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from PIL import Image
import torch
import torchvision.transforms as transforms
def preprocess_img_from_path(path_to_image, img_size, normalize=False):
img = Image.open(path_to_image)
return preprocess_img(img, img_size, normalize)
def preprocess_img(img: Image, img_size, normalize=False):
original_size = img.size
if normalize:
transform = transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
else:
transform = transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ToTensor()
])
img = transform(img).unsqueeze(0)
return img, original_size
def postprocess_img(img, original_size, normalize=False):
img = img.detach().cpu().squeeze(0)
# Denormalize the image
if normalize:
mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
img = img * std + mean
img = torch.clamp(img, 0, 1)
img = transforms.ToPILImage()(img)
img = img.resize(original_size, Image.Resampling.LANCZOS)
return img