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");
println!("🧹 IMAGE CLEANUP RECIPES");
image_cleanup_recipes()?;
println!("\n🔍 OBJECT DETECTION RECIPES");
object_detection_recipes()?;
println!("\n📏 MEASUREMENT AND ANALYSIS RECIPES");
measurement_recipes()?;
println!("\n🔄 IMAGE TRANSFORMATION RECIPES");
transformation_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!("========================================");
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!();
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!();
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!();
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!();
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!("========================================");
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!();
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!();
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!();
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!();
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!("========================================");
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!();
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!();
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!();
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!();
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!("========================================");
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!();
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!();
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!();
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!();
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(¤t, &[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!("========================================");
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!();
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!();
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!();
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!();
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(())
}
#[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>> {
Ok(image.clone())
}
#[allow(dead_code)]
fn estimate_perimeter(prop: &scirs2_ndimage::measurements::RegionProperties<f64>) -> f64 {
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>> {
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);
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)
}