Generate human image data for vision model training with comprehensive data loading capabilities
This project aims to develop a comprehensive pipeline for generating high-quality human image datasets specifically designed for training vision models. The system includes both data generation and efficient data loading capabilities, focusing on creating diverse, realistic human imagery data that can be used for various computer vision tasks including person detection, pose estimation, action recognition, and human behavior analysis.
Key Components:
- Data Generation Pipeline: Create photorealistic synthetic human datasets
- SyntheticDataLoader: Efficient PostgreSQL + MinIO data loading toolkit
- AIGC GUI: AI-powered image generation interface
- Synthetic Human Data Generation: Create photorealistic human images with diverse demographics, poses, clothing, and environments
- Crowd Scene Simulation: Generate complex multi-person scenes with realistic interactions and spatial relationships
- Annotation Pipeline: Provide rich annotations including bounding boxes, keypoints, segmentation masks, and behavioral labels
- Quality Assurance: Ensure generated data meets the quality standards required for effective model training
- Person detection and tracking systems
- Pose estimation and human action recognition
- Crowd analysis and behavior understanding
- Surveillance and security applications
- Sports analytics and movement analysis
- Retail and customer behavior analysis
- Machine Learning Training: Efficient data loading for PyTorch/TensorFlow workflows
- Large-scale Dataset Management: Multi-database synthetic data organization
- 3D Human Models: Utilize advanced 3D human modeling techniques
- Procedural Generation: Implement algorithms for diverse scene creation
- Style Transfer: Apply various artistic and photographic styles
- Augmentation Techniques: Enhance dataset diversity through intelligent augmentation
- Demographic Diversity: Age, gender, ethnicity, body types
- Pose Variability: Walking, running, sitting, standing, gesturing
- Environmental Contexts: Indoor, outdoor, various lighting conditions
- Clothing Variations: Seasonal, cultural, professional, casual attire
- Crowd Dynamics: Group interactions, crowd density variations
- Realistic Physics: Natural human movement and physics simulation
- Resolution: High-resolution images (minimum 1080p, up to 4K)
- Format: PNG/JPG with optional depth maps
- Color Space: RGB with optional multi-spectral channels
- Frame Rate: Support for video sequences (30fps/60fps)
- Bounding Boxes: Person-level detection boxes
- Keypoints: 2D/3D skeletal pose annotations
- Segmentation: Instance and semantic segmentation masks
- Attributes: Age group, gender, clothing, activity labels
- Metadata: Camera parameters, lighting conditions, scene context
- 3D Engines: Blender, Unreal Engine, or Unity for scene rendering
- AI/ML Frameworks: PyTorch/TensorFlow for procedural generation
- Computer Graphics: OpenGL/DirectX for real-time rendering
- Data Processing: Python ecosystem (NumPy, OpenCV, PIL)
- Database Systems: PostgreSQL with pgvector extension for vector operations
- Object Storage: MinIO for scalable binary file storage
- Data Loading: SQLAlchemy ORM with optimized query patterns
- ML Integration: Native PyTorch Dataset/DataLoader support
- Cloud Computing: Scalable rendering pipeline
- Storage: Efficient dataset storage and distribution
- Version Control: Dataset versioning and management
- Quality Control: Automated validation and human review
crowd-human-data-gen/ ├── src/ │ ├── generators/ # Core generation algorithms │ ├── models/ # 3D human models and assets │ ├── scenes/ # Scene composition logic │ ├── annotations/ # Annotation generation │ └── utils/ # Utility functions ├── datasets/ │ ├── samples/ # Sample generated data │ ├── benchmarks/ # Evaluation datasets │ └── exports/ # Final dataset releases ├── configs/ │ ├── generation/ # Generation parameters │ ├── environments/ # Scene configurations │ └── models/ # Model configurations ├── tools/ │ ├── visualization/ # Data visualization tools │ ├── validation/ # Quality assurance tools │ └── conversion/ # Format conversion utilities ├── docs/ │ ├── api/ # API documentation │ ├── tutorials/ # Usage tutorials │ └── research/ # Research and methodology └── tests/ ├── unit/ # Unit tests ├── integration/ # Integration tests └── performance/ # Performance benchmarks
This project is currently in the planning and design phase. Implementation details and setup instructions will be provided as development progresses.
- Python 3.8+
- GPU with CUDA support (recommended)
- Sufficient storage space for dataset generation
- 3D rendering software (Blender/Unity/Unreal)
- PostgreSQL with pgvector extension
- MinIO object storage server
`�ash
git clone https://github.com/CodeGandee/crowd-human-data-gen.git cd crowd-human-data-gen
pip install -r requirements.txt
python setup.py configure `
- Requirements analysis and specification
- Technology stack selection
- Architecture design
- Prototype development
- Basic human model integration
- Scene generation pipeline
- Annotation system development
- Quality assurance framework
- Advanced pose generation
- Crowd simulation algorithms
- Performance optimization
- Comprehensive testing
- Large-scale dataset generation
- Validation and benchmarking
- Documentation and tutorials
- Community release
We welcome contributions from the community! This project is in early development, and we're looking for:
- Computer vision researchers and practitioners
- 3D graphics and animation experts
- Machine learning engineers
- Data scientists and analysts
Please see our contributing guidelines (coming soon) for more information.
This project will be released under an open-source license (to be determined).
For questions, suggestions, or collaboration opportunities, please reach out through:
- GitHub Issues for technical discussions
- Project discussions for general inquiries
Note: This project is currently in the conceptual and planning phase. Implementation details, timelines, and specifications are subject to change as development progresses.