License Plate Detection and Scraping Project

This project involves scraping license plate images from a website and using a pre-trained YOLO model to detect license plates in the images. The detected license plates are then saved to a CSV file along with their coordinates.

Table of Contents

• Features
• Installation
• Usage
• Scripts
• Dependencies
• License

Features

• Scrape license plate images from a specified website.
• Detect license plates in the images using a pre-trained YOLO model.
• Save the detection results to a CSV file.
• Generate commands to run the scraper for multiple countries.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/license-plate-detection.git
   cd license-plate-detection

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Generate Scraping Commands:

   Run the script to generate commands for scraping license plate images for multiple countries:

   python generate_cmd.py

   This will create a cmd.txt file with the commands to run the scraper for each country.

2. Run the Scraper:

   Execute the commands in cmd.txt to scrape the images:

   source cmd.txt

3. Detect License Plates:

   Run the detection script to detect license plates in the scraped images and save the results to a CSV file:

   python generate_detections.py

Scripts

generate_cmd.py

This script generates commands to run the scraper for multiple countries and saves them to a cmd.txt file.

scraper.py

This script scrapes license plate images from a specified website and saves them to a directory.

generate_detections.py

This script uses a pre-trained YOLO model license_plate_detector.pt to detect license plates in the scraped images and saves the detection results to a CSV file.