https://github.com/bfactory-ai/zignal
Zero-dependency image processing library
https://github.com/bfactory-ai/zignal
drawing geometry image-processing svd-matrix-factorisation webassembly zero-dependency zig zig-package
Last synced: 4 months ago
JSON representation
Zero-dependency image processing library
- Host: GitHub
- URL: https://github.com/bfactory-ai/zignal
- Owner: bfactory-ai
- License: mit
- Created: 2024-04-13T06:47:14.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-07-21T05:20:21.000Z (4 months ago)
- Last Synced: 2025-07-21T05:41:59.617Z (4 months ago)
- Topics: drawing, geometry, image-processing, svd-matrix-factorisation, webassembly, zero-dependency, zig, zig-package
- Language: Zig
- Homepage: https://bfactory-ai.github.io/zignal/
- Size: 689 KB
- Stars: 120
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- awesome-zig - zignal🗒️Image processing library
- awesome-zig - bfactory-ai/zignal
README
# Zignal
[](https://github.com/bfactory-ai/zignal/actions/workflows/test.yml)
[](https://github.com/bfactory-ai/zignal/actions/workflows/documentation.yml)
Zignal is a zero-dependency image processing library heavily inspired by the amazing [dlib](https://dlib.net).
## Disclaimer
This library is in early stages of development and being used internally.
As a result, the API might change often.
## Installation
```console
zig fetch --save git+https://github.com/bfactory-ai/zignal
```
Then, in your `build.zig`
```zig
const zignal = b.dependency("zignal", .{ .target = target, .optimize = optimize });
// And assuming that your b.addExecutable `exe`:
exe.root_module.addImport("zignal", zignal.module("zignal"));
// If you're creating a `module` using b.createModule, then:
module.addImport("zignal", zignal.module("zignal"));
```
## Motivation
This library is used by [Ameli](https://ameli.co.kr/) for their makeup virtual try on.
## Example
```zig
const std = @import("std");
const zignal = @import("zignal");
const Canvas = zignal.Canvas;
const Image = zignal.Image;
const Point2d = zignal.Point2d;
const Rgba = zignal.Rgba;
const savePng = zignal.savePng;
pub fn main() !void {
const allocator = std.heap.page_allocator;
// Create a 800x600 RGBA image
var image: Image(Rgba) = try .initAlloc(allocator, 600, 800);
defer image.deinit(allocator);
// Create a drawing canvas
const canvas: Canvas(Rgba) = .init(allocator, image);
canvas.fill(Rgba.white);
const red: Rgba = .{ .r = 255, .g = 0, .b = 0, .a = 255 };
const start: Point2d(f32) = .init2d(50, 50);
const end: Point2d(f32) = .init2d(750, 100);
const center: Point2d(f32) = .init2d(400, 300);
canvas.drawLine(start, end, red, 5, .soft);
canvas.drawCircle(center, 100, red, 3, .soft);
// Save the result to a PNG file
try savePng(Rgba, allocator, image, "drawing.png");
}
```
## Features
Initially, the features in this library are the ones required to get the virtual try on for makeup working.
However, we hope that it can be a foundation from which we can build a high quality image processing library, collaboratively.
Current features include:
- **Principal Component Analysis (PCA)** with generic SIMD-accelerated implementation
- works on arbitrary-dimensional data (colors, spatial features, etc.)
- automatic color-to-point conversion using reflection
- image-to-points utilities for computer vision applications
- efficient batch processing and reconstruction
- **color space conversions** (RGB, HSL, HSV, Lab, XYZ, Oklab, Oklch, etc.)
- **matrix operations** with common linear algebra functions
- **singular value decomposition (SVD)** ported from dlib
- geometry
- unified Point system with SIMD acceleration supporting arbitrary dimensions
- 2D/3D/4D points with convenient accessors (x(), y(), z(), w())
- vector operations (add, subtract, scale, dot product, norm, distance)
- dimension conversion and projection methods
- rectangles and geometric primitives
- projective, affine and similarity transforms
- convex hull algorithms
- simple image struct with common operations
- resize
- rotate
- crop
- blur
- sharpen
- views (called `sub_image` in dlib or `roi` in OpenCV.)
- Canvas drawing API
- lines with variable width and antialiasing
- circles (filled and outlined) with soft edges
- polygons (filled and outlined)
- rectangles with customizable borders
- BĂ©zier curves (quadratic and cubic) with adaptive subdivision
- spline polygons with tension control for soft curved shapes
- multiple drawing modes: fast (hard edges) and soft (antialiased edges)
## Examples
One of the greatest things about dlib is the large amount of examples illustrating how to use many of that library features.
I plan to showcase most of the features of this library as simple HTML/JS + Wasm examples, which can be accessed from [here](https://bfactory-ai.github.io/zignal/examples/).
Currently, there are examples for:
- [Color space conversions](https://bfactory-ai.github.io/zignal/examples/colorspaces.html)
- [Face alignment](https://bfactory-ai.github.io/zignal/examples/face-alignment.html)
- [Perlin noise generation](https://bfactory-ai.github.io/zignal/examples/perlin-noise.html)
- [Seam carving](https://bfactory-ai.github.io/zignal/examples/seam-carving.html)
- [Feature distribution matching](https://bfactory-ai.github.io/zignal/examples/fdm.html)
- [White balance](https://bfactory-ai.github.io/zignal/examples/white-balance.html)
## Acknowledgements
First of all, this project would not have been possible without the existence of [dlib](http://dlib.net).
In fact, the first version of the virtual makeup try on was written in C++ with dlib and Emscripten.
However, we decided to give Zig a go, even if that meant rewriting everything. As a result, we have no dependencies now.
Finally, [B factory, Inc](https://www.bfactory.ai/), which is my employer and graciously agreed to release this library to the public.