Displaying images with imshow in Matplotlib - Mini Project: Build & Apply

Displaying Images with imshow

📖 Scenario: You work as a data analyst and need to visualize image data to understand it better. Visualizing images helps you see patterns and details that numbers alone can't show.

🎯 Goal: You will create a simple image using numbers and display it using matplotlib's imshow function.

📋 What You'll Learn

Create a 2D list representing a grayscale image

Set a color map configuration variable

Use imshow to display the image with the chosen color map

Show the image using plt.show()

💡 Why This Matters

🌍 Real World

Displaying images is important in fields like medical imaging, satellite data analysis, and computer vision to visually inspect data.

💼 Career

Data scientists often need to visualize image data to explore patterns and communicate findings effectively.

1

Create a 2D list representing a grayscale image

Create a variable called image_data and assign it a 2D list with these exact values: [[0, 50, 100], [150, 200, 250], [255, 125, 75]]

Matplotlib

# Create the 2D list called image_data
# Your code here

Hint

Think of image_data as a small grid of pixel brightness values from 0 (black) to 255 (white).

2

Set the color map configuration

Create a variable called color_map and set it to the string 'gray' to display the image in grayscale.

Matplotlib

image_data = [[0, 50, 100], [150, 200, 250], [255, 125, 75]]
# Create the color_map variable and set it to 'gray'
# Your code here

Hint

The color_map tells imshow how to color the image. 'gray' means black and white.

3

Display the image using imshow

Import matplotlib.pyplot as plt. Then use plt.imshow with image_data and cmap=color_map to display the image.

Matplotlib

image_data = [[0, 50, 100], [150, 200, 250], [255, 125, 75]]
color_map = 'gray'
# Import matplotlib.pyplot as plt
# Use plt.imshow with image_data and cmap=color_map
# Your code here

Hint

Remember to import matplotlib.pyplot as plt before using imshow.

4

Show the image

Add a line to call plt.show() to display the image window.

Matplotlib

import matplotlib.pyplot as plt

image_data = [[0, 50, 100], [150, 200, 250], [255, 125, 75]]
color_map = 'gray'

plt.imshow(image_data, cmap=color_map)
# Call plt.show() to display the image
# Your code here

Hint

plt.show() opens a window with the image. Without it, the image won't appear.

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