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A SDK for tumour study

Project description

TumourKit

Ubuntu testsWindows testspyversion torchversion dglversion

Python package from my Bachelor's thesis. It is dedicated to aid in the study of tumours.

Installation

Just do:

pip install git+https://github.com/marcown/imgaug.git@74e63f2#egg=imgaug
pip install tumourkit

It's that simple, dependencies will be automatically installed and several command line scripts will be readily available. It is recommended that you install it inside a virtual environment. For that, type in a shell:

python3.10 -m venv [ENV_NAME]
source [ENV_NAME]/bin/activate
pip install git+https://github.com/marcown/imgaug.git@74e63f2#egg=imgaug
pip install tumourkit

PyTorch and Deep Graph Library are not included as dependencies since they change rapidly and require extra links. To install PyTorch and Deep Graph Library please go to their official installation pages.

Also, don't mind the imgaug package being installed manually, that is to avoid deprecation with newer versions of numpy.

Known errors

If you try to use the GPU version of Deep Graph Library but have installed the CPU you will receive the following error.

Check failed: allow_missing: Device API gpu is not enabled. Please install the cuda version of dgl.

Just install the GPU build and you will be fine.

If you come accross something like

OSError: libcusparse.so.11: cannot open shared object file: No such file or directory

That means your python environment does not link correctly to your CUDA installation. You will have to edit the LD_LIBRARY_PATH environmental variable so that the dynamic library libcusparse.so.11 can be found. Typically it is found under nvidia/cublas/lib so a possible fix is

export LD_LIBRARY_PATH=[ENV_NAME]/lib/python3.10/site-packages/nvidia/cublas/lib/:$LD_LIBRARY_PATH

Main features

With this package you'll be able to easily convert between different data formats, train models and make inference. As an example, if you want to convert GeoJSON data exported from QuPath into the standard PNG / CSV format you can simply type:

geojson2pngcsv --gson-dir [...] --png-dir [...] --csv-dir [...]

Substituting the dots by the input and output folders respectively.

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