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

形态学运算的基础是腐蚀和膨胀

腐蚀运算通过降低边缘噪点实现平滑，膨胀通过扩大边缘实现消除内部噪点

o = cv2.imread("erode_hand.jpg")  #原图为黑底白色mask
kernel = np.ones((6, 6), np.uint8)  #定义6*6的算子
erd = cv2.erode(o, kernel, iterations=2)  #此处定义迭代运算2次
dil = cv2.dilate(o, kernel, iterations=2)

plt.figure("original - erode - dilate")  
#调用plot中的imshow函数来显示多图对比
plt.subplot(131), plt.imshow(o, cmap=plt.cm.gray), plt.axis("off")  
#cmap为色彩空间，原图为灰度图，故cmap=plt.cm.gray
plt.subplot(132), plt.imshow(erd, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(133), plt.imshow(dil, cmap=plt.cm.gray), plt.axis("off")
plt.show()  #显示图像

original - erode - dilate

通用形态学运算

dst = cv2.morphologyEX(scr, op, kernel[, anchor[, iterations[, borderType[, borderValye]]]]])

op====> cv2.MORPH_OPEN 开运算

首先腐蚀然后膨胀，达到外部去噪目的

open = cv2.morphologyEx(o, cv2.MORPH_OPEN, kernel, iterations=2)

plt.figure("original - open")
plt.subplot(121), plt.imshow(o, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(122), plt.imshow(open, cmap=plt.cm.gray), plt.axis("off")
plt.show()

original - open

op====> cv2.MORPH_CLOSE 闭运算

首先膨胀然后腐蚀，达到内部去噪目的

close = cv2.morphologyEx(o, cv2.MORPH_CLOSE, kernel, iterations=2)

plt.figure("original - close")
plt.subplot(121), plt.imshow(o, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(122), plt.imshow(close, cmap=plt.cm.gray), plt.axis("off")
plt.show()

original - close

op====> cv2.MORPH_TOPHAT 礼帽运算

原始图像减去开运算图像，通常得到比原图更亮的边缘噪点图像

tophat = cv2.morphologyEx(o, cv2.MORPH_TOPHAT, kernel)

plt.figure("original - open - tophat")
plt.subplot(131), plt.imshow(o, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(132), plt.imshow(open, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(133), plt.imshow(tophat, cmap=plt.cm.gray), plt.axis("off")
plt.show()

original - open - tophat

op====> cv2.MORPH_BLACKHAT 黑帽运算

闭运算减去原始图像，得到原始图像中的小孔，通常得到比原图暗的噪点图像

blackhat = cv2.morphologyEx(o, cv2.MORPH_BLACKHAT, kernel)

plt.figure("original - close - blackhat")
plt.subplot(131), plt.imshow(o, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(132), plt.imshow(close, cmap=plt.cm.gray), plt.axis("off")
plt.subplot(133), plt.imshow(blackhat, cmap=plt.cm.gray), plt.axis("off")
plt.show()

original - close - blackhat