%global _empty_manifest_terminate_build 0
Name:		python-DeepCell-Tracking
Version:	0.6.4
Release:	1
Summary:	Tracking cells and lineage with deep learning.
License:	LICENSE
URL:		https://github.com/vanvalenlab/deepcell-tracking
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2a/4f/6a773227856fd11c4a0b9a00f014c487329b87222c1874012b43c611c0f3/DeepCell_Tracking-0.6.4.tar.gz
BuildArch:	noarch


%description
# ![DeepCell Tracking Banner](https://raw.githubusercontent.com/vanvalenlab/deepcell-tracking/master/docs/images/DeepCell_tracking_Banner.png)

[![PyPI version](https://badge.fury.io/py/DeepCell-Tracking.svg)](https://badge.fury.io/py/DeepCell-Tracking)
[![Build Status](https://github.com/vanvalenlab/deepcell-tracking/workflows/build/badge.svg)](https://github.com/vanvalenlab/deepcell-tracking/actions)
[![Coverage Status](https://coveralls.io/repos/github/vanvalenlab/deepcell-tracking/badge.svg?branch=master)](https://coveralls.io/github/vanvalenlab/deepcell-tracking?branch=master)

`deepcell-tracking` uses deep learning models from [deepcell-tf](https://github.com/vanvalenlab/deepcell-tf) within an assignment problem framework to [track cells through time-lapse sequences](https://www.biorxiv.org/content/10.1101/803205v2) and build cell lineages. The assignment problem is solved using the [Hungarian algorithm.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747604/)

## Getting Started

`deepcell-tracking` is a Python package that can be installed with `pip`:

```bash
pip install deepcell-tracking
```

Or it can be installed from source:

```bash
git clone https://github.com/vanvalenlab/deepcell-tracking.git

cd deepcell-tracking

# install the dependencies
pip install .
```

## How to Use

```python
from deepcell_tracking import CellTracker

# X and y are the time-sequence data and their corresponding segmentations (labels), respectively.
# model is a deepcell-tf tracking model.
tracker = CellTracker(X, y, model)

tracker.track_cells()  # runs in place, builds tracks

# Save all tracked data and lineage files to a .trk file
tracker.dump('./results.trk')

# Open the track file
from deepcell_tracking.utils import load_trks

data = load_trks('./results.trk')

lineage = data['lineages']  # linage information
X = data['X']  # raw X data
y = data['y']  # tracked y data
```


%package -n python3-DeepCell-Tracking
Summary:	Tracking cells and lineage with deep learning.
Provides:	python-DeepCell-Tracking
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-DeepCell-Tracking
# ![DeepCell Tracking Banner](https://raw.githubusercontent.com/vanvalenlab/deepcell-tracking/master/docs/images/DeepCell_tracking_Banner.png)

[![PyPI version](https://badge.fury.io/py/DeepCell-Tracking.svg)](https://badge.fury.io/py/DeepCell-Tracking)
[![Build Status](https://github.com/vanvalenlab/deepcell-tracking/workflows/build/badge.svg)](https://github.com/vanvalenlab/deepcell-tracking/actions)
[![Coverage Status](https://coveralls.io/repos/github/vanvalenlab/deepcell-tracking/badge.svg?branch=master)](https://coveralls.io/github/vanvalenlab/deepcell-tracking?branch=master)

`deepcell-tracking` uses deep learning models from [deepcell-tf](https://github.com/vanvalenlab/deepcell-tf) within an assignment problem framework to [track cells through time-lapse sequences](https://www.biorxiv.org/content/10.1101/803205v2) and build cell lineages. The assignment problem is solved using the [Hungarian algorithm.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747604/)

## Getting Started

`deepcell-tracking` is a Python package that can be installed with `pip`:

```bash
pip install deepcell-tracking
```

Or it can be installed from source:

```bash
git clone https://github.com/vanvalenlab/deepcell-tracking.git

cd deepcell-tracking

# install the dependencies
pip install .
```

## How to Use

```python
from deepcell_tracking import CellTracker

# X and y are the time-sequence data and their corresponding segmentations (labels), respectively.
# model is a deepcell-tf tracking model.
tracker = CellTracker(X, y, model)

tracker.track_cells()  # runs in place, builds tracks

# Save all tracked data and lineage files to a .trk file
tracker.dump('./results.trk')

# Open the track file
from deepcell_tracking.utils import load_trks

data = load_trks('./results.trk')

lineage = data['lineages']  # linage information
X = data['X']  # raw X data
y = data['y']  # tracked y data
```


%package help
Summary:	Development documents and examples for DeepCell-Tracking
Provides:	python3-DeepCell-Tracking-doc
%description help
# ![DeepCell Tracking Banner](https://raw.githubusercontent.com/vanvalenlab/deepcell-tracking/master/docs/images/DeepCell_tracking_Banner.png)

[![PyPI version](https://badge.fury.io/py/DeepCell-Tracking.svg)](https://badge.fury.io/py/DeepCell-Tracking)
[![Build Status](https://github.com/vanvalenlab/deepcell-tracking/workflows/build/badge.svg)](https://github.com/vanvalenlab/deepcell-tracking/actions)
[![Coverage Status](https://coveralls.io/repos/github/vanvalenlab/deepcell-tracking/badge.svg?branch=master)](https://coveralls.io/github/vanvalenlab/deepcell-tracking?branch=master)

`deepcell-tracking` uses deep learning models from [deepcell-tf](https://github.com/vanvalenlab/deepcell-tf) within an assignment problem framework to [track cells through time-lapse sequences](https://www.biorxiv.org/content/10.1101/803205v2) and build cell lineages. The assignment problem is solved using the [Hungarian algorithm.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747604/)

## Getting Started

`deepcell-tracking` is a Python package that can be installed with `pip`:

```bash
pip install deepcell-tracking
```

Or it can be installed from source:

```bash
git clone https://github.com/vanvalenlab/deepcell-tracking.git

cd deepcell-tracking

# install the dependencies
pip install .
```

## How to Use

```python
from deepcell_tracking import CellTracker

# X and y are the time-sequence data and their corresponding segmentations (labels), respectively.
# model is a deepcell-tf tracking model.
tracker = CellTracker(X, y, model)

tracker.track_cells()  # runs in place, builds tracks

# Save all tracked data and lineage files to a .trk file
tracker.dump('./results.trk')

# Open the track file
from deepcell_tracking.utils import load_trks

data = load_trks('./results.trk')

lineage = data['lineages']  # linage information
X = data['X']  # raw X data
y = data['y']  # tracked y data
```


%prep
%autosetup -n DeepCell-Tracking-0.6.4

%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-DeepCell-Tracking -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.4-1
- Package Spec generated