Why image handling matters in Matplotlib - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this image plot code?

Consider the following Python code using matplotlib to display an image. What will be the shape of the displayed image array?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(100, 150)
plt.imshow(image, cmap='gray')
plt.show()
print(image.shape)

A (100, 150)

B (150, 100)

C (100, 150, 3)

D (150, 100, 3)

💡 Hint

Remember that numpy arrays for grayscale images have 2 dimensions: height and width.

The image array is created with shape (100, 150), meaning 100 rows (height) and 150 columns (width). This matches the shape printed and the image displayed.

❓ data_output

intermediate

What is the pixel value at position (50, 75)?

Given this code that creates a grayscale image with a gradient, what is the pixel value at row 50, column 75?

Matplotlib

import numpy as np
image = np.tile(np.linspace(0, 1, 150), (100, 1))
print(round(image[50, 75], 2))

A 0.75

B 1.0

C 0.25

D 0.5

💡 Hint

The image is a horizontal gradient from 0 to 1 over 150 columns.

The pixel value at column 75 is about halfway through the gradient, so approximately 0.5.

❓ visualization

advanced

Which plot shows the correct color mapping for this image?

You have an image array with values from 0 to 255. Which matplotlib code snippet correctly displays it as a grayscale image?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
image = np.random.randint(0, 256, (50, 50))

A

plt.imshow(image, cmap='hot')
plt.show()

B

plt.imshow(image)
plt.show()

C

plt.imshow(image, cmap='gray')
plt.show()

D

plt.imshow(image, cmap='jet')
plt.show()

💡 Hint

Grayscale images need the 'gray' colormap to show shades of gray.

Option C uses cmap='gray' which maps pixel values to grayscale colors correctly. Other colormaps show colors, not grayscale.

🔧 Debug

advanced

Why does this image display incorrectly?

This code tries to display a color image but the colors look wrong. What is the cause?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
image = np.random.randint(0, 256, (100, 100, 3))
plt.imshow(image)
plt.show()

A The pixel values must be normalized between 0 and 1

B The color channels are in BGR order instead of RGB

C The image shape should be (3, 100, 100) not (100, 100, 3)

D The image array dtype should be float, not int

💡 Hint

Matplotlib expects RGB order for color images.

If the image was loaded from OpenCV, it might be in BGR order, causing wrong colors in matplotlib which expects RGB.

🚀 Application

expert

How to efficiently resize a large image array for faster plotting?

You have a large image array of shape (4000, 6000). You want to display a smaller version quickly using matplotlib. Which approach is best?

A Use numpy slicing: image_small = image[::10, ::10]

B Use plt.imshow(image, interpolation='nearest') without resizing

C Use plt.imshow(image, cmap='gray') directly on large image

D Use image_small = image.resize((400, 600))

💡 Hint

Reducing image size by sampling pixels speeds up plotting.

Option A downsamples the image by taking every 10th pixel, reducing size and speeding up display. Option A is invalid because numpy arrays don't have resize method like that.