retail-store-analysis

🛒 Retail Store Analytics System using YOLOv8 & Streamlit

An end-to-end real-time object detection and analytics solution designed for retail stores. Built using Python, YOLOv8, OpenCV, and Streamlit, this system monitors customer activity, detects footfall, and visualizes key retail metrics.

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🚀 Features

• 📸 Image, Video, and Real-Time Webcam Input
• 🧠 Object detection powered by YOLOv8
• 👥 Person count and activity tracking
• 📊 Analytics dashboard with:
  • Total visitors
  • Peak time of the day
  • Line chart: Footfall over time
  • Bar chart: Hourly visitor distribution
  • CSV download of visitor logs
• 🗃️ Logs stored in analytics/footfall.csv
• 🎯 Designed for hackathons & real-world deployment

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🖼️ Screenshots

🎥 Real-Time Detection Interface

📈 Analytics Dashboard

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🛠️ Tech Stack

Tool	Purpose
Python	Core programming language
YOLOv8	Object detection (via Ultralytics)
OpenCV	Video and webcam frame processing
Streamlit	Web UI and dashboard
Pandas	Data logging & CSV export
Matplotlib	Graph plotting in dashboard

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📁 Folder Structure

Retail-Analytics/
|-- app.py                  # Main Streamlit app
|-- detect.py               # Image/video/webcam detection logic
|-- dashboard.py            # Analytics dashboard view
|-- models/
     yolov8n.pt          # YOLOv8 pretrained weights
|-- analytics/
     footfall.csv        # Visitor count log
|-- requirements.txt        # Python dependencies
|-- README.md               # Project documentation
|-- presentation.pptx       # (Optional) Hackathon PPT

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✅ How to Run

1️⃣ Clone the Repository

git clone https://github.com/your-username/retail-analytics.git
cd retail-analytics

2️⃣ Create a Virtual Environment

python -m venv venv
venv\Scripts\activate       # Windows
# OR
source venv/bin/activate    # macOS/Linux

3️⃣ Install Requirements

pip install -r requirements.txt

4️⃣ Run the App

streamlit run app.py

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🎥 Inputs Supported

• .jpg, .jpeg, .png image formats
• .mp4, .avi, .mov video formats
• Live webcam (click Start Detection)

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📊 Analytics Output

All footfall data is saved in:

analytics/footfall.csv

Sample format:

timestamp,person_count
2025-06-24 14:05:01,3
2025-06-24 14:05:02,2

This data feeds the analytics dashboard automatically.

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🎯 Use Cases

• Retail store footfall analytics
• Mall crowd monitoring
• Showroom heatmap analysis
• Queue optimization
• Security surveillance support

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🌟 Future Enhancements

• Zone-based detection & heatmap overlays
• Product shelf stock monitoring
• Gender & age group analytics
• Alert system for overcapacity

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👨‍💻 Author

Ankush M., Anjali
Project developed for hackathon & real-world deployment.

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📄 License

This project is licensed for educational and non-commercial use only.