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| from __future__ import print_function | |
| import os | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| print('?') | |
| # 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 = (image_numpy + 1) / 2.0 | |
| image_numpy = np.clip(image_numpy, 0, 1) | |
| if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3: | |
| image_numpy = image_numpy[:, :, 0] | |
| return image_numpy | |
| # 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)) | |
| label_numpy = label_tensor.numpy() | |
| label_numpy = label_numpy / 255.0 | |
| return label_numpy | |
| def save_image(image_numpy, image_path, grayscale=False): | |
| image_pil = Image.fromarray(image_numpy) | |
| image_pil.save(image_path) | |
| def save_tensor_as_image(image_tensor, image_path, grayscale=False): | |
| image_numpy = tensor_to_image(image_tensor, grayscale) | |
| save_image(image_numpy, image_path, grayscale) | |
| def tensor_to_image(img_tensor, grayscale=False): | |
| if grayscale: | |
| tensor = img_tensor.cpu().clamp(0, 255) | |
| else: | |
| tensor = (img_tensor.clone() + 1) * 0.5 * 255 | |
| tensor = tensor.cpu().clamp(0, 255) | |
| try: | |
| array = tensor.numpy().astype('uint8') | |
| except: | |
| array = tensor.detach().numpy().astype('uint8') | |
| if array.shape[0] == 1: | |
| array = array.squeeze(0) | |
| elif array.shape[0] == 3: | |
| array = array.swapaxes(0, 1).swapaxes(1, 2) | |
| return array | |
| 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) | |
| ############################################################################### | |
| # Code from | |
| # https://github.com/ycszen/pytorch-seg/blob/master/transform.py | |
| # Modified so it complies with the Citscape label map colors | |
| ############################################################################### | |
| def uint82bin(n, count=8): | |
| """returns the binary of integer n, count refers to amount of bits""" | |
| return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) | |
| def labelcolormap(N): | |
| if N == 35: # cityscape | |
| cmap = np.array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (111, 74, 0), (81, 0, 81), | |
| (128, 64, 128), (244, 35, 232), (250, 170, 160), (230, | |
| 150, 140), (70, 70, 70), (102, 102, 156), (190, 153, 153), | |
| (180, 165, 180), (150, 100, 100), (150, 120, 90), (153, | |
| 153, 153), (153, 153, 153), (250, 170, 30), (220, 220, 0), | |
| (107, 142, 35), (152, 251, 152), (70, 130, 180), (220, | |
| 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), | |
| (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100), (0, 0, 230), (119, 11, 32), (0, 0, 142)], | |
| dtype=np.uint8) | |
| else: | |
| cmap = np.zeros((N, 3), dtype=np.uint8) | |
| for i in range(N): | |
| r, g, b = 0, 0, 0 | |
| id = i | |
| for j in range(7): | |
| str_id = uint82bin(id) | |
| r = r ^ (np.uint8(str_id[-1]) << (7-j)) | |
| g = g ^ (np.uint8(str_id[-2]) << (7-j)) | |
| b = b ^ (np.uint8(str_id[-3]) << (7-j)) | |
| id = id >> 3 | |
| cmap[i, 0] = r | |
| cmap[i, 1] = g | |
| cmap[i, 2] = b | |
| return cmap | |
| class Colorize(object): | |
| def __init__(self, n=35): | |
| self.cmap = labelcolormap(n) | |
| self.cmap = torch.from_numpy(self.cmap[:n]) | |
| def __call__(self, gray_image): | |
| size = gray_image.size() | |
| color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) | |
| for label in range(0, len(self.cmap)): | |
| mask = (label == gray_image[0]).cpu() | |
| color_image[0][mask] = self.cmap[label][0] | |
| color_image[1][mask] = self.cmap[label][1] | |
| color_image[2][mask] = self.cmap[label][2] | |
| return color_image | |