Metadata-Version: 2.1
Name: py-SAINT
Version: 1.4.0
Summary: SAINT
Home-page: UNKNOWN
Author: SMK
Author-email: 454297329@qq.com
License: UNKNOWN
Description: ##### This is SAINT(Spatially Aware Interpolation Network for Medical Slice Synthesis)
        
        1. .nii->.pt 
        
           ```python
           from py_SAINT.STAGE1 import nii2pickle
           
           nii2pickle.nii2pt(ori_dir_path,output_file_path)
           ```
        
           | parameters       | description                    |
           | ---------------- | ------------------------------ |
           | ori_dir_path     | file path containing .nii      |
           | output_file_path | file path of the generated .pt |
        
           ```python
           #eg:
           
           nii2pickle.nii2pt("/home1/xx/xx_data/273data-yscl/1T2/1/002_OCor_T2_FRFSE/","/home1/xx/SAINT/Data/Stage1_Input/TEST/HR/")
           ```
        
        2. Interpolate with sag and cor view respectively
        
           ```python
           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')
           ```
        
           | parameters | description                                                  |
           | ---------- | ------------------------------------------------------------ |
           | 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 orientation of the data.) |
        
           ```python
           #eg:
           
           interpolation.get_Stage1_result (scale ='4',save ="/home1/xx/SAINT/Data/Stage1_output_sag_cor/" ,dir_data ='/home1/xx/SAINT/Data/Stage1_Input/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='cor',gpu='0')
           
           interpolation.get_Stage1_result (scale ='4',save ="/home1/xx/SAINT/Data/Stage1_output_sag_cor/" ,dir_data ='/home1/xx/SAINT/Data/Stage1_Input/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='sag',gpu='0')
           ```
        
           
        
        3. 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
        
           ```python
           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)
           ```
        
           | parameters        | description                               |
           | ----------------- | ----------------------------------------- |
           | 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                    |
        
           ```python
           #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)
           ```
        
        4. Residual-Fusion
        
           ```python
           from py_SAINT.STAGE2 import fuse
           fuse.get_Stage2_result(save ='/path/',dir_data ='/path/' ,n_GPUs =1 ,rgb_range =4000,gpu='0')
           ```
        
           | parameters | description          |
           | ---------- | -------------------- |
           | save       | file path of save    |
           | dir_data   | step3_out_dir        |
           | n_GPUs     | number of GPUs       |
           | rgb_range  | maximum value of RGB |
        
           ```python
           #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')
           ```
        
        5. .pt->.nii(option)
        
           ```python
           from py_SAINT.STAGE1 import pt2nii
           pt2nii.pt2nii(ori_nii_dir_path, pt_dir_path,nii_dir_path)
           ```
        
           | parameters   | description    |
           | ------------ | -------------- |
           | nii_dir_path | nii_output_dir |
        
           ```python
           #eg:
           
           pt2nii.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
