vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b
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// //////////////////////////////////////////////////////////////////////////////////////
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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
|
||||
// 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
|
||||
// names of its contributors may be used to endorse or promote products
|
||||
// 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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QUnit.module('Image Processing', {});
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QUnit.test('applyColorMap', function(assert) {
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{
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let src = cv.matFromArray(2, 1, cv.CV_8U, [50,100]);
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cv.applyColorMap(src, src, cv.COLORMAP_BONE);
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// Verify result.
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let expected = new Uint8Array([60,44,44,119,89,87]);
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assert.deepEqual(src.data, expected);
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src.delete();
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}
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});
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QUnit.test('blendLinear', function(assert) {
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{
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let src1 = cv.matFromArray(2, 1, cv.CV_8U, [50,100]);
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let src2 = cv.matFromArray(2, 1, cv.CV_8U, [200,20]);
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let weights1 = cv.matFromArray(2, 1, cv.CV_32F, [0.4,0.5]);
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let weights2 = cv.matFromArray(2, 1, cv.CV_32F, [0.6,0.5]);
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let dst = new cv.Mat();
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cv.blendLinear(src1, src2, weights1, weights2, dst);
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// Verify result.
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let expected = new Uint8Array([140,60]);
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assert.deepEqual(dst.data, expected);
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src1.delete();
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src2.delete();
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weights1.delete();
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weights2.delete();
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dst.delete();
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}
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});
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QUnit.test('createHanningWindow', function(assert) {
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{
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let dst = new cv.Mat();
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cv.createHanningWindow(dst, new cv.Size(5, 3), cv.CV_32F);
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// Verify result.
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let expected = cv.matFromArray(3, 5, cv.CV_32F, [0.,0.,0.,0.,0.,0.,0.70710677,1.,0.70710677,0.,0.,0.,0.,0.,0.]);
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assert.deepEqual(dst.data, expected.data);
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dst.delete();
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expected.delete();
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}
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});
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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, 1);
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assert.equal(size.width, 256);
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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, 1);
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assert.equal(size.width, 256);
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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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});
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QUnit.test('Drawing Functions', function(assert) {
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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);
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_shape', function(assert) {
|
||||
// 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);
|
||||
assert.equal(area, 0);
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||||
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||||
// default parameters
|
||||
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);
|
||||
assert.equal(area, 0);
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||||
|
||||
points.delete();
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_min_enclosing', function(assert) {
|
||||
// minEnclosingCircle
|
||||
{
|
||||
let points = new cv.Mat(4, 1, cv.CV_32FC2);
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||||
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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;
|
||||
points.data32F[4] = 1;
|
||||
points.data32F[5] = 1;
|
||||
points.data32F[6] = 0;
|
||||
points.data32F[7] = 1;
|
||||
|
||||
let circle = cv.minEnclosingCircle(points);
|
||||
|
||||
assert.deepEqual(circle.center, {x: 0.5, y: 0.5});
|
||||
assert.ok(Math.abs(circle.radius - Math.sqrt(2) / 2) < 0.001);
|
||||
|
||||
points.delete();
|
||||
}
|
||||
|
||||
// minEnclosingTriangle
|
||||
{
|
||||
let dst = cv.Mat.zeros(80, 80, cv.CV_8U);
|
||||
let contours = new cv.MatVector();
|
||||
let hierarchy = new cv.Mat();
|
||||
let triangle = new cv.Mat();
|
||||
|
||||
cv.drawMarker(dst, new cv.Point(40, 40), new cv.Scalar(255));
|
||||
cv.findContoursLinkRuns(dst,contours,hierarchy);
|
||||
cv.minEnclosingTriangle(contours.get(0),triangle);
|
||||
|
||||
// Verify result.
|
||||
const triangleData = triangle.data32F;
|
||||
assert.deepEqual(triangleData[0], triangleData[4]);
|
||||
assert.deepEqual(triangleData[1], 20);
|
||||
assert.deepEqual(triangleData[2], 30);
|
||||
assert.deepEqual(triangleData[3], 40);
|
||||
assert.deepEqual(triangleData[5], 60);
|
||||
|
||||
dst.delete();
|
||||
contours.delete();
|
||||
hierarchy.delete();
|
||||
triangle.delete();
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_filter', function(assert) {
|
||||
// blur
|
||||
{
|
||||
let mat1 = cv.Mat.ones(5, 5, cv.CV_8UC3);
|
||||
let mat2 = new cv.Mat();
|
||||
|
||||
cv.blur(mat1, mat2, {height: 3, width: 3}, {x: -1, y: -1}, cv.BORDER_DEFAULT);
|
||||
|
||||
// Verify result.
|
||||
let size = mat2.size();
|
||||
assert.equal(mat2.channels(), 3);
|
||||
assert.equal(size.height, 5);
|
||||
assert.equal(size.width, 5);
|
||||
|
||||
cv.blur(mat1, mat2, {height: 3, width: 3}, {x: -1, y: -1});
|
||||
|
||||
// Verify result.
|
||||
size = mat2.size();
|
||||
assert.equal(mat2.channels(), 3);
|
||||
assert.equal(size.height, 5);
|
||||
assert.equal(size.width, 5);
|
||||
|
||||
cv.blur(mat1, mat2, {height: 3, width: 3});
|
||||
|
||||
// Verify result.
|
||||
size = mat2.size();
|
||||
assert.equal(mat2.channels(), 3);
|
||||
assert.equal(size.height, 5);
|
||||
assert.equal(size.width, 5);
|
||||
|
||||
mat1.delete();
|
||||
mat2.delete();
|
||||
}
|
||||
|
||||
// GaussianBlur
|
||||
{
|
||||
let mat1 = cv.Mat.ones(7, 7, cv.CV_8UC1);
|
||||
let mat2 = new cv.Mat();
|
||||
|
||||
cv.GaussianBlur(mat1, mat2, new cv.Size(3, 3), 0, 0, // eslint-disable-line new-cap
|
||||
cv.BORDER_DEFAULT);
|
||||
|
||||
// Verify result.
|
||||
let size = mat2.size();
|
||||
assert.equal(mat2.channels(), 1);
|
||||
assert.equal(size.height, 7);
|
||||
assert.equal(size.width, 7);
|
||||
mat1.delete();
|
||||
mat2.delete();
|
||||
}
|
||||
|
||||
// spatialGradient
|
||||
{
|
||||
let src = cv.matFromArray(4, 4, cv.CV_8U, [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]);
|
||||
let dx = new cv.Mat();
|
||||
let dy = new cv.Mat();
|
||||
cv.spatialGradient(src, dx, dy);
|
||||
|
||||
// Verify result.
|
||||
let expected_dx = new cv.Mat();
|
||||
let expected_dy = new cv.Mat();
|
||||
cv.Sobel(src, expected_dx, cv.CV_16SC1, 1, 0, 3);
|
||||
cv.Sobel(src, expected_dy, cv.CV_16SC1, 0, 1, 3);
|
||||
|
||||
assert.deepEqual(dx.data, expected_dx.data);
|
||||
assert.deepEqual(dy.data, expected_dy.data);
|
||||
|
||||
src.delete();
|
||||
dx.delete();
|
||||
dy.delete();
|
||||
expected_dx.delete();
|
||||
expected_dy.delete();
|
||||
}
|
||||
|
||||
// sqrBoxFilter
|
||||
{
|
||||
let src = cv.matFromArray(2, 3, cv.CV_8U, [1,2,1,1,2,1]);
|
||||
let dst = new cv.Mat();
|
||||
cv.sqrBoxFilter(src, dst, cv.CV_32F, new cv.Size(3, 3));
|
||||
|
||||
// Verify result.
|
||||
let expected = cv.matFromArray(2, 3, cv.CV_32F,[3.0,2.0,3.0,3.0,2.0,3.0]);
|
||||
|
||||
assert.deepEqual(dst.data, expected.data);
|
||||
src.delete();
|
||||
dst.delete();
|
||||
expected.delete();
|
||||
}
|
||||
|
||||
// stackBlur
|
||||
{
|
||||
let src = cv.matFromArray(2, 3, cv.CV_8U, [10,25,30,45,50,60]);
|
||||
cv.stackBlur(src, src, new cv.Size(3, 3));
|
||||
|
||||
// Verify result.
|
||||
let expected = new Uint8Array([14,22,29,46,51,58]);
|
||||
|
||||
assert.deepEqual(src.data, expected);
|
||||
src.delete();
|
||||
}
|
||||
|
||||
// medianBlur
|
||||
{
|
||||
let mat1 = cv.Mat.ones(9, 9, cv.CV_8UC3);
|
||||
let mat2 = new cv.Mat();
|
||||
|
||||
cv.medianBlur(mat1, mat2, 3);
|
||||
|
||||
// Verify result.
|
||||
let size = mat2.size();
|
||||
|
||||
assert.equal(mat2.channels(), 3);
|
||||
assert.equal(size.height, 9);
|
||||
assert.equal(size.width, 9);
|
||||
mat1.delete();
|
||||
mat2.delete();
|
||||
}
|
||||
|
||||
// bilateralFilter
|
||||
{
|
||||
let mat1 = cv.Mat.ones(11, 11, cv.CV_8UC3);
|
||||
let mat2 = new cv.Mat();
|
||||
|
||||
cv.bilateralFilter(mat1, mat2, 3, 6, 1.5, cv.BORDER_DEFAULT);
|
||||
|
||||
// Verify result.
|
||||
let size = mat2.size();
|
||||
assert.equal(mat2.channels(), 3);
|
||||
assert.equal(size.height, 11);
|
||||
assert.equal(size.width, 11);
|
||||
|
||||
// default parameters
|
||||
cv.bilateralFilter(mat1, mat2, 3, 6, 1.5);
|
||||
// Verify result.
|
||||
size = mat2.size();
|
||||
assert.equal(mat2.channels(), 3);
|
||||
assert.equal(size.height, 11);
|
||||
assert.equal(size.width, 11);
|
||||
|
||||
mat1.delete();
|
||||
mat2.delete();
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_watershed', function(assert) {
|
||||
{
|
||||
let mat = cv.Mat.ones(11, 11, cv.CV_8UC3);
|
||||
let out = new cv.Mat(11, 11, cv.CV_32SC1);
|
||||
|
||||
cv.watershed(mat, out);
|
||||
|
||||
// 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();
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_distanceTransform', function(assert) {
|
||||
{
|
||||
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();
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_integral', function(assert) {
|
||||
{
|
||||
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();
|
||||
}
|
||||
});
|
||||
|
||||
QUnit.test('test_rotatedRectangleIntersection', function(assert) {
|
||||
{
|
||||
let dst = cv.Mat.zeros(80, 80, cv.CV_8U);
|
||||
let contours = new cv.MatVector();
|
||||
let hierarchy = new cv.Mat();
|
||||
let intersectionPoints = new cv.Mat();
|
||||
|
||||
cv.drawMarker(dst, new cv.Point(40, 40), new cv.Scalar(255));
|
||||
cv.findContoursLinkRuns(dst,contours,hierarchy);
|
||||
let rr1 = cv.minAreaRect(contours.get(0));
|
||||
let rr2 = cv.minAreaRect(contours.get(0));
|
||||
let rr3 = new cv.RotatedRect({x: 40, y: 40}, {height: 10, width: 20}, 45);
|
||||
|
||||
let intersectionType = cv.rotatedRectangleIntersection(rr1, rr2, intersectionPoints);
|
||||
|
||||
// Verify result.
|
||||
assert.deepEqual(intersectionType, cv.INTERSECT_FULL);
|
||||
intersectionPoints.convertTo(intersectionPoints, cv.CV_32S);
|
||||
let intersectionPointsData = intersectionPoints.data32S;
|
||||
assert.deepEqual(intersectionPointsData[0], 40);
|
||||
assert.deepEqual(intersectionPointsData[1], 50);
|
||||
assert.deepEqual(intersectionPointsData[2], 30);
|
||||
assert.deepEqual(intersectionPointsData[3], 40);
|
||||
assert.deepEqual(intersectionPointsData[4], 40);
|
||||
assert.deepEqual(intersectionPointsData[5], 30);
|
||||
assert.deepEqual(intersectionPointsData[6], 50);
|
||||
assert.deepEqual(intersectionPointsData[7], 40);
|
||||
|
||||
intersectionType = cv.rotatedRectangleIntersection(rr1, rr3, intersectionPoints);
|
||||
|
||||
// Verify result.
|
||||
assert.deepEqual(intersectionType, cv.INTERSECT_PARTIAL);
|
||||
intersectionPoints.convertTo(intersectionPoints, cv.CV_32S);
|
||||
intersectionPointsData = intersectionPoints.data32S;
|
||||
assert.deepEqual(intersectionPointsData[0], 39);
|
||||
assert.deepEqual(intersectionPointsData[1], 31);
|
||||
assert.deepEqual(intersectionPointsData[2], 49);
|
||||
assert.deepEqual(intersectionPointsData[3], 41);
|
||||
assert.deepEqual(intersectionPointsData[4], 41);
|
||||
assert.deepEqual(intersectionPointsData[5], 49);
|
||||
assert.deepEqual(intersectionPointsData[6], 31);
|
||||
assert.deepEqual(intersectionPointsData[7], 39);
|
||||
|
||||
dst.delete();
|
||||
contours.delete();
|
||||
hierarchy.delete();
|
||||
intersectionPoints.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);
|
||||
|
||||
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());
|
||||
});
|
||||
Reference in New Issue
Block a user