openCV学习笔记（二十） —— 图像滤波 —— 线性滤波（方框滤波、均值滤波、高斯滤波）

淩亂°似流年 2021-09-27 07:28 432阅读 0赞

# 图像滤波简介 #

![1199089-20170722184657621-495116822.png][]

![1199089-20170722184737387-124091566.png][]

# 方框滤波——boxFilter（）  #

## 原理 ##

![1199089-20170722185405043-1333650065.png][]

![1199089-20170722185417840-254023067.png][]

## 方框滤波程序  ##

#include<opencv2/opencv.hpp>
    #include <vector>
    #include <time.h>
    
    using namespace std;
    using namespace cv;
    
    #define BOX_FILTER_ORIGINAL_WINDOW_NAME	"方框滤波【原图】"
    #define BOX_FILTER_RESULT_WINDOW_NAME	"方框滤波【效果图】"
    
    int main()
    {
    	//载入原图
    	Mat image = imread("test.jpg");
    
    	//创建窗口
    	namedWindow(BOX_FILTER_ORIGINAL_WINDOW_NAME);
    	namedWindow(BOX_FILTER_RESULT_WINDOW_NAME);
    
    	//显示原图
    	imshow(BOX_FILTER_ORIGINAL_WINDOW_NAME, image);
    
    	//进行滤波操作
    	Mat out;
    	boxFilter(image, out, -1, Size(5, 5));
    
    	//显示效果图
    	imshow(BOX_FILTER_RESULT_WINDOW_NAME, out);
    
    	waitKey(0);
    
    	return 0;
    }

# 均值滤波 —— blur（） #

## 原理 ##

![1199089-20170722191054512-1020278462.png][]

![1199089-20170722191122543-1974268601.png][]

##  均值滤波程序 ##

#include<opencv2/opencv.hpp>
    #include <vector>
    #include <time.h>
    
    using namespace std;
    using namespace cv;
    
    #define BOX_FILTER_ORIGINAL_WINDOW_NAME	"均值滤波【原图】"
    #define BOX_FILTER_RESULT_WINDOW_NAME	"均值滤波【效果图】"
    
    int main()
    {
    	//载入原图
    	Mat image = imread("test.jpg");
    
    	//创建窗口
    	namedWindow(BOX_FILTER_ORIGINAL_WINDOW_NAME);
    	namedWindow(BOX_FILTER_RESULT_WINDOW_NAME);
    
    	//显示原图
    	imshow(BOX_FILTER_ORIGINAL_WINDOW_NAME, image);
    
    	//进行滤波操作
    	Mat out;
    	blur(image, out, Size(5, 5));
    
    	//显示效果图
    	imshow(BOX_FILTER_RESULT_WINDOW_NAME, out);
    
    	waitKey(0);
    
    	return 0;
    }

# 高斯滤波——GaussianBlur（） #

## 原理 ##

![1199089-20170722191448934-1962366556.png][]

![1199089-20170722192426262-1953599862.png][]

## 高斯滤波程序  ##

#include<opencv2/opencv.hpp>
    #include <vector>
    #include <time.h>
    
    using namespace std;
    using namespace cv;
    
    #define BOX_FILTER_ORIGINAL_WINDOW_NAME	"高斯滤波【原图】"
    #define BOX_FILTER_RESULT_WINDOW_NAME	"高斯滤波【效果图】"
    
    int main()
    {
    	//载入原图
    	Mat image = imread("test.jpg");
    
    	//创建窗口
    	namedWindow(BOX_FILTER_ORIGINAL_WINDOW_NAME);
    	namedWindow(BOX_FILTER_RESULT_WINDOW_NAME);
    
    	//显示原图
    	imshow(BOX_FILTER_ORIGINAL_WINDOW_NAME, image);
    
    	//进行滤波操作
    	Mat out;
    	GaussianBlur(image, out, Size(5, 5), 0, 0);
    
    	//显示效果图
    	imshow(BOX_FILTER_RESULT_WINDOW_NAME, out);
    
    	waitKey(0);
    
    	return 0;
    }

# 图像线性滤波综合示例程序 #

#include <opencv2/opencv.hpp>
    #include <iostream>
    
    using namespace std;
    using namespace cv;
    
    #define BOX_FILTER_WINDOW	"方框滤波"
    #define MEAN_BLUR_WINDOW	"均值滤波"
    #define GAUSSIAN_BLUR_WINDOW	"高斯滤波"
    
    Mat g_srcImage, g_dstImage1, g_dstImage2, g_dstImage3;	//存储图片的Mat类型
    int g_nBoxFilterValue = 3;	//方框滤波参数值
    int g_nMeanBlurValue = 3;	//均值滤波参数值
    int g_nGaussianBlurValue = 3;	//高斯滤波参数值
    
    //轨迹条的回调函数
    static void on_BoxFilter(int, void *);	//方框滤波
    static void on_MeanBlur(int, void *);	//均值滤波
    static void on_GaussianBlur(int, void *);	//高斯滤波
    
    int main()
    {
    	//改变console字体颜色
    	system("color5E");
    
    	//载入原图
    	g_srcImage = imread("test.jpg");
    	if (!g_srcImage.data)
    	{
    		printf("读取srcImage错误！\n");
    		return -1;
    	}
    
    	//复制原图到三个Mat类型中
    	g_dstImage1 = g_srcImage.clone();
    	g_dstImage2 = g_srcImage.clone();
    	g_dstImage3 = g_srcImage.clone();
    
    	//显示原图
    	imshow("原图窗口", g_srcImage);
    
    	//1.方框滤波
    	//创建窗口
    	namedWindow(BOX_FILTER_WINDOW);
    	//创建轨迹条
    	createTrackbar("内核值：", BOX_FILTER_WINDOW, &g_nBoxFilterValue, 40, on_BoxFilter);
    	on_BoxFilter(g_nBoxFilterValue, 0);
    	imshow(BOX_FILTER_WINDOW, g_dstImage1);
    
    	//2.均值滤波
    	//创建窗口
    	namedWindow(MEAN_BLUR_WINDOW);
    	//创建轨迹条
    	createTrackbar("内核值：", MEAN_BLUR_WINDOW, &g_nMeanBlurValue, 40, on_MeanBlur);
    	on_MeanBlur(g_nMeanBlurValue, 0);
    
    	//3.高斯滤波
    	//创建窗口
    	namedWindow(GAUSSIAN_BLUR_WINDOW);
    	createTrackbar("内核值：", GAUSSIAN_BLUR_WINDOW, &g_nGaussianBlurValue, 40, on_GaussianBlur);
    	on_GaussianBlur(g_nGaussianBlurValue, 0);
    
    	//输出一些帮助信息
    	cout << endl << "\t请调整滚动条观察图像效果\n"
    		<< "\t按下“q”键时，程序退出!\n";
    
    	//按下"q"键时，程序退出
    	while (char(waitKey(1)) != 'q');
    	destroyAllWindows();
    
    	return 0;
    }
    
    static void on_BoxFilter(int, void *)
    {
    	boxFilter(g_srcImage, g_dstImage1, -1, Size(g_nBoxFilterValue + 1, g_nBoxFilterValue + 1));
    	imshow("方框滤波", g_dstImage1);
    }
    
    static void on_MeanBlur(int, void *)
    {
    	blur(g_srcImage, g_dstImage2, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1), Point(-1, -1));
    	imshow("均值滤波", g_dstImage2);
    }
    
    static void on_GaussianBlur(int, void *)
    {
    	GaussianBlur(g_srcImage, g_dstImage3, Size(g_nGaussianBlurValue*2 + 1, g_nGaussianBlurValue*2 + 1), 0, 0);
    	imshow("高斯滤波", g_dstImage3);
    }

[1199089-20170722184657621-495116822.png]: /images/20210923/b793442e011149e48a746c7f491517bc.png
[1199089-20170722184737387-124091566.png]: /images/20210923/9c37329dc73f4ce2b5b2b2227db05a4a.png
[1199089-20170722185405043-1333650065.png]: /images/20210923/a1d46a0a87af4bdba63ed08efbd265f5.png
[1199089-20170722185417840-254023067.png]: /images/20210923/25aeedfc2f074180b4baefbccc204f33.png
[1199089-20170722191054512-1020278462.png]: /images/20210923/0eeae12a130749f1a89e56c9b778e52c.png
[1199089-20170722191122543-1974268601.png]: /images/20210923/9ab79f0807284111a8b3fe827bcc70a4.png
[1199089-20170722191448934-1962366556.png]: /images/20210923/79a0443492a64984b6e1ae5bf6fbf5b1.png
[1199089-20170722192426262-1953599862.png]: /images/20210923/c4c6982785014f1fbd51ecc9f1fa6c2a.png