colorbalance module added, now it contains the only WHITE_BALANCE_SIMPLE implementation

doc/tutorials/ximpgroc/prediction/prediction.rst

@@ -111,11 +111,10 @@ Explanation
         return -1;
     }
 
-2. **Convert source image to [0;1] range and RGB colospace**
+2. **Convert source image to [0;1] range**
 
   .. code-block:: cpp
 
-    cv::cvtColor(image, image, CV_BGR2RGB);
     image.convertTo(image, cv::DataType<float>::type, 1/255.0);
 
 3. **Run main algorithm**

modules/colorbalance/CMakeLists.txt

@@ -0,0 +1,3 @@
+set(the_description "Color balance algorithms")
+ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
+ocv_define_module(colorbalance opencv_core opencv_imgproc OPTIONAL opencv_highgui)

modules/colorbalance/doc/colorbalance.rst

@@ -0,0 +1,8 @@
+********************************************
+colorbalance. Color balance
+********************************************
+
+.. toctree::
+    :maxdepth: 2
+
+    Color balance <colorbalance/whitebalance>

modules/colorbalance/doc/colorbalance/whitebalance.rst

@@ -0,0 +1,31 @@
+Automatic white balance correction
+**********************************
+
+.. highlight:: cpp
+
+balanceWhite
+------------
+.. ocv:function:: (const Mat &src, Mat &dst, const int algorithmType,
+    const float inputMin  = 0.0f, const float inputMax  = 255.0f,
+    const float outputMin = 0.0f, const float outputMax = 255.0f);
+
+The function implements different algorithm of automatic white balance, i.e.
+it tries to map image's white color to perceptual white (this can be violated
+due to specific illumination or camera settings).
+
+    :param src : source image
+    :param dst : destination image
+    :param algorithmType : type of the algorithm to use.
+                           Use WHITE_BALANCE_SIMPLE to perform
+                           smart histogram adjustments
+                           (ignoring 4% pixels with minimal
+                           and maximal values) for each channel.
+    :param inputMin : minimum value in the input image
+    :param inputMax : maximum value in the input image
+    :param outputMin : minimum value in the output image
+    :param outputMax : maximum value in the output image
+
+.. seealso::
+
+    :ocv:class:`cvtColor`,
+    :ocv:class:`equalizeHist`

modules/colorbalance/include/opencv2/colorbalance.hpp

@@ -0,0 +1,48 @@
+/*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*/
+
+#ifndef __OPENCV_EDGEDETECTION_HPP__
+#define __OPENCV_EDGEDETECTION_HPP__
+
+#include "opencv2/core.hpp"
+#include "opencv2/colorbalance/simple_color_balance.hpp"
+#endif

modules/colorbalance/include/opencv2/colorbalance/simple_color_balance.hpp

@@ -0,0 +1,81 @@
+/*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) 2009-2011, 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*/
+
+#ifndef __OPENCV_SIMPLE_COLOR_BALANCE_HPP__
+#define __OPENCV_SIMPLE_COLOR_BALANCE_HPP__
+
+/*
+* simple_color_balance.hpp
+*
+*  Created on: Jun 26, 2014
+*      Author: Yury Gitman
+*/
+
+#include <opencv2/core.hpp>
+
+/*! \namespace cv
+Namespace where all the C++ OpenCV functionality resides
+*/
+namespace cv
+{
+    //! various white balance algorithms
+    enum
+    {
+        WHITE_BALANCE_SIMPLE = 0,
+        WHITE_BALANCE_GRAYWORLD = 1
+    };
+
+    /*! This function implements different white balance algorithms
+    *  \param src : source image
+    *  \param dst : destination image
+    *  \param algorithmType : type of the algorithm to use
+    *  \param inputMin : minimum input value
+    *  \param inputMax : maximum output value
+    *  \param outputMin : minimum input value
+    *  \param outputMax : maximum output value
+    */
+    CV_EXPORTS_W void balanceWhite(const Mat &src, Mat &dst, const int algorithmType,
+        const float inputMin  = 0.0f, const float inputMax  = 255.0f,
+        const float outputMin = 0.0f, const float outputMax = 255.0f);
+}
+
+#endif // __OPENCV_SIMPLE_COLOR_BALANCE_HPP__

modules/colorbalance/samples/simple_color_balance.cpp

@@ -0,0 +1,60 @@
+#include "opencv2/colorbalance.hpp"
+
+#include "opencv2/imgproc.hpp"
+#include "opencv2/highgui.hpp"
+
+#include "opencv2/core/utility.hpp"
+#include "opencv2/imgproc/types_c.h"
+
+const char* keys =
+{
+    "{i || input image name}"
+    "{o || output image name}"
+};
+
+int main( int argc, const char** argv )
+{
+    bool printHelp = ( argc == 1 );
+    printHelp = printHelp || ( argc == 2 && std::string(argv[1]) == "--help" );
+    printHelp = printHelp || ( argc == 2 && std::string(argv[1]) == "-h" );
+
+    if ( printHelp )
+    {
+        printf("\nThis sample demonstrates simple color balance algorithm\n"
+            "Call:\n"
+            "    simple_color_blance -i=in_image_name [-o=out_image_name]\n\n");
+        return 0;
+    }
+
+    cv::CommandLineParser parser(argc, argv, keys);
+    if ( !parser.check() )
+    {
+        parser.printErrors();
+        return -1;
+    }
+
+    std::string inFilename = parser.get<std::string>("i");
+    std::string outFilename = parser.get<std::string>("o");
+
+    cv::Mat src = cv::imread(inFilename, 1);
+    if ( src.empty() )
+    {
+        printf("Cannot read image file: %s\n", inFilename.c_str());
+        return -1;
+    }
+
+    cv::Mat res(src.size(), src.type());
+    cv::balanceWhite(src, res, cv::WHITE_BALANCE_SIMPLE);
+
+    if ( outFilename == "" )
+    {
+        cv::namedWindow("after white balance", 1);
+        cv::imshow("after white balance", res);
+
+        cv::waitKey(0);
+    }
+    else
+        cv::imwrite(outFilename, res);
+
+    return 0;
+}

modules/colorbalance/src/simple_color_balance.cpp

@@ -0,0 +1,205 @@
+/*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) 2009-2011, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+//   * 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.
+//
+//M*/
+
+#include <vector>
+#include <algorithm>
+#include <iterator>
+#include <iostream>
+
+#include "opencv2/colorbalance.hpp"
+
+#include "opencv2/imgproc.hpp"
+
+#include "opencv2/core.hpp"
+#include "opencv2/core/core_c.h"
+
+#include "opencv2/core/types.hpp"
+#include "opencv2/core/types_c.h"
+
+namespace cv
+{
+    template <typename T>
+    void balanceWhite(std::vector < Mat_<T> > &src, Mat &dst,
+        const float inputMin, const float inputMax,
+        const float outputMin, const float outputMax, const int algorithmType)
+    {
+        switch ( algorithmType )
+        {
+        case WHITE_BALANCE_SIMPLE:
+            {
+                /********************* Simple white balance *********************/
+                float s1 = 2.0f; // low quantile
+                float s2 = 2.0f; // high quantile
+
+                int depth = 2; // depth of histogram tree
+                if (src[0].depth() != CV_8U)
+                    ++depth;
+                int bins = 16; // number of bins at each histogram level
+
+                int nElements = int( pow(bins, depth) );
+                 // number of elements in histogram tree
+
+                for (size_t i = 0; i < src.size(); ++i)
+                {
+                    std::vector <int> hist(nElements, 0);
+
+                    Mat_<T>::iterator beginIt = src[i].begin();
+                    Mat_<T>::iterator endIt = src[i].end();
+
+                    for (Mat_<T>::iterator it = beginIt; it != endIt; ++it)
+                     // histogram filling
+                    {
+                        int pos = 0;
+                        float minValue = inputMin - 0.5f;
+                        float maxValue = inputMax + 0.5f;
+                        T val = *it;
+
+                        float interval = float(maxValue - minValue) / bins;
+
+                        for (int j = 0; j < depth; ++j)
+                        {
+                            int currentBin = (val - minValue + 1e-4) / interval;
+                            ++hist[pos + currentBin];
+
+                            pos = (pos + currentBin)*bins;
+
+                            minValue = minValue + currentBin*interval;
+                            maxValue = minValue + interval;
+
+                            interval /= bins;
+                        }
+                    }
+
+                    int total = src[i].total();
+
+                    int p1 = 0, p2 = bins - 1;
+                    int n1 = 0, n2 = total;
+
+                    float minValue = inputMin - 0.5f;
+                    float maxValue = inputMax + 0.5f;
+
+                    float interval = (maxValue - minValue) / float(bins);
+
+                    for (int j = 0; j < depth; ++j)
+                     // searching for s1 and s2
+                    {
+                        while (n1 + hist[p1] < s1 * total / 100.0f)
+                        {
+                            n1 += hist[p1++];
+                            minValue += interval;
+                        }
+                        p1 *= bins;
+
+                        while (n2 - hist[p2] > (100.0f - s2) * total / 100.0f)
+                        {
+                            n2 -= hist[p2--];
+                            maxValue -= interval;
+                        }
+                        p2 = p2*bins - 1;
+
+                        interval /= bins;
+                    }
+
+                    src[i] = (outputMax - outputMin) * (src[i] - minValue)
+                        / (maxValue - minValue) + outputMin;
+                }
+                /****************************************************************/
+                break;
+            }
+        default:
+            CV_Assert( false );
+        }
+
+        dst.create(/**/ src[0].size(), CV_MAKETYPE( src[0].depth(), src.size() ) /**/);
+        cv::merge(src, dst);
+    }
+
+    /*!
+    * Wrappers over different white balance algorithm
+    *
+    * \param src : source image (RGB)
+    * \param dst : destination image
+    *
+    * \param inputMin : minimum input value
+    * \param inputMax : maximum input value
+    * \param outputMin : minimum output value
+    * \param outputMax : maximum output value
+    *
+    * \param algorithmType : type of the algorithm to use
+    */
+    void balanceWhite(const Mat &src, Mat &dst, const int algorithmType,
+        const float inputMin, const float inputMax,
+        const float outputMin, const float outputMax)
+    {
+        switch ( src.depth() )
+        {
+        case CV_8U:
+            {
+                std::vector < Mat_<uchar> > mv;
+                split(src, mv);
+                balanceWhite(mv, dst, inputMin, inputMax, outputMin, outputMax, algorithmType);
+                break;
+            }
+        case CV_16S:
+            {
+                std::vector < Mat_<short> > mv;
+                split(src, mv);
+                balanceWhite(mv, dst, inputMin, inputMax, outputMin, outputMax, algorithmType);
+                break;
+            }
+        case CV_32S:
+            {
+                std::vector < Mat_<int> > mv;
+                split(src, mv);
+                balanceWhite(mv, dst, inputMin, inputMax, outputMin, outputMax, algorithmType);
+                break;
+            }
+        case CV_32F:
+            {
+                std::vector < Mat_<float> > mv;
+                split(src, mv);
+                balanceWhite(mv, dst, inputMin, inputMax, outputMin, outputMax, algorithmType);
+                break;
+            }
+        default:
+            CV_Assert( false );
+            break;
+        }
+    }
+}

modules/colorbalance/test/simple_color_balance.cpp

@@ -0,0 +1,29 @@
+#include "test_precomp.hpp"
+
+namespace cvtest
+{
+    TEST(colorbalance_SimpleColorBalance, regression)
+    {
+        cv::String dir = cvtest::TS::ptr()->get_data_path();
+        int nTests = 12;
+        float threshold = 0.005;
+
+        for (int i = 0; i < nTests; ++i)
+        {
+            cv::String srcName = dir + cv::format( "sources/%02d.png", i + 1);
+            cv::Mat src = cv::imread( srcName, 1 );
+
+            cv::String previousResultName = dir + cv::format( "results/%02d.png", i + 1 );
+            cv::Mat previousResult = cv::imread( previousResultName, 1 );
+
+            cv::Mat currentResult;
+            cv::balanceWhite(src, currentResult, cv::WHITE_BALANCE_SIMPLE);
+
+            cv::Mat sqrError = ( currentResult - previousResult )
+                .mul( currentResult - previousResult );
+            cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( sqrError.total()*sqrError.channels() );
+
+            EXPECT_LE( mse[0]+mse[1]+mse[2]+mse[3], threshold );
+        }
+    }
+}

modules/colorbalance/test/test_main.cpp

@@ -0,0 +1,3 @@
+#include "test_precomp.hpp"
+
+CV_TEST_MAIN("colorbalance")

modules/colorbalance/test/test_precomp.hpp

@@ -0,0 +1,20 @@
+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_TEST_PRECOMP_HPP__
+#define __OPENCV_TEST_PRECOMP_HPP__
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/imgproc/types_c.h"
+#include "opencv2/highgui.hpp"
+#include "opencv2/colorbalance.hpp"
+#include "opencv2/ts.hpp"
+#include <iostream>
+
+#endif

modules/ximpgroc/doc/ximpgroc.rst

@@ -5,4 +5,4 @@ ximpgroc. Advanced edge-detection algorithms
 .. toctree::
     :maxdepth: 2
 
-    Structured forests for fast edge detection <structured_edge_detection/index>
+    Structured forests for fast edge detection <ximpgroc/structured_edge_detection>

modules/ximpgroc/doc/ximpgroc/structured_edge_detection.rst

@@ -3,7 +3,8 @@ Structured forests for fast edge detection
 
 .. highlight:: cpp
 
-...
+This module contains implementations of modern structured edge detection algorithms,
+i.e. algorithms which somehow takes into account pixel affinities in natural images.
 
 StructuredEdgeDetection
 -----------------------
@@ -40,12 +41,12 @@ StructuredEdgeDetection::detectEdges
 ++++++++++++++++++++++++++++++++++++
 .. ocv:function:: void detectEdges(const Mat src, Mat dst)
 
-The function detects edges in src and draw them to dst. The algorithm underlies this function
-is much more robust to texture presence, than common approaches, e.g. Sobel
+    The function detects edges in src and draw them to dst. The algorithm underlies this function
+    is much more robust to texture presence, than common approaches, e.g. Sobel
 
-    :param src : source image (RGB, float, in [0;1]) to detect edges
-    :param dst : destination image (grayscale, float, in [0;1])
-                 where edges are drawn
+    :param src: source image (RGB, float, in [0;1]) to detect edges
+    :param dst: destination image (grayscale, float, in [0;1])
+                where edges are drawn
 
 .. seealso::
 
@@ -56,15 +57,11 @@ createStructuredEdgeDetection
 +++++++++++++++++++++++++++++
 .. ocv:function:: Ptr<cv::StructuredEdgeDetection> createStructuredEdgeDetection(String model)
 
-The only available constructor
+    The only available constructor
 
     :param model: model file name
 
 
-Literature
-----------
-
 .. [Dollar2013] Dollár P., Zitnick C. L., "Structured forests for fast edge detection",
                 IEEE International Conference on Computer Vision (ICCV), 2013,
                 pp. 1841-1848. `DOI <http://dx.doi.org/10.1109/ICCV.2013.231>`_
-

modules/ximpgroc/samples/structured_edge_detection.cpp

@@ -6,8 +6,6 @@
 #include "opencv2/core/utility.hpp"
 #include "opencv2/imgproc/types_c.h"
 
-#include <iostream>
-
 const char* keys =
 {
     "{i || input image name}"
@@ -47,7 +45,6 @@ int main( int argc, const char** argv )
         return -1;
     }
 
-    cv::cvtColor(image, image, CV_BGR2RGB);
     image.convertTo(image, cv::DataType<float>::type, 1/255.0);
 
     cv::Mat edges(image.size(), image.type());

modules/ximpgroc/src/structured_edge_detection.cpp

@@ -88,7 +88,7 @@ static cv::Mat imsmooth(const cv::Mat &src, const int rad)
         return src;
     else
     {
-        const float p = 12/rad/(rad + 2) - 2;
+        const float p = 12.0f/rad/(rad + 2) - 2;
         cv::Mat dst;
 
         if (rad <= 1)
@@ -98,7 +98,7 @@ static cv::Mat imsmooth(const cv::Mat &src, const int rad)
         }
         else
         {
-            float nrml = CV_SQR(rad + 1);
+            float nrml = CV_SQR(rad + 1.0f);
 
             std::vector <float> kernelXY(2*rad + 1);
             for (int i = 0; i <= rad; ++i)
@@ -164,7 +164,7 @@ static cv::Mat rgb2luv(const cv::Mat &src)
             const float xyz[] = {mX[0]*rgb[0] + mX[1]*rgb[1] + mX[2]*rgb[2],
                                  mY[0]*rgb[0] + mY[1]*rgb[1] + mY[2]*rgb[2],
                                  mZ[0]*rgb[0] + mZ[1]*rgb[1] + mZ[2]*rgb[2]};
-            const float nz = 1.0f/(xyz[0] + 15*xyz[1] + 3*xyz[2] + 1e-35);
+            const float nz = 1.0f / float(xyz[0] + 15*xyz[1] + 3*xyz[2] + 1e-35);
 
             const float l = pDst[j] = lTable[cvFloor(1024*xyz[1])];
 
@@ -217,7 +217,7 @@ static void gradientHist(const cv::Mat &src, cv::Mat &magnitude, cv::Mat &histog
 
         for (int j = 0; j < src.cols*nchannels; j += nchannels)
         {
-            float fMagn = -1e-5, fdx, fdy;
+            float fMagn = float(-1e-5), fdx, fdy;
             for (int k = 0; k < nchannels; ++k)
             {
                 float cMagn = CV_SQR( pDx[j + k] ) + CV_SQR( pDy[j + k] );
@@ -534,7 +534,7 @@ protected:
         // half of cell
         std::vector <int> gridPositions;
         for(int i = 0; i < gridSize; i++)
-            gridPositions.push_back( (i+1)*(pSize/shrink + 2*hc - 1)/(gridSize + 1.0) - hc + 0.5 );
+            gridPositions.push_back( int( (i+1)*(pSize/shrink + 2*hc - 1)/(gridSize + 1.0) - hc + 0.5f ) );
 
         for (int i = 0, n = 0; i < CV_SQR(gridSize)*nchannels; ++i)
             for (int j = (i%CV_SQR(gridSize)) + 1; j < CV_SQR(gridSize); ++j, ++n)
@@ -623,7 +623,7 @@ protected:
                 }
         }
 
-        cv::reduce( dstM.reshape(1, dstM.total() ), dstM, 2, CV_REDUCE_SUM);
+        cv::reduce( dstM.reshape(1, int( dstM.total() ) ), dstM, 2, CV_REDUCE_SUM);
         imsmooth( dstM.reshape(1, dst.rows), 1 ).copyTo(dst);
     }
 

modules/ximpgroc/test/structured_edge_detection.cpp

@@ -5,9 +5,9 @@ namespace cvtest
 
 TEST(ximpgroc_StructuredEdgeDetection, regression)
 {
-    cv::String dir = "C:/Users/Yury/Projects/opencv/sources/opencv_contrib/modules/ximpgroc/testdata/";//cvtest::TS::ptr()->get_data_path();
+    cv::String dir = cvtest::TS::ptr()->get_data_path();
     int nTests = 12;
-    float threshold = 0.01;
+    float threshold = 0.01f;
 
     cv::String modelName = dir + "model.yml.gz";
     cv::Ptr<cv::StructuredEdgeDetection> pDollar =
@@ -22,7 +22,6 @@ TEST(ximpgroc_StructuredEdgeDetection, regression)
         cv::Mat previousResult = cv::imread( previousResultName, 0 );
         previousResult.convertTo( previousResult, cv::DataType<float>::type, 1/255.0 );
 
-        cv::cvtColor( src, src, CV_BGR2RGB );
         src.convertTo( src, cv::DataType<float>::type, 1/255.0 );
 
         cv::Mat currentResult( src.size(), src.type() );
@@ -30,7 +29,7 @@ TEST(ximpgroc_StructuredEdgeDetection, regression)
 
         cv::Mat sqrError = ( currentResult - previousResult )
             .mul( currentResult - previousResult );
-        cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( sqrError.total() );
+        cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( double( sqrError.total() ) );
 
         EXPECT_LE( mse[0], threshold );
     }