Color channel handling in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of color channel extraction

What is the shape and data type of the red channel extracted from this RGB image array?

Matplotlib

import numpy as np
image = np.array([[[255, 0, 0], [0, 255, 0]],
                  [[0, 0, 255], [255, 255, 0]]], dtype=np.uint8)
red_channel = image[:, :, 0]
print(red_channel.shape, red_channel.dtype)

A (2, 2) uint8

B (2, 2, 3) uint8

C (3, 2) int32

D (2, 2) float64

💡 Hint

Remember that extracting one color channel from an RGB image reduces the last dimension.

The image has shape (2, 2, 3). Extracting the red channel (index 0) keeps the first two dimensions, resulting in shape (2, 2). The data type remains uint8 as the original array.

❓ data_output

intermediate

Result of swapping color channels

Given an RGB image array, what is the resulting pixel value after swapping the red and blue channels for the pixel [100, 150, 200]?

Matplotlib

pixel = [100, 150, 200]
swapped_pixel = [pixel[2], pixel[1], pixel[0]]
print(swapped_pixel)

A [150, 100, 200]

B [100, 150, 200]

C [200, 100, 150]

D [200, 150, 100]

💡 Hint

Swapping red and blue means the first and last values switch places.

The original pixel is [R=100, G=150, B=200]. Swapping red and blue channels results in [B=200, G=150, R=100].

❓ visualization

advanced

Visualizing individual color channels

Which option correctly plots the green channel of an RGB image using matplotlib?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt
image = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8)
green_channel = image[:, :, 1]
plt.imshow(green_channel, cmap='Greens')
plt.show()

A plt.imshow(image[:, :, 1])

B plt.imshow(image[:, :, 1], cmap='Greens')

C plt.imshow(image[:, :, 1], cmap='Reds')

D plt.imshow(image[:, :, 0], cmap='Greens')

💡 Hint

Use the correct channel index and a matching colormap for green.

The green channel is at index 1. Using cmap='Greens' colors the grayscale green channel appropriately.

🔧 Debug

advanced

Identify the error in color channel normalization

What error will this code raise when normalizing the blue channel of an image?

Matplotlib

import numpy as np
image = np.array([[[10, 20, 30], [40, 50, 60]]], dtype=np.uint8)
blue_channel = image[:, :, 2]
normalized = blue_channel / 255
print(normalized.dtype)

A No error; dtype is float64

B TypeError: unsupported operand type(s) for /: 'uint8' and 'int'

C ValueError: operands could not be broadcast together

D AttributeError: 'numpy.ndarray' object has no attribute 'dtype'

💡 Hint

Dividing uint8 by int converts to float automatically.

Dividing a uint8 numpy array by an int converts the result to float64 without error.

🚀 Application

expert

Calculate average intensity of red channel in an image

Given a 3x3 RGB image array, what is the average intensity of the red channel?

Matplotlib

import numpy as np
image = np.array([
  [[10, 20, 30], [40, 50, 60], [70, 80, 90]],
  [[15, 25, 35], [45, 55, 65], [75, 85, 95]],
  [[20, 30, 40], [50, 60, 70], [80, 90, 100]]
], dtype=np.uint8)
red_channel = image[:, :, 0]
avg_red = np.mean(red_channel)
print(round(avg_red, 2))

A 40.0

B 50.0

C 45.0

D 55.0

💡 Hint

Sum all red values and divide by 9 pixels.

Sum of red channel values: 10+40+70+15+45+75+20+50+80=405. Average = 405/9 = 45.0.

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