Skip to main content

Openslide/libtiff/GDAL ndarray-like interface and lazy parallel tile-based processing

Reason this release was yanked:

invalid caching

Project description

BIPL is a Big Image Python Library

Library to read big pyramidal images like in formats like BigTiff, Aperio SVS, Leica MRXS.

bipl.Slide - ndarray-like reader for multiscale images (svs, tiff, etc...)

import numpy as np
from bipl import Slide

slide = Slide.open('test.svs')
shape: tuple[int, ...] = slide.shape  # Native shape
downsamples: tuple[int, ...] = slide.downsamples  # List of pre-existing sub-resolution levels

# Get native miniature
tmb: np.ndarray = slide.thumbnail()

mpp: float = slide.mpp  # X um per pixel, native resolution
image: np.ndarray = slide[:2048, :2048]  # Get numpy.ndarray of 2048x2048 from full resolution

MPP = 16.  # Let's say we want slide at 16 um/px resolution
downsample = MPP / slide.mpp
mini = slide.pool(downsample)  # Gives `downsample`-times smaller image
mini = slide.resample(MPP)  # Gives the same result

# Those ones trigger ndarray conversion
image: np.ndarray
image = mini[:512, :512]  # Take a part of
image = mini.numpy()  # Take a whole resolution level
image = np.array(mini, copy=False)  # Use __array__ API

bipl.Mosaic - apply function for each tile of big image on desired scale.

import numpy as np
from bipl import Mosaic, Slide

m = Mosaic(step=512, overlap=0)  # Read at [0:512], [512:1024], ...

# Open slide at 1:1 scale
s = Slide.open('test.svs')

# Target at 4 um/px resolution
# If `test.svs` has some pyramid in it (i.e. 1:1, 1:4, 1:16), it will be used to speed up reads.
s4 = s.resample(mpp=4.0)

# Get iterator over tiles.
# Reads will be at [0:512], [512:1024] ... @ MPP
tiles = m.iterate(s4)

# Read only subset of tiles according to binary mask (1s are read, 0s are not).
# `s4.shape * scale = mask.shape`, `scale <= 1`
tiles = tiles.select(mask, scale)

# Read all data, trigger I/O. All the previous calls do not trigger any disk reads beyond metadata.
images: list[np.ndarray] = [*tiles]

Installation

pip install bipl

bipl is compatible with: Python 3.10+. Tested on ArchLinux, Ubuntu 20.04/22.04, Windows 10/11.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bipl-0.6.0.tar.gz (37.9 kB view hashes)

Uploaded Source

Built Distribution

bipl-0.6.0-py3-none-win_amd64.whl (4.6 MB view hashes)

Uploaded Python 3 Windows x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page