Keras generator function now returns categorical label for each sample.

Author: mdbloice

Augmentor/ImageUtilities.py

@@ -47,6 +47,7 @@ def __init__(self, image_path, output_directory):
         self._class_label = None
         self._class_label_int = None
         self._label_pair = None
+        self._categorical_label = None
 
         # Now we call the setters that we require.
         self.image_path = image_path
@@ -124,6 +125,14 @@ def class_label_int(self):
     def class_label_int(self, value):
         self._class_label_int = value
 
+    @property
+    def categorical_label(self):
+        return self._categorical_label
+
+    @categorical_label.setter
+    def categorical_label(self, value):
+        self._categorical_label = value
+
     @property
     def ground_truth(self):
         """
@@ -199,6 +208,7 @@ def scan(source_directory, abs_output_directory):
             a = AugmentorImage(image_path=image_path, output_directory=abs_output_directory)
             a.class_label = parent_directory_name
             a.class_label_int = label_counter
+            a.categorical_label = np.ndarray(1, dtype=np.uint32)  # TODO: Fix, as this is not good. Maybe leave as None.
             augmentor_images.append(a)
 
             class_labels.append((label_counter, parent_directory_name))
@@ -211,9 +221,12 @@ def scan(source_directory, abs_output_directory):
         for d in directories:
             output_directory = os.path.join(abs_output_directory, os.path.split(d)[1])
             for image_path in scan_directory(d):
+                categorical_label = np.zeros(directory_count, dtype=np.uint32)
                 a = AugmentorImage(image_path=image_path, output_directory=output_directory)
                 a.class_label = os.path.split(d)[1]
                 a.class_label_int = label_counter
+                categorical_label[label_counter] = 1
+                a.categorical_label = categorical_label
                 augmentor_images.append(a)
             class_labels.append((os.path.split(d)[1], label_counter))
             label_counter += 1

Augmentor/Pipeline.py

@@ -342,6 +342,8 @@ def keras_image_generator(self, image_format='channels_first'):
         :return: An image generator.
         """
 
+        # TODO: Always return at least the original dataset as well as the augmented dataset
+
         while True:
             batch_indices = list(range(0, len(self.augmentor_images)))
             for i in range(0, len(self.augmentor_images)):
@@ -352,19 +354,20 @@ def keras_image_generator(self, image_format='channels_first'):
 
                 if image_format == 'channels_first':
                     num_of_channels = len(im_PIL.getbands())
-                    im_array = im_array.reshape(num_of_channels, im_PIL.width, im_PIL.height)
-                    yield im_array, self.augmentor_images[im_index].class_label_int
+                    im_array = im_array.reshape(1, num_of_channels, im_PIL.width, im_PIL.height)
+                    yield im_array, self.augmentor_images[im_index].categorical_label
                 elif image_format == 'channels_last':
                     num_of_channels = len(im_PIL.getbands())
-                    im_array = im_array.reshape(im_PIL.width, im_PIL.height, num_of_channels)
-                    yield im_array, self.augmentor_images[im_index].class_label_int
+                    im_array = im_array.reshape(1, im_PIL.width, im_PIL.height, num_of_channels)
+                    yield im_array, self.augmentor_images[im_index].categorical_label
 
     def keras_image_generator_with_replacement(self, image_format='channels_first'):
-        while True:
-            im_index = random.randint(0, len(self.augmentor_images))
-            im_PIL = self._execute(self.augmentor_images[im_index], save_to_disk=False)
-            im_array = np.asarray(im_PIL)
-            yield im_array, self.augmentor_images[im_index].class_label_int
+        raise NotImplementedError("This method is currently not implemented. Use keras_image_generator().")
+        #while True:
+        #    im_index = random.randint(0, len(self.augmentor_images))
+        #    im_PIL = self._execute(self.augmentor_images[im_index], save_to_disk=False)
+        #    im_array = np.asarray(im_PIL)
+        #    yield im_array, self.augmentor_images[im_index].class_label_int
 
     def add_operation(self, operation):
         """