[opencv] - comprehensive examples of five image filters

preface ： This article is about the previous study Linear filtering and Nonlinear filtering A review of . Before that, we introduced the filter's processing of pictures in chapters , In this section, the knowledge points introduced above are carried by code , Let's show you , Use the sliding bar to control all kinds of filtering learned （ Box filtering 、 Mean filtering 、 Gauss filtering 、 median filtering 、 Bilateral filtering ） Parameter values for . Through the scroll bar to control the blur of the image under various smoothing processing , Not only can we see the effect by comparing with the original picture , And it also has certain playability , ha-ha .

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Don't talk much , Code up

#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<iostream>
using namespace cv;
using namespace std;
//---------------------------------
// Global variable declaration 
//-------------------------------
Mat g_srcIamge, g_dstImage1, g_dstImage2, g_dstImage3,g_dstImage4, g_dstImage5;// Store pictures Mat type 
int g_nBoxFilterValue = 3;// Block filter parameter value 
int g_nMeanBlurValue = 3;// Mean filter parameter value 
int g_nGaussianBlurValue = 3;// Gaussian filter parameter value 
int g_nMedianBlurValue = 10;// Median filter parameter value 
int g_nBilateralFilterValue = 10;// Bilateral filtering parameter value 
//---------------------------------
// Global function declaration 
//-------------------------------
static void on_BoxFilter(int, void *);// Box filtering 
static void on_MeanBlur(int, void *);// Mean filtering 
static void on_GaussianBlur(int, void *);// Gauss filtering 
static void on_MedianBlur(int, void *);// Median filter 
static void on_BilateralFilter(int, void *);// Bilateral filter 
int main()
{
    
	// change console The font color 
	system("color 5E");
	// Load original 
	g_srcIamge = imread("E:\\Pec\\shihao.jpg", 1);
	if (!g_srcIamge.data)
	{
    
		printf(" Error reading picture \n");
		return false;
	}
	// Copy the original image to three Mat Type in the 
	g_dstImage1 = g_srcIamge.clone();
	g_dstImage2 = g_srcIamge.clone();
	g_dstImage3 = g_srcIamge.clone();
	g_dstImage4 = g_srcIamge.clone();
	g_dstImage5 = g_srcIamge.clone();
	// Show the original 
	namedWindow("【<0> Original window 】", 1);
	imshow("【<0> Original window 】", g_srcIamge);

	//=========1、 Box filtering ===========
	// create a window 
	namedWindow("【<1> Box filtering 】", 1);
	// Create a trackbar 
	createTrackbar(" Kernel value ：", "【<1> Box filtering 】", &g_nBoxFilterValue, 40, on_BoxFilter);
	on_MeanBlur(g_nBoxFilterValue, 0);


	//======2、 Mean filtering ===========
	namedWindow("【<2> Mean filtering 】", 1);
	// Create a trackbar 
	createTrackbar(" Kernel value ：", "【<2> Mean filtering 】", &g_nMeanBlurValue, 40, on_BoxFilter);
	on_MeanBlur(g_nMeanBlurValue, 0);


	//=====3、 Gauss filtering ===============
	namedWindow("【<3> Gauss filtering 】", 1);
	// Create a trackbar 
	createTrackbar(" Kernel value ：", "【<3> Gauss filtering 】", &g_nGaussianBlurValue, 40, on_BoxFilter);
	on_GaussianBlur(g_nGaussianBlurValue, 0);

	//=====4、 median filtering ===============
	namedWindow("【<4> median filtering 】", 1);
	// Create a trackbar 
	createTrackbar(" Kernel value ：", "【<4> median filtering 】", &g_nMedianBlurValue, 50, on_MeanBlur);
	on_MedianBlur(g_nMedianBlurValue, 0);

	
	//=====5、 Bilateral filtering ===============
	namedWindow("【<5> Bilateral filtering 】", 1);
	// Create a trackbar 
	createTrackbar(" Kernel value ：", "【<5> Bilateral filtering 】", &g_nBilateralFilterValue, 50, on_BilateralFilter);
	on_BilateralFilter(g_nBilateralFilterValue, 0);

	// Output some help information 
	cout << endl << " Please adjust the scroll bar to observe the image effect ~\n\n" << "\t Press down “q” Key time , Program exit ~\n";
	// stay waitKey(1) Then enter a value 
	while (char(waitKey(1)) != 'q') {
    }
	return 0;
}
//----------------------
// Callback function of block filter operation 
//---------------------------
static void on_BoxFilter(int, void *)// Box filtering 
{
    
	boxFilter(g_srcIamge, g_dstImage1, -1, Size(g_nBoxFilterValue + 1, g_nBoxFilterValue + 1));
	imshow("【<1> Box filtering 】", g_dstImage1);
}
static void on_MeanBlur(int, void *)// Mean filtering 
{
    
	blur(g_srcIamge, g_dstImage2, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1), Point(-1, -1));
	imshow("【<2> Mean filtering 】", g_dstImage2);
}
static void on_GaussianBlur(int, void *)// Gauss filtering 
{
    
	GaussianBlur(g_srcIamge, g_dstImage3,
		Size(g_nGaussianBlurValue * 2 + 1, g_nGaussianBlurValue * 2 + 1), 0, 0);
	imshow("【<3> Gauss filtering 】", g_dstImage3);
}
static void on_MedianBlur(int, void *)// Median filter 
{
    
	medianBlur(g_srcIamge, g_dstImage4, g_nMedianBlurValue * 2 + 1);
	imshow("【<4> median filtering 】", g_dstImage4);
}
static void on_BilateralFilter(int, void *)// Bilateral filter 
{
    
	bilateralFilter(g_srcIamge, g_dstImage5, g_nBilateralFilterValue, g_nBilateralFilterValue * 2, 
	g_nBilateralFilterValue / 2);
	imshow("【<5> Bilateral filtering 】", g_dstImage5);
}

(1) Compare the original image with the block filter

(2) Comparison between original image and mean filtering

(3) Comparison between original image and Gaussian filter

(4) Comparison between original image and median filter

(5) The original image is compared with bilateral filtering

Sum up ： Observation found that ： The effect of block filtering and mean filtering is similar , Median filtering has a great impact on the original image , Bilateral filtering is almost no different from the original image .