This project is an assignment for the IISC Bangalore Summer Internship. The website utilizes YOLOv5s, a state-of-the-art object detection system, to detect, annotate, and count vehicles in traffic. This technology can be instrumental in managing and analyzing traffic flow, potentially contributing to smarter and more efficient transportation systems.

1. Clone the repository:

2. git clone https://github.com/sd1p/IISCB_Assignment.git

3. Install dependencies for both backend and frontend:

4. cd backend

5. pip install -r requirements.txt

6. cd ../frontend

7. npm install

8. Configure environment variables:

   • Create a .env.local file in the frontend directory and set the required variables.

     NEXT_PUBLIC_BACKEND_URI=http://localhost:5000

9. Run the backend server:

10. cd backend

11. python app.py

12. Run the frontend application:

13. cd frontend

14. npm run dev

15. Run the app at http://localhost:3000/

• Method: POST

• Description: Uploads a Image file to the server.

• Parameters:

• file (multipart/form-data): The Image file to upload.

• Response:

• response (json) : Contains fileName, namespace, URI

    {
        "annotated_image": "", // base64 image
        "class_count": "", // vehicles and counts
    }