Add option to ignore problematic files.

By default problematic files (non-image files, or image files that
can't be read by a specified imageIO or corrupt image files) are
listed in the output csv. Adding a flag --ignore_problematic. If
given on the commandline the problematic files will not be listed in
the csv.

Author: zivy

Python/71_Trust_But_Verify.ipynb

@@ -737,7 +737,9 @@
    "source": [
     "faux_volume_image_files = sitk.ReadImage(faux_series_volume_file_name)\n",
     "# convert string representation of list of file names to a list of file names\n",
-    "image_file_list = pd.read_csv(faux_series_file_list_name)[\"files\"].apply(ast.literal_eval).tolist()\n",
+    "image_file_list = (\n",
+    "    pd.read_csv(faux_series_file_list_name)[\"files\"].apply(ast.literal_eval).tolist()\n",
+    ")\n",
     "\n",
     "image_selection_gui2 = ImageSelection(\n",
     "    faux_volume_image_files,\n",

Python/scripts/characterize_data.py

@@ -452,6 +452,11 @@ def characterize_data(argv=None):
         nargs="*",
         help="titles of the results columns for the metadata_keys",
     )
+    parser.add_argument(
+        "--ignore_problems",
+        action="store_true",
+        help="problematic files will not be listed if parameter is given on commandline",
+    )
 
     args = parser.parse_args(argv)
     if len(args.external_applications) != len(args.external_applications_headings):
@@ -479,6 +484,9 @@ def characterize_data(argv=None):
             meta_data_keys=args.metadata_keys,
             additional_column_names=args.metadata_keys_headings,
         )
+    # remove all rows associated with problematic files (non-image files or image files with problems)
+    if args.ignore_problems:
+        df.dropna(inplace=True)
     # save the raw information, create directory structure if it doesn't exist
     os.makedirs(os.path.dirname(args.output_file), exist_ok=True)
     df.to_csv(args.output_file, index=False)