Color channel handling in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the red channel from the image array.

Matplotlib

red_channel = image[:, :, [1]]

Drag options to blanks, or click blank then click option'

A1

B0

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 or 2 instead of 0 for the red channel index.

Confusing channel order with BGR instead of RGB.

The red channel is the first channel in an RGB image, so its index is 0.

2fill in blank

medium

Complete the code to create a grayscale image by averaging the RGB channels.

Matplotlib

gray_image = image.mean(axis=[1])

Drag options to blanks, or click blank then click option'

A-1

B1

C0

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Averaging over axis 0 or 1 which are spatial dimensions.

Using -1 which also works but is less explicit.

The color channels are along axis 2 in the image array, so averaging over axis 2 converts RGB to grayscale.

3fill in blank

hard

Fix the error in the code to swap the red and blue channels of the image.

Matplotlib

swapped_image = image[:, :, [[1], 1, 0]]

Drag options to blanks, or click blank then click option'

A2

B3

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 or 1 incorrectly in the first position.

Including an invalid index like 3.

The red channel is at index 0 and blue at index 2, so swapping means placing blue first (2), then green (1), then red (0).

4fill in blank

hard

Fill both blanks to create a new image with only the green channel and zeros for red and blue.

Matplotlib

new_image = np.zeros_like(image)
new_image[:, :, [1]] = image[:, :, [2]]

Drag options to blanks, or click blank then click option'

A1

B0

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using different indices for source and destination channels.

Using indices for red or blue instead of green.

The green channel is at index 1, so we copy the green channel from the original image to the green channel of the new image.

5fill in blank

hard

Fill all three blanks to normalize each color channel separately to the range 0-1.

Matplotlib

normalized = np.empty_like(image, dtype=float)
for i in range(3):
    channel = image[:, :, [1]]
    min_val = channel.min()
    max_val = channel.max()
    normalized[:, :, [2]] = (channel - min_val) / (max_val - min_val)

result = normalized[:, :, [3]]

Drag options to blanks, or click blank then click option'

Ai

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using fixed indices like 0 instead of the loop variable.

Mixing indices between source and destination channels.

We loop over each channel index i, extract that channel, normalize it, and assign it back to the same channel index i. Finally, we select channel i from the normalized 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