🚦 Vision Flow - LPR System for Traffic Management

Traffic management and priority vehicle guidance

This project detects vehicles and license plates from traffic videos using YOLOv5 and EasyOCR, identifies the plate color, and simulates smart traffic signals. Detection data is stored in a CSV file and frames are saved locally.

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📁 Project Structure

visionflow/
│
├── visionflow.py              # Main application file
├── ViewCSV.py                 # Optional CSV viewer (not used in this README)
├── traffic_video2.mp4         # Sample traffic video input
├── yolov5su.pt                # YOLOv5 model weights (custom/small)
├── frames/                    # Saved image frames with detections
├── TrafficRecords.csv         # Output file with detection logs
├── requirements.txt           # Required Python dependencies

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🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/visionflow.git
cd visionflow

2. Create and Activate a Virtual Environment (optional but recommended)

python -m venv venv
venv\Scripts\activate  # On Windows
source venv/bin/activate  # On macOS/Linux

3. Install Dependencies

Make sure to have Python 3.8 or above.

pip install -r requirements.txt
pip install ultralytics, easyocr

4. Download or Place Required Files

• ✅ Ensure that the video is added in the import placeholder for the program.
• ✅ Place your YOLOv5 model file (yolov5su.pt) in the same directory.
• 🚫 No need to create frames/ or TrafficRecords.csv manually; they will be auto-created.

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▶️ Run the Project

python visionflow.py

The program will:

• Process video frames.
• Detect vehicles using YOLO.
• Detect license plate text using EasyOCR.
• Determine plate color (Red, Yellow, White).
• Assign a random lane.
• Save frame and detection data to CSV and local image folder.
• Display live traffic signals on video feed.

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📝 Output

• TrafficRecords.csv: Contains all detections.
• frames/: Contains saved image frames with annotated plates and vehicle info.

Each row in CSV includes:

• Timestamp
• License Plate Text
• Plate Color
• Lane
• Vehicle Type
• Confidence
• Path to Saved Frame Image

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📦 requirements.txt Sample

opencv-python
numpy
easyocr
ultralytics

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⚠️ Notes

• You must have a GPU for optimal performance (for real-time YOLO + OCR) or you can use online freely available compilers.
• If running on Google Colab, replace cv2.imshow with cv2_imshow (already done in code).
• yolov5su.pt is a lightweight YOLO model; you can use your custom-trained one.

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📸 Sample Output Preview

You can check the saved frame images in the frames/ directory after running.