gen_csv_anon.py 1.4 KB

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  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Thu Mar 10 13:38:24 2022
  4. @author: katja
  5. """
  6. from __future__ import print_function
  7. import os
  8. import glob
  9. import pandas as pd
  10. head = ['ID','Visit','Image','Mask']
  11. #Initialize datadict
  12. datadict = pd.DataFrame([], columns=head)
  13. path = r"C:/Users/strah/OneDrive/Namizje/Raziskave/Melanoma/Retrospective/Patients/*/*.nii.gz"
  14. print("path: ", path)
  15. outDir = r'C:/Users/strah/OneDrive/Namizje/Raziskave/Melanoma/Retrospective'
  16. PETNames = []
  17. MaskNames = []
  18. for filename in glob.iglob(path, recursive=True):
  19. file=os.path.basename(filename)
  20. print(file)
  21. name = file.split('.')[0]
  22. modal= "_".join(name.split('_')[2:])
  23. print(modal)
  24. if (modal =='PET_notCropped_2mmVoxel'):
  25. PETNames.append(filename)
  26. elif (modal == 'Segm_v5'):
  27. MaskNames.append(filename)
  28. j=0
  29. for (PETname,MaskName) in zip(PETNames,MaskNames):
  30. base = os.path.basename(PETname)
  31. print(PETname)
  32. name = base.split('.')[0]
  33. ID= "-".join(name.split('-')[0:5])
  34. allelse="-".join(name.split('-')[5:])
  35. Visit="_".join(allelse.split('_')[0:2])
  36. print(Visit)
  37. #MaskName = os.path.join(MaskDir,'*-label.nrrd')
  38. print('Now processing:', ID)
  39. datadict.loc[j] = [ID, Visit, PETname, MaskName]
  40. j+=1
  41. datadict.to_csv(os.path.join(outDir, 'cases_retroMManon.csv'))