Car Detection in Video using OpenCV

This project demonstrates real-time car detection in a video using OpenCV and a pre-trained Haar Cascade classifier.

Overview

The script car_detection.py reads a video file (cars.mp4) frame-by-frame, detects cars using a Haar Cascade classifier (cars.xml), and draws bounding boxes around them. It provides a simple example of object detection using computer vision techniques.

Features

• Real-time car detection in a video stream.
• Efficient use of OpenCV library for video processing.
• Simple user interface with intuitive exit functionality (press 'z' to exit).

Requirements

• Python 3.x
• OpenCV (opencv-python package)
• cars.mp4 (or replace with your own video file)
• cars.xml (Haar Cascade classifier for car detection)

Installation

1. Clone the repository:

git clone https://github.com/flameAutomation/Car-Detection-OpenCV.git
cd Car-Detection-OpenCV

2. Install dependencies:

pip install opencv-python

3. Run the Script:

python car_detection.py

Usage

• Ensure cars.mp4 and cars.xml are in the same directory as car_detection.py.
• The video will open in a new window showing detected cars with bounding boxes.
• Press z on your keyboard to exit the video display.

Notes:

• cars.mp4 - Example video used for car detection.
• cars.xml - Pre-trained Haar Cascade classifier for car detection.