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Open Source Computer Vision Library
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804 lines
31 KiB
804 lines
31 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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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) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., 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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//M*/ |
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#include "test_precomp.hpp" |
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namespace opencv_test { |
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namespace { |
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class CV_ConnectedComponentsTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_ConnectedComponentsTest(); |
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~CV_ConnectedComponentsTest(); |
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protected: |
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void run(int); |
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}; |
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CV_ConnectedComponentsTest::CV_ConnectedComponentsTest() {} |
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CV_ConnectedComponentsTest::~CV_ConnectedComponentsTest() {} |
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// This function force a row major order for the labels |
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void normalizeLabels(Mat1i& imgLabels, int iNumLabels) { |
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vector<int> vecNewLabels(iNumLabels + 1, 0); |
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int iMaxNewLabel = 0; |
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for (int r = 0; r < imgLabels.rows; ++r) { |
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for (int c = 0; c < imgLabels.cols; ++c) { |
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int iCurLabel = imgLabels(r, c); |
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if (iCurLabel > 0) { |
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if (vecNewLabels[iCurLabel] == 0) { |
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vecNewLabels[iCurLabel] = ++iMaxNewLabel; |
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} |
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imgLabels(r, c) = vecNewLabels[iCurLabel]; |
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} |
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} |
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} |
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} |
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void CV_ConnectedComponentsTest::run(int /* start_from */) |
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{ |
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int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
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string exp_path = string(ts->get_data_path()) + "connectedcomponents/ccomp_exp.png"; |
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Mat exp = imread(exp_path, IMREAD_GRAYSCALE); |
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Mat orig = imread(string(ts->get_data_path()) + "connectedcomponents/concentric_circles.png", 0); |
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if (orig.empty()) |
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{ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return; |
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} |
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Mat bw = orig > 128; |
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for (uint cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) |
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{ |
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Mat1i labelImage; |
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int nLabels = connectedComponents(bw, labelImage, 8, CV_32S, ccltype[cclt]); |
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normalizeLabels(labelImage, nLabels); |
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// Validate test results |
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for (int r = 0; r < labelImage.rows; ++r) { |
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for (int c = 0; c < labelImage.cols; ++c) { |
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int l = labelImage.at<int>(r, c); |
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bool pass = l >= 0 && l <= nLabels; |
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if (!pass) { |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return; |
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} |
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} |
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} |
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if (exp.empty() || orig.size() != exp.size()) |
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{ |
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imwrite(exp_path, labelImage); |
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exp = labelImage; |
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} |
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if (0 != cvtest::norm(labelImage > 0, exp > 0, NORM_INF)) |
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{ |
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); |
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return; |
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} |
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if (nLabels != cvtest::norm(labelImage, NORM_INF) + 1) |
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{ |
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); |
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return; |
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} |
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} |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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TEST(Imgproc_ConnectedComponents, regression) { CV_ConnectedComponentsTest test; test.safe_run(); } |
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TEST(Imgproc_ConnectedComponents, grana_buffer_overflow) |
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{ |
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cv::Mat darkMask; |
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darkMask.create(31, 87, CV_8U); |
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darkMask = 0; |
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cv::Mat labels; |
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cv::Mat stats; |
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cv::Mat centroids; |
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int nbComponents = cv::connectedComponentsWithStats(darkMask, labels, stats, centroids, 8, CV_32S, cv::CCL_GRANA); |
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EXPECT_EQ(1, nbComponents); |
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} |
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static cv::Mat createCrashMat(int numThreads) { |
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const int h = numThreads * 4 * 2 + 8; |
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const double nParallelStripes = std::max(1, std::min(h / 2, numThreads * 4)); |
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const int w = 4; |
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const int nstripes = cvRound(nParallelStripes <= 0 ? h : MIN(MAX(nParallelStripes, 1.), h)); |
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const cv::Range stripeRange(0, nstripes); |
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const cv::Range wholeRange(0, h); |
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cv::Mat m(h, w, CV_8U); |
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m = 0; |
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// Look for a range that starts with odd value and ends with even value |
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cv::Range bugRange; |
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for (int s = stripeRange.start; s < stripeRange.end; s++) { |
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cv::Range sr(s, s + 1); |
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cv::Range r; |
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r.start = (int)(wholeRange.start + |
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((uint64)sr.start * (wholeRange.end - wholeRange.start) + nstripes / 2) / nstripes); |
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r.end = sr.end >= nstripes ? |
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wholeRange.end : |
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(int)(wholeRange.start + |
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((uint64)sr.end * (wholeRange.end - wholeRange.start) + nstripes / 2) / nstripes); |
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if (r.start > 0 && r.start % 2 == 1 && r.end % 2 == 0 && r.end >= r.start + 2) { |
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bugRange = r; |
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break; |
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} |
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} |
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if (bugRange.empty()) { // Could not create a buggy range |
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return m; |
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} |
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// Fill in bug Range |
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for (int x = 1; x < w; x++) { |
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m.at<char>(bugRange.start - 1, x) = 1; |
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} |
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m.at<char>(bugRange.start + 0, 0) = 1; |
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m.at<char>(bugRange.start + 0, 1) = 1; |
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m.at<char>(bugRange.start + 0, 3) = 1; |
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m.at<char>(bugRange.start + 1, 1) = 1; |
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m.at<char>(bugRange.start + 2, 1) = 1; |
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m.at<char>(bugRange.start + 2, 3) = 1; |
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m.at<char>(bugRange.start + 3, 0) = 1; |
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m.at<char>(bugRange.start + 3, 1) = 1; |
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return m; |
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} |
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TEST(Imgproc_ConnectedComponents, parallel_wu_labels) |
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{ |
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cv::Mat mat = createCrashMat(cv::getNumThreads()); |
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if (mat.empty()) { |
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return; |
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} |
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const int nbPixels = cv::countNonZero(mat); |
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cv::Mat labels; |
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cv::Mat stats; |
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cv::Mat centroids; |
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int nb = 0; |
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EXPECT_NO_THROW(nb = cv::connectedComponentsWithStats(mat, labels, stats, centroids, 8, CV_32S, cv::CCL_WU)); |
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int area = 0; |
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for (int i = 1; i < nb; ++i) { |
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area += stats.at<int32_t>(i, cv::CC_STAT_AREA); |
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} |
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EXPECT_EQ(nbPixels, area); |
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} |
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TEST(Imgproc_ConnectedComponents, missing_background_pixels) |
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{ |
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cv::Mat m = Mat::ones(10, 10, CV_8U); |
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cv::Mat labels; |
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cv::Mat stats; |
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cv::Mat centroids; |
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EXPECT_NO_THROW(cv::connectedComponentsWithStats(m, labels, stats, centroids, 8, CV_32S, cv::CCL_WU)); |
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EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_WIDTH), 0); |
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EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_HEIGHT), 0); |
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EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_LEFT), -1); |
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EXPECT_TRUE(std::isnan(centroids.at<double>(0, 0))); |
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EXPECT_TRUE(std::isnan(centroids.at<double>(0, 1))); |
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} |
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TEST(Imgproc_ConnectedComponents, spaghetti_bbdt_sauf_stats) |
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{ |
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cv::Mat1b img(16, 16); |
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img << 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, |
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0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, |
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0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, |
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0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, |
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0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, |
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0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, |
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0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, |
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0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, |
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0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, |
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1; |
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cv::Mat1i labels; |
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cv::Mat1i stats; |
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cv::Mat1d centroids; |
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int ccltype[] = { cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
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for (uint cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
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EXPECT_NO_THROW(cv::connectedComponentsWithStats(img, labels, stats, centroids, 8, CV_32S, ccltype[cclt])); |
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EXPECT_EQ(stats(0, cv::CC_STAT_LEFT), 0); |
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EXPECT_EQ(stats(0, cv::CC_STAT_TOP), 0); |
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EXPECT_EQ(stats(0, cv::CC_STAT_WIDTH), 16); |
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EXPECT_EQ(stats(0, cv::CC_STAT_HEIGHT), 15); |
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EXPECT_EQ(stats(0, cv::CC_STAT_AREA), 144); |
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EXPECT_EQ(stats(1, cv::CC_STAT_LEFT), 1); |
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EXPECT_EQ(stats(1, cv::CC_STAT_TOP), 1); |
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EXPECT_EQ(stats(1, cv::CC_STAT_WIDTH), 3); |
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EXPECT_EQ(stats(1, cv::CC_STAT_HEIGHT), 3); |
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EXPECT_EQ(stats(1, cv::CC_STAT_AREA), 9); |
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EXPECT_EQ(stats(2, cv::CC_STAT_LEFT), 1); |
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EXPECT_EQ(stats(2, cv::CC_STAT_TOP), 1); |
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EXPECT_EQ(stats(2, cv::CC_STAT_WIDTH), 8); |
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EXPECT_EQ(stats(2, cv::CC_STAT_HEIGHT), 7); |
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EXPECT_EQ(stats(2, cv::CC_STAT_AREA), 40); |
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EXPECT_EQ(stats(3, cv::CC_STAT_LEFT), 10); |
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EXPECT_EQ(stats(3, cv::CC_STAT_TOP), 2); |
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EXPECT_EQ(stats(3, cv::CC_STAT_WIDTH), 5); |
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EXPECT_EQ(stats(3, cv::CC_STAT_HEIGHT), 2); |
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EXPECT_EQ(stats(3, cv::CC_STAT_AREA), 8); |
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EXPECT_EQ(stats(4, cv::CC_STAT_LEFT), 11); |
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EXPECT_EQ(stats(4, cv::CC_STAT_TOP), 5); |
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EXPECT_EQ(stats(4, cv::CC_STAT_WIDTH), 3); |
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EXPECT_EQ(stats(4, cv::CC_STAT_HEIGHT), 3); |
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EXPECT_EQ(stats(4, cv::CC_STAT_AREA), 9); |
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EXPECT_EQ(stats(5, cv::CC_STAT_LEFT), 2); |
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EXPECT_EQ(stats(5, cv::CC_STAT_TOP), 9); |
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EXPECT_EQ(stats(5, cv::CC_STAT_WIDTH), 1); |
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EXPECT_EQ(stats(5, cv::CC_STAT_HEIGHT), 1); |
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EXPECT_EQ(stats(5, cv::CC_STAT_AREA), 1); |
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EXPECT_EQ(stats(6, cv::CC_STAT_LEFT), 12); |
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EXPECT_EQ(stats(6, cv::CC_STAT_TOP), 9); |
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EXPECT_EQ(stats(6, cv::CC_STAT_WIDTH), 1); |
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EXPECT_EQ(stats(6, cv::CC_STAT_HEIGHT), 1); |
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EXPECT_EQ(stats(6, cv::CC_STAT_AREA), 1); |
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// Labels' order could be different! |
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if (cclt == cv::CCL_WU || cclt == cv::CCL_SAUF) { |
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// CCL_SAUF, CCL_WU |
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EXPECT_EQ(stats(9, cv::CC_STAT_LEFT), 1); |
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EXPECT_EQ(stats(9, cv::CC_STAT_TOP), 11); |
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EXPECT_EQ(stats(9, cv::CC_STAT_WIDTH), 4); |
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EXPECT_EQ(stats(9, cv::CC_STAT_HEIGHT), 2); |
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EXPECT_EQ(stats(9, cv::CC_STAT_AREA), 8); |
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EXPECT_EQ(stats(7, cv::CC_STAT_LEFT), 6); |
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EXPECT_EQ(stats(7, cv::CC_STAT_TOP), 10); |
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EXPECT_EQ(stats(7, cv::CC_STAT_WIDTH), 4); |
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EXPECT_EQ(stats(7, cv::CC_STAT_HEIGHT), 2); |
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EXPECT_EQ(stats(7, cv::CC_STAT_AREA), 8); |
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EXPECT_EQ(stats(8, cv::CC_STAT_LEFT), 0); |
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EXPECT_EQ(stats(8, cv::CC_STAT_TOP), 10); |
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EXPECT_EQ(stats(8, cv::CC_STAT_WIDTH), 16); |
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EXPECT_EQ(stats(8, cv::CC_STAT_HEIGHT), 6); |
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EXPECT_EQ(stats(8, cv::CC_STAT_AREA), 21); |
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} |
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else { |
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// CCL_BBDT, CCL_GRANA, CCL_SPAGHETTI, CCL_BOLELLI |
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EXPECT_EQ(stats(7, cv::CC_STAT_LEFT), 1); |
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EXPECT_EQ(stats(7, cv::CC_STAT_TOP), 11); |
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EXPECT_EQ(stats(7, cv::CC_STAT_WIDTH), 4); |
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EXPECT_EQ(stats(7, cv::CC_STAT_HEIGHT), 2); |
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EXPECT_EQ(stats(7, cv::CC_STAT_AREA), 8); |
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EXPECT_EQ(stats(8, cv::CC_STAT_LEFT), 6); |
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EXPECT_EQ(stats(8, cv::CC_STAT_TOP), 10); |
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EXPECT_EQ(stats(8, cv::CC_STAT_WIDTH), 4); |
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EXPECT_EQ(stats(8, cv::CC_STAT_HEIGHT), 2); |
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EXPECT_EQ(stats(8, cv::CC_STAT_AREA), 8); |
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EXPECT_EQ(stats(9, cv::CC_STAT_LEFT), 0); |
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EXPECT_EQ(stats(9, cv::CC_STAT_TOP), 10); |
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EXPECT_EQ(stats(9, cv::CC_STAT_WIDTH), 16); |
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EXPECT_EQ(stats(9, cv::CC_STAT_HEIGHT), 6); |
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EXPECT_EQ(stats(9, cv::CC_STAT_AREA), 21); |
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} |
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EXPECT_EQ(stats(10, cv::CC_STAT_LEFT), 9); |
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EXPECT_EQ(stats(10, cv::CC_STAT_TOP), 12); |
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EXPECT_EQ(stats(10, cv::CC_STAT_WIDTH), 5); |
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EXPECT_EQ(stats(10, cv::CC_STAT_HEIGHT), 2); |
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EXPECT_EQ(stats(10, cv::CC_STAT_AREA), 7); |
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} |
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} |
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TEST(Imgproc_ConnectedComponents, chessboard_even) |
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{ |
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cv::Size size(16, 16); |
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cv::Mat1b input(size); |
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cv::Mat1i output_8c(size); |
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cv::Mat1i output_4c(size); |
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// Chessboard image with even number of rows and cols |
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// Note that this is the maximum number of labels for 4-way connectivity |
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{ |
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input << |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
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output_8c << |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
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0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
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output_4c << |
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1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, |
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0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, |
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17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0, |
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0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, |
|
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0, |
|
0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, |
|
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0, |
|
0, 57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64, |
|
65, 0, 66, 0, 67, 0, 68, 0, 69, 0, 70, 0, 71, 0, 72, 0, |
|
0, 73, 0, 74, 0, 75, 0, 76, 0, 77, 0, 78, 0, 79, 0, 80, |
|
81, 0, 82, 0, 83, 0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0, |
|
0, 89, 0, 90, 0, 91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96, |
|
97, 0, 98, 0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0, |
|
0, 105, 0, 106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112, |
|
113, 0, 114, 0, 115, 0, 116, 0, 117, 0, 118, 0, 119, 0, 120, 0, |
|
0, 121, 0, 122, 0, 123, 0, 124, 0, 125, 0, 126, 0, 127, 0, 128; |
|
} |
|
|
|
int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
|
|
|
cv::Mat1i labels; |
|
cv::Mat diff; |
|
int nLabels = 0; |
|
for (size_t cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 8, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_8c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
|
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 4, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_4c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
} |
|
|
|
} |
|
|
|
TEST(Imgproc_ConnectedComponents, chessboard_odd) |
|
{ |
|
cv::Size size(15, 15); |
|
cv::Mat1b input(size); |
|
cv::Mat1i output_8c(size); |
|
cv::Mat1i output_4c(size); |
|
|
|
// Chessboard image with odd number of rows and cols |
|
// Note that this is the maximum number of labels for 4-way connectivity |
|
{ |
|
input << |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
|
|
|
output_8c << |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
|
|
|
output_4c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, |
|
0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, |
|
16, 0, 17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, |
|
0, 24, 0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, |
|
31, 0, 32, 0, 33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, |
|
0, 39, 0, 40, 0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0, |
|
46, 0, 47, 0, 48, 0, 49, 0, 50, 0, 51, 0, 52, 0, 53, |
|
0, 54, 0, 55, 0, 56, 0, 57, 0, 58, 0, 59, 0, 60, 0, |
|
61, 0, 62, 0, 63, 0, 64, 0, 65, 0, 66, 0, 67, 0, 68, |
|
0, 69, 0, 70, 0, 71, 0, 72, 0, 73, 0, 74, 0, 75, 0, |
|
76, 0, 77, 0, 78, 0, 79, 0, 80, 0, 81, 0, 82, 0, 83, |
|
0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0, 89, 0, 90, 0, |
|
91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96, 0, 97, 0, 98, |
|
0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0, 105, 0, |
|
106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112, 0, 113; |
|
} |
|
|
|
int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
|
|
|
cv::Mat1i labels; |
|
cv::Mat diff; |
|
int nLabels = 0; |
|
for (size_t cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 8, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_8c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
|
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 4, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_4c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
} |
|
|
|
} |
|
|
|
TEST(Imgproc_ConnectedComponents, maxlabels_8conn_even) |
|
{ |
|
cv::Size size(16, 16); |
|
cv::Mat1b input(size); |
|
cv::Mat1i output_8c(size); |
|
cv::Mat1i output_4c(size); |
|
|
|
{ |
|
input << |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; |
|
|
|
output_8c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; |
|
|
|
output_4c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64, 0, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; |
|
} |
|
|
|
int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
|
|
|
cv::Mat1i labels; |
|
cv::Mat diff; |
|
int nLabels = 0; |
|
for (size_t cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 8, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_8c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
|
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 4, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_4c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
} |
|
|
|
} |
|
|
|
TEST(Imgproc_ConnectedComponents, maxlabels_8conn_odd) |
|
{ |
|
cv::Size size(15, 15); |
|
cv::Mat1b input(size); |
|
cv::Mat1i output_8c(size); |
|
cv::Mat1i output_4c(size); |
|
|
|
{ |
|
input << |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
|
|
|
output_8c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64; |
|
|
|
output_4c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, |
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
|
57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64; |
|
} |
|
|
|
int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
|
|
|
cv::Mat1i labels; |
|
cv::Mat diff; |
|
int nLabels = 0; |
|
for (size_t cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 8, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_8c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
|
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 4, CV_32S, ccltype[cclt])); |
|
normalizeLabels(labels, nLabels); |
|
|
|
diff = labels != output_4c; |
|
EXPECT_EQ(cv::countNonZero(diff), 0); |
|
} |
|
|
|
} |
|
|
|
TEST(Imgproc_ConnectedComponents, single_row) |
|
{ |
|
cv::Size size(1, 15); |
|
cv::Mat1b input(size); |
|
cv::Mat1i output_8c(size); |
|
cv::Mat1i output_4c(size); |
|
|
|
{ |
|
input << |
|
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
|
|
|
|
|
output_8c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8; |
|
|
|
|
|
output_4c << |
|
1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8; |
|
|
|
} |
|
|
|
int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
|
|
|
cv::Mat1i labels; |
|
cv::Mat diff; |
|
int nLabels = 0; |
|
for (size_t cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
|
|
|
EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 8, CV_32S, ccltype[cclt])); |
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normalizeLabels(labels, nLabels); |
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diff = labels != output_8c; |
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EXPECT_EQ(cv::countNonZero(diff), 0); |
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EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 4, CV_32S, ccltype[cclt])); |
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normalizeLabels(labels, nLabels); |
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diff = labels != output_4c; |
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EXPECT_EQ(cv::countNonZero(diff), 0); |
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} |
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} |
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TEST(Imgproc_ConnectedComponents, single_column) |
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{ |
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cv::Size size(15, 1); |
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cv::Mat1b input(size); |
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cv::Mat1i output_8c(size); |
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cv::Mat1i output_4c(size); |
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{ |
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input << |
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1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1; |
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output_8c << |
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1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8; |
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output_4c << |
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1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8; |
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} |
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int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI }; |
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cv::Mat1i labels; |
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cv::Mat diff; |
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int nLabels = 0; |
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for (size_t cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt) { |
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EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 8, CV_32S, ccltype[cclt])); |
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normalizeLabels(labels, nLabels); |
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diff = labels != output_8c; |
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EXPECT_EQ(cv::countNonZero(diff), 0); |
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EXPECT_NO_THROW(nLabels = cv::connectedComponents(input, labels, 4, CV_32S, ccltype[cclt])); |
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normalizeLabels(labels, nLabels); |
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diff = labels != output_4c; |
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EXPECT_EQ(cv::countNonZero(diff), 0); |
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} |
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} |
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TEST(Imgproc_ConnectedComponents, 4conn_regression_21366) |
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{ |
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Mat src = Mat::zeros(Size(10, 10), CV_8UC1); |
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{ |
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Mat labels, stats, centroids; |
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EXPECT_NO_THROW(cv::connectedComponentsWithStats(src, labels, stats, centroids, 4)); |
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} |
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} |
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} |
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} // namespace
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