"Onion-skin" visual differences between a reference document image and a scanned copy
"Onion-skin" visual differences between a reference document image and a scanned copy.
Given an image of a reference document, docoskin will attempt to find key points of the document in a scanned copy, align the two, auto-correct the image contrasts and display a combined image, featuring sections in red which were "removed from" the reference document and sections in green which were added to the document.
The intended use for this is comparing a signed, returned scan of a contract with the original version as provided to the signer to check for unagreed amendments.
Implemented as a python library with a command-line interface. Significantly more power and flexibility is achievable through accessing the python components.
Depends on opencv and six (and, on python2.7, the
opencv is always going to be a slightly painful dependency from a python point of view because a) it's a native library
and b) it doesn't really play by python packaging rules supplying an egg. I advocate use of Nix to
solve both these problems and provide a
default.nix so that
nix users can simply perform a
$ nix-shell .
in the source directory for a development-ready shell (somthing like a non-python-specific
Those wishing to attempt using the pypi
opencv-python package can try installing the package with the 'extra'
pypi_opencv, which hypothetically should download & install opencv from pypi
$ pip install -e .[pypi_opencv]
but the author has never had any luck with that and has unfavourable opinions of
pip in general, not to mention the
concept of downloading & installing binaries from pypi.
Preliminary license is GPLv3.
The test suite makes use of images which are kept in a git submodule due to their size. Performing a
$ git submodule init $ git submodule update
should fetch the required data. If this data is not present, the tests will quietly skip tests requiring images (which is most of them, though).
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