（二十六）skimage 形态学去噪

参考博客

本文内容引用自 python数字图像处理（13）：基本形态学滤波 ，有部分修改

导入依赖

from skimage import data
import skimage.morphology as sm
import matplotlib.pyplot as plt

结构元素

print('正方形')
print(sm.square(5))

print('平面圆形')
print(sm.disk(5))

print('球形')
print(sm.ball(5))

print('立方体形')
print(sm.cube(5))

print('钻石形')
print(sm.diamond(5))

print('矩形')
print(sm.rectangle(5, 4))

print('星形')
print(sm.star(5, int))

print('八角形')
print(sm.octagon(4, 3, int))

print('八面体')
print(sm.octahedron(5))

正方形
[[1 1 1 1 1]
 [1 1 1 1 1]
 [1 1 1 1 1]
 [1 1 1 1 1]
 [1 1 1 1 1]]
平面圆形
[[0 0 0 0 0 1 0 0 0 0 0]
 [0 0 1 1 1 1 1 1 1 0 0]
 [0 1 1 1 1 1 1 1 1 1 0]
 [0 1 1 1 1 1 1 1 1 1 0]
 [0 1 1 1 1 1 1 1 1 1 0]
 [1 1 1 1 1 1 1 1 1 1 1]
 [0 1 1 1 1 1 1 1 1 1 0]
 [0 1 1 1 1 1 1 1 1 1 0]
 [0 1 1 1 1 1 1 1 1 1 0]
 [0 0 1 1 1 1 1 1 1 0 0]
 [0 0 0 0 0 1 0 0 0 0 0]]
球形
[[[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 ...

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]]
立方体形
[[[1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]]

 [[1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]]

 [[1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]]

 [[1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]]

 [[1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]
  [1 1 1 1 1]]]
钻石形
[[0 0 0 0 0 1 0 0 0 0 0]
 [0 0 0 0 1 1 1 0 0 0 0]
 [0 0 0 1 1 1 1 1 0 0 0]
 [0 0 1 1 1 1 1 1 1 0 0]
 [0 1 1 1 1 1 1 1 1 1 0]
 [1 1 1 1 1 1 1 1 1 1 1]
 [0 1 1 1 1 1 1 1 1 1 0]
 [0 0 1 1 1 1 1 1 1 0 0]
 [0 0 0 1 1 1 1 1 0 0 0]
 [0 0 0 0 1 1 1 0 0 0 0]
 [0 0 0 0 0 1 0 0 0 0 0]]
矩形
[[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]
星形
[[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 1 1 1 0 0 0 0 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 1 1 1 1 1 1 1 1 1 1 1 1 1 0]
 [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
 [0 1 1 1 1 1 1 1 1 1 1 1 1 1 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 1 1 1 1 1 1 1 1 1 1 1 0 0]
 [0 0 0 0 0 0 1 1 1 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]]
八角形
[[0 0 0 1 1 1 1 0 0 0]
 [0 0 1 1 1 1 1 1 0 0]
 [0 1 1 1 1 1 1 1 1 0]
 [1 1 1 1 1 1 1 1 1 1]
 [1 1 1 1 1 1 1 1 1 1]
 [1 1 1 1 1 1 1 1 1 1]
 [1 1 1 1 1 1 1 1 1 1]
 [0 1 1 1 1 1 1 1 1 0]
 [0 0 1 1 1 1 1 1 0 0]
 [0 0 0 1 1 1 1 0 0 0]]
八面体
[[[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 ...

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]

 [[0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  ...
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]
  [0 0 0 ... 0 0 0]]]

腐蚀

# 读入黑白棋盘图
img = data.checkerboard()
# 用结构元素腐蚀
dst1 = sm.erosion(img, sm.square(5))
dst2 = sm.erosion(img, sm.square(25))
# 绘图
plt.figure('morphology', figsize=(8, 8))
plt.subplot(131)
plt.title('origin image')
plt.imshow(img, plt.cm.gray)

plt.subplot(132)
plt.title('morphological_5 image')
plt.imshow(dst1, plt.cm.gray)

plt.subplot(133)
plt.title('morphological_25 image')
plt.imshow(dst2, plt.cm.gray)

plt.show()

膨胀

# 读入黑白棋盘图
img = data.checkerboard()
# 用结构元素膨胀
dst1 = sm.dilation(img, sm.square(5))
dst2 = sm.dilation(img, sm.square(25))
# 绘图
plt.figure('morphology', figsize=(8, 8))
plt.subplot(131)
plt.title('origin image')
plt.imshow(img, plt.cm.gray)

plt.subplot(132)
plt.title('morphological_5 image')
plt.imshow(dst1, plt.cm.gray)

plt.subplot(133)
plt.title('morphological_25 image')
plt.imshow(dst2, plt.cm.gray)

plt.show()

开运算

# 先腐蚀再膨胀
# 可以消除小物体或小斑块
# 黑色中去掉白色
img = color.rgb2gray(io.imread('3.jpg'))
dst = sm.opening(img, sm.disk(9))

plt.figure('morphology', figsize=(8, 8))
plt.subplot(121)
plt.title('origin image')
plt.imshow(img, plt.cm.gray)
plt.axis('off')

plt.subplot(122)
plt.title('morphological image')
plt.imshow(dst, plt.cm.gray)
plt.axis('off')
plt.show()

闭运算

# 先膨胀再腐蚀
# 可用来填充孔洞
# 白色中去掉黑色
img = color.rgb2gray(io.imread('3.jpg'))
dst = sm.closing(img, sm.disk(9))

plt.figure('morphology', figsize=(8, 8))
plt.subplot(121)
plt.title('origin image')
plt.imshow(img, plt.cm.gray)
plt.axis('off')

plt.subplot(122)
plt.title('morphological image')
plt.imshow(dst, plt.cm.gray)
plt.axis('off')
plt.show()

白帽

# 原图减去开运算
# 查找原图中比邻近点更亮的区域
img = color.rgb2gray(io.imread('3.jpg'))
dst = sm.white_tophat(img, sm.disk(9))

plt.figure('morphology', figsize=(8, 8))
plt.subplot(121)
plt.title('origin image')
plt.imshow(img, plt.cm.gray)
plt.axis('off')

plt.subplot(122)
plt.title('morphological image')
plt.imshow(dst, plt.cm.gray)
plt.axis('off')
plt.show()

黑帽

# 原图减去闭运算
# 查找原图中比邻近点较暗的区域
img = color.rgb2gray(io.imread('3.jpg'))
dst = sm.black_tophat(img, sm.disk(9))

plt.figure('morphology', figsize=(8, 8))
plt.subplot(121)
plt.title('origin image')
plt.imshow(img, plt.cm.gray)
plt.axis('off')

plt.subplot(122)
plt.title('morphological image')
plt.imshow(dst, plt.cm.gray)
plt.axis('off')
plt.show()