OpenCV.js - detectMultiScale“无法捕获此异常" [英] OpenCV.js - detectMultiScale "This Exception cannot be caught"
问题描述
我正在尝试通过 OpenCV.js 使用面部识别，但是当我调用 CascadeClassifier 对象的 detectMultiScale() 方法时，我收到错误:
I'm trying to use facial recognition via OpenCV.js, however when I call on the detectMultiScale() method of the CascadeClassifier object I receive the error:
未捕获 6446128 - 异常捕获已禁用，无法捕获此异常.使用 -s DISABLE_EXCEPTION_CATCHING=0 或 DISABLE_EXCEPTION_CATCHING=2 编译以捕获.
Uncaught 6446128 - Exception catching is disabled, this exception cannot be caught. Compile with -s DISABLE_EXCEPTION_CATCHING=0 or DISABLE_EXCEPTION_CATCHING=2 to catch.
问题是我直接从 opencv.org - 它不是构建版本，因为我无法自己构建它，因此无法按照错误说明进行操作.
The problem is I'm leveraging a hosted version of opencv.js directly from opencv.org - it's not a build version because I'm unable to build it myself, and therefore cannot follow the error's instructions.
我参考了他们 GitHub 上的一个例子 在这里并根据我的需要调整了代码，如下所示:
I've followed an example from their GitHub here and adapted the code to suit my needs, as follows:
<html>
<head>
<script src="https://docs.opencv.org/master/opencv.js"></script>
<script src="https://docs.opencv.org/master/utils.js"></script>
</head>
<body>
<img id="test" src="image/with/face.jpg" alt=""/>
<canvas id="output"></canvas>
<script>
let face_cascade = new cv.CascadeClassifier();
face_cascade.load("https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades_cuda/haarcascade_frontalface_default.xml");
function face_detector() {
let imgEl = document.getElementById("test");
let img = cv.imread(imgEl);
cv.imshow("output", img);
let src = cv.imread("output");
let gray = new cv.Mat();
let msize = new cv.Size(0,0);
cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY, 0);
let faces = new cv.RectVector();
face_cascade.detectMultiScale(gray, faces, 1.1, 3, 0, msize, msize); //Error occurs here
}
face_detector();
</script>
</body>
</html>
有使用 OpenCV.js 和面部识别经验的人可以提供帮助吗?
Anyone with experience with OpenCV.js and facial recognition that could help?
推荐答案
遵循这个 主题:
xml 文件是预先构建的"；在使用 load 函数加载它们之前.为了实现这一点，它使用了 utils.js 中的 createFileFromUrl 函数.之后，我们终于可以从文件中加载我们的分类器了.
The xml files are "pre-built" before loading them with the load function. To achieve this it's used the function createFileFromUrl from utils.js. After that we can finally load our classifier from file.
let classifier = new cv.CascadeClassifier(); // initialize classifier
let utils = new Utils('errorMessage'); //use utils class
let faceCascadeFile = 'haarcascade_frontalface_default.xml'; // path to xml
// use createFileFromUrl to "pre-build" the xml
utils.createFileFromUrl(faceCascadeFile, faceCascadeFile, () => {
classifier.load(faceCascadeFile); // in the callback, load the cascade from file
});
人脸检测其他示例
let src = cv.imread('canvasInput');
let gray = new cv.Mat();
cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY, 0);
let faces = new cv.RectVector();
let eyes = new cv.RectVector();
let faceCascade = new cv.CascadeClassifier();
// load pre-trained classifiers
faceCascade.load('haarcascade_frontalface_default.xml');
// detect faces
let msize = new cv.Size(0, 0);
// try to change scaleFactor and minNeighbors values
faceCascade.detectMultiScale(gray, faces,1.05,0);
for (let i = 0; i < faces.size(); ++i) {
let roiGray = gray.roi(faces.get(i));
let roiSrc = src.roi(faces.get(i));
let point1 = new cv.Point(faces.get(i).x, faces.get(i).y);
let point2 = new cv.Point(faces.get(i).x + faces.get(i).width,
faces.get(i).y + faces.get(i).height);
cv.rectangle(src, point1, point2, [255, 0, 0, 255]);
roiGray.delete(); roiSrc.delete();
}
cv.imshow('canvasOutput', src);
src.delete(); gray.delete(); faceCascade.delete();
faces.delete(); eyes.delete();
尝试更改 faceCascade.detectMultiScale 参数，如下面的示例所示:
Try to change faceCascade.detectMultiScale parameters like given examples below:
faceCascade.detectMultiScale(gray, faces,1.05,0);
faceCascade.detectMultiScale(gray, faces,1.05,1);
faceCascade.detectMultiScale(gray, faces,2,0);
faceCascade.detectMultiScale(gray, faces,2,1);
faceCascade.detectMultiScale(gray, faces,3,0);
faceCascade.detectMultiScale(gray, faces,3,1);
faceCascade.detectMultiScale(gray, faces,4,0);
faceCascade.detectMultiScale(gray, faces,4,1);
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