def __call__(self, **data_dict):
seg = data_dict.get("seg")
if seg is not None:
new_seg = np.zeros([seg.shape[0], len(self.classes)] + list(seg.shape[2:]), dtype=seg.dtype)
for b in range(seg.shape[0]):
new_seg[b] = convert_seg_image_to_one_hot_encoding(seg[b, self.seg_channel], self.classes)
data_dict[self.output_key] = new_seg
else:
from warnings import warn
warn("calling ConvertSegToOnehotTransform but there is no segmentation")
return data_dict
def __call__(self, **data_dict):
seg = data_dict.get("seg")
if seg is not None:
new_seg = np.zeros([seg.shape[0], len(self.classes)] + list(seg.shape[2:]), dtype=seg.dtype)
for b in range(seg.shape[0]):
new_seg[b] = convert_seg_image_to_one_hot_encoding(seg[b, self.seg_channel], self.classes)
data_dict[self.output_key] = new_seg
else:
from warnings import warn
warn("calling ConvertSegToOnehotTransform but there is no segmentation")
return data_dict
def __call__(self, **data_dict):
seg = data_dict.get("seg")
if seg is not None:
new_seg = np.zeros([seg.shape[0], len(self.classes) * seg.shape[1]] + list(seg.shape[2:]), dtype=seg.dtype)
for b in range(seg.shape[0]):
for c in range(seg.shape[1]):
new_seg[b, c*len(self.classes):(c+1)*len(self.classes)] = convert_seg_image_to_one_hot_encoding(seg[b, c], self.classes)
data_dict["seg"] = new_seg
else:
from warnings import warn
warn("calling ConvertMultiSegToOnehotTransform but there is no segmentation")
return data_dict
