Скачать или смотреть How to Reduce Latency When Reading Frames from OpenCV Using Multithreading

Discover effective ways to `minimize latency` in frame reading from OpenCV by leveraging multithreading techniques in your Python applications.
---
This video is based on the question https://stackoverflow.com/q/74280157/ asked by the user 'Mr. Fractals' ( https://stackoverflow.com/u/12037615/ ) and on the answer https://stackoverflow.com/a/74280828/ provided by the user 'Burak' ( https://stackoverflow.com/u/12447766/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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: multithreading reading frames from opencv reducing latency

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Reduce Latency When Reading Frames from OpenCV Using Multithreading

Introduction

In the world of computer vision and video processing, latency can often become an obstacle. This is particularly the case when using OpenCV to read video streams. As frame capture speeds increase, it’s crucial to ensure that your application can keep up with the flow of data without lagging behind. In this guide, we’ll tackle the problem of high latency when reading frames from OpenCV and explore an effective solution using multithreading.

Understanding the Problem

The primary concern raised is the noticeable latency in reading video frames from a VideoCapture object in OpenCV. The user noted that their current solution involves creating copies of the frames, which may be causing unnecessary delays. The challenge lies in finding an optimal way to acquire frames from the stream so the application remains responsive.

Why Latency Matters

User Experience: High latency can lead to a laggy interface, making the application feel unresponsive.

Real-time Applications: In fields like robotics or gaming, minimizing latency is critical to maintain synchronization with real-world dynamics.

Efficiency: Reducing latency can lead to more efficient processing and resource utilization.

The Solution: Multithreading

To address the latency issue, the solution provided involves implementing a multithreading approach for reading frames. Here’s a breakdown of how this system functions:

1. Using Threading

By creating a dedicated thread for capturing frames, the main application can continue processing without waiting for each frame to be read. This setup involves:

Frame Update: A separate thread continuously reads frames from the video stream.

Buffered Frames: The thread updates the latest frame in a synchronized manner, which is then accessed by the main program as needed.

2. Example Implementation

The script provided uses a Python class, VideoCaptureThreading, to encapsulate the multithreaded frame reading. Here’s how it’s structured:

Initialization: Set up the video capture object and initial configurations.

Start Method: Begins the frame reading thread.

Update Method: Continuously grabs frames and updates the buffer.

Read Method: Retrieves a copy of the latest frame for processing.

3. Important Considerations

While using copying of frames with the copy() method may introduce some overhead, this cost is generally minimal (within a few milliseconds). The critical factor is ensuring that your video capturing and processing threads operate in a manner that minimizes bottlenecks.

Observations:

Frame Latestness: Always guarantees access to the most recent frame even if the capture frequency exceeds the processing rate.

Real-Time Configuration: Ensuring the listener is set appropriately can help further reduce latency.

Conclusion

Reducing latency when reading frames from a video capture object in OpenCV can essentially be tackled with the power of multithreading. By effectively managing how frames are captured and ensuring that your application processes them efficiently, you can enhance performance and deliver a faster, more responsive experience. Remember that while copying frames has its costs, the overall benefits of multithreading far outweigh these bounds in most applications.

Next time you’re faced with latency issues in your video processing tasks, consider applying the multithreading approach for a smoother experience.