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301 lines
11 KiB
JavaScript
301 lines
11 KiB
JavaScript
// //////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//
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// //////////////////////////////////////////////////////////////////////////////////////
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// Author: Sajjad Taheri, University of California, Irvine. sajjadt[at]uci[dot]edu
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//
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// LICENSE AGREEMENT
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// Copyright (c) 2015 The Regents of the University of California (Regents)
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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// 1. Redistributions of source code must retain the above copyright
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// notice, this list of conditions and the following disclaimer.
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// 2. Redistributions in binary form must reproduce the above copyright
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// notice, this list of conditions and the following disclaimer in the
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// documentation and/or other materials provided with the distribution.
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// 3. Neither the name of the University nor the
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// names of its contributors may be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ''AS IS'' AND ANY
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// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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// WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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// DISCLAIMED. IN NO EVENT SHALL CONTRIBUTORS BE LIABLE FOR ANY
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// DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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// ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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//
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if (typeof module !== 'undefined' && module.exports) {
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// The environment is Node.js
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var cv = require('./opencv.js'); // eslint-disable-line no-var
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cv.FS_createLazyFile('/', 'haarcascade_frontalface_default.xml', // eslint-disable-line new-cap
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'haarcascade_frontalface_default.xml', true, false);
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}
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QUnit.module('Object Detection', {});
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QUnit.test('Cascade classification', function(assert) {
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// Group rectangle
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{
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let rectList = new cv.RectVector();
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let weights = new cv.IntVector();
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let groupThreshold = 1;
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const eps = 0.2;
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let rect1 = new cv.Rect(1, 2, 3, 4);
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let rect2 = new cv.Rect(1, 4, 2, 3);
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rectList.push_back(rect1);
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rectList.push_back(rect2);
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cv.groupRectangles(rectList, weights, groupThreshold, eps);
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rectList.delete();
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weights.delete();
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}
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// CascadeClassifier
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{
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let classifier = new cv.CascadeClassifier();
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const modelPath = '/haarcascade_frontalface_default.xml';
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assert.equal(classifier.empty(), true);
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classifier.load(modelPath);
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assert.equal(classifier.empty(), false);
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let image = cv.Mat.eye({height: 10, width: 10}, cv.CV_8UC3);
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let objects = new cv.RectVector();
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let numDetections = new cv.IntVector();
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const scaleFactor = 1.1;
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const minNeighbors = 3;
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const flags = 0;
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const minSize = {height: 0, width: 0};
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const maxSize = {height: 10, width: 10};
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classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
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minNeighbors, flags, minSize, maxSize);
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// test default parameters
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classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
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minNeighbors, flags, minSize);
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classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
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minNeighbors, flags);
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classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
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minNeighbors);
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classifier.detectMultiScale2(image, objects, numDetections, scaleFactor);
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classifier.delete();
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objects.delete();
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numDetections.delete();
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}
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// HOGDescriptor
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{
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let hog = new cv.HOGDescriptor();
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let mat = new cv.Mat({height: 10, width: 10}, cv.CV_8UC1);
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let descriptors = new cv.FloatVector();
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let locations = new cv.PointVector();
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assert.equal(hog.winSize.height, 128);
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assert.equal(hog.winSize.width, 64);
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assert.equal(hog.nbins, 9);
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assert.equal(hog.derivAperture, 1);
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assert.equal(hog.winSigma, -1);
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assert.equal(hog.histogramNormType, 0);
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assert.equal(hog.nlevels, 64);
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hog.nlevels = 32;
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assert.equal(hog.nlevels, 32);
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hog.delete();
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mat.delete();
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descriptors.delete();
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locations.delete();
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}
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});
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QUnit.test('QR code detect and decode', function (assert) {
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{
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const detector = new cv.QRCodeDetector();
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let mat = cv.Mat.ones(800, 600, cv.CV_8U);
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assert.ok(mat);
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// test detect
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let points = new cv.Mat();
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let qrCodeFound = detector.detect(mat, points);
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assert.equal(points.rows, 0)
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assert.equal(points.cols, 0)
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assert.equal(qrCodeFound, false);
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// test detectMult
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qrCodeFound = detector.detectMulti(mat, points);
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assert.equal(points.rows, 0)
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assert.equal(points.cols, 0)
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assert.equal(qrCodeFound, false);
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// test decode (with random numbers)
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let decodeTestPoints = cv.matFromArray(1, 4, cv.CV_32FC2, [10, 20, 30, 40, 60, 80, 90, 100]);
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let qrCodeContent = detector.decode(mat, decodeTestPoints);
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assert.equal(typeof qrCodeContent, 'string');
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assert.equal(qrCodeContent, '');
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//test detectAndDecode
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qrCodeContent = detector.detectAndDecode(mat);
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assert.equal(typeof qrCodeContent, 'string');
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assert.equal(qrCodeContent, '');
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// test decodeCurved
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qrCodeContent = detector.decodeCurved(mat, decodeTestPoints);
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assert.equal(typeof qrCodeContent, 'string');
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assert.equal(qrCodeContent, '');
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decodeTestPoints.delete();
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points.delete();
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mat.delete();
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}
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});
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QUnit.test('Aruco-based QR code detect', function (assert) {
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{
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let qrcode_params = new cv.QRCodeDetectorAruco_Params();
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let detector = new cv.QRCodeDetectorAruco();
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let mat = cv.Mat.ones(800, 600, cv.CV_8U);
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assert.ok(mat);
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detector.setDetectorParameters(qrcode_params);
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let points = new cv.Mat();
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let qrCodeFound = detector.detect(mat, points);
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assert.equal(points.rows, 0)
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assert.equal(points.cols, 0)
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assert.equal(qrCodeFound, false);
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qrcode_params.delete();
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detector.delete();
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points.delete();
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mat.delete();
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}
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});
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QUnit.test('Bar code detect', function (assert) {
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{
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let detector = new cv.barcode_BarcodeDetector();
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let mat = cv.Mat.ones(800, 600, cv.CV_8U);
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assert.ok(mat);
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let points = new cv.Mat();
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let codeFound = detector.detect(mat, points);
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assert.equal(points.rows, 0)
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assert.equal(points.cols, 0)
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assert.equal(codeFound, false);
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codeContent = detector.detectAndDecode(mat);
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assert.equal(typeof codeContent, 'string');
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assert.equal(codeContent, '');
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detector.delete();
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points.delete();
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mat.delete();
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}
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});
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QUnit.test('Aruco detector', function (assert) {
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{
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let dictionary = cv.getPredefinedDictionary(cv.DICT_4X4_50);
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let aruco_image = new cv.Mat();
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let detectorParameters = new cv.aruco_DetectorParameters();
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let refineParameters = new cv.aruco_RefineParameters(10, 3, true);
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let detector = new cv.aruco_ArucoDetector(dictionary, detectorParameters,refineParameters);
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let corners = new cv.MatVector();
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let ids = new cv.Mat();
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dictionary.generateImageMarker(10, 128, aruco_image);
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assert.ok(!aruco_image.empty());
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detector.detectMarkers(aruco_image, corners, ids);
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dictionary.delete();
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aruco_image.delete();
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detectorParameters.delete();
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refineParameters.delete();
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detector.delete();
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corners.delete();
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ids.delete();
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}
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});
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QUnit.test('Charuco detector', function (assert) {
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{
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let dictionary = new cv.getPredefinedDictionary(cv.DICT_4X4_50);
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let boardIds = new cv.Mat();
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let board = new cv.aruco_CharucoBoard(new cv.Size(3, 5), 64, 32, dictionary, boardIds);
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let charucoParameters = new cv.aruco_CharucoParameters();
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let detectorParameters = new cv.aruco_DetectorParameters();
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let refineParameters = new cv.aruco_RefineParameters(10, 3, true);
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let detector = new cv.aruco_CharucoDetector(board, charucoParameters, detectorParameters, refineParameters);
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let board_image = new cv.Mat();
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let corners = new cv.Mat();
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let ids = new cv.Mat();
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board.generateImage(new cv.Size(300, 500), board_image);
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assert.ok(!board_image.empty());
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detector.detectBoard(board_image, corners, ids);
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assert.ok(!corners.empty());
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assert.ok(!ids.empty());
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dictionary.delete();
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boardIds.delete();
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board.delete();
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board_image.delete();
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charucoParameters.delete();
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detectorParameters.delete();
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refineParameters.delete();
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detector.delete();
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corners.delete();
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ids.delete();
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}
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});
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