Merge branch 'master' of https://github.com/nevion/opencv into cc

modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst

@@ -118,6 +118,48 @@ These values are proved to be invariants to the image scale, rotation, and refle
 
 .. seealso:: :ocv:func:`matchShapes`
 
+connectedComponents
+-----------
+computes the connected components labeled image of boolean image I with 4 or 8 way connectivity - returns N, the total
+number of labels [0, N-1] where 0 represents the background label. L's value type determines the label type, an important
+consideration based on the total number of labels or alternatively the total number of pixels.
+
+.. ocv:function:: uint64 connectedComponents(Mat &L, const Mat &I, int connectivity = 8)
+
+.. ocv:function:: uint64 connectedComponentsWithStats(Mat &L, const Mat &I, std::vector<ConnectedComponentStats> &statsv, int connectivity = 8)
+
+    :param L: destitination Labeled image
+
+    :param I: the image to be labeled
+
+    :param connectivity: 8 or 4 for 8-way or 4-way connectivity respectively
+
+    :param statsv: statistics for each label, including the background label
+
+Statistics information such as bounding box, area, and centroid is exported via the ``ConnectComponentStats`` structure defined as: ::
+
+    class CV_EXPORTS ConnectedComponentStats
+    {
+        public:
+        //! lower left corner column
+        int lower_x;
+        //! lower left corner row
+        int lower_y;
+        //! upper right corner column
+        int upper_x;
+        //! upper right corner row
+        int upper_y;
+        //! centroid column
+        double centroid_x;
+        //! centroid row
+        double centroid_y;
+        //! sum of all columns where the image was non-zero
+        uint64 integral_x;
+        //! sum of all rows where the image was non-zero
+        uint64 integral_y;
+        //! count of all non-zero pixels
+        unsigned int area;
+    };
 
 findContours
 ----------------

modules/imgproc/include/opencv2/imgproc/imgproc.hpp

@@ -1102,6 +1102,15 @@ enum { TM_SQDIFF=0, TM_SQDIFF_NORMED=1, TM_CCORR=2, TM_CCORR_NORMED=3, TM_CCOEFF
 CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
                                  OutputArray result, int method );
 
+enum { CC_STAT_LEFT=0, CC_STAT_TOP=1, CC_STAT_WIDTH=2, CC_STAT_HEIGHT=3, CC_STAT_AREA=4, CC_STAT_MAX = 5};
+
+//! computes the connected components labeled image of boolean image I with 4 or 8 way connectivity - returns N, the total
+//number of labels [0, N-1] where 0 represents the background label. L's value type determines the label type, an important
+//consideration based on the total number of labels or alternatively the total number of pixels.
+CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, int connectivity = 8, int ltype=CV_32S);
+CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, OutputArray stats, OutputArray centroids, int connectivity = 8, int ltype=CV_32S);
+
+
 //! mode of the contour retrieval algorithm
 enum
 {

modules/imgproc/src/connectedcomponents.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+// 2011 Jason Newton <nevion@gmail.com>
+//M*/
+//
+#include "precomp.hpp"
+#include <vector>
+
+#if defined _MSC_VER
+#pragma warning(disable: 4127)
+#endif
+
+namespace cv{
+    namespace connectedcomponents{
+
+    template<typename LabelT>
+    struct NoOp{
+        NoOp(){
+        }
+        void init(const LabelT labels){
+            (void) labels;
+        }
+        inline
+        void operator()(int r, int c, LabelT l){
+            (void) r;
+            (void) c;
+            (void) l;
+        }
+        void finish(){}
+    };
+    struct Point2ui64{
+        uint64 x, y;
+        Point2ui64(uint64 _x, uint64 _y):x(_x), y(_y){}
+    };
+    template<typename LabelT>
+    struct CCStatsOp{
+        OutputArray _mstatsv;
+        cv::Mat statsv;
+        OutputArray _mcentroidsv;
+        cv::Mat centroidsv;
+        std::vector<Point2ui64> integrals;
+
+        CCStatsOp(OutputArray _statsv, OutputArray _centroidsv): _mstatsv(_statsv), _mcentroidsv(_centroidsv){
+        }
+        inline
+        void init(const LabelT nlabels){
+            _mstatsv.create(cv::Size(nlabels, CC_STAT_MAX), cv::DataType<int>::type);
+            statsv = _mstatsv.getMat();
+            _mcentroidsv.create(cv::Size(nlabels, 2), cv::DataType<double>::type);
+            centroidsv = _mcentroidsv.getMat();
+
+            for(int l = 0; l < (int) nlabels; ++l){
+                unsigned int *row = (unsigned int *) &statsv.at<int>(l, 0);
+                row[CC_STAT_LEFT] = std::numeric_limits<LabelT>::max();
+                row[CC_STAT_TOP] = std::numeric_limits<LabelT>::max();
+                row[CC_STAT_WIDTH] = std::numeric_limits<LabelT>::min();
+                row[CC_STAT_HEIGHT] = std::numeric_limits<LabelT>::min();
+                //row[CC_STAT_CX] = 0;
+                //row[CC_STAT_CY] = 0;
+                row[CC_STAT_AREA] = 0;
+            }
+            integrals.resize(nlabels, Point2ui64(0, 0));
+        }
+        void operator()(int r, int c, LabelT l){
+            int *row = &statsv.at<int>(l, 0);
+            unsigned int *urow = (unsigned int *) row;
+            if(c > row[CC_STAT_WIDTH]){
+                row[CC_STAT_WIDTH] = c;
+            }else{
+                if(c < row[CC_STAT_LEFT]){
+                    row[CC_STAT_LEFT] = c;
+                }
+            }
+            if(r > row[CC_STAT_HEIGHT]){
+                row[CC_STAT_HEIGHT] = r;
+            }else{
+                if(r < row[CC_STAT_TOP]){
+                    row[CC_STAT_TOP] = r;
+                }
+            }
+            urow[CC_STAT_AREA]++;
+            Point2ui64 &integral = integrals[l];
+            integral.x += c;
+            integral.y += r;
+        }
+        void finish(){
+            for(int l = 0; l < statsv.rows; ++l){
+                unsigned int *row = (unsigned int *) &statsv.at<int>(l, 0);
+                row[CC_STAT_LEFT] = std::min(row[CC_STAT_LEFT], row[CC_STAT_WIDTH]);
+                row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
+                row[CC_STAT_TOP] = std::min(row[CC_STAT_TOP], row[CC_STAT_HEIGHT]);
+                row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1;
+
+                Point2ui64 &integral = integrals[l];
+                double *centroid = &centroidsv.at<double>(l, 0);
+                centroid[0] = double(integral.x) / row[CC_STAT_AREA];
+                centroid[1] = double(integral.y) / row[CC_STAT_AREA];
+            }
+        }
+    };
+
+    //Find the root of the tree of node i
+    template<typename LabelT>
+    inline static
+    LabelT findRoot(const LabelT *P, LabelT i){
+        LabelT root = i;
+        while(P[root] < root){
+            root = P[root];
+        }
+        return root;
+    }
+
+    //Make all nodes in the path of node i point to root
+    template<typename LabelT>
+    inline static
+    void setRoot(LabelT *P, LabelT i, LabelT root){
+        while(P[i] < i){
+            LabelT j = P[i];
+            P[i] = root;
+            i = j;
+        }
+        P[i] = root;
+    }
+
+    //Find the root of the tree of the node i and compress the path in the process
+    template<typename LabelT>
+    inline static
+    LabelT find(LabelT *P, LabelT i){
+        LabelT root = findRoot(P, i);
+        setRoot(P, i, root);
+        return root;
+    }
+
+    //unite the two trees containing nodes i and j and return the new root
+    template<typename LabelT>
+    inline static
+    LabelT set_union(LabelT *P, LabelT i, LabelT j){
+        LabelT root = findRoot(P, i);
+        if(i != j){
+            LabelT rootj = findRoot(P, j);
+            if(root > rootj){
+                root = rootj;
+            }
+            setRoot(P, j, root);
+        }
+        setRoot(P, i, root);
+        return root;
+    }
+
+    //Flatten the Union Find tree and relabel the components
+    template<typename LabelT>
+    inline static
+    LabelT flattenL(LabelT *P, LabelT length){
+        LabelT k = 1;
+        for(LabelT i = 1; i < length; ++i){
+            if(P[i] < i){
+                P[i] = P[P[i]];
+            }else{
+                P[i] = k; k = k + 1;
+            }
+        }
+        return k;
+    }
+
+    //Based on "Two Strategies to Speed up Connected Components Algorithms", the SAUF (Scan array union find) variant
+    //using decision trees
+    //Kesheng Wu, et al
+    //Note: rows are encoded as position in the "rows" array to save lookup times
+    //reference for 4-way: {{-1, 0}, {0, -1}};//b, d neighborhoods
+    const int G4[2][2] = {{1, 0}, {0, -1}};//b, d neighborhoods
+    //reference for 8-way: {{-1, -1}, {-1, 0}, {-1, 1}, {0, -1}};//a, b, c, d neighborhoods
+    const int G8[4][2] = {{1, -1}, {1, 0}, {1, 1}, {0, -1}};//a, b, c, d neighborhoods
+    template<typename LabelT, typename PixelT, typename StatsOp = NoOp<LabelT>, int connectivity = 8>
+    struct LabelingImpl{
+    LabelT operator()(const cv::Mat &I, cv::Mat &L, StatsOp &sop){
+        CV_Assert(L.rows == I.rows);
+        CV_Assert(L.cols == I.cols);
+        const int rows = L.rows;
+        const int cols = L.cols;
+        size_t Plength = (size_t(rows + 3 - 1)/3) * (size_t(cols + 3 - 1)/3);
+        if(connectivity == 4){
+            Plength = 4 * Plength;//a quick and dirty upper bound, an exact answer exists if you want to find it
+            //the 4 comes from the fact that a 3x3 block can never have more than 4 unique labels
+        }
+        LabelT *P = (LabelT *) fastMalloc(sizeof(LabelT) * Plength);
+        P[0] = 0;
+        LabelT lunique = 1;
+        //scanning phase
+        for(int r_i = 0; r_i < rows; ++r_i){
+            LabelT *Lrow = (LabelT *)(L.data + L.step.p[0] * r_i);
+            LabelT *Lrow_prev = (LabelT *)(((char *)Lrow) - L.step.p[0]);
+            const PixelT *Irow = (PixelT *)(I.data + I.step.p[0] * r_i);
+            const PixelT *Irow_prev = (const PixelT *)(((char *)Irow) - I.step.p[0]);
+            LabelT *Lrows[2] = {
+                Lrow,
+                Lrow_prev
+            };
+            const PixelT *Irows[2] = {
+                Irow,
+                Irow_prev
+            };
+            if(connectivity == 8){
+                const int a = 0;
+                const int b = 1;
+                const int c = 2;
+                const int d = 3;
+                const bool T_a_r = (r_i - G8[a][0]) >= 0;
+                const bool T_b_r = (r_i - G8[b][0]) >= 0;
+                const bool T_c_r = (r_i - G8[c][0]) >= 0;
+                for(int c_i = 0; Irows[0] != Irow + cols; ++Irows[0], c_i++){
+                    if(!*Irows[0]){
+                        Lrow[c_i] = 0;
+                        continue;
+                    }
+                    Irows[1] = Irow_prev + c_i;
+                    Lrows[0] = Lrow + c_i;
+                    Lrows[1] = Lrow_prev + c_i;
+                    const bool T_a = T_a_r && (c_i + G8[a][1]) >= 0   && *(Irows[G8[a][0]] + G8[a][1]);
+                    const bool T_b = T_b_r                            && *(Irows[G8[b][0]] + G8[b][1]);
+                    const bool T_c = T_c_r && (c_i + G8[c][1]) < cols && *(Irows[G8[c][0]] + G8[c][1]);
+                    const bool T_d =          (c_i + G8[d][1]) >= 0   && *(Irows[G8[d][0]] + G8[d][1]);
+
+                    //decision tree
+                    if(T_b){
+                        //copy(b)
+                        *Lrows[0] = *(Lrows[G8[b][0]] + G8[b][1]);
+                    }else{//not b
+                        if(T_c){
+                            if(T_a){
+                                //copy(c, a)
+                                *Lrows[0] = set_union(P, *(Lrows[G8[c][0]] + G8[c][1]), *(Lrows[G8[a][0]] + G8[a][1]));
+                            }else{
+                                if(T_d){
+                                    //copy(c, d)
+                                    *Lrows[0] = set_union(P, *(Lrows[G8[c][0]] + G8[c][1]), *(Lrows[G8[d][0]] + G8[d][1]));
+                                }else{
+                                    //copy(c)
+                                    *Lrows[0] = *(Lrows[G8[c][0]] + G8[c][1]);
+                                }
+                            }
+                        }else{//not c
+                            if(T_a){
+                                //copy(a)
+                                *Lrows[0] = *(Lrows[G8[a][0]] + G8[a][1]);
+                            }else{
+                                if(T_d){
+                                    //copy(d)
+                                    *Lrows[0] = *(Lrows[G8[d][0]] + G8[d][1]);
+                                }else{
+                                    //new label
+                                    *Lrows[0] = lunique;
+                                    P[lunique] = lunique;
+                                    lunique = lunique + 1;
+                                }
+                            }
+                        }
+                    }
+                }
+            }else{
+                //B & D only
+                assert(connectivity == 4);
+                const int b = 0;
+                const int d = 1;
+                const bool T_b_r = (r_i - G4[b][0]) >= 0;
+                for(int c_i = 0; Irows[0] != Irow + cols; ++Irows[0], c_i++){
+                    if(!*Irows[0]){
+                        Lrow[c_i] = 0;
+                        continue;
+                    }
+                    Irows[1] = Irow_prev + c_i;
+                    Lrows[0] = Lrow + c_i;
+                    Lrows[1] = Lrow_prev + c_i;
+                    const bool T_b = T_b_r                            && *(Irows[G4[b][0]] + G4[b][1]);
+                    const bool T_d =          (c_i + G4[d][1]) >= 0   && *(Irows[G4[d][0]] + G4[d][1]);
+                    if(T_b){
+                        if(T_d){
+                            //copy(d, b)
+                            *Lrows[0] = set_union(P, *(Lrows[G4[d][0]] + G4[d][1]), *(Lrows[G4[b][0]] + G4[b][1]));
+                        }else{
+                            //copy(b)
+                            *Lrows[0] = *(Lrows[G4[b][0]] + G4[b][1]);
+                        }
+                    }else{
+                        if(T_d){
+                            //copy(d)
+                            *Lrows[0] = *(Lrows[G4[d][0]] + G4[d][1]);
+                        }else{
+                            //new label
+                            *Lrows[0] = lunique;
+                            P[lunique] = lunique;
+                            lunique = lunique + 1;
+                        }
+                    }
+                }
+            }
+        }
+
+        //analysis
+        LabelT nLabels = flattenL(P, lunique);
+        sop.init(nLabels);
+
+        for(int r_i = 0; r_i < rows; ++r_i){
+            LabelT *Lrow_start = (LabelT *)(L.data + L.step.p[0] * r_i);
+            LabelT *Lrow_end = Lrow_start + cols;
+            LabelT *Lrow = Lrow_start;
+            for(int c_i = 0; Lrow != Lrow_end; ++Lrow, ++c_i){
+                const LabelT l = P[*Lrow];
+                *Lrow = l;
+                sop(r_i, c_i, l);
+            }
+        }
+
+        sop.finish();
+        fastFree(P);
+
+        return nLabels;
+    }//End function LabelingImpl operator()
+
+    };//End struct LabelingImpl
+}//end namespace connectedcomponents
+
+//L's type must have an appropriate depth for the number of pixels in I
+template<typename StatsOp>
+static
+int connectedComponents_sub1(const cv::Mat &I, cv::Mat &L, int connectivity, StatsOp &sop){
+    CV_Assert(L.channels() == 1 && I.channels() == 1);
+    CV_Assert(connectivity == 8 || connectivity == 4);
+
+    int lDepth = L.depth();
+    int iDepth = I.depth();
+    using connectedcomponents::LabelingImpl;
+    //warn if L's depth is not sufficient?
+
+    if(lDepth == CV_8U){
+        if(iDepth == CV_8U || iDepth == CV_8S){
+            if(connectivity == 4){
+                return (int) LabelingImpl<uchar, uchar, StatsOp, 4>()(I, L, sop);
+            }else{
+                return (int) LabelingImpl<uchar, uchar, StatsOp, 8>()(I, L, sop);
+            }
+        }else{
+            CV_Assert(false);
+        }
+    }else if(lDepth == CV_16U){
+        if(iDepth == CV_8U || iDepth == CV_8S){
+            if(connectivity == 4){
+                return (int) LabelingImpl<ushort, uchar, StatsOp, 4>()(I, L, sop);
+            }else{
+                return (int) LabelingImpl<ushort, uchar, StatsOp, 8>()(I, L, sop);
+            }
+        }else{
+            CV_Assert(false);
+        }
+    }else if(lDepth == CV_32S){
+        //note that signed types don't really make sense here and not being able to use unsigned matters for scientific projects
+        //OpenCV: how should we proceed?  .at<T> typechecks in debug mode
+        if(iDepth == CV_8U || iDepth == CV_8S){
+            if(connectivity == 4){
+                return (int) LabelingImpl<int, uchar, StatsOp, 4>()(I, L, sop);
+            }else{
+                return (int) LabelingImpl<int, uchar, StatsOp, 8>()(I, L, sop);
+            }
+        }else{
+            CV_Assert(false);
+        }
+    }
+
+    CV_Error(CV_StsUnsupportedFormat, "unsupported label/image type");
+    return -1;
+}
+
+int connectedComponents(InputArray _I, OutputArray _L, int connectivity, int ltype){
+    const cv::Mat I = _I.getMat();
+    _L.create(I.size(), CV_MAT_TYPE(ltype));
+    cv::Mat L = _L.getMat();
+    if(ltype == CV_16U){
+        connectedcomponents::NoOp<ushort> sop; return connectedComponents_sub1(I, L, connectivity, sop);
+    }else if(ltype == CV_32S){
+        connectedcomponents::NoOp<unsigned> sop; return connectedComponents_sub1(I, L, connectivity, sop);
+    }else{
+        CV_Assert(false);
+        return 0;
+    }
+}
+
+int connectedComponentsWithStats(InputArray _I, OutputArray _L, OutputArray statsv, OutputArray centroids, int connectivity, int ltype){
+    const cv::Mat I = _I.getMat();
+    _L.create(I.size(), CV_MAT_TYPE(ltype));
+    cv::Mat L = _L.getMat();
+    if(ltype == CV_16U){
+        connectedcomponents::CCStatsOp<ushort> sop(statsv, centroids); return connectedComponents_sub1(I, L, connectivity, sop);
+    }else if(ltype == CV_32S){
+        connectedcomponents::CCStatsOp<unsigned> sop(statsv, centroids); return connectedComponents_sub1(I, L, connectivity, sop);
+    }else{
+        CV_Assert(false);
+        return 0;
+    }
+}
+
+}

modules/imgproc/test/test_connectedcomponents.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include <string>
+
+using namespace cv;
+using namespace std;
+
+class CV_ConnectedComponentsTest : public cvtest::BaseTest
+{
+public:
+    CV_ConnectedComponentsTest();
+    ~CV_ConnectedComponentsTest();
+protected:
+    void run(int);
+};
+
+CV_ConnectedComponentsTest::CV_ConnectedComponentsTest() {}
+CV_ConnectedComponentsTest::~CV_ConnectedComponentsTest() {}
+
+void CV_ConnectedComponentsTest::run( int /* start_from */)
+{
+    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 );
+        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;
+            }
+        }
+    }
+
+    if( exp.empty() || orig.size() != exp.size() )
+    {
+        imwrite(exp_path, labelImage);
+        exp = labelImage;
+    }
+
+    if (0 != norm(labelImage > 0, exp > 0, NORM_INF))
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+        return;
+    }
+    if (nLabels != norm(labelImage, NORM_INF)+1)
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+        return;
+    }
+    ts->set_failed_test_info(cvtest::TS::OK);
+}
+
+TEST(Imgproc_ConnectedComponents, regression) { CV_ConnectedComponentsTest test; test.safe_run(); }
+

modules/python/src2/cv2.cpp

@@ -410,7 +410,7 @@ static bool pyopencv_to(PyObject* obj, bool& value, const char* name = "<unknown
 
 static PyObject* pyopencv_from(size_t value)
 {
-    return PyLong_FromUnsignedLong((unsigned long)value);
+    return PyLong_FromSize_t(value);
 }
 
 static bool pyopencv_to(PyObject* obj, size_t& value, const char* name = "<unknown>")
@@ -497,9 +497,16 @@ static bool pyopencv_to(PyObject* obj, float& value, const char* name = "<unknow
 
 static PyObject* pyopencv_from(int64 value)
 {
-    return PyFloat_FromDouble((double)value);
+    return PyLong_FromLongLong(value);
 }
 
+#if !defined(__LP64__)
+static PyObject* pyopencv_from(uint64 value)
+{
+    return PyLong_FromUnsignedLongLong(value);
+}
+#endif
+
 static PyObject* pyopencv_from(const string& value)
 {
     return PyString_FromString(value.empty() ? "" : value.c_str());

samples/cpp/connected_components.cpp

@@ -11,23 +11,19 @@ int threshval = 100;
 static void on_trackbar(int, void*)
 {
     Mat bw = threshval < 128 ? (img < threshval) : (img > threshval);
-
-    vector<vector<Point> > contours;
-    vector<Vec4i> hierarchy;
-
-    findContours( bw, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
-
-    Mat dst = Mat::zeros(img.size(), CV_8UC3);
-
-    if( !contours.empty() && !hierarchy.empty() )
-    {
-        // iterate through all the top-level contours,
-        // draw each connected component with its own random color
-        int idx = 0;
-        for( ; idx >= 0; idx = hierarchy[idx][0] )
-        {
-            Scalar color( (rand()&255), (rand()&255), (rand()&255) );
-            drawContours( dst, contours, idx, color, CV_FILLED, 8, hierarchy );
+    Mat labelImage(img.size(), CV_32S);
+    int nLabels = connectedComponents(bw, labelImage, 8);
+    std::vector<Vec3b> colors(nLabels);
+    colors[0] = Vec3b(0, 0, 0);//background
+    for(int label = 1; label < nLabels; ++label){
+        colors[label] = Vec3b( (rand()&255), (rand()&255), (rand()&255) );
+    }
+    Mat dst(img.size(), CV_8UC3);
+    for(int r = 0; r < dst.rows; ++r){
+        for(int c = 0; c < dst.cols; ++c){
+            int label = labelImage.at<int>(r, c);
+            Vec3b &pixel = dst.at<Vec3b>(r, c);
+            pixel = colors[label];
          }
      }
 
@@ -45,14 +41,14 @@ static void help()
 
 const char* keys =
 {
-    "{@image |stuff.jpg|image for converting to a grayscale}"
+    "{@image|stuff.jpg|image for converting to a grayscale}"
 };
 
 int main( int argc, const char** argv )
 {
     help();
     CommandLineParser parser(argc, argv, keys);
-    string inputImage = parser.get<string>(1);
+    string inputImage = parser.get<string>("@image");
     img = imread(inputImage.c_str(), 0);
 
     if(img.empty())