vascx-simplify 0.1.9

A PyTorch library for vessel and fundus image analysis (simplified rewrite)

vascx_simplify

A PyTorch library for vessel and fundus image analysis, providing GPU-accelerated preprocessing and inference utilities for medical imaging tasks.

Note: This is a simplified rewrite of rtnls_vascx_models by Eyened.

AI Usage Disclaimer

This project was developed with significant assistance from AI tools (GitHub Copilot, ChatGPT, Claude) for code organization, refactoring, documentation, and packaging.

Features

• GPU-Accelerated Preprocessing: Fast fundus image contrast enhancement with mixed precision support
• Sliding Window Inference: Efficient inference for large images
• Ensemble Models: Segmentation, classification, regression, and heatmap-based models
• HuggingFace Integration: Easy model loading from HuggingFace Hub
• Minimal Dependencies: Uses fewer dependency libraries for easier installation and maintenance

Installation

pip install vascx_simplify

From source:

git clone https://github.com/kapong/vascx_simplify.git
cd vascx_simplify
pip install -e .

Requirements

• Python >= 3.12
• PyTorch >= 1.10.0
• kornia >= 0.6.0
• scikit-learn >= 1.0.0
• scipy >= 1.7.0
• numpy >= 1.21.0
• huggingface-hub >= 0.10.0

Usage Examples

Artery/Vein Segmentation

Segment arteries (red), veins (blue), and crossings (green) from fundus images:

from vascx_simplify import EnsembleSegmentation, VASCXTransform, from_huggingface
from PIL import Image
import torch

# Load model
model_path = from_huggingface('Eyened/vascx:artery_vein/av_july24.pt')
model = EnsembleSegmentation(model_path, VASCXTransform())

# Predict
rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, H, W] with class values

Full example: See examples/01_artery_vein.py

Optic Disc Segmentation

Detect and segment the optic disc:

from vascx_simplify import EnsembleSegmentation, VASCXTransform, from_huggingface
from PIL import Image

model_path = from_huggingface('Eyened/vascx:disc/disc_july24.pt')
model = EnsembleSegmentation(model_path, VASCXTransform(512))

rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, H, W] with class values

Full example: See examples/02_disc_segment.py

Fovea Detection

Locate the fovea center using heatmap regression:

from vascx_simplify import HeatmapRegressionEnsemble, VASCXTransform, from_huggingface
from PIL import Image

model_path = from_huggingface('Eyened/vascx:fovea/fovea_july24.pt')
model = HeatmapRegressionEnsemble(model_path, VASCXTransform())

rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, M, 2] with (x, y) coordinates

fovea_x = prediction[0, 0, 0].item()
fovea_y = prediction[0, 0, 1].item()

Full example: See examples/03_fovea_regression.py

Image Quality Assessment

Classify fundus image quality (Reject/Usable/Good):

from vascx_simplify import ClassificationEnsemble, VASCXTransform, from_huggingface
from PIL import Image

model_path = from_huggingface('Eyened/vascx:quality/quality.pt')
model = ClassificationEnsemble(model_path, VASCXTransform(use_ce=False))

rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, 3] with quality scores (already softmaxed)

# Get probabilities (already normalized)
q1_reject, q2_usable, q3_good = prediction[0].tolist()

Full example: See examples/04_quality_classify.py

Contrast Enhancement

Enhance fundus image contrast with GPU-accelerated preprocessing:

from vascx_simplify.preprocess import FundusContrastEnhance
from PIL import Image
import torch
import numpy as np

# Initialize enhancer
enhancer = FundusContrastEnhance(
    use_fp16=True,      # Use mixed precision for faster processing
    square_size=512,    # Optional: crop to square size
)

# Load and convert image to tensor
rgb_image = Image.open('fundus.jpg')
img_tensor = torch.from_numpy(np.array(rgb_image)).permute(2, 0, 1).cuda()  # you can use CPU/GPU to process

# Apply contrast enhancement
original, enhanced, bounds = enhancer(img_tensor)

# Convert back to numpy for saving/visualization
enhanced_np = enhanced.cpu().permute(1, 2, 0).numpy()

Full example: See examples/07_contrast_enhancement.py for detailed visualization with before/after comparison, zoomed regions, and statistics.

Batch Processing

Process multiple images with automatic batch splitting to prevent out-of-memory errors:

⚠️ Important: Batch processing speed benefit depends on model type:

• Segmentation/Classification/Regression: 2-3x faster than sequential
• Heatmap Regression (fovea): NO speed improvement (due to sliding window complexity)

from vascx_simplify import EnsembleSegmentation, VASCXTransform, from_huggingface
from PIL import Image

# Load model once
model_path = from_huggingface('Eyened/vascx:artery_vein/av_july24.pt')
model = EnsembleSegmentation(model_path, VASCXTransform())

# Load multiple images
images = [Image.open(f'fundus_{i}.jpg') for i in range(10)]

# Batch prediction (much faster for segmentation/classification)
predictions = model.predict(images)  # Returns [10, H, W]
# Automatically splits into chunks of 4 (default for segmentation)

# Process each result
for i, pred in enumerate(predictions):
    # pred is [H, W] - no need for [0] indexing
    save_prediction(pred, f'result_{i}.png')

# For large datasets, automatic splitting prevents OOM
large_images = [Image.open(f'img_{i}.jpg') for i in range(100)]
predictions = model.predict(large_images)  # Auto-splits into chunks
# Returns [100, H, W] - seamlessly handles large batches

# Override batch size for your GPU memory
predictions = model.predict(images, batch_size=8)  # Process 8 at once

Key Features:

• ️ Automatic OOM prevention - large batches split automatically
• 🔄 100% backward compatible - existing single-image code works unchanged
• ⚙️ Configurable - override batch size per call or use smart defaults
• 📊 Flexible inputs - accepts PIL.Image, torch.Tensor, or lists of either

Default Batch Sizes by Model Type:

• All Models: batch_size=1 (no speed benefit from batching observed in testing)

Usage Patterns:

# Pattern 1: Batch processing a directory
import glob
from pathlib import Path

image_paths = glob.glob('fundus_images/*.jpg')
images = [Image.open(p) for p in image_paths]
predictions = model.predict(images)  # Efficient batch processing

# Pattern 2: Process with torch tensors
tensors = [torch.randn(3, 512, 512) for _ in range(20)]
predictions = model.predict(tensors)  # Works with tensors too

# Pattern 3: Memory-constrained environment
predictions = model.predict(images, batch_size=2)  # Smaller batches

# Pattern 4: High-memory GPU
predictions = model.predict(images, batch_size=16)  # Larger batches

Backward Compatibility: All existing single-image code works without modification:

# This still works exactly as before
image = Image.open('fundus.jpg')
pred = model.predict(image)  # Returns [1, H, W]
result = pred[0]  # Access with [0] as before

Performance Tips:

• Default batch size is set to 1 for all models (no speed benefit observed from batching)
• You can override batch_size parameter for memory management if processing large numbers of images
• Increase batch_size for high-memory GPUs (24GB+) to process more images simultaneously
• Decrease batch_size if you encounter OOM errors
• List of tensors is slightly faster than list of PIL.Images

Full examples:

• examples/05_batch_fovea.py - Batch processing for heatmap regression
• examples/06_utils_demo.py - Utility functions demonstration

License

MIT License - see LICENSE file for details.

Author

Phongphan Phienphanich [e-mail]

Acknowledgments

This is a simplified rewrite of rtnls_vascx_models by Eyened.