Node.js 的 OpenCV 绑定

# node-opencv [](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.