This project utilizes FastAPI and YOLOv5 to develop a traffic control system that processes four short videos and returns traffic control parameters for both X lane and Y lane.

The system returns the following data:

{
  "id": "20240413060929emkax651ip2ff3e1acea50443b9b6ed854613634e6",
  "x1_vehicles": 0,
  "x2_vehicles": 0,
  "y1_vehicles": 0,
  "y2_vehicles": 0,
  "x_green_time": 60,
  "y_green_time": 60
}

Where:

• x1_vehicles, x2_vehicles, y1_vehicles, y2_vehicles represent the vehicle count in X and Y lanes respectively.
• x_green_time and y_green_time represent the green light duration for X and Y lanes respectively.

1. Clone the legacy YOLOv5 Repository

git clone https://github.com/ultralytics/yolov5.git

2. Install the required libraries

pip install -r requirements.txt && cd yolov5  && pip install -r requirements.txt

After completing the above steps, your directory structure should resemble the following:

project ├── LICENSE ├── README.md ├── assets │ └── output.gif ├── deep_sort # Folder containing Deep Sort tracking code │ └── ... ( contains other Deep Sort related files) ├── inference # Folder potentially related to model inference │ └── ... ( contains other inference related files) ├── runs # Folder potentially related to model training runs │ └── ... ( contains other run related files) ├── templates # Folder potentially related to template files │ └── ... ( contains other template related files) ├── uploads │ └── ...sample videos for testing ( contains other uploaded files) ├── yolov5 # Folder containing YOLOv5 object detection code │ ├── .env # Likely an environment variable file │ ├── light.py # Script potentially related to light control │ ├── main.py # Main script for the project │ ├── Pipfile # Likely a lockfile for Pip package manager │ ├── raspberry_code.py # Script potentially related to Raspberry Pi code │ ├── requirements.txt # File containing required Python libraries │ ├── test.py # Script for testing purposes │ ├── tracker.py # Script for running the vehicle counting algorithm │ ├── traffic_time.py # Script potentially related to traffic light timing │ ├── videos.py # Script potentially related to video processing │ ├── x.jpg # Image file │ └── x.txt # Text file └── yolov5s.pt # YOLOv5 model weights file

Run the server using the following command:

uvicorn main:app --reload

you can upload to a cloud storage immediately