图像处理——（源）均值滤波（blue）、高斯滤波（GaussianBlue）函数编程实现

  1 #include <opencv2/opencv.hpp>
  2 #include <opencv2/core/core.hpp>
  3 #include <opencv2/highgui/highgui.hpp>
  4 #include<iostream>
  5 #include<stdlib.h>
  6 using namespace cv;
  7 using namespace std;
  8 
  9 
 10 void MeanFilter_my1(const Mat &src, Mat &dst, int ksize)//均值滤波
 11 {
 12     CV_Assert(ksize % 2 == 1);
 13 
 14     int *kernel = new int[ksize*ksize];
 15     for (int i = 0; i < ksize*ksize; i++)
 16         kernel[i] = 1;
 17     Mat tmp;
 18     int len = ksize / 2;
 19     tmp.create(Size(src.cols + len, src.rows + len), src.type());//添加边框
 20     dst.create(Size(src.cols, src.rows), src.type());
 21 
 22     int channel = src.channels();
 23     uchar *ps = src.data;
 24     uchar *pt = tmp.data;
 25 
 26     for (int row = 0; row < tmp.rows; row++)//添加边框的过程
 27     {
 28         for (int col = 0; col < tmp.cols; col++)
 29         {
 30             for (int c = 0; c < channel; c++)
 31             {
 32                 if (row >= len && row < tmp.rows - len && col >= len && col < tmp.cols - len)
 33                     pt[(tmp.cols * row + col)*channel + c] = ps[(src.cols * (row - len) + col - len) * channel + c];
 34                 else
 35                     pt[(tmp.cols * row + col)*channel + c] = 0;
 36             }
 37         }
 38     }
 39 
 40 
 41     uchar *pd = dst.data;
 42     pt = tmp.data;
 43     for (int row = len; row < tmp.rows - len; row++)//卷积的过程
 44     {
 45         for (int col = len; col < tmp.cols - len; col++)
 46         {
 47             for (int c = 0; c < channel; c++)
 48             {
 49                 short t = 0;
 50                 for (int x = -len; x <= len; x++)
 51                 {
 52                     for (int y = -len; y <= len; y++)
 53                     {
 54                         t += kernel[(len + x) * ksize + y + len] * pt[((row + x) * tmp.cols + col + y) * channel + c];
 55                     }
 56                 }
 57 
 58                 pd[(dst.cols * (row - len) + col - len) * channel + c] = saturate_cast<ushort> (t/(ksize*ksize));//防止数据溢出ushort是16为数据
 59             }
 60         }
 61     }
 62     delete[] kernel;
 63 }
 64 //方差可调节
 65 void GaussFilter_my(const Mat &src, Mat &dst, int ksize, double sigmaX, double sigmaY = 0)
 66 {
 67     CV_Assert(ksize % 2 == 1);
 68 
 69     if (fabs(sigmaY) < 1e-5)
 70         sigmaY = sigmaX;
 71 
 72     double *kernel = new double[ksize*ksize];
 73     int center = ksize / 2;
 74     double sum = 0;
 75     for (int i = 0; i < ksize; i++)
 76     {
 77         for (int j = 0; j < ksize; j++)
 78         {
 79             kernel[i * ksize + j] = exp(-(i - center)*(i - center) / (2 * sigmaX*sigmaX) - (j - center)*(j - center) / (2 * sigmaY*sigmaY));
 80             sum += kernel[i*ksize + j];
 81         }
 82     }
 83     for (int i = 0; i < ksize; i++)
 84     {
 85         for (int j = 0; j < ksize; j++)
 86         {
 87             kernel[i*ksize + j] /= sum;
 88         }
 89     }
 90     Mat tmp;
 91     int len = ksize / 2;
 92     tmp.create(Size(src.cols + len, src.rows + len), src.type());//添加边框
 93     dst.create(Size(src.cols, src.rows), src.type());
 94 
 95 
 96     int channel = src.channels();
 97     uchar *ps = src.data;
 98     uchar *pt = tmp.data;
 99 
100     for (int row = 0; row < tmp.rows; row++)//添加边框的过程
101     {
102         for (int col = 0; col < tmp.cols; col++)
103         {
104             for (int c = 0; c < channel; c++)
105             {
106                 if (row >= len && row < tmp.rows - len && col >= len && col < tmp.cols - len)
107                     pt[(tmp.cols * row + col)*channel + c] = ps[(src.cols * (row - len) + col - len) * channel + c];
108                 else
109                     pt[(tmp.cols * row + col)*channel + c] = 0;
110             }
111         }
112     }
113 
114     uchar *pd = dst.data;
115     pt = tmp.data;
116     for (int row = len; row < tmp.rows - len; row++)//卷积的过程
117     {
118         for (int col = len; col < tmp.cols - len; col++)
119         {
120             for (int c = 0; c < channel; c++)
121             {
122                 short t = 0;
123                 for (int x = -len; x <= len; x++)
124                 {
125                     for (int y = -len; y <= len; y++)
126                     {
127                         t += kernel[(len + x) * ksize + y + len] * pt[((row + x) * tmp.cols + col + y) * channel + c];
128                     }
129                 }
130 
131                 pd[(dst.cols * (row - len) + col - len) * channel + c] = saturate_cast<ushort> (t);//防止数据溢出ushort是16为数据
132             }
133         }
134     }
135     delete[] kernel;
136 }
137 
138 int main()
139 {
140     Mat img=imread("E://lena.jpg");
141     Mat dst=imread("E://heibai.jpg");    
142     imshow("img",img);
143     imshow("dst",dst);
144     int k=7;
145     double sigmaX=0.001;
146     double sigmaY = 0.001;
147     MeanFilter_my1(img,dst,k);
148     imshow("dst1",dst);
149     GaussFilter_my(img,dst,k,sigmaX,sigmaY );
150     imshow("dst1",dst);
151 
152     
153     waitKey(0);
154 }
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