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Merge remote-tracking branch 'origin/2.4' into merge-2.4
Conflicts: cmake/OpenCVDetectAndroidSDK.cmake cmake/OpenCVGenAndroidMK.cmake cmake/OpenCVModule.cmake cmake/templates/OpenCV.mk.in cmake/templates/OpenCVConfig.cmake.in doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.rst modules/cudabgsegm/src/cuda/mog.cu modules/imgproc/perf/opencl/perf_filters.cpp modules/imgproc/src/opencl/filterSep_singlePass.cl modules/nonfree/CMakeLists.txt modules/nonfree/perf/perf_precomp.hpp modules/ocl/perf/perf_haar.cpp modules/ocl/src/filtering.cpp modules/ocl/src/opencl/bgfg_mog.cl modules/superres/CMakeLists.txt modules/superres/src/btv_l1_cuda.cpp modules/superres/src/cuda/btv_l1_gpu.cu modules/superres/src/frame_source.cpp modules/superres/src/input_array_utility.cpp modules/superres/src/optical_flow.cpp modules/superres/src/precomp.hpp samples/gpu/CMakeLists.txt samples/gpu/brox_optical_flow.cpp samples/gpu/super_resolution.cpp
This commit is contained in:
Roman Donchenko
@@ -6,12 +6,12 @@ Adding (blending) two images using OpenCV
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Goal
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=====
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In this tutorial you will learn how to:
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In this tutorial you will learn:
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.. container:: enumeratevisibleitemswithsquare
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* What is *linear blending* and why it is useful.
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* Add two images using :add_weighted:`addWeighted <>`
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* what is *linear blending* and why it is useful;
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* how to add two images using :add_weighted:`addWeighted <>`
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Theory
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=======
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@@ -18,7 +18,7 @@ We'll seek answers for the following questions:
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Our test case
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=============
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Let us consider a simple color reduction method. Using the unsigned char C and C++ type for matrix item storing a channel of pixel may have up to 256 different values. For a three channel image this can allow the formation of way too many colors (16 million to be exact). Working with so many color shades may give a heavy blow to our algorithm performance. However, sometimes it is enough to work with a lot less of them to get the same final result.
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Let us consider a simple color reduction method. By using the unsigned char C and C++ type for matrix item storing, a channel of pixel may have up to 256 different values. For a three channel image this can allow the formation of way too many colors (16 million to be exact). Working with so many color shades may give a heavy blow to our algorithm performance. However, sometimes it is enough to work with a lot less of them to get the same final result.
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In this cases it's common that we make a *color space reduction*. This means that we divide the color space current value with a new input value to end up with fewer colors. For instance every value between zero and nine takes the new value zero, every value between ten and nineteen the value ten and so on.
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@@ -84,88 +84,10 @@ Code
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* **Code at glance:**
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.. code-block:: cpp
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.. literalinclude:: ../../../../../samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp
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:language: cpp
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:tab-width: 4
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <stdio.h>
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using namespace std;
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using namespace cv;
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/** @function main */
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int main( int argc, char** argv )
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{
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Mat src_base, hsv_base;
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Mat src_test1, hsv_test1;
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Mat src_test2, hsv_test2;
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Mat hsv_half_down;
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/// Load three images with different environment settings
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if( argc < 4 )
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{ printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_setting1> <image_settings2>\n");
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return -1;
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}
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src_base = imread( argv[1], 1 );
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src_test1 = imread( argv[2], 1 );
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src_test2 = imread( argv[3], 1 );
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/// Convert to HSV
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cvtColor( src_base, hsv_base, CV_BGR2HSV );
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cvtColor( src_test1, hsv_test1, CV_BGR2HSV );
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cvtColor( src_test2, hsv_test2, CV_BGR2HSV );
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hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
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/// Using 30 bins for hue and 32 for saturation
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int h_bins = 50; int s_bins = 60;
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int histSize[] = { h_bins, s_bins };
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// hue varies from 0 to 256, saturation from 0 to 180
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float h_ranges[] = { 0, 256 };
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float s_ranges[] = { 0, 180 };
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const float* ranges[] = { h_ranges, s_ranges };
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// Use the o-th and 1-st channels
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int channels[] = { 0, 1 };
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/// Histograms
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MatND hist_base;
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MatND hist_half_down;
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MatND hist_test1;
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MatND hist_test2;
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/// Calculate the histograms for the HSV images
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calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
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normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );
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calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );
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normalize( hist_half_down, hist_half_down, 0, 1, NORM_MINMAX, -1, Mat() );
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calcHist( &hsv_test1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false );
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normalize( hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat() );
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calcHist( &hsv_test2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false );
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normalize( hist_test2, hist_test2, 0, 1, NORM_MINMAX, -1, Mat() );
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/// Apply the histogram comparison methods
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for( int i = 0; i < 4; i++ )
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{ int compare_method = i;
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double base_base = compareHist( hist_base, hist_base, compare_method );
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double base_half = compareHist( hist_base, hist_half_down, compare_method );
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double base_test1 = compareHist( hist_base, hist_test1, compare_method );
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double base_test2 = compareHist( hist_base, hist_test2, compare_method );
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printf( " Method [%d] Perfect, Base-Half, Base-Test(1), Base-Test(2) : %f, %f, %f, %f \n", i, base_base, base_half , base_test1, base_test2 );
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}
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printf( "Done \n" );
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return 0;
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}
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Explanation
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@@ -211,11 +133,11 @@ Explanation
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.. code-block:: cpp
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int h_bins = 50; int s_bins = 32;
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int h_bins = 50; int s_bins = 60;
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int histSize[] = { h_bins, s_bins };
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float h_ranges[] = { 0, 256 };
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float s_ranges[] = { 0, 180 };
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float h_ranges[] = { 0, 180 };
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float s_ranges[] = { 0, 256 };
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const float* ranges[] = { h_ranges, s_ranges };
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