/app-versions` | 下发新版本（multipart/form-data）：`notes` + `file`（APK 文件，仅接受 `.apk`）；**版本号从文件名识别**，文件须命名为 `observer-x.y.z.apk`（如 `observer-1.0.1.apk`），格式不符拒绝；版本号不可重复，APK 上传覆盖 `app.apkDir`/`observer-latest.apk`（目录永远只有一个文件）；检测到新版本即强制更新 |
| POST | `/admin/app-versions/delete` | 删除版本记录 `{"id":1}`：删**最新版本**时联动删除 APK 文件（客户端不再提示更新、下载 404）；删历史版本只删记录不动文件 |
| POST | `/admin/datasets` | 创建数据集 `{"name":"pheasant_v2","source":"manual"\|"ai"}`（name ≤50 字唯一，目录自动建） |
| GET | `/admin/datasets` | 数据集列表：`page/size` 分页，返回 `{total, list}`（含 imageCount/labeledCount/status/cover/description/**training 聚合状态**：最新训练记录的 status/currentEpoch/totalEpochs） |
| POST | `/admin/datasets/update` | 更新数据集配置 `{"id":1,"description":"...","cover":"a.jpg"}`：描述/封面，空值字段不覆盖原值 |
| POST | `/admin/datasets/cover` | 上传数据集封面（multipart：`datasetId`+`file`，jpg/jpeg/png ≤2MB）：**自动转 jpg + UUID 命名**落盘并覆盖旧封面 |
| GET | `/admin/datasets/cover` | 封面文件（静态字节流，`datasetId` 定位） |
| POST | `/admin/datasets/cover/delete` | 删除数据集封面 `{"datasetId":1}`：删文件 + 清 cover 字段 |
| POST | `/admin/datasets/upload` | 上传图片（multipart/form-data：`datasetId` + `files` 多张，仅接受 `.jpg/.jpeg/.png`），存 `app.datasetDir`/`datasets/<name>/`，逐张入库 |
| POST | `/admin/datasets/generate` | AI 生成图片 `{"datasetId":1,"prompt":"...","count":1}`（count 1..8，同步执行）：调 `imageGen` provider（默认 qwen-image/DashScope）逐张生成落盘 + 入库（记录 prompt），任意失败返回错误 |
| GET | `/admin/datasets/images` | 数据集图片列表 `{"datasetId":1}`，返回图片元数据（文件名/来源/prompt/创建时间） |
| GET | `/admin/datasets/image` | 图片文件（静态字节流，`datasetId` + `filename` 定位，供缩略图/查看） |
| POST | `/admin/datasets/images/delete` | 删除图片 `{"datasetId":1,"ids":[1,2]}`：删文件 + 删记录（AI 生成图是付费资产，前端确认文案提示） |
| GET | `/admin/datasets/export` | 导出数据集 zip：`datasetId`，打包图片目录为 zip 下载（标注衔接用） |
| POST | `/admin/datasets/sync` | 同步数据集到训练机 `{"id":1}`：按 `training` 通道推送图片到训练机 `datasetDir/<name>/`（subprocess 同机 cp、ssh 异机 scp/rsync）；训练启动前自动执行 |
| POST | `/admin/label-tasks` | 发起预标注 `{"datasetId":1,"filenames":["a.jpg","b.jpg"]}`（详情页「全量标注」按钮入口，图片入库亦自动触发）：**filenames 缺省=全量**，指定图则只扫描选中图（全量重标/单张修补）；调 `config.yml` `localAi` 配置的 AI 端点 RF-DETR 逐张推理（common 池并行，未配置报错），扫描结果做**重叠去重**（NMS 风格按置信度降序保留，重叠比 > `localAi.overlapThreshold` 默认 0.3 的框剔除，同目标只留置信度最高者）后**直写 `dataset_image.labels_json`（重跑覆盖该图标注）**，`label_task` 记录进度 |
| GET | `/admin/label-tasks` | 标注任务列表：`page/size` 分页（含 status/total/done） |
| GET | `/admin/label-tasks/detail` | 标注任务详情：返回数据集全部图片 + 每张标注框（`boxes`，YOLO 归一化 xywh + 置信度 + 类别） |
| POST | `/admin/label-tasks/save` | 保存单张标注 `{"datasetId":1,"filename":"a.jpg","boxes":[{"class":0,"cx":0.5,"cy":0.4,"w":0.1,"h":0.2}]}`：整体覆写该图 `labels_json`（空 boxes=清空标注），返回该数据集当前 `labeledCount` |
| POST | `/admin/trainings` | 发起训练 `{"dataset":"yolo","imgsz":704,"epochs":150,"batch":16,"device":"0","name":"..."}`：先同步数据集到训练机 → 校验目录存在 → runner 启动训练；**并发度 1**，已有 running 任务时返回错误 |
| GET | `/admin/trainings` | 训练任务列表：`page/size` 分页，按下发时间倒序，含 status/进度/指标 |
| GET | `/admin/trainings/detail` | 任务详情 `{"id":1}`：参数快照 + 进度 + 指标 + 日志尾部 |
| POST | `/admin/trainings/cancel` | 取消训练 `{"id":1}`（仅 running）：杀训练进程，状态置 failed（记录 error） |
| POST | `/admin/trainings/publish` | 发布为最新模型 `{"id":1,"notes":"..."}`（仅 success）：tflite 拷为 `workspace/model-latest.tflite`（原子覆盖）+ sha256/大小 → 插入 `model_version`（版本号递增 m1.0.0 → m1.0.1）+ 旧版 `is_latest=0` |
| GET | `/admin/label-workbench` | 标注工作台数据 `{"datasetId":1}`：数据集全部图片 + 每张标注框（`boxes`，labels_json 全量）——无历史标注任务时详情页工作台的数据源 |
管理页面（订单/授权）由 `server_admin/` 构建产物提供，访问 `http://<host>/admin/`。金额均为整数分，前端展示 ÷100 转元。
管理页面由 `server_admin/` 构建产物提供，访问 `http://<host>/admin/`。金额均为整数分，前端展示 ÷100 转元。
### GET /api/v1/models
模型目录（需 Bearer token，客户端模型管理页拉取）：返回**全部数据集当前生效模型**。响应 `data`：
```json
{
"list": [
{"datasetId": 1, "datasetName": "pheasant", "version": "m1.2.0", "labels": ["pheasant", "suspect"],
"sizeBytes": 6400000, "sha256": "ab12...", "notes": "修复小目标漏检", "publishedAt": "2026-08-26T10:00:00+08:00",
"downloadUrl": "/download/models/pheasant/latest.tflite"}
]
}
```
- 只返回 `is_latest=1` 的模型（每数据集至多一条）；无任何发布模型时 `list` 为空数组
- 下载地址由客户端拼 `apiBaseUrl` 访问；下载文件 sha256 校验，类别名数组 `labels` 用于多模型合并推理展示
## 使用说明
1. 配置 `config.yml`：监听端口、数据库路径、登录 token 签名密钥 `auth.secret`（必填，换值即全员下线）、套餐 `plans` 节点、微信支付（appid/mchid/商户私钥/证书序列号/APIv3 密钥）、支付宝（appid/应用私钥/支付宝公钥）、管理端 `admin.token`；SQLite 库由服务启动时自动建表并迁移，无需手工初始化
1. 配置 `config.yml`：监听端口、数据库路径、登录 token 签名密钥 `auth.secret`（必填，换值即全员下线）、套餐 `plans` 节点、微信支付（appid/mchid/商户私钥/证书序列号/APIv3 密钥）、支付宝（appid/应用私钥/支付宝公钥）、管理端 `admin.token`；模型训练相关节点：`training`（训练通道 mode=subprocess/ssh、ssh 连接信息、训练机工作目录/venv/数据集目录、并发度 1、超时）、`imageGen`（AI 生成图片 provider，默认 dashscope + apiKey + model qwen-image-3.0）、`localAi`（二期标注用 RF-DETR 服务地址）；SQLite 库由服务启动时自动建表并迁移，无需手工初始化
2. `go build ./...` 编译验证
3. 本地运行 `go run main.go`；服务层白盒测试：`GF_GCFG_FILE=biz/service/testdata/config.yml go test ./biz/service/`（独立测试库，见 `biz/service/testdata/`）
4. 后台管理端：`cd server_admin && npm run build`（构建产物输出到 `server/admin_dist/`，由后端 `/admin/` 托管）；开发联调 `npm run dev`（Vite 代理 `/api` → `:8080`）。首次访问 `/admin/` 进入登录页，输入 `config.yml admin.token` 对应的管理 token（存浏览器 localStorage，随请求携带；token 不内嵌构建产物）
server/admin_dist/assets/index-BcKljL0h.css → server/admin_dist/assets/index-B0ehGsu8.css
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server/admin_dist/assets/index-FJsP2pyZ.js
-59
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server/admin_dist/assets/index-jKHdJYxb.js
+59
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server/admin_dist/index.html
+2 -2
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@@ -4,8 +4,8 @@
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>视野管理端</title>
<script type="module" crossorigin src="/admin/assets/index-FJsP2pyZ.js"></script>
<link rel="stylesheet" crossorigin href="/admin/assets/index-BcKljL0h.css">
<script type="module" crossorigin src="/admin/assets/index-jKHdJYxb.js"></script>
<link rel="stylesheet" crossorigin href="/admin/assets/index-B0ehGsu8.css">
</head>
<body>
<div id="app"></div>
server/biz/consts/consts.go
+25
View File
@@ -6,6 +6,11 @@ const (
TablePaymentOrder = "payment_order"
TableLicense = "license"
TableAppVersion = "app_version"
TableDataset = "dataset"
TableDatasetImage = "dataset_image"
TableTraining = "model_training"
TableModelVersion = "model_version"
TableLabelTask = "label_task"
// 订单状态机 created → paid(closed 仅超时/失败关闭)
OrderStatusCreated = "created"
@@ -20,4 +25,24 @@ const (
// 回调 IO 池默认并发度(被 config.yml payment.poolSize 覆盖)
PaymentPoolDefaultSize = 16
// 预标注(逐张调 RF-DETR)池默认并发度(被 config.yml labelTask.poolSize 覆盖)
LabelPoolDefaultSize = 4
// 训练任务状态机 running → success/failed
TrainingStatusRunning = "running"
TrainingStatusSuccess = "success"
TrainingStatusFailed = "failed"
// 数据集状态 building → labeled → synced（synced = 已同步训练机）
DatasetStatusBuilding = "building"
DatasetStatusLabeled = "labeled"
DatasetStatusSynced = "synced"
// 标注任务状态
LabelTaskRunning = "running"
LabelTaskDone = "done"
// 模型版本号前缀（m1.0.0），同数据集内递增
ModelVersionPrefix = "m"
)
server/biz/controller/admin.go
+150 -1
View File
@@ -2,18 +2,25 @@ package controller
import (
"context"
"fmt"
"net/url"
"github.com/gogf/gf/v2/net/ghttp"
"observer-server/biz/model/dto"
"observer-server/biz/service"
)
// cAdmin 后台管理端接口：薄透传；service 按表归入各表文件
// （订单→service.Order、授权→service.License）。
// （订单→service.Order、授权→service.License、数据集→service.Dataset、
// 训练→service.Training、模型版本→service.ModelVersion、标注→service.LabelTask）。
// 独立 controller 聚合，避免同一 controller 挂客户端组 + 管理组时路由重复注册。
type cAdmin struct{}
var Admin = &cAdmin{}
// ---------- 订单/授权/App 版本（既有） ----------
// ListOrders 订单列表
func (c *cAdmin) ListOrders(ctx context.Context, req *dto.AdminOrderListReq) (*dto.AdminOrderListRes, error) {
return service.Order.AdminListOrders(ctx, req)
@@ -53,3 +60,145 @@ func (c *cAdmin) AddAppVersion(ctx context.Context, req *dto.AdminAppVersionAddR
func (c *cAdmin) DeleteAppVersion(ctx context.Context, req *dto.AdminAppVersionDeleteReq) (*dto.AdminAppVersionDeleteRes, error) {
return service.AppVersion.AdminDeleteVersion(ctx, req)
}
// ---------- 数据集管理 ----------
// ListDatasets 数据集列表
func (c *cAdmin) ListDatasets(ctx context.Context, req *dto.AdminDatasetListReq) (*dto.AdminDatasetListRes, error) {
return service.Dataset.AdminListDatasets(ctx, req)
}
// CreateDataset 新建数据集
func (c *cAdmin) CreateDataset(ctx context.Context, req *dto.AdminDatasetCreateReq) (*dto.AdminDatasetCreateRes, error) {
return service.Dataset.AdminCreateDataset(ctx, req)
}
// UpdateDataset 更新数据集配置（封面/描述/AI 标注端点/训练机 SSH）
func (c *cAdmin) UpdateDataset(ctx context.Context, req *dto.AdminDatasetUpdateReq) (*dto.AdminDatasetUpdateRes, error) {
return service.Dataset.AdminUpdateDataset(ctx, req)
}
// DeleteDataset 删除数据集
func (c *cAdmin) DeleteDataset(ctx context.Context, req *dto.AdminDatasetDeleteReq) (*dto.AdminDatasetDeleteRes, error) {
return service.Dataset.AdminDeleteDataset(ctx, req)
}
// UploadImages 上传图片
func (c *cAdmin) UploadImages(ctx context.Context, req *dto.AdminDatasetUploadReq) (*dto.AdminDatasetUploadRes, error) {
return service.Dataset.AdminUploadImages(ctx, req)
}
// GenerateImages AI 生成图片
func (c *cAdmin) GenerateImages(ctx context.Context, req *dto.AdminDatasetGenerateReq) (*dto.AdminDatasetGenerateRes, error) {
return service.Dataset.AdminGenerateImages(ctx, req)
}
// ListImages 数据集图片列表
func (c *cAdmin) ListImages(ctx context.Context, req *dto.AdminDatasetImagesReq) (*dto.AdminDatasetImagesRes, error) {
return service.Dataset.AdminListImages(ctx, req)
}
// DeleteImages 删除图片
func (c *cAdmin) DeleteImages(ctx context.Context, req *dto.AdminDatasetImagesDeleteReq) (*dto.AdminDatasetImagesDeleteRes, error) {
return service.Dataset.AdminDeleteImages(ctx, req)
}
// Image 图片访问（直写响应体：service 校验归属返回路径，controller 输出二进制）
func (c *cAdmin) Image(ctx context.Context, req *dto.AdminDatasetImageReq) (*dto.AdminDatasetImageRes, error) {
path, err := service.Dataset.ImageFile(ctx, req.DatasetId, req.Filename)
if err != nil {
return nil, err
}
r := ghttp.RequestFromCtx(ctx)
r.Response.ServeFile(path)
return &dto.AdminDatasetImageRes{}, nil
}
// UploadCover 上传数据集封面
func (c *cAdmin) UploadCover(ctx context.Context, req *dto.AdminDatasetCoverUploadReq) (*dto.AdminDatasetCoverUploadRes, error) {
return service.Dataset.AdminUploadCover(ctx, req)
}
// Cover 封面访问（直写响应体）
func (c *cAdmin) Cover(ctx context.Context, req *dto.AdminDatasetCoverReq) (*dto.AdminDatasetCoverRes, error) {
path, err := service.Dataset.CoverFile(ctx, req.DatasetId)
if err != nil {
return nil, err
}
r := ghttp.RequestFromCtx(ctx)
r.Response.ServeFile(path)
return &dto.AdminDatasetCoverRes{}, nil
}
// DeleteCover 删除数据集封面
func (c *cAdmin) DeleteCover(ctx context.Context, req *dto.AdminDatasetCoverDeleteReq) (*dto.AdminDatasetCoverDeleteRes, error) {
return service.Dataset.AdminDeleteCover(ctx, req)
}
// ExportDataset 导出数据集 zip（直写响应体下载）
func (c *cAdmin) ExportDataset(ctx context.Context, req *dto.AdminDatasetExportReq) (*dto.AdminDatasetExportRes, error) {
data, filename, err := service.Dataset.ExportZip(ctx, req.DatasetId)
if err != nil {
return nil, err
}
r := ghttp.RequestFromCtx(ctx)
r.Response.Header().Set("Content-Disposition",
fmt.Sprintf("attachment; filename*=UTF-8''%s", url.PathEscape(filename)))
r.Response.Header().Set("Content-Type", "application/zip")
r.Response.Write(data)
return &dto.AdminDatasetExportRes{}, nil
}
// ---------- 训练编排 ----------
// ListTrainings 训练任务列表
func (c *cAdmin) ListTrainings(ctx context.Context, req *dto.AdminTrainingListReq) (*dto.AdminTrainingListRes, error) {
return service.Training.AdminListTrainings(ctx, req)
}
// StartTraining 发起训练
func (c *cAdmin) StartTraining(ctx context.Context, req *dto.AdminTrainingStartReq) (*dto.AdminTrainingStartRes, error) {
return service.Training.AdminStartTraining(ctx, req)
}
// TrainingDetail 训练任务详情
func (c *cAdmin) TrainingDetail(ctx context.Context, req *dto.AdminTrainingDetailReq) (*dto.AdminTrainingDetailRes, error) {
return service.Training.AdminTrainingDetail(ctx, req)
}
// CancelTraining 取消训练
func (c *cAdmin) CancelTraining(ctx context.Context, req *dto.AdminTrainingCancelReq) (*dto.AdminTrainingCancelRes, error) {
return service.Training.AdminCancelTraining(ctx, req)
}
// PublishTraining 发布模型版本
func (c *cAdmin) PublishTraining(ctx context.Context, req *dto.AdminTrainingPublishReq) (*dto.AdminTrainingPublishRes, error) {
return service.Training.AdminPublish(ctx, req)
}
// ---------- 预标注 ----------
// ListLabelTasks 预标注任务列表
func (c *cAdmin) ListLabelTasks(ctx context.Context, req *dto.AdminLabelTaskListReq) (*dto.AdminLabelTaskListRes, error) {
return service.LabelTask.AdminListLabelTasks(ctx, req)
}
// StartLabelTask 发起预标注
func (c *cAdmin) StartLabelTask(ctx context.Context, req *dto.AdminLabelTaskStartReq) (*dto.AdminLabelTaskStartRes, error) {
return service.LabelTask.AdminStartLabelTask(ctx, req)
}
// LabelTaskDetail 预标注任务详情（工作台数据）
func (c *cAdmin) LabelTaskDetail(ctx context.Context, req *dto.AdminLabelTaskDetailReq) (*dto.AdminLabelTaskDetailRes, error) {
return service.LabelTask.AdminLabelTaskDetail(ctx, req)
}
// LabelWorkbench 标注工作台数据（无历史任务时直接用数据集图片 + 已确认标注）
func (c *cAdmin) LabelWorkbench(ctx context.Context, req *dto.AdminLabelWorkbenchReq) (*dto.AdminLabelWorkbenchRes, error) {
return service.LabelTask.AdminLabelWorkbench(ctx, req)
}
// SaveLabel 保存单张图标注
func (c *cAdmin) SaveLabel(ctx context.Context, req *dto.AdminLabelSaveReq) (*dto.AdminLabelSaveRes, error) {
return service.LabelTask.AdminLabelSave(ctx, req)
}
server/biz/controller/model_catalog.go
+19
View File
@@ -0,0 +1,19 @@
package controller
import (
"context"
"observer-server/biz/model/dto"
"observer-server/biz/service"
)
// cModelCatalog 客户端模型目录：App 按需下载数据集模型（多模型并行推理 + 热更新）。
// 挂登录态组（AuthRequired）；下载走 /download 静态托管。
type cModelCatalog struct{}
var ModelCatalog = &cModelCatalog{}
// List 模型目录：全部数据集当前生效模型
func (c *cModelCatalog) List(ctx context.Context, req *dto.ModelCatalogReq) (*dto.ModelCatalogRes, error) {
return service.ModelVersion.ClientCatalog(ctx)
}
server/biz/dao/dataset.go
+186
View File
@@ -0,0 +1,186 @@
package dao
import (
"context"
"strconv"
"github.com/gogf/gf/v2/database/gdb"
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/os/gtime"
"observer-server/biz/consts"
"observer-server/biz/model/entity"
"observer-server/common"
)
// Dataset 数据集表 DAO：表小、读写低频，不做查询缓存；
// 图片计数由 service 在增删图片时维护，查询走普通读。
type datasetDao struct{}
var Dataset = &datasetDao{}
func init() {
ctx := context.Background()
_, err := g.DB().Exec(ctx, `CREATE TABLE IF NOT EXISTS dataset (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT NOT NULL UNIQUE,
source TEXT NOT NULL DEFAULT 'manual',
image_count INTEGER NOT NULL DEFAULT 0,
labeled_count INTEGER NOT NULL DEFAULT 0,
status TEXT NOT NULL DEFAULT 'building',
cover TEXT,
description TEXT,
ai_endpoint TEXT,
ai_model TEXT,
train_host TEXT,
train_user TEXT,
train_password TEXT,
train_key TEXT,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
)`)
if err != nil {
panic(err)
}
}
// Insert 新建数据集（name UNIQUE 由库兜底）
func (d *datasetDao) Insert(ctx context.Context, m *entity.Dataset) (int64, error) {
res, err := g.DB().Model(consts.TableDataset).Ctx(ctx).Data(g.Map{
"name": m.Name,
"source": m.Source,
"image_count": m.ImageCount,
"labeled_count": m.LabeledCount,
"status": m.Status,
"created_at": m.CreatedAt,
"updated_at": m.UpdatedAt,
}).Insert()
if err != nil {
return 0, err
}
return res.LastInsertId()
}
// GetById 按主键查询，不存在返回 nil
func (d *datasetDao) GetById(ctx context.Context, id int64) (*entity.Dataset, error) {
var e entity.Dataset
err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("id", id).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// GetByName 按名称查询（目录名即数据集名），不存在返回 nil
func (d *datasetDao) GetByName(ctx context.Context, name string) (*entity.Dataset, error) {
var e entity.Dataset
err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("name", name).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// UpdateCounters 更新图片数/已标注数/状态（imgDelta 增量；labeledCount ≥0 时覆盖写）
func (d *datasetDao) UpdateCounters(ctx context.Context, id int64, imgDelta, labeledCount int64, status string) error {
data := g.Map{"updated_at": gtime.Now()}
if imgDelta != 0 {
data["image_count"] = gdb.Raw("image_count + " + strconv.FormatInt(imgDelta, 10))
}
if labeledCount >= 0 {
data["labeled_count"] = labeledCount
}
if status != "" {
data["status"] = status
}
_, err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("id", id).Data(data).Update()
return err
}
// UpdateConfigs 更新数据集展示配置（封面/描述；仅更新非零字段，留空字段保留原值——
// 前端提交整包配置，未填项不覆盖。AI 端点/训练机 SSH 为全局训练配置，不在此表维护）
func (d *datasetDao) UpdateConfigs(ctx context.Context, id int64, m *entity.Dataset) error {
data := g.Map{"updated_at": gtime.Now()}
if m.Cover != "" {
data["cover"] = m.Cover
}
if m.Description != "" {
data["description"] = m.Description
}
_, err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("id", id).Data(data).Update()
return err
}
// ClearCover 清空封面字段（删除封面用；UpdateConfigs 空值不覆盖，无法复用）
func (d *datasetDao) ClearCover(ctx context.Context, id int64) error {
_, err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("id", id).Data(g.Map{"cover": "", "updated_at": gtime.Now()}).Update()
return err
}
// UpdateStatus 更新状态（building|labeled|synced）
func (d *datasetDao) UpdateStatus(ctx context.Context, id int64, status string) error {
_, err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("id", id).
Data(g.Map{"status": status, "updated_at": gtime.Now()}).Update()
return err
}
// DeleteById 删除数据集记录
func (d *datasetDao) DeleteById(ctx context.Context, id int64) error {
_, err := g.DB().Model(consts.TableDataset).Ctx(ctx).Where("id", id).Delete()
return err
}
// PageByKeyword 数据集分页：名称模糊匹配（Like 命中目录名，数据库内仅元数据）
func (d *datasetDao) PageByKeyword(ctx context.Context, keyword string, page, size int) ([]*entity.Dataset, int64, error) {
base := g.DB().Model(consts.TableDataset).Ctx(ctx).WhereLike("name", "%"+keyword+"%")
total, err := base.Count()
if err != nil {
return nil, 0, err
}
var list []*entity.Dataset
err = base.OrderDesc("id").Limit((page-1)*size, size).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.Dataset{}, int64(total), nil
}
return nil, 0, err
}
return list, int64(total), nil
}
// ListAll 全量数据集（模型/训练列表组装数据集名用，数据集表小）
func (d *datasetDao) ListAll(ctx context.Context) ([]*entity.Dataset, error) {
var list []*entity.Dataset
err := g.DB().Model(consts.TableDataset).Ctx(ctx).OrderAsc("id").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.Dataset{}, nil
}
return nil, err
}
return list, nil
}
// Page 数据集分页：创建时间倒序
func (d *datasetDao) Page(ctx context.Context, page, size int) ([]*entity.Dataset, int64, error) {
base := g.DB().Model(consts.TableDataset).Ctx(ctx)
total, err := base.Count()
if err != nil {
return nil, 0, err
}
var list []*entity.Dataset
err = base.OrderDesc("id").Limit((page-1)*size, size).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.Dataset{}, int64(total), nil
}
return nil, 0, err
}
return list, int64(total), nil
}
server/biz/dao/dataset_image.go
+141
View File
@@ -0,0 +1,141 @@
package dao
import (
"context"
"github.com/gogf/gf/v2/frame/g"
"observer-server/biz/consts"
"observer-server/biz/model/entity"
"observer-server/common"
)
// DatasetImage 数据集图片表 DAO：文件名唯一（防重名），按数据集查询；表小不做缓存。
type datasetImageDao struct{}
var DatasetImage = &datasetImageDao{}
func init() {
ctx := context.Background()
_, err := g.DB().Exec(ctx, `CREATE TABLE IF NOT EXISTS dataset_image (
id INTEGER PRIMARY KEY AUTOINCREMENT,
dataset_id INTEGER NOT NULL,
filename TEXT NOT NULL,
source TEXT NOT NULL DEFAULT 'manual',
prompt TEXT,
labels_json TEXT,
created_at TEXT NOT NULL,
UNIQUE (dataset_id, filename)
)`)
if err != nil {
panic(err)
}
// 存量库迁移：标注列缺失时追加（新库建表已含列，幂等跳过）；candidates_json 已随 v10 删除
common.EnsureColumn(ctx, consts.TableDatasetImage, "labels_json", "labels_json TEXT")
}
// Insert 插入图片记录
func (d *datasetImageDao) Insert(ctx context.Context, m *entity.DatasetImage) (int64, error) {
res, err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).Data(g.Map{
"dataset_id": m.DatasetId,
"filename": m.Filename,
"source": m.Source,
"prompt": m.Prompt,
"created_at": m.CreatedAt,
}).Insert()
if err != nil {
return 0, err
}
return res.LastInsertId()
}
// ListByDataset 某数据集全部图片（文件名倒序，详情页逐行展示用）
func (d *datasetImageDao) ListByDataset(ctx context.Context, datasetId int64) ([]*entity.DatasetImage, error) {
var list []*entity.DatasetImage
err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).
Where("dataset_id", datasetId).OrderDesc("filename").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.DatasetImage{}, nil
}
return nil, err
}
return list, nil
}
// ListUnlabeledByDataset 未标注图片（labels_json 为空/null/'[]'；自动补标轮次用）
func (d *datasetImageDao) ListUnlabeledByDataset(ctx context.Context, datasetId int64) ([]*entity.DatasetImage, error) {
var list []*entity.DatasetImage
err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).
Where("dataset_id", datasetId).
Where("labels_json IS NULL OR labels_json = '' OR labels_json = '[]'").
OrderAsc("id").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.DatasetImage{}, nil
}
return nil, err
}
return list, nil
}
// GetByIds 按 id 批量取（删除图片定位文件用）
func (d *datasetImageDao) GetByIds(ctx context.Context, ids []int64) ([]*entity.DatasetImage, error) {
var list []*entity.DatasetImage
err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).WhereIn("id", ids).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.DatasetImage{}, nil
}
return nil, err
}
return list, nil
}
// GetByFilename 按文件名取（图片访问校验归属）
func (d *datasetImageDao) GetByFilename(ctx context.Context, datasetId int64, filename string) (*entity.DatasetImage, error) {
var e entity.DatasetImage
err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).
Where("dataset_id", datasetId).Where("filename", filename).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// CountByDataset 某数据集图片数
func (d *datasetImageDao) CountByDataset(ctx context.Context, datasetId int64) (int64, error) {
n, err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).Where("dataset_id", datasetId).Count()
return int64(n), err
}
// UpdateLabels 覆写标注 JSON（整图粒度；空串=清空标注；AI 自动标注与人工保存共用）
func (d *datasetImageDao) UpdateLabels(ctx context.Context, id int64, labelsJson string) error {
_, err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).Where("id", id).
Data(g.Map{"labels_json": labelsJson}).Update()
return err
}
// CountLabeledByDataset 某数据集已标注图片数（labels_json 非空数组的图片行数）
func (d *datasetImageDao) CountLabeledByDataset(ctx context.Context, datasetId int64) (int64, error) {
n, err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).
Where("dataset_id", datasetId).
Where("labels_json IS NOT NULL AND labels_json != '' AND labels_json != '[]'").
Count()
return int64(n), err
}
// DeleteByIds 按 id 批量删除（标注随行删除）
func (d *datasetImageDao) DeleteByIds(ctx context.Context, ids []int64) error {
_, err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).WhereIn("id", ids).Delete()
return err
}
// DeleteByDataset 删除某数据集全部图片记录（删数据集联动）
func (d *datasetImageDao) DeleteByDataset(ctx context.Context, datasetId int64) error {
_, err := g.DB().Model(consts.TableDatasetImage).Ctx(ctx).Where("dataset_id", datasetId).Delete()
return err
}
server/biz/dao/label_task.go
+149
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@@ -0,0 +1,149 @@
package dao
import (
"context"
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/os/gtime"
"observer-server/biz/consts"
"observer-server/biz/model/entity"
"observer-server/common"
)
// LabelTask 标注任务表 DAO：进度更新（done 计数）高频，单行 UPDATE 原子。
type labelTaskDao struct{}
var LabelTask = &labelTaskDao{}
func init() {
ctx := context.Background()
_, err := g.DB().Exec(ctx, `CREATE TABLE IF NOT EXISTS label_task (
id INTEGER PRIMARY KEY AUTOINCREMENT,
dataset_id INTEGER NOT NULL,
status TEXT NOT NULL DEFAULT 'running',
total INTEGER NOT NULL DEFAULT 0,
done INTEGER NOT NULL DEFAULT 0,
boxes_file TEXT,
filenames TEXT,
error TEXT,
created_at TEXT NOT NULL,
finished_at TEXT
)`)
if err != nil {
panic(err)
}
}
// Insert 创建标注任务，返回自增 id
func (d *labelTaskDao) Insert(ctx context.Context, m *entity.LabelTask) (int64, error) {
res, err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).Data(g.Map{
"dataset_id": m.DatasetId,
"status": m.Status,
"total": m.Total,
"done": m.Done,
"boxes_file": m.BoxesFile,
"filenames": m.Filenames,
"error": m.Error,
"created_at": m.CreatedAt,
"finished_at": m.FinishedAt,
}).Insert()
if err != nil {
return 0, err
}
return res.LastInsertId()
}
// GetById 按主键查询，不存在返回 nil
func (d *labelTaskDao) GetById(ctx context.Context, id int64) (*entity.LabelTask, error) {
var e entity.LabelTask
err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).Where("id", id).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// ListRunning 全部 running 任务（服务重启恢复用）
func (d *labelTaskDao) ListRunning(ctx context.Context) ([]*entity.LabelTask, error) {
var list []*entity.LabelTask
err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).
Where("status", consts.LabelTaskRunning).OrderAsc("id").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.LabelTask{}, nil
}
return nil, err
}
return list, nil
}
// GetRunningByDataset 某数据集 running 任务（并发检查用）
func (d *labelTaskDao) GetRunningByDataset(ctx context.Context, datasetId int64) (*entity.LabelTask, error) {
var e entity.LabelTask
err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).
Where("dataset_id", datasetId).Where("status", consts.LabelTaskRunning).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// UpdateProgress 更新处理进度
func (d *labelTaskDao) UpdateProgress(ctx context.Context, id int64, done int) error {
_, err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).Where("id", id).
Data(g.Map{"done": done}).Update()
return err
}
// Finish 完成任务（done 状态 + 完成时间 + 候选框文件路径 + 失败原因）
func (d *labelTaskDao) Finish(ctx context.Context, id int64, boxesFile, errMsg string) error {
_, err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).Where("id", id).
Data(g.Map{"status": consts.LabelTaskDone, "finished_at": gtime.Now(), "boxes_file": boxesFile, "error": errMsg}).Update()
return err
}
// LatestByDataset 某数据集最近一次任务（无则 nil）
func (d *labelTaskDao) LatestByDataset(ctx context.Context, datasetId int64) (*entity.LabelTask, error) {
var e entity.LabelTask
err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).
Where("dataset_id", datasetId).OrderDesc("id").Limit(1).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// DeleteByDataset 删除数据集关联的全部标注任务（数据集删除时级联清理孤儿记录）
func (d *labelTaskDao) DeleteByDataset(ctx context.Context, datasetId int64) error {
_, err := g.DB().Model(consts.TableLabelTask).Ctx(ctx).
Where("dataset_id", datasetId).Delete()
return err
}
// Page 标注任务分页：创建时间倒序
func (d *labelTaskDao) Page(ctx context.Context, page, size int) ([]*entity.LabelTask, int64, error) {
base := g.DB().Model(consts.TableLabelTask).Ctx(ctx)
total, err := base.Count()
if err != nil {
return nil, 0, err
}
var list []*entity.LabelTask
err = base.OrderDesc("id").Limit((page-1)*size, size).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.LabelTask{}, int64(total), nil
}
return nil, 0, err
}
return list, int64(total), nil
}
server/biz/dao/model_training.go
+233
View File
@@ -0,0 +1,233 @@
package dao
import (
"context"
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/os/gtime"
"observer-server/biz/consts"
"observer-server/biz/model/entity"
"observer-server/common"
)
// ModelTraining 训练任务表 DAO：进度/日志尾部为高频更新（独立小事务，不走 Serial 串行，
// 单行 UPDATE 天然原子，无并发写冲突）；状态流转（发起/结束）走 service 单写者。
type modelTrainingDao struct{}
var Training = &modelTrainingDao{}
func init() {
ctx := context.Background()
_, err := g.DB().Exec(ctx, `CREATE TABLE IF NOT EXISTS model_training (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT NOT NULL,
status TEXT NOT NULL DEFAULT 'running',
dataset_id INTEGER NOT NULL,
imgsz INTEGER NOT NULL DEFAULT 704,
epochs INTEGER NOT NULL DEFAULT 150,
batch INTEGER NOT NULL DEFAULT 16,
device TEXT NOT NULL DEFAULT '0',
current_epoch INTEGER NOT NULL DEFAULT 0,
total_epochs INTEGER NOT NULL DEFAULT 0,
metrics TEXT,
log_tail TEXT,
pid INTEGER,
error TEXT,
started_at TEXT NOT NULL,
finished_at TEXT,
created_at TEXT NOT NULL
)`)
if err != nil {
panic(err)
}
}
// Insert 创建训练任务，返回自增 id
func (d *modelTrainingDao) Insert(ctx context.Context, m *entity.ModelTraining) (int64, error) {
res, err := g.DB().Model(consts.TableTraining).Ctx(ctx).Data(g.Map{
"name": m.Name,
"status": m.Status,
"dataset_id": m.DatasetId,
"imgsz": m.Imgsz,
"epochs": m.Epochs,
"batch": m.Batch,
"device": m.Device,
"current_epoch": m.CurrentEpoch,
"total_epochs": m.TotalEpochs,
"metrics": m.Metrics,
"log_tail": m.LogTail,
"pid": m.Pid,
"error": m.Error,
"started_at": m.StartedAt,
"finished_at": m.FinishedAt,
"created_at": m.CreatedAt,
}).Insert()
if err != nil {
return 0, err
}
return res.LastInsertId()
}
// GetById 按主键查询，不存在返回 nil
func (d *modelTrainingDao) GetById(ctx context.Context, id int64) (*entity.ModelTraining, error) {
var e entity.ModelTraining
err := g.DB().Model(consts.TableTraining).Ctx(ctx).Where("id", id).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// UpdateProgress 更新进度/指标/日志尾部（训练轮询高频调用）
func (d *modelTrainingDao) UpdateProgress(ctx context.Context, id int64, currentEpoch, totalEpochs int, metrics, logTail string) error {
data := g.Map{}
if currentEpoch > 0 {
data["current_epoch"] = currentEpoch
}
if totalEpochs > 0 {
data["total_epochs"] = totalEpochs
}
if metrics != "" {
data["metrics"] = metrics
}
if logTail != "" {
data["log_tail"] = logTail
}
if len(data) == 0 {
return nil
}
_, err := g.DB().Model(consts.TableTraining).Ctx(ctx).Where("id", id).Data(data).Update()
return err
}
// UpdatePid 记录训练进程 pid（启动后写入，恢复扫描用）
func (d *modelTrainingDao) UpdatePid(ctx context.Context, id int64, pid int) error {
_, err := g.DB().Model(consts.TableTraining).Ctx(ctx).Where("id", id).
Data(g.Map{"pid": pid}).Update()
return err
}
// PageByStatus 训练任务分页：按状态筛选（创建时间倒序）
func (d *modelTrainingDao) PageByStatus(ctx context.Context, status string, page, size int) ([]*entity.ModelTraining, int64, error) {
base := g.DB().Model(consts.TableTraining).Ctx(ctx).Where("status", status)
total, err := base.Count()
if err != nil {
return nil, 0, err
}
var list []*entity.ModelTraining
err = base.OrderDesc("id").Limit((page-1)*size, size).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.ModelTraining{}, int64(total), nil
}
return nil, 0, err
}
return list, int64(total), nil
}
// Finish 结束任务（success/failed）：状态 + 结束时间 + 指标 + 日志尾部 + 失败原因
func (d *modelTrainingDao) Finish(ctx context.Context, id int64, status, metrics, logTail, errMsg string) error {
data := g.Map{"status": status, "finished_at": gtime.Now(), "log_tail": logTail}
if metrics != "" {
data["metrics"] = metrics
}
if errMsg != "" {
data["error"] = errMsg
}
_, err := g.DB().Model(consts.TableTraining).Ctx(ctx).Where("id", id).Data(data).Update()
return err
}
// Running 当前 running 任务（并发度 1 检查用；异常终态的失败任务同表记录，不算 running）
func (d *modelTrainingDao) Running(ctx context.Context) (*entity.ModelTraining, error) {
var e entity.ModelTraining
err := g.DB().Model(consts.TableTraining).Ctx(ctx).
Where("status", consts.TrainingStatusRunning).OrderAsc("id").Limit(1).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// RunningByDataset 某数据集 running 任务（删数据集前检查用）
func (d *modelTrainingDao) RunningByDataset(ctx context.Context, datasetId int64) (*entity.ModelTraining, error) {
var e entity.ModelTraining
err := g.DB().Model(consts.TableTraining).Ctx(ctx).
Where("dataset_id", datasetId).Where("status", consts.TrainingStatusRunning).Limit(1).Scan(&e)
if err != nil {
if common.IsNoRows(err) {
return nil, nil
}
return nil, err
}
return &e, nil
}
// ListRunning 全部 running 任务（Go 重启后恢复扫描用）
func (d *modelTrainingDao) ListRunning(ctx context.Context) ([]*entity.ModelTraining, error) {
var list []*entity.ModelTraining
err := g.DB().Model(consts.TableTraining).Ctx(ctx).
Where("status", consts.TrainingStatusRunning).OrderAsc("id").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.ModelTraining{}, nil
}
return nil, err
}
return list, nil
}
// LatestByDatasets 批量取各数据集最新一条训练记录（列表卡片训练状态用；
// IN 一次取回按 id 倒序，应用层按 dataset_id 去重；数据集表小、记录少，单次查询足够）
func (d *modelTrainingDao) LatestByDatasets(ctx context.Context, datasetIds []int64) (map[int64]*entity.ModelTraining, error) {
out := make(map[int64]*entity.ModelTraining)
if len(datasetIds) == 0 {
return out, nil
}
for start := 0; start < len(datasetIds); start += 100 {
end := start + 100
if end > len(datasetIds) {
end = len(datasetIds)
}
var list []*entity.ModelTraining
err := g.DB().Model(consts.TableTraining).Ctx(ctx).
WhereIn("dataset_id", datasetIds[start:end]).OrderDesc("id").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
continue
}
return nil, err
}
for _, t := range list {
if _, ok := out[t.DatasetId]; !ok {
out[t.DatasetId] = t
}
}
}
return out, nil
}
// Page 训练任务分页：创建时间倒序
func (d *modelTrainingDao) Page(ctx context.Context, page, size int) ([]*entity.ModelTraining, int64, error) {
base := g.DB().Model(consts.TableTraining).Ctx(ctx)
total, err := base.Count()
if err != nil {
return nil, 0, err
}
var list []*entity.ModelTraining
err = base.OrderDesc("id").Limit((page-1)*size, size).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.ModelTraining{}, int64(total), nil
}
return nil, 0, err
}
return list, int64(total), nil
}
server/biz/dao/model_version.go
+110
View File
@@ -0,0 +1,110 @@
package dao
import (
"context"
"github.com/gogf/gf/v2/frame/g"
"observer-server/biz/consts"
"observer-server/biz/model/entity"
"observer-server/common"
)
// ModelVersion 模型版本表 DAO：每数据集独立版本序列；表小不做查询缓存。
type modelVersionDao struct{}
var ModelVersion = &modelVersionDao{}
func init() {
ctx := context.Background()
_, err := g.DB().Exec(ctx, `CREATE TABLE IF NOT EXISTS model_version (
id INTEGER PRIMARY KEY AUTOINCREMENT,
dataset_id INTEGER NOT NULL,
version TEXT NOT NULL,
training_id INTEGER,
artifact_file TEXT,
metrics TEXT,
labels TEXT NOT NULL,
sha256 TEXT NOT NULL,
size_bytes INTEGER NOT NULL,
is_latest INTEGER NOT NULL DEFAULT 0,
notes TEXT,
created_at TEXT NOT NULL,
UNIQUE (dataset_id, version)
)`)
if err != nil {
panic(err)
}
}
// Insert 插入模型版本（is_latest 由 service 先置 0 再插新行置 1）
func (d *modelVersionDao) Insert(ctx context.Context, m *entity.ModelVersion) (int64, error) {
res, err := g.DB().Model(consts.TableModelVersion).Ctx(ctx).Data(g.Map{
"dataset_id": m.DatasetId,
"version": m.Version,
"training_id": m.TrainingId,
"artifact_file": m.ArtifactFile,
"metrics": m.Metrics,
"labels": m.Labels,
"sha256": m.Sha256,
"size_bytes": m.SizeBytes,
"is_latest": m.IsLatest,
"notes": m.Notes,
"created_at": m.CreatedAt,
}).Insert()
if err != nil {
return 0, err
}
return res.LastInsertId()
}
// ClearLatest 某数据集所有版本置 is_latest=0（发布前调用）
func (d *modelVersionDao) ClearLatest(ctx context.Context, datasetId int64) error {
_, err := g.DB().Model(consts.TableModelVersion).Ctx(ctx).
Where("dataset_id", datasetId).Data(g.Map{"is_latest": 0}).Update()
return err
}
// DeleteByDataset 删除某数据集全部版本记录（删数据集级联）
func (d *modelVersionDao) DeleteByDataset(ctx context.Context, datasetId int64) error {
_, err := g.DB().Model(consts.TableModelVersion).Ctx(ctx).Where("dataset_id", datasetId).Delete()
return err
}
// ListAllLatest 全部数据集的当前生效版本（模型目录/热更新目录）
func (d *modelVersionDao) ListAllLatest(ctx context.Context) ([]*entity.ModelVersion, error) {
var list []*entity.ModelVersion
err := g.DB().Model(consts.TableModelVersion).Ctx(ctx).
Where("is_latest", 1).OrderAsc("dataset_id").Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.ModelVersion{}, nil
}
return nil, err
}
return list, nil
}
// PageByDataset 某数据集版本分页：发布时间倒序
func (d *modelVersionDao) PageByDataset(ctx context.Context, datasetId int64, page, size int) ([]*entity.ModelVersion, int64, error) {
base := g.DB().Model(consts.TableModelVersion).Ctx(ctx).Where("dataset_id", datasetId)
total, err := base.Count()
if err != nil {
return nil, 0, err
}
var list []*entity.ModelVersion
err = base.OrderDesc("id").Limit((page-1)*size, size).Scan(&list)
if err != nil {
if common.IsNoRows(err) {
return []*entity.ModelVersion{}, int64(total), nil
}
return nil, 0, err
}
return list, int64(total), nil
}
// DeleteById 删除版本记录
func (d *modelVersionDao) DeleteById(ctx context.Context, id int64) error {
_, err := g.DB().Model(consts.TableModelVersion).Ctx(ctx).Where("id", id).Delete()
return err
}
server/biz/model/dto/app_version.go
+6 -3
View File
@@ -12,8 +12,11 @@ type AppUpdateReq struct {
}
// AppUpdateRes 服务器最新版本记录（无记录时全字段零值，客户端视为无需更新）；
// 检测到新版本即强制更新，无普通/强制之分
// 检测到新版本即强制更新，无普通/强制之分。
// Models 为模型热更新目录（全部数据集当前生效模型，与 GET /api/v1/models 同构）；
// 无发布模型时不返回该字段（旧 App 忽略、新 App 兼容旧服务器）。
type AppUpdateRes struct {
Version string `json:"version"`
Notes string `json:"notes"`
Version string `json:"version"`
Notes string `json:"notes"`
Models []*ModelCatalogItem `json:"models,omitempty"`
}
server/biz/model/dto/training.go
+378
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@@ -0,0 +1,378 @@
package dto
import (
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/net/ghttp"
"github.com/gogf/gf/v2/os/gtime"
)
// 模型训练体系接口（数据集 → 标注 → 训练 → 模型版本），组前缀 /api/v1/admin。
// 图片/标注/模型文件均在服务器磁盘（app.datasetDir），DB 只存元数据。
// 图片访问与 zip 导出为直接写响应体场景，由 controller 以 *ghttp.Request 方法实现
// （见 controller/admin.go 的 Image/ExportDataset 方法）。
// ---------- 数据集管理 ----------
// AdminDatasetListReq 数据集列表（创建时间倒序）
type AdminDatasetListReq struct {
g.Meta `path:"/datasets" method:"get" summary:"数据集列表" tags:"管理端"`
Keyword string `json:"keyword" v:"length:0,50" dc:"名称模糊匹配"`
Page int `json:"page" v:"integer|min:1" dc:"页码,默认 1"`
Size int `json:"size" v:"integer|min:1|max:100" dc:"每页条数,默认 20"`
}
// AdminDatasetItem 数据集条目（卡片展示用；AI 端点/训练机 SSH 走 config.yml 全局配置（localAi / training.ssh））
type AdminDatasetItem struct {
Id int64 `json:"id"`
Name string `json:"name"`
Source string `json:"source"` // manual | ai
ImageCount int64 `json:"imageCount"`
LabeledCount int64 `json:"labeledCount"`
Status string `json:"status"` // building | labeled | synced
Cover string `json:"cover"` // 封面文件名
Description string `json:"description"` // 描述
TrainingId int64 `json:"trainingId"` // 最新训练记录 id（发布/详情用）
TrainingStatus string `json:"trainingStatus"` // 最新训练记录状态 running|success|failed|空
TrainingCurrentEpoch int `json:"trainingCurrentEpoch"`
TrainingTotalEpochs int `json:"trainingTotalEpochs"`
CreatedAt *gtime.Time `json:"createdAt"`
UpdatedAt *gtime.Time `json:"updatedAt"`
}
type AdminDatasetListRes struct {
Total int64 `json:"total"`
List []*AdminDatasetItem `json:"list"`
}
// AdminDatasetUpdateReq 更新数据集展示配置（空值字段不覆盖原值，保留现有配置；
// AI 端点/训练机 SSH 走 config.yml 全局配置（localAi / training.ssh））
type AdminDatasetUpdateReq struct {
g.Meta `path:"/datasets/update" method:"post" summary:"更新数据集配置" tags:"管理端"`
Id int64 `json:"id" v:"required|min:1" dc:"数据集 id"`
Description string `json:"description" v:"length:0,500" dc:"描述(非空才更新)"`
Cover string `json:"cover" v:"length:0,200" dc:"封面文件名(非空才更新)"`
}
type AdminDatasetUpdateRes struct {
Id int64 `json:"id"`
}
// AdminDatasetCreateReq 新建数据集（名称同时是磁盘目录名：中文/字母/数字/下划线/短横线，唯一）
type AdminDatasetCreateReq struct {
g.Meta `path:"/datasets" method:"post" summary:"新建数据集" tags:"管理端"`
Name string `json:"name" v:"required|regex:^[a-zA-Z0-9_一-龥-]+$|length:1,50" dc:"数据集名称(同是目录名,唯一)"`
Source string `json:"source" v:"required|in:manual,ai" dc:"图片来源 manual|ai"`
}
type AdminDatasetCreateRes struct {
Id int64 `json:"id"`
}
// AdminDatasetDeleteReq 删除数据集（删除图片文件+标注目录+记录；AI 生成图为付费资产，前端须带确认文案）
type AdminDatasetDeleteReq struct {
g.Meta `path:"/datasets/delete" method:"post" summary:"删除数据集" tags:"管理端"`
Id int64 `json:"id" v:"required|min:1" dc:"数据集 id"`
}
type AdminDatasetDeleteRes struct{}
// AdminDatasetUploadReq 上传图片（multipart 多文件；重名跳过并计数）
type AdminDatasetUploadReq struct {
g.Meta `path:"/datasets/upload" method:"post" summary:"上传图片" tags:"管理端" mime:"multipart/form-data"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Files []*ghttp.UploadFile `json:"files" dc:"图片文件(jpg/jpeg/png,多选)"`
}
type AdminDatasetUploadRes struct {
Added int `json:"added"`
Skipped int `json:"skipped"` // 重名跳过数
}
// AdminDatasetGenerateReq AI 生成图片（同步执行；prompt 禁止含目标位置描述，前端模板+文案约束）
type AdminDatasetGenerateReq struct {
g.Meta `path:"/datasets/generate" method:"post" summary:"AI 生成图片" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Prompt string `json:"prompt" v:"required|length:5,500" dc:"生成提示词(禁止含位置描述)"`
Count int `json:"count" v:"required|integer|min:1|max:8" dc:"生成张数 1-8"`
Size string `json:"size" v:"required|in:1152x2048,704x704" dc:"图片尺寸"`
}
type AdminDatasetGenerateRes struct {
Generated int `json:"generated"` // 成功张数（失败时可能 < count，已成功的图保留）
}
// AdminDatasetImagesReq 数据集图片列表（标注工作台/网格预览用）
type AdminDatasetImagesReq struct {
g.Meta `path:"/datasets/images" method:"get" summary:"数据集图片列表" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
}
// AdminImageItem 图片条目（url 为管理端图片访问地址，前端拼 apiBaseUrl 使用）
type AdminImageItem struct {
Id int64 `json:"id"`
Filename string `json:"filename"`
Source string `json:"source"` // manual | ai
Prompt string `json:"prompt"`
Url string `json:"url"`
CreatedAt *gtime.Time `json:"createdAt"`
}
type AdminDatasetImagesRes struct {
List []*AdminImageItem `json:"list"`
}
// AdminDatasetImagesDeleteReq 删除图片（删文件+删记录；付费资产，前端须带确认文案）
type AdminDatasetImagesDeleteReq struct {
g.Meta `path:"/datasets/images/delete" method:"post" summary:"删除图片" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Ids []int64 `json:"ids" v:"required|min-length:1" dc:"图片记录 id 列表"`
}
type AdminDatasetImagesDeleteRes struct{}
// AdminDatasetImageReq 图片访问（直写响应体，例外场景：controller 经 service 校验归属后输出二进制）
type AdminDatasetImageReq struct {
g.Meta `path:"/datasets/image" method:"get" summary:"图片访问" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Filename string `json:"filename" v:"required" dc:"图片文件名"`
}
type AdminDatasetImageRes struct{}
// AdminDatasetExportReq 数据集 zip 导出（直写响应体下载；标注衔接与备份用）
type AdminDatasetExportReq struct {
g.Meta `path:"/datasets/export" method:"get" summary:"导出数据集 zip" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
}
type AdminDatasetExportRes struct{}
// AdminDatasetCoverUploadReq 上传数据集封面（multipart 单文件；覆盖旧封面，存图片目录 cover<ext>）
type AdminDatasetCoverUploadReq struct {
g.Meta `path:"/datasets/cover" method:"post" summary:"上传数据集封面" tags:"管理端" mime:"multipart/form-data"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
File *ghttp.UploadFile `json:"file" dc:"封面图片(jpg/jpeg/png,≤2MB)"`
}
type AdminDatasetCoverUploadRes struct{}
// AdminDatasetCoverReq 封面访问（直写响应体，与 Image 同模式）
type AdminDatasetCoverReq struct {
g.Meta `path:"/datasets/cover" method:"get" summary:"数据集封面访问" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
}
type AdminDatasetCoverRes struct{}
// AdminDatasetCoverDeleteReq 删除数据集封面（删文件+清字段；列表卡片恢复占位图）
type AdminDatasetCoverDeleteReq struct {
g.Meta `path:"/datasets/cover/delete" method:"post" summary:"删除数据集封面" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
}
type AdminDatasetCoverDeleteRes struct{}
// ---------- 训练编排 ----------
// AdminTrainingListReq 训练任务列表（创建时间倒序）
type AdminTrainingListReq struct {
g.Meta `path:"/trainings" method:"get" summary:"训练任务列表" tags:"管理端"`
Status string `json:"status" v:"in:running,success,failed" dc:"状态筛选"`
Page int `json:"page" v:"integer|min:1" dc:"页码,默认 1"`
Size int `json:"size" v:"integer|min:1|max:100" dc:"每页条数,默认 20"`
}
// AdminTrainingItem 训练任务条目（列表不含日志尾部，详情接口返回）
type AdminTrainingItem struct {
Id int64 `json:"id"`
Name string `json:"name"`
DatasetId int64 `json:"datasetId"`
DatasetName string `json:"datasetName"`
Status string `json:"status"` // running | success | failed
Imgsz int `json:"imgsz"`
Epochs int `json:"epochs"`
Batch int `json:"batch"`
Device string `json:"device"`
CurrentEpoch int `json:"currentEpoch"`
TotalEpochs int `json:"totalEpochs"`
Metrics string `json:"metrics"`
Error string `json:"error"`
StartedAt *gtime.Time `json:"startedAt"`
FinishedAt *gtime.Time `json:"finishedAt"`
CreatedAt *gtime.Time `json:"createdAt"`
}
type AdminTrainingListRes struct {
Total int64 `json:"total"`
List []*AdminTrainingItem `json:"list"`
}
// AdminTrainingStartReq 发起训练（并发度 1：已有 running 任务时拒绝）
type AdminTrainingStartReq struct {
g.Meta `path:"/trainings" method:"post" summary:"发起训练" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Name string `json:"name" v:"required|length:1,50" dc:"任务名称"`
Imgsz int `json:"imgsz" v:"integer|min:64|max:2048" dc:"输入尺寸,默认 704"`
Epochs int `json:"epochs" v:"integer|min:1|max:1000" dc:"训练轮数,默认 150"`
Batch int `json:"batch" v:"integer|min:1|max:128" dc:"批大小,默认 16"`
Device string `json:"device" v:"length:0,16" dc:"设备,默认 0"`
}
type AdminTrainingStartRes struct {
Id int64 `json:"id"`
}
// AdminTrainingDetailReq 训练任务详情（含日志尾部）
type AdminTrainingDetailReq struct {
g.Meta `path:"/trainings/detail" method:"get" summary:"训练任务详情" tags:"管理端"`
Id int64 `json:"id" v:"required|min:1" dc:"训练任务 id"`
}
// AdminTrainingDetailRes 详情 = 条目 + 日志尾部
type AdminTrainingDetailRes struct {
AdminTrainingItem
LogTail string `json:"logTail"`
}
// AdminTrainingCancelReq 取消训练（杀进程，任务置 failed）
type AdminTrainingCancelReq struct {
g.Meta `path:"/trainings/cancel" method:"post" summary:"取消训练" tags:"管理端"`
Id int64 `json:"id" v:"required|min:1" dc:"训练任务 id"`
}
type AdminTrainingCancelRes struct{}
// AdminTrainingPublishReq 发布模型版本（仅 success 任务；按数据集版本号 m<major>.<minor>.<patch> 自增）
type AdminTrainingPublishReq struct {
g.Meta `path:"/trainings/publish" method:"post" summary:"发布模型版本" tags:"管理端"`
Id int64 `json:"id" v:"required|min:1" dc:"训练任务 id"`
}
type AdminTrainingPublishRes struct {
Version string `json:"version"`
}
// ---------- 模型版本 ----------
// ---------- 预标注 ----------
// AdminLabelTaskListReq 预标注任务列表（创建时间倒序）
type AdminLabelTaskListReq struct {
g.Meta `path:"/label-tasks" method:"get" summary:"预标注任务列表" tags:"管理端"`
Page int `json:"page" v:"integer|min:1" dc:"页码,默认 1"`
Size int `json:"size" v:"integer|min:1|max:100" dc:"每页条数,默认 20"`
}
type AdminLabelTaskItem struct {
Id int64 `json:"id"`
DatasetId int64 `json:"datasetId"`
DatasetName string `json:"datasetName"`
Status string `json:"status"` // running | done
Total int `json:"total"`
Done int `json:"done"`
Error string `json:"error"`
CreatedAt *gtime.Time `json:"createdAt"`
FinishedAt *gtime.Time `json:"finishedAt"`
}
type AdminLabelTaskListRes struct {
Total int64 `json:"total"`
List []*AdminLabelTaskItem `json:"list"`
}
// AdminLabelTaskStartReq 发起预标注（RF-DETR 扫描，候选框合并入 datasets/<name>/boxes.json）
type AdminLabelTaskStartReq struct {
g.Meta `path:"/label-tasks" method:"post" summary:"发起预标注" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Filenames []string `json:"filenames" v:"length:0,500" dc:"选中图片列表(多选批量);缺省=全量扫描"`
}
type AdminLabelTaskStartRes struct {
Id int64 `json:"id"`
}
// AdminLabelTaskDetailReq 预标注任务详情（每张图候选框 + 已确认标注，供工作台 canvas 叠框）
type AdminLabelTaskDetailReq struct {
g.Meta `path:"/label-tasks/detail" method:"get" summary:"预标注任务详情" tags:"管理端"`
Id int64 `json:"id" v:"required|min:1" dc:"标注任务 id"`
}
// AdminLabelBox 单框（归一化 xywh，坐标 0~1）
type AdminLabelBox struct {
Cx float64 `json:"cx"`
Cy float64 `json:"cy"`
W float64 `json:"w"`
H float64 `json:"h"`
Confidence float64 `json:"confidence"` // 人工框为 1
Class int `json:"class"` // 0 确认 | 1 疑似
}
// AdminLabelImageItem 工作台单张图：全部标注（labels_json，AI 自动标注与人工框同层，人工可修改/清理）
type AdminLabelImageItem struct {
Filename string `json:"filename"`
Url string `json:"url"`
Width int `json:"width"`
Height int `json:"height"`
Boxes []*AdminLabelBox `json:"boxes"`
Labeled bool `json:"labeled"` // 有标注（AI 自动标注或人工保存）
}
type AdminLabelTaskDetailRes struct {
TaskId int64 `json:"taskId"`
DatasetId int64 `json:"datasetId"`
DatasetName string `json:"datasetName"`
Status string `json:"status"`
Total int `json:"total"`
Done int `json:"done"`
Error string `json:"error"`
Images []*AdminLabelImageItem `json:"images"`
}
// AdminLabelWorkbenchReq 标注工作台数据（无历史任务时直接用数据集图片 + 已确认标注，
// 候选框读数据集目录 boxes.json）
type AdminLabelWorkbenchReq struct {
g.Meta `path:"/label-workbench" method:"get" summary:"标注工作台数据" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
}
type AdminLabelWorkbenchRes struct {
DatasetId int64 `json:"datasetId"`
DatasetName string `json:"datasetName"`
Images []*AdminLabelImageItem `json:"images"`
}
// AdminLabelSaveReq 保存单张图标注（工作台「保存本张」；覆写 dataset_image.labels_json）
type AdminLabelSaveReq struct {
g.Meta `path:"/label-tasks/save" method:"post" summary:"保存图片标注" tags:"管理端"`
DatasetId int64 `json:"datasetId" v:"required|min:1" dc:"数据集 id"`
Filename string `json:"filename" v:"required|length:1,200" dc:"图片文件名"`
Boxes []*AdminLabelBox `json:"boxes" dc:"确认后的框列表(空=清空标注)"`
}
type AdminLabelSaveRes struct {
LabeledCount int64 `json:"labeledCount"` // 该数据集当前已标注图数
}
// ---------- 客户端模型目录 ----------
// ModelCatalogReq 模型目录（客户端登录态）：全部数据集的当前生效模型
type ModelCatalogReq struct {
g.Meta `path:"/models" method:"get" summary:"模型目录" tags:"客户端"`
}
// ModelCatalogItem 客户端模型条目（App 按需下载，多模型并行推理合并）
type ModelCatalogItem struct {
DatasetId int64 `json:"datasetId"`
DatasetName string `json:"datasetName"`
Version string `json:"version"`
Labels []string `json:"labels"` // 类别名，App 推理结果展示用
SizeBytes int64 `json:"sizeBytes"`
Sha256 string `json:"sha256"`
Notes string `json:"notes"`
PublishedAt *gtime.Time `json:"publishedAt"`
DownloadUrl string `json:"downloadUrl"` // /download/models/<name>/latest.tflite
}
type ModelCatalogRes struct {
List []*ModelCatalogItem `json:"list"`
}
server/biz/model/entity/dataset.go
+18
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@@ -0,0 +1,18 @@
package entity
import "github.com/gogf/gf/v2/os/gtime"
// Dataset 训练数据集：图片文件在 app.datasetDir/datasets/<name>/（DB 只存元数据 + 标注 labels_json）。
// 每数据集训练一个模型。
type Dataset struct {
Id int64 `json:"id" orm:"id" description:"自增主键"`
Name string `json:"name" orm:"name" description:"数据集名(≤50字,同是目录名,唯一)"`
Source string `json:"source" orm:"source" description:"manual|ai"`
ImageCount int64 `json:"imageCount" orm:"image_count" description:"图片数(冗余计数)"`
LabeledCount int64 `json:"labeledCount" orm:"labeled_count" description:"已标注数"`
Status string `json:"status" orm:"status" description:"building|labeled|synced"`
Cover string `json:"cover" orm:"cover" description:"封面文件名(卡片展示)"`
Description string `json:"description" orm:"description" description:"描述(卡片展示)"`
CreatedAt *gtime.Time `json:"createdAt" orm:"created_at" description:"创建时间"`
UpdatedAt *gtime.Time `json:"updatedAt" orm:"updated_at" description:"更新时间"`
}
server/biz/model/entity/dataset_image.go
+16
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@@ -0,0 +1,16 @@
package entity
import "github.com/gogf/gf/v2/os/gtime"
// DatasetImage 数据集图片：文件在 datasets/<数据集名>/<filename>，
// AI 生成图记录 prompt（付费资产，追溯用）；标注为 JSON 数组存本表
// （元素同 dto.AdminLabelBox：class/cx/cy/w/h/confidence，YOLO 归一化）。
type DatasetImage struct {
Id int64 `json:"id" orm:"id" description:"自增主键"`
DatasetId int64 `json:"datasetId" orm:"dataset_id" description:"所属数据集"`
Filename string `json:"filename" orm:"filename" description:"唯一文件名(防重名加时间戳后缀)"`
Source string `json:"source" orm:"source" description:"manual|ai"`
Prompt string `json:"prompt" orm:"prompt" description:"AI 生成图提示词"`
LabelsJson string `json:"labelsJson" orm:"labels_json" description:"标注 JSON 数组(AI 自动标注与人工标注同存,null/''/'[]'=未标注)"`
CreatedAt *gtime.Time `json:"createdAt" orm:"created_at" description:"创建时间"`
}
server/biz/model/entity/label_task.go
+19
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@@ -0,0 +1,19 @@
package entity
import "github.com/gogf/gf/v2/os/gtime"
// LabelTask 标注任务：RF-DETR 预标注批量推理进度；候选框存
// datasets/<数据集名>/boxes.json（YOLO 归一化 xywh + 置信度 + 建议类别）。
// Filenames 为选中图片列表（JSON 数组串；空 = 全量扫描）。
type LabelTask struct {
Id int64 `json:"id" orm:"id" description:"自增主键"`
DatasetId int64 `json:"datasetId" orm:"dataset_id" description:"所属数据集"`
Status string `json:"status" orm:"status" description:"running|done"`
Total int `json:"total" orm:"total" description:"待标注图片数"`
Done int `json:"done" orm:"done" description:"已处理数"`
BoxesFile string `json:"boxesFile" orm:"boxes_file" description:"候选框 JSON 相对路径"`
Filenames string `json:"filenames" orm:"filenames" description:"选中图片JSON数组(空=全量)"`
Error string `json:"error" orm:"error" description:"失败原因(部分失败/全部失败)"`
CreatedAt *gtime.Time `json:"createdAt" orm:"created_at" description:"创建时间"`
FinishedAt *gtime.Time `json:"finishedAt" orm:"finished_at" description:"完成时间"`
}
server/biz/model/entity/model_training.go
+25
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@@ -0,0 +1,25 @@
package entity
import "github.com/gogf/gf/v2/os/gtime"
// ModelTraining 训练任务：runner 启动训练进程，轮询解析 epoch 日志更新进度/指标，
// 日志尾部截断存 log_tail；pid 用于取消与存活探测。
type ModelTraining struct {
Id int64 `json:"id" orm:"id" description:"自增主键"`
Name string `json:"name" orm:"name" description:"任务名"`
Status string `json:"status" orm:"status" description:"running|success|failed"`
DatasetId int64 `json:"datasetId" orm:"dataset_id" description:"来源数据集"`
Imgsz int `json:"imgsz" orm:"imgsz" description:"训练分辨率"`
Epochs int `json:"epochs" orm:"epochs" description:"目标轮数"`
Batch int `json:"batch" orm:"batch" description:"batch size"`
Device string `json:"device" orm:"device" description:"训练设备"`
CurrentEpoch int `json:"currentEpoch" orm:"current_epoch" description:"当前轮数"`
TotalEpochs int `json:"totalEpochs" orm:"total_epochs" description:"实际总轮数(完成时写)"`
Metrics string `json:"metrics" orm:"metrics" description:"JSON {p,r,map50}"`
LogTail string `json:"logTail" orm:"log_tail" description:"日志尾部(截断)"`
Pid int `json:"pid" orm:"pid" description:"训练进程 pid"`
Error string `json:"error" orm:"error" description:"失败原因"`
StartedAt *gtime.Time `json:"startedAt" orm:"started_at" description:"开始时间"`
FinishedAt *gtime.Time `json:"finishedAt" orm:"finished_at" description:"结束时间"`
CreatedAt *gtime.Time `json:"createdAt" orm:"created_at" description:"创建时间"`
}
server/biz/model/entity/model_version.go
+21
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@@ -0,0 +1,21 @@
package entity
import "github.com/gogf/gf/v2/os/gtime"
// ModelVersion 模型版本：每数据集独立版本序列（m1.0.0 递增，UNIQUE(dataset_id, version)）。
// 模型文件不落表：发布即写当前生效副本 models/<数据集>/latest.tflite（无存档回退机制）；
// labels 为类别名数组 JSON（App 多模型合并推理依赖）。
type ModelVersion struct {
Id int64 `json:"id" orm:"id" description:"自增主键"`
DatasetId int64 `json:"datasetId" orm:"dataset_id" description:"所属数据集"`
Version string `json:"version" orm:"version" description:"m1.0.0 递增"`
TrainingId int64 `json:"trainingId" orm:"training_id" description:"来源训练任务"`
ArtifactFile string `json:"artifactFile" orm:"artifact_file" description:"归档 zip 相对路径"`
Metrics string `json:"metrics" orm:"metrics" description:"JSON 指标"`
Labels string `json:"labels" orm:"labels" description:"JSON 类别名数组"`
Sha256 string `json:"sha256" orm:"sha256" description:"tflite 文件校验"`
SizeBytes int64 `json:"sizeBytes" orm:"size_bytes" description:"文件大小"`
IsLatest int `json:"isLatest" orm:"is_latest" description:"1=该数据集当前生效"`
Notes string `json:"notes" orm:"notes" description:"备注"`
CreatedAt *gtime.Time `json:"createdAt" orm:"created_at" description:"发布时间"`
}
server/biz/service/app_version.go
+14 -7
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@@ -27,19 +27,26 @@ var apkNameRe = regexp.MustCompile(`^observer-(\d+\.\d+\.\d+)\.apk$`)
var AppVersion = &appVersionService{}
// GetUpdate 客户端版本更新检查（公开接口，无需登录态；仅 Android 调用）：
// 返回最新一条版本记录，无记录时返回空结构（客户端视为无需更新）。
// 返回最新一条版本记录 + 模型热更新目录（多模型体系，与 GET /api/v1/models 同构）。
// 无版本记录时返回空结构（客户端视为无需更新）；无发布模型时 models 不返回。
func (s *appVersionService) GetUpdate(ctx context.Context, req *dto.AppUpdateReq) (*dto.AppUpdateRes, error) {
res := &dto.AppUpdateRes{}
latest, err := dao.AppVersion.Latest(ctx)
if err != nil {
return nil, err
}
if latest == nil {
return &dto.AppUpdateRes{}, nil
if latest != nil {
res.Version = latest.Version
res.Notes = latest.Notes
}
return &dto.AppUpdateRes{
Version: latest.Version,
Notes: latest.Notes,
}, nil
catalog, err := ModelVersion.ClientCatalog(ctx)
if err != nil {
return nil, err
}
if len(catalog.List) > 0 {
res.Models = catalog.List
}
return res, nil
}
// AdminListVersions 管理端版本记录分页（按下发时间倒序）
server/biz/service/dataset.go
+678
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@@ -0,0 +1,678 @@
package service
import (
"archive/zip"
"bytes"
"context"
"fmt"
"image"
"image/jpeg"
"net/url"
"os"
"path/filepath"
"regexp"
"strings"
"time"
"github.com/gogf/gf/v2/errors/gerror"
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/net/ghttp"
"github.com/gogf/gf/v2/os/gtime"
"observer-server/biz/consts"
"observer-server/biz/dao"
"observer-server/biz/model/dto"
"observer-server/biz/model/entity"
"observer-server/common"
)
// datasetService 数据集业务：管理端「数据集管理」模块。
// 图片文件在 app.datasetDir/datasets/<name>/（平铺、文件名唯一），DB 只存元数据；
// AI 生成为付费资产，删除类接口由前端带确认文案（后端不额外拦截）。
type datasetService struct{}
var Dataset = &datasetService{}
// imgExts 允许上传/保存的图片扩展名
var imgExts = map[string]bool{".jpg": true, ".jpeg": true, ".png": true}
// AdminListDatasets 数据集分页列表（卡片展示：封面/描述/训练配置标记 + 最新训练状态聚合）
func (s *datasetService) AdminListDatasets(ctx context.Context, req *dto.AdminDatasetListReq) (*dto.AdminDatasetListRes, error) {
page, size := common.NormalizePage(req.Page, req.Size)
var list []*entity.Dataset
var total int64
var err error
if req.Keyword != "" {
list, total, err = dao.Dataset.PageByKeyword(ctx, req.Keyword, page, size)
} else {
list, total, err = dao.Dataset.Page(ctx, page, size)
}
if err != nil {
return nil, err
}
ids := make([]int64, 0, len(list))
for _, v := range list {
ids = append(ids, v.Id)
}
latest, err := dao.Training.LatestByDatasets(ctx, ids)
if err != nil {
return nil, err
}
items := make([]*dto.AdminDatasetItem, 0, len(list))
for _, v := range list {
item := &dto.AdminDatasetItem{
Id: v.Id,
Name: v.Name,
Source: v.Source,
ImageCount: v.ImageCount,
LabeledCount: v.LabeledCount,
Status: v.Status,
Cover: v.Cover,
Description: v.Description,
CreatedAt: v.CreatedAt,
UpdatedAt: v.UpdatedAt,
}
if t, ok := latest[v.Id]; ok {
item.TrainingId = t.Id
item.TrainingStatus = t.Status
item.TrainingCurrentEpoch = t.CurrentEpoch
item.TrainingTotalEpochs = t.TotalEpochs
}
items = append(items, item)
}
return &dto.AdminDatasetListRes{Total: total, List: items}, nil
}
// AdminUpdateDataset 更新数据集展示配置（空值字段不覆盖；封面/描述。
// AI 端点/训练机 SSH 走 config.yml 全局配置（localAi / training.ssh））
func (s *datasetService) AdminUpdateDataset(ctx context.Context, req *dto.AdminDatasetUpdateReq) (*dto.AdminDatasetUpdateRes, error) {
existing, err := dao.Dataset.GetById(ctx, req.Id)
if err != nil {
return nil, err
}
if existing == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
// 封面须属于该数据集图片（防伪造文件名指向任意文件）
if req.Cover != "" {
img, err := dao.DatasetImage.GetByFilename(ctx, req.Id, req.Cover)
if err != nil {
return nil, err
}
if img == nil {
return nil, gerror.NewCode(common.CodeImageNotFound)
}
}
if err := dao.Dataset.UpdateConfigs(ctx, req.Id, &entity.Dataset{
Cover: req.Cover,
Description: req.Description,
}); err != nil {
return nil, err
}
return &dto.AdminDatasetUpdateRes{Id: req.Id}, nil
}
// AdminUploadCover 上传数据集封面：解码校验图片 → 转 jpg（Quality 92）→ UUID 命名落盘，
// 删除旧封面文件（UUID 互不覆盖，但 DB 指向切换）→ 更新 cover 字段。
func (s *datasetService) AdminUploadCover(ctx context.Context, req *dto.AdminDatasetCoverUploadReq) (*dto.AdminDatasetCoverUploadRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
if req.File == nil {
return nil, gerror.New("缺少封面文件")
}
ext := strings.ToLower(filepath.Ext(req.File.Filename))
if !imgExts[ext] {
return nil, gerror.New("封面仅支持 jpg/jpeg/png")
}
if req.File.Size > 2*1024*1024 {
return nil, gerror.New("封面不能超过 2MB")
}
raw, err := req.File.Open()
if err != nil {
return nil, gerror.Wrap(err, "读取封面文件失败")
}
defer func() { _ = raw.Close() }()
img, _, err := image.Decode(raw)
if err != nil {
return nil, gerror.New("封面文件不是有效图片")
}
var buf bytes.Buffer
if err := jpeg.Encode(&buf, img, &jpeg.Options{Quality: 92}); err != nil {
return nil, gerror.Wrap(err, "封面转 jpg 失败")
}
dir := common.DatasetImagesDir(ctx, dataset.Name)
if err := os.MkdirAll(dir, 0o755); err != nil {
return nil, gerror.Wrap(err, "创建图片目录失败")
}
if dataset.Cover != "" {
_ = os.Remove(filepath.Join(dir, filepath.Base(dataset.Cover)))
}
name := common.UuidV4() + ".jpg"
if err := common.WriteFileAtomic(filepath.Join(dir, name), buf.Bytes()); err != nil {
return nil, gerror.Wrap(err, "保存封面失败")
}
if err := dao.Dataset.UpdateConfigs(ctx, dataset.Id, &entity.Dataset{Cover: name}); err != nil {
_ = os.Remove(filepath.Join(dir, name))
return nil, err
}
return &dto.AdminDatasetCoverUploadRes{}, nil
}
// coverNameRe 封面命名规范：UUIDv4 + .jpg（固定命名 cover* 为历史遗留，迁移见 MigrateLegacyCovers）
var coverNameRe = regexp.MustCompile(`^[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}\.jpg$`)
// isCoverName 校验 DB cover 值是否符合 UUID jpg 规范（防御库内被写入任意文件名）
func isCoverName(name string) bool {
return coverNameRe.MatchString(filepath.Base(name))
}
// CoverFile 封面文件定位（校验归属；controller 直写响应体输出）
func (s *datasetService) CoverFile(ctx context.Context, datasetId int64) (string, error) {
dataset, err := dao.Dataset.GetById(ctx, datasetId)
if err != nil {
return "", err
}
if dataset == nil {
return "", gerror.NewCode(common.CodeDatasetNotFound)
}
if dataset.Cover == "" {
return "", gerror.NewCode(common.CodeImageNotFound)
}
name := filepath.Base(dataset.Cover)
if !isCoverName(name) {
return "", gerror.NewCode(common.CodeImageNotFound)
}
path := filepath.Join(common.DatasetImagesDir(ctx, dataset.Name), name)
if _, err := os.Stat(path); err != nil {
return "", gerror.NewCode(common.CodeImageNotFound)
}
return path, nil
}
// AdminDeleteCover 删除数据集封面：删文件 + 清 cover 字段（列表卡片恢复占位图）
func (s *datasetService) AdminDeleteCover(ctx context.Context, req *dto.AdminDatasetCoverDeleteReq) (*dto.AdminDatasetCoverDeleteRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
if dataset.Cover != "" && isCoverName(dataset.Cover) {
_ = os.Remove(filepath.Join(common.DatasetImagesDir(ctx, dataset.Name), filepath.Base(dataset.Cover)))
}
if err := dao.Dataset.ClearCover(ctx, dataset.Id); err != nil {
return nil, err
}
return &dto.AdminDatasetCoverDeleteRes{}, nil
}
// MigrateLegacyCovers 存量封面迁移：cover 非空且不符合 UUID jpg 规范（历史 cover.jpg/cover.png 等）
// → 重命名为 <uuid>.jpg（保留原图字节）并更新 cover 字段。幂等：已符合规范的行跳过。
func (s *datasetService) MigrateLegacyCovers(ctx context.Context) error {
list, err := dao.Dataset.ListAll(ctx)
if err != nil {
return err
}
migrated := 0
for _, d := range list {
if d.Cover == "" || isCoverName(d.Cover) {
continue
}
oldName := filepath.Base(d.Cover)
src := filepath.Join(common.DatasetImagesDir(ctx, d.Name), oldName)
data, err := os.ReadFile(src)
if err != nil {
g.Log().Warningf(ctx, "数据集 %s 封面迁移跳过（文件不存在: %s）: %v", d.Name, src, err)
continue
}
newName := common.UuidV4() + ".jpg"
if err := common.WriteFileAtomic(filepath.Join(common.DatasetImagesDir(ctx, d.Name), newName), data); err != nil {
g.Log().Errorf(ctx, "数据集 %s 封面迁移写新文件失败: %+v", d.Name, err)
continue
}
if err := dao.Dataset.UpdateConfigs(ctx, d.Id, &entity.Dataset{Cover: newName}); err != nil {
g.Log().Errorf(ctx, "数据集 %s 封面迁移更新字段失败: %+v", d.Name, err)
continue
}
_ = os.Remove(src)
migrated++
g.Log().Infof(ctx, "数据集 %s 封面迁移: %s → %s", d.Name, oldName, newName)
}
if migrated > 0 {
g.Log().Infof(ctx, "封面存量迁移完成: 共 %d 个数据集", migrated)
}
return nil
}
// AdminCreateDataset 新建数据集：名称唯一（UNIQUE 兜底）+ 创建图片目录
func (s *datasetService) AdminCreateDataset(ctx context.Context, req *dto.AdminDatasetCreateReq) (*dto.AdminDatasetCreateRes, error) {
now := gtime.Now()
var id int64
err := common.Serial().Submit(ctx, func() error {
exists, err := dao.Dataset.GetByName(ctx, req.Name)
if err != nil {
return err
}
if exists != nil {
return gerror.NewCode(common.CodeDatasetNameDuplicate)
}
id, err = dao.Dataset.Insert(ctx, &entity.Dataset{
Name: req.Name,
Source: req.Source,
Status: consts.DatasetStatusBuilding,
CreatedAt: now,
UpdatedAt: now,
})
return err
})
if err != nil {
return nil, err
}
if err := os.MkdirAll(common.DatasetImagesDir(ctx, req.Name), 0o755); err != nil {
return nil, gerror.Wrap(err, "创建数据集目录失败")
}
return &dto.AdminDatasetCreateRes{Id: id}, nil
}
// AdminDeleteDataset 删除数据集：有 running 标注任务 / 该数据集训练进行中 / 已发布模型版本时拒绝
// （训练产物与模型为付费资产，需先删除模型版本再删数据集）。
// 删除 = 删图片/模型目录 + 删记录（标注随图片行删除，Serial 单写者串行）。
func (s *datasetService) AdminDeleteDataset(ctx context.Context, req *dto.AdminDatasetDeleteReq) (*dto.AdminDatasetDeleteRes, error) {
var name string
err := common.Serial().Submit(ctx, func() error {
d, err := dao.Dataset.GetById(ctx, req.Id)
if err != nil {
return err
}
if d == nil {
return gerror.NewCode(common.CodeDatasetNotFound)
}
name = d.Name
// 预标注任务进行中（RF-DETR 正在扫该数据集图片）
if t, err := dao.LabelTask.GetRunningByDataset(ctx, d.Id); err != nil {
return err
} else if t != nil {
return gerror.NewCode(common.CodeLabelTaskRunning)
}
// 该数据集训练进行中（并发度 1，防文件被删训练中断）
if t, err := dao.Training.RunningByDataset(ctx, d.Id); err != nil {
return err
} else if t != nil {
return gerror.New("该数据集有训练任务进行中，无法删除")
}
// 模型版本记录随数据集级联删除（管理端无模型管理界面，2026-08-26 决策；
// 若需保留已下发模型，删除数据集前先确认客户端不再需要）
if err := dao.ModelVersion.DeleteByDataset(ctx, d.Id); err != nil {
return err
}
if err := dao.DatasetImage.DeleteByDataset(ctx, d.Id); err != nil {
return err
}
return dao.Dataset.DeleteById(ctx, d.Id)
})
if err != nil {
return nil, err
}
// 文件清理（图片 + 模型文件；删除失败仅记日志，记录已删）
dirs := []string{common.DatasetImagesDir(ctx, name), common.DatasetModelsDir(ctx, name)}
for _, dir := range dirs {
if err := os.RemoveAll(dir); err != nil {
g.Log().Errorf(ctx, "删除数据集 %s 目录失败: %+v", dir, err)
}
}
return &dto.AdminDatasetDeleteRes{}, nil
}
// AdminUploadImages 上传图片（多文件）：重名跳过（文件名唯一约束），逐张落盘 + 批量入库。
func (s *datasetService) AdminUploadImages(ctx context.Context, req *dto.AdminDatasetUploadReq) (*dto.AdminDatasetUploadRes, error) {
if len(req.Files) == 0 {
return nil, gerror.New("请选择图片文件")
}
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
dir := common.DatasetImagesDir(ctx, dataset.Name)
if err := os.MkdirAll(dir, 0o755); err != nil {
return nil, gerror.Wrap(err, "创建图片目录失败")
}
res := &dto.AdminDatasetUploadRes{}
now := gtime.Now()
saved := make([]string, 0, len(req.Files)) // 本次已落盘文件名（入库失败时清理）
for _, f := range req.Files {
name := filepath.Base(f.Filename)
if !imgExts[strings.ToLower(filepath.Ext(name))] {
continue
}
exists, err := dao.DatasetImage.GetByFilename(ctx, dataset.Id, name)
if err != nil {
return nil, err
}
if exists != nil {
res.Skipped++
continue
}
if err := saveImageFile(f, dir); err != nil {
return nil, gerror.Wrapf(err, "图片 %s 保存失败", name)
}
saved = append(saved, name)
}
if len(saved) == 0 {
return res, nil
}
// 标注强语义：未配置标注服务时不允许产生无标注图（文件已落盘，失败则清掉）
if common.LocalAiClient(ctx) == nil {
for _, name := range saved {
_ = os.Remove(filepath.Join(dir, name))
}
return nil, gerror.NewCode(common.CodeLocalAiNotConfigured)
}
addedIds := make([]int64, 0, len(saved))
err = common.Serial().Submit(ctx, func() error {
for _, name := range saved {
// 重查重（并发上传兜底）+ 入库
exists, err := dao.DatasetImage.GetByFilename(ctx, dataset.Id, name)
if err != nil {
return err
}
if exists != nil {
continue
}
id, err := dao.DatasetImage.Insert(ctx, &entity.DatasetImage{
DatasetId: dataset.Id,
Filename: name,
Source: "manual",
CreatedAt: now,
})
if err != nil {
return err
}
addedIds = append(addedIds, id)
res.Added++
}
return dao.Dataset.UpdateCounters(ctx, dataset.Id, int64(res.Added), -1, "")
})
if err != nil {
// 入库失败：清掉已落盘文件，保证「记录存在 ⟺ 文件存在」
for _, name := range saved {
_ = os.Remove(filepath.Join(dir, name))
}
return nil, err
}
// 自动触发标注：忙（已有 running 任务）不报错，由任务完成后的自动补标轮兜底；其他失败回滚本次入库
newImages, err := dao.DatasetImage.GetByIds(ctx, addedIds)
if err != nil {
return nil, err
}
if err := LabelTask.AutoLabel(ctx, dataset, newImages); err != nil {
// 触发失败回滚本次入库（manual 上传文件非付费资产，可删）
rollbackIds := make([]int64, 0, len(newImages))
for _, img := range newImages {
rollbackIds = append(rollbackIds, img.Id)
if rErr := os.Remove(filepath.Join(dir, img.Filename)); rErr != nil {
g.Log().Warningf(ctx, "回滚删除图片文件失败: %s: %v", img.Filename, rErr)
}
}
if rErr := common.Serial().Submit(ctx, func() error {
if dErr := dao.DatasetImage.DeleteByIds(ctx, rollbackIds); dErr != nil {
return dErr
}
return dao.Dataset.UpdateCounters(ctx, dataset.Id, -int64(len(rollbackIds)), 0, "")
}); rErr != nil {
g.Log().Errorf(ctx, "自动标注触发失败后的入库回滚失败: %v", rErr)
}
return nil, err
}
return res, nil
}
// saveImageFile 上传文件保存（ghttp.UploadFile.Save 已按原始文件名落盘）
func saveImageFile(f *ghttp.UploadFile, dir string) error {
saved, err := f.Save(dir)
if err != nil {
return err
}
if saved != filepath.Base(f.Filename) {
_ = os.Remove(filepath.Join(dir, saved))
}
return nil
}
// AdminGenerateImages AI 生成图片：同步逐张生成（每张 2min 超时），逐张落盘 + 入库；
// 中途失败返回错误并附已成功张数（已生成的图为付费资产，保留不删除）。
func (s *datasetService) AdminGenerateImages(ctx context.Context, req *dto.AdminDatasetGenerateReq) (*dto.AdminDatasetGenerateRes, error) {
provider := common.ImageGen(ctx)
if provider == nil {
return nil, gerror.NewCode(common.CodeImageGenNotConfigured)
}
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
// 标注强语义：未配置标注服务时不允许开始生成（避免付费资产生成后无法标注）
if common.LocalAiClient(ctx) == nil {
return nil, gerror.NewCode(common.CodeLocalAiNotConfigured)
}
dir := common.DatasetImagesDir(ctx, dataset.Name)
if err := os.MkdirAll(dir, 0o755); err != nil {
return nil, gerror.Wrap(err, "创建图片目录失败")
}
now := gtime.Now()
generated := 0
addedIds := make([]int64, 0, req.Count)
for i := 0; i < req.Count; i++ {
genCtx, cancel := context.WithTimeout(ctx, 120*time.Second)
data, genErr := provider.Generate(genCtx, req.Prompt, req.Size)
cancel()
if genErr != nil {
// 付费资产保留原则：已生成的不删除
return &dto.AdminDatasetGenerateRes{Generated: generated},
gerror.NewCode(common.CodeImageGenFailed, fmt.Sprintf("第 %d 张生成失败(已生成 %d 张): %v", i+1, generated, genErr))
}
filename := fmt.Sprintf("gen_%s_%d.jpg", now.Format("20060102150405"), i)
if err := common.WriteFileAtomic(filepath.Join(dir, filename), data); err != nil {
return &dto.AdminDatasetGenerateRes{Generated: generated},
gerror.Wrapf(err, "第 %d 张保存失败(已生成 %d 张)", i+1, generated)
}
var imgId int64
insErr := common.Serial().Submit(ctx, func() error {
id, err := dao.DatasetImage.Insert(ctx, &entity.DatasetImage{
DatasetId: dataset.Id,
Filename: filename,
Source: "ai",
Prompt: req.Prompt,
CreatedAt: now,
})
imgId = id
return err
})
if insErr != nil {
return &dto.AdminDatasetGenerateRes{Generated: generated},
gerror.Wrap(insErr, "生成图片入库失败")
}
addedIds = append(addedIds, imgId)
generated++
}
if err := common.Serial().Submit(ctx, func() error {
return dao.Dataset.UpdateCounters(ctx, dataset.Id, int64(generated), -1, "")
}); err != nil {
return nil, err
}
// 自动触发标注：忙（已有 running 任务）不报错，由任务完成后的自动补标轮兜底；
// 其他失败报错但保留已生成图（付费资产，不可删）
newImages, err := dao.DatasetImage.GetByIds(ctx, addedIds)
if err != nil {
return nil, err
}
if err := LabelTask.AutoLabel(ctx, dataset, newImages); err != nil {
return &dto.AdminDatasetGenerateRes{Generated: generated},
gerror.Wrap(err, "图片已生成入库，但自动标注触发失败")
}
return &dto.AdminDatasetGenerateRes{Generated: generated}, nil
}
// AdminListImages 数据集图片列表（创建时间正序，标注工作台/网格预览）
func (s *datasetService) AdminListImages(ctx context.Context, req *dto.AdminDatasetImagesReq) (*dto.AdminDatasetImagesRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
list, err := dao.DatasetImage.ListByDataset(ctx, dataset.Id)
if err != nil {
return nil, err
}
items := make([]*dto.AdminImageItem, 0, len(list))
for _, v := range list {
items = append(items, &dto.AdminImageItem{
Id: v.Id,
Filename: v.Filename,
Source: v.Source,
Prompt: v.Prompt,
Url: datasetImageUrl(ctx, dataset.Id, v.Filename),
CreatedAt: v.CreatedAt,
})
}
return &dto.AdminDatasetImagesRes{List: items}, nil
}
// AdminDeleteImages 删除图片：删图片文件 + 记录 + 更新计数
// （付费资产，前端带确认文案；标注/候选框 JSON 随图片行删除）
func (s *datasetService) AdminDeleteImages(ctx context.Context, req *dto.AdminDatasetImagesDeleteReq) (*dto.AdminDatasetImagesDeleteRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
images, err := dao.DatasetImage.GetByIds(ctx, req.Ids)
if err != nil {
return nil, err
}
var del []*entity.DatasetImage
for _, img := range images {
// 只删本数据集内的记录（防跨数据集误删）
if img.DatasetId == dataset.Id {
del = append(del, img)
}
}
if len(del) == 0 {
return &dto.AdminDatasetImagesDeleteRes{}, nil
}
// 文件清理（删除失败仅记日志，记录照删）：图片文件（标注/候选随行删除）
imgDir := common.DatasetImagesDir(ctx, dataset.Name)
for _, img := range del {
_ = os.Remove(filepath.Join(imgDir, img.Filename))
}
// Serial 内：删行 → 重算已标注数 → 同步计数与状态
err = common.Serial().Submit(ctx, func() error {
ids := make([]int64, 0, len(del))
for _, img := range del {
ids = append(ids, img.Id)
}
if err := dao.DatasetImage.DeleteByIds(ctx, ids); err != nil {
return err
}
labeled, err := dao.DatasetImage.CountLabeledByDataset(ctx, dataset.Id)
if err != nil {
return err
}
status := ""
if labeled > 0 {
status = consts.DatasetStatusLabeled
}
return dao.Dataset.UpdateCounters(ctx, dataset.Id, -int64(len(del)), labeled, status)
})
if err != nil {
return nil, err
}
return &dto.AdminDatasetImagesDeleteRes{}, nil
}
// datasetImageUrl 管理端图片访问地址（controller 直写响应体，见 admin.go Image 方法）
func datasetImageUrl(ctx context.Context, datasetId int64, filename string) string {
return fmt.Sprintf("/api/v1/admin/datasets/image?%s",
url.Values{"datasetId": {fmt.Sprintf("%d", datasetId)}, "filename": {filename}}.Encode())
}
// ImageFile 图片文件定位（校验归属后返回绝对路径；controller 直写响应体）
func (s *datasetService) ImageFile(ctx context.Context, datasetId int64, filename string) (string, error) {
if filename != filepath.Base(filename) || !imgExts[strings.ToLower(filepath.Ext(filename))] {
return "", gerror.NewCode(common.CodeImageNotFound)
}
dataset, err := dao.Dataset.GetById(ctx, datasetId)
if err != nil {
return "", err
}
if dataset == nil {
return "", gerror.NewCode(common.CodeDatasetNotFound)
}
img, err := dao.DatasetImage.GetByFilename(ctx, dataset.Id, filename)
if err != nil {
return "", err
}
if img == nil {
return "", gerror.NewCode(common.CodeImageNotFound)
}
path := filepath.Join(common.DatasetImagesDir(ctx, dataset.Name), filename)
if _, err := os.Stat(path); err != nil {
return "", gerror.NewCode(common.CodeImageNotFound)
}
return path, nil
}
// ExportZip 打包数据集图片目录为 zip（内存构建；controller 直写响应体下载）
func (s *datasetService) ExportZip(ctx context.Context, datasetId int64) ([]byte, string, error) {
dataset, err := dao.Dataset.GetById(ctx, datasetId)
if err != nil {
return nil, "", err
}
if dataset == nil {
return nil, "", gerror.NewCode(common.CodeDatasetNotFound)
}
dir := common.DatasetImagesDir(ctx, dataset.Name)
var buf bytes.Buffer
zw := zip.NewWriter(&buf)
entries, err := os.ReadDir(dir)
if err != nil {
if os.IsNotExist(err) {
return nil, "", gerror.New("数据集图片目录不存在")
}
return nil, "", err
}
for _, e := range entries {
if e.IsDir() {
continue
}
data, err := os.ReadFile(filepath.Join(dir, e.Name()))
if err != nil {
continue
}
w, err := zw.Create(e.Name())
if err != nil {
return nil, "", err
}
if _, err := w.Write(data); err != nil {
return nil, "", err
}
}
if err := zw.Close(); err != nil {
return nil, "", err
}
return buf.Bytes(), dataset.Name + ".zip", nil
}
server/biz/service/label_task.go
+566
View File
@@ -0,0 +1,566 @@
package service
import (
"bytes"
"context"
"encoding/json"
"fmt"
"image"
_ "image/jpeg"
_ "image/png"
"math"
"os"
"path/filepath"
"sort"
"strings"
"time"
"github.com/gogf/gf/v2/errors/gerror"
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/os/gtime"
"observer-server/biz/consts"
"observer-server/biz/dao"
"observer-server/biz/model/dto"
"observer-server/biz/model/entity"
"observer-server/common"
)
// labelTaskService 预标注工作台后端：RF-DETR 全图扫描，AI 标注结果直写
// dataset_image.labels_json（与人工标注同存同编辑），人工可修改/清理全部框；
// 训练前整理 YOLO 训练集。
// 全图扫描规则（项目既定）：不套用生成规格的位置裁剪，候选宁多勿漏。
type labelTaskService struct{}
var LabelTask = &labelTaskService{}
// recoverLabelTasks 服务重启恢复：孤儿 running 预标注任务置 done + 错误提示
// （RF-DETR 检测无状态，重新发起即可重新生成标注）。
func (s *labelTaskService) recoverLabelTasks(ctx context.Context) {
list, err := dao.LabelTask.ListRunning(ctx)
if err != nil {
g.Log().Errorf(ctx, "恢复预标注任务失败: %+v", err)
return
}
for _, t := range list {
if err := dao.LabelTask.Finish(ctx, t.Id, "", "服务重启，任务中断，可重新发起"); err != nil {
g.Log().Errorf(ctx, "恢复预标注任务 %d 失败: %+v", t.Id, err)
}
}
}
// AdminListLabelTasks 预标注任务分页（组装数据集名）
func (s *labelTaskService) AdminListLabelTasks(ctx context.Context, req *dto.AdminLabelTaskListReq) (*dto.AdminLabelTaskListRes, error) {
page, size := common.NormalizePage(req.Page, req.Size)
list, total, err := dao.LabelTask.Page(ctx, page, size)
if err != nil {
return nil, err
}
names := Training.datasetNameMap(ctx)
items := make([]*dto.AdminLabelTaskItem, 0, len(list))
for _, v := range list {
items = append(items, &dto.AdminLabelTaskItem{
Id: v.Id,
DatasetId: v.DatasetId,
DatasetName: names[v.DatasetId],
Status: v.Status,
Total: v.Total,
Done: v.Done,
Error: v.Error,
CreatedAt: v.CreatedAt,
FinishedAt: v.FinishedAt,
})
}
return &dto.AdminLabelTaskListRes{Total: total, List: items}, nil
}
// AdminStartLabelTask 发起预标注：串行检查（数据集存在 + 无 running 任务）→ 插任务 →
// 池内逐张调 RF-DETR（全图扫描，AI 端点/模型取 config.yml localAi 节点），
// 完成后标注直写 dataset_image.labels_json 置 done；任一图片失败则任务置 done + error。
// 多选批量：req.Filenames 非空时只扫选中图。
func (s *labelTaskService) AdminStartLabelTask(ctx context.Context, req *dto.AdminLabelTaskStartReq) (*dto.AdminLabelTaskStartRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
if common.LocalAiClient(ctx) == nil {
return nil, gerror.NewCode(common.CodeLocalAiNotConfigured)
}
images, err := dao.DatasetImage.ListByDataset(ctx, dataset.Id)
if err != nil {
return nil, err
}
if len(images) == 0 {
return nil, gerror.New("数据集暂无图片")
}
// 多选批量：只处理选中图片（缺省全量）
subset := len(req.Filenames) > 0
filenamesJSON := ""
if subset {
sel := make(map[string]bool, len(req.Filenames))
for _, f := range req.Filenames {
sel[f] = true
}
filtered := make([]*entity.DatasetImage, 0, len(sel))
for _, img := range images {
if sel[img.Filename] {
filtered = append(filtered, img)
}
}
if len(filtered) == 0 {
return nil, gerror.New("选中的图片不在该数据集内")
}
images = filtered
names := make([]string, 0, len(images))
for _, img := range images {
names = append(names, img.Filename)
}
if raw, jErr := json.Marshal(names); jErr == nil {
filenamesJSON = string(raw)
}
}
taskId, err := s.startDetection(ctx, dataset, images, filenamesJSON)
if err != nil {
return nil, err
}
return &dto.AdminLabelTaskStartRes{Id: taskId}, nil
}
// startDetection 发起预标注任务：Serial 内检查并发（已有 running 任务报错）→ 插任务 → 启动检测协程
func (s *labelTaskService) startDetection(ctx context.Context, dataset *entity.Dataset, images []*entity.DatasetImage, filenamesJSON string) (int64, error) {
now := gtime.Now()
var taskId int64
err := common.Serial().Submit(ctx, func() error {
running, err := dao.LabelTask.GetRunningByDataset(ctx, dataset.Id)
if err != nil {
return err
}
if running != nil {
return gerror.NewCode(common.CodeLabelTaskRunning)
}
taskId, err = dao.LabelTask.Insert(ctx, &entity.LabelTask{
DatasetId: dataset.Id,
Status: consts.LabelTaskRunning,
Total: len(images),
Filenames: filenamesJSON,
CreatedAt: now,
})
return err
})
if err != nil {
return 0, err
}
s.runDetection(ctx, taskId, dataset, images, common.LocalAiClient(ctx))
return taskId, nil
}
// AutoLabel 图片入库自动触发标注（上传/AI 生成成功后调用）：只标本次新增图。
// localAi 未配置 → 报错（调用方拒绝/回滚，不允许产生无标注图）；
// 已有 running 任务（忙）→ 不报错：本次跳过，由任务成功完成后的自动补标轮兜底。
func (s *labelTaskService) AutoLabel(ctx context.Context, dataset *entity.Dataset, images []*entity.DatasetImage) error {
if len(images) == 0 {
return nil
}
if common.LocalAiClient(ctx) == nil {
return gerror.NewCode(common.CodeLocalAiNotConfigured)
}
names := make([]string, 0, len(images))
for _, img := range images {
names = append(names, img.Filename)
}
filenamesJSON := ""
if raw, jErr := json.Marshal(names); jErr == nil {
filenamesJSON = string(raw)
}
_, err := s.startDetection(ctx, dataset, images, filenamesJSON)
if err != nil && gerror.HasCode(err, common.CodeLabelTaskRunning) {
return nil // 忙：不报错
}
return err
}
// autoSupplement 自动补标：任务成功完成后，该数据集仍有未标注图（忙时入库的图/人工清空的图）
// 则续一轮只标未标注图；失败任务不续（防配置故障时无限重试）。
func (s *labelTaskService) autoSupplement(ctx context.Context, dataset *entity.Dataset) {
unlabeled, err := dao.DatasetImage.ListUnlabeledByDataset(ctx, dataset.Id)
if err != nil || len(unlabeled) == 0 {
return
}
if err := s.AutoLabel(ctx, dataset, unlabeled); err != nil {
g.Log().Infof(ctx, "自动补标未触发（数据集 %d）: %v", dataset.Id, err)
}
}
// runDetection 预标注执行协程（生命周期任务）：池内逐张检测，进度经 Serial 更新。
// 每张：读图 → 全图检测（等比缩放提交，坐标映射回原图归一化）→ 标注（conf≥confConfirmed 为 class 0）。
// 全部成功后 Serial 内逐张覆写 labels_json（重跑覆盖该图标注）。
func (s *labelTaskService) runDetection(ctx context.Context, taskId int64, dataset *entity.Dataset, images []*entity.DatasetImage, client *common.LocalAi) {
// 生命周期任务必须脱离请求 ctx：请求结束即取消，会让 Submit 秒退 + Finish 静默失败 → 任务悬挂
bgCtx := context.Background()
go func() {
if client == nil {
_ = dao.LabelTask.Finish(bgCtx, taskId, "", "标注服务未配置")
return
}
dir := common.DatasetImagesDir(bgCtx, dataset.Name)
results := make([][]*dto.AdminLabelBox, len(images))
failed := ""
doneCount := 0
for i, img := range images {
// 单张 120s 超时兜底（无超时 + 无取消的独立 ctx 下防检测服务悬挂拖死任务）
detCtx, cancel := context.WithTimeout(bgCtx, 120*time.Second)
err := common.LabelTaskPoolInstance().Submit(detCtx, func(ctx context.Context) error {
data, err := os.ReadFile(filepath.Join(dir, img.Filename))
if err != nil {
return gerror.Wrapf(err, "读取图片失败")
}
w, h := imageSize(data)
if w <= 0 || h <= 0 {
return gerror.New("无法识别图片尺寸")
}
mime := imageMime(img.Filename)
detections, err := client.Detect(ctx, data, mime, w, h)
if err != nil {
return err
}
cands := make([]*dto.AdminLabelBox, 0, len(detections))
for _, d := range detections {
// 坐标映射回原图像素后归一化，越界轻微裁剪
box := &dto.AdminLabelBox{
Cx: clamp01((d.X + d.Width/2) / float64(w)),
Cy: clamp01((d.Y + d.Height/2) / float64(h)),
W: clamp01(d.Width / float64(w)),
H: clamp01(d.Height / float64(h)),
Confidence: d.Confidence,
Class: 1,
}
if d.Confidence >= client.ConfConfirmed {
box.Class = 0
}
cands = append(cands, box)
}
results[i] = suppressOverlap(cands, client.OverlapThreshold)
return nil
})
cancel()
if err != nil {
failed = fmt.Sprintf("第 %d 张(%s)检测失败: %v", doneCount+1, img.Filename, err)
break
}
doneCount++
_ = common.Serial().Submit(bgCtx, func() error {
return dao.LabelTask.UpdateProgress(bgCtx, taskId, doneCount)
})
}
if failed != "" {
_ = dao.LabelTask.Finish(bgCtx, taskId, "", failed)
return
}
// 全部成功：Serial 内逐张覆写标注（重跑覆盖该图标注，人工保存同走 UpdateLabels）
err := common.Serial().Submit(bgCtx, func() error {
for i := range results {
raw, jErr := json.Marshal(results[i])
if jErr != nil {
return gerror.Wrap(jErr, "标注序列化失败")
}
if uErr := dao.DatasetImage.UpdateLabels(bgCtx, images[i].Id, string(raw)); uErr != nil {
return uErr
}
}
return nil
})
if err != nil {
_ = dao.LabelTask.Finish(bgCtx, taskId, "", err.Error())
return
}
_ = dao.LabelTask.Finish(bgCtx, taskId, "", "")
// 成功完成：自动补标该数据集未标注图（忙时入库的图等），失败任务不续
s.autoSupplement(bgCtx, dataset)
}()
}
// AdminLabelTaskDetail 预标注任务详情：标注（labels_json）输出，供工作台 canvas 叠框；
// 图片尺寸读取文件头。
func (s *labelTaskService) AdminLabelTaskDetail(ctx context.Context, req *dto.AdminLabelTaskDetailReq) (*dto.AdminLabelTaskDetailRes, error) {
t, err := dao.LabelTask.GetById(ctx, req.Id)
if err != nil {
return nil, err
}
if t == nil {
return nil, gerror.New("标注任务不存在")
}
dataset, err := dao.Dataset.GetById(ctx, t.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
items, err := s.buildWorkbenchItems(ctx, dataset)
if err != nil {
return nil, err
}
return &dto.AdminLabelTaskDetailRes{
TaskId: t.Id,
DatasetId: t.DatasetId,
DatasetName: dataset.Name,
Status: t.Status,
Total: t.Total,
Done: t.Done,
Error: t.Error,
Images: items,
}, nil
}
// buildWorkbenchItems 工作台单张图数据：全部标注（labels_json，AI 自动标注与人工框同层）；
// 尺寸统一读文件头。
func (s *labelTaskService) buildWorkbenchItems(ctx context.Context, dataset *entity.Dataset) ([]*dto.AdminLabelImageItem, error) {
images, err := dao.DatasetImage.ListByDataset(ctx, dataset.Id)
if err != nil {
return nil, err
}
imgDir := common.DatasetImagesDir(ctx, dataset.Name)
items := make([]*dto.AdminLabelImageItem, 0, len(images))
for _, img := range images {
item := &dto.AdminLabelImageItem{
Filename: img.Filename,
Url: datasetImageUrl(ctx, dataset.Id, img.Filename),
}
if img.LabelsJson != "" && img.LabelsJson != "[]" {
var boxes []*dto.AdminLabelBox
if json.Unmarshal([]byte(img.LabelsJson), &boxes) == nil && len(boxes) > 0 {
item.Boxes = boxes
item.Labeled = true
}
}
if data, rErr := os.ReadFile(filepath.Join(imgDir, img.Filename)); rErr == nil {
item.Width, item.Height = imageSize(data)
}
items = append(items, item)
}
return items, nil
}
// AdminLabelWorkbench 标注工作台数据：数据集无历史任务时直接输出图片 + 全部标注，
// 标注读 dataset_image.labels_json（与任务详情同一组装）。
func (s *labelTaskService) AdminLabelWorkbench(ctx context.Context, req *dto.AdminLabelWorkbenchReq) (*dto.AdminLabelWorkbenchRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
items, err := s.buildWorkbenchItems(ctx, dataset)
if err != nil {
return nil, err
}
return &dto.AdminLabelWorkbenchRes{
DatasetId: dataset.Id,
DatasetName: dataset.Name,
Images: items,
}, nil
}
// AdminLabelSave 保存单张图标注：校验坐标 → json.Marshal 覆写 labels_json（空框=清空），
// 并刷新数据集 labeled_count（labels_json 非空数组的图片数）。
func (s *labelTaskService) AdminLabelSave(ctx context.Context, req *dto.AdminLabelSaveReq) (*dto.AdminLabelSaveRes, error) {
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
img, err := dao.DatasetImage.GetByFilename(ctx, dataset.Id, req.Filename)
if err != nil {
return nil, err
}
if img == nil {
return nil, gerror.NewCode(common.CodeImageNotFound)
}
for _, box := range req.Boxes {
if box.Cx < 0 || box.Cy < 0 || box.W <= 0 || box.H <= 0 || box.Cx > 1 || box.Cy > 1 {
return nil, gerror.New("标注框坐标非法（需 0~1 归一化）")
}
}
// 空框 = 清空标注
raw := ""
if len(req.Boxes) > 0 {
b, err := json.Marshal(req.Boxes)
if err != nil {
return nil, gerror.Wrap(err, "标注序列化失败")
}
raw = string(b)
}
var labeled int64
if err := common.Serial().Submit(ctx, func() error {
if err := dao.DatasetImage.UpdateLabels(ctx, img.Id, raw); err != nil {
return err
}
labeled, err = dao.DatasetImage.CountLabeledByDataset(ctx, dataset.Id)
if err != nil {
return err
}
status := ""
if labeled > 0 {
status = consts.DatasetStatusLabeled
}
return dao.Dataset.UpdateCounters(ctx, dataset.Id, 0, labeled, status)
}); err != nil {
return nil, err
}
return &dto.AdminLabelSaveRes{LabeledCount: labeled}, nil
}
// prepareYoloSet 训练前组装内存 YOLO 训练集包：已标注图（labels_json 非空）按 80/20 拆 train/val，
// 标注 txt 内存生成、原图仅记源路径（由训练通道读取，不落本地暂存盘）；无标注报错。
// data.yaml 由训练发起方追加进包（path 需指向训练机）。
func (s *labelTaskService) prepareYoloSet(ctx context.Context, dataset *entity.Dataset) (*common.YoloPackage, error) {
images, err := dao.DatasetImage.ListByDataset(ctx, dataset.Id)
if err != nil {
return nil, err
}
type item struct {
filename string
lines string
}
var kept []item
for _, img := range images {
if img.LabelsJson == "" || img.LabelsJson == "[]" {
continue // 空标注图剔除
}
var boxes []*dto.AdminLabelBox
if json.Unmarshal([]byte(img.LabelsJson), &boxes) != nil || len(boxes) == 0 {
continue
}
var b strings.Builder
for _, box := range boxes {
fmt.Fprintf(&b, "%d %.6f %.6f %.6f %.6f\n", box.Class, box.Cx, box.Cy, box.W, box.H)
}
kept = append(kept, item{filename: img.Filename, lines: strings.TrimSpace(b.String())})
}
if len(kept) == 0 {
return nil, gerror.New("数据集无有效标注，请先在标注工作台完成标注")
}
// 固定随机种子 + 20% val（至少 1 张，语义沿用原 prepare_yolo.py）
idx := make([]int, len(kept))
for i := range idx {
idx[i] = i
}
randShuffle(idx)
nVal := len(kept) / 5
if nVal < 1 {
nVal = 1
}
imgSrc := common.DatasetImagesDir(ctx, dataset.Name)
pkg := &common.YoloPackage{}
addSplit := func(split string, items []item) {
for _, it := range items {
pkg.Files = append(pkg.Files,
common.YoloFile{Name: filepath.Join("images", split, it.filename), ImagePath: filepath.Join(imgSrc, it.filename)},
common.YoloFile{Name: filepath.Join("labels", split, strings.TrimSuffix(it.filename, filepath.Ext(it.filename))+".txt"), Content: []byte(it.lines + "\n")},
)
}
}
var trainItems, valItems []item
for i, it := range kept {
if i < nVal {
valItems = append(valItems, it)
} else {
trainItems = append(trainItems, it)
}
}
addSplit("train", trainItems)
addSplit("val", valItems)
return pkg, nil
}
// randShuffle Fisher-Yates 伪随机（固定种子，沿用原 prepare_yolo.py random.seed(42) 语义）
func randShuffle(n []int) {
state := uint32(42)
seed := func() uint32 {
state = state*1664525 + 1013904223
return state
}
for i := len(n) - 1; i > 0; i-- {
j := int(seed() % uint32(i+1))
n[i], n[j] = n[j], n[i]
}
}
// suppressOverlap 重叠去重（NMS 风格）：按置信度降序依次保留，与已保留框重叠比 > overlapThreshold
// 的框剔除（RF-DETR 同一目标重复检出时多个高度重叠框，只留置信度最高者；跨 class 去重）。
func suppressOverlap(boxes []*dto.AdminLabelBox, overlapThreshold float64) []*dto.AdminLabelBox {
if len(boxes) <= 1 {
return boxes
}
sorted := append([]*dto.AdminLabelBox(nil), boxes...)
sort.Slice(sorted, func(i, j int) bool {
return sorted[i].Confidence > sorted[j].Confidence
})
kept := make([]*dto.AdminLabelBox, 0, len(sorted))
for i := range sorted {
dup := false
for _, k := range kept {
if boxOverlap(sorted[i], k) > overlapThreshold {
dup = true
break
}
}
if !dup {
kept = append(kept, sorted[i])
}
}
return kept
}
// boxOverlap 两个归一化框（cx,cy,w,h）的重叠比（minIoU）：交叠面积 / 两框较小面积。
// 用 minIoU 而非 IoU：RF-DETR 对同一目标常输出一大一小两个框（IoU 仅 0.3~0.5），
// 大框套小框时小框被覆盖比例高（0.3~0.9）能命中；相邻目标两框互有外露，minIoU 通常 < 0.3。
func boxOverlap(a, b *dto.AdminLabelBox) float64 {
ax1, ay1, ax2, ay2 := a.Cx-a.W/2, a.Cy-a.H/2, a.Cx+a.W/2, a.Cy+a.H/2
bx1, by1, bx2, by2 := b.Cx-b.W/2, b.Cy-b.H/2, b.Cx+b.W/2, b.Cy+b.H/2
ix1, iy1 := math.Max(ax1, bx1), math.Max(ay1, by1)
ix2, iy2 := math.Min(ax2, bx2), math.Min(ay2, by2)
if ix2 <= ix1 || iy2 <= iy1 {
return 0
}
inter := (ix2 - ix1) * (iy2 - iy1)
minArea := math.Min(a.W*a.H, b.W*b.H)
if minArea <= 0 {
return 0
}
return inter / minArea
}
// imageSize 读取图片尺寸（文件头，不解码全图）
func imageSize(data []byte) (int, int) {
cfg, _, err := image.DecodeConfig(bytes.NewReader(data))
if err != nil {
return 0, 0
}
return cfg.Width, cfg.Height
}
func imageMime(filename string) string {
switch strings.ToLower(filepath.Ext(filename)) {
case ".png":
return "image/png"
default:
return "image/jpeg"
}
}
func clamp01(v float64) float64 {
if v < 0 {
return 0
}
if v > 1 {
return 1
}
return v
}
server/biz/service/model_version.go
+53
View File
@@ -0,0 +1,53 @@
package service
import (
"context"
"encoding/json"
"observer-server/biz/dao"
"observer-server/biz/model/dto"
)
// modelVersionService 模型版本业务：管理端无模型管理界面（2026-08-26 决策），
// 版本记录仅支撑客户端模型目录（App 多模型下载热更新）。文件布局见 common/workspace.go。
type modelVersionService struct{}
var ModelVersion = &modelVersionService{}
// ClientCatalog 客户端模型目录：全部数据集当前生效模型（App 多模型下载热更新）。
// downloadUrl 复用 /download 静态托管（/download/models/<数据集名>/latest.tflite）。
func (s *modelVersionService) ClientCatalog(ctx context.Context) (*dto.ModelCatalogRes, error) {
list, err := dao.ModelVersion.ListAllLatest(ctx)
if err != nil {
return nil, err
}
names := Training.datasetNameMap(ctx)
items := make([]*dto.ModelCatalogItem, 0, len(list))
for _, v := range list {
datasetName := names[v.DatasetId]
if datasetName == "" {
continue
}
items = append(items, &dto.ModelCatalogItem{
DatasetId: v.DatasetId,
DatasetName: datasetName,
Version: v.Version,
Labels: parseLabels(v.Labels),
SizeBytes: v.SizeBytes,
Sha256: v.Sha256,
Notes: v.Notes,
PublishedAt: v.CreatedAt,
DownloadUrl: "/download/models/" + datasetName + "/latest.tflite",
})
}
return &dto.ModelCatalogRes{List: items}, nil
}
// parseLabels 解析类别名 JSON 数组
func parseLabels(s string) []string {
var out []string
if err := json.Unmarshal([]byte(s), &out); err != nil || len(out) == 0 {
return []string{"class0", "class1"}
}
return out
}
server/biz/service/training.go
+632
View File
@@ -0,0 +1,632 @@
package service
import (
"context"
"encoding/json"
"fmt"
"os"
"path/filepath"
"strconv"
"strings"
"time"
"github.com/gogf/gf/v2/errors/gerror"
"github.com/gogf/gf/v2/frame/g"
"github.com/gogf/gf/v2/os/gtime"
"observer-server/biz/consts"
"observer-server/biz/dao"
"observer-server/biz/model/dto"
"observer-server/biz/model/entity"
"observer-server/common"
)
// trainingService 训练编排业务：发起训练（同步数据集 → 写任务参数 → 起进程）、
// 后台轮询进度/判定结束、取消、发布模型版本。并发度 1（GPU 独占）。
type trainingService struct{}
var Training = &trainingService{}
// StartBackgroundJobs 启动后台协程：训练进度轮询 + 孤儿预标注任务恢复（main.go 启动时调用）。
// 单协程生命周期任务（非并行工作负载），不做池封装。
func (s *trainingService) StartBackgroundJobs(ctx context.Context) {
LabelTask.recoverLabelTasks(ctx)
go s.pollTrainings(ctx)
}
// pollTrainings 训练轮询：每 10s 扫描 running 任务，更新进度/日志，按 result.json 或进程
// 存活判定结束；超时无结果判死。Go 重启后自动恢复扫描（进程已死 → 置 failed）。
func (s *trainingService) pollTrainings(ctx context.Context) {
ticker := time.NewTicker(10 * time.Second)
defer ticker.Stop()
for {
select {
case <-ctx.Done():
return
case <-ticker.C:
}
runner := common.Runner(ctx)
if runner == nil {
continue
}
cfg, ok := common.TrainingConfigOf(ctx)
if !ok {
continue
}
running, err := dao.Training.ListRunning(ctx)
if err != nil {
g.Log().Errorf(ctx, "训练轮询读取 running 任务失败: %+v", err)
continue
}
for _, t := range running {
job, err := s.buildJob(ctx, t, cfg)
if err != nil {
g.Log().Errorf(ctx, "训练 %d 构建任务参数失败: %+v", t.Id, err)
continue
}
s.pollOne(ctx, runner, job, t)
}
}
}
func (s *trainingService) pollOne(ctx context.Context, runner common.TrainingRunner, job *common.TrainingJob, t *entity.ModelTraining) {
tail, err := runner.FetchLogTail(ctx, job)
if err != nil {
tail = ""
}
// 结束判定：result.json 存在 = 训练完成（先于存活判定，进程可能已退出）
result, err := runner.FetchResult(ctx, job)
if err != nil {
g.Log().Errorf(ctx, "训练 %d 读取结果失败: %+v", t.Id, err)
return
}
if result != "" {
// result.json 带 error 字段 = 脚本异常退出（如 tflite 导出失败），置失败并带出原因
var resErr struct {
Error string `json:"error"`
}
if json.Unmarshal([]byte(result), &resErr) == nil && resErr.Error != "" {
_ = s.finishFailed(ctx, t, "训练失败: %s", resErr.Error)
return
}
s.finishSuccess(ctx, runner, job, t, result, tail)
return
}
alive, err := runner.IsAlive(ctx, job)
if err != nil {
g.Log().Errorf(ctx, "训练 %d 存活探测失败: %+v", t.Id, err)
return
}
// 超时判死（started_at 起算）
cfg, _ := common.TrainingConfigOf(ctx)
timeout := time.Duration(cfg.TimeoutMins) * time.Minute
if t.StartedAt != nil && time.Since(t.StartedAt.Time) > timeout {
_ = runner.Cancel(ctx, job)
_ = s.finishFailed(ctx, t, "训练超时（超过 %d 分钟无结果，已终止）", cfg.TimeoutMins)
return
}
if !alive {
_ = s.finishFailed(ctx, t, "训练进程已退出（无结果文件）")
return
}
// 进度：日志尾解析最后一条 epoch 行
epoch, total, metrics := parseEpochTail(tail)
if epoch > 0 {
_ = dao.Training.UpdateProgress(ctx, t.Id, epoch, total, metrics, truncateTail(tail))
}
}
// finishSuccess 训练成功：解析 result.json（最终指标 + 类别名）→ 更新任务 + 拉取产物到服务器
func (s *trainingService) finishSuccess(ctx context.Context, runner common.TrainingRunner, job *common.TrainingJob, t *entity.ModelTraining, result, tail string) {
var res struct {
Metrics map[string]float64 `json:"metrics"`
Names []string `json:"names"`
BestTflite string `json:"best_tflite"`
TfliteCheck *struct {
OK bool `json:"ok"`
Reason string `json:"reason"`
} `json:"tflite_check"`
}
_ = json.Unmarshal([]byte(result), &res)
metricsJSON := ""
if res.Metrics != nil {
if b, err := json.Marshal(res.Metrics); err == nil {
metricsJSON = string(b)
}
}
// tflite 产物自检失败（训练脚本内嵌检查，旧任务无该字段不校验）：坏产物不进发布链路
if res.TfliteCheck != nil && !res.TfliteCheck.OK {
reason := res.TfliteCheck.Reason
if reason == "" {
reason = "shape 校验未通过"
}
_ = s.finishFailed(ctx, t, "tflite 产物自检失败: %s", reason)
return
}
// 先拉产物再置成功：产物拉取失败则置失败（发布依赖 tflite 存在）
dest := common.TrainingArtifactsDir(ctx, t.Id)
if res.BestTflite == "" {
_ = s.finishFailed(ctx, t, "训练完成但 result.json 缺少 best_tflite")
return
}
if err := runner.FetchArtifact(ctx, job, res.BestTflite, filepath.Join(dest, "best.tflite")); err != nil {
g.Log().Errorf(ctx, "训练 %d 拉取 best.tflite 失败: %+v", t.Id, err)
_ = s.finishFailed(ctx, t, "拉取训练产物失败: %v", err)
return
}
zipPath := fmt.Sprintf("artifacts/%d.zip", t.Id)
if err := runner.FetchArtifact(ctx, job, zipPath, filepath.Join(dest, "artifact.zip")); err != nil {
g.Log().Warningf(ctx, "训练 %d 拉取 artifact.zip 失败(不阻断): %+v", t.Id, err)
}
// 指标尾部带上类别名，发布时解析 labels
if len(res.Names) > 0 {
if names, err := json.Marshal(res.Names); err == nil {
metricsJSON = mergeNamesIntoMetrics(metricsJSON, string(names))
}
}
if err := dao.Training.Finish(ctx, t.Id, consts.TrainingStatusSuccess, metricsJSON, truncateTail(tail), ""); err != nil {
g.Log().Errorf(ctx, "训练 %d 置成功失败: %+v", t.Id, err)
}
}
// mergeNamesIntoMetrics 把 names 数组并入 metrics JSON（names 字段供发布解析类别名）
func mergeNamesIntoMetrics(metricsJSON, namesJSON string) string {
if metricsJSON == "" {
return `{"names":` + namesJSON + `}`
}
var m map[string]any
if json.Unmarshal([]byte(metricsJSON), &m) != nil {
return metricsJSON
}
m["names"] = json.RawMessage(namesJSON)
if b, err := json.Marshal(m); err == nil {
return string(b)
}
return metricsJSON
}
func (s *trainingService) finishFailed(ctx context.Context, t *entity.ModelTraining, format string, args ...any) error {
return dao.Training.Finish(ctx, t.Id, consts.TrainingStatusFailed, "", "", fmt.Sprintf(format, args...))
}
// buildJob 组装训练机路径布局的 runner 任务
func (s *trainingService) buildJob(ctx context.Context, t *entity.ModelTraining, cfg common.TrainingConfig) (*common.TrainingJob, error) {
dataset, err := dao.Dataset.GetById(ctx, t.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
job := &common.TrainingJob{
TaskId: t.Id,
DatasetName: dataset.Name,
Python: cfg.Python,
Workdir: cfg.Workdir,
DatasetDir: cfg.DatasetDir,
}
return job, nil
}
// parseEpochTail 从日志尾部解析最后一条 epoch 进度行（{"epoch":N,"total":M,"metrics":{...}}）
func parseEpochTail(tail string) (epoch, total int, metrics string) {
lines := strings.Split(tail, "\n")
for i := len(lines) - 1; i >= 0; i-- {
line := strings.TrimSpace(lines[i])
if line == "" {
continue
}
var e struct {
Epoch int `json:"epoch"`
Total int `json:"total"`
Metrics map[string]float64 `json:"metrics"`
}
if err := json.Unmarshal([]byte(line), &e); err != nil || e.Epoch <= 0 {
continue
}
metrics = ""
if e.Metrics != nil {
if b, err := json.Marshal(e.Metrics); err == nil {
metrics = string(b)
}
}
return e.Epoch, e.Total, metrics
}
return 0, 0, ""
}
func truncateTail(s string) string {
if len(s) > 8*1024 {
return s[len(s)-8*1024:]
}
return s
}
// AdminListTrainings 训练任务分页（组装数据集名）
func (s *trainingService) AdminListTrainings(ctx context.Context, req *dto.AdminTrainingListReq) (*dto.AdminTrainingListRes, error) {
page, size := common.NormalizePage(req.Page, req.Size)
var list []*entity.ModelTraining
var total int64
var err error
if req.Status != "" {
list, total, err = dao.Training.PageByStatus(ctx, req.Status, page, size)
} else {
list, total, err = dao.Training.Page(ctx, page, size)
}
if err != nil {
return nil, err
}
names := s.datasetNameMap(ctx)
items := make([]*dto.AdminTrainingItem, 0, len(list))
for _, v := range list {
items = append(items, &dto.AdminTrainingItem{
Id: v.Id,
Name: v.Name,
DatasetId: v.DatasetId,
DatasetName: names[v.DatasetId],
Status: v.Status,
Imgsz: v.Imgsz,
Epochs: v.Epochs,
Batch: v.Batch,
Device: v.Device,
CurrentEpoch: v.CurrentEpoch,
TotalEpochs: v.TotalEpochs,
Metrics: v.Metrics,
Error: v.Error,
StartedAt: v.StartedAt,
FinishedAt: v.FinishedAt,
CreatedAt: v.CreatedAt,
})
}
return &dto.AdminTrainingListRes{Total: total, List: items}, nil
}
// datasetNameMap 全量数据集 id → 名称（列表组装用，避免 N+1）
func (s *trainingService) datasetNameMap(ctx context.Context) map[int64]string {
m := map[int64]string{}
list, err := dao.Dataset.ListAll(ctx)
if err != nil {
return m
}
for _, d := range list {
m[d.Id] = d.Name
}
return m
}
// AdminStartTraining 发起训练：并发度 1（已有 running 拒绝）；先本地整理 yolo 训练集
// （80/20 拆 train/val）再同步训练机 → 写任务参数 → 启动进程；任何一步失败置任务 failed。
func (s *trainingService) AdminStartTraining(ctx context.Context, req *dto.AdminTrainingStartReq) (*dto.AdminTrainingStartRes, error) {
runner := common.Runner(ctx)
if runner == nil {
return nil, gerror.NewCode(common.CodeTrainingNotConfigured)
}
cfg, ok := common.TrainingConfigOf(ctx)
if !ok {
return nil, gerror.NewCode(common.CodeTrainingNotConfigured)
}
dataset, err := dao.Dataset.GetById(ctx, req.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
// 组装 yolo 训练集包（有标注才可训练；内存组装，不落本地暂存盘）
pkg, err := LabelTask.prepareYoloSet(ctx, dataset)
if err != nil {
return nil, err
}
imgsz := req.Imgsz
if imgsz <= 0 {
imgsz = 704
}
epochs := req.Epochs
if epochs <= 0 {
epochs = 150
}
batch := req.Batch
if batch <= 0 {
batch = 16
}
device := req.Device
if device == "" {
device = "0"
}
now := gtime.Now()
var taskId int64
err = common.Serial().Submit(ctx, func() error {
running, err := dao.Training.Running(ctx)
if err != nil {
return err
}
if running != nil {
return gerror.NewCode(common.CodeTrainingRunning)
}
taskId, err = dao.Training.Insert(ctx, &entity.ModelTraining{
Name: req.Name,
Status: consts.TrainingStatusRunning,
DatasetId: dataset.Id,
Imgsz: imgsz,
Epochs: epochs,
Batch: batch,
Device: device,
StartedAt: now,
CreatedAt: now,
})
return err
})
if err != nil {
return nil, err
}
// 准备阶段（训练机侧）失败 → 任务置 failed（记录保留便于排查）
job := &common.TrainingJob{
TaskId: taskId,
DatasetName: dataset.Name,
Python: cfg.Python,
Workdir: cfg.Workdir,
DatasetDir: cfg.DatasetDir,
}
// data.yaml 的 path 指向训练机路径，随包一起同步
trainPath := filepath.Join(cfg.Workdir, cfg.DatasetDir, "yolo", dataset.Name)
pkg.Files = append(pkg.Files, common.YoloFile{
Name: "dataset.yaml",
Content: []byte(yoloYamlContent(trainPath, localAiClassNames(ctx))),
})
taskJSON, _ := json.Marshal(map[string]any{
"workdir": cfg.Workdir,
"yolo": filepath.ToSlash(filepath.Join(cfg.DatasetDir, "yolo", dataset.Name)),
"imgsz": imgsz,
"epochs": epochs,
"batch": batch,
"device": device,
"project": filepath.ToSlash(filepath.Join("runs", "tasks", strconv.FormatInt(taskId, 10))),
"log_file": filepath.ToSlash(filepath.Join("logs", strconv.FormatInt(taskId, 10)+".jsonl")),
"result_file": filepath.ToSlash(filepath.Join("results", strconv.FormatInt(taskId, 10)+".json")),
"artifact_zip": filepath.ToSlash(filepath.Join("artifacts", strconv.FormatInt(taskId, 10)+".zip")),
})
if err := runner.WriteTaskJson(ctx, job, string(taskJSON)); err != nil {
_ = s.finishFailed(ctx, &entity.ModelTraining{Id: taskId}, "写任务参数失败: %v", err)
return nil, err
}
if err := runner.SyncYoloDataset(ctx, job, pkg); err != nil {
_ = s.finishFailed(ctx, &entity.ModelTraining{Id: taskId}, "同步数据集失败: %v", err)
return nil, err
}
pid, err := runner.Start(ctx, job)
if err != nil {
_ = s.finishFailed(ctx, &entity.ModelTraining{Id: taskId}, "启动训练失败: %v", err)
return nil, err
}
if err := dao.Training.UpdatePid(ctx, taskId, pid); err != nil {
g.Log().Errorf(ctx, "训练 %d 记录 pid 失败: %+v", taskId, err)
}
return &dto.AdminTrainingStartRes{Id: taskId}, nil
}
// yoloYamlContent 生成训练集 data.yaml 内容（path 为训练机绝对路径）
func yoloYamlContent(trainPath string, names []string) string {
var b strings.Builder
fmt.Fprintf(&b, "path: %s\n", trainPath)
b.WriteString("train: images/train\nval: images/val\nnames:\n")
for i, n := range names {
fmt.Fprintf(&b, " %d: %s\n", i, n)
}
return b.String()
}
// localAiClassNames 标注类别名（config.yml localAi.classNames，默认 class0/class1）
func localAiClassNames(ctx context.Context) []string {
names := g.Cfg().MustGet(ctx, "localAi.classNames").Strings()
if len(names) == 0 {
return []string{"class0", "class1"}
}
return names
}
// AdminTrainingDetail 训练任务详情（含日志尾部）
func (s *trainingService) AdminTrainingDetail(ctx context.Context, req *dto.AdminTrainingDetailReq) (*dto.AdminTrainingDetailRes, error) {
t, err := dao.Training.GetById(ctx, req.Id)
if err != nil {
return nil, err
}
if t == nil {
return nil, gerror.NewCode(common.CodeTrainingNotFound)
}
names := s.datasetNameMap(ctx)
return &dto.AdminTrainingDetailRes{
AdminTrainingItem: dto.AdminTrainingItem{
Id: t.Id,
Name: t.Name,
DatasetId: t.DatasetId,
DatasetName: names[t.DatasetId],
Status: t.Status,
Imgsz: t.Imgsz,
Epochs: t.Epochs,
Batch: t.Batch,
Device: t.Device,
CurrentEpoch: t.CurrentEpoch,
TotalEpochs: t.TotalEpochs,
Metrics: t.Metrics,
Error: t.Error,
StartedAt: t.StartedAt,
FinishedAt: t.FinishedAt,
CreatedAt: t.CreatedAt,
},
LogTail: t.LogTail,
}, nil
}
// AdminCancelTraining 取消训练：杀进程 + 置 failed
func (s *trainingService) AdminCancelTraining(ctx context.Context, req *dto.AdminTrainingCancelReq) (*dto.AdminTrainingCancelRes, error) {
var t *entity.ModelTraining
err := common.Serial().Submit(ctx, func() error {
var err error
t, err = dao.Training.GetById(ctx, req.Id)
if err != nil {
return err
}
if t == nil {
return gerror.NewCode(common.CodeTrainingNotFound)
}
if t.Status != consts.TrainingStatusRunning {
return gerror.New("仅运行中的训练任务可取消")
}
return nil
})
if err != nil {
return nil, err
}
runner := common.Runner(ctx)
if runner != nil {
if cfg, ok := common.TrainingConfigOf(ctx); ok {
if job, jErr := s.buildJob(ctx, t, cfg); jErr == nil {
_ = runner.Cancel(ctx, job)
}
}
}
if err := s.finishFailed(ctx, t, "用户取消"); err != nil {
return nil, err
}
return &dto.AdminTrainingCancelRes{}, nil
}
// AdminPublish 发布模型版本：仅 success 任务 + 本地 best.tflite 存在；
// 版本号同数据集内 m<major>.<minor>.<patch> 自增（无记录从 m1.0.0 起）。
// 落库（置旧版 is_latest=0 + 插新版）后写 latest 副本（无存档回退机制），文件失败补偿删记录，
// 保证「记录存在 ⟺ 文件存在」（同 APK 版本管理）。
func (s *trainingService) AdminPublish(ctx context.Context, req *dto.AdminTrainingPublishReq) (*dto.AdminTrainingPublishRes, error) {
t, err := dao.Training.GetById(ctx, req.Id)
if err != nil {
return nil, err
}
if t == nil {
return nil, gerror.NewCode(common.CodeTrainingNotFound)
}
if t.Status != consts.TrainingStatusSuccess {
return nil, gerror.NewCode(common.CodeTrainingNotSuccess)
}
dataset, err := dao.Dataset.GetById(ctx, t.DatasetId)
if err != nil {
return nil, err
}
if dataset == nil {
return nil, gerror.NewCode(common.CodeDatasetNotFound)
}
bestTflite := filepath.Join(common.TrainingArtifactsDir(ctx, t.Id), "best.tflite")
data, err := os.ReadFile(bestTflite)
if err != nil {
return nil, gerror.New("训练产物 best.tflite 缺失，无法发布")
}
labels := labelsFromMetrics(t.Metrics)
sha256, err := common.Sha256Hex(data)
if err != nil {
return nil, err
}
version, err := s.nextVersion(ctx, t.DatasetId)
if err != nil {
return nil, err
}
now := gtime.Now()
var mv *entity.ModelVersion
err = common.Serial().Submit(ctx, func() error {
// 该数据集旧版全部置 0，再插新版本（is_latest=1）
if err := dao.ModelVersion.ClearLatest(ctx, t.DatasetId); err != nil {
return err
}
id, err := dao.ModelVersion.Insert(ctx, &entity.ModelVersion{
DatasetId: t.DatasetId,
Version: version,
TrainingId: t.Id,
Metrics: t.Metrics,
Labels: labelsJSON(labels),
Sha256: sha256,
SizeBytes: int64(len(data)),
IsLatest: 1,
Notes: t.Name,
CreatedAt: now,
})
if err != nil {
return err
}
mv = &entity.ModelVersion{Id: id, Version: version, DatasetId: t.DatasetId}
return nil
})
if err != nil {
return nil, err
}
// 文件：当前生效副本 latest.tflite（tmp+rename 原子覆盖），客户端固定下载
dir := common.DatasetModelsDir(ctx, dataset.Name)
if err := os.MkdirAll(dir, 0o755); err != nil {
return nil, gerror.Wrap(err, "创建模型目录失败")
}
if err := common.WriteFileAtomic(filepath.Join(dir, "latest.tflite"), data); err != nil {
_ = dao.ModelVersion.DeleteById(ctx, mv.Id)
return nil, gerror.Wrap(err, "写当前生效模型失败")
}
return &dto.AdminTrainingPublishRes{Version: version}, nil
}
// nextVersion 同数据集内版本号自增：取最大 m<x>.<y>.<z>，patch+1；无记录从 m1.0.0 起
func (s *trainingService) nextVersion(ctx context.Context, datasetId int64) (string, error) {
list, _, err := dao.ModelVersion.PageByDataset(ctx, datasetId, 1, 1000)
if err != nil {
return "", err
}
maxPatch := 0
maxMinor := 0
maxMajor := 0
for _, v := range list {
major, minor, patch, ok := parseModelVersion(v.Version)
if !ok {
continue
}
if major > maxMajor || major == maxMajor && (minor > maxMinor || minor == maxMinor && patch > maxPatch) {
maxMajor, maxMinor, maxPatch = major, minor, patch
}
}
if maxMajor == 0 && maxMinor == 0 && maxPatch == 0 {
return consts.ModelVersionPrefix + "1.0.0", nil
}
return fmt.Sprintf("%s%d.%d.%d", consts.ModelVersionPrefix, maxMajor, maxMinor, maxPatch+1), nil
}
// parseModelVersion 解析 m1.2.3 → (1,2,3,true)
func parseModelVersion(v string) (major, minor, patch int, ok bool) {
trimmed := strings.TrimPrefix(v, consts.ModelVersionPrefix)
parts := strings.Split(trimmed, ".")
if len(parts) != 3 {
return 0, 0, 0, false
}
major, err1 := strconv.Atoi(parts[0])
minor, err2 := strconv.Atoi(parts[1])
patch, err3 := strconv.Atoi(parts[2])
if err1 != nil || err2 != nil || err3 != nil {
return 0, 0, 0, false
}
return major, minor, patch, true
}
// labelsFromMetrics 从任务 metrics JSON 解析类别名（names 字段），缺省 class0/class1
func labelsFromMetrics(metrics string) []string {
if metrics != "" {
var m map[string]json.RawMessage
if json.Unmarshal([]byte(metrics), &m) == nil {
if raw, ok := m["names"]; ok {
var names []string
if json.Unmarshal(raw, &names) == nil && len(names) > 0 {
return names
}
}
}
}
return []string{"class0", "class1"}
}
func labelsJSON(labels []string) string {
b, err := json.Marshal(labels)
if err != nil {
return `["class0","class1"]`
}
return string(b)
}
server/common/admin_auth.go
+6 -1
View File
@@ -10,9 +10,14 @@ import (
// AdminAuth 管理端静态 token 鉴权中间件：请求头 X-Admin-Token 须与 config.yml
// admin.token 一致；token 未配置（空）时管理接口全部拒绝。
// 图片等 <img>/<video> 标签无法携带自定义请求头，允许 query 参数 token 兜底。
func AdminAuth(r *ghttp.Request) {
want := g.Cfg().MustGet(r.GetCtx(), "admin.token", "").String()
if want == "" || r.Header.Get("X-Admin-Token") != want {
token := r.Header.Get("X-Admin-Token")
if token == "" {
token = r.Get("token").String()
}
if want == "" || token != want {
r.Response.WriteStatusExit(http.StatusUnauthorized, g.Map{
"code": gcode.CodeNotAuthorized.Code(),
"message": "管理端未授权",
server/common/errors.go
+20 -9
View File
@@ -5,13 +5,24 @@ import "github.com/gogf/gf/v2/errors/gcode"
// 业务错误码（1000+，框架保留 <1000）：统一 HTTP 200 + body code!=0 表示失败，
// 客户端按 code 分支。错误统一用 gerror.NewCode(common.CodeXxx, "...") 构造。
var (
CodePlanNotConfigured = gcode.New(1001, "套餐不存在或未配置", nil)
CodePaymentNotConfigured = gcode.New(1002, "支付渠道未配置", nil)
CodeOrderNotFound = gcode.New(1003, "订单不存在", nil)
CodeOrderClosed = gcode.New(1004, "订单已关闭，需重新下单", nil)
CodeCallbackVerifyFailed = gcode.New(1005, "回调验签失败", nil)
CodeCallbackMismatch = gcode.New(1006, "回调商户/金额不匹配", nil)
CodeVersionDuplicate = gcode.New(1007, "该版本号已存在，请勿重复下发", nil)
CodeApkInvalid = gcode.New(1008, "请上传 APK 文件（.apk 后缀）", nil)
CodeVersionNotFound = gcode.New(1009, "版本记录不存在", nil)
CodePlanNotConfigured = gcode.New(1001, "套餐不存在或未配置", nil)
CodePaymentNotConfigured = gcode.New(1002, "支付渠道未配置", nil)
CodeOrderNotFound = gcode.New(1003, "订单不存在", nil)
CodeOrderClosed = gcode.New(1004, "订单已关闭，需重新下单", nil)
CodeCallbackVerifyFailed = gcode.New(1005, "回调验签失败", nil)
CodeCallbackMismatch = gcode.New(1006, "回调商户/金额不匹配", nil)
CodeVersionDuplicate = gcode.New(1007, "该版本号已存在，请勿重复下发", nil)
CodeApkInvalid = gcode.New(1008, "请上传 APK 文件（.apk 后缀）", nil)
CodeVersionNotFound = gcode.New(1009, "版本记录不存在", nil)
CodeDatasetNotFound = gcode.New(1010, "数据集不存在", nil)
CodeDatasetNameDuplicate = gcode.New(1011, "数据集名称已存在", nil)
CodeImageGenNotConfigured = gcode.New(1012, "图像生成服务未配置（imageGen 节点）", nil)
CodeImageGenFailed = gcode.New(1013, "图像生成失败", nil)
CodeTrainingNotConfigured = gcode.New(1014, "训练通道未配置（training 节点）", nil)
CodeTrainingRunning = gcode.New(1015, "已有训练任务进行中（并发度 1）", nil)
CodeTrainingNotFound = gcode.New(1016, "训练任务不存在", nil)
CodeTrainingNotSuccess = gcode.New(1017, "仅训练成功的任务可发布", nil)
CodeLabelTaskRunning = gcode.New(1020, "该数据集已有预标注任务进行中", nil)
CodeLocalAiNotConfigured = gcode.New(1021, "标注服务未配置（localAi 节点）", nil)
CodeImageNotFound = gcode.New(1022, "图片不存在", nil)
)
server/common/imagegen.go
+160
View File
@@ -0,0 +1,160 @@
package common
import (
"context"
"encoding/json"
"fmt"
"io"
"net/http"
"strings"
"time"
"github.com/gogf/gf/v2/frame/g"
)
// ImageGenProvider 图像生成 provider 抽象：管理端「AI 生成图片」按 config.yml imageGen.provider 选择实现。
type ImageGenProvider interface {
// Generate 生成一张图片，返回图片字节；prompt 由调用方保证不含位置描述（项目提示词规范）。
Generate(ctx context.Context, prompt, size string) ([]byte, error)
}
// ImageGen 当前配置的图像生成 provider 单例（未配置返回 nil，调用方判 CodeImageGenNotConfigured）。
func ImageGen(ctx context.Context) ImageGenProvider {
if g.Cfg().MustGet(ctx, "imageGen.apiKey").String() == "" {
return nil
}
switch g.Cfg().MustGet(ctx, "imageGen.provider", "dashscope").String() {
case "dashscope":
return &dashScopeImageGen{apiKey: g.Cfg().MustGet(ctx, "imageGen.apiKey").String()}
default:
return nil
}
}
// dashScopeImageGen 通义万相（DashScope）实现：提交异步任务 → 轮询 task 状态 → 下载产物图片。
// 文档：https://help.aliyun.com/zh/model-studio/text-to-image-api-reference
type dashScopeImageGen struct {
apiKey string
}
const (
dashScopeSynthUrl = "https://dashscope.aliyuncs.com/api/v1/services/aigc/text2image/image-synthesis"
dashScopeTaskUrlFmt = "https://dashscope.aliyuncs.com/api/v1/tasks/%s"
)
func (p *dashScopeImageGen) Generate(ctx context.Context, prompt, size string) ([]byte, error) {
model := g.Cfg().MustGet(ctx, "imageGen.model", "qwen-image-3.0").String()
// 前端尺寸格式 1152x2048 → API 规格 1152*2048
apiSize := strings.ReplaceAll(size, "x", "*")
body, err := json.Marshal(map[string]any{
"model": model,
"input": map[string]string{"prompt": prompt, "size": apiSize},
"parameters": map[string]any{"n": 1, "watermark": false},
})
if err != nil {
return nil, err
}
resp, err := p.post(ctx, dashScopeSynthUrl, body)
if err != nil {
return nil, err
}
taskId, ok := resp["task_id"].(string)
if !ok || taskId == "" {
return nil, fmt.Errorf("DashScope 提交失败: %v", resp)
}
// 轮询任务结果：异步生成通常 10~60s，上限 120s（与设计一致：超时 2min/张）
deadline := time.Now().Add(120 * time.Second)
for {
if ctx.Err() != nil {
return nil, ctx.Err()
}
if time.Now().After(deadline) {
return nil, fmt.Errorf("DashScope 生成超时(120s)")
}
task, err := p.post(ctx, fmt.Sprintf(dashScopeTaskUrlFmt, taskId), nil)
if err != nil {
return nil, err
}
status, _ := task["task_status"].(string)
switch status {
case "SUCCEEDED":
results, _ := task["results"].([]any)
if len(results) == 0 {
return nil, fmt.Errorf("DashScope 成功但无产物图片")
}
url, _ := results[0].(map[string]any)["url"].(string)
if url == "" {
return nil, fmt.Errorf("DashScope 成功但无图片 URL")
}
return p.download(ctx, url)
case "FAILED", "CANCELED":
msg, _ := task["message"].(string)
return nil, fmt.Errorf("DashScope 生成失败: %s", msg)
}
select {
case <-time.After(2 * time.Second):
case <-ctx.Done():
return nil, ctx.Err()
}
}
}
// post 调用 DashScope HTTP 接口并解析统一 JSON（无 body 时为空 GET，轮询任务用）
func (p *dashScopeImageGen) post(ctx context.Context, url string, body []byte) (map[string]any, error) {
method := http.MethodPost
var rd io.Reader
if body == nil {
method = http.MethodGet
} else {
rd = strings.NewReader(string(body))
}
req, err := http.NewRequestWithContext(ctx, method, url, rd)
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+p.apiKey)
if body != nil {
req.Header.Set("Content-Type", "application/json")
}
resp, err := http.DefaultClient.Do(req)
if err != nil {
return nil, err
}
defer func() { _ = resp.Body.Close() }()
raw, err := io.ReadAll(io.LimitReader(resp.Body, 8<<20))
if err != nil {
return nil, err
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("DashScope HTTP %d: %s", resp.StatusCode, truncateStr(string(raw), 200))
}
var m map[string]any
if err := json.Unmarshal(raw, &m); err != nil {
return nil, err
}
return m, nil
}
// download 下载生成产物图片（存于阿里云 OSS，无需鉴权）
func (p *dashScopeImageGen) download(ctx context.Context, url string) ([]byte, error) {
req, err := http.NewRequestWithContext(ctx, http.MethodGet, url, nil)
if err != nil {
return nil, err
}
resp, err := http.DefaultClient.Do(req)
if err != nil {
return nil, err
}
defer func() { _ = resp.Body.Close() }()
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("下载生成图片失败: HTTP %d", resp.StatusCode)
}
return io.ReadAll(io.LimitReader(resp.Body, 16<<20))
}
func truncateStr(s string, n int) string {
if len(s) <= n {
return s
}
return s[:n] + "..."
}
server/common/localai.go
+176
View File
@@ -0,0 +1,176 @@
package common
import (
"bytes"
"context"
"encoding/base64"
"encoding/json"
"fmt"
"image"
"image/color"
"image/jpeg"
"image/png"
"io"
"net/http"
"strings"
"github.com/gogf/gf/v2/frame/g"
)
// LocalAi RF-DETR 检测服务客户端（local-ai 兼容 /v1/detection 协议）：
// POST {baseUrl}/v1/detection，body {"model","image":"data:image/jpeg;base64,...","threshold"}，
// 响应 {"detections":[{"x","y","width","height","confidence","class_name"}]}，坐标单位 = 提交图片像素。
type LocalAi struct {
BaseUrl string
Model string
Threshold float64
ConfConfirmed float64
// 重叠去重阈值（minIoU = 交叠面积/两框较小面积）：与高置信框重叠超过该值的框剔除（同目标只留一个）。
// 用 minIoU 而非 IoU：RF-DETR 对同一目标常输出一大一小两个框，标准 IoU 可能仅 0.3~0.5 而漏杀，
// 大框套小框时小框被覆盖比例高，minIoU 能命中；相邻目标两框互有外露，minIoU 通常 < 0.3。
OverlapThreshold float64
// 提交前整图等比缩放到的最长边（RF-DETR 对小图更稳）
InputSize int
}
// Detection RF-DETR 单目标检测结果（提交图比例尺下的像素坐标）
type Detection struct {
X float64 `json:"x"`
Y float64 `json:"y"`
Width float64 `json:"width"`
Height float64 `json:"height"`
Confidence float64 `json:"confidence"`
ClassName string `json:"class_name"`
}
// LocalAiClient 当前配置的标注服务客户端（未配置 baseUrl 返回 nil，调用方判 CodeLocalAiNotConfigured）
func LocalAiClient(ctx context.Context) *LocalAi {
base := g.Cfg().MustGet(ctx, "localAi.baseUrl").String()
if base == "" {
return nil
}
return &LocalAi{
BaseUrl: strings.TrimRight(base, "/"),
Model: g.Cfg().MustGet(ctx, "localAi.model", "rfdetr-xlarge").String(),
Threshold: g.Cfg().MustGet(ctx, "localAi.threshold", 0.08).Float64(),
ConfConfirmed: g.Cfg().MustGet(ctx, "localAi.confConfirmed", 0.2).Float64(),
OverlapThreshold: g.Cfg().MustGet(ctx, "localAi.overlapThreshold", 0.3).Float64(),
InputSize: g.Cfg().MustGet(ctx, "localAi.inputSize", 700).Int(),
}
}
// Detect 对单张图片做全图检测（不做任何裁剪，位置由模型自行推理）：
// 整图等比缩放至最长边 InputSize 提交，坐标映射回原图像素后返回。
// imgW/imgH 为原图尺寸；返回坐标均为原图像素尺度。
func (c *LocalAi) Detect(ctx context.Context, data []byte, mime string, imgW, imgH int) ([]*Detection, error) {
sub, scale := c.prepare(data, mime, imgW, imgH)
body, err := json.Marshal(map[string]any{
"model": c.Model,
"image": fmt.Sprintf("data:%s;base64,%s", mime, base64.StdEncoding.EncodeToString(sub)),
"threshold": c.Threshold,
})
if err != nil {
return nil, err
}
req, err := http.NewRequestWithContext(ctx, http.MethodPost, c.BaseUrl+"/v1/detection",
bytes.NewReader(body))
if err != nil {
return nil, err
}
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil {
return nil, err
}
defer func() { _ = resp.Body.Close() }()
raw, err := io.ReadAll(io.LimitReader(resp.Body, 8<<20))
if err != nil {
return nil, err
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("RF-DETR HTTP %d: %s", resp.StatusCode, truncateStr(string(raw), 200))
}
var out struct {
Detections []*Detection `json:"detections"`
}
if err := json.Unmarshal(raw, &out); err != nil {
return nil, err
}
// 坐标从提交图比例尺映射回原图
for _, d := range out.Detections {
d.X /= scale
d.Y /= scale
d.Width /= scale
d.Height /= scale
}
return out.Detections, nil
}
// prepare 整图等比缩放至最长边 InputSize（等比，不裁剪），返回提交字节与缩放比（原图/提交图）。
func (c *LocalAi) prepare(data []byte, mime string, imgW, imgH int) ([]byte, float64) {
if imgW <= 0 || imgH <= 0 || imgW <= c.InputSize && imgH <= c.InputSize {
return data, 1
}
scale := float64(c.InputSize) / float64(maxInt(imgW, imgH))
w, h := maxInt(1, int(float64(imgW)*scale)), maxInt(1, int(float64(imgH)*scale))
src, _, err := image.Decode(bytes.NewReader(data))
if err != nil {
return data, 1
}
dst := bilinearResize(src, w, h)
var buf bytes.Buffer
if strings.Contains(mime, "png") {
_ = png.Encode(&buf, dst)
} else {
_ = jpeg.Encode(&buf, dst, &jpeg.Options{Quality: 92})
}
return buf.Bytes(), scale
}
// bilinearResize 双线性缩放（RF-DETR 对小图鲁棒，检测场景无需高质量插值）
func bilinearResize(src image.Image, w, h int) *image.RGBA {
b := src.Bounds()
dst := image.NewRGBA(image.Rect(0, 0, w, h))
if b.Dx() == 0 || b.Dy() == 0 {
return dst
}
for y := 0; y < h; y++ {
sy := float64(y) * float64(b.Dy()-1) / float64(maxInt(h-1, 1))
y0, y1 := int(sy), minInt(int(sy)+1, b.Dy()-1)
fy := sy - float64(y0)
for x := 0; x < w; x++ {
sx := float64(x) * float64(b.Dx()-1) / float64(maxInt(w-1, 1))
x0, x1 := int(sx), minInt(int(sx)+1, b.Dx()-1)
fx := sx - float64(x0)
r00, g00, b00, _ := src.At(b.Min.X+x0, b.Min.Y+y0).RGBA()
r10, g10, b10, _ := src.At(b.Min.X+x1, b.Min.Y+y0).RGBA()
r01, g01, b01, _ := src.At(b.Min.X+x0, b.Min.Y+y1).RGBA()
r11, g11, b11, _ := src.At(b.Min.X+x1, b.Min.Y+y1).RGBA()
top := func(v00, v10 uint32) uint8 {
return uint8((float64(v00)*(1-fx) + float64(v10)*fx) / 257)
}
bot := func(v01, v11 uint32) uint8 {
return uint8((float64(v01)*(1-fx) + float64(v11)*fx) / 257)
}
r := uint8((float64(top(r00, r10))*(1-fy) + float64(bot(r01, r11))*fy))
gx := uint8((float64(top(g00, g10))*(1-fy) + float64(bot(g01, g11))*fy))
bb := uint8((float64(top(b00, b10))*(1-fy) + float64(bot(b01, b11))*fy))
dst.Set(x, y, color.RGBA{R: r, G: gx, B: bb, A: 255})
}
}
return dst
}
func maxInt(a, b int) int {
if a > b {
return a
}
return b
}
func minInt(a, b int) int {
if a < b {
return a
}
return b
}
server/common/pool.go
+41
View File
@@ -50,3 +50,44 @@ func (p *CallbackPool) Submit(ctx context.Context, fn func(ctx context.Context)
return ctx.Err()
}
}
// LabelTaskPool 预标注任务并发池：逐张图片调 RF-DETR（IO 等待为主），
// 并发度来自 config.yml labelTask.poolSize，缺失或非法时回退 consts 默认值。
// 池内任务禁止提交本池（防 worker 饿死死锁）；DB 写仍走 Serial 单写者。
type LabelTaskPool struct {
pool *grpool.Pool
}
var (
labelTaskPoolOnce sync.Once
labelTaskPool *LabelTaskPool
)
// LabelTaskPoolInstance 进程级预标注池单例（懒初始化，读取配置）。
func LabelTaskPoolInstance() *LabelTaskPool {
labelTaskPoolOnce.Do(func() {
ctx := context.Background()
size := g.Cfg().MustGet(ctx, "labelTask.poolSize", consts.LabelPoolDefaultSize).Int()
if size <= 0 {
size = consts.LabelPoolDefaultSize
}
labelTaskPool = &LabelTaskPool{pool: grpool.New(size, size)}
})
return labelTaskPool
}
// Submit 提交单张图片的预标注任务并等待完成，返回任务的 error。
func (p *LabelTaskPool) Submit(ctx context.Context, fn func(ctx context.Context) error) error {
res := make(chan error, 1)
if err := p.pool.Add(ctx, func(ctx context.Context) {
res <- fn(ctx)
}); err != nil {
return err
}
select {
case err := <-res:
return err
case <-ctx.Done():
return ctx.Err()
}
}
server/common/training_runner.go
+472
View File
@@ -0,0 +1,472 @@
package common
import (
"archive/tar"
"bytes"
"context"
"fmt"
"io"
"os"
"os/exec"
"path/filepath"
"strconv"
"strings"
"sync"
"syscall"
"github.com/gogf/gf/v2/errors/gerror"
"github.com/gogf/gf/v2/frame/g"
"golang.org/x/crypto/ssh"
)
// TrainingRunner 训练通道抽象（config.yml training.mode 选择实现）：训练机与 Go 服务器
// 可同机（subprocess）或异机（ssh）。任务脚本 server/training/train_server.py 以 --task-json 驱动，
// 产物约定（相对训练机 workdir）：
//
// tasks/<taskId>.json 任务参数（Go 侧写入）
// logs/<taskId>.jsonl 每 epoch 一行 JSON：{"epoch","total","metrics"}
// results/<taskId>.json 结束结果：{"metrics","names","best_tflite"(相对路径)}
// artifacts/<taskId>.zip 打包产物（best.pt + results.csv + 曲线）
type TrainingRunner interface {
// Start 启动训练进程，返回可探测存活的 pid（subprocess 本机 pid；ssh 远程 pid）
Start(ctx context.Context, job *TrainingJob) (int, error)
// IsAlive 进程存活探测
IsAlive(ctx context.Context, job *TrainingJob) (bool, error)
// Cancel 终止训练（杀进程组，含 ultralytics 子进程）
Cancel(ctx context.Context, job *TrainingJob) error
// FetchLogTail 拉取日志尾部（截断 N KB 返回，供轮询解析 epoch 进度）
FetchLogTail(ctx context.Context, job *TrainingJob) (string, error)
// FetchResult 读取结束结果 JSON 内容；不存在返回 nil（任务仍在跑）
FetchResult(ctx context.Context, job *TrainingJob) (string, error)
// FetchArtifact 把训练机产物文件拉回服务器本地路径
FetchArtifact(ctx context.Context, job *TrainingJob, remoteName, localPath string) error
// SyncYoloDataset 把训练集包落到训练机（subprocess 直写 workdir；ssh 走 tar 流式管道，本地不落盘）
SyncYoloDataset(ctx context.Context, job *TrainingJob, pkg *YoloPackage) error
// WriteTaskJson 把任务参数文件写到训练机（随 Start 前的准备阶段调用）
WriteTaskJson(ctx context.Context, job *TrainingJob, content string) error
}
// YoloFile 训练集包内单个文件：Name 为包内相对路径（如 images/train/x.jpg / dataset.yaml），
// ImagePath 非空时内容取自该文件（原图直接读数据集目录，不复制暂存），否则用 Content。
type YoloFile struct {
Name string
ImagePath string
Content []byte
}
// YoloPackage 内存中的 YOLO 训练集包（训练前按 80/20 拆 train/val 组装，不落本地磁盘）
type YoloPackage struct {
Files []YoloFile
}
// TrainingJob 训练任务运行信息（runner 视角；字段为训练机路径布局）。
// SSH 凭据不随任务传递：sshRunner 直接读 config.yml training.ssh 节点。
type TrainingJob struct {
TaskId int64
DatasetName string // yolo 数据集目录名（训练机 datasetDir/yolo/<name>）
Python string // 训练机 venv python 路径
Workdir string // 训练机工作目录（train_server.py / yolov8n.pt 所在）
DatasetDir string // 训练机数据集根目录（相对 workdir）
}
// Runner 按 config.yml training.mode 返回训练通道（未配置返回 nil，调用方判 CodeTrainingNotConfigured）
func Runner(ctx context.Context) TrainingRunner {
mode := g.Cfg().MustGet(ctx, "training.mode", "").String()
switch mode {
case "subprocess":
return &subprocessRunner{}
case "ssh":
return &sshRunner{}
default:
return nil
}
}
// TrainingConfig 训练通道配置快照
type TrainingConfig struct {
Workdir string
DatasetDir string
Python string
TimeoutMins int
}
// TrainingConfigOf 读取训练通道配置（未配置返回 ok=false）
func TrainingConfigOf(ctx context.Context) (TrainingConfig, bool) {
cfg := TrainingConfig{
Workdir: g.Cfg().MustGet(ctx, "training.workdir").String(),
DatasetDir: g.Cfg().MustGet(ctx, "training.datasetDir", "datasets").String(),
Python: g.Cfg().MustGet(ctx, "training.venvPython").String(),
TimeoutMins: g.Cfg().MustGet(ctx, "training.timeoutMinutes", 600).Int(),
}
return cfg, cfg.Workdir != "" && cfg.Python != ""
}
// ---------------- subprocess 实现（同机） ----------------
type subprocessRunner struct {
mu sync.Mutex
cmds map[int64]*exec.Cmd // taskId → 进程（并发度 1，实际最多一条）
}
func (r *subprocessRunner) Start(ctx context.Context, job *TrainingJob) (int, error) {
script := filepath.Join(job.Workdir, "train_server.py")
taskJson := filepath.Join(job.Workdir, "tasks", fmt.Sprintf("%d.json", job.TaskId))
cmd := exec.Command(job.Python, script, "--task-json", taskJson)
cmd.Dir = job.Workdir
// 独立进程组：取消时 kill 整个组，连带 ultralytics 的子进程
cmd.SysProcAttr = &syscall.SysProcAttr{Setpgid: true}
if err := cmd.Start(); err != nil {
return 0, gerror.Wrap(err, "启动训练进程失败")
}
r.mu.Lock()
if r.cmds == nil {
r.cmds = map[int64]*exec.Cmd{}
}
r.cmds[job.TaskId] = cmd
r.mu.Unlock()
return cmd.Process.Pid, nil
}
func (r *subprocessRunner) IsAlive(ctx context.Context, job *TrainingJob) (bool, error) {
r.mu.Lock()
cmd := r.cmds[job.TaskId]
r.mu.Unlock()
if cmd == nil || cmd.Process == nil {
return false, nil
}
err := cmd.Process.Signal(syscall.Signal(0))
if err != nil {
if err == os.ErrProcessDone {
return false, nil
}
return false, nil
}
return true, nil
}
func (r *subprocessRunner) Cancel(ctx context.Context, job *TrainingJob) error {
r.mu.Lock()
cmd := r.cmds[job.TaskId]
r.mu.Unlock()
if cmd == nil || cmd.Process == nil {
return nil
}
// 杀进程组（负 pid），覆盖 python + ultralytics 子进程
_ = syscall.Kill(-cmd.Process.Pid, syscall.SIGKILL)
return nil
}
func (r *subprocessRunner) FetchLogTail(ctx context.Context, job *TrainingJob) (string, error) {
path := filepath.Join(job.Workdir, "logs", fmt.Sprintf("%d.jsonl", job.TaskId))
return tailFile(path, 8*1024)
}
func (r *subprocessRunner) FetchResult(ctx context.Context, job *TrainingJob) (string, error) {
path := filepath.Join(job.Workdir, "results", fmt.Sprintf("%d.json", job.TaskId))
data, err := os.ReadFile(path)
if err != nil {
if os.IsNotExist(err) {
return "", nil
}
return "", err
}
return string(data), nil
}
func (r *subprocessRunner) FetchArtifact(ctx context.Context, job *TrainingJob, remoteName, localPath string) error {
src := filepath.Join(job.Workdir, filepath.FromSlash(remoteName))
data, err := os.ReadFile(src)
if err != nil {
return gerror.Wrap(err, "读取训练机产物失败")
}
return WriteFileAtomic(localPath, data)
}
func (r *subprocessRunner) SyncYoloDataset(ctx context.Context, job *TrainingJob, pkg *YoloPackage) error {
// 同机训练：训练进程直接读 workdir 下文件，包直写目标目录（无中间暂存）
dst := filepath.Join(job.Workdir, job.DatasetDir, "yolo", job.DatasetName)
_ = os.RemoveAll(dst)
for _, f := range pkg.Files {
data := f.Content
if f.ImagePath != "" {
imgData, rErr := os.ReadFile(f.ImagePath)
if rErr != nil {
return gerror.Wrapf(rErr, "读取原图失败: %s", f.ImagePath)
}
data = imgData
}
if err := WriteFileAtomic(filepath.Join(dst, filepath.FromSlash(f.Name)), data); err != nil {
return err
}
}
return nil
}
func (r *subprocessRunner) WriteTaskJson(ctx context.Context, job *TrainingJob, content string) error {
dir := filepath.Join(job.Workdir, "tasks")
if err := os.MkdirAll(dir, 0o755); err != nil {
return err
}
return WriteFileAtomic(filepath.Join(dir, fmt.Sprintf("%d.json", job.TaskId)), []byte(content))
}
// ---------------- ssh 实现（异机） ----------------
type sshRunner struct {
mu sync.Mutex
pids map[int64]int // taskId → 远程 pid
}
// dial 建立 SSH 连接（凭据直读 config.yml training.ssh：privateKeyPath 与 password 二选一）
func (r *sshRunner) dial(ctx context.Context) (*ssh.Client, error) {
host := g.Cfg().MustGet(ctx, "training.ssh.host").String()
if host == "" {
return nil, gerror.New("training.ssh 未配置 host")
}
user := g.Cfg().MustGet(ctx, "training.ssh.user").String()
port := g.Cfg().MustGet(ctx, "training.ssh.port", 22).Int()
password := g.Cfg().MustGet(ctx, "training.ssh.password").String()
keyPath := g.Cfg().MustGet(ctx, "training.ssh.privateKeyPath").String()
cfg := &ssh.ClientConfig{
User: user,
HostKeyCallback: ssh.InsecureIgnoreHostKey(), // 内网训练机，信任首次连接
Timeout: 15e9,
}
if keyPath != "" {
data, err := os.ReadFile(keyPath)
if err != nil {
return nil, gerror.Wrap(err, "读取 SSH 私钥失败")
}
signer, err := ssh.ParsePrivateKey(data)
if err != nil {
return nil, gerror.Wrap(err, "解析 SSH 私钥失败")
}
cfg.Auth = []ssh.AuthMethod{ssh.PublicKeys(signer)}
} else if password != "" {
cfg.Auth = []ssh.AuthMethod{ssh.Password(password)}
} else {
return nil, gerror.New("training.ssh 未配置认证方式（私钥或密码）")
}
return ssh.Dial("tcp", fmt.Sprintf("%s:%d", host, port), cfg)
}
// runCmd 远程执行单条命令，返回 stdout
func (r *sshRunner) runCmd(ctx context.Context, job *TrainingJob, cmd string) (string, error) {
client, err := r.dial(ctx)
if err != nil {
return "", err
}
defer func() { _ = client.Close() }()
session, err := client.NewSession()
if err != nil {
return "", err
}
defer func() { _ = session.Close() }()
var buf bytes.Buffer
session.Stdout = &buf
session.Stderr = &buf
if err := session.Run(cmd); err != nil {
return "", gerror.Wrapf(err, "远程命令失败: %s", cmd)
}
return buf.String(), nil
}
func (r *sshRunner) Start(ctx context.Context, job *TrainingJob) (int, error) {
taskJson := filepath.Join(job.Workdir, "tasks", fmt.Sprintf("%d.json", job.TaskId))
cmd := fmt.Sprintf("cd %s && nohup %s train_server.py --task-json %s > logs/%d.stdout 2>&1 & echo $!",
job.Workdir, job.Python, taskJson, job.TaskId)
out, err := r.runCmd(ctx, job, cmd)
if err != nil {
return 0, err
}
pid, err := strconv.Atoi(strings.TrimSpace(out))
if err != nil {
return 0, gerror.New("SSH 启动未返回 pid: " + out)
}
r.mu.Lock()
if r.pids == nil {
r.pids = map[int64]int{}
}
r.pids[job.TaskId] = pid
r.mu.Unlock()
return pid, nil
}
func (r *sshRunner) IsAlive(ctx context.Context, job *TrainingJob) (bool, error) {
r.mu.Lock()
pid := r.pids[job.TaskId]
r.mu.Unlock()
if pid == 0 {
return false, nil
}
out, err := r.runCmd(ctx, job, fmt.Sprintf("kill -0 %d 2>/dev/null && echo alive || echo dead", pid))
if err != nil {
return false, nil // 连接故障按"状态未知"处理，不误判任务结束
}
return strings.TrimSpace(out) == "alive", nil
}
func (r *sshRunner) Cancel(ctx context.Context, job *TrainingJob) error {
r.mu.Lock()
pid := r.pids[job.TaskId]
r.mu.Unlock()
if pid == 0 {
return nil
}
_, err := r.runCmd(ctx, job, fmt.Sprintf("kill -9 %d 2>/dev/null; pkill -9 -P %d 2>/dev/null; true", pid, pid))
return err
}
func (r *sshRunner) FetchLogTail(ctx context.Context, job *TrainingJob) (string, error) {
return r.runCmd(ctx, job, fmt.Sprintf("tail -c 8192 %s/logs/%d.jsonl", job.Workdir, job.TaskId))
}
func (r *sshRunner) FetchResult(ctx context.Context, job *TrainingJob) (string, error) {
out, err := r.runCmd(ctx, job, fmt.Sprintf("cat %s/results/%d.json 2>/dev/null", job.Workdir, job.TaskId))
if err != nil || out == "" {
return "", nil // 文件不存在 = 仍在跑
}
return out, nil
}
func (r *sshRunner) FetchArtifact(ctx context.Context, job *TrainingJob, remoteName, localPath string) error {
client, err := r.dial(ctx)
if err != nil {
return err
}
defer func() { _ = client.Close() }()
session, err := client.NewSession()
if err != nil {
return err
}
defer func() { _ = session.Close() }()
src := filepath.Join(job.Workdir, filepath.FromSlash(remoteName))
data, err := session.Output(fmt.Sprintf("cat %s", src))
if err != nil {
return gerror.Wrap(err, "拉取训练机产物失败")
}
return WriteFileAtomic(localPath, data)
}
func (r *sshRunner) SyncYoloDataset(ctx context.Context, job *TrainingJob, pkg *YoloPackage) error {
// tar 流式管道：原图直接读数据集目录打包，stdin 推远端解包，本地不落盘
client, err := r.dial(ctx)
if err != nil {
return err
}
defer func() { _ = client.Close() }()
session, err := client.NewSession()
if err != nil {
return err
}
defer func() { _ = session.Close() }()
parent := fmt.Sprintf("%s/%s", job.Workdir, job.DatasetDir)
dst := fmt.Sprintf("%s/%s/yolo/%s", job.Workdir, job.DatasetDir, job.DatasetName)
// tar 解包到 parent，条目前缀 yolo/<name>/ 即落到 dst
cmd := fmt.Sprintf("mkdir -p %s && rm -rf %s && tar -xf - -C %s", parent, dst, parent)
pr, pw := io.Pipe()
session.Stdin = pr
tarErr := make(chan error, 1)
go func() {
defer pw.Close() // 失败路径也必须关管道，否则远端 tar 等不到 EOF
tarErr <- writeYoloTar(pw, job.DatasetName, pkg)
}()
runErr := session.Run(cmd)
_ = pr.Close()
if werr := <-tarErr; werr != nil {
return werr
}
if runErr != nil {
return gerror.Wrap(runErr, "tar 同步数据集失败")
}
return nil
}
// writeYoloTar 把训练集包写入 tar 流（条目前缀 yolo/<name>/，供远端 tar -xf 解包）
func writeYoloTar(w io.Writer, datasetName string, pkg *YoloPackage) error {
tw := tar.NewWriter(w)
for _, f := range pkg.Files {
data := f.Content
if f.ImagePath != "" {
imgData, rErr := os.ReadFile(f.ImagePath)
if rErr != nil {
return gerror.Wrapf(rErr, "读取原图失败: %s", f.ImagePath)
}
data = imgData
}
hdr := &tar.Header{
Name: filepath.ToSlash(filepath.Join("yolo", datasetName, f.Name)),
Mode: 0o644,
Size: int64(len(data)),
}
if err := tw.WriteHeader(hdr); err != nil {
return gerror.Wrap(err, "写 tar 头失败")
}
if _, err := tw.Write(data); err != nil {
return gerror.Wrap(err, "写 tar 内容失败")
}
}
if err := tw.Close(); err != nil {
return gerror.Wrap(err, "关闭 tar 流失败")
}
return nil
}
func (r *sshRunner) WriteTaskJson(ctx context.Context, job *TrainingJob, content string) error {
// 通过 ssh stdin 管道写文件（避免命令行转义地狱）
client, err := r.dial(ctx)
if err != nil {
return err
}
defer func() { _ = client.Close() }()
session, err := client.NewSession()
if err != nil {
return err
}
defer func() { _ = session.Close() }()
dir := filepath.Join(job.Workdir, "tasks")
path := filepath.Join(dir, fmt.Sprintf("%d.json", job.TaskId))
cmd := fmt.Sprintf("mkdir -p %s && cat > %s", dir, path)
session.Stdin = strings.NewReader(content)
return session.Run(cmd)
}
// ---------------- 文件工具 ----------------
// atomicWriteFile tmp + rename 原子写（避免中断产生半截文件）
func WriteFileAtomic(path string, data []byte) error {
dir := filepath.Dir(path)
if err := os.MkdirAll(dir, 0o755); err != nil {
return err
}
tmp := path + ".tmp"
if err := os.WriteFile(tmp, data, 0o644); err != nil {
return err
}
return os.Rename(tmp, path)
}
// tailFile 读文件末尾最多 maxBytes 字节
func tailFile(path string, maxBytes int64) (string, error) {
f, err := os.Open(path)
if err != nil {
if os.IsNotExist(err) {
return "", nil
}
return "", err
}
defer func() { _ = f.Close() }()
st, err := f.Stat()
if err != nil {
return "", err
}
start := st.Size() - maxBytes
if start < 0 {
start = 0
}
buf := make([]byte, st.Size()-start)
if _, err := f.ReadAt(buf, start); err != nil && err != io.EOF {
return "", err
}
return string(buf), nil
}
server/common/uuid.go
+28
View File
@@ -0,0 +1,28 @@
package common
import "crypto/rand"
// UuidV4 生成 UUIDv4 字符串（crypto/rand 16 字节 + 版本/变体位，无第三方依赖）；
// 用于封面等文件名，避免固定命名碰撞。
func UuidV4() string {
var b [16]byte
if _, err := rand.Read(b[:]); err != nil {
panic(err) // crypto/rand 失败属系统级故障，直接崩溃重启
}
b[6] = (b[6] & 0x0f) | 0x40 // version 4
b[8] = (b[8] & 0x3f) | 0x80 // variant 10
const hex = "0123456789abcdef"
out := make([]byte, 36)
j := 0
for i := 0; i < 16; i++ {
if i == 4 || i == 6 || i == 8 || i == 10 {
out[j] = '-'
j++
}
out[j] = hex[b[i]>>4]
j++
out[j] = hex[b[i]&0x0f]
j++
}
return string(out)
}
server/common/workspace.go
+45
View File
@@ -0,0 +1,45 @@
package common
import (
"context"
"crypto/sha256"
"encoding/hex"
"path/filepath"
"strconv"
"github.com/gogf/gf/v2/frame/g"
)
// 训练体系运行时数据布局（config.yml app.datasetDir，默认 ./workspace，挂载持久化、不提交 git）：
//
// datasets/<name>/ 数据集图片（平铺，文件名唯一，标注存 DB dataset_image.labels_json）
// models/<name>/ latest.tflite 当前生效副本（无存档，客户端固定下载）
// trainings/<taskId>/ 训练产物（best.tflite + artifact.zip）
//
// 训练机与 Go 服务器异机时，数据集经 training 通道同步（见 common/training_runner.go）。
// DatasetDir 训练体系运行时数据根目录：config.yml app.datasetDir（默认 ./workspace）
func DatasetDir(ctx context.Context) string {
return g.Cfg().MustGet(ctx, "app.datasetDir", "./workspace").String()
}
// DatasetImagesDir 某数据集图片目录
func DatasetImagesDir(ctx context.Context, datasetName string) string {
return filepath.Join(DatasetDir(ctx), "datasets", datasetName)
}
// DatasetModelsDir 某数据集模型目录
func DatasetModelsDir(ctx context.Context, datasetName string) string {
return filepath.Join(DatasetDir(ctx), "models", datasetName)
}
// TrainingArtifactsDir 某训练任务产物目录（拉回的 best.tflite + artifact.zip）
func TrainingArtifactsDir(ctx context.Context, taskId int64) string {
return filepath.Join(DatasetDir(ctx), "trainings", strconv.FormatInt(taskId, 10))
}
// Sha256Hex 计算文件内容 SHA-256 十六进制（模型版本校验用）
func Sha256Hex(data []byte) (string, error) {
sum := sha256.Sum256(data)
return hex.EncodeToString(sum[:]), nil
}
server/config.yml
+37
View File
@@ -28,6 +28,43 @@ app:
# Android APK 上传目录（运行时数据，与 ./data 平级、docker-compose 挂载持久化；
# 目录下永远只保留最新一个文件 observer-latest.apk，下载地址固定 /download/observer-latest.apk）
apkDir: "./workspace"
# 模型训练体系运行时数据根目录（图片/标注/模型/训练产物；与 apkDir 同目录时共用 workspace）
datasetDir: "./workspace"
# 图像生成（管理端「AI 生成图片」）：未配置 apiKey 时生成接口返回「图像生成服务未配置」
imageGen:
provider: dashscope # 当前唯一实现
apiKey: "" # DashScope API Key
model: qwen-image-3.0
# 训练通道（并发度 1：GPU 独占，同时仅一个 running 任务）：
# mode=subprocess 训练机与服务器同机；mode=ssh 异机（训练脚本/数据集经 ssh 通道同步）
training:
mode: subprocess # subprocess | ssh
ssh:
host: "192.168.3.210" # 训练机地址（mode=ssh 必填）
user: "root"
port: 22
privateKeyPath: "" # 私钥路径与 password 二选一
password: "123"
workdir: /opt/pheasant_data # 训练机工作目录（train_server.py / yolov8n.pt 所在）
venvPython: /opt/pheasant_data/venv/bin/python
datasetDir: datasets # 训练机数据集根目录（相对 workdir，yolo/<name> 为子目录）
timeoutMinutes: 600 # 训练超时判死
# RF-DETR 预标注服务（管理端标注工作台）：需从服务器可达，未配置时预标注接口返回「标注服务未配置」
localAi:
baseUrl: "http://192.168.3.210:18080" # 如 http://127.0.0.1:18080（local-ai 兼容 /v1/detection）
model: rfdetr-xlarge
threshold: 0.08 # 候选置信度阈值（宁多勿漏）
confConfirmed: 0.2 # 高于此视为确认(class 0)，否则疑似(class 1)
overlapThreshold: 0.3 # 重叠去重阈值（交叠/较小框面积，NMS 风格；RF-DETR 同目标常输出一大一小两框，minIoU 比 IoU 更能命中）
inputSize: 700 # 提交前整图等比缩放最长边
classNames: [pheasant, suspect] # 标注类别名（写入 data.yaml，随 result.json 存模型 labels）
labelTask:
# 预标注并发度（逐张调 RF-DETR，缺失或非法时回退默认值）
poolSize: 4
# 套餐定价（静态配置，改价 = 改本节点后重启服务；金额单位为分，展示名由 days 派生"N天"）
plans:
server/data/observer.db
BIN
View File
Binary file not shown.
server/go.mod
+1 -1
View File
@@ -7,6 +7,7 @@ require (
github.com/gogf/gf/v2 v2.10.2
github.com/smartwalle/alipay/v3 v3.2.31
github.com/wechatpay-apiv3/wechatpay-go v0.2.21
golang.org/x/crypto v0.55.0
)
require (
@@ -39,7 +40,6 @@ require (
go.opentelemetry.io/otel/metric v1.38.0 // indirect
go.opentelemetry.io/otel/sdk v1.38.0 // indirect
go.opentelemetry.io/otel/trace v1.38.0 // indirect
golang.org/x/crypto v0.55.0 // indirect
golang.org/x/net v0.57.0 // indirect
golang.org/x/sys v0.47.0 // indirect
golang.org/x/text v0.41.0 // indirect
server/go.sum
+2 -6
View File
@@ -98,18 +98,14 @@ go.uber.org/goleak v1.3.0 h1:2K3zAYmnTNqV73imy9J1T3WC+gmCePx2hEGkimedGto=
go.uber.org/goleak v1.3.0/go.mod h1:CoHD4mav9JJNrW/WLlf7HGZPjdw8EucARQHekz1X6bE=
golang.org/x/crypto v0.55.0 h1:+KWHjbgOaAQ66dh/YlkZKHlz9ZUlq61AFirAR9ntP8M=
golang.org/x/crypto v0.55.0/go.mod h1:uq0V9dE/fzQuJtbnL+2EhWOE63vo164FY8xqEnV9xis=
golang.org/x/net v0.40.0 h1:79Xs7wF06Gbdcg4kdCCIQArK11Z1hr5POQ6+fIYHNuY=
golang.org/x/net v0.40.0/go.mod h1:y0hY0exeL2Pku80/zKK7tpntoX23cqL3Oa6njdgRtds=
golang.org/x/net v0.57.0 h1:K5+3DljvIuDG9/Jv9rvyMywYNFCQ9RSUY6OOTTkT+tE=
golang.org/x/net v0.57.0/go.mod h1:KpXc8iv+r3XplLAG/f7Jsf9RPszJzdR0f58q9vGOuEU=
golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.35.0 h1:vz1N37gP5bs89s7He8XuIYXpyY0+QlsKmzipCbUtyxI=
golang.org/x/sys v0.35.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
golang.org/x/sys v0.47.0 h1:o7XGOvZQCADBQQ4Y7VNq2dRWQR7JmOUW8Kxx4ZsNgWs=
golang.org/x/sys v0.47.0/go.mod h1:4GL1E5IUh+htKOUEOaiffhrAeqysfVGipDYzABqnCmw=
golang.org/x/text v0.25.0 h1:qVyWApTSYLk/drJRO5mDlNYskwQznZmkpV2c8q9zls4=
golang.org/x/text v0.25.0/go.mod h1:WEdwpYrmk1qmdHvhkSTNPm3app7v4rsT8F2UD6+VHIA=
golang.org/x/term v0.45.0 h1:NwWyBmoJCbfTHpxrWoZ9C6/VxOf7ic219I8xZZFdrf0=
golang.org/x/term v0.45.0/go.mod h1:9aqxs0blBcrm/n0L9QW0aRVD+ktan8ssZromtqJC43w=
golang.org/x/text v0.41.0 h1:vz/seA0lnX87Othu2f/0L24RcgrXD9/YFTSuGjj3rH8=
golang.org/x/text v0.41.0/go.mod h1:jvf1O8ajNzZqhSrQBPbutR/EB83Cc0CFrezNQIwbb5M=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
server/main.go
+101 -3
View File
@@ -9,29 +9,35 @@ import (
"github.com/gogf/gf/v2/util/gconv"
"observer-server/biz/controller"
"observer-server/biz/service"
"observer-server/common"
)
func main() {
ctx := context.Background()
initDatabase(ctx)
// 封面存量迁移：历史固定命名 cover* → UUID jpg（幂等，新库空跑）
if err := service.Dataset.MigrateLegacyCovers(ctx); err != nil {
g.Log().Errorf(ctx, "封面存量迁移失败: %+v", err)
}
s := g.Server()
// Android APK 下载静态托管：app.apkDir 目录下固定文件 observer-latest.apk，
// URL 固定 /download/observer-latest.apk（绕过统一响应包装，纯二进制流）
// URL 固定 /download/observer-latest.apk（绕过统一响应包装，纯二进制流）；
// 模型热更新文件同根 /download/models/<数据集名>/latest.tflite（datasetDir 下 models 目录）
if err := os.MkdirAll(common.ApkDir(ctx), 0o755); err != nil {
g.Log().Fatalf(ctx, "创建 APK 目录失败: %+v", err)
}
s.AddStaticPath("/download", common.ApkDir(ctx))
// 客户端接口组：统一响应包装 {"code":0,"message":"ok","data":...}
// 账号组公开（注册/登录），业务组（订单/授权）需登录态（Authorization: Bearer token）
// 账号组公开（注册/登录），业务组（订单/授权/模型目录）需登录态（Authorization: Bearer token）
s.Group("/api/v1", func(group *ghttp.RouterGroup) {
group.Middleware(common.UnifiedResponse)
common.BindController(group, controller.Auth, controller.AppVersion)
})
s.Group("/api/v1", func(group *ghttp.RouterGroup) {
group.Middleware(common.UnifiedResponse, common.AuthRequired)
common.BindController(group, controller.Order, controller.License)
common.BindController(group, controller.Order, controller.License, controller.ModelCatalog)
})
// 支付回调组：不做统一包装，按渠道应答格式直接返回（微信 SUCCESS/FAIL JSON、支付宝 success/failure 文本）
s.Group("/api/v1/payment", func(group *ghttp.RouterGroup) {
@@ -56,6 +62,8 @@ func main() {
} else {
g.Log().Warningf(ctx, "admin_dist 不存在，跳过管理端静态托管（cd server_admin && npm run build）")
}
// 后台协程：训练进度轮询 + 孤儿预标注任务恢复
service.Training.StartBackgroundJobs(ctx)
s.Run()
}
@@ -104,4 +112,94 @@ func initDatabase(ctx context.Context) {
}
g.Log().Infof(ctx, "数据库初始化完成(version=3)")
}
if version < 8 {
// v8：dataset 加训练配置/展示列 + label_task 加 filenames（多选批量标注）。
// PRAGMA table_info 逐列检测缺失才 ADD COLUMN，全新库建表自带全列直接跳过
addCols := []struct{ table, col, ddl string }{
{"dataset", "cover", "TEXT"},
{"dataset", "description", "TEXT"},
{"dataset", "ai_endpoint", "TEXT"},
{"dataset", "ai_model", "TEXT"},
{"dataset", "train_host", "TEXT"},
{"dataset", "train_user", "TEXT"},
{"dataset", "train_password", "TEXT"},
{"dataset", "train_key", "TEXT"},
{"label_task", "filenames", "TEXT"},
}
for _, c := range addCols {
cols, err := g.DB().GetAll(ctx, "PRAGMA table_info("+c.table+")")
if err != nil {
g.Log().Fatalf(ctx, "读取 %s 表结构失败: %+v", c.table, err)
}
exists := false
for _, col := range cols {
if gconv.String(col["name"]) == c.col {
exists = true
break
}
}
if exists {
continue
}
if _, err := g.DB().Exec(ctx, "ALTER TABLE "+c.table+" ADD COLUMN "+c.col+" "+c.ddl); err != nil {
g.Log().Fatalf(ctx, "给 %s 加列 %s 失败: %+v", c.table, c.col, err)
}
}
if _, err := g.DB().Exec(ctx, "PRAGMA user_version = 8"); err != nil {
g.Log().Fatalf(ctx, "库版本写入失败: %+v", err)
}
g.Log().Infof(ctx, "数据库初始化完成(version=8)")
}
if version < 10 {
// v10：标注流程简化，撤销候选确认两阶段 —— dataset_image 删 candidates_json 列
//（存量候选数据为空直接删；全新库建表已无此列、PRAGMA table_info 检测后跳过）
cols, err := g.DB().GetAll(ctx, "PRAGMA table_info(dataset_image)")
if err != nil {
g.Log().Fatalf(ctx, "读取 dataset_image 表结构失败: %+v", err)
}
hasCandidates := false
for _, col := range cols {
if gconv.String(col["name"]) == "candidates_json" {
hasCandidates = true
break
}
}
if hasCandidates {
if _, err := g.DB().Exec(ctx, "ALTER TABLE dataset_image DROP COLUMN candidates_json"); err != nil {
g.Log().Fatalf(ctx, "删除 candidates_json 列失败: %+v", err)
}
}
if _, err := g.DB().Exec(ctx, "PRAGMA user_version = 10"); err != nil {
g.Log().Fatalf(ctx, "库版本写入失败: %+v", err)
}
g.Log().Infof(ctx, "数据库初始化完成(version=10)")
}
if version < 11 {
// v11：移除模型存档回退机制 —— model_version 删 model_file 列
//（模型文件不落表：发布即写 latest.tflite，客户端固定下载；存量库删列、新库建表已无此列自动跳过）
cols, err := g.DB().GetAll(ctx, "PRAGMA table_info(model_version)")
if err != nil {
g.Log().Fatalf(ctx, "读取 model_version 表结构失败: %+v", err)
}
hasModelFile := false
for _, col := range cols {
if gconv.String(col["name"]) == "model_file" {
hasModelFile = true
break
}
}
if hasModelFile {
if _, err := g.DB().Exec(ctx, "ALTER TABLE model_version DROP COLUMN model_file"); err != nil {
g.Log().Fatalf(ctx, "删除 model_file 列失败: %+v", err)
}
}
if _, err := g.DB().Exec(ctx, "PRAGMA user_version = 11"); err != nil {
g.Log().Fatalf(ctx, "库版本写入失败: %+v", err)
}
g.Log().Infof(ctx, "数据库初始化完成(version=11)")
}
// 死表清理：app_config 全局训练配置表已撤销（配置走 config.yml），存量库残留表启动即删
if _, err := g.DB().Exec(ctx, "DROP TABLE IF EXISTS app_config"); err != nil {
g.Log().Fatalf(ctx, "删除残留 app_config 表失败: %+v", err)
}
}
training/dump_graph.py → server/training/dump_graph.py
View File
server/training/train_server.py
+311
View File
@@ -0,0 +1,311 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""训练任务脚本（Go 后端 --task-json 驱动，产物契约见 server/common/training_runner.go）。
用法：
python train_server.py --task-json tasks/<taskId>.json
任务参数（Go 侧写入，字段相对训练机 workdir）：
workdir 训练机工作目录（脚本 / yolov8n.pt / venv 所在），启动即 chdir
yolo 训练集目录（含 dataset.yaml），相对 workdir
imgsz 训练/导出分辨率（默认 704，与端侧推理对齐）
epochs 训练轮数
batch 批大小
device GPU 编号或 cpu
project 训练输出目录（相对 workdir，末尾自动拼 name）
log_file 每 epoch 一行 JSON 的进度日志（相对 workdir）
result_file 结束结果 JSON（相对 workdir）
artifact_zip 产物打包（best.pt + results.csv + 曲线，相对 workdir）
产物契约：
log_file {"epoch":1,"total":150,"metrics":{"metrics/mAP50(B)":0.87,...}}
result_file {"metrics":{...},"names":["pheasant","suspect"],"best_tflite":"runs/tasks/<id>/weights/best.tflite",
"tflite_check":{"ok":true,"reason":"","inputs":[...],"outputs":[...]}}
result_file 存在 = 训练完成；异常时写 {"error":"..."}，Go 侧据以置失败并展示原因。
tflite_check.ok=false（产物 shape 异常）时 Go 侧置训练失败并带出 reason。
"""
import argparse
import json
import math
import os
import struct
import sys
import traceback
import zipfile
from pathlib import Path
# 抗尺度漂移（与人工训练基线一致）：随机缩放输入 0.5~1.5x
MULTI_SCALE = 0.5
# 早停耐心（连续 N 轮无提升即停）
PATIENCE = 30
def on_fit_epoch_end(trainer):
"""ultralytics 回调：每 epoch 结束写一行进度 JSON（行缓冲，进程被杀不丢行）。"""
metrics = {}
for k, v in (trainer.metrics or {}).items():
if isinstance(v, (int, float)) and math.isfinite(v):
metrics[k] = round(float(v), 5)
if _LOG is None:
return
total = getattr(trainer, "epochs", None) or getattr(trainer, "total_epochs", 0) or 0
_LOG.write(json.dumps({
"epoch": trainer.epoch + 1,
"total": total,
"metrics": metrics,
}, ensure_ascii=False) + "\n")
def register_callback():
from ultralytics.utils.callbacks import callbacks
callbacks["on_fit_epoch_end"].append(on_fit_epoch_end)
def write_result(result_file, payload):
Path(result_file).parent.mkdir(parents=True, exist_ok=True)
# tmp + rename 原子写，避免 Go 侧读到半截文件
tmp = result_file + ".tmp"
with open(tmp, "w", encoding="utf-8") as f:
json.dump(payload, f, ensure_ascii=False, indent=2)
os.replace(tmp, result_file)
def read_names(yolo_dir):
"""从 data.yaml 读类别名（有序列表，ultralytics 依赖 pyyaml）"""
try:
import yaml
with open(Path(yolo_dir) / "dataset.yaml", encoding="utf-8") as f:
cfg = yaml.safe_load(f)
names = cfg.get("names") or {}
if isinstance(names, dict):
return [names[k] for k in sorted(names, key=lambda k: int(k))]
return list(names)
except Exception:
return []
def find_best_tflite(save_dir):
"""定位导出的 tflite（ultralytics 各版本产物位置不一）：
优先 fp32 best.tflite（与人工基线一致），再 float32/int8，最后兜底全局搜。"""
weights = save_dir / "weights"
for name in ("best.tflite", "best_float32.tflite", "best_int8.tflite"):
p = weights / name
if p.exists():
return p
candidates = sorted(weights.rglob("*.tflite"))
if candidates:
return candidates[0]
return None
# TFLite flatbuffer 张量类型（tensorflow/lite/schema/schema.fbs，字段顺序敏感）
TENSOR_TYPE = {0: "FLOAT32", 1: "FLOAT16", 2: "INT32", 3: "UINT8", 4: "INT64",
5: "STRING", 6: "BOOL", 7: "INT16", 8: "COMPLEX64", 9: "INT8",
10: "FLOAT64", 11: "COMPLEX128", 12: "UINT64", 13: "RESOURCE",
14: "VARIANT", 15: "UINT32", 16: "UINT16", 17: "INT4",
18: "BFLOAT16", 19: "FLOAT8_E4M3FN", 20: "FLOAT8_E4M3FNUZ",
21: "FLOAT8_E5M2", 22: "FLOAT8_E5M2FNUZ"}
class _FB:
"""极简 flatbuffer 读取器（仅 Model/SubGraph/Tensor 表所需字段）"""
def __init__(self, data):
self.d = data
def u32(self, pos): return struct.unpack_from("<I", self.d, pos)[0]
def i32(self, pos): return struct.unpack_from("<i", self.d, pos)[0]
def u16(self, pos): return struct.unpack_from("<H", self.d, pos)[0]
def field(self, t, i):
vto = self.i32(t)
vt = t - vto
vs = self.u16(vt)
off = vt + 4 + 2 * i
if off + 2 > vt + vs:
return None
f = self.u16(off)
return None if f == 0 else t + f
def deref(self, pos):
return pos + self.u32(pos) if pos is not None else None
def vec(self, pos):
pos = self.deref(pos)
if pos is None:
return None
return pos + 4, self.u32(pos)
def vec_table(self, pos):
start, n = self.vec(pos)
return [start + i * 4 + self.u32(start + i * 4) for i in range(n)]
def string(self, pos):
pos = self.deref(pos)
if pos is None:
return None
n = self.u32(pos)
return self.d[pos + 4:pos + 4 + n].decode("utf-8", "replace")
def int_vec(self, pos):
start, n = self.vec(pos)
return [self.i32(start + 4 * i) for i in range(n)]
def check_tflite(path, imgsz):
"""TFLite 产物自检（原 inspect_tflite.py，2026-08-26 并入）：
输入恰 1 张且 4 维、元素总数 == imgsz²×3（NCHW/NHWC 皆可）、输出 ≥ 1 张且 batch 维 = 1。
返回 {"ok","reason","inputs":[{"name","shape","type"}],"outputs":[...]}。"""
try:
with open(path, "rb") as f:
fb = _FB(f.read())
root = fb.u32(0) # Model 表
subgraphs = fb.vec_table(fb.field(root, 2)) # Model.subgraphs [2]
sg = subgraphs[0]
tensors = fb.vec_table(fb.field(sg, 0)) # SubGraph.tensors [0]
inputs = fb.int_vec(fb.field(sg, 1)) # SubGraph.inputs [1]
outputs = fb.int_vec(fb.field(sg, 2)) # SubGraph.outputs [2]
def describe(i):
t = tensors[i]
shape = fb.int_vec(fb.field(t, 0))
ttype = fb.field(t, 1)
return {"name": fb.string(fb.field(t, 3)) or "?",
"shape": shape,
"type": TENSOR_TYPE.get(fb.d[ttype], fb.d[ttype]) if ttype else "?"}
in_desc = [describe(i) for i in inputs]
out_desc = [describe(i) for i in outputs]
problems = []
if len(in_desc) != 1:
problems.append(f"输入张量 {len(in_desc)} 个（期望 1）")
else:
shape = in_desc[0]["shape"]
n = 1
for d in shape:
n *= d
if len(shape) != 4:
problems.append(f"输入 shape {shape} 非 4 维")
elif n != imgsz * imgsz * 3:
problems.append(f"输入元素数 {n} ≠ imgsz²×3={imgsz * imgsz * 3}（shape {shape}，imgsz={imgsz}）")
if not out_desc:
problems.append("无输出张量")
else:
for o in out_desc:
if o["shape"] and o["shape"][0] != 1:
problems.append(f"输出 batch 维 ≠ 1: {o['shape']}")
return {"ok": not problems, "reason": "；".join(problems),
"inputs": in_desc, "outputs": out_desc}
except Exception as e:
return {"ok": False, "reason": f"解析失败: {e}", "inputs": [], "outputs": []}
def build_artifact_zip(zip_path, save_dir):
Path(zip_path).parent.mkdir(parents=True, exist_ok=True)
with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
for p in (save_dir / "weights" / "best.pt", save_dir / "results.csv"):
if p.exists():
zf.write(p, arcname=p.name)
# 曲线/混淆矩阵等绘图产物在 save_dir 根目录
for p in sorted(save_dir.glob("*.png")) + sorted(save_dir.glob("*.jpg")):
zf.write(p, arcname=p.name)
def main():
ap = argparse.ArgumentParser(description="observer 训练任务脚本（Go --task-json 驱动）")
ap.add_argument("--task-json", required=True, help="任务参数 JSON 文件路径")
args = ap.parse_args()
with open(args.task_json, encoding="utf-8") as f:
task = json.load(f)
global _LOG
base = os.path.abspath(task["workdir"])
def task_path(key):
"""任务参数里的路径全部相对 workdir，这里绝对化（chdir 失败也能定位）"""
p = task[key]
return p if os.path.isabs(p) else os.path.join(base, p)
try:
os.chdir(base)
except Exception:
pass # 目录不可用则后续训练必然失败，错误路径仍尽力写 result.json
log_file = task_path("log_file")
result_file = task_path("result_file")
artifact_zip = task_path("artifact_zip")
global _LOG
_LOG = None
try:
Path(log_file).parent.mkdir(parents=True, exist_ok=True)
_LOG = open(log_file, "a", encoding="utf-8", buffering=1)
except Exception:
pass # 日志不可写（如 workdir 缺失）不阻断错误路径，traceback 仍走 stderr
imgsz = int(task.get("imgsz") or 704)
epochs = int(task.get("epochs") or 150)
batch = int(task.get("batch") or 16)
device = task.get("device") or "0"
data = os.path.join(task["yolo"], "dataset.yaml")
names = read_names(task["yolo"])
try:
from ultralytics import YOLO
register_callback()
model = YOLO("yolov8n.pt")
model.train(
data=data, imgsz=imgsz, epochs=epochs,
patience=PATIENCE, batch=batch, device=device, workers=4,
# project 必须绝对路径，相对路径会被拼到默认 runs/detect 下造成双层嵌套
project=task_path("project"), name="train",
exist_ok=True, plots=True, multi_scale=MULTI_SCALE,
)
trainer = getattr(model, "trainer", None)
if trainer is None:
raise RuntimeError("训练完成但无法获取 trainer（save_dir 未知）")
save_dir = Path(trainer.save_dir)
best_tflite = find_best_tflite(save_dir)
if best_tflite is None:
model.export(format="tflite", imgsz=imgsz)
best_tflite = find_best_tflite(save_dir)
if best_tflite is None:
raise RuntimeError("tflite 导出失败：weights 目录下未找到任何 .tflite 产物")
# tflite 产物自检：shape 异常也写进 result.json（ok=false），Go 侧据此置失败
tflite_check = check_tflite(str(best_tflite), imgsz)
if not tflite_check["ok"]:
print(f"[tflite-check] FAIL: {tflite_check['reason']}", file=sys.stderr)
# 结果指标取 best 轮（发布的是 best.pt），缺省回退末轮
metrics = getattr(trainer, "best_metrics", None) or trainer.metrics
clean_metrics = {}
for k, v in (metrics or {}).items():
if isinstance(v, (int, float)) and math.isfinite(v):
clean_metrics[k] = round(float(v), 5)
build_artifact_zip(artifact_zip, save_dir)
# best_tflite 相对 workdir，Go 侧按此路径拉取
write_result(result_file, {
"metrics": clean_metrics,
"names": names,
"best_tflite": os.path.relpath(best_tflite, base),
"tflite_check": tflite_check,
})
except Exception:
err = traceback.format_exc()
if _LOG is not None:
_LOG.write("\n" + err + "\n")
try:
write_result(result_file, {"error": err[-2000:]})
except Exception:
pass
sys.exit(1)
if __name__ == "__main__":
main()
server/技术设计.md
+185
View File
@@ -62,7 +62,96 @@ CREATE TABLE IF NOT EXISTS app_version (
-- 无下载地址列：APK 为固定文件 app.apkDir/observer-latest.apk，上传即覆盖，目录永远只有一个文件
);
CREATE TABLE IF NOT EXISTS dataset (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT NOT NULL UNIQUE, -- 数据集名（≤50 字），同时是服务器目录名
source TEXT NOT NULL DEFAULT 'manual', -- manual | ai
image_count INTEGER NOT NULL DEFAULT 0, -- 图片数（冗余计数，随增删更新）
labeled_count INTEGER NOT NULL DEFAULT 0, -- 已标注数（二期标注任务完成累计）
status TEXT NOT NULL DEFAULT 'building',-- building | labeled | synced
cover TEXT, -- 封面文件名（上传自动转 jpg + UUID 命名，卡片展示）
description TEXT, -- 描述（卡片展示）
-- 遗留列（ai_endpoint/ai_model/train_host/train_user/train_password/train_key）：
-- AI 标注/训练机 SSH 配置统一走 config.yml（localAi / training.ssh），这 6 个覆盖列新代码不再读写，
-- 保留不迁移（存量库列不动、无数据迁移）
ai_endpoint TEXT,
ai_model TEXT,
train_host TEXT,
train_user TEXT,
train_password TEXT,
train_key TEXT,
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
-- 图片文件在 app.datasetDir/datasets/<name>/；DB 存元数据 + 标注（dataset_image.labels_json）
);
-- 存量库 v8 迁移：逐列检测补列（PRAGMA table_info），新库 CREATE 自带全列
CREATE TABLE IF NOT EXISTS dataset_image (
id INTEGER PRIMARY KEY AUTOINCREMENT,
dataset_id INTEGER NOT NULL, -- → dataset.id
filename TEXT NOT NULL, -- 唯一文件名（防重名加时间戳后缀）
source TEXT NOT NULL DEFAULT 'manual', -- manual | ai
prompt TEXT, -- AI 生成图记录提示词（追溯用）
labels_json TEXT, -- 标注 JSON 数组（YOLO 归一化 xywh+类别+置信度），AI 自动标注与人工标注同存（人工可修改/清理），null/''/'[]'=未标注
created_at TEXT NOT NULL,
UNIQUE (dataset_id, filename)
);
-- 存量库迁移：EnsureColumn 逐列检测补列（v9）；candidates_json 列已随 v10 删除（DROP COLUMN）；
-- model_version.model_file 列已随 v11 删除（模型无存档回退机制，只留 latest.tflite）
CREATE TABLE IF NOT EXISTS model_training (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT NOT NULL, -- 任务名（默认「数据集+时间」）
status TEXT NOT NULL DEFAULT 'running', -- running | success | failed
dataset TEXT NOT NULL, -- 训练机数据集名（datasetDir 下子目录名）
imgsz INTEGER NOT NULL DEFAULT 704,
epochs INTEGER NOT NULL DEFAULT 150,
batch INTEGER NOT NULL DEFAULT 16,
device TEXT NOT NULL DEFAULT '0',
current_epoch INTEGER NOT NULL DEFAULT 0,
total_epochs INTEGER NOT NULL DEFAULT 0,
metrics TEXT, -- JSON {p, r, map50}（成功后写）
log_tail TEXT, -- 日志尾部（截断 N KB，轮询更新）
pid INTEGER, -- 训练进程 pid（取消/存活探测用）
error TEXT, -- 失败原因
started_at TEXT NOT NULL,
finished_at TEXT,
created_at TEXT NOT NULL,
UNIQUE (dataset, imgsz, epochs, batch, started_at) -- 防止重复提交同参任务（宽松防呆）
);
CREATE TABLE IF NOT EXISTS model_version (
id INTEGER PRIMARY KEY AUTOINCREMENT,
dataset_id INTEGER NOT NULL, -- → dataset.id，**每个数据集独立模型版本序列**
version TEXT NOT NULL, -- m1.0.0 递增（每次发布 patch+1，同数据集内唯一）
training_id INTEGER, -- 来源训练任务 → model_training.id
artifact_file TEXT, -- 归档 zip（best.pt + results 曲线），可选
metrics TEXT, -- JSON，与来源任务一致
labels TEXT NOT NULL, -- JSON 类别名数组（随模型下发，App 合并/展示用）
sha256 TEXT NOT NULL, -- tflite 文件校验
size_bytes INTEGER NOT NULL,
is_latest INTEGER NOT NULL DEFAULT 0, -- 1=该数据集当前生效（客户端拉取对象），每数据集至多一条
notes TEXT,
created_at TEXT NOT NULL,
UNIQUE (dataset_id, version)
);
CREATE TABLE IF NOT EXISTS label_task (
id INTEGER PRIMARY KEY AUTOINCREMENT,
dataset_id INTEGER NOT NULL, -- → dataset.id
filenames TEXT, -- JSON 选中图片列表（多选批量标注）；NULL = 全量扫描
status TEXT NOT NULL DEFAULT 'running', -- running | done
total INTEGER NOT NULL DEFAULT 0, -- 待标注图片数
done INTEGER NOT NULL DEFAULT 0, -- 已标注数
boxes_file TEXT, -- 遗留列：候选框 JSON 路径（boxes.json 已废弃，新代码不读写）
error TEXT, -- 失败原因（检测中途失败/服务重启中断）
created_at TEXT NOT NULL,
finished_at TEXT
);
CREATE INDEX IF NOT EXISTS idx_payment_order_phone ON payment_order(phone_num);
CREATE INDEX IF NOT EXISTS idx_dataset_image_dataset ON dataset_image(dataset_id);
CREATE INDEX IF NOT EXISTS idx_model_training_status ON model_training(status);
```
存量库迁移以 `PRAGMA user_version` 版本化标记，禁止重复执行。迁移记录：
@@ -72,6 +161,17 @@ CREATE INDEX IF NOT EXISTS idx_payment_order_phone ON payment_order(phone_num);
- v4 = `license` 加 `remark` 列（管理端备注；`PRAGMA table_info` 检测列缺失才 `ALTER TABLE ... ADD COLUMN remark TEXT`，新库直接建表跳过）
- v5 = `app_version` 新表（版本管理；`dao` init `CREATE TABLE IF NOT EXISTS` 自动建，新库/存量库均无需 `user_version` 迁移，此处记录 DDL 变更）
- v6 = `app_version` 删 `url` 列（下载地址改为固定文件 `app.apkDir`/`observer-latest.apk`，表内不再记录；`PRAGMA table_info` 检测列存在才 `ALTER TABLE ... DROP COLUMN url`，新库建表已无此列直接跳过）
- v7 = `dataset` / `dataset_image` / `model_training` / `model_version` / `label_task` 新表（模型训练体系；各 `dao` init `CREATE TABLE IF NOT EXISTS` 自动建，此处记录 DDL 变更）
- v8 = `dataset` 加 9 列（`cover`/`description`/`ai_endpoint`/`ai_model`/`train_host`/`train_user`/`train_password`/`train_key`）+ `label_task` 加 `filenames` 列（多选批量标注；`PRAGMA table_info` 逐列检测缺失才 `ALTER TABLE ... ADD COLUMN`，新库建表自带跳过）——**其中 6 列（ai/train 覆盖字段）为遗留列**：AI 标注/训练机 SSH 配置统一走 `config.yml`（`localAi` / `training.ssh`），新代码不读写，存量库保留不迁移
- v9 = 标注存储从文件迁移入库：`dataset_image` 加 `labels_json`/`candidates_json` 两列（`common.EnsureColumn` 迁移），启动时把历史 `labels/<数据集>/*.txt` 解析入 `labels_json`（幂等：仅未迁移行处理），`boxes.json` 废弃；**历史 labels/ 目录与迁移代码已删除（2026-08-26：数据全部入表后无保留价值）**
- v10 = 标注流程简化（撤销候选确认两阶段）：`ALTER TABLE dataset_image DROP COLUMN candidates_json`（`PRAGMA table_info` 检测列存在才 DROP，新库建表已无此列直接跳过；存量候选数据为空直接删）——AI 预标注结果直写 `labels_json`，人工可修改/清理全部标注框，`label_task.boxes_file` 与 `dataset` 的 ai/train 六列同为遗留列保留不迁移
## 全局训练配置（config.yml 直读）
**决策（2026-08-26）**：AI 标注端点（`localAi`）与训练机 SSH 凭据（`training.ssh`）是全局配置、与数据集无关——**不设独立存储**（曾尝试 `app_config` KV 表 + 管理端「训练配置」入口，2026-08-26 撤销）：标注与训练直接读 `config.yml` 的 `localAi` / `training.ssh` 节点，改配置需重启服务。数据集表 6 个覆盖列（ai_endpoint 等）为遗留列，代码不读写。
- AI 客户端：`common.LocalAiClient(ctx)` 直读 `localAi.baseUrl`/`model`，未配置返回 nil（预标注接口报「标注服务未配置」）
- SSH 凭据：`common/training_runner.go` 的 sshRunner 直读 `training.ssh.host`/`user`/`port`/`privateKeyPath`/`password`，未配置 host 报「training.ssh 未配置 host」
## 账号体系（注册/登录）
@@ -208,9 +308,94 @@ Android 客户端启动 GET /api/v1/app/update（公开，无需 token；iOS 不
- **写入串行**：新增/删除记录均走 `common.Serial()` 单写者（与授权链路一致，SQLite 无 WAL）；列表/最新查询读走普通读，表极小不设查询缓存
- **写入串行**：新增记录走 `common.Serial()` 单写者（与授权链路一致，SQLite 无 WAL）；列表/最新查询读走普通读，表极小不设查询缓存
## 模型训练体系（数据集 → 训练 → 模型版本）
**背景**：识别模型（YOLOv8n → TFLite 704x704）此前在仓库根 `training/` 目录人工命令行训练（本机 MPS / GPU 服务器跑 `train_server.py`，best.tflite 手动拷进 App assets 重打包 APK 下发）。本模块将「数据集管理 → 训练编排 → 模型版本发布 → 客户端热更新」闭环搬进后台管理端，模型迭代不再依赖开发者本机与 APK 发版。**Python 训练脚本随项目整合进 `server/training/`（2026-08-26：能 Go 化的已 Go 化——prepare_yolo→prepareYoloSet、analyze_rfdetr→LocalAi.Detect，脚本删除；train_server.py/yolov8n.pt/调试工具迁移入 server/training/，yolov8n.pt 权重不进 git，由 .gitignore 排除；2026-08-26 再收口：inspect_tflite.py 并入 train_server.py 自检、dump_graph.py 保留）**。
**整体数据流**：
```
管理端 数据集管理（上传 / AI 生成）→ 图片落服务器 workspace/datasets/<name>/
├─► 二期：标注工作台（RF-DETR 预标注 + 人工确认）→ labels/
└─► 发起训练 → 同步数据集到训练机 → runner 起训练 → 进度/日志/指标
└─► 产物（best.tflite + best.pt 归档）拉回服务器
└─► 发布 → model_version + workspace/model-latest.tflite（sha256）
└─► 三期：客户端启动 GET /app/update → 模型热更新下载替换
```
### 数据集存储与流转（决策：图片不进 DB、不提交 git）
- 图片目录：`app.datasetDir`（默认 `./workspace/`）下 `datasets/<数据集名>/`（图片平铺，文件名唯一防重名）；标注存 `dataset_image.labels_json`（JSON 数组，每元素 `{class,cx,cy,w,h,confidence}` 归一化，AI 自动标注与人工标注同存、人工可修改/清理）；**历史 labels/ txt 目录与 yolo 暂存目录已删除（2026-08-26，标注全在 DB，训练集流式化不落本地盘）**
- **封面命名规范（2026-08-26）**：封面文件不得固定命名 `cover`，上传时解码为 **jpg 格式 + UUIDv4 命名**（`common.UuidV4()` 生成，crypto/rand 无第三方依赖，落盘 `datasets/<name>/<uuid>.jpg`，旧封面删除）；DB `cover` 列只存文件名，前端用 DB 值拼 URL 零改动；删除/校验按 `isCoverName` 正则（UUID v4 + `.jpg` 后缀）识别，不认固定名——历史 `cover.jpg` 等存量封面启动时 `MigrateLegacyCovers` 幂等重命名迁移
- DB 只存文件名/来源/prompt 等元数据（`dataset` / `dataset_image`），**禁止图片进库**
- 生成/上传图片是**付费资产**：删除接口必须带前端确认文案（提示 AI 生成图有成本）；删除 = 删文件 + 删记录，目录清理
- 训练机与 Go 服务器可能异机：训练前按 `training` 通道同步（subprocess 同机 cp、ssh 异机 scp）；**数据集在训练机上的权威路径** `training.workdir`/`training.datasetDir`/`<name>/`，DB `model_training.dataset` 只存数据集名
- zip 导出（`GET /admin/datasets/export`）：打包图片目录为 zip 下载——用于标注衔接（二期前的人工标注路径）与备份
### AI 生成图片（provider 抽象）
- `imageGen` 配置节点：`provider: dashscope`（当前唯一实现）+ `apiKey` + `model: qwen-image-3.0`；接口在 `common` 层抽象 `ImageGenProvider`（`Generate(ctx, prompt, size) ([]byte, error)`），后续可加本地 SD 等实现，config 切换
- 尺寸参数由前端传（下拉可选，默认 1152x2048 竖图，与既有训练图一致；另提供 704x704 档）
- 生成同步执行（count 1..8，超时 2min/张），逐张落盘 + 入库（记录 prompt 便于追溯）；中途失败返回错误（已成功的图保留，不删除——付费资产原则）
- prompt 由前端预设模板 + 用户微调；**遵守项目提示词规范：不得包含目标位置描述**（位置由模型自行推理，前端模板与校验文案落实此约束）
### 训练通道（runner 抽象，决策：同机/异机不确定 → 可配置）
```yaml
training:
mode: subprocess # subprocess | ssh
ssh: { host: "", user: "", port: 22, privateKeyPath: "", password: "" }
workdir: /opt/pheasant_data # 训练机工作目录
venvPython: /opt/pheasant_data/venv/bin/python
datasetDir: datasets # 训练机数据集根目录（相对 workdir，数据集为子目录）
concurrency: 1 # GPU 独占：同时仅一个 running，新任务排队
timeoutMinutes: 600 # 超时判死
```
- service 内 `Runner` 接口：`Start(ctx, *TrainingJob) (pid, error)` / `FetchLogTail(ctx, job)` / `IsAlive(ctx, job) bool` / `Cancel(ctx, job)` / `FetchArtifacts(ctx, job, destDir)`；`subprocess` 与 `ssh` 两个实现，按 config `mode` 选择；**ssh 凭据直接读本节点 `training.ssh` 配置**（见「全局训练配置」节）
- **训练脚本**（`server/training/train_server.py`，随项目迁移）：支持 `--task-json <file>`（含 dataset/imgsz/epochs/batch/device/project 名），每 epoch 输出一行机器可读 JSON 到 `--log-file`（`{"epoch":1,"total":150,"metrics":{...}}`），结束写 `result.json`（最终指标）+ 自动打包 `artifact.zip`（best.pt + results.csv + 曲线）；Go 侧解析日志行更新进度、轮询日志尾部截断 N KB 存 `model_training.log_tail`
- **tflite 产物自检**（2026-08-26）：`inspect_tflite.py` 的 flatbuffer 解析逻辑内嵌进 `train_server.py`（`check_tflite`），训练收尾定位 `best.tflite` 后自动校验并写 `result.json` 的 `tflite_check` 字段：`{"ok":bool,"reason":string,"inputs":[{"name","shape","type"}],"outputs":[...]}`；校验规则 = 输入恰 1 张且 4 维、元素总数 == imgsz²×3（兼容 NCHW/NHWC）、输出 ≥ 1 张且 batch 维 = 1；`ok=false`（如 shape 漂移、导出异常）时 Go 侧在拉产物前直接置训练失败并带出 reason，杜绝坏产物进入发布链路；`dump_graph.py` 保留作训练机人工深度调试
- **任务生命周期**：`running → success/failed`；取消 = 杀进程（ssh 模式远程 kill pid）；超时无心跳判死；**Go 服务重启后启动扫描** running 任务按 pid 存活探测（subprocess 本机、ssh 远程 `kill -0`），进程已死则置 failed
- **并发度 1**：发起训练时若已有 running 任务返回错误「训练进行中」；训练任务不排队（简化，管理端人工再点一次）
- 产物拉取：成功后拉 `best.tflite` + `artifact.zip` 到服务器 `workspace/trainings/<taskId>/`，发布时引用
- 写操作走 `common.Serial()` 单写者（SQLite 无 WAL，与既有链路一致）；任务状态更新（进度轮询）为高频写，单独小事务
### 模型版本（每数据集一个模型，多模型体系）
**核心决策：每个数据集训练一个模型，模型按数据集独立版本化，App 多模型并行推理合并**——用户按需下载若干数据集的模型，加载全部已下载模型共同推理标注（类别名不同则自然互补，同类名跨模型 NMS 去重）。
- 版本号规则：`m<major>.<minor>.<patch>`，**同一数据集内**每次发布 patch+1（取该数据集最大版本号解析自增，无记录从 m1.0.0 起）；`UNIQUE(dataset_id, version)` 防重复
- 文件布局：当前生效副本 `workspace/models/<数据集名>/latest.tflite`（tmp + rename 原子覆盖），客户端固定下载该文件；**无 `<version>.tflite` 存档（2026-08-26 决策：不需要模型回退机制，模型只增不删不回滚）**；每数据集一个目录互不影响
- 类别名：发布时从训练任务/数据集记录类别（训练脚本 result.json 输出 `names`），存 `model_version.labels`（JSON 数组），**App 合并推理依赖它**
- **发布**（`POST /admin/trainings/publish`）：校验任务 success + tflite 产物存在 → sha256 → 写 latest 副本 → 插 `model_version` + 该数据集旧版 `is_latest=0`；「记录存在 ⟺ 文件存在」补偿逻辑同 APK 版本管理
- **管理端无模型管理界面**（2026-08-26 决策）：删 `AdminListModels`/`AdminActivateModel`/`AdminDeleteModel` 三个管理接口，`model_version` 表保留——仅支撑客户端下发目录；版本只增不删不回滚（发布即最新）
- **模型目录（客户端拉取）**：`GET /api/v1/models`（公开，登录态即可）返回所有数据集当前生效模型：`{datasetId, datasetName, version, labels, sizeBytes, sha256, notes, publishedAt, downloadUrl}`；下载 URL `/download/models/<数据集名>/latest.tflite`（复用 `/download` 静态托管）
### 标注工作台（依赖 local-ai 可达；2026-08-26 布局重构）
- `localAi` 配置节点：`{ baseUrl: "http://127.0.0.1:18080", model: "rfdetr-xlarge", threshold: 0.08, confConfirmed: 0.2, overlapThreshold: 0.3 }`——**部署前提：RF-DETR 服务须从 Go 服务器可达**（现跑在 Mac 上，部署时搬服务器/训练机；**未配置时图片上传/生成接口直接报错**——标注是强语义，不允许产生无标注图片；配置调不通则任务 failed，页顶错误条展示，不自动重试）；**端点为配置唯一来源，无运行时覆盖**
- **预标注自动触发（2026-08-26 决策，无手动按钮）**：手动上传/AI 生成图片入库成功后，自动对**本次新增图**发起标注；**`localAi` 未配置 → 上传/生成接口直接报错**（同步检查，生成场景在第一张生成前检查避免付费资产生成后标不了）；**已有 running 标注任务（忙）→ 不报错**：上传/生成照常成功，新图由「任务成功完成后自动补标未标注图」机制兜底（每轮任务成功结束时检查该数据集未标注图，有则自动续一轮只标未标注图——失败任务不续，防配置坏时无限重试）；service 逐张调 local-ai 推理（common 池并行，`label_task` 记录 total/done 进度）→ 扫描结果（YOLO 归一化 xywh + 置信度 + 建议类别，conf≥confConfirmed 为 class 0）**直写 `dataset_image.labels_json`（重跑覆盖该图标注）**；**重叠去重（2026-08-26 修订）**：NMS 风格、按置信度降序依次保留，与已保留框**重叠比（minIoU = 交叠面积/两框较小面积）> `localAi.overlapThreshold`（默认 0.3）**的框剔除（同目标被重复检出只留置信度最高者，跨 class 去重；人工画框不参与去重）——**用 minIoU 而非 IoU**：RF-DETR 对同一目标常输出一大一小两个框（标准 IoU 仅 0.3~0.5 会漏杀，实测红黄重叠即此形态），大框套小框时小框被覆盖比例高，minIoU 命中；相邻目标两框互有外露，minIoU 通常 < 0.3；**全图扫描**（项目既定规则：不套用生成规格的位置裁剪，候选宁多勿漏）；不设候选确认两阶段（2026-08-26 撤销 candidates_json）；`POST /admin/label-tasks` 接口保留（详情页「全量标注」按钮，2026-08-26 恢复），可手动全量/指定图重标
- **详情页布局（无选项卡）**：分页（每页 20 条）逐行「原图 ‖ 标注图」对照；页顶标注任务进度条（自动触发任务进度，含错误展示）；标注图 = 原图 + 标注框叠加（只读 canvas，与工作台同一绘制逻辑）
- **弹窗标注**：点击原图/标注图打开放大弹窗（大 canvas，1152x2048 原尺寸），支持两点画框/点框删除/清空/类别切换（确认 0/疑似 1）/上下一张/保存——**AI 自动标注与人工框同层可编辑**（含清理 AI 框）；保存即整体覆写 `dataset_image.labels_json`（JSON 数组，YOLO 归一化 xywh+类别+置信度，空=清空标注）；未保存修改切换图片有确认
- 无历史标注任务时详情页工作台数据源为 `GET /admin/label-workbench?dataset_id=`（图片 + 全量标注框），避免依赖人工先跑一次任务
- 训练前自动整理（prepare_yolo 逻辑服务端，**内存组装不落本地盘**）：数据集有 labels → 按 80/20 拆 train/val 生成 YOLO 训练集包（标注 txt 内存生成、原图只记数据集目录源路径）→ 随同步推训练机（subprocess 直写 `workdir/datasetDir/yolo/<name>/`；ssh 走 **tar 流式管道**本地不落盘）；**自动标注图直接进训练集**（人工修改/清理后即为训练数据）；本地无暂存目录（旧 `workspace/datasets/yolo/` 与 `labels/` 已删除）
### 模型热更新与多模型推理（客户端 Flutter）
- `GET /api/v1/app/update` 扩展：响应追加 `models` 数组（`GET /api/v1/models` 同构：datasetId/datasetName/version/labels/sizeBytes/sha256/downloadUrl）——**多模型目录，不再有单一 modelUrl**；无发布模型时不返回该字段（旧 App 忽略、新 App 兼容旧服务器）
- **模型管理**：App 设置页「模型管理」——列出服务器全部可用模型 + 本地已下载状态（版本/大小/说明），用户**自由下载/删除/启用**；下载到应用私有目录 `.tmp` → sha256 校验 → 原子替换；已下载模型跨启动持久
- **并行推理合并**：`tflite_detector` 启动时加载**全部已启用且已下载**的模型（每模型独立 interpreter + 独立 isolate 线程并行推理）；单帧结果按**类别名**合并 + 跨模型 NMS 去重（IoU 阈值 0.45，同类名才合并）；类别名冲突以合并结果展示、标签文本随模型 labels
- 内置模型兜底：assets `model.tflite` + `labels.txt` 作为无任何下载模型时的默认；有下载模型则并行加载下载模型（内置不重复加载，避免重复检测同类别）
- **非强制**：下载失败/校验不过删 tmp 用旧状态，下次启动重试；不弹阻塞窗
- 性能：N 模型并行推理墙钟 ≈ 单模型 1.2~1.5 倍（多核并行）；用户按需下载控制 N；帧率不足时可降采样
- 版本比较规则同 APK（数字段比较，m 前缀剥离后按 x.y.z）
## 待办/风险
- 微信支付需商户号（APP 支付权限）、APIv3 密钥与平台证书；支付宝需商户应用与密钥 —— 当前均未配置，接口按真实 SDK 契约实现，配置走 `config.yml` 占位
- iOS App Store 对数字内容强制 IAP，微信/支付宝直连有被拒风险（已决策，记录在案）
- 管理端静态 token 由登录页输入（浏览器 localStorage），属内网管理凭据：`config.yml admin.token` 失配/未配置时管理接口全部拒绝；token 不进入前端构建产物与 git
- 手动授权等同免费发卡，仅限运营/客诉排查场景，前端页面带确认弹窗，后端不限制频率（按需再加审计）
- 模型训练体系依赖外部环境：训练机 GPU + ultralytics venv（`training.venvPython`）、RF-DETR 服务从服务器可达（`localAi.baseUrl`）、DashScope API key（`imageGen.apiKey`）——未配置对应节点时相关管理接口返回配置缺失错误，不影响支付/授权主链路
- subprocess 模式下训练与在线服务同机，训练吃满 GPU 可能影响识别类在线任务（当前在线服务无 GPU 推理，风险低）；异机部署切换 `training.mode: ssh`
- 训练产物与数据集为磁盘占用大户（图片百 MB~GB、每轮产物几十 MB），`workspace/` 已挂载持久化；清理入口：删除数据集（付费资产需确认）；模型无存档，仅 latest.tflite 被新发布覆盖，无需单独清理
server_admin/src/App.vue
+12 -10
View File
@@ -1,15 +1,22 @@
<script setup>
import { computed } from 'vue'
import { useRoute, useRouter } from 'vue-router'
import {
List as ListIcon,
Key as KeyIcon,
Refresh as RefreshIcon,
SwitchButton as SwitchIcon,
Picture as PictureIcon,
} from '@element-plus/icons-vue'
const route = useRoute()
const router = useRouter()
// 详情页 /datasets/:id 仍高亮「数据训练」菜单项
const activeMenu = computed(() =>
route.path.startsWith('/datasets') ? '/datasets' : route.path,
)
function logout() {
localStorage.removeItem('adminToken')
router.push('/login')
@@ -22,8 +29,11 @@ function logout() {
<el-container v-else class="app-layout">
<el-aside width="200px" class="app-aside">
<div class="app-logo">视野管理端</div>
<el-menu :default-active="route.path" router>
<el-menu :default-active="activeMenu" router>
<el-menu-item index="/datasets">
<el-icon><PictureIcon /></el-icon>
<span>数据训练</span>
</el-menu-item>
<el-menu-item index="/orders">
<el-icon><ListIcon /></el-icon>
<span>订单管理</span>
@@ -57,14 +67,6 @@ function logout() {
.app-aside {
background: #001529;
}
.app-logo {
height: 56px;
line-height: 56px;
text-align: center;
color: #fff;
font-size: 16px;
font-weight: 600;
}
.app-aside :deep(.el-menu) {
border-right: none;
background: #001529;
server_admin/src/router/index.js
+5 -1
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@@ -3,13 +3,17 @@ import Login from '../views/Login.vue'
import Orders from '../views/Orders.vue'
import Licenses from '../views/Licenses.vue'
import AppVersions from '../views/AppVersions.vue'
import Datasets from '../views/Datasets.vue'
import DatasetDetail from '../views/DatasetDetail.vue'
// 生产部署于 /admin/ 前缀（后端静态托管），history 路由由后端 SPA fallback 兜底
const router = createRouter({
history: createWebHistory('/admin/'),
routes: [
{ path: '/login', name: 'login', component: Login, meta: { title: '登录' } },
{ path: '/', redirect: '/orders' },
{ path: '/', redirect: '/datasets' },
{ path: '/datasets', name: 'datasets', component: Datasets, meta: { title: '数据训练' } },
{ path: '/datasets/:id', name: 'datasetDetail', component: DatasetDetail, meta: { title: '数据训练详情' } },
{ path: '/orders', name: 'orders', component: Orders, meta: { title: '订单管理' } },
{ path: '/licenses', name: 'licenses', component: Licenses, meta: { title: '授权管理' } },
{ path: '/app-versions', name: 'appVersions', component: AppVersions, meta: { title: '版本管理' } },
server_admin/src/views/DatasetDetail.vue
+1009
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File diff suppressed because it is too large Load Diff
server_admin/src/views/Datasets.vue
+565
View File
@@ -0,0 +1,565 @@
<script setup>
import { onBeforeUnmount, onMounted, reactive, ref } from 'vue'
import { ElMessage, ElMessageBox } from 'element-plus'
import { Plus, Picture, Setting, VideoPlay, Download } from '@element-plus/icons-vue'
import { useRouter } from 'vue-router'
import request from '../api/request'
const router = useRouter()
const loading = ref(false)
const list = ref([])
const total = ref(0)
const page = ref(1)
const size = ref(12)
const keyword = ref('')
const createVisible = ref(false)
const createForm = reactive({ name: '', source: 'manual' })
const creating = ref(false)
const configVisible = ref(false)
const configSaving = ref(false)
const configForm = reactive({
id: 0,
description: '',
cover: '',
})
const coverFile = ref(null) // 新选择的封面文件（el-upload 单文件）
const coverFileList = ref([]) // 封面回显：已有封面（服务端 url）或新选文件（本地预览）
const coverDeleted = ref(false) // 用户删除了已有封面（保存时调删除接口）
const trainVisible = ref(false)
const training = ref(false)
const trainForm = reactive({ datasetId: 0, name: '', imgsz: 704, epochs: 150, batch: 16, device: '0' })
const statusMap = { building: '建设中', labeled: '已标注', synced: '已同步' }
const statusTag = { building: 'info', labeled: 'success', synced: 'primary' }
const trainStatusMap = { running: '训练中', success: '已训练', failed: '训练失败' }
const trainStatusTag = { running: 'primary', success: 'success', failed: 'danger' }
function load() {
loading.value = true
request
.get('/datasets', { params: { page: page.value, size: size.value, keyword: keyword.value || undefined } })
.then((data) => {
list.value = data.list || []
total.value = data.total || 0
})
.finally(() => {
loading.value = false
})
}
function search() {
page.value = 1
load()
}
function openCreate() {
createForm.name = ''
createForm.source = 'manual'
createVisible.value = true
}
function submitCreate() {
if (!createForm.name.trim()) {
ElMessage.warning('请输入数据集名称')
return
}
creating.value = true
request
.post('/datasets', { name: createForm.name.trim(), source: createForm.source })
.then(() => {
ElMessage.success('数据集已创建，请上传或生成图片')
createVisible.value = false
page.value = 1
load()
})
.finally(() => {
creating.value = false
})
}
// ---------- 数据集级配置（描述 + 封面；AI 端点/训练机 SSH 走 config.yml，与数据集无关） ----------
function openConfig(row) {
Object.assign(configForm, {
id: row.id,
description: row.description || '',
cover: row.cover || '',
})
coverFile.value = null
coverDeleted.value = false
// 已有封面回显：el-upload picture-card 直接以 url 项展示
coverFileList.value = row.cover ? [{ name: '当前封面', url: imgUrl(`/api/v1/admin/datasets/cover?datasetId=${row.id}`) }] : []
configVisible.value = true
}
// 封面文件选中：多选时只保留最后一个（替换语义）；新文件选中即视为不再删除
function onCoverChange(file, fileList) {
if (fileList.length > 1) coverFileList.value = [fileList[fileList.length - 1]]
coverFile.value = coverFileList.value.length ? coverFileList.value[0].raw : null
if (coverFile.value) coverDeleted.value = false
}
// 删除项：删服务端已有封面（无 raw）→ 标记待删除；删新选文件且列表清空 → 同样按删除处理（服务端旧封面仍在）
function onCoverRemove(file) {
coverFile.value = null
if (!file.raw) {
coverDeleted.value = true
} else if (!coverFileList.value.length && configForm.cover) {
coverDeleted.value = true
}
}
async function submitConfig() {
configSaving.value = true
try {
if (coverFile.value) {
const fd = new FormData()
fd.append('datasetId', configForm.id)
fd.append('file', coverFile.value)
await request.post('/datasets/cover', fd, { timeout: 60000 })
} else if (coverDeleted.value) {
await request.post('/datasets/cover/delete', { datasetId: configForm.id })
}
// cover 由上传接口单独维护，update 不携带
await request.post('/datasets/update', { ...configForm, cover: '' })
ElMessage.success('配置已保存')
configVisible.value = false
load()
} finally {
configSaving.value = false
}
}
// ---------- 开始训练 ----------
function openTrain(row) {
trainForm.datasetId = row.id
trainForm.name = `${row.name} 训练`
trainForm.imgsz = 704
trainForm.epochs = 150
trainForm.batch = 16
trainForm.device = '0'
trainVisible.value = true
}
function submitTrain() {
if (!trainForm.name.trim()) {
ElMessage.warning('请输入任务名称')
return
}
training.value = true
request
.post('/trainings', { ...trainForm })
.then(() => {
ElMessage.success('训练任务已发起，完成后可发布为模型版本')
trainVisible.value = false
load()
})
.catch(() => {})
.finally(() => {
training.value = false
})
}
// ---------- 导出 / 删除 ----------
function exportDataset(row) {
window.open(`${location.origin}/api/v1/admin/datasets/export?datasetId=${row.id}`, '_blank')
}
function removeDataset(row) {
const warn =
row.source === 'ai' || row.imageCount > 0
? '将删除图片文件、标注与记录，AI 生成图属付费资产，删除后无法恢复。'
: ''
ElMessageBox.confirm(`确定删除数据集「${row.name}」？${warn}`, '删除数据集', {
confirmButtonText: '删除',
cancelButtonText: '取消',
type: 'warning',
})
.then(() => request.post('/datasets/delete', { id: row.id }))
.then(() => {
ElMessage.success('已删除')
load()
})
.catch(() => {})
}
// <img> 标签无法带自定义请求头，图片地址以 query 参数携带 admin token
function imgUrl(u) {
return `${location.origin}${u}&token=${encodeURIComponent(localStorage.getItem('adminToken') || '')}`
}
function coverUrl(row) {
if (!row.cover) return ''
return imgUrl(`/api/v1/admin/datasets/cover?datasetId=${row.id}`)
}
// 训练进度（百分比）：有总轮数才显示比例
function trainPercent(row) {
if (row.trainingStatus !== 'running' || !row.trainingTotalEpochs) return 0
return Math.min(100, Math.round((row.trainingCurrentEpoch / row.trainingTotalEpochs) * 100))
}
function publishModel(row) {
ElMessageBox.confirm(
`确定将「${row.name}」的最新训练结果发布为模型版本？将按数据集版本号自增（m<主>.<次>.<修订>），客户端下次启动热更新下载。`,
'发布模型版本',
{ confirmButtonText: '发布', cancelButtonText: '取消', type: 'warning' },
)
.then(() => request.post('/trainings/publish', { id: row.trainingId }))
.then((data) => {
ElMessage.success(`已发布版本 ${data.version}`)
load()
})
.catch(() => {})
}
// 有训练中的任务时周期刷新，卡片进度条保持最新
let trainPoll = null
onMounted(() => {
load()
trainPoll = setInterval(() => {
if (list.value.some((r) => r.trainingStatus === 'running')) load()
}, 10000)
})
onBeforeUnmount(() => {
if (trainPoll) clearInterval(trainPoll)
})
</script>
<template>
<el-card shadow="never">
<div class="toolbar">
<div class="toolbar-left">
<el-input
v-model="keyword"
class="kw"
placeholder="按名称搜索"
clearable
@keyup.enter="search"
@clear="search"
/>
<el-button @click="search">搜索</el-button>
</div>
<div class="toolbar-right">
<el-button type="primary" :icon="Plus" @click="openCreate">新建数据集</el-button>
</div>
</div>
<div v-loading="loading" class="ds-grid">
<div v-for="row in list" :key="row.id" class="ds-card" @click="router.push(`/datasets/${row.id}`)">
<div class="ds-cover">
<img v-if="row.cover" :src="coverUrl(row)" :alt="row.name" loading="lazy" />
<div v-else class="ds-cover-placeholder"><el-icon :size="34"><Picture /></el-icon></div>
<div v-if="row.source === 'ai'" class="ds-cover-source">AI 生成</div>
</div>
<div class="ds-body">
<div class="ds-name" :title="row.name">{{ row.name }}</div>
<div class="ds-desc">{{ row.description || '暂无描述，点击进入管理图片与标注' }}</div>
<div class="ds-stats">
<span>图片 {{ row.imageCount }}</span>
<span>已标注 {{ row.labeledCount }}</span>
<el-tag :type="statusTag[row.status] || 'info'" size="small">{{ statusMap[row.status] || row.status }}</el-tag>
</div>
</div>
<div class="ds-actions" @click.stop>
<el-button type="primary" size="small" :icon="VideoPlay" :disabled="row.trainingStatus === 'running'" @click="openTrain(row)">
{{ row.trainingStatus === 'running' ? '训练中' : '开始训练' }}
</el-button>
<el-button size="small" :icon="Setting" @click="openConfig(row)">配置</el-button>
<el-button size="small" :icon="Download" @click="exportDataset(row)">导出</el-button>
<el-button size="small" type="danger" @click="removeDataset(row)">删除</el-button>
</div>
<!-- 训练任务进度与状态（最新一条训练记录） -->
<div v-if="row.trainingStatus" class="ds-train" @click.stop>
<template v-if="row.trainingStatus === 'running'">
<el-progress :percentage="trainPercent(row)" :stroke-width="6" :show-text="false" class="ds-train-bar" />
<div class="ds-train-text">
<span>训练中 {{ row.trainingCurrentEpoch || 0 }}/{{ row.trainingTotalEpochs || '-' }} 轮</span>
</div>
</template>
<template v-else>
<el-tag :type="trainStatusTag[row.trainingStatus] || 'info'" size="small">
{{ trainStatusMap[row.trainingStatus] || row.trainingStatus }}
</el-tag>
<el-button v-if="row.trainingStatus === 'success' && row.trainingId" size="small" type="success" @click="publishModel(row)">
发布模型
</el-button>
</template>
</div>
</div>
<el-empty v-if="!loading && !list.length" description="暂无数据集，点击右上角新建" />
</div>
<el-pagination
class="pager"
v-model:current-page="page"
v-model:page-size="size"
:total="total"
:page-sizes="[12, 24, 48]"
layout="total, sizes, prev, pager, next"
@change="load"
/>
<!-- 新建数据集 -->
<el-dialog v-model="createVisible" title="新建数据集" width="min(440px, 92vw)">
<el-form label-width="90px">
<el-form-item label="名称">
<el-input
v-model="createForm.name"
maxlength="50"
show-word-limit
placeholder="中文/字母/数字/下划线/短横线，唯一且作磁盘目录名"
/>
</el-form-item>
<el-form-item label="图片来源">
<el-radio-group v-model="createForm.source">
<el-radio value="manual">手动上传</el-radio>
<el-radio value="ai">AI 生成</el-radio>
</el-radio-group>
</el-form-item>
</el-form>
<template #footer>
<el-button @click="createVisible = false">取消</el-button>
<el-button type="primary" :loading="creating" @click="submitCreate">创建</el-button>
</template>
</el-dialog>
<!-- 数据集配置 -->
<el-dialog v-model="configVisible" title="数据集配置" width="min(720px, 94vw)">
<el-form label-width="120px">
<el-form-item label="描述">
<el-input v-model="configForm.description" type="textarea" :rows="6" maxlength="500" show-word-limit placeholder="数据集说明，展示在卡片上" />
</el-form-item>
<el-form-item label="封面">
<div class="cover-row">
<el-upload
v-model:file-list="coverFileList"
:auto-upload="false"
accept=".jpg,.jpeg,.png"
list-type="picture-card"
:on-change="onCoverChange"
:on-remove="onCoverRemove"
>
<div v-if="coverFileList.length === 0" class="upload-tile">
<el-icon :size="22"><Plus /></el-icon>
</div>
</el-upload>
<span class="add-tip">jpg/jpeg/png，≤2MB；悬停预览右上角 × 可删除当前封面</span>
</div>
</el-form-item>
</el-form>
<template #footer>
<el-button @click="configVisible = false">取消</el-button>
<el-button type="primary" :loading="configSaving" @click="submitConfig">保存</el-button>
</template>
</el-dialog>
<!-- 开始训练 -->
<el-dialog v-model="trainVisible" title="开始训练" width="min(480px, 92vw)">
<el-form label-width="90px">
<el-form-item label="任务名称">
<el-input v-model="trainForm.name" maxlength="50" show-word-limit placeholder="如：雉鸡数据集 v2 训练" />
</el-form-item>
<el-form-item label="分辨率">
<el-input-number v-model="trainForm.imgsz" :min="64" :max="2048" :step="32" />
<span class="field-tip">与端侧推理对齐，默认 704</span>
</el-form-item>
<el-form-item label="轮数">
<el-input-number v-model="trainForm.epochs" :min="1" :max="1000" />
</el-form-item>
<el-form-item label="Batch">
<el-input-number v-model="trainForm.batch" :min="1" :max="128" />
</el-form-item>
<el-form-item label="设备">
<el-input v-model="trainForm.device" maxlength="16" placeholder="GPU 编号 0，CPU 填 cpu" />
</el-form-item>
<div class="start-tip">训练前自动整理 80/20 训练/验证集，须先完成图片标注；训练机并发度 1，已有运行中任务会被拒绝。</div>
</el-form>
<template #footer>
<el-button @click="trainVisible = false">取消</el-button>
<el-button type="primary" :loading="training" @click="submitTrain">发起训练</el-button>
</template>
</el-dialog>
</el-card>
</template>
<style scoped>
.toolbar {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 14px;
gap: 10px;
}
.toolbar-left {
display: flex;
gap: 8px;
}
.toolbar-right {
display: flex;
gap: 8px;
}
.kw {
width: 220px;
}
.pager {
margin-top: 14px;
justify-content: flex-end;
}
.ds-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(300px, 1fr));
gap: 14px;
min-height: 120px;
}
.ds-card {
background: #fff;
border: 1px solid #ebeef5;
border-radius: 8px;
overflow: hidden;
cursor: pointer;
transition: box-shadow 0.2s;
}
.ds-card:hover {
box-shadow: 0 4px 16px rgba(0, 0, 0, 0.08);
}
.ds-cover {
position: relative;
height: 150px;
background: #f5f7fa;
}
.ds-cover img {
width: 100%;
height: 100%;
object-fit: contain;
display: block;
}
.ds-cover-placeholder {
height: 100%;
display: flex;
align-items: center;
justify-content: center;
color: #c0c4cc;
}
.ds-cover-source {
position: absolute;
left: 8px;
top: 8px;
background: rgba(0, 0, 0, 0.55);
color: #fff;
font-size: 11px;
padding: 2px 8px;
border-radius: 10px;
}
.ds-train {
display: flex;
align-items: center;
gap: 10px;
padding: 8px 12px 12px;
border-top: 1px dashed #ebeef5;
}
.ds-train-bar {
flex: 1;
}
.ds-train-text {
font-size: 12px;
color: #606266;
white-space: nowrap;
}
.ds-body {
padding: 10px 12px 6px;
}
.ds-name {
font-size: 15px;
font-weight: 600;
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
.ds-desc {
font-size: 12px;
color: #909399;
line-height: 1.5;
height: 36px;
overflow: hidden;
margin-top: 4px;
}
.ds-stats {
display: flex;
align-items: center;
gap: 10px;
font-size: 12px;
color: #606266;
margin-top: 8px;
}
.ds-actions {
display: flex;
flex-wrap: wrap;
gap: 4px;
padding: 8px 12px 12px;
border-top: 1px dashed #ebeef5;
margin-top: 8px;
}
.field-tip {
margin-left: 8px;
font-size: 12px;
color: #909399;
}
.start-tip {
font-size: 12px;
color: #909399;
line-height: 1.6;
margin-left: 90px;
}
.cover-row {
display: flex;
align-items: center;
gap: 10px;
width: 100%;
}
/* 上传方块：虚线边框 + 居中加号（el-upload picture-card 触发块） */
.upload-tile {
width: 100%;
height: 100%;
display: flex;
align-items: center;
justify-content: center;
color: #8c939d;
}
.add-tip {
font-size: 12px;
color: #909399;
line-height: 1.6;
align-self: flex-start;
margin-top: 12px;
}
@media (max-width: 767px) {
.toolbar {
flex-wrap: wrap;
align-items: flex-start;
}
.toolbar-left {
flex: 1;
min-width: 200px;
}
.toolbar-right {
width: 100%;
justify-content: flex-end;
}
.kw {
flex: 1;
width: auto;
}
.ds-grid {
grid-template-columns: 1fr;
}
}
</style>
server_admin/src/views/Login.vue
+1 -1
View File
@@ -23,7 +23,7 @@ function login() {
.then(() => {
localStorage.setItem('adminToken', token.value.trim())
ElMessage.success('登录成功')
router.push('/orders')
router.push('/datasets')
})
.catch(() => {
errorMsg.value = 'token 无效或服务端未配置（检查 config.yml admin.token）'
server_admin/vite.config.js
+1 -1
View File
@@ -12,7 +12,7 @@ export default defineConfig({
},
server: {
proxy: {
'/api': 'http://127.0.0.1:8080',
'/api': 'http://127.0.0.1:18080',
},
},
})
training/analyze_rfdetr.py
-278
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@@ -1,278 +0,0 @@
#!/usr/bin/env python3
"""RF-DETR 整图批量检测野鸡,生成候选标注(初标)
- 整图等比缩放至最长边 INPUT_SIZE(700)送检测,**不做任何裁剪**,坐标按比例映射回原图
- 只保留 COCO bird 类;同目标重复框用 NMS 去重
- 藏身点:图像分析(植被密度/地形边缘/光线)生成 class 1 疑似藏匿位置
- 输出: YOLO txt (class 0 = 确认野鸡, class 1 = 疑似(低置信检测+藏身点))
-> datasets/labels/pheasant_rfdetr/
- 分析JSON -> datasets/analysis/pheasant_rfdetr/
- 预览图 -> datasets/previews/pheasant_rfdetr/
"""
import argparse
import base64
import glob
import json
import os
import urllib.request
from concurrent.futures import ThreadPoolExecutor, as_completed
import cv2
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
SRC_IMAGES = os.path.join(BASE_DIR, "datasets/images/pheasant")
OUT_LABELS = os.path.join(BASE_DIR, "datasets/labels/pheasant_rfdetr")
OUT_JSON = os.path.join(BASE_DIR, "datasets/analysis/pheasant_rfdetr")
OUT_PREVIEW = os.path.join(BASE_DIR, "datasets/previews/pheasant_rfdetr")
ENDPOINT = "http://192.168.3.210:18080/v1/detection"
MODEL = "rfdetr-xlarge"
TIMEOUT = 300
THRESHOLD = 0.08
CONF_CONFIRMED = 0.2 # 置信度高于此为明确(class 0 红框),否则为疑似(class 1 蓝框)
# RF-DETR 置信度分布:强目标 0.3-0.9,0.05-0.14 多为噪声尾;
# THRESHOLD=0.08 砍掉纯噪声,CONF_CONFIRMED=0.2 只把强目标标红,宁多勿漏
INPUT_SIZE = 700 # 整图等比缩放后的最长边,不裁剪
NMS_IOU = 0.4
WORKERS = 4
KEEP_CLASSES = set() # 空 = 保留全部类别(RF-DETR 对小目标类别不稳定,定位一致即可)
IMG_MIME = {".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".png": "image/png"}
def detect(buf, mime):
body = json.dumps({"model": MODEL, "image": f"data:{mime};base64,{base64.b64encode(buf.tobytes()).decode()}",
"threshold": THRESHOLD}).encode()
opener = urllib.request.build_opener(urllib.request.ProxyHandler({}))
req = urllib.request.Request(ENDPOINT, data=body,
headers={"Content-Type": "application/json"}, method="POST")
with opener.open(req, timeout=TIMEOUT) as r:
resp = json.loads(r.read().decode())
return resp.get("detections", [])
def nms(boxes, iou_thr):
"""boxes: [(x, y, w, h, conf)], 按置信度降序去重"""
boxes = sorted(boxes, key=lambda b: -b[4])
keep = []
for b in boxes:
overlap = False
for k in keep:
ix = min(b[0] + b[2], k[0] + k[2]) - max(b[0], k[0])
iy = min(b[1] + b[3], k[1] + k[3]) - max(b[1], k[1])
if ix <= 0 or iy <= 0:
continue
inter = ix * iy
union = b[2] * b[3] + k[2] * k[3] - inter
if inter / union > iou_thr:
overlap = True
break
if not overlap:
keep.append(b)
return keep
def find_hiding_spots(img, H, W, n_spots=3):
"""图像分析疑似藏身点:植被密度/地形边缘(田埂、林田交界、沟渠)/光线调制。
野鸡习性:地面活动,藏身于茂密低矮遮蔽物,避开开阔裸地;
强光场景偏好阴影遮蔽,弱光场景偏好相对亮且有遮蔽的区域。
返回 [(x, y, w, h)] 像素坐标,框面积约占画面 2%~8%。
"""
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
s, v = hsv[..., 1], hsv[..., 2]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32) / 255.0
b = img[..., 0].astype(np.float32) / 255.0
g = img[..., 1].astype(np.float32) / 255.0
r = img[..., 2].astype(np.float32) / 255.0
veg = np.clip(g - np.maximum(r, b), 0, 1)
veg[s / 255.0 < 0.12] = 0
tex = np.abs(gray - cv2.GaussianBlur(gray, (0, 0), 3))
base = veg + tex
grad = cv2.magnitude(cv2.Sobel(base, cv2.CV_32F, 1, 0, ksize=3),
cv2.Sobel(base, cv2.CV_32F, 0, 1, ksize=3))
light = v / 255.0
mean_light = float(gray.mean())
if mean_light > 0.55:
light_w = 1.0 - np.abs(light - 0.25)
else:
light_w = 1.0 - np.abs(light - 0.6)
cover = (veg * 0.55 + tex * 0.30 + grad * 0.15) * (0.4 + 0.6 * light_w)
cover[light < 0.12] = 0
cover[light > 0.93] *= 0.15
cover = cv2.GaussianBlur(cover, (0, 0), 7)
windows = []
for area_frac in (0.02, 0.04, 0.07):
for aspect in (1.0, 1.6, 2.5):
bw = int(round((area_frac * aspect * H * W) ** 0.5))
bh = int(round((area_frac * H * W / aspect) ** 0.5))
bw, bh = min(bw, W), min(bh, H)
windows.append((cv2.boxFilter(cover, -1, (bw, bh)), bw, bh))
stack = np.stack([w[0] for w in windows])
best = stack.max(axis=0)
best_idx = stack.argmax(axis=0)
floor = max(float(best.max()) * 0.35, 0.02)
spots = []
for _ in range(n_spots):
idx = np.unravel_index(int(np.argmax(best)), best.shape)
y, x = idx
if best[y, x] < floor:
break
_, bw, bh = windows[int(best_idx[y, x])]
x0 = max(0, min(x - bw // 2, W - bw))
y0 = max(0, min(y - bh // 2, H - bh))
spots.append((x0, y0, bw, bh))
mx, my = max(bw, 64), max(bh, 64)
best[max(0, y - my):min(H, y + my), max(0, x - mx):min(W, x + mx)] = -1
return [(int(x), int(y), int(w), int(h)) for x, y, w, h in spots]
def suppress_overlap(lines):
"""class 1(疑似)框与任一 class 0(确认)框重叠时删除,只保留 class 0"""
cls0 = [l for l in lines if l.startswith("0 ")]
if not cls0:
return lines
boxes0 = [tuple(map(float, l.split())) for l in cls0]
keep1 = []
for l in lines:
if l.startswith("0 "):
continue
c, cx, cy, bw, bh = map(float, l.split())
x1, y1, x2, y2 = cx - bw / 2, cy - bh / 2, cx + bw / 2, cy + bh / 2
overlap = False
for kc, kx, ky, kw, kh in boxes0:
ix = min(x2, kx + kw / 2) - max(x1, kx - kw / 2)
iy = min(y2, ky + kh / 2) - max(y1, ky - kh / 2)
if ix > 0 and iy > 0:
overlap = True
break
if not overlap:
keep1.append(l)
return cls0 + keep1
def process(name):
path = os.path.join(SRC_IMAGES, name)
img = cv2.imread(path)
H, W = img.shape[:2]
ext = os.path.splitext(path)[1].lower()
mime = IMG_MIME.get(ext, "image/jpeg")
jpeg_params = [cv2.IMWRITE_JPEG_QUALITY, 100] if ext in (".jpg", ".jpeg") else []
# 整图等比缩放至最长边 INPUT_SIZE,不裁剪;检测坐标按比例映射回原图
scale = min(1.0, INPUT_SIZE / max(H, W))
if scale < 1.0:
small = cv2.resize(img, (round(W * scale), round(H * scale)),
interpolation=cv2.INTER_AREA)
else:
small = img
ok, buf = cv2.imencode(ext, small, jpeg_params)
boxes = []
for d in detect(buf, mime):
if not KEEP_CLASSES or d.get("class_name") in KEEP_CLASSES:
boxes.append((d["x"] / scale, d["y"] / scale,
d["width"] / scale, d["height"] / scale, d["confidence"]))
# 野鸡目标 20-100px(近大远小);排除大物体误检(田地纹理)与细条误检
boxes = [b for b in nms(boxes, NMS_IOU)
if 4 <= b[3] <= H * 0.06 and 4 <= b[2] <= W * 0.1
and 0.3 <= b[2] / b[3] <= 3.0]
hiding = find_hiding_spots(img, H, W)
return name, W, H, boxes, hiding
def reclassify():
"""不重新检测:按当前 CONF_CONFIRMED 从分析 JSON 重出 txt 标签与预览"""
total_boxes = total_empty = 0
for p in sorted(glob.glob(os.path.join(OUT_JSON, "*.json"))):
j = json.load(open(p, encoding="utf-8"))
W, H = j["size"]
stem = os.path.splitext(os.path.basename(p))[0]
lines = []
for b in j["candidates"]:
x, y, w, h, conf = b["x"], b["y"], b["width"], b["height"], b["confidence"]
cx, cy, bw, bh = (x + w / 2) / W, (y + h / 2) / H, w / W, h / H
cls = 0 if conf >= CONF_CONFIRMED else 1
lines.append(f"{cls} {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
for b in j.get("hiding_spots", []):
x, y, w, h = b["x"], b["y"], b["width"], b["height"]
cx, cy, bw, bh = (x + w / 2) / W, (y + h / 2) / H, w / W, h / H
lines.append(f"1 {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
lines = suppress_overlap(lines)
with open(os.path.join(OUT_LABELS, f"{stem}.txt"), "w", encoding="utf-8") as f:
f.write("\n".join(lines) + ("\n" if lines else ""))
draw_preview(os.path.join(SRC_IMAGES, j["image"]), lines,
os.path.join(OUT_PREVIEW, f"{stem}.jpg"))
total_boxes += len(lines)
if not lines:
total_empty += 1
print(f"重分类完成: {total_boxes} 框, 空图 {total_empty} 张", flush=True)
def main():
ap = argparse.ArgumentParser(description="RF-DETR 切块放大批量检测野鸡候选框")
ap.add_argument("--names", type=str, default="", help="只处理指定编号,逗号分隔")
ap.add_argument("--reclassify", action="store_true",
help="不重新检测,按当前 CONF_CONFIRMED 从分析JSON重出标签与预览")
args = ap.parse_args()
if args.reclassify:
reclassify()
return
for d in (OUT_LABELS, OUT_JSON, OUT_PREVIEW):
os.makedirs(d, exist_ok=True)
names = sorted(p.name for p in os.scandir(SRC_IMAGES)
if p.name.startswith("pheasant_") and p.name.endswith(".jpg"))
if args.names:
want = {f"pheasant_{n}.jpg" for n in args.names.split(",")}
names = [n for n in names if n in want]
total_boxes = total_empty = 0
with ThreadPoolExecutor(max_workers=WORKERS) as pool:
futures = [pool.submit(process, n) for n in names]
for fut in as_completed(futures):
name, W, H, boxes, hiding = fut.result()
stem = os.path.splitext(name)[0]
lines = []
for x, y, w, h, conf in boxes:
cx, cy, bw, bh = (x + w / 2) / W, (y + h / 2) / H, w / W, h / H
cls = 0 if conf >= CONF_CONFIRMED else 1
lines.append(f"{cls} {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
for x, y, w, h in hiding:
cx, cy, bw, bh = (x + w / 2) / W, (y + h / 2) / H, w / W, h / H
lines.append(f"1 {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
lines = suppress_overlap(lines)
with open(os.path.join(OUT_JSON, f"{stem}.json"), "w", encoding="utf-8") as f:
json.dump({"image": name, "size": [W, H],
"candidates": [{"x": b[0], "y": b[1], "width": b[2],
"height": b[3], "confidence": b[4]} for b in boxes],
"hiding_spots": [{"x": b[0], "y": b[1], "width": b[2],
"height": b[3]} for b in hiding]},
f, ensure_ascii=False, indent=1)
with open(os.path.join(OUT_LABELS, f"{stem}.txt"), "w", encoding="utf-8") as f:
f.write("\n".join(lines) + ("\n" if lines else ""))
draw_preview(os.path.join(SRC_IMAGES, name), lines,
os.path.join(OUT_PREVIEW, f"{stem}.jpg"))
total_boxes += len(lines)
if not lines:
total_empty += 1
print(f"{name}: 候选{len(lines)}", flush=True)
print(f"完成: {len(names)} 张, 候选框 {total_boxes}, 无候选 {total_empty} 张", flush=True)
def draw_preview(img_path, lines, out_path):
img = cv2.imread(img_path)
h, w = img.shape[:2]
for line in lines:
cls, cx, cy, bw, bh = map(float, line.split())
x1, y1 = int((cx - bw / 2) * w), int((cy - bh / 2) * h)
x2, y2 = int((cx + bw / 2) * w), int((cy + bh / 2) * h)
color = (0, 0, 255) if cls == 0 else (255, 0, 0)
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
cv2.imwrite(out_path, img)
if __name__ == "__main__":
main()
training/prepare_yolo.py
-48
View File
@@ -1,48 +0,0 @@
#!/usr/bin/env python3
"""从 RF-DETR 候选标注整理 YOLO 训练集(class 0 确认野鸡 + class 1 疑似/藏身点, 80/20 拆分)"""
import os
import random
import shutil
BASE = os.path.dirname(os.path.abspath(__file__))
IMG_SRC = os.path.join(BASE, "datasets/images/pheasant")
LBL_SRC = os.path.join(BASE, "datasets/labels/pheasant_rfdetr")
OUT = os.path.join(BASE, "datasets/yolo")
random.seed(42)
stems = sorted(f[:-4] for f in os.listdir(LBL_SRC) if f.endswith(".txt"))
kept, dropped = [], []
for s in stems:
src_lbl = os.path.join(LBL_SRC, s + ".txt")
with open(src_lbl, encoding="utf-8") as f:
lines = [l for l in f.read().splitlines() if l]
if not lines:
dropped.append(s)
continue
kept.append((s, lines))
random.shuffle(kept)
n_val = max(1, round(len(kept) * 0.2))
val, train = kept[:n_val], kept[n_val:]
for split, items in (("train", train), ("val", val)):
for d in (os.path.join(OUT, "images", split), os.path.join(OUT, "labels", split)):
os.makedirs(d, exist_ok=True)
for s, lines in items:
shutil.copy(os.path.join(IMG_SRC, s + ".jpg"), os.path.join(OUT, "images", split, s + ".jpg"))
with open(os.path.join(OUT, "labels", split, s + ".txt"), "w", encoding="utf-8") as f:
f.write("\n".join(lines) + "\n")
yaml = """path: /opt/pheasant_data/datasets/yolo
train: images/train
val: images/val
names:
0: pheasant
1: suspect
"""
with open(os.path.join(OUT, "dataset.yaml"), "w", encoding="utf-8") as f:
f.write(yaml)
print(f"train: {len(train)} 张, val: {len(val)} 张, 空标注被剔除: {len(dropped)} 张")
print("剔除:", ", ".join(dropped) if dropped else "无")
training/train_local.py
-21
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@@ -1,21 +0,0 @@
from ultralytics import YOLO
import os
BASE = os.path.dirname(os.path.abspath(__file__))
YOLO_DIR = os.path.join(BASE, "datasets/yolo")
data = os.path.join(YOLO_DIR, "dataset_local.yaml")
with open(data, "w", encoding="utf-8") as f:
f.write(f"""path: {YOLO_DIR}
train: images/train
val: images/val
names:
0: pheasant
1: suspect
""")
model = YOLO("yolov8n.pt")
model.train(data=data, imgsz=1024, epochs=150, patience=30, batch=8,
device="mps", workers=4,
# project 必须绝对路径，相对路径会被拼到默认 runs/detect 下造成双层嵌套
project=os.path.join(BASE, "runs/pheasant"), name="yolov8n_1024",
exist_ok=True, plots=True)
training/train_server.py
-18
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@@ -1,18 +0,0 @@
from ultralytics import YOLO
import os
# 训练分辨率与端侧推理对齐：tflite 导出/真机推理均为 704x704。
# 训练 1280 会让模型对"清晰大目标"自信，推理时目标变小置信度崩坏（误报根源）。
# multi_scale=0.5 随机缩放输入 0.5~1.5x，进一步抗尺度漂移。
# 训练完成后导出 tflite（imgsz=704，与训练一致）。
os.chdir("/opt/pheasant_data")
model = YOLO("yolov8n.pt")
model.train(data="datasets/yolo/dataset.yaml", imgsz=704, epochs=150,
patience=30, batch=16, device=0, workers=4,
# project 必须绝对路径，相对路径会被拼到默认 runs/detect 下造成双层嵌套
project="/opt/pheasant_data/runs/pheasant", name="yolov8n_704",
exist_ok=True, plots=True, multi_scale=0.5)
# 导出 tflite：输入固定 [1,3,704,704]，与 flutter_app/assets/model.tflite 一致
model.export(format="tflite", imgsz=704)
print("EXPORT_DONE: /opt/pheasant_data/runs/pheasant/yolov8n_704/weights/best.tflite")