Installation Guide

Step 1: Clone or Extract the Project

Navigate to the project directory in your terminal or command prompt:

cd "c:\Users\Admin\Desktop\cccoddee\students Emgaments Detection"

For Mac/Linux users, use forward slashes in the path.

Step 2: Activate Virtual Environment

For Windows:

.\venv\Scripts\activate

For Mac/Linux:

source venv/bin/activate

You should see (venv) prefix in your command prompt after activation.

Step 3: Install Required Dependencies

Install all necessary packages using pip:

pip install -r requirements.txt

This will install Django 4.2, YOLOv8, MediaPipe, OpenCV, Celery, and all other dependencies. Installation may take 5-10 minutes.

Key packages that will be installed:

• Django 4.2 - Web framework
• ultralytics - YOLOv8 for object detection
• mediapipe - Pose estimation
• opencv-python - Computer vision library
• Pillow - Image processing
• celery - Async task processing
• redis - Cache and message broker

Step 4: Database Setup

Create database tables by running migrations:

python manage.py makemigrations
python manage.py migrate

This creates tables for students, video sessions, analytics, and user authentication.

Step 5: Create Superuser Account

Create an admin account to access the Django admin panel:

python manage.py createsuperuser

You will be prompted to enter:

• Username (e.g., admin)
• Email address (optional)
• Password (enter twice for confirmation)

Remember these credentials - you'll need them to access the admin panel.

Step 6: Collect Static Files (Production Only)

If deploying to production, collect all static files:

python manage.py collectstatic

For development, this step is optional.

Step 7: Start Development Server

Launch the Django development server:

python manage.py runserver

The application will be available at:

http://127.0.0.1:8000/

Keep this terminal window open while using the application.

Step 8: Access Admin Panel

Open your web browser and navigate to:

http://127.0.0.1:8000/admin/

Login using the superuser credentials you created in Step 5.

Step 9: Add Students to Database

In the admin panel:

1. Click on "Students" section
2. Click "Add Student" button
3. Fill in student details:
   • Student ID (unique identifier)
   • Student Name
   • Upload student photo (optional but recommended)
   • Set seating position (row and column numbers)
4. Click "Save" to add the student
5. Repeat for all students in the classroom

Accurate seating positions improve tracking accuracy.

Step 10: Configure Project Settings

Open engagement_monitor/settings.py to customize:

Database Configuration:

# For SQLite (default)
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.sqlite3',
        'NAME': BASE_DIR / 'db.sqlite3',
    }
}

# For PostgreSQL (production)
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'engagement_db',
        'USER': 'your_username',
        'PASSWORD': 'your_password',
        'HOST': 'localhost',
        'PORT': '5432',
    }
}

Media Upload Limits:

DATA_UPLOAD_MAX_MEMORY_SIZE = 524288000  # 500MB
FILE_UPLOAD_MAX_MEMORY_SIZE = 524288000  # 500MB

Frame Processing Rate:

# Process every Nth frame (higher = faster but less accurate)
PROCESS_EVERY_N_FRAMES = 5  # Default: 5

Step 11: Optional - Setup Background Processing

For production environments handling multiple large videos:

Install Redis:

Windows: Download from redis.io

Mac: brew install redis

Linux: sudo apt-get install redis-server

Start Redis Server:

redis-server

Start Celery Worker (in new terminal):

celery -A engagement_monitor worker -l info

This enables asynchronous video processing for better performance.

Step 12: Upload and Analyze Video

1. Navigate to the "Upload Video" page in the web interface
2. Enter a session title (e.g., "Mathematics Class - Grade 10 - January 5")
3. Click "Choose File" or drag and drop your classroom video
4. Supported formats: MP4, AVI, MOV, MKV
5. Maximum file size: 500MB
6. Click "Start Analysis" button

Ensure the video has good lighting and clear view of students for best results.

Step 13: Monitor Processing Status

After uploading, you'll be redirected to the processing status page where you can:

• View real-time progress percentage
• See current frame being processed
• Monitor detection events in real-time
• Estimated time remaining

Processing time varies based on video length and system specs. A 10-minute video typically takes 3-5 minutes.

Step 14: View Results and Analytics

Once processing completes, access the results dashboard to view:

• Behavior distribution pie chart (mobile usage, sleeping, talking, etc.)
• Engagement timeline graph showing activity over time
• Student-wise breakdown with individual metrics
• Session summary with overall statistics
• Heatmaps showing attention patterns

Step 15: Generate and Download Reports

From the results page, you can:

• Click "Generate PDF Report" for a professional PDF document
• Click "Generate Excel Report" for detailed data analysis
• Download processed video with annotations
• Share reports with teachers, parents, or administrators

Reports include charts, statistics, and recommendations for improving engagement.