image-match/python/opencv/modules/js/test/test_objdetect.js

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// //////////////////////////////////////////////////////////////////////////////////////
// Author: Sajjad Taheri, University of California, Irvine. sajjadt[at]uci[dot]edu
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var haarcascade_data = undefined;
if (typeof module !== 'undefined' && module.exports) {
    // The environment is Node.js
    let fs = require("fs");
    haarcascade_data = fs.readFileSync("haarcascade_frontalface_default.xml");
}

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
    {
        if (haarcascade_data) {
            cv.FS_createDataFile("/", "haarcascade_frontalface_default.xml", haarcascade_data, true, false, false);
        }

        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();
        let mat = cv.Mat.ones(800, 600, cv.CV_8U);
        assert.ok(mat);

        // test detect
        let points = new cv.Mat();
        let qrCodeFound = detector.detect(mat, points);
        assert.equal(points.rows, 0)
        assert.equal(points.cols, 0)
        assert.equal(qrCodeFound, false);

        // test detectMult
        qrCodeFound = detector.detectMulti(mat, points);
        assert.equal(points.rows, 0)
        assert.equal(points.cols, 0)
        assert.equal(qrCodeFound, false);

        // test decode (with random numbers)
        let decodeTestPoints = cv.matFromArray(1, 4, cv.CV_32FC2, [10, 20, 30, 40, 60, 80, 90, 100]);
        let qrCodeContent = detector.decode(mat, decodeTestPoints);
        assert.equal(typeof qrCodeContent, 'string');
        assert.equal(qrCodeContent, '');

        //test detectAndDecode
        qrCodeContent = detector.detectAndDecode(mat);
        assert.equal(typeof qrCodeContent, 'string');
        assert.equal(qrCodeContent, '');

        // test decodeCurved
        qrCodeContent = detector.decodeCurved(mat, decodeTestPoints);
        assert.equal(typeof qrCodeContent, 'string');
        assert.equal(qrCodeContent, '');

        decodeTestPoints.delete();
        points.delete();
        mat.delete();

    }
});
QUnit.test('Aruco-based QR code detect', function (assert) {
    {
        let qrcode_params = new cv.QRCodeDetectorAruco_Params();
        let detector = new cv.QRCodeDetectorAruco();
        let mat = cv.Mat.ones(800, 600, cv.CV_8U);
        assert.ok(mat);

        detector.setDetectorParameters(qrcode_params);

        let points = new cv.Mat();
        let qrCodeFound = detector.detect(mat, points);
        assert.equal(points.rows, 0)
        assert.equal(points.cols, 0)
        assert.equal(qrCodeFound, false);

        qrcode_params.delete();
        detector.delete();
        points.delete();
        mat.delete();
    }
});
QUnit.test('Bar code detect', function (assert) {
    {
        let detector = new cv.barcode_BarcodeDetector();
        let mat = cv.Mat.ones(800, 600, cv.CV_8U);
        assert.ok(mat);

        let points = new cv.Mat();
        let codeFound = detector.detect(mat, points);
        assert.equal(points.rows, 0)
        assert.equal(points.cols, 0)
        assert.equal(codeFound, false);

        codeContent = detector.detectAndDecode(mat);
        assert.equal(typeof codeContent, 'string');
        assert.equal(codeContent, '');

        detector.delete();
        points.delete();
        mat.delete();
    }
});
QUnit.test('Aruco detector', function (assert) {
    {
        let dictionary = cv.getPredefinedDictionary(cv.DICT_4X4_50);
        let aruco_image = new cv.Mat();
        let detectorParameters = new cv.aruco_DetectorParameters();
        let refineParameters = new cv.aruco_RefineParameters(10, 3, true);
        let detector = new cv.aruco_ArucoDetector(dictionary, detectorParameters,refineParameters);
        let corners = new cv.MatVector();
        let ids = new cv.Mat();

        dictionary.generateImageMarker(10, 128, aruco_image);
        assert.ok(!aruco_image.empty());

        detector.detectMarkers(aruco_image, corners, ids);

        dictionary.delete();
        aruco_image.delete();
        detectorParameters.delete();
        refineParameters.delete();
        detector.delete();
        corners.delete();
        ids.delete();
    }
});
QUnit.test('Charuco detector', function (assert) {
    {
        let dictionary = new cv.getPredefinedDictionary(cv.DICT_4X4_50);
        let boardIds = new cv.Mat();
        let board = new cv.aruco_CharucoBoard(new cv.Size(3, 5), 64, 32, dictionary, boardIds);
        let charucoParameters = new cv.aruco_CharucoParameters();
        let detectorParameters = new cv.aruco_DetectorParameters();
        let refineParameters = new cv.aruco_RefineParameters(10, 3, true);
        let detector = new cv.aruco_CharucoDetector(board, charucoParameters, detectorParameters, refineParameters);
        let board_image = new cv.Mat();
        let corners = new cv.Mat();
        let ids = new cv.Mat();

        board.generateImage(new cv.Size(300, 500), board_image);
        assert.ok(!board_image.empty());

        let chess_corners = board.getChessboardCorners();

        detector.detectBoard(board_image, corners, ids);
        assert.ok(!corners.empty());
        assert.ok(!ids.empty());

        dictionary.delete();
        boardIds.delete();
        board.delete();
        board_image.delete();
        charucoParameters.delete();
        detectorParameters.delete();
        refineParameters.delete();
        detector.delete();
        corners.delete();
        ids.delete();
        chess_corners.delete();
    }
});