oximedia-scene 0.1.2

//! Composition rules analysis (rule of thirds, golden ratio, phi grid, etc.).

use crate::common::Point;
use crate::error::{SceneError, SceneResult};
use serde::{Deserialize, Serialize};

/// Composition analysis result.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompositionScore {
    /// Overall composition quality (0.0-1.0).
    pub overall_score: f32,
    /// Rule of thirds adherence (0.0-1.0).
    pub rule_of_thirds: f32,
    /// Golden ratio adherence (0.0-1.0).
    pub golden_ratio: f32,
    /// Phi grid adherence (0.0-1.0).
    pub phi_grid: f32,
    /// Symmetry score (0.0-1.0).
    pub symmetry: f32,
    /// Leading lines present (0.0-1.0).
    pub leading_lines: f32,
    /// Frame within frame (0.0-1.0).
    pub frame_in_frame: f32,
    /// Points of interest.
    pub interest_points: Vec<Point>,
    /// Golden spiral convergence region score (0.0-1.0).
    pub golden_spiral: f32,
}

/// Phi grid configuration constants.
///
/// The phi grid divides the frame using the golden ratio (1.618...).
/// Each dimension is split into three parts of sizes proportional to:
///   1 / (1 + phi)  ≈ 0.382
///   1 / (phi * (1 + phi)) ≈ 0.236
///   1 - above ≈ 0.382  (symmetric)
///
/// In practice the grid produces intersection points at multiples of
/// 1/phi² ≈ 0.382 and phi - 1 ≈ 0.618 (like rule of thirds but with
/// golden-ratio proportions instead of equal thirds).
const PHI: f32 = 1.618_034;
/// 1.0 / phi
const PHI_INV: f32 = 0.618_034;
/// 1.0 / phi²
const PHI_INV2: f32 = 0.381_966;

/// Composition analyzer using classical rules.
pub struct CompositionAnalyzer;

impl CompositionAnalyzer {
    /// Create a new composition analyzer.
    #[must_use]
    pub fn new() -> Self {
        Self
    }

    /// Analyze composition of an image.
    ///
    /// # Errors
    ///
    /// Returns error if analysis fails.
    pub fn analyze(
        &self,
        rgb_data: &[u8],
        width: usize,
        height: usize,
    ) -> SceneResult<CompositionScore> {
        if rgb_data.len() != width * height * 3 {
            return Err(SceneError::InvalidDimensions(
                "RGB data size mismatch".to_string(),
            ));
        }

        // Detect interest points using edge density
        let interest_points = self.detect_interest_points(rgb_data, width, height);

        // Analyze rule of thirds
        let rule_of_thirds = self.analyze_rule_of_thirds(&interest_points, width, height);

        // Analyze golden ratio
        let golden_ratio = self.analyze_golden_ratio(&interest_points, width, height);

        // Analyze phi grid
        let phi_grid = self.analyze_phi_grid(&interest_points, width, height);

        // Analyze golden spiral convergence
        let golden_spiral = self.analyze_golden_spiral(&interest_points, width, height);

        // Analyze symmetry
        let symmetry = self.analyze_symmetry(rgb_data, width, height);

        // Detect leading lines
        let leading_lines = self.detect_leading_lines(rgb_data, width, height);

        // Detect frames within frame
        let frame_in_frame = self.detect_frame_in_frame(rgb_data, width, height);

        // Calculate overall score
        let overall_score = (rule_of_thirds * 0.25
            + golden_ratio * 0.15
            + phi_grid * 0.15
            + golden_spiral * 0.05
            + symmetry * 0.2
            + leading_lines * 0.1
            + frame_in_frame * 0.1)
            .clamp(0.0, 1.0);

        Ok(CompositionScore {
            overall_score,
            rule_of_thirds,
            golden_ratio,
            phi_grid,
            symmetry,
            leading_lines,
            frame_in_frame,
            interest_points,
            golden_spiral,
        })
    }

    /// Detect points of interest using edge density.
    fn detect_interest_points(&self, rgb_data: &[u8], width: usize, height: usize) -> Vec<Point> {
        let mut points = Vec::new();
        let block_size = width.min(height) / 10;

        for y in (0..height - block_size).step_by(block_size) {
            for x in (0..width - block_size).step_by(block_size) {
                let edge_density = self.compute_edge_density(rgb_data, width, x, y, block_size);

                if edge_density > 0.02 {
                    points.push(Point::new(
                        (x + block_size / 2) as f32,
                        (y + block_size / 2) as f32,
                    ));
                }
            }
        }

        points
    }

    /// Compute edge density in a block.
    fn compute_edge_density(
        &self,
        rgb_data: &[u8],
        width: usize,
        x: usize,
        y: usize,
        size: usize,
    ) -> f32 {
        let mut edge_count = 0;
        let mut total = 0;

        for dy in 0..size {
            for dx in 0..size.saturating_sub(1) {
                let idx = ((y + dy) * width + (x + dx)) * 3;
                let idx_next = ((y + dy) * width + (x + dx + 1)) * 3;

                if idx + 2 < rgb_data.len() && idx_next + 2 < rgb_data.len() {
                    let diff = ((rgb_data[idx] as i32 - rgb_data[idx_next] as i32).abs()
                        + (rgb_data[idx + 1] as i32 - rgb_data[idx_next + 1] as i32).abs()
                        + (rgb_data[idx + 2] as i32 - rgb_data[idx_next + 2] as i32).abs())
                        as u32;

                    if diff > 30 {
                        edge_count += 1;
                    }
                    total += 1;
                }
            }
        }

        if total > 0 {
            edge_count as f32 / total as f32
        } else {
            0.0
        }
    }

    /// Analyze adherence to rule of thirds.
    fn analyze_rule_of_thirds(
        &self,
        interest_points: &[Point],
        width: usize,
        height: usize,
    ) -> f32 {
        // Rule of thirds divides image into 3x3 grid
        let third_w = width as f32 / 3.0;
        let third_h = height as f32 / 3.0;

        let power_points = [
            Point::new(third_w, third_h),
            Point::new(third_w * 2.0, third_h),
            Point::new(third_w, third_h * 2.0),
            Point::new(third_w * 2.0, third_h * 2.0),
        ];

        let threshold = width.min(height) as f32 * 0.1;
        let mut score = 0.0;

        for power_point in &power_points {
            let mut closest_dist = f32::MAX;
            for interest_point in interest_points {
                let dist = power_point.distance(interest_point);
                closest_dist = closest_dist.min(dist);
            }

            if closest_dist < threshold {
                score += 0.25;
            }
        }

        score
    }

    /// Analyze adherence to golden ratio.
    fn analyze_golden_ratio(&self, interest_points: &[Point], width: usize, height: usize) -> f32 {
        const GOLDEN_RATIO: f32 = 1.618;
        let golden_w = width as f32 / GOLDEN_RATIO;
        let golden_h = height as f32 / GOLDEN_RATIO;

        let golden_points = [
            Point::new(golden_w, golden_h),
            Point::new(width as f32 - golden_w, golden_h),
            Point::new(golden_w, height as f32 - golden_h),
            Point::new(width as f32 - golden_w, height as f32 - golden_h),
        ];

        let threshold = width.min(height) as f32 * 0.1;
        let mut score = 0.0;

        for golden_point in &golden_points {
            let mut closest_dist = f32::MAX;
            for interest_point in interest_points {
                let dist = golden_point.distance(interest_point);
                closest_dist = closest_dist.min(dist);
            }

            if closest_dist < threshold {
                score += 0.25;
            }
        }

        score
    }

    /// Analyze adherence to the phi grid.
    ///
    /// The phi grid uses golden-ratio proportions (≈0.382 and ≈0.618) instead of equal thirds.
    /// Intersection points at phi-proportional positions act as compositional power points.
    fn analyze_phi_grid(&self, interest_points: &[Point], width: usize, height: usize) -> f32 {
        let w = width as f32;
        let h = height as f32;

        // Phi grid intersections at PHI_INV2 (≈0.382) and PHI_INV (≈0.618) in each axis
        let phi_xs = [w * PHI_INV2, w * PHI_INV];
        let phi_ys = [h * PHI_INV2, h * PHI_INV];

        let threshold = width.min(height) as f32 * 0.1;
        let mut score: f32 = 0.0;

        for &px in &phi_xs {
            for &py in &phi_ys {
                let power = Point::new(px, py);
                let mut closest_dist = f32::MAX;
                for ip in interest_points {
                    let dist = power.distance(ip);
                    if dist < closest_dist {
                        closest_dist = dist;
                    }
                }
                if closest_dist < threshold {
                    score += 0.25;
                }
            }
        }

        score.clamp(0.0, 1.0)
    }

    /// Analyze how well interest points cluster near the golden spiral convergence region.
    ///
    /// The Fibonacci / golden spiral has its tight convergence zone near one of the
    /// four "eyes" of the spiral — each located at a phi-grid intersection shifted
    /// inward by PHI_INV2 in both axes from the nearest corner.
    fn analyze_golden_spiral(&self, interest_points: &[Point], width: usize, height: usize) -> f32 {
        if interest_points.is_empty() {
            return 0.0;
        }
        let w = width as f32;
        let h = height as f32;

        // Four possible spiral eye positions (one per corner orientation)
        let eyes = [
            Point::new(w * PHI_INV2, h * PHI_INV2),
            Point::new(w * PHI_INV, h * PHI_INV2),
            Point::new(w * PHI_INV2, h * PHI_INV),
            Point::new(w * PHI_INV, h * PHI_INV),
        ];

        let eye_radius = width.min(height) as f32 * 0.12;

        // Find the eye that has the most interest points within radius
        let best_count = eyes
            .iter()
            .map(|eye| {
                interest_points
                    .iter()
                    .filter(|ip| eye.distance(ip) < eye_radius)
                    .count()
            })
            .max()
            .unwrap_or(0);

        (best_count as f32 / 3.0).clamp(0.0, 1.0)
    }

    /// Analyze symmetry.
    fn analyze_symmetry(&self, rgb_data: &[u8], width: usize, height: usize) -> f32 {
        let mut diff_sum = 0u64;
        let mut count = 0u64;

        // Check horizontal symmetry
        for y in 0..height {
            for x in 0..width / 2 {
                let left_idx = (y * width + x) * 3;
                let right_idx = (y * width + (width - 1 - x)) * 3;

                if right_idx + 2 < rgb_data.len() {
                    for c in 0..3 {
                        diff_sum += (rgb_data[left_idx + c] as i32 - rgb_data[right_idx + c] as i32)
                            .unsigned_abs() as u64;
                    }
                    count += 3;
                }
            }
        }

        if count > 0 {
            let avg_diff = diff_sum as f32 / count as f32;
            (1.0 - avg_diff / 255.0).clamp(0.0, 1.0)
        } else {
            0.0
        }
    }

    /// Detect leading lines.
    fn detect_leading_lines(&self, rgb_data: &[u8], width: usize, height: usize) -> f32 {
        // Simplified: look for strong diagonal edges
        let mut diagonal_strength = 0.0;
        let mut count = 0;

        for y in 1..height - 1 {
            for x in 1..width - 1 {
                let idx = (y * width + x) * 3;
                let diag1_idx = ((y - 1) * width + (x - 1)) * 3;
                let diag2_idx = ((y - 1) * width + (x + 1)) * 3;

                if diag1_idx + 2 < rgb_data.len() && diag2_idx + 2 < rgb_data.len() {
                    let mut diag_diff = 0.0;
                    for c in 0..3 {
                        diag_diff += ((rgb_data[idx + c] as i32 - rgb_data[diag1_idx + c] as i32)
                            .abs()
                            + (rgb_data[idx + c] as i32 - rgb_data[diag2_idx + c] as i32).abs())
                            as f32;
                    }
                    diagonal_strength += diag_diff;
                    count += 1;
                }
            }
        }

        if count > 0 {
            (diagonal_strength / count as f32 / 255.0 / 6.0).clamp(0.0, 1.0)
        } else {
            0.0
        }
    }

    /// Detect frame within frame.
    fn detect_frame_in_frame(&self, rgb_data: &[u8], width: usize, height: usize) -> f32 {
        // Look for rectangular structures in the image
        let border_width = width / 10;
        let border_height = height / 10;

        let mut edge_density_border = 0.0;
        let mut edge_density_center = 0.0;

        // Check border regions
        for y in 0..border_height {
            for x in 0..width {
                let idx = (y * width + x) * 3;
                if idx + width * 3 < rgb_data.len() {
                    edge_density_border += self.compute_pixel_edge_strength(rgb_data, width, x, y);
                }
            }
        }

        // Check center
        for y in border_height..height - border_height {
            for x in border_width..width - border_width {
                edge_density_center += self.compute_pixel_edge_strength(rgb_data, width, x, y);
            }
        }

        let border_pixels = (border_height * width * 2) as f32;
        let center_pixels = ((height - 2 * border_height) * (width - 2 * border_width)) as f32;

        if border_pixels > 0.0 && center_pixels > 0.0 {
            let border_avg = edge_density_border / border_pixels;
            let center_avg = edge_density_center / center_pixels;

            // Frame within frame has strong edges at border
            if border_avg > center_avg * 1.5 {
                (border_avg / center_avg / 3.0).clamp(0.0, 1.0)
            } else {
                0.0
            }
        } else {
            0.0
        }
    }

    /// Compute edge strength for a pixel.
    fn compute_pixel_edge_strength(
        &self,
        rgb_data: &[u8],
        width: usize,
        x: usize,
        y: usize,
    ) -> f32 {
        let idx = (y * width + x) * 3;
        if idx + width * 3 + 3 < rgb_data.len() && x + 1 < width {
            let mut edge = 0.0;
            for c in 0..3 {
                edge += ((rgb_data[idx + c] as i32 - rgb_data[idx + 3 + c] as i32).abs()
                    + (rgb_data[idx + c] as i32 - rgb_data[idx + width * 3 + c] as i32).abs())
                    as f32;
            }
            edge / 6.0 / 255.0
        } else {
            0.0
        }
    }
}

impl Default for CompositionAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    /// Build a uniform grey image.
    fn uniform_image(w: usize, h: usize, v: u8) -> Vec<u8> {
        vec![v; w * h * 3]
    }

    /// Build an image with a high-contrast vertical stripe placed at a given x position.
    fn stripe_image(w: usize, h: usize, stripe_x: usize, stripe_width: usize) -> Vec<u8> {
        let mut data = vec![50u8; w * h * 3];
        for y in 0..h {
            for dx in 0..stripe_width {
                let x = (stripe_x + dx).min(w - 1);
                let idx = (y * w + x) * 3;
                data[idx] = 255;
                data[idx + 1] = 255;
                data[idx + 2] = 255;
            }
        }
        data
    }

    /// Build an image with a single bright spot at (cx, cy).
    fn spot_image(w: usize, h: usize, cx: usize, cy: usize, radius: usize) -> Vec<u8> {
        let mut data = vec![30u8; w * h * 3];
        for y in 0..h {
            for x in 0..w {
                let dx = (x as i32 - cx as i32).unsigned_abs() as usize;
                let dy = (y as i32 - cy as i32).unsigned_abs() as usize;
                if dx * dx + dy * dy <= radius * radius {
                    let idx = (y * w + x) * 3;
                    data[idx] = 240;
                    data[idx + 1] = 240;
                    data[idx + 2] = 240;
                }
            }
        }
        data
    }

    #[test]
    fn test_composition_analyzer_uniform() {
        let analyzer = CompositionAnalyzer::new();
        let w = 320;
        let h = 240;
        let rgb_data = uniform_image(w, h, 128);

        let result = analyzer.analyze(&rgb_data, w, h);
        assert!(result.is_ok());
        let score = result.expect("ok");
        assert!(score.overall_score >= 0.0 && score.overall_score <= 1.0);
    }

    #[test]
    fn test_phi_grid_fields_present() {
        let analyzer = CompositionAnalyzer::new();
        let w = 200;
        let h = 200;
        let rgb_data = uniform_image(w, h, 100);
        let score = analyzer.analyze(&rgb_data, w, h).expect("ok");
        assert!(score.phi_grid >= 0.0 && score.phi_grid <= 1.0);
        assert!(score.golden_spiral >= 0.0 && score.golden_spiral <= 1.0);
    }

    #[test]
    fn test_phi_grid_detects_phi_positioned_interest() {
        // Place a high-edge spot at the phi grid intersection (≈0.382*w, ≈0.382*h)
        let w = 300;
        let h = 300;
        let cx = (w as f32 * PHI_INV2) as usize;
        let cy = (h as f32 * PHI_INV2) as usize;
        let rgb_data = spot_image(w, h, cx, cy, 15);

        let analyzer = CompositionAnalyzer::new();
        let score = analyzer.analyze(&rgb_data, w, h).expect("ok");
        // Phi grid score should be non-zero when interest point is near phi intersection
        assert!(
            score.phi_grid > 0.0,
            "phi_grid should be > 0, got {}",
            score.phi_grid
        );
    }

    #[test]
    fn test_rule_of_thirds_with_positioned_spot() {
        // Place a bright spot at a rule-of-thirds power point (w/3, h/3)
        let w = 300;
        let h = 300;
        let cx = w / 3;
        let cy = h / 3;
        let rgb_data = spot_image(w, h, cx, cy, 15);

        let analyzer = CompositionAnalyzer::new();
        let score = analyzer.analyze(&rgb_data, w, h).expect("ok");
        assert!(
            score.rule_of_thirds > 0.0,
            "rule_of_thirds should be > 0, got {}",
            score.rule_of_thirds
        );
    }

    #[test]
    fn test_symmetry_perfect() {
        // Perfect horizontally-symmetric image
        let w = 200;
        let h = 100;
        let mut data = vec![0u8; w * h * 3];
        for y in 0..h {
            for x in 0..w {
                let v = (x * 200 / w) as u8;
                let idx = (y * w + x) * 3;
                let idx_mirror = (y * w + (w - 1 - x)) * 3;
                data[idx] = v;
                data[idx + 1] = v;
                data[idx + 2] = v;
                data[idx_mirror] = v;
                data[idx_mirror + 1] = v;
                data[idx_mirror + 2] = v;
            }
        }
        let analyzer = CompositionAnalyzer::new();
        let score = analyzer.analyze(&data, w, h).expect("ok");
        // Symmetry should be high for a symmetric image
        assert!(score.symmetry > 0.5, "symmetry={}", score.symmetry);
    }

    #[test]
    fn test_leading_lines_stripe() {
        // A vertical stripe creates strong diagonal-like edges
        let w = 200;
        let h = 200;
        let rgb_data = stripe_image(w, h, 10, 5);
        let analyzer = CompositionAnalyzer::new();
        let score = analyzer.analyze(&rgb_data, w, h).expect("ok");
        assert!(score.overall_score >= 0.0 && score.overall_score <= 1.0);
    }

    #[test]
    fn test_invalid_dimensions() {
        let analyzer = CompositionAnalyzer::new();
        let result = analyzer.analyze(&[0u8; 10], 10, 10);
        assert!(result.is_err());
    }

    #[test]
    fn test_golden_ratio_vs_rule_of_thirds() {
        // The two metrics should give different scores for the same image
        let w = 300;
        let h = 300;
        // Spot exactly at rule-of-thirds intersection (not at golden ratio position)
        let rgb_data = spot_image(w, h, w / 3, h / 3, 10);
        let analyzer = CompositionAnalyzer::new();
        let score = analyzer.analyze(&rgb_data, w, h).expect("ok");

        // The metrics exist and are in range regardless of which is higher
        assert!(score.rule_of_thirds >= 0.0 && score.rule_of_thirds <= 1.0);
        assert!(score.golden_ratio >= 0.0 && score.golden_ratio <= 1.0);
        // They should not be identical for a spot at rule-of-thirds position only
        // (golden ratio position differs by ~5% of image dimension)
    }
}