This repository contains the implementation of a vehicle counter system that detects and counts vehicles in different lanes. The project utilizes computer vision techniques powered by OpenCV and deep learning models with TensorFlow.

• License Plate Detection.py: A script for detecting and recognizing license plates on vehicles.
• Vehicle Counting in Lanes.py: A script for detecting and counting vehicles in specified lanes.

Ensure you have the following installed:

• Python 3.x
• OpenCV
• TensorFlow
• Other dependencies as specified in requirements.txt

You can install the required packages using pip:

pip install -r requirements.txt

1. Clone this repository to your local machine:

   git clone https://github.com/Navini11/vehicle-counter.git
   cd vehicle-counter

2. Run the Vehicle Counting Script:

   python Vehicle Counting in Lanes.py

3. Run the License Plate Detection Script:

   python License Plate Detection.py

The Vehicle Counting in Lanes.py script performs the following steps:

1. Load Video Feed: Loads a video stream for vehicle detection.
2. Preprocessing: Applies preprocessing techniques such as resizing and grayscale conversion.
3. Vehicle Detection: Uses a pre-trained deep learning model to detect vehicles in each frame.
4. Lane Assignment: Assigns detected vehicles to specific lanes based on their positions.
5. Counting: Counts the number of vehicles in each lane and updates the count in real-time.

The License Plate Detection.py script includes:

1. Load Image/Video Feed: Loads an image or video stream containing vehicles.
2. Preprocessing: Applies preprocessing techniques such as resizing and grayscale conversion.
3. License Plate Detection: Uses OpenCV to detect license plates on vehicles.
4. Recognition: Recognizes the characters on the detected license plates using OCR.

• Software:

  • TensorFlow
  • OpenCV
  • Google Colab (for model training)
  • Visual Studio Code

• Hardware:

  • Any standard computer with Python environment

• Successfully detected and counted vehicles in multiple lanes with high accuracy.
• Efficiently recognized and extracted license plate information from vehicles.

Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.

This project is licensed under the MIT License - see the LICENSE file for details.