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Merge pull request #25324 from kaingwade:clean_haarcascades_jsbindings

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Fix broken js build after moving HaarCascades to contrib #25324

The HaarCascades related are not completely cleaned up #25311 after #25198, which breaks the JavaScript build. The PR is to fix the issue.

Related PR: opencv/opencv_contrib#3712
This commit is contained in:
WU Jia
2024-04-27 19:37:40 +08:00
committed by GitHub
parent 72ad06bcf3
commit 94f4678d3a
7 changed files with 117 additions and 303 deletions
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doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown
+116 -41
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@@ -227,52 +227,128 @@ In the browser, this filesystem is emulated in memory while in Node.js there's a
### The example ###
The following is an adaptation of @ref tutorial_js_face_detection.
@code{.js}
const { Canvas, createCanvas, Image, ImageData, loadImage } = require('canvas');
const { JSDOM } = require('jsdom');
const { writeFileSync, existsSync, mkdirSync } = require('fs');
const https = require('https');
(async () => {
await loadOpenCV();
const createFileFromUrl = function (path, url, maxRedirects = 10) {
console.log('Downloading ' + url + '...');
return new Promise((resolve, reject) => {
const download = (url, redirectCount) => {
if (redirectCount > maxRedirects) {
reject(new Error('Too many redirects'));
} else {
let connection = https.get(url, (response) => {
if (response.statusCode === 200) {
let data = [];
response.on('data', (chunk) => {
data.push(chunk);
});
const image = await loadImage('lena.jpg');
const src = cv.imread(image);
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();
let eyeCascade = new cv.CascadeClassifier();
response.on('end', () => {
try {
writeFileSync(path, Buffer.concat(data));
resolve();
} catch (err) {
reject(new Error('Failed to write file ' + path));
}
});
} else if (response.statusCode === 302 || response.statusCode === 301) {
connection.abort();
download(response.headers.location, redirectCount + 1);
} else {
reject(new Error('Failed to load ' + url + ' status: ' + response.statusCode));
}
}).on('error', (err) => {
reject(new Error('Network Error: ' + err.message));
});
}
};
download(url, 0);
});
};
// Load pre-trained classifier files. Notice how we reference local files using relative paths just
// like we normally would do
faceCascade.load('./haarcascade_frontalface_default.xml');
eyeCascade.load('./haarcascade_eye.xml');
if (!existsSync('./face_detection_yunet_2023mar.onnx')) {
await createFileFromUrl('./face_detection_yunet_2023mar.onnx', 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx')
}
let mSize = new cv.Size(0, 0);
faceCascade.detectMultiScale(gray, faces, 1.1, 3, 0, mSize, mSize);
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]);
eyeCascade.detectMultiScale(roiGray, eyes);
for (let j = 0; j < eyes.size(); ++j) {
let point1 = new cv.Point(eyes.get(j).x, eyes.get(j).y);
let point2 = new cv.Point(eyes.get(j).x + eyes.get(j).width, eyes.get(j).y + eyes.get(j).height);
cv.rectangle(roiSrc, point1, point2, [0, 0, 255, 255]);
}
roiGray.delete();
roiSrc.delete();
if (!existsSync('./opencv.js')) {
await createFileFromUrl('./opencv.js', 'https://docs.opencv.org/5.x/opencv.js')
}
if (!existsSync('./lena.jpg')) {
await createFileFromUrl('./lena.jpg', 'https://docs.opencv.org/5.x/lena.jpg')
}
await loadOpenCV();
const image = await loadImage('./lena.jpg');
const src = cv.imread(image);
let srcBGR = new cv.Mat();
cv.cvtColor(src, srcBGR, cv.COLOR_RGBA2BGR);
// Load the deep learning model file. Notice how we reference local files using relative paths just
// like we normally would do
let netDet = new cv.FaceDetectorYN("./face_detection_yunet_2023mar.onnx", "", new cv.Size(320, 320), 0.9, 0.3, 5000);
netDet.setInputSize(new cv.Size(src.cols, src.rows));
let out = new cv.Mat();
netDet.detect(srcBGR, out);
let faces = [];
for (let i = 0, n = out.data32F.length; i < n; i += 15) {
let left = out.data32F[i];
let top = out.data32F[i + 1];
let right = (out.data32F[i] + out.data32F[i + 2]);
let bottom = (out.data32F[i + 1] + out.data32F[i + 3]);
left = Math.min(Math.max(0, left), src.cols - 1);
top = Math.min(Math.max(0, top), src.rows - 1);
right = Math.min(Math.max(0, right), src.cols - 1);
bottom = Math.min(Math.max(0, bottom), src.rows - 1);
if (left < right && top < bottom) {
faces.push({
x: left,
y: top,
width: right - left,
height: bottom - top,
x1: out.data32F[i + 4] < 0 || out.data32F[i + 4] > src.cols - 1 ? -1 : out.data32F[i + 4],
y1: out.data32F[i + 5] < 0 || out.data32F[i + 5] > src.rows - 1 ? -1 : out.data32F[i + 5],
x2: out.data32F[i + 6] < 0 || out.data32F[i + 6] > src.cols - 1 ? -1 : out.data32F[i + 6],
y2: out.data32F[i + 7] < 0 || out.data32F[i + 7] > src.rows - 1 ? -1 : out.data32F[i + 7],
x3: out.data32F[i + 8] < 0 || out.data32F[i + 8] > src.cols - 1 ? -1 : out.data32F[i + 8],
y3: out.data32F[i + 9] < 0 || out.data32F[i + 9] > src.rows - 1 ? -1 : out.data32F[i + 9],
x4: out.data32F[i + 10] < 0 || out.data32F[i + 10] > src.cols - 1 ? -1 : out.data32F[i + 10],
y4: out.data32F[i + 11] < 0 || out.data32F[i + 11] > src.rows - 1 ? -1 : out.data32F[i + 11],
x5: out.data32F[i + 12] < 0 || out.data32F[i + 12] > src.cols - 1 ? -1 : out.data32F[i + 12],
y5: out.data32F[i + 13] < 0 || out.data32F[i + 13] > src.rows - 1 ? -1 : out.data32F[i + 13],
confidence: out.data32F[i + 14]
})
}
}
out.delete();
const canvas = createCanvas(image.width, image.height);
cv.imshow(canvas, src);
writeFileSync('output3.jpg', canvas.toBuffer('image/jpeg'));
src.delete(); gray.delete(); faceCascade.delete(); eyeCascade.delete(); faces.delete(); eyes.delete()
faces.forEach(function(rect) {
cv.rectangle(src, {x: rect.x, y: rect.y}, {x: rect.x + rect.width, y: rect.y + rect.height}, [0, 255, 0, 255]);
if(rect.x1>0 && rect.y1>0)
cv.circle(src, {x: rect.x1, y: rect.y1}, 2, [255, 0, 0, 255], 2)
if(rect.x2>0 && rect.y2>0)
cv.circle(src, {x: rect.x2, y: rect.y2}, 2, [0, 0, 255, 255], 2)
if(rect.x3>0 && rect.y3>0)
cv.circle(src, {x: rect.x3, y: rect.y3}, 2, [0, 255, 0, 255], 2)
if(rect.x4>0 && rect.y4>0)
cv.circle(src, {x: rect.x4, y: rect.y4}, 2, [255, 0, 255, 255], 2)
if(rect.x5>0 && rect.y5>0)
cv.circle(src, {x: rect.x5, y: rect.y5}, 2, [0, 255, 255, 255], 2)
});
const canvas = createCanvas(image.width, image.height);
cv.imshow(canvas, src);
writeFileSync('output3.jpg', canvas.toBuffer('image/jpeg'));
console.log('The result is saved.')
src.delete(); srcBGR.delete();
})();
/**
@@ -287,7 +363,7 @@ const { writeFileSync, existsSync, mkdirSync } = require('fs');
*/
function loadOpenCV(rootDir = '/work', localRootDir = process.cwd()) {
if(global.Module && global.Module.onRuntimeInitialized && global.cv && global.cv.imread) {
return Promise.resolve()
Promise.resolve()
}
return new Promise(resolve => {
installDOM()
@@ -333,13 +409,12 @@ function installDOM(){
### Execute it ###
- Save the file as `exampleNodeCanvasData.js`.
- Make sure the files `aarcascade_frontalface_default.xml` and `haarcascade_eye.xml` are present in project's directory. They can be obtained from [OpenCV sources](https://github.com/opencv/opencv/tree/5.x/data/haarcascades).
- Make sure a sample image file `lena.jpg` exists in project's directory. It should display people's faces for this example to make sense. The following image is known to work:
- The files `face_detection_yunet_2023mar.onnx`, `lena.jpg` and `opencv.js` will be downloaded if they not present in project's directory.
![image](js_assets/lena.jpg)
The following command should generate the file `output3.jpg`:
The following command should generate the file `output3.jpg` look the image below:
@code{.bash}
node exampleNodeCanvasData.js
@endcode
![image](js_assets/lena_yunet.jpg)