how to carryon after matplotlib has been called

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Summary

The issue at hand is that the code execution stops after displaying an image using matplotlib. This is due to the way matplotlib handles its event loop, particularly when plt.show() is called. By default, plt.show() blocks the execution of the code until the plot is closed. To continue execution after displaying the image, we need to find a way to circumvent this blocking behavior.

Root Cause

The root cause of this problem lies in the nature of the matplotlib event loop and how it interacts with the Python interpreter. When plt.show() is called, it starts an event loop that waits for user interactions (like closing the plot window). This event loop is blocking, meaning it prevents the execution of any code that comes after it until the plot window is closed.

Why This Happens in Real Systems

This behavior is common in graphical and event-driven programming. Libraries like matplotlib need to handle user interactions, which requires running an event loop. However, when integrated into scripts or programs that are not primarily graphical, this can lead to unexpected behavior, such as the blocking issue described.

Real-World Impact

The real-world impact is that scripts or programs that use matplotlib for displaying images or plots during execution can halt or appear to freeze, awaiting user interaction to continue. This can be particularly problematic in automated workflows or when running scripts in non-interactive environments.

Example or Code

To demonstrate how to continue execution after displaying an image with matplotlib, consider using the following approach:

import matplotlib.pyplot as plt
import cv2

# Load an image
image = cv2.imread("sample.jpg")

# Display the image without blocking
plt.ion()  # Turn on interactive mode
plt.figure(figsize=[10, 10])
plt.title("Image")
plt.axis("off")
plt.imshow(image[:, :, ::-1])
plt.draw()  # Draw the plot
plt.pause(0.001)  # Pause for a moment
plt.show(block=False)  # Show the plot without blocking

# Continue with the rest of the code
image_resized = cv2.resize(image, None, fx=0.5, fy=0.5)
#... rest of your image processing code...

# When you're ready to display again
plt.figure(figsize=[30, 30])
plt.subplot(131)
plt.imshow(image_sharp[:, :, ::-1])
plt.title("Final Image")
plt.axis("off")
plt.subplot(132)
plt.imshow(image_cleared[:, :, ::-1])
plt.title("Clear Impurities")
plt.axis("off")
plt.subplot(133)
plt.imshow(image[:, :, ::-1])
plt.title("Original")
plt.axis("off")
plt.show()  # This will block until the window is closed

How Senior Engineers Fix It

Senior engineers often handle this issue by understanding the event loop mechanism of matplotlib and using it to their advantage. Techniques include:

  • Using non-blocking shows (plt.show(block=False)) to allow the script to continue running.
  • Employing matplotlib in interactive mode (plt.ion()) for dynamic updates.
  • Utilizing matplotlib‘s ability to update plots (plt.draw(), plt.pause()) for controlled plotting.

Why Juniors Miss It

Juniors may miss this because:

  • Lack of experience with graphical libraries and their event loops.
  • Not fully understanding the blocking nature of plt.show().
  • Insufficient knowledge of matplotlib‘s interactive mode and non-blocking functions.
  • Key takeaway: Understanding the fundamentals of event-driven programming and library-specific functionalities is crucial for effective debugging and coding.