YOLO Object Detection - Real-time & Image Processing

This Python project is a case study developed and presented during a lesson I conducted as a tutor at an ITIS school as part of a PCTO (Percorsi per le Competenze Trasversali e l'Orientamento) program. The objective of this lesson was to introduce students to computer vision, object detection, and practical applications of YOLO (You Only Look Once) in real-world scenarios.

Presentation link: https://pitch.com/v/computer-vision-evqtzw

Features

• Real-time Object Detection using a webcam.
• Batch Processing of Images from a specified folder.
• Color-coded Bounding Boxes for detected objects.
• Navigation System to compare original and processed images.

How It Works

1. Real-time Detection: Opens the webcam, detects objects, and overlays bounding boxes.
2. Image Processing: Loads images from a folder, applies object detection, and saves results.
3. Image Viewer: Allows users to navigate through processed images.

Requirements

• Python 3.10
• OpenCV (cv2)
• Ultralytics YOLO (ultralytics)
• A YOLO model file (yolo11n.pt)

Installation

Before running the script, install the necessary dependencies:

pip install opencv-python ultralytics

Usage

Run the script and choose between real-time detection or image processing:

python object_detector.py

Configuration

• Change MODEL_PATH to use a different YOLO model.
• Modify INPUT_FOLDER and OUTPUT_FOLDER for custom image paths.
• Adjust the color palette for bounding boxes.