| from torchvision.transforms import transforms | |
| import torch | |
| def process_image(image, shape=(500, 500)): | |
| """ | |
| This function takes an image and transforms it into a tensor | |
| """ | |
| transform = transforms.Compose( | |
| [ | |
| transforms.Resize(shape), | |
| transforms.ToTensor(), | |
| transforms.Normalize( | |
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | |
| ), | |
| ] | |
| ) | |
| image = transform(image).unsqueeze(0) | |
| return image | |
| def tensor_to_image(tensor): | |
| """ | |
| This function takes a tensor and transforms it into an image | |
| """ | |
| inverse_normalize = transforms.Normalize( | |
| mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225], | |
| std=[1 / 0.229, 1 / 0.224, 1 / 0.225], | |
| ) | |
| tensor = inverse_normalize(tensor) | |
| tensor = torch.clamp(tensor, 0, 1) | |
| return transforms.ToPILImage()(tensor) |