Yolov8 to react-native app

[BekirrUgur]

Hello, I have a project that accesses IP cameras through Python via Yolov8 and counts the objects it identifies when they pass certain points. I want to turn this into a mobile application via "React-Native". What is the best method to do this?

[sheeehy]

You can't run Python directly on mobile, so you'll need to set up a server that does the YOLO processing. You can use Flask or Django to make a simple API. Then, in your React Native app, you'll use fetch or Axios to get the processed data from the server.

For the IP camera access, you could use a package that can display the camera feed in React Native. Once you've got the feed and data showing up in the app, you can start adding features like object counting based on the data you get from the server. So, in short: set up a server for YOLO, get your IP camera feed into React Native, and connect the two.

[pderrenger]

@sheeehy to integrate YOLOv8 into a React Native app, you would typically follow these steps:

1. Backend Server: Set up a backend server with an API endpoint that can receive image data and return YOLOv8 predictions. You can use frameworks like Flask or FastAPI for Python.

2. YOLOv8 Inference: On the server, load your trained YOLOv8 model and create an inference function that processes images and returns the detection results.

3. React Native App: Develop your React Native app to capture or stream video from the IP cameras. Use a library like react-native-camera for camera integration.

4. API Communication: From the app, send the relevant image data to your backend server's API endpoint and receive the object detection results.

5. Display Results: Present the detection results in your app, potentially overlaying them on the video feed or displaying counts and other statistics.

6. Optimization: Consider performance optimizations, such as reducing the frequency of API calls, resizing images before sending, and ensuring your server can handle the load.

Remember to handle network requests and responses efficiently to provide a smooth user experience. 🚀

[HasithaKutala]

Hi @pderrenger thats very amazing information about integration, it would be really great and helpful if you could attach GitHub repo or code for this integration setup. Thankyou in advance.

[pderrenger]

Hi @HasithaKutala, I'm glad you found the information helpful! Currently, we don't have a specific GitHub repository set up for this exact integration scenario. However, the general approach involves setting up a server with Flask or FastAPI to handle YOLOv8 inference, and then connecting it to a React Native app via API calls. For the server setup, you can refer to the Flask or FastAPI documentation for creating API endpoints, and the YOLOv8 documentation for model inference. On the React Native side, using libraries like react-native-camera will be key for handling camera input. If you have any specific questions or run into issues while setting this up, feel free to ask here! 🚀

[CodeGetters]

Hello, I have a project that accesses IP cameras through Python via Yolov8 and counts the objects it identifies when they pass certain points. I want to turn this into a mobile application via "React-Native". What is the best method to do this?您好，我有一个项目，它通过 Python 通过 Yolov8 访问 IP 摄像机，并在它们通过某些点时计算它识别的对象。我想通过“React-Native”将其转换为移动应用程序。最好的方法是什么？

Hello brother, I also have the same idea as you, may I ask if you have solved this problem? If so, can you tell me the solution in a simple way? Thank you!👀

[pderrenger]

@CodeGetters integrating YOLOv8 into a React Native app for object detection involves a few steps. Since React Native is a JavaScript framework and YOLOv8 is a Python-based model, you'll need to set up a server that can handle the object detection processing. Here's a high-level overview of the process:

1. Server Setup: Deploy a Python server with an API endpoint that uses YOLOv8 for object detection. This server will receive image data from your mobile app, run the detection, and send back the results.

2. API Communication: From your React Native app, send requests to the server's API endpoint with the image data from the IP cameras. You can use libraries like Axios or the native fetch API for this.

3. Handling Responses: Process the server's response in your React Native app to display the detection results, such as the object count and their locations.

4. Optimization: Consider performance optimizations, such as compressing images before sending them to the server, to reduce latency and bandwidth usage.

5. Security: Ensure secure transmission of data by using HTTPS and implementing proper authentication mechanisms.

6. User Interface: Develop a user-friendly interface in React Native to display the camera feed and detection results.

Remember to adhere to the licensing terms of YOLOv8 when deploying it in your application. If you need more detailed guidance on each step, you can refer to the Ultralytics Docs for information on using YOLOv8, and consult React Native documentation for app development specifics. Good luck with your project! 🚀

[CodeGetters]

Thanks for your help🥰

[pderrenger]

@CodeGetters of course, happy to help! 🌟 If you're encountering issues or have specific questions about using YOLOv8, could you provide a bit more detail or share the error messages you're seeing? This way, I can offer more targeted advice. For example, if you're trying to run a prediction and facing issues, make sure your code looks something like this:

from ultralytics import YOLO

# Load the model
model = YOLO('yolov8n.pt')  # Adjust model path as needed

# Run a prediction
results = model.predict('path/to/your/image.jpg')

# View results
results.show()

If this doesn't solve your problem, please share more details so we can dive deeper! 🛠

[WrishavSett]

Hello @BekirrUgur ,
While this may not be an answer to your question, I would like to ask about the original project you were working on if thats alright.
I wanted to know how you accessed your IP cameras (whether it was using Ffmpeg or not) and while running your code or multiple instances of it for various cameras if you faced a hardware resource utilization problem where you faced high CPU usage and slow inference times.
I also wanted to know how you implemented your tracking and counting if it not proprietary.
Thank you.