try to fix more errors

modules/ximpgroc/src/structured_edge_detection.cpp

@@ -342,10 +342,10 @@ public:
      * \param filename : name of the file where the model is stored
      */
     StructuredEdgeDetectionImpl(const cv::String &filename,
-        const RFFeatureGetter *howToGetFeatures)
+        const RFFeatureGetter *_howToGetFeatures)
         : name("StructuredEdgeDetection"),
-          howToGetFeatures( howToGetFeatures != NULL
-                          ? howToGetFeatures
+          howToGetFeatures( _howToGetFeatures != NULL
+                          ? _howToGetFeatures
                           : createRFFeatureGetter() )
     {
         cv::FileStorage modelFile(filename, FileStorage::READ);

modules/xphoto/doc/colorbalance/whitebalance.rst

@@ -5,9 +5,7 @@ Automatic white balance correction
 
 balanceWhite
 ------------
-.. ocv:function:: 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)
+.. ocv:function:: 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)
 
 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
@@ -15,9 +13,7 @@ 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 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
@@ -25,5 +21,5 @@ due to specific illumination or camera settings).
 
 .. seealso::
 
-    :ocv:function:`cvtColor`,
-    :ocv:function:`equalizeHist`
+    :ocv:func:`cvtColor`,
+    :ocv:func:`equalizeHist`
\ No newline at end of file

modules/xphoto/doc/denoising/denoising.rst

@@ -17,4 +17,4 @@ link: http://www.ipol.im/pub/art/2011/ys-dct/.
 
 .. seealso::
 
-    :ocv:function:`fastNLMeansDenoising`
+    :ocv:func:`fastNLMeansDenoising`
\ No newline at end of file

modules/xphoto/src/dct_image_denoising.cpp

@@ -134,21 +134,21 @@ namespace cv
     {
         CV_Assert( src.type() == CV_MAKE_TYPE(CV_32F, 3) );
 
-        Mat m = Mat_<float>(3, 3) << (cvInvSqrt(3),  cvInvSqrt(3),       cvInvSqrt(3),
+        cv::Matx33f mt(cvInvSqrt(3),  cvInvSqrt(3),       cvInvSqrt(3),
                        cvInvSqrt(2),  0.0f,              -cvInvSqrt(2),
                        cvInvSqrt(6), -2.0f*cvInvSqrt(6),  cvInvSqrt(6));
 
-        cv::transform(src, dst, m);
+        cv::transform(src, dst, mt);
 
         std::vector <Mat> mv;
         split(dst, mv);
 
-        for (int i = 0; i < mv.size(); ++i)
+        for (size_t i = 0; i < mv.size(); ++i)
             grayDctDenoising(mv[i], mv[i], sigma, psize);
 
         merge(mv, dst);
 
-        cv::transform( dst, dst, m.inv() );
+        cv::transform( dst, dst, mt.inv() );
     }
 
     /*! This function implements simple dct-based image denoising,

modules/xphoto/src/inpainting.cpp

@@ -38,6 +38,7 @@
 //M*/
 
 #include <vector>
+#include <limits>
 #include <algorithm>
 #include <iterator>
 #include <iostream>

modules/xphoto/src/simple_color_balance.cpp

@@ -94,7 +94,7 @@ namespace cv
 
                             for (int j = 0; j < depth; ++j)
                             {
-                            int currentBin = (val - minValue + 1e-4) / interval;
+                                int currentBin = int( (val - minValue + 1e-4f) / interval );
                                 ++hist[pos + currentBin];
 
                                 pos = (pos + currentBin)*bins;
@@ -106,7 +106,7 @@ namespace cv
                             }
                         }
 
-                    int total = src[i].total();
+                        int total = int( src[i].total() );
 
                         int p1 = 0, p2 = bins - 1;
                         int n1 = 0, n2 = total;
@@ -143,10 +143,11 @@ namespace cv
                     break;
                 }
             default:
-            CV_Assert( false );
+                CV_Error_( CV_StsNotImplemented,
+                    ("Unsupported algorithm type (=%d)", algorithmType) );
         }
 
-        dst.create(/**/ src[0].size(), CV_MAKETYPE( src[0].depth(), src.size() ) /**/);
+        dst.create(/**/ src[0].size(), CV_MAKETYPE( src[0].depth(), int( src.size() ) ) /**/);
         cv::merge(src, dst);
     }
 
@@ -198,7 +199,8 @@ namespace cv
                     break;
                 }
             default:
-            CV_Assert( false );
+                CV_Error_( CV_StsNotImplemented,
+                    ("Unsupported source image format (=%d)", src.type()) );
                 break;
         }
     }

modules/xphoto/test/dct_image_denoising.cpp

@@ -7,7 +7,7 @@ namespace cvtest
         cv::String dir = cvtest::TS::ptr()->get_data_path() + "dct_image_denoising/";
         int nTests = 1;
 
-        float psnrThreshold[] = {0.5};
+        float thresholds[] = {0.1};
 
         int psize[] = {8};
         double sigma[] = {9.0};
@@ -26,10 +26,9 @@ namespace cvtest
 
             cv::Mat sqrError = ( currentResult - previousResult )
                 .mul( currentResult - previousResult );
-            cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( sqrError.total()*sqrError.channels() );
-            double psnr = 10*log10(3*255*255/(mse[0] + mse[1] + mse[2])) - psnr;
+            cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( double(sqrError.total()*sqrError.channels()) );
 
-            EXPECT_GE( psnr, psnrThreshold[i] );
+            EXPECT_LE( mse[0] + mse[1] + mse[2] + mse[3], thresholds[i] );
         }
     }
 }
\ No newline at end of file

modules/xphoto/test/simple_color_balance.cpp

@@ -6,7 +6,7 @@ namespace cvtest
     {
         cv::String dir = cvtest::TS::ptr()->get_data_path() + "simple_white_balance/";
         int nTests = 12;
-        float threshold = 0.005;
+        float threshold = 0.005f;
 
         for (int i = 0; i < nTests; ++i)
         {
@@ -21,7 +21,7 @@ namespace cvtest
 
             cv::Mat sqrError = ( currentResult - previousResult )
                 .mul( currentResult - previousResult );
-            cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( sqrError.total()*sqrError.channels() );
+            cv::Scalar mse = cv::sum(sqrError) / cv::Scalar::all( double( sqrError.total()*sqrError.channels() ) );
 
             EXPECT_LE( mse[0]+mse[1]+mse[2]+mse[3], threshold );
         }