Merge branch 4.x

Author: asmorkalov

modules/cudafilters/include/opencv2/cudafilters.hpp

@@ -359,13 +359,18 @@ CV_EXPORTS_W Ptr<Filter> createColumnSumFilter(int srcType, int dstType, int ksi
 
 /** @brief Performs median filtering for each point of the source image.
 
-@param srcType type of of source image. Only CV_8UC1 images are supported for now.
+@param srcType type of of source image. Only CV_8UC1 if CUDA_VERSION < 11. CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32FC1, CV_32FC3 or CV_32FC4 if CUDA_VERSION >= 11.
 @param windowSize Size of the kernerl used for the filtering. Uses a (windowSize x windowSize) filter.
-@param partition Specifies the parallel granularity of the workload. This parameter should be used GPU experts when optimizing performance.
+@param partition Specifies the parallel granularity of the workload. Only used if CUDA_VERSION < 11.
 
 Outputs an image that has been filtered using a median-filtering formulation.
 
-Details on this algorithm can be found in:
+If compiled with CUDA 11 or greater, a wavelet based algorithm is used as described at:
+https://cgenglab.github.io/en/publication/sigga22_wmatrix_median/
+Yuji Moroto, Nobuyuki Umetani, 2022, "Constant Time Median Filter Using 2D Wavelet Matrix",
+                                     ACM Transactions on Graphics, Volume 41, Issue 6.
+
+For earlier versions of CUDA, details on the algorithm used can be found in:
 Green, O., 2017. "Efficient scalable median filtering using histogram-based operations",
                   IEEE Transactions on Image Processing, 27(5), pp.2217-2228.
 

modules/cudaimgproc/doc/cudaimgproc.bib

@@ -8,3 +8,12 @@ @article{Allegretti2019
   year={2019},
   publisher={IEEE}
 }
+
+@article{BT.709,
+  title={Recommendation ITU-R BT.709-6},
+  author={ITU},
+  pages={3},
+  year={2015},
+  publisher={ITU}
+  url={https://www.itu.int/dms_pubrec/itu-r/rec/bt/R-REC-BT.709-6-201506-I!!PDF-E.pdf}
+}

modules/cudaimgproc/doc/pics/gammacorrection.png

@@ -154,6 +154,19 @@ CV_EXPORTS void swapChannels(InputOutputArray image, const int dstOrder[4], Stre
 @param dst Destination image.
 @param forward true for forward gamma correction or false for inverse gamma correction.
 @param stream Stream for the asynchronous version.
+
+Gamma correction is conformant to BT.709 @cite BT.709 with γ=0.45.
+
+For the forward transform, RGB values are normalised to fit in the range L=[0..1], then:
+- For L < 0.018
+  + V = 4.5*L
+- For L >= 0.018
+  + V = 1.099 * L^0.45 - 0.099
+
+With V then being scaled back to [0..255].
+
+![image](pics/gammacorrection.png)
+
  */
 CV_EXPORTS_W void gammaCorrection(InputArray src, OutputArray dst, bool forward = true, Stream& stream = Stream::Null());
 

modules/cudaimgproc/include/opencv2/cudaimgproc.hpp

@@ -4,6 +4,6 @@ endif()
 
 set(the_description "Saliency API")
 
-ocv_define_module(saliency opencv_imgproc opencv_features WRAP python)
+ocv_define_module(saliency opencv_imgproc opencv_features WRAP python java)
 
 ocv_warnings_disable(CMAKE_CXX_FLAGS -Woverloaded-virtual)

modules/saliency/CMakeLists.txt

@@ -87,7 +87,7 @@ class CV_EXPORTS_W StaticSaliencySpectralResidual : public StaticSaliency
   }
 
   CV_WRAP void read( const FileNode& fn ) CV_OVERRIDE;
-  void write( FileStorage& fs ) const CV_OVERRIDE;
+  CV_WRAP void write( FileStorage& fs ) const CV_OVERRIDE;
 
   CV_WRAP int getImageWidth() const
   {
@@ -308,9 +308,6 @@ class CV_EXPORTS_W ObjectnessBING : public Objectness
     return computeSaliencyImpl( image, saliencyMap );
   }
 
-  CV_WRAP void read();
-  CV_WRAP void write() const;
-
   /** @brief Return the list of the rectangles' objectness value,
 
     in the same order as the *vector\<Vec4i\> objectnessBoundingBox* returned by the algorithm (in

modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp

@@ -448,21 +448,12 @@ bool ObjectnessBING::matRead( const std::string& filename, Mat& _M )
   M.copyTo( _M );
   return true;
 }
+
 std::vector<float> ObjectnessBING::getobjectnessValues()
 {
   return objectnessValues;
 }
 
-void ObjectnessBING::read()
-{
-
-}
-
-void ObjectnessBING::write() const
-{
-
-}
-
 bool ObjectnessBING::computeSaliencyImpl( InputArray image, OutputArray objectnessBoundingBox )
 {
   ValStructVec<float, Vec4i> finalBoxes;