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

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

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

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

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.

Practice

1. What does the last dimension in a matplotlib image array usually represent?

easy

A. The image width

B. The image height

C. The color channels like red, green, and blue

D. The number of images in a batch

Solution

1. Step 1: Understand image array structure

   Matplotlib images are stored as arrays where the last dimension holds color information.

2. Step 2: Identify what the last dimension holds

   This last dimension typically contains the red, green, and blue channels for each pixel.

3. Final Answer:

   The color channels like red, green, and blue -> Option C

4. Quick Check:

   Last dimension = color channels [OK]

Hint: Remember: last dimension = RGB colors in image arrays [OK]

Common Mistakes:

• Confusing width or height with the last dimension
• Thinking the last dimension is batch size
• Assuming grayscale images have 3 channels

2. Which of the following is the correct way to extract the green channel from a 3D image array named img in matplotlib?

easy

A. green = img[2, :, :]

B. green = img[1, :, :]

C. green = img[:, 1, :]

D. green = img[:, :, 1]

Solution

1. Step 1: Recall channel indexing in image arrays

   Color channels are stored in the last dimension, with red=0, green=1, blue=2.

2. Step 2: Extract green channel correctly

   To get green, select all rows and columns, but only index 1 in the last dimension: img[:, :, 1].

3. Final Answer:

   green = img[:, :, 1] -> Option D

4. Quick Check:

   Green channel index = 1 [OK]

Hint: Use img[:, :, 1] to get green channel [OK]

Common Mistakes:

• Mixing up axis order and indexing rows or columns
• Using wrong channel index for green
• Selecting wrong dimensions

3. Given the code below, what will be the shape of red_channel?

import numpy as np
img = np.random.rand(100, 150, 3)
red_channel = img[:, :, 0]

medium

A. (100, 150)

B. (100, 150, 3)

C. (3, 100, 150)

D. (150, 3)

Solution

1. Step 1: Understand original image shape

   img has shape (100, 150, 3) meaning 100 rows, 150 columns, 3 color channels.

2. Step 2: Extract red channel shape

   Extracting img[:, :, 0] selects all rows and columns but only the first channel, so shape becomes (100, 150).

3. Final Answer:

   (100, 150) -> Option A

4. Quick Check:

   Channel extraction removes last dimension [OK]

Hint: Extracting one channel drops last dimension [OK]

Common Mistakes:

• Expecting 3 channels after extraction
• Confusing axis order
• Misreading shape tuple

4. What is wrong with this code snippet that tries to swap the red and blue channels of an image array img?

img[:, :, 0] = img[:, :, 2]
img[:, :, 2] = img[:, :, 0]

medium

A. The syntax for slicing is invalid

B. It overwrites red channel before saving it, losing original data

C. The channel indices are incorrect for red and blue

D. It should use img[0, :, :] instead of img[:, :, 0]

Solution

1. Step 1: Analyze channel swapping logic

   The code assigns blue channel to red, then red channel to blue without temporary storage.

2. Step 2: Identify data overwrite problem

   After first line, original red data is lost, so second line copies new red (which is blue) back to blue channel.

3. Final Answer:

   It overwrites red channel before saving it, losing original data -> Option B

4. Quick Check:

   Swap needs temp variable to avoid overwrite [OK]

Hint: Use a temp variable when swapping channels [OK]

Common Mistakes:

• Not using a temporary variable for swap
• Mixing up channel indices
• Incorrect slicing syntax

5. You want to create a grayscale image by averaging the red, green, and blue channels of an image array img. Which code correctly does this and keeps the result as a 2D array?

hard

A. gray = img.mean(axis=2)

B. gray = img.mean(axis=0)

C. gray = img[:, :, 0] + img[:, :, 1] + img[:, :, 2]

D. gray = img.sum(axis=1)

Solution

1. Step 1: Understand axis for color channels

   Color channels are in the last dimension (axis=2) of img.

2. Step 2: Average across color channels

   Using img.mean(axis=2) averages red, green, and blue for each pixel, resulting in a 2D array.

3. Final Answer:

   gray = img.mean(axis=2) -> Option A

4. Quick Check:

   Mean over axis=2 gives grayscale 2D image [OK]

Hint: Use mean(axis=2) to average RGB channels [OK]

Common Mistakes:

• Averaging over wrong axis
• Summing channels without dividing
• Resulting in 3D array instead of 2D