scirs2-vision

Computer vision module for SciRS2, providing functionality for image processing, feature detection, segmentation, and color transformations.

Overview

The scirs2-vision module is designed to provide comprehensive computer vision capabilities for scientific computing applications. It builds upon the foundation of the scirs2-ndimage module but provides additional specialized functionality specifically for computer vision tasks.

Features

Feature Detection and Description

• Edge detection with Sobel operator
• Corner detection with Harris detector
• Feature point extraction
• SIFT-like feature descriptors and matching

Image Segmentation

• Binary thresholding
• Otsu's automatic thresholding
• Adaptive thresholding (mean, Gaussian)
• Connected component labeling

Preprocessing

• Brightness and contrast normalization
• Histogram equalization
• Gaussian blur
• Unsharp masking

Color Processing

• RGB ↔ HSV conversion
• RGB ↔ LAB conversion
• Channel splitting and merging
• Weighted grayscale conversion

Morphological Operations

• Erosion and dilation
• Opening and closing
• Morphological gradient
• Top-hat and black-hat transforms

Examples

Feature Detection

use scirs2_vision::feature::{sobel_edges, harris_corners};
use scirs2_vision::preprocessing::gaussian_blur;

// Load an image
let img = image::open("input.jpg")?;

// Preprocess
let blurred = gaussian_blur(&img, 1.0)?;

// Detect edges
let edges = sobel_edges(&blurred, 0.1)?;

// Detect corners
let corners = harris_corners(&blurred, 3, 0.04, 0.01)?;

Color Transformations

use scirs2_vision::color::{rgb_to_hsv, rgb_to_lab, split_channels};

// Load an image
let img = image::open("input.jpg")?;

// Convert to HSV
let hsv = rgb_to_hsv(&img)?;

// Convert to LAB
let lab = rgb_to_lab(&img)?;

// Split into channels
let (r_channel, g_channel, b_channel) = split_channels(&img)?;

Image Segmentation

use scirs2_vision::segmentation::{
    threshold_binary, otsu_threshold, adaptive_threshold, connected_components,
    AdaptiveMethod,
};

// Load an image
let img = image::open("input.jpg")?;

// Apply Otsu's thresholding
let (binary, threshold) = otsu_threshold(&img)?;

// Apply adaptive thresholding
let adaptive = adaptive_threshold(&img, 11, 0.02, AdaptiveMethod::Gaussian)?;

// Perform connected component labeling
let (labeled, num_components) = connected_components(&binary)?;

Morphological Operations

use scirs2_vision::preprocessing::{
    erode, dilate, opening, closing, morphological_gradient, StructuringElement,
};

// Load an image
let img = image::open("input.jpg")?;

// Define a structuring element
let se = StructuringElement::Ellipse(5, 5);

// Apply opening (erosion followed by dilation)
let opened = opening(&img, se)?;

// Calculate morphological gradient
let gradient = morphological_gradient(&img, se)?;

Usage

Add scirs2-vision to your dependencies in Cargo.toml:

[dependencies]
scirs2-vision = "0.1.0"

Documentation

For detailed documentation, examples, and API reference, please refer to the API documentation and the examples directory.

Testing

The module includes a comprehensive test suite to ensure functionality works as expected:

# Run all tests
cargo test

# Run specific test
cargo test test_grayscale_conversion

Dependencies

• scirs2-core: Core functionality for SciRS2
• scirs2-ndimage: N-dimensional image processing
• ndarray: N-dimensional array manipulation
• image: Basic image processing in Rust
• num-traits and num-complex: Numerical type traits

License

This project is licensed under the terms specified in the repository's LICENSE file.

Contributing

Contributions are welcome! Please see the project's CONTRIBUTING.md file for guidelines.