Displaying images with imshow in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the imshow function do in matplotlib?

The imshow function displays an image on a plot using pixel data, showing the image as it appears in an array or matrix form.

beginner

Which data types can imshow display as images?

It can display 2D arrays for grayscale images and 3D arrays for color images (like RGB), where pixel values represent brightness or color intensity.

beginner

How do you add a color map to an image displayed with imshow?

Use the cmap parameter in imshow, for example, imshow(image, cmap='gray') to show a grayscale color map.

intermediate

What does the interpolation parameter control in imshow?

It controls how pixel values are smoothed or stretched when the image is resized, affecting the sharpness or blur of the displayed image.

beginner

Why might you use plt.axis('off') after imshow?

To hide the axis ticks and labels, making the image display cleaner and focusing only on the image itself.

What type of array does imshow expect for a color image?

A 3D array with RGB values

B 2D array with grayscale values

C 1D array of pixel intensities

D 4D array with alpha channel

imshow expects a 3D array for color images, where the last dimension represents RGB color channels.

Which parameter changes the color scheme of an image in imshow?

A palette

B color

C color_map

D cmap

The cmap parameter sets the color map for the image display.

What does plt.axis('off') do after showing an image?

A Removes axis ticks and labels

B Changes image brightness

C Turns off the image

D Saves the image

It hides the axis ticks and labels for a cleaner image display.

If you want a sharper image display with imshow, which parameter should you adjust?

A dpi

B interpolation

C resolution

D sharpness

The interpolation parameter controls how pixels are smoothed or sharpened.

Which library provides the imshow function?

A pandas

B numpy

C matplotlib

D seaborn

imshow is part of the matplotlib.pyplot module.

Explain how to display a grayscale image using imshow and how to remove axis labels for a cleaner look.

Think about the image data shape and how to hide axes.

Describe the role of the interpolation parameter in imshow and how it affects image appearance.

Consider what happens when you resize an 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