Image as array concept in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 3x3 grayscale image as a NumPy array.

NumPy

import numpy as np
image = np.array([1])

Drag options to blanks, or click blank then click option'

A[[0, 128], [64, 128], [0, 64]]

B[0, 128, 255, 64, 128, 192, 0, 64, 255]

C[[255, 255, 255]]

D[[0, 128, 255], [64, 128, 192], [0, 64, 255]]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a flat list instead of nested lists.

Creating arrays with wrong dimensions.

The image is represented as a 2D array with 3 rows and 3 columns, each value representing a pixel intensity.

2fill in blank

medium

Complete the code to access the pixel value at row 1, column 2 in the image array.

NumPy

pixel_value = image[[1]]

Drag options to blanks, or click blank then click option'

A1, 2

B[1, 2]

C(1, 2)

Dimage[1][2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list instead of a tuple for indexing.

Trying to use double brackets like image[1][2] which works but is not the best practice here.

NumPy arrays use tuple indexing for multi-dimensional access like image[row, column].

3fill in blank

hard

Fix the error in the code to convert a color image array to grayscale by averaging the color channels.

NumPy

grayscale = np.mean(image[1], axis=[2])

Drag options to blanks, or click blank then click option'

A(,)

B[,]

C[:, :, :]

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect slicing syntax.

Averaging over the wrong axis.

To average color channels, select all rows and columns and all color channels with [:, :, :], then take mean along axis 2.

4fill in blank

hard

Fill both blanks to create a dictionary mapping pixel positions to their grayscale values for pixels with value greater than 100.

NumPy

pixel_dict = [1] for i in range(image.shape[0]) for j in range(image.shape[1]) if image[i, j] [2] 100

Drag options to blanks, or click blank then click option'

A{(i, j): image[i, j]}

B[i, j]

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using list instead of dictionary comprehension.

Using wrong comparison operator.

This dictionary comprehension creates keys as pixel coordinates and values as pixel intensities for pixels above 100.

5fill in blank

hard

Fill all three blanks to create a new image array where pixels below 50 are set to 0, others remain unchanged.

NumPy

new_image = np.array([[image[i, j] if image[i, j] [1] 50 else [2] for j in range([3])] for i in range(image.shape[0])])

Drag options to blanks, or click blank then click option'

A>

B0

Cimage.shape[1]

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operator.

Using wrong range for inner loop.

The code uses a nested list comprehension to check each pixel; if pixel is greater than 50, keep it, else set to 0. The inner loop runs over columns using image.shape[1].

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