【OpenCV】Canny 边缘检测
Canny 边缘检测算法
1986年，JOHN CANNY 提出一个很好的边缘检测算法，被称为Canny编边缘检测器[1]。
Canny边缘检测根据对信噪比与定位乘积进行测度，得到最优化逼近算子，也就是Canny算子。类似与 LoG 边缘检测方法，也属于先平滑后求导数的方法。
使用Canny边缘检测器，图象边缘检测必须满足两个条件：
- 能有效地抑制噪声；
- 必须尽量精确确定边缘的位置。
算法大致流程：
1、求图像与高斯平滑滤波器卷积:
2、使用一阶有限差分计算偏导数的两个阵列P与Q：
3、幅值和方位角:
4、非极大值抑制（NMS ） ：细化幅值图像中的屋脊带，即只保留幅值局部变化最大的点。
将梯度角的变化范围减小到圆周的四个扇区之一，方向角和幅值分别为：
非极大值抑制通过抑制梯度线上所有非屋脊峰值的幅值来细化M[i,j],中的梯度幅值屋脊．这一算法首先将梯度角θ[i,j]的变化范围减小到圆周的四个扇区之一,如下图所示：
5、取阈值
-
将低于阈值的所有值赋零，得到图像的边缘阵列
- 阈值τ取得太低->假边缘
- 阈值τ取得太高->部分轮廊丢失
- 选用两个阈值: 更有效的阈值方案．
相关代码
Canny算法实现：
- 用高斯滤波器平滑图像（在调用Canny之前自己用blur平滑）
- 用一阶偏导的有限差分来计算梯度的幅值和方向．
- 对梯度幅值应用非极大值抑制 ．
- 用双阈值算法检测和连接边缘．
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();
if (!L2gradient && (aperture_size & CV_CANNY_L2_GRADIENT) == CV_CANNY_L2_GRADIENT)
{
//backward compatibility
aperture_size &= ~CV_CANNY_L2_GRADIENT;
L2gradient = true;
}
if ((aperture_size & 1) == 0 || (aperture_size != -1 && (aperture_size < 3 || aperture_size > 7)))
CV_Error(CV_StsBadFlag, "");
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::canny(src, dst, low_thresh, high_thresh, aperture_size, L2gradient))
return;
#endif
const int cn = src.channels();
cv::Mat dx(src.rows, src.cols, CV_16SC(cn));
cv::Mat dy(src.rows, src.cols, CV_16SC(cn));
cv::Sobel(src, dx, CV_16S, 1, 0, aperture_size, 1, 0, cv::BORDER_REPLICATE);
cv::Sobel(src, dy, CV_16S, 0, 1, aperture_size, 1, 0, cv::BORDER_REPLICATE);
if (low_thresh > high_thresh)
std::swap(low_thresh, high_thresh);
if (L2gradient)
{
low_thresh = std::min(32767.0, low_thresh);
high_thresh = std::min(32767.0, high_thresh);
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);
ptrdiff_t mapstep = src.cols + 2;
cv::AutoBuffer buffer((src.cols+2)*(src.rows+2) + cn * mapstep * 3 * sizeof(int));
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));
uchar* map = (uchar*)(mag_buf[2] + mapstep*cn);
memset(map, 1, mapstep);
memset(map + mapstep*(src.rows + 1), 1, mapstep);
int maxsize = std::max(1 << 10, src.cols * src.rows / 10);
std::vector stack(maxsize);
uchar **stack_top = &stack[0];
uchar **stack_bottom = &stack[0];
/* sector numbers
(Top-Left Origin)
1 2 3
* * *
* * *
0*******0
* * *
* * *
3 2 1
*/
#define CANNY_PUSH(d) *(d) = uchar(2), *stack_top++ = (d)
#define CANNY_POP(d) (d) = *--stack_top
// calculate magnitude and angle of gradient, perform non-maxima supression.
// fill the map with one of the following values:
// 0 - the pixel might belong to an edge
// 1 - the pixel can not belong to an edge
// 2 - the pixel does belong to an edge
for (int i = 0; i <= src.rows; i++)
{
int* _norm = mag_buf[(i > 0) + 1] + 1;
if (i < src.rows)
{
short* _dx = dx.ptr(i);
short* _dy = dy.ptr(i);
if (!L2gradient)
{
for (int j = 0; j < src.cols*cn; j++)
_norm[j] = std::abs(int(_dx[j])) + std::abs(int(_dy[j]));
}
else
{
for (int j = 0; j < src.cols*cn; j++)
_norm[j] = int(_dx[j])*_dx[j] + int(_dy[j])*_dy[j];
}
if (cn > 1)
{
for(int j = 0, jn = 0; j < src.cols; ++j, jn += cn)
{
int maxIdx = jn;
for(int k = 1; k < cn; ++k)
if(_norm[jn + k] > _norm[maxIdx]) maxIdx = jn + k;
_norm[j] = _norm[maxIdx];
_dx[j] = _dx[maxIdx];
_dy[j] = _dy[maxIdx];
}
}
_norm[-1] = _norm[src.cols] = 0;
}
else
memset(_norm-1, 0, /* cn* */mapstep*sizeof(int));
// at the very beginning we do not have a complete ring
// buffer of 3 magnitude rows for non-maxima suppression
if (i == 0)
continue;
uchar* _map = map + mapstep*i + 1;
_map[-1] = _map[src.cols] = 1;
int* _mag = mag_buf[1] + 1; // take the central row
ptrdiff_t magstep1 = mag_buf[2] - mag_buf[1];
ptrdiff_t magstep2 = mag_buf[0] - mag_buf[1];
const short* _x = dx.ptr(i-1);
const short* _y = dy.ptr(i-1);
if ((stack_top - stack_bottom) + src.cols > maxsize)
{
int sz = (int)(stack_top - stack_bottom);
maxsize = maxsize * 3/2;
stack.resize(maxsize);
stack_bottom = &stack[0];
stack_top = stack_bottom + sz;
}
int prev_flag = 0;
for (int j = 0; j < src.cols; j++)
{
#define CANNY_SHIFT 15
const int TG22 = (int)(0.4142135623730950488016887242097*(1< 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
{
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);
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);
}
}
Canny() 调用接口（C++）：
void Canny(InputArray image, OutputArray edges, double threshold1, double threshold2,
int apertureSize=3, bool L2gradient=false )
实践示例
Mat src, src_gray;
Mat dst, detected_edges;
int edgeThresh = 1;
int lowThreshold;
int const max_lowThreshold = 100;
int ratio = 3;
int kernel_size = 3;
char* window_name = "Edge Map";
void CannyThreshold(int, void*)
{
/// Reduce noise with a kernel 3x3
blur( src_gray, detected_edges, Size(3,3) );
/// Canny detector
Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );
dst = Scalar::all(0);
src.copyTo( dst, detected_edges);
imshow( window_name, dst );
}
int main( )
{
src = imread( "images\\happycat.png" );
if( !src.data )
{ return -1; }
dst.create( src.size(), src.type() );
cvtColor( src, src_gray, CV_BGR2GRAY );
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );
CannyThreshold(0, 0);
waitKey(0);
return 0;
}原图：
边缘检测效果图：
（从左到右lowThread分别为0、50、100）
参考文献：
[1] Canny. A Computational Approach to Edge Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6), pp. 679-698 (1986).
转载请注明出处：http://blog.csdn.net/xiaowei_cqu/article/details/7839140
资源下载：http://download.csdn.net/detail/xiaowei_cqu/4483966
[1] Canny. A Computational Approach to Edge Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6), pp. 679-698 (1986).