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5ddd25313f
* Add Grana's connected components algorithm for 8-way connectivity. That algorithm is faster than Wu's one (currently implemented in opencv). For more details see https://github.com/prittt/YACCLAB. * New functions signature and distance transform compatibility * Add tests to imgproc/test/test_connectedcomponents.cpp * Change of test_connectedcomponents.cpp for c++98 support
141 lines
4.8 KiB
C++
141 lines
4.8 KiB
C++
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// copy or use the software.
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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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// * 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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// This software is provided by the copyright holders and contributors "as is" and
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//M*/
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#include "test_precomp.hpp"
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#include <string>
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#include <vector>
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using namespace cv;
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using namespace std;
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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_WU, cv::CCL_DEFAULT, cv::CCL_GRANA };
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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, 0);
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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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