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

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

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)

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

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)

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

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.

Practice

1. What is the main purpose of connected component labeling in image processing?

easy

A. To enhance image contrast

B. To convert color images to grayscale

C. To identify and label groups of connected pixels in binary images

D. To compress image file size

Solution

1. Step 1: Understand connected component labeling

   It finds groups of connected pixels in binary images and assigns unique labels to each group.

2. Step 2: Compare with other image tasks

   Other options like grayscale conversion, contrast enhancement, and compression do not involve labeling connected pixels.

3. Final Answer:

   To identify and label groups of connected pixels in binary images -> Option C

4. Quick Check:

   Connected component labeling = identify connected pixel groups [OK]

Hint: Remember: labeling means assigning unique IDs to connected pixels [OK]

Common Mistakes:

• Confusing labeling with color conversion
• Thinking it compresses images
• Mixing it up with image enhancement

2. Which of the following is the correct way to import the connected component labeling function from scipy.ndimage?

easy

A. import scipy.ndimage.label

B. from scipy.ndimage import label

C. from scipy import label

D. import label from scipy.ndimage

Solution

1. Step 1: Recall correct import syntax in Python

   The correct syntax to import a function is 'from module import function'.

2. Step 2: Match with scipy.ndimage.label

   The function 'label' is inside 'scipy.ndimage', so 'from scipy.ndimage import label' is correct.

3. Final Answer:

   from scipy.ndimage import label -> Option B

4. Quick Check:

   Correct import syntax = from module import function [OK]

Hint: Use 'from module import function' to import specific functions [OK]

Common Mistakes:

• Using 'import scipy.ndimage.label' which is invalid
• Trying 'from scipy import label' which misses submodule
• Incorrect 'import label from scipy.ndimage' syntax

3. Given the following code, what will be the output of num_features?

import numpy as np
from scipy.ndimage import label

array = np.array([[1, 0, 0, 1],
                  [1, 1, 0, 0],
                  [0, 0, 1, 1],
                  [0, 0, 1, 0]])

labeled_array, num_features = label(array)
print(num_features)

medium

A. 1

B. 3

C. 4

D. 2

Solution

1. Step 1: Identify connected groups in the array

   There are two groups of connected 1s: one cluster at top-left (positions (0,0),(1,0),(1,1)) and one cluster at bottom-right (positions (0,3),(2,2),(2,3),(3,2)).

2. Step 2: Count the connected components

   Counting these groups gives 2 connected components.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Count connected 1s groups = 2 [OK]

Hint: Count distinct connected 1s clusters in the array [OK]

Common Mistakes:

• Counting isolated 1s as separate components incorrectly
• Ignoring connectivity rules
• Misreading array layout

4. What is wrong with the following code snippet for connected component labeling?

import numpy as np
from scipy.ndimage import label

binary_image = np.array([[1, 1, 0],
                         [0, 1, 0],
                         [1, 0, 1]])

labeled, num = label(binary_image, structure=1)
print(num)

medium

A. The 'structure' parameter should be an array, not an integer

B. The input array must be float type, not integer

C. The 'label' function does not accept a 'structure' parameter

D. The print statement syntax is incorrect

Solution

1. Step 1: Check 'structure' parameter type

   The 'structure' parameter expects an array defining connectivity, not a single integer.

2. Step 2: Identify correct usage

   Passing 'structure=1' is invalid; it should be an array like np.ones((3,3)) for full connectivity.

3. Final Answer:

   The 'structure' parameter should be an array, not an integer -> Option A

4. Quick Check:

   'structure' must be array, not int [OK]

Hint: Remember: 'structure' needs an array defining connectivity [OK]

Common Mistakes:

• Passing integer instead of array for 'structure'
• Assuming 'label' lacks 'structure' parameter
• Confusing data types of input array

5. You have a binary image with noise: small isolated pixels scattered randomly. How can you use connected component labeling with scipy to remove noise by keeping only components larger than 2 pixels?

hard

A. Label components, count sizes, then remove components with size ≤ 2

B. Apply Gaussian blur before labeling to remove noise

C. Use label function with parameter 'min_size=3' to filter components

D. Invert the image and label the background instead

Solution

1. Step 1: Label connected components in the binary image

   Use scipy.ndimage.label to assign unique labels to each connected group of pixels.

2. Step 2: Count the size of each labeled component and filter

   Calculate the size of each component and remove those with size less than or equal to 2 pixels to eliminate noise.

3. Final Answer:

   Label components, count sizes, then remove components with size ≤ 2 -> Option A

4. Quick Check:

   Filter small components after labeling to remove noise [OK]

Hint: Label, count sizes, remove small components to clean noise [OK]

Common Mistakes:

• Expecting label() to filter by size automatically
• Using image blur instead of component filtering
• Inverting image does not remove noise directly