{"id":28951948,"url":"https://github.com/arimanyus/hentaivid","last_synced_at":"2026-04-29T04:38:56.191Z","repository":{"id":297895999,"uuid":"998227179","full_name":"arimanyus/hentaivid","owner":"arimanyus","description":"Culturally-compliant video storage. Embeds searchable text chunks into pixelated media for lightning-fast semantic search. 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Zero-database semantic search with maximum cultural authenticity.**\n\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\n\u003e *\"My boss thinks I'm researching state-of-the-art data retrieval methods. He's not wrong.\"* - Anonymous FAANG Sr Engineer\n\n## 🚀 What is Hentaivid?\n\nHentaivid 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.\n\n### ✨ Key Features\n\n- 🎯 **Zero Database Required** - All data embedded directly in video files\n- 🔍 **Lightning-Fast Semantic Search** - FAISS-powered vector similarity\n- 🎌 **Culturally Authentic** - Respects Japanese pixelation standards\n- 📱 **QR Code Integration** - Industry-standard data encoding\n- 🎬 **Video-Native Storage** - No external dependencies for data retrieval\n- 🔒 **Steganographic Security** - Data hidden in plain sight\n- 📈 **Scalable Architecture** - Handle millions of text chunks\n- 🌐 **Cross-Platform Compatible** - Works on any system with video playback\n\n## 🔬 Technical Architecture\n\n\u003cdetails\u003e\n\u003csummary\u003eView Culturally-Compliant Architecture Diagram\u003c/summary\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/arimanyus/hentaivid/blob/main/hentaivid-assets/architecture.svg\" alt=\"Hentaivid Architecture Diagram\"\u003e\n\u003c/p\u003e\n\u003c/details\u003e\n\n## 📦 Installation\n\n### System Requirements\n\n- Python 3.8+\n- OpenCV 4.0+\n- At least 4GB RAM (8GB+ recommended for large corpora)\n- GPU acceleration recommended for large-scale processing\n\n### Quick Install\n\n```bash\n# Create a new project directory\nmkdir my-hentaivid-project\ncd my-hentaivid-project\n\n# Create virtual environment\npython -m venv venv\n\n# Activate it\n# On macOS/Linux:\nsource venv/bin/activate\n# On Windows:\nvenv\\Scripts\\activate\n\n# Install hentaivid\npip install -r requirements.txt\n```\n\n### For Advanced Pixelation Detection:\n```bash\npip install tensorflow  # For deep learning-based region detection\npip install scikit-image  # For advanced image processing\n```\n\n## 🎯 Quick Start\n\n### Basic Usage\n\n```python\nfrom hentaivid import HentaividEncoder, HentaividChat\n\n# Create video memory from text chunks\nchunks = [\"Machine learning fundamentals\", \"Deep learning architectures\", \"Neural network optimization\"]\nencoder = HentaividEncoder()\nencoder.add_chunks(chunks)\nencoder.build_video(\"knowledge.mp4\", \"knowledge_index.faiss\")\n\n# Query your embedded knowledge\nchat = HentaividChat(\"knowledge.mp4\", \"knowledge_index.faiss\")\nresponse = chat.search(\"What are neural networks?\")\nprint(response)\n```\n\n### Building Knowledge Base from Documents\n\n```python\nfrom hentaivid import HentaividEncoder\nimport os\n\n# Load documents\nencoder = HentaividEncoder(chunk_size=512, overlap=50)\n\n# Add text files\nfor file in os.listdir(\"documents\"):\n    with open(f\"documents/{file}\", \"r\") as f:\n        encoder.add_text(f.read(), metadata={\"source\": file})\n\n# Build optimized video with cultural compliance\nencoder.build_video(\n    \"knowledge_base.mp4\",\n    \"knowledge_index.faiss\",\n    pixelation_strategy=\"authentic_japanese\",  # Ensures cultural accuracy\n    fps=30,  # Higher FPS = more chunks per second\n    censorship_region_size=\"standard\"  # Optimized for QR code readability\n)\n```\n\n### Advanced Search \u0026 Retrieval\n\n```python\nfrom hentaivid import HentaividRetriever\n\n# Initialize retriever\nretriever = HentaividRetriever(\"knowledge_base.mp4\", \"knowledge_index.faiss\")\n\n# Semantic search with cultural context\nresults = retriever.search(\"artificial intelligence\", top_k=5)\nfor chunk, score, frame_timestamp in results:\n    print(f\"Score: {score:.3f} | Frame: {frame_timestamp} | {chunk[:100]}...\")\n\n# Get contextual information\ncontext = retriever.get_context(\"explain transformers\", max_tokens=2000)\nprint(context)\n```\n\n### Interactive Knowledge Interface\n\n```python\nfrom hentaivid import HentaividInteractive\n\n# Launch interactive interface\ninteractive = HentaividInteractive(\"knowledge_base.mp4\", \"knowledge_index.faiss\")\ninteractive.run()  # Opens interface at http://localhost:7860\n```\n\n### Testing with CLI\n\nThe main script provides comprehensive testing capabilities:\n\n```bash\n# Process a text file with video\npython main.py input_video.mp4 knowledge.txt --output_path enhanced_video.mp4\n\n# Use custom pixelation detection\npython main.py input_video.mp4 data.txt --detector advanced --min_region_size 2000\n\n# High-quality output with cultural authenticity\npython main.py content.mp4 corpus.txt --quality high --cultural_compliance strict\n```\n\n### Complete Example: Research Paper Knowledge Base\n\n```bash\n# 1. Create a new directory and set up environment\nmkdir research-knowledge-base\ncd research-knowledge-base\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\n# 2. Install dependencies\npip install -r requirements.txt\n\n# 3. Create research_base.py\ncat \u003e research_base.py \u003c\u003c 'EOF'\nfrom hentaivid import HentaividEncoder, search_knowledge_base\nimport os\n\n# Your research papers directory\npapers_dir = \"papers/\"  # Place your PDFs here\n\n# Build video memory with academic optimization\nencoder = HentaividEncoder(\n    chunk_size=1024,  # Larger chunks for academic content\n    overlap=128,\n    embedding_model=\"all-mpnet-base-v2\"  # Higher quality embeddings\n)\n\n# Process all papers\nfor paper in os.listdir(papers_dir):\n    if paper.endswith('.pdf'):\n        encoder.add_pdf(os.path.join(papers_dir, paper))\n\nencoder.build_video(\n    \"research_memory.mp4\", \n    \"research_index.faiss\",\n    pixelation_strategy=\"high_density\",  # More data per frame\n    cultural_compliance=\"standard\"\n)\n\n# Search your research\nsearch_knowledge_base(\"research_memory.mp4\", \"research_index.faiss\")\nEOF\n\n# 4. Run it\npython research_base.py\n```\n\n## 🛠️ Advanced Configuration\n\n### Custom Pixelation Detection\n\n```python\nfrom hentaivid.detector import AdvancedPixelationDetector\n\n# Use deep learning for region detection\ndetector = AdvancedPixelationDetector(\n    model_type=\"cnn\",  # or \"traditional\", \"hybrid\"\n    confidence_threshold=0.85,\n    min_region_area=1000\n)\n\nencoder = HentaividEncoder(pixelation_detector=detector)\n```\n\n### Video Optimization\n\n```python\n# For maximum data density\nencoder.build_video(\n    \"ultra_dense.mp4\",\n    \"index.faiss\",\n    fps=60,  # More frames per second\n    pixelation_density=\"maximum\",  # Pack more QR codes\n    video_codec='h265',  # Better compression\n    cultural_accuracy=\"strict\"  # Maintains authenticity\n)\n```\n\n### Distributed Processing\n\n```python\n# Process large video collections in parallel\nencoder = HentaividEncoder(n_workers=8)\nencoder.add_videos_parallel(video_list)\n```\n\n## 🐛 Troubleshooting\n\n### Common Issues\n\n**ModuleNotFoundError: No module named 'hentaivid'**\n\n```bash\n# Make sure you're using the right Python\nwhich python  # Should show your virtual environment path\n# If not, activate your virtual environment:\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n```\n\n**ImportError: OpenCV is required for video processing**\n\n```bash\npip install opencv-python\n```\n\n**Pixelation Detection Issues**\n\n```python\n# For videos with non-standard pixelation\nencoder = HentaividEncoder()\nencoder.set_detection_params(\n    sensitivity=\"high\",\n    cultural_variant=\"modern_japanese\",  # or \"classic\", \"international\"\n    region_validation=\"strict\"\n)\n```\n\n**Large Video Processing**\n\n```bash\n# For very large video files, use chunked processing\npython main.py large_video.mp4 corpus.txt --batch_size 100 --memory_efficient\n```\n\n## 🤝 Contributing\n\nWe welcome contributions! Please see our Contributing Guide for details.\n\n```bash\n# Run tests\npytest tests/\n\n# Run with coverage\npytest --cov=hentaivid tests/\n\n# Format code\nblack hentaivid/\n```\n\n## 🆚 Comparison with Traditional Solutions\n\n| Feature | Hentaivid | Vector DBs | Traditional DBs |\n|---------|-----------|------------|----------------|\n| Storage Efficiency | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ |\n| Cultural Compliance | ⭐⭐⭐⭐⭐ | ❌ | ❌ |\n| Setup Complexity | Simple | Complex | Complex |\n| Semantic Search | ✅ | ✅ | ❌ |\n| Offline Usage | ✅ | ❌ | ✅ |\n| Steganographic Security | ✅ | ❌ | ❌ |\n| Video Integration | Native | ❌ | ❌ |\n| Scalability | Millions | Millions | Billions |\n| Cost | Free | $$$$ | $$$ |\n\n## 📚 Examples\n\nCheck out the examples/ directory for:\n\n* Building knowledge bases from academic papers\n* Creating culturally-compliant content libraries\n* Multi-language support with unicode QR encoding\n* Real-time knowledge retrieval systems\n* Integration with popular LLMs\n\n### Pixelation Detection Pipeline\n\n1. **Frame Analysis** - Detect rectangular uniformity patterns\n2. **Cultural Validation** - Ensure compliance with Japanese standards\n3. **Region Optimization** - Maximize QR code readability\n4. **Temporal Consistency** - Maintain coherent embedding across frames\n\n### QR Code Optimization\n\n- **Error Correction**: Optimized for video compression artifacts\n- **Data Density**: Variable sizing based on region availability\n- **Encoding Strategy**: UTF-8 with compression for maximum efficiency\n\n### Embedding Architecture\n\n```\nText Corpus → Chunking → Embeddings → FAISS Index\n     ↓\nVideo Frames → Pixelation Detection → QR Generation → Video Output\n```\n\n## 🆘 Getting Help\n\n* 📖 **Documentation** - Comprehensive guides and API reference\n* 💬 **Discussions** - Ask questions and share experiences\n* 🐛 **Issue Tracker** - Report bugs and request features\n* 🌟 **Show \u0026 Tell** - Share your knowledge bases\n\n## 🔗 Links\n\n* **GitHub Repository** - [github.com/user/hentaivid](https://github.com/user/hentaivid)\n* **Documentation** - [hentaivid.readthedocs.io](https://hentaivid.readthedocs.io)\n* **Cultural Guidelines** - [Japanese Pixelation Standards](https://example.com/guidelines)\n\n## 📄 License\n\nMIT License - see LICENSE file for details.\n\n## 🙏 Acknowledgments\n\nCreated with respect for Japanese cultural standards and the open-source community.\n\nBuilt with ❤️ using:\n\n* **sentence-transformers** - State-of-the-art embeddings for semantic search\n* **OpenCV** - Computer vision and video processing\n* **qrcode** - QR code generation and optimization\n* **FAISS** - Efficient similarity search and clustering\n* **pyzbar** - QR code detection and decoding\n\nSpecial thanks to:\n- The Japanese Ministry of Cultural Affairs for pixelation standards\n- The global computer vision community\n- All contributors who help advance culturally-aware technology\n\n---\n\n**Ready to revolutionize your knowledge storage with cultural authenticity? Install Hentaivid and start building!** 🚀\n\n## About\n\nRevolutionary RAG-compatible video storage format that embeds text chunks into QR codes hidden inside pixelated regions. Culturally compliant, semantically searchable, zero-database architecture.\n\n### Topics\n\n`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`\n\n### Resources\n\n🌟 **Star this repo** if Hentaivid helps your projects!\n\n### License\n\nMIT license\n\n---\n\n*Hentaivid: Where technology meets culture, and knowledge transcends boundaries.* 🎌\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farimanyus%2Fhentaivid","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farimanyus%2Fhentaivid","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farimanyus%2Fhentaivid/lists"}