Uhm-azing Classroom (엄청난 강의실) is a smart classroom platform that automates and assists repetitive tasks in lecture environments using cutting-edge computer vision technologies. Our system supports face-recognition attendance, real-time lecture material interaction, and random presenter selection—all in one seamless pipeline.

• 🎯 Automated Attendance: Face recognition-based attendance system
• 🖐️ Interactive Lectures: Hand gesture-controlled pointer and slides
• 🎲 Fair Selection: Random presenter selection from detected students
• 🚁 Flexible Input: Supports both drone and Raspberry Pi camera inputs

🎓 Uhm-Tendance Face Recognition Attendance Automated attendance checking using PyTorch CNN models

🖐️ Pow-Uhm Point Interactive Lecture System Hand gesture recognition for pointer and slide control

🎲 Pick Me, Uhm! Presenter Selection System Random selection from crowd-detected students

graph LR
A[🚁 Drone/PiCam] -->|Video Stream| B[📹 ZMQ Server]
B -->|Frames| C[🤖 CV Processing]
C -->|Recognition Results| D[📡 WebSocket Server]
D -->|Real-time Data| E[🖥️ Web Dashboard]
C -->|Attendance Data| F[📊 CSV Reports]

김태화 📝 Documentation	김태량 🖐️ Gesture Recognition	박형빈 🖐️ Hand Tracking	이찬 🎓 Attendance System	홍석진 🎲 Presenter Selection	손찬수 🚁 Hardware Setup	손인화 🎓 Face Recognition
이가은 💻 Web Development

# Real-time attendance tracking-PyTorch-basedCNNclassification-ZMQvideostreamprocessing-WebSocketbroadcasting-CSVreportgeneration

Technologies: OpenCV, PyTorch, ZMQ, WebSocket

# Gesture-based slide control-Handkeypointdetection-Gestureclassification-Pointercoordinatemapping-Slidenavigationcontrol

Technologies: OpenCV, MediaPipe, Hand Tracking

# Fair student selection-Crowddetection-Studentcounting-Randomselectionalgorithm-Visualfeedbacksystem

Technologies: OpenCV, Object Detection, DNN

# Python 3.8 or higher
python --version
# Install dependencies
pip install -r requirements.txt

# Clone the repository
git clone https://github.com/your-org/uhm-azing-classroom.git
cd uhm-azing-classroom
# Set up virtual environment
conda create -n icv python=3.8
conda activate icv
# Install packages
pip install opencv-python torch torchvision mediapipe websockets pyzmq

# 1. Start AI Server
python 03_run_attendance_server.py
# 2. Start Camera Client (Drone/PiCam)
python zmq_client.py
# 3. Open Web Dashboard# Navigate to ws://localhost:5556

• 📖 Full Documentation
• 🎥 Demo Video
• 📊 Project Proposal
• 🔧 API Reference

We welcome contributions! Please check out our Contributing Guidelines.

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

This project is licensed under the MIT License - see the LICENSE file for details.

• Computer Vision Course, University
• Professor Uhm and Teaching Assistants
• All team members for their dedication
• Open source community