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from data.base_dataset import BaseDataset, get_params, get_transform
from PIL import Image
import util.util as util
import os
class Pix2pixDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.add_argument('--no_pairing_check', action='store_true',
help='If specified, skip sanity check of correct label-image file pairing')
return parser
def initialize(self, opt):
self.opt = opt
label_paths, image_paths, instance_paths = self.get_paths(opt)
util.natural_sort(label_paths)
util.natural_sort(image_paths)
if not opt.no_instance:
util.natural_sort(instance_paths)
label_paths = label_paths[:opt.max_dataset_size]
image_paths = image_paths[:opt.max_dataset_size]
instance_paths = instance_paths[:opt.max_dataset_size]
if not opt.no_pairing_check:
for path1, path2 in zip(label_paths, image_paths):
assert self.paths_match(path1, path2), \
"The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this." % (path1, path2)
self.label_paths = label_paths
self.image_paths = image_paths
self.instance_paths = instance_paths
size = len(self.label_paths)
self.dataset_size = size
def get_paths(self, opt):
label_paths = []
image_paths = []
instance_paths = []
assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)"
return label_paths, image_paths, instance_paths
def paths_match(self, path1, path2):
filename1_without_ext = os.path.splitext(os.path.basename(path1))[0]
filename2_without_ext = os.path.splitext(os.path.basename(path2))[0]
return filename1_without_ext == filename2_without_ext
def __getitem__(self, index):
# Label (Content) Image
label_path = self.label_paths[index]
label = Image.open(label_path)
if self.opt.task != 'SIS':
label = label.convert('RGB')
params = get_params(self.opt, label.size)
if self.opt.task != 'SIS':
transform_label = get_transform(self.opt, params)
label_tensor = transform_label(label)
else:
transform_label = get_transform(self.opt, params, method=Image.NEAREST, normalize=False)
label_tensor = transform_label(label) * 255.0
label_tensor[label_tensor == 255] = self.opt.label_nc # 'unknown' is opt.label_nc
# Real (Style) Image
image_path = self.image_paths[index]
print("Images_path",image_path)
assert self.paths_match(label_path, image_path), \
"The label_path %s and image_path %s don't match." % \
(label_path, image_path)
image = Image.open(image_path)
image = image.convert('RGB')
transform_image = get_transform(self.opt, params)
image_tensor = transform_image(image)
# if using instance maps
if self.opt.no_instance:
instance_tensor = 0
else:
instance_path = self.instance_paths[index]
instance = Image.open(instance_path)
if instance.mode == 'L':
instance_tensor = transform_label(instance) * 255
instance_tensor = instance_tensor.long()
else:
instance_tensor = transform_label(instance)
input_dict = {'label': label_tensor,
'instance': instance_tensor,
'image': image_tensor,
'path': image_path,
'cpath': label_path
}
# Give subclasses a chance to modify the final output
len_dict=len(input_dict['label'])
print("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",len_dict)
self.postprocess(input_dict)
return input_dict
def postprocess(self, input_dict):
return input_dict
def __len__(self):
return self.dataset_size
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