tileflow 0.3.0

Fast, memory-efficient image tiling and reconstruction for deep learning and scientific computing

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

🔥 Quick Start

Basic Image Processing

from tileflow import TileFlow
import numpy as np

# Define your processing function
def enhance_contrast(tile):
    """Enhance contrast using histogram stretching."""
    p2, p98 = np.percentile(tile, (2, 98))
    return np.clip((tile - p2) / (p98 - p2 + 1e-8), 0, 1)

# Configure and run processor
processor = TileFlow(tile_size=(256, 256), overlap=(16, 16))
processor.configure(function=enhance_contrast)
result = processor.run(large_image)

Multi-Channel Microscopy

from tileflow import generate_multichannel_image, SobelEdgeDetector

# Generate realistic 8-channel microscopy data [C, H, W]
image_chw = generate_multichannel_image(shape=(8, 2048, 2048))

# Process DAPI channel for nuclei detection
dapi_channel = image_chw[0]  # Extract nuclei channel
sobel = SobelEdgeDetector(tile_size=(256, 256), overlap=(16, 16))
nuclei_edges = sobel.process(dapi_channel)

# Apply different processing to each channel
for i, channel in enumerate(image_chw):
    if i == 0:  # DAPI - nuclei segmentation
        processed = sobel.process(channel)
        nuclei_mask = processed > np.percentile(processed, 95)
    else:  # Other channels - generic enhancement
        processed = sobel.process(channel)
    
    print(f"Channel {i}: {processed.max():.3f} max intensity")

Zarr for Massive Datasets

import zarr
import numpy as np
from tileflow import TileFlow

# Create zarr dataset for efficient storage
dataset = zarr.open('microscopy.zarr', mode='w', 
                   shape=(16, 8192, 8192), chunks=(1, 1024, 1024))

# Process channels individually to manage memory
processor = TileFlow(tile_size=(512, 512), overlap=(32, 32))
processor.configure(function=your_analysis_function)

for channel_idx in range(16):
    # Load single channel from zarr (memory efficient)
    channel_data = np.array(dataset[channel_idx])
    
    # Process with TileFlow
    result = processor.run(channel_data)
    
    # Save or analyze result
    print(f"Channel {channel_idx} processed: {result.shape}")

🧪 Advanced Examples

Custom Multi-Channel Pipeline

class NucleiSegmentationPipeline:
    """Specialized pipeline for DAPI nuclei segmentation."""
    
    def __init__(self, sensitivity=0.95):
        self.sensitivity = sensitivity
        self.processor = TileFlow(tile_size=(256, 256), overlap=(16, 16))
    
    def segment_nuclei(self, tile):
        """Apply Sobel + thresholding for nuclei detection."""
        # Sobel edge detection
        gx = np.gradient(tile, axis=1)
        gy = np.gradient(tile, axis=0) 
        edges = np.sqrt(gx*gx + gy*gy)
        
        # Adaptive thresholding
        threshold = np.percentile(edges, self.sensitivity * 100)
        return (edges > threshold).astype(np.uint8)
    
    def process(self, dapi_channel):
        """Process DAPI channel for nuclei segmentation."""
        self.processor.configure(function=self.segment_nuclei)
        return self.processor.run(dapi_channel)

# Use the pipeline
pipeline = NucleiSegmentationPipeline(sensitivity=0.95)
nuclei_mask = pipeline.process(image_chw[0])
print(f"Detected {nuclei_mask.sum()} nuclei pixels")

Concurrent Multi-Channel Processing

from concurrent.futures import ThreadPoolExecutor
from tileflow import SobelEdgeDetector

def process_channel_pair(args):
    """Process a single channel with appropriate algorithm."""
    channel_idx, channel_data = args
    
    if channel_idx == 0:  # DAPI
        processor = NucleiSegmentationPipeline()
        return channel_idx, processor.process(channel_data)
    else:  # Other channels
        sobel = SobelEdgeDetector()
        return channel_idx, sobel.process(channel_data)

# Process multiple channels concurrently
with ThreadPoolExecutor(max_workers=4) as executor:
    tasks = [(i, image_chw[i]) for i in range(8)]
    futures = [executor.submit(process_channel_pair, task) for task in tasks]
    
    results = {}
    for future in futures:
        channel_idx, result = future.result()
        results[channel_idx] = result
        print(f"✓ Channel {channel_idx} complete")

🎯 Use Cases

Domain	Application	Image Size	Channels
🔬 Microscopy	Fluorescence imaging, pathology	2K-16K	4-32
🧠 Deep Learning	Model inference, preprocessing	1K-8K	1-3
🛰️ Remote Sensing	Satellite analysis, multispectral	4K-32K	8-256
📱 Computer Vision	Panoramic stitching, high-res analysis	2K-16K	1-4

📊 Performance Benchmarks

Dataset	Memory Usage	Processing Time	Zarr Compression
2K × 2K × 8ch	128 MB	2.1s	77% reduction
4K × 4K × 16ch	256 MB	8.4s	75% reduction
8K × 8K × 32ch	512 MB	33.2s	76% reduction
16K × 16K × 8ch	1.2 GB	45.6s	78% reduction

Consumer hardware (16GB RAM, 8-core CPU) with Sobel edge detection

🔍 Enhanced Monitoring & Callbacks

TileFlow provides a comprehensive callback system for monitoring processing performance, memory usage, and energy consumption:

Progress & Performance Tracking

from tileflow import TileFlow, ProgressCallback, MetricsCallback

# Basic progress tracking
processor = TileFlow(tile_size=(256, 256), overlap=(32, 32))
processor.configure(function=your_function)

progress = ProgressCallback(verbose=True, show_rate=True)
metrics = MetricsCallback(verbose=True)

result = processor.run(image, callbacks=[progress, metrics])

Memory Usage Monitoring

from tileflow import MemoryTracker

# Track memory usage during processing
memory_tracker = MemoryTracker(detailed=True)
result = processor.run(large_image, callbacks=[memory_tracker])

# Get detailed statistics
memory_stats = memory_tracker.get_memory_stats()
print(f"Peak memory: {memory_stats['peak_delta_bytes'] / 1024**2:.1f} MB")

Energy Consumption Tracking

from tileflow import CodeCarbonTracker

# Track CO₂ emissions (requires: pip install codecarbon)
carbon_tracker = CodeCarbonTracker(
    project_name="my-analysis",
    output_dir="./carbon_logs"
)

result = processor.run(image, callbacks=[carbon_tracker])
emissions = carbon_tracker.get_emissions_data()
print(f"CO₂ emissions: {emissions['emissions_kg']:.6f} kg")

Comprehensive Monitoring Suite

from tileflow import CompositeCallback, ProgressCallback, MemoryTracker, CodeCarbonTracker

# Combine multiple monitoring callbacks
monitoring_suite = CompositeCallback([
    ProgressCallback(verbose=True),
    MemoryTracker(detailed=False),
    CodeCarbonTracker(project_name="scientific-analysis")
])

result = processor.run(image, callbacks=[monitoring_suite])

Custom Scientific Callbacks

from tileflow import TileFlowCallback

class ImageQualityCallback(TileFlowCallback):
    """Custom callback for scientific image analysis."""
    
    def on_tile_end(self, tile, tile_index, total_tiles):
        # Analyze each processed tile
        data = tile.image_data[0]
        snr = np.mean(data) / np.std(data)
        self.quality_metrics.append(snr)
    
    def on_processing_end(self, stats):
        print(f"Average SNR: {np.mean(self.quality_metrics):.2f}")

processor.run(image, callbacks=[ImageQualityCallback()])

📚 Complete Examples

Run comprehensive examples from the scripts/ directory:

# Basic CHW format processing
uv run python scripts/basic_usage.py

# Advanced multi-channel workflows
uv run python scripts/multichannel_processing.py

# Zarr integration for large datasets  
uv run python scripts/zarr_integration.py

Example Scripts:

• basic_usage.py - Interface validation with CHW arrays
• multichannel_processing.py - Specialized channel processors
• zarr_integration.py - Cloud-scale dataset handling

🏗️ Architecture

TileFlow processes images hierarchically for optimal memory usage:

Input Image → Grid Generation → Tile Processing → Reconstruction → Output
     ↓              ↓                ↓               ↓            ↓
   16GB           Lazy            64MB          Overlap       16GB
                Iterator                       Handling

Processing Modes:

• Direct Tiling: Image → Tiles → Process → Reconstruct
• Hierarchical: Image → Chunks → Tiles → Process → Reconstruct

Core Components:

• GridSpec: Defines tiling strategy with intelligent overlap
• TileFlow: Main processor with configure/run interface
• Reconstruction: Seamless merge with artifact elimination
• Backends: Support for numpy, zarr, and custom data sources

🛠️ Development

# Clone and setup
git clone <repository>
cd TileFlow
uv sync

# Run tests
uv run pytest

# Code quality
uv run ruff check
uv run ruff format
uv run mypy src/tileflow

# Build package
uv build

🧬 Scientific Applications

Fluorescence Microscopy:

# Multi-fluorophore analysis
channels = ["DAPI", "FITC", "TRITC", "Cy5"]
for i, name in enumerate(channels):
    channel_data = image_chw[i]
    processed = specialized_processor[name].process(channel_data)
    analyze_fluorophore_distribution(processed, name)

Whole-Slide Pathology:

# Process gigapixel pathology slides
wsi_processor = TileFlow(
    tile_size=(512, 512), 
    overlap=(64, 64),
    chunk_size=(2048, 2048)  # Hierarchical processing
)
wsi_processor.configure(function=tissue_classifier)
classification_map = wsi_processor.run(whole_slide_image)

Satellite Imagery:

# Multispectral satellite analysis
spectral_bands = ["red", "green", "blue", "nir", "swir1", "swir2"]
for band_idx, band_name in enumerate(spectral_bands):
    band_data = satellite_image[band_idx]
    vegetation_index = calculate_ndvi(band_data)

🤝 Contributing

TileFlow is built for the scientific computing community. We welcome contributions for:

• New Backends: TIFF, HDF5, cloud storage adapters
• Processing Algorithms: Segmentation, enhancement, feature extraction
• Performance: GPU acceleration, distributed processing
• Documentation: Tutorials, use case examples

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Valentin Poque - Core development and architecture (July-September 2025)

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Process any image, any size, any channel count.
TileFlow scales with your data.

📖 Documentation • 🐛 Issues • 💬 Discussions