opencv学习之中值滤波和双边滤波

继上一篇线性滤波之方框滤波，均值滤波，高斯滤波之后，加入非线性滤波操作中值滤波和双边滤波。

http://blog.youkuaiyun.com/oliverkingli/article/details/54022075

代码;

#include"stdafx.h"
#include <opencv2/core/utility.hpp>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include"opencv2/core/core.hpp"
#include <iostream>

using namespace cv;
using namespace std;

// 定义全局变量
Mat image, dstImage1, dstImage2, dstImage3, dstImage4, dstImage5;
int g_nBoxFilterValue = 3;
int g_nMeanBlurValue = 3;
int g_nGaussianBlurValue = 3;
int g_nMedianBlurValue = 3;
int g_nBilateralFilterValue = 3;
//函数声明
void salt(Mat image, int n);
void pepper(Mat image, int n);
void on_BoxFilter(int, void*);
void on_MeanBlur(int, void*);
void on_GaussianBlur(int, void*);
void on_MedianBlur(int, void*);
void on_BilateralFilter(int, void*);


int main() {
	system("color 5E");
	//读取图片
	image = imread("E:\\pictures\\For_Project\\New_opencv\\Fruits.jpg");
	namedWindow("【原始图像】", 1);
	imshow("【原始图像】", image);

	Mat dstImage1 = image.clone();
	Mat dstImage2 = image.clone();
	Mat dstImage3 = image.clone();
	Mat dstImage4 = image.clone();
	Mat dstImage5 = image.clone();
	//分别添加盐椒噪声
	salt(dstImage1, 1000);
	pepper(dstImage1, 1000);
	salt(dstImage2, 1000);
	pepper(dstImage2, 1000);
	salt(dstImage3, 1000);
	pepper(dstImage3, 1000);
	salt(dstImage4, 1000);
	pepper(dstImage4, 1000);
	salt(dstImage5, 1000);
	pepper(dstImage5, 1000);
	imshow("【添加盐椒噪声图片】", dstImage1);
	
	double time1 = static_cast<double>(getTickCount());
	namedWindow("【方框滤波】", 1);
	createTrackbar("内核值:", "【方框滤波】", &g_nBoxFilterValue, 40, on_BoxFilter);
	on_BoxFilter(g_nBoxFilterValue, 0);
	//计算运行时间并输出  
	time1 = ((double)getTickCount() - time1) / getTickFrequency();
	cout << "方框滤波方法运行时间为：" << time1 << "秒" << endl;

	double time2 = static_cast<double>(getTickCount());
	namedWindow("【均值滤波】", 1);
	createTrackbar("内核值:", "【均值滤波】", &g_nMeanBlurValue, 40, on_MeanBlur);
	on_MeanBlur(g_nMeanBlurValue, 0);
	//计算运行时间并输出  
	time2 = ((double)getTickCount() - time2) / getTickFrequency();
	cout << "均值滤波方法运行时间为：" << time2 << "秒" << endl;

	double time3 = static_cast<double>(getTickCount());
	namedWindow("【高斯滤波】", 1);
	createTrackbar("内核值:", "【高斯滤波】", &g_nGaussianBlurValue, 40, on_GaussianBlur);
	on_GaussianBlur(g_nGaussianBlurValue, 0);
	//计算运行时间并输出  
	time3 = ((double)getTickCount() - time3) / getTickFrequency();
	cout << "高斯滤波方法运行时间为：" << time3 << "秒" << endl;

	double time4 = static_cast<double>(getTickCount());
	namedWindow("【中值滤波】", 1);
	createTrackbar("内核值:", "【中值滤波】", &g_nMedianBlurValue, 40, on_MedianBlur);
	on_MedianBlur(g_nMedianBlurValue, 0);
	//计算运行时间并输出  
	time4 = ((double)getTickCount() - time4) / getTickFrequency();
	cout << "中值滤波方法运行时间为：" << time4 << "秒" << endl;

	double time5 = static_cast<double>(getTickCount());
	namedWindow("【双边滤波】", 1);
	createTrackbar("内核值:", "【双边滤波】", &g_nBilateralFilterValue, 40, on_BilateralFilter);
	on_BilateralFilter(g_nBilateralFilterValue, 0);
	//计算运行时间并输出  
	time5 = ((double)getTickCount() - time5) / getTickFrequency();
	cout << "高斯滤波方法运行时间为：" << time5 << "秒" << endl;


	//等待键盘按键‘q’退出
	while (char(waitKey(1)) != 'q') {}
	return 0;
}

void on_BoxFilter(int, void*) {
	boxFilter(image, dstImage1, -1, Size(g_nBoxFilterValue + 1, g_nBoxFilterValue + 1));
	imshow("【方框滤波】", dstImage1);
}

void on_MeanBlur(int, void*) {
	blur(image, dstImage2, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1),Point(-1,-1));
	imshow("【均值滤波】", dstImage2);
}

void on_GaussianBlur(int, void*) {
	GaussianBlur(image, dstImage3, Size(g_nGaussianBlurValue*2 + 1, g_nGaussianBlurValue*2 + 1),0,0);
	imshow("【高斯滤波】", dstImage3);
}

void on_MedianBlur(int, void*) {
	medianBlur(image, dstImage4, g_nMedianBlurValue * 2 + 1);
	imshow("【中值滤波】", dstImage4);
}

void on_BilateralFilter(int, void*) {
	bilateralFilter(image, dstImage5, g_nBilateralFilterValue, g_nBilateralFilterValue * 2, g_nBilateralFilterValue / 2);
	imshow("【双边滤波】", dstImage5);
}

void salt(Mat image, int n) {

	int i, j;
	for (int k = 0; k<n / 2; k++) {

		// rand() is the random number generator  
		i = std::rand() % image.cols; // % 整除取余数运算符,rand=1022,cols=1000,rand%cols=22  
		j = std::rand() % image.rows;

		if (image.type() == CV_8UC1) { // gray-level image  

			image.at<uchar>(j, i) = 255; //at方法需要指定Mat变量返回值类型,如uchar等  

		}
		else if (image.type() == CV_8UC3) { // color image  

			image.at<cv::Vec3b>(j, i)[0] = 255; //cv::Vec3b为opencv定义的一个3个值的向量类型  
			image.at<cv::Vec3b>(j, i)[1] = 255; //[]指定通道，B:0，G:1，R:2  
			image.at<cv::Vec3b>(j, i)[2] = 255;
		}
	}
}

//椒噪声  
void pepper(Mat image, int n) {

	int i, j;
	for (int k = 0; k<n; k++) {

		// rand() is the random number generator  
		i = std::rand() % image.cols; // % 整除取余数运算符,rand=1022,cols=1000,rand%cols=22  
		j = std::rand() % image.rows;

		if (image.type() == CV_8UC1) { // gray-level image  

			image.at<uchar>(j, i) = 0; //at方法需要指定Mat变量返回值类型,如uchar等  

		}
		else if (image.type() == CV_8UC3) { // color image  

			image.at<cv::Vec3b>(j, i)[0] = 0; //cv::Vec3b为opencv定义的一个3个值的向量类型  
			image.at<cv::Vec3b>(j, i)[1] = 0; //[]指定通道，B:0，G:1，R:2  
			image.at<cv::Vec3b>(j, i)[2] = 0;
		}
	}
}