🎌 Hentaivid

Revolutionary RAG-compatible video storage format that embeds text chunks into QR codes hidden inside pixelated regions of adult content. Zero-database semantic search with maximum cultural authenticity.

"My boss thinks I'm researching state-of-the-art data retrieval methods. He's not wrong." - Anonymous FAANG Sr Engineer

🚀 What is Hentaivid?

Hentaivid is a breakthrough video-based knowledge storage system that leverages legally mandated pixelation in Japanese adult content as a steganographic medium for embedding searchable text data. By utilizing the culturally-required censorship regions as QR code carriers, Hentaivid creates a novel form of contextual data storage that is both semantically searchable and culturally compliant.

✨ Key Features

• 🎯 Zero Database Required - All data embedded directly in video files
• 🔍 Lightning-Fast Semantic Search - FAISS-powered vector similarity
• 🎌 Culturally Authentic - Respects Japanese pixelation standards
• 📱 QR Code Integration - Industry-standard data encoding
• 🎬 Video-Native Storage - No external dependencies for data retrieval
• 🔒 Steganographic Security - Data hidden in plain sight
• 📈 Scalable Architecture - Handle millions of text chunks
• 🌐 Cross-Platform Compatible - Works on any system with video playback

🔬 Technical Architecture

📦 Installation

System Requirements

• Python 3.8+
• OpenCV 4.0+
• At least 4GB RAM (8GB+ recommended for large corpora)
• GPU acceleration recommended for large-scale processing

Quick Install

# Create a new project directory
mkdir my-hentaivid-project
cd my-hentaivid-project

# Create virtual environment
python -m venv venv

# Activate it
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install hentaivid
pip install -r requirements.txt

For Advanced Pixelation Detection:

pip install tensorflow  # For deep learning-based region detection
pip install scikit-image  # For advanced image processing

🎯 Quick Start

Basic Usage

from hentaivid import HentaividEncoder, HentaividChat

# Create video memory from text chunks
chunks = ["Machine learning fundamentals", "Deep learning architectures", "Neural network optimization"]
encoder = HentaividEncoder()
encoder.add_chunks(chunks)
encoder.build_video("knowledge.mp4", "knowledge_index.faiss")

# Query your embedded knowledge
chat = HentaividChat("knowledge.mp4", "knowledge_index.faiss")
response = chat.search("What are neural networks?")
print(response)

Building Knowledge Base from Documents

from hentaivid import HentaividEncoder
import os

# Load documents
encoder = HentaividEncoder(chunk_size=512, overlap=50)

# Add text files
for file in os.listdir("documents"):
    with open(f"documents/{file}", "r") as f:
        encoder.add_text(f.read(), metadata={"source": file})

# Build optimized video with cultural compliance
encoder.build_video(
    "knowledge_base.mp4",
    "knowledge_index.faiss",
    pixelation_strategy="authentic_japanese",  # Ensures cultural accuracy
    fps=30,  # Higher FPS = more chunks per second
    censorship_region_size="standard"  # Optimized for QR code readability
)

Advanced Search & Retrieval

from hentaivid import HentaividRetriever

# Initialize retriever
retriever = HentaividRetriever("knowledge_base.mp4", "knowledge_index.faiss")

# Semantic search with cultural context
results = retriever.search("artificial intelligence", top_k=5)
for chunk, score, frame_timestamp in results:
    print(f"Score: {score:.3f} | Frame: {frame_timestamp} | {chunk[:100]}...")

# Get contextual information
context = retriever.get_context("explain transformers", max_tokens=2000)
print(context)

Interactive Knowledge Interface

from hentaivid import HentaividInteractive

# Launch interactive interface
interactive = HentaividInteractive("knowledge_base.mp4", "knowledge_index.faiss")
interactive.run()  # Opens interface at http://localhost:7860

Testing with CLI

The main script provides comprehensive testing capabilities:

# Process a text file with video
python main.py input_video.mp4 knowledge.txt --output_path enhanced_video.mp4

# Use custom pixelation detection
python main.py input_video.mp4 data.txt --detector advanced --min_region_size 2000

# High-quality output with cultural authenticity
python main.py content.mp4 corpus.txt --quality high --cultural_compliance strict

Complete Example: Research Paper Knowledge Base

# 1. Create a new directory and set up environment
mkdir research-knowledge-base
cd research-knowledge-base
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 2. Install dependencies
pip install -r requirements.txt

# 3. Create research_base.py
cat > research_base.py << 'EOF'
from hentaivid import HentaividEncoder, search_knowledge_base
import os

# Your research papers directory
papers_dir = "papers/"  # Place your PDFs here

# Build video memory with academic optimization
encoder = HentaividEncoder(
    chunk_size=1024,  # Larger chunks for academic content
    overlap=128,
    embedding_model="all-mpnet-base-v2"  # Higher quality embeddings
)

# Process all papers
for paper in os.listdir(papers_dir):
    if paper.endswith('.pdf'):
        encoder.add_pdf(os.path.join(papers_dir, paper))

encoder.build_video(
    "research_memory.mp4", 
    "research_index.faiss",
    pixelation_strategy="high_density",  # More data per frame
    cultural_compliance="standard"
)

# Search your research
search_knowledge_base("research_memory.mp4", "research_index.faiss")
EOF

# 4. Run it
python research_base.py

🛠️ Advanced Configuration

Custom Pixelation Detection

from hentaivid.detector import AdvancedPixelationDetector

# Use deep learning for region detection
detector = AdvancedPixelationDetector(
    model_type="cnn",  # or "traditional", "hybrid"
    confidence_threshold=0.85,
    min_region_area=1000
)

encoder = HentaividEncoder(pixelation_detector=detector)

Video Optimization

# For maximum data density
encoder.build_video(
    "ultra_dense.mp4",
    "index.faiss",
    fps=60,  # More frames per second
    pixelation_density="maximum",  # Pack more QR codes
    video_codec='h265',  # Better compression
    cultural_accuracy="strict"  # Maintains authenticity
)

Distributed Processing

# Process large video collections in parallel
encoder = HentaividEncoder(n_workers=8)
encoder.add_videos_parallel(video_list)

🐛 Troubleshooting

Common Issues

ModuleNotFoundError: No module named 'hentaivid'

# Make sure you're using the right Python
which python  # Should show your virtual environment path
# If not, activate your virtual environment:
source venv/bin/activate  # On Windows: venv\Scripts\activate

ImportError: OpenCV is required for video processing

pip install opencv-python

Pixelation Detection Issues

# For videos with non-standard pixelation
encoder = HentaividEncoder()
encoder.set_detection_params(
    sensitivity="high",
    cultural_variant="modern_japanese",  # or "classic", "international"
    region_validation="strict"
)

Large Video Processing

# For very large video files, use chunked processing
python main.py large_video.mp4 corpus.txt --batch_size 100 --memory_efficient

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

# Run tests
pytest tests/

# Run with coverage
pytest --cov=hentaivid tests/

# Format code
black hentaivid/

🆚 Comparison with Traditional Solutions

📚 Examples

Check out the examples/ directory for:

• Building knowledge bases from academic papers
• Creating culturally-compliant content libraries
• Multi-language support with unicode QR encoding
• Real-time knowledge retrieval systems
• Integration with popular LLMs

Pixelation Detection Pipeline

1. Frame Analysis - Detect rectangular uniformity patterns
2. Cultural Validation - Ensure compliance with Japanese standards
3. Region Optimization - Maximize QR code readability
4. Temporal Consistency - Maintain coherent embedding across frames

QR Code Optimization

• Error Correction: Optimized for video compression artifacts
• Data Density: Variable sizing based on region availability
• Encoding Strategy: UTF-8 with compression for maximum efficiency

Embedding Architecture

Text Corpus → Chunking → Embeddings → FAISS Index
     ↓
Video Frames → Pixelation Detection → QR Generation → Video Output

🆘 Getting Help

• 📖 Documentation - Comprehensive guides and API reference
• 💬 Discussions - Ask questions and share experiences
• 🐛 Issue Tracker - Report bugs and request features
• 🌟 Show & Tell - Share your knowledge bases

🔗 Links

• GitHub Repository - github.com/user/hentaivid
• Documentation - hentaivid.readthedocs.io
• Cultural Guidelines - Japanese Pixelation Standards

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Created with respect for Japanese cultural standards and the open-source community.

Built with ❤️ using:

• sentence-transformers - State-of-the-art embeddings for semantic search
• OpenCV - Computer vision and video processing
• qrcode - QR code generation and optimization
• FAISS - Efficient similarity search and clustering
• pyzbar - QR code detection and decoding

Special thanks to:

• The Japanese Ministry of Cultural Affairs for pixelation standards
• The global computer vision community
• All contributors who help advance culturally-aware technology

Ready to revolutionize your knowledge storage with cultural authenticity? Install Hentaivid and start building! 🚀

About

Revolutionary RAG-compatible video storage format that embeds text chunks into QR codes hidden inside pixelated regions. Culturally compliant, semantically searchable, zero-database architecture.

Topics

python nlp opencv machine-learning ai cultural-compliance video-processing knowledge-base semantic-search faiss rag vector-database llm qr-codes steganography japanese-standards pixelation adult-content

Resources

🌟 Star this repo if Hentaivid helps your projects!

License

MIT license

Hentaivid: Where technology meets culture, and knowledge transcends boundaries. 🎌