OpenCV中的improc组件——三种线性滤波实例及综合实例(14)

1 线性滤波API函数
1.1 方框滤波函数：boxFilter函数
<1> . boxFilter()函数的作用：使用方框滤波来模糊一张图像。
<2> . 函数原型：void boxFilter(InputArray src, OutputArray dst, int ddepth, Size ksize,Point anchor=Point(-1, -1), boolnormalize=true, int borderType=BORDER_DEFAULT);
<3>. 调用代码的示例：

Mat image = imread("2.jpg");
Mat out;
boxFilter(image, out, -1, Size(5, 5));

<4> 实例
实例代码：

#include "opencv2/opencv.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
using namespace std;

int main()
{
    Mat image = imread("../1.jpg");

    namedWindow("原图");
    namedWindow("均值滤波效果图");

    imshow("原图", image);

    Mat out;
    boxFilter(image, out, -1, Size(3, 3));

    imshow("均值滤波效果图", out);

    // waitKey(0);
    while(char (waitKey(1)) != 'q') {}
}

运行效果：

1.2 均值滤波：blur()函数
<1> blur函数的作用：对图像进行均值滤波后输出；
<2> 函数原型：void blur(InputArray src, OutputArraydst, Size ksize, Point anchor=Point(-1, -1), int borderType=BORDER_DEFAULT);
<3> 调用代码示例：

Mat image = imread("1.jpg");
Mat out;
blur(image, out, Size(7, 7));

<4> 实例
实例代码：

#include "opencv2/opencv.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;
int main()
{
    Mat image = imread("../1.jpg");
    Mat out;

    namedWindow("原图");
    namedWindow("均值滤波图");

    blur(image, out, Size(3, 3));
    imshow("原图", image);
    imshow("均值滤波图", out);
    while (char (waitKey(1)) != 'q') {}   
}

运行效果：

1.3 高斯滤波：GaussianBlur()函数
<1>GaussianBlur函数作用：模糊一张图像；
<2> 原型：void GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY=0, intborderType=BORDER_DEFAULT);
<3> 调用示例

Mat image= imread("1.jpg");
Mat out;
GaussianBlur(image, out, Size(5, 5), 0, 0);

<4> 实例
实例代码：

#include "opencv2/opencv.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;
int main()
{
    Mat image = imread("../1.jpg");
    Mat out;

    namedWindow("原图");
    namedWindow("高斯滤波图");

    GaussianBlur(image, out, Size(3, 3), 0, 0);
    imshow("原图", image);
    imshow("高斯滤波图", out);
    while (char (waitKey(1)) != 'q') {}
    
}

运行效果：

1.4 图像滤波综合实例化
使用滑动条来控制三种线性滤波的核参数值，通过滑动条，就可以控制图像的在不同的滤波方法下的模糊度。
实例代码：

#include "opencv2/opencv.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;
Mat g_srcImage, g_dstImage1, g_dstImage2, g_dstImage3;
int g_nBoxFilterValue = 3; //方框滤波参数值
int g_nMeanBlurValue = 3;
int g_nGaussianValue = 3;


static void On_BoxFilter(int, void*); // 方框滤波
static void On_MeanBlur(int , void*);
static void On_GaussianBlur(int, void*);
int main()
{
     g_srcImage = imread("../1.jpg");
     if(!g_srcImage.data){
         printf("获取图像错误!\n");
         return false;
     }

     //复制原图到三个Mat 中
     g_dstImage1 = g_srcImage.clone();
     g_dstImage2 = g_srcImage.clone();
     g_dstImage3 = g_srcImage.clone();

     //显示原图
     namedWindow("原图", 1);
     imshow("原图", g_srcImage);

     //方框滤波
     namedWindow("方框滤波");
     createTrackbar("内核值:", "方框滤波", &g_nBoxFilterValue, 40, On_BoxFilter);
     On_BoxFilter(g_nBoxFilterValue, 0);

     //均值滤波
     namedWindow("均值滤波");
     createTrackbar("内核值:", "均值滤波", &g_nMeanBlurValue, 40, On_MeanBlur);
     On_MeanBlur(g_nMeanBlurValue, 0);

     //高斯滤波
     namedWindow("高斯滤波");
     createTrackbar("内核值:", "高斯滤波", &g_nGaussianValue, 40, On_GaussianBlur);

    while (char (waitKey(1)) != 'q') {}
 	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_nGaussianValue * 2 + 1, g_nGaussianValue * 2 + 1), 0, 0);  //一定是正数和奇数
    imshow("高斯滤波", g_dstImage3);
}

运行效果：