//! Image Processing Cookbook - Quick Solutions for Common Tasks
//!
//! This cookbook provides ready-to-use recipes for common image processing tasks.
//! Each recipe is self-contained and can be copied directly into your code.
//!
//! ## Recipe Categories:
//!
//! ### 🧹 Image Cleanup
//! - Remove noise from images
//! - Fix broken/missing pixels  
//! - Enhance contrast and brightness
//! - Remove background patterns
//!
//! ### 🔍 Object Detection
//! - Find circles, rectangles, and custom shapes
//! - Count objects in images
//! - Measure object properties
//! - Separate touching objects
//!
//! ### 📏 Measurement and Analysis
//! - Calculate distances and areas
//! - Measure texture properties
//! - Analyze shape characteristics
//! - Compare images
//!
//! ### 🔄 Image Transformation
//! - Rotate and scale images
//! - Correct perspective distortion
//! - Register multiple images
//! - Create image pyramids
//!
//! ### 🎨 Enhancement and Effects
//! - Sharpen blurry images
//! - Create artistic effects
//! - Adjust colors and tones
//! - Generate visualizations
//!
//! Each recipe includes:
//! - Problem description
//! - Complete working code
//! - Parameter explanations
//! - Common variations
//! - Troubleshooting tips

use scirs2_core::ndarray::{s, Array2, Array3, ArrayView2};
use scirs2_ndimage::{
    error::NdimageResult, features::*, filters::*, interpolation::*, measurements::*,
    morphology::*, segmentation::*,
};

#[allow(dead_code)]
fn main() -> NdimageResult<()> {
    println!("📚 Image Processing Cookbook");
    println!("============================\n");

    // Image Cleanup Recipes
    println!("🧹 IMAGE CLEANUP RECIPES");
    image_cleanup_recipes()?;

    // Object Detection Recipes
    println!("\n🔍 OBJECT DETECTION RECIPES");
    object_detection_recipes()?;

    // Measurement Recipes
    println!("\n📏 MEASUREMENT AND ANALYSIS RECIPES");
    measurement_recipes()?;

    // Transformation Recipes
    println!("\n🔄 IMAGE TRANSFORMATION RECIPES");
    transformation_recipes()?;

    // Enhancement Recipes
    println!("\n🎨 ENHANCEMENT AND EFFECTS RECIPES");
    enhancement_recipes()?;

    println!("\n✨ Cookbook Complete!");
    println!("Copy any recipe and adapt the parameters for your specific needs.");

    Ok(())
}

#[allow(dead_code)]
fn image_cleanup_recipes() -> NdimageResult<()> {
    println!("========================================");

    // Recipe 1: Remove Salt-and-Pepper Noise
    println!("🧂 RECIPE 1: Remove Salt-and-Pepper Noise");
    println!("Problem: Image has random black and white pixels (salt & pepper noise)");
    println!("Solution: Use median filter to replace each pixel with neighborhood median");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::filters::median_filter;");
    println!();
    println!("// Quick fix for light noise");
    println!("let cleaned = median_filter(&noisyimage.view(), Some(&[3, 3]), None, None)?;");
    println!();
    println!("// Stronger cleaning for heavy noise");
    println!("let cleaned = median_filter(&noisyimage.view(), Some(&[5, 5]), None, None)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Use 3x3 for light noise, 5x5 for moderate, 7x7 for heavy");
    println!("- Median filter preserves edges better than Gaussian");
    println!("- Don't go larger than 7x7 unless absolutely necessary");
    println!();

    // Recipe 2: Reduce Gaussian Noise
    println!("🌫️  RECIPE 2: Reduce Gaussian Noise");
    println!("Problem: Image is grainy/fuzzy due to sensor noise");
    println!("Solution: Use Gaussian filter for smooth noise reduction");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::filters::gaussian_filter;");
    println!();
    println!("// Light smoothing (preserves details)");
    println!("let denoised = gaussian_filter(&noisyimage, &[0.8, 0.8], None, None, None)?;");
    println!();
    println!("// Medium smoothing");
    println!("let denoised = gaussian_filter(&noisyimage, &[1.5, 1.5], None, None, None)?;");
    println!();
    println!("// Heavy smoothing (removes fine details)");
    println!("let denoised = gaussian_filter(&noisyimage, &[3.0, 3.0], None, None, None)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Start with sigma=1.0 and adjust based on results");
    println!("- Higher sigma = more blur but less noise");
    println!("- Use different sigma for X and Y if needed: &[sigma_x, sigma_y]");
    println!();

    // Recipe 3: Smart Noise Reduction (Edge-Preserving)
    println!("🧠 RECIPE 3: Smart Noise Reduction (Edge-Preserving)");
    println!("Problem: Need to reduce noise while keeping edges sharp");
    println!("Solution: Use bilateral filter that considers both space and intensity");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::filters::bilateral_filter;");
    println!();
    println!("// Balanced noise reduction and edge preservation");
    println!("let cleaned = bilateral_filter(");
    println!("    noisyimage.view(),");
    println!("    2.0,    // Spatial sigma (neighborhood size)");
    println!("    0.1,    // Intensity sigma (edge threshold)");
    println!("    Some(5) // Window size");
    println!(")?;");
    println!();
    println!("// For high-contrast images");
    println!("let cleaned = bilateral_filter(noisyimage.view(), 1.5, 0.2, Some(5))?;");
    println!();
    println!("// For low-contrast images");
    println!("let cleaned = bilateral_filter(noisyimage.view(), 3.0, 0.05, Some(7))?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Lower intensity sigma = preserves more edges");
    println!("- Higher spatial sigma = stronger smoothing");
    println!("- Window size should be 2-3 times spatial sigma");
    println!();

    // Recipe 4: Fix Broken/Missing Pixels
    println!("🔧 RECIPE 4: Fix Broken/Missing Pixels");
    println!("Problem: Image has dead pixels or missing data");
    println!("Solution: Use morphological operations to fill gaps");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::morphology::{{binary_closing, generate_binary_structure}};");
    println!();
    println!("// For binary images - fill small holes");
    println!("let structure = generate_binary_structure(2, 1)?;  // 4-connected");
    println!("let fixed = binary_closing(&brokenimage.view(), Some(&structure.view()), None, None, None)?;");
    println!();
    println!("// For grayscale images - interpolate missing values");
    println!("let mask = missing_pixels_mask;  // 1 where pixels are missing");
    println!("let fixed = interpolate_missing_pixels(&image, &mask)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Use closing for small gaps, opening for small noise");
    println!("- Create mask manually or detect outliers automatically");
    println!("- Consider using inpainting algorithms for large missing areas");
    println!();

    // Recipe 5: Enhance Contrast
    println!("🌟 RECIPE 5: Enhance Contrast");
    println!("Problem: Image looks flat/washed out");
    println!("Solution: Stretch histogram or apply adaptive enhancement");
    println!();

    println!("```rust");
    println!("// Simple contrast stretching");
    println!("fn enhance_contrast(image: &Array2<f64>, factor: f64) -> Array2<f64> {{");
    println!("    let mean = image.sum() / image.len() as f64;");
    println!("    image.mapv(|x| ((x - mean) * factor + mean).clamp(0.0, 1.0))");
    println!("}}");
    println!();
    println!("let enhanced = enhance_contrast(&dullimage, 1.5);  // 50% more contrast");
    println!();
    println!("// Histogram equalization using rank operations");
    println!("use scirs2_ndimage::filters::rank_filter;");
    println!(
        "let local_rank = rank_filter(&image.view(), None, 0.5, None, None)?;  // Local median"
    );
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Factor > 1.0 increases contrast, < 1.0 decreases");
    println!("- Check histogram before and after adjustment");
    println!("- Use local operations for non-uniform lighting");

    Ok(())
}

#[allow(dead_code)]
fn object_detection_recipes() -> NdimageResult<()> {
    println!("========================================");

    // Recipe 1: Find Circles
    println!("⭕ RECIPE 1: Find Circles");
    println!("Problem: Detect circular objects in an image");
    println!("Solution: Use edge detection + template matching or Hough transform");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::{{features::canny, measurements::region_properties}};");
    println!();
    println!("// Method 1: Edge-based circle detection");
    println!("let edges = canny(image.view(), 1.0, 0.1, 0.3, None)?;");
    println!();
    println!("// Method 2: Threshold + roundness filter");
    println!("let binary = threshold_binary(&image.view(), 0.5)?;");
    println!("let (labeled_) = label(&binary.view(), None)?;");
    println!("let props = region_properties(&labeled.view(), Some(&image.view()))?;");
    println!();
    println!("let circles: Vec<_> = props.iter().filter(|prop| {{");
    println!("    let bbox_area = (prop.bbox[2] - prop.bbox[0]) * (prop.bbox[3] - prop.bbox[1]);");
    println!("    let extent = prop.area / bbox_area as f64;");
    println!("    extent > 0.7  // Round objects have high extent");
    println!("}}).collect();");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Adjust extent threshold (0.6-0.8 for circles)");
    println!("- Combine with size filtering: prop.area > min_area");
    println!("- Use morphological operations to clean binary image first");
    println!();

    // Recipe 2: Count Objects
    println!("🔢 RECIPE 2: Count Objects");
    println!("Problem: Count how many objects are in the image");
    println!("Solution: Threshold + connected component labeling + filtering");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::{{segmentation::{{threshold_binary, otsu_threshold}}, morphology::label}};");
    println!();
    println!("// Automatic thresholding");
    println!("let binary = otsu_threshold(&image.view())?;");
    println!();
    println!("// Manual thresholding");
    println!("// let binary = threshold_binary(&image.view(), 0.5)?;");
    println!();
    println!("// Label connected components");
    println!("let (labeled, num_objects) = label(&binary.view(), None)?;");
    println!();
    println!("// Filter by size");
    println!("let props = region_properties(&labeled.view(), Some(&image.view()))?;");
    println!("let validobjects: Vec<_> = props.iter()");
    println!("    .filter(|prop| prop.area >= min_size && prop.area <= max_size)");
    println!("    .collect();");
    println!();
    println!("println!(\"Found {{}} objects\", valid_objects.len());");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Try Otsu threshold first, adjust manually if needed");
    println!("- Always filter by minimum size to remove noise");
    println!("- Use watershed if objects are touching");
    println!();

    // Recipe 3: Separate Touching Objects
    println!("🫱🏻‍🫲🏾 RECIPE 3: Separate Touching Objects");
    println!("Problem: Objects are connected and counted as one");
    println!("Solution: Use watershed segmentation with distance transform");
    println!();

    println!("```rust");
    println!(
        "use scirs2_ndimage::{{morphology::distance_transform_edt, segmentation::watershed}};"
    );
    println!("use scirs2_core::ndarray::IxDyn;");
    println!();
    println!("// Step 1: Create binary mask");
    println!("let binary = threshold_binary(&image.view(), 0.5)?;");
    println!();
    println!("// Step 2: Distance transform to find object centers");
    println!("let binary_dyn = binary.clone().into_dimensionality::<IxDyn>().expect(\"Operation failed\");");
    println!("let (distances_) = distance_transform_edt(&binary_dyn, None, true, false)?;");
    println!(
        "let distances_2d = distances.into_dimensionality::<scirs2_core::ndarray::Ix2>().expect(\"Operation failed\");"
    );
    println!();
    println!("// Step 3: Find peaks as markers");
    println!("let markers = find_local_maxima(&distances_2d, 2.0);");
    println!();
    println!("// Step 4: Watershed segmentation");
    println!("let segmented = watershed(&image.view(), &markers.view(), None, None)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Adjust threshold in find_local_maxima for marker sensitivity");
    println!("- Pre-smooth binary image if objects have rough boundaries");
    println!("- Use morphological opening to separate slightly touching objects");
    println!();

    // Recipe 4: Find Rectangles/Squares
    println!("⬜ RECIPE 4: Find Rectangles/Squares");
    println!("Problem: Detect rectangular or square objects");
    println!("Solution: Use corner detection + geometric constraints");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::features::harris_corners;");
    println!();
    println!("// Method 1: Corner-based detection");
    println!("let corners = harris_corners(image.view(), 0.04, 3, 0.01, None)?;");
    println!();
    println!("// Method 2: Shape analysis after thresholding");
    println!("let binary = threshold_binary(&image.view(), 0.5)?;");
    println!("let (labeled_) = label(&binary.view(), None)?;");
    println!("let props = region_properties(&labeled.view(), Some(&image.view()))?;");
    println!();
    println!("let rectangles: Vec<_> = props.iter().filter(|prop| {{");
    println!("    // Check if bounding box is well-filled");
    println!("    let bbox_area = (prop.bbox[2] - prop.bbox[0]) * (prop.bbox[3] - prop.bbox[1]);");
    println!("    let solidity = prop.area / bbox_area as f64;");
    println!("    solidity > 0.8  // Rectangular objects fill their bounding box");
    println!("}}).collect();");
    println!();
    println!("// Filter for squares specifically");
    println!("let squares: Vec<_> = rectangles.iter().filter(|prop| {{");
    println!("    let width = prop.bbox[3] - prop.bbox[1];");
    println!("    let height = prop.bbox[2] - prop.bbox[0];");
    println!("    let aspect_ratio = width.max(height) as f64 / width.min(height) as f64;");
    println!("    aspect_ratio < 1.2  // Nearly square");
    println!("}}).collect();");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Solidity > 0.8 for rectangles, > 0.9 for perfect rectangles");
    println!("- Aspect ratio ~1.0 for squares, check range for rectangles");
    println!("- Consider rotation - objects might not be axis-aligned");
    println!();

    // Recipe 5: Custom Shape Detection
    println!("🎯 RECIPE 5: Custom Shape Detection");
    println!("Problem: Find objects with specific shape/pattern");
    println!("Solution: Use template matching or hit-or-miss transform");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::morphology::binary_hit_or_miss;");
    println!();
    println!("// Create template for your target shape");
    println!("let template = Array2::from_shape_vec((5, 5), vec![");
    println!("    0, 1, 1, 1, 0,");
    println!("    1, 1, 1, 1, 1,");
    println!("    1, 1, 0, 1, 1,  // Shape with hole in center");
    println!("    1, 1, 1, 1, 1,");
    println!("    0, 1, 1, 1, 0,");
    println!("    ])?;");
    println!();
    println!("// Find matches using hit-or-miss");
    println!(
        "let matches = binary_hit_or_miss(&binary_image.view(), &template.view(), None, None)?;"
    );
    println!();
    println!("// Count matches");
    println!("let num_matches = matches.iter().filter(|&&x| x > 0).count();");
    println!("println!(\"Found {{}} instances of target shape\", num_matches);");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Design template to capture essential shape features");
    println!("- Consider multiple templates for different orientations");
    println!("- Use don't-care values (neither 0 nor 1) for flexible matching");

    Ok(())
}

#[allow(dead_code)]
fn measurement_recipes() -> NdimageResult<()> {
    println!("========================================");

    // Recipe 1: Measure Object Areas
    println!("📐 RECIPE 1: Measure Object Areas");
    println!("Problem: Calculate area of objects in pixels or real units");
    println!("Solution: Use region properties after object labeling");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::{{morphology::label, measurements::region_properties}};");
    println!();
    println!("// Label objects");
    println!("let binary = threshold_binary(&image.view(), 0.5)?;");
    println!("let (labeled_) = label(&binary.view(), None)?;");
    println!();
    println!("// Measure areas");
    println!("let props = region_properties(&labeled.view(), Some(&image.view()))?;");
    println!();
    println!("for (i, prop) in props.iter().enumerate() {{");
    println!("    let area_pixels = prop.area;");
    println!("    let area_mm2 = area_pixels * pixel_size_mm * pixel_size_mm;");
    println!(
        "    println!(\"Object {{}}: {{:.1}} pixels ({{:.2}} mm²)\", i+1, area_pixels, area_mm2);"
    );
    println!("}}");
    println!();
    println!("// Statistics");
    println!("let totalarea: f64 = props.iter().map(|p| p.area).sum();");
    println!("let mean_area = total_area / props.len() as f64;");
    println!("let largest_area = props.iter().map(|p| p.area).fold(0.0, f64::max);");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Define pixel_size_mm based on your imaging setup");
    println!("- Filter out noise objects by minimum area");
    println!("- Consider using equivalent diameter: 2 * sqrt(area / π)");
    println!();

    // Recipe 2: Calculate Distances
    println!("📏 RECIPE 2: Calculate Distances");
    println!("Problem: Measure distances between points or object centers");
    println!("Solution: Use centroids and Euclidean distance formula");
    println!();

    println!("```rust");
    println!("// Get object centroids");
    println!("let props = region_properties(&labeled.view(), Some(&image.view()))?;");
    println!();
    println!("// Distance between two specific objects");
    println!(
        "fn distance_between_objects(obj1: &RegionProperties, obj2: &RegionProperties) -> f64 {{"
    );
    println!("    let dx = obj1.centroid[0] - obj2.centroid[0];");
    println!("    let dy = obj1.centroid[1] - obj2.centroid[1];");
    println!("    (dx * dx + dy * dy).sqrt()");
    println!("}}");
    println!();
    println!("// Find all pairwise distances");
    println!("for i in 0..props.len() {{");
    println!("    for j in i+1..props.len() {{");
    println!("        let dist = distance_between_objects(&props[i], &props[j]);");
    println!("        println!(\"Distance {{}} to {{}}: {{:.1}} pixels\", i+1, j+1, dist);");
    println!("    }}");
    println!("}}");
    println!();
    println!("// Find nearest neighbor for each object");
    println!("for (i, obj) in props.iter().enumerate() {{");
    println!("    let nearest_dist = props.iter().enumerate()");
    println!("        .filter(|(j_)| *j != i)");
    println!("        .map(|(_, other)| distance_between_objects(obj, other))");
    println!("        .fold(f64::INFINITY, f64::min);");
    println!(
        "    println!(\"Object {{}}: nearest neighbor at {{:.1}} pixels\", i+1, nearest_dist);"
    );
    println!("}}");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Convert to real units: distance_mm = distance_pixels * pixel_size_mm");
    println!("- Use bounding box edges for edge-to-edge distances");
    println!("- Consider 3D distance for volume data: sqrt(dx² + dy² + dz²)");
    println!();

    // Recipe 3: Analyze Shape Properties
    println!("🔍 RECIPE 3: Analyze Shape Properties");
    println!("Problem: Characterize object shapes (roundness, elongation, etc.)");
    println!("Solution: Calculate geometric descriptors from region properties");
    println!();

    println!("```rust");
    println!("// Extended shape analysis");
    println!("for (i, prop) in props.iter().enumerate() {{");
    println!("    // Basic measurements");
    println!("    let area = prop.area;");
    println!("    let perimeter = estimate_perimeter(prop);");
    println!("    ");
    println!("    // Shape descriptors");
    println!("    let circularity = 4.0 * std::f64::consts::PI * area / (perimeter * perimeter);");
    println!("    let aspect_ratio = {{");
    println!("        let width = (prop.bbox[3] - prop.bbox[1]) as f64;");
    println!("        let height = (prop.bbox[2] - prop.bbox[0]) as f64;");
    println!("        width.max(height) / width.min(height)");
    println!("    }};");
    println!("    let solidity = {{");
    println!(
        "        let bbox_area = (prop.bbox[2] - prop.bbox[0]) * (prop.bbox[3] - prop.bbox[1]);"
    );
    println!("        area / bbox_area as f64");
    println!("    }};");
    println!("    let extent = {{");
    println!(
        "        let bbox_area = (prop.bbox[2] - prop.bbox[0]) * (prop.bbox[3] - prop.bbox[1]);"
    );
    println!("        area / bbox_area as f64");
    println!("    }};");
    println!("    ");
    println!("    println!(\"Object {{}}: \", i+1);");
    println!("    println!(\"  Circularity: {{:.3}} (1.0 = perfect circle)\", circularity);");
    println!("    println!(\"  Aspect ratio: {{:.2}} (1.0 = square)\", aspect_ratio);");
    println!("    println!(\"  Solidity: {{:.3}} (convexity measure)\", solidity);");
    println!("    println!(\"  Extent: {{:.3}} (bbox filling)\", extent);");
    println!("    ");
    println!("    // Shape classification");
    println!("    if circularity > 0.8 {{");
    println!("        println!(\"  Shape: Circular\");");
    println!("    }} else if aspect_ratio > 3.0 {{");
    println!("        println!(\"  Shape: Linear/Elongated\");");
    println!("    }} else if solidity > 0.9 {{");
    println!("        println!(\"  Shape: Rectangular\");");
    println!("    }} else {{");
    println!("        println!(\"  Shape: Irregular\");");
    println!("    }}");
    println!("}}");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Circularity: >0.8 round, 0.6-0.8 oval, <0.6 irregular");
    println!("- Aspect ratio: ~1 square, 2-3 rectangular, >3 linear");
    println!("- Solidity: >0.95 convex, 0.8-0.95 slightly concave, <0.8 very concave");
    println!();

    // Recipe 4: Texture Analysis
    println!("🌊 RECIPE 4: Texture Analysis");
    println!("Problem: Quantify surface texture or pattern roughness");
    println!("Solution: Use local variance and edge density measures");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::filters::{{generic_filter, sobel}};");
    println!();
    println!("// Method 1: Local variance (texture roughness)");
    println!("let variance_filter = |window: &ArrayView2<f64>| -> f64 {{");
    println!("    let mean = window.sum() / window.len() as f64;");
    println!("    let variance = window.fold(0.0, |acc, &x| acc + (x - mean).powi(2)) / window.len() as f64;");
    println!("    variance");
    println!("}};");
    println!();
    println!("let texture_map = generic_filter(");
    println!("    &image.view(),");
    println!("    Some(&Array2::ones((5, 5))),  // 5x5 window");
    println!("    variance_filter,");
    println!("    None, None, None, None");
    println!(")?;");
    println!();
    println!("let meantexture = texture_map.sum() / texture_map.len() as f64;");
    println!("println!(\"Average texture roughness: {{:.4}}\", meantexture);");
    println!();
    println!("// Method 2: Edge density (pattern complexity)");
    println!("let edges = sobel(&image.view(), None, None, None)?;");
    println!("let edge_threshold = 0.1;");
    println!("let edge_pixels = edges.iter().filter(|&&x| x > edge_threshold).count();");
    println!("let edge_density = edge_pixels as f64 / edges.len() as f64;");
    println!("println!(\"Edge density: {{:.3}} (pattern complexity)\", edge_density);");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Higher variance = rougher texture");
    println!("- Higher edge density = more complex patterns");
    println!("- Use different window sizes for different texture scales");
    println!("- Consider directional filters for oriented textures");
    println!();

    // Recipe 5: Compare Images
    println!("🔄 RECIPE 5: Compare Images");
    println!("Problem: Quantify similarity/difference between two images");
    println!("Solution: Use statistical measures and correlation metrics");
    println!();

    println!("```rust");
    println!("// Statistical comparison");
    println!("fn compareimages(img1: &Array2<f64>, img2: &Array2<f64>) -> ImageComparison {{");
    println!("    assert_eq!(_img1.dim(), img2.dim(), \"Images must have same dimensions\");");
    println!("    ");
    println!("    // Mean Squared Error");
    println!("    let mse = img1.iter().zip(img2.iter())");
    println!("        .map(|(&a, &b)| (a - b).powi(2))");
    println!("        .sum::<f64>() / img1.len() as f64;");
    println!("    ");
    println!("    // Mean Absolute Error");
    println!("    let mae = img1.iter().zip(img2.iter())");
    println!("        .map(|(&a, &b)| (a - b).abs())");
    println!("        .sum::<f64>() / img1.len() as f64;");
    println!("    ");
    println!("    // Correlation coefficient");
    println!("    let mean1 = img1.sum() / img1.len() as f64;");
    println!("    let mean2 = img2.sum() / img2.len() as f64;");
    println!("    ");
    println!("    let numerator: f64 = img1.iter().zip(img2.iter())");
    println!("        .map(|(&a, &b)| (a - mean1) * (b - mean2))");
    println!("        .sum();");
    println!("    ");
    println!("    let var1: f64 = img1.iter().map(|&x| (x - mean1).powi(2)).sum();");
    println!("    let var2: f64 = img2.iter().map(|&x| (x - mean2).powi(2)).sum();");
    println!("    ");
    println!("    let correlation = numerator / (var1 * var2).sqrt();");
    println!("    ");
    println!("    ImageComparison {{ mse, mae, correlation }}");
    println!("}}");
    println!();
    println!("let comparison = compareimages(&image1, &image2);");
    println!("println!(\"MSE: {{:.6}} (lower = more similar)\", comparison.mse);");
    println!("println!(\"MAE: {{:.6}} (lower = more similar)\", comparison.mae);");
    println!("println!(\"Correlation: {{:.3}} (1.0 = identical, 0.0 = uncorrelated)\", comparison.correlation);");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- MSE/MAE close to 0 = very similar images");
    println!("- Correlation > 0.9 = strong similarity");
    println!("- Consider normalized cross-correlation for shifted images");
    println!("- Use histogram comparison for intensity distribution differences");

    Ok(())
}

#[allow(dead_code)]
fn transformation_recipes() -> NdimageResult<()> {
    println!("========================================");

    // Recipe 1: Rotate Image
    println!("🔄 RECIPE 1: Rotate Image");
    println!("Problem: Rotate image by specific angle while maintaining quality");
    println!("Solution: Use rotation with appropriate interpolation");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::interpolation::{{rotate, InterpolationOrder, BoundaryMode}};");
    println!();
    println!("// Basic rotation");
    println!("let rotated = rotate(");
    println!("    &image.view(),");
    println!("    45.0,                              // Angle in degrees");
    println!("    None,                              // Use image center");
    println!("    Some(InterpolationOrder::Linear),  // Good quality");
    println!("    Some(BoundaryMode::Reflect),       // Handle edges");
    println!("    None,                              // Auto background");
    println!("    None                               // No output shape specified");
    println!(")?;");
    println!();
    println!("// High-quality rotation");
    println!("let rotated_hq = rotate(");
    println!("    &image.view(),");
    println!("    30.0,");
    println!("    None,");
    println!("    Some(InterpolationOrder::Cubic),   // Higher quality");
    println!("    Some(BoundaryMode::Constant),");
    println!("    Some(0.0),                         // Black background");
    println!("    None");
    println!(")?;");
    println!();
    println!("// Fast rotation (lower quality)");
    println!("let rotated_fast = rotate(");
    println!("    &image.view(),");
    println!("    90.0,");
    println!("    None,");
    println!("    Some(InterpolationOrder::Nearest), // Fast but blocky");
    println!("    Some(BoundaryMode::Constant),");
    println!("    Some(0.0),");
    println!("    None");
    println!(")?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Use Cubic for best quality, Linear for good balance, Nearest for speed");
    println!("- Reflect boundary often looks most natural");
    println!("- Positive angles = counterclockwise rotation");
    println!("- For 90° rotations, consider using array transpose for perfect quality");
    println!();

    // Recipe 2: Scale/Resize Image
    println!("📏 RECIPE 2: Scale/Resize Image");
    println!("Problem: Change image size while preserving aspect ratio or specific dimensions");
    println!("Solution: Use zoom function with scale factors");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::interpolation::{{zoom, InterpolationOrder, BoundaryMode}};");
    println!();
    println!("// Uniform scaling (preserve aspect ratio)");
    println!("let scale_factor = 2.0;  // 2x larger");
    println!("let scaled = zoom(");
    println!("    &image,");
    println!("    &[scale_factor, scale_factor],");
    println!("    InterpolationOrder::Linear,");
    println!("    BoundaryMode::Reflect,");
    println!("    None");
    println!(")?;");
    println!();
    println!("// Non-uniform scaling (stretch)");
    println!("let stretched = zoom(");
    println!("    &image,");
    println!("    &[1.5, 0.8],  // 1.5x height, 0.8x width");
    println!("    InterpolationOrder::Linear,");
    println!("    BoundaryMode::Reflect,");
    println!("    None");
    println!(")?;");
    println!();
    println!("// Resize to specific dimensions");
    println!("let target_height = 256;");
    println!("let target_width = 256;");
    println!("let (current_h, current_w) = image.dim();");
    println!("let scale_h = target_height as f64 / current_h as f64;");
    println!("let scale_w = target_width as f64 / current_w as f64;");
    println!("let resized = zoom(&image, &[scale_h, scale_w], InterpolationOrder::Linear, BoundaryMode::Reflect, None)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Scale > 1.0 enlarges, < 1.0 shrinks");
    println!("- Use same scale factor for both dimensions to preserve aspect ratio");
    println!("- Consider anti-aliasing filters before downsampling");
    println!("- Check output dimensions: new_size = original_size * scale_factor");
    println!();

    // Recipe 3: Correct Perspective
    println!("📐 RECIPE 3: Correct Perspective Distortion");
    println!("Problem: Fix perspective distortion (e.g., photo of document taken at angle)");
    println!("Solution: Use affine or perspective transformation matrix");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::interpolation::{{affine_transform, InterpolationOrder, BoundaryMode}};");
    println!();
    println!("// Simple affine correction (shear + scale)");
    println!("let affine_matrix = Array2::from_shape_vec((2, 3), vec![");
    println!("    1.0,  0.2,  0.0,   // Scale X + shear + translation X");
    println!("    -0.1, 1.0,  0.0,   // Shear + scale Y + translation Y");
    println!("])?;");
    println!();
    println!("let corrected = affine_transform(");
    println!("    &distortedimage.view(),");
    println!("    &affine_matrix.view(),");
    println!("    InterpolationOrder::Linear,");
    println!("    BoundaryMode::Constant,");
    println!("    Some(0.0)");
    println!(")?;");
    println!();
    println!("// Perspective correction using coordinate mapping");
    println!("// Define source and target corner points");
    println!("let src_corners = [(50.0, 30.0), (200.0, 40.0), (190.0, 150.0), (60.0, 140.0)];");
    println!("let dst_corners = [(0.0, 0.0), (150.0, 0.0), (150.0, 100.0), (0.0, 100.0)];");
    println!();
    println!("// Calculate perspective transform matrix (simplified)");
    println!("let perspective_corrected = apply_perspective_correction(&distortedimage, &src_corners, &dst_corners)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Identify 4 corner points of rectangular object in distorted image");
    println!("- Map to desired rectangular coordinates");
    println!("- Use higher-order interpolation for better quality");
    println!("- Consider using coordinate mapping for complex corrections");
    println!();

    // Recipe 4: Register/Align Images
    println!("🎯 RECIPE 4: Register/Align Multiple Images");
    println!("Problem: Align images taken at different times or with slight movements");
    println!("Solution: Find optimal translation/rotation to maximize overlap");
    println!();

    println!("```rust");
    println!("// Method 1: Translation-only registration");
    println!("fn find_translation(reference: &Array2<f64>, moving: &Array2<f64>) -> (f64, f64) {{");
    println!("    let mut best_correlation = -1.0;");
    println!("    let mut best_shift = (0.0, 0.0);");
    println!("    ");
    println!("    // Search over reasonable shift range");
    println!("    for dy in -20..=20 {{");
    println!("        for dx in -20..=20 {{");
    println!("            let shifted = shift(&moving.view(), &[dy as f64, dx as f64], ");
    println!("                                InterpolationOrder::Linear, BoundaryMode::Constant, Some(0.0))?;");
    println!("            ");
    println!("            let correlation = calculate_correlation(_reference, &shifted);");
    println!("            if correlation > best_correlation {{");
    println!("                best_correlation = correlation;");
    println!("                best_shift = (dy as f64, dx as f64);");
    println!("            }}");
    println!("        }}");
    println!("    }}");
    println!("    ");
    println!("    best_shift");
    println!("}}");
    println!();
    println!("// Apply registration");
    println!("let (shift_y, shift_x) = find_translation(&referenceimage, &movingimage);");
    println!("let registered = shift(&movingimage.view(), &[shift_y, shift_x], ");
    println!(
        "                      InterpolationOrder::Linear, BoundaryMode::Constant, Some(0.0))?;"
    );
    println!();
    println!("println!(\"Best alignment at shift: ({{:.1}}, {{:.1}})\", shift_y, shift_x);");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Start with coarse grid search, refine around best match");
    println!("- Use feature-based registration for large movements");
    println!("- Consider rotation and scaling if needed");
    println!("- Normalize images before correlation calculation");
    println!();

    // Recipe 5: Create Image Pyramid
    println!("🏔️  RECIPE 5: Create Multi-Scale Image Pyramid");
    println!("Problem: Analyze image at multiple resolutions for hierarchical processing");
    println!("Solution: Create pyramid by iterative smoothing and downsampling");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::filters::gaussian_filter;");
    println!();
    println!("fn create_pyramid(image: &Array2<f64>, levels: usize) -> NdimageResult<Vec<Array2<f64>>> {{");
    println!("    let mut pyramid = vec![image.clone()];");
    println!("    let mut current = image.clone();");
    println!("    ");
    println!("    for level in 1..levels {{");
    println!("        // Smooth before downsampling to avoid aliasing");
    println!("        let smoothed = gaussian_filter(&current, &[1.0, 1.0], None, None, None)?;");
    println!("        ");
    println!("        // Downsample by 2x");
    println!("        let downsampled = zoom(&smoothed, &[0.5, 0.5], ");
    println!(
        "                              InterpolationOrder::Linear, BoundaryMode::Reflect, None)?;"
    );
    println!("        ");
    println!("        pyramid.push(downsampled.clone());");
    println!("        current = downsampled;");
    println!("    }}");
    println!("    ");
    println!("    Ok(pyramid)");
    println!("}}");
    println!();
    println!("// Create 4-level pyramid");
    println!("let pyramid = create_pyramid(&image, 4)?;");
    println!();
    println!("for (level, img) in pyramid.iter().enumerate() {{");
    println!("    println!(\"Level {{}}: {{}}x{{}}\", level, img.nrows(), img.ncols());");
    println!("}}");
    println!();
    println!("// Process each level");
    println!("for (level, img) in pyramid.iter().enumerate() {{");
    println!("    let edges = sobel(&img.view(), None, None, None)?;");
    println!("    let edge_count = edges.iter().filter(|&&x| x > 0.1).count();");
    println!("    println!(\"Level {{}}: {{}} edge pixels\", level, edge_count);");
    println!("}}");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Always smooth before downsampling to prevent aliasing");
    println!("- Typical pyramid has 3-5 levels");
    println!("- Use for multi-scale feature detection or coarse-to-fine optimization");
    println!("- Consider Laplacian pyramids for edge-preserving analysis");

    Ok(())
}

#[allow(dead_code)]
fn enhancement_recipes() -> NdimageResult<()> {
    println!("========================================");

    // Recipe 1: Sharpen Blurry Image
    println!("✨ RECIPE 1: Sharpen Blurry Image");
    println!("Problem: Image is blurry and needs edge enhancement");
    println!("Solution: Use unsharp masking or Laplacian sharpening");
    println!();

    println!("```rust");
    println!("use scirs2_ndimage::filters::{{gaussian_filter, laplace}};");
    println!();
    println!("// Method 1: Unsharp masking");
    println!("fn unsharp_mask(image: &Array2<f64>, sigma: f64, strength: f64) -> NdimageResult<Array2<f64>> {{");
    println!("    // Create blurred version");
    println!("    let blurred = gaussian_filter(image, &[sigma, sigma], None, None, None)?;");
    println!("    ");
    println!("    // Calculate mask (difference between original and blurred)");
    println!("    let mask = image - &blurred;");
    println!("    ");
    println!("    // Add weighted mask back to original");
    println!("    let sharpened = image + &(&mask * strength);");
    println!("    ");
    println!("    // Clamp values to valid range");
    println!("    Ok(sharpened.mapv(|x| x.clamp(0.0, 1.0)))");
    println!("}}");
    println!();
    println!("let sharpened = unsharp_mask(&blurryimage, 1.0, 0.5)?;  // Moderate sharpening");
    println!("let sharp_aggressive = unsharp_mask(&blurryimage, 0.5, 1.0)?;  // Strong sharpening");
    println!();
    println!("// Method 2: Laplacian sharpening");
    println!("let laplacian = laplace(&blurryimage.view(), None, None)?;");
    println!("let sharpened_lap = &blurryimage - &(&laplacian * 0.3);");
    println!("let sharpened_lap = sharpened_lap.mapv(|x| x.clamp(0.0, 1.0));");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Start with strength 0.3-0.5, increase carefully to avoid artifacts");
    println!("- Smaller sigma = sharper details, larger sigma = broader enhancement");
    println!("- Watch for oversharpening: halos around edges");
    println!("- Apply selectively to avoid amplifying noise");
    println!();

    // Recipe 2: Remove Background Pattern
    println!("🌊 RECIPE 2: Remove Background Pattern");
    println!("Problem: Image has unwanted background pattern or uneven illumination");
    println!("Solution: Use background subtraction with large-scale smoothing");
    println!();

    println!("```rust");
    println!("// Method 1: Rolling ball background subtraction");
    println!("fn remove_background(image: &Array2<f64>, backgroundsize: f64) -> NdimageResult<Array2<f64>> {{");
    println!("    // Estimate background with heavy smoothing");
    println!("    let background = gaussian_filter(image, &[background_size, background_size], None, None, None)?;");
    println!("    ");
    println!("    // Subtract background");
    println!("    let corrected = image - &background;");
    println!("    ");
    println!("    // Add back mean intensity to maintain brightness");
    println!("    let mean_intensity = image.sum() / image.len() as f64;");
    println!("    Ok(corrected.mapv(|x| (x + mean_intensity).clamp(0.0, 1.0)))");
    println!("}}");
    println!();
    println!(
        "let corrected = remove_background(&unevenimage, 50.0)?;  // Remove large-scale variations"
    );
    println!();
    println!("// Method 2: Top-hat filtering (remove bright background)");
    println!("use scirs2_ndimage::morphology::{{white_tophat, generate_binary_structure}};");
    println!("let structure = generate_binary_structure(2, 1)?;");
    println!("let tophat = white_tophat(&image.view(), Some(&structure.view()), None)?;");
    println!();
    println!("// Method 3: High-pass filtering");
    println!("let lowpass = gaussian_filter(&image, &[10.0, 10.0], None, None, None)?;");
    println!("let highpass = &image - &lowpass + 0.5;  // Add 0.5 to center around gray");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Background size should be 2-3x larger than features to preserve");
    println!("- Use morphological operations for textured backgrounds");
    println!("- Consider median filter for background estimation if image has outliers");
    println!("- Adjust mean intensity to maintain overall brightness");
    println!();

    // Recipe 3: Create False Color Visualization
    println!("🎨 RECIPE 3: Create False Color Visualization");
    println!("Problem: Enhance visualization of scientific data with color mapping");
    println!("Solution: Map intensity values to color lookup table");
    println!();

    println!("```rust");
    println!("// Create false color image from grayscale");
    println!("fn apply_colormap(image: &Array2<f64>, colormap: &str) -> Array3<f64> {{");
    println!("    let (height, width) = image.dim();");
    println!("    let mut colored = Array3::zeros((height, width, 3));");
    println!("    ");
    println!("    for i in 0..height {{");
    println!("        for j in 0..width {{");
    println!("            let intensity = image[[i, j]];");
    println!("            let (r, g, b) = match colormap {{");
    println!("                \"jet\" => jet_colormap(intensity),");
    println!("                \"hot\" => hot_colormap(intensity),");
    println!("                \"viridis\" => viridis_colormap(intensity),");
    println!("                _ => (intensity, intensity, intensity),  // Grayscale fallback");
    println!("            }};");
    println!("            colored[[i, j, 0]] = r;");
    println!("            colored[[i, j, 1]] = g;");
    println!("            colored[[i, j, 2]] = b;");
    println!("        }}");
    println!("    }}");
    println!("    ");
    println!("    colored");
    println!("}}");
    println!();
    println!("// Colormap functions");
    println!("fn jet_colormap(value: f64) -> (f64, f64, f64) {{");
    println!("    let v = value.clamp(0.0, 1.0);");
    println!("    if v < 0.25 {{");
    println!("        (0.0, 4.0 * v, 1.0)");
    println!("    }} else if v < 0.5 {{");
    println!("        (0.0, 1.0, 4.0 * (0.5 - v))");
    println!("    }} else if v < 0.75 {{");
    println!("        (4.0 * (v - 0.5), 1.0, 0.0)");
    println!("    }} else {{");
    println!("        (1.0, 4.0 * (1.0 - v), 0.0)");
    println!("    }}");
    println!("}}");
    println!();
    println!("let coloredimage = apply_colormap(&dataimage, \"jet\");");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Jet: Good for general scientific data");
    println!("- Hot: Good for thermal/intensity data");
    println!("- Viridis: Perceptually uniform, colorblind-friendly");
    println!("- Normalize data to 0-1 range before applying colormap");
    println!();

    // Recipe 4: Enhance Local Contrast
    println!("🔦 RECIPE 4: Enhance Local Contrast");
    println!("Problem: Image has both dark and bright regions that need different enhancement");
    println!("Solution: Use adaptive histogram equalization or CLAHE");
    println!();

    println!("```rust");
    println!("// Simplified adaptive contrast enhancement");
    println!("fn adaptive_contrast_enhancement(image: &Array2<f64>, windowsize: usize) -> NdimageResult<Array2<f64>> {{");
    println!("    let (height, width) = image.dim();");
    println!("    let mut enhanced = image.clone();");
    println!("    let half_window = window_size / 2;");
    println!("    ");
    println!("    for i in half_window..height-half_window {{");
    println!("        for j in half_window..width-half_window {{");
    println!("            // Extract local window");
    println!("            let window = image.slice(s![");
    println!("                i-half_window..i+half_window+1,");
    println!("                j-half_window..j+half_window+1");
    println!("            ]);");
    println!("            ");
    println!("            // Calculate local statistics");
    println!("            let local_mean = window.sum() / window.len() as f64;");
    println!("            let local_std = {{");
    println!("                let variance = window.fold(0.0, |acc, &x| acc + (x - local_mean).powi(2)) / window.len() as f64;");
    println!("                variance.sqrt()");
    println!("            }};");
    println!("            ");
    println!("            // Enhance based on local contrast");
    println!("            let current_value = image[[i, j]];");
    println!("            let enhancement_factor = if local_std < 0.1 {{ 2.0 }} else {{ 1.0 }};");
    println!("            let enhanced_value = local_mean + (current_value - local_mean) * enhancement_factor;");
    println!("            ");
    println!("            enhanced[[i, j]] = enhanced_value.clamp(0.0, 1.0);");
    println!("        }}");
    println!("    }}");
    println!("    ");
    println!("    Ok(enhanced)");
    println!("}}");
    println!();
    println!("let enhanced = adaptive_contrast_enhancement(&low_contrastimage, 15)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Window size: 10-20 pixels for local details, 30-50 for regional enhancement");
    println!("- Avoid over-enhancement in uniform regions");
    println!("- Consider limiting enhancement strength to prevent artifacts");
    println!("- Process in linear color space for best results");
    println!();

    // Recipe 5: Create Artistic Effects
    println!("🎭 RECIPE 5: Create Artistic Effects");
    println!("Problem: Transform regular photo into artistic rendition");
    println!("Solution: Combine multiple filters for creative effects");
    println!();

    println!("```rust");
    println!("// Effect 1: Oil painting effect");
    println!("fn oil_painting_effect(image: &Array2<f64>) -> NdimageResult<Array2<f64>> {{");
    println!("    // Quantize intensities");
    println!("    let levels = 8;");
    println!("    let quantized = image.mapv(|x| ((x * levels as f64).round() / levels as f64));");
    println!("    ");
    println!("    // Apply median filter for smoothing");
    println!("    let smoothed = median_filter(&quantized.view(), Some(&[3, 3]), None, None)?;");
    println!("    ");
    println!("    Ok(smoothed)");
    println!("}}");
    println!();
    println!("// Effect 2: Edge enhancement (cartoon-like)");
    println!("fn cartoon_effect(image: &Array2<f64>) -> NdimageResult<Array2<f64>> {{");
    println!("    // Strong bilateral filter for flat regions");
    println!("    let smooth = bilateral_filter(image.view(), 5.0, 0.05, Some(9))?;");
    println!("    ");
    println!("    // Detect edges");
    println!("    let edges = sobel(&smooth.view(), None, None, None)?;");
    println!("    let edge_mask = edges.mapv(|x| if x > 0.1 {{ 0.0 }} else {{ 1.0 }});");
    println!("    ");
    println!("    // Combine smooth regions with edge mask");
    println!("    Ok(&smooth * &edge_mask)");
    println!("}}");
    println!();
    println!("// Effect 3: Pencil sketch");
    println!("fn pencil_sketch(image: &Array2<f64>) -> NdimageResult<Array2<f64>> {{");
    println!("    // Create inverted image");
    println!("    let inverted = image.mapv(|x| 1.0 - x);");
    println!("    ");
    println!("    // Blur inverted image");
    println!("    let blurred = gaussian_filter(&inverted, &[5.0, 5.0], None, None, None)?;");
    println!("    ");
    println!("    // Color dodge blend mode");
    println!("    let sketch = image.iter().zip(blurred.iter())");
    println!("        .map(|(&orig, &blur)| {{");
    println!("            if blur >= 1.0 {{ 1.0 }} else {{ (orig / (1.0 - blur)).min(1.0) }}");
    println!("        }})");
    println!("        .collect::<Vec<_>>();");
    println!("    ");
    println!("    Ok(Array2::from_shape_vec(image.dim(), sketch)?)");
    println!("}}");
    println!();
    println!("// Apply effects");
    println!("let oil_painting = oil_painting_effect(&photo)?;");
    println!("let cartoon = cartoon_effect(&photo)?;");
    println!("let sketch = pencil_sketch(&photo)?;");
    println!("```");
    println!();
    println!("💡 Tips:");
    println!("- Combine multiple effects for unique styles");
    println!("- Adjust quantization levels for different artistic intensities");
    println!("- Consider color space conversions for more realistic effects");
    println!("- Experiment with different blend modes and filter combinations");

    Ok(())
}

// Helper functions and structures

#[derive(Debug)]
#[allow(dead_code)]
struct ImageComparison {
    mse: f64,
    mae: f64,
    correlation: f64,
}

#[allow(dead_code)]
fn interpolate_missing_pixels(
    image: &Array2<f64>,
    mask: &Array2<u8>,
) -> NdimageResult<Array2<f64>> {
    // Simplified missing pixel interpolation
    Ok(image.clone())
}

#[allow(dead_code)]
fn estimate_perimeter(prop: &scirs2_ndimage::measurements::RegionProperties<f64>) -> f64 {
    // Simplified perimeter estimation
    4.0 * (prop.area as f64 / std::f64::consts::PI).sqrt()
}

#[allow(dead_code)]
fn find_local_maxima(image: &Array2<f64>, threshold: f64) -> Array2<u32> {
    let mut markers = Array2::zeros(image.dim());
    let mut label = 1u32;

    let (height, width) = image.dim();
    for i in 1..height - 1 {
        for j in 1..width - 1 {
            if image[[i, j]] > threshold
                && image[[i, j]] > image[[i - 1, j]]
                && image[[i, j]] > image[[i + 1, j]]
                && image[[i, j]] > image[[i, j - 1]]
                && image[[i, j]] > image[[i, j + 1]]
            {
                markers[[i, j]] = label;
                label += 1;
            }
        }
    }

    markers
}

#[allow(dead_code)]
fn apply_perspective_correction(
    image: &Array2<f64>,
    src_corners: &[(f64, f64)],
    _dst_corners: &[(f64, f64)],
) -> NdimageResult<Array2<f64>> {
    // Simplified perspective correction
    Ok(image.clone())
}

#[allow(dead_code)]
fn calculate_correlation(img1: &Array2<f64>, img2: &Array2<f64>) -> f64 {
    let mean1 = img1.sum() / img1.len() as f64;
    let mean2 = img2.sum() / img2.len() as f64;

    let numerator: f64 = img1
        .iter()
        .zip(img2.iter())
        .map(|(&a, &b)| (a - mean1) * (b - mean2))
        .sum();

    let var1: f64 = img1.iter().map(|&x| (x - mean1).powi(2)).sum();
    let var2: f64 = img2.iter().map(|&x| (x - mean2).powi(2)).sum();

    numerator / (var1 * var2).sqrt()
}

#[allow(dead_code)]
fn hot_colormap(value: f64) -> (f64, f64, f64) {
    let v = value.clamp(0.0, 1.0);
    if v < 1.0 / 3.0 {
        (3.0 * v, 0.0, 0.0)
    } else if v < 2.0 / 3.0 {
        (1.0, 3.0 * (v - 1.0 / 3.0), 0.0)
    } else {
        (1.0, 1.0, 3.0 * (v - 2.0 / 3.0))
    }
}

#[allow(dead_code)]
fn viridis_colormap(value: f64) -> (f64, f64, f64) {
    let v = value.clamp(0.0, 1.0);
    // Simplified viridis approximation
    let r = (0.267 + v * (0.975 - 0.267)).clamp(0.0, 1.0);
    let g = (0.005 + v * (0.906 - 0.005)).clamp(0.0, 1.0);
    let b = (0.329 + v * (0.110 - 0.329)).clamp(0.0, 1.0);
    (r, g, b)
}
