Merge branch 'GSOC_text_detect_DNN_backend' of https://github.com/sghoshcvc/opencv_contrib into GSOC_text_detect_DNN_backend

merge conflict

Author: sghoshcvc

modules/aruco/src/charuco.cpp

@@ -738,7 +738,7 @@ void detectCharucoDiamond(InputArray _image, InputArrayOfArrays _markerCorners,
 
     CV_Assert(_markerIds.total() > 0 && _markerIds.total() == _markerCorners.total());
 
-    const float minRepDistanceRate = 0.12f;
+    const float minRepDistanceRate = 1.302455f;
 
     // create Charuco board layout for diamond (3x3 layout)
     Ptr<Dictionary> dict = getPredefinedDictionary(PREDEFINED_DICTIONARY_NAME(0));
@@ -771,7 +771,7 @@ void detectCharucoDiamond(InputArray _image, InputArrayOfArrays _markerCorners,
           perimeterSq += edge.x*edge.x + edge.y*edge.y;
         }
         // maximum reprojection error relative to perimeter
-        float minRepDistance = perimeterSq * minRepDistanceRate * minRepDistanceRate;
+        float minRepDistance = sqrt(perimeterSq) * minRepDistanceRate;
 
         int currentId = _markerIds.getMat().at< int >(i);
 

modules/bgsegm/include/opencv2/bgsegm.hpp

@@ -189,6 +189,8 @@ CV_EXPORTS_W Ptr<BackgroundSubtractorGMG> createBackgroundSubtractorGMG(int init
 
   About as fast as MOG2 on a high end system.
   More than twice faster than MOG2 on cheap hardware (benchmarked on Raspberry Pi3).
+
+  %Algorithm by Sagi Zeevi ( https://github.com/sagi-z/BackgroundSubtractorCNT )
 */
 class CV_EXPORTS_W BackgroundSubtractorCNT  : public BackgroundSubtractor
 {

modules/bioinspired/include/opencv2/bioinspired/retina.hpp

@@ -422,32 +422,30 @@ class CV_EXPORTS_W Retina : public Algorithm {
     Retina::getParvo methods
      */
     CV_WRAP virtual void activateContoursProcessing(const bool activate)=0;
-};
-
-//! @relates bioinspired::Retina
-//! @{
-
-/** @overload */
-CV_EXPORTS_W Ptr<Retina> createRetina(Size inputSize);
-/** @brief Constructors from standardized interfaces : retreive a smart pointer to a Retina instance
-
-@param inputSize the input frame size
-@param colorMode the chosen processing mode : with or without color processing
-@param colorSamplingMethod specifies which kind of color sampling will be used :
--   cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice
--   cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
--   cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling
-@param useRetinaLogSampling activate retina log sampling, if true, the 2 following parameters can
-be used
-@param reductionFactor only usefull if param useRetinaLogSampling=true, specifies the reduction
-factor of the output frame (as the center (fovea) is high resolution and corners can be
-underscaled, then a reduction of the output is allowed without precision leak
-@param samplingStrenght only usefull if param useRetinaLogSampling=true, specifies the strenght of
-the log scale that is applied
- */
-CV_EXPORTS_W Ptr<Retina> createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f);
 
-//! @}
+    /** @overload */
+    CV_WRAP static Ptr<Retina> create(Size inputSize);
+    /** @brief Constructors from standardized interfaces : retreive a smart pointer to a Retina instance
+
+    @param inputSize the input frame size
+    @param colorMode the chosen processing mode : with or without color processing
+    @param colorSamplingMethod specifies which kind of color sampling will be used :
+    -   cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice
+    -   cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
+    -   cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling
+    @param useRetinaLogSampling activate retina log sampling, if true, the 2 following parameters can
+    be used
+    @param reductionFactor only usefull if param useRetinaLogSampling=true, specifies the reduction
+    factor of the output frame (as the center (fovea) is high resolution and corners can be
+    underscaled, then a reduction of the output is allowed without precision leak
+    @param samplingStrenght only usefull if param useRetinaLogSampling=true, specifies the strenght of
+    the log scale that is applied
+     */
+    CV_WRAP static Ptr<Retina> create(Size inputSize, const bool colorMode,
+                                           int colorSamplingMethod=RETINA_COLOR_BAYER,
+                                           const bool useRetinaLogSampling=false,
+                                           const float reductionFactor=1.0f, const float samplingStrenght=10.0f);
+};
 
 //! @}
 

modules/bioinspired/include/opencv2/bioinspired/retinafasttonemapping.hpp

@@ -126,10 +126,10 @@ class CV_EXPORTS_W RetinaFastToneMapping : public Algorithm
     (default is 1, see reference paper)
      */
     CV_WRAP virtual void setup(const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0;
+
+    CV_WRAP static Ptr<RetinaFastToneMapping> create(Size inputSize);
 };
 
-//! @relates bioinspired::RetinaFastToneMapping
-CV_EXPORTS_W Ptr<RetinaFastToneMapping> createRetinaFastToneMapping(Size inputSize);
 
 //! @}
 

modules/bioinspired/include/opencv2/bioinspired/transientareassegmentationmodule.hpp

@@ -187,13 +187,12 @@ class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
     /** @brief cleans all the buffers of the instance
     */
     CV_WRAP virtual void clearAllBuffers()=0;
-};
 
-/** @brief allocator
-@param inputSize : size of the images input to segment (output will be the same size)
-@relates bioinspired::TransientAreasSegmentationModule
- */
-CV_EXPORTS_W Ptr<TransientAreasSegmentationModule> createTransientAreasSegmentationModule(Size inputSize);
+    /** @brief allocator
+    @param inputSize : size of the images input to segment (output will be the same size)
+     */
+    CV_WRAP static Ptr<TransientAreasSegmentationModule> create(Size inputSize);
+};
 
 //! @}
 

modules/bioinspired/perf/opencl/perf_retina.ocl.cpp

@@ -29,7 +29,7 @@ OCL_PERF_TEST_P(RetinaFixture, Retina,
     UMat ocl_parvo, ocl_magno;
 
     {
-        Ptr<cv::bioinspired::Retina> retina = cv::bioinspired::createRetina(
+        Ptr<cv::bioinspired::Retina> retina = cv::bioinspired::Retina::create(
             input.size(), colorMode, colorSamplingMethod, useLogSampling,
             reductionFactor, samplingStrength);
 

modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp

@@ -220,10 +220,10 @@ int main(int argc, char* argv[])
 		 */
 		if (useLogSampling)
 		{
-			retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
+            retina = cv::bioinspired::Retina::create(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
 		}
 		else// -> else allocate "classical" retina :
-			retina = cv::bioinspired::createRetina(inputImage.size());
+            retina = cv::bioinspired::Retina::create(inputImage.size());
 
 		// create a fast retina tone mapper (Meyla&al algorithm)
 		std::cout<<"Allocating fast tone mapper..."<<std::endl;