Crate torsh_vision

Expand description

Computer vision operations for ToRSh

This crate provides PyTorch-compatible computer vision functionality including:

Image transformations and augmentations
Pre-trained models and model architectures
Dataset loaders for common vision datasets
Spatial operations and feature matching
Video processing capabilities
3D visualization utilities

Built on top of the SciRS2 ecosystem for high-performance image processing.

§Examples

use torsh_vision::transforms::*;

// Create a standard image transformation pipeline
let transform = Compose::new(vec![
    Box::new(Resize::new((224, 224))),
    Box::new(ToTensor::new()),
    Box::new(Normalize::new(vec![0.485, 0.456, 0.406], vec![0.229, 0.224, 0.225])),
]);

Re-exports§

pub use datasets::DatasetConfig;
pub use datasets::DatasetError;
pub use datasets::DatasetStats;
pub use datasets_impl::CifarDataset;
pub use datasets_impl::CocoDataset;
pub use datasets_impl::ImageFolder;
pub use datasets_impl::MnistDataset;
pub use datasets_impl::VocDataset;
pub use explainability::AttentionVisualizer;
pub use explainability::BaselineType;
pub use explainability::FeatureVisualizer;
pub use explainability::GradCAM;
pub use explainability::IntegratedGradients;
pub use explainability::SaliencyMap;
pub use ops::adjust_brightness;
pub use ops::adjust_contrast;
pub use ops::adjust_hue;
pub use ops::adjust_saturation;
pub use ops::calculate_iou;
pub use ops::center_crop;
pub use ops::edge_detection;
pub use ops::gaussian_blur;
pub use ops::generate_anchors;
pub use ops::histogram_equalization;
pub use ops::horizontal_flip;
pub use ops::morphological_operation;
pub use ops::nms;
pub use ops::normalize;
pub use ops::random_crop;
pub use ops::resize;
pub use ops::rgb_to_grayscale;
pub use ops::rotate;
pub use ops::sobel_edge_detection;
pub use ops::vertical_flip;
pub use spatial::distance::PatchMatcher;
pub use spatial::interpolation::ImageWarper;
pub use spatial::interpolation::OpticalFlowInterpolator;
pub use spatial::interpolation::SpatialInterpolator;
pub use spatial::matching::Feature;
pub use spatial::matching::FeatureMatcher;
pub use spatial::matching::Keypoint;
pub use spatial::matching::TemplateMatcher;
pub use spatial::structures::BoundingBox;
pub use spatial::structures::PointCloudProcessor;
pub use spatial::structures::SpatialObjectTracker;
pub use spatial::transforms::GeometricProcessor;
pub use spatial::transforms::ImageRegistrar;
pub use spatial::transforms::PoseEstimator;
pub use spatial::FeatureMatch;
pub use spatial::SpatialConfig;
pub use spatial::SpatialPoint;
pub use spatial::SpatialProcessor;
pub use spatial::TransformResult;
pub use scirs2_integration::ContrastMethod;
pub use scirs2_integration::CornerPoint;
pub use scirs2_integration::DenoiseMethod;
pub use scirs2_integration::DisparityMap;
pub use scirs2_integration::EdgeDetectionMethod;
pub use scirs2_integration::Keypoint as SciKeypoint;
pub use scirs2_integration::MemoryStrategy;
pub use scirs2_integration::OpticalFlow;
pub use scirs2_integration::OrbFeatures;
pub use scirs2_integration::QualityLevel;
pub use scirs2_integration::SciRS2VisionProcessor;
pub use scirs2_integration::SiftFeatures;
pub use scirs2_integration::SimdLevel;
pub use scirs2_integration::SurfFeatures;
pub use scirs2_integration::VisionConfig;
pub use self_supervised::BYOLAugmentation;
pub use self_supervised::DINOAugmentation;
pub use self_supervised::GaussianBlur;
pub use self_supervised::MoCoAugmentation;
pub use self_supervised::RandomGrayscale;
pub use self_supervised::SimCLRAugmentation;
pub use self_supervised::Solarize;
pub use self_supervised::SwAVAugmentation;
pub use segmentation_advanced::graph_cuts;
pub use segmentation_advanced::region_growing;
pub use segmentation_advanced::watershed;
pub use segmentation_advanced::Connectivity;
pub use segmentation_advanced::GraphCutsConfig;
pub use segmentation_advanced::RegionGrowingConfig;
pub use segmentation_advanced::WatershedConfig;
pub use segmentation_advanced::WatershedMarkers;
pub use feature_detection_advanced::apply_ratio_test;
pub use feature_detection_advanced::AttentionMatcher;
pub use feature_detection_advanced::AttentionMatcherConfig;
pub use feature_detection_advanced::BruteForceMatcher;
pub use feature_detection_advanced::DistanceMetric;
pub use feature_detection_advanced::Feature as AdvancedFeature;
pub use feature_detection_advanced::FeatureMatch as AdvancedFeatureMatch;
pub use feature_detection_advanced::LearnedSiftConfig;
pub use feature_detection_advanced::LearnedSiftDetector;
pub use feature_detection_advanced::MultiScaleConfig;
pub use feature_detection_advanced::MultiScaleDetector;
pub use feature_detection_advanced::SuperPointConfig;
pub use feature_detection_advanced::SuperPointDetector;
pub use streaming::BatchProcessor;
pub use streaming::Frame;
pub use streaming::FrameMetadata;
pub use streaming::FramePreprocessor;
pub use streaming::QualityAdaptation;
pub use streaming::StreamConfig;
pub use streaming::StreamProcessor;
pub use streaming::StreamStats;
pub use benchmarks::run_full_benchmark_suite;
pub use benchmarks::run_quick_benchmark;
pub use benchmarks::AccuracyMetrics;
pub use benchmarks::BenchmarkConfig;
pub use benchmarks::BenchmarkResult;
pub use benchmarks::VisionBenchmarkSuite;
pub use advanced_transforms::*;
pub use error_handling::*;
pub use examples::*;
pub use hardware::*;
pub use interactive::*;
pub use io::*;
pub use memory::*;
pub use models::*;
pub use transforms::*;
pub use unified_transforms::*;
pub use utils::*;
pub use video::*;
pub use viz3d::*;

Modules§

advanced_transforms: Advanced SciRS2-Powered Image Transformations and Augmentations
benchmarks: Comprehensive SciRS2-Powered Vision Benchmarks
datasets: Dataset loading and management for torsh-vision
datasets_impl: Dataset loading and management for torsh-vision
error_handling
examples: Comprehensive examples for torsh-vision
explainability: Model Explainability and Interpretability Tools
feature_detection_advanced: Advanced Feature Detection and Description
hardware
interactive: Interactive visualization tools for torsh-vision
io: Consolidated I/O operations for torsh-vision
memory: Memory optimization utilities for torsh-vision
models: Computer vision models
ops: Computer vision operations for image processing and analysis
optimized_impl: Optimized dataset implementations with lazy loading and memory management
prelude
scirs2_integration: Comprehensive SciRS2-Vision Integration for Computer Vision
segmentation_advanced: Advanced Segmentation Algorithms for Computer Vision
self_supervised: Self-Supervised Learning Augmentation Strategies
spatial: Spatial algorithms integration for computer vision
streaming: Real-Time Video Stream Processing
transforms: ToRSh Vision Transforms
unified_transforms
utils: Vision utilities module
video
viz3d: 3D visualization tools for torsh-vision