Open lauraporta opened 4 days ago
From previous PR:
Improve tests, suggestions from @JoeZiminski in #25 :
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Description
What is this PR
Why is this PR needed? See #12
What does this PR do?
Creates methods and pipeline to find the centre of rotation in the incremental dataset.
⚠️ It does not fix the wobbling issues. The pipeline works well with simulated data but worsens the real datasets.
Pipeline
Find the centre
Find the centre of rotation using the incremental rotations datasets, in which the sample was rotated at discrete angles with 10deg intervals. Centre of rotation is found by fitting an ellipse to the brightest ROI location at each of the 10deg configurations. New methods include:
find_center_of_rotation
get_coords_of_largest_blob
plot_blob_detection
fit_ellipse_to_points
plot_ellipse_fit_and_centers
Use the new centre
In order to use the new centre of rotation, the
derotate_an_image_array_line_by_line
needed to be totally rewritten. I am discarding the usage ofscipy.ndimage.rotate
and using classic linear algebra to rotate the matrices. With this new implementation the derotation is so much faster! 🚀How has this PR been tested?
The performance of the de-rotation when the centre of rotation is shifted has been tested with simulated data generated by the class
Rotator
.I created a stepwise array of angles to imitate the incremental rotation dataset, and a sinusoidal array as the main experimental session dataset.
I then rotated a sample image that has two blobs that imitate two ROIs (one brighter than the other).
Ellipse fit works well with a shifted centre:
And derotation works smothly. This is the proof that in principle my derotation with shifted centres works.
I added tests to cover this new functionality and the pipeline.
Is this a breaking change?
No
Checklist: