%global _empty_manifest_terminate_build 0
Name: python-upolygon
Version: 0.1.10
Release: 1
Summary: Collection of fast polygon operations for DL
License: MIT License
URL: https://github.com/v7labs/upolygon
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/59/c7/68a4a1124465b8bc8cd3cd8ea127dcdaf61ddc6f21ec92916d31a09b244e/upolygon-0.1.10.tar.gz
%description
# uPolygon
Library of handy polygon related functions to speed up machine learning projects.
It was born as a replacement for `cv2.fillPoly` when generating masks for instance segmentation, without having to bring in all of opencv.
## TODO
- [x] draw_polygon
- [x] find_contours
- [ ] polygon_area
- [ ] point_in_polygon
## Usage
This library expects all polygons to be model as a list of paths, each path is a list of alternating x and y coordinates (`[x1,y1,x2,y2,...]`).
A simple triangle would be declared as:
```python
triangle = [[50,50, 100,0, 0,0]]
```
Complex polygons (holes and/or disjoints) follow the even-odd rule.
## draw_polygon
`draw_polygon(mask: array[:, :], paths: path[]) -> array[:, :]`
```python
from upolygon import draw_polygon
import numpy as np
mask = np.zeros((100,100), dtype=np.int32)
draw_polygon(mask, [[50,50, 100,0, 0,0]], 1)
```
Equivalent of calling `cv2.fillPoly(mask, [np.array([[50,50], [100,0], [0,0]])], 1)` or `cv2.drawContours(mask, [np.array([[50,50], [100,0], [0,0]])], -1, 1, cv2.FILLED)` when using opencv.
uPolygon is ~ 6 times faster than opencv for large random polygons with many intersecting lines.
For smaller polygons or few intersections, uPolygon is half as fast as opencv.
## find_contours
`find_contours(mask: array[:, :]) -> (array[:, :], path[:], path[:])`
0 is treated as background, 1 is treated as foreground.
```python
from upolygon import find_contours
import numpy as np
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0]
], dtype=np.uint8)
_labels, external_paths, internal_paths = find_contours(mask)
```
Similar to OpenCV's `cv2.findContours` but lacking hierarchies. Also similar to BoofCV's `LinearContourLabelChang2004` which is based on the same [algorithm](https://www.iis.sinica.edu.tw/papers/fchang/1362-F.pdf).
Note that currently the input mask to find_contour needs to be uint8.
## rle_encode
`rle_encode(mask: array[:,:]) -> list`
Takes a 2-dim binary mask and generates a run length encoding according to the coco specs
~ 15 times faster than written in plain python
%package -n python3-upolygon
Summary: Collection of fast polygon operations for DL
Provides: python-upolygon
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-upolygon
# uPolygon
Library of handy polygon related functions to speed up machine learning projects.
It was born as a replacement for `cv2.fillPoly` when generating masks for instance segmentation, without having to bring in all of opencv.
## TODO
- [x] draw_polygon
- [x] find_contours
- [ ] polygon_area
- [ ] point_in_polygon
## Usage
This library expects all polygons to be model as a list of paths, each path is a list of alternating x and y coordinates (`[x1,y1,x2,y2,...]`).
A simple triangle would be declared as:
```python
triangle = [[50,50, 100,0, 0,0]]
```
Complex polygons (holes and/or disjoints) follow the even-odd rule.
## draw_polygon
`draw_polygon(mask: array[:, :], paths: path[]) -> array[:, :]`
```python
from upolygon import draw_polygon
import numpy as np
mask = np.zeros((100,100), dtype=np.int32)
draw_polygon(mask, [[50,50, 100,0, 0,0]], 1)
```
Equivalent of calling `cv2.fillPoly(mask, [np.array([[50,50], [100,0], [0,0]])], 1)` or `cv2.drawContours(mask, [np.array([[50,50], [100,0], [0,0]])], -1, 1, cv2.FILLED)` when using opencv.
uPolygon is ~ 6 times faster than opencv for large random polygons with many intersecting lines.
For smaller polygons or few intersections, uPolygon is half as fast as opencv.
## find_contours
`find_contours(mask: array[:, :]) -> (array[:, :], path[:], path[:])`
0 is treated as background, 1 is treated as foreground.
```python
from upolygon import find_contours
import numpy as np
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0]
], dtype=np.uint8)
_labels, external_paths, internal_paths = find_contours(mask)
```
Similar to OpenCV's `cv2.findContours` but lacking hierarchies. Also similar to BoofCV's `LinearContourLabelChang2004` which is based on the same [algorithm](https://www.iis.sinica.edu.tw/papers/fchang/1362-F.pdf).
Note that currently the input mask to find_contour needs to be uint8.
## rle_encode
`rle_encode(mask: array[:,:]) -> list`
Takes a 2-dim binary mask and generates a run length encoding according to the coco specs
~ 15 times faster than written in plain python
%package help
Summary: Development documents and examples for upolygon
Provides: python3-upolygon-doc
%description help
# uPolygon
Library of handy polygon related functions to speed up machine learning projects.
It was born as a replacement for `cv2.fillPoly` when generating masks for instance segmentation, without having to bring in all of opencv.
## TODO
- [x] draw_polygon
- [x] find_contours
- [ ] polygon_area
- [ ] point_in_polygon
## Usage
This library expects all polygons to be model as a list of paths, each path is a list of alternating x and y coordinates (`[x1,y1,x2,y2,...]`).
A simple triangle would be declared as:
```python
triangle = [[50,50, 100,0, 0,0]]
```
Complex polygons (holes and/or disjoints) follow the even-odd rule.
## draw_polygon
`draw_polygon(mask: array[:, :], paths: path[]) -> array[:, :]`
```python
from upolygon import draw_polygon
import numpy as np
mask = np.zeros((100,100), dtype=np.int32)
draw_polygon(mask, [[50,50, 100,0, 0,0]], 1)
```
Equivalent of calling `cv2.fillPoly(mask, [np.array([[50,50], [100,0], [0,0]])], 1)` or `cv2.drawContours(mask, [np.array([[50,50], [100,0], [0,0]])], -1, 1, cv2.FILLED)` when using opencv.
uPolygon is ~ 6 times faster than opencv for large random polygons with many intersecting lines.
For smaller polygons or few intersections, uPolygon is half as fast as opencv.
## find_contours
`find_contours(mask: array[:, :]) -> (array[:, :], path[:], path[:])`
0 is treated as background, 1 is treated as foreground.
```python
from upolygon import find_contours
import numpy as np
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0]
], dtype=np.uint8)
_labels, external_paths, internal_paths = find_contours(mask)
```
Similar to OpenCV's `cv2.findContours` but lacking hierarchies. Also similar to BoofCV's `LinearContourLabelChang2004` which is based on the same [algorithm](https://www.iis.sinica.edu.tw/papers/fchang/1362-F.pdf).
Note that currently the input mask to find_contour needs to be uint8.
## rle_encode
`rle_encode(mask: array[:,:]) -> list`
Takes a 2-dim binary mask and generates a run length encoding according to the coco specs
~ 15 times faster than written in plain python
%prep
%autosetup -n upolygon-0.1.10
%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-upolygon -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.10-1
- Package Spec generated