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])

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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]]

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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])

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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

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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])])

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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].