👁️ YOLOv8 & OpenCV Computer Vision Suite

🚀 Overview

Welcome to the YOLOv8 & OpenCV Computer Vision Suite. This repository houses a professional collection of computer vision implementations, bridging the gap between classical image processing techniques and state-of-the-art deep learning detection models.

From real-time security monitoring systems capable of detecting loitering in restricted zones, to interactive facial analysis tools and comprehensive object tracking benchmarks, this suite serves as both a practical toolkit and an advanced educational resource.

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🌟 Key Features

1. 🚨 YOLOv8 Night-Time Security Tracker

Located in: YOLOv8 and OpenCV built-in tracking/

A robust surveillance system designed for low-light environments.

• Real-time Person Detection: Leverages yolov8n (Nano) for high-speed tracking on consumer hardware.
• Smart Loitering Detection: Define custom polygon zones (e.g., driveways, porches). The system tracks individuals and triggers alerts if they linger beyond a set threshold (default: 5s).
• Persistent Tracking: Utilizes YOLO's native tracking capabilities to maintain subject identity across frames.
• Visual Analytics: dynamic bounding boxes, alert zone overlays, and on-screen status warnings.

2. 👤 YuNet Face Detection & Analytics

Located in: Open CV/face_Detection_OpenCV.py

A modern, GUI-based application for facial analysis.

• Deep Learning Backend: Powered by OpenCV's FaceDetectorYN (YuNet) for superior accuracy over Haar cascades.
• Interactive GUI: Built with standard libraries to allow drag-and-drop image analysis and live webcam switching.
• Detailed Landmarks: Detects and visualizes 5 key facial landmarks (eyes, nose, mouth corners).
• Configurable: Adjust Confidence Threshold, NMS (Non-Maximum Suppression), and Top-K parameters in real-time.

3. 👥 AI People Counter & Tracker

Located in: yolov5/GetCounting.py

A lightweight, custom implementation of object tracking for counting applications.

• Centroid Tracking Algorithm: Implements a custom pure-Python centroid tracker to assign and maintain IDs.
• Video Analysis: Select any video file via system dialog to automatically count unique persons.
• Filtered Detection: Intelligent filtering based on confidence scores and bounding box size to reduce false positives.
• Real-time Visualization: Displays dynamic bounding boxes, unique IDs, and a live total count overlay.

4. 🎯 Universal Object Tracking Benchmark

Located in: Open CV/Object_trackeng_OpenCV.py

A comprehensive suite allowing side-by-side comparison of 7+ tracking algorithms.

• Supported Algorithms: CSRT (High Accuracy), KCF (Fast), MOSSE (Ultra Fast), MIL, TLD, MEDIANFLOW, BOOSTING.
• ROI Selection: Interactive Region-of-Interest selection to start tracking any arbitrary object.
• Performance Metrics: Real-time FPS display to evaluate tracker efficiency.

5. 🛠️ Classical Computer Vision Utilities

A treasure trove of essential image processing scripts:

• Panorama Stitching: Automatically stitch multiple overlapping photos into a single seamless panorama.
• Sketch Artist: Convert standard video/images into high-quality pencil sketches using bilateral filtering.
• Hough Line Detection: Detect structural lines in architectural or geometric images.
• Image Segmentation: Implement Watershed algorithm for complex object separation.

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📦 Installation

Prerequisites

• Python 3.8+
• NVIDIA GPU (Recommended for YOLOv8 performance)
• Webcam (For live demos)

Setup

1. Clone the Repository

   git clone https://github.com/yourusername/yolov5-yolov8-computer-vision-main.git
   cd yolov5-yolov8-computer-vision-main

2. Install Dependencies

   pip install numpy opencv-python ultralytics pillow torch torchvision torchaudio ipywidgets

3. Model Setup (For Face Detection)

   • Download the YuNet ONNX model.
   • Place face_detection_yunet_2023mar.onnx in the Open CV/ directory.

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💻 Usage Guide

Running the Security Tracker

Monitor your video feeds for suspicious activity.

cd "YOLOv8 and OpenCV built-in tracking"
# Edit the video path in the script if not using a webcam
python Live_Tracking.py

Launching the Face Detection App

Start the GUI for image and video analysis.

cd "Open CV"
python face_Detection_OpenCV.py

Running the AI People Counter

Select a video file to count unique individuals.

cd yolov5
python GetCounting.py

Testing Object Trackers

Compare different tracking algorithms on your own footage.

cd "Open CV"
python Object_trackeng_OpenCV.py

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📂 Project Structure

root/
├── YOLOv8 and OpenCV built-in tracking/   # Modern Deep Learning Tracking
│   ├── Live_Tracking.py                   # Main Security Script
│   └── Live_Tracking.ipynb                # Jupyter Notebook demonstration
│
├── Open CV/                               # Classical & DNN OpenCV Tools
│   ├── face_Detection_OpenCV.py           # YuNet Face Detection GUI
│   ├── Object_trackeng_OpenCV.py          # Multi-algorithm Tracker
│   ├── image_Stitching_OpenCV.py          # Panorama Creator
│   ├── Pencil_sketch_conversion_OpenCV.py # Artistic Filters
│   └── ... (various utility scripts)
│
└── README.md                              # This file

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🔮 Future Roadmap

• Web Interface: Porting the Security Tracker to a Streamlit Dashboard.
• Multi-Camera Support: Enabling simultaneous streams for larger area coverage.
• Action Recognition: Integrating Pose Estimation to detect specific behaviors (falling, running).
• Docker Support: Containerizing the application for easy deployment.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

Distributed under the MIT License. See LICENSE for more information.