Parking Spot Detection using OpenCV and SVC Classifier

Overview

This project uses computer vision techniques to detect available parking spots in images or video streams. The system utilizes the OpenCV library for image processing and employs a Support Vector Classifier (SVC) for object classification.

Features

Detects and marks empty parking spots in images or video frames.
Utilizes an SVC classifier for efficient spot classification.
Provides visual feedback by highlighting detected spots.

Installation

Clone the repository to your local machine:

git clone https://github.com/nina96/parking-spots-detection.git

Navigate to the project directory:
```
cd parking-spot-detection
```
Install the required dependencies:
```
pip install -r requirements.txt
```

Usage

Ensure you have Python installed on your machine.
Replace the video path with the path to the image or video file you want to process. This path is in the 'main.py' file.
Run the parking spot detection script:
```
python main.py
```
The script will process the input and display the result, marking available parking spots.

Project Structure

main.py: The main script for parking spot detection.
model.p: The trained SVC classifier model.
mp4 file: example video for testing.
utils.py: Script having utility functions.

![Demo Video]

Download the file by clicking on the link and downloading it.