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| from __future__ import print_function | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| # 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) | |
| ############################################################################### | |
| # 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 | |