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. This project targets OpenCV 3 and provides an asynchronous as well as an synchronous API.
The ultimate goal of this project is to provide a comprehensive collection of Node.js bindings to the API of OpenCV and the OpenCV-contrib modules. An overview of available bindings can be found in the API Documentation. 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
- Usage with NW.js
- Quick Start
- Async API
- With TypeScript
- External Memory Tracking (v4.0.0)
- Available Modules
See examples for implementation.
Check out Node.js + OpenCV for Face Recognition.
Face Recognition with face-recognition.js
Check out Node.js + face-recognition.js : Simple and Robust Face Recognition using Deep Learning.
Check out Simple Hand Gesture Recognition using OpenCV and JavaScript.
Check out Node.js meets OpenCV’s Deep Neural Networks — Fun with Tensorflow and Caffe.
Check out Machine Learning with OpenCV and JavaScript: Recognizing Handwritten Letters using HOG and SVM.
- cmake (unless you are using a prebuilt OpenCV release)
On windows you will need Windows Build Tools to compile OpenCV and opencv4nodejs. If you don't have Visual Studio or Windows Build Tools installed, you can easily install the VS2015 build tools:
npm install --global windows-build-toolsIf you do not want to set up OpenCV on your own you can simply let this package auto install OpenCV 3.4 + OpenCV contrib 3.4 (might take some time):
$ npm install --save opencv4nodejsSetting up OpenCV on your own will require you to set an environment variable: OPENCV4NODEJS_DISABLE_AUTOBUILD=1.
You can either install any of the OpenCV 3+ releases (note, this will come without contrib) or build OpenCV with or without OpenCV contrib from source on your own. On Linux and MacOSX the library should be installed under usr/local (which is the default).
If you choose to set up OpenCV on your own you have to set the following environment variables before installing opencv4nodejs:
- OPENCV_INCLUDE_DIR pointing to the directory with the subfolders opencv and opencv2 containing the header files
- OPENCV_LIB_DIR pointing to the lib directory containing the OpenCV .lib files
Also you will need to add the OpenCV binaries to your system path:
- add an environment variable OPENCV_BIN_DIR pointing to the binary directory containing the OpenCV .dll files
- append
;%OPENCV_BIN_DIR%;to your system 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.
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
Note: The aforementioned Docker image already has opencv4nodejs installed globally. In order to prevent build errors during an npm install, your package.json should not include opencv4nodejs, and instead should include/require the global package either by requiring it by absolute path or setting the NODE_PATH environment variable to /usr/lib/node_modules in your Dockerfile and requiring the package as you normally would.
Different OpenCV 3.x base images can be found here: https://hub.docker.com/r/justadudewhohacks/.
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-rebuildRequire it in the application:
const cv = require('opencv4nodejs');Any native modules, including opencv4nodejs, must be recompiled to be used with NW.js. Instructions on how to do this are available in the [Use Native Modules] (http://docs.nwjs.io/en/latest/For%20Users/Advanced/Use%20Native%20Node%20Modules/) section of the the NW.js documentation.
Once recompiled, the module can be installed and required as usual:
const cv = require('opencv4nodejs');const cv = require('opencv4nodejs');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(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);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();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();// 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();
// load base64 encoded image
const base64text='data:image/png;base64,R0lGO..';//Base64 encoded string
const base64data =base64text.replace('data:image/jpeg;base64','')
.replace('data:image/png;base64','');//Strip image type prefix
const buffer = Buffer.from(base64data,'base64');
const image = cv.imdecode(buffer); //Image is now represented as Mat
// convert Mat to base64 encoded jpg image
const outBase64 = cv.imencode('.jpg', croppedImage).toString('base64'); // Perform base64 encoding
const htmlImg='<img src=data:image/jpeg;base64,'+outBase64 + '>'; //Create insert into HTML compatible <img> tag
// 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;
}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]);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);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);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.
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);
}import * as cv from 'opencv4nodejs'Check out the TypeScript examples.
Since version 4.0.0 was released, external memory tracking has been enabled by default. Simply put, the memory allocated for Matrices (cv.Mat) will be manually reported to the node process. This solves the issue of inconsistent Garbage Collection, which could have resulted in spiking memory usage of the node process eventually leading to overflowing the RAM of your system, prior to version 4.0.0.
Note, that in doubt this feature can be disabled by setting an environment variable OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING before requiring the module:
export OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING=1 // linux
set OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING=1 // windowsOr directly in your code:
process.env.OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING = 1
const cv = require('opencv4nodejs')