node-opencv

$ npm install opencv

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');
  });
})

new Matrix(rows, cols)

new Matrix(height, width)

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

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

var s = new cv.ImageDataStream()

s.on('load', function(matrix){
  ...
})

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

var s = new cv.ImageStream()

s.on('data', function(matrix){
   ...
})

ardrone.createPngStream().pipe(s);

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')

var buff = mat.toBuffer()

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

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

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

mat.goodFeaturesToTrack

mat.findCountours
mat.drawContour
mat.drawAllContours

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);
contours.fitEllipse(index);

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

const fs = require('fs');
const path = require('path');
const cv = require('opencv');

function forEachFileInDir(dir, cb) {
  let f = fs.readdirSync(dir);
  f.forEach(function (fpath, index, array) {
    if (fpath != '.DS_Store')
     cb(path.join(dir, fpath));
  });
}

let dataDir = "./_training";
function trainIt (fr) {
  // if model existe, load it
  if ( fs.existsSync('./trained.xml') ) {
    fr.loadSync('./trained.xml');
    return;
  }

  // else train a model
  let samples = [];
  forEachFileInDir(dataDir, (f)=>{
      cv.readImage(f, function (err, im) {
          // Assume all training photo are named as id_xxx.jpg
          let labelNumber = parseInt(path.basename(f).substring(3));
          samples.push([labelNumber, im]);
      })
  })

  if ( samples.length > 3 ) {
    // There are async and sync version of training method:
    // .train(info, cb)
    //     cb : standard Nan::Callback
    //     info : [[intLabel,matrixImage],...])
    // .trainSync(info)
    fr.trainSync(samples);
    fr.saveSync('./trained.xml');
  }else {
    console.log('Not enough images uploaded yet', cvImages)
  }
}

function predictIt(fr, f){
  cv.readImage(f, function (err, im) {
    let result = fr.predictSync(im);
    console.log(`recognize result:(${f}) id=${result.id} conf=${100.0-result.confidence}`);
  });
}

//using defaults: .createLBPHFaceRecognizer(radius=1, neighbors=8, grid_x=8, grid_y=8, threshold=80)
const fr = new cv.FaceRecognizer();
trainIt(fr);
forEachFileInDir('./_bench', (f) => predictIt(fr, f));