def preprocess(opt, image_path, label_path): ## __getitem__
# label
label = Image.open(label_path)
params = get_params(opt, label.size)
transform_label = get_transform(opt,
params,
method=Image.NEAREST,
normalize=False)
label_tensor = transform_label(label) * 255.0
label_tensor[label_tensor == 255] = opt.label_nc
# image (Real)
for path in os.listdir(image_path):
image = Image.open(osp.join(image_path, path))
image = image.convert('RGB')
transform_image = get_transform(opt, params)
image_tensor = transform_image(image)
instance_tensor = torch.Tensor([0])
return {
'label': label_tensor,
'instance': instance_tensor,
'image': image_tensor,
'path': image_path
}
def get_input_by_names(self, image_path, image, label_img):
label = Image.fromarray(label_img)
params = get_params(self.opt, label.size)
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
label_tensor.unsqueeze_(0)
# input image (real images)]
# image = Image.open(image_path)
# image = image.convert('RGB')
transform_image = get_transform(self.opt, params)
image_tensor = transform_image(image)
image_tensor.unsqueeze_(0)
# if using instance maps
if self.opt.no_instance:
instance_tensor = torch.Tensor([0])
input_dict = {
'label': label_tensor,
'instance': instance_tensor,
'image': image_tensor,
'path': image_path,
}
# Give subclasses a chance to modify the final output
self.postprocess(input_dict)
return input_dict
def __getitem__(self, index):
# Label Image
label_path = self.label_paths[index]
label = Image.open(label_path)
params = get_params(self.opt, label.size)
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
# input image (real images)
image_path = self.image_paths[index]
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,
}
# Give subclasses a chance to modify the final output
self.postprocess(input_dict)
return input_dict