Post-Registration Evaluation
============================

Find the best list of transformations given multiple lists and the images they were registered from

.. image:: img/2D_posteval.png

Parameters
----------

image_stacks
    Image stacks as a dictionary of numpy arrays or list of HDF5 dataset URI's

transformations
    The resulting transformations for each of the image_stacks

url
    Url of an hdf5 Dataset to save the chosen transformed image stack

reference_stack
    Most relevant stack name

Outputs
-------

image_stacks
    Image stacks as a dictionary of numpy arrays or list of HDF5 dataset URI's

transformations
    The resulting transformations for each of the image_stacks

reference_stack
    Most relevant stack name

ranked_stack_names
    Stack names ordered from most to least relevant

.. ewokstasks:: ewoksndreg.tasks.reg2d_posteval
    :task-type: class

Notes
-----

Deciding whether an alignment is "good" is very hard to solve algorithmically, therefore the attemps to find the best alignment in this task
are in no way definitive.

For every image_stacks there are two measures used:
    - Mean squared error between the images
    - Smoothness of the transformations using change of corner coordinates when applying transformations