Connected component labeling in SciPy - Mini Project: Build & Apply

Connected Component Labeling with SciPy

📖 Scenario: Imagine you have a black and white image represented as a grid of pixels. Some pixels are black (value 1), and others are white (value 0). You want to find groups of connected black pixels. Each group is called a connected component.This is useful in many real-world cases like counting objects in a photo or finding clusters in data.

🎯 Goal: You will create a small 2D array representing an image, then use SciPy's connected component labeling to find and count the groups of connected black pixels.

📋 What You'll Learn

Create a 2D NumPy array called image with specific values

Create a connectivity structure variable called structure

Use scipy.ndimage.label to label connected components in image

Print the number of connected components found

💡 Why This Matters

🌍 Real World

Connected component labeling helps in image processing tasks like counting objects, detecting shapes, and segmenting images.

💼 Career

This technique is used in computer vision, medical imaging, and quality control jobs where analyzing images is important.

1

Create the image array

Create a 2D NumPy array called image with these exact values: [[1, 0, 0, 1], [1, 1, 0, 0], [0, 0, 1, 1], [0, 0, 1, 0]].

SciPy

import numpy as np
# Create the 2D array called image with the given values
# Your code here

Hint

Use np.array to create a 2D array with the exact values shown.

2

Create the connectivity structure

Create a variable called structure using np.ones((3, 3)) to define connectivity for labeling.

SciPy

import numpy as np
image = np.array([[1, 0, 0, 1], [1, 1, 0, 0], [0, 0, 1, 1], [0, 0, 1, 0]])
# Create the structure variable with np.ones((3, 3))
# Your code here

Hint

The structure variable tells the labeling function how to connect pixels. Use a 3x3 matrix of ones.

3

Label connected components

Import label from scipy.ndimage and use it with image and structure to create labeled_array and num_features.

SciPy

import numpy as np
image = np.array([[1, 0, 0, 1], [1, 1, 0, 0], [0, 0, 1, 1], [0, 0, 1, 0]])
structure = np.ones((3, 3))
# Import label from scipy.ndimage and label the image
# Your code here

Hint

Use label function to find connected components. It returns the labeled array and the number of features.

4

Print the number of connected components

Print the variable num_features to show how many connected components were found.

SciPy

import numpy as np
image = np.array([[1, 0, 0, 1], [1, 1, 0, 0], [0, 0, 1, 1], [0, 0, 1, 0]])
structure = np.ones((3, 3))
from scipy.ndimage import label
labeled_array, num_features = label(image, structure=structure)
# Print the number of connected components
# Your code here

Hint

Use print(num_features) to display the count of connected components.

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