Tensorflow implementation of Shearlab, including a python wrapper of the Julia Shearlab APi
tfShearlab can be easily installed by running
$ pip install https://github.com/arsenal9971/tfshearlab/archive/master.zipThis package has the next dependencies
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Julia language: One can either precompiled packages or build from source. This package requires the Julia version 0.6 or higher.
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Shearlab.jl: To install the library in Julia 0.6.x one needs to run the command
julia -e 'Pkg.add("Shearlab"). -
Pyjulia: One can install the Python API of Julia with the command
pip install julia, for more details on installation check the documentation.- One also needs to make the Julia and Python enviroment to coincide running the command
julia -e 'ENV["PYTHON"]="<your-python-executable>"; Pkg.add("PyCall"); Pkg.build("PyCall")'. One can find its python executable path by running on the terminal$(which python).
- One also needs to make the Julia and Python enviroment to coincide running the command
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SSL certificates: Sometimes you need to give (and add to bashrc.) the SSL certificates path using
export SSL_CERT_FILE=/etc/ssl/ca-bundle.pem.
Shearlab.jl is a Julia Library with toolbox for two- and threedimensional data processing using the Shearlet system as basis functions which generates an sparse representation of cartoon-like functions.
tfShearlab is a tensorflow implementation of the Shearlet transform using the Julia API as backend. The reason for this implementation lies in mainly in the GPU-functionalities of tensorflow that accelerates the fft-based convolutions; in comparison with the version without tensorflow, the Shearlet decomposition and recosntruction are about 30x faster in a GTX 1080 graphic card.
This package also contains a python wrapper of the Julia API, so one can perform the Shearlet transform without tensorflow.
For the 2D version one has three important functions:
- Generate the Shearlet System.
getshearletsystem2D(rows,cols,nScales,shearLevels,full,directionalFilter,scalingFilter);- Decoding of a signal X.
tfsheardec2D(Xtf, tfshearlets) - Reconstruction of a signal Xtf.
tfshearrec2D(coeffstf,tfshearlets,tfdualFrameWeights )For more detailed usage functionalities check the original Shearlab manual, or examples for scientific reference one can also read "ShearLab 3D: Faithful Digital Shearlet Transforms Based on Compactly Supported Shearlets".