图像边缘提取图像的边界信息⼀般通过灰度值突变来体现，所以图像边缘提取⼀般通过捕捉灰度突变的⽅法来实现，捕捉灰度突变可以通过求微分来实现导数越⼤说明变化越⼤，边缘信号越强1.Sobel算⼦也叫离散微分算⼦，⼀阶微分算⼦，求导算⼦，先做⾼斯平滑在做微分求导可以在各个⽅向上求图像的梯度如⽔平⽅向 Gx=[-1,0,1,-2,0,2,-1,0,1]，垂直⽅向Gy=[-1,-2,-1,0,0,0,1,2,1]最终G=sqrt(Gx^2+Gy^2),或者G=|Gx|+|Gy|第⼆种的运算速度要快于第⼀种，所以⼀般采⽤第⼆种⽅法Sobel算⼦的改进版叫Scharr算⼦[-3，0，3，-10，0，10，-3，0，3]#include<iostream>#include<opencv2/opencv.hpp>using namespace std;using namespace cv;int main(int argc, char **argv){Mat src, dst;src = imread("b.png");if (src.empty()){cout << "load img failed" << endl;return -1;}imshow("input img", src);Mat gaussian,gray_src;GaussianBlur(src, gaussian, Size(3, 3), 0, 0);cvtColor(gaussian, gray_src, CV_BGR2GRAY);imshow("blur gray", gray_src);Mat xgrad, ygrad;Sobel(gray_src, xgrad, CV_16S, 1, 0, 3);Sobel(gray_src, ygrad, CV_16S, 0, 1, 3);convertScaleAbs(xgrad, xgrad);convertScaleAbs(ygrad, ygrad);imshow("x grade", xgrad);imshow("y grade", ygrad);addWeighted(xgrad, 0.5, ygrad, 0.5, 0, dst);imshow("output img", dst);/*dst = Mat(xgrad.size(), xgrad.type());int width = dst.cols;int height = dst.rows;for(int i=0;i<height;++i)for (int j = 0; j < width; ++j){int xg = xgrad.at<char>(i, j);int yg = ygrad.at<char>(i, j);int xy = xg + yg;dst.at<char>(i, j) = saturate_cast<uchar>(xy);}imshow("output img", dst);*/waitKey(0);return0;}plance算⼦求⼆阶导数，在⼆阶导数的时候，最⼤变化处的值为0，即边缘的⼆阶导数是0流程：⾼斯模糊去噪GaussianBlur()转为灰度值cvtColor()Laplance⼆阶导数计算Laplancian()取绝对值convertScaleAbs()显⽰结果#include<iostream>#include<opencv2/opencv.hpp>using namespace std;using namespace cv;int main(int argc, char **argv){Mat src, dst;src = imread("b.png");if (src.empty()){cout << "load img failed" << endl;return -1;}imshow("input img", src);Mat gaussian,gray_src;GaussianBlur(src, gaussian, Size(3, 3), 0, 0);cvtColor(gaussian, gray_src, CV_BGR2GRAY);imshow("blur gray", gray_src);Laplacian(gray_src, dst, CV_16S,3);convertScaleAbs(dst, dst);imshow("Laplacian", dst);threshold(dst, dst, 0, 255, THRESH_OTSU | THRESH_BINARY);imshow("output img", dst);/*dst = Mat(xgrad.size(), xgrad.type());int width = dst.cols;int height = dst.rows;for(int i=0;i<height;++i)for (int j = 0; j < width; ++j){int xg = xgrad.at<char>(i, j);int yg = ygrad.at<char>(i, j);int xy = xg + yg;dst.at<char>(i, j) = saturate_cast<uchar>(xy);}imshow("output img", dst);*/waitKey(0);return0;}3.Canny边缘检测步骤：⾼斯模糊 GaussianBlur灰度转换cvtColor计算梯度Sobel/Scharr⾮最⼤信号抑制⾼低阈值输出⼆值图像⾮最⼤信号抑制需要计算梯度⽅向T1为低阈值，T2为⾼阈值，凡是⾼于T2的都保留，凡是低于T1的都丢弃，从⾼于T2的像素出发，凡是⼤于T1且相互连接的都保留，最终得到⼀个输出⼆值图像推荐的⾼低阈值⽐为3：1或2：1Canny(src,dst,threshold_low,threshold_high,Sobel_size,Lwgradient)最后⼀个如果是true就⽤L2归⼀化（开根），如果不是就L1归⼀化（绝对值），⼀般⽤L1 #include<iostream>#include<opencv2/opencv.hpp>using namespace std;using namespace cv;Mat src, dst, gray_src, gaussian;int t1_value = 50;int max_value = 255;const char* OUTPUT_TITLE = "Canny Result";void Canny_Demo(int,void*);int main(int argc, char **argv){//Mat src, dst;src = imread("b.png");if (src.empty()){cout << "load img failed" << endl;return -1;}imshow("input img", src);//Mat gaussian,gray_src;//GaussianBlur(src, gaussian, Size(3, 3), 0, 0);namedWindow(OUTPUT_TITLE, CV_WINDOW_AUTOSIZE);cvtColor(src, gray_src, CV_BGR2GRAY);createTrackbar("Threshold Value :", OUTPUT_TITLE, &t1_value, max_value, Canny_Demo);Canny_Demo(0, 0);waitKey(0);return0;}void Canny_Demo(int, void*){Mat edge_output;blur(gray_src, gray_src, Size(3, 3), Point(-1, -1), BORDER_DEFAULT);Canny(gray_src, edge_output, t1_value, t1_value * 2, 3, false);//dst.create(src.size(), src.type());//src.copyTo(dst, edge_output);imshow(OUTPUT_TITLE, ~edge_output);}去掉注释会变成彩⾊图，注意修改imshow中的输出变量。
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