/CV_detect_cars

Primary LanguagePython

Car Detection System

Overview

This Python application is designed to detect cars in video feeds using OpenCV and store the count in a PostgreSQL database. It uses Haar cascades for object detection.

Requirements

  • Python 3.11
  • OpenCV
  • PostgreSQL
  • psycopg2
  • Additional requirements can be found in requirements.txt.

Setup

  1. Install Dependencies: Run pip install -r requirements.txt to install the required Python packages.
  2. Database Setup: Ensure PostgreSQL is installed and running. Create a database and update the settings.json file with your database credentials.
  3. Haar Cascade: Place the haarcascade_car.xml file in the project directory.
  4. Docker (Optional): Use the provided Dockerfile for containerization.

Configuration

Edit the settings.json file to set up your configuration. Example configuration:

{
    "cascade_name": "haarcascade_car.xml",
    "db_name": "your_db_name",
    "db_user": "your_db_user",
    "db_pass": "your_db_password",
    "db_host": "localhost",
    "video_link": "path_to_video_file_or_stream"
}

Running the Application

Execute the script using:

python application.py

Files Description

  • application.py: The main entry point of the application.
  • computer_vision.py: Contains the CarCount class for car detection and database operations.
  • controls.py: Utility script to load configuration settings.
  • requirements.txt: Lists all Python dependencies.
  • haarcascade_car.xml: Haar cascade file for car detection.
  • settings.json: Configuration file for database and other settings.

Usage

The application will start processing the video feed specified in the configuration file. Detected cars will be displayed in a window, and the count will be stored in the PostgreSQL database.