%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. original foreground background ## 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 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. original foreground background ## 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 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. original foreground background ## 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 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 - 0.2-1 - Package Spec generated