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]]

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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])

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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]]

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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]]

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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]]

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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.