Halcon之图像梯度、图像边缘、USM锐化

$$图像梯度、图像边缘、USM锐化$$

图像卷积&＃xff1a;
1.模糊
2.梯度
3.边缘
4.锐化

1.视频教程&＃xff1a;
B站、网易云课堂、腾讯课堂
2.代码地址&＃xff1a;
Gitee
Github
3.存储地址&＃xff1a;
Google云
百度云&＃xff1a;
提取码&＃xff1a;

• 1.图像梯度
• 2.图像边缘
• 3.USM锐化

1.图像梯度

1.1 梯度算子

利用梯度算子进行梯度的计算

常见的图像梯度算子

Sobel、Scharr算子&＃xff0c;计算x和y方向的差异

一阶导数算子

#include
#include using namespace cv;
using namespace std;int main(int argc, char** argv) {Mat src &＃61; imread("E:/cats.jpg", IMREAD_COLOR);if (src.empty()) {printf("image is empty!!!");return -1;}imshow("src", src);// 非均值滤波Mat robot_x &＃61; (Mat_ <int>(2, 2) << 1, 0, 0, -1);Mat robot_y &＃61; (Mat_ <int>(2, 2) << 0, 1, -1, 0);Mat grad_x,grad_y;filter2D(src, grad_x, CV_32F, robot_x, Point(-1, -1), 0, BORDER_DEFAULT);filter2D(src, grad_y, CV_32F, robot_y, Point(-1, -1), 0, BORDER_DEFAULT);convertScaleAbs(grad_x, grad_x);convertScaleAbs(grad_y, grad_y);Mat result;add(grad_x, grad_y, result);imshow("robot gradient", result);waitKey(0);destroyAllWindows();return 0;
}

#include
#include using namespace cv;
using namespace std;int main(int argc, char** argv) {Mat src &＃61; imread("E:/cats.jpg", IMREAD_COLOR);if (src.empty()) {printf("image is empty!!!");return -1;}imshow("src", src);// robot算子Mat robot_x &＃61; (Mat_ <int>(2, 2) << 1, 0, 0, -1);Mat robot_y &＃61; (Mat_ <int>(2, 2) << 0, 1, -1, 0);Mat grad_x,grad_y;filter2D(src, grad_x, CV_32F, robot_x, Point(-1, -1), 0, BORDER_DEFAULT);filter2D(src, grad_y, CV_32F, robot_y, Point(-1, -1), 0, BORDER_DEFAULT);convertScaleAbs(grad_x, grad_x);convertScaleAbs(grad_y, grad_y);Mat result;add(grad_x, grad_y, result);imshow("robot gradient", result);// Sobel算子Sobel(src, grad_x, CV_32F, 1, 0);Sobel(src, grad_y, CV_32F, 0, 1);convertScaleAbs(grad_x, grad_x);convertScaleAbs(grad_y, grad_y);Mat result2;addWeighted(grad_x, 0.5, grad_y, 0.5, 0, result2);imshow("sobel gradient", result2);// ScharrScharr(src, grad_x, CV_32F, 1, 0);Scharr(src, grad_y, CV_32F, 0, 1);convertScaleAbs(grad_x, grad_x);convertScaleAbs(grad_y, grad_y);Mat result3;addWeighted(grad_x, 0.5, grad_y, 0.5, 0, result3);imshow("Scharr gradient", result3);waitKey(0);destroyAllWindows();return 0;
}

2.图像边缘

2.1 二阶导数算子

锐化的常用梯度算子&＃xff1a;拉普拉斯算子

#include
#include using namespace cv;
using namespace std;int main(int argc, char** argv) {Mat src &＃61; imread("E:/cats.jpg", IMREAD_COLOR);if (src.empty()) {printf("image is empty!!!");return -1;}imshow("src", src);Mat dst;Laplacian(src, dst, -1, 3, 1.0, 0, BORDER_DEFAULT);imshow("laplacian", dst);waitKey(0);destroyAllWindows();return 0;
}

#include
#include using namespace cv;
using namespace std;int main(int argc, char** argv) {Mat src &＃61; imread("E:/cats.jpg", IMREAD_COLOR);if (src.empty()) {printf("image is empty!!!");return -1;}imshow("src", src);// 拉普拉斯算子Mat dst;Laplacian(src, dst, -1, 3, 1.0, 0, BORDER_DEFAULT);imshow("laplacian", dst);// 锐化Mat sh_op &＃61; (Mat_ <int>(3,3) <<0,-1,0,-1,5,-1,0,-1,0);Mat result;filter2D(src, result, CV_32F, sh_op, Point(-1, -1), 0, BORDER_DEFAULT);convertScaleAbs(result, result);imshow("sharpen",result);waitKey(0);destroyAllWindows();return 0;
}

拉普拉斯容易被噪声影响&＃xff0c;所以在使用之前&＃xff0c;需要进行去噪

3.USM锐化

#include
#include using namespace cv;
using namespace std;int main(int argc, char** argv) {Mat src &＃61; imread("E:/cats.jpg", IMREAD_COLOR);if (src.empty()) {printf("image is empty!!!");return -1;}imshow("src", src);// Unsharpen mask filterMat blur_image, dst;GaussianBlur(src, blur_image, Size(3, 3), 0);Laplacian(src, dst, -1, 3, 1.0, 0, BORDER_DEFAULT);imshow("laplacian", dst);Mat usm_image;addWeighted(blur_image, 1.0, dst, -1.0, 0, usm_image);imshow("usm filter", usm_image);waitKey(0);destroyAllWindows();return 0;
}