Merge remote-tracking branch 'origin/2.4' into merge-2.4

Conflicts:
	CMakeLists.txt
	cmake/OpenCVDetectCUDA.cmake
	doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst
	modules/core/src/cmdparser.cpp
	modules/gpu/CMakeLists.txt
	modules/gpu/doc/introduction.rst
	modules/gpu/perf/perf_video.cpp
	modules/highgui/doc/reading_and_writing_images_and_video.rst
	modules/ocl/src/cl_context.cpp
	modules/video/include/opencv2/video/background_segm.hpp
	samples/cpp/image_sequence.cpp
	samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp
	samples/python/chessboard.py
	samples/python/cvutils.py
	samples/python/demhist.py
	samples/python/dft.py
	samples/python/distrans.py
	samples/python/edge.py
	samples/python/ffilldemo.py
	samples/python/fitellipse.py
	samples/python/houghlines.py
	samples/python/inpaint.py
	samples/python/logpolar.py
	samples/python/morphology.py
	samples/python/numpy_array.py
	samples/python/watershed.py

samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp

@@ -7,50 +7,102 @@
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"
 #include <iostream>
-#include <stdio.h>
 
 using namespace std;
 using namespace cv;
 
-/**
- * @function main
- */
-int main(int, char** argv)
+namespace
 {
-   Mat src, src_gray;
+    // windows and trackbars name
+    const std::string windowName = "Hough Circle Detection Demo";
+    const std::string cannyThresholdTrackbarName = "Canny threshold";
+    const std::string accumulatorThresholdTrackbarName = "Accumulator Threshold";
+    const std::string usage = "Usage : tutorial_HoughCircle_Demo <path_to_input_image>\n";
 
-   /// Read the image
-   src = imread( argv[1], 1 );
+    // initial and max values of the parameters of interests.
+    const int cannyThresholdInitialValue = 200;
+    const int accumulatorThresholdInitialValue = 50;
+    const int maxAccumulatorThreshold = 200;
+    const int maxCannyThreshold = 255;
 
-   if( !src.data )
-     { return -1; }
-
-   /// Convert it to gray
-    cvtColor( src, src_gray, COLOR_BGR2GRAY );
-
-   /// Reduce the noise so we avoid false circle detection
-    GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
-
-    vector<Vec3f> circles;
-
-   /// Apply the Hough Transform to find the circles
-    HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );
-
-   /// Draw the circles detected
-    for( size_t i = 0; i < circles.size(); i++ )
+    void HoughDetection(const Mat& src_gray, const Mat& src_display, int cannyThreshold, int accumulatorThreshold)
     {
-         Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
-         int radius = cvRound(circles[i][2]);
-         // circle center
-         circle( src, center, 3, Scalar(0,255,0), -1, 8, 0 );
-         // circle outline
-         circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );
+        // will hold the results of the detection
+        std::vector<Vec3f> circles;
+        // runs the actual detection
+        HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, src_gray.rows/8, cannyThreshold, accumulatorThreshold, 0, 0 );
+
+        // clone the colour, input image for displaying purposes
+        Mat display = src_display.clone();
+        for( size_t i = 0; i < circles.size(); i++ )
+        {
+            Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
+            int radius = cvRound(circles[i][2]);
+            // circle center
+            circle( display, center, 3, Scalar(0,255,0), -1, 8, 0 );
+            // circle outline
+            circle( display, center, radius, Scalar(0,0,255), 3, 8, 0 );
+        }
+
+        // shows the results
+        imshow( windowName, display);
+    }
+}
+
+
+int main(int argc, char** argv)
+{
+    Mat src, src_gray;
+
+    if (argc < 2)
+    {
+        std::cerr<<"No input image specified\n";
+        std::cout<<usage;
+        return -1;
     }
 
-   /// Show your results
-    namedWindow( "Hough Circle Transform Demo", WINDOW_AUTOSIZE );
-    imshow( "Hough Circle Transform Demo", src );
+    // Read the image
+    src = imread( argv[1], 1 );
+
+    if( !src.data )
+    {
+        std::cerr<<"Invalid input image\n";
+        std::cout<<usage;
+        return -1;
+    }
+
+    // Convert it to gray
+    cvtColor( src, src_gray, COLOR_BGR2GRAY );
+
+    // Reduce the noise so we avoid false circle detection
+    GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
+
+    //declare and initialize both parameters that are subjects to change
+    int cannyThreshold = cannyThresholdInitialValue;
+    int accumulatorThreshold = accumulatorThresholdInitialValue;
+
+    // create the main window, and attach the trackbars
+    namedWindow( windowName, WINDOW_AUTOSIZE );
+    createTrackbar(cannyThresholdTrackbarName, windowName, &cannyThreshold,maxCannyThreshold);
+    createTrackbar(accumulatorThresholdTrackbarName, windowName, &accumulatorThreshold, maxAccumulatorThreshold);
+
+    // infinite loop to display
+    // and refresh the content of the output image
+    // until the user presses q or Q
+    int key = 0;
+    while(key != 'q' && key != 'Q')
+    {
+        // those paramaters cannot be =0
+        // so we must check here
+        cannyThreshold = std::max(cannyThreshold, 1);
+        accumulatorThreshold = std::max(accumulatorThreshold, 1);
+
+        //runs the detection, and update the display
+        HoughDetection(src_gray, src, cannyThreshold, accumulatorThreshold);
+
+        // get user key
+        key = waitKey(10);
+    }
 
-    waitKey(0);
     return 0;
 }

samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp

@@ -66,12 +66,14 @@ int main( int argc, char** argv )
   printf("-- Max dist : %f \n", max_dist );
   printf("-- Min dist : %f \n", min_dist );
 
-  //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
+  //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
+  //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
+  //-- small)
   //-- PS.- radiusMatch can also be used here.
   std::vector< DMatch > good_matches;
 
   for( int i = 0; i < descriptors_1.rows; i++ )
-  { if( matches[i].distance <= 2*min_dist )
+  { if( matches[i].distance <= max(2*min_dist, 0.02) )
     { good_matches.push_back( matches[i]); }
   }
 

samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp

@@ -73,7 +73,7 @@ int main(int, char *argv[])
     BufferPSNR bufferPSNR;
     BufferMSSIM bufferMSSIM;
 
-    int TIMES;
+    int TIMES = 10;
     stringstream sstr(argv[3]);
     sstr >> TIMES;
     double time, result = 0;