OpenCV中的形态学8

形态学概述

开运算：先做腐蚀后做膨胀（腐蚀可以理解为缩小）
闭运算：先膨胀后腐蚀

图像全局二值化

低于threshold就置为0，高于threshold就置为255

# -*- coding: utf-8 -*-
import cv2
import numpy as np

img = cv2.imread('E://pic//9.jpg')
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, dst = cv2.threshold(img2, 180, 255, cv2.THRESH_BINARY)
print(dst.shape)

cv2.imshow('img', img)
cv2.imshow('gray', img2)
cv2.imshow('bin', dst)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as np

img = cv2.imread('E://pic//9.jpg')
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, dst = cv2.threshold(img2, 180, 255, cv2.THRESH_BINARY_INV)
print(dst.shape)

cv2.imshow('img', img)
cv2.imshow('gray', img2)
cv2.imshow('bin', dst)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as np

img = cv2.imread('E://pic//9.jpg')
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, dst = cv2.threshold(img2, 60, 255, cv2.THRESH_BINARY)
ret, dst2 = cv2.threshold(img2, 60, 255, cv2.THRESH_BINARY_INV)
print(dst.shape)

cv2.imshow('img', img)
cv2.imshow('gray', img2)
cv2.imshow('bin', dst)
cv2.imshow('bin2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

阈值类型

自适应阈值二值化

# -*- coding: utf-8 -*-
import cv2
import numpy as np

# img = cv2.imread('E://pic//9.jpg')
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('gray', cv2.WINDOW_NORMAL)
cv2.namedWindow('bin', cv2.WINDOW_NORMAL)
cv2.namedWindow('bin2', cv2.WINDOW_NORMAL)
img = cv2.imread('./math.png')
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, dst = cv2.threshold(img2, 180, 255, cv2.THRESH_BINARY)
ret, dst2 = cv2.threshold(img2, 180, 255, cv2.THRESH_BINARY_INV)
print(dst.shape)

cv2.imshow('img', img)
cv2.imshow('gray', img2)
cv2.imshow('bin', dst)
cv2.imshow('bin2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('gray', cv2.WINDOW_NORMAL)
cv2.namedWindow('bin', cv2.WINDOW_NORMAL)
cv2.namedWindow('bin2', cv2.WINDOW_NORMAL)
img = cv2.imread('./math.png')
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

dst = cv2.adaptiveThreshold(img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 0)
dst2 = cv2.adaptiveThreshold(img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 0)

cv2.imshow('img', img)
cv2.imshow('gray', img2)
cv2.imshow('bin', dst)
cv2.imshow('bin2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

OpenCV腐蚀

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
kernel = np.ones((3, 3), np.uint8)
dst = cv2.erode(img, kernel, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

获取形态学卷积核

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
print(kernel)
dst = cv2.erode(img, kernel, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

OpenCV膨胀

原来黑色区域变成白色

卷积核大小意味着膨胀的速度，卷积核越大膨胀越快

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
dst = cv2.dilate(img, kernel, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
print(kernel)
# 腐蚀
dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
dst2 = cv2.dilate(dst, kernel, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
print(kernel)
# 腐蚀
dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
dst2 = cv2.dilate(dst, kernel, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

开运算

开运算能达到消除噪点的效果


对噪点比较大的图像，我们用大的卷积核，消除噪点能力更强

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./dotj.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
# print(kernel)
# 腐蚀
dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
dst2 = cv2.dilate(dst, kernel, iterations=1)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./dotj.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
# print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
# dst2 = cv2.dilate(dst, kernel, iterations=1)

dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
# cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

闭运算

闭运算消除里面的噪点，开运算消除外面的噪点

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./dotinj.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
# print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
# dst2 = cv2.dilate(dst, kernel, iterations=1)

# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
# cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

噪点比较大就要大一点的卷积核

形态学梯度

可以求边缘

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
# print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
# dst2 = cv2.dilate(dst, kernel, iterations=1)

# 开运算
# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
# 闭运算
# dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

dst = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
# cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

因为7x7的kernel太大，腐蚀太多，造成里面的黑线很少

现在我们换成3x3的kernel

# -*- coding: utf-8 -*-
import cv2
import numpy as np

cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./j.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
# dst2 = cv2.dilate(dst, kernel, iterations=1)

# 开运算
# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
# 闭运算
# dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

dst = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
# cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

顶帽运算

获取的是外面的噪点

# -*- coding: utf-8 -*-
import cv2
import numpy as np

# cv2.namedWindow('img', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./tophat.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (19, 19))
# print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
# dst2 = cv2.dilate(dst, kernel, iterations=1)

# 开运算
# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
# 闭运算
# dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
# 形态学梯度
# dst = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)

# 顶帽运算
dst = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
# cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

黑帽操作

获取图内的噪点

# -*- coding: utf-8 -*-
import cv2
import numpy as np

# cv2.namedWindow('img', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
# cv2.namedWindow('dst2', cv2.WINDOW_NORMAL)
img = cv2.imread('./dotinj.png')
# kernel = np.ones((7, 7), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
# print(kernel)
# 腐蚀
# dst = cv2.erode(img, kernel, iterations=1)
# 膨胀
# dst2 = cv2.dilate(dst, kernel, iterations=1)

# 开运算
# dst = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
# 闭运算
# dst = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
# 形态学梯度
# dst = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)

# 顶帽运算
# dst = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

# 黑帽操作
dst = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel)

cv2.imshow('img', img)
cv2.imshow('dst', dst)
# cv2.imshow('dst2', dst2)

key = cv2.waitKey(0) & 0xff
if key == ord('q'):
    cv2.destroyAllWindows()

小结

之后我会持续更新，如果喜欢我的文章，请记得一键三连哦，点赞关注收藏，你的每一个赞每一份关注每一次收藏都将是我前进路上的无限动力 ！！！↖(▔▽▔)↗感谢支持！