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

Need a 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

Need a 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

Need a 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

Need a hint?

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