🎯 Gunny Bag Detection using YOLOv8 (Windows)

This project uses the YOLOv8 object detection model to perform real-time gunny bag detection on video files. It is designed to work seamlessly on Windows, using Python and OpenCV.

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📦 Features

• 🎥 Real-time detection on video (data/video1.mp4)
• 🧠 YOLOv8 inference (Ultralytics API)
• 📏 Bounding boxes with labels and detection speed
• 💻 Windows command-line compatible

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🛠️ Requirements

• Python 3.10 (recommended)
• Git
• OpenCV with GUI support
• GPU (optional but recommended)

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📁 Folder Structure

GunnyBagCounter/ ├── Gunny-Bags-Counting/ │ ├── gunny_test.py # Real-time video detection script │ ├── yolov8n.pt # YOLOv8 pretrained model │ ├── data/ │ │ └── video1.mp4 # Test video │ ├── runs/ # Output directory for YOLO results │ └── README.md # You are here

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🚀 Setup & Run (Windows CMD)

1️⃣ Clone the Repository

git clone https://github.com/YourUsername/Gunny-Bags-Counting.git
cd Gunny-Bags-Counting
2️⃣ Create Virtual Environment
bash
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python -m venv venv
venv\Scripts\activate
3️⃣ Install Dependencies
bash
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pip install --upgrade pip
pip install ultralytics opencv-python numpy
4️⃣ Place Files
Place yolov8n.pt (or best.pt) in the same folder.

Place video1.mp4 inside the data/ folder.

5️⃣ Run the Detection Script
bash
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python gunny_test.py
👁️ Press Q to quit the video window.

📜 Sample Output
bash
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0: 384x640 3 persons, 48.5ms
Speed: 1.2ms preprocess, 48.5ms inference, 1.3ms postprocess per image