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.
- 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.
- Python 3.8 or higher
- Dependencies:
streamlittorchultralyticsopencv-pythonmatplotlibnumpy
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Clone the repository (if applicable) or download the code.
git clone <repository-link> cd Traffic-Management-System
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Install dependencies: Ensure you have Python 3.8 or higher installed, and then run:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run main.py
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Upload Images:
- Use the sidebar to upload 4 images, each representing traffic in one of the lanes.
- Images should be in
.jpg,.jpeg, or.pngformat.
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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.
- Once images are uploaded, the app will:
You can access the app directly at Traffic Management App