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|
%global _empty_manifest_terminate_build 0
Name: python-whitening
Version: 0.2
Release: 1
Summary: Document whitening (foreground separation)
License: MIT
URL: https://github.com/rossumai/whitening
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5d/61/eb6442e547f6e429fe325955e6bd4e33b27095156eb4db4579e56fe0561c/whitening-0.2.tar.gz
BuildArch: noarch
%description
# Document whitening (foreground separation)
This package tries to separate text/line foreground and background by 2D median
filter.
<img src="data/IMG_3262.jpg" alt="original" width="250">
<img src="data/IMG_3262_fg.jpg" alt="foreground" width="250">
<img src="data/IMG_3262_bg.jpg" alt="background" width="250">
## Installation
Install from PyPI. Works on Python 3.
```bash
pip install whitening
```
## Example usage
### Python API
It works with images represented as `PIL.Image` or as a numpy array. Images can
be either RGB or grayscale.
```python
import numpy as np
import PIL.Image
from whitening import whiten
# possible to use numpy array as input/output
image = np.asarray(PIL.Image.open('image.jpg'), dtype='uint8')
foreground, background = whiten(image, kernel_size=20, downsample=4)
PIL.Image.fromarray(foreground).save('foreground.jpg', 'jpeg')
# or directly a PIL image
image = PIL.Image.open('image.jpg')
foreground, background = whiten(image, kernel_size=20, downsample=4)
foreground.save('foreground.jpg', 'jpeg')
```
### CLI
It install an entry point called `whiten`.
```bash
# help
$ whiten -h
# whiten an image and save the foreground output
$ whiten input.jpg foreground.jpg
# specify the kernel size
$ whiten input.jpg foreground.jpg -k 100
# work in grayscale instead of RGB (3x faster)
$ whiten input.jpg foreground.jpg -g
# downsample the image 4x (faster, but a bit less precise)
$ whiten input.jpg foreground.jpg -d 4
# save also the background
$ whiten input.jpg foreground.jpg -b background.jpg
```
We assume the original images is a product of foreground and background,
thus we can recover the foreground by dividing the image by the background:
`I = F * B => F = I / B`. We try to approximate the background by 2D median
filtering the original image which suppresses sparse features such as text and
lines.
Select kernel size that's enough for not making artifacts while small enough
to keep computation fast. A good starting point is 50 pixels.
A 9.5 Mpx image can be processed on a MacBook in 15 s, with grayscale and
downsampling 4x the run time can be reduced to 1 s! Quite good results can be
obtained even with kernel size 10 and downsampling 16x.
More info: http://bohumirzamecnik.cz/blog/2015/image-whitening/
## Development
See the `Makefile` for various development tasks.
## License
Author: Bohumír Zámečník <bohumir.zamecnik@gmail.com>
Supported by [Rossum](https://rossum.ai), creating a world without manual data entry.
%package -n python3-whitening
Summary: Document whitening (foreground separation)
Provides: python-whitening
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-whitening
# Document whitening (foreground separation)
This package tries to separate text/line foreground and background by 2D median
filter.
<img src="data/IMG_3262.jpg" alt="original" width="250">
<img src="data/IMG_3262_fg.jpg" alt="foreground" width="250">
<img src="data/IMG_3262_bg.jpg" alt="background" width="250">
## Installation
Install from PyPI. Works on Python 3.
```bash
pip install whitening
```
## Example usage
### Python API
It works with images represented as `PIL.Image` or as a numpy array. Images can
be either RGB or grayscale.
```python
import numpy as np
import PIL.Image
from whitening import whiten
# possible to use numpy array as input/output
image = np.asarray(PIL.Image.open('image.jpg'), dtype='uint8')
foreground, background = whiten(image, kernel_size=20, downsample=4)
PIL.Image.fromarray(foreground).save('foreground.jpg', 'jpeg')
# or directly a PIL image
image = PIL.Image.open('image.jpg')
foreground, background = whiten(image, kernel_size=20, downsample=4)
foreground.save('foreground.jpg', 'jpeg')
```
### CLI
It install an entry point called `whiten`.
```bash
# help
$ whiten -h
# whiten an image and save the foreground output
$ whiten input.jpg foreground.jpg
# specify the kernel size
$ whiten input.jpg foreground.jpg -k 100
# work in grayscale instead of RGB (3x faster)
$ whiten input.jpg foreground.jpg -g
# downsample the image 4x (faster, but a bit less precise)
$ whiten input.jpg foreground.jpg -d 4
# save also the background
$ whiten input.jpg foreground.jpg -b background.jpg
```
We assume the original images is a product of foreground and background,
thus we can recover the foreground by dividing the image by the background:
`I = F * B => F = I / B`. We try to approximate the background by 2D median
filtering the original image which suppresses sparse features such as text and
lines.
Select kernel size that's enough for not making artifacts while small enough
to keep computation fast. A good starting point is 50 pixels.
A 9.5 Mpx image can be processed on a MacBook in 15 s, with grayscale and
downsampling 4x the run time can be reduced to 1 s! Quite good results can be
obtained even with kernel size 10 and downsampling 16x.
More info: http://bohumirzamecnik.cz/blog/2015/image-whitening/
## Development
See the `Makefile` for various development tasks.
## License
Author: Bohumír Zámečník <bohumir.zamecnik@gmail.com>
Supported by [Rossum](https://rossum.ai), creating a world without manual data entry.
%package help
Summary: Development documents and examples for whitening
Provides: python3-whitening-doc
%description help
# Document whitening (foreground separation)
This package tries to separate text/line foreground and background by 2D median
filter.
<img src="data/IMG_3262.jpg" alt="original" width="250">
<img src="data/IMG_3262_fg.jpg" alt="foreground" width="250">
<img src="data/IMG_3262_bg.jpg" alt="background" width="250">
## Installation
Install from PyPI. Works on Python 3.
```bash
pip install whitening
```
## Example usage
### Python API
It works with images represented as `PIL.Image` or as a numpy array. Images can
be either RGB or grayscale.
```python
import numpy as np
import PIL.Image
from whitening import whiten
# possible to use numpy array as input/output
image = np.asarray(PIL.Image.open('image.jpg'), dtype='uint8')
foreground, background = whiten(image, kernel_size=20, downsample=4)
PIL.Image.fromarray(foreground).save('foreground.jpg', 'jpeg')
# or directly a PIL image
image = PIL.Image.open('image.jpg')
foreground, background = whiten(image, kernel_size=20, downsample=4)
foreground.save('foreground.jpg', 'jpeg')
```
### CLI
It install an entry point called `whiten`.
```bash
# help
$ whiten -h
# whiten an image and save the foreground output
$ whiten input.jpg foreground.jpg
# specify the kernel size
$ whiten input.jpg foreground.jpg -k 100
# work in grayscale instead of RGB (3x faster)
$ whiten input.jpg foreground.jpg -g
# downsample the image 4x (faster, but a bit less precise)
$ whiten input.jpg foreground.jpg -d 4
# save also the background
$ whiten input.jpg foreground.jpg -b background.jpg
```
We assume the original images is a product of foreground and background,
thus we can recover the foreground by dividing the image by the background:
`I = F * B => F = I / B`. We try to approximate the background by 2D median
filtering the original image which suppresses sparse features such as text and
lines.
Select kernel size that's enough for not making artifacts while small enough
to keep computation fast. A good starting point is 50 pixels.
A 9.5 Mpx image can be processed on a MacBook in 15 s, with grayscale and
downsampling 4x the run time can be reduced to 1 s! Quite good results can be
obtained even with kernel size 10 and downsampling 16x.
More info: http://bohumirzamecnik.cz/blog/2015/image-whitening/
## Development
See the `Makefile` for various development tasks.
## License
Author: Bohumír Zámečník <bohumir.zamecnik@gmail.com>
Supported by [Rossum](https://rossum.ai), creating a world without manual data entry.
%prep
%autosetup -n whitening-0.2
%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-whitening -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2-1
- Package Spec generated
|
