This method is used to calculate a 1-D spline filter along the given axis. These are filtered by a spline filter.
Syntax: scipy.ndimage.spline_filter1d(input, order=3, axis=-1, output=<class ‘numpy.float64’>)
Parameters
input: array_like – The input array
order: int – The order of the spline, default is 3.
axis: int, – The axis along which the spline filter is applied. Default is the last axis.
output: ndarray – The array in which to place the output, or the dtype of the returned array. Default is numpy.float64.
Example 1:
Python3
# importing spline filter with one dimension. from scipy.ndimage import spline_filter1d # importing matplot library for visualization import matplotlib.pyplot as plt # importing numpy module import numpy as np # creating an image geek_image = np.eye( 80 ) # returns an image array format geek_image[ 40 , :] = 1.0 print (geek_image) |
Output:
Example 2:
Python3
# importing spline filter with one dimension. from scipy.ndimage import spline_filter1d # importing matplot library for visualization import matplotlib.pyplot as plt # importing numpy module import numpy as np # creating an image geek_image = np.eye( 80 ) geek_image[ 40 , :] = 1.0 # in axis=0 axis_0 = spline_filter1d(geek_image, axis = 0 ) # in axis=1 axis_1 = spline_filter1d(geek_image, axis = 1 ) f, ax = plt.subplots( 1 , 3 , sharex = True ) for ind, data in enumerate ([[geek_image, "geek_image original" ], [axis_0, "spline filter in axis 0" ], [axis_1, "spline filter in axis 1" ]]): ax[ind].imshow(data[ 0 ], cmap = 'gray_r' ) # giving title ax[ind].set_title(data[ 1 ]) # orientation layout of our image plt.tight_layout() # to show image plt.show() |
Output: