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Merge pull request #21275 from stal12:CCL_improvements
Improve CCL with new algorithms and tests * Improve CCL with new algorithms and tests * Split CCL test into dedicated tests cases
This commit is contained in:
Stefano Allegretti
GitHub
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49bef1cdbf
commit
91817bffe1
@ -1278,3 +1278,11 @@
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pages={281--305},
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year={1987}
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}
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@article{Bolelli2021,
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title={One DAG to Rule Them All},
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author={Bolelli, Federico and Allegretti, Stefano and Grana, Costantino},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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year={2021},
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publisher={IEEE},
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doi = {10.1109/TPAMI.2021.3055337}
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}
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@ -406,10 +406,10 @@ enum ConnectedComponentsTypes {
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//! connected components algorithm
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enum ConnectedComponentsAlgorithmsTypes {
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CCL_DEFAULT = -1, //!< BBDT @cite Grana2010 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both BBDT and SAUF.
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CCL_DEFAULT = -1, //!< Spaghetti @cite Bolelli2019 algorithm for 8-way connectivity, Spaghetti4C @cite Bolelli2021 algorithm for 4-way connectivity.
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CCL_WU = 0, //!< SAUF @cite Wu2009 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for SAUF.
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CCL_GRANA = 1, //!< BBDT @cite Grana2010 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both BBDT and SAUF.
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CCL_BOLELLI = 2, //!< Spaghetti @cite Bolelli2019 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity.
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CCL_BOLELLI = 2, //!< Spaghetti @cite Bolelli2019 algorithm for 8-way connectivity, Spaghetti4C @cite Bolelli2021 algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both Spaghetti and Spaghetti4C.
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CCL_SAUF = 3, //!< Same as CCL_WU. It is preferable to use the flag with the name of the algorithm (CCL_SAUF) rather than the one with the name of the first author (CCL_WU).
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CCL_BBDT = 4, //!< Same as CCL_GRANA. It is preferable to use the flag with the name of the algorithm (CCL_BBDT) rather than the one with the name of the first author (CCL_GRANA).
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CCL_SPAGHETTI = 5, //!< Same as CCL_BOLELLI. It is preferable to use the flag with the name of the algorithm (CCL_SPAGHETTI) rather than the one with the name of the first author (CCL_BOLELLI).
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@ -3912,9 +3912,10 @@ image with 4 or 8 way connectivity - returns N, the total number of labels [0, N
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represents the background label. ltype specifies the output label image type, an important
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consideration based on the total number of labels or alternatively the total number of pixels in
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the source image. ccltype specifies the connected components labeling algorithm to use, currently
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Grana (BBDT) and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes
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for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
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This function uses parallel version of both Grana and Wu's algorithms if at least one allowed
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Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms
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are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces
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a row major ordering of labels while Spaghetti and BBDT do not.
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This function uses parallel version of the algorithms if at least one allowed
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parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
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@param image the 8-bit single-channel image to be labeled
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@ -3944,9 +3945,10 @@ image with 4 or 8 way connectivity - returns N, the total number of labels [0, N
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represents the background label. ltype specifies the output label image type, an important
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consideration based on the total number of labels or alternatively the total number of pixels in
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the source image. ccltype specifies the connected components labeling algorithm to use, currently
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Grana's (BBDT) and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes
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for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
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This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed
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Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms
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are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces
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a row major ordering of labels while Spaghetti and BBDT do not.
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This function uses parallel version of the algorithms (statistics included) if at least one allowed
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parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
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@param image the 8-bit single-channel image to be labeled
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File diff suppressed because it is too large
Load Diff
@ -42,7 +42,8 @@
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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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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@ -61,10 +62,10 @@ 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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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 (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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@ -74,7 +75,7 @@ void normalizeLabels(Mat1i& imgLabels, int iNumLabels) {
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}
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}
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void CV_ConnectedComponentsTest::run( int /* start_from */)
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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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@ -91,7 +92,7 @@ void CV_ConnectedComponentsTest::run( int /* start_from */)
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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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for (uint cclt = 0; cclt < sizeof(ccltype) / sizeof(int); ++cclt)
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{
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Mat1i labelImage;
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@ -100,11 +101,11 @@ void CV_ConnectedComponentsTest::run( int /* start_from */)
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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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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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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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@ -166,12 +167,12 @@ static cv::Mat createCrashMat(int numThreads) {
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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.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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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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@ -203,7 +204,7 @@ static cv::Mat createCrashMat(int numThreads) {
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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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if (mat.empty()) {
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return;
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}
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@ -213,10 +214,10 @@ TEST(Imgproc_ConnectedComponents, parallel_wu_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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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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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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@ -229,7 +230,7 @@ TEST(Imgproc_ConnectedComponents, missing_background_pixels)
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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_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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@ -241,21 +242,21 @@ 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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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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@ -357,4 +358,436 @@ TEST(Imgproc_ConnectedComponents, spaghetti_bbdt_sauf_stats)
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}
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}
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}} // namespace
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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,
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33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0,
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0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48,
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49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0,
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0, 57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64,
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65, 0, 66, 0, 67, 0, 68, 0, 69, 0, 70, 0, 71, 0, 72, 0,
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0, 73, 0, 74, 0, 75, 0, 76, 0, 77, 0, 78, 0, 79, 0, 80,
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81, 0, 82, 0, 83, 0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0,
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0, 89, 0, 90, 0, 91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96,
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97, 0, 98, 0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0,
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0, 105, 0, 106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112,
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113, 0, 114, 0, 115, 0, 116, 0, 117, 0, 118, 0, 119, 0, 120, 0,
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0, 121, 0, 122, 0, 123, 0, 124, 0, 125, 0, 126, 0, 127, 0, 128;
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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]));
|
||||
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]));
|
||||
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_column)
|
||||
{
|
||||
cv::Size size(15, 1);
|
||||
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]));
|
||||
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);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
} // namespace
|
||||
|
||||
Loading…
Reference in New Issue
Block a user