Person Counter System 🚶‍♂️📹

Welcome to the Person Counter System! 🎉
This Flask-based web application uses the powerful YOLOv11 model to detect and count people in real-time video streams from various sources, including local MP4 files, RTSP cameras, and Android phone cameras.

It tracks the current and maximum number of people detected, logs data in a JSON file (limited to 200 entries), and provides a user-friendly web interface with options to upload videos or use a pre-uploaded sample. 🖥️

---

🌟 Features

• Real-Time Detection: Uses YOLOv11 to identify and count people in video streams with high accuracy. 🔍
• Multiple Sources: Supports local MP4 files 📁, RTSP camera streams 📹, and Android phone cameras 📱.
• Video Upload: Upload your own MP4 video or use a pre-uploaded sample video for person counting. 🎥
• Interactive Web Interface: Displays live video feeds with bounding boxes, current/maximum counts, source type, and recent activity logs. 📊
• Secure Camera Access: Login interface for entering RTSP or Android camera URLs securely. 🔒
• Data Management: Stores up to 200 detection records in data.json, including timestamp, person count, change type, max count, and source type. 📝
• Responsive Design: Built with Tailwind CSS for a modern, mobile-friendly interface. 📱💻

---

📋 Prerequisites

To run this project, you’ll need:

• Python 3.8+ 🐍
• A compatible video source:
  • Local MP4 file (e.g., sample.mp4) or uploaded MP4 file 📁
  • RTSP camera with a valid URL (e.g., rtsp://username:password@host:port/path) 📹
  • Android phone with an IP Webcam app (e.g., http://host:port/videofeed) 📱
• YOLOv11 Model File (yolo11n.pt) for person detection. 🧠
• A modern web browser (Chrome, Firefox, etc.) 🌐

---

🛠️ Setup Instructions

1. Clone the Repository 📥

git clone https://github.com/your-username/person-counter-system.git
cd person-counter-system

2. Install Dependencies 🛠️

pip install -r requirements.txt

This installs Flask, OpenCV, Ultralytics (YOLO), and NumPy.

3. Download YOLO Model 🧠

• Download yolo11n.pt from the Ultralytics YOLOv11 repository or use a pre-trained model.
• Place yolo11n.pt in the project root directory (person-counter-system/).

4. Prepare Video Sources 🎥

• Local Video: Place a sample MP4 file in the sample/ directory (e.g., sample/sample.mp4).
• Uploaded Video: Use the web interface to upload your own MP4 video.
• RTSP Camera: Obtain the RTSP URL and enter it in the web interface.
• Android Camera: Use an IP Webcam app and ensure the phone is on the same network.

5. Run the Application 🚀

python main.py

Then open http://localhost:5000 in your browser. 🌐

---

🌐 Usage

Select a Video Source 📡

• Sample Video (MP4) 📁
• IP Camera Feed 📹
• Android Phone Camera 📱

Enter the camera URL in the login form when needed and click Connect.

View the Feed 📺

• Green bounding boxes around detected people.
• Text overlays show:
  • Current Count ✅
  • Maximum Count 🔴
  • Source Type 🟨

Monitor Data 📊

• Current: People detected right now.
• Max: Highest count detected.
• Source: FILE, RTSP, ANDROID.
• Recent Activity: Table of last 5 detection events (with timestamp, person count, change type, max count, and source).

---

🚀 Deployment

Prepare the Repository 📦

git add .
git commit -m "Ready for deployment"
git push origin main

Deploy on Render 🌍

• Build Command: pip install -r requirements.txt
• Start Command: python main.py
• Env Vars:
  • SAMPLE_VIDEO_PATH = sample/sample.mp4
  • PORT = 5000

⚠️ Note: YOLO + video = resource heavy. Paid plan may be required.

---

📂 Project Structure

person-counter-system/
├── backend/          # Detection data
│   └── data.json     # Logs up to 200 records
├── sample/           # Sample videos
│   └── sample.mp4
├── uploads/          # Uploaded videos
├── templates/        # HTML
│   └── index.html
├── main.py           # Flask app
├── maxperson.py      # YOLO detection logic
├── requirements.txt  # Dependencies
└── README.md         # Docs

---

📊 Data Storage

data.json stores detection records:

[
  {
    "timestamp": "2025-08-26 11:42:00",
    "person_count": 9,
    "change_type": "increase",
    "max_count": 9,
    "source": "file"
  }
]

---

🛡️ Notes

• YOLO Model: yolo11n.pt excluded from repo → download manually.
• Sample Video: Place sample.mp4 yourself.
• Uploads: Stored in uploads/, excluded from repo.
• Data Limit: Only last 200 records saved.
• Performance: Heavy on CPU/GPU, optimize for deployment.

---

🐛 Troubleshooting

• Video Feed Not Loading → Check video path / URL / network.
• Counts Not Updating → Ensure data.json is being written.
• Upload Errors → Only valid MP4 files allowed.
• YOLO Model Error → Place yolo11n.pt in root.
• Slow Performance → Use better hardware / reduce detection interval.

---

🤝 Contributing

1. Fork the repo
2. Create branch → git checkout -b feature/awesome-feature
3. Commit changes → git commit -m "Add awesome feature"
4. Push branch → git push origin feature/awesome-feature
5. Open Pull Request 🙌

---

📜 License

This project is licensed under the MIT License. See the LICENSE file.

---

🙏 Acknowledgments

• Ultralytics for YOLOv11
• Flask
• OpenCV
• Tailwind CSS

---

✨ Happy Counting! 🚶‍♀️🚶‍♂️