Canny 边缘检测算法【OpenCV】Canny 边缘检测分类：【OpenCV】2012-08-08 10:17 490人阅读评论(10) 收藏举报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．[cpp] view plaincopyprint?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 compatibilityaperture_size &= ~CV_CANNY_L2_GRADIENT;L2gradient = true;}if ((aperture_size & 1) == 0 || (aperture_size != -1 && (aperture_size 7)))CV_Error(CV_StsBadFlag, "");#ifdef HA VE_TEGRA_OPTIMIZATIONif (tegra::canny(src, dst, low_thresh, high_thresh, aperture_size, L2gradient))return;#endifconst 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(1std::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, performnon-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 edgefor (int i = 0; i{int* _norm = mag_buf[(i > 0) + 1] + 1;if (i{short* _dx = dx.ptrshort>(i);short* _dy = dy.ptrshort>(i);if (!L2gradient){for (int j = 0; j_norm[j] = std::abs(int(_dx[j])) + std::abs(int(_dy[j])); }else{for (int j = 0; j_norm[j] = int(_dx[j])*_dx[j] + int(_dy[j])*_dy[j];}if (cn > 1){for(int j = 0, jn = 0; j{int maxIdx = jn;for(int k = 1; kif(_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;}elsememset(_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 suppressionif (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.ptrshort>(i-1);const short* _y = dy.ptrshort>(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{#define CANNY_SHIFT 15const int TG22 =(int)(0.4142135623730950488016887242097*(1 int m = _mag[j];if (m > low){int xs = _x[j];int ys = _y[j];int x = std::abs(xs);int y = std::abs(ys)int tg22x = x * TG22;if (y{if (m > _mag[j-1] && m >= _mag[j+1]) goto __ocv_canny_push;}else{int tg67x = tg22x + (xif (y > tg67x){if (m > _mag[j+magstep2] && m >= _mag[j+magstep1]) goto __ocv_canny_push;}else{int s = (xs ^ ys)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 imageconst uchar* pmap = map + mapstep + 1;uchar* pdst = dst.ptr();for (int i = 0; i{for (int j = 0; jpdst[j] = (uchar)-(pmap[j] >> 1);}} 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 compatibilityaperture_size &= ~CV_CANNY_L2_GRADIENT;L2gradient = true;} if ((aperture_size & 1) == 0 || (aperture_size != -1 && (aperture_size 7)))CV_Error(CV_StsBadFlag, "");#ifdefHA VE_TEGRA_OPTIMIZATIONif (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 edgefor (int i = 0; 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 1){for(int j = 0, jn = 0; j _norm[maxIdx]) maxIdx = jn + k;_norm[j] = _norm[maxIdx];_dx[j] = _dx[maxIdx];_dy[j] = _dy[maxIdx];}}_norm[-1] = _norm[src.cols] = 0;}elsememset(_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 suppressionif (i == 0)continue; uchar* _map = map +mapstep*i + 1;_map[-1] = _map[src.cols] = 1; int* _mag = mag_buf[1] + 1; // take the central rowptrdiff_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 15const 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) = _mag[j+1]) goto __ocv_canny_push;}else{int tg67x = tg22x + (x =_mag[j+magstep1]) goto __ocv_canny_push;}else{int s = (xs ^ ys)_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 imageconst uchar* pmap = map + mapstep + 1;uchar* pdst = dst.ptr();for (int i = 0; i 1);}}Canny() 调用接口（C++）：[cpp] viewplaincopyprint?void Canny(InputArray image, OutputArray edges, double threshold1, double threshold2,int apertureSize=3, bool L2gradient=false ) voidCanny(InputArray image, OutputArray edges, double threshold1, double threshold2,int apertureSize=3, boolL2gradient=false )实践示例[cpp] viewplaincopyprint?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 3x3blur( src_gray, detected_edges, Size(3,3) );/// Canny detectorCanny( 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;} 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 3x3blur( src_gray, detected_edges, Size(3,3) );/// Canny detectorCanny( 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 PatternAnalysis and Machine Intelligence, 8(6), pp. 679-698 (1986).转载请注明出处：/xiaowei_cqu/article/details/7839140资源下载：/detail/xiaowei_cqu/4483966。