123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100 |
- 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
|