Running Multiple Cameras Simultaneously with Python Threading

Learn how to leverage Python's threading capabilities to run multiple cameras and display their video streams separately using OpenCV.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: how to run multiple camera in threading using python

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Running Multiple Cameras Simultaneously with Python Threading

Do you want to simultaneously display video streams from multiple cameras using Python? Many developers encounter challenges when managing multiple video feeds, typically ending up with only one feed displayed at a time. In this post, we’ll address how to run multiple cameras in threading using Python, allowing each video to open separately without overlapping.

The Problem

While using the OpenCV library in conjunction with Python's threading capabilities, you may find that your current implementation only allows one video stream to play at a time. What you need is a solution that allows each camera (or video feed) to run independently in its own window. This can lead to a more engaging GUI experience, especially in applications requiring multiple video streams.

Example of the Initial Code

Here is an example you might have encountered in your initial attempts:

[[See Video to Reveal this Text or Code Snippet]]

As this code demonstrates, even with the use of threading, each video is displayed in the same window, ultimately resulting in the limitation of viewing just one stream at a time.

The Solution

To solve this issue, we need to modify the code so that each video stream is displayed in a separate window with a unique title. Additionally, we will manage the positioning of each window to prevent overlap. Here's how you can achieve this:

Step 1: Update the RTSP List

Instead of using a list of URLs, create a list of tuples that include both the URL and an index to represent each stream:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Change the Video Function to Accept Tuples

Adjust the url_to_video function to unpack the index from its parameters, allowing each window to be named differently and positioned distinctly:

[[See Video to Reveal this Text or Code Snippet]]

This will create separate windows for each camera, labeled RTSP 0, RTSP 1, and so on.

Step 3: Execute in a Thread Pool

You can continue using a thread pool to manage the video streams concurrent with the following structure:

[[See Video to Reveal this Text or Code Snippet]]

Final Code

Combining these updates, your final code will look as follows:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following the steps provided, you can effectively run multiple camera feeds simultaneously in Python using threading with OpenCV. Each camera will have its own window, allowing for a clear and separate view of each video stream. Feel free to experiment with additional settings, such as window size and layout, to optimize your application further.

Now you can enjoy watching multiple video feeds at the same time, enhancing your project and user experience!