Smart Checkout System

This project implements a smart checkout system using YOLO V8 object detection to track and calculate the prices of products crossing a designated checkout line in a video feed.

Overview

The system uses the YOLO V8 model for real-time object detection and tracking. It identifies products as they move through a video frame. The system then calculates the total cost of detected products based on predefined prices.

Features

• Object detection and tracking using YOLO V8.
• Cost calculation for specific product classes as they cross a defined line on the screen.
• Visual annotation of detected objects on the video frames.
• Output video with annotated frames showing tracked objects and total cost.

Components

1. Python Libraries Used

• ultralytics: YOLO V8 model for object detection and tracking.
• cv2 (OpenCV): For video capture, frame manipulation, and annotation.
• supervision: Custom library/module for video processing and output.

2. Files

• main.py: Python script implementing the smart checkout system.
• utils.py: Utility functions including get_product_cost for calculating product costs.
• model/yolov8n.pt: Pre-trained YOLO V8 model file.
• test_videos/: Directory containing input video files for testing.
• test_output/: Directory for storing output videos with annotated frames.

3. Usage

To run the smart checkout system:

1. Ensure all dependencies are installed (ultralytics, cv2, supervision).
2. Place the input video (video4.mp4 or other test videos) in the test_videos/ directory.
3. Run main.py to process the video and generate the output.