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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.

License Status

Docs Changelog

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Sponsor Security Policy

Contents

Before vs After

Input (Original Raster) Output (SVG)
Original input raster image (Aerial view of mountains) Final output SVG image (Aerial view of mountains)
Original input raster image (A garden in Margate, South Africa) Final output SVG image (A garden in Margate, South Africa)
Original input raster image (A ring on a woman's hand) Final output SVG image  (A ring on a woman's hand)

What are you waiting for?

Try one of our example-apps!


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
C logo C package releases Docs C Changelog
C++ logo C++ package releases Docs C++ Changelog
JavaScript logo npm jsDelivr CDN unpkg CDN Downloads JavaScript package releases Docs JavaScript Changelog
Python logo PyPI PyPI Downloads Supported Versions Python package releases Docs Python Changelog

Changelog Contributing Security Policy License Sponsor Issues Good First Issues Blog GitHub Discussions

Installation

pip install img2num
uv add 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

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.


Type Hints

This package is fully typed. Wheels include a PEP 561 py.typed marker and generated .pyi stubs for the native _img2num extension, so mypy, pyright, and IDE autocomplete work out of the box — no extra stub packages needed.

Examples

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

Type stubs are generated with pybind11-stubgen during the CMake build and bundled into the wheel automatically.

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

This package (the library you install from PyPI) is MIT-licensed. Documentation, example apps, and CI/config in the main repository are AGPLv3 — see the repository license overview for details.

Supporting Us

GitHub Sponsors Ko-fi

Img2Num is free and open source. If it saves you time or you'd like to support its continued development:


Release files for img2num 0.3.1

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Source distribution (sdist)

Source distribution for img2num 0.3.1
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img2num-0.3.1.tar.gz 5.8 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for img2num 0.3.1
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img2num-0.3.1-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
img2num-0.3.1-cp313-cp313-win32.whl CPython 3.13 CPython 3.13 Windows x86-32 Details
img2num-0.3.1-cp313-cp313-musllinux_1_2_x86_64.whl CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-64 Details
img2num-0.3.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
img2num-0.3.1-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
img2num-0.3.1-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
img2num-0.3.1-cp312-cp312-win32.whl CPython 3.12 CPython 3.12 Windows x86-32 Details
img2num-0.3.1-cp312-cp312-musllinux_1_2_x86_64.whl CPython 3.12 CPython 3.12 Linux musl 1.2+ x86-64 Details
img2num-0.3.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
img2num-0.3.1-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
img2num-0.3.1-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
img2num-0.3.1-cp311-cp311-win32.whl CPython 3.11 CPython 3.11 Windows x86-32 Details
img2num-0.3.1-cp311-cp311-musllinux_1_2_x86_64.whl CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-64 Details
img2num-0.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
img2num-0.3.1-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
img2num-0.3.1-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
img2num-0.3.1-cp310-cp310-win32.whl CPython 3.10 CPython 3.10 Windows x86-32 Details
img2num-0.3.1-cp310-cp310-musllinux_1_2_x86_64.whl CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-64 Details
img2num-0.3.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
img2num-0.3.1-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details

Total release size: 122.3 MB

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This release

0.3.1 This release

21 release files

0.3.0

21 release files

0.2.2

21 release files

0.0.0

21 release files

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