| import numpy as np | |
| import io | |
| from PIL import Image | |
| from torchvision import transforms | |
| def softmax(vector): | |
| e = np.exp(vector - np.max(vector)) # for numerical stability | |
| return e / e.sum() | |
| def augment_image(img_pil): | |
| transform_flip = transforms.Compose([ | |
| transforms.RandomHorizontalFlip(p=1.0) | |
| ]) | |
| transform_rotate = transforms.Compose([ | |
| transforms.RandomRotation(degrees=(90, 90)) | |
| ]) | |
| augmented_img_flip = transform_flip(img_pil) | |
| augmented_img_rotate = transform_rotate(img_pil) | |
| return augmented_img_flip, augmented_img_rotate | |
| def convert_pil_to_bytes(image, format='JPEG'): | |
| img_byte_arr = io.BytesIO() | |
| image.save(img_byte_arr, format=format) | |
| img_byte_arr = img_byte_arr.getvalue() | |
| return img_byte_arr |