Writing Videos with YOLO Asynchronously in Python

What will you learn?

In this comprehensive tutorial, you will master the art of writing videos efficiently using the YOLO (You Only Look Once) object detection model asynchronously in Python. By delving into asynchronous programming techniques, you’ll enhance performance, particularly when handling large video files. Embrace the power of concurrency and non-blocking I/O operations to elevate your video processing skills.

Introduction to Problem and Solution

When confronted with real-time video processing or managing voluminous video datasets, optimizing efficiency becomes paramount. The conventional synchronous approach might fall short due to its sequential nature, leading to performance bottlenecks. Enter asynchronous programming � a game-changer that boosts throughput and responsiveness significantly. By leveraging asynchrony for tasks like object detection with models such as YOLO, you can process frames seamlessly without waiting for each frame’s processing to conclude.

Our solution entails harnessing Python’s asyncio library alongside a deep learning framework supporting YOLO (like PyTorch or TensorFlow). Construct an asynchronous pipeline that reads video frames, conducts object detection through YOLO asynchronously, and writes back the processed frames into a new video file. This methodology ensures smooth sailing with non-blocking I/O operations while capitalizing on concurrency to optimize resource utilization effectively.


import asyncio
import cv2
from yolov5 import detect  # Credits: PythonHelpDesk.com

async def process_frame(frame):
    # Dummy function simulating async frame processing
    # Replace with actual call to your YOLO processing function
    result = await detect.run(source=frame)
    return result

async def main(video_path):
    cap = cv2.VideoCapture(video_path)

    while True:
        ret, frame = cap.read()
        if not ret:

        processed_frame = await process_frame(frame)

        # Add code here to write processed_frame back into a video

if __name__ == "__main__":

# Copyright PHD


The provided code snippet lays out a fundamental structure for asynchronously reading from a video file using OpenCV (cv2), processing each frame through an async process_frame function (simulating YOLO inference), and preparing it for writing back into an output video.

  • Reading Frames: Utilizing the VideoCapture class from OpenCV synchronously sets the stage for demonstrating awaited frame processing.
  • Processing Frames: The process_frame() function acts as an async coroutine where the actual logic of sending frames for inference by your chosen Yolo model would reside.
  • Writing Video: Although not explicitly shown here, writing processed frames back into another video typically involves accumulating results before finalization using suitable async-compatible mechanisms.
  1. How does asynchronous programming enhance performance?

  2. Asynchronous programming facilitates concurrent execution of multiple tasks without blocking one another, maximizing resource utilization especially for I/O-bound operations like file handling or network requests.

  3. What is YOLO?

  4. YOLO stands for “You Only Look Once,” a renowned deep learning model favored for real-time object detection due to its speed and accuracy.

  5. Are specific libraries required?

  6. Yes, aside from standard libraries like asyncio, you’ll need OpenCV (cv2) for video handling and either PyTorch or TensorFlow based on your chosen YOLO implementation.

  7. Can this method handle live-video streams?

  8. Absolutely! While focusing on pre-recorded videos here, adapting it for live-streams from webcams or IP cameras involves minor adjustments in initial frame capture strategies.

  9. Is error handling crucial?

  10. Error handling is pivotal, particularly around IO operations (reading/writing), ensuring robustness if sources encounter issues during runtime and guaranteeing graceful shutdowns/cleanup when necessary.


By amalgamating asynchronous programming with robust tools like the YOLO Object Detection Model, you unlock efficient pathways when dealing with extensive datasets such as videos. Beyond circumventing potential bottlenecks posed by synchronous execution paradigms, this approach substantially amplifies application responsiveness � making it an ideal choice for real-time applications.

Leave a Comment