ndimage.labelで識別される機能の処理例を次に示します。これが役立つかどうかは、機能で何をしたいかによって異なります。
import numpy as np
import scipy.ndimage as ndi
import matplotlib.pyplot as plt
# Make a small array for the demonstration.
# The ndimage.label() function treats 0 as the "background".
a = np.zeros((16, 16), dtype=int)
a[:6, :8] = 1
a[9:, :5] = 1
a[8:, 13:] = 2
a[5:13, 6:12] = 3
struct = ndi.generate_binary_structure(2, 2)
lbl, n = ndi.label(a, struct)
# Plot the original array.
plt.figure(figsize=(11, 4))
plt.subplot(1, n + 1, 1)
plt.imshow(a, interpolation='nearest')
plt.title("Original")
plt.axis('off')
# Plot the isolated features found by label().
for i in range(1, n + 1):
# Make an array of zeros the same shape as `a`.
feature = np.zeros_like(a, dtype=int)
# Set the elements that are part of feature i to 1.
# Feature i consists of elements in `lbl` where the value is i.
# This statement uses numpy's "fancy indexing" to set the corresponding
# elements of `feature` to 1.
feature[lbl == i] = 1
# Make an image plot of the feature.
plt.subplot(1, n + 1, i + 1)
plt.imshow(feature, interpolation='nearest', cmap=plt.cm.copper)
plt.title("Feature {:d}".format(i))
plt.axis('off')
plt.show()
スクリプトによって生成された画像は次のとおりです。