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

System Message: ERROR/3 (/home/docs/checkouts/readthedocs.org/user_builds/savu/checkouts/latest/savu/plugins/alignment/projection_2d_alignment.py:docstring of plugins.alignment.projection_2d_alignment, line 1)

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.. module:: projection_2d_alignment
   :platform: Unix
   :synopsis: either calculates horizontal-vertical shift vectors for fixing misaligned projection data
   or register misiligned projections explicitly

class Projection2dAlignment

get_max_frames()

nInput_datasets()

The number of datasets required as input to the plugin

Returns

Number of input datasets

nOutput_datasets()

The number of datasets created by the plugin

Returns

Number of output datasets

post_process()

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)

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()

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).