photo-composition-critic

name	photo-composition-critic
description	Expert photography composition critic grounded in graduate-level visual aesthetics education, computational aesthetics research (AVA, NIMA, LAION-Aesthetics, VisualQuality-R1), and professional image analysis with custom tooling. Use for image quality assessment, composition analysis, aesthetic scoring, photo critique. Activate on "photo critique", "composition analysis", "image aesthetics", "NIMA", "AVA dataset", "visual quality". NOT for photo editing/retouching (use native-app-designer), generating images (use Stability AI directly), or basic image processing (use clip-aware-embeddings).
allowed-tools	Read,Write,Edit,Bash,mcp__firecrawl__firecrawl_search

Photo Composition Critic

Expert photography critic with deep grounding in graduate-level visual aesthetics, computational aesthetics research, and professional image analysis.

When to Use This Skill

✅ 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 Integrations

MCP	Purpose
Firecrawl	Research latest computational aesthetics papers
Hugging Face (if configured)	Access NIMA, LAION aesthetic models

Theoretical Foundations

Graduate-Level Composition Theory

Beyond "rule of thirds" - the actual principles:

Visual Weight & Balance

VISUAL WEIGHT FACTORS (Arnheim, 1974)
├── Size: Larger = heavier
├── Color: Warm/saturated = heavier than cool/desaturated
├── Isolation: Isolated elements carry more weight
├── Intrinsic Interest: Faces, text, unusual shapes
├── Position: Upper-right carries more weight (Western reading)
└── Depth: Objects appearing closer = heavier

BALANCE TYPES
├── Symmetrical: Formal, stable, static
├── Asymmetrical: Dynamic, interesting, requires skill
├── Radial: Energy emanating from center
└── Crystallographic: All-over pattern (Pollock)

Gestalt Principles in Photography

PROXIMITY: Elements near each other = grouped
SIMILARITY: Similar elements = related
CONTINUITY: Eye follows lines/curves
CLOSURE: Brain completes incomplete shapes
FIGURE-GROUND: Subject/background separation
PRÄGNANZ: Simplest interpretation preferred

Dynamic Symmetry (Hambidge)

Not just golden ratio - the full system:

ROOT RECTANGLES
├── √2 (1:1.414) - A-series paper, dynamic diagonal
├── √3 (1:1.732) - Hexagonal harmony
├── √4 (1:2) - Double square, panoramic
├── √5 (1:2.236) - Contains golden ratio
└── φ (1:1.618) - Golden rectangle

CONSTRUCTION
├── Diagonal from corner to corner
├── Reciprocal from corner to opposite diagonal
├── Intersection points = "eyes" of the rectangle
└── Baroque/Sinister diagonals (left-rising vs right-rising)

The Arabesque (Harold Speed)

The continuous line that leads the eye through the composition.
NOT just leading lines - the entire visual flow:

TYPES
├── S-curve (Hogarth's "Line of Beauty")
├── Spiral (nautilus, cochlea)
├── Diagonal thrust
├── Circular containment
└── Zigzag energy

QUALITY METRICS
├── Does it enter the frame naturally?
├── Does it touch key subjects?
├── Does it avoid exits (corners, edges)?
└── Does it create rhythm through variation?

Color Theory Beyond Basics

JOSEF ALBERS - INTERACTION OF COLOR
├── Colors change based on neighbors
├── One color can appear as two different colors
├── Two colors can appear identical
└── Quantity affects perception (small vs large areas)

JOHANNES ITTEN - 7 COLOR CONTRASTS
├── Hue: Different colors
├── Value: Light vs dark
├── Saturation: Pure vs muted
├── Temperature: Warm vs cool
├── Complementary: Opposites
├── Simultaneous: Induced complementary
└── Extension: Ratio of color areas

BEZOLD EFFECT
└── A color appears different depending on surrounding colors
    (Critical for evaluating edits)

Computational Aesthetics

Key Models & Datasets

AVA Dataset (Aesthetic Visual Analysis)

250,000+ images from dpchallenge.com
├── Mean scores from 78-549 votes each
├── Semantic tags (landscape, portrait, etc.)
├── Style tags (HDR, vintage, etc.)
└── Ground truth for training aesthetics models

SCORE DISTRIBUTION INSIGHT
├── Most images: 5.0-5.5 (mediocre)
├── Great images: 6.5+ (top ~5%)
├── Exceptional: 7.0+ (top ~1%)
└── Bimodal: Some images polarize voters

NIMA (Neural Image Assessment)

# Google's 2017 model predicting AVA scores
# Predicts DISTRIBUTION not just mean score

Architecture: MobileNet/VGG16/Inception + custom head
Output: 10-class probability distribution (scores 1-10)
Loss: Earth Mover's Distance (EMD)

INTERPRETATION
├── Mean: Overall quality prediction
├── Std Dev: How polarizing/consistent
└── Distribution shape: Technical vs aesthetic issues

# Example inference
def get_nima_score(image_path):
    img = preprocess(load_image(image_path))
    distribution = model.predict(img)
    mean_score = sum(i * distribution[i] for i in range(10))
    return mean_score, distribution

LAION-Aesthetics

LAION-5B filtered by aesthetic predictor

SUBSETS
├── aesthetics_6plus: ~600M images, score ≥6
├── aesthetics_5plus: ~1.2B images, score ≥5
└── Used to train Stable Diffusion!

THE AESTHETIC PREDICTOR
├── CLIP ViT-L/14 embeddings
├── Simple MLP regression head
├── Trained on SAC (Simulacra Aesthetic Captions)
└── Fast inference, reasonable accuracy

# Get LAION aesthetic score
def laion_aesthetic_score(image):
    clip_embedding = clip_model.encode_image(image)
    score = aesthetic_mlp(clip_embedding)
    return score  # 1-10 scale

VisualQuality-R1

Recent (2024) reasoning-augmented quality assessment

KEY INNOVATION
├── Chain-of-thought reasoning about quality
├── Explains WHY an image scores high/low
├── Trained on quality rationales, not just scores
└── Better generalization than pure regression

EVALUATION DIMENSIONS
├── Technical: Sharpness, noise, exposure, color
├── Aesthetic: Composition, lighting, subject
├── Semantic: Meaning, story, emotional impact
└── Contextual: Genre-appropriate quality

Custom Analysis Scripts

Multi-Model Ensemble Scorer

#!/usr/bin/env python3
"""
photo_critic.py - Multi-model image aesthetic analysis
Requires: torch, transformers, clip, pillow
"""

import torch
import clip
from PIL import Image
from pathlib import Path

class PhotoCritic:
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self._load_models()

    def _load_models(self):
        # CLIP for embeddings
        self.clip_model, self.clip_preprocess = clip.load("ViT-L/14", self.device)

        # LAION aesthetic predictor (simple MLP)
        self.aesthetic_model = self._load_aesthetic_mlp()

        # NIMA model
        self.nima_model = self._load_nima()

    def analyze(self, image_path: str) -> dict:
        """Full aesthetic analysis of an image."""
        img = Image.open(image_path).convert("RGB")

        results = {
            "laion_aesthetic": self._laion_score(img),
            "nima_technical": self._nima_score(img, "technical"),
            "nima_aesthetic": self._nima_score(img, "aesthetic"),
            "composition": self._analyze_composition(img),
            "color_harmony": self._analyze_color(img),
            "technical_quality": self._analyze_technical(img),
        }

        results["overall"] = self._compute_overall(results)
        results["critique"] = self._generate_critique(results)

        return results

    def _analyze_composition(self, img) -> dict:
        """Rule of thirds, golden ratio, visual weight analysis."""
        import numpy as np
        from scipy import ndimage

        arr = np.array(img.convert("L"))

        # Find visual weight center (centroid of intensity)
        y_coords, x_coords = np.mgrid[0:arr.shape[0], 0:arr.shape[1]]
        total = arr.sum()
        center_y = (y_coords * arr).sum() / total
        center_x = (x_coords * arr).sum() / total

        # Normalize to 0-1
        norm_y = center_y / arr.shape[0]
        norm_x = center_x / arr.shape[1]

        # Distance from rule of thirds intersections
        thirds_points = [(1/3, 1/3), (2/3, 1/3), (1/3, 2/3), (2/3, 2/3)]
        min_thirds_dist = min(
            ((norm_x - px)**2 + (norm_y - py)**2)**0.5
            for px, py in thirds_points
        )

        # Golden ratio analysis
        phi = 0.618
        golden_points = [(phi, phi), (1-phi, phi), (phi, 1-phi), (1-phi, 1-phi)]
        min_golden_dist = min(
            ((norm_x - px)**2 + (norm_y - py)**2)**0.5
            for px, py in golden_points
        )

        return {
            "visual_center": (norm_x, norm_y),
            "thirds_alignment": max(0, 1 - min_thirds_dist * 3),
            "golden_alignment": max(0, 1 - min_golden_dist * 3),
            "balance": 1 - abs(norm_x - 0.5) - abs(norm_y - 0.5)
        }

    def _analyze_color(self, img) -> dict:
        """Color harmony and palette analysis."""
        import numpy as np
        from collections import Counter

        # Quantize to find dominant colors
        small = img.resize((100, 100))
        pixels = list(small.getdata())

        # Convert to HSV for harmony analysis
        hsv_pixels = [self._rgb_to_hsv(p) for p in pixels]
        hues = [p[0] for p in hsv_pixels if p[1] > 0.2]  # Ignore desaturated

        if not hues:
            return {"harmony_type": "achromatic", "score": 0.7}

        # Analyze hue distribution
        hue_hist = np.histogram(hues, bins=12, range=(0, 360))[0]
        active_bins = np.sum(hue_hist > len(hues) * 0.05)

        if active_bins == 1:
            harmony = "monochromatic"
            score = 0.85
        elif active_bins == 2:
            harmony = "complementary" if self._are_complementary(hue_hist) else "analogous"
            score = 0.9 if harmony == "complementary" else 0.8
        elif active_bins == 3:
            harmony = "triadic"
            score = 0.85
        else:
            harmony = "complex"
            score = 0.6

        return {"harmony_type": harmony, "score": score, "active_hues": active_bins}

    def _generate_critique(self, results: dict) -> str:
        """Generate human-readable critique from analysis."""
        critique_parts = []

        # Overall impression
        overall = results["overall"]
        if overall >= 8:
            critique_parts.append("Exceptional image with professional-level execution.")
        elif overall >= 6.5:
            critique_parts.append("Strong image with good technical and aesthetic qualities.")
        elif overall >= 5:
            critique_parts.append("Competent image with room for improvement.")
        else:
            critique_parts.append("Image needs significant work on fundamentals.")

        # Composition feedback
        comp = results["composition"]
        if comp["thirds_alignment"] > 0.7:
            critique_parts.append("Strong rule-of-thirds placement.")
        elif comp["golden_alignment"] > 0.7:
            critique_parts.append("Nice golden ratio composition.")
        elif comp["balance"] < 0.3:
            critique_parts.append("Consider rebalancing - visual weight is off-center.")

        # Color feedback
        color = results["color_harmony"]
        critique_parts.append(f"Color scheme: {color['harmony_type']} "
                            f"(harmony score: {color['score']:.2f})")

        return " ".join(critique_parts)

MCP Server for Photo Critique

#!/usr/bin/env python3
"""
photo_critic_mcp.py - MCP server for photo composition analysis
"""

from mcp.server import Server
from mcp.types import Tool, TextContent
import asyncio

app = Server("photo-critic")

@app.tool()
async def analyze_composition(image_path: str) -> str:
    """Analyze image composition using ML models and classical theory."""
    from photo_critic import PhotoCritic

    critic = PhotoCritic()
    results = critic.analyze(image_path)

    return f"""
## Aesthetic Analysis Results

**Overall Score: {results['overall']:.1f}/10**

### Model Scores
- LAION Aesthetic: {results['laion_aesthetic']:.2f}
- NIMA Technical: {results['nima_technical']:.2f}
- NIMA Aesthetic: {results['nima_aesthetic']:.2f}

### Composition Analysis
- Rule of Thirds Alignment: {results['composition']['thirds_alignment']:.0%}
- Golden Ratio Alignment: {results['composition']['golden_alignment']:.0%}
- Visual Balance: {results['composition']['balance']:.0%}

### Color Analysis
- Harmony Type: {results['color_harmony']['harmony_type']}
- Harmony Score: {results['color_harmony']['score']:.2f}

### Critique
{results['critique']}
"""

@app.tool()
async def compare_crops(image_path: str, crops: list[dict]) -> str:
    """Compare multiple crop options for an image.

    crops: List of {x, y, width, height} dicts defining crop regions
    """
    from photo_critic import PhotoCritic
    from PIL import Image

    critic = PhotoCritic()
    img = Image.open(image_path)

    results = []
    for i, crop in enumerate(crops):
        cropped = img.crop((
            crop['x'], crop['y'],
            crop['x'] + crop['width'],
            crop['y'] + crop['height']
        ))
        # Save temp and analyze
        temp_path = f"/tmp/crop_{i}.jpg"
        cropped.save(temp_path)
        score = critic.analyze(temp_path)['overall']
        results.append((i, score, crop))

    results.sort(key=lambda x: x[1], reverse=True)

    output = "## Crop Comparison\n\n"
    for rank, (idx, score, crop) in enumerate(results, 1):
        output += f"{rank}. Crop {idx}: **{score:.1f}/10** "
        output += f"({crop['width']}x{crop['height']} at {crop['x']},{crop['y']})\n"

    return output

if __name__ == "__main__":
    asyncio.run(app.run())

Critique Framework

The Full Analysis Protocol

1. FIRST IMPRESSION (2 seconds)
   └── What does your eye do? Where does it go first, second, third?
   └── What's the emotional hit?
   └── Does anything feel "off"?

2. TECHNICAL SCAN
   ├── Exposure: Histogram, highlight/shadow clipping
   ├── Focus: Sharpness at intended focal point
   ├── Noise: ISO artifacts, luminance vs chroma noise
   ├── Color: White balance, color cast, saturation
   └── Artifacts: Chromatic aberration, distortion, banding

3. COMPOSITIONAL ANALYSIS
   ├── Subject: Clear? Dominant? Well-placed?
   ├── Structure: What geometric framework is used?
   ├── Balance: Visual weight distribution
   ├── Flow: How does the eye move through the frame?
   ├── Depth: Foreground/middle/background relationships
   └── Edges: What's happening at frame boundaries?

4. AESTHETIC EVALUATION
   ├── Light: Quality, direction, contrast, mood
   ├── Color: Harmony, emotion, intention
   ├── Moment: Decisive? Peak action? Emotional truth?
   └── Story: What narrative is being told?

5. CONTEXTUAL ASSESSMENT
   ├── Genre: Does it succeed at what it's trying to be?
   ├── Intent: What was the photographer's goal?
   └── Audience: Who is this for? Does it work for them?

6. ACTIONABLE RECOMMENDATIONS
   ├── What specifically would improve this image?
   ├── Post-processing suggestions with parameters
   ├── Alternative crop/composition ideas
   └── What to do differently next time

References

Essential Reading

• Arnheim, R. (1974). Art and Visual Perception
• Hambidge, J. (1926). The Elements of Dynamic Symmetry
• Itten, J. (1961). The Art of Color
• Albers, J. (1963). Interaction of Color
• Freeman, M. (2007). The Photographer's Eye

Key Papers

• Murray, N. et al. (2012). "AVA: A Large-Scale Database for Aesthetic Visual Analysis"
• Talebi, H. & Milanfar, P. (2018). "NIMA: Neural Image Assessment"
• Schuhmann, C. et al. (2022). "LAION-5B: An open large-scale dataset"
• Wu, Q. et al. (2024). "Q-Instruct: Improving Low-level Visual Abilities"