| | from __future__ import print_function |
| |
|
| | import torch |
| | from PIL import Image |
| | import numpy as np |
| | import os |
| |
|
| | 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() |
| |
|
| | 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 |
| |
|
| | 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 = label_tensor.numpy() |
| | label_numpy = label_numpy / 255.0 |
| |
|
| | return label_numpy |
| |
|
| | 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) |
| |
|
| |
|
| | 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: |
| | 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 |
| |
|
