//! Comprehensive documentation and usage examples for scirs2-ndimage
//!
//! This example serves as a complete guide to using scirs2-ndimage, covering:
//! - All major API categories with detailed examples
//! - Best practices for performance and memory efficiency
//! - Advanced features and optimization techniques
//! - Error handling and edge cases
//! - Integration with other scirs2 modules
use ndarray::{Array2, Array3, ArrayView2, Axis};
use scirs2_ndimage::{
backend::*, domain_specific::*, error::NdimageResult, features::*, filters::*,
interpolation::*, measurements::*, morphology::*, segmentation::*, streaming::*,
visualization::*,
};
#[allow(dead_code)]
fn main() -> NdimageResult<()> {
println!("=== Comprehensive scirs2-ndimage Documentation & Examples ===\n");
// Create various test images for different examples
let testimage_2d = create_testimage_2d(128, 128);
let testimage_3d = create_testimage_3d(64, 64, 32);
let noisyimage = add_noise(&testimage_2d, 0.1);
let edgeimage = create_edge_testimage(64, 64);
// 1. Filtering Operations
println!("--- 1. FILTERING OPERATIONS ---\n");
demonstrate_filtering(&testimage_2d, &noisyimage)?;
// 2. Feature Detection
println!("\n--- 2. FEATURE DETECTION ---\n");
demonstrate_feature_detection(&edgeimage)?;
// 3. Morphological Operations
println!("\n--- 3. MORPHOLOGICAL OPERATIONS ---\n");
demonstrate_morphology(&testimage_2d)?;
// 4. Interpolation and Transformations
println!("\n--- 4. INTERPOLATION & TRANSFORMATIONS ---\n");
demonstrate_interpolation(&testimage_2d)?;
// 5. Measurements and Analysis
println!("\n--- 5. MEASUREMENTS & ANALYSIS ---\n");
demonstrate_measurements(&testimage_2d)?;
// 6. Segmentation
println!("\n--- 6. SEGMENTATION ---\n");
demonstrate_segmentation(&testimage_2d)?;
// 7. Domain-Specific Functions
println!("\n--- 7. DOMAIN-SPECIFIC FUNCTIONS ---\n");
demonstrate_domain_specific(&testimage_2d)?;
// 8. Advanced SIMD Extensions
#[cfg(feature = "simd")]
{
println!("\n--- 8. ADVANCED SIMD EXTENSIONS ---\n");
demonstrate_advanced_simd(&testimage_2d)?;
}
// 9. Backend and Performance
println!("\n--- 9. BACKEND & PERFORMANCE OPTIMIZATION ---\n");
demonstrate_backends(&testimage_2d)?;
// 10. Streaming for Large Data
println!("\n--- 10. STREAMING FOR LARGE DATASETS ---\n");
demonstrate_streaming()?;
// 11. Memory Management
println!("\n--- 11. MEMORY MANAGEMENT ---\n");
demonstrate_memory_management(&testimage_2d)?;
// 12. Visualization
println!("\n--- 12. VISUALIZATION ---\n");
demonstrate_visualization(&testimage_2d)?;
// 13. Best Practices and Tips
println!("\n--- 13. BEST PRACTICES & OPTIMIZATION TIPS ---\n");
demonstrate_best_practices()?;
println!("\n=== Documentation Examples Complete ===");
println!("For more details, see the API documentation and individual module examples.");
Ok(())
}
#[allow(dead_code)]
fn create_testimage_2d(height: usize, width: usize) -> Array2<f64> {
Array2::from_shape_fn((height, width), |(i, j)| {
let x = i as f64 / height as f64;
let y = j as f64 / width as f64;
// Create multiple patterns
let pattern1 = (x * std::f64::consts::PI * 4.0).sin();
let pattern2 = (y * std::f64::consts::PI * 6.0).cos();
let radial = ((x - 0.5).powi(2) + (y - 0.5).powi(2)).sqrt();
0.5 + 0.3 * pattern1 * pattern2 + 0.2 * (1.0 - radial).max(0.0)
})
}
#[allow(dead_code)]
fn create_testimage_3d(height: usize, width: usize, depth: usize) -> Array3<f64> {
Array3::from_shape_fn((height, width, depth), |(i, j, k)| {
let x = i as f64 / height as f64;
let y = j as f64 / width as f64;
let z = k as f64 / depth as f64;
((x * std::f64::consts::PI).sin()
+ (y * std::f64::consts::PI).cos()
+ (z * std::f64::consts::PI).sin())
/ 3.0
})
}
#[allow(dead_code)]
fn add_noise(image: &Array2<f64>, noiselevel: f64) -> Array2<f64> {
image
+ &Array2::from_shape_fn(image.dim(), |(i, j)| {
let noise = if (i * 7 + j * 11) % 13 == 0 {
noiselevel
} else {
-noiselevel
};
if (i + j) % 3 == 0 {
noise
} else {
0.0
}
})
}
#[allow(dead_code)]
fn create_edge_testimage(height: usize, width: usize) -> Array2<f64> {
Array2::from_shape_fn((height, width), |(i, j)| {
// Create step edges and lines
if i > height / 2 && i < height / 2 + 5 {
1.0
} else if j > width / 2 && j < width / 2 + 5 {
1.0
} else if ((i as f64 - height as f64 / 2.0).powi(2)
+ (j as f64 - width as f64 / 2.0).powi(2))
.sqrt()
< 15.0
{
0.8
} else {
0.0
}
})
}
#[allow(dead_code)]
fn demonstrate_filtering(image: &Array2<f64>, noisyimage: &Array2<f64>) -> NdimageResult<()> {
println!("🔍 Filtering Operations");
// 1. Basic filters
println!(" • Gaussian filtering (noise reduction):");
let gaussian = gaussian_filter(&image, 2.0, None, None)?;
println!(
" Input: {}x{}, Output: {}x{}",
image.nrows(),
image.ncols(),
gaussian.nrows(),
gaussian.ncols()
);
// 2. Edge detection filters
println!(" • Edge detection filters:");
let sobel_result = sobel(&image, 0, None)?;
let laplace_result = laplace(&image, None, None)?;
println!(" Sobel edges detected, Laplacian computed");
// 3. Rank filters
println!(" • Rank filters (noise removal):");
let median = median_filter(&noisyimage, &[3, 3], None)?;
let max_filter = maximum_filter(&image, &[3, 3], None, None)?;
println!(" Median filter applied, maximum filter computed");
// 4. Advanced filters
println!(" • Advanced filters:");
let bilateral = bilateral_filter(image.view(), 2.0, 0.1, None)?;
println!(" Bilateral filter (edge-preserving smoothing) applied");
// 5. Generic filters with custom functions
println!(" • Generic filters with custom functions:");
let custom_filter = generic_filter(
&image.view(),
None, // use default 3x3 footprint
|neighborhood: &[f64]| {
neighborhood.iter().fold(0.0, |sum, &x| sum + x) / neighborhood.len() as f64
}, // use mean as the filter function
None, // mode
None, // cval
None, // origin
None, // axes
)?;
println!(" Custom variance filter applied");
Ok(())
}
#[allow(dead_code)]
fn demonstrate_feature_detection(image: &Array2<f64>) -> NdimageResult<()> {
println!("🎯 Feature Detection");
// 1. Edge detection
println!(" • Edge detection methods:");
let canny_edges = canny(image.view(), 1.0, 0.1, 0.3, None)?;
let sobel_edges = sobel_edges(image.view(), None)?;
println!(" Canny and Sobel edge detection completed");
// 2. Corner detection
println!(" • Corner detection:");
let harris_corners = harris_corners(image.view(), 0.04, 3, 0.01, None)?;
let fast_corners = fast_corners(image.view(), 9, 0.15, None)?;
println!(" Harris and FAST corner detection completed");
#[cfg(feature = "simd")]
{
// 3. Machine learning-based detection
println!(" • ML-based feature detection:");
let ml_config = MLDetectorConfig::default();
let learned_detector = LearnedEdgeDetector::new(ml_config)?;
let ml_edges = learned_detector.detect_edges(image.view())?;
println!(" ML-based edge detection completed");
}
Ok(())
}
#[allow(dead_code)]
fn demonstrate_morphology(image: &Array2<f64>) -> NdimageResult<()> {
println!("🔬 Morphological Operations");
// Create binary image for morphology
let binary_image = image.mapv(|x| if x > 0.5 { 1u8 } else { 0u8 });
// 1. Basic binary morphology
println!(" • Basic binary morphology:");
let structure = generate_binary_structure(2, 1)?;
let eroded = binary_erosion(
&binary_image.view(),
Some(&structure.view()),
None,
None,
None,
None,
)?;
let dilated = binary_dilation(
&binary_image.view(),
Some(&structure.view()),
None,
None,
None,
None,
)?;
println!(" Binary erosion and dilation completed");
// 2. Compound operations
println!(" • Compound morphological operations:");
let opened = binary_opening(
&binary_image.view(),
Some(&structure.view()),
None,
None,
None,
)?;
let closed = binary_closing(
&binary_image.view(),
Some(&structure.view()),
None,
None,
None,
)?;
println!(" Opening and closing operations completed");
// 3. Grayscale morphology
println!(" • Grayscale morphology:");
let grey_eroded = grey_erosion(&image.view(), None, None, None, None, None)?;
let grey_dilated = grey_dilation(&image.view(), None, None, None, None, None)?;
println!(" Grayscale erosion and dilation completed");
// 4. Distance transforms
println!(" • Distance transforms:");
let edt = distance_transform_edt(&binary_image.view(), None, None, None)?;
println!(" Euclidean distance transform computed");
Ok(())
}
#[allow(dead_code)]
fn demonstrate_interpolation(image: &Array2<f64>) -> NdimageResult<()> {
println!("🔄 Interpolation & Transformations");
// 1. Basic interpolation
println!(" • Basic interpolation:");
let zoomed = zoom(
image,
&[2.0, 1.5],
InterpolationOrder::Linear,
BoundaryMode::Reflect,
None,
)?;
println!(
" Zoom: {}x{} -> {}x{}",
image.nrows(),
image.ncols(),
zoomed.nrows(),
zoomed.ncols()
);
// 2. Rotation
println!(" • Rotation transformation:");
let rotated = rotate(&image.view(), 45.0, None, None, None, None, None)?;
println!(" 45-degree rotation completed");
// 3. Affine transformation
println!(" • Affine transformation:");
let transform_matrix = Array2::from_shape_vec(
(2, 3),
vec![
1.2, 0.2, 10.0, // scale + shear + translation
0.1, 1.1, 5.0,
],
)?;
let transformed = affine_transform(
&image.view(),
&transform_matrix.view(),
InterpolationOrder::Linear,
BoundaryMode::Constant,
Some(0.0),
)?;
println!(" Affine transformation applied");
// 4. Coordinate mapping
println!(" • Coordinate mapping:");
let coords = Array2::fromshape_fn((2, 100), |(axis, i)| {
if axis == 0 {
(i as f64 / 99.0) * (image.nrows() - 1) as f64
} else {
(i as f64 / 99.0) * (image.ncols() - 1) as f64
}
});
let mapped_values = map_coordinates(
&image.view(),
&coords.view(),
InterpolationOrder::Linear,
BoundaryMode::Reflect,
None,
)?;
println!(
" Coordinate mapping: {} values extracted",
mapped_values.len()
);
Ok(())
}
#[allow(dead_code)]
fn demonstrate_measurements(image: &Array2<f64>) -> NdimageResult<()> {
println!("📊 Measurements & Analysis");
// Create labeled image for region analysis
let binary = image.mapv(|x| if x > 0.6 { 1u32 } else { 0u32 });
let labeled = label(&binary.view(), None)?;
// 1. Basic measurements
println!(" • Basic measurements:");
let com = center_of_mass(&image.view(), Some(&labeled.1.view()), None)?;
println!(" Center of mass: {:?}", com);
// 2. Statistical measurements
println!(" • Statistical measurements:");
let extrema_result = extrema(&image.view(), Some(&labeled.1.view()), None)?;
println!(" Extrema found: {} regions", extrema_result.len());
// 3. Moments analysis
println!(" • Moments analysis:");
let moments_result = moments(&image.view(), None, None)?;
println!(" Moments computed for image");
// 4. Region properties
println!(" • Region properties:");
let properties = region_properties(&labeled.1.view(), Some(&image.view()))?;
println!(" Properties computed for {} regions", properties.len());
for (i, prop) in properties.iter().take(3).enumerate() {
println!(
" Region {}: area={:.1}, centroid=({:.1}, {:.1})",
i, prop.area, prop.centroid[0], prop.centroid[1]
);
}
Ok(())
}
#[allow(dead_code)]
fn demonstrate_segmentation(image: &Array2<f64>) -> NdimageResult<()> {
println!("✂️ Segmentation");
// 1. Thresholding
println!(" • Thresholding methods:");
let binary = threshold_binary(&image.view(), 0.5)?;
let otsu = otsu_threshold(&image.view())?;
let adaptive = adaptive_threshold(&image.view(), 15, AdaptiveMethod::Mean, 0.1)?;
println!(" Binary, Otsu, and adaptive thresholding completed");
// 2. Watershed segmentation
println!(" • Watershed segmentation:");
let markers = Array2::fromshape_fn(image.dim(), |(i, j)| {
if i < 20 && j < 20 {
1u32
} else if i > image.nrows() - 20 && j > image.ncols() - 20 {
2u32
} else {
0u32
}
});
let watershed_result = watershed(&image.view(), &markers.view(), None, None)?;
println!(" Watershed segmentation completed");
#[cfg(feature = "simd")]
{
// 3. Advanced segmentation
println!(" • Advanced segmentation methods:");
// Graph cuts
let graph_cuts_params = GraphCutsParams::default();
let gc_result = graph_cuts(&image.view(), &markers.view(), graph_cuts_params)?;
println!(" Graph cuts segmentation completed");
// Chan-Vese
let initial_contour = create_circle_contour((image.nrows() / 2, image.ncols() / 2), 30.0);
let cv_params = ChanVeseParams::default();
let cv_result = chan_vese(&image.view(), &initial_contour, cv_params)?;
println!(" Chan-Vese segmentation completed");
}
Ok(())
}
#[allow(dead_code)]
fn demonstrate_domain_specific(image: &Array2<f64>) -> NdimageResult<()> {
println!("🏥 Domain-Specific Functions");
// 1. Medical imaging
println!(" • Medical imaging functions:");
let vesselness = medical::frangi_vesselness(&image.view(), None)?;
println!(" Frangi vesselness filter applied");
let enhanced_bone = medical::enhance_bone_structure(&image.view(), 1.0, 2.0)?;
println!(" Bone structure enhancement completed");
// 2. Satellite/remote sensing
println!(" • Satellite/remote sensing:");
let fake_nir = image + &Array2::ones(image.dim()) * 0.3; // Simulate NIR band
let ndvi = satellite::compute_ndvi(&fake_nir.view(), &image.view())?;
println!(" NDVI computation completed");
let water_mask = satellite::detect_water_bodies(&image.view(), 0.3)?;
println!(" Water body detection completed");
// 3. Microscopy
println!(" • Microscopy functions:");
let cell_params = microscopy::CellSegmentationParams::default();
let cells = microscopy::segment_cells(&image.view(), cell_params)?;
println!(" Cell segmentation: {} cells detected", cells.len());
Ok(())
}
#[cfg(feature = "simd")]
#[allow(dead_code)]
fn demonstrate_advanced_simd(image: &Array2<f64>) -> NdimageResult<()> {
println!("⚡ Advanced SIMD Extensions");
// 1. Wavelet pyramid
println!(" • Advanced-SIMD wavelet pyramid:");
let pyramid = advanced_simd_wavelet_pyramid(image.view(), 3, WaveletType::Daubechies4)?;
println!(
" Wavelet pyramid: {} levels generated",
pyramid.levels.len()
);
// 2. Local Binary Patterns
println!(" • Advanced-SIMD multi-scale LBP:");
let lbp = advanced_simd_multi_scale_lbp(image.view(), &[1, 2, 3], &[8, 16, 24])?;
println!(" Multi-scale LBP texture analysis completed");
// 3. Advanced edge detection
println!(" • Advanced-SIMD advanced edge detection:");
let advanced_edges = advanced_simd_advanced_edge_detection(image.view(), 1.0, 0.1, 0.3)?;
println!(" Advanced edge detection with multi-directional gradients completed");
Ok(())
}
#[allow(dead_code)]
fn demonstrate_backends(image: &Array2<f64>) -> NdimageResult<()> {
println!("🖥️ Backend & Performance Optimization");
// 1. Backend selection
println!(" • Backend configuration:");
let backend = BackendBuilder::new()
.backend(Backend::Cpu)
.gpu_threshold(10000)
.allow_fallback(true)
.build()?;
println!(" Backend configured with automatic GPU fallback");
// 2. Auto backend selection
println!(" • Automatic backend selection:");
let auto_backend = auto_backend(&[image.nrows(), image.ncols()])?;
println!(" Auto-selected backend: {:?}", auto_backend);
#[cfg(feature = "cuda")]
{
println!(" • GPU acceleration available");
// GPU-specific operations would go here
}
#[cfg(not(feature = "cuda"))]
{
println!(" • GPU acceleration not available (compile with --features cuda)");
}
Ok(())
}
#[allow(dead_code)]
fn demonstrate_streaming() -> NdimageResult<()> {
println!("🌊 Streaming for Large Datasets");
// Create a temporary large file simulation
println!(" • Streaming configuration:");
let config = StreamConfig {
chunk_size: (512, 512),
overlap: (32, 32),
num_workers: 4,
buffer_size: 1024 * 1024 * 100, // 100MB buffer
};
println!(
" Stream config: {}x{} chunks, {} workers",
config.chunk_size.0, config.chunk_size.1, config.num_workers
);
// Create streaming processor
println!(" • Streaming Gaussian filter setup:");
let streaming_filter = StreamingGaussianFilter::new([2.0, 2.0]);
println!(" Streaming Gaussian filter configured");
println!(" • For real file processing, use:");
println!(" stream_process_file(input_path, output_path, operation, config)");
Ok(())
}
#[allow(dead_code)]
fn demonstrate_memory_management(image: &Array2<f64>) -> NdimageResult<()> {
println!("🧠 Memory Management");
use scirs2_ndimage::memory_management::*;
// 1. Memory estimation
println!(" • Memory usage estimation:");
let memory_needed = estimate_memory_usage(&[image.nrows(), image.ncols()], 8)?; // 8 bytes per f64
println!(
" Estimated memory for processing: {:.2} MB",
memory_needed as f64 / 1_000_000.0
);
// 2. Memory configuration
println!(" • Memory configuration:");
let memory_config = MemoryConfig {
max_memory_gb: 4.0,
chunk_size_mb: 100.0,
enable_memory_mapping: true,
buffer_pool_size: 10,
};
println!(
" Memory limit: {:.1} GB, chunk size: {:.1} MB",
memory_config.max_memory_gb, memory_config.chunk_size_mb
);
// 3. Buffer pool
println!(" • Buffer pool management:");
let buffer_pool = BufferPool::new(5, 1024 * 1024)?; // 5 buffers of 1MB each
println!(" Buffer pool created with 5 buffers");
Ok(())
}
#[allow(dead_code)]
fn demonstrate_visualization(image: &Array2<f64>) -> NdimageResult<()> {
println!("📊 Visualization");
// 1. Colormaps
println!(" • Colormap creation:");
let colormap = create_colormap(ColorMap::Viridis, 256)?;
println!(" Viridis colormap created with 256 colors");
// 2. Statistical plots
println!(" • Statistical visualization:");
let plot_config = PlotConfig::default();
let histogram_data = plot_histogram(&image.view(), 50, plot_config.clone())?;
println!(" Histogram data generated for 50 bins");
// 3. Report generation
println!(" • Report generation:");
let report_config = ReportConfig {
format: ReportFormat::Html,
include_statistics: true,
include_plots: true,
output_path: "analysis_report.html".into(),
};
println!(" Report configuration created for HTML output");
Ok(())
}
#[allow(dead_code)]
fn demonstrate_best_practices() -> NdimageResult<()> {
println!("💡 Best Practices & Optimization Tips");
println!(" 1. Performance Tips:");
println!(" • Use SIMD features: compile with --features simd");
println!(" • Enable parallel processing: --features parallel");
println!(" • Choose appropriate data types (f32 vs f64)");
println!(" • Use in-place operations when possible");
println!(" 2. Memory Optimization:");
println!(" • Use streaming for large datasets (> 1GB)");
println!(" • Configure appropriate chunk sizes");
println!(" • Enable memory mapping for file processing");
println!(" • Use buffer pools for repeated operations");
println!(" 3. Error Handling:");
println!(" • Always handle NdimageResult errors");
println!(" • Check input dimensions and data types");
println!(" • Validate parameters before processing");
println!(" 4. API Selection:");
println!(" • Use SciPy-compatible API for migration");
println!(" • Use native Rust API for new code");
println!(" • Combine with other scirs2 modules as needed");
println!(" 5. Backend Selection:");
println!(" • Use auto backend selection for adaptive performance");
println!(" • Configure GPU thresholds based on your hardware");
println!(" • Enable fallback mechanisms for robustness");
Ok(())
}
#[cfg(not(feature = "simd"))]
#[allow(dead_code)]
fn demonstrate_advanced_simd(image: &Array2<f64>) -> NdimageResult<()> {
println!("⚡ Advanced SIMD Extensions");
println!(" Note: SIMD features not available (compile with --features simd)");
Ok(())
}