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# multiview-stitcher
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[`multiview-stitcher`](https://github.com/multiview-stitcher/multiview-sticher) 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`](https://github.com/multiview-stitcher/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](https://github.com/ome/ngff).
It leverages [`xarray`](https://github.com/xarray) in combination with [`spatial-image`](https://github.com/spatial-data) and [`multiscale-spatial-image`](https://github.com/spatial-image/multiscale-spatial-image) for image handling and [`dask`](https://github.com/dask) and [`dask-image`](https://github.com/dask-image) for chunked and distributed image processing.
## Quickstart
### Notebooks
Check out the [example notebooks](https://github.com/multiview-stitcher/multiview-stitcher/tree/main/notebooks).
### Napari plugin
There's an associated napari plugin: [napari-stitcher](https://github.com/multiview-stitcher/napari-stitcher).
Image data by [Arthur Michaut](https://research.pasteur.fr/fr/member/arthur-michaut/) @ [Jérôme Gros Lab](https://research.pasteur.fr/fr/team/dynamic-regulation-of-morphogenesis/) @ Institut Pasteur.
### Code example
These code snippets walk you through a small stitching workflow consisting of
1) Preparing the input image data and metadata (tile positions, spacing, channels)
2) Registering the tiles
3) Stitching / fusing the tiles
#### 1) Prepare data for stitching
```python
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)
```
#### 2) Register the tiles
```python
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)
```
#### 3) Stitch / fuse the tiles
```python
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()
```
----------------------------------
## Installation
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
## Stitching in the browser
`multiview-stitcher` can run without installation in your browser.
### Try it out
- open [JupyterLite](https://jupyter.org/try-jupyter/lab/) in a private browser window
- upload this notebook into the jupyter lab window: [notebooks/stitching_in_the_browser.ipynb](https://github.com/multiview-stitcher/multiview-stitcher/tree/main/notebooks/stitching_in_the_browser.ipynb)
- upload files to stitch into a 'data' folder in the jupyter lab window
- follow the notebook
#### Limitations
- 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
## Work in progress
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.
## Previous work
`multiview-stitcher` improves and replaces [MVRegFUS](https://github.com/m-albert/MVRegFus).
## Issues
If you encounter any problems, please [file an issue](https://github.com/multiview-stitcher/multiview-stitcher/issues) along with a detailed description.
## Contributing
Contributions are welcome.
## License
Distributed under the terms of the BSD-3 license,
"multiview-stitcher" is free and open source software.
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"description": "[](https://github.com/multiview-stitcher/multiview-stitcher/raw/main/LICENSE)\n[](https://pypi.org/project/multiview-stitcher)\n[](https://python.org)\n[](https://github.com/multiview-stitcher/multiview-stitcher/actions)\n[](https://zenodo.org/doi/10.5281/zenodo.13151252)\n\n\n# multiview-stitcher\n\n<!--\n[](https://github.com/multiview-stitcher/multiview-stitcher/raw/main/LICENSE)\n[](https://pypi.org/project/multiview-stitcher)\n[](https://python.org)\n[](https://github.com/multiview-stitcher/multiview-stitcher/actions)\n[](https://codecov.io/gh/multiview-stitcher/multiview-stitcher)\n-->\n\n[`multiview-stitcher`](https://github.com/multiview-stitcher/multiview-sticher) 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.\n\nFor visualization, the associated [`napari-stitcher`](https://github.com/multiview-stitcher/napari-stitcher) provides visualization functionality using the Napari viewer, including a standalone widget.\n\nWith 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](https://github.com/ome/ngff).\n\nIt leverages [`xarray`](https://github.com/xarray) in combination with [`spatial-image`](https://github.com/spatial-data) and [`multiscale-spatial-image`](https://github.com/spatial-image/multiscale-spatial-image) for image handling and [`dask`](https://github.com/dask) and [`dask-image`](https://github.com/dask-image) for chunked and distributed image processing.\n\n## Quickstart\n\n### Notebooks\n\nCheck out the [example notebooks](https://github.com/multiview-stitcher/multiview-stitcher/tree/main/notebooks).\n\n### Napari plugin\n\nThere's an associated napari plugin: [napari-stitcher](https://github.com/multiview-stitcher/napari-stitcher).\n\n\n\nImage data by [Arthur Michaut](https://research.pasteur.fr/fr/member/arthur-michaut/) @ [J\u00e9r\u00f4me Gros Lab](https://research.pasteur.fr/fr/team/dynamic-regulation-of-morphogenesis/) @ Institut Pasteur.\n\n### Code example\n\nThese code snippets walk you through a small stitching workflow consisting of\n1) Preparing the input image data and metadata (tile positions, spacing, channels)\n2) Registering the tiles\n3) Stitching / fusing the tiles\n\n#### 1) Prepare data for stitching\n\n\n```python\nimport numpy as np\nfrom multiview_stitcher import msi_utils\nfrom multiview_stitcher import spatial_image_utils as si_utils\n\n# input data (can be any numpy compatible array: numpy, dask, cupy, etc.)\ntile_arrays = [np.random.randint(0, 100, (2, 10, 100, 100)) for _ in range(3)]\n\n# indicate the tile offsets and spacing\ntile_translations = [\n {\"z\": 2.5, \"y\": -10, \"x\": 30},\n {\"z\": 2.5, \"y\": 30, \"x\": 10},\n {\"z\": 2.5, \"y\": 30, \"x\": 50},\n]\nspacing = {\"z\": 2, \"y\": 0.5, \"x\": 0.5}\n\nchannels = [\"DAPI\", \"GFP\"]\n\n# build input for stitching\nmsims = []\nfor tile_array, tile_translation in zip(tile_arrays, tile_translations):\n sim = si_utils.get_sim_from_array(\n tile_array,\n dims=[\"c\", \"z\", \"y\", \"x\"],\n scale=spacing,\n translation=tile_translation,\n transform_key=\"stage_metadata\",\n c_coords=channels,\n )\n msims.append(msi_utils.get_msim_from_sim(sim, scale_factors=[]))\n\n# plot the tile configuration\n# from multiview_stitcher import vis_utils\n# fig, ax = vis_utils.plot_positions(msims, transform_key='stage_metadata', use_positional_colors=False)\n```\n\n\n\n#### 2) Register the tiles\n\n```python\nfrom dask.diagnostics import ProgressBar\nfrom multiview_stitcher import registration\n\nwith ProgressBar():\n params = registration.register(\n msims,\n reg_channel=\"DAPI\", # channel to use for registration\n transform_key=\"stage_metadata\",\n new_transform_key=\"translation_registered\",\n )\n\n# plot the tile configuration after registration\n# vis_utils.plot_positions(msims, transform_key='translation_registered', use_positional_colors=False)\n```\n\n#### 3) Stitch / fuse the tiles\n```python\nfrom multiview_stitcher import fusion\n\nfused_sim = fusion.fuse(\n [msi_utils.get_sim_from_msim(msim) for msim in msims],\n transform_key=\"translation_registered\",\n)\n\n# get fused array as a dask array\nfused_sim.data\n\n# get fused array as a numpy array\nfused_sim.data.compute()\n```\n\n----------------------------------\n## Installation\n\nYou can install `multiview-stitcher` via `pip` from PyPI:\n\n pip install multiview-stitcher\n\nor from the source code in this github repository:\n\n pip install git+https://github.com/multiview-stitcher/multiview-stitcher.git\n\n## Stitching in the browser\n\n`multiview-stitcher` can run without installation in your browser.\n\n### Try it out\n\n- open [JupyterLite](https://jupyter.org/try-jupyter/lab/) in a private browser window\n- upload this notebook into the jupyter lab window: [notebooks/stitching_in_the_browser.ipynb](https://github.com/multiview-stitcher/multiview-stitcher/tree/main/notebooks/stitching_in_the_browser.ipynb)\n- upload files to stitch into a 'data' folder in the jupyter lab window\n- follow the notebook\n\n#### Limitations\n- stitching will run with a single thread\n- while the code runs locally, your local file system is not directly accessible from within the browser environment\n\n## Work in progress\n\nWARNING: 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.\n\n## Previous work\n\n`multiview-stitcher` improves and replaces [MVRegFUS](https://github.com/m-albert/MVRegFus).\n\n## Issues\n\nIf you encounter any problems, please [file an issue](https://github.com/multiview-stitcher/multiview-stitcher/issues) along with a detailed description.\n\n## Contributing\n\nContributions are welcome.\n\n## License\n\nDistributed under the terms of the BSD-3 license,\n\"multiview-stitcher\" is free and open source software.\n",
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