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| import gc | |
| import torch | |
| from scipy.signal import fftconvolve | |
| from PIL import Image | |
| import numpy as np | |
| def flush(): | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def convolution(mask: Image.Image, size=9) -> Image: | |
| """Method to blur the mask | |
| Args: | |
| mask (Image): masking image | |
| size (int, optional): size of the blur. Defaults to 9. | |
| Returns: | |
| Image: blurred mask | |
| """ | |
| mask = np.array(mask.convert("L")) | |
| conv = np.ones((size, size)) / size**2 | |
| mask_blended = fftconvolve(mask, conv, 'same') | |
| mask_blended = mask_blended.astype(np.uint8).copy() | |
| border = size | |
| # replace borders with original values | |
| mask_blended[:border, :] = mask[:border, :] | |
| mask_blended[-border:, :] = mask[-border:, :] | |
| mask_blended[:, :border] = mask[:, :border] | |
| mask_blended[:, -border:] = mask[:, -border:] | |
| return Image.fromarray(mask_blended).convert("L") | |
| def postprocess_image_masking(inpainted: Image, image: Image, mask: Image) -> Image: | |
| """Method to postprocess the inpainted image | |
| Args: | |
| inpainted (Image): inpainted image | |
| image (Image): original image | |
| mask (Image): mask | |
| Returns: | |
| Image: inpainted image | |
| """ | |
| final_inpainted = Image.composite(inpainted.convert("RGBA"), image.convert("RGBA"), mask) | |
| return final_inpainted.convert("RGB") | |