Question

I am standing in front of a huge problem. Using the python libraries NumPy and SciPy, I identified several features in large array. For this purpose, I created a 3×3 neighbor structure and used it for a connected component analysis –> see docs.

struct = scipy.ndimage.generate_binary_structure(2,2)
labeled_array, num_features = ndimage.label(array,struct)

My problem now is that I want to iterate through all identified features in a loop. Someone has an idea how to address individual features in the resulting NumPy array?

Answer 1

Here’s an example of handling features identified by ndimage.label. Whether this helps you or not depends on what you want to do with the features.

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()

Here’s the image generated by the script: