Fast, memory-efficient image tiling and reconstruction for deep learning and scientific computing
Project description
TileFlow
High-performance tile-based image processing for scientific computing
Process gigapixel images with minimal memory footprint through intelligent tiling and reconstruction. Designed for microscopy, whole-slide imaging, and large-scale computer vision workflows.
🚀 Key Features
- 🧠 Memory Efficient: Process images larger than RAM using intelligent tiling
- 🔬 Multi-Channel Ready: Native CHW format support for microscopy workflows
- ⚡ Zero-Copy Views: Leverages numpy slicing for maximum performance
- 🔧 Seamless Reconstruction: Intelligent overlap handling eliminates artifacts
- ☁️ Cloud-Scale: Built-in zarr integration for massive datasets
- 🎯 Pluggable Pipeline: Custom processing functions integrate seamlessly
📦 Installation
pip install tileflow
📄 License
MIT License - see LICENSE file for details.
🙏 Acknowledgments
Valentin Poque - Core development and architecture (July-September 2025)
Process any image, any size, any channel count.
TileFlow scales with your data.
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