// ////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2013, OpenCV Foundation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // // // ////////////////////////////////////////////////////////////////////////////////////// // Author: Sajjad Taheri, University of California, Irvine. sajjadt[at]uci[dot]edu // // LICENSE AGREEMENT // Copyright (c) 2015 The Regents of the University of California (Regents) // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // 1. Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // 2. Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // 3. Neither the name of the University nor the // names of its contributors may be used to endorse or promote products // derived from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ''AS IS'' AND ANY // EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED // WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE // DISCLAIMED. IN NO EVENT SHALL CONTRIBUTORS BE LIABLE FOR ANY // DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES // (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; // LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND // ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. // 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(); 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(); } });