canny算法:边界检测算法C++代码
一、算法原理
canny边缘检测算法原理
二、程序分块介绍
1、高斯模糊。
float GasArray[9]= {0.0751,0.1238,0.0571,0.1238,0.2043,0.1238,0.0751,0.1238,0.0751};\\高斯模糊算子
两个矩阵的卷积（未采用傅里叶变换）
float *Array_Cov_f_f_f(float *input,int input_x,int input_y,float *cov,int cov_x,int cov_y)
{
if(input_x<cov_x||input_y<cov_y)
{
printf("The size of input array is smaller than cov's.\n");
return NULL;
}
float *output;
int x,y;
float sum = 0;
x = input_x - cov_x + 1,y = input_y - cov_y + 1;
output = (float*)malloc(x*y*sizeof(float));
for(int i=0; i<x; i++)
for(int j=0; j<y; j++)
{
sum = 0;
for( int k=0; k<cov_x; k++)
for(int l=0; l<cov_y; l++)
sum+=input[(i+k)*input_y+j+l]*cov[k*cov_y+l];
output[i*y+j] = sum;`在这里插入代码片`
}
return output;
}
2、计算梯度幅值和方向。
float soble_x[9] = {-1,0,1,-2,0,2,-1,0,1};
float soble_y[9] = {1,2,1,0,0,0,-1,-2,-1};\\soble算子
先分别用两个算子与原图像矩阵进行卷积，再用两个矩阵计算梯度幅值和方向
幅值
float *Two_array_geometry_avg(float *input_1,float *input_2,int input_x,int input_y)
{
float *output;
output = (float*)malloc(input_x*input_y*sizeof(float));
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
output[i*input_y+j] = sqrt(input_1[i*input_y+j]*input_1[i*input_y+j]+input_2[i*input_y+j]+input_2[i*input_y+j]);
return output;
}
方向
int *Gradient_direction(float *input_1,float *input_2,int input_x,int input_y)
{
float direction;
int *output;
output = (int*)malloc(input_x*input_y*sizeof(int));
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
{
direction = atan(input_2[i*input_y+j]/input_1[i*input_y+j]);
if(direction >= -0.3926990816987 && direction < 0.3926990816987)
output[i*input_y+j] = 0;
else if(direction >= 0.3926990816987 &&direction < 1.1780972450961)
output[i*input_y+j] = 1;
else if(direction >= 1.1780972450961 && direction < -1.1780972450961)
output[i*input_y+j] =2;
else
output[i*input_y+j] =3;
}
return output;
}
3、 非最大值抑制。
void Non_maximum_suppression(float *input_1,int *input_2,int input_x,int input_y)
{
int x,y;
x=input_x-1;
y=input_y-1;
for(int i=1; i<x; i++)
for(int j=1; j<y; j++)
{
switch(input_2[i*input_y+j])
{
case 0:
if(input_1[i*input_y+j+1]<input_1[i*input_y+j] &&input_1[i*input_y+j-1]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
break;
case 1:
if(input_1[(i+1)*input_y+j+1]<input_1[i*input_y+j] &&input_1[(i-1)*input_y+j-1]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
break;
case 2:
if(input_1[(i+1)*input_y+j]<input_1[i*input_y+j] &&input_1[(i-1)*input_y+j]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
case 3:
if(input_1[(i+1)*input_y+j-1]<input_1[i*input_y+j] &&input_1[(i-1)*input_y+j+1]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
}
}
return ;
}
4、双阈值
void Double_threshold(float *input_1,int input_x,int input_y,float big,float small)
{
int x,y;
float smaller = (big + small)/2;
x=input_x-1;
y=input_y-1;
for(int i=1; i<x; i++)
for(int j=1; j<y; j++)
{
if(small<input_1[i*input_y+j] &&big>input_1[i*input_y+j])
input_1[i*input_y+j] = small;
else if(input_1[i*input_y+j]>big)
input_1[i*input_y+j] = 255;
else
input_1[i*input_y+j] = 0;
}
return ;
}
5、滞后边界跟踪
算法感觉有很大问题
void Neighborhood_tracking(float *input,int input_x,int input_y,float big,float small)
{
int *sign;
int x,y;
x = input_x -1, y =input_y-1;
sign = (int*)malloc(input_x*input_y*sizeof(int));
memset(sign,0,sizeof(sign));
for(int i=1; i<x; i++)
for(int j=1; j<y; j++)
{
if(!sign[i*input_y+j])
{
if(input[i*input_y+j] == 255)
{
dfs(input,input_x,input_y,i,j,small,sign);
}
}
}
free(sign);
return ;
}
void dfs(float *input,int input_x,int input_y,int now_x,int now_y,float small,int *sign)
{
if(sign[now_x*input_y+now_y] == 0)
{
sign[now_x*input_y+now_y] = 1;
int i,j;
for(int k=0; k<8; k++)
{
i=now_x+dx[k],j=now_y+dy[k];
if(i>=input_x||j>=input_y||i<=0 ||j<=0)
continue;
if(input[i*input_y+j] == small)
{
input[i*input_y+j] = 255;
dfs(input,input_x,input_y,i,j,small,sign);
}
}
}
return ;
}
6、其他函数
将矩阵转化为图片
Mat Tr_array_Mat_gray(float *input,int input_x,int input_y)
{
Mat output;
output = Mat(input_x,input_y,0);
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
{
output.at<uchar>(i,j) = (uchar)input[i*input_y+j];
}
return output;
}
将图片转化为矩阵
float *Tr_Mat_array_gray(Mat input)
{
float *output;
int x,y;
x = input.rows,y = input.cols;
output = (float *)malloc(x*y*sizeof(float));
for(int i=0; i<x; i++)
for(int j=0; j<y; j++)
{
output[i*y+j] = (float)input.at<uchar>(i,j);
}
return output;
}
三、全部代码
#include <iostream>
#include<opencv2/opencv.hpp>
#include<cstdio>
#include<cmath>
#include<cstring>
using namespace std;
using namespace cv;
float GasArray[9]= {0.0751,0.1238,0.0571,0.1238,0.2043,0.1238,0.0751,0.1238,0.0751};
float soble_x[9] = {-1,0,1,-2,0,2,-1,0,1};
float soble_y[9] = {1,2,1,0,0,0,-1,-2,-1};
int dx[8]= {-1,-1,-1,1,1,1,0,0};
int dy[8]= {0,1,-1,-1,1,0,-1,1};
int gas_x = 3,gas_y = 3;
int soble = 3;
float *Array_Cov_f_f_f(float *input,int input_x,int input_y,float *cov,int cov_x,int cov_y);
float *Tr_Mat_array_gray(Mat input);
float *Two_array_geometry_avg(float *input_1,float *input_2,int input_x,int input_y);
int *Gradient_direction(float *input_1,float *input_2,int input_x,int input_y);
void Non_maximum_suppression(float *input_1,int *input_2,int input_x,int input_y);
void Double_threshold(float *input_1,int input_x,int input_y,float big,float small);
void Neighborhood_tracking(float *input,int input_x,int input_y,float big,float small);
Mat Tr_array_Mat_gray(float *input,int input_x,int intput_y);
void dfs(float *input,int input_x,int input_y,int now_x,int now_y,float small,int *sign);
float *Two_array_arithmetic_avg(float *input_1,float *input_2,int input_x,int input_y);
int main()
{
Mat img,img_gas,img_soble_x,img_soble_y,img_soble,img_nms,img_dt,img_nt;
float *img_array;
float *img_gas_array;
float *img_soble_x_array,*img_soble_y_array,*img_soble_array;
int *direction;
img_array = NULL;
img_gas_array = NULL;
img = imread("1.jpg",0);
img_array = Tr_Mat_array_gray(img);
img_gas_array = Array_Cov_f_f_f(img_array,img.rows,img.cols,GasArray,gas_x,gas_y);
img_gas = Tr_array_Mat_gray(img_gas_array,img.rows-gas_x+1,img.cols-gas_y+1);
img_soble_x_array = Array_Cov_f_f_f(img_gas_array,img_gas.rows,img_gas.cols,soble_x,soble,soble);
img_soble_x = Tr_array_Mat_gray(img_soble_x_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
img_soble_y_array = Array_Cov_f_f_f(img_gas_array,img_gas.rows,img_gas.cols,soble_y,soble,soble);
img_soble_y = Tr_array_Mat_gray(img_soble_y_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
img_soble_array = Two_array_arithmetic_avg(img_soble_x_array,img_soble_y_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
img_soble = Tr_array_Mat_gray(img_soble_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
direction = Gradient_direction(img_soble_array,img_soble_y_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
Non_maximum_suppression(img_soble_array,direction,img_gas.rows-soble+1,img_gas.cols-soble+1);
img_nms = Tr_array_Mat_gray(img_soble_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
Double_threshold(img_soble_array,img_gas.rows-soble+1,img_gas.cols-soble+1,100,30);
img_dt = Tr_array_Mat_gray(img_soble_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
Neighborhood_tracking(img_soble_array,img_gas.rows-soble+1,img_gas.cols-soble+1,100,30);
img_nt = Tr_array_Mat_gray(img_soble_array,img_gas.rows-soble+1,img_gas.cols-soble+1);
free(img_array);
free(img_gas_array);
free(img_soble_x_array);
free(img_soble_y_array);
free(img_soble_array);
free(direction);
imshow("origl:",img);
imshow("gas:",img_gas);
imshow("soble_x:",img_soble_x);
imshow("soble_y:",img_soble_y);
imshow("soble:",img_soble);
imshow("nms:",img_nms);
imshow("dt:",img_dt);
imshow("nt:",img_nt);
waitKey(90000);
return 0;
}
void Neighborhood_tracking(float *input,int input_x,int input_y,float big,float small)
{
int *sign;
int x,y;
x = input_x -1, y =input_y-1;
sign = (int*)malloc(input_x*input_y*sizeof(int));
memset(sign,0,sizeof(sign));
for(int i=1; i<x; i++)
for(int j=1; j<y; j++)
{
if(!sign[i*input_y+j])
{
if(input[i*input_y+j] == 255)
{
dfs(input,input_x,input_y,i,j,small,sign);
}
}
}
free(sign);
return ;
}
void dfs(float *input,int input_x,int input_y,int now_x,int now_y,float small,int *sign)
{
if(sign[now_x*input_y+now_y] == 0)
{
sign[now_x*input_y+now_y] = 1;
int i,j;
for(int k=0; k<8; k++)
{
i=now_x+dx[k],j=now_y+dy[k];
if(i>=input_x||j>=input_y||i<=0 ||j<=0)
continue;
if(input[i*input_y+j] == small)
{
input[i*input_y+j] = 255;
dfs(input,input_x,input_y,i,j,small,sign);
}
}
}
return ;
}
void Double_threshold(float *input_1,int input_x,int input_y,float big,float small)
{
int x,y;
float smaller = (big + small)/2;
x=input_x-1;
y=input_y-1;
for(int i=1; i<x; i++)
for(int j=1; j<y; j++)
{
if(small<input_1[i*input_y+j] &&big>input_1[i*input_y+j])
input_1[i*input_y+j] = small;
else if(input_1[i*input_y+j]>big)
input_1[i*input_y+j] = 255;
else
input_1[i*input_y+j] = 0;
}
return ;
}
void Non_maximum_suppression(float *input_1,int *input_2,int input_x,int input_y)
{
int x,y;
x=input_x-1;
y=input_y-1;
for(int i=1; i<x; i++)
for(int j=1; j<y; j++)
{
switch(input_2[i*input_y+j])
{
case 0:
if(input_1[i*input_y+j+1]<input_1[i*input_y+j] &&input_1[i*input_y+j-1]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
break;
case 1:
if(input_1[(i+1)*input_y+j+1]<input_1[i*input_y+j] &&input_1[(i-1)*input_y+j-1]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
break;
case 2:
if(input_1[(i+1)*input_y+j]<input_1[i*input_y+j] &&input_1[(i-1)*input_y+j]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
case 3:
if(input_1[(i+1)*input_y+j-1]<input_1[i*input_y+j] &&input_1[(i-1)*input_y+j+1]<input_1[i*input_y+j]);
else
input_1[i*input_y+j] = 0;
}
}
return ;
}
int *Gradient_direction(float *input_1,float *input_2,int input_x,int input_y)
{
float direction;
int *output;
output = (int*)malloc(input_x*input_y*sizeof(int));
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
{
direction = atan(input_2[i*input_y+j]/input_1[i*input_y+j]);
if(direction >= -0.3926990816987 && direction < 0.3926990816987)
output[i*input_y+j] = 0;
else if(direction >= 0.3926990816987 &&direction < 1.1780972450961)
output[i*input_y+j] = 1;
else if(direction >= 1.1780972450961 && direction < -1.1780972450961)
output[i*input_y+j] =2;
else
output[i*input_y+j] =3;
}
return output;
}
float *Two_array_geometry_avg(float *input_1,float *input_2,int input_x,int input_y)
{
float *output;
output = (float*)malloc(input_x*input_y*sizeof(float));
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
output[i*input_y+j] = sqrt(input_1[i*input_y+j]*input_1[i*input_y+j]+input_2[i*input_y+j]+input_2[i*input_y+j]);
return output;
}
float *Two_array_arithmetic_avg(float *input_1,float *input_2,int input_x,int input_y)
{
float *output;
output = (float*)malloc(input_x*input_y*sizeof(float));
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
output[i*input_y+j] = abs(input_1[i*input_y+j]) + abs(input_2[i*input_y+j]);
return output;
}
float *Tr_Mat_array_gray(Mat input)
{
float *output;
int x,y;
x = input.rows,y = input.cols;
output = (float *)malloc(x*y*sizeof(float));
for(int i=0; i<x; i++)
for(int j=0; j<y; j++)
{
output[i*y+j] = (float)input.at<uchar>(i,j);
}
return output;
}
Mat Tr_array_Mat_gray(float *input,int input_x,int input_y)
{
Mat output;
output = Mat(input_x,input_y,0);
for(int i=0; i<input_x; i++)
for(int j=0; j<input_y; j++)
{
output.at<uchar>(i,j) = (uchar)input[i*input_y+j];
}
return output;
}
float *Array_Cov_f_f_f(float *input,int input_x,int input_y,float *cov,int cov_x,int cov_y)
{
if(input_x<cov_x||input_y<cov_y)
{
printf("The size of input array is smaller than cov's.\n");
return NULL;
}
float *output;
int x,y;
float sum = 0;
x = input_x - cov_x + 1,y = input_y - cov_y + 1;
output = (float*)malloc(x*y*sizeof(float));
for(int i=0; i<x; i++)
for(int j=0; j<y; j++)
{
sum = 0;
for( int k=0; k<cov_x; k++)
for(int l=0; l<cov_y; l++)
sum+=input[(i+k)*input_y+j+l]*cov[k*cov_y+l];
output[i*y+j] = sum;
}
return output;
}