add augmentation methods

@@ -15,6 +15,7 @@ class F3Classification(BaseModel):
         super().__init__(*args, **kwargs)
         self.class_count = len(class_name_list) if not class_other_first else len(class_name_list) - 1
         self.class_label_map = self.get_class_label_map(class_name_list, class_other_first)
+        self.image_ext_set = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
 
     @staticmethod
     def history_save(history, save_path):
@@ -60,6 +61,8 @@ class F3Classification(BaseModel):
             label = self.class_label_map[class_name]
             for file_name in os.listdir(class_dir_path):
                 # TODO image check
+                if os.path.splitext(file_name)[1] not in self.image_ext_set:
+                    continue
                 file_path = os.path.join(class_dir_path, file_name)
                 image_path_list.append(file_path)
                 label_list.append(tf.one_hot(label, depth=self.class_count))
@@ -68,21 +71,42 @@ class F3Classification(BaseModel):
     @staticmethod
     # @tf.function
     def random_rgb_2_bgr(image, label):
-        if random.random() > 0.2:
-            return image, label
+        # 1/5
+        if random.random() < 0.1:
             image = image[:, :, ::-1]
         return image, label
 
     @staticmethod
     # @tf.function
     def random_grayscale_expand(image, label):
-        if random.random() > 0.1:
-            return image, label
+        # 1/10
+        if random.random() < 0.1:
             image = tf.image.rgb_to_grayscale(image)
             image = tf.image.grayscale_to_rgb(image)
         return image, label
 
     @staticmethod
+    def random_flip_left_right(image, label):
+        # 1/10
+        if random.random() < 0.2:
+            image = tf.image.random_flip_left_right(image)
+        return image
+
+    @staticmethod
+    def random_flip_up_down(image, label):
+        # 1/10
+        if random.random() < 0.2:
+            image = tf.image.random_flip_up_down(image)
+        return image
+
+    @staticmethod
+    def random_rot90(image, label):
+        # 1/10
+        if random.random() < 0.1:
+            image = tf.image.rot90(image, k=random.randint(1, 3))
+        return image
+
+    @staticmethod
     # @tf.function
     def load_image(image_path, label):
         image = tf.io.read_file(image_path)
@@ -163,7 +187,13 @@ class F3Classification(BaseModel):
             name=train_dir_name,
             batch_size=batch_size,
             # augmentation_methods=[],
-            augmentation_methods=['random_rgb_2_bgr', 'random_grayscale_expand'],
+            augmentation_methods=[
+                'random_flip_left_right',
+                'random_flip_up_down',
+                'random_rot90',
+                'random_rgb_2_bgr',
+                'random_grayscale_expand'
+            ],
         )
         validate_dataset = self.load_dataset(
             dataset_dir=os.path.join(dataset_dir, validate_dir_name),