Traffic Lane Image Processing and Analysis App

This Streamlit application processes traffic lane images, detects vehicles using a YOLOv8 model, calculates green signal times for each lane, and visualizes the results through images and graphs.

Features

• Upload Traffic Lane Images: Supports up to 4 images, one per lane.
• Vehicle Detection: Counts vehicles in each lane using the YOLOv8 model.
• Green Signal Calculation: Calculates green signal durations based on vehicle counts.
• Visualization: Displays original and processed images with bounding boxes, along with bar, pie, and line plots for analysis.

Requirements

• Python 3.8 or higher
• Dependencies:
  • streamlit
  • torch
  • ultralytics
  • opencv-python
  • matplotlib
  • numpy

Installation

1. Clone the repository (if applicable) or download the code.

   git clone <repository-link>
   cd Traffic-Management-System

2. Install dependencies: Ensure you have Python 3.8 or higher installed, and then run:

   pip install -r requirements.txt

Usage

1. Run the Streamlit app:

   streamlit run main.py

2. Upload Images:

   • Use the sidebar to upload 4 images, each representing traffic in one of the lanes.
   • Images should be in .jpg, .jpeg, or .png format.

3. Process and View Results:

   • Once images are uploaded, the app will:
     • Detect and count vehicles in each lane.
     • Calculate green signal times based on vehicle counts.
     • Display original and processed images with bounding boxes.
     • Visualize data using:
       • Bar Chart: Vehicle count per lane.
       • Pie Chart: Green signal time distribution across lanes.
       • Line Plot: Relationship between vehicle count and green signal time.

Deployed Link

You can access the app directly at Traffic Management App