Conflict resolved for 2.0 releaase merge

Dockerfile

@@ -85,3 +85,5 @@ RUN cp bin/3.2_DTIConnectivity/dsi_main.py dsi_main
 COPY lib/ lib/
 COPY test/ test/
 RUN echo "/aida/bin/dsi_studio_ubuntu_1804/dsi-studio/dsi_studio" > bin/3.2_DTIConnectivity/dsi_studioPath.txt
+COPY test/ test/
+

bin/2.1_T2PreProcessing/applyMICO.py

@@ -26,13 +26,15 @@
 import sys,os
 import MICO
 import progressbar
+from tqdm import tqdm
 
 
 def run_MICO(IMGdata,outputPath):
     data = nii.load(IMGdata)
     v = 8
 
-    vol = data.get_data()
+    # get UNSCALED img data
+    vol = data.get_fdata()
     biasCorrectedVol = np.zeros(vol.shape[0:3])
     ImgMe = np.mean(vol)
     if ImgMe > 10000:
@@ -42,11 +44,8 @@ def run_MICO(IMGdata,outputPath):
     else:
         nCvalue = 1
 
-    bar = progressbar.ProgressBar()
-    for idx in bar(range(vol.shape[2])):
-        #ImgMe = np.mean(vol,3)
-        #Img = ImgMe[:,:,idx]/10
-
+    progressbar = tqdm(total=vol.shape[2], desc='Biasfieldcorrection')
+    for idx in range(vol.shape[2]):
         if np.size(vol.shape) == 4:
             Img = vol[:, :, idx, 0] / nCvalue
         else:
@@ -107,23 +106,22 @@ def run_MICO(IMGdata,outputPath):
                         # smV = img_bc > 1200
                         # img_bc[smV] = 0
 
-
-
-
         M, C = sortMemC(M, C)
         seg = np.zeros([nrow,ncol])
         for k in range(N_region):
             seg = seg + k * M[:,:, k] # label  the k-th region
 
         biasCorrectedVol[:,:,idx]=img_bc
 
+        progressbar.update(1)
+
+    progressbar.close()
+
     unscaledNiiData = nii.Nifti1Image(biasCorrectedVol, data.affine)
     hdrOut = unscaledNiiData.header
     hdrOut.set_xyzt_units('mm')
 
-    #outputData = os.path.join(os.path.dirname(IMGdata),os.path.basename(IMGdata).split('.')[0]+'BiasMico.nii.gz')
     outputData = os.path.join(outputPath,os.path.basename(IMGdata).split('.')[0]+'Bias.nii.gz')
-    # hdrOut['sform_code'] = 1
     nii.save(unscaledNiiData,outputData)
 
     return outputData

bin/2.1_T2PreProcessing/preProcessing_T2.py

@@ -14,6 +14,36 @@
 import nibabel as nii
 import numpy as np
 import applyMICO
+import subprocess
+import shutil
+import logging
+
+
+def reset_orientation(input_file):
+
+    brkraw_dir = os.path.join(os.path.dirname(input_file), "brkraw")
+    if os.path.exists(brkraw_dir):
+        return 
+
+    os.mkdir(brkraw_dir)
+    dst_path = os.path.join(brkraw_dir, os.path.basename(input_file))
+
+    shutil.copyfile(input_file, dst_path)
+
+    data = nii.load(input_file)
+    raw_img = data.dataobj.get_unscaled()
+
+    raw_nii = nii.Nifti1Image(raw_img, data.affine)
+    nii.save(raw_nii, input_file)
+
+    delete_orient_command = f"fslorient -deleteorient {input_file}"
+    subprocess.run(delete_orient_command, shell=True)
+
+    # Befehl zum Festlegen der radiologischen Orientierung
+    forceradiological_command = f"fslorient -forceradiological {input_file}"
+    subprocess.run(forceradiological_command, shell=True)
+
+
 
 #%% Functions
 
@@ -24,7 +54,7 @@ def applyBET(input_file, frac, radius, vertical_gradient):
     """
     # scale Nifti data by factor 10
     data = nii.load(input_file)
-    imgTemp = data.get_data()
+    imgTemp = data.get_fdata()
     scale = np.eye(4)* 10
     scale[3][3] = 1
 
@@ -37,23 +67,21 @@ def applyBET(input_file, frac, radius, vertical_gradient):
     hdrIn = scaledNiiData.header
     hdrIn.set_xyzt_units('mm')
     scaledNiiData = nii.as_closest_canonical(scaledNiiData)
-    print('Orientation:' + str(nii.aff2axcodes(scaledNiiData.affine)))
 
-    fslPath = os.path.join(os.getcwd(),'fslScaleTemp.nii.gz')
+    fslPath = os.path.join(os.path.dirname(input_file),'fslScaleTemp.nii.gz')
     nii.save(scaledNiiData, fslPath)
 
     # extract brain
     output_file = os.path.join(os.path.dirname(input_file),os.path.basename(input_file).split('.')[0] + 'Bet.nii.gz')
 
     myBet = fsl.BET(in_file=fslPath, out_file=output_file,frac=frac,radius=radius,
                     vertical_gradient=vertical_gradient,robust=True, mask = True)
-    print(myBet.cmdline)
     myBet.run()
     os.remove(fslPath)
 
     # unscale result data by factor 10ˆ(-1)
     dataOut = nii.load(output_file)
-    imgOut = dataOut.get_data()
+    imgOut = dataOut.get_fdata()
     scale = np.eye(4)/ 10
     scale[3][3] = 1
 
@@ -72,7 +100,7 @@ def applyBET(input_file, frac, radius, vertical_gradient):
     parser = argparse.ArgumentParser(description='Preprocessing of T2 Data')
 
     requiredNamed = parser.add_argument_group('Required named arguments')
-    requiredNamed.add_argument('-i','--inputFile', help='path to input file',required=True)
+    requiredNamed.add_argument('-i','--input_file', help='path to input file',required=True)
 
     parser.add_argument(
         '-f',
@@ -110,20 +138,24 @@ def applyBET(input_file, frac, radius, vertical_gradient):
     args = parser.parse_args()
 
     # set Parameters
-    inputFile = None
-    if args.inputFile is not None and args.inputFile is not None:
-        inputFile = args.inputFile
-    if not os.path.exists(inputFile):
-        sys.exit("Error: '%s' is not an existing directory or file %s is not in directory." % (inputFile, args.file,))
+    input_file = None
+    if args.input_file is not None and args.input_file is not None:
+        input_file = args.input_file
+    if not os.path.exists(input_file):
+        sys.exit("Error: '%s' is not an existing directory or file %s is not in directory." % (input_file, args.file,))
+
+    #Konfiguriere das Logging-Modul
+    log_file_path = os.path.join(os.path.dirname(input_file), "preprocess.txt")
+    logging.basicConfig(filename=log_file_path, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 
 
     frac = args.frac
     radius = args.radius
     vertical_gradient = args.vertical_gradient
     bias_skip = args.bias_skip
 
-    # 1) Process MRI
-    print("T2 Preprocessing  \33[5m...\33[0m (wait!)", end="\r")
+    logging.info(f"Frac: {frac} Radius: {radius} Gradient {vertical_gradient}")
 
+<<<<<<< HEAD
     # generate log - file
     sys.stdout = open(os.path.join(os.path.dirname(inputFile), 'preprocessing.log'), 'w')
     sys.stderr = open(os.path.join(os.path.dirname(inputFile), 'preprocessing_err.log'), 'w')
@@ -132,15 +164,51 @@ def applyBET(input_file, frac, radius, vertical_gradient):
     print("Frac: %s" % frac)
     print("Radius: %s" % radius)
     print("Gradient: %s" %vertical_gradient)
+=======
+    reset_orientation(input_file)
+    logging.info("Orientation resetted to RAS")
+>>>>>>> production
 
     #intensity correction using non parametric bias field correction algorithm
+    logging.info("Starting Biasfieldcorrection:")
     if bias_skip == 0:
-        outputMICO = applyMICO.run_MICO(inputFile,os.path.dirname(inputFile))
+        try:
+            outputMICO = applyMICO.run_MICO(input_file,os.path.dirname(input_file))
+            logging.info("Biasfieldcorrecttion was successful")
+        except Exception as e:
+            logging.error(f'Fehler in der Biasfieldcorrecttion\nFehlermeldung: {str(e)}')
+            raise
     else:
+<<<<<<< HEAD
         outputMICO = inputFile
     # get rid of your skull
     outputBET = applyBET(input_file=outputMICO,frac=frac,radius=radius,vertical_gradient=vertical_gradient)
 
     sys.stdout = sys.__stdout__
     sys.stderr = sys.__stderr__
-    print('T2 Preprocessing  \033[0;30;42m COMPLETED \33[0m')
+    print('T2 Preprocessing  \033[0;30;42m COMPLETED \33[0m')
+=======
+        outputMICO = input_file
+
+    # brain extraction
+    logging.info("Starting brain extraction")
+    try:
+        outputBET = applyBET(input_file=outputMICO,frac=frac,radius=radius,vertical_gradient=vertical_gradient)
+        logging.info("Brain extraction was successful")
+    except Exception as e:
+        logging.error(f'Error in brain extraction\nFehlermeldung: {str(e)}')
+        raise
+
+    logging.info("Preprocessing completed")
+
+
+
+
+
+
+
+
+
+
+
+>>>>>>> production