multiview-stitcher
is an open-source modular toolbox for distributed and tiled stitching of 2-3D image data in python. It is a collection of algorithms (under development) to register and fuse small and large datasets from multi-positioning and multi-view light sheet microscopy, as well as other modalities such as correlative cryo-EM datasets.
For visualization, the associated napari-stitcher
provides visualization functionality using the Napari viewer, including a standalone widget.
With a focus on interoperability and integration with existing tools and the ecosystem, the package intends to integrate as tightly as possible with the NGFF specification.
It leverages xarray
in combination with spatial-image
and multiscale-spatial-image
for image handling and dask
and dask-image
for chunked and distributed image processing.
Check out the example notebooks.
There's an associated napari plugin: napari-stitcher.
Image data by Arthur Michaut @ Jérôme Gros Lab @ Institut Pasteur.
These code snippets walk you through a small stitching workflow consisting of
- Preparing the input image data and metadata (tile positions, spacing, channels)
- Registering the tiles
- Stitching / fusing the tiles
import numpy as np
from multiview_stitcher import msi_utils
from multiview_stitcher import spatial_image_utils as si_utils
# input data (can be any numpy compatible array: numpy, dask, cupy, etc.)
tile_arrays = [np.random.randint(0, 100, (2, 10, 100, 100)) for _ in range(3)]
# indicate the tile offsets and spacing
tile_translations = [
{"z": 2.5, "y": -10, "x": 30},
{"z": 2.5, "y": 30, "x": 10},
{"z": 2.5, "y": 30, "x": 50},
]
spacing = {"z": 2, "y": 0.5, "x": 0.5}
channels = ["DAPI", "GFP"]
# build input for stitching
msims = []
for tile_array, tile_translation in zip(tile_arrays, tile_translations):
sim = si_utils.get_sim_from_array(
tile_array,
dims=["c", "z", "y", "x"],
scale=spacing,
translation=tile_translation,
transform_key="stage_metadata",
c_coords=channels,
)
msims.append(msi_utils.get_msim_from_sim(sim, scale_factors=[]))
# plot the tile configuration
# from multiview_stitcher import vis_utils
# fig, ax = vis_utils.plot_positions(msims, transform_key='stage_metadata', use_positional_colors=False)
from dask.diagnostics import ProgressBar
from multiview_stitcher import registration
with ProgressBar():
params = registration.register(
msims,
reg_channel="DAPI", # channel to use for registration
transform_key="stage_metadata",
new_transform_key="translation_registered",
)
# plot the tile configuration after registration
# vis_utils.plot_positions(msims, transform_key='translation_registered', use_positional_colors=False)
from multiview_stitcher import fusion
fused_sim = fusion.fuse(
[msi_utils.get_sim_from_msim(msim) for msim in msims],
transform_key="translation_registered",
)
# get fused array as a dask array
fused_sim.data
# get fused array as a numpy array
fused_sim.data.compute()
You can install multiview-stitcher
via pip
from PyPI:
pip install multiview-stitcher
or from the source code in this github repository:
pip install git+https://github.com/multiview-stitcher/multiview-stitcher.git
multiview-stitcher
can run without installation in your browser.
- open JupyterLite in a private browser window
- upload this notebook into the jupyter lab window: notebooks/stitching_in_the_browser.ipynb
- upload files to stitch into a 'data' folder in the jupyter lab window
- follow the notebook
- stitching will run with a single thread
- while the code runs locally, your local file system is not directly accessible from within the browser environment
WARNING: THIS IS WORK IN PROGRESS. multiview-stitcher
is being developed in the open and has not reached a stable release yet. The API is subject to change.
multiview-stitcher
improves and replaces MVRegFUS.
If you encounter any problems, please file an issue along with a detailed description.
Contributions are welcome.
Distributed under the terms of the BSD-3 license, "multiview-stitcher" is free and open source software.