Projection 2D Alignment¶
Description¶
A plugin to perform alignment (registration) if two images, e.g. two projections. The result is horizontal-vertical shift vectors written into the experimental metadata.
Parameters
in_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description: "Two datasets to register to each other, given as [static_reference, dataset_to_register_to_reference]. The order of datasets in the list is important to avoid divergence in the iterative alignment method."
default: "[]"
out_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description: Default out dataset names.
default: "['shifts']"
upsample_factor:
visibility: advanced
dtype: int
description: The upsampling factor. Registration accuracy is inversely propotional to upsample_factor.
default: "10"
Key
visibility: The visibility level of the parameter
dtype: The type of data
description: A short explanation of the parameter
default: The default value
options: A list of permitted values
dependency: The name of the parameter and value which this parameter depends upon
range: A guide for the range of the parameter
Citations
No citations
API
-
class
Projection2dAlignment
[source] -
get_max_frames
()[source]
-
nInput_datasets
()[source] The number of datasets required as input to the plugin
- Returns
Number of input datasets
-
nOutput_datasets
()[source] The number of datasets created by the plugin
- Returns
Number of output datasets
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post_process
()[source] This method is called after the process function in the pipeline framework as a post-processing step. All processes will have finished performing the main processing at this stage.
- Parameters
exp (experiment class instance) – An experiment object, holding input and output datasets
-
process_frames
(data)[source] This method is called after the plugin has been created by the pipeline framework and forms the main processing step
- Parameters
data (list(np.array)) – A list of numpy arrays for each input dataset.
-
setup
()[source] This method is first to be called after the plugin has been created. It determines input/output datasets and plugin specific dataset information such as the pattern (e.g. sinogram/projection).
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