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type-safe, modular, chainable image processing library built on top of OpenCV.js with a fluent API leveraging pipeline processing.","maintainers":[{"name":"snowfluke","email":"awalariansyah7@gmail.com"}],"readme":"# ppu-ocv\n\n[](https://www.npmjs.com/package/ppu-ocv) [](https://jsr.io/@snowfluke/ppu-ocv) [](https://www.npmjs.com/package/ppu-ocv) [](https://www.npmjs.com/package/ppu-ocv#provenance) [](./LICENSE) [](https://scorecard.dev/viewer/?uri=github.com/PT-Perkasa-Pilar-Utama/ppu-ocv) [](https://socket.dev/npm/package/ppu-ocv) [](https://www.bestpractices.dev/projects/12961)\n\nA type-safe, modular, chainable image processing library built on top of OpenCV.js with a fluent API leveraging pipeline processing. Decoupled canvas utilities run anywhere — Node, Bun, browsers, browser extensions, and service workers — with or without OpenCV.\n\n\n\n```ts\nconst processor = new ImageProcessor(canvas);\n\nconst result = processor\n .grayscale()\n .blur({ size: [5, 5] })\n .threshold()\n .invert()\n .dilate({ size: [20, 20], iter: 5 })\n .toCanvas();\n\nprocessor.destroy();\n```\n\nBased on [TechStark/opencv-js](https://github.com/TechStark/opencv-js).\n\n## Table of Contents\n\n- [Why ppu-ocv?](#why-ppu-ocv)\n- [Installation](#installation)\n- [Usage (Node.js / Bun)](#usage-nodejs--bun)\n- [Canvas-only Usage (no OpenCV)](#canvas-only-usage-no-opencv)\n- [Web / Browser Support](#web--browser-support)\n- [React Native / Mobile Support](#react-native--mobile-support)\n- [Built-in Pipeline Operations](#built-in-pipeline-operations)\n- [Extending Operations](#extending-operations)\n- [Class Documentation](#class-documentation)\n- [Migrating from v2](#migrating-from-v2)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Why ppu-ocv?\n\n- **Simplified API** — chainable methods that hide OpenCV's verbose Mat allocation\n- **No memory management** — automatic Mat lifecycle within the pipeline\n- **Type-safe** — full TypeScript inference for operations and options\n- **Extensible** — register custom operations with `registry.register(...)` without forking\n- **Cross-platform** — same API in Node, Bun, browsers, and constrained runtimes\n- **Loosely coupled** — canvas utilities work standalone; OpenCV is only loaded when actually needed\n\n## Installation\n\nInstall using your preferred package manager:\n\n```bash\nnpm install ppu-ocv\nyarn add ppu-ocv\nbun add ppu-ocv\n```\n\n## Usage (Node.js / Bun)\n\nNote that operation order matters — you should have at least basic familiarity with OpenCV. See the operations table below.\n\n```ts\nimport { CanvasProcessor, ImageProcessor } from \"ppu-ocv\";\n\nconst file = Bun.file(\"./assets/receipt.jpg\");\nconst image = await file.arrayBuffer();\n\nawait ImageProcessor.initRuntime(); // init opencv\nconst canvas = await CanvasProcessor.prepareCanvas(image);\n\nconst processor = new ImageProcessor(canvas);\nprocessor\n .grayscale()\n .blur({ size: [5, 5] })\n .threshold();\n\nconst resultCanvas = processor.toCanvas();\nprocessor.destroy();\n```\n\nOr use the `execute` API directly:\n\n```ts\nimport { CanvasProcessor, CanvasToolkit, ImageProcessor, cv } from \"ppu-ocv\";\n\nconst file = Bun.file(\"./assets/receipt.jpg\");\nconst image = await file.arrayBuffer();\n\nconst canvasToolkit = CanvasToolkit.getInstance();\nawait ImageProcessor.initRuntime();\nconst canvas = await CanvasProcessor.prepareCanvas(image);\n\nconst processor = new ImageProcessor(canvas);\nconst grayscaleImg = processor.execute(\"grayscale\").toCanvas();\n\n// The pipeline continues from the grayscaled image\nconst thresholdImg = processor\n .execute(\"blur\")\n .execute(\"threshold\", {\n type: cv.THRESH_BINARY_INV + cv.THRESH_OTSU,\n })\n .toCanvas();\n\nawait canvasToolkit.saveImage({\n canvas: thresholdImg,\n filename: \"threshold\",\n path: \"out\",\n});\n```\n\nFor more advanced usage, see: [Example usage of ppu-ocv](./examples)\n\n## Canvas-only usage (no OpenCV)\n\nStarting from v3.0.0, canvas utilities are fully decoupled from OpenCV. If you only need canvas I/O (e.g. loading/saving images, cropping, drawing) without any image processing, import from `ppu-ocv/canvas` (Node) or `ppu-ocv/canvas-web` (browser). OpenCV is **never imported or initialised** by these entry points, making them safe for use in Browser Extensions, Service Workers, and edge runtimes.\n\n```ts\n// Node.js — zero OpenCV dependency\nimport { CanvasProcessor, CanvasToolkit } from \"ppu-ocv/canvas\";\n\nconst file = Bun.file(\"./assets/image.jpg\");\nconst canvas = await CanvasProcessor.prepareCanvas(await file.arrayBuffer());\n\nconst toolkit = CanvasToolkit.getInstance();\nconst cropped = toolkit.crop({\n canvas,\n bbox: { x0: 0, y0: 0, x1: 100, y1: 100 },\n});\n\nconst buffer = await CanvasProcessor.prepareBuffer(cropped);\n```\n\n```ts\n// Browser Extension background script — zero OpenCV dependency\nimport { CanvasProcessor, CanvasToolkit } from \"ppu-ocv/canvas-web\";\n\nconst response = await fetch(\"/image.jpg\");\nconst canvas = await CanvasProcessor.prepareCanvas(await response.arrayBuffer());\n```\n\n## Web / Browser Support\n\nImport from `ppu-ocv/web` to use the browser-native canvas APIs (`HTMLCanvasElement` / `OffscreenCanvas`) instead of `@napi-rs/canvas`.\n\n### With a bundler (Vite, webpack, etc.)\n\n```ts\nimport { CanvasProcessor, ImageProcessor, cv } from \"ppu-ocv/web\";\n\nawait ImageProcessor.initRuntime();\n\nconst response = await fetch(\"/my-image.jpg\");\nconst buffer = await response.arrayBuffer();\n\nconst canvas = await CanvasProcessor.prepareCanvas(buffer);\nconst processor = new ImageProcessor(canvas);\n\nprocessor\n .grayscale()\n .blur({ size: [5, 5] })\n .threshold();\n\nconst result = processor.toCanvas(); // returns HTMLCanvasElement\ndocument.body.appendChild(result);\n\nprocessor.destroy();\n```\n\n### Vanilla HTML (no bundler)\n\nThe web build does not bundle OpenCV — load `opencv.js` yourself so it is on\n`globalThis.cv`, then `initRuntime()` waits for the WASM to finish initializing:\n\n```html\n\n\n\n```\n\n> With a bundler, importing `@techstark/opencv-js` once (it self-registers on\n> `globalThis.cv`) is enough — no script tag needed.\n\n> **Note:** ES modules require HTTP/HTTPS — use a local server (`npx serve .`) for dev, or deploy to GitHub Pages.\n\nSee the [interactive demo](./index.html) for a full working example.\n\n### Entry point reference\n\n| Import path | OpenCV | Canvas backend | `CanvasToolkit` | Use case |\n| ----------------------- | ------ | ------------------------------------- | -------------------- | ------------------------------------------ |\n| `ppu-ocv` | ✅ | `@napi-rs/canvas` | Full (with file I/O) | Full pipeline, Node.js / Bun |\n| `ppu-ocv/web` | ✅ | `HTMLCanvasElement`/`OffscreenCanvas` | Base only | Full pipeline, browser |\n| `ppu-ocv/canvas` | ❌ | `@napi-rs/canvas` | Full (with file I/O) | Canvas-only, Node (extensions, edge, etc.) |\n| `ppu-ocv/canvas-web` | ❌ | `HTMLCanvasElement`/`OffscreenCanvas` | Base only | Canvas-only, browser extensions / SW |\n| `ppu-ocv/canvas-mobile` | ❌ | `@shopify/react-native-skia` | Base only | Canvas-only, React Native / Expo |\n\n### Platform abstraction\n\nUnder the hood, ppu-ocv uses a platform abstraction layer. Each entry point auto-registers its platform. You can also register a custom platform:\n\n```ts\nimport { setPlatform, type CanvasPlatform } from \"ppu-ocv/web\";\n\nconst myPlatform: CanvasPlatform = {\n createCanvas(width, height) {\n /* ... */\n },\n loadImage(source) {\n /* ... */\n },\n isCanvas(value) {\n /* ... */\n },\n};\n\nsetPlatform(myPlatform);\n```\n\n## React Native / Mobile Support\n\nImport from `ppu-ocv/canvas-mobile` for React Native apps (iOS / Android). This\nentry point is backed by [`@shopify/react-native-skia`](https://shopify.github.io/react-native-skia/)\nand is functionally equivalent to `ppu-ocv/canvas-web` — it exposes `CanvasProcessor`,\n`CanvasToolkitBase`, and canvas factory types **without any OpenCV or WASM dependency**.\n\n### Installation\n\n```bash\n# In your React Native / Expo project\nnpm install ppu-ocv @shopify/react-native-skia\n# or\nbun add ppu-ocv @shopify/react-native-skia\n```\n\nFollow the [react-native-skia setup guide](https://shopify.github.io/react-native-skia/docs/getting-started/installation)\nto complete native installation (pod install / Gradle sync). Skia **must** be\ninitialised by the time you call any `ppu-ocv` API.\n\n### Usage\n\n```ts\nimport { CanvasProcessor } from \"ppu-ocv/canvas-mobile\";\n\n// Load from an ArrayBuffer (e.g. from expo-file-system or fetch)\nconst response = await fetch(\"https://example.com/receipt.jpg\");\nconst canvas = await CanvasProcessor.prepareCanvas(await response.arrayBuffer());\n\n// Or load directly from a URI (file:// or https://)\nconst canvas = await CanvasProcessor.prepareCanvas(\"file:///path/to/photo.jpg\");\n\n// Chainable canvas-only pipeline (no OpenCV)\nconst regions = new CanvasProcessor(canvas)\n .grayscale()\n .threshold({ thresh: 127 })\n .findRegions({ foreground: \"light\", minArea: 20 });\n\n// Export back to bytes\nconst buffer = await CanvasProcessor.prepareBuffer(canvas);\n```\n\n### Requirements\n\n- `@shopify/react-native-skia` ≥ 1.0.0\n- React Native ≥ 0.74 / Expo SDK ≥ 51 (Hermes engine)\n\n> **Note:** OpenCV (`@techstark/opencv-js`) is never loaded by this entry point.\n> If you need full OpenCV operations in a React Native context, consider running\n> the processing server-side and streaming results to the device.\n\n## Built-in pipeline operations\n\nTo avoid bloat, we only ship essential operations for chaining. Currently shipped operations are:\n\n| Operation | Depends on… | Why |\n| ------------------------- | ------------------------------------------- | ---------------------------------------------------------------------------------------- |\n| **grayscale** | – | Converts to single‐channel; many ops expect a gray image first. |\n| **blur** | _(ideally after)_ grayscale | Noise reduction works best on 1-channel data. |\n| **equalize** | _(after)_ grayscale | Histogram equalisation (CLAHE or global) for contrast normalisation before thresholding. |\n| **threshold** | _(after)_ grayscale | Produces a binary image; needs gray levels. |\n| **adaptiveThreshold** | _(after)_ grayscale (and optionally blur) | Local thresholding on gray values (smoother if blurred first). |\n| **invert** | _(after)_ threshold or adaptiveThreshold | Inverting a binary mask flips foreground/background. |\n| **canny** | _(after)_ grayscale + blur | Edge detection expects a smoothed gray image. |\n| **dilate** | _(after)_ threshold or edge detection | Expands foreground regions—usually on a binary mask. |\n| **erode** | _(after)_ threshold or edge detection | Shrinks or cleans up binary regions. |\n| **morphologicalGradient** | _(after)_ dilation + erosion (or threshold) | Highlights boundaries by subtracting eroded from dilated image. |\n| **warp** | – | Geometric transform; can be applied at any point. |\n| **resize** | – | Also independent; purely geometry. |\n| **border** | – | Independent; purely geometry. |\n| **rotate** | – | Independent. |\n\n## Extending operations\n\nYou can easily add your own by creating a prototype method or extending the `ImageProcessor` class.\n\nSee: [How to extend ppu-ocv operations](./docs/how-to-extend-ppu-ocv-operations.md)\n\n## Class documentation\n\n#### `CanvasProcessor`\n\nCanvas-native image processing with **no OpenCV dependency**. Available from all entry points including `ppu-ocv/canvas` and `ppu-ocv/canvas-web`. Provides a chainable instance API alongside static I/O helpers.\n\n```ts\nconst result = new CanvasProcessor(canvas)\n .resize({ width: 360, height: 640 })\n .grayscale()\n .threshold({ thresh: 127 })\n .invert()\n .border({ size: 10, color: \"white\" })\n .toCanvas();\n\n// Detect connected white regions on a binary image\nconst regions = new CanvasProcessor(binaryCanvas).findRegions({\n foreground: \"light\",\n minArea: 20,\n // thresh: 0 ← use on resized binary images to match OpenCV (any non-zero pixel = foreground)\n // padding: { vertical: 0.4, horizontal: 0.6 } ← expand bbox by fraction of height\n // scale: 1 / resizeRatio ← map coords back to original image space\n});\nregions.sort((a, b) => b.area - a.area); // largest first\n// regions[0] → { bbox: { x0, y0, x1, y1 }, area }\n```\n\n**Static I/O**\n\n| Method | Args | Description |\n| ---------------------- | ----------- | ----------------------------------------------------- |\n| static `prepareCanvas` | ArrayBuffer | Load image bytes into a `CanvasLike` |\n| static `prepareBuffer` | CanvasLike | Export a `CanvasLike` to an `ArrayBuffer` (PNG bytes) |\n\n**Instance operations** (chainable, return `this`)\n\n| Method | Options | OpenCV equivalent | Fidelity |\n| ----------- | ---------------------------------- | ------------------------- | -------------- |\n| `resize` | `width`, `height` | `cv.resize` INTER_LINEAR | 1:1 (↓), ≈ (↑) |\n| `grayscale` | — | `COLOR_RGBA2GRAY` | **1:1** |\n| `convert` | `alpha?`, `beta?` | `Mat.convertTo` (α·x + β) | **1:1** |\n| `invert` | — | `cv.bitwise_not` | **1:1** ¹ |\n| `threshold` | `thresh?` (127), `maxValue?` (255) | `THRESH_BINARY` | **1:1** |\n| `border` | `size?` (10), `color?` (CSS) | `BORDER_CONSTANT` | **1:1** |\n| `rotate` | `angle`, `cx?`, `cy?` | `warpAffine` | ≈ (±6 px) ² |\n| `toCanvas` | — | — | — |\n\n**Region detection** (returns data, does not mutate)\n\n| Method | Options | Description |\n| ------------- | ---------------------------------------------------------------------------------------- | -------------------------------------------------------------- |\n| `findRegions` | `foreground?` (`\"light\"`), `thresh?` (127), `minArea?`, `maxArea?`, `padding?`, `scale?` | 8-connected flood-fill on a binary canvas → `DetectedRegion[]` |\n\n`DetectedRegion` shape: `{ bbox: BoundingBox, area: number }` where `bbox` is `{ x0, y0, x1, y1 }` (x1/y1 exclusive). Equivalent to OpenCV's `findContours(RETR_EXTERNAL) + boundingRect` — all matched bboxes agree within ±1 px on solid binary images. ³\n\n**`thresh` option** — pixel value threshold for foreground detection (default `127`). For resized binary images, use `thresh: 0` so anti-aliased border pixels (values 1–127) are included as foreground, matching OpenCV's non-zero threshold. With `thresh: 0` + `padding` + `scale`, full-pipeline IoU vs OpenCV is **98.4%** (all 21/21 boxes matched).\n\n> ¹ Canvas `invert` preserves the alpha channel; OpenCV `bitwise_not` also inverts alpha. Results are identical when the source is opaque (alpha=255).\n>\n> ² Canvas uses anti-aliased bilinear interpolation; OpenCV uses plain bilinear. Difference is visually imperceptible and has no impact on OCR quality.\n>\n> ³ `RETR_LIST` may return additional inner-hole contours for white regions that contain dark sub-regions; `findRegions` counts each connected white component once regardless of interior holes.\n\n#### `ImageProcessor`\n\nRequires OpenCV. Available from `ppu-ocv` and `ppu-ocv/web`.\n\n| Method | Args | Description |\n| -------------------- | ---------------- | ------------------------------------------------------------------ |\n| constructor | cv.Mat or Canvas | Instantiate processor with initial image |\n| static `initRuntime` | | OpenCV runtime initialization — required once per runtime |\n| operations | depends | Chainable operations like `blur`, `grayscale`, `resize`, and so on |\n| `execute` | name, options | Chainable operations via the `execute` API |\n| `toMat` | | Return the current image as a `cv.Mat` |\n| `toCanvas` | | Return the current image as a `CanvasLike` |\n| `destroy` | | Clean up `cv.Mat` memory |\n\n#### `CanvasToolkit`\n\n| Method | Args | Description |\n| ------------- | ---------------------- | ----------------------------------------------------------------------------------------- |\n| `crop` | BoundingBox, Canvas | Crop a part of source canvas and return a new canvas of the cropped part |\n| `isDirty` | Canvas, threshold | Check whether a binary canvas is dirty (full of major color either black or white) or not |\n| `saveImage` | Canvas, filename, path | Save a canvas to an image file _(Node only)_ |\n| `clearOutput` | path | Clear the output folder _(Node only)_ |\n| `drawLine` | ctx, coordinate, style | Draw a non-filled rectangle outline on the canvas |\n| `drawContour` | ctx, contour, style | Draw a contour on the canvas — accepts any `ContourLike` (`{ data32S }`) |\n\n#### `DeskewService`\n\nDetects and corrects text skew in document images using a multi-method consensus approach (minAreaRect, baseline analysis, Hough transform). Requires OpenCV. Available from `ppu-ocv` and `ppu-ocv/web`.\n\n| Method | Args | Description |\n| -------------------- | ------------- | ------------------------------------ |\n| constructor | DeskewOptions | `verbose`, `minimumAreaThreshold` |\n| `calculateSkewAngle` | CanvasLike | Detect skew angle in degrees |\n| `deskewImage` | CanvasLike | Return a deskewed copy of the canvas |\n\n#### `Contours`\n\n| Method | Args | Description |\n| -------------------------------- | --------------- | ---------------------------------------------------------------- |\n| constructor | cv.Mat, options | Instantiate Contours and automatically find & store contour list |\n| `getAll` | | Return the full `cv.MatVector` of contours |\n| `getFromIndex` | index | Get contour at a specific index |\n| `getRect` | contour | Get the bounding rectangle of a contour |\n| `iterate` | callback | Iterate over all contours |\n| `getLargestContourArea` | | Return the contour with the largest area |\n| `getCornerPoints` | options | Get four corner points for perspective transformation (warp) |\n| `getApproximateRectangleContour` | options | Simplify a contour to an approximate rectangle |\n| `destroy` | | Destroy and clean up contour memory |\n\n#### `ImageAnalysis`\n\nA collection of utility functions for analyzing image properties (requires OpenCV).\n\n- `calculateMeanNormalizedLabLightness`: Calculates the mean normalized lightness of an image using the L channel of the Lab color space.\n- `calculateMeanGrayscaleValue`: Calculates the mean pixel value after converting to grayscale.\n\n## Contributing\n\nSee [CONTRIBUTING.md](./CONTRIBUTING.md) for the full guide — setup, commit conventions, quality checks, and PR flow. Also:\n\n- [Code of Conduct](./CODE_OF_CONDUCT.md) — community standards.\n- [Security policy](./SECURITY.md) — how to report vulnerabilities privately.\n- [Issue tracker](https://github.com/PT-Perkasa-Pilar-Utama/ppu-ocv/issues) — bug reports, feature requests, and docs gaps each have a template.\n\nQuick local commands:\n\n```bash\nbun install\nbun test # run unit tests\nbun run fmt # check formatting\nbun run lint # check lint\nbun run type-check # tsgo --noEmit\nbun task build # emit ./lib\nbun task bench # micro-bench the operations registry\n```\n\n## Migrating from v2\n\nSee [MIGRATION.md](./MIGRATION.md) for a full guide. The short version:\n\n```diff\n- import { ImageProcessor } from \"ppu-ocv\";\n- const canvas = await ImageProcessor.prepareCanvas(buffer);\n- const buf = await ImageProcessor.prepareBuffer(canvas);\n\n+ import { CanvasProcessor } from \"ppu-ocv\";\n+ const canvas = await CanvasProcessor.prepareCanvas(buffer);\n+ const buf = await CanvasProcessor.prepareBuffer(canvas);\n```\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Support\n\nIf you encounter any issues or have suggestions, please open an issue in the repository.\n\nHappy coding!\n","readmeFilename":"README.md"}