# Zignal Python Bindings
Zero-dependency image processing library written in Zig with Python bindings.
## Installation
```console
pip install zignal-processing
```
## Quick Start
```python
import zignal
# Create RGB color
red = zignal.Rgb(255, 0, 0)
print(f"Red color: {red}")
# Convert to HSV
hsv = red.to_hsv()
print(f"HSV: {hsv}")
# Convert to other color spaces
lab = red.to_lab()
oklab = red.to_oklab()
xyz = red.to_xyz()
```
## Features
- **12 Color Spaces**: RGB, RGBA, HSV, HSL, Lab, XYZ, Oklab, Oklch, LCH, LMS, XYB, YCbCr
- **Seamless Conversions**: Convert between any supported color spaces
- **Type Safety**: Strong typing with validation for color components
- **Zero Dependencies**: No external dependencies, pure Zig implementation
- **High Performance**: Native performance with minimal overhead
- **Cross Platform**: Works on Linux, macOS, and Windows (x86_64 and ARM64)
## Supported Color Spaces
- **RGB/RGBA**: Standard RGB with optional alpha channel (0-255)
- **HSV**: Hue, Saturation, Value (0-360°, 0-100%, 0-100%)
- **HSL**: Hue, Saturation, Lightness (0-360°, 0-100%, 0-100%)
- **Lab**: CIELAB perceptual color space
- **XYZ**: CIE 1931 XYZ color space
- **Oklab**: Perceptually uniform color space by Björn Ottosson
- **Oklch**: Cylindrical representation of Oklab
- **LCH**: Cylindrical representation of Lab
- **LMS**: Long, Medium, Short cone response
- **XYB**: Color space used in JPEG XL
- **YCbCr**: Luma and chroma components
## Examples
### Color Space Conversions
```python
# Create a color in any space
hsl = zignal.Hsl(180.0, 50.0, 50.0) # Cyan-ish color
# Convert to any other space
rgb = hsl.to_rgb()
lab = hsl.to_lab()
oklab = hsl.to_oklab()
# Chain conversions
original = zignal.Rgb(128, 64, 192)
hsv = original.to_hsv()
back_to_rgb = hsv.to_rgb()
```
### Working with Alpha Channel
```python
# Create RGBA color
rgba = zignal.Rgba(255, 128, 0, 200) # Orange with transparency
# Convert RGB to RGBA (default alpha=255)
rgb = zignal.Rgb(255, 128, 0)
rgba = rgb.to_rgba()
```
### Modern Color Spaces
```python
# Oklab - perceptually uniform color space
oklab = zignal.Oklab(0.5, 0.1, -0.05)
oklch = oklab.to_oklch() # Convert to cylindrical form
# Work with perceptual properties
print(f"Lightness: {oklch.l}")
print(f"Chroma: {oklch.c}")
print(f"Hue: {oklch.h}")
```
### NumPy Integration (Optional)
If NumPy is installed, you can work with image arrays:
```python
import numpy as np
import zignal
# Create numpy array (supports both RGB and RGBA)
arr_rgb = np.zeros((100, 200, 3), dtype=np.uint8)
arr_rgb[50, 100] = [255, 0, 0] # Red pixel
# Convert to Image (allocates RGBA internally for SIMD performance)
img = zignal.Image.from_numpy(arr_rgb)
# For zero-copy with 4-channel arrays, use the helper:
arr_rgba = zignal.Image.add_alpha(arr_rgb) # Adds alpha=255
img = zignal.Image.from_numpy(arr_rgba) # Zero-copy!
# Convert back to numpy
arr2 = img.to_numpy() # Returns RGBA (4 channels) by default
arr3 = img.to_numpy(include_alpha=False) # Returns RGB (3 channels)
# Load and save images
img = zignal.Image.load("input.png") # Supports PNG and JPEG
img.save("output.png")
```
**Performance Note**: The Image class uses RGBA storage internally for SIMD-optimized operations.
This provides 2-5x performance improvements for resize and interpolation operations.
When using 3-channel RGB arrays, they are automatically converted to RGBA with alpha=255.
Note: NumPy is not required for basic color operations.
## Building from Source
Requires Zig compiler:
```bash
git clone https://github.com/bfactory-ai/zignal
cd zignal/bindings/python
pip install -e .
```
## License
MIT License
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