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Node Bindings to OpenCV


OpenCV bindings for Node.js. OpenCV is the defacto computer vision library - by interfacing with it natively in node, we get powerful real time vision in js.

People are using node-opencv to fly control quadrocoptors, detect faces from webcam images and annotate video streams. If you're using it for something cool, I'd love to hear about it!

You'll need OpenCV 2.3.1 or newer installed before installing node-opencv. Note that OpenCV 3.x is not yet fully supported.

  1. Download and install OpenCV (Be sure to use a 2.4 version) @ For these instructions we will assume OpenCV is put at C:\OpenCV, but you can adjust accordingly.

  2. If you haven't already, create a system variable called OPENCV_DIR and set it to C:\OpenCV\build\x64\vc12

    Make sure the "x64" part matches the version of NodeJS you are using.

    Also add the following to your system PATH ;%OPENCV_DIR%\bin

  3. Install Visual Studio 2013. Make sure to get the C++ components. You can use a different edition, just make sure OpenCV supports it, and you set the "vcxx" part of the variables above to match.

  4. Download peterbraden/node-opencv fork git clone

  5. run npm install

$ npm install opencv

Run the examples from the parent directory.

cv.readImage("./examples/files/mona.png", function(err, im){
  im.detectObject(cv.FACE_CASCADE, {}, function(err, faces){
    for (var i=0;i<faces.length; i++){
      var x = faces[i]
      im.ellipse(x.x + x.width/2, x.y + x.height/2, x.width/2, x.height/2);

The matrix is the most useful base datastructure in OpenCV. Things like images are just matrices of pixels.

new Matrix(rows, cols)

Or if you're thinking of a Matrix as an image:

new Matrix(height, width)

Or you can use opencv to read in image files. Supported formats are in the OpenCV docs, but jpgs etc are supported.

cv.readImage(filename, function(err, mat){
cv.readImage(buffer, function(err, mat){

If you need to pipe data into an image, you can use an ImageDataStream:

var s = new cv.ImageDataStream()
s.on('load', function(matrix){

If however, you have a series of images, and you wish to stream them into a stream of Matrices, you can use an ImageStream. Thus:

var s = new cv.ImageStream()
s.on('data', function(matrix){

Note: Each 'data' event into the ImageStream should be a complete image buffer.

var mat = new cv.Matrix.Eye(4,4); // Create identity matrix 
mat.get(0,0) // 1 
mat.row(0)  // [1,0,0,0] 
mat.col(4)  // [0,0,0,1]'./pic.jpg')


var buff = mat.toBuffer()
im.canny(5, 300)
im.ellipse(x, y)
im.line([x1,y1], [x2, y2])

There is a shortcut method for Viola-Jones Haar Cascade object detection. This can be used for face detection etc.

mat.detectObject(haar_cascade_xml, opts, function(err, matches){})

For convenience in face detection, cv.FACE_CASCADE is a cascade that can be used for frontal face detection.



findContours returns a Contours collection object, not a native array. This object provides functions for accessing, computing with, and altering the contours contained in it. See relevant source code and examples

var contours = im.findContours();
// Count of contours in the Contours object 
// Count of corners(verticies) of contour `index` 
// Access vertex data of contours 
for(var c = 0; c < contours.size(); ++c) {
  console.log("Contour " + c);
  for(var i = 0; i < contours.cornerCount(c); ++i) {
    var point = contours.point(c, i);
    console.log("(" + point.x + "," + point.y + ")");
// Computations of contour `index` 
contours.arcLength(index, isClosed);
// Destructively alter contour `index` 
contours.approxPolyDP(index, epsilon, isClosed);
contours.convexHull(index, clockwise);

Using tape. Run with command:

npm test.

Using istanbul and lcov. Run with command:

make cover

Build version of opencv.node will be generated, and coverage files will be put in coverage/ directory. These files can be remvoved automatically by running make clean.

The library is distributed under the MIT License - if for some reason that doesn't work for you please get in touch.