OpenCV 边缘检测 Canny源码剖析
Canny 算子源码见我的下载资源
下面是我对Canny 算子源码部分细节功能的注释，代码详见源码。
void cv::Canny( InputArray _src, OutputArray _dst,
double low_thresh, double high_thresh,
int aperture_size, bool L2gradient )
{
Mat src = _src.getMat();
CV_Assert( src.depth() == CV_8U );
_dst.create(src.size(), CV_8U);
Mat dst = _dst.getMat();
....
//aperture--空隙大小/尺寸
if ((aperture_size & 1) == 0 || (aperture_size != -1 && (aperture_size < 3 || aperture_size > 7)))
CV_Error(CV_StsBadFlag, "");
if (low_thresh > high_thresh)
std::swap(low_thresh, high_thresh);
....
//CV_16SC--#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))，其中CV_16S表示16位短整型，详见types_c.h中
const int cn = src.channels();
Mat dx(src.rows, src.cols, CV_16SC(cn));
Mat dy(src.rows, src.cols, CV_16SC(cn));
//这里两次计算x轴，y轴方向上的像素点的导数估计值并存在dx ,dy 数组中
Sobel(src, dx, CV_16S, 1, 0, aperture_size, 1, 0, cv::BORDER_REPLICATE);
Sobel(src, dy, CV_16S, 0, 1, aperture_size, 1, 0, cv::BORDER_REPLICATE);
//L2gradient这里用于指示梯度估计使用的公式
1-- = \sqrt{(dI/dx)^2 + (dI/dy)^2}
if (L2gradient)
0/default-- = |dI/dx|+|dI/dy|
{
low_thresh = std::min(32767.0, low_thresh);
high_thresh = std::min(32767.0, high_thresh);
//平方
if (low_thresh > 0) low_thresh *= low_thresh;
if (high_thresh > 0) high_thresh *= high_thresh;
}
int low = cvFloor(low_thresh);
int high = cvFloor(high_thresh);
//这里以及后面对行，列扩展2表示图像边缘扩展
ptrdiff_t mapstep = src.cols + 2;
AutoBuffer buffer((src.cols+2)*(src.rows+2) + cn * mapstep * 3 * sizeof(int));
//初始化图像中三行数据，并分配存储空间
int* mag_buf[3];
mag_buf[0] = (int*)(uchar*)buffer;
mag_buf[1] = mag_buf[0] + mapstep*cn;
mag_buf[2] = mag_buf[1] + mapstep*cn;
memset(mag_buf[0], 0, /* cn* */mapstep*sizeof(int));
//这里map指向第三行最后一个元素的地址
uchar* map = (uchar*)(mag_buf[2] + mapstep*cn);
memset(map, 1, mapstep);
memset(map + mapstep*(src.rows + 1), 1, mapstep);
//通过max函数显式确定一个存储空间的最大取值，这里设置为源图像的像素点个数十分之一
int maxsize = std::max(1 << 10, src.cols * src.rows / 10);
std::vector0.5);
int m = _mag[j];
//下面的条件分支语句段用于完成非极大值抑制，magstep1/2用于表示相邻两行在buffer中的步长，即分段后每段的长度
if (m > low)
{
int xs = _x[j];
int ys = _y[j];
int x = std::abs(xs);
int y = std::abs(ys) << CANNY_SHIFT;
int tg22x = x * TG22;
if (y < tg22x)
{
if (m > _mag[j-1] && m >= _mag[j+1]) goto __ocv_canny_push;
}
else
{
//这里tg67表示tan67.5用于表示领域中的梯度角度
int tg67x = tg22x + (x << (CANNY_SHIFT+1));
if (y > tg67x)
{
if (m > _mag[j+magstep2] && m >= _mag[j+magstep1]) goto __ocv_canny_push;
}
else
{
int s = (xs ^ ys) < 0 ? -1 : 1;
if (m > _mag[j+magstep2-s] && m > _mag[j+magstep1+s]) goto __ocv_canny_push;
}
}
}
prev_flag = 0;
_map[j] = uchar(1);
continue;
__ocv_canny_push:
if (!prev_flag && m > high && _map[j-mapstep] != 2)
{
CANNY_PUSH(_map + j);
prev_flag = 1;
}
else
_map[j] = 0;
}
// scroll the ring buffer
_mag = mag_buf[0];
mag_buf[0] = mag_buf[1];
mag_buf[1] = mag_buf[2];
mag_buf[2] = _mag;
}
// now track the edges (hysteresis thresholding)
while (stack_top > stack_bottom)
{
uchar* m;
if ((stack_top - stack_bottom) + 8 > maxsize)
{
int sz = (int)(stack_top - stack_bottom);
maxsize = maxsize * 3/2;
stack.resize(maxsize);
stack_bottom = &stack[0];
stack_top = stack_bottom + sz;
}
CANNY_POP(m);
//根据CANNY_PUSH的说明，这一次操作可以将从Stack存储空间（完全对应于图像）中的相应位置进行了修改，所以每个边缘像素点的坐标已知
if (!m[-1]) CANNY_PUSH(m - 1);
if (!m[1]) CANNY_PUSH(m + 1);
if (!m[-mapstep-1]) CANNY_PUSH(m - mapstep - 1);
if (!m[-mapstep]) CANNY_PUSH(m - mapstep);
if (!m[-mapstep+1]) CANNY_PUSH(m - mapstep + 1);
if (!m[mapstep-1]) CANNY_PUSH(m + mapstep - 1);
if (!m[mapstep]) CANNY_PUSH(m + mapstep);
if (!m[mapstep+1]) CANNY_PUSH(m + mapstep + 1);
}
// the final pass, form the final image
const uchar* pmap = map + mapstep + 1;
uchar* pdst = dst.ptr();
for (int i = 0; i < src.rows; i++, pmap += mapstep, pdst += dst.step)
{
for (int j = 0; j < src.cols; j++)
pdst[j] = (uchar)-(pmap[j] >> 1);
}
}