def __create_transformation(self, target_shape):
transformation = augment.create_identity_transformation(
target_shape, subsample=self.subsample)
if sum(self.jitter_sigma) > 0:
transformation += augment.create_elastic_transformation(
target_shape,
self.control_point_spacing,
self.jitter_sigma,
subsample=self.subsample,
)
rotation = random.random(
) * self.rotation_max_amount + self.rotation_start
if rotation != 0:
transformation += augment.create_rotation_transformation(
target_shape, rotation, subsample=self.subsample)
if self.subsample > 1:
transformation = augment.upscale_transformation(
transformation, target_shape)
if self.prob_slip + self.prob_shift > 0:
self.__misalign(transformation)
return transformation
def prepare(self, request):
total_roi = request.get_total_roi()
logger.debug("total ROI is %s" % total_roi)
dims = len(total_roi.get_shape())
# create a transformation for the total ROI
rotation = random.random(
) * self.rotation_max_amount + self.rotation_start
self.total_transformation = augment.create_identity_transformation(
total_roi.get_shape(), subsample=self.subsample)
self.total_transformation += augment.create_elastic_transformation(
total_roi.get_shape(),
self.control_point_spacing,
self.jitter_sigma,
subsample=self.subsample)
self.total_transformation += augment.create_rotation_transformation(
total_roi.get_shape(), rotation, subsample=self.subsample)
if self.subsample > 1:
self.total_transformation = augment.upscale_transformation(
self.total_transformation, total_roi.get_shape())
if self.prob_slip + self.prob_shift > 0:
self.__misalign()
# crop the parts corresponding to the requested volume ROIs
self.transformations = {}
logger.debug("total ROI is %s" % total_roi)
for (volume_type, roi) in request.volumes.items():
logger.debug("downstream request ROI for %s is %s" %
(volume_type, roi))
roi_in_total_roi = roi.shift(-total_roi.get_offset())
transformation = np.copy(
self.total_transformation[(slice(None), ) +
roi_in_total_roi.get_bounding_box()])
self.transformations[volume_type] = transformation
# update request ROI to get all voxels necessary to perfrom
# transformation
roi = self.__recompute_roi(roi, transformation)
request.volumes[volume_type] = roi
logger.debug("upstream request roi for %s = %s" %
(volume_type, roi))