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 visualizationimport matplotlib.pyplot as plt# importing numpy moduleimport numpy as np# creating an imagegeek_image = np.eye(80)# returns an image array formatgeek_image[40, :] = 1.0print(geek_image) |
Output:
Example 2:
Python3
# importing spline filter with one dimension.from scipy.ndimage import spline_filter1d# importing matplot library for visualizationimport matplotlib.pyplot as plt# importing numpy moduleimport numpy as np# creating an imagegeek_image = np.eye(80)geek_image[40, :] = 1.0# in axis=0axis_0 = spline_filter1d(geek_image, axis=0)# in axis=1axis_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 imageplt.tight_layout()# to show imageplt.show() |
Output:
