photo-composition-critic

Use for:

• Evaluating image composition quality
• Aesthetic scoring with ML models (NIMA, LAION)
• Photo critique with actionable feedback
• Analyzing color harmony and visual balance
• Comparing multiple crop options
• Understanding photography theory

Do NOT use for:

• Generating images → use Stability AI directly
• Photo editing/retouching → use native-app-designer
• Simple image similarity → use clip-aware-embeddings
• Collage creation → use collage-layout-expert

| MCP | Purpose |

|-----|---------|

| Firecrawl | Research latest computational aesthetics papers |

| Hugging Face (if configured) | Access NIMA, LAION aesthetic models |

Compositional Frameworks

| Framework | Key Points |

|-----------|------------|

| Visual Weight | Size, color warmth, isolation, intrinsic interest, position |

| Gestalt | Proximity, similarity, continuity, closure, figure-ground |

| Dynamic Symmetry | Root rectangles (√2, √3, φ), baroque/sinister diagonals |

| Arabesque | S-curve, spiral, diagonal thrust - eye flow through frame |

Color Harmony Types

| Type | Score | Notes |

|------|-------|-------|

| Complementary | 0.9 | High visual interest |

| Monochromatic | 0.85 | Safe, cohesive |

| Triadic | 0.85 | Balanced, vibrant |

| Analogous | 0.8 | Natural, harmonious |

| Achromatic | 0.7 | B&W or desaturated |

| Complex | 0.6 | May be chaotic or intentional |

ML Model Score Interpretation

| Score Range | Meaning |

|-------------|---------|

| 7.0+ | Exceptional (top ~1%) |

| 6.5+ | Great (top ~5%) |

| 5.0-5.5 | Mediocre (most images) |

| <5.0 | Below average |

```

1. FIRST IMPRESSION (2 seconds)

└── Where does the eye go? Emotional hit? Anything "off"?

1. TECHNICAL SCAN

└── Exposure, focus, noise, color, artifacts

1. COMPOSITIONAL ANALYSIS

└── Subject clarity, structure, balance, flow, depth, edges

1. AESTHETIC EVALUATION

└── Light quality, color harmony, decisive moment, story

1. CONTEXTUAL ASSESSMENT

└── Genre success, photographer intent, audience fit

1. ACTIONABLE RECOMMENDATIONS

└── Specific improvements, post-processing, alt crops

```

"Just use rule of thirds"

| What it looks like | Why it's wrong |

|--------------------|----------------|

| Blindly placing subjects on thirds intersections | Oversimplification ignores visual weight, gestalt, dynamic symmetry |

| Instead: Analyze visual weight center, consider multiple frameworks |

"Higher NIMA score = better photo"

| What it looks like | Why it's wrong |

|--------------------|----------------|

| Using ML score as sole quality metric | Models trained on averages, miss artistic intent, polarizing works |

| Instead: Use ML as one input alongside theoretical analysis |

"Color harmony means matching colors"

| What it looks like | Why it's wrong |

|--------------------|----------------|

| Recommending monochromatic or matchy palettes | Ignores Itten's contrasts, Albers' interaction effects |

| Instead: Evaluate harmony type AND contextual appropriateness |

Ignoring genre context

| What it looks like | Why it's wrong |

|--------------------|----------------|

| Applying portrait criteria to documentary | Different genres have different quality signals |

| Instead: Assess against genre-appropriate standards |

Load these for detailed implementations:

| File | Contents |

|------|----------|

| references/composition-theory.md | Arnheim visual weight, Gestalt, Dynamic Symmetry, Arabesque |

| references/color-theory.md | Albers interaction, Itten's 7 contrasts, harmony detection algo |

| references/ml-models.md | AVA dataset, NIMA, LAION-Aesthetics, VisualQuality-R1 |

| references/analysis-scripts.md | PhotoCritic class, MCP server implementation |

Theory: Arnheim (1974), Hambidge (1926), Itten (1961), Albers (1963), Freeman (2007)

Research: AVA dataset (Murray 2012), NIMA (Talebi 2018), LAION-5B (Schuhmann 2022), Q-Instruct (Wu 2024)