Module ndimage

Expand description

N-Dimensional Image Processing Module

This module provides comprehensive multidimensional image processing functionality, built on top of scirs2-ndimage. It includes:

Filters: Gaussian, median, rank, and edge detection filters
Morphology: Binary and grayscale morphological operations
Measurements: Region properties, moments, and extrema detection
Segmentation: Thresholding and watershed algorithms
Features: Corner and edge detection
Interpolation: Spline and geometric transformations

§Examples

use numrs2::ndimage;
use scirs2_core::ndarray::Array2;

// Create a sample 2D image
let image = Array2::<f64>::from_shape_fn((10, 10), |(i, j)| {
    if (i > 3 && i < 7) && (j > 3 && j < 7) {
        1.0
    } else {
        0.0
    }
});

// Apply Gaussian filter
let sigma = 1.0;
let filtered = ndimage::filters::gaussian_filter(&image, sigma, None, None).expect("gaussian_filter should succeed");

// Create binary image and apply binary dilation
let binary_image: Array2<bool> = Array2::from_shape_fn((10, 10), |(i, j)| {
    i > 3 && i < 7 && j > 3 && j < 7
});
let dilated = ndimage::morphology::binary_dilation(&binary_image, None, None, None, None, None, None).expect("binary_dilation should succeed");

§Filters

The filters module provides various image filtering operations:

gaussian_filter: Apply Gaussian filter for smoothing
median_filter: Apply median filter for noise reduction
minimum_filter, maximum_filter: Morphological filters
sobel, prewitt: Edge detection filters
laplace: Laplace filter for edge enhancement

§Morphology

Morphological operations for binary and grayscale images:

binary_erosion, binary_dilation: Basic morphological operations
binary_opening, binary_closing: Composite operations
binary_fill_holes: Fill holes in binary objects
grey_erosion, grey_dilation: Grayscale morphology

§Measurements

Functions for measuring properties of labeled regions:

label: Connected component labeling
center_of_mass: Compute center of mass
sum, mean, variance: Statistics over labeled regions
minimum, maximum: Extrema over labeled regions

§Segmentation

Image segmentation algorithms:

threshold_otsu: Otsu’s automatic thresholding
threshold_adaptive: Adaptive local thresholding
watershed: Watershed segmentation

§Features

Feature detection algorithms:

corner_harris: Harris corner detector
canny: Canny edge detector
sobel: Sobel edge detector

§Interpolation

Geometric transformations and interpolation:

rotate, shift, zoom: Basic geometric transformations
affine_transform: General affine transformations
map_coordinates: Custom coordinate mapping

Modules§

active_contours: Advanced active contour (snake) models for image segmentation
adaptive_image_optimizer: Adaptive Advanced Optimizer - Dynamic Performance Tuning
advanced_fusion_algorithms: Advanced Fusion Core - Ultimate Image Processing Engine
advanced_streaming_ai: Advanced AI-Driven Streaming with Predictive Chunking
ai_driven_adaptive_processing: AI-Driven Adaptive Processing - Next-Generation Intelligent Image Processing
analysis: Advanced image analysis and quality assessment functions
api_compatibility_verification: API Compatibility Verification with SciPy ndimage
array4d: 4D spatiotemporal array support for scirs2-ndimage.
backend: Backend delegation system for GPU acceleration
biological_vision_inspired: Biological Vision System Inspired Algorithms
chunked: Chunked processing for memory-efficient operations on large arrays
chunked_morphology: Memory-efficient morphological operations with chunked processing
chunked_processing: Enhanced chunked image processing for large images
chunked_v2: Enhanced chunked processing using scirs2-core’s memory-efficient features
co_occurrence: Co-occurrence Matrix Features and Run-Length Matrix
comprehensive_examples: Comprehensive Examples and Documentation for scirs2-ndimage
comprehensive_scipy_benchmarks: Comprehensive benchmarking suite comparing scirs2-ndimage with SciPy ndimage
comprehensive_scipy_validation: Comprehensive Numerical Validation Against SciPy ndimage
comprehensive_validation: Enhanced Validation Framework for Advanced Mode
convolution3d: 3D Convolution and Volumetric Filtering
crf: Conditional Random Field (CRF) post-processing module
deep_features: CNN-inspired deep image feature extraction (no neural network training required).
distance_transforms: Distance transform algorithms for 2D binary images
documentation: Documentation Generation System
domain_specific: Domain-specific imaging functions
error: Error types for the ndimage module
feature_detection: Advanced Image Feature Detection
features: Feature detection module
filters: Filtering functions for n-dimensional arrays
filters3d: 3D Image Filters
freq_filter: Frequency Domain Filtering Module
frequency_domain: Frequency Domain Image Processing
gpu_chunked: GPU-accelerated chunked image processing
gpu_operations: GPU-accelerated implementations for intensive ndimage operations
hyperdimensional_computing: Hyperdimensional Computing for Advanced-Efficient Pattern Recognition
interpolation: Interpolation functions for n-dimensional arrays
level_set: Geodesic active contours and Chan-Vese level-set segmentation.
measurements: Measurement functions for n-dimensional arrays
memory_management: Memory management utilities for ndimage operations
meta_learning_algorithms: Advanced Enhanced Meta-Learning and Transfer Learning System
mmap_io: Memory-mapped I/O operations for large images
moment_invariants: Image moments and invariant descriptors.
morphology: Morphological operations on n-dimensional arrays
morphology3d: 3D Morphological Operations
morphology_advanced: Advanced mathematical morphology transforms
morphology_ext: Extended mathematical morphology operations.
neuromorphic_computing: Neuromorphic Computing for Image Processing
optical_flow: Optical flow algorithms for dense motion estimation
out_of_core: Out-of-core image processing pipeline
performance_profiler: Advanced performance profiling and optimization tools for ndimage operations
profiling: Performance profiling and optimization tools
python_interop: Python Interoperability Support
quality: Image quality assessment metrics.
quantum_ai_consciousness: Quantum-AI Consciousness Processor - Beyond Human-Level Image Understanding
quantum_enhanced_gpu: Quantum-Enhanced GPU Acceleration Framework
quantum_inspired: Quantum-Inspired Image Processing Algorithms
quantum_neuromorphic_fusion: Quantum-Neuromorphic Fusion for Image Processing
radiomics: Radiomics feature extraction module
regionprops: Region properties analysis for labeled images
registration: Image Registration Module
scipy_compat: SciPy ndimage compatibility layer
scipy_compat_layer: SciPy ndimage compatibility layer
scipy_migration_layer: SciPy ndimage API compatibility layer for seamless migration
scipy_performance_comparison: Comprehensive Performance Comparison with SciPy ndimage
segmentation: Image segmentation module
segmentation_advanced: Advanced segmentation algorithms for scirs2-ndimage
segmentation_ext: Extended image segmentation algorithms.
shape_analysis: Shape analysis: convex hull, contour extraction, shape descriptors, ellipse fitting, and minimum bounding rectangle.
skeletonize: Morphological skeletonization and binary shape analysis
slice_processing: Multi-Planar Reconstruction and Volume Rendering
streaming: Streaming operations for processing large datasets
template_matching: Template Matching and Sliding Window Detection
texture_segmentation: Texture-Based Image Segmentation
threading: Thread pool integration for shared worker management
topology: Topological analysis of binary images.
utils: Common utility functions for ndimage processing
visualization: Visualization and Reporting Utilities for Image Processing Results
volume_analysis: 3D Volumetric Image Analysis
volumetric_ops: Volumetric Image Operations
watershed: Advanced watershed segmentation algorithms
watershed3d: 3D watershed segmentation with topological constraints.
zero_copy: Zero-copy transformations for memory-efficient image processing

Macros§

profile_op: Profile an operation

Structs§

AccuracyResult: Numerical accuracy comparison results
ActiveContourParams: Parameters for active contour evolution
AdaptiveMemoryConsolidation: Adaptive Memory Consolidation
AdaptiveThreadPool: Adaptive thread pool that adjusts based on workload
AdvancedConfig: Advanced Processing Configuration
AdvancedState: Advanced Processing State
ApiCompatibilityResult: API compatibility test result
ApiCompatibilityTester: Comprehensive API compatibility tester
BackendBuilder: Builder for creating backend executors
BackendConfig: Configuration for backend selection and operation
BackendExecutor: Backend executor that handles delegation
BatchNormParams: Batch normalization parameters
BenchmarkConfig: Comprehensive benchmark suite configuration
BoundingBox2D: 2D bounding box for a labeled region
BufferPool: Buffer pool for reusing allocated arrays
CellInfo: Information about a segmented cell
CellSegmentationParams: Parameters for cell segmentation
ChanVeseParams: Parameters for Chan-Vese segmentation
ChunkConfig: Configuration for chunked processing
ColocalizationMetrics: Colocalization analysis results
CompactWatershedConfig: Configuration for compact (regularized) watershed
CompatibilityConfig: Configuration for compatibility testing
CompatibilityResult: API compatibility test result
ComprehensiveValidator: Comprehensive validation framework for Advanced operations
ConsciousnessComplexity: Consciousness Complexity
ConsciousnessState: Consciousness State
Contour: A contour extracted from a binary image
ContourHierarchy: Contour hierarchy information
ContourMoments: Calculate moments of a contour
DamResult: Result of dam (boundary) extraction
EdgeDetectionConfig: Configuration for edge detection algorithms
EnhancedMetaLearningSystem: Enhanced Meta-Learning System
Event: Event representation for address-event processing
ExampleTutorial: Comprehensive tutorial and example collection
FeatureDetectorWeights: Pre-trained model weights for feature detection
GaborParams: Gabor filter parameters
GaussianChunkProcessor: Example chunk processor for Gaussian filtering
GraphCutsParams: Parameters for graph cuts segmentation
HierarchicalLearner: Hierarchical Learner
ImageQualityMetrics: Comprehensive image quality metrics
InteractiveGraphCuts: Interactive graph cuts segmentation with iterative refinement
LearnedEdgeDetector: Learned edge detector using convolutional filters
LearnedKeypointDescriptor: Keypoint descriptor using learned features
MLDetectorConfig: Configuration for ML-based feature detection
MemoryConfig: Configuration for memory-efficient operations
MemoryEfficientOp: Memory-efficient wrapper for array operations
MetaLearningTracker: Meta-Learning Tracker
MultiScaleBilateralConfig: Configuration for multi-scale bilateral filtering
MultiScaleConfig: Multi-scale analysis configuration
MultiScaleMorphConfig: Advanced morphological operations for texture analysis and feature extraction
NeuromorphicConfig: Neuromorphic processing configuration
Nodule: Detected nodule information
ObjectProposal: Object proposal with location and score
ObjectProposalGenerator: Object proposal generator using learned objectness scores
OversegmentationConfig: Configuration for oversegmentation control
ParameterTest: Parameter compatibility test
PerformanceBenchmark: Performance benchmark data
PerformanceResult: Performance comparison results for a single operation
PerformanceSummary: Performance summary
PlasticSynapse: Synaptic connection with plasticity
PlotConfig: Configuration for plotting operations
QuantumAwareResourceScheduler: Quantum-Aware Resource Scheduler
QuantumCoherenceOptimizer: Quantum Coherence Optimizer
QuantumConfig: Configuration for quantum-inspired algorithms
QuantumConsciousnessEvolution: Quantum consciousness evolution system
QuantumState: Quantum state representation for image processing
RegionProperties: Properties structure for labeled regions
RegionProperties2D: Comprehensive properties of a labeled region in a 2D image
RegionProps: Comprehensive properties of a single labeled region
ReportConfig: Configuration for report generation
ResourceSchedulingDecision: Resource Scheduling Decision
SciPyBenchmarkSuite: Main benchmark suite for comprehensive performance analysis
SciPyValidationConfig: Configuration for validation testing
SciPyValidationSuite: Comprehensive numerical validation suite
SemanticFeatureExtractor: Semantic feature extractor using pre-trained deep features
SnakeParams: Parameters controlling active contour evolution
SnakeResult: Result of active contour evolution
SpikingNeuron: Spiking neuron state
StrategyEvolution: Strategy Evolution
StreamConfig: Configuration for streaming operations
StreamProcessor: Streaming processor for large arrays
StreamingGaussianFilter: Example: Streaming Gaussian filter
TemporalMemoryFusion: Temporal Memory Fusion
TextureMetrics: Texture analysis results
ThreadPoolConfig: Thread pool configuration
ThreadPoolContext: Thread pool aware execution context
TutorialStep: Individual tutorial step
ValidationConfig: Validation configuration parameters
ValidationError: Validation error information
ValidationReport: Validation report
ValidationResult: Numerical validation result for a single test case
VesselEnhancementParams: Parameters for vessel enhancement
WatershedConfig: Configuration for watershed segmentation
WaveletFilter: Wavelet filter coefficients
WorkStealingQueue: Work-stealing queue for load balancing
WorkerInfo: Thread-local worker information
WorkloadCharacteristics: Workload Characteristics

Enums§

AdaptiveMethod: Apply adaptive thresholding
ApproximationMethod: Contour approximation method
Backend: Available computation backends
BorderMode: Border handling modes for filters
BoundaryMode: Boundary mode for handling points outside the input array
CoherenceStrategy: Coherence Strategy
ColorMap: Color map types for visualization
Connectivity: Structuring element connectivity for binary morphology
DistanceMetric: Distance metrics for distance transforms
EdgeDetectionAlgorithm: Edge detection algorithm options
GradientMethod: Gradient calculation method for edge detection
InterpolationOrder: Order of interpolation
MemoryStrategy: Memory allocation strategy for operations
MorphBorderMode: Border handling modes for morphological operations
MorphOperation: Types of morphological operations
NdimageError: Error type for ndimage operations
PanSharpenMethod: Pan-sharpening method
RegionConnectivity: Connectivity mode for connected component labeling
RegionFilter: Predicate for filtering regions by property
ReportFormat: Report output format options
RetrievalMode: Contour retrieval mode
StructureType: Types of structuring elements
ThresholdMethod
WatershedConnectivity: Connectivity mode for watershed segmentation
WatershedNeighborhood: Connectivity mode for watershed neighbor traversal
WaveletFamily: Wavelet family enumeration

Traits§

BackendOp: Trait for operations that can be delegated to different backends
ChunkProcessor: Trait for operations that can be applied to chunks
InPlaceOp: Trait for operations that can be performed in-place
StreamableOp: Trait for operations that can be applied in a streaming fashion

Functions§

active_contour: Active contour segmentation using parametric snakes
adaptive_bilateral_filter: Adaptive bilateral filter that automatically adjusts parameters based on local characteristics
adaptive_threshold
adaptive_wiener_filter: Apply adaptive Wiener filtering for noise reduction
affine_transform: Apply an affine transformation to an array using interpolation
anisotropic_diffusion: Apply anisotropic diffusion filtering for edge-preserving smoothing
area_opening_2d: Area opening - removes connected components smaller than a given area
auto_backend: Convenience function to create an auto-selecting backend executor
auto_markers: Generate automatic markers from local minima of the image
batch_quality_assessment: High-performance batch quality assessment for multiple image pairs
bilateral_filter: Apply a bilateral filter to preserve edges while smoothing
bilateral_gradient_filter: Apply a bilateral edge-preserving gradient filter
binary_closing: Close an array using a structuring element
binary_dilation: Dilate a binary array using a structuring element
binary_dilation_2d_optimized: Optimized binary dilation for 2D arrays
binary_erosion: Erode a binary array using a structuring element
binary_erosion_2d_optimized: Optimized binary erosion for 2D arrays
binary_fill_holes: Fill holes in a binary array
binary_hit_or_miss: Apply a binary hit-or-miss transform to an array
binary_opening: Open an array using a structuring element
black_tophat: Apply black tophat transformation to a grayscale array
black_tophat_2d_fast: Fast 2D black top-hat transform (closing - image)
box_structure: Create a box structuring element of given size
bspline: Evaluate a B-spline at given positions
calculate_accuracy_metrics: Calculate numerical accuracy metrics between two arrays
canny: Canny edge detector
center_of_mass: Find the center of mass (centroid) of an array
central_moments: Calculate central moments (moments around centroid)
chan_vese: Chan-Vese segmentation using level sets
chan_vese_multiphase: Multi-phase Chan-Vese segmentation for multiple regions
check_memory_limit: Check if an operation would exceed memory limit
checkerboard_level_set: Create checkerboard initialization for multi-phase segmentation
chessboard_distance_transform: Optimized chessboard distance transform
chessboard_dt: Chessboard (Chebyshev / L-infinity) distance transform
cityblock_distance_transform: Optimized city-block (Manhattan) distance transform
cityblock_dt: City block (Manhattan / L1) distance transform
coherence_enhancing_diffusion: Apply coherence enhancing diffusion for line-like structure enhancement
colocalization_analysis: Colocalization analysis for multi-channel microscopy
compact_watershed: Compact (regularized) watershed segmentation
compute_local_variance: Compute local variance in a sliding window
compute_ndvi: Compute Normalized Difference Vegetation Index (NDVI)
configure_parallel_ops: Integration with scirs2-core parallel operations
contour_area: Compute the area enclosed by a closed contour using the shoelace formula
contour_energy: Compute the total energy of a contour on an image
contour_perimeter: Compute the perimeter (total arc length) of a closed contour
contrast_to_noise_ratio: Compute Contrast-to-Noise Ratio
convolve: Convolve an n-dimensional array with a filter kernel
count_labels: Count the number of pixels/elements in each labeled region
count_labels_2d: Count pixels per label in a labeled image
create_circle_contour: Generate initial circular contour
create_circular_contour: Create a circular initial contour
create_colormap: Create a color map for visualization
create_ellipse_contour: Generate initial elliptical contour
create_elliptical_contour: Create an elliptical initial contour
create_output_array: Create a memory-efficient view or copy based on configuration
create_rectangular_contour: Create a rectangular initial contour
createimage_montage: Create an image montage/grid from multiple 2D arrays
detect_clouds: Cloud detection in satellite imagery
detect_lung_nodules: Lung nodule detection (simplified)
detect_nuclei: Detect and count nuclei in fluorescence microscopy
detect_water_bodies: Detect water bodies using spectral indices
disk_structure: Create a disk structuring element
distance_transform_bf: Calculate the distance transform of a binary image using a brute force algorithm.
distance_transform_cdt: Calculate the city block (Manhattan) distance transform of a binary image.
distance_transform_chessboard_2d: Efficient 2D Chessboard distance transform
distance_transform_cityblock_2d: Efficient 2D Manhattan (city-block) distance transform
distance_transform_edt: Calculate the Euclidean distance transform of a binary image.
distance_transform_edt_2d: Efficient 2D Euclidean distance transform using Meijster’s algorithm
distance_transform_edt_full: Compute the distance transform with both distances and indices
draw_contours: Draw contours on an image
dwt_1d: 1D Discrete Wavelet Transform
dwt_2d: 2D Discrete Wavelet Transform
edge_detector: Unified edge detector function that works with different algorithms
edge_detector_simple: Enhanced edge detector (compatible with previous API)
enhance_bone_structure: Bone structure enhancement using morphological operations
enhanced_meta_learning_with_temporal_fusion: Enhanced Meta-Learning with Temporal Memory Fusion
enhanced_quantum_consciousness_evolution: Enhanced Quantum Consciousness Processing with Evolution
estimate_fractal_dimension: Estimate fractal dimension using box-counting method
estimate_memory_usage: Estimate memory usage for an operation
euclidean_distance_transform: Compute the Euclidean distance transform (not squared)
euclidean_distance_transform_separable: Compute the squared Euclidean distance transform using the Felzenszwalb & Huttenlocher algorithm
euclidean_dt: Exact Euclidean Distance Transform using the Meijster separable algorithm
event_driven_processing: Event-Driven Image Processing
evolve_snake: Evolve an active contour (snake) on an image
extract_dams: Extract dam (boundary) pixels from a watershed segmentation
extrema: Find the extrema (min, max, min_loc, max_loc) of an array
fast_corners: Fast corner detector (FAST: Features from Accelerated Segment Test)
fill_holes_2d: Fill holes in a binary 2D image
filter_regions: Filter regions by one or more property criteria
find_boundaries: Find the boundaries of objects in a labeled array
find_contours: Find contours in a binary image using the Suzuki-Abe algorithm
find_objects: Find and locate objects in a labeled array
find_objects_2d: Find objects (bounding boxes) in a 2D labeled image
frangi_vesselness: Enhance blood vessels using Frangi filter
fusion_processing: Advanced Quantum-Conscious Image Processing
gabor_filter: Apply a Gabor filter to a 2D array
gabor_filter_bank: Apply a bank of Gabor filters with different orientations
gaussian_filter: Apply a Gaussian filter to an n-dimensional array of f64 values
gaussian_filter_chunked: Memory-efficient Gaussian filter
gaussian_filter_f32: Apply a gaussian filter to an n-dimensional array of f32 values
gaussian_filter_f64: Specialized Gaussian filter implementation for f64 arrays
generate_binary_structure: Generate a binary structure for morphological operations
generate_report: Generate a comprehensive analysis report
generic_filter: Apply a generic filter to an n-dimensional array
geodesic_dilation_2d: Geodesic dilation - dilation constrained by a reference image
geodesic_dt: Geodesic distance transform
geodesic_erosion_2d: Geodesic erosion - erosion constrained by a reference image
geometric_transform: Apply a general geometric transform to an array
get_thread_pool_config: Get the current thread pool configuration
gradient_edges: Gradient-based edge detection
granulometry_2d: Granulometry analysis for texture characterization
graph_cuts: Perform graph cuts segmentation on an image
grey_closing: Close a grayscale array using a structuring element
grey_dilation: Dilate a grayscale array using a structuring element
grey_dilation_2d_optimized: Optimized grayscale dilation for 2D arrays using SIMD and parallel processing
grey_erosion: Erode a grayscale array using a structuring element
grey_erosion_2d_optimized: Optimized grayscale erosion for 2D arrays using SIMD and parallel processing
grey_opening: Open a grayscale array using a structuring element
h_minima_transform: H-minima transform: suppress all minima whose depth is less than h
harris_corners: Harris corner detector
histogram: Calculate histogram of values within labeled regions
homeostatic_adaptive_filter: Adaptive Homeostatic Filtering
idwt_1d: 1D Inverse Discrete Wavelet Transform
idwt_2d: 2D Inverse Discrete Wavelet Transform
image_entropy: Compute image entropy (information content)
image_quality_assessment: Compute comprehensive image quality metrics
image_sharpness: Compute image sharpness using Laplacian variance
init_thread_pool: Initialize the global thread pool configuration
iterate_structure: Iterate binary erosion or dilation until convergence
label: Label connected components in a binary array
label_2d: Optimized 2D connected component labeling using two-pass algorithm with union-find
label_components: Label connected components in a binary image
laplace: Apply a Laplace filter to detect edges in an n-dimensional array
laplacian_edges: Laplacian of Gaussian (LoG) edge detector
liquidstate_machine: Liquid State Machine for Temporal Image Processing
local_extrema: Find the local extrema of an array
local_feature_analysis: Optimized local feature analysis using sliding window statistics
log_gabor_filter: Apply a Log-Gabor filter to a 2D array
map_coordinates: Map coordinates from one array to another
marker_watershed: Marker-controlled watershed for 2D arrays with configurable connectivity
mask_to_contour: Extract contour from segmentation mask
mask_to_level_set: Initialize level set from binary mask
maximum_filter: Apply a maximum filter to an n-dimensional array
mean_absolute_error: Compute Mean Absolute Error
mean_labels: Calculate the mean (average) value for each labeled region
mean_squared_error: Compute Mean Squared Error
median_filter: Apply a median filter to an n-dimensional array
median_filter_chunked: Memory-efficient median filter
merge_small_regions: Control oversegmentation by merging small regions
minimum_filter: Apply a minimum filter to an n-dimensional array
moments: Calculate image moments
moments_inertia_tensor: Find the moment of inertia tensor of an array
morphological_gradient: Apply morphological gradient to a grayscale array
morphological_gradient_2d_fast: Fast 2D morphological gradient (dilation - erosion)
morphological_laplace: Apply morphological Laplace to a grayscale array
morphological_reconstruction_2d: Morphological reconstruction using geodesic operations
morphological_reconstruction_by_dilation: Morphological reconstruction by dilation using fast hybrid algorithm
morphological_reconstruction_by_erosion: Morphological reconstruction by erosion using fast hybrid algorithm
multi_scale_analysis: Perform multi-scale image analysis
multi_scale_bilateral_filter: Multi-scale bilateral filter for enhanced edge preservation and noise reduction
multi_scale_morphology_2d: Multi-scale morphological analysis
nearest_background: Compute the nearest background pixel indices for each foreground pixel
non_local_means: Apply non-local means denoising filter
normalized_moments: Calculate normalized moments
otsu_threshold: Apply Otsu’s thresholding method
pan_sharpen: Pan-sharpening: merge high-resolution panchromatic with low-resolution multispectral
peak_prominences: Calculate peak prominence for peaks in a 1D array
peak_signal_to_noise_ratio: Compute Peak Signal-to-Noise Ratio (PSNR)
peak_widths: widths - Width of each peak at the specified heightwidth_heights - Actual height values at which widths were measuredleft_ips - Left interpolation points (x-coordinates) of width measurementsright_ips - Right interpolation points (x-coordinates) of width measurements
percentile_filter: Apply a percentile filter to an n-dimensional array
plot_contour: Generate a contour plot representation of a 2D array
plot_heatmap: Generate a heatmap visualization of a 2D array
plot_histogram: Generate a histogram plot representation
plot_profile: Generate a profile plot (line plot) representation
plot_statistical_comparison: Generate a comparative statistical plot for multiple datasets
plot_surface: Generate a 3D surface plot representation of a 2D array
point_in_contour: Check if a point is inside a contour using ray casting
process_chunked: Process an array in chunks using the given processor
quantum_amplitude_amplification: Quantum Amplitude Amplification for Feature Detection
quantum_annealing_segmentation: Quantum Annealing-Based Segmentation
quantum_aware_resource_scheduling_optimization: Quantum-Aware Resource Scheduling and Optimization
quantum_entanglement_correlation: Quantum Entanglement Correlation Analysis
quantum_error_correction: Quantum Error Correction for Image Processing
quantum_fourier_enhancement: Quantum Fourier Transform Enhancement
quantum_machine_learning_classifier: Quantum Machine Learning for Image Classification
quantum_superposition_filter: Quantum Superposition Filtering
quantum_tensor_network_processing: Quantum Tensor Network Image Processing
quantum_variational_enhancement: Quantum Variational Image Enhancement
quantum_walk_edge_detection: Quantum Walk-Based Edge Detection
rank_filter: Apply a rank filter to an n-dimensional array
region_properties: Extract comprehensive properties of labeled regions
region_props_advanced: Compute region properties for all labeled regions in a 2D image
regionprops_2d: Compute comprehensive properties for each labeled region in a 2D image
remove_small_holes: Remove small holes from a labeled array
remove_small_objects: Remove small objects from a labeled array
resample_contour: Resample a contour to have a specified number of uniformly spaced points
rotate: Rotate an array using interpolation
safe_f64_to_float: Helper function for safe conversion of hardcoded constants from f64 to generic float type
safe_float_to_f64: Helper function for safe float to f64 conversion
safe_usize_to_float: Helper function for safe usize conversion
segment_cells: Segment cells in microscopy images
shift: Shift an array using interpolation
shock_filter: Apply shock filter for edge enhancement
signal_to_noise_ratio: Compute Signal-to-Noise Ratio
signed_distance_function: Signed distance function (SDF) for a binary image
smooth_contour: Smooth a contour using B-spline interpolation
sobel: Apply a Sobel filter to calculate gradients in an n-dimensional array
sobel_edges: Sobel edge detector (for backward compatibility)
spiking_neural_network_filter: Spiking Neural Network for Image Processing
spline_filter: Spline filter for use in interpolation
spline_filter1d: Spline filter 1D for use in separable interpolation
stdp_unsupervised_learning: STDP-based Unsupervised Learning Filter
steerable_filter: Apply a steerable filter to a 2D array
stream_process_file: Stream process a file-based array
structural_similarity_index: Compute Structural Similarity Index Measure (SSIM)
sum_labels: Sum the values of an array for each labeled region
temporal_coding_feature_extraction: Temporal Coding Feature Extraction
texture_analysis: Perform comprehensive texture analysis
threshold_binary: Apply a threshold to an image to create a binary image
uniform_filter: Apply a uniform filter to an n-dimensional array
uniform_filter_chunked: Memory-efficient uniform filter
update_thread_pool_config: Update thread pool configuration
validate_all_examples: Utility function to run all examples and validate they work
validate_api_compatibility: Validate API compatibility for a specific function
validated_advanced_processing: Enhanced Advanced processing with validation
value_at_coordinates: Find values at given indices in an array
variance_labels: Calculate the variance of an array for each labeled region
visualize_gradient: Visualize gradient information as a vector field
watershed: Watershed segmentation for 2D arrays (Meyer’s flooding algorithm)
watershed_from_distance: Automatic watershed segmentation from distance transform
watershed_with_config: Full watershed segmentation with configuration
wavelet_decompose: Multi-level wavelet decomposition
wavelet_denoise: Wavelet-based denoising using soft thresholding
wavelet_reconstruct: Multi-level wavelet reconstruction
white_tophat: Apply white tophat transformation to a grayscale array
white_tophat_2d_fast: Fast 2D white top-hat transform (image - opening)
zoom: Zoom an array using interpolation

Type Aliases§

ComprehensiveSummary: Comprehensive summary of validation and performance results
NdimageResult: Result type for ndimage operations
PeakWidthsResult: Calculate peak widths at specified height levels in a 1D array

Module ndimage

Module ndimage Copy item path

§Examples

§Filters

§Morphology

§Measurements

§Segmentation

§Features

§Interpolation

Modules§

Macros§

Structs§

Enums§

Traits§

Functions§

Type Aliases§

Module ndimage