%global _empty_manifest_terminate_build 0 Name: python-vollseg Version: 10.8.9 Release: 1 Summary: Segmentation tool for biological cells of irregular size and shape in 3D and 2D. License: BSD-3-Clause URL: https://github.com/kapoorlab/vollseg/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dd/b4/0309484cd5351270c72b6bf12ffc4c5c1ea90de95347f2539dc9ec515111/vollseg-10.8.9.tar.gz BuildArch: noarch Requires: python3-pandas Requires: python3-stardist Requires: python3-scipy Requires: python3-tifffile Requires: python3-matplotlib Requires: python3-napari Requires: python3-cellpose-vollseg Requires: python3-torch Requires: python3-test-tube Requires: python3-lightning Requires: python3-tox Requires: python3-pytest Requires: python3-pytest-cov %description # VollSeg [![Build Status](https://travis-ci.com/kapoorlab/vollseg.svg?branch=master)](https://travis-ci.com/github/kapoorlab/vollseg) [![PyPI version](https://img.shields.io/pypi/v/vollseg.svg?maxAge=2591000)](https://pypi.org/project/vollseg/) [![License](https://img.shields.io/pypi/l/napari-metroid.svg?color=green)](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE) [![Twitter Badge](https://badgen.net/badge/icon/twitter?icon=twitter&label)](https://twitter.com/entracod) 3D segmentation tool for irregular shaped cells ![Segmentation](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_compare-big.png) ## Installation This package can be installed by `pip install --user vollseg` `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` If you are building this from the source, clone the repository and install via ```bash git clone https://github.com/kapoorlab/vollseg/ cd vollseg pip install --user -e . `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` ``` ### Pipenv install Pipenv allows you to install dependencies in a virtual environment. ```bash # install pipenv if you don't already have it installed pip install --user pipenv # clone the repository and sync the dependencies git clone https://github.com/kapoorlab/vollseg/ cd vollseg pipenv sync # make the current package available pipenv run python setup.py develop `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` # you can run the example notebooks by starting the jupyter notebook inside the virtual env pipenv run jupyter notebook ``` Access the `example` folder and run the cells. ## Algorithm ![Algorithm](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_pipe-git.png) Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view. ## Example To try the provided notebooks we provide an example dataset of Arabidopsis, [Binary Images](https://doi.org/10.5281/zenodo.5217367), [Raw Images](https://doi.org/10.5281/zenodo.5217394) and [Labelled images](https://doi.org/10.5281/zenodo.5217341) and trained models: [stardist](https://doi.org/10.5281/zenodo.5227304), [Denoising](https://doi.org/10.5281/zenodo.5227316), [U-Net](https://doi.org/10.5281/zenodo.5227301). For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb) ## Docker A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with: ~~~bash docker build -t voll . ~~~ Now to run the `track` command: ~~~bash # show help docker run --rm -it voll ~~~ ## Requirements - Python 3.7 and above. ## License Under MIT license. See [LICENSE](LICENSE). ## Authors - Varun Kapoor - Claudia Carabaña - Mari Tolonen %package -n python3-vollseg Summary: Segmentation tool for biological cells of irregular size and shape in 3D and 2D. Provides: python-vollseg BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-vollseg # VollSeg [![Build Status](https://travis-ci.com/kapoorlab/vollseg.svg?branch=master)](https://travis-ci.com/github/kapoorlab/vollseg) [![PyPI version](https://img.shields.io/pypi/v/vollseg.svg?maxAge=2591000)](https://pypi.org/project/vollseg/) [![License](https://img.shields.io/pypi/l/napari-metroid.svg?color=green)](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE) [![Twitter Badge](https://badgen.net/badge/icon/twitter?icon=twitter&label)](https://twitter.com/entracod) 3D segmentation tool for irregular shaped cells ![Segmentation](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_compare-big.png) ## Installation This package can be installed by `pip install --user vollseg` `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` If you are building this from the source, clone the repository and install via ```bash git clone https://github.com/kapoorlab/vollseg/ cd vollseg pip install --user -e . `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` ``` ### Pipenv install Pipenv allows you to install dependencies in a virtual environment. ```bash # install pipenv if you don't already have it installed pip install --user pipenv # clone the repository and sync the dependencies git clone https://github.com/kapoorlab/vollseg/ cd vollseg pipenv sync # make the current package available pipenv run python setup.py develop `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` # you can run the example notebooks by starting the jupyter notebook inside the virtual env pipenv run jupyter notebook ``` Access the `example` folder and run the cells. ## Algorithm ![Algorithm](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_pipe-git.png) Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view. ## Example To try the provided notebooks we provide an example dataset of Arabidopsis, [Binary Images](https://doi.org/10.5281/zenodo.5217367), [Raw Images](https://doi.org/10.5281/zenodo.5217394) and [Labelled images](https://doi.org/10.5281/zenodo.5217341) and trained models: [stardist](https://doi.org/10.5281/zenodo.5227304), [Denoising](https://doi.org/10.5281/zenodo.5227316), [U-Net](https://doi.org/10.5281/zenodo.5227301). For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb) ## Docker A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with: ~~~bash docker build -t voll . ~~~ Now to run the `track` command: ~~~bash # show help docker run --rm -it voll ~~~ ## Requirements - Python 3.7 and above. ## License Under MIT license. See [LICENSE](LICENSE). ## Authors - Varun Kapoor - Claudia Carabaña - Mari Tolonen %package help Summary: Development documents and examples for vollseg Provides: python3-vollseg-doc %description help # VollSeg [![Build Status](https://travis-ci.com/kapoorlab/vollseg.svg?branch=master)](https://travis-ci.com/github/kapoorlab/vollseg) [![PyPI version](https://img.shields.io/pypi/v/vollseg.svg?maxAge=2591000)](https://pypi.org/project/vollseg/) [![License](https://img.shields.io/pypi/l/napari-metroid.svg?color=green)](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE) [![Twitter Badge](https://badgen.net/badge/icon/twitter?icon=twitter&label)](https://twitter.com/entracod) 3D segmentation tool for irregular shaped cells ![Segmentation](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_compare-big.png) ## Installation This package can be installed by `pip install --user vollseg` `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` If you are building this from the source, clone the repository and install via ```bash git clone https://github.com/kapoorlab/vollseg/ cd vollseg pip install --user -e . `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` ``` ### Pipenv install Pipenv allows you to install dependencies in a virtual environment. ```bash # install pipenv if you don't already have it installed pip install --user pipenv # clone the repository and sync the dependencies git clone https://github.com/kapoorlab/vollseg/ cd vollseg pipenv sync # make the current package available pipenv run python setup.py develop `mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia` # you can run the example notebooks by starting the jupyter notebook inside the virtual env pipenv run jupyter notebook ``` Access the `example` folder and run the cells. ## Algorithm ![Algorithm](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_pipe-git.png) Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view. ## Example To try the provided notebooks we provide an example dataset of Arabidopsis, [Binary Images](https://doi.org/10.5281/zenodo.5217367), [Raw Images](https://doi.org/10.5281/zenodo.5217394) and [Labelled images](https://doi.org/10.5281/zenodo.5217341) and trained models: [stardist](https://doi.org/10.5281/zenodo.5227304), [Denoising](https://doi.org/10.5281/zenodo.5227316), [U-Net](https://doi.org/10.5281/zenodo.5227301). For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb) ## Docker A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with: ~~~bash docker build -t voll . ~~~ Now to run the `track` command: ~~~bash # show help docker run --rm -it voll ~~~ ## Requirements - Python 3.7 and above. ## License Under MIT license. See [LICENSE](LICENSE). ## Authors - Varun Kapoor - Claudia Carabaña - Mari Tolonen %prep %autosetup -n vollseg-10.8.9 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-vollseg -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 10.8.9-1 - Package Spec generated