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108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
from __future__ import print_function
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print ('?')
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import torch
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import numpy as np
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from PIL import Image
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# import numpy as np
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import os
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# Converts a Tensor into a Numpy array
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# |imtype|: the desired type of the converted numpy array
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def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
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if isinstance(image_tensor, list):
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image_numpy = []
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for i in range(len(image_tensor)):
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image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
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return image_numpy
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image_numpy = image_tensor.cpu().float().numpy()
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#if normalize:
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# image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
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#else:
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# image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
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image_numpy = (image_numpy + 1) / 2.0
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image_numpy = np.clip(image_numpy, 0, 1)
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if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
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image_numpy = image_numpy[:,:,0]
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return image_numpy
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# Converts a one-hot tensor into a colorful label map
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def tensor2label(label_tensor, n_label, imtype=np.uint8):
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if n_label == 0:
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return tensor2im(label_tensor, imtype)
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label_tensor = label_tensor.cpu().float()
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if label_tensor.size()[0] > 1:
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label_tensor = label_tensor.max(0, keepdim=True)[1]
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label_tensor = Colorize(n_label)(label_tensor)
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#label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
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label_numpy = label_tensor.numpy()
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label_numpy = label_numpy / 255.0
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return label_numpy
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def save_image(image_numpy, image_path):
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image_pil = Image.fromarray(image_numpy)
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image_pil.save(image_path)
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def mkdirs(paths):
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if isinstance(paths, list) and not isinstance(paths, str):
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for path in paths:
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mkdir(path)
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else:
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mkdir(paths)
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def mkdir(path):
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if not os.path.exists(path):
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os.makedirs(path)
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###############################################################################
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# Code from
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# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
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# Modified so it complies with the Citscape label map colors
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###############################################################################
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def uint82bin(n, count=8):
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"""returns the binary of integer n, count refers to amount of bits"""
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return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
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def labelcolormap(N):
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if N == 35: # cityscape
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cmap = np.array([( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), (111, 74, 0), ( 81, 0, 81),
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(128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153),
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(180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220, 0),
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(107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255, 0, 0), ( 0, 0,142), ( 0, 0, 70),
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( 0, 60,100), ( 0, 0, 90), ( 0, 0,110), ( 0, 80,100), ( 0, 0,230), (119, 11, 32), ( 0, 0,142)],
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dtype=np.uint8)
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else:
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cmap = np.zeros((N, 3), dtype=np.uint8)
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for i in range(N):
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r, g, b = 0, 0, 0
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id = i
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for j in range(7):
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str_id = uint82bin(id)
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r = r ^ (np.uint8(str_id[-1]) << (7-j))
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g = g ^ (np.uint8(str_id[-2]) << (7-j))
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b = b ^ (np.uint8(str_id[-3]) << (7-j))
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id = id >> 3
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cmap[i, 0] = r
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cmap[i, 1] = g
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cmap[i, 2] = b
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return cmap
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class Colorize(object):
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def __init__(self, n=35):
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self.cmap = labelcolormap(n)
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self.cmap = torch.from_numpy(self.cmap[:n])
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def __call__(self, gray_image):
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size = gray_image.size()
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color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
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for label in range(0, len(self.cmap)):
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mask = (label == gray_image[0]).cpu()
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color_image[0][mask] = self.cmap[label][0]
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color_image[1][mask] = self.cmap[label][1]
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color_image[2][mask] = self.cmap[label][2]
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return color_image
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