Basic image manipulation with arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 3x3 grayscale image filled with zeros.

NumPy

import numpy as np
image = np.[1]((3, 3))

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

Azeros

Bfull

Cempty

Dones

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.ones creates an image filled with white pixels, not black.

Using np.empty creates an uninitialized array with random values.

Using np.zeros creates an array filled with zeros, which represents a black image in grayscale.

2fill in blank

medium

Complete the code to set the pixel at row 1, column 2 to white (255) in a grayscale image.

NumPy

image = np.zeros((3, 3), dtype=np.uint8)
image[1] = 255

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A(1, 2)

B[1, 2]

C[2, 1]

D(2, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping row and column indices.

Using square brackets inside the indexing which is invalid syntax.

In NumPy, the first index is the row and the second is the column. So image[1, 2] accesses row 1, column 2.

3fill in blank

hard

Fix the error in the code to invert a grayscale image (255 - pixel value).

NumPy

inverted = 255 - image[1]

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A[]

B()

C.astype(int)

D.copy()

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses or brackets after the array name causes syntax errors.

Not copying the array modifies the original image unintentionally.

Using .copy() creates a new array so the inversion does not affect the original image.

4fill in blank

hard

Fill both blanks to create a dictionary with pixel positions as keys and their values for pixels greater than 100.

NumPy

pixels = {(row, col): image[row, col] for row in range(image.shape[[1]]) for col in range(image.shape[[2]]) if image[row, col] > 100}

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A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the indices for rows and columns.

Using an index that is out of range for the shape tuple.

In NumPy, shape[0] is the number of rows and shape[1] is the number of columns.

5fill in blank

hard

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

NumPy

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

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

A>=

B50

C0

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>=' reverses the condition.

Setting the else value incorrectly changes pixel values.

The condition keeps pixels greater or equal to 50 unchanged; pixels less than 50 are set to 0.