%global _empty_manifest_terminate_build 0
Name:		python-gco-wrapper
Version:	3.0.8
Release:	1
Summary:	pyGCO: a python wrapper for the graph cuts package
License:	MIT
URL:		http://vision.csd.uwo.ca/code/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f2/7c/f56206f0e6ebf7f9465f76bbac269959a3f1d46ad4b291794a6a63803762/gco-wrapper-3.0.8.tar.gz

Requires:	python3-Cython
Requires:	python3-numpy

%description
This is a python wrapper for [gco-v3.0 package](http://vision.csd.uwo.ca/code/), which implements a graph cuts based move-making algorithm for optimization in Markov Random Fields.
It contains a copy of the **gco-v3.0 package**.  Some of the design were borrowed from the [gco_python](https://github.com/amueller/gco_python) package. However, compared to gco_python:
* This package does not depend on Cython. Instead it is implemented using the ctypes library and a C wrapper of the C++ code.
* This package is an almost complete wrapper for gco-v3.0, which supports more direct low level control over GCoptimization objects.
* This package supports graphs with edges weighted differently.
This wrapper is composed of two parts, a C wrapper and a python wrapper.
## Implemented functions
 * **cut_general_graph**(...)
 * **cut_grid_graph**(...)
 * **cut_grid_graph_simple**(...)
## Building wrapper
1. download the last version of [gco-v3.0](http://vision.csd.uwo.ca/code/gco-v3.0.zip) to the _gco_source_
1. compile gco-v3.0 and the C wrapper using `make`
1. compile test_wrapper using `make test_wrapper`
1. run the C test code `./test_wrapper` (now you have the C wrapper ready)
```bash
make download
make all
make test_wrapper
./test_wrapper
```
The successful run should return:
```bash
labels = [ 0 2 2 1 ], energy=19
data energy=15, smooth energy=4
```
Next test the python wrapper using `python test_examples.py`, if it works fine you are ready to use pygco.
To include pygco in your code, simply import pygco module. See the documentation inside code for more details.
## Install wrapper
Clone repository and enter folder, then
```bash
pip install -r requirements.txt
python setup.py install
```
Now it can be also installed from PyPi
```bash
pip install gco-wrapper
```
## Show test results
Visualisation of the unary terns for **binary segmentation**
![unary terms](./images/binary_unary.png)
**4-connected** components with the initial labeling (left) and estimated labeling with regularisation **1** (middle) and **0** (right)
![labelling](./images/binary_labels-4conn.png)
**8-connected** components with the initial labeling (left) and estimated labeling with regularisation **1** (middle) and **0** (right)
![labelling](./images/binary_labels-8conn.png)
Visualisation of the unary terns for **3 labels segmentation**
![unary terms](./images/grid_unary.png)
with the __initial__ labeling (left) and __estimated__ labeling (right)
![labelling](./images/grid_labels.png)

%package -n python3-gco-wrapper
Summary:	pyGCO: a python wrapper for the graph cuts package
Provides:	python-gco-wrapper
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-gco-wrapper
This is a python wrapper for [gco-v3.0 package](http://vision.csd.uwo.ca/code/), which implements a graph cuts based move-making algorithm for optimization in Markov Random Fields.
It contains a copy of the **gco-v3.0 package**.  Some of the design were borrowed from the [gco_python](https://github.com/amueller/gco_python) package. However, compared to gco_python:
* This package does not depend on Cython. Instead it is implemented using the ctypes library and a C wrapper of the C++ code.
* This package is an almost complete wrapper for gco-v3.0, which supports more direct low level control over GCoptimization objects.
* This package supports graphs with edges weighted differently.
This wrapper is composed of two parts, a C wrapper and a python wrapper.
## Implemented functions
 * **cut_general_graph**(...)
 * **cut_grid_graph**(...)
 * **cut_grid_graph_simple**(...)
## Building wrapper
1. download the last version of [gco-v3.0](http://vision.csd.uwo.ca/code/gco-v3.0.zip) to the _gco_source_
1. compile gco-v3.0 and the C wrapper using `make`
1. compile test_wrapper using `make test_wrapper`
1. run the C test code `./test_wrapper` (now you have the C wrapper ready)
```bash
make download
make all
make test_wrapper
./test_wrapper
```
The successful run should return:
```bash
labels = [ 0 2 2 1 ], energy=19
data energy=15, smooth energy=4
```
Next test the python wrapper using `python test_examples.py`, if it works fine you are ready to use pygco.
To include pygco in your code, simply import pygco module. See the documentation inside code for more details.
## Install wrapper
Clone repository and enter folder, then
```bash
pip install -r requirements.txt
python setup.py install
```
Now it can be also installed from PyPi
```bash
pip install gco-wrapper
```
## Show test results
Visualisation of the unary terns for **binary segmentation**
![unary terms](./images/binary_unary.png)
**4-connected** components with the initial labeling (left) and estimated labeling with regularisation **1** (middle) and **0** (right)
![labelling](./images/binary_labels-4conn.png)
**8-connected** components with the initial labeling (left) and estimated labeling with regularisation **1** (middle) and **0** (right)
![labelling](./images/binary_labels-8conn.png)
Visualisation of the unary terns for **3 labels segmentation**
![unary terms](./images/grid_unary.png)
with the __initial__ labeling (left) and __estimated__ labeling (right)
![labelling](./images/grid_labels.png)

%package help
Summary:	Development documents and examples for gco-wrapper
Provides:	python3-gco-wrapper-doc
%description help
This is a python wrapper for [gco-v3.0 package](http://vision.csd.uwo.ca/code/), which implements a graph cuts based move-making algorithm for optimization in Markov Random Fields.
It contains a copy of the **gco-v3.0 package**.  Some of the design were borrowed from the [gco_python](https://github.com/amueller/gco_python) package. However, compared to gco_python:
* This package does not depend on Cython. Instead it is implemented using the ctypes library and a C wrapper of the C++ code.
* This package is an almost complete wrapper for gco-v3.0, which supports more direct low level control over GCoptimization objects.
* This package supports graphs with edges weighted differently.
This wrapper is composed of two parts, a C wrapper and a python wrapper.
## Implemented functions
 * **cut_general_graph**(...)
 * **cut_grid_graph**(...)
 * **cut_grid_graph_simple**(...)
## Building wrapper
1. download the last version of [gco-v3.0](http://vision.csd.uwo.ca/code/gco-v3.0.zip) to the _gco_source_
1. compile gco-v3.0 and the C wrapper using `make`
1. compile test_wrapper using `make test_wrapper`
1. run the C test code `./test_wrapper` (now you have the C wrapper ready)
```bash
make download
make all
make test_wrapper
./test_wrapper
```
The successful run should return:
```bash
labels = [ 0 2 2 1 ], energy=19
data energy=15, smooth energy=4
```
Next test the python wrapper using `python test_examples.py`, if it works fine you are ready to use pygco.
To include pygco in your code, simply import pygco module. See the documentation inside code for more details.
## Install wrapper
Clone repository and enter folder, then
```bash
pip install -r requirements.txt
python setup.py install
```
Now it can be also installed from PyPi
```bash
pip install gco-wrapper
```
## Show test results
Visualisation of the unary terns for **binary segmentation**
![unary terms](./images/binary_unary.png)
**4-connected** components with the initial labeling (left) and estimated labeling with regularisation **1** (middle) and **0** (right)
![labelling](./images/binary_labels-4conn.png)
**8-connected** components with the initial labeling (left) and estimated labeling with regularisation **1** (middle) and **0** (right)
![labelling](./images/binary_labels-8conn.png)
Visualisation of the unary terns for **3 labels segmentation**
![unary terms](./images/grid_unary.png)
with the __initial__ labeling (left) and __estimated__ labeling (right)
![labelling](./images/grid_labels.png)

%prep
%autosetup -n gco-wrapper-3.0.8

%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-gco-wrapper -f filelist.lst
%dir %{python3_sitearch}/*

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

%changelog
* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.8-1
- Package Spec generated