帮我写用c++和mfc实现canny算子边缘检测的函数Canny(pUnchImage, lSrcWidth, lSrcHeight, 0.4, 0.4, 0.79, pUnchEdge);不用openCV库
时间: 2023-10-14 09:04:21 浏览: 25
好的,以下是基于C++和MFC实现Canny算子边缘检测的函数:
```c++
void Canny(unsigned char* pUnchImage, int lSrcWidth, int lSrcHeight, double lowThreshold, double highThreshold, double sigma, unsigned char* pUnchEdge)
{
// 高斯滤波
int kernelSize = (int)(sigma * 3) * 2 + 1;
double* kernel = new double[kernelSize];
double sum = 0;
for (int i = 0; i < kernelSize; i++)
{
kernel[i] = exp(-pow(i - kernelSize / 2, 2) / (2 * pow(sigma, 2)));
sum += kernel[i];
}
for (int i = 0; i < kernelSize; i++)
{
kernel[i] /= sum;
}
unsigned char* pUnchTemp = new unsigned char[lSrcWidth * lSrcHeight];
memset(pUnchTemp, 0, lSrcWidth * lSrcHeight * sizeof(unsigned char));
for (int i = kernelSize / 2; i < lSrcHeight - kernelSize / 2; i++)
{
for (int j = kernelSize / 2; j < lSrcWidth - kernelSize / 2; j++)
{
double sum = 0;
for (int k = 0; k < kernelSize; k++)
{
for (int l = 0; l < kernelSize; l++)
{
sum += pUnchImage[(i + k - kernelSize / 2) * lSrcWidth + j + l - kernelSize / 2] * kernel[k] * kernel[l];
}
}
pUnchTemp[i * lSrcWidth + j] = (unsigned char)sum;
}
}
// 计算梯度幅值和方向
int* gradientMagnitude = new int[lSrcWidth * lSrcHeight];
int* gradientDirection = new int[lSrcWidth * lSrcHeight];
memset(gradientMagnitude, 0, lSrcWidth * lSrcHeight * sizeof(int));
memset(gradientDirection, 0, lSrcWidth * lSrcHeight * sizeof(int));
for (int i = 1; i < lSrcHeight - 1; i++)
{
for (int j = 1; j < lSrcWidth - 1; j++)
{
int gx = pUnchTemp[(i + 1) * lSrcWidth + j - 1] - pUnchTemp[(i - 1) * lSrcWidth + j - 1]
+ 2 * pUnchTemp[(i + 1) * lSrcWidth + j] - 2 * pUnchTemp[(i - 1) * lSrcWidth + j]
+ pUnchTemp[(i + 1) * lSrcWidth + j + 1] - pUnchTemp[(i - 1) * lSrcWidth + j + 1];
int gy = pUnchTemp[(i - 1) * lSrcWidth + j + 1] - pUnchTemp[(i - 1) * lSrcWidth + j - 1]
+ 2 * pUnchTemp[i * lSrcWidth + j + 1] - 2 * pUnchTemp[i * lSrcWidth + j - 1]
+ pUnchTemp[(i + 1) * lSrcWidth + j + 1] - pUnchTemp[(i + 1) * lSrcWidth + j - 1];
gradientMagnitude[i * lSrcWidth + j] = (int)sqrt(pow(gx, 2) + pow(gy, 2));
gradientDirection[i * lSrcWidth + j] = (int)(atan2(gy, gx) * 180 / 3.14159265);
}
}
// 非极大值抑制
for (int i = 1; i < lSrcHeight - 1; i++)
{
for (int j = 1; j < lSrcWidth - 1; j++)
{
int direction = gradientDirection[i * lSrcWidth + j];
int magnitude = gradientMagnitude[i * lSrcWidth + j];
int magnitude1 = 0, magnitude2 = 0;
if (direction < 0)
{
direction += 180;
}
if ((direction >= 0 && direction < 22.5) || (direction >= 157.5 && direction < 180))
{
magnitude1 = gradientMagnitude[i * lSrcWidth + j - 1];
magnitude2 = gradientMagnitude[i * lSrcWidth + j + 1];
}
else if (direction >= 22.5 && direction < 67.5)
{
magnitude1 = gradientMagnitude[(i - 1) * lSrcWidth + j - 1];
magnitude2 = gradientMagnitude[(i + 1) * lSrcWidth + j + 1];
}
else if (direction >= 67.5 && direction < 112.5)
{
magnitude1 = gradientMagnitude[(i - 1) * lSrcWidth + j];
magnitude2 = gradientMagnitude[(i + 1) * lSrcWidth + j];
}
else if (direction >= 112.5 && direction < 157.5)
{
magnitude1 = gradientMagnitude[(i - 1) * lSrcWidth + j + 1];
magnitude2 = gradientMagnitude[(i + 1) * lSrcWidth + j - 1];
}
if (magnitude < magnitude1 || magnitude < magnitude2)
{
gradientMagnitude[i * lSrcWidth + j] = 0;
}
}
}
// 双阈值处理和连接边缘
int lowThresholdValue = (int)(lowThreshold * 255);
int highThresholdValue = (int)(highThreshold * 255);
for (int i = 0; i < lSrcHeight; i++)
{
for (int j = 0; j < lSrcWidth; j++)
{
int magnitude = gradientMagnitude[i * lSrcWidth + j];
if (magnitude >= highThresholdValue)
{
pUnchEdge[i * lSrcWidth + j] = 255;
}
else if (magnitude < lowThresholdValue)
{
pUnchEdge[i * lSrcWidth + j] = 0;
}
else
{
bool isConnected = false;
for (int k = -1; k <= 1; k++)
{
for (int l = -1; l <= 1; l++)
{
if (i + k >= 0 && i + k < lSrcHeight && j + l >= 0 && j + l < lSrcWidth)
{
if (gradientMagnitude[(i + k) * lSrcWidth + j + l] >= highThresholdValue)
{
pUnchEdge[i * lSrcWidth + j] = 255;
isConnected = true;
break;
}
}
}
if (isConnected)
{
break;
}
}
if (!isConnected)
{
pUnchEdge[i * lSrcWidth + j] = 0;
}
}
}
}
// 释放内存
delete[] kernel;
delete[] pUnchTemp;
delete[] gradientMagnitude;
delete[] gradientDirection;
}
```
这里首先实现了高斯滤波函数,然后计算出梯度幅值和方向,并进行非极大值抑制。接着根据双阈值进行边缘连接,最终将结果存储到pUnchEdge中。由于没有使用OpenCV库,需要手动计算各个参数的值,可能会比较麻烦。