I want to setup a livestream using the RTMP and NGINX server for a DJI drone.

[ShabanWaris1234]

I want to set up a live stream using the RTMP and NGINX server for a DJI drone. I have already trained a custom yolov8 model. However, I am having a problem setting up a live stream with the RTMP and NGINX server for my drone. Are there any tutorials or codes that can guide me in setting up a real-time detection using a drone? I also need to make a web app for my project I plan to use flask. Any tutorials would help.

[glenn-jocher]

Certainly! Setting up a live stream with RTMP and NGINX for your DJI drone and integrating it with your custom YOLOv8 model for real-time detection sounds like an exciting project! Here’s a brief guide to get you started:

1. RTMP Server Setup with NGINX:

   • Install NGINX and the RTMP module. You can find a good tutorial on how to do this here.
   • Configure NGINX to receive the RTMP stream from your drone. You'll need to modify the nginx.conf file to include the RTMP module settings.

2. Integrate YOLOv8 for Real-Time Detection:

   • Use the Python cv2 library to capture video frames from the RTMP stream.
   • Load your trained YOLOv8 model and pass the video frames to it for detection.

   import cv2
   from ultralytics import YOLO
   
   # Load model
   model = YOLO('path/to/your/custom_model.pt')
   
   # Capture RTMP stream
   cap = cv2.VideoCapture('rtmp://localhost/live/stream_name')
   while cap.isOpened():
       ret, frame = cap.read()
       if ret:
           # Make predictions
           results = model(frame)
           results.show()  # Display the frame with detections
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break
   cap.release()
   cv2.destroyAllWindows()

3. Developing a Web App with Flask:

   • Flask is a great choice for setting up a web server. You can start by creating a basic Flask app that displays the video stream.
   • Integrate the Flask app with the detection model to display the processed frames.
   • Check out the Flask documentation for basic setup and this tutorial for streaming video through Flask.

These steps should help you set up your project. For specific issues or errors, feel free to ask for more detailed help! Good luck with your real-time detection system! 🚀

[mordka]

@ShabanWaris1234 just a note, you won't get realtime detection, because RTMP has got few seconds latency embedded in protocol design. I've built realtime AI drone streaming pipeline with <1s AI latency and I'm open to share the details. If you are interested in this reach out to me on mordka@proton.me

[ccamacho]

you could share it here

[glenn-jocher]

Hello @ccamacho,

Thank you for your interest! For setting up a live stream with RTMP and NGINX for your DJI drone, you can follow these general steps:

1. Set up NGINX with RTMP Module: Configure NGINX on your server to handle RTMP streams. This involves installing the NGINX server with the RTMP module and configuring it to accept live streams.

2. Stream from Drone to NGINX: Use your DJI drone's software to send the video feed to your NGINX server's RTMP URL. This typically involves setting the RTMP stream URL in the drone's broadcasting settings.

3. Process Stream with YOLOv8: Capture the RTMP stream in your application using a library like OpenCV, and then process the frames with your custom-trained YOLOv8 model for real-time object detection.

4. Web App Integration: For integrating with a Flask web app, you can set up Flask to serve the processed video stream to a frontend. You might use WebSocket or another method to push the video frames to the client browser in near real-time.

For detailed code examples and step-by-step guides, you might want to check out resources specific to each component (NGINX RTMP setup, OpenCV video capture, Flask streaming). Unfortunately, I don't have a specific tutorial to share here, but these keywords should help you find relevant resources.

If you encounter specific issues or have questions about integrating these components, feel free to ask here!

Best of luck with your project! 🚀

— The Ultralytics Team

[ShabanWaris1234]

Thanks for the help @glenn-jocher.
Was able to setup the stream.
Any tips on how to lower the latency. The latency normally is around 3-4seconda delay. But when the model is inferring the latency is 40 seconds+