How to implement basic morphological filtering in Python
There are four most basic morphological operations, namely corrosion, expansion, open calculation and closed calculation. `scipy.ndimage implements these four operations for binary arrays and grayscale arrays respectively
Binary | Grayscale | |
---|---|---|
grey_erosion | Corrosion | |
grey_dilation | Inflation | |
grey_closing | Close (expand first and then corrode) | |
grey_opening | Open (corrode first and then expand) |
ij represents the set of all values that are 1 in B when the origin of B BB is at (i, j).
This formula means that when structure B is used to corrode A, when the origin of B is translated to the pixel (i,j) of image A, if B is completely surrounded by the overlapping area of the two, then Assigned a value of 1, otherwise assigned a value of 0. When a certain element in B is 1, if the corresponding position in A is also 1, then the value at (i, j) is 1. This is a more intuitive example. Inflation is the opposite, and can be expressed asimport numpy as np import matplotlib.pyplot as plt import scipy.ndimage as sn x = np.zeros([20,20]) x[5:15, 5:15] = 1 x_ero = sn.binary_erosion(x) x_dil = sn.binary_dilation(x) fig = plt.figure() ax = fig.add_subplot(1,3,1) ax.imshow(x) plt.title("original") ax = fig.add_subplot(1,3,2) ax.imshow(x_ero) plt.title("erosion") ax = fig.add_subplot(1,3,3) ax.imshow(x_dil) plt.title("dilation") plt.show()
x = np.zeros([20,20]) x[5:15, 5:15] = 1 x[10:12,10:12] = 0 x[2:4, 2:4] = 1 x_open = sn.binary_opening(x) x_close = sn.binary_closing(x) fig = plt.figure() ax = fig.add_subplot(1,3,1) ax.imshow(x) plt.title("original") ax = fig.add_subplot(1,3,2) ax.imshow(x_open) plt.title("opening") ax = fig.add_subplot(1,3,3) ax.imshow(x_close) plt.title("closing") plt.show()
scipy, and the erosion, expansion, and opening and closing operations are performed in sequence.
from scipy.misc import ascent img = ascent() funcs = { "original": lambda x, tmp:x, "erosion" : sn.grey_erosion, "dilation" : sn.grey_dilation, "opening" : sn.grey_opening, "closing" : sn.grey_closing } fig = plt.figure() for i, key in enumerate(funcs): ax = fig.add_subplot(2,3,i+1) plt.imshow(funcs[key](img, (10,10)), cmap=plt.cm.gray) plt.title(key) plt.show()
Parameter list
The parameters of the binary function and the grayscale function are not the same. The following takes the closing operation as an example. All parameters of the binary and grayscale functions , in addition to the input input, the parameters shared by both are- structure is an array type, indicating the construction element, which can be understood as a convolution template
- output is an array with the same dimensions as the input, and can save the result
- orgin filter setting, the default is 0
binary_closing(input, iterations=1, mask=None, border_value=0, brute_force=False)
- iterations is the number of executions
- mask mask array, which is bool type Array, the position corresponding to False will not change
- border_value The value at the edge
- brute_force If it is False, only the last iteration Only the changed values in will be updated
- size is the filter template
- mode optional reflect,constant, nearest, mirror, wrap, edge filling method
- cval edge filling value
grey_closing(input, size=None, footprint=None, mode='reflect', cval=0.0)
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