src/machine_learning/neural_network/database.py

@@ -0,0 +1,25 @@
+import os
+
+import cv2
+
+IMG_SIZE = 28
+CATEGORIES = ["bomby", "other"]
+
+temp_path = os.path.abspath('../../..')
+DIR = 'images\learning'
+DIR = os.path.join(temp_path, DIR)
+
+
+def create_training_data():
+    training_data = []
+    for category in CATEGORIES:
+        path = os.path.join(DIR, category)
+        category_index = CATEGORIES.index(category)
+        for img in os.listdir(path):
+            try:
+                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
+                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
+                training_data.append((new_array, category_index))
+            except Exception as e:
+                print(e)
+    return training_data

src/neural_network/database.py

@@ -1,27 +0,0 @@
-import numpy as np
-import os
-import cv2
-
-IMG_SIZE = 28
-CATEGORIES = ["bomby","other"]
-
-temp_path =os.path.abspath('../..')
-DIR = 'images\learning'
-DIR = os.path.join(temp_path, DIR)
-
-
-def create_training_data():
-	training_data_label = []
-	training_data_images = []
-	for category in CATEGORIES:
-		path = os.path.join(DIR, category)
-		category_index = CATEGORIES.index(category)
-		for img in os.listdir(path):
-			try:
-				img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
-				new_array = cv2.resize(img_array, (IMG_SIZE,IMG_SIZE))
-				training_data_label.append(category_index)
-				training_data_images.append(new_array)
-			except Exception as e:
-				pass
-	return [training_data_images, training_data_label]