Python allows multiprocessing to efficiently utilize multiple CPU cores for concurrent tasks. Displaying an image in a child process involves spawning a separate process using libraries like Tkinter or Matplotlib. This approach makes sure that image loading and display operations can run concurrently with other tasks, enhancing overall performance and responsiveness in applications handling visual data.

Display an Image in a Child Process in Python

• Using multiprocessing and Tkinter
• Using multiprocessing and Matplotlib

Display an Image in a Child Process using Multiprocessing and Tkinter

In this approach, we are using multiprocessing and Tkinter to display an image in a child process. The approach1Fn function creates a Tkinter window (root) and loads an image from image_path using PIL (ImageTk). This child process communicates back to the main process using a Queue (queue.put) to signal when the image is displayed, ensuring synchronization (queue.get) before joining (p.join()) the child process.

import multiprocessing as mp
import tkinter as tk
from PIL import Image, ImageTk

def approach1Fn(image_path, queue):
    root = tk.Tk()
    root.title("Image Display")

    image = Image.open(image_path)
    photo = ImageTk.PhotoImage(image)

    label = tk.Label(root, image=photo)
    label.pack()

    queue.put("Image displayed")
    
    root.mainloop()

if __name__ == "__main__":
    image_path = "gfglogo.png"
    queue = mp.Queue()
    p = mp.Process(target=approach1Fn, args=(image_path, queue))
    p.start()
    message = queue.get()
    print(message)
    p.join()

Output:

Display an Image in a Child Process using Multiprocessing and Matplotlib

In this approach, we are using multiprocessing (mp.Process) to create a child process that loads and displays an image (gfglogo.png) using Matplotlib (matplotlib.pyplot). The imshow function displays the image, axis(‘off’) removes axis labels, and title sets a window title. After showing the image, the process communicates back to the main process via a Queue to indicate successful display (“Image displayed”).

import multiprocessing as mp
import matplotlib.pyplot as plt
from matplotlib.image import imread

def approach2Fn(image_path, queue):
    image = imread(image_path)
    plt.imshow(image)
    plt.axis('off') 
    plt.title('Image Display')
    plt.show()
    queue.put("Image displayed")

if __name__ == "__main__":
    image_path = "gfglogo.png"
    queue = mp.Queue()
    
    p = mp.Process(target=approach2Fn, args=(image_path, queue))
    p.start()
    
    message = queue.get()
    print(message)
    
    p.join()

Output:

Conclusion

In conclusion, displaying an image in a child process in Python can be effectively achieved using multiprocessing with either tkinter or matplotlib. Both approaches allow for parallel execution of image display tasks while maintaining communication with the main process through queues for synchronization. Choosing between tkinter for GUI-based applications or matplotlib for more data-centric visualizations depends on the specific requirements of the project, offering flexibility in handling image display tasks asynchronously.