torchvtk 0.2.4

Efficient data loading and visualization for volumes in PyTorch

# torchvtk PyTorch volume data loading framework

## Installation Instructions ` pip install git+https://github.com/xeTaiz/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.