Color channel handling in Matplotlib - Mini Project: Build & Apply

Color Channel Handling

📖 Scenario: You are working with a simple image represented as a small grid of pixels. Each pixel has three color channels: Red, Green, and Blue (RGB). You want to learn how to extract and work with these color channels separately.

🎯 Goal: Build a program that creates a small RGB image as a list of lists, extracts the red color channel into a separate structure, and then displays the red channel as a grayscale image using matplotlib.

📋 What You'll Learn

Create a 3x3 image as a list of lists with RGB tuples

Create a variable to hold the red channel values

Use a loop to extract the red channel from each pixel

Display the red channel as a grayscale image using matplotlib

💡 Why This Matters

🌍 Real World

Handling color channels is important in image processing tasks like photo editing, computer vision, and graphics design.

💼 Career

Data scientists and machine learning engineers often manipulate image color channels to prepare data for models or to analyze images.

1

Create the RGB image data

Create a variable called image that is a 3x3 list of lists. Each element should be a tuple representing RGB values exactly as follows: top-left pixel (255, 0, 0), top-middle pixel (0, 255, 0), top-right pixel (0, 0, 255), middle-left pixel (255, 255, 0), center pixel (0, 255, 255), middle-right pixel (255, 0, 255), bottom-left pixel (192, 192, 192), bottom-middle pixel (128, 128, 128), bottom-right pixel (64, 64, 64).

Matplotlib

# Create the 3x3 image variable called image with the exact RGB tuples
# Your code here

Need a hint?

Think of image as a list of rows, where each row is a list of RGB tuples.

2

Create a variable for the red channel

Create an empty list called red_channel that will hold the red values from each pixel in the image.

Matplotlib

image = [
    [(255, 0, 0), (0, 255, 0), (0, 0, 255)],
    [(255, 255, 0), (0, 255, 255), (255, 0, 255)],
    [(192, 192, 192), (128, 128, 128), (64, 64, 64)]
]

# Create an empty list called red_channel
# Your code here

Need a hint?

Just create an empty list named red_channel to store red values later.

3

Extract the red channel values

Use a for loop with the variable row to iterate over each row in image. Inside it, use another for loop with the variable pixel to iterate over each pixel in the row. Append the red value (the first element of the pixel tuple) to red_channel.

Matplotlib

image = [
    [(255, 0, 0), (0, 255, 0), (0, 0, 255)],
    [(255, 255, 0), (0, 255, 255), (255, 0, 255)],
    [(192, 192, 192), (128, 128, 128), (64, 64, 64)]
]

red_channel = []

# Use nested for loops to extract red values and append to red_channel
# Your code here

Need a hint?

Remember, each pixel is a tuple like (R, G, B). The red value is pixel[0].

4

Display the red channel as a grayscale image

Import matplotlib.pyplot as plt. Convert red_channel into a 3x3 2D list called red_image where each inner list is a row of red values. Use plt.imshow(red_image, cmap='gray') to show the red channel as a grayscale image. Then call plt.show() to display it.

Matplotlib

image = [
    [(255, 0, 0), (0, 255, 0), (0, 0, 255)],
    [(255, 255, 0), (0, 255, 255), (255, 0, 255)],
    [(192, 192, 192), (128, 128, 128), (64, 64, 64)]
]

red_channel = []

for row in image:
    for pixel in row:
        red_channel.append(pixel[0])

# Import matplotlib.pyplot as plt
# Create red_image as a 3x3 list of lists from red_channel
# Use plt.imshow with cmap='gray' to display red_image
# Call plt.show() to display the plot
# Your code here

Need a hint?

Use list slicing to split red_channel into rows of 3 pixels each.

Use plt.imshow(red_image, cmap='gray') and plt.show() to display the image.