def initialize(self, opt):
self.opt = opt
self.A_paths = []
self.B_paths = []
for root_dir_item in opt.root_dir:
dir_A_tmp = os.path.join(root_dir_item, opt.phase + '_A')
dir_B_tmp = os.path.join(root_dir_item, opt.phase + '_B')
img_list = os.path.join(root_dir_item, opt.name_img_list)
A_labels_tmp, A_paths_tmp = get_list(dir_A_tmp, img_list)
B_labels_tmp, B_paths_tmp = get_list(dir_B_tmp, img_list)
self.A_paths += A_paths_tmp
self.B_paths += B_paths_tmp
assert (len(self.A_paths) == len(self.B_paths))
assert (opt.resize_or_crop == 'resize_and_crop')
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
self.transform = transforms.Compose(transform_list)
self.crop_type = opt.crop_type
def initialize(self, opt):
self.opt = opt
self.A_paths = []
self.B_paths = []
for root_dir_item in opt.root_dir:
dir_A_tmp = os.path.join(root_dir_item, opt.phase + '_A')
dir_B_tmp = os.path.join(root_dir_item, opt.phase + '_B')
img_list = os.path.join(root_dir_item, opt.name_img_list)
A_labels_tmp, A_paths_tmp = get_list(dir_A_tmp, img_list)
B_labels_tmp, B_paths_tmp = get_list(dir_B_tmp, img_list)
self.A_paths += A_paths_tmp
self.B_paths += B_paths_tmp
self.color_A = opt.color_A
self.color_B = opt.color_B
self.heatmap_size = opt.fineSize_B
self.transform = AffineCompose(
rotation_range=opt.rotate_range,
translation_range=opt.translate_range,
zoom_range=opt.zoom_range,
output_img_width=opt.fineSize_A,
output_img_height=opt.fineSize_A,
mirror=opt.mirror,
corr_list=None,
normalise=opt.normalise,
normalisation_type=opt.normalisation_type)
def initialize(self, opt):
self.opt = opt
self.dir_A = os.path.join(opt.dir_landmarks_list_A, opt.name_landmarks_list)
self.dir_B = os.path.join(opt.dir_landmarks_list_B, opt.name_landmarks_list)
self.A_items = get_list(self.dir_A)
self.B_items = get_list(self.dir_B)
self.A_size = len(self.A_items)
self.B_size = len(self.B_items)
def initialize(self, opt):
self.opt = opt
self.F1_paths = []
self.F1_labels = []
for root_dir_item in opt.root_dir:
dir_F1_tmp = os.path.join(root_dir_item, 'train' + '_F1')
img_list = os.path.join(root_dir_item, opt.name_landmarks_list)
F1_labels_tmp, F1_paths_tmp = get_list(dir_F1_tmp, img_list)
self.F1_paths += F1_paths_tmp
self.F1_labels += F1_labels_tmp
self.color_F1 = opt.color_F1
self.fineSize_F1 = opt.fineSize_F1
self.sigma = opt.sigma
self.label_num = opt.label_num
self.transform = AffineCompose(
rotation_range=opt.rotate_range,
translation_range=opt.translate_range,
zoom_range=opt.zoom_range,
output_img_width=self.fineSize_F1,
output_img_height=self.fineSize_F1,
mirror=opt.mirror,
corr_list=opt.corr_list,
normalise=opt.normalise,
normalisation_type=opt.normalisation_type)
def initialize(self, opt):
self.opt = opt
self.A_paths = []
self.A_labels = []
for root_dir_item in opt.root_dir:
dir_A_tmp = os.path.join(root_dir_item, 'Image')
img_list = os.path.join(root_dir_item, opt.name_landmarks_list)
A_labels_tmp, A_paths_tmp = get_list(dir_A_tmp, img_list)
self.A_paths += A_paths_tmp
self.A_labels += A_labels_tmp
self.color_A = opt.color_A
self.label_size = opt.fineSize_A
self.heatmap_size = opt.fineSize_B
self.heatmap_num = opt.output_nc
self.sigma = opt.sigma
self.label_num = opt.label_num
self.transform = AffineCompose(
rotation_range=opt.rotate_range,
translation_range=opt.translate_range,
zoom_range=opt.zoom_range,
output_img_width=self.label_size,
output_img_height=self.label_size,
mirror=opt.mirror,
corr_list=opt.corr_list,
normalise=opt.normalise,
normalisation_type=opt.normalisation_type)