Displaying images with imshow in Matplotlib

Introduction

We use imshow to show pictures or image data on the screen. It helps us see what the image looks like in a simple way.

You want to check how an image looks after loading it from a file.

You need to visualize the results of image processing or editing.

You want to display a heatmap or matrix as an image.

You are exploring data that is stored as pixels or colors.

You want to compare two images side by side visually.

Syntax

Matplotlib

matplotlib.pyplot.imshow(X, cmap=None, interpolation=None)

# X is the image data (array or matrix)
# cmap sets the color map (for grayscale or color)
# interpolation smooths the image display

X is usually a 2D or 3D array representing the image pixels.

cmap is useful for grayscale images to choose how shades appear.

Examples

Shows a random 10x10 grayscale image with default colors.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(10, 10)
plt.imshow(image)
plt.show()

Displays the same image in grayscale colors.

Matplotlib

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

Shows a random 10x10 color image with RGB channels.

Matplotlib

color_image = np.random.rand(10, 10, 3)
plt.imshow(color_image)
plt.show()

Sample Program

This program creates a small 5x5 image where pixel values increase from top-left to bottom-right. It shows the image with a color map and a color bar to explain the colors.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Create a simple 5x5 image with a gradient
image = np.array([[i + j for j in range(5)] for i in range(5)])

plt.imshow(image, cmap='viridis')
plt.colorbar()  # Show color scale
plt.title('Simple Gradient Image')
plt.show()

Important Notes

Always call plt.show() to display the image window.

You can add plt.colorbar() to show the color scale next to the image.

Images can be grayscale (2D arrays) or color (3D arrays with 3 channels for RGB).

Summary

imshow shows image data as a picture.

You can control colors with cmap and smoothness with interpolation.

Use plt.show() to see the image on screen.

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