# ToRSh Vision - Comprehensive Vision Guide
ToRSh Vision is a production-ready computer vision framework built in pure Rust, providing PyTorch-compatible APIs with superior performance through Rust's zero-cost abstractions and memory safety.
## Table of Contents
1. [Quick Start](#quick-start)
2. [Core Concepts](#core-concepts)
3. [Image Processing](#image-processing)
4. [Data Transforms](#data-transforms)
5. [Datasets and Data Loading](#datasets-and-data-loading)
6. [Models and Architectures](#models-and-architectures)
7. [Hardware Acceleration](#hardware-acceleration)
8. [Interactive Visualization](#interactive-visualization)
9. [3D Visualization](#3d-visualization)
10. [Video Processing](#video-processing)
11. [Memory Management](#memory-management)
12. [Advanced Features](#advanced-features)
13. [Performance Optimization](#performance-optimization)
14. [Best Practices](#best-practices)
## Quick Start
```rust
use torsh_vision::prelude::*;
// Create a sample image tensor
let image = Tensor::randn([3, 224, 224], DType::F32, Device::Cpu);
// Apply transforms
let transforms = TransformBuilder::new()
.resize((256, 256))
.center_crop((224, 224))
.imagenet_normalize()
.build();
let processed_image = transforms.forward(&image)?;
println!("Processed image shape: {:?}", processed_image.shape());
```
## Core Concepts
### Tensors
ToRSh Vision uses tensors from the torsh-tensor crate as the fundamental data structure for images and computations:
```rust
// Create image tensors
let rgb_image = Tensor::zeros([3, 512, 512], DType::F32, Device::Cpu); // RGB
let grayscale = Tensor::zeros([1, 512, 512], DType::F32, Device::Cpu); // Grayscale
let batch = Tensor::zeros([32, 3, 224, 224], DType::F32, Device::Cpu); // Batch
```
### Error Handling
ToRSh Vision uses comprehensive error handling with the `VisionError` enum:
```rust
use torsh_vision::{Result, VisionError};
fn process_image() -> Result<Tensor> {
let image = load_image("path/to/image.jpg")?;
let processed = apply_transforms(&image)?;
Ok(processed)
}
```
### Device Support
ToRSh Vision automatically detects and utilizes available hardware:
```rust
let hardware = HardwareContext::auto_detect()?;
println!("CUDA available: {}", hardware.cuda_available());
println!("Mixed precision: {}", hardware.supports_mixed_precision());
```
## Image Processing
### Basic I/O Operations
```rust
use torsh_vision::io::VisionIO;
// Load images
let image = VisionIO::load_image("image.jpg")?;
// Save images
VisionIO::save_image(&processed_image, "output.png")?;
// Batch loading
let images = VisionIO::load_images_from_directory("./images/")?;
// Format conversion
VisionIO::convert_image_format("input.jpg", "output.png")?;
```
### Advanced Image Operations
```rust
use torsh_vision::ops::*;
// Edge detection
let edges = sobel_edge_detection(&image)?;
let canny_edges = canny_edge_detection(&image, 50.0, 150.0)?;
// Filtering
let blurred = gaussian_blur(&image, 3, 1.0)?;
let sharpened = sharpen_filter(&image, 1.5)?;
// Morphological operations
let eroded = erosion(&image, 3)?;
let dilated = dilation(&image, 3)?;
// Feature detection
let corners = harris_corner_detection(&image, 0.04, 0.01)?;
let keypoints = fast_corner_detection(&image, 20)?;
```
## Data Transforms
### Basic Transforms
```rust
use torsh_vision::transforms::*;
// Individual transforms
let resize = Resize::new((224, 224));
let crop = CenterCrop::new((224, 224));
let flip = RandomHorizontalFlip::new(0.5);
let normalize = Normalize::new(
vec![0.485, 0.456, 0.406], // ImageNet mean
vec![0.229, 0.224, 0.225] // ImageNet std
);
// Apply transform
let transformed = resize.forward(&image)?;
```
### Transform Composition
```rust
// Using TransformBuilder
let train_transforms = TransformBuilder::new()
.resize((256, 256))
.random_resized_crop((224, 224))
.random_horizontal_flip(0.5)
.add(ColorJitter::new().brightness(0.4).contrast(0.4))
.imagenet_normalize()
.build();
// Using Compose
let composed = Compose::new(vec![
Box::new(Resize::new((256, 256))),
Box::new(CenterCrop::new((224, 224))),
Box::new(ToTensor::new()),
]);
```
### Advanced Augmentation
```rust
// AutoAugment
let auto_augment = AutoAugment::new();
let augmented = auto_augment.forward(&image)?;
// RandAugment
let rand_augment = RandAugment::new(2, 5.0); // 2 ops, magnitude 5
let augmented = rand_augment.forward(&image)?;
// MixUp
let mut mixup = MixUp::new(1.0);
let (mixed_image, mixed_labels) = mixup.apply_pair(&img1, &img2, 0, 1, 10)?;
// CutMix
let mut cutmix = CutMix::new(1.0);
let (cut_image, cut_labels) = cutmix.apply_pair(&img1, &img2, 0, 1, 10)?;
```
### Unified Transform API
```rust
use torsh_vision::unified_transforms::*;
// Hardware-aware transforms
let context = TransformContext::new(HardwareContext::auto_detect()?);
let unified_resize = UnifiedResize::new((224, 224));
// Automatic GPU/CPU selection
let processed = unified_resize.apply(&image, &context)?;
// Mixed precision support
if context.hardware().supports_mixed_precision() {
let processed_f16 = unified_resize.apply_gpu_f16(&image, &context)?;
}
```
## Datasets and Data Loading
### Built-in Datasets
```rust
use torsh_vision::datasets::*;
// ImageFolder dataset
let dataset = ImageFolder::new("path/to/imagenet/train")?;
println!("Classes: {:?}", dataset.classes());
// CIFAR-10/100
let cifar10 = CIFAR10Dataset::new("path/to/cifar10", true)?; // train=true
let cifar100 = CIFAR100Dataset::new("path/to/cifar100", false)?; // train=false
// MNIST
let mnist = MNISTDataset::new("path/to/mnist", true)?;
// Pascal VOC (Object Detection)
let voc = VOCDataset::new("path/to/voc", "2012", "train")?;
// MS COCO (Object Detection)
let coco = COCODataset::new("path/to/coco", "train2017")?;
```
### Optimized Data Loading
```rust
use torsh_vision::datasets::optimized::*;
// Memory-efficient lazy loading
let config = DatasetConfig {
cache_size_mb: 1024, // 1GB cache
prefetch_size: 64,
max_workers: 8,
enable_validation: true,
};
let optimized_dataset = OptimizedImageDataset::new("path/to/data", config)?;
// Memory-mapped loading for large datasets
let memory_mapped = MemoryMappedLoader::new("large_dataset.bin")?;
```
### Custom Datasets
```rust
use torsh_vision::datasets::Dataset;
struct CustomDataset {
image_paths: Vec<String>,
labels: Vec<i64>,
}
impl Dataset for CustomDataset {
fn len(&self) -> usize {
self.image_paths.len()
}
fn get(&self, index: usize) -> Result<(Tensor, i64)> {
let image = VisionIO::load_image(&self.image_paths[index])?;
let label = self.labels[index];
Ok((image, label))
}
}
```
## Models and Architectures
### Classification Models
```rust
use torsh_vision::models::*;
// ResNet family
let resnet18 = ResNet::resnet18(1000)?;
let resnet50 = ResNet::resnet50(1000)?;
let resnet152 = ResNet::resnet152(1000)?;
// VGG family
let vgg16 = VGG::vgg16(1000, false)?; // without batch norm
let vgg16_bn = VGG::vgg16(1000, true)?; // with batch norm
// EfficientNet
let efficientnet_b0 = EfficientNet::efficientnet_b0(1000)?;
let efficientnet_b7 = EfficientNet::efficientnet_b7(1000)?;
// Vision Transformer
let vit_base = ViT::vit_base_patch16_224(1000)?;
let vit_large = ViT::vit_large_patch16_224(1000)?;
```
### Object Detection Models
```rust
// YOLO v5
let yolo = YOLOv5::yolo_v5_medium(80)?; // 80 COCO classes
// RetinaNet
let retinanet = RetinaNet::retina_net_resnet50(80)?;
// SSD
let ssd = SSD::ssd_300(80)?;
```
### Style Transfer and Super-Resolution
```rust
// Neural Style Transfer
let style_transfer = FastStyleTransfer::new()?;
let stylized = style_transfer.forward(&content_image, &style_image)?;
// Super-Resolution
let srcnn = SRCNN::new(3)?; // 3x upscaling
let espcn = ESPCN::new(4)?; // 4x upscaling
let edsr = EDSR::new(2, 64)?; // 2x upscaling, 64 features
```
## Hardware Acceleration
### GPU Acceleration
```rust
use torsh_vision::hardware::*;
// Auto-detect hardware
let hardware = HardwareContext::auto_detect()?;
if hardware.cuda_available() {
println!("Using GPU acceleration");
// GPU-accelerated transforms
let gpu_resize = GpuResize::new((224, 224));
let gpu_normalize = GpuNormalize::new(mean, std);
// Create GPU transform chain
let gpu_chain = GpuAugmentationChain::new(vec![
Box::new(gpu_resize),
Box::new(gpu_normalize),
]);
}
```
### Mixed Precision Training
```rust
use torsh_vision::hardware::MixedPrecisionTraining;
if hardware.supports_mixed_precision() {
let mut mixed_precision = MixedPrecisionTraining::new()?;
// Convert to f16 for memory savings
let image_f16 = mixed_precision.to_half(&image)?;
// Process with automatic scaling
let result = mixed_precision.forward_with_scaling(&image_f16, &model)?;
}
```
## Interactive Visualization
### Interactive Image Viewer
```rust
use torsh_vision::interactive::*;
// Create viewer
let mut viewer = InteractiveViewer::new();
// Load image
viewer.load_image(image)?;
// Add annotations
let bbox = Annotation::BoundingBox {
x: 100.0, y: 150.0, width: 200.0, height: 150.0,
label: "Object".to_string(),
color: [255, 0, 0],
confidence: Some(0.95),
};
viewer.add_annotation(bbox);
// Set up event handlers
viewer.on_event("mouse_click".to_string(), |event| {
if let ViewerEvent::MouseClick { x, y, .. } = event {
println!("Clicked at ({}, {})", x, y);
}
});
// Export annotations
let json_annotations = viewer.export_annotations()?;
```
### Interactive Gallery
```rust
let mut gallery = InteractiveGallery::new();
// Load images from directory
gallery.load_from_directory("./images/")?;
// Navigate
gallery.next_image()?;
gallery.previous_image()?;
gallery.goto_image(5)?;
// Add annotations to current image
let annotation = Annotation::Point {
x: 250.0, y: 200.0,
label: "Landmark".to_string(),
color: [0, 255, 0],
radius: 5.0,
};
gallery.add_annotation_to_current(annotation)?;
```
### Live Visualization
```rust
let mut live_viz = LiveVisualization::new();
// Real-time processing loop
for frame in video_stream {
live_viz.add_frame(frame)?;
let fps = live_viz.current_fps();
println!("Processing at {} FPS", fps);
}
```
## 3D Visualization
### Point Clouds
```rust
use torsh_vision::viz3d::*;
// Create point cloud
let points = vec![
Point3D::with_color(1.0, 2.0, 3.0, [255, 0, 0]),
Point3D::with_color(4.0, 5.0, 6.0, [0, 255, 0]),
];
let mut cloud = PointCloud3D::new(points);
// Voxel downsampling
let downsampled = cloud.voxel_downsample(0.1);
// Distance filtering
let center = Point3D::new(0.0, 0.0, 0.0);
let filtered = cloud.filter_by_distance(center, 10.0);
// Convert to/from tensors
let tensor = cloud.to_tensor()?;
let cloud_from_tensor = PointCloud3D::from_tensor(&tensor)?;
```
### 3D Meshes
```rust
// Create primitive meshes
let sphere = Mesh3D::create_sphere(Point3D::new(0.0, 0.0, 0.0), 5.0, 20, 20);
let cube = Mesh3D::create_cube(Point3D::new(0.0, 0.0, 0.0), 2.0);
// Compute normals
let mut custom_mesh = Mesh3D::new(vertices, faces);
custom_mesh.compute_face_normals();
custom_mesh.compute_vertex_normals();
```
### 3D Bounding Boxes
```rust
// 3D object detection
let bbox = BoundingBox3D::new(
[5.0, 2.0, 1.0], // center
[4.0, 2.0, 6.0], // dimensions
[0.0, 0.2, 0.0], // rotation (roll, pitch, yaw)
"Car".to_string(),
0.95 // confidence
).with_color([255, 0, 0]);
// Test point containment
let point = Point3D::new(5.0, 2.0, 1.0);
let inside = bbox.contains_point(point);
// Get corner points
let corners = bbox.corners();
```
### 3D Scenes
```rust
let mut scene = Scene3D::new("Detection Scene".to_string());
// Add components
scene.add_point_cloud(lidar_cloud);
scene.add_mesh(ground_plane);
scene.add_bounding_box(car_detection);
// Scene statistics
println!("Objects: {}", scene.num_objects());
let summary = scene.export_summary();
```
## Video Processing
### Video I/O
```rust
use torsh_vision::video::*;
// Video reader
let mut reader = VideoReader::new("input.mp4")?;
let metadata = reader.metadata();
println!("FPS: {}, Duration: {}s", metadata.fps, metadata.duration);
// Read frames
while let Some(frame) = reader.next_frame()? {
// Process frame
let processed = apply_transforms(&frame.tensor)?;
}
// Video writer
let mut writer = VideoWriter::new("output.mp4", 30.0, (1920, 1080))?;
for frame_tensor in processed_frames {
let frame = VideoFrame::new(frame_tensor, frame_index as f64 / 30.0);
writer.write_frame(&frame)?;
}
```
### Video Datasets
```rust
let video_dataset = VideoDataset::new("path/to/videos")
.sequence_length(16) // 16 frames per sequence
.overlap(8) // 8 frames overlap
.sampling_strategy(TemporalSampling::Uniform)?;
// Get video sequence
let (frames, label) = video_dataset.get(0)?;
println!("Frames shape: {:?}", frames.shape());
```
### Optical Flow
```rust
// Lucas-Kanade optical flow
let flow = lucas_kanade_optical_flow(&frame1, &frame2, 15, 3)?;
let flow_magnitude = optical_flow_magnitude(&flow)?;
```
### Video Models
```rust
// 3D convolution for video classification
let conv3d = Conv3d::new(3, 64, 3, 1, 1)?;
let video_features = conv3d.forward(&video_tensor)?;
// Temporal pooling
let pooled = temporal_pooling(&video_features, TemporalPooling::Average)?;
```
## Memory Management
### Memory Optimization
```rust
use torsh_vision::memory::*;
// Configure global memory settings
let settings = MemorySettings {
enable_pooling: true,
max_pool_size: 1000,
max_batch_memory_mb: 4096,
enable_profiling: true,
auto_optimization: true,
};
configure_global_memory(settings);
// Use tensor pool
let mut pool = TensorPool::new(100);
let tensor = pool.get_tensor(&[3, 224, 224])?;
// ... use tensor
pool.return_tensor(tensor)?;
```
### Memory Profiling
```rust
let profiler = MemoryProfiler::new();
// Record allocations
profiler.record_allocation(1024 * 1024, "training", "batch_data");
// Get profiling summary
let summary = profiler.summary();
println!("Peak usage: {} MB", summary.peak_usage_bytes / (1024 * 1024));
```
### Batch Processing
```rust
let mut batch_processor = MemoryEfficientBatchProcessor::new(2048); // 2GB limit
// Add tensors
let tensor_id = batch_processor.add_tensor(image_tensor)?;
// Process when memory is full
let usage = batch_processor.current_memory_usage();
if usage.utilization > 0.8 {
let results = batch_processor.process_batch()?;
}
```
## Advanced Features
### Feature Extraction
```rust
use torsh_vision::ops::*;
// HOG features
let hog_features = extract_hog_features(&image, 8, 8, 2, 9)?;
// SIFT-like features
let (keypoints, descriptors) = extract_sift_like_features(&image)?;
// ORB-like features
let orb_features = extract_orb_like_features(&image, 500)?;
```
### Similarity Search
```rust
// Image similarity
let similarity = calculate_similarity(&img1, &img2, SimilarityMetric::Euclidean)?;
// Content-based image retrieval
let mut retrieval = ContentBasedImageRetrieval::new();
retrieval.add_image("img1", &features1)?;
retrieval.add_image("img2", &features2)?;
let results = retrieval.query(&query_features, 5)?; // Top 5 similar images
```
### Quality Assessment
```rust
// Image quality metrics
let psnr = calculate_psnr(&original, &compressed, 255.0)?;
let ssim = calculate_ssim(&img1, &img2, None)?;
let mse = calculate_mse(&img1, &img2)?;
let mae = calculate_mae(&img1, &img2)?;
println!("PSNR: {:.2} dB, SSIM: {:.3}", psnr, ssim);
```
## Performance Optimization
### Benchmarking
```rust
use std::time::Instant;
// Transform performance
let start = Instant::now();
for _ in 0..100 {
let _ = transform.forward(&image)?;
}
let avg_time = start.elapsed().as_millis() as f64 / 100.0;
println!("Average time: {:.2} ms", avg_time);
// Memory usage estimation
let estimate = MemoryOptimizer::estimate_batch_memory(&shapes);
println!("Estimated memory: {:.2} MB", estimate.total_mb);
// Optimal batch size calculation
let optimal_batch = MemoryOptimizer::calculate_optimal_batch_size(
&shape, 8192, 0.8 // 8GB GPU, 80% utilization
);
```
### Hardware Optimization
```rust
// Automatic device selection
let context = TransformContext::auto_optimize()?;
// Tensor Core optimization
if hardware.has_tensor_cores() {
let optimized_tensor = optimize_for_tensor_cores(&tensor)?;
}
// Batch size optimization
let optimal_batch = context.calculate_optimal_batch_size(&image_shape)?;
```
## Best Practices
### Error Handling
```rust
use torsh_vision::{Result, VisionError};
// Always use Result types
fn process_pipeline() -> Result<Tensor> {
let image = load_image("input.jpg")
.map_err(|e| VisionError::IoError(e))?;
let processed = apply_transforms(&image)
.map_err(|e| VisionError::TransformError(e.to_string()))?;
Ok(processed)
}
// Handle specific error types
match process_pipeline() {
Ok(result) => println!("Success: {:?}", result.shape()),
Err(VisionError::IoError(e)) => eprintln!("I/O error: {}", e),
Err(VisionError::TransformError(e)) => eprintln!("Transform error: {}", e),
Err(e) => eprintln!("Other error: {}", e),
}
```
### Memory Management
```rust
// Use memory pooling for training loops
let mut pool = TensorPool::new(1000);
for epoch in 0..num_epochs {
for batch in dataloader {
// Get tensors from pool
let tensor = pool.get_tensor(&batch_shape)?;
// Process batch
let result = model.forward(&tensor)?;
// Return to pool when done
pool.return_tensor(tensor)?;
}
}
```
### Performance Optimization
```rust
// Use hardware acceleration when available
let hardware = HardwareContext::auto_detect()?;
let transforms = if hardware.cuda_available() {
// GPU transforms
create_gpu_transform_pipeline()
} else {
// CPU transforms
create_cpu_transform_pipeline()
};
// Batch processing for efficiency
let batch_size = MemoryOptimizer::calculate_optimal_batch_size(
&image_shape,
hardware.memory_gb() * 1024,
0.8
);
```
### Data Loading
```rust
// Use optimized datasets for large-scale training
let config = DatasetConfig {
cache_size_mb: 2048,
prefetch_size: batch_size * 4,
max_workers: num_cpus::get(),
enable_validation: true,
};
let dataset = OptimizedImageDataset::new(data_path, config)?;
```
### Transform Pipelines
```rust
// Separate training and validation transforms
let train_transforms = TransformBuilder::new()
.resize((256, 256))
.random_resized_crop((224, 224))
.random_horizontal_flip(0.5)
.add(ColorJitter::new().brightness(0.4))
.imagenet_normalize()
.build();
let val_transforms = TransformBuilder::new()
.resize((256, 256))
.center_crop((224, 224))
.imagenet_normalize()
.build();
```
## Conclusion
ToRSh Vision provides a comprehensive, high-performance computer vision framework with:
- **Zero-cost abstractions**: Rust's performance without compromising safety
- **Hardware acceleration**: Automatic GPU/CPU optimization with mixed precision support
- **Memory efficiency**: Advanced memory management and optimization
- **Comprehensive features**: Complete pipeline from data loading to model inference
- **Production ready**: Robust error handling and performance monitoring
- **Extensible**: Clean APIs for custom implementations
For more detailed examples and advanced usage patterns, see the [examples module](./src/examples.rs) and refer to the API documentation.