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preprocessing.py

+import os
+import cv2
+import numpy as np
+
+path = 'Data'
+
+categories = os.listdir(path)
+labels = list(range(len(categories)))
+
+categorDict = dict(zip(categories, labels))
+
+print(categories)
+print(labels)
+print(categorDict)
+
+size = 50
+dataset = []
+
+for category in categories:
+    imgs_path = os.path.join(path, category)
+    imgs_names = os.listdir(imgs_path)
+
+    for img_name in imgs_names:
+        img_path = os.path.join(imgs_path, img_name)
+        img = cv2.imread(img_path)
+
+        try:
+
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            gray = cv2.resize(gray, (size, size))
+            dataset.append([gray, categorDict[category]])
+
+        except Exception as e:
+
+            print(e)
+
+len(dataset)
+
+from random import shuffle
+
+shuffle(dataset)
+
+data = []
+target = []
+
+for img, label in dataset:
+    data.append(img)
+    target.append(label)
+
+data = np.array(data)
+target = np.array(target)
+
+data = data.reshape(data.shape[0], size, size, 1) / 255
+
+from keras.utils import np_utils
+
+target = np_utils.to_categorical(target)
+
+print(data.shape)
+print(target.shape)
+
+np.save('Processed_Data/data', data)
+np.save('Processed_Data/target', target)