Metadata-Version: 2.1
Name: py-SAINT
Version: 1.3.0
Summary: SAINT
Home-page: UNKNOWN
Author: SMK
Author-email: 454297329@qq.com
License: UNKNOWN
Description: This is SAINT. 
        Environment configuration:  pytorch:0.4.1 python:3.6 cuda:9.0
        SAINT:  Spatially Aware Interpolation NeTwork for Medical Slice Synthesis
        step1:  .nii->.pt 
                   from py_SAINT.STAGE1 import nii2pickle
                   nii2pickle.nii2pt(ori_dir_path,output_file_path)
        eg: nii2pickle.nii2pt("/home1/mksun/xh_data/273data-yscl/1T2/1/002_OCor_T2_FRFSE/","/home1/mksun/SAINT/Data/Stage1_Input/TEST/HR/")
        
                   ori_dir_path:  file path containing .nii
                   output_file_path:  file path of the generated .pt
        step2:  Interpolate with sag and cor view respectively
                   from py_SAINT.STAGE1 import interpolation
                   interpolation.get_Stage1_result (scale ='4',save =/path/ ,dir_data ='/path/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='sag',gpu='0')
                   interpolation.get_Stage1_result (scale ='4',save =/path/ ,dir_data ='/path/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='cor',gpu='0')
        eg:interpolation.get_Stage1_result (scale ='4',save ="/home1/mksun/SAINT/Data/Stage1_output_sag_cor/" ,dir_data ='/home1/mksun/SAINT/Data/Stage1_Input/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='cor',gpu='0')
        eg:interpolation.get_Stage1_result (scale ='4',save ="/home1/mksun/SAINT/Data/Stage1_output_sag_cor/" ,dir_data ='/home1/mksun/SAINT/Data/Stage1_Input/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='sag',gpu='0')
        
                   scale:  super resolution scale (eg:2,3,4,6)
                   save:  file path of save           
                   dir_data:  dataset directory (Note that the dir_data path should point to a folder that contains subfolders named  'TEST', each of which needs to have a 'HR' and 'LR' subfolder, 
                               'HR' is high resolution file , 'LR' is low resolution file. Data should go accordingly in this structure.
                   n_colors:  number of channels to use
                   n_GPUs:  number of GPUs
                   rgb_range:  maximum value of RGB
                   view:  view of interpolation (Note the --view option performs inference on the volume from either the sagittal or coronal axis. Note that the whether it's actually sagittal or coronal depends on the oritentation of the data.)
        step3:  Before going to the RFN stage, sagittal and coronal-wise SR'ed volume needs to be recombine into a single volume for inference. In simple terms just concatenate them in the first dimension, coronal SR goes in channel 0 and             sagittal SR goes in channel 1. 
                   from py_SAINT.STAGE1.process import cor_sag_comb_test
                   cor_sag_comb_test.comb_cor_sag(files_dir='/path/',input_sag_cor_dir='/path/',out_dir='/path/', scale=4)
                   eg:cor_sag_comb_test.comb_cor_sag(files_dir='/home1/mksun/SAINT/Data/Stage1_Input/TEST/HR/',input_sag_cor_dir='/home1/mksun/SAINT/Data/Stage1_output_sag_cor/results/raw/',out_dir='/home1/mksun/SAINT/Data/combine_cor_sag_out/TEST/', scale=4)
                   files_dir:  dataset directory
                   input_sag_cor_dir:  path to the folder containing sag and cor
                   out_dir:  generated combine path
                   scale:  super resolution scale
        step4:  Residual-Fusion
                   from py_SAINT.STAGE2 import fuse
                   fuse.get_Stage2_result(save ='/path/',dir_data ='/path/' ,n_GPUs =1 ,rgb_range =4000,gpu='0')
        eg: fuse.get_Stage2_result(save ='/home1/mksun/SAINT/Data/out_fuse/',dir_data ='/home1/mksun/SAINT/Data/combine_cor_sag_out/' ,n_GPUs =1 ,rgb_range =4000,gpu='0')
                   save:  file path of save  
                   dir_data:  step3_out_dir
                   n_GPUs:  number of GPUs
                   rgb_range:  maximum value of RGB
        (option):  .pt->.nii
                   from py_SAINT.STAGE1 import pt2nii
                   pt2nii.pt2nii(ori_nii_dir_path, pt_dir_path,nii_dir_path)
                   nii_dir_path: nii_output_dir
        eg锛歱t2nii.pt2nii(ori_nii_dir_path='/home1/mksun/xh_data/273data-yscl/1T2/1/002_OCor_T2_FRFSE/',pt_dir_path='/home1/mksun/SAINT/Data/out_fuse/results/raw/',nii_dir_path='/home1/mksun/SAINT/Data/final_nii/')
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
