Img2Num

Img2Num is a fast and accurate raster vectorizer.

It converts raster images (like PNGs and JPGs) into clean SVGs with high accuracy and performance.

_{Img2Num is optimized for natural images.}

Status

Before vs After
Why Img2Num?
Features
Multi-Language Support
Community Links

Installation
Quick Start
API Reference
Examples
Building and Publishing

Before vs After

Input (Original Raster)	Output (SVG)

What are you waiting for?

Try our image to color-by-number demo!

[!IMPORTANT]

Why Img2Num?

Most raster-to-SVG vectorizers were designed for clean, synthetic input images such as logos, icons, diagrams, and flat illustrations. When applied to real-world photographs, they often struggle with noise, gradients, fine detail, and complex textures, resulting in less accurate vectorizations.

Img2Num takes the opposite approach. It was designed from the ground up for natural images, combining color quantization, contour extraction, and GPU-accelerated processing to produce high-quality SVGs from photographs while still performing well on synthetic artwork.

If your input images are photographs rather than logos or illustrations, Img2Num was built specifically for that use case.

_{What is Img2Num? Think of tools like Potrace or imagetracerjs, but designed with first-class support for natural photographs and other real-world imagery.}

Features

Built for real-world photos - Designed from the ground up to handle natural, noisy raster images (photographs, scans, etc.), unlike many vectorization libraries that are optimized for clean, synthetic source images (icons, logos, flat illustrations).
Raster to SVG vectorization - Converts PNG/JPEG images into clean, layered SVG paths using color quantization, contour tracing, and an integrated SVG writer.
GPU-accelerated processing - Leverages Dawn (Google's WebGPU implementation) for hardware-accelerated quantization and image processing.
Color quantization & palette control - Reduce an image to any K number of colors (K-Means), with output SVGs organized into logical color groups.
Precise contour extraction - Edge detection and polygon simplification with tunable fidelity for accuracy vs. performance trade-offs.
Multi-language bindings - Native C++17 core with first-class bindings for:
- C - lightweight C API (add as a submodule)
- Python (pip install img2num) - NumPy arrays in, SVG strings out
- JavaScript (npm i img2num) - same C++ core compiled to WebAssembly, works in browser and Node
WebAssembly-powered - The native C++ core is compiled to WebAssembly (WASM) for high-performance execution in browsers.
Zero-copy bindings - Direct memory access via NumPy in Python and TypedArrays in JS, avoiding unnecessary data copying.
Minimal dependencies - Core library built for speed with only one external runtime dependency (Google's Dawn).
Cross-platform CI - Tested on Linux, macOS, Windows, and WASM.
Flexible distribution - Available via PyPI, npm, and Docker Hub.
Permissive licensing - MIT-licensed core (libraries, packages, build tools), with AGPLv3 covering docs, example apps, and CI/config - see below for details.

Multi-Language Support

Language	Package Info

Community Links

Installation

pip install img2num

Runtime dependency: numpy>=1.23.5

Supported Python versions: 3.10, 3.11, 3.12

Quick Start

[!Important] Input images must be 4 channel uint8 arrays with channel order RGBA

All-in-one (recommended)

import cv2
from img2num import image_to_svg, ImageToSvgConfig

img = cv2.imread("input.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA)  # VERY IMPORTANT

cfg = ImageToSvgConfig(kmeans={"k": 16}, min_thickness=10)
svg = image_to_svg(img, config=cfg)

with open("output.svg", "w") as f:
    f.write(svg)

API Reference

width and height are automatically injected from the image shape, do not pass them manually.

For full API details see the Python API reference.

Examples

Console app: example-apps/console-py

A formal test suite is not yet present. Verification is done via linting and build/import smoke checks.

Building and Publishing

# Build wheel locally
uv build
# or
python -m build

Release wheels are built automatically via cibuildwheel and published to PyPI using OIDC trusted publishing through the GitHub Actions release workflow.

License

MIT © Ryan Millard

GitHub · Documentation · Changelog · Issues · Discussions

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Tags: CPython 3.10, manylinux: glibc 2.27+ x86-64, manylinux: glibc 2.28+ x86-64
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Uploaded via: twine/6.1.0 CPython/3.13.14

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Publisher: release.yml on Ryan-Millard/Img2Num

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- Predicate type: https://docs.pypi.org/attestations/publish/v1
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- Branch / Tag: refs/heads/main
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Download URL: img2num-0.3.0-cp310-cp310-macosx_11_0_arm64.whl
Upload date: Aug 15, 2026
Size: 2.5 MB
Tags: CPython 3.10, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.14

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The following attestation bundles were made for img2num-0.3.0-cp310-cp310-macosx_11_0_arm64.whl:

Publisher: release.yml on Ryan-Millard/Img2Num

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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- Branch / Tag: refs/heads/main
- Owner: https://github.com/Ryan-Millard
- Access: public
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- Runner Environment: github-hosted
- Publication workflow: release.yml@28e83fdb6d3059302c53cb5462b15f45e56e951f
- Trigger Event: push

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This release

0.3.0

Aug 15, 2026

0.2.2

Jul 30, 2026

0.2.1

Jul 2, 2026

0.0.0

Jun 27, 2026

img2num 0.3.0

Img2Num

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