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BytesAgain › Skills › 摄影照片评分Aesthetic Scorer
🦀 ClawHub

摄影照片评分Aesthetic Scorer

by @kooui

给你的照片打分、评价反馈、给出改进建议或美学分析 / Aesthetic photo scorer with detailed analysis

Versionv1.4.9
Downloads824
#clawhub
TERMINAL
clawhub install aesthetic-scorer

📖 About This Skill

name: aesthetic-scorer version: "1.7.0" description: | 给你的照片打分、评价反馈、给出改进建议或美学分析 / Aesthetic photo scorer with detailed analysis author: "WorkBuddy Community" tags: [照片评分, 照片分析, 美学评分, 摄影评分, 图片评分, 视觉评分, photo-scoring, aesthetic-scoring, photography, image, vision, offline]

Aesthetic Scorer Skill

This skill provides comprehensive aesthetic evaluation and improvement suggestions for images and photographs through a dynamic weighted two-tier architecture.

Architecture Overview

Two Evaluation Sources with Dynamic Weight:

1. Improved Aesthetic Predictor: CLIP ViT-L/14 + MLP for content-level aesthetic scoring - Understands image semantics and visual impact - Base Weight: 45% (adjustable 45%-70% based on NIMA consensus)

2. NIMA (Neural Image Assessment): MobileNet for technical quality scoring - Provides detailed quality distribution and standard deviation - Base Weight: 55% (adjustable 30%-55% based on NIMA consensus)

Dynamic Weight Logic:

  • NIMA returns a standard deviation (std) indicating score distribution spread
  • High std = controversial image = more weight to IAP (content-based)
  • Low std = consensus = use balanced weights

    • Dynamic Weight Formula

    normalized_std = min(nima_std / 2.5, 1.0)
weight_iap = 0.45 + normalized_std * 0.25    // Range: 45% - 70%
weight_nima = 1.0 - weight_iap
    // Penalty when scores diverge significantly (diff >= 2.0)
penalty = 0.05 * |IAP_score - NIMA_score|
    weighted_score = weight_iap * IAP + weight_nima * NIMA - penalty

    | Parameter | Value | Description | |-----------|-------|-------------| | Base IAP Weight | 45% | Balanced starting point | | Base NIMA Weight | 55% | Balanced starting point | | Max Adjustment | ±25% | IAP weight increases with controversy | | IAP Weight Range | 45% - 70% | Dynamic adjustment | | Divergence Threshold | 2.0 | Score difference triggers penalty | | Divergence Penalty | 0.05 | Per point of difference |

    Evaluation Text Generation:

  • The detailed evaluation text (composition, color, lighting, technical quality, improvement suggestions) is generated by the AI (WorkBuddy) based on:
    • - The weighted scores from both models - Visual understanding of the photo content - Professional photography knowledge and best practices
  • This provides professional-grade analysis without requiring external API calls

    • Workflow

    Phase 1: Execute Both Evaluations

    Step 1: Improved Aesthetic Predictor (dynamic weight) 1. Execute scripts/score_improved_predictor.py 2. Parse output score (0-10 scale) 3. Record as score_improved

    Step 2: NIMA Model (dynamic weight) 1. Execute scripts/score_nima.py 2. Parse mean score AND standard deviation 3. Record as score_nima and nima_std

    Phase 2: Calculate Weighted Comprehensive Score (Dynamic)

    Step 2.1: Calculate Dynamic Weights

  • Extract NIMA's standard deviation (std_score)
  • Normalize: normalized_std = min(std_score / 2.5, 1.0)
  • Calculate weights: weight_iap = 0.45 + normalized_std * 0.25

    • Step 2.2: Apply Penalty if Needed

  • If |IAP - NIMA| >= 2.0, apply penalty: penalty = 0.05 * |IAP - NIMA|

    • Step 2.3: Calculate Final Score 

    weighted_score = weight_iap * IAP + (1-weight_iap) * NIMA - penalty

    Example:

  • IAP = 6.44, NIMA = 4.52, NIMA_std = 1.87
  • normalized_std = 1.87/2.5 = 0.75
  • weight_iap = 0.45 + 0.75×0.25 = 0.637 (63.7%)
  • weight_nima = 0.363 (36.3%)
  • Score_diff = 1.92 < 2.0, penalty = 0
  • Final = 0.637×6.44 + 0.363×4.52 = 5.74

    • Security Note: All processing is 100% local - no data leaves your device

    Phase 3: Generate Evaluation at Appropriate Detail Level

    CRITICAL: Three Detail Levels Available

    Always generate the detailed evaluation (10分) in the background first and save it. Then present the evaluation at the requested detail level:

    | Level | Name | Word Count per Photo | Description | |-------|------|---------------------|-------------| | 1 | 简要 | ~200字 | Concise overview, key points only | | 3 | 中等 (默认) | ~300字 | Balanced, covers all aspects | ⭐ DEFAULT | | 10 | 详细 | ~4000字 | Comprehensive, in-depth analysis |

    How User Requests Different Levels:

  • 默认/未指定 → 使用 3分（中等），约300字
  • "详细评价" / "详细版" / "完整评价" → 使用 10分（详细），约4000字
  • "简要评价" / "简洁版" / "简要说明" → 使用 1分（简要），约200字

    • Important:

  • ALWAYS generate detailed evaluation (10分) in background first
  • Save detailed evaluation so it can be retrieved immediately if user requests it
  • Present the appropriate level based on user request (default: 3分)
  • If user requests detailed evaluation after seeing summary, retrieve the saved detailed version

    • Output Format by Detail Level

    Level 1: 简要 (~200字 per photo)

    ## [Photo Name]
    综合评分: X.XX/10 (等级: 夯/顶级/人上人/NPC/拉完了)
构图: [2-3句话]
色彩: [2-3句话]
光线: [2-3句话]
技术: [2-3句话]
建议: [3-4条关键建议]

    Level 3: 中等 (默认, ~300字 per photo)

    ## [Photo Name]
    综合评分: X.XX/10 (夯/顶级/人上人/NPC/拉完了)
    综合分析
    #### 构图评价
[3-4句话]
    #### 色彩评价
[3-4句话]
    #### 光线评价
[3-4句话]
    #### 技术质量评价
[3-4句话]
    改进建议
    #### 拍摄技巧
[3条建议]
    #### 后期处理
[3条建议]
    #### 构图优化
[3条建议]
    总体评价
    
[2-3句话]

    Level 10: 详细 (~4000字 per photo)

    ## [Photo Name]
    综合评分: X.XX/10 (夯/顶级/人上人/NPC/拉完了)
    评分解读
    
[3-4句话]
    综合分析
    #### 构图评价
[6-10详细句话]
    #### 色彩评价
[6-10详细句话]
    #### 光线评价
[6-10详细句话]
    #### 技术质量评价
[6-10详细句话]
    改进建议
    #### 拍摄技巧
[5-7详细条建议]
    #### 后期处理
[5-7详细条建议]
    #### 构图优化
[5-7详细条建议]
    总体评价
    
[3-4段，每段6-8句]

    Multiple Photos Comparison (Level 3, ~600字 total)

    ## 照片对比分析
    照片 1: [Name]
    
[Level 3 evaluation as above, ~300字]
    照片 2: [Name]
    
[Level 3 evaluation as above, ~300字]
    对比总结
    | 对比项 | 照片1 | 照片2 | 胜出 |
|--------|-------|-------|------|
| 综合评分 | X.XX/10 | X.XX/10 | 照片X |
| 构图 | [评级] | [评级] | 照片X |
| 色彩 | [评级] | [评级] | 照片X |
| 光线 | [评级] | [评级] | 照片X |
| 技术质量 | [评级] | [评级] | 照片X |
    综合建议
    
[3-4句话]

    Score Interpretation Guide

    "从夯到拉" Rating System / "从夯到拉" 评分系统

    | Score Range | 等级 / Level | Description / 描述 | |-------------|-------------|-------------------| | 9.0-10.0 | 夯 (Hāng) | 好到没话说,顶级水平 / Exceptional, top-tier, perfect | | 8.0-8.9 | 顶级 | 极好,专业水准 / Excellent, professional level | | 7.0-7.9 | 人上人 | 很好,超越常人 / Very good, above average, outstanding | | 6.0-6.9 | NPC | 不起眼,普普通通 / Average, unremarkable, plain | | 0.0-5.9 | 拉完了 | 差到没法再差 / Terrible, needs major improvement |

    Traditional Rating / 传统评分

    | Score Range | Level | Description | |-------------|-------|-------------| | 9.0-10.0 | 优秀 | Exceptional quality, professional level | | 8.0-8.9 | 很好 | High quality with minor improvements needed | | 7.0-7.9 | 良好 | Solid quality, above average | | 6.0-6.9 | 一般 | Average quality, noticeable room for improvement | | 4.0-5.9 | 较差 | Below average, significant improvements needed | | 0.0-3.9 | 很差 | Poor quality, substantial improvements needed |

    重要说明: 综合评分格式示例: 

    综合评分: X.XX/10 (夯)
综合评分: X.XX/10 (顶级)
综合评分: X.XX/10 (人上人)
综合评分: X.XX/10 (NPC)
综合评分: X.XX/10 (拉完了)

    Error Handling

    If any evaluation source fails:

    1. Improved Predictor fails: Use NIMA only - Score: NIMA score only - Note in report: "Improved Predictor 不可用，仅使用 NIMA 评分"

    2. NIMA fails: Use Improved Predictor only - Score: Improved Predictor score only - Note in report: "NIMA 不可用，仅使用 Improved Predictor 评分"

    3. Both sources fail: Inform user and suggest trying again later

    Script Dependencies

    All scripts in scripts/ directory must be executable:

  • score_improved_predictor.py: Fast aesthetic scoring
  • score_nima.py: Detailed quality analysis
  • comprehensive_score.py: Integrated weighted scoring

    • Model Paths (Local Installation)

    Default Paths (Windows)

    | Model | Location | |-------|----------| | Improved Aesthetic Predictor (.pth) | F:\software\skill\aesthetic-scorer\models\improved-aesthetic-predictor\sac+logos+ava1-l14-linearMSE.pth | | NIMA MobileNet weights (.h5) | F:\software\skill\aesthetic-scorer\models\neural-image-assessment\weights\mobilenet_weights.h5 |

    Environment Variables (Override Default Paths)

    You can override model paths by setting environment variables:

    | Variable | Description | Default | |----------|-------------|---------| | AESTHETIC_SCORER_MODEL_DIR | Override IAP model directory | F:\software\skill\aesthetic-scorer\models\improved-aesthetic-predictor | | AESTHETIC_SCORER_NIMA_DIR | Override NIMA model directory | F:\software\skill\aesthetic-scorer\models\neural-image-assessment |

    Python runtime: F:\software\python\python.exe (Python 3.12)

    Required packages (install via pip install -r requirements.txt): torch>=2.0.0, torchvision>=0.15.0, transformers>=4.30.0, tensorflow>=2.12.0, tf_keras>=2.12.0, pillow>=10.0.0, numpy>=1.23.0

    Usage Examples

    User: "请评价这张照片" Action: Execute both evaluations, calculate weighted score, generate Level 3 evaluation (default, ~300字)

    User: "详细评价这张照片" Action: Execute both evaluations, calculate weighted score, generate Level 10 evaluation (~4000字)

    User: "简要评价这张照片" Action: Execute both evaluations, calculate weighted score, generate Level 1 evaluation (~200字)

    User: "对比这两张照片" Action: Evaluate both photos, generate Level 3 comparison (~600字 total)

    User: [评价后] "给我看详细版" Action: Retrieve the saved Level 10 evaluation and present it immediately

    Notes

  • Always execute both evaluation sources when available
  • Present evaluation as unified expert opinion
  • Default to Level 3 (medium detail) unless user specifies otherwise
  • Always generate and save Level 10 (detailed) evaluation in background
  • Retrieve saved detailed evaluation when requested, don't regenerate
  • Avoid repetitive references to evaluation sources
  • Use natural, flowing language
  • Adjust detail level based on user request
  • Support both Chinese and English
  • Always display "从夯到拉" rating level in the score

    • 📋 Tips & Best Practices

  • Always execute both evaluation sources when available
  • Present evaluation as unified expert opinion
  • Default to Level 3 (medium detail) unless user specifies otherwise
  • Always generate and save Level 10 (detailed) evaluation in background
  • Retrieve saved detailed evaluation when requested, don't regenerate
  • Avoid repetitive references to evaluation sources
  • Use natural, flowing language
  • Adjust detail level based on user request
  • Support both Chinese and English
  • Always display "从夯到拉" rating level in the score