| import numpy as np | |
| import io | |
| from PIL import Image, ImageFilter, ImageChops | |
| from torchvision import transforms | |
| def softmax(vector): | |
| e = np.exp(vector - np.max(vector)) # for numerical stability | |
| probabilities = e / e.sum() | |
| return [float(p.item()) for p in probabilities] # Convert numpy array elements to Python floats using .item() | |
| def augment_image(img_pil, methods, rotate_degrees=0, noise_level=0, sharpen_strength=1): | |
| for method in methods: | |
| if method == "rotate": | |
| img_pil = img_pil.rotate(rotate_degrees) | |
| elif method == "add_noise": | |
| noise = np.random.normal(0, noise_level, img_pil.size[::-1] + (3,)).astype(np.uint8) | |
| img_pil = Image.fromarray(np.clip(np.array(img_pil) + noise, 0, 255).astype(np.uint8)) | |
| elif method == "sharpen": | |
| img_pil = img_pil.filter(ImageFilter.UnsharpMask(radius=2, percent=sharpen_strength, threshold=3)) | |
| return img_pil, img_pil | |
| 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 | |