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(This fork defaults to disabled auto build, and checks for OPENCV4NODEJS_ENABLE_AUTOBUILD)","homepage":"https://github.com/justadudewhohacks/opencv4nodejs#readme","keywords":["opencv","cv","computer vision","face","detection","recognition","machine","learning","neural","network","async","typescript"],"repository":{"type":"git","url":"git+https://github.com/justadudewhohacks/opencv4nodejs.git"},"author":{"name":"justadudewhohacks"},"bugs":{"url":"https://github.com/justadudewhohacks/opencv4nodejs/issues"},"license":"MIT","readme":"opencv4nodejs\n=============\n\n\n\n\n[](http://travis-ci.org/justadudewhohacks/opencv4nodejs)\n[](https://ci.appveyor.com/project/justadudewhohacks/opencv4nodejs/branch/master)\n[](https://codecov.io/gh/justadudewhohacks/opencv4nodejs)\n[](https://www.npmjs.com/package/opencv4nodejs)\n[](http://nodejs.org/download/)\n[](https://slack.bri.im/)\n\n**By its nature, JavaScript lacks the performance to implement Computer Vision tasks efficiently. Therefore this package brings the performance of the native OpenCV library to your Node.js application. This project targets OpenCV 3 and provides an asynchronous as well as an synchronous API.**\n\n**The ultimate goal of this project is to provide a comprehensive collection of Node.js bindings to the API of OpenCV and the OpenCV-contrib modules. An overview of available bindings can be found in the [API Documentation](https://justadudewhohacks.github.io/opencv4nodejs). Furthermore, contribution is highly appreciated. If you want to get involved you can have a look at the contribution guide.**\n\n* **[Examples](#examples)**\n* **[How to install](#how-to-install)**\n* **[Usage with Docker](#usage-with-docker)**\n* **[Usage with Electron](#usage-with-electron)**\n* **[Usage with NW.js](#usage-with-nwjs)**\n* **[Quick Start](#quick-start)**\n* **[Async API](#async-api)**\n* **[With TypeScript](#with-typescript)**\n* **[External Memory Tracking (v4.0.0)](#external-mem-tracking)**\n* **[Available Modules](#available-modules)**\n\n\n\n# Examples\n\nSee examples for implementation.\n\n### Face Detection\n\n\n\n\n### Face Recognition with the OpenCV face module\n\nCheck out Node.js + OpenCV for Face Recognition.\n\n\n\n### Face Landmarks with the OpenCV face module\n\n\n\n### Face Recognition with face-recognition.js\n\nCheck out Node.js + face-recognition.js : Simple and Robust Face Recognition using Deep Learning.\n\n[](https://www.youtube.com/watch?v=ArcFHpX-usQ \"Nodejs Face Recognition using face-recognition.js and opencv4nodejs\")\n\n### Hand Gesture Recognition\nCheck out Simple Hand Gesture Recognition using OpenCV and JavaScript.\n\n\n\n### Object Recognition with Deep Neural Networks\nCheck out Node.js meets OpenCV’s Deep Neural Networks — Fun with Tensorflow and Caffe.\n\n#### Tensorflow Inception\n\n\n\n\n\n\n#### Single Shot Multibox Detector with COCO\n\n\n\n\n### Machine Learning\nCheck out Machine Learning with OpenCV and JavaScript: Recognizing Handwritten Letters using HOG and SVM.\n\n\n\n### Object Tracking\n\n\n\n\n### Feature Matching\n\n\n\n### Image Histogram\n\n\n\n\n\n\n# How to install\n\n**Important note:** node-gyp won't handle whitespaces properly, thus make sure, that the path to your project directory does **not contain any whitespaces**. Installing opencv4nodejs under \"C:\\Program Files\\some_dir\" or similar will not work and will fail with: \"fatal error C1083: Cannot open include file: 'opencv2/core.hpp'\"!**\n\n## Requirements\n- cmake (unless you are using a prebuilt OpenCV release)\n\n### On Windows\nOn windows you will need Windows Build Tools to compile OpenCV and opencv4nodejs. If you don't have Visual Studio or Windows Build Tools installed, you can easily install the VS2015 build tools:\n``` bash\nnpm install --global windows-build-tools\n```\n\n## Auto build\nIf you do not want to set up OpenCV on your own you can simply let this package auto install OpenCV 3.4 + OpenCV contrib 3.4 (might take some time):\n``` bash\n$ npm install --save opencv4nodejs\n```\n\n## Manual build\nSetting up OpenCV on your own will require you to set an environment variable: *OPENCV4NODEJS_DISABLE_AUTOBUILD=1*.\n\nYou can either install any of the OpenCV 3+ releases (note, this will come without contrib) or build OpenCV with or without OpenCV contrib from source on your own. On Linux and MacOSX the library should be installed under *usr/local* (which is the default).\n\n### On Windows\nIf you choose to set up OpenCV on your own you have to set the following environment variables before installing opencv4nodejs:\n- *OPENCV_INCLUDE_DIR* pointing to the directory with the subfolders *opencv* and *opencv2* containing the header files\n- *OPENCV_LIB_DIR* pointing to the lib directory containing the OpenCV .lib files\n\nAlso you will need to add the OpenCV binaries to your system path:\n- add an environment variable *OPENCV_BIN_DIR* pointing to the binary directory containing the OpenCV .dll files\n- append `;%OPENCV_BIN_DIR%;` to your system path variable\n\nNote: Restart your current console session after making changes to your environment.\n\nIf you are running into issues also check the requirements for node-gyp specific to your OS: https://github.com/nodejs/node-gyp.\n\n\n\n## Usage with Docker\n\n### [opencv-express](https://github.com/justadudewhohacks/opencv-express) - example for opencv4nodejs with express.js and docker\n\nOr simply pull from [justadudewhohacks/opencv-nodejs](https://hub.docker.com/r/justadudewhohacks/opencv-nodejs/) for opencv-3.2 + contrib-3.2 with opencv4nodejs globally installed:\n\n``` docker\nFROM justadudewhohacks/opencv-nodejs\n```\n\n**Note**: The aforementioned Docker image already has ```opencv4nodejs``` installed globally. In order to prevent build errors during an ```npm install```, your ```package.json``` should not include ```opencv4nodejs```, and instead should include/require the global package either by requiring it by absolute path or setting the ```NODE_PATH``` environment variable to ```/usr/lib/node_modules``` in your Dockerfile and requiring the package as you normally would.\n\nDifferent OpenCV 3.x base images can be found here: https://hub.docker.com/r/justadudewhohacks/.\n\n\n\n## Usage with Electron\n\n### [opencv-electron](https://github.com/justadudewhohacks/opencv-electron) - example for opencv4nodejs with electron\n\nAdd the following script to your package.json:\n``` python\n\"electron-rebuild\": \"electron-rebuild -w opencv4nodejs\"\n```\n\nRun the script:\n``` bash\n$ npm run electron-rebuild\n```\n\nRequire it in the application:\n``` javascript\nconst cv = require('opencv4nodejs');\n```\n\n\n\n## Usage with NW.js\n\nAny native modules, including opencv4nodejs, must be recompiled to be used with [NW.js](https://nwjs.io/). Instructions on how to do this are available in the **[Use Native Modules] (http://docs.nwjs.io/en/latest/For%20Users/Advanced/Use%20Native%20Node%20Modules/)** section of the the NW.js documentation.\n\nOnce recompiled, the module can be installed and required as usual:\n\n``` javascript\nconst cv = require('opencv4nodejs');\n```\n\n\n\n# Quick Start\n\n``` javascript\nconst cv = require('opencv4nodejs');\n```\n\n### Initializing Mat (image matrix), Vec, Point\n\n``` javascript\nconst rows = 100; // height\nconst cols = 100; // width\n\n// empty Mat\nconst emptyMat = new cv.Mat(rows, cols, cv.CV_8UC3);\n\n// fill the Mat with default value\nconst whiteMat = new cv.Mat(rows, cols, cv.CV_8UC1, 255);\nconst blueMat = new cv.Mat(rows, cols, cv.CV_8UC3, [255, 0, 0]);\n\n// from array (3x3 Matrix, 3 channels)\nconst matData = [\n [[255, 0, 0], [255, 0, 0], [255, 0, 0]],\n [[0, 0, 0], [0, 0, 0], [0, 0, 0]],\n [[255, 0, 0], [255, 0, 0], [255, 0, 0]]\n];\nconst matFromArray = new cv.Mat(matData, cv.CV_8UC3);\n\n// from node buffer\nconst charData = [255, 0, ...];\nconst matFromArray = new cv.Mat(Buffer.from(charData), rows, cols, cv.CV_8UC3);\n\n// Point\nconst pt2 = new cv.Point(100, 100);\nconst pt3 = new cv.Point(100, 100, 0.5);\n\n// Vector\nconst vec2 = new cv.Vec(100, 100);\nconst vec3 = new cv.Vec(100, 100, 0.5);\nconst vec4 = new cv.Vec(100, 100, 0.5, 0.5);\n```\n\n### Mat and Vec operations\n\n``` javascript\nconst mat0 = new cv.Mat(...);\nconst mat1 = new cv.Mat(...);\n\n// arithmetic operations for Mats and Vecs\nconst matMultipliedByScalar = mat0.mul(0.5); // scalar multiplication\nconst matDividedByScalar = mat0.div(2); // scalar division\nconst mat0PlusMat1 = mat0.add(mat1); // addition\nconst mat0MinusMat1 = mat0.sub(mat1); // subtraction\nconst mat0MulMat1 = mat0.hMul(mat1); // elementwise multiplication\nconst mat0DivMat1 = mat0.hDiv(mat1); // elementwise division\n\n// logical operations Mat only\nconst mat0AndMat1 = mat0.and(mat1);\nconst mat0OrMat1 = mat0.or(mat1);\nconst mat0bwAndMat1 = mat0.bitwiseAnd(mat1);\nconst mat0bwOrMat1 = mat0.bitwiseOr(mat1);\nconst mat0bwXorMat1 = mat0.bitwiseXor(mat1);\nconst mat0bwNot = mat0.bitwiseNot();\n```\n\n### Accessing Mat data\n\n``` javascript\nconst matBGR = new cv.Mat(..., cv.CV_8UC3);\nconst matGray = new cv.Mat(..., cv.CV_8UC1);\n\n// get pixel value as vector or number value\nconst vec3 = matBGR.at(200, 100);\nconst grayVal = matGray.at(200, 100);\n\n// get raw pixel value as array\nconst [b, g, r] = matBGR.atRaw(200, 100);\n\n// set single pixel values\nmatBGR.set(50, 50, [255, 0, 0]);\nmatBGR.set(50, 50, new Vec(255, 0, 0));\nmatGray.set(50, 50, 255);\n\n// get a 25x25 sub region of the Mat at offset (50, 50)\nconst width = 25;\nconst height = 25;\nconst region = matBGR.getRegion(new cv.Rect(50, 50, width, height));\n\n// get a node buffer with raw Mat data\nconst matAsBuffer = matBGR.getData();\n\n// get entire Mat data as JS array\nconst matAsArray = matBGR.getDataAsArray();\n```\n\n### IO\n\n``` javascript\n// load image from file\nconst mat = cv.imread('./path/img.jpg');\ncv.imreadAsync('./path/img.jpg', (err, mat) => {\n ...\n})\n\n// save image\ncv.imwrite('./path/img.png', mat);\ncv.imwriteAsync('./path/img.jpg', mat,(err) => {\n ...\n})\n\n// show image\ncv.imshow('a window name', mat);\ncv.waitKey();\n\n// load base64 encoded image\nconst base64text='data:image/png;base64,R0lGO..';//Base64 encoded string\nconst base64data =base64text.replace('data:image/jpeg;base64','')\n .replace('data:image/png;base64','');//Strip image type prefix\nconst buffer = Buffer.from(base64data,'base64');\nconst image = cv.imdecode(buffer); //Image is now represented as Mat\n\n// convert Mat to base64 encoded jpg image\nconst outBase64 = cv.imencode('.jpg', croppedImage).toString('base64'); // Perform base64 encoding\nconst htmlImg='
'; //Create insert into HTML compatible ![]()
tag\n\n// open capture from webcam\nconst devicePort = 0;\nconst wCap = new cv.VideoCapture(devicePort);\n\n// open video capture\nconst vCap = new cv.VideoCapture('./path/video.mp4');\n\n// read frames from capture\nconst frame = vCap.read();\nvCap.readAsync((err, frame) => {\n ...\n});\n\n// loop through the capture\nconst delay = 10;\nlet done = false;\nwhile (!done) {\n let frame = vCap.read();\n // loop back to start on end of stream reached\n if (frame.empty) {\n vCap.reset();\n frame = vCap.read();\n }\n\n // ...\n\n const key = cv.waitKey(delay);\n done = key !== 255;\n}\n```\n\n### Useful Mat methods\n\n``` javascript\nconst matBGR = new cv.Mat(..., cv.CV_8UC3);\n\n// convert types\nconst matSignedInt = matBGR.convertTo(cv.CV_32SC3);\nconst matDoublePrecision = matBGR.convertTo(cv.CV_64FC3);\n\n// convert color space\nconst matGray = matBGR.bgrToGray();\nconst matHSV = matBGR.cvtColor(cv.COLOR_BGR2HSV);\nconst matLab = matBGR.cvtColor(cv.COLOR_BGR2Lab);\n\n// resize\nconst matHalfSize = matBGR.rescale(0.5);\nconst mat100x100 = matBGR.resize(100, 100);\nconst matMaxDimIs100 = matBGR.resizeToMax(100);\n\n// extract channels and create Mat from channels\nconst [matB, matG, matR] = matBGR.splitChannels();\nconst matRGB = new cv.Mat([matR, matB, matG]);\n```\n\n### Drawing a Mat into HTML Canvas\n\n``` javascript\nconst img = ...\n\n// convert your image to rgba color space\nconst matRGBA = img.channels === 1\n ? img.cvtColor(cv.COLOR_GRAY2RGBA)\n : img.cvtColor(cv.COLOR_BGR2RGBA);\n\n// create new ImageData from raw mat data\nconst imgData = new ImageData(\n new Uint8ClampedArray(matRGBA.getData()),\n img.cols,\n img.rows\n);\n\n// set canvas dimensions\nconst canvas = document.getElementById('myCanvas');\ncanvas.height = img.rows;\ncanvas.width = img.cols;\n\n// set image data\nconst ctx = canvas.getContext('2d');\nctx.putImageData(imgData, 0, 0);\n```\n\n### Method Interface\n\nOpenCV method interface from official docs or src:\n``` c++\nvoid GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT);\n```\n\ntranslates to:\n\n``` javascript\nconst src = new cv.Mat(...);\n// invoke with required arguments\nconst dst0 = src.gaussianBlur(new cv.Size(5, 5), 1.2);\n// with optional paramaters\nconst dst2 = src.gaussianBlur(new cv.Size(5, 5), 1.2, 0.8, cv.BORDER_REFLECT);\n// or pass specific optional parameters\nconst optionalArgs = {\n borderType: cv.BORDER_CONSTANT\n};\nconst dst2 = src.gaussianBlur(new cv.Size(5, 5), 1.2, optionalArgs);\n```\n\n\n\n# Async API\n\nThe async API can be consumed by passing a callback as the last argument of the function call. By default, if an async method is called without passing a callback, the function call will yield a Promise.\n\n### Async Face Detection\n\n``` javascript\nconst classifier = new cv.CascadeClassifier(cv.HAAR_FRONTALFACE_ALT2);\n\n// by nesting callbacks\ncv.imreadAsync('./faceimg.jpg', (err, img) => {\n if (err) { return console.error(err); }\n\n const grayImg = img.bgrToGray();\n classifier.detectMultiScaleAsync(grayImg, (err, res) => {\n if (err) { return console.error(err); }\n\n const { objects, numDetections } = res;\n ...\n });\n});\n\n// via Promise\ncv.imreadAsync('./faceimg.jpg')\n .then(img =>\n img.bgrToGrayAsync()\n .then(grayImg => classifier.detectMultiScaleAsync(grayImg))\n .then((res) => {\n const { objects, numDetections } = res;\n ...\n })\n )\n .catch(err => console.error(err));\n\n// using async await\ntry {\n const img = await cv.imreadAsync('./faceimg.jpg');\n const grayImg = await img.bgrToGrayAsync();\n const { objects, numDetections } = await classifier.detectMultiScaleAsync(grayImg);\n ...\n} catch (err) {\n console.error(err);\n}\n```\n\n\n\n# With TypeScript\n\n``` javascript\nimport * as cv from 'opencv4nodejs'\n```\n\nCheck out the TypeScript [examples](https://github.com/justadudewhohacks/opencv4nodejs/tree/master/examples/typed).\n\n\n\n# External Memory Tracking (v4.0.0)\n\nSince version 4.0.0 was released, external memory tracking has been enabled by default. Simply put, the memory allocated for Matrices (cv.Mat) will be manually reported to the node process. This solves the issue of inconsistent Garbage Collection, which could have resulted in spiking memory usage of the node process eventually leading to overflowing the RAM of your system, prior to version 4.0.0.\n\nNote, that in doubt this feature can be **disabled** by setting an environment variable `OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING` before requiring the module:\n\n``` bash\nexport OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING=1 // linux\nset OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING=1 // windows\n```\n\nOr directly in your code:\n``` javascript\nprocess.env.OPENCV4NODEJS_DISABLE_EXTERNAL_MEM_TRACKING = 1\nconst cv = require('opencv4nodejs')\n```\n\n\n\n# Available Modules\n\n### API doc overview\n\n* core\n* io\n* imgproc\n* ximgproc\n* objdetect\n* machinelearning\n* deepneuralnetworks\n* video\n* calib3d\n* photo\n* features2d\n* xfeatures2d\n* tracking\n* text\n* face\n","readmeFilename":"README.md"}