Witcher 3 Gameplay Analysis System

A deep learning pipeline for automated analysis of Witcher 3 gameplay footage using CNN-LSTM architecture. The system analyzes combat efficiency, resource management, and quest progression in real-time.

🎯 Features

• Data Collection: YouTube gameplay footage scraping with filters for specific content types
• Frame Analysis: CNN-based feature extraction for gameplay frames
• Temporal Analysis: LSTM/Transformer for sequence understanding
• Multi-task Learning: Simultaneous analysis of multiple gameplay aspects
• Memory-Efficient Processing: Optimized for processing long gameplay videos

🔧 System Components

1. Data Collection (Witcher_Data_Scrapper.py)

• YouTube gameplay footage scraping system
• Configurable search queries for different gameplay types
• Automatic categorization of downloaded content
• Built-in rate limiting and error handling

2. Feature Extraction (CNN_Feature_Extraction.py & Feature_Extraction_Prototype.py)

• ResNet18-based frame feature extraction
• Memory-efficient processing with gradient checkpointing
• Temporal feature aggregation
• Real-time feature visualization capabilities

3. Data Handling (data_handling.py)

• Custom PyTorch datasets for video processing
• Efficient caching mechanism for faster training
• Parallel processing support
• Robust error handling and recovery

4. CNN Model (CNN_Model.py)

• Memory-efficient architecture combining ResNet and LSTM
• Multi-task output heads
• Checkpoint system for training recovery
• Mixed precision training support

📊 Model Architecture

Input Video -> CNN Frame Analysis -> Temporal Processing -> Multi-task Outputs
                    ↓                        ↓                     ↓
              ResNet Features     →    LSTM/Transformer   →    Combat Score
                                                        →    Resource Rating
                                                        →    Quest Progress

⚙️ Requirements

• Python 3.8+
• PyTorch 1.8+
• CUDA-capable GPU
• Required packages in requirements.txt

🚀 Quick Start

1. Install dependencies:

pip install -r requirements.txt

2. Download training data:

python Witcher_Data_Scrapper.py

3. Train the model:

python CNN_Model.py

4. Run analysis on a video:

python Feature_Extraction_Prototype.py --video path/to/video.mp4

📝 Project Structure

witcher3-gameplay-ai-analysis-ml-cv/
├── Witcher_Data_Scrapper.py    # YouTube data collection
├── Feature_Extraction_Prototype.py  # Main feature extraction
├── CNN_Feature_Extraction.py    # CNN architecture
├── CNN_Model.py                # Training pipeline
├── data_handling.py            # Dataset management
└── training_checkpoints/       # Model checkpoints

🔄 Current Status

• CNN Frame Analysis
• LSTM/Transformer Integration (In Progress)
• Multi-task Learning Heads
• Real-time Analysis System

📈 Performance Notes

The system is designed for memory efficiency, handling long gameplay videos through:

• Gradient checkpointing
• Batch processing
• Mixed precision training
• Efficient caching mechanisms

🛠️ Development Notes

• The CNN model achieves ~96% accuracy on combat detection
• Current focus on LSTM/Transformer integration
• Future plans include real-time analysis capabilities
• Memory optimization is a key priority

📜 License

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