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Move objdetect HaarCascadeClassifier and HOGDescriptor to contrib xobjdetect (#25198)

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* Move objdetect parts to contrib

* Move objdetect parts to contrib

* Minor fixes.
This commit is contained in:
WU Jia
2024-03-22 04:40:10 +08:00
committed by GitHub
parent 213e1a4d9d
commit aa5ea340f7
111 changed files with 22 additions and 741097 deletions
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modules/js/test/test_objdetect.js
-85
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@@ -71,94 +71,9 @@
if (typeof module !== 'undefined' && module.exports) {
// The environment is Node.js
var cv = require('./opencv.js'); // eslint-disable-line no-var
cv.FS_createLazyFile('/', 'haarcascade_frontalface_default.xml', // eslint-disable-line new-cap
'haarcascade_frontalface_default.xml', true, false);
}
QUnit.module('Object Detection', {});
QUnit.test('Cascade classification', function(assert) {
// Group rectangle
{
let rectList = new cv.RectVector();
let weights = new cv.IntVector();
let groupThreshold = 1;
const eps = 0.2;
let rect1 = new cv.Rect(1, 2, 3, 4);
let rect2 = new cv.Rect(1, 4, 2, 3);
rectList.push_back(rect1);
rectList.push_back(rect2);
cv.groupRectangles(rectList, weights, groupThreshold, eps);
rectList.delete();
weights.delete();
}
// CascadeClassifier
{
let classifier = new cv.CascadeClassifier();
const modelPath = '/haarcascade_frontalface_default.xml';
assert.equal(classifier.empty(), true);
classifier.load(modelPath);
assert.equal(classifier.empty(), false);
let image = cv.Mat.eye({height: 10, width: 10}, cv.CV_8UC3);
let objects = new cv.RectVector();
let numDetections = new cv.IntVector();
const scaleFactor = 1.1;
const minNeighbors = 3;
const flags = 0;
const minSize = {height: 0, width: 0};
const maxSize = {height: 10, width: 10};
classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
minNeighbors, flags, minSize, maxSize);
// test default parameters
classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
minNeighbors, flags, minSize);
classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
minNeighbors, flags);
classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
minNeighbors);
classifier.detectMultiScale2(image, objects, numDetections, scaleFactor);
classifier.delete();
objects.delete();
numDetections.delete();
}
// HOGDescriptor
{
let hog = new cv.HOGDescriptor();
let mat = new cv.Mat({height: 10, width: 10}, cv.CV_8UC1);
let descriptors = new cv.FloatVector();
let locations = new cv.PointVector();
assert.equal(hog.winSize.height, 128);
assert.equal(hog.winSize.width, 64);
assert.equal(hog.nbins, 9);
assert.equal(hog.derivAperture, 1);
assert.equal(hog.winSigma, -1);
assert.equal(hog.histogramNormType, 0);
assert.equal(hog.nlevels, 64);
hog.nlevels = 32;
assert.equal(hog.nlevels, 32);
hog.delete();
mat.delete();
descriptors.delete();
locations.delete();
}
});
QUnit.test('QR code detect and decode', function (assert) {
{
const detector = new cv.QRCodeDetector();