Clean up of DataFramePipeline class for consistency.

todaysky1234 · Dec 17, 2018 · 0c321ad · 0c321ad
1 parent 0f7118e
commit 0c321ad
Showing 1 changed file with 11 additions and 18 deletions.
diff --git a/Augmentor/Pipeline.py b/Augmentor/Pipeline.py
@@ -25,7 +25,12 @@
 import uuid
 import warnings
 import numpy as np
-from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+from concurrent.futures import ThreadPoolExecutor
+
+# NOTE:
+# https://pypi.org/project/futures/ mentions:
+# The ProcessPoolExecutor class has known (unfixable) problems on Python 2 and
+# should not be relied on for mission critical work.
 
 from tqdm import tqdm
 from PIL import Image
@@ -1758,24 +1763,13 @@ def __init__(self, source_dataframe, image_col, category_col, output_directory="
         super(DataFramePipeline, self).__init__(source_directory=None,
                                                 output_directory=output_directory,
                                                 save_format=save_format)
-        self._populate(source_dataframe,
-                  image_col,
-                  category_col,
-                  output_directory,
-                  save_format)
-
-    def _populate(self,
-                  source_dataframe,
-                  image_col,
-                  category_col,
-                  output_directory,
-                  save_format):
+
+        self._populate(source_dataframe, image_col, category_col, output_directory, save_format)
+
+    def _populate(self, source_dataframe, image_col, category_col, output_directory, save_format):
         # Assume we have an absolute path for the output
         # Scan the directory that user supplied.
-        self.augmentor_images, self.class_labels = scan_dataframe(source_dataframe,
-                                                                   image_col,
-                                                                   category_col,
-                                                                  output_directory)
+        self.augmentor_images, self.class_labels = scan_dataframe(source_dataframe, image_col, category_col, output_directory)
 
         self._check_images(output_directory)
 
@@ -1908,7 +1902,6 @@ def sample(self, n):
                 if r <= operation.probability:
                     images_to_return = operation.perform_operation(images_to_return)
 
-            # Convert to array data
             images_to_return = [np.asarray(x) for x in images_to_return]
 
             if self.labels: