# Aesthetic Scorer

> 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

- Skill: `lord1egypt/aesthetic-scorer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lord1egypt/aesthetic-scorer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lord1egypt/aesthetic-scorer/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Lord1Egypt (https://skillmd.com/u/lord1egypt)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/lord1egypt/aesthetic-scorer

---


# 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 <image_path>`
2. Parse output score (0-10 scale)
3. Record as `score_improved`

**Step 2: NIMA Model (dynamic weight)**
1. Execute `scripts/score_nima.py <image_path>`
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)

```markdown
## [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)

```markdown
## [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)

```markdown
## [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)

```markdown
## 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