Image as array concept in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the shape of the image array?

Given a color image loaded as a NumPy array with shape (480, 640, 3), what does the shape represent?

NumPy

import numpy as np
image = np.zeros((480, 640, 3))
print(image.shape)

A (480, 640, 3) means 480 pixels width, 640 pixels height, and 3 color channels (RGB)

B (480, 640, 3) means 480 pixels height, 640 pixels width, and 3 color channels (RGB)

C (480, 640, 3) means 3 pixels height, 480 pixels width, and 640 color channels

D (480, 640, 3) means 3 pixels width, 640 pixels height, and 480 color channels

💡 Hint

Remember the shape order is (height, width, channels) for images.

The shape (480, 640, 3) means the image has 480 rows (height), 640 columns (width), and 3 color channels (Red, Green, Blue).

❓ data_output

intermediate

What is the pixel value at position (2, 3)?

Consider this grayscale image array:

import numpy as np
image = np.array([[10, 20, 30, 40],
                  [50, 60, 70, 80],
                  [90, 100, 110, 120]])
print(image[2, 3])

What is the output?

NumPy

import numpy as np
image = np.array([[10, 20, 30, 40],
                  [50, 60, 70, 80],
                  [90, 100, 110, 120]])
print(image[2, 3])

A 120

B 110

C 80

D 90

💡 Hint

Remember indexing is zero-based and rows come first.

The pixel at row 2, column 3 is 120 because indexing starts at 0.

❓ visualization

advanced

Visualize a grayscale image from a 2D array

Which code snippet correctly displays a 5x5 grayscale image using matplotlib from a NumPy array?

NumPy

import numpy as np
import matplotlib.pyplot as plt
image = np.array([[0, 50, 100, 150, 200],
                  [10, 60, 110, 160, 210],
                  [20, 70, 120, 170, 220],
                  [30, 80, 130, 180, 230],
                  [40, 90, 140, 190, 240]])
plt.imshow(image, cmap='gray')
plt.show()

A plt.imshow(image, cmap='jet') followed by plt.show()

B plt.imshow(image) without cmap argument followed by plt.show()

C plt.imshow(image, cmap='gray') followed by plt.show()

D plt.plot(image) followed by plt.show()

💡 Hint

Use the grayscale colormap to show intensity properly.

Using cmap='gray' tells matplotlib to display the array as a grayscale image.

🔧 Debug

advanced

Why does this code raise an error?

What error does this code raise and why?

import numpy as np
image = np.zeros((100, 100, 3))
print(image[50, 50])
print(image[50, 50, 3])

NumPy

import numpy as np
image = np.zeros((100, 100, 3))
print(image[50, 50])
print(image[50, 50, 3])

A No error, prints pixel values

B TypeError: too many indices for array

C ValueError: invalid index

D IndexError: index 3 is out of bounds for axis 2 with size 3

💡 Hint

Remember Python indexing starts at 0 and max index is size-1.

The last index 3 is out of bounds because valid channel indices are 0, 1, 2.

🚀 Application

expert

Calculate average color of an RGB image

Given a NumPy array representing an RGB image of shape (100, 100, 3), which code correctly calculates the average color across all pixels?

NumPy

import numpy as np
image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)

A avg_color = image.mean(axis=(0,1))

B avg_color = image.mean(axis=2)

C avg_color = image.mean(axis=0)

D avg_color = image.mean()

💡 Hint

Average color means average over height and width, keep channels separate.

Using axis=(0,1) averages over rows and columns, leaving the 3 color channels.