chromapath 0.2.0

# ChromaPath - Ray Tracing in One Weekend: Rust & Hardware RT

![ChromaPath Output](img/cover.png)

**ChromaPath** is a complete Rust implementation of Peter Shirley's ["Ray Tracing in One Weekend"](https://raytracing.github.io/books/RayTracingInOneWeekend.html) with GPU hardware ray tracing acceleration.

## What is this?

This project implements the exact same path tracer from the famous book, but with two key improvements:
- **Pure Rust**: Using modern Rust idioms and the `glam` math library
- **Hardware RT**: Optional GPU acceleration using Vulkan ray tracing extensions

Both implementations produce identical images - the GPU version is just orders of magnitude faster!

## Quick Start

```bash
# Install dependencies (Vulkan SDK required for default GPU mode)
# Then build and run:

# Hardware RT (default, orders of magnitude faster)
cargo run --release -- -s 100

# CPU mode (naive book implementation, for comparison/learning)
cargo run --release -- --cpu -s 100

# Compare both with benchmark
cargo run --release -- --bench
```

## Performance

The GPU hardware ray tracing version delivers **orders of magnitude** performance improvements over the naive CPU implementation, especially for complex scenes with many objects.

### Real-world Example
*RTX 3090, 800×600, 4000 samples per pixel, complex scene with Lucy model (448k triangles) + random spheres*

```
🚀 Hardware Ray Tracing: 11.43s
   ├─ Ray tracing dispatch: 4.40s (436.6M rays/sec)
   ├─ Scene setup: ~2s
   └─ Image readback: ~5s

📊 Performance: 436.6 million rays per second
🎯 Total pixels: 480,000 (4000 samples each = 1.92 billion rays)
```

The same scene on CPU would take **days** due to the naive O(n) intersection testing without acceleration structures.

## Output Formats

```bash
# PNG output (hardware RT is default)
cargo run --release -- -s 100 -o image.png

# HDR/EXR for professional workflows  
cargo run --release -- -s 100 -o image.exr

# Send directly to TEV viewer
cargo run --release -- -s 100 --tev

# CPU mode for comparison
cargo run --release -- --cpu -s 100 -o cpu_image.png
```

## How it works

The CPU implementation faithfully follows "Ray Tracing in One Weekend":
- Same sphere scene from Chapter 1
- Same camera, materials, and ray tracing logic
- **Deliberately naive**: No BVH, no advanced optimizations - just like the book
- Simple linear search through all objects (O(n) complexity)
- Perfect for learning, but slow for large scenes

The GPU version translates these concepts to Vulkan hardware RT:
- `ray_color()` function → Ray generation + hit/miss shaders
- Manual ray-sphere intersections → Hardware acceleration structures (O(log n))
- CPU loops → GPU parallel execution across thousands of cores
- **Massive speedup** comes from both parallelization AND better algorithms

## Requirements

- **Rust 1.70+**
- **Vulkan SDK** (for hardware RT mode)
- **GPU with RT support** (RTX 20xx+ or RDNA2+) for hardware acceleration

## Book Chapters Implemented

✅ **Chapter 1**: Output an Image  
✅ **Chapter 2**: The vec3 Class → `glam::Vec3A`  
✅ **Chapter 3**: Rays, a Simple Camera  
✅ **Chapter 4**: Adding a Sphere  
✅ **Chapter 5**: Surface Normals and Multiple Objects  
✅ **Chapter 6**: Antialiasing  
✅ **Chapter 7**: Diffuse Materials  
✅ **Chapter 8**: Metal  
✅ **Chapter 9**: Dielectrics  
✅ **Chapter 10**: Positionable Camera  
🚀 **Bonus**: Hardware Ray Tracing with Vulkan

## References

- [Ray Tracing in One Weekend](https://raytracing.github.io/books/RayTracingInOneWeekend.html) - The original book
- [Vulkano](https://github.com/vulkano-rs/vulkano) - Rust Vulkan bindings
- [glam](https://docs.rs/glam) - Fast 3D math library
- [TEV](https://github.com/Tom94/tev) - HDR image viewer