patchly 0.0.17

A grid sampler for N-dimensional images

Patchly

Patchly is a grid sampler for N-dimensional images enabling inference and other processing steps on extremely large images. Especially for 3D images, it has been proven successfully to inference large images patch-wise in a sliding-window approach. Patchly does just that with a very simple interface to sample and aggregate images.

The main functionalities of Patchly consist of a Sampler, which samples patches from an image based on a grid, and an Aggregator, which assembles the patches back into the shape of the original image. There is a multitude of libraries providing similar functionality already. However, they tend to work only for a limited number of usage scenarios before becoming unusable.

Patchly is the first library providing an advanced set of features for users working with sophisticated image processing pipelines requiring patch-based processing.

A complete overview of how the Sampler and Aggregator work and an in-depth explanation of the features can be found here.

Feature Summary

Patchly provides the following advanced features:

• N-dimensional image handling (1D, 2D, 3D, ...)
• Multiple border-handling strategies
• Support for any array-like images (Numpy, Tensor, Zarr, Dask, ...)
• Memory-mapped image support
• Patch overlap
• Gaussian patch averaging
• Support for images with non-spatial dimensions (channel dimension, batch dimension, ...)
• Chunk aggregation to minimize memory consumption
• Numpy padding techniques

Installation

You can install patchly via pip:

pip install patchly

Usage

Demonstration on how to use Patchly for sliding-window patchification and subsequent aggregation:

sampler = GridSampler(image, spatial_size, patch_size, step_size)
aggregator = Aggregator(sampler, output_size)

for patch, patch_bbox in sampler:
    aggregator.append(patch, patch_bbox)

prediction = aggregator.get_output()

Example

Working example for inference of a 2D RGB image with Patchly in PyTorch:

import numpy as np
from patchly.sampler import GridSampler
from patchly.aggregator import Aggregator
from torch.utils.data import DataLoader, Dataset
import torch

class ExampleDataset(Dataset):
    def __init__(self, sampler):
        self.sampler = sampler

    def __getitem__(self, idx):
        # Get patch
        patch, patch_bbox = self.sampler.__getitem__(idx)
        # Preprocess patch
        patch = patch.transpose(2, 0, 1)
        return patch, patch_bbox

    def __len__(self):
        return len(self.sampler)

def model(x):
    y = torch.rand((x.shape[0], 8, x.shape[2], x.shape[3]))  # Batch, Class, Width, Height
    return y

# Init GridSampler
sampler = GridSampler(image=np.random.random((1000, 1000, 3)), spatial_size=(1000, 1000), patch_size=(100, 100), step_size=(50, 50))
# Init dataloader
loader = DataLoader(ExampleDataset(sampler), batch_size=4, num_workers=0, shuffle=False)
# Init aggregator
aggregator = Aggregator(sampler=sampler, output_size=(8, 1000, 1000), spatial_first=False, has_batch_dim=True)

# Run inference
with torch.no_grad():
    for patch, patch_bbox in loader:
        patch_prediction = model(patch)
        aggregator.append(patch_prediction, patch_bbox)

# Finalize aggregation
prediction = aggregator.get_output()
print("Inference completed!")
print("Prediction shape: ", prediction.shape)

Further examples can be found in examples.

License

Distributed under the terms of the Apache Software License 2.0 license, "Patchly" is free and open source software

Acknowledgements

Patchly is developed and maintained by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ).