Canny边缘检测主要包括：
图像的灰度化；
图像的高斯滤波；
计算出每一个像素点位置的梯度（X方向梯度、Y方向梯度、已经该点的梯度幅值）和方向角度；
局部非极大值抑制处理；
双阈值处理和连接处理；
Canny算法思路参考下面的博客：
https://blog.csdn.net/dcrmg/article/details/52344902
https://www.cnblogs.com/love6tao/p/5152020.html
我在下面直接给出可以运行的C++代码（Opencv2.4.9）
#include <iostream> #include "opencv2/opencv.hpp" using namespace std; using namespace cv; /* 生成高斯卷积核 kernel */ void Gaussian_kernel(int kernel_size, int sigma, Mat &kernel) { const double PI = 3.1415926; int m = kernel_size / 2; kernel = Mat(kernel_size, kernel_size, CV_32FC1); float s = 2 * sigma*sigma; for (int i = 0; i < kernel_size; i++) { for (int j = 0; j < kernel_size; j++) { int x = i - m; int y = j - m; kernel.at<float>(i, j) = exp(-(x*x + y*y) / s) / (PI*s); } } } /* 计算梯度值和方向 imageSource 原始灰度图 imageX X方向梯度图像 imageY Y方向梯度图像 gradXY XY方向的梯度值 pointDirection 梯度方向角度 */ void GradDirection(const Mat imageSource, Mat &imageX, Mat &imageY,Mat &gradXY, Mat &theta) { imageX = Mat::zeros(imageSource.size(), CV_32SC1); imageY = Mat::zeros(imageSource.size(), CV_32SC1); gradXY = Mat::zeros(imageSource.size(), CV_32SC1); theta = Mat::zeros(imageSource.size(), CV_32SC1); int rows = imageSource.rows; int cols = imageSource.cols; int stepXY = imageX.step; int step = imageSource.step; /* Mat.step参数指图像的一行实际占用的内存长度，因为opencv中的图像会对每行的长度自动补齐（8的倍数） */ uchar *PX = imageX.data; uchar *PY = imageY.data; uchar *P = imageSource.data; uchar *XY = gradXY.data; for (int i = 1; i < rows - 1; i++) { for (int j = 1; j < cols - 1; j++) { int a00 = P[(i - 1)*step + j - 1]; int a01 = P[(i - 1)*step + j]; int a02 = P[(i - 1)*step + j + 1]; int a10 = P[i*step + j - 1]; int a11 = P[i*step + j]; int a12 = P[i*step + j + 1]; int a20 = P[(i + 1)*step + j - 1]; int a21 = P[(i + 1)*step + j]; int a22 = P[(i + 1)*step + j + 1]; double gradY = double(a02 + 2 * a12 + a22 - a00 - 2 * a10 - a20); double gradX = double(a00 + 2 * a01 + a02 - a20 - 2 * a21 - a22); //PX[i*stepXY + j*(stepXY / step)] = abs(gradX); //PY[i*stepXY + j*(stepXY / step)] = abs(gradY); imageX.at<int>(i, j) = abs(gradX); imageY.at<int>(i, j) = abs(gradY); if (gradX == 0) { gradX = 0.000000000001; } theta.at<int>(i, j) = atan(gradY / gradX)*57.3; theta.at<int>(i, j) = (theta.at<int>(i, j) + 360) % 360; gradXY.at<int>(i, j) = sqrt(gradX*gradX + gradY*gradY); //XY[i*stepXY + j*(stepXY / step)] = sqrt(gradX*gradX + gradY*gradY); } } convertScaleAbs(imageX, imageX); convertScaleAbs(imageY, imageY); convertScaleAbs(gradXY, gradXY); } /* 局部非极大值抑制 沿着该点梯度方向，比较前后两个点的幅值大小，若该点大于前后两点，则保留，若该点小于前后两点，则置为0； imageInput 输入得到梯度图像 imageOutput 输出的非极大值抑制图像 theta 每个像素点的梯度方向角度 imageX X方向梯度 imageY Y方向梯度 */ void NonLocalMaxValue(const Mat imageInput, Mat &imageOutput, const Mat &theta, const Mat &imageX, const Mat &imageY) { imageOutput = imageInput.clone(); int cols = imageInput.cols; int rows = imageInput.rows; for (int i = 1; i < rows - 1; i++) { for (int j = 1; j < cols - 1; j++) { if (0 == imageInput.at<uchar>(i, j))continue; int g00 = imageInput.at<uchar>(i - 1, j - 1); int g01 = imageInput.at<uchar>(i - 1, j); int g02 = imageInput.at<uchar>(i - 1, j + 1); int g10 = imageInput.at<uchar>(i , j - 1); int g11 = imageInput.at<uchar>(i, j); int g12 = imageInput.at<uchar>(i , j + 1); int g20 = imageInput.at<uchar>(i + 1, j - 1); int g21 = imageInput.at<uchar>(i + 1, j); int g22 = imageInput.at<uchar>(i + 1, j + 1); int direction = theta.at<int>(i, j); //该点梯度的角度值 int g1 = 0; int g2 = 0; int g3 = 0; int g4 = 0; double tmp1 = 0.0; //保存亚像素点插值得到的灰度数 double tmp2 = 0.0; double weight = fabs((double)imageY.at<uchar>(i, j) / (double)imageX.at<uchar>(i, j)); if (weight == 0)weight = 0.0000001; if (weight > 1) { weight = 1 / weight; } if ((0 <= direction && direction < 45) || 180 <= direction &&direction < 225) { tmp1 = g10*(1 - weight) + g20*(weight); tmp2 = g02*(weight)+g12*(1 - weight); } if ((45 <= direction && direction < 90) || 225 <= direction &&direction < 270) { tmp1 = g01*(1 - weight) + g02*(weight); tmp2 = g20*(weight)+g21*(1 - weight); } if ((90 <= direction && direction < 135) || 270 <= direction &&direction < 315) { tmp1 = g00*(weight)+g01*(1 - weight); tmp2 = g21*(1 - weight) + g22*(weight); } if ((135 <= direction && direction < 180) || 315 <= direction &&direction < 360) { tmp1 = g00*(weight)+g10*(1 - weight); tmp2 = g12*(1 - weight) + g22*(weight); } if (imageInput.at<uchar>(i, j) < tmp1 || imageInput.at<uchar>(i, j) < tmp2) { imageOutput.at<uchar>(i,j) = 0; } } } } /* 双阈值的机理是： 指定一个低阈值A，一个高阈值B，一般取B为图像整体灰度级分布的70%，且B为1.5到2倍大小的A； 灰度值小于A的，置为0,灰度值大于B的，置为255； 连接处理: 灰度值介于A和B之间的，考察改像素点临近的8像素是否有灰度值为255的，若没有255的，表示这是一个孤立的局部极大值点，予以排除，置为0；若有255的，表示这是一个跟其他边缘有“接壤”的可造之材，置为255，之后重复执行该步骤，直到考察完之后一个像素点。 */ void DoubleThreshold(Mat &imageInput, const double lowThreshold, const double highThreshold) { int cols = imageInput.cols; int rows = imageInput.rows; for (int i = 0; i < rows; i++) { for (int j = 0; j < cols; j++) { double temp = imageInput.at<uchar>(i, j); temp = temp>highThreshold ? (255) : (temp); temp = temp < lowThreshold ? (0) : (temp); imageInput.at<uchar>(i, j) = temp; } } for (int i = 1; i < rows - 1; i++) { for (int j = 1; j < cols - 1; j++) { double temp = imageInput.at<uchar>(i, j); if (temp<lowThreshold || temp>highThreshold)continue; bool change = false; for (int k = -1; k <= 1; k++) { for (int u = -1; u <= 1; u++) { if (k == 0 && u == 0)continue; if (imageInput.at<uchar>(i + k, j + u) == 255) { change = true; break; } } if (change)break; } imageInput.at<uchar>(i, j) = (change) ? (255) : (0); } } } int main() { Mat image = imread("test.jpg"); imshow("origin image", image); //转换为灰度图 Mat grayImage; cvtColor(image, grayImage, CV_RGB2GRAY); imshow("gray image", grayImage); //高斯滤波 Mat gausKernel; int kernel_size = 5; double sigma = 1; Gaussian_kernel(kernel_size, sigma, gausKernel); Mat gausImage; filter2D(grayImage, gausImage, grayImage.depth(), gausKernel); imshow("gaus image", gausImage); //计算XY方向梯度 Mat imageX, imageY, imageXY; Mat theta; GradDirection(grayImage, imageX, imageY, imageXY , theta); imshow("XY grad", imageXY); //对梯度幅值进行非极大值抑制 Mat localImage; NonLocalMaxValue(imageXY, localImage, theta,imageX,imageY);; imshow("Non local maxinum image", localImage); //双阈值算法检测和边缘连接 DoubleThreshold(localImage, 60, 100); imshow("canny image", localImage); Mat temMat; Canny(image, temMat, 60, 100); imshow("opencv canny image", temMat); waitKey(0); return 0; }文章来源: Opencv和C++实现canny边缘检测