Add utils

utils.py

@@ -0,0 +1,65 @@
+import torch
+import numpy as np
+import torch
+from PIL import Image, ImageOps
+from torchvision.transforms import ToPILImage, ToTensor
+totensor = ToTensor()
+topil = ToPILImage()
+
+
+
+def resize_and_crop(img, size, crop_type="center"):
+    '''Resize and crop the image to the given size.'''
+    if crop_type == "top":
+        center = (0, 0)
+    elif crop_type == "center":
+        center = (0.5, 0.5)
+    else:
+        raise ValueError
+
+    resize = list(size)
+    if size[0] is None:
+        resize[0] = img.size[0]
+    if size[1] is None:
+        resize[1] = img.size[1]
+    return ImageOps.fit(img, resize, centering=center)
+
+
+def recover_image(image, init_image, mask, background=False):
+    image = totensor(image)
+    mask = totensor(mask)[0]
+    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