evenly divides the data in pred_file with the constraint that no dimension of any subvolume is longer than max_len
Zwatershed the subvolumes
eval_with_spark(partition_data[0])
with spark
eval_with_par_map(partition_data[0],NUM_WORKERS)
with python multiprocessing map
after evaluating, subvolumes will be saved into the out_folder directory named based on their smallest indices in each dimension (ex. path/to/out_folder/0_0_0_vol)
Stitch the subvolumes together
stitch_and_save(partition_data,outname)
stitch together the subvolumes in partition_data
save to the hdf5 file outname
outname['starts'] = list of min_indices of each subvolume
outname['ends'] = list of max_indices of each subvolume
outname['seg'] = full stitched segmentation
outname['seg_sizes'] = array of size of each segmentation