前端使用opencv

最近了解了下opencv,看了下官方的实例和文档 opencvjs文档入口 3.3.1版本看了官方示例的网页结构基本上所有的实例都用到了两个js 核心js：opencv.js 工具js：utils.js 官方给出的方案是

Installing Emscripten Emscripten is an LLVM-to-JavaScript compiler. We will use Emscripten to build OpenCV.js. 安装Emscripten Emscripten是LLVM到JavaScript的编译器。我们将使用Emscripten构建OpenCV.js。

但是它官方示例中肯定要用到js对吧我看了很多csdn文件分享都需要c币或者积分而且自己编译的也容易出问题官方给出的能运行示例那么为啥不拿来用呢同时还有官方训练的人脸识别xml文件一样可以爬取那就去网页爬取老方法右键检查元素 opencv.js人脸识别简单使用

下载这三个文件放到项目目录下

分析网页结构 opencv.js人脸识别简单使用将关键代码复制下来后放到自己的文件内

代码来自爬取官方


<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Face Detection Camera Example</title>
<link href="https://docs.opencv.org/3.3.1/js_example_style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<!-- <h2>Face Detection Camera Example</h2> -->
<div>
<div class="control"><button id="startAndStop">Start</button></div>
<textarea class="code" rows="29" cols="80" id="codeEditor" spellcheck="false">

</textarea>
</div>
<p class="err" id="errorMessage"></p>
<div>
    <table cellpadding="0" cellspacing="0" width="0" border="0">
    <tr>
        <td>
            <video id="videoInput" width=320 height=240></video>
        </td>
        <td>
            <canvas id="canvasOutput" width=320 height=240></canvas>
        </td>
        <td></td>
        <td></td>
    </tr>
    <tr>
        <td>
            <div class="caption">videoInput</div>
        </td>
        <td>
            <div class="caption">canvasOutput</div>
        </td>
        <td></td>
        <td></td>
    </tr>
    </table>
</div>
<script type="text/javascript" src="js/opencv.js"></script>
<!-- <script src="js/adapter-5.0.4.js" type="text/javascript"></script> -->
<script src="js/utils.js" type="text/javascript"></script>
<script id="codeSnippet" type="text/code-snippet">
let video = document.getElementById('videoInput');
let src = new cv.Mat(video.height, video.width, cv.CV_8UC4);
let dst = new cv.Mat(video.height, video.width, cv.CV_8UC4);
let gray = new cv.Mat();
let cap = new cv.VideoCapture(video);
let faces = new cv.RectVector();
let classifier = new cv.CascadeClassifier();

// load pre-trained classifiers
classifier.load('haarcascade_frontalface_default.xml');

const FPS = 30;
function processVideo() {
    try {
        if (!streaming) {
            // clean and stop.
            src.delete();
            dst.delete();
            gray.delete();
            faces.delete();
            classifier.delete();
            return;
        }
        let begin = Date.now();
        // start processing.
        cap.read(src);
        src.copyTo(dst);
        cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
        // detect faces.
        classifier.detectMultiScale(gray, faces, 1.1, 3, 0);
        // draw faces.
        for (let i = 0; i < faces.size(); ++i) {
            let face = faces.get(i);
            let point1 = new cv.Point(face.x, face.y);
            let point2 = new cv.Point(face.x + face.width, face.y + face.height);
            cv.rectangle(dst, point1, point2, [255, 0, 0, 255]);
        }
        cv.imshow('canvasOutput', dst);
        // schedule the next one.
        let delay = 1000/FPS - (Date.now() - begin);
        setTimeout(processVideo, delay);
    } catch (err) {
        utils.printError(err);
    }
};

// schedule the first one.
setTimeout(processVideo, 0);
</script>
<script type="text/javascript">

    let utils = new Utils('errorMessage');

    utils.loadCode('codeSnippet', 'codeEditor');

    let streaming = false;
    let videoInput = document.getElementById('videoInput');
    let startAndStop = document.getElementById('startAndStop');
    let canvasOutput = document.getElementById('canvasOutput');
    let canvasContext = canvasOutput.getContext('2d');

    startAndStop.addEventListener('click', () => {
        if (!streaming) {
            utils.clearError();
            utils.startCamera('qvga', onVideoStarted, 'videoInput');
        } else {
            utils.stopCamera();
            onVideoStopped();
        }
    });

    function onVideoStarted() {
        streaming = true;
        startAndStop.innerText = 'Stop';
        videoInput.width = videoInput.videoWidth;
        videoInput.height = videoInput.videoHeight;
        utils.executeCode('codeEditor');
    }

    function onVideoStopped() {
        streaming = false;
        canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
        startAndStop.innerText = 'Start';
    }

    utils.loadOpenCv(() => {
        let faceCascadeFile = 'haarcascade_frontalface_default.xml';
        utils.createFileFromUrl(faceCascadeFile, faceCascadeFile, () => {
            // startAndStop.removeAttribute('disabled');
        });
    });


</script>
</body>
</html>

效果

opencv.js人脸识别简单使用提示这个页面的好处有很多，可以将自己的js写入来测试自己的功能，稍微修改代码即可跑通官方给出的所有示例。