opencv

Node Bindings to 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!

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

Then:

$ npm install opencv

Run the examples from the parent directory.

cv.readImage("./examples/files/mona.png", function(errim){
  im.detectObject(cv.FACE_CASCADE, {}, function(errfaces){
    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');
  });
})

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(errmat){
  ...
})
 
cv.readImage(buffer, function(errmat){
  ...
})

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.

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] 
mat.save('./pic.jpg')

or:

var buff = mat.toBuffer()
im.convertGrayscale()
im.canny(5, 300)
im.houghLinesP()
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(errmatches){})

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

Also:

mat.goodFeaturesToTrack
mat.findCountours
mat.drawContour
mat.drawAllContours

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);

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