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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
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