mirror of
https://github.com/visioncortex/vtracer.git
synced 2026-09-10 06:08:48 -07:00
Chris Tsang
89 lines
3.6 KiB
Markdown
89 lines
3.6 KiB
Markdown
<div align="center">
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<img src="https://raw.githubusercontent.com/visioncortex/vtracer/master/docs/images/visioncortex-banner.png">
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<h1>VTracer</h1>
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<p>
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<strong>Raster to Vector Graphics Converter</strong>
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</p>
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<h3>
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<a href="https://github.com/visioncortex/vtracer/releases/download/1.0.0-alpha.4/VTracer_1.0.0-alpha.4_x64-setup.exe">Windows App</a>
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<span> | </span>
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<a href="https://github.com/visioncortex/vtracer/releases/download/1.0.0-alpha.4/VTracer_1.0.0-alpha.4_universal.dmg">macOS App</a>
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<span> | </span>
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<a href="https://github.com/visioncortex/vtracer/releases/download/1.0.0-alpha.4/VTracer_1.0.0-alpha.4_x64.AppImage">Linux App</a>
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</h3>
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</div>
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# vtracer (Node.js)
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Raster → vector (SVG) for Node, a WebAssembly build of
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[`vtracer`](https://github.com/visioncortex/vtracer). Image decoding and
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vectorization both happen in wasm, so there is **no native dependency** — just
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`npm install`.
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## Introduction
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visioncortex VTracer is an open source software to convert raster images (like jpg & png) into vector graphics (svg). It can vectorize graphics and photographs and trace the curves to output compact vector files.
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Comparing to Potrace, VTracer has an image processing pipeline which can handle colored images. VTracer skips Potrace's expensive optimal-polygon search in favor of a fast, linear pipeline that stays faithful to high-resolution images.
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Comparing to Adobe Illustrator's Image Trace, VTracer's output is much more compact as we adopt a stacking strategy and avoid producing shapes with holes.
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VTracer is originally designed for processing high resolution scans of historic blueprints up to gigapixels. At the same time, VTracer can also handle low resolution pixel art, simulating `image-rendering: pixelated` for retro game artworks.
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Technical descriptions of the [tracing algorithm](https://www.visioncortex.org/vtracer-docs) and [clustering algorithm](https://www.visioncortex.org/impression-docs).
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## Install
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```sh
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npm install @visioncortex/vtracer
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```
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## Usage
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```js
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const fs = require('fs');
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const vtracer = require('@visioncortex/vtracer');
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// file in, file out
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await vtracer.convertFile('in.png', 'out.svg');
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await vtracer.convertFile('in.jpg', 'out.svg', { mode: 'polygon', hierarchical: 'cutout' });
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// buffers
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const svg = vtracer.convertBuffer(fs.readFileSync('in.png'), { preset: 'poster' });
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// raw RGBA8 pixels
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const svg2 = vtracer.convertPixels(rgba, width, height, { clustering: 'bw' });
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```
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## API
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- `convertBuffer(buffer, options?) => string` — encoded image (PNG/JPEG/GIF/BMP) → SVG.
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- `convertPixels(rgba, width, height, options?) => string` — raw RGBA8 → SVG.
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- `convertFile(input, output, options?) => Promise<void>` — read, trace, write.
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- `convertFileSync(input, output, options?) => void`.
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### `Options` (all optional, camelCase)
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`preset` (`"bw" | "poster" | "photo"`, applied first), `clustering`
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(`"color-cluster" | "bw" | "watershed"`), `hierarchical` (`"stacked" | "cutout"`
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for the seam-free mosaic), `mode` (`"pixel" | "polygon" | "spline"`),
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`filterSpeckle`, `colorPrecision`, `layerDifference`, `cornerThreshold`,
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`lengthThreshold`, `maxIterations`, `spliceThreshold`, `simplify` (curve
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simplification tolerance in px, try 1–2.5), `pathPrecision`, `palette` (list of
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`#rrggbb`), `maxColors`, `optimize` (`0 | 1 | 2`), `binaryThreshold` /
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`adaptive` / `adaptiveWindow` / `adaptiveT` (binary mode), `watershedDetail`
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(default 128; higher = more regions, uncapped).
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## Build from source
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Requires the Rust toolchain and [`wasm-pack`](https://rustwasm.github.io/wasm-pack/):
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```sh
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npm run build # wasm-pack build --target nodejs --out-dir pkg
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npm test
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```
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