Image interpolation methods in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of interpolation method in matplotlib.imshow

What is the output of this code snippet that uses matplotlib to display an image with 'nearest' interpolation?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

image = np.array([[0, 1], [2, 3]])
plt.imshow(image, interpolation='nearest')
plt.axis('off')
plt.savefig('output.png')
plt.close()

print(image)

A SyntaxError

B

[[0 1]
 [2 3]]

C

[[0 1 1]
 [2 3 3]]

D

[[0 0]
 [0 0]]

💡 Hint

The 'nearest' interpolation shows the original pixel values without smoothing.

The code prints the original numpy array representing the image. The interpolation affects the display, not the array content.

🧠 Conceptual

intermediate

Effect of 'bilinear' interpolation on image edges

Which statement best describes the effect of using 'bilinear' interpolation in matplotlib's imshow on image edges?

A Edges appear smoother because pixel values are averaged linearly.

B Edges become pixelated due to nearest neighbor selection.

C Edges appear sharper because pixels are duplicated exactly.

D Edges are removed by cropping the image.

💡 Hint

Think about how averaging affects transitions between pixels.

'Bilinear' interpolation calculates pixel values by linear averaging of neighbors, smoothing edges.

🔧 Debug

advanced

Identify the error in interpolation parameter

What error does this code raise? import matplotlib.pyplot as plt import numpy as np img = np.random.rand(5,5) plt.imshow(img, interpolation='bilinearr') plt.show()

A SyntaxError: invalid syntax

B TypeError: interpolation must be a boolean

C ValueError: Unknown interpolation method 'bilinearr'

D No error, image displays with default interpolation

💡 Hint

Check the spelling of the interpolation method.

The interpolation parameter value 'bilinearr' is invalid and causes a ValueError.

❓ data_output

advanced

Number of pixels after resizing with 'nearest' interpolation

Given a 3x3 image array resized to 6x6 using 'nearest' interpolation in matplotlib, how many unique pixel values will the resized image have?

Matplotlib

import numpy as np
from matplotlib import pyplot as plt

original = np.array([[1,2,3],[4,5,6],[7,8,9]])
resized = np.kron(original, np.ones((2,2)))
unique_values = np.unique(resized).size
print(unique_values)

A 9

B 36

C 4

D 6

💡 Hint

Nearest interpolation duplicates pixels, so unique values remain the same.

Nearest interpolation duplicates pixels, so the number of unique pixel values does not change.

🚀 Application

expert

Choosing interpolation for a heatmap visualization

You want to display a heatmap of temperature data with smooth color transitions using matplotlib. Which interpolation method should you choose to best achieve this?

A 'hanning' which is not a valid interpolation method in matplotlib

B 'nearest' to keep exact data values visible

C 'none' to disable interpolation and show raw pixels

D 'bicubic' to create smooth gradients between data points

💡 Hint

Smooth gradients require interpolation methods that blend pixel values smoothly.

'bicubic' interpolation creates smooth gradients by considering surrounding pixels with cubic functions.