c81307004d
- CURRENT-STATE 更新至D158 - 生成脚本: char004/char004_r3/prop002/prop002_r3/prop003 全量 - QC脚本: qc_char004_r2/r3 + qc_prop_r2 - QC报告: CHAR-004 R2~R5 + PROP全量 - generation-meta: 所有候选批次元数据 - 候选图(JPG)保留本地不入库
100 lines
4.7 KiB
Python
100 lines
4.7 KiB
Python
#!/usr/bin/env python3
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"""CHAR-004 R3 候选质检 · Qwen-VL · 反乞丐滑坡版标准"""
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import sys, os, json, base64, subprocess
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from secrets_loader import secret, endpoint
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API_KEY = secret("SC-004")
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EP = endpoint("EPT-002")
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FALLBACK_EP = "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation"
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CANDIDATES_DIR = os.path.expanduser("~/guanghulab/video-ai-system/assets/candidates/D157-VA-05-001-R3/CHAR-004-ZhugeFeng")
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CANDIDATES = [f"{CANDIDATES_DIR}/CHAR-004-R3-candidate-{i:02d}-1080x1920.jpg" for i in range(1, 5)]
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QC_PROMPT = """你是动画资产质量检查员。检查这张CHAR-004诸葛风立绘候选图。规范要求：清贫但整洁、有骨气、不是乞丐不是流民。服装干净，无破洞无补丁无污渍。
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逐项检查并输出严格JSON：
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1. 裤子膝盖有没有破洞/破损？(hole_in_pants: true/false)
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2. 衣服下摆有没有毛边/撕裂？(frayed_hem: true/false)
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3. 鞋子干不干净？有泥污没？(dirty_shoes: true/false)
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4. 衣服有没有污渍？(stains: true/false)
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5. 有没有补丁？(patches: true/false)
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6. 整体气质像不像乞丐/流浪汉？(beggar_look: true/false)
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7. 五官清晰无崩坏吗？(face_intact: true/false)
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8. 肢体比例正常吗？(anatomy_ok: true/false)
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9. 画面清晰吗？(clear_image: true/false)
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10. 全身从头到鞋完整吗？(full_body: true/false)
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11. 和苏白CHAR-003的脸像不像？(similar_to_sb: true/false)
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注意：
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- "磨白"不等于"破洞"——裤子膝盖只有颜色变浅、没有破损开口，hole_in_pants就是false
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- 鞋子没有明显泥污就是dirty_shoes=false
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- 衣领轻微磨毛不等于毛边撕裂——frayed_hem特指明显破损/散开/撕裂
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输出严格JSON，不要额外文字：
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{"candidate":N,"pass":true/false,"score":0-100,"hole_in_pants":bool,"frayed_hem":bool,"dirty_shoes":bool,"stains":bool,"patches":bool,"beggar_look":bool,"face_intact":bool,"anatomy_ok":bool,"clear_image":bool,"full_body":bool,"similar_to_sb":bool,"summary":"一句话"}"""
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def encode_image(path):
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with open(path, "rb") as f:
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b64 = base64.b64encode(f.read()).decode()
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ext = path.rsplit(".", 1)[-1].lower()
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mime = {"jpg": "jpeg", "jpeg": "jpeg", "png": "png"}.get(ext, "jpeg")
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return f"data:image/{mime};base64,{b64}"
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def call_qwen(img):
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body = json.dumps({"model":"qwen-vl-max","input":{"messages":[{"role":"user","content":[{"image":img},{"text":QC_PROMPT}]}]}})
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for ep in [EP, FALLBACK_EP]:
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try:
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proc = subprocess.run(["curl","-s","-m","60","--noproxy","*","-X","POST",ep,"-H",f"Authorization: Bearer {API_KEY}","-H","Content-Type: application/json","--data-binary","@-"],input=body,capture_output=True,text=True,timeout=65)
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if not proc.stdout.strip(): continue
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resp = json.loads(proc.stdout)
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if "output" in resp:
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content = resp["output"]["choices"][0]["message"]["content"][0]["text"]
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if "```" in content:
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content = content.split("```")[1]
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if content.startswith("json"): content = content[4:]
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content = content.split("```")[0]
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return json.loads(content.strip())
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except: continue
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return None
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def main():
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print("CHAR-004 R3 QC · Qwen-VL")
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results = []
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for i, p in enumerate(CANDIDATES):
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print(f"[{i+1}/4]", end=" ", flush=True)
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qc = call_qwen(encode_image(p))
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if qc:
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s = qc.get("score","?")
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ok = qc.get("pass","?")
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h = qc.get("hole_in_pants","?")
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print(f"score={s} pass={ok} 破洞={h}")
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results.append(qc)
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else:
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print("✗")
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results.append({"candidate":i+1,"pass":False,"error":"api"})
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ok = sum(1 for r in results if r.get("pass"))
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print(f"\n通过: {ok}/4")
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for r in results:
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if not r.get("pass"):
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issues = []
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if r.get("hole_in_pants"): issues.append("破洞")
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if r.get("frayed_hem"): issues.append("毛边")
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if r.get("dirty_shoes"): issues.append("脏鞋")
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if r.get("stains"): issues.append("污渍")
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if r.get("beggar_look"): issues.append("乞丐化")
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print(f" 候选{r.get('candidate')} ✗: {', '.join(issues) if issues else '评分不足'} (score={r.get('score')})")
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report = {"spec":"VA-05-001-R3","asset":"CHAR-004","tool":"qwen-vl-max","results":results,"summary":{"passed":ok,"total":4}}
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with open(os.path.join(CANDIDATES_DIR,"QC-R3-BATCH.json"),"w") as f:
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json.dump(report,f,ensure_ascii=False,indent=2)
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print(f"\n报告: QC-R3-BATCH.json")
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return 0
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if __name__=="__main__":
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sys.exit(main())
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