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data_augmentation.py

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+from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
+import os
+import shutil
+
+def data_augmentation(input_dir, output_dir):
+    # List all classes (assuming each subdirectory represents a class)
+    classes = os.listdir(input_dir)
+
+    # Create the output directory if it doesn't exist
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    else:
+        # Clear existing files in the directory
+        shutil.rmtree(output_dir)
+        os.makedirs(output_dir)
+
+    # Dictionary to store file paths for each class
+    data = {cls: [] for cls in classes}
+
+    # Gather file paths for each class
+    for cls in classes:
+        cls_dir = os.path.join(input_dir, cls)
+        data[cls] = [os.path.join(cls_dir, file) for file in os.listdir(cls_dir)]
+
+    # ImageDataGenerator for augmentation
+    datagen = ImageDataGenerator(
+        rescale=1.0 / 255,
+        rotation_range=40,
+        width_shift_range=0.2,
+        height_shift_range=0.2,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True,
+        fill_mode='nearest'
+    )
+
+    # Generate and save augmented images
+    for cls, files in data.items():
+        cls_augmented_dir = os.path.join(output_dir, cls)
+        os.makedirs(cls_augmented_dir)
+
+        for file in files:
+            img = load_img(file)
+            x = img_to_array(img)
+            x = x.reshape((1,) + x.shape)
+
+            i = 0
+            for batch in datagen.flow(x, batch_size=1, save_to_dir=cls_augmented_dir, save_prefix=cls, save_format='jpg'):
+                i += 1
+                if i >= 5:  # Generate 5 augmented images for each original image
+                    break
+
+# Usage example:
+input_directory = "D:/Drone/New folder (2)/asl_alphabet_train"
+output_directory = "D:/Drone/New folder (2)/asl_alphabet_train_augmented"
+
+data_augmentation(input_directory, output_directory)