12, opencv核心操作学习-形态学转换

1, 腐蚀

卷积核沿着图像滑动,如果与卷积核对应的原图像的所有像素值均是 1, 那么中心元素就保持原来的像素值虽否则就变为0。

# coding:utf-8

import cv2
import numpy as np
from matplotlib import pyplot as plt


def show(imgs, titles):
    j = 121
    for i in range(len(imgs)):
        plt.subplot(j), plt.imshow(imgs[i]), plt.title(titles[i])
        plt.xticks([]), plt.yticks([])
        j = j +1
    plt.show()

if __name__ == '__main__':
    img = cv2.imread('./jing.jpg', cv2.IMREAD_UNCHANGED)
    kernel = np.ones((9,9),np.uint8)
    out = cv2.erode(img, kernel,iterations=1)
    show([img, out], ['original', 'erode'])

erode(src, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None)

2, 膨胀

卷积核沿着图像滑动,如果与卷积核对应的原图像的像素值有为 1, 那么中心元素就为1

# coding:utf-8

import cv2
import numpy as np
from matplotlib import pyplot as plt


def show(img):
    # cv2.namedWindow('aa', cv2.WINDOW_NORMAL)
    cv2.imshow('aa', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == '__main__':
    img = cv2.imread('./jing.jpg', cv2.IMREAD_GRAYSCALE)
    show(img)
    kernel = np.ones((9,9),np.uint8)
    out = cv2.dilate(img, kernel,iterations=4)
    show(out)

dilate(src, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None)

形态学运算的变种

3,morphologyEx(src, op, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None)

op

# coding:utf-8

import cv2
import numpy as np
from matplotlib import pyplot as plt


def show(img):
    # cv2.namedWindow('aa', cv2.WINDOW_NORMAL)
    cv2.imshow('aa', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == '__main__':
    img = cv2.imread('./jing.jpg', cv2.IMREAD_GRAYSCALE)
    show(img)
    kernel = np.ones((9,9),np.uint8)
    out = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
    show(out)

4, 结构化元素

之前我们使用的结构化元素(理解为卷积核形状), 但是有时我们需要构建圆形或者椭圆形的核, 我们就需要使用opencv提供的函数来构建卷积核 cv2.getStructuringElement()。

getStructuringElement(shape, ksize, anchor=None)

构建结构化元素(卷积核)

shape