diff options
| -rwxr-xr-x | Wrappers/Python/ccpi/processors.py | 105 | ||||
| -rw-r--r-- | Wrappers/Python/conda-recipe/meta.yaml | 1 |
2 files changed, 0 insertions, 106 deletions
diff --git a/Wrappers/Python/ccpi/processors.py b/Wrappers/Python/ccpi/processors.py index b65722e..a2d0446 100755 --- a/Wrappers/Python/ccpi/processors.py +++ b/Wrappers/Python/ccpi/processors.py @@ -19,7 +19,6 @@ from ccpi.framework import DataProcessor, DataContainer, AcquisitionData,\
AcquisitionGeometry, ImageGeometry, ImageData
-from ccpi.reconstruction.parallelbeam import alg as pbalg
import numpy
from scipy import ndimage
@@ -389,107 +388,3 @@ class CenterOfRotationFinder(DataProcessor): return cor
-
-class AcquisitionDataPadder(DataProcessor):
- '''Normalization based on flat and dark
-
- This processor read in a AcquisitionData and normalises it based on
- the instrument reading with and without incident photons or neutrons.
-
- Input: AcquisitionData
- Parameter: 2D projection with flat field (or stack)
- 2D projection with dark field (or stack)
- Output: AcquisitionDataSetn
- '''
-
- def __init__(self,
- center_of_rotation = None,
- acquisition_geometry = None,
- pad_value = 1e-5):
- kwargs = {
- 'acquisition_geometry' : acquisition_geometry,
- 'center_of_rotation' : center_of_rotation,
- 'pad_value' : pad_value
- }
-
- super(AcquisitionDataPadder, self).__init__(**kwargs)
-
- def check_input(self, dataset):
- if self.acquisition_geometry is None:
- self.acquisition_geometry = dataset.geometry
- if dataset.number_of_dimensions == 3:
- return True
- else:
- raise ValueError("Expected input dimensions is 2 or 3, got {0}"\
- .format(dataset.number_of_dimensions))
-
- def process(self):
- projections = self.get_input()
- w = projections.get_dimension_size('horizontal')
- delta = w - 2 * self.center_of_rotation
-
- padded_width = int (
- numpy.ceil(abs(delta)) + w
- )
- delta_pix = padded_width - w
-
- voxel_per_pixel = 1
- geom = pbalg.pb_setup_geometry_from_acquisition(projections.as_array(),
- self.acquisition_geometry.angles,
- self.center_of_rotation,
- voxel_per_pixel )
-
- padded_geometry = self.acquisition_geometry.clone()
-
- padded_geometry.pixel_num_h = geom['n_h']
- padded_geometry.pixel_num_v = geom['n_v']
-
- delta_pix_h = padded_geometry.pixel_num_h - self.acquisition_geometry.pixel_num_h
- delta_pix_v = padded_geometry.pixel_num_v - self.acquisition_geometry.pixel_num_v
-
- if delta_pix_h == 0:
- delta_pix_h = delta_pix
- padded_geometry.pixel_num_h = padded_width
- #initialize a new AcquisitionData with values close to 0
- out = AcquisitionData(geometry=padded_geometry)
- out = out + self.pad_value
-
-
- #pad in the horizontal-vertical plane -> slice on angles
- if delta > 0:
- #pad left of middle
- command = "out.array["
- for i in range(out.number_of_dimensions):
- if out.dimension_labels[i] == 'horizontal':
- value = '{0}:{1}'.format(delta_pix_h, delta_pix_h+w)
- command = command + str(value)
- else:
- if out.dimension_labels[i] == 'vertical' :
- value = '{0}:'.format(delta_pix_v)
- command = command + str(value)
- else:
- command = command + ":"
- if i < out.number_of_dimensions -1:
- command = command + ','
- command = command + '] = projections.array'
- #print (command)
- else:
- #pad right of middle
- command = "out.array["
- for i in range(out.number_of_dimensions):
- if out.dimension_labels[i] == 'horizontal':
- value = '{0}:{1}'.format(0, w)
- command = command + str(value)
- else:
- if out.dimension_labels[i] == 'vertical' :
- value = '{0}:'.format(delta_pix_v)
- command = command + str(value)
- else:
- command = command + ":"
- if i < out.number_of_dimensions -1:
- command = command + ','
- command = command + '] = projections.array'
- #print (command)
- #cleaned = eval(command)
- exec(command)
- return out
\ No newline at end of file diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml index c6b1940..f72c980 100644 --- a/Wrappers/Python/conda-recipe/meta.yaml +++ b/Wrappers/Python/conda-recipe/meta.yaml @@ -18,7 +18,6 @@ requirements: run: - python - numpy - #- ccpi-reconstruction - matplotlib about: |