Flatten skill category directory structure
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#!/usr/bin/env python3
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"""Seedance 视频片段质量审查模板。
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此脚本不直接调用 API，而是：
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1. 输出标准化的 read_media 审查 prompt
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2. 解析审查结果 JSON 并给出 PASS/FAIL 判定
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3. 将审查记录写入 review_log.json
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使用方式：
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# 生成审查 prompt（供 Claude 调用 read_media 时使用）
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python3 review_segment.py prompt <segment.mp4>
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# 记录审查结果
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python3 review_segment.py record <segment.mp4> <PASS|FAIL> <reason> [--log review_log.json]
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# 检查所有片段是否通过审查
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python3 review_segment.py check [--log review_log.json]
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"""
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import json
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import sys
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from datetime import datetime
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from pathlib import Path
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# ═══════════════════════════════════════════
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# 审查标准定义
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# ═══════════════════════════════════════════
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REVIEW_CRITERIA = {
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"style": {
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"name": "风格一致性",
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"name_en": "Style Consistency",
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"description": "必须是 3D 皮克斯风格动画，不能是 2D 扁平/写实/其他风格",
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"weight": "CRITICAL", # CRITICAL = 不通过即 FAIL
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},
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"camera": {
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"name": "分镜切换",
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"name_en": "Camera Transitions",
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"description": "15s 片段内必须有至少 2 次镜头切换（全景↔特写），不能全程固定机位",
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"weight": "CRITICAL",
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},
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"audio": {
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"name": "音频质量",
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"name_en": "Audio Quality",
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"description": "对话清晰无乱码、无重叠/重复/AI 噪声，全程可懂",
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"weight": "CRITICAL",
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},
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"character": {
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"name": "角色一致性",
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"name_en": "Character Consistency",
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"description": "角色外观（服装、配饰、体型）与参考图一致",
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"weight": "IMPORTANT", # IMPORTANT = 警告但可酌情通过
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},
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"content": {
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"name": "内容匹配",
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"name_en": "Content Match",
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"description": "对话内容与脚本大致吻合，不必逐字匹配但意思要对",
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"weight": "IMPORTANT",
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},
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}
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# ═══════════════════════════════════════════
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# 标准化审查 Prompt
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# ═══════════════════════════════════════════
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REVIEW_PROMPT = """Evaluate this video segment against these 5 criteria. For each criterion, give a PASS or FAIL verdict with a brief reason.
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## Criteria
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1. **STYLE** (Critical): Is this 3D Pixar-style animation? NOT 2D flat/realistic/other styles. Rate: PASS or FAIL.
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2. **CAMERA TRANSITIONS** (Critical): Count the number of distinct camera angle changes (e.g. wide shot → close-up → wide shot). A 15s segment MUST have at least 2 camera changes. List each shot with timestamp. Rate: PASS or FAIL.
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3. **AUDIO QUALITY** (Critical): Is ALL dialogue clear and understandable throughout the entire clip? Check for: garbled speech, overlapping/doubled audio tracks, AI noise/artifacts, nonsensical words. Even a brief 2-second garble = FAIL. Rate: PASS or FAIL.
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4. **CHARACTER CONSISTENCY** (Important): Do characters maintain consistent appearance (clothes, accessories, body type) across shots? Rate: PASS or FAIL.
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5. **CONTENT MATCH** (Important): Does the spoken dialogue roughly match expected content? (Don't need exact words, but the topic/meaning should be right.) Rate: PASS or FAIL.
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## Output Format
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Respond in this exact JSON format:
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```json
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{
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"style": {"verdict": "PASS/FAIL", "reason": "..."},
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"camera": {"verdict": "PASS/FAIL", "reason": "...", "shot_count": N, "shots": ["0:00-0:05 wide shot", ...]},
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"audio": {"verdict": "PASS/FAIL", "reason": "...", "garbled_sections": []},
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"character": {"verdict": "PASS/FAIL", "reason": "..."},
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"content": {"verdict": "PASS/FAIL", "reason": "..."},
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"overall": "PASS/FAIL",
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"summary": "One sentence summary"
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}
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```
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OVERALL is PASS only if ALL Critical criteria (style, camera, audio) pass. Important criteria failures are warnings but don't block overall PASS."""
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def cmd_prompt(segment_path: str):
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"""输出标准化的审查 prompt。"""
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print("=" * 60)
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print(f"审查片段: {segment_path}")
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print("=" * 60)
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print()
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print("请使用以下 prompt 调用 read_media:")
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print()
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print(REVIEW_PROMPT)
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print()
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print("审查标准:")
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for key, c in REVIEW_CRITERIA.items():
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print(f" [{c['weight']}] {c['name']} ({c['name_en']}): {c['description']}")
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def cmd_record(segment_path: str, verdict: str, reason: str, log_path: str = "review_log.json"):
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"""记录审查结果到 JSON 日志。"""
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log_file = Path(log_path)
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if log_file.exists():
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log = json.loads(log_file.read_text())
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else:
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log = {"reviews": [], "segments": {}}
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segment_name = Path(segment_path).name
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entry = {
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"segment": segment_name,
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"path": str(segment_path),
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"verdict": verdict.upper(),
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"reason": reason,
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"timestamp": datetime.now().isoformat(),
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}
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log["reviews"].append(entry)
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log["segments"][segment_name] = entry
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log_file.write_text(json.dumps(log, indent=2, ensure_ascii=False))
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icon = "✅" if verdict.upper() == "PASS" else "❌"
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print(f"{icon} {segment_name}: {verdict.upper()} — {reason}")
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def cmd_check(log_path: str = "review_log.json"):
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"""检查所有片段审查状态。"""
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log_file = Path(log_path)
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if not log_file.exists():
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print("未找到审查日志，请先进行审查")
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sys.exit(1)
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log = json.loads(log_file.read_text())
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segments = log.get("segments", {})
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if not segments:
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print("无审查记录")
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sys.exit(1)
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all_pass = True
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print("=== 审查状态汇总 ===\n")
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for name, entry in sorted(segments.items()):
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icon = "✅" if entry["verdict"] == "PASS" else "❌"
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print(f" {icon} {name}: {entry['verdict']} — {entry['reason']}")
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if entry["verdict"] != "PASS":
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all_pass = False
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print()
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if all_pass:
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print("ALL PASS — 所有片段通过审查，可以进行拼接合成")
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else:
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failed = [n for n, e in segments.items() if e["verdict"] != "PASS"]
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print(f"BLOCKED — {len(failed)} 个片段未通过: {', '.join(failed)}")
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sys.exit(1)
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def main():
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if len(sys.argv) < 2:
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print(__doc__)
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sys.exit(1)
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cmd = sys.argv[1]
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if cmd == "prompt":
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if len(sys.argv) < 3:
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print("用法: python review_segment.py prompt <segment.mp4>")
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sys.exit(1)
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cmd_prompt(sys.argv[2])
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elif cmd == "record":
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if len(sys.argv) < 5:
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print("用法: python review_segment.py record <segment.mp4> <PASS|FAIL> <reason>")
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sys.exit(1)
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log_path = "review_log.json"
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for i, arg in enumerate(sys.argv):
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if arg == "--log" and i + 1 < len(sys.argv):
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log_path = sys.argv[i + 1]
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cmd_record(sys.argv[2], sys.argv[3], sys.argv[4], log_path)
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elif cmd == "check":
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log_path = "review_log.json"
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for i, arg in enumerate(sys.argv):
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if arg == "--log" and i + 1 < len(sys.argv):
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log_path = sys.argv[i + 1]
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cmd_check(log_path)
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else:
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print(f"未知命令: {cmd}")
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print(__doc__)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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