#12 create dataframe of all files from disk

rmsandu · Aug 4, 2019 · f39f62a · f39f62a
1 parent efb95c5
commit f39f62a
Showing 1 changed file with 46 additions and 37 deletions.
diff --git a/A_01_read_files_info.py b/A_01_read_files_info.py
@@ -8,57 +8,66 @@
 import pandas as pd
 import B_01_ResampleSegmentations as ResizerClass
 
+
 def create_paths(rootdir):
     list_all_ct_series = []
     for subdir, dirs, files in os.walk(rootdir):
-        path, foldername = os.path.split(subdir)
-        for file in sorted(files):
-            try:
-                dcm_file = os.path.join(subdir, file)
-                ds = pydicom.read_file(dcm_file)
-            except Exception:
-                # not dicom file so continue until you find one
-                continue
-            path_img_folder = dcm_file
-            source_series_instance_uid = ds.SeriesInstanceUID
-            source_study_instance_uid = ds.StudyInstanceUID
-            source_series_number = ds.SeriesNumber
-            try:
-                path_reference_segm = ds.ReferencedImageSequence[0].ReferencedSOPInstanceUID
-                path_reference_src = ds.SourceImageSequence[0].ReferencedSOPInstanceUID
-                segment_label = ds.SegmentLabel
-            except Exception:
-                path_reference_segm = None
-                path_reference_src = None
+        if not len(files) > 1:
+            continue
+        else:
+            for file in sorted(files):
+                try:
+                    dcm_file = os.path.join(subdir, file)
+                    ds = pydicom.read_file(dcm_file)
+                except Exception:
+                    # not dicom file so continue until you find one
+                    continue
+                path_img_folder = dcm_file
+                source_series_instance_uid = ds.SeriesInstanceUID
+                source_study_instance_uid = ds.StudyInstanceUID
+                source_series_number = ds.SeriesNumber
 
-            # if the ct series is not found in the dictionary, add it
-            result = next((item for item in list_all_ct_series if
-                           item["SeriesInstanceNumberUID"] == source_series_instance_uid), None)
+                try:
+                    path_reference_segm = ds.ReferencedImageSequence[0].ReferencedSOPInstanceUID
+                    path_reference_src = ds.SourceImageSequence[0].ReferencedSOPInstanceUID
+                    lesion_number = ds.ReferencedImageSequence[0].ReferencedSegmentNumber
+                    segment_label = ds.SegmentLabel
+                except AttributeError:
+                    path_reference_segm = None
+                    path_reference_src = None
+                    lesion_number = None
+                    segment_label = None
 
-            if result is None:  # that means that that the img is not yet in the dictionary
-                dict_series_folder = {
-                    "PathSeries": path_img_folder,
-                    "SegmentLabel": segment_label,
-                    "ReferenceSourceImgSeriesInstanceUID": path_reference_src,
-                    "ReferenceSegmentationImgSeriesInstanceUID": path_reference_segm,
-                    "SeriesNumber": source_series_number,
-                    "SeriesInstanceNumberUID": source_series_instance_uid,
-                    "StudyInstanceUID": source_study_instance_uid,
-                }
-                list_all_ct_series.append(dict_series_folder)
+                # if the ct series is not found in the dictionary, add it
+                result = next((item for item in list_all_ct_series if
+                               item["SeriesInstanceNumberUID"] == source_series_instance_uid), None)
 
-        df_paths_mapping = pd.DataFrame(list_all_ct_series)
-        return df_paths_mapping
+                if result is None:  # that means that that the img is not yet in the dictionary
+                    dict_series_folder = {
+                        "PathSeries": path_img_folder,
+                        "SegmentLabel": segment_label,
+                        "LesionNumber": lesion_number,
+                        "ReferenceSourceImgSeriesInstanceUID": path_reference_src,
+                        "ReferenceSegmentationImgSeriesInstanceUID": path_reference_segm,
+                        "SeriesNumber": source_series_number,
+                        "SeriesInstanceNumberUID": source_series_instance_uid,
+                        "StudyInstanceUID": source_study_instance_uid,
+                    }
+                    list_all_ct_series.append(dict_series_folder)
 
+    df_paths_mapping = pd.DataFrame(list_all_ct_series)
+    return df_paths_mapping
 
-if __name__ == '__main__':
 
-    rootdir = r"C:\tmp_patients\Pat_MAV_BE_B03\Study_0"  # start with single patient folder, then load all the folders in the memory with glob
+if __name__ == '__main__':
+    #  start with single patient folder, then load all the folders in the memory with glob
+    rootdir = r"C:\tmp_patients\Pat_MAV_BE_B01_\Study_0"
     folder_path_new_resized_images = r" "
     flag_resize_only_segmentations = 'Y'
     flag_match_with_patient_studyID = 'N'
     flag_extract_max_size =  'N'
     df_paths_mapping = create_paths(rootdir)
+    print('Success')
 
     # now that we have the paths, we should read the folders into simpleitk objects
     # we need a loop for all the tumors and the ablations