卷积公式：
卷积的作用：模糊图像、提取边缘、图像增强（锐化）等。
常见卷积核算子：Robert算子，对45度和135度的像素变化敏感。
Sobel算子：分别对水平方向和数值方向的像素变化敏感
拉普拉斯算子：用于边缘提取
#include<opencv2/opencv.hpp> #include<iostream> #include<string> using namespace cv; Mat src, dst, dst1, dst2, dst3,dst4; const String path = "C:\\Users\\admin\\Desktop\\demo.jpg"; const String Output = "Src window"; const String Output1 = "Robert window"; const String Output2 = "Sobel window"; const String Output21 = "Sobel1 window"; const String Output3 = "Laplace window"; int main(int argv, char** argc) { src = imread(path, IMREAD_REDUCED_COLOR_2); if (!src.data) { printf("Could not load the pic demo..."); return false; } namedWindow(Output, CV_WINDOW_AUTOSIZE); Mat kernel1 = (Mat_<int>(2, 2) << 1, 0, 0, 1); Mat kernel2 = (Mat_<int>(2, 2) << 0, 1, 1, 0); Mat kernel3 = (Mat_<int>(3, 3) << -1,0,1,-2,0,2,-1,0,1); Mat kernel4 = (Mat_<int>(3, 3) << -1,-2 ,-1, 0, 0,0, 1, 2, 1); Mat kernel5 = (Mat_<int>(3, 3) << 0, -1, 0, -1, 4,-1, 0, -1, 0); cvtColor(src, src, CV_BGR2GRAY); imshow(Output, src); //Robert filter2D(src, dst, -1, kernel2, Point(-1, -1)); namedWindow(Output1, CV_WINDOW_AUTOSIZE); imshow(Output1, dst); //Sobel filter2D(src, dst2, -1, kernel3, Point(-1, -1)); namedWindow(Output2, CV_WINDOW_AUTOSIZE); imshow(Output2, dst2); //Sobel1 filter2D(src, dst3, -1, kernel4, Point(-1, -1)); namedWindow(Output21, CV_WINDOW_AUTOSIZE); imshow(Output21, dst3); //Laplace filter2D(src, dst4, -1, kernel5, Point(-1, -1)); namedWindow(Output3, CV_WINDOW_AUTOSIZE); imshow(Output3, dst4); waitKey(0); return 0; }效果：
卷积：
int c = 0; int ksize = 3; int index = 0; namedWindow(Output4, CV_WINDOW_AUTOSIZE); while (true) { waitKey(500); if ((char) c==27)//ESC { break; } ksize = 4 + (index % 5) * 2 + 1; Mat kernels = Mat::ones(Size(ksize, ksize), CV_32F) / (float)(ksize*ksize); filter2D(src, dst, -1, kernels, Point(-1, -1)); index++; imshow(Output4, dst); }