opencv

node-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!

Install

You'll need OpenCV 2.3.1 or newer installed before installing node-opencv.

Then:

$ npm install opencv

Examples

Face Detection

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);
    }
    im.save('./out.jpg');
  });
})

API Documentation

Matrix

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

Creation

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){
  ...
})

fs.createReadStream('./examples/files/mona.png').pipe(s);

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){
   ...
})

ardrone.createPngStream().pipe(s);

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

Accessing Data

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]

Save

mat.save('./pic.jpg')

or:

var buff = mat.toBuffer()

Image Processing

im.convertGrayscale()
im.canny(5, 300)
im.houghLinesP()

Simple Drawing

im.ellipse(x, y)
im.line([x1,y1], [x2, y2])

Object Detection

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 recognition, cv.FACE_CASCADE is a cascade that can be used for frontal face recognition.

Also:

mat.goodFeaturesToTrack

Contours

mat.findCountours
mat.drawContour
mat.drawAllContours

Using Contours

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
contours.size();

// Count of corners(verticies) of contour `index`
contours.cornerCount(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.area(index);
contours.arcLength(index, isClosed);
contours.boundingRect(index);
contours.minAreaRect(index);
contours.isConvex(index);

// Destructively alter contour `index`
contours.approxPolyDP(index, epsilon, isClosed);
contours.convexHull(index, clockwise);

MIT License

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