# node-opencv

[![Build Status](https://secure.travis-ci.org/peterbraden/node-opencv.svg)](http://travis-ci.org/peterbraden/node-opencv)

[OpenCV](http://opencv.org) 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.

## Specific for macOS
Install OpenCV using brew
```bash
brew install pkg-config
brew install opencv@2
brew link --force opencv@2
```


## Specific for Windows
1. Download and install OpenCV (Be sure to use a 2.4 version) @
http://opencv.org/releases.html
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 https://github.com/peterbraden/node-opencv

5. run npm install

```bash
$ npm install opencv
```

## Examples
Run the examples from the parent directory.

### Face Detection

```javascript
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](http://opencv.jp/opencv-2svn_org/cpp/core_basic_structures.html#mat) is the most useful
base data structure in OpenCV. Things like images are just matrices of pixels.

#### Creation

```javascript
new Matrix(rows, cols)
```

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

```javascript
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.

```javascript
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:

```javascript
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:

```javascript
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

```javascript
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(3)  // [0,0,0,1]
```

##### Save

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

or:

```javascript
var buff = mat.toBuffer()
```

#### Image Processing

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


#### Simple Drawing

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

#### Object Detection

There is a shortcut method for
[Viola-Jones Haar Cascade](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) object
detection. This can be used for face detection etc.

```javascript
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.

Also:

```javascript
mat.goodFeaturesToTrack
```

#### Contours

```javascript
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](src/Contours.cc) and [examples](examples/)

```javascript
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);
```

#### Face Recognization

It requires to `train` then `predict`. For acceptable result, the face should be cropped, grayscaled and aligned, I ignore this part so that we may focus on the api usage.

** Please ensure your OpenCV 3.2+ is configured with contrib. MacPorts user may `port install opencv +contrib` **

```javascript
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));
```

## Test

Using [tape](https://github.com/substack/tape). Run with command:

`npm test`.

## Contributing

I (@peterbraden) don't spend much time maintaining this library, it runs
primarily on contributor support. I'm happy to accept most PR's if the tests run
green, all new functionality is tested, and there are no objections in the PR.

Because I haven't got much time for maintenance, I'd prefer to keep an absolute
minimum of dependencies.


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