Create utils.py

utils.py

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+from PIL import Image
+
+import numpy as np
+
+import torch
+
+import torchvision.transforms as T
+
+
+
+totensor = T.ToTensor()
+
+topil = T.ToPILImage()
+
+
+
+def recover_image(image, init_image, mask, background=False):
+
+image = totensor(image)
+
+mask = totensor(mask)
+
+init_image = totensor(init_image)
+
+if background:
+
+result = mask * init_image + (1 - mask) * image
+
+else:
+
+result = mask * image + (1 - mask) * init_image
+
+return topil(result)
+
+
+
+def preprocess(image):
+
+w, h = image.size
+
+w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
+
+image = image.resize((w, h), resample=Image.LANCZOS)
+
+image = np.array(image).astype(np.float32) / 255.0
+
+image = image[None].transpose(0, 3, 1, 2)
+
+image = torch.from_numpy(image)
+
+return 2.0 * image - 1.0
+
+
+
+def prepare_mask_and_masked_image(image, mask):
+
+image = np.array(image.convert("RGB"))
+
+image = image[None].transpose(0, 3, 1, 2)
+
+image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+
+
+mask = np.array(mask.convert("L"))
+
+mask = mask.astype(np.float32) / 255.0
+
+mask = mask[None, None]
+
+mask[mask < 0.5] = 0
+
+mask[mask >= 0.5] = 1
+
+mask = torch.from_numpy(mask)
+
+
+
+masked_image = image * (mask < 0.5)
+
+
+
+return mask, masked_image
+
+
+
+def prepare_image(image):
+
+image = np.array(image.convert("RGB"))
+
+image = image[None].transpose(0, 3, 1, 2)
+
+image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+
+
+return image[0]