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| # Copyright (c) Microsoft Corporation. | |
| # Licensed under the MIT License. | |
| from __future__ import print_function | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| import torch.nn as nn | |
| # Converts a Tensor into a Numpy array | |
| # |imtype|: the desired type of the converted numpy array | |
| def tensor2im(image_tensor, imtype=np.uint8, normalize=True): | |
| if isinstance(image_tensor, list): | |
| image_numpy = [] | |
| for i in range(len(image_tensor)): | |
| image_numpy.append(tensor2im(image_tensor[i], imtype, normalize)) | |
| return image_numpy | |
| image_numpy = image_tensor.cpu().float().numpy() | |
| if normalize: | |
| image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 | |
| else: | |
| image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 | |
| image_numpy = np.clip(image_numpy, 0, 255) | |
| if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3: | |
| image_numpy = image_numpy[:, :, 0] | |
| return image_numpy.astype(imtype) | |
| # Converts a one-hot tensor into a colorful label map | |
| def tensor2label(label_tensor, n_label, imtype=np.uint8): | |
| if n_label == 0: | |
| return tensor2im(label_tensor, imtype) | |
| label_tensor = label_tensor.cpu().float() | |
| if label_tensor.size()[0] > 1: | |
| label_tensor = label_tensor.max(0, keepdim=True)[1] | |
| label_tensor = Colorize(n_label)(label_tensor) | |
| label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0)) | |
| return label_numpy.astype(imtype) | |
| def save_image(image_numpy, image_path): | |
| image_pil = Image.fromarray(image_numpy) | |
| image_pil.save(image_path) | |
| def mkdirs(paths): | |
| if isinstance(paths, list) and not isinstance(paths, str): | |
| for path in paths: | |
| mkdir(path) | |
| else: | |
| mkdir(paths) | |
| def mkdir(path): | |
| if not os.path.exists(path): | |
| os.makedirs(path) | |