| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100 | from __future__ import print_functionimport torchimport numpy as npfrom PIL import Imageimport numpy as npimport os# Converts a Tensor into a Numpy array# |imtype|: the desired type of the converted numpy arraydef 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 mapdef 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 cmapclass 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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