[OpenCV] Image Filtering

[Theory]

where

f(i+k, j+l) : source image

h(k, l) : kernel

g(i, j) : filtered image

 

---

[File Types]

• Box Filter
• Gaussian Filter
• Median Filter
• Bilateral Filter : refer to this link

---

[Source Code]

#include "stdafx.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"

using namespace std;
using namespace cv;

// Global Variables
int DELAY_CAPTION = 1500;
int DELAY_BLUR = 100;
int MAX_KERNEL_LENGTH = 31;

Mat src; Mat dst;
char window_name[] = "Filter Demo 1";

/// Function headers
int display_caption( char* caption );
int display_dst( int delay );

//////////////////////////////////////////////////////////////////
// function : main
int _tmain(int argc, _TCHAR* argv[])
{
	namedWindow( window_name, CV_WINDOW_AUTOSIZE );

	/// Load the source image
	src = imread( [YOUR_IMAGE_PATH], 1 );

	if( ( src.rows == 0 ) && ( src.cols == 0 ) ) {
		return 0;
	}

	if( display_caption( "Original Image" ) != 0 ) {
		return 0;
	}

	dst = src.clone();
	
	if( display_dst( DELAY_CAPTION ) != 0 ) {
		return 0;
	}

	/// Applying Homogeneous blur
	if( display_caption( "Homogeneous Blur" ) != 0 ) {
		return 0;
	}
	
	for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
		blur( src, dst, Size( i, i ), Point( -1, -1 ) );

		if( display_dst( DELAY_BLUR ) != 0 ) {
			return 0;
		}
	}

	/// Applying Gaussian blur
	if( display_caption( "Gaussian Blur" ) != 0 ) {
		return 0;
	}
	
	for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
		GaussianBlur( src, dst, Size( i, i ), 0, 0 );

		if( display_dst( DELAY_BLUR ) != 0 ) {
			return 0;
		}
	}

	/// Applying Median blur
	if( display_caption( "Median Blur" ) != 0 ) {
		return 0;
	}

	for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
		medianBlur ( src, dst, i );

		if( display_dst( DELAY_BLUR ) != 0 ) {
			return 0;
		}
	}

	/// Applying Bilateral Filter
	if( display_caption( "Bilateral Blur" ) != 0 ) {
		return 0;
	}
	
	for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
		bilateralFilter ( src, dst, i, i*2, i/2 );
		
		if( display_dst( DELAY_BLUR ) != 0 ) {
			return 0;
		}
	}
	
	/// Wait until user press a key
	display_caption( "End: Press a key!" );
	waitKey(0);
	
	return 0;
}


//////////////////////////////////////////////////////////////////
// function : display_caption
int display_caption( char* caption )
{
	dst = Mat::zeros( src.size(), src.type() );
	putText( dst, caption, Point( src.cols/4, src.rows/2), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(255, 255, 255) );

	imshow( window_name, dst );
	
	int c = waitKey( DELAY_CAPTION );
	
	if( c >= 0 ) {
		return -1;
	}
	
	return 0;
}


//////////////////////////////////////////////////////////////////
// function : display_dst
int display_dst( int delay )
{
	imshow( window_name, dst );

	int c = waitKey ( delay );

	if( c >= 0 ) {
		return -1;
	}
	
	return 0;
}

---

[Normalized Block Filter]

for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
	blur( src, dst, Size( i, i ), Point( -1, -1 ) );

	if( display_dst( DELAY_BLUR ) != 0 ) {
		return 0;
	}
}

• src : Source image.
• dst : Destination image.
• Size(w, h) : Size of the kernel to be used.
• Point(-1, -1) : anchor point location. Negative value denotes the center of the kernel is considered.

[Gaussian Filter]

for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
	GaussianBlur( src, dst, Size( i, i ), 0, 0 );

	if( display_dst( DELAY_BLUR ) != 0 ) {
		return 0;
	}
}

• src : Source image.
• dst : Destination image.
• Size(w, h) : Size of the kernel to be used.
•  : Standard deviation in x. 0 implies  uses kernel size.
•  : Standard deviation in x. 0 implies  uses kernel size.

[Median Filter]

for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
	medianBlur ( src, dst, i );

	if( display_dst( DELAY_BLUR ) != 0 ) {
		return 0;
	}
}

• src : Source image.
• dst : Destination image.
• i : Size of the kernel.

[Bilateral Filter]

for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 ) {
	bilateralFilter ( src, dst, i, i*2, i/2 );
	
	if( display_dst( DELAY_BLUR ) != 0 ) {
		return 0;
	}
}

• src : Source image.
• dst : Destination image.
• d : The diameter of each pixel neighborhood.
•  : Standard deviation in color space.
•  : Standard deviation in the coordinate space.