Metadata-Version: 2.1
Name: torchvtk
Version: 0.4.0
Summary: Efficient data loading and visualization for volumes in PyTorch
Home-page: https://github.com/torchvtk/torchvtk
Author: Dominik Engel, Marc Fabian Mezger
License: MIT
Description: # torchvtk
        PyTorch volume data loading framework
        ## [Documentation](https://torchvtk.github.io)
        
        
        ## Installation Instructions
        The latest GitHub release is pushed to PyPi:
        ```
        pip install torchvtk
        ```
        
        To get the master run:
        ```
        pip install git+https://github.com/torchvtk/torchvtk.git@master#egg=torchvtk
        ```
        
        ### Optional for DICOM stuff only:
        ```
        conda create --name "tvtk" python=3.6 && conda activate tvtk
        conda install gdcm -c conda-forge
        pip install pydicom dicom_numpy h5py numpy matplotlib
        ```
        If you need DICOM, and thus gdcm, your Python version needs to be <=3.6
        Modify `tvtk` in the third line (both after `--name` and at the end of the line) to your preferred environment name or just add the required packages to your existing environment.
        Note that the restriction to Python <= 3.6 is due to `gdcm` and higher version should work as well if you don't need DICOM loading capabilities.
        
        ### Creating the Numpy Files for the HDF 5 File generation.
        `hdf5/nifiti_crawler.py` This script generates out of the nifti Files of the Medical Decathlon Challenge numpy arrays that contain the images and the segmentation groundtruths.
        
        ### Creating the HDF5 Files
        `hdf5/hdf5_crawler.py` This script generates hdf5 Files with different compression techniques. These files contain the image as well as the groundtruth. The image is normalized between [0,1] and is stored in the Float32 Format. The Groundtruth is saved as an Int16 Format. The used compressions are gzip, szip  and lzf.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
