Smoothing OpenCV

smoothing.cpp

#include <opencv2/imgproc/imgproc.hpp>
#include "opencv2/highgui/highgui.hpp"
#include <iostream>

using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
	
	// This tutorial will used the newly defined C++ interfaces which use memory managed containers instead of the 
	// older IplImage format which was based on the older C interfaces and had issues of memory management.
	Mat src,result;	
	int size;
	cout<<"Enter the kernel size for the operations\n";
	cin>>size;

	namedWindow( "source image", CV_WINDOW_AUTOSIZE );

	src = imread(argv[1],-1);
	imshow( "source image", src );


	/*
	Apply blur or averaging filter:
	Size( w,h ): Defines the size of the kernel to be used ( of width w pixels and height h pixels)
	Point(-1, -1): Indicates where the anchor point (the pixel evaluated) is located with respect to the neighborhood.
	If there is a negative value, then the center of the kernel is considered the anchor point.
	*/

	cout<<"Normalized blurring the image\n";
	namedWindow( "normalized image", CV_WINDOW_AUTOSIZE );
	blur(src, result, Size(size,size),Point(-1,-1));
	imshow("normalized image", result);

	cout<<"Gaussian blurring the image\n";
	namedWindow("gaussian blurred image", CV_WINDOW_AUTOSIZE );
	GaussianBlur(src,result,Size(size,size),0,0);
	imshow("gaussian blurred image", result);
	
	cout<<"median blurring the image\n";
	namedWindow("median blurred image", CV_WINDOW_AUTOSIZE );
	medianBlur ( src, result, size );
	imshow("median blurred image", result);

	cout<<"bilateral filtering the image\n";
	namedWindow("bilateral blurred image", CV_WINDOW_AUTOSIZE );
	bilateralFilter ( src, result, size, size*2, size/2 );
	imshow("bilateral blurred image", result);
	waitKey();

}