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https://github.com/peterbraden/node-opencv

OpenCV Bindings for node.js
https://github.com/peterbraden/node-opencv

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OpenCV Bindings for node.js

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# 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{

      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.