def auto_find_minimisation_sqsum(self, slices: List[int], recon_params: ReconstructionParameters,
initial_cor: Union[float, List[float]], progress: Progress) -> List[float]:
"""
:param slices: Slice indices to be reconstructed
:param recon_params: Reconstruction parameters
:param initial_cor: Initial COR for the slices. Will be used as the start for the minimisation.
If a float is passed it will be used for all slices.
If a list is passed, the COR will be retrieved for each slice.
:param progress: Progress reporter
"""
# Ensure we have some sample data
if self.images is None:
return [0.0]
if isinstance(initial_cor, list):
assert len(slices) == len(initial_cor), "A COR for each slice index being reconstructed must be provided"
else:
# why be efficient when you can be lazy?
initial_cor = [initial_cor] * len(slices)
reconstructor = get_reconstructor_for(recon_params.algorithm)
progress = Progress.ensure_instance(progress, num_steps=len(slices))
progress.update(0, msg=f"Calculating COR for slice {slices[0]}")
cors = []
for idx, slice in enumerate(slices):
cor = reconstructor.find_cor(self.images, slice, initial_cor[idx], recon_params)
cors.append(cor)
progress.update(msg=f"Calculating COR for slice {slice}")
return cors
def run_full_recon(self, recon_params: ReconstructionParameters, progress: Progress) -> Optional[Images]:
# Ensure we have some sample data
images = self.images
if images is None:
return None
reconstructor = get_reconstructor_for(recon_params.algorithm)
# get the image height based on the current ROI
recon = reconstructor.full(images, self.data_model.get_all_cors_from_regression(images.height), recon_params,
progress)
recon = self._apply_pixel_size(recon, recon_params, progress)
return recon
def __init__(self, images: Images, slice_idx: int, initial_cor: ScalarCoR,
recon_params: ReconstructionParameters):
self.image_width = images.width
self.sino = images.sino(slice_idx)
# Initial parameters
self.centre_cor = initial_cor.value
self.cor_step = 50
# Cache projection angles
self.proj_angles = images.projection_angles()
self.recon_params = recon_params
self.reconstructor = get_reconstructor_for(recon_params.algorithm)
def run_preview_recon(
self, slice_idx, cor: ScalarCoR,
recon_params: ReconstructionParameters) -> Optional[Images]:
# Ensure we have some sample data
if self.images is None:
return None
# Perform single slice reconstruction
reconstructor = get_reconstructor_for(recon_params.algorithm)
output_shape = (1, self.images.width, self.images.width)
recon: Images = Images.create_empty_images(output_shape,
self.images.dtype,
self.images.metadata)
recon.data[0] = reconstructor.single_sino(
self.images.sino(slice_idx), cor,
self.images.projection_angles(recon_params.max_projection_angle),
recon_params)
recon = self._apply_pixel_size(recon, recon_params)
return recon
def __init__(self, images: Images, slice_idx: int, initial_cor: ScalarCoR, recon_params: ReconstructionParameters,
iters_mode: bool):
self.image_width = images.width
self.sino = images.sino(slice_idx)
# Initial parameters
if iters_mode:
self.centre_value: Union[int, float] = INIT_ITERS_CENTRE_VALUE
self.step = INIT_ITERS_STEP
self.initial_cor = initial_cor
self._recon_preview = self._recon_iters_preview
self._divide_step = self._divide_iters_step
else:
self.centre_value = initial_cor.value
self.step = self.image_width * 0.05
self._recon_preview = self._recon_cor_preview
self._divide_step = self._divide_cor_step
# Cache projection angles
self.proj_angles = images.projection_angles(recon_params.max_projection_angle)
self.recon_params = recon_params
self.reconstructor = get_reconstructor_for(recon_params.algorithm)
def get_allowed_filters(alg_name: str):
reconstructor = get_reconstructor_for(alg_name)
return reconstructor.allowed_filters()
