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 3.13+. Tested on Ubuntu & Windows.

DeepZoom server

To start use:

pip install bipl[deepzoom]
export SLIDES=~/slides
uvicorn bipl.dzi:app --workers 4 --host 0.0.0.0 --port 8000

Or you can just use a router from there:

from bipl.dzi import router

your_fastapi_app.include_router(router)

Project links

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Credits

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Author/Maintainer: Paul Maevskikh

GitHub Statistics

Data verified by PyPI on Jul 29, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT License)

Requires

Python <3.15, >=3.13

Provides Extra

cov deepzoom dev dev-core dev-wemake gdal

Tags

aperio batch bigtiff gdal geotiff hamamatsu images lazy libtiff medical mrxs ndpi openslide osgeo parallel pathology processing pyramid slide svs tiff tile whole-slide wsi

Classifiers

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