Displaying images with imshow in Matplotlib - Step-by-Step Execution

Concept Flow - Displaying images with imshow

Load Image Data

↓

Call imshow() with data

↓

Render Image on Plot

↓

Show Plot with plt.show()

↓

Image Displayed on Screen

The flow starts by loading image data, then imshow() is called to prepare the image, followed by rendering and displaying it on screen.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
image = np.array([[0, 255], [255, 0]])
plt.imshow(image, cmap='gray')
plt.show()

This code creates a simple 2x2 grayscale image and displays it using imshow.

Execution Table

Step	Action	Input/Parameter	Internal State	Output/Effect
1	Import matplotlib.pyplot as plt	-	plt module loaded	Ready to plot
2	Import numpy as np	-	np module loaded	Ready for array operations
3	Create image array	[[0, 255], [255, 0]]	image variable holds 2x2 array	Image data ready
4	Call plt.imshow()	image array, cmap='gray'	Prepare image for display	Image plot object created
5	Call plt.show()	-	Render plot window	Image displayed on screen
6	End	-	-	Execution complete

💡 All steps executed; image displayed successfully.

Variable Tracker

Variable	Start	After Step 3	After Step 4	After Step 5
plt	Not imported	Imported	Imported	Imported
np	Not imported	Imported	Imported	Imported
image	Undefined	[[0, 255], [255, 0]]	[[0, 255], [255, 0]]	[[0, 255], [255, 0]]
imshow_object	Undefined	Undefined	Undefined	Undefined

Key Moments - 2 Insights

Why do we need to call plt.show() after plt.imshow()?

plt.imshow() prepares the image plot but does not display it. plt.show() opens the window to actually show the image, as seen in execution_table step 5.

What does the cmap='gray' parameter do in imshow()?

cmap='gray' tells imshow to display the image in grayscale colors instead of default colors. This is shown in step 4 where imshow prepares the image with grayscale.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of the variable 'image' after step 3?

A A 2x2 numpy array with values [[0, 255], [255, 0]]

B Undefined

C A plot object

D A grayscale colormap

💡 Hint

Check the 'Internal State' column in row for step 3.

At which step is the image actually displayed on the screen?

A Step 3

B Step 4

C Step 5

D Step 6

💡 Hint

Look at the 'Output/Effect' column for when the image is shown.

If we remove plt.show(), what will happen according to the execution flow?

A Image will still display automatically

B Image will not display on screen

C Code will error out

D Image will display but in color

💡 Hint

Refer to key moment about plt.show() necessity and execution_table step 5.

Concept Snapshot

imshow(image, cmap=None) displays image data as a plot.
Use plt.show() to open the window and see the image.
cmap='gray' shows grayscale images.
Image data is usually a 2D or 3D numpy array.
imshow prepares the image; show renders it.

Full Transcript

This visual execution traces how to display images using matplotlib's imshow function. First, image data is created as a numpy array. Then plt.imshow() is called with this data and optional parameters like cmap='gray' to set grayscale colors. However, imshow only prepares the image plot internally. The actual display happens when plt.show() is called, which opens a window showing the image. Variables like 'image' hold the pixel data, and 'imshow_object' represents the plot. Key points include the need for plt.show() to see the image and how cmap controls colors. The execution table shows each step from imports to display, helping beginners see the process clearly.

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