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image_augmentation.py

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+import torch
+from torchvision import transforms
+from PIL import Image
+import random
+from glob import glob
+from os import path
+
+
+def random_augmented_image(
+        image: Image,
+        seed: int,
+) -> torch.Tensor:
+    candidates = [transforms.RandomRotation(180), transforms.RandomVerticalFlip(),
+                  transforms.RandomHorizontalFlip(), transforms.ColorJitter()]
+    random.seed(seed)
+    x = image
+    random_transforms = random.sample(candidates, k=2)
+    for transform in random_transforms:
+        x = transform(x)
+    x = transforms.ToTensor()(x)
+    x = torch.nn.Dropout(p=0.01)(x)
+    return transforms.ToPILImage()(x)
+
+
+if __name__ == "__main__":
+    image_dir = r"training_raw"
+    augmented_image_dir = r"training_raw/augmented"
+    image_files = sorted(path.abspath(f) for f in glob(path.join(image_dir, "**", "*.jpg"), recursive=True))
+    i = 0
+    for img in image_files:
+        with Image.open(img) as image:
+            augmented_image = random_augmented_image(image, seed=3)
+            filename = path.basename(img)
+            augmented_image_path = path.join(augmented_image_dir, f"augmented_{i}.jpg")
+            augmented_image.save(augmented_image_path)
+            i += 1