Merge pull request #1 from Itseez/2.4

Add calculating integral image using IPP

modules/core/include/opencv2/core/version.hpp

@@ -50,7 +50,7 @@
 #define CV_VERSION_EPOCH    2
 #define CV_VERSION_MAJOR    4
 #define CV_VERSION_MINOR    6
-#define CV_VERSION_REVISION 0
+#define CV_VERSION_REVISION 1
 
 #define CVAUX_STR_EXP(__A)  #__A
 #define CVAUX_STR(__A)      CVAUX_STR_EXP(__A)

modules/features2d/src/brisk.cpp

@@ -525,7 +525,11 @@ BRISK::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& keypoi
   bool doOrientation=true;
   if (useProvidedKeypoints)
     doOrientation = false;
-  computeDescriptorsAndOrOrientation(_image, _mask, keypoints, _descriptors, true, doOrientation,
+
+  // If the user specified cv::noArray(), this will yield false. Otherwise it will return true.
+  bool doDescriptors = _descriptors.needed();
+
+  computeDescriptorsAndOrOrientation(_image, _mask, keypoints, _descriptors, doDescriptors, doOrientation,
                                        useProvidedKeypoints);
 }
 

modules/highgui/src/cap.cpp

@@ -220,8 +220,8 @@ CV_IMPL CvCapture * cvCreateCameraCapture (int index)
                 return capture;
             break;
 #endif
-#ifdef HAVE_VFW
         case CV_CAP_VFW:
+#ifdef HAVE_VFW
             capture = cvCreateCameraCapture_VFW (index);
             if (capture)
                 return capture;

modules/imgproc/src/templmatch.cpp

@@ -248,6 +248,8 @@ void cv::matchTemplate( InputArray _img, InputArray _templ, OutputArray _result,
     CV_Assert( (img.depth() == CV_8U || img.depth() == CV_32F) &&
                img.type() == templ.type() );
 
+    CV_Assert( img.rows >= templ.rows && img.cols >= templ.cols);
+
     Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1);
     _result.create(corrSize, CV_32F);
     Mat result = _result.getMat();

modules/ocl/include/opencv2/ocl/ocl.hpp

@@ -834,6 +834,18 @@ namespace cv
         CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
             int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
 
+        /////////////////////////////////// ML ///////////////////////////////////////////
+
+        //! Compute closest centers for each lines in source and lable it after center's index
+        // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
+        CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat &centers);
+
+        //!Does k-means procedure on GPU 
+        // supports CV_32FC1/CV_32FC2/CV_32FC4 data type
+        CV_EXPORTS double kmeans(const oclMat &src, int K, oclMat &bestLabels,
+                                     TermCriteria criteria, int attemps, int flags, oclMat &centers);
+
+
         ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
         ///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
         ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

modules/ocl/src/initialization.cpp

@@ -319,7 +319,7 @@ namespace cv
             char clVersion[256];
             for (unsigned i = 0; i < numPlatforms; ++i)
             {
-                cl_uint numsdev;
+                cl_uint numsdev = 0;
                 cl_int status = clGetDeviceIDs(platforms[i], devicetype, 0, NULL, &numsdev);
                 if(status != CL_DEVICE_NOT_FOUND)
                     openCLVerifyCall(status);

modules/ocl/src/kmeans.cpp

@@ -0,0 +1,438 @@
+/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//     Xiaopeng Fu, fuxiaopeng2222@163.com
+//
+// 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 oclMaterials 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 <iomanip>
+#include "precomp.hpp"
+
+using namespace cv;
+using namespace ocl;
+
+namespace cv
+{
+namespace ocl
+{
+////////////////////////////////////OpenCL kernel strings//////////////////////////
+extern const char *kmeans_kernel;
+}
+}
+
+static void generateRandomCenter(const vector<Vec2f>& box, float* center, RNG& rng)
+{
+    size_t j, dims = box.size();
+    float margin = 1.f/dims;
+    for( j = 0; j < dims; j++ )
+        center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
+}
+
+// This class is copied from matrix.cpp in core module.
+class KMeansPPDistanceComputer : public ParallelLoopBody
+{
+public:
+    KMeansPPDistanceComputer( float *_tdist2,
+                              const float *_data,
+                              const float *_dist,
+                              int _dims,
+                              size_t _step,
+                              size_t _stepci )
+        : tdist2(_tdist2),
+          data(_data),
+          dist(_dist),
+          dims(_dims),
+          step(_step),
+          stepci(_stepci) { }
+
+    void operator()( const cv::Range& range ) const
+    {
+        const int begin = range.start;
+        const int end = range.end;
+
+        for ( int i = begin; i<end; i++ )
+        {
+            tdist2[i] = std::min(normL2Sqr_(data + step*i, data + stepci, dims), dist[i]);
+        }
+    }
+
+private:
+    KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
+
+    float *tdist2;
+    const float *data;
+    const float *dist;
+    const int dims;
+    const size_t step;
+    const size_t stepci;
+};
+/*
+k-means center initialization using the following algorithm:
+Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
+*/
+static void generateCentersPP(const Mat& _data, Mat& _out_centers,
+                              int K, RNG& rng, int trials)
+{
+    int i, j, k, dims = _data.cols, N = _data.rows;
+    const float* data = (float*)_data.data;
+    size_t step = _data.step/sizeof(data[0]);
+    vector<int> _centers(K);
+    int* centers = &_centers[0];
+    vector<float> _dist(N*3);
+    float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
+    double sum0 = 0;
+
+    centers[0] = (unsigned)rng % N;
+
+    for( i = 0; i < N; i++ )
+    {
+        dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
+        sum0 += dist[i];
+    }
+
+    for( k = 1; k < K; k++ )
+    {
+        double bestSum = DBL_MAX;
+        int bestCenter = -1;
+
+        for( j = 0; j < trials; j++ )
+        {
+            double p = (double)rng*sum0, s = 0;
+            for( i = 0; i < N-1; i++ )
+                if( (p -= dist[i]) <= 0 )
+                    break;
+            int ci = i;
+
+            parallel_for_(Range(0, N),
+                          KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
+            for( i = 0; i < N; i++ )
+            {
+                s += tdist2[i];
+            }
+
+            if( s < bestSum )
+            {
+                bestSum = s;
+                bestCenter = ci;
+                std::swap(tdist, tdist2);
+            }
+        }
+        centers[k] = bestCenter;
+        sum0 = bestSum;
+        std::swap(dist, tdist);
+    }
+
+    for( k = 0; k < K; k++ )
+    {
+        const float* src = data + step*centers[k];
+        float* dst = _out_centers.ptr<float>(k);
+        for( j = 0; j < dims; j++ )
+            dst[j] = src[j];
+    }
+}
+
+void cv::ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat &centers)
+{
+    //if(src.clCxt -> impl -> double_support == 0 && src.type() == CV_64F)
+    //{
+    //    CV_Error(CV_GpuNotSupported, "Selected device don't support double\r\n");
+    //    return;
+    //}
+
+    Context  *clCxt = src.clCxt;
+    int labels_step = (int)(labels.step/labels.elemSize());
+    string kernelname = "distanceToCenters";
+    int threadNum = src.rows > 256 ? 256 : src.rows;
+    size_t localThreads[3]  = {1, threadNum, 1};
+    size_t globalThreads[3] = {1, src.rows, 1};
+
+    vector<pair<size_t, const void *> > args;
+    args.push_back(make_pair(sizeof(cl_int), (void *)&labels_step));
+    args.push_back(make_pair(sizeof(cl_int), (void *)&centers.rows));
+    args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
+    args.push_back(make_pair(sizeof(cl_mem), (void *)&labels.data));
+    args.push_back(make_pair(sizeof(cl_int), (void *)&centers.cols));
+    args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
+    args.push_back(make_pair(sizeof(cl_mem), (void *)&centers.data));
+    args.push_back(make_pair(sizeof(cl_mem), (void*)&dists.data));
+
+    openCLExecuteKernel(clCxt, &kmeans_kernel, kernelname, globalThreads, localThreads, args, -1, -1, NULL);
+}
+///////////////////////////////////k - means /////////////////////////////////////////////////////////
+double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
+                       TermCriteria criteria, int attempts, int flags, oclMat &_centers)
+{
+    const int SPP_TRIALS = 3;
+    bool isrow = _src.rows == 1 && _src.oclchannels() > 1;
+    int N = !isrow ? _src.rows : _src.cols;
+    int dims = (!isrow ? _src.cols : 1) * _src.oclchannels();
+    int type = _src.depth();
+
+    attempts = std::max(attempts, 1);
+    CV_Assert(type == CV_32F && K > 0 );
+    CV_Assert( N >= K );
+
+    Mat _labels;
+    if( flags & CV_KMEANS_USE_INITIAL_LABELS )
+    {
+        CV_Assert( (_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
+                   _bestLabels.cols * _bestLabels.rows == N &&
+                   _bestLabels.type() == CV_32S );
+        _bestLabels.download(_labels);
+    }
+    else
+    {
+        if( !((_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
+                _bestLabels.cols * _bestLabels.rows == N &&
+                _bestLabels.type() == CV_32S &&
+                _bestLabels.isContinuous()))
+            _bestLabels.create(N, 1, CV_32S);
+        _labels.create(_bestLabels.size(), _bestLabels.type());
+    }
+    int* labels = _labels.ptr<int>();
+
+    Mat data;
+    _src.download(data);
+    Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
+    vector<int> counters(K);
+    vector<Vec2f> _box(dims);
+    Vec2f* box = &_box[0];
+    double best_compactness = DBL_MAX, compactness = 0;
+    RNG& rng = theRNG();
+    int a, iter, i, j, k;
+
+    if( criteria.type & TermCriteria::EPS )
+        criteria.epsilon = std::max(criteria.epsilon, 0.);
+    else
+        criteria.epsilon = FLT_EPSILON;
+    criteria.epsilon *= criteria.epsilon;
+
+    if( criteria.type & TermCriteria::COUNT )
+        criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
+    else
+        criteria.maxCount = 100;
+
+    if( K == 1 )
+    {
+        attempts = 1;
+        criteria.maxCount = 2;
+    }
+
+    const float* sample = data.ptr<float>();
+    for( j = 0; j < dims; j++ )
+        box[j] = Vec2f(sample[j], sample[j]);
+
+    for( i = 1; i < N; i++ )
+    {
+        sample = data.ptr<float>(i);
+        for( j = 0; j < dims; j++ )
+        {
+            float v = sample[j];
+            box[j][0] = std::min(box[j][0], v);
+            box[j][1] = std::max(box[j][1], v);
+        }
+    }
+
+    for( a = 0; a < attempts; a++ )
+    {
+        double max_center_shift = DBL_MAX;
+        for( iter = 0;; )
+        {
+            swap(centers, old_centers);
+
+            if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
+            {
+                if( flags & KMEANS_PP_CENTERS )
+                    generateCentersPP(data, centers, K, rng, SPP_TRIALS);
+                else
+                {
+                    for( k = 0; k < K; k++ )
+                        generateRandomCenter(_box, centers.ptr<float>(k), rng);
+                }
+            }
+            else
+            {
+                if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
+                {
+                    for( i = 0; i < N; i++ )
+                        CV_Assert( (unsigned)labels[i] < (unsigned)K );
+                }
+
+                // compute centers
+                centers = Scalar(0);
+                for( k = 0; k < K; k++ )
+                    counters[k] = 0;
+
+                for( i = 0; i < N; i++ )
+                {
+                    sample = data.ptr<float>(i);
+                    k = labels[i];
+                    float* center = centers.ptr<float>(k);
+                    j=0;
+#if CV_ENABLE_UNROLLED
+                    for(; j <= dims - 4; j += 4 )
+                    {
+                        float t0 = center[j] + sample[j];
+                        float t1 = center[j+1] + sample[j+1];
+
+                        center[j] = t0;
+                        center[j+1] = t1;
+
+                        t0 = center[j+2] + sample[j+2];
+                        t1 = center[j+3] + sample[j+3];
+
+                        center[j+2] = t0;
+                        center[j+3] = t1;
+                    }
+#endif
+                    for( ; j < dims; j++ )
+                        center[j] += sample[j];
+                    counters[k]++;
+                }
+
+                if( iter > 0 )
+                    max_center_shift = 0;
+
+                for( k = 0; k < K; k++ )
+                {
+                    if( counters[k] != 0 )
+                        continue;
+
+                    // if some cluster appeared to be empty then:
+                    //   1. find the biggest cluster
+                    //   2. find the farthest from the center point in the biggest cluster
+                    //   3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
+                    int max_k = 0;
+                    for( int k1 = 1; k1 < K; k1++ )
+                    {
+                        if( counters[max_k] < counters[k1] )
+                            max_k = k1;
+                    }
+
+                    double max_dist = 0;
+                    int farthest_i = -1;
+                    float* new_center =  centers.ptr<float>(k);
+                    float* old_center =  centers.ptr<float>(max_k);
+                    float* _old_center = temp.ptr<float>(); // normalized
+                    float scale = 1.f/counters[max_k];
+                    for( j = 0; j < dims; j++ )
+                        _old_center[j] = old_center[j]*scale;
+
+                    for( i = 0; i < N; i++ )
+                    {
+                        if( labels[i] != max_k )
+                            continue;
+                        sample = data.ptr<float>(i);
+                        double dist = normL2Sqr_(sample, _old_center, dims);
+
+                        if( max_dist <= dist )
+                        {
+                            max_dist = dist;
+                            farthest_i = i;
+                        }
+                    }
+
+                    counters[max_k]--;
+                    counters[k]++;
+                    labels[farthest_i] = k;
+                    sample = data.ptr<float>(farthest_i);
+
+                    for( j = 0; j < dims; j++ )
+                    {
+                        old_center[j] -= sample[j];
+                        new_center[j] += sample[j];
+                    }
+                }
+
+                for( k = 0; k < K; k++ )
+                {
+                    float* center = centers.ptr<float>(k);
+                    CV_Assert( counters[k] != 0 );
+
+                    float scale = 1.f/counters[k];
+                    for( j = 0; j < dims; j++ )
+                        center[j] *= scale;
+
+                    if( iter > 0 )
+                    {
+                        double dist = 0;
+                        const float* old_center = old_centers.ptr<float>(k);
+                        for( j = 0; j < dims; j++ )
+                        {
+                            double t = center[j] - old_center[j];
+                            dist += t*t;
+                        }
+                        max_center_shift = std::max(max_center_shift, dist);
+                    }
+                }
+            }
+
+            if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
+                break;
+
+            // assign labels
+            oclMat _dists(1, N, CV_64F);
+
+            _bestLabels.upload(_labels);
+            _centers.upload(centers);
+            distanceToCenters(_dists, _bestLabels, _src, _centers);
+
+            Mat dists;
+            _dists.download(dists);
+            _bestLabels.download(_labels);
+
+            double* dist = dists.ptr<double>(0);
+            compactness = 0;
+            for( i = 0; i < N; i++ )
+            {
+                compactness += dist[i];
+            }
+        }
+
+        if( compactness < best_compactness )
+        {
+            best_compactness = compactness;
+        }
+    }
+
+    return best_compactness;
+}
+

modules/ocl/src/opencl/kmeans_kernel.cl

@@ -0,0 +1,84 @@
+/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Xiaopeng Fu, fuxiaopeng2222@163.com
+//
+// 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 GpuMaterials 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*/
+
+__kernel void distanceToCenters(
+    int label_step, int K,
+    __global float *src,
+    __global int *labels, int dims, int rows,
+    __global float *centers,
+    __global float *dists)
+{
+    int gid = get_global_id(1);
+
+    float dist, euDist, min;
+    int minCentroid;
+
+    if(gid >= rows)
+        return;
+
+    for(int i = 0 ; i < K; i++)
+    {
+        euDist = 0;
+        for(int j = 0; j < dims; j++)
+        {
+            dist = (src[j + gid * dims]
+                    - centers[j + i * dims]);
+            euDist += dist * dist;
+        }
+
+        if(i == 0)
+        {
+            min = euDist;
+            minCentroid = 0;
+        }
+        else if(euDist < min)
+        {
+            min = euDist;
+            minCentroid = i;
+        }
+    }
+    dists[gid] = min;
+    labels[label_step * gid] = minCentroid;
+}

modules/ocl/test/main.cpp

@@ -73,14 +73,12 @@ void print_info()
 #endif
 
 }
-std::string workdir;
 int main(int argc, char **argv)
 {
-    TS::ptr()->init("ocl");
+    TS::ptr()->init(".");
     InitGoogleTest(&argc, argv);
     const char *keys =
         "{ h | help     | false              | print help message }"
-        "{ w | workdir  | ../../../samples/c/| set working directory }"
         "{ t | type     | gpu                | set device type:cpu or gpu}"
         "{ p | platform | 0                  | set platform id }"
         "{ d | device   | 0                  | set device id }";
@@ -92,7 +90,6 @@ int main(int argc, char **argv)
         cmd.printParams();
         return 0;
     }
-    workdir = cmd.get<string>("workdir");
     string type = cmd.get<string>("type");
     unsigned int pid = cmd.get<unsigned int>("platform");
     int device = cmd.get<int>("device");

modules/ocl/test/test_calib3d.cpp

@@ -50,7 +50,6 @@
 
 using namespace cv;
 
-extern std::string workdir;
 PARAM_TEST_CASE(StereoMatchBM, int, int)
 {
     int n_disp;
@@ -66,9 +65,9 @@ PARAM_TEST_CASE(StereoMatchBM, int, int)
 TEST_P(StereoMatchBM, Regression)
 {
 
-    Mat left_image  = readImage("stereobm/aloe-L.png", IMREAD_GRAYSCALE);
+    Mat left_image  = readImage("gpu/stereobm/aloe-L.png", IMREAD_GRAYSCALE);
-    Mat right_image = readImage("stereobm/aloe-R.png", IMREAD_GRAYSCALE);
+    Mat right_image = readImage("gpu/stereobm/aloe-R.png", IMREAD_GRAYSCALE);
-    Mat disp_gold   = readImage("stereobm/aloe-disp.png", IMREAD_GRAYSCALE);
+    Mat disp_gold   = readImage("gpu/stereobm/aloe-disp.png", IMREAD_GRAYSCALE);
     ocl::oclMat d_left, d_right;
     ocl::oclMat d_disp(left_image.size(), CV_8U);
     Mat  disp;
@@ -113,9 +112,9 @@ PARAM_TEST_CASE(StereoMatchBP, int, int, int, float, float, float, float)
 };
 TEST_P(StereoMatchBP, Regression)
 {
-    Mat left_image  = readImage("stereobp/aloe-L.png");
+    Mat left_image  = readImage("gpu/stereobp/aloe-L.png");
-    Mat right_image = readImage("stereobp/aloe-R.png");
+    Mat right_image = readImage("gpu/stereobp/aloe-R.png");
-    Mat disp_gold   = readImage("stereobp/aloe-disp.png", IMREAD_GRAYSCALE);
+    Mat disp_gold   = readImage("gpu/stereobp/aloe-disp.png", IMREAD_GRAYSCALE);
     ocl::oclMat d_left, d_right;
     ocl::oclMat d_disp;
     Mat  disp;
@@ -166,9 +165,9 @@ PARAM_TEST_CASE(StereoMatchConstSpaceBP, int, int, int, int, float, float, float
 };
 TEST_P(StereoMatchConstSpaceBP, Regression)
 {
-    Mat left_image  = readImage("csstereobp/aloe-L.png");
+    Mat left_image  = readImage("gpu/csstereobp/aloe-L.png");
-    Mat right_image = readImage("csstereobp/aloe-R.png");
+    Mat right_image = readImage("gpu/csstereobp/aloe-R.png");
-    Mat disp_gold   = readImage("csstereobp/aloe-disp.png", IMREAD_GRAYSCALE);
+    Mat disp_gold   = readImage("gpu/csstereobp/aloe-disp.png", IMREAD_GRAYSCALE);
 
     ocl::oclMat d_left, d_right;
     ocl::oclMat d_disp;

modules/ocl/test/test_canny.cpp

@@ -48,7 +48,6 @@
 
 ////////////////////////////////////////////////////////
 // Canny
-extern std::string workdir;
 IMPLEMENT_PARAM_CLASS(AppertureSize, int);
 IMPLEMENT_PARAM_CLASS(L2gradient, bool);
 
@@ -67,7 +66,7 @@ PARAM_TEST_CASE(Canny, AppertureSize, L2gradient)
 
 TEST_P(Canny, Accuracy)
 {
-    cv::Mat img = readImage(workdir + "fruits.jpg", cv::IMREAD_GRAYSCALE);
+    cv::Mat img = readImage("cv/shared/fruits.png", cv::IMREAD_GRAYSCALE);
     ASSERT_FALSE(img.empty());
 
     double low_thresh = 50.0;

modules/ocl/test/test_kmeans.cpp

@@ -0,0 +1,162 @@
+/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Erping Pang,   pang_er_ping@163.com
+//    Xiaopeng Fu,   fuxiaopeng2222@163.com
+//
+// 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 oclMaterials 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 "precomp.hpp"
+
+#ifdef HAVE_OPENCL
+
+using namespace cvtest;
+using namespace testing;
+using namespace std;
+using namespace cv;
+
+#define OCL_KMEANS_USE_INITIAL_LABELS 1
+#define OCL_KMEANS_PP_CENTERS         2
+
+PARAM_TEST_CASE(Kmeans, int, int, int)
+{
+    int type;
+    int K;
+    int flags;
+    cv::Mat src ;
+    ocl::oclMat d_src, d_dists;
+
+    Mat labels, centers;
+    ocl::oclMat d_labels, d_centers;
+    cv::RNG rng ;
+    virtual void SetUp(){
+        K = GET_PARAM(0);
+        type = GET_PARAM(1);
+        flags = GET_PARAM(2);
+        rng = TS::ptr()->get_rng();
+
+        // MWIDTH=256, MHEIGHT=256. defined in utility.hpp
+        cv::Size size = cv::Size(MWIDTH, MHEIGHT);
+        src.create(size, type);
+        int row_idx = 0;
+        const int max_neighbour = MHEIGHT / K - 1;
+        CV_Assert(K <= MWIDTH);
+        for(int i = 0; i < K; i++ )
+        {
+            Mat center_row_header = src.row(row_idx);
+            center_row_header.setTo(0);
+            int nchannel = center_row_header.channels();
+            for(int j = 0; j < nchannel; j++)
+                center_row_header.at<float>(0, i*nchannel+j) = 50000.0;
+
+            for(int j = 0; (j < max_neighbour) || 
+                           (i == K-1 && j < max_neighbour + MHEIGHT%K); j ++)
+            {
+                Mat cur_row_header = src.row(row_idx + 1 + j);
+                center_row_header.copyTo(cur_row_header);
+                Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), -200, 200, false);
+                cur_row_header += tmpmat;
+            }
+            row_idx += 1 + max_neighbour;
+        }
+    }
+};
+TEST_P(Kmeans, Mat){
+
+    if(flags & KMEANS_USE_INITIAL_LABELS)
+    {
+        // inital a given labels
+        labels.create(src.rows, 1, CV_32S);
+        int *label = labels.ptr<int>();
+        for(int i = 0; i < src.rows; i++)
+            label[i] = rng.uniform(0, K);
+        d_labels.upload(labels);
+    }
+    d_src.upload(src);
+
+    for(int j = 0; j < LOOP_TIMES; j++)
+    {
+        kmeans(src, K, labels,
+            TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 100, 0),
+            1, flags, centers);
+
+        ocl::kmeans(d_src, K, d_labels,
+            TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 100, 0),
+            1, flags, d_centers);
+  
+        Mat dd_labels(d_labels);
+        Mat dd_centers(d_centers);
+        if(flags & KMEANS_USE_INITIAL_LABELS)
+        {
+            EXPECT_MAT_NEAR(labels, dd_labels, 0);
+            EXPECT_MAT_NEAR(centers, dd_centers, 1e-3);
+        } 
+        else 
+        {
+            int row_idx = 0;
+            for(int i = 0; i < K; i++)
+            {
+                // verify lables with ground truth resutls
+                int label = labels.at<int>(row_idx);
+                int header_label = dd_labels.at<int>(row_idx);
+                for(int j = 0; (j < MHEIGHT/K)||(i == K-1 && j < MHEIGHT/K+MHEIGHT%K); j++)
+                {
+                    ASSERT_NEAR(labels.at<int>(row_idx+j), label, 0);
+                    ASSERT_NEAR(dd_labels.at<int>(row_idx+j), header_label, 0);
+                }
+
+                // verify centers
+                float *center = centers.ptr<float>(label);
+                float *header_center = dd_centers.ptr<float>(header_label);
+                for(int t = 0; t < centers.cols; t++)
+                    ASSERT_NEAR(center[t], header_center[t], 1e-3);
+
+                row_idx += MHEIGHT/K;
+            }
+        }
+    }
+}
+INSTANTIATE_TEST_CASE_P(OCL_ML, Kmeans, Combine(
+    Values(3, 5, 8),
+    Values(CV_32FC1, CV_32FC2, CV_32FC4),
+    Values(OCL_KMEANS_USE_INITIAL_LABELS/*, OCL_KMEANS_PP_CENTERS*/))); 
+
+#endif

modules/ocl/test/test_moments.cpp

@@ -45,7 +45,7 @@ TEST_P(MomentsTest, Mat)
     {
         if(test_contours)
         {
-            Mat src = imread( workdir + "../cpp/pic3.png", IMREAD_GRAYSCALE );
+            Mat src = readImage( "cv/shared/pic3.png", IMREAD_GRAYSCALE );
             ASSERT_FALSE(src.empty());
             Mat canny_output;
             vector<vector<Point> > contours;

modules/ocl/test/test_objdetect.cpp

@@ -63,11 +63,8 @@ PARAM_TEST_CASE(HOG, Size, int)
     {
         winSize = GET_PARAM(0);
         type = GET_PARAM(1);
-        img_rgb = readImage(workdir + "../gpu/road.png");
+        img_rgb = readImage("gpu/hog/road.png");
-        if(img_rgb.empty())
+        ASSERT_FALSE(img_rgb.empty());
-        {
-            std::cout << "Couldn't read road.png" << std::endl;
-        }
     }
 };
 
@@ -211,18 +208,11 @@ PARAM_TEST_CASE(Haar, int, CascadeName)
     virtual void SetUp()
     {
         flags = GET_PARAM(0);
-        cascadeName = (workdir + "../../data/haarcascades/").append(GET_PARAM(1));
+        cascadeName = (string(cvtest::TS::ptr()->get_data_path()) + "cv/cascadeandhog/cascades/").append(GET_PARAM(1));
-        if( (!cascade.load( cascadeName )) || (!cpucascade.load(cascadeName)) )
+        ASSERT_TRUE(cascade.load( cascadeName ));
-        {
+        ASSERT_TRUE(cpucascade.load(cascadeName));
-            std::cout << "ERROR: Could not load classifier cascade" << std::endl;
+        img = readImage("cv/shared/lena.png", IMREAD_GRAYSCALE);
-            return;
+        ASSERT_FALSE(img.empty());
-        }
-        img = readImage(workdir + "lena.jpg", IMREAD_GRAYSCALE);
-        if(img.empty())
-        {
-            std::cout << "Couldn't read lena.jpg" << std::endl;
-            return ;
-        }
         equalizeHist(img, img);
         d_img.upload(img);
     }

modules/ocl/test/test_optflow.cpp

@@ -75,7 +75,7 @@ PARAM_TEST_CASE(GoodFeaturesToTrack, MinDistance)
 
 TEST_P(GoodFeaturesToTrack, Accuracy)
 {
-    cv::Mat frame = readImage(workdir + "../gpu/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
+    cv::Mat frame = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
     ASSERT_FALSE(frame.empty());
 
     int maxCorners = 1000;
@@ -146,10 +146,10 @@ PARAM_TEST_CASE(TVL1, bool)
 
 TEST_P(TVL1, Accuracy)
 {
-    cv::Mat frame0 = readImage(workdir + "../gpu/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
+    cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
     ASSERT_FALSE(frame0.empty());
 
-    cv::Mat frame1 = readImage(workdir + "../gpu/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
+    cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
     ASSERT_FALSE(frame1.empty());
 
     cv::ocl::OpticalFlowDual_TVL1_OCL d_alg;
@@ -188,10 +188,10 @@ PARAM_TEST_CASE(Sparse, bool, bool)
 
 TEST_P(Sparse, Mat)
 {
-    cv::Mat frame0 = readImage(workdir + "../gpu/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
     ASSERT_FALSE(frame0.empty());
 
-    cv::Mat frame1 = readImage(workdir + "../gpu/rubberwhale2.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
+    cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
     ASSERT_FALSE(frame1.empty());
 
     cv::Mat gray_frame;
@@ -301,10 +301,10 @@ PARAM_TEST_CASE(Farneback, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
 
 TEST_P(Farneback, Accuracy)
 {
-    cv::Mat frame0 = imread(workdir + "/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
+    cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
     ASSERT_FALSE(frame0.empty());
 
-    cv::Mat frame1 = imread(workdir + "/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
+    cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
     ASSERT_FALSE(frame1.empty());
 
     double polySigma = polyN <= 5 ? 1.1 : 1.5;