2019-07-13（day031_Mat模板类：(Mat_(rows, cols) << v1, v2, v3, ......, vn);，其中n==rows * cols 边缘提...

python

import cv2 as cv
import numpy as np

img = cv.imread('../images/test.jpg')
h, w = img.shape[0 : 2]

grad_x = cv.Sobel(img, cv.CV_32F, 1, 0)
grad_y = cv.Sobel(img, cv.CV_32F, 0, 1)

grad_x = cv.convertScaleAbs(grad_x)
grad_y = cv.convertScaleAbs(grad_y)

grad_xy = cv.add(grad_x, grad_y, dtype = cv.CV_16S)
grad_xy = cv.convertScaleAbs(grad_xy)

result = np.zeros([h, w * 2, 3], dtype = img.dtype)
result[0:h, 0 : w, :] = img
result[0:h, w : 2 * w, :] = grad_xy
cv.imshow('result', result)
cv.waitKey(0)
cv.destroyAllWindows()

python中新知识点：
此处注意python中不用filter2D()方法，替代方法为Sobel

c++

#include "all.h"
using namespace std;
using namespace cv;
void MyClass::day031() {
    Mat img = MyClass::read(PATH + "images\\test.jpg");
    imshow("input", img);
    Mat robert_x = (Mat_(2, 2) << 1, 0, 0, -1);
    Mat robert_y = (Mat_(2, 2) << 0, -1, 1, 0);

    Mat prowitt_x = (Mat_(3, 3) << -1, 0, 1, -1, 0, 1, -1, 0, 1);
    Mat prowitt_y = (Mat_(3, 3) << -1, -1, -1, 0, 0, 0, 1, 1, 1);

    Mat robert_grad_x, robert_grad_y, prowitt_grad_x, prowitt_grad_y;

    filter2D(img, robert_grad_x, CV_16S, robert_x);
    filter2D(img, robert_grad_y, CV_16S, robert_y);

    convertScaleAbs(robert_grad_x, robert_grad_x);
    convertScaleAbs(robert_grad_y, robert_grad_y);

    imshow("robert_x", robert_grad_x);
    imshow("robert_y", robert_grad_y);

    filter2D(img, prowitt_grad_x, CV_32F, prowitt_x);
    filter2D(img, prowitt_grad_y, CV_32F, prowitt_y);

    convertScaleAbs(prowitt_grad_x, prowitt_grad_x);
    convertScaleAbs(prowitt_grad_y, prowitt_grad_y);

    imshow("prowitt_x", prowitt_grad_x);
    imshow("prowitt_y", prowitt_grad_y);

    waitKey(0);

}

c++新知识点：
Mat模板类：(Mat_(rows, cols) << v1, v2, v3, ......, vn);，其中n==rows * cols
边缘提取算子：Sobel, Isotropic Sobel, Roberts, Prewitt, Laplacian, Canny
filter2D()：根据边缘提取算子来过滤图像，效果是暴露图像的梯度
convertScaleAbs()：该函数经常用于图像增强，args1:input，args2:output，args3:alpha，args4:belta