def __init__(self, sub_list):
self.sub_list = sub_list
self.skipcrop = True
if self.skipcrop:
osize = [512, 512]
else:
osize = [576, 576]
fineSize = [512,512]
transform_list = []
transform_list.append(transforms.Scale(osize, Image.BICUBIC))
self.transforms_scale = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.Scale(osize, Image.NEAREST))
self.transforms_seg_scale = transforms.Compose(transform_list)
transform_list = []
transform_list.append(random_crop_yh.randomcrop_yh(fineSize))
self.transforms_crop = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.ToTensor())
self.transforms_toTensor = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)))
self.transforms_normalize = transforms.Compose(transform_list)
def __init__(self, sub_list):
self.sub_list = sub_list
self.finesize = [128, 128]
transform_list = []
transform_list.append(transforms.Scale(self.finesize, Image.BICUBIC))
self.transforms_scale = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.Scale(self.finesize, Image.NEAREST))
self.transforms_seg_scale = transforms.Compose(transform_list)
transform_list = []
transform_list.append(random_crop_yh.randomcrop_yh(self.finesize))
self.transforms_crop = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.ToTensor())
self.transforms_toTensor = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.Normalize([0.5],[0.5]))
self.transforms_normalize = transforms.Compose(transform_list)
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.dir_A = opt.raw_MRI_dir
self.dir_B = opt.raw_CT_dir
self.dir_Seg = opt.raw_MRI_seg_dir
self.A_paths = opt.imglist_MRI
self.B_paths = opt.imglist_CT
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
if not self.opt.isTrain:
self.skipcrop = True
else:
self.skipcrop = False
# self.transform = get_transform(opt)
if self.skipcrop:
osize = [opt.fineSize, opt.fineSize]
else:
osize = [opt.loadSize, opt.loadSize]
transform_list = []
transform_list.append(transforms.Scale(osize, Image.BICUBIC))
self.transforms_scale = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.Scale(osize, Image.NEAREST))
self.transforms_seg_scale = transforms.Compose(transform_list)
transform_list = []
transform_list.append(random_crop_yh.randomcrop_yh(opt.fineSize))
self.transforms_crop = transforms.Compose(transform_list)
transform_list = []
transform_list.append(transforms.ToTensor())
self.transforms_toTensor = transforms.Compose(transform_list)
transform_list = []
transform_list.append(
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
self.transforms_normalize = transforms.Compose(transform_list)