Connected component labeling in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the connected components function from scipy.ndimage.

SciPy

from scipy.ndimage import [1]

Drag options to blanks, or click blank then click option'

Alabel

Bconnect_components

Ccomponent_label

Dconnected_label

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent function name like 'connect_components'.

Confusing the function with other similar names.

The function label from scipy.ndimage is used to find connected components in an array.

2fill in blank

medium

Complete the code to create a binary 2D numpy array named 'image' with a 3x3 block of ones in the center.

SciPy

import numpy as np
image = np.zeros((7, 7), dtype=int)
image[[1]] = 1

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A2:5, 2:5

B1:4, 1:4

C3:6, 3:6

D0:3, 0:3

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing slices that are too small or too large.

Off-by-one errors in slicing.

The slice 2:5, 2:5 selects a 3x3 block in the center of a 7x7 array.

3fill in blank

hard

Fix the error in the code to label connected components in 'image' using 4-connectivity.

SciPy

labeled_array, num_features = label(image, [1]=1)

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Aconnect

Bconnectivity

Cconnectivity_structure

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'connect' or 'connectivity_structure'.

Omitting the parameter and getting default connectivity.

The correct parameter name is connectivity which defines the neighborhood connectivity.

4fill in blank

hard

Fill both blanks to create a structuring element for 8-connectivity and label the image accordingly.

SciPy

structure = np.array([[[1], [2], [1]],
                      [[2], 1, [2]],
                      [[1], [2], [1]]])
labeled_array, num_features = label(image, structure=structure)

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A1

B0

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0s for corners which breaks 8-connectivity.

Using values other than 0 or 1.

For 8-connectivity, the structuring element uses 1s for all neighbors including diagonals and the center.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each label to the count of pixels in that component, excluding background (label 0).

SciPy

counts = {label: (labeled_array == label).[1]() for label in range(1, [2] + [3])}

Drag options to blanks, or click blank then click option'

Asum

Bnum_features

C1

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Including label 0 which is background.

Using incorrect range limits.

Using a non-existent method like count().

The sum() counts pixels for each label. The range goes from 1 to num_features + 1 to exclude background label 0.