学习c++版opencv3.4之18-sobel算子

sobel算子得到的是图像在x方向和y方向的梯度图像。拉普拉斯算子得到二阶梯度。

#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>

using namespace std;
using namespace cv;

int threshold_value = 100, threshold_max = 255;
int threshold_type = 0, threshold_type_max = 4;
string outwindow = "threshold img";
Mat src, dst;
void mythreshold(int, void*);
int main(){
    Mat src1;
    src1 = imread("/Users/ming/Documents/test.jpg");
    resize(src1, src, Size(src1.cols/2, src1.rows/2));
    if (!src.data){
        printf("cannot load image ...");
        return -1;
    }
    namedWindow("src img", CV_WINDOW_AUTOSIZE);
    imshow("src img", src);
    
    GaussianBlur(src, dst, Size(3,3), 0);
    Mat src_gray;
    cvtColor(src, src_gray, CV_BGR2GRAY);
    imshow("gaussian+gray", src_gray);
    //sobel算子
    Mat dst_x, dst_y;
    Sobel(src_gray, dst_x, -1, 1, 0); // x方向导数运算参数，可取0,1,2
    Sobel(src_gray, dst_y, -1, 0, 1); // y方向导数运算参数
    imshow("gaussian+gray+sobel_x", dst_x);
    imshow("gaussian+gray+sobel_y", dst_y);
    addWeighted(dst_x, 0.5, dst_y, 0.5, 0, dst);
    imshow("sobel", dst);
    
    waitKey(0);
    return 0;
}

Scharr算子：

#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>

using namespace std;
using namespace cv;

int threshold_value = 100, threshold_max = 255;
int threshold_type = 0, threshold_type_max = 4;
string outwindow = "threshold img";
Mat src, dst;
void mythreshold(int, void*);
int main(){
    Mat src1;
    src1 = imread("/Users/ming/Documents/test.jpg");
    resize(src1, src, Size(src1.cols/2, src1.rows/2));
    if (!src.data){
        printf("cannot load image ...");
        return -1;
    }
    namedWindow("src img", CV_WINDOW_AUTOSIZE);
    imshow("src img", src);
    
    GaussianBlur(src, dst, Size(3,3), 0);
    Mat src_gray;
    cvtColor(src, src_gray, CV_BGR2GRAY);
    imshow("gaussian+gray", src_gray);
    //Scharr算子
    Mat dst_x, dst_y;
//    Sobel(src_gray, dst_x, -1, 1, 0); // x方向导数运算参数，可取0,1,2
//    Sobel(src_gray, dst_y, -1, 0, 1); // y方向导数运算参数
    Scharr(src_gray, dst_x, -1, 1, 0); // x方向导数运算参数，可取0,1,2
    Scharr(src_gray, dst_y, -1, 0, 1); // y方向导数运算参数
    imshow("gaussian+gray+Scharr_x", dst_x);
    imshow("gaussian+gray+Scharr_y", dst_y);
    addWeighted(dst_x, 0.5, dst_y, 0.5, 0, dst);
    imshow("Scharr", dst);
    
    waitKey(0);
    return 0;
}