Displaying images with imshow in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What does this imshow code display?

Consider this Python code using matplotlib to display an image:

import matplotlib.pyplot as plt
import numpy as np

image = np.array([[0, 1], [2, 3]])
plt.imshow(image, cmap='gray')
plt.colorbar()
plt.show()

What will the displayed image look like?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.array([[0, 1], [2, 3]])
plt.imshow(image, cmap='gray')
plt.colorbar()
plt.show()

A A 2x2 grayscale image with all pixels the same shade of gray

B A 2x2 grayscale image with the darkest pixel at top-left and brightest at bottom-right

C An error because the image array is not 3-dimensional

D A 2x2 color image with random colors

💡 Hint

Think about how imshow maps numbers to colors using the grayscale colormap.

The imshow function shows the 2x2 array as a grayscale image. The smallest value (0) is black, the largest (3) is white, and values in between are shades of gray. So top-left is darkest, bottom-right is brightest.

❓ data_output

intermediate

What is the shape of the image shown?

Given this code snippet:

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(100, 150, 3)
plt.imshow(image)
plt.show()

What is the shape of the image array being displayed?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(100, 150, 3)
plt.imshow(image)
plt.show()

A (100, 150, 3)

B (150, 100, 3)

C (100, 150)

D (3, 100, 150)

💡 Hint

Remember the shape format for color images is (height, width, color_channels).

The image array has 100 rows (height), 150 columns (width), and 3 color channels (RGB), so its shape is (100, 150, 3).

❓ visualization

advanced

Which option produces a heatmap with a colorbar?

Which code snippet correctly displays a 10x10 heatmap with a colorbar using imshow?

A

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data, cmap='hot')
plt.colorbar(data)
plt.show()

B

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data)
plt.colorbar('hot')
plt.show()

C

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data, cmap='hot')
plt.colorbar(cmap='hot')
plt.show()

D

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data, cmap='hot')
plt.colorbar()
plt.show()

💡 Hint

Check how colorbar() is called and how colormaps are assigned.

Option D correctly sets the colormap in imshow and calls plt.colorbar() without arguments to add the colorbar. Other options misuse colorbar arguments causing errors or ignoring the colormap.

🔧 Debug

advanced

What error does this code raise?

Analyze this code snippet:

import matplotlib.pyplot as plt
import numpy as np

image = np.array([1, 2, 3, 4])
plt.imshow(image)
plt.show()

What error will occur when running this?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.array([1, 2, 3, 4])
plt.imshow(image)
plt.show()

A No error, displays a 1D image

B TypeError: Image data must be 2D or 3D array

C ValueError: Invalid shape for image data

D IndexError: index out of bounds

💡 Hint

Check the shape requirements for imshow input arrays.

imshow requires a 2D array (grayscale) or 3D array (color). A 1D array like [1,2,3,4] is invalid and raises a ValueError about shape.

🚀 Application

expert

How to display an RGB image with transparency using imshow?

You have an RGB image stored as a NumPy array img with shape (100, 100, 3). You want to display it with 50% transparency over a white background using imshow. Which code snippet achieves this?

A

plt.imshow(img, alpha=0.5)
plt.gca().set_facecolor('white')
plt.show()

B

plt.imshow(img)
plt.alpha(0.5)
plt.show()

C

plt.imshow(img, transparency=0.5)
plt.show()

D

plt.imshow(img, alpha=0.5, background='white')
plt.show()

💡 Hint

Check how to set transparency and background color in matplotlib.

Option A correctly sets the alpha parameter for transparency and sets the axes background color to white. Other options use invalid parameters or functions.

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