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.