opencv4nodejs

By its nature, JavaScript lacks the performance to implement Computer Vision tasks efficiently. Therefore this package brings the performance of the native OpenCV library to your Node.js application. Supports all OpenCV 3 versions. The bindings are available as an asynchronous (via promises or callbacks) and synchronous API.

The ultimate goal of this project is to provide a collection of Node.js bindings, which supports the entire OpenCV and OpenCV-contrib API. An overview of available bindings can be found in the API Documentation. The implementation of missing bindings can be specifically prioritized by requesting new features. Furthermore, contribution is highly appreciated. If you want to get involved you can have a look at the contribution guide.

• Examples
• How to install
• Usage with Docker
• Usage with Electron
• Quick Start
• Async API
• Available Modules
• Request new Features

Examples

See examples for implementation.

Face Detection

Face Recognition

Check out my article about face recognition.

Hand Gesture Recognition

Check out my article about fingertip detection.

Object Recognition with Deep Neural Networks

Check out my article about using OpenCV's Deep Neural Networks with Node.js.

Tensorflow Inception

Single Shot Multibox Detector with COCO

Machine Learning

Check out my article about recognition of handwritten letters using Histogram of oriented Gradients (HOG) and Support Vector Machines (SVM).

Object Tracking

Feature Matching

Image Histogram

How to install

$ npm install --save opencv4nodejs

Make sure to have OpenCV 3+ ( extra modules are optional ) installed on your System https://github.com/opencv/opencv/releases/. In case you are running on Windows or have OpenCV set up in a custom directory make sure to set the following environment variables:

1. OPENCV_DIR pointing to the root path containing include directory or set OPENCV_INCLUDE_DIR explicitly.
2. OPENCV_LIB_DIR pointing to the library dir containing the OpenCV .lib, .so or .dylib files.

On Windows

On windows you will need the windows build tools to compile opencv4nodejs:

npm install --global windows-build-tools

Also you will need to add the OpenCV binaries to your system path:

• Add an environment variable OPENCV_BIN_DIR pointing to the library dir containing the OpenCV .dll files.
• Append ;%OPENCV_BIN_DIR%; to your path variable.

Note: Restart your current console session after making changes to your environment.

If you are running into issues also check the requirements for node-gyp specific to your OS: https://github.com/nodejs/node-gyp.

Usage with Docker

opencv-express - example for opencv4nodejs with express.js and docker

Or simply pull from justadudewhohacks/opencv-nodejs for opencv-3.2 + contrib-3.2 with opencv4nodejs globally installed:

FROM justadudewhohacks/opencv-nodejs

Different OpenCV 3.x base images can be found here: https://hub.docker.com/r/justadudewhohacks/.

Usage with Electron

opencv-electron - example for opencv4nodejs with electron

Add the following script to your package.json:

"electron-rebuild": "electron-rebuild -w opencv4nodejs"

Run the script:

$ npm run electron-rebuild

Require it in the application:

const electron = require('electron');
const cv = electron.remote.require('opencv4nodejs');

Quick Start

const cv = require('opencv4nodejs');

Initializing Mat (image matrix), Vec, Point

const rows = 100; // height
const cols = 100; // width

// empty Mat
const emptyMat = new cv.Mat(rows, cols, cv.CV_8UC3);

// fill the Mat with default value
const whiteMat = new cv.Mat(rows, cols, cv.CV_8UC1, 255);
const blueMat = new cv.Mat(rows, cols, cv.CV_8UC3, [255, 0, 0]);

// from array (3x3 Matrix, 3 channels)
const matData = [
  [[255, 0, 0], [255, 0, 0], [255, 0, 0]],
  [[0, 0, 0], [0, 0, 0], [0, 0, 0]],
  [[255, 0, 0], [255, 0, 0], [255, 0, 0]]
];
const matFromArray = new cv.Mat(matData, cv.CV_8UC3);

// from node buffer
const charData = [255, 0, ...];
const matFromArray = new cv.Mat(new Buffer.from(charData), rows, cols, cv.CV_8UC3);

// Point
const pt2 = new cv.Point(100, 100);
const pt3 = new cv.Point(100, 100, 0.5);

// Vector
const vec2 = new cv.Vec(100, 100);
const vec3 = new cv.Vec(100, 100, 0.5);
const vec4 = new cv.Vec(100, 100, 0.5, 0.5);

Mat and Vec operations

const mat0 = new cv.Mat(...);
const mat1 = new cv.Mat(...);

// arithmetic operations for Mats and Vecs
const matMultipliedByScalar = mat0.mul(0.5);  // scalar multiplication
const matDividedByScalar = mat0.div(2);       // scalar division
const mat0PlusMat1 = mat0.add(mat1);          // addition
const mat0MinusMat1 = mat0.sub(mat1);         // subtraction
const mat0MulMat1 = mat0.hMul(mat1);          // elementwise multiplication
const mat0DivMat1 = mat0.hDiv(mat1);          // elementwise division

// logical operations Mat only
const mat0AndMat1 = mat0.and(mat1);
const mat0OrMat1 = mat0.or(mat1);
const mat0bwAndMat1 = mat0.bitwiseAnd(mat1);
const mat0bwOrMat1 = mat0.bitwiseOr(mat1);
const mat0bwXorMat1 = mat0.bitwiseXor(mat1);
const mat0bwNot = mat0.bitwiseNot();

Accessing Mat data

const matBGR = new cv.Mat(..., cv.CV_8UC3);
const matGray = new cv.Mat(..., cv.CV_8UC1);

// get pixel value as vector or number value
const vec3 = matBGR.at(200, 100);
const grayVal = matGray.at(200, 100);

// get raw pixel value as array
const [b, g, r] = matBGR.atRaw(200, 100);

// set single pixel values
matBGR.set(50, 50, [255, 0, 0]);
matBGR.set(50, 50, new Vec(255, 0, 0));
matGray.set(50, 50, 255);

// get a 25x25 sub region of the Mat at offset (50, 50)
const width = 25;
const height = 25;
const region = matBGR.getRegion(new cv.Rect(50, 50, width, height));

// get a node buffer with raw Mat data
const matAsBuffer = matBGR.getData();

// get entire Mat data as JS array
const matAsArray = matBGR.getDataAsArray();

IO

// load image from file
const mat = cv.imread('./path/img.jpg');
cv.imreadAsync('./path/img.jpg', (err, mat) => {
  ...
})

// save image
cv.imwrite('./path/img.png', mat);
cv.imwriteAsync('./path/img.jpg', mat,(err) => {
  ...
})

// show image
cv.imshow('a window name', mat);
cv.waitKey();

// open capture from webcam
const devicePort = 0;
const wCap = new cv.VideoCapture(devicePort);

// open video capture
const vCap = new cv.VideoCapture('./path/video.mp4');

// read frames from capture
const frame = vCap.read();
vCap.readAsync((err, frame) => {
  ...
});

// loop through the capture
const delay = 10;
let done = false;
while (!done) {
  let frame = vCap.read();
  // loop back to start on end of stream reached
  if (frame.empty) {
    vCap.reset();
    frame = vCap.read();
  }

  // ...

  const key = cv.waitKey(delay);
  done = key !== 255;
}

Useful Mat methods

const matBGR = new cv.Mat(..., cv.CV_8UC3);

// convert types
const matSignedInt = matBGR.convertTo(cv.CV_32SC3);
const matDoublePrecision = matBGR.convertTo(cv.CV_64FC3);

// convert color space
const matGray = matBGR.bgrToGray();
const matHSV = matBGR.cvtColor(cv.COLOR_BGR2HSV);
const matLab = matBGR.cvtColor(cv.COLOR_BGR2Lab);

// resize
const matHalfSize = matBGR.rescale(0.5);
const mat100x100 = matBGR.resize(100, 100);
const matMaxDimIs100 = matBGR.resizeToMax(100);

// extract channels and create Mat from channels
const [matB, matG, matR] = matBGR.splitChannels();
const matRGB = new cv.Mat([matR, matB, matG]);

Drawing a Mat into HTML Canvas

const img = ...

// convert your image to rgba color space
const matRGBA = img.channels === 1
  ? img.cvtColor(cv.COLOR_GRAY2RGBA)
  : img.cvtColor(cv.COLOR_BGR2RGBA);

// create new ImageData from raw mat data
const imgData = new ImageData(
  new Uint8ClampedArray(matRGBA.getData()),
  img.cols,
  img.rows
);

// set canvas dimensions
const canvas = document.getElementById('myCanvas');
canvas.height = img.rows;
canvas.width = img.cols;

// set image data
const ctx = canvas.getContext('2d');
ctx.putImageData(imgData, 0, 0);

Method Interface

OpenCV method interface from official docs or src:

void GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT);

translates to:

const src = new cv.Mat(...);
// invoke with required arguments
const dst0 = src.gaussianBlur(new cv.Size(5, 5), 1.2);
// with optional paramaters
const dst2 = src.gaussianBlur(new cv.Size(5, 5), 1.2, 0.8, cv.BORDER_REFLECT);
// or pass specific optional parameters
const optionalArgs = {
  borderType: cv.BORDER_CONSTANT
};
const dst2 = src.gaussianBlur(new cv.Size(5, 5), 1.2, optionalArgs);

Async API

The async API can be consumed by passing a callback as the last argument of the function call. By default, if an async method is called without passing a callback, the function call will yield a Promise.

Async Face Detection

const classifier = new cv.CascadeClassifier(cv.HAAR_FRONTALFACE_ALT2);

// by nesting callbacks
cv.imreadAsync('./faceimg.jpg', (err, img) => {
  if (err) { return console.error(err); }

  const grayImg = img.bgrToGray();
  classifier.detectMultiScaleAsync(grayImg, (err, res) => {
    if (err) { return console.error(err); }

    const { objects, numDetections } = res;
    ...
  });
});

// via Promise
cv.imreadAsync('./faceimg.jpg')
  .then(img =>
    img.bgrToGrayAsync()
      .then(grayImg => classifier.detectMultiScaleAsync(grayImg))
      .then((res) => {
        const { objects, numDetections } = res;
        ...
      })
  )
  .catch(err => console.error(err));

// using async await
try {
  const img = await cv.imreadAsync('./faceimg.jpg');
  const grayImg = await img.bgrToGrayAsync();
  const { objects, numDetections } = await classifier.detectMultiScaleAsync(grayImg);
  ...
} catch (err) {
  console.error(err);
}

Available Modules

API doc overview

• core
• io
• imgproc
• ximgproc
• objdetect
• machinelearning
• deepneuralnetworks
• video
• calib3d
• photo
• features2d
• xfeatures2d
• tracking
• text
• face

Request new Features

Are you missing some functions from OpenCV (overview of available bindings) or have an idea for utility in regards to the OpenCV API, which you would like to be added to this package? No problem! Open a new issue with a listing of the desired function bindings or features and you will find them in this package soon.