Displaying images with imshow in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib module used for plotting images.

Matplotlib

import matplotlib.[1] as plt

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

Aimshow

Bimage

Cplot

Dpyplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'image' instead of 'pyplot' for import.

Trying to import 'imshow' directly.

Using 'plot' which is not the correct module.

The pyplot module in matplotlib is used for plotting images and graphs. We import it as plt by convention.

2fill in blank

medium

Complete the code to display an image stored in the variable 'img' using imshow.

Matplotlib

plt.[1](img)
plt.show()

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

Aimshow

Bplot

Cimage

Ddisplay

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'plot' which is for line plots.

Using 'display' which is not a matplotlib function.

Using 'image' which is not a function.

The imshow function is used to display images in matplotlib.

3fill in blank

hard

Fix the error in the code to correctly display a grayscale image stored in 'gray_img'.

Matplotlib

plt.imshow(gray_img, cmap=[1])
plt.show()

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

A'color'

B'gray'

C'rgb'

D'hsv'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rgb' which is for color images.

Using 'color' which is not a valid colormap.

Using 'hsv' which changes colors.

To display a grayscale image correctly, the colormap 'gray' must be used.

4fill in blank

hard

Fill both blanks to create a figure of size 8x6 inches and display the image 'img'.

Matplotlib

plt.figure(figsize=([1], [2]))
plt.imshow(img)
plt.show()

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

A8

B6

C4

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping width and height values.

Using very small or very large numbers.

Forgetting to use a tuple.

The figsize parameter takes width and height in inches. Here, 8 and 6 create a wider figure.

5fill in blank

hard

Fill all three blanks to display the image 'img' without axis ticks and with a title 'My Image'.

Matplotlib

plt.imshow([1])
plt.axis([2])
plt.title([3])
plt.show()

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

Aimg

B'off'

C'My Image'

D'on'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'on' instead of 'off' for axis.

Passing the title without quotes.

Using wrong variable name for the image.

We display the image variable img, turn off axis ticks with 'off', and set the title to 'My Image'.

Practice

1. What does the imshow function in matplotlib do?

easy

A. Displays image data as a picture

B. Creates a line plot from data points

C. Generates a histogram of values

D. Saves an image file to disk

Solution

1. Step 1: Understand the purpose of imshow

   imshow is designed to display image data visually as a picture.

2. Step 2: Compare with other plotting functions

   Other functions like line plots or histograms serve different purposes, so they don't match imshow's role.

3. Final Answer:

   Displays image data as a picture -> Option A

4. Quick Check:

   imshow = display image [OK]

Hint: Remember: imshow means 'image show' [OK]

Common Mistakes:

• Confusing imshow with plot or hist functions
• Thinking imshow saves images instead of displaying
• Assuming imshow creates charts, not images

2. Which of the following is the correct way to display a 2D numpy array named img as an image using matplotlib?

easy

A. plt.hist(img)

B. plt.plot(img)

C. plt.imshow(img)

D. plt.show(img)

Solution

1. Step 1: Identify the function to display images

   To show an image from a 2D array, plt.imshow() is the correct function.

2. Step 2: Check other options for correctness

   plt.plot() is for line plots, plt.hist() for histograms, and plt.show() displays the current figure but does not take data as argument.

3. Final Answer:

   plt.imshow(img) -> Option C

4. Quick Check:

   Image display = plt.imshow() [OK]

Hint: Use imshow to display arrays as images [OK]

Common Mistakes:

• Using plt.plot for image data
• Passing data to plt.show() incorrectly
• Confusing histogram with image display

3. What will the following code display?

import matplotlib.pyplot as plt
import numpy as np
img = np.array([[0, 1], [1, 0]])
plt.imshow(img, cmap='gray')
plt.show()

medium

A. A 2x2 image with black and white pixels

B. A line plot of the array values

C. An error because cmap='gray' is invalid

D. A blank plot with no image

Solution

1. Step 1: Understand the array and cmap

   The array has values 0 and 1 arranged in a 2x2 grid. Using cmap='gray' maps 0 to black and 1 to white.

2. Step 2: Predict the image output

   The image will show a 2x2 grid with black and white pixels arranged as per the array.

3. Final Answer:

   A 2x2 image with black and white pixels -> Option A

4. Quick Check:

   Array + cmap='gray' = black/white image [OK]

Hint: cmap='gray' shows 0 as black, 1 as white [OK]

Common Mistakes:

• Expecting a line plot instead of image
• Thinking cmap='gray' causes error
• Assuming image will be blank

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
import numpy as np
img = np.random.rand(5,5)
plt.imshow(img, cmap='viridis', interpolation='none')
plt.show()

medium

A. The interpolation value 'none' is invalid

B. The cmap 'viridis' does not exist

C. np.random.rand cannot create 2D arrays

D. The code runs without error and shows the image

Solution

1. Step 1: Check interpolation parameter

   In matplotlib, interpolation='none' is valid and means no smoothing.

2. Step 2: Verify cmap and array creation

   'viridis' is a standard colormap, and np.random.rand(5,5) creates a 5x5 array of floats between 0 and 1.

3. Step 3: Confirm code behavior

   The code runs without error and displays a 5x5 colored image with viridis colors and no interpolation smoothing.

4. Final Answer:

   The code runs without error and shows the image -> Option D

5. Quick Check:

   interpolation='none' and cmap='viridis' are valid [OK]

Hint: Check docs: 'none' is valid interpolation [OK]

Common Mistakes:

• Assuming 'none' is invalid interpolation
• Thinking 'viridis' cmap is missing
• Believing np.random.rand can't make 2D arrays

5. You have a grayscale image stored as a 2D numpy array with values from 0 to 255. You want to display it with matplotlib so that the darkest pixel is black and the brightest is white. Which code snippet achieves this correctly?

hard

A. plt.imshow(image_array, cmap='viridis', vmin=0, vmax=255)

B. plt.imshow(image_array, cmap='gray', vmin=0, vmax=255)

C. plt.imshow(image_array, cmap='gray', vmin=255, vmax=0)

D. plt.imshow(image_array)

Solution

1. Step 1: Understand grayscale display with imshow

   To show grayscale correctly, use cmap='gray' and set vmin=0 (black) and vmax=255 (white) to map pixel values properly.

2. Step 2: Evaluate other options

   plt.imshow(image_array, cmap='gray', vmin=255, vmax=0) reverses vmin and vmax, causing inverted colors. plt.imshow(image_array, cmap='viridis', vmin=0, vmax=255) uses wrong colormap 'viridis'. plt.imshow(image_array) lacks vmin/vmax, so colors may not map correctly.

3. Final Answer:

   plt.imshow(image_array, cmap='gray', vmin=0, vmax=255) -> Option B

4. Quick Check:

   Grayscale with correct vmin/vmax = plt.imshow(image_array, cmap='gray', vmin=0, vmax=255) [OK]

Hint: Set vmin=0 and vmax=255 for correct grayscale [OK]

Common Mistakes:

• Reversing vmin and vmax values
• Using wrong colormap for grayscale
• Not setting vmin and vmax for pixel range