Basic image manipulation with arrays in NumPy - Mini Project: Build & Apply

Basic image manipulation with arrays

📖 Scenario: You have a small grayscale image represented as a 2D array of pixel brightness values. Each value ranges from 0 (black) to 255 (white). You want to perform simple image manipulations using arrays.

🎯 Goal: Learn how to create a numpy array to represent an image, set a brightness threshold, create a mask to highlight bright pixels, and display the result.

📋 What You'll Learn

Use numpy to create and manipulate arrays

Create a 2D numpy array with exact pixel values

Define a brightness threshold variable

Use array operations to create a mask of bright pixels

Print the resulting mask array

💡 Why This Matters

🌍 Real World

Image processing is used in photography, medical imaging, and computer vision to analyze and enhance pictures.

💼 Career

Understanding how to manipulate images as arrays is a key skill for data scientists working with image data or machine learning.

1

Create the image array

Create a 2D numpy array called image with these exact pixel values: [[10, 200, 30], [255, 100, 50], [0, 180, 220]].

NumPy

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

Need a hint?

Use np.array() to create the 2D array with the exact values.

2

Set the brightness threshold

Create a variable called threshold and set it to 150.

NumPy

import numpy as np
image = np.array([[10, 200, 30], [255, 100, 50], [0, 180, 220]])
# Create the variable threshold and set it to 150
# Your code here

Need a hint?

Just assign the number 150 to the variable threshold.

3

Create a mask for bright pixels

Create a variable called bright_mask that is a boolean numpy array. It should be True where image pixels are greater than threshold, and False otherwise.

NumPy

import numpy as np
image = np.array([[10, 200, 30], [255, 100, 50], [0, 180, 220]])
threshold = 150
# Create bright_mask as a boolean array where image > threshold
# Your code here

Need a hint?

Use the comparison operator > between image and threshold to get a boolean array.

4

Print the bright pixel mask

Print the variable bright_mask to display the boolean mask array.

NumPy

import numpy as np
image = np.array([[10, 200, 30], [255, 100, 50], [0, 180, 220]])
threshold = 150
bright_mask = image > threshold
# Print the bright_mask array
# Your code here

Need a hint?

Use print(bright_mask) to show the boolean array.