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.

Practice

1. What does each element in a NumPy array representing an image usually stand for?

easy

A. A pixel's color value

B. The image file size

C. The image format type

D. The number of images

Solution

1. Step 1: Understand image representation in arrays

   Images are stored as arrays where each element corresponds to a pixel's color information.

2. Step 2: Identify what each element represents

   Each element holds the color value (like red, green, or blue intensity) for that pixel.

3. Final Answer:

   A pixel's color value -> Option A

4. Quick Check:

   Pixel color = element value [OK]

Hint: Pixels map to array elements holding color values [OK]

Common Mistakes:

• Confusing pixel values with image size
• Thinking array stores image format
• Assuming array length equals number of images

2. Which NumPy code correctly creates a 3x3 image array with 3 color channels (RGB)?

easy

A. np.zeros((3, 3, 3))

B. np.zeros([3, 3])

C. np.zeros(3, 3, 3)

D. np.zeros((3, 3))

Solution

1. Step 1: Understand array shape for RGB image

   An RGB image array shape is (height, width, 3) where 3 is for color channels.

2. Step 2: Check correct NumPy syntax for shape

   NumPy expects shape as a tuple inside zeros(), so (3, 3, 3) is correct.

3. Final Answer:

   np.zeros((3, 3, 3)) -> Option A

4. Quick Check:

   Shape tuple with 3 channels = np.zeros((3,3,3)) [OK]

Hint: Use tuple shape with 3 for RGB channels [OK]

Common Mistakes:

• Passing shape as separate arguments instead of tuple
• Missing the third dimension for color channels
• Using list instead of tuple for shape

3. What is the output shape of this code?

import numpy as np
img = np.ones((4, 5, 3))
print(img.shape)

medium

A. (5, 4, 3)

B. (4, 5, 3)

C. (4, 5)

D. (3, 4, 5)

Solution

1. Step 1: Analyze the array creation

   np.ones((4, 5, 3)) creates an array with shape (4, 5, 3).

2. Step 2: Understand shape attribute output

   Printing img.shape returns the tuple describing the array dimensions exactly as given.

3. Final Answer:

   (4, 5, 3) -> Option B

4. Quick Check:

   Shape matches input tuple (4,5,3) [OK]

Hint: Shape prints exactly as array was created [OK]

Common Mistakes:

• Mixing up width and height order
• Ignoring the color channel dimension
• Assuming shape returns total size

4. Find the error in this code that tries to create a 2x2 RGB image array:

import numpy as np
img = np.zeros(2, 2, 3)
print(img.shape)

medium

A. np.zeros cannot create 3D arrays

B. Shape dimensions are too large

C. Missing tuple for shape in np.zeros

D. print statement syntax is wrong

Solution

1. Step 1: Check np.zeros syntax

   np.zeros expects a single shape argument as a tuple, but here three separate arguments are given.

2. Step 2: Identify correct shape input

   Shape should be passed as np.zeros((2, 2, 3)) with parentheses around the shape tuple.

3. Final Answer:

   Missing tuple for shape in np.zeros -> Option C

4. Quick Check:

   Shape must be tuple in np.zeros() [OK]

Hint: Always pass shape as one tuple to np.zeros [OK]

Common Mistakes:

• Passing multiple arguments instead of one tuple
• Assuming np.zeros can't create 3D arrays
• Incorrect print syntax

5. You have a 2x2 image array with RGB values:

import numpy as np
img = np.array([[[255, 0, 0], [0, 255, 0]],
                [[0, 0, 255], [255, 255, 0]]])

How do you change the bottom-right pixel to white (255, 255, 255)?

hard

A. img[0, 0] = [255, 255, 255]

B. img[2, 2] = [255, 255, 255]

C. img[1][1][3] = 255

D. img[1, 1] = [255, 255, 255]

Solution

1. Step 1: Identify pixel position in array

   Bottom-right pixel in a 2x2 array is at index [1, 1] (zero-based indexing).

2. Step 2: Assign new RGB values correctly

   Assign the list [255, 255, 255] to img[1, 1] to set it to white.

3. Final Answer:

   img[1, 1] = [255, 255, 255] -> Option D

4. Quick Check:

   Index [1,1] changes bottom-right pixel [OK]

Hint: Use zero-based index for pixel, assign RGB list [OK]

Common Mistakes:

• Using out-of-range indices like [2, 2]
• Trying to assign to a non-existing channel index
• Changing wrong pixel like top-left