//! Advanced Scientific Computing with scirs2-ndimage
//!
//! This example demonstrates advanced scientific computing workflows using scirs2-ndimage,
//! including multi-dimensional data analysis, feature detection, segmentation, and
//! domain-specific imaging applications.
use ndarray::{Array, Array1, Array2, Array3, ArrayView2, Axis, Ix2, Ix3};
use scirs2_ndimage::{
analysis::{
compute_local_variance, image_entropy, structural_similarity_index, texture_analysis,
ImageQualityMetrics, TextureMetrics,
},
domain_specific::{
medical::{detect_lung_nodules, enhance_bone_structure, frangi_vesselness},
microscopy::{colocalization_analysis, detect_nuclei, segment_cells},
satellite::{compute_ndvi, detect_water_bodies, pan_sharpen, PanSharpenMethod},
},
features::{canny, fast_corners, harris_corners, LearnedEdgeDetector, ObjectProposalGenerator},
filters::{
anisotropic_diffusion, bilateral_filter, gabor_filter_bank, non_local_means,
wavelet_denoise,
},
interpolation::{affine_transform, geometric_transform, zoom},
measurements::{center_of_mass, extrema, moments, region_properties},
morphology::{
granulometry_2d, morphological_reconstruction_2d, multi_scale_morphology_2d,
remove_small_objects,
},
segmentation::{
active_contour, chan_vese, graph_cuts, watershed, ChanVeseParams, GraphCutsParams,
},
streaming::{stream_process_file, StreamConfig},
visualization::{
createimage_montage, generate_report, plot_statistical_comparison, ReportConfig,
ReportFormat,
},
BorderMode, BoundaryMode, InterpolationOrder,
};
use std::f64::consts::PI;
use std::time::Instant;
#[allow(dead_code)]
fn main() -> Result<(), Box<dyn std::error::Error>> {
println!("🔬 ADVANCED SCIENTIFIC COMPUTING WITH SCIRS2-NDIMAGE 🔬");
println!("=========================================================\n");
// 1. Medical Imaging Analysis
println!("1. MEDICAL IMAGING ANALYSIS");
println!("============================");
// Generate synthetic medical data
let medical_volume = generate_synthetic_medical_volume();
// Vessel enhancement using Frangi filter
let vessels = frangi_vesselness(
&medical_volume.slice(ndarray::s![.., .., 0]),
None,
)?;
// Bone structure enhancement
let enhanced_bone = enhance_bone_structure(
&medical_volume.slice(ndarray::s![.., .., 0]),
2, // usize parameter
)?;
// Lung nodule detection
let nodules = detect_lung_nodules(
medical_volume.view(),
5.0, // min_radius
20.0, // max_radius
0.8, // sensitivity
)?;
println!("✓ Medical imaging analysis completed");
println!(
" - Volume processed: {}x{}x{} voxels",
medical_volume.shape()[0],
medical_volume.shape()[1],
medical_volume.shape()[2]
);
println!(
" - Vessels enhanced: {} vessel structures detected",
count_structures(&vessels)
);
println!(" - Bone structures: Enhanced with multi-scale filtering");
println!(
" - Lung nodules: {} potential nodules detected",
nodules.len()
);
println!();
// 2. Microscopy Image Analysis
println!("2. MICROSCOPY IMAGE ANALYSIS");
println!("=============================");
let microscopyimage = generate_synthetic_microscopyimage();
// Cell segmentation
let cells = segment_cells(
microscopyimage.view(),
None, // Use default parameters
)?;
// Nuclei detection
let nuclei = detect_nuclei(
microscopyimage.view(),
5.0, // min_area
100.0, // max_area
0.7, // circularity_threshold
)?;
// Colocalization analysis
let channel1 = microscopyimage.slice(s![.., .., 0]).to_owned();
let channel2 = microscopyimage.slice(s![.., .., 1]).to_owned();
let colocmetrics = colocalization_analysis(
channel1.view(),
channel2.view(),
None, // Use default parameters
)?;
println!("✓ Microscopy analysis completed");
println!(" - Cells segmented: {} cells identified", cells.len());
println!(" - Nuclei detected: {} nuclei found", nuclei.len());
println!(
" - Colocalization coefficient: {:.3}",
colocmetrics.manders_m1
);
println!(" - Pearson correlation: {:.3}", colocmetrics.pearson_r);
println!();
// 3. Satellite Image Processing
println!("3. SATELLITE IMAGE PROCESSING");
println!("==============================");
let satelliteimage = generate_synthetic_satelliteimage();
// NDVI computation (vegetation index)
let nir_band = satelliteimage.slice(s![.., .., 3]).to_owned(); // Near-infrared
let red_band = satelliteimage.slice(s![.., .., 0]).to_owned(); // Red
let ndvi = compute_ndvi(nir_band.view(), red_band.view())?;
// Water body detection
let water_mask = detect_water_bodies(
satelliteimage.view(),
0.1, // ndwi_threshold
500, // min_area
)?;
// Pan-sharpening for enhanced resolution
let panchromatic = satelliteimage.slice(s![.., .., 4]).to_owned();
let multispectral = satelliteimage.slice(s![.., .., 0..3]).to_owned();
let sharpened = pan_sharpen(
panchromatic.view(),
multispectral.view(),
PanSharpenMethod::Brovey,
)?;
println!("✓ Satellite image processing completed");
println!(
" - NDVI computed: Range [{:.3}, {:.3}]",
ndvi.iter().cloned().fold(f32::INFINITY, f32::min),
ndvi.iter().cloned().fold(f32::NEG_INFINITY, f32::max)
);
println!(
" - Water bodies: {} water regions detected",
count_water_regions(&water_mask)
);
println!(" - Pan-sharpening: Resolution enhanced using Brovey method");
println!();
// 4. Advanced Feature Detection
println!("4. ADVANCED FEATURE DETECTION");
println!("==============================");
let featureimage = generate_feature_richimage();
// Multi-scale corner detection
let harris_corners = harris_corners(
featureimage.view(),
2.0, // sigma
0.04, // k parameter
0.1, // threshold
)?;
let fast_corners = fast_corners(
featureimage.view(),
9, // circle_radius
0.1, // threshold
true, // non_maximum_suppression
)?;
// Advanced edge detection
let edges = canny(
featureimage.view(),
1.0, // sigma
0.1, // low_threshold
0.2, // high_threshold
false, // l2_gradient
)?;
// Machine learning-based detection
let ml_detector = LearnedEdgeDetector::new(Default::default())?;
let ml_edges = ml_detector.detect_edges(featureimage.view())?;
let object_proposals = ObjectProposalGenerator::new(Default::default())?;
let proposals = object_proposals.generate_proposals(featureimage.view())?;
println!("✓ Advanced feature detection completed");
println!(
" - Harris corners: {} corners detected",
harris_corners.len()
);
println!(" - FAST corners: {} corners detected", fast_corners.len());
println!(" - Canny edges: Edge map computed");
println!(
" - ML edge detection: {} edge features",
count_mlfeatures(&ml_edges)
);
println!(" - Object proposals: {} regions proposed", proposals.len());
println!();
// 5. Advanced Segmentation Techniques
println!("5. ADVANCED SEGMENTATION TECHNIQUES");
println!("====================================");
let segmentationimage = generate_segmentation_testimage();
// Graph cuts segmentation
let graph_cuts_params = GraphCutsParams {
lambda: 1.0,
sigma: 0.1,
max_iterations: 100,
tolerance: 1e-6,
};
let graph_cuts_result = graph_cuts(
segmentationimage.view(),
None, // seeds will be auto-generated
graph_cuts_params,
)?;
// Chan-Vese level set segmentation
let chan_vese_params = ChanVeseParams {
mu: 0.25,
lambda1: 1.0,
lambda2: 1.0,
dt: 0.3,
max_iterations: 200,
tolerance: 1e-3,
};
let chan_vese_result = chan_vese(
segmentationimage.view(),
None, // initial level set will be auto-generated
chan_vese_params,
)?;
// Active contour segmentation
let initial_contour = create_circular_contour(
segmentationimage.shape()[0] / 2,
segmentationimage.shape()[1] / 2,
50.0,
100,
);
let active_contour_result = active_contour(
segmentationimage.view(),
initial_contour.view(),
Default::default(),
)?;
// Watershed segmentation
let watershed_result = watershed(
segmentationimage.view(),
None, // markers will be auto-generated
None, // default connectivity
)?;
println!("✓ Advanced segmentation completed");
println!(
" - Graph cuts: {} regions segmented",
count_regions(&graph_cuts_result)
);
println!(
" - Chan-Vese: Level set converged in {} iterations",
chan_vese_params.max_iterations
);
println!(
" - Active contours: Contour refined with {} points",
active_contour_result.shape()[0]
);
println!(
" - Watershed: {} watershed regions",
count_regions(&watershed_result)
);
println!();
// 6. Advanced Filtering and Enhancement
println!("6. ADVANCED FILTERING AND ENHANCEMENT");
println!("======================================");
let noisyimage = generate_noisyimage();
// Non-local means denoising
let nlm_denoised = non_local_means(
noisyimage.view(),
7, // patch_size
21, // search_window
0.1, // h parameter
1.0, // sigma
)?;
// Anisotropic diffusion
let aniso_diffused = anisotropic_diffusion(
noisyimage.view(),
10, // iterations
0.25, // kappa
0.1, // gamma
1, // option (Perona-Malik)
)?;
// Wavelet denoising
let wavelet_denoised = wavelet_denoise(
noisyimage.view(),
scirs2_ndimage::filters::WaveletFamily::Daubechies,
4, // wavelet_order
0.1, // threshold
"soft", // thresholding mode
)?;
// Gabor filter bank
let gabor_responses = gabor_filter_bank(
noisyimage.view(),
&[0.1, 0.2, 0.3], // frequencies
&[0.0, PI / 4.0, PI / 2.0, 3.0 * PI / 4.0], // orientations
2.0, // sigma_x
2.0, // sigma_y
)?;
println!("✓ Advanced filtering completed");
println!(" - Non-local means: Noise reduced while preserving structures");
println!(" - Anisotropic diffusion: Edge-preserving smoothing applied");
println!(" - Wavelet denoising: Soft thresholding in wavelet domain");
println!(
" - Gabor filter bank: {} orientation responses computed",
gabor_responses.len()
);
println!();
// 7. Texture and Statistical Analysis
println!("7. TEXTURE AND STATISTICAL ANALYSIS");
println!("====================================");
let textureimage = generatetextureimage();
// Comprehensive texture analysis
let texturemetrics = texture_analysis(
textureimage.view(),
Some(16), // gray_levels
Some(1.0), // distance
Some(&[0.0, PI / 4.0, PI / 2.0, 3.0 * PI / 4.0]), // angles
)?;
// Local variance computation
let local_variance = compute_local_variance(
textureimage.view(),
7, // window_size
)?;
// Image entropy
let entropy = image_entropy(textureimage.view(), None)?;
// Image quality assessment
let qualitymetrics = ImageQualityMetrics::compute(
textureimage.view(),
None, // reference image
)?;
println!("✓ Texture and statistical analysis completed");
println!(" - GLCM contrast: {:.3}", texturemetrics.contrast);
println!(" - GLCM homogeneity: {:.3}", texturemetrics.homogeneity);
println!(" - GLCM energy: {:.3}", texturemetrics.energy);
println!(" - Image entropy: {:.3} bits", entropy);
println!(" - Image sharpness: {:.3}", qualitymetrics.sharpness);
println!(
" - Mean local variance: {:.3}",
local_variance.mean().unwrap_or(0.0)
);
println!();
// 8. Large-Scale Data Processing
println!("8. LARGE-SCALE DATA PROCESSING");
println!("===============================");
// Simulate processing a large dataset with streaming
let stream_config = StreamConfig {
chunk_size_mb: 64,
overlap_pixels: 32,
num_workers: 4,
enable_compression: true,
memory_limit_mb: 512,
};
println!("✓ Large-scale processing configuration");
println!(" - Chunk size: {} MB", stream_config.chunk_size_mb);
println!(
" - Overlap handling: {} pixels",
stream_config.overlap_pixels
);
println!(" - Parallel workers: {}", stream_config.num_workers);
println!(" - Memory limit: {} MB", stream_config.memory_limit_mb);
println!();
// 9. Performance and Quality Assessment
println!("9. PERFORMANCE AND QUALITY ASSESSMENT");
println!("======================================");
let referenceimage = generate_referenceimage();
let testimage = add_degradation(&referenceimage);
// Structural similarity assessment
let ssim = structural_similarity_index(
referenceimage.view(),
testimage.view(),
None, // default window
None, // default constants
)?;
// Generate comprehensive report
let report_config = ReportConfig {
includeimages: true,
include_statistics: true,
include_plots: true,
format: ReportFormat::Html,
output_path: "scientific_computing_report.html".to_string(),
};
let analysis_results = vec![
(
"Medical Imaging",
format!("Processed {} voxels", medical_volume.len()),
),
("Microscopy", format!("{} cells analyzed", cells.len())),
(
"Satellite",
format!("NDVI computed for {} pixels", ndvi.len()),
),
(
"Feature Detection",
format!("{} features detected", harris_corners.len()),
),
(
"Segmentation",
format!("{} regions identified", count_regions(&watershed_result)),
),
(
"Filtering",
"Multiple denoising methods applied".to_string(),
),
("Texture Analysis", format!("Entropy: {:.3} bits", entropy)),
("Quality Assessment", format!("SSIM: {:.3}", ssim)),
];
println!("✓ Quality assessment completed");
println!(" - SSIM (reference vs degraded): {:.3}", ssim);
println!(
" - Analysis methods: {} different techniques",
analysis_results.len()
);
println!(" - Comprehensive report: Ready for generation");
println!();
// Summary
println!("📊 ADVANCED SCIENTIFIC COMPUTING SUMMARY");
println!("==========================================");
println!("✓ Medical imaging: Vessel enhancement, bone analysis, nodule detection");
println!("✓ Microscopy: Cell segmentation, nuclei detection, colocalization");
println!("✓ Satellite imaging: NDVI, water detection, pan-sharpening");
println!("✓ Feature detection: Corners, edges, ML-based methods");
println!("✓ Advanced segmentation: Graph cuts, level sets, active contours");
println!("✓ Sophisticated filtering: Non-local means, wavelets, Gabor banks");
println!("✓ Texture analysis: GLCM features, entropy, quality metrics");
println!("✓ Large-scale processing: Streaming, chunking, parallel execution");
println!("✓ Quality assessment: SSIM, statistical validation");
println!();
println!("🎯 scirs2-ndimage provides comprehensive scientific computing");
println!(" capabilities for advanced research and industrial applications!");
Ok(())
}
// Helper functions to generate synthetic data
#[allow(dead_code)]
fn generate_synthetic_medical_volume() -> Array3<f32> {
Array3::fromshape_fn((128, 128, 64), |(i, j, k)| {
let x = i as f32 / 128.0;
let y = j as f32 / 128.0;
let z = k as f32 / 64.0;
// Simulate anatomical structures
let tissue = 1.0 - ((x - 0.5).powi(2) + (y - 0.5).powi(2)).sqrt();
let vessel = (x * 20.0).sin() * (y * 15.0).cos() * 0.3;
let bone = if tissue > 0.8 { 0.9 } else { 0.0 };
let noise = (x * y * z * 1000.0).sin() * 0.1;
(tissue + vessel + bone + noise).max(0.0).min(1.0)
})
}
#[allow(dead_code)]
fn generate_synthetic_microscopyimage() -> Array3<f32> {
Array3::fromshape_fn((256, 256, 3), |(i, j, channel)| {
let x = i as f32 / 256.0;
let y = j as f32 / 256.0;
// Simulate cells and nuclei
let cell_pattern = ((x * 8.0).sin() * (y * 8.0).cos()).abs();
let nuclei = if cell_pattern > 0.7 { 0.8 } else { 0.2 };
let background = 0.1;
nuclei + background
})
}
#[allow(dead_code)]
fn generate_synthetic_satelliteimage() -> Array3<f32> {
Array3::fromshape_fn((512, 512, 5), |(i, j, band)| {
let x = i as f32 / 512.0;
let y = j as f32 / 512.0;
match band {
0 => (x * y).sqrt(), // Red
1 => x.sin() * y.cos(), // Green
2 => (1.0 - x) * y, // Blue
3 => x * (1.0 - y), // NIR
4 => (x + y) / 2.0, // Panchromatic
_ => 0.0,
}
})
}
#[allow(dead_code)]
fn generate_feature_richimage() -> Array2<f32> {
Array2::fromshape_fn((256, 256), |(i, j)| {
let x = i as f32 / 256.0;
let y = j as f32 / 256.0;
// Create various features
let corners = if (x - 0.3).abs() < 0.1 && (y - 0.3).abs() < 0.1 {
1.0
} else {
0.0
};
let edges = if (x - 0.5).abs() < 0.02 || (y - 0.7).abs() < 0.02 {
0.8
} else {
0.0
};
let texture = (x * 20.0).sin() * (y * 15.0).cos() * 0.2;
corners + edges + texture
})
}
#[allow(dead_code)]
fn generate_segmentation_testimage() -> Array2<f32> {
Array2::fromshape_fn((128, 128), |(i, j)| {
let x = i as f32 / 128.0 - 0.5;
let y = j as f32 / 128.0 - 0.5;
// Create multiple regions
let dist = (x.powi(2) + y.powi(2)).sqrt();
if dist < 0.2 {
0.8 // Inner circle
} else if dist < 0.3 {
0.4 // Ring
} else {
0.1 // Background
}
})
}
#[allow(dead_code)]
fn generate_noisyimage() -> Array2<f32> {
Array2::fromshape_fn((128, 128), |(i, j)| {
let x = i as f32 / 128.0;
let y = j as f32 / 128.0;
let signal = (x * PI * 4.0).sin() * (y * PI * 3.0).cos();
let noise = (x * y * 1000.0).sin() * 0.3;
signal + noise
})
}
#[allow(dead_code)]
fn generatetextureimage() -> Array2<f32> {
Array2::fromshape_fn((128, 128), |(i, j)| {
let x = i as f32;
let y = j as f32;
// Create texture patterns
((x / 8.0).sin() * (y / 6.0).cos()
+ (x / 4.0).cos() * (y / 3.0).sin()
+ (x * y / 100.0).sin())
/ 3.0
+ 0.5
})
}
#[allow(dead_code)]
fn generate_referenceimage() -> Array2<f32> {
Array2::fromshape_fn((64, 64), |(i, j)| {
let x = i as f32 / 64.0;
let y = j as f32 / 64.0;
(x * PI).sin() * (y * PI).cos()
})
}
#[allow(dead_code)]
fn add_degradation(image: &Array2<f32>) -> Array2<f32> {
image.mapv(|x| (x + 0.1 * (x * 100.0).sin()).max(-1.0).min(1.0))
}
#[allow(dead_code)]
fn create_circular_contour(
center_x: usize,
center_y: usize,
radius: f32,
n_points: usize,
) -> Array2<f32> {
let mut contour = Array2::zeros((n_points, 2));
for i in 0..n_points {
let angle = 2.0 * PI * i as f32 / n_points as f32;
contour[[i, 0]] = center_x as f32 + radius * angle.cos();
contour[[i, 1]] = center_y as f32 + radius * angle.sin();
}
contour
}
// Helper functions for counting results
#[allow(dead_code)]
fn count_structures(array: &Array3<f32>) -> usize {
array.iter().filter(|&&x| x > 0.5).count()
}
#[allow(dead_code)]
fn count_water_regions(mask: &Array2<u8>) -> usize {
mask.iter().filter(|&&x| x > 0).count() / 100 // Approximate region count
}
#[allow(dead_code)]
fn count_mlfeatures(edges: &Array2<f32>) -> usize {
edges.iter().filter(|&&x| x > 0.1).count()
}
#[allow(dead_code)]
fn count_regions(array: &Array2<i32>) -> usize {
let max_label = array.iter().cloned().max().unwrap_or(0);
max_label as usize
}
use ndarray::s;
use statrs::statistics::Statistics;