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Add Grana's connected components algorithm for 8-way connectivity. (#6823)

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* 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
This commit is contained in:
Vadim Pisarevsky
2016-08-26 16:01:00 +04:00
committed by GitHub
parent 4f0f5a24ef
commit 5ddd25313f
4 changed files with 1486 additions and 53 deletions
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modules/imgproc/test/test_connectedcomponents.cpp
+59 -26
View File
@@ -42,6 +42,7 @@
#include "test_precomp.hpp"
#include <string>
#include <vector>
using namespace cv;
using namespace std;
@@ -58,49 +59,81 @@ protected:
CV_ConnectedComponentsTest::CV_ConnectedComponentsTest() {}
CV_ConnectedComponentsTest::~CV_ConnectedComponentsTest() {}
// This function force a row major order for the labels
void normalizeLabels(Mat1i& imgLabels, int iNumLabels) {
vector<int> vecNewLabels(iNumLabels + 1, 0);
int iMaxNewLabel = 0;
for (int r = 0; r<imgLabels.rows; ++r) {
for (int c = 0; c<imgLabels.cols; ++c) {
int iCurLabel = imgLabels(r, c);
if (iCurLabel>0) {
if (vecNewLabels[iCurLabel] == 0) {
vecNewLabels[iCurLabel] = ++iMaxNewLabel;
}
imgLabels(r, c) = vecNewLabels[iCurLabel];
}
}
}
}
void CV_ConnectedComponentsTest::run( int /* start_from */)
{
int ccltype[] = { cv::CCL_WU, cv::CCL_DEFAULT, cv::CCL_GRANA };
string exp_path = string(ts->get_data_path()) + "connectedcomponents/ccomp_exp.png";
Mat exp = imread(exp_path, 0);
Mat orig = imread(string(ts->get_data_path()) + "connectedcomponents/concentric_circles.png", 0);
if (orig.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
Mat bw = orig > 128;
Mat labelImage;
int nLabels = connectedComponents(bw, labelImage, 8, CV_32S);
for(int r = 0; r < labelImage.rows; ++r){
for(int c = 0; c < labelImage.cols; ++c){
int l = labelImage.at<int>(r, c);
bool pass = l >= 0 && l <= nLabels;
if(!pass){
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
for (uint cclt = 0; cclt < sizeof(ccltype)/sizeof(int); ++cclt)
{
Mat1i labelImage;
int nLabels = connectedComponents(bw, labelImage, 8, CV_32S, ccltype[cclt]);
normalizeLabels(labelImage, nLabels);
// Validate test results
for (int r = 0; r < labelImage.rows; ++r){
for (int c = 0; c < labelImage.cols; ++c){
int l = labelImage.at<int>(r, c);
bool pass = l >= 0 && l <= nLabels;
if (!pass){
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
}
}
if (exp.empty() || orig.size() != exp.size())
{
imwrite(exp_path, labelImage);
exp = labelImage;
}
if (0 != cvtest::norm(labelImage > 0, exp > 0, NORM_INF))
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
return;
}
if (nLabels != cvtest::norm(labelImage, NORM_INF) + 1)
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
return;
}
}
if( exp.empty() || orig.size() != exp.size() )
{
imwrite(exp_path, labelImage);
exp = labelImage;
}
if (0 != cvtest::norm(labelImage > 0, exp > 0, NORM_INF))
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
return;
}
if (nLabels != cvtest::norm(labelImage, NORM_INF)+1)
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
return;
}
ts->set_failed_test_info(cvtest::TS::OK);
}