Harris特征提取算法的C语言程序实现
时间: 2023-07-10 11:22:04 浏览: 52
Harris特征提取算法是一种常用的基于角点的特征提取算法,以下是一个简单的C语言程序实现示例:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define IMG_WIDTH 640
#define IMG_HEIGHT 480
#define WINDOW_SIZE 3
#define THRESHOLD 1000
int main()
{
// 读取图像数据
unsigned char img[IMG_WIDTH*IMG_HEIGHT];
FILE* fp = fopen("image.raw", "rb");
fread(img, sizeof(unsigned char), IMG_WIDTH*IMG_HEIGHT, fp);
fclose(fp);
// 计算图像灰度梯度
int gx, gy;
int gradient[IMG_WIDTH*IMG_HEIGHT];
for (int i = 1; i < IMG_HEIGHT - 1; i++) {
for (int j = 1; j < IMG_WIDTH - 1; j++) {
gx = img[(i-1)*IMG_WIDTH + (j+1)] + 2*img[i*IMG_WIDTH + (j+1)] + img[(i+1)*IMG_WIDTH + (j+1)]
- img[(i-1)*IMG_WIDTH + (j-1)] - 2*img[i*IMG_WIDTH + (j-1)] - img[(i+1)*IMG_WIDTH + (j-1)];
gy = img[(i+1)*IMG_WIDTH + (j-1)] + 2*img[(i+1)*IMG_WIDTH + j] + img[(i+1)*IMG_WIDTH + (j+1)]
- img[(i-1)*IMG_WIDTH + (j-1)] - 2*img[(i-1)*IMG_WIDTH + j] - img[(i-1)*IMG_WIDTH + (j+1)];
gradient[i*IMG_WIDTH + j] = gx*gx + gy*gy;
}
}
// 计算Harris响应函数
float A, B, C, det, trace, response;
int windowSum;
float harris[IMG_WIDTH*IMG_HEIGHT];
for (int i = 1; i < IMG_HEIGHT - 1; i++) {
for (int j = 1; j < IMG_WIDTH - 1; j++) {
windowSum = 0;
for (int m = -WINDOW_SIZE/2; m <= WINDOW_SIZE/2; m++) {
for (int n = -WINDOW_SIZE/2; n <= WINDOW_SIZE/2; n++) {
windowSum += gradient[(i+m)*IMG_WIDTH + (j+n)];
}
}
A = B = C = 0;
for (int m = -WINDOW_SIZE/2; m <= WINDOW_SIZE/2; m++) {
for (int n = -WINDOW_SIZE/2; n <= WINDOW_SIZE/2; n++) {
A += pow(m, 2)*gradient[(i+m)*IMG_WIDTH + (j+n)];
B += m*n*gradient[(i+m)*IMG_WIDTH + (j+n)];
C += pow(n, 2)*gradient[(i+m)*IMG_WIDTH + (j+n)];
}
}
det = A*C - B*B;
trace = A + C;
response = det - 0.04*pow(trace, 2);
if (response > THRESHOLD) {
harris[i*IMG_WIDTH + j] = response;
} else {
harris[i*IMG_WIDTH + j] = 0;
}
}
}
// 输出检测到的角点
int numCorners = 0;
for (int i = 1; i < IMG_HEIGHT - 1; i++) {
for (int j = 1; j < IMG_WIDTH - 1; j++) {
if (harris[i*IMG_WIDTH + j] > 0) {
printf("Corner at (%d, %d), response = %f\n", j, i, harris[i*IMG_WIDTH + j]);
numCorners++;
}
}
}
printf("Total number of corners: %d\n", numCorners);
return 0;
}
```
这个示例中,我们首先读取图像数据,然后计算图像的灰度梯度。接着,我们在每个像素周围的窗口内计算Harris响应函数,并将响应函数大于阈值的像素标记为角点。最后,我们输出检测到的角点的坐标和响应函数值。
需要注意的是,Harris特征提取算法中有很多细节需要处理,具体实现需要根据实际情况进行调整。