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 }
转载于:https://www.cnblogs.com/fpzs/p/11183426.html
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