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

* 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

modules/imgproc/include/opencv2/imgproc.hpp

@@ -413,6 +413,13 @@ enum ConnectedComponentsTypes {
     CC_STAT_MAX    = 5
 };
 
+//! connected components algorithm
+enum ConnectedComponentsAlgorithmsTypes {
+    CCL_WU      = 0,  //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
+    CCL_DEFAULT = -1, //!< BBDT algortihm for 8-way connectivity, SAUF algorithm for 4-way connectivity
+    CCL_GRANA   = 1   //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
+};
+
 //! mode of the contour retrieval algorithm
 enum RetrievalModes {
     /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for
@@ -3648,16 +3655,56 @@ CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
 image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
 represents the background label. ltype specifies the output label image type, an important
 consideration based on the total number of labels or alternatively the total number of pixels in
-the source image.
+the source image. ccltype specifies the connected components labeling algorithm to use, currently
+Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes
+for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
 
 @param image the 8-bit single-channel image to be labeled
 @param labels destination labeled image
 @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
 @param ltype output image label type. Currently CV_32S and CV_16U are supported.
- */
+@param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes).
+*/
+CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels,
+                                                                        int connectivity, int ltype, int ccltype);
+
+
+/** @overload
+
+@param image the 8-bit single-channel image to be labeled
+@param labels destination labeled image
+@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
+@param ltype output image label type. Currently CV_32S and CV_16U are supported.
+*/
 CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels,
                                      int connectivity = 8, int ltype = CV_32S);
 
+
+/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label
+
+image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
+represents the background label. ltype specifies the output label image type, an important
+consideration based on the total number of labels or alternatively the total number of pixels in
+the source image. ccltype specifies the connected components labeling algorithm to use, currently
+Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes
+for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
+
+
+@param image the 8-bit single-channel image to be labeled
+@param labels destination labeled image
+@param stats statistics output for each label, including the background label, see below for
+available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
+cv::ConnectedComponentsTypes. The data type is CV_32S.
+@param centroids centroid output for each label, including the background label. Centroids are
+accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
+@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
+@param ltype output image label type. Currently CV_32S and CV_16U are supported.
+@param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes).
+*/
+CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels,
+                                                                                          OutputArray stats, OutputArray centroids,
+                                                                                          int connectivity, int ltype, int ccltype);
+
 /** @overload
 @param image the 8-bit single-channel image to be labeled
 @param labels destination labeled image

modules/imgproc/src/distransform.cpp

@@ -827,7 +827,7 @@ void cv::distanceTransform( InputArray _src, OutputArray _dst, OutputArray _labe
         if( labelType == CV_DIST_LABEL_CCOMP )
         {
             Mat zpix = src == 0;
-            connectedComponents(zpix, labels, 8, CV_32S);
+            connectedComponents(zpix, labels, 8, CV_32S, CCL_WU);
         }
         else
         {

modules/imgproc/test/test_connectedcomponents.cpp

@@ -42,6 +42,7 @@
 
 #include "test_precomp.hpp"
 #include <string>
+#include <vector>
 
 using namespace cv;
 using namespace std;
@@ -58,34 +59,63 @@ 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 (uint cclt = 0; cclt < sizeof(ccltype)/sizeof(int); ++cclt)
-        for(int c = 0; c < labelImage.cols; ++c){
+    {
+
+        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){
+                if (!pass){
-                ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
+                    ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
                     return;
                 }
             }
         }
 
-    if( exp.empty() || orig.size() != exp.size() )
+        if (exp.empty() || orig.size() != exp.size())
         {
             imwrite(exp_path, labelImage);
             exp = labelImage;
@@ -93,14 +123,17 @@ void CV_ConnectedComponentsTest::run( int /* start_from */)
 
         if (0 != cvtest::norm(labelImage > 0, exp > 0, NORM_INF))
         {
-        ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+            ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
             return;
         }
-    if (nLabels != cvtest::norm(labelImage, NORM_INF)+1)
+        if (nLabels != cvtest::norm(labelImage, NORM_INF) + 1)
         {
-        ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+            ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
             return;
         }
+
+    }
+
     ts->set_failed_test_info(cvtest::TS::OK);
 }