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Currently, it is based on OpenCV version 3.3. It includes more than 100 select functions form the following OpenCV modules.\n\n* Core\n* Image processing\n* Video\n* Object detection\n* Image codecs\n\n#### Installation\n\n```\nnpm install opencv.js\n```\n\n#### How to Use\n```js\ncv = require('opencv.js');\n```\n\n#### Demos\nUse this [pointer](https://huningxin.github.io/opencv.js/samples/index.html) to access the demos.\n\n#### API\nPlease refer to  the [OpenCV.js documentations](https://huningxin.github.io/opencv_docs/tutorial_js_table_of_contents_core.html).\n\n#### Basic Types\n```cv.Mat``` is the main data structure that is used by the functions. The following constructors are available to create matrices.\n\n```\ncv.Mat()\ncv.Mat(another_mat)\ncv.Mat(size, type)\ncv.Mat(rows, cols, type, data, step)\n```\n\nSimple types such as ```cv::Rect```, ```cv::Point``` are represented using JavaScript value objects.\n\n#### Examples\n##### Working with Images - Canny Edge Detection\nThis examples demonstrates how to find edges in a given image file.\n\n```js\nvar cv = require('opencv.js');\nvar jpeg = require('jpeg-js');\nvar fs = require('fs');\n\n// Load an image\nvar jpeg_data = fs.readFileSync(\"in_img.jpg\");\nvar raw_data = jpeg.decode(jpeg_data);\n\n// Create a matrix from image. input image expected to be in RGBA format\nvar src = cv.matFromImageData(raw_data);\ncv.cvtColor(src, src, cv.COLOR_RGBA2GRAY); // Convert to grayscale\n\ndst = new cv.Mat();\ncv.Canny(src, dst, 50, 150);\ncv.cvtColor(dst, dst, cv.COLOR_GRAY2RGBA); // Convert back to RGBA to display\n\n// Save the result\nraw_data = {\n  data: dst.data,\n  width: dst.size().width,\n  height: dst.size().height\n};\nvar jpeg_data = jpeg.encode(raw_data, 50);\nfs.writeFileSync(\"out_img.jpg\", jpeg_data.data);\n\n```\n##### Working with videos - Face detection using HARR Cascades\nThis example demonstrates how to use HAAR cascades to find objects in video frames. We will rely on pre-trained HAAR models that are specialized in finding certain objects. OpenCV provides several models that can be used.\n\n```js\nlet v4l2camera = require( \"v4l2camera\" );\nvar jpeg = require('jpeg-js');\nvar fs = require('fs');\nlet cv = require('opencv.js');\n\n// Loading classifier with the frontal face model\ncv.FS_createLazyFile('/', 'haarcascade_frontalface_default.xml',\n                         'haarcascade_frontalface_default.xml', true, false);\nlet faceClassifier = new cv.CascadeClassifier();\nfaceClassifier.load('haarcascade_frontalface_default.xml');\n\n// Start the camera\nlet cam = new v4l2camera.Camera(\"/dev/video0\");\nif (cam.configGet().formatName !== \"YUYV\") {\n    console.log(\"YUYV camera required\");\n    process.exit(1);\n}\n\n// Configure and start the camera\ncam.configSet({width: 320, height: 240});\nlet format = cam.configGet();\nconsole.log(\"Camera config [ \" + format.formatName + \" \" + format.width + \"x\" +\n    format.height + \" \" + format.interval.numerator + \"/\" +\n    format.interval.denominator + \"]\");\ncam.start();\n\nlet yuvMat = null;\nlet rgbMat = null;\nlet grayMat = null;\n\nlet stopped = false;\nlet frameIndex = 0;\n\ncam.capture(function detectFace(success) {\n  let frame = cam.frameRaw();\n  let videoHeight = cam.height;\n  let videoWidth = cam.width;\n  if (!yuvMat)\n    yuvMat = new cv.Mat(videoHeight, videoWidth, cv.CV_8UC2);\n  yuvMat.data.set(frame);\n  if (!rgbMat)\n    rgbMat = new cv.Mat(videoHeight, videoWidth, cv.CV_8UC4);\n  cv.cvtColor(yuvMat, rgbMat, cv.COLOR_YUV2RGBA_YUYV);\n  if (!grayMat)\n    grayMat = new cv.Mat(videoHeight, videoWidth, cv.CV_8UC1);\n  cv.cvtColor(rgbMat, grayMat, cv.COLOR_RGBA2GRAY);\n\n  let faces = [];\n  let eyes = [];\n  let size;\n  let faceVect = new cv.RectVector();\n  let faceMat = new cv.Mat();\n\n  // Scale down the input frame\n  cv.pyrDown(grayMat, faceMat);\n  if (videoWidth > 320)\n    cv.pyrDown(faceMat, faceMat);\n  size = faceMat.size();\n\n  // Processing the frame to find faces\n  faceClassifier.detectMultiScale(faceMat, faceVect);\n\n  // Draw rectangle around faces\n  for (let i = 0; i < faceVect.size(); i++) {\n    let xRatio = videoWidth/size.width;\n    let yRatio = videoHeight/size.height;\n    let face = faceVect.get(i);\n    let x = face.x*xRatio;\n    let y = face.y*yRatio;\n    let w = face.width*xRatio;\n    let h = face.height*yRatio;\n    let point1 = new cv.Point(x, y);\n    let point2 = new cv.Point(x + w, y + h);\n    cv.rectangle(rgbMat, point1, point2, [255, 0, 0, 255]);\n    console.log('\\tFace detected : ' + '[' + i + ']' +\n        ' (' + x + ', ' + y + ', ' + w + ', ' + h + ')');\n  }\n\n  // Free the memory used by vectors\n  faceMat.delete();\n  faceVect.delete();\n\n  if (stopped) {\n    cam.stop();\n    console.log('Stopped');\n    rawData = {\n      data: rgbMat.data,\n      width: rgbMat.size().width,\n      height: rgbMat.size().height\n    };\n    var jpegData = jpeg.encode(rawData, 50);\n    const filename = 'result.jpg';\n    fs.writeFileSync(filename, jpegData.data);\n    console.log('Written into ' + filename);\n    yuvMat.delete();\n    rgbMat.delete();\n    grayMat.delete();\n    process.exit();\n  }\n  cam.capture(detectFace);\n});\n\nconst ESC_KEY = '\\u001b';\nconst CTRL_C = '\\u0003';\nlet stdin = process.stdin;\nstdin.setRawMode(true);\nstdin.resume();\nstdin.setEncoding('utf8');\nstdin.on('data', function(key) {\n  if (key === ESC_KEY || key === CTRL_C) {\n    stopped = true;\n  }\n});\n```\n\n\n#### Contributers\n1. Sajjad Taheri (Architect of the initial version and GSoC mentor, University of California, Irvine)\n2. Congxiang Pan (GSoC student, Shanghai Jiao Tong University)\n3. Gang Song (GSoC student, Shanghai Jiao Tong University)\n4. Wenyao Gan (Student intern, Shanghai Jiao Tong University)\n5. Mohammad Reza Haghighat (Project initiator & sponsor, Intel Corporation)\n6. Ningxin Hu (Students' supervisor, Intel Corporation)\n\nThis project was supported by the Intel corporation and Google through 2017 Google Summer of Code program.\n\n## License\nBSD 3 Clause\n","maintainers":[{"email":"gurunio@gmail.com","name":"sajjadt"}],"time":{"modified":"2022-05-12T06:17:24.294Z","created":"2017-07-19T06:57:48.585Z","1.0.1":"2017-07-19T06:57:48.585Z","0.0.1-security":"2017-08-01T15:19:13.854Z","1.0.2":"2017-08-01T23:43:15.721Z","0.0.2-security":"2017-08-02T17:53:29.418Z","1.0.0":"2017-08-25T00:45:17.429Z","1.1.0":"2017-08-25T02:04:58.392Z","1.1.1":"2017-08-25T02:28:40.839Z","1.1.2":"2017-08-25T02:40:01.574Z","1.1.3":"2017-08-25T02:57:50.895Z","1.1.4":"2017-08-25T04:15:04.214Z","1.1.5":"2017-08-25T05:07:07.179Z","1.1.6":"2017-08-25T05:54:51.838Z","1.1.7":"2017-08-25T08:06:38.154Z","1.1.8":"2017-08-26T05:55:17.885Z","1.1.9":"2017-08-26T07:12:34.775Z","1.1.10":"2017-08-26T07:22:58.761Z","1.1.11":"2017-09-13T00:30:06.689Z","1.2.0":"2017-10-27T04:37:26.240Z","1.2.1":"2017-11-01T08:25:35.021Z"},"readmeFilename":"README.md","repository":{"type":"git","url":"git+https://github.com/huningxin/opencv.git"},"keywords":["Computer vision","OpenCV"],"author":{"name":"Sajjad Taheri"},"license":"BSD-3-Clause","homepage":"https://github.com/huningxin/opencv#readme","bugs":{"url":"https://github.com/huningxin/opencv/issues"}}