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1003 lines
32 KiB
JavaScript
1003 lines
32 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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}
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QUnit.module('Image Processing', {});
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QUnit.test('test_imgProc', function(assert) {
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// calcHist
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{
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let vec1 = new cv.Mat.ones(new cv.Size(20, 20), cv.CV_8UC1); // eslint-disable-line new-cap
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let source = new cv.MatVector();
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source.push_back(vec1);
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let channels = [0];
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let histSize = [256];
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let ranges =[0, 256];
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let hist = new cv.Mat();
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let mask = new cv.Mat();
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let binSize = cv._malloc(4);
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let binView = new Int32Array(cv.HEAP8.buffer, binSize);
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binView[0] = 10;
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cv.calcHist(source, channels, mask, hist, histSize, ranges, false);
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// hist should contains a N X 1 array.
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let size = hist.size();
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assert.equal(size.height, 256);
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assert.equal(size.width, 1);
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// default parameters
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cv.calcHist(source, channels, mask, hist, histSize, ranges);
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size = hist.size();
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assert.equal(size.height, 256);
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assert.equal(size.width, 1);
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// Do we need to verify data in histogram?
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// let dataView = hist.data;
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// Free resource
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cv._free(binSize);
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mask.delete();
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hist.delete();
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}
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// cvtColor
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{
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let source = new cv.Mat(10, 10, cv.CV_8UC3);
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let dest = new cv.Mat();
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cv.cvtColor(source, dest, cv.COLOR_BGR2GRAY, 0);
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assert.equal(dest.channels(), 1);
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cv.cvtColor(source, dest, cv.COLOR_BGR2GRAY);
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assert.equal(dest.channels(), 1);
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cv.cvtColor(source, dest, cv.COLOR_BGR2BGRA, 0);
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assert.equal(dest.channels(), 4);
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cv.cvtColor(source, dest, cv.COLOR_BGR2BGRA);
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assert.equal(dest.channels(), 4);
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dest.delete();
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source.delete();
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}
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// equalizeHist
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{
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let source = new cv.Mat(10, 10, cv.CV_8UC1);
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let dest = new cv.Mat();
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cv.equalizeHist(source, dest);
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// eualizeHist changes the content of a image, but does not alter meta data
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// of it.
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assert.equal(source.channels(), dest.channels());
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assert.equal(source.type(), dest.type());
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dest.delete();
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source.delete();
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}
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// floodFill
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{
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let center = new cv.Point(5, 5);
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let rect = new cv.Rect(0, 0, 0, 0);
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let img = new cv.Mat.zeros(10, 10, cv.CV_8UC1);
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let color = new cv.Scalar (255);
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cv.circle(img, center, 3, color, 1);
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let edge = new cv.Mat();
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cv.Canny(img, edge, 100, 255);
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cv.copyMakeBorder(edge, edge, 1, 1, 1, 1, cv.BORDER_REPLICATE);
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let expected_img_data = new Uint8Array([
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 255, 0, 0, 0, 0,
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0, 0, 0, 255, 255, 255, 255, 255, 0, 0,
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0, 0, 0, 255, 0, 255, 0, 255, 0, 0,
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0, 0, 255, 255, 255, 255, 0, 0, 255, 0,
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0, 0, 0, 255, 0, 0, 0, 255, 0, 0,
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0, 0, 0, 255, 255, 0, 255, 255, 0, 0,
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0, 0, 0, 0, 0, 255, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
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let img_elem = 10*10*1;
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let expected_img_data_ptr = cv._malloc(img_elem);
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let expected_img_data_heap = new Uint8Array(cv.HEAPU8.buffer,
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expected_img_data_ptr,
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img_elem);
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expected_img_data_heap.set(new Uint8Array(expected_img_data.buffer));
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let expected_img = new cv.Mat( 10, 10, cv.CV_8UC1, expected_img_data_ptr, 0);
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let expected_rect = new cv.Rect(3,3,3,3);
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let compare_result = new cv.Mat(10, 10, cv.CV_8UC1);
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cv.floodFill(img, edge, center, color, rect);
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cv.compare (img, expected_img, compare_result, cv.CMP_EQ);
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// expect every pixels are the same.
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assert.equal (cv.countNonZero(compare_result), img.total());
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assert.equal (rect.x, expected_rect.x);
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assert.equal (rect.y, expected_rect.y);
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assert.equal (rect.width, expected_rect.width);
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assert.equal (rect.height, expected_rect.height);
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img.delete();
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edge.delete();
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expected_img.delete();
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compare_result.delete();
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}
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// fillPoly
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{
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let img_width = 6;
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let img_height = 6;
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let img = new cv.Mat.zeros(img_height, img_width, cv.CV_8UC1);
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let npts = 4;
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let square_point_data = new Uint8Array([
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1, 1,
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4, 1,
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4, 4,
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1, 4]);
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let square_points = cv.matFromArray(npts, 1, cv.CV_32SC2, square_point_data);
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let pts = new cv.MatVector();
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pts.push_back (square_points);
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let color = new cv.Scalar (255);
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let expected_img_data = new Uint8Array([
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0, 0, 0, 0, 0, 0,
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0, 255, 255, 255, 255, 0,
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0, 255, 255, 255, 255, 0,
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0, 255, 255, 255, 255, 0,
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0, 255, 255, 255, 255, 0,
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0, 0, 0, 0, 0, 0]);
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let expected_img = cv.matFromArray(img_height, img_width, cv.CV_8UC1, expected_img_data);
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cv.fillPoly(img, pts, color);
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let compare_result = new cv.Mat(img_height, img_width, cv.CV_8UC1);
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cv.compare (img, expected_img, compare_result, cv.CMP_EQ);
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// expect every pixels are the same.
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assert.equal (cv.countNonZero(compare_result), img.total());
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img.delete();
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square_points.delete();
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pts.delete();
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expected_img.delete();
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compare_result.delete();
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}
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// fillConvexPoly
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{
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let img_width = 6;
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let img_height = 6;
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let img = new cv.Mat.zeros(img_height, img_width, cv.CV_8UC1);
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let npts = 4;
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let square_point_data = new Uint8Array([
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1, 1,
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4, 1,
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4, 4,
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1, 4]);
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let square_points = cv.matFromArray(npts, 1, cv.CV_32SC2, square_point_data);
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let color = new cv.Scalar (255);
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let expected_img_data = new Uint8Array([
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0, 0, 0, 0, 0, 0,
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0, 255, 255, 255, 255, 0,
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0, 255, 255, 255, 255, 0,
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0, 255, 255, 255, 255, 0,
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0, 255, 255, 255, 255, 0,
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0, 0, 0, 0, 0, 0]);
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let expected_img = cv.matFromArray(img_height, img_width, cv.CV_8UC1, expected_img_data);
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cv.fillConvexPoly(img, square_points, color);
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let compare_result = new cv.Mat(img_height, img_width, cv.CV_8UC1);
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cv.compare (img, expected_img, compare_result, cv.CMP_EQ);
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// expect every pixels are the same.
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assert.equal (cv.countNonZero(compare_result), img.total());
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img.delete();
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square_points.delete();
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expected_img.delete();
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compare_result.delete();
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}
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});
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QUnit.test('test_segmentation', function(assert) {
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const THRESHOLD = 127.0;
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const THRESHOLD_MAX = 210.0;
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// threshold
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{
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let source = new cv.Mat(1, 5, cv.CV_8UC1);
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let sourceView = source.data;
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sourceView[0] = 0; // < threshold
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sourceView[1] = 100; // < threshold
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sourceView[2] = 200; // > threshold
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let dest = new cv.Mat();
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cv.threshold(source, dest, THRESHOLD, THRESHOLD_MAX, cv.THRESH_BINARY);
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let destView = dest.data;
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assert.equal(destView[0], 0);
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assert.equal(destView[1], 0);
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assert.equal(destView[2], THRESHOLD_MAX);
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}
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// adaptiveThreshold
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{
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let source = cv.Mat.zeros(1, 5, cv.CV_8UC1);
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let sourceView = source.data;
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sourceView[0] = 50;
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sourceView[1] = 150;
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sourceView[2] = 200;
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let dest = new cv.Mat();
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const C = 0;
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const blockSize = 3;
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cv.adaptiveThreshold(source, dest, THRESHOLD_MAX,
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cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, blockSize, C);
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let destView = dest.data;
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assert.equal(destView[0], 0);
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assert.equal(destView[1], THRESHOLD_MAX);
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assert.equal(destView[2], THRESHOLD_MAX);
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}
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});
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QUnit.test('test_shape', function(assert) {
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// moments
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{
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let points = new cv.Mat(1, 4, cv.CV_32SC2);
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let data32S = points.data32S;
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data32S[0]=50;
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data32S[1]=56;
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data32S[2]=53;
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data32S[3]=53;
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data32S[4]=46;
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data32S[5]=54;
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data32S[6]=49;
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data32S[7]=51;
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let m = cv.moments(points, false);
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let area = cv.contourArea(points, false);
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assert.equal(m.m00, 0);
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assert.equal(m.m01, 0);
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assert.equal(m.m10, 0);
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assert.equal(area, 0);
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// default parameters
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m = cv.moments(points);
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area = cv.contourArea(points);
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assert.equal(m.m00, 0);
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assert.equal(m.m01, 0);
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assert.equal(m.m10, 0);
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assert.equal(area, 0);
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points.delete();
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}
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});
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QUnit.test('test_min_enclosing', function(assert) {
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{
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let points = new cv.Mat(4, 1, cv.CV_32FC2);
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points.data32F[0] = 0;
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points.data32F[1] = 0;
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points.data32F[2] = 1;
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points.data32F[3] = 0;
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points.data32F[4] = 1;
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points.data32F[5] = 1;
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points.data32F[6] = 0;
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points.data32F[7] = 1;
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let circle = cv.minEnclosingCircle(points);
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assert.deepEqual(circle.center, {x: 0.5, y: 0.5});
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assert.ok(Math.abs(circle.radius - Math.sqrt(2) / 2) < 0.001);
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points.delete();
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}
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});
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QUnit.test('test_filter', function(assert) {
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// blur
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{
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let mat1 = cv.Mat.ones(5, 5, cv.CV_8UC3);
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let mat2 = new cv.Mat();
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cv.blur(mat1, mat2, {height: 3, width: 3}, {x: -1, y: -1}, cv.BORDER_DEFAULT);
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// Verify result.
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let size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 5);
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assert.equal(size.width, 5);
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cv.blur(mat1, mat2, {height: 3, width: 3}, {x: -1, y: -1});
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// Verify result.
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size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 5);
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assert.equal(size.width, 5);
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cv.blur(mat1, mat2, {height: 3, width: 3});
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// Verify result.
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size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 5);
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assert.equal(size.width, 5);
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mat1.delete();
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mat2.delete();
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}
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// GaussianBlur
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{
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let mat1 = cv.Mat.ones(7, 7, cv.CV_8UC1);
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let mat2 = new cv.Mat();
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cv.GaussianBlur(mat1, mat2, new cv.Size(3, 3), 0, 0, // eslint-disable-line new-cap
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cv.BORDER_DEFAULT);
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// Verify result.
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let size = mat2.size();
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assert.equal(mat2.channels(), 1);
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assert.equal(size.height, 7);
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assert.equal(size.width, 7);
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}
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// medianBlur
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{
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let mat1 = cv.Mat.ones(9, 9, cv.CV_8UC3);
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let mat2 = new cv.Mat();
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cv.medianBlur(mat1, mat2, 3);
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// Verify result.
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let size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 9);
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assert.equal(size.width, 9);
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}
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// Transpose
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{
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let mat1 = cv.Mat.eye(9, 9, cv.CV_8UC3);
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let mat2 = new cv.Mat();
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cv.transpose(mat1, mat2);
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// Verify result.
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let size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 9);
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assert.equal(size.width, 9);
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}
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// bilateralFilter
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{
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let mat1 = cv.Mat.ones(11, 11, cv.CV_8UC3);
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let mat2 = new cv.Mat();
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cv.bilateralFilter(mat1, mat2, 3, 6, 1.5, cv.BORDER_DEFAULT);
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// Verify result.
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let size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 11);
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assert.equal(size.width, 11);
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// default parameters
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cv.bilateralFilter(mat1, mat2, 3, 6, 1.5);
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// Verify result.
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size = mat2.size();
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assert.equal(mat2.channels(), 3);
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assert.equal(size.height, 11);
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assert.equal(size.width, 11);
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mat1.delete();
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mat2.delete();
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}
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// Watershed
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{
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let mat = cv.Mat.ones(11, 11, cv.CV_8UC3);
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let out = new cv.Mat(11, 11, cv.CV_32SC1);
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cv.watershed(mat, out);
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|
|
// Verify result.
|
|
let size = out.size();
|
|
assert.equal(out.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(out.elemSize1(), 4);
|
|
|
|
mat.delete();
|
|
out.delete();
|
|
}
|
|
|
|
// Concat
|
|
{
|
|
let mat = cv.Mat.ones({height: 10, width: 5}, cv.CV_8UC3);
|
|
let mat2 = cv.Mat.eye({height: 10, width: 5}, cv.CV_8UC3);
|
|
let mat3 = cv.Mat.eye({height: 10, width: 5}, cv.CV_8UC3);
|
|
|
|
|
|
let out = new cv.Mat();
|
|
let input = new cv.MatVector();
|
|
input.push_back(mat);
|
|
input.push_back(mat2);
|
|
input.push_back(mat3);
|
|
|
|
cv.vconcat(input, out);
|
|
|
|
// Verify result.
|
|
let size = out.size();
|
|
assert.equal(out.channels(), 3);
|
|
assert.equal(size.height, 30);
|
|
assert.equal(size.width, 5);
|
|
assert.equal(out.elemSize1(), 1);
|
|
|
|
cv.hconcat(input, out);
|
|
|
|
// Verify result.
|
|
size = out.size();
|
|
assert.equal(out.channels(), 3);
|
|
assert.equal(size.height, 10);
|
|
assert.equal(size.width, 15);
|
|
assert.equal(out.elemSize1(), 1);
|
|
|
|
input.delete();
|
|
out.delete();
|
|
}
|
|
|
|
|
|
// distanceTransform letiants
|
|
{
|
|
let mat = cv.Mat.ones(11, 11, cv.CV_8UC1);
|
|
let out = new cv.Mat(11, 11, cv.CV_32FC1);
|
|
let labels = new cv.Mat(11, 11, cv.CV_32FC1);
|
|
const maskSize = 3;
|
|
cv.distanceTransform(mat, out, cv.DIST_L2, maskSize, cv.CV_32F);
|
|
|
|
// Verify result.
|
|
let size = out.size();
|
|
assert.equal(out.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(out.elemSize1(), 4);
|
|
|
|
|
|
cv.distanceTransformWithLabels(mat, out, labels, cv.DIST_L2, maskSize,
|
|
cv.DIST_LABEL_CCOMP);
|
|
|
|
// Verify result.
|
|
size = out.size();
|
|
assert.equal(out.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(out.elemSize1(), 4);
|
|
|
|
size = labels.size();
|
|
assert.equal(labels.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(labels.elemSize1(), 4);
|
|
|
|
mat.delete();
|
|
out.delete();
|
|
labels.delete();
|
|
}
|
|
|
|
// Min, Max
|
|
{
|
|
let data1 = new Uint8Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
|
|
let data2 = new Uint8Array([0, 4, 0, 8, 0, 12, 0, 16, 0]);
|
|
|
|
let expectedMin = new Uint8Array([0, 2, 0, 4, 0, 6, 0, 8, 0]);
|
|
let expectedMax = new Uint8Array([1, 4, 3, 8, 5, 12, 7, 16, 9]);
|
|
|
|
let dataPtr = cv._malloc(3*3*1);
|
|
let dataPtr2 = cv._malloc(3*3*1);
|
|
|
|
let dataHeap = new Uint8Array(cv.HEAPU8.buffer, dataPtr, 3*3*1);
|
|
dataHeap.set(new Uint8Array(data1.buffer));
|
|
|
|
let dataHeap2 = new Uint8Array(cv.HEAPU8.buffer, dataPtr2, 3*3*1);
|
|
dataHeap2.set(new Uint8Array(data2.buffer));
|
|
|
|
|
|
let mat1 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr, 0);
|
|
let mat2 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr2, 0);
|
|
|
|
let mat3 = new cv.Mat();
|
|
|
|
cv.min(mat1, mat2, mat3);
|
|
// Verify result.
|
|
let size = mat2.size();
|
|
assert.equal(mat2.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(mat3.data, expectedMin);
|
|
|
|
|
|
cv.max(mat1, mat2, mat3);
|
|
// Verify result.
|
|
size = mat2.size();
|
|
assert.equal(mat2.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(mat3.data, expectedMax);
|
|
|
|
cv._free(dataPtr);
|
|
cv._free(dataPtr2);
|
|
}
|
|
|
|
// Bitwise operations
|
|
{
|
|
let data1 = new Uint8Array([0, 1, 2, 4, 8, 16, 32, 64, 128]);
|
|
let data2 = new Uint8Array([255, 255, 255, 255, 255, 255, 255, 255, 255]);
|
|
|
|
let expectedAnd = new Uint8Array([0, 1, 2, 4, 8, 16, 32, 64, 128]);
|
|
let expectedOr = new Uint8Array([255, 255, 255, 255, 255, 255, 255, 255, 255]);
|
|
let expectedXor = new Uint8Array([255, 254, 253, 251, 247, 239, 223, 191, 127]);
|
|
|
|
let expectedNot = new Uint8Array([255, 254, 253, 251, 247, 239, 223, 191, 127]);
|
|
|
|
let dataPtr = cv._malloc(3*3*1);
|
|
let dataPtr2 = cv._malloc(3*3*1);
|
|
|
|
let dataHeap = new Uint8Array(cv.HEAPU8.buffer, dataPtr, 3*3*1);
|
|
dataHeap.set(new Uint8Array(data1.buffer));
|
|
|
|
let dataHeap2 = new Uint8Array(cv.HEAPU8.buffer, dataPtr2, 3*3*1);
|
|
dataHeap2.set(new Uint8Array(data2.buffer));
|
|
|
|
|
|
let mat1 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr, 0);
|
|
let mat2 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr2, 0);
|
|
|
|
let mat3 = new cv.Mat();
|
|
let none = new cv.Mat();
|
|
|
|
cv.bitwise_not(mat1, mat3, none);
|
|
// Verify result.
|
|
let size = mat3.size();
|
|
assert.equal(mat3.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(mat3.data, expectedNot);
|
|
|
|
cv.bitwise_and(mat1, mat2, mat3, none);
|
|
// Verify result.
|
|
size = mat3.size();
|
|
assert.equal(mat3.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(mat3.data, expectedAnd);
|
|
|
|
|
|
cv.bitwise_or(mat1, mat2, mat3, none);
|
|
// Verify result.
|
|
size = mat3.size();
|
|
assert.equal(mat3.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(mat3.data, expectedOr);
|
|
|
|
cv.bitwise_xor(mat1, mat2, mat3, none);
|
|
// Verify result.
|
|
size = mat3.size();
|
|
assert.equal(mat3.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(mat3.data, expectedXor);
|
|
|
|
cv._free(dataPtr);
|
|
cv._free(dataPtr2);
|
|
}
|
|
|
|
// Arithmetic operations
|
|
{
|
|
let data1 = new Uint8Array([0, 1, 2, 3, 4, 5, 6, 7, 8]);
|
|
let data2 = new Uint8Array([0, 2, 4, 6, 8, 10, 12, 14, 16]);
|
|
let data3 = new Uint8Array([0, 1, 0, 1, 0, 1, 0, 1, 0]);
|
|
|
|
// |data1 - data2|
|
|
let expectedAbsDiff = new Uint8Array([0, 1, 2, 3, 4, 5, 6, 7, 8]);
|
|
let expectedAdd = new Uint8Array([0, 3, 6, 9, 12, 15, 18, 21, 24]);
|
|
|
|
const alpha = 4;
|
|
const beta = -1;
|
|
const gamma = 3;
|
|
// 4*data1 - data2 + 3
|
|
let expectedWeightedAdd = new Uint8Array([3, 5, 7, 9, 11, 13, 15, 17, 19]);
|
|
|
|
let dataPtr = cv._malloc(3*3*1);
|
|
let dataPtr2 = cv._malloc(3*3*1);
|
|
let dataPtr3 = cv._malloc(3*3*1);
|
|
|
|
let dataHeap = new Uint8Array(cv.HEAPU8.buffer, dataPtr, 3*3*1);
|
|
dataHeap.set(new Uint8Array(data1.buffer));
|
|
let dataHeap2 = new Uint8Array(cv.HEAPU8.buffer, dataPtr2, 3*3*1);
|
|
dataHeap2.set(new Uint8Array(data2.buffer));
|
|
let dataHeap3 = new Uint8Array(cv.HEAPU8.buffer, dataPtr3, 3*3*1);
|
|
dataHeap3.set(new Uint8Array(data3.buffer));
|
|
|
|
let mat1 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr, 0);
|
|
let mat2 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr2, 0);
|
|
let mat3 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr3, 0);
|
|
|
|
let dst = new cv.Mat();
|
|
let none = new cv.Mat();
|
|
|
|
cv.absdiff(mat1, mat2, dst);
|
|
// Verify result.
|
|
let size = dst.size();
|
|
assert.equal(dst.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(dst.data, expectedAbsDiff);
|
|
|
|
cv.add(mat1, mat2, dst, none, -1);
|
|
// Verify result.
|
|
size = dst.size();
|
|
assert.equal(dst.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(dst.data, expectedAdd);
|
|
|
|
cv.addWeighted(mat1, alpha, mat2, beta, gamma, dst, -1);
|
|
// Verify result.
|
|
size = dst.size();
|
|
assert.equal(dst.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(dst.data, expectedWeightedAdd);
|
|
|
|
// default parameter
|
|
cv.addWeighted(mat1, alpha, mat2, beta, gamma, dst);
|
|
// Verify result.
|
|
size = dst.size();
|
|
assert.equal(dst.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
|
|
assert.deepEqual(dst.data, expectedWeightedAdd);
|
|
|
|
mat1.delete();
|
|
mat2.delete();
|
|
mat3.delete();
|
|
dst.delete();
|
|
none.delete();
|
|
}
|
|
|
|
// Integral letiants
|
|
{
|
|
let mat = cv.Mat.eye({height: 100, width: 100}, cv.CV_8UC3);
|
|
let sum = new cv.Mat();
|
|
let sqSum = new cv.Mat();
|
|
let title = new cv.Mat();
|
|
|
|
cv.integral(mat, sum, -1);
|
|
|
|
// Verify result.
|
|
let size = sum.size();
|
|
assert.equal(sum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
cv.integral2(mat, sum, sqSum, -1, -1);
|
|
// Verify result.
|
|
size = sum.size();
|
|
assert.equal(sum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
size = sqSum.size();
|
|
assert.equal(sqSum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
mat.delete();
|
|
sum.delete();
|
|
sqSum.delete();
|
|
title.delete();
|
|
}
|
|
|
|
// Mean, meanSTDev
|
|
{
|
|
let mat = cv.Mat.eye({height: 100, width: 100}, cv.CV_8UC3);
|
|
let sum = new cv.Mat();
|
|
let sqSum = new cv.Mat();
|
|
let title = new cv.Mat();
|
|
|
|
cv.integral(mat, sum, -1);
|
|
|
|
// Verify result.
|
|
let size = sum.size();
|
|
assert.equal(sum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
cv.integral2(mat, sum, sqSum, -1, -1);
|
|
// Verify result.
|
|
size = sum.size();
|
|
assert.equal(sum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
size = sqSum.size();
|
|
assert.equal(sqSum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
mat.delete();
|
|
sum.delete();
|
|
sqSum.delete();
|
|
title.delete();
|
|
}
|
|
|
|
// Invert
|
|
{
|
|
let inv1 = new cv.Mat();
|
|
let inv2 = new cv.Mat();
|
|
let inv3 = new cv.Mat();
|
|
let inv4 = new cv.Mat();
|
|
|
|
|
|
let data1 = new Float32Array([1, 0, 0,
|
|
0, 1, 0,
|
|
0, 0, 1]);
|
|
let data2 = new Float32Array([0, 0, 0,
|
|
0, 5, 0,
|
|
0, 0, 0]);
|
|
let data3 = new Float32Array([1, 1, 1, 0,
|
|
0, 3, 1, 2,
|
|
2, 3, 1, 0,
|
|
1, 0, 2, 1]);
|
|
let data4 = new Float32Array([1, 4, 5,
|
|
4, 2, 2,
|
|
5, 2, 2]);
|
|
|
|
let expected1 = new Float32Array([1, 0, 0,
|
|
0, 1, 0,
|
|
0, 0, 1]);
|
|
// Inverse does not exist!
|
|
let expected3 = new Float32Array([-3, -1/2, 3/2, 1,
|
|
1, 1/4, -1/4, -1/2,
|
|
3, 1/4, -5/4, -1/2,
|
|
-3, 0, 1, 1]);
|
|
let expected4 = new Float32Array([0, -1, 1,
|
|
-1, 23/2, -9,
|
|
1, -9, 7]);
|
|
|
|
let dataPtr1 = cv._malloc(3*3*4);
|
|
let dataPtr2 = cv._malloc(3*3*4);
|
|
let dataPtr3 = cv._malloc(4*4*4);
|
|
let dataPtr4 = cv._malloc(3*3*4);
|
|
|
|
let dataHeap = new Float32Array(cv.HEAP32.buffer, dataPtr1, 3*3);
|
|
dataHeap.set(new Float32Array(data1.buffer));
|
|
let dataHeap2 = new Float32Array(cv.HEAP32.buffer, dataPtr2, 3*3);
|
|
dataHeap2.set(new Float32Array(data2.buffer));
|
|
let dataHeap3 = new Float32Array(cv.HEAP32.buffer, dataPtr3, 4*4);
|
|
dataHeap3.set(new Float32Array(data3.buffer));
|
|
let dataHeap4 = new Float32Array(cv.HEAP32.buffer, dataPtr4, 3*3);
|
|
dataHeap4.set(new Float32Array(data4.buffer));
|
|
|
|
let mat1 = new cv.Mat(3, 3, cv.CV_32FC1, dataPtr1, 0);
|
|
let mat2 = new cv.Mat(3, 3, cv.CV_32FC1, dataPtr2, 0);
|
|
let mat3 = new cv.Mat(4, 4, cv.CV_32FC1, dataPtr3, 0);
|
|
let mat4 = new cv.Mat(3, 3, cv.CV_32FC1, dataPtr4, 0);
|
|
|
|
QUnit.assert.deepEqualWithTolerance = function( value, expected, tolerance ) {
|
|
for (let i = 0; i < value.length; i= i+1) {
|
|
this.pushResult( {
|
|
result: Math.abs(value[i]-expected[i]) < tolerance,
|
|
actual: value[i],
|
|
expected: expected[i],
|
|
} );
|
|
}
|
|
};
|
|
|
|
cv.invert(mat1, inv1, 0);
|
|
// Verify result.
|
|
let size = inv1.size();
|
|
assert.equal(inv1.channels(), 1);
|
|
assert.equal(size.height, 3);
|
|
assert.equal(size.width, 3);
|
|
assert.deepEqualWithTolerance(inv1.data32F, expected1, 0.0001);
|
|
|
|
|
|
cv.invert(mat2, inv2, 0);
|
|
// Verify result.
|
|
assert.deepEqualWithTolerance(inv3.data32F, expected3, 0.0001);
|
|
|
|
cv.invert(mat3, inv3, 0);
|
|
// Verify result.
|
|
size = inv3.size();
|
|
assert.equal(inv3.channels(), 1);
|
|
assert.equal(size.height, 4);
|
|
assert.equal(size.width, 4);
|
|
assert.deepEqualWithTolerance(inv3.data32F, expected3, 0.0001);
|
|
|
|
cv.invert(mat3, inv3, 1);
|
|
// Verify result.
|
|
assert.deepEqualWithTolerance(inv3.data32F, expected3, 0.0001);
|
|
|
|
cv.invert(mat4, inv4, 2);
|
|
// Verify result.
|
|
assert.deepEqualWithTolerance(inv4.data32F, expected4, 0.0001);
|
|
|
|
cv.invert(mat4, inv4, 3);
|
|
// Verify result.
|
|
assert.deepEqualWithTolerance(inv4.data32F, expected4, 0.0001);
|
|
|
|
mat1.delete();
|
|
mat2.delete();
|
|
mat3.delete();
|
|
mat4.delete();
|
|
inv1.delete();
|
|
inv2.delete();
|
|
inv3.delete();
|
|
inv4.delete();
|
|
}
|
|
//Rotate
|
|
{
|
|
let dst = new cv.Mat();
|
|
let src = cv.matFromArray(3, 2, cv.CV_8U, [1,2,3,4,5,6]);
|
|
|
|
cv.rotate(src, dst, cv.ROTATE_90_CLOCKWISE);
|
|
|
|
let size = dst.size();
|
|
assert.equal(size.height, 2, "ROTATE_HEIGHT");
|
|
assert.equal(size.width, 3, "ROTATE_WIGTH");
|
|
|
|
let expected = new Uint8Array([5,3,1,6,4,2]);
|
|
|
|
assert.deepEqual(dst.data, expected);
|
|
|
|
dst.delete();
|
|
src.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('warpPolar', function(assert) {
|
|
const lines = new cv.Mat(255, 255, cv.CV_8U, new cv.Scalar(0));
|
|
for (let r = 0; r < lines.rows; r++) {
|
|
lines.row(r).setTo(new cv.Scalar(r));
|
|
}
|
|
cv.warpPolar(lines, lines, { width: 5, height: 5 }, new cv.Point(2, 2), 3,
|
|
cv.INTER_CUBIC | cv.WARP_FILL_OUTLIERS | cv.WARP_INVERSE_MAP);
|
|
assert.ok(lines instanceof cv.Mat);
|
|
assert.deepEqual(Array.from(lines.data), [
|
|
159, 172, 191, 210, 223,
|
|
146, 159, 191, 223, 236,
|
|
128, 128, 0, 0, 0,
|
|
109, 96, 64, 32, 19,
|
|
96, 83, 64, 45, 32
|
|
]);
|
|
});
|
|
|
|
|
|
QUnit.test('IntelligentScissorsMB', function(assert) {
|
|
const lines = new cv.Mat(50, 100, cv.CV_8U, new cv.Scalar(0));
|
|
lines.row(10).setTo(new cv.Scalar(255));
|
|
assert.ok(lines instanceof cv.Mat);
|
|
|
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let tool = new cv.segmentation_IntelligentScissorsMB();
|
|
tool.applyImage(lines);
|
|
assert.ok(lines instanceof cv.Mat);
|
|
lines.delete();
|
|
|
|
tool.buildMap(new cv.Point(10, 10));
|
|
|
|
let contour = new cv.Mat();
|
|
tool.getContour(new cv.Point(50, 10), contour);
|
|
assert.equal(contour.type(), cv.CV_32SC2);
|
|
assert.ok(contour.total() == 41, contour.total());
|
|
|
|
tool.getContour(new cv.Point(80, 10), contour);
|
|
assert.equal(contour.type(), cv.CV_32SC2);
|
|
assert.ok(contour.total() == 71, contour.total());
|
|
});
|