#!/usr/bin/env python3 """ thumbnail_audit.py - 基于MrBeast缩略图理论的检查清单脚本 检查维度（基于MrBeast公开分享的缩略图原则）： 1. 标题与缩略图互补性：是否「互补而非重复」？ 2. 焦点数量：是否只有1-2个视觉焦点？ 3. 文字量：缩略图文字是否少于5个词？ 4. 情绪表达：是否有明确的面部表情/情绪？ 5. 颜色对比：颜色对比是否足够醒目？ 如果提供了图片文件，会用PIL分析颜色分布和亮度对比。 用法: python thumbnail_audit.py --title "I Spent 50 Hours Buried Alive" python thumbnail_audit.py --title "..." --image thumbnail.jpg python thumbnail_audit.py --title "..." --image thumbnail.jpg -o report.md 依赖: Pillow（仅图片分析时需要，纯文本检查无依赖） """ import argparse import sys from pathlib import Path # ---------- MrBeast缩略图原则 ---------- REDUNDANCY_WORDS = { # 如果标题中的关键词大量出现在缩略图文字中，说明重复而非互补 "challenge", "survive", "hours", "days", "dollars", "money", "biggest", "world", "first", "last", "never", "impossible", } # 情绪相关词汇（用于标题分析） EMOTION_WORDS = [ "shocked", "scared", "amazed", "crying", "screaming", "laughing", "surprised", "angry", "terrified", "excited", "happy", "sad", "emotional", "insane", "crazy", "unbelievable", "incredible", # 中文 "震惊", "害怕", "惊讶", "哭", "尖叫", "笑", "疯狂", "不敢相信", ] def check_title_thumbnail_complementarity(title: str, thumb_text: str = "") -> dict: """检查标题与缩略图是否互补（而非重复）""" title_words = set(title.lower().split()) thumb_words = set(thumb_text.lower().split()) if thumb_text else set() if not thumb_text: return { "score": 3, "max": 5, "note": "未提供缩略图文字，无法完整评估。建议：缩略图应补充标题没说的信息。", } overlap = title_words & thumb_words & REDUNDANCY_WORDS overlap_ratio = len(overlap) / max(len(thumb_words), 1) if overlap_ratio > 0.5: score = 1 note = f"缩略图文字与标题高度重复（重复词: {', '.join(overlap)}）。MrBeast原则：缩略图应该补充标题，而不是重复标题。" elif overlap_ratio > 0.2: score = 3 note = f"有部分重复（{', '.join(overlap)}），但还可以。考虑让缩略图传递标题没说的信息。" else: score = 5 note = "标题与缩略图互补性好，各自传递不同信息。" return {"score": score, "max": 5, "note": note, "overlap": list(overlap)} def check_text_amount(thumb_text: str = "") -> dict: """检查缩略图文字量""" if not thumb_text: return { "score": 4, "max": 5, "word_count": 0, "note": "未提供缩略图文字。MrBeast的缩略图通常文字极少（0-3词）或不用文字。", } word_count = len(thumb_text.split()) if word_count == 0: score, note = 5, "无文字，干净利落。" elif word_count <= 3: score, note = 5, f"仅{word_count}词，符合MrBeast标准。" elif word_count <= 5: score, note = 3, f"{word_count}词，接近上限。考虑精简到3词以内。" else: score, note = 1, f"{word_count}词，太多了！MrBeast缩略图极少超过3-5个词。文字越少，点击率越高。" return {"score": score, "max": 5, "word_count": word_count, "note": note} def check_emotion_in_title(title: str) -> dict: """检查标题是否暗示明确的情绪（间接评估缩略图情绪需求）""" title_lower = title.lower() found_emotions = [w for w in EMOTION_WORDS if w in title_lower] # 检查感叹号和问号 has_exclamation = "!" in title or "？" in title or "!" in title has_question = "?" in title or "？" in title if found_emotions: score = 5 note = f"标题有明确情绪暗示（{', '.join(found_emotions[:3])}）。缩略图应该用面部表情呼应这种情绪。" elif has_exclamation or has_question: score = 3 note = "标题有情绪标点，但缺少明确情绪词。缩略图需要用面部表情补充情绪。" else: score = 2 note = "标题情绪不明显。MrBeast原则：缩略图必须有一张表情夸张的人脸，或者明确的情绪视觉元素。" return { "score": score, "max": 5, "emotions_found": found_emotions, "note": note, } def check_title_curiosity_gap(title: str) -> dict: """检查标题是否制造好奇心缺口""" curiosity_patterns = [ ("数字对比", ["vs", "versus", "$", "比"]), ("悬念词", ["secret", "mystery", "hidden", "never", "impossible", "秘密", "不可能"]), ("挑战框架", ["challenge", "survive", "last", "endure", "挑战", "坚持"]), ("极端词", ["world", "biggest", "smallest", "most", "least", "最大", "最小", "最"]), ("时间压力", ["hours", "days", "minutes", "seconds", "小时", "天", "分钟"]), ] found = [] title_lower = title.lower() for pattern_name, keywords in curiosity_patterns: if any(k in title_lower for k in keywords): found.append(pattern_name) if len(found) >= 3: score, note = 5, f"标题有{len(found)}个好奇心元素（{', '.join(found)}），非常强！" elif len(found) >= 2: score, note = 4, f"标题有{len(found)}个好奇心元素（{', '.join(found)}），不错。" elif len(found) == 1: score, note = 3, f"标题有1个好奇心元素（{found[0]}），可以更强。" else: score, note = 1, "标题缺少好奇心缺口。MrBeast标题通常至少包含2-3个好奇心元素。" return {"score": score, "max": 5, "patterns_found": found, "note": note} def analyze_image(image_path: str) -> dict: """用PIL分析图片的颜色和对比度""" try: from PIL import Image, ImageStat except ImportError: return { "available": False, "note": "Pillow未安装，跳过图片分析。安装: pip install Pillow", } path = Path(image_path) if not path.exists(): return {"available": False, "note": f"图片文件不存在: {image_path}"} try: img = Image.open(path) except Exception as e: return {"available": False, "note": f"无法打开图片: {e}"} # 转换为RGB if img.mode != "RGB": img = img.convert("RGB") stat = ImageStat.Stat(img) width, height = img.size # 平均亮度 avg_brightness = sum(stat.mean) / 3 # 亮度标准差（对比度指标） avg_stddev = sum(stat.stddev) / 3 # 颜色饱和度分析 hsv_img = img.convert("HSV") hsv_stat = ImageStat.Stat(hsv_img) avg_saturation = hsv_stat.mean[1] # 主色调分析（简化版：取中心区域和边缘区域对比） center_crop = img.crop((width // 4, height // 4, 3 * width // 4, 3 * height // 4)) center_stat = ImageStat.Stat(center_crop) center_brightness = sum(center_stat.mean) / 3 # 评估 results = { "available": True, "size": f"{width}x{height}", "brightness": { "average": round(avg_brightness, 1), "score": 5 if 80 < avg_brightness < 200 else 3 if 50 < avg_brightness < 230 else 1, "note": "亮度适中" if 80 < avg_brightness < 200 else "偏暗或偏亮", }, "contrast": { "stddev": round(avg_stddev, 1), "score": 5 if avg_stddev > 60 else 3 if avg_stddev > 40 else 1, "note": "对比度强" if avg_stddev > 60 else "对比度中等" if avg_stddev > 40 else "对比度不足，缩略图在小尺寸下可能不够醒目", }, "saturation": { "average": round(avg_saturation, 1), "score": 5 if avg_saturation > 100 else 3 if avg_saturation > 60 else 2, "note": "色彩饱和度高" if avg_saturation > 100 else "色彩饱和度中等" if avg_saturation > 60 else "色彩偏淡，考虑增加饱和度", }, "center_focus": { "center_brightness": round(center_brightness, 1), "edge_contrast": round(abs(center_brightness - avg_brightness), 1), "note": "中心区域与边缘有明显对比" if abs(center_brightness - avg_brightness) > 15 else "中心与边缘对比不明显，焦点可能不够突出", }, } return results def generate_report(title: str, thumb_text: str = "", image_path: str = None) -> str: """生成完整审核报告""" complementarity = check_title_thumbnail_complementarity(title, thumb_text) text_amount = check_text_amount(thumb_text) emotion = check_emotion_in_title(title) curiosity = check_title_curiosity_gap(title) image_analysis = analyze_image(image_path) if image_path else None # 计算总分 scores = [complementarity["score"], text_amount["score"], emotion["score"], curiosity["score"]] if image_analysis and image_analysis.get("available"): scores.append(image_analysis["brightness"]["score"]) scores.append(image_analysis["contrast"]["score"]) scores.append(image_analysis["saturation"]["score"]) total = sum(scores) max_total = len(scores) * 5 lines = [] lines.append("# 缩略图审核报告\n") lines.append(f"**标题**: {title}") if thumb_text: lines.append(f"**缩略图文字**: {thumb_text}") if image_path: lines.append(f"**图片**: {image_path}") lines.append(f"\n**总分**: {total}/{max_total} ({total/max_total*100:.0f}%)\n") # 评级 pct = total / max_total * 100 if pct >= 80: grade = "A - 优秀，点击率潜力高" elif pct >= 60: grade = "B - 良好，有优化空间" elif pct >= 40: grade = "C - 及格，需要重点改进" else: grade = "D - 需要重做" lines.append(f"**评级**: {grade}\n") # 各项检查 lines.append("## 1. 标题-缩略图互补性 ({}/{})\n".format(complementarity["score"], complementarity["max"])) lines.append(complementarity["note"]) lines.append("") lines.append("## 2. 缩略图文字量 ({}/{})\n".format(text_amount["score"], text_amount["max"])) lines.append(text_amount["note"]) lines.append("") lines.append("## 3. 情绪表达 ({}/{})\n".format(emotion["score"], emotion["max"])) lines.append(emotion["note"]) lines.append("") lines.append("## 4. 好奇心缺口 ({}/{})\n".format(curiosity["score"], curiosity["max"])) lines.append(curiosity["note"]) lines.append("") # 图片分析 if image_analysis: if image_analysis.get("available"): lines.append(f"## 5. 图片技术分析 (尺寸: {image_analysis['size']})\n") b = image_analysis["brightness"] c = image_analysis["contrast"] s = image_analysis["saturation"] cf = image_analysis["center_focus"] lines.append(f"- **亮度** ({b['score']}/5): 平均 {b['average']} - {b['note']}") lines.append(f"- **对比度** ({c['score']}/5): 标准差 {c['stddev']} - {c['note']}") lines.append(f"- **饱和度** ({s['score']}/5): 平均 {s['average']} - {s['note']}") lines.append(f"- **焦点**: 中心-边缘差 {cf['edge_contrast']} - {cf['note']}") else: lines.append(f"## 5. 图片分析\n") lines.append(f"跳过: {image_analysis['note']}") lines.append("") # MrBeast缩略图清单 lines.append("## MrBeast缩略图黄金法则\n") lines.append("- [ ] 缩略图在手机小屏上是否清晰可辨？") lines.append("- [ ] 是否只有1-2个视觉焦点（不杂乱）？") lines.append("- [ ] 是否有一张情绪强烈的人脸？") lines.append("- [ ] 缩略图是否让人产生「我必须点进去看」的冲动？") lines.append("- [ ] 标题和缩略图组合是否创造了信息缺口？") lines.append("- [ ] 与同时段其他视频放在一起时是否够醒目？") lines.append("") # 改进建议 lines.append("## 改进建议\n") suggestions = [] if complementarity["score"] < 4: suggestions.append("让缩略图传递标题没说的信息（比如标题说挑战，缩略图展示结果或最戏剧性的瞬间）") if text_amount["score"] < 4: suggestions.append("减少缩略图文字，理想是0-3个词，用视觉而非文字讲故事") if emotion["score"] < 4: suggestions.append("缩略图加入表情夸张的人脸照片，情绪越强烈越好") if curiosity["score"] < 4: suggestions.append("标题加入数字/极端词/时间压力等好奇心元素") if image_analysis and image_analysis.get("available"): if image_analysis["contrast"]["score"] < 4: suggestions.append("提高图片对比度，确保缩略图在小尺寸下也清晰醒目") if image_analysis["saturation"]["score"] < 4: suggestions.append("增加色彩饱和度，让图片在YouTube首页中跳出来") if suggestions: for i, s in enumerate(suggestions, 1): lines.append(f"{i}. {s}") else: lines.append("整体表现优秀，继续保持！") return "\n".join(lines) def main(): parser = argparse.ArgumentParser( description="基于MrBeast缩略图理论的审核工具", formatter_class=argparse.RawDescriptionHelpFormatter, epilog='示例:\n python thumbnail_audit.py --title "I Survived 50 Hours In Antarctica"\n python thumbnail_audit.py --title "..." --thumb-text "50 HOURS" --image thumb.jpg', ) parser.add_argument("--title", required=True, help="视频标题") parser.add_argument("--thumb-text", default="", help="缩略图上的文字（如果有）") parser.add_argument("--image", help="缩略图图片文件路径（可选）") parser.add_argument("-o", "--output", help="输出报告文件路径") args = parser.parse_args() report = generate_report(args.title, args.thumb_text, args.image) if args.output: Path(args.output).write_text(report, encoding="utf-8") print(f"[OK] 报告已保存到: {args.output}") else: print(report) if __name__ == "__main__": main()