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depends on params sparsityXXX > 0
instead of doing it manually
replace self.output_diameter with self.retina.getOutputSize() replace self.retina_diameter with self.retina.getInputSize()
require same dims for all images make new_image_() a static method, used internally by compute()
TODO avoid that, work with rectangles?
processing.
This reverts commit 40dd4b30781c96f8b5bae9545f258c73e75592ce.
This reverts commit e46d5f3.
breznak
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Oct 22, 2019
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CC @ctrl-z-9000-times please have a look at these, and esp the issues in this PR's description, thank you.
| self.resolution_factor = 3 | ||
| self.retina_diameter = int(self.resolution_factor * output_diameter) | ||
| # Argument fovea_scale ... represents "zoom" aka distance from the object/image. | ||
| self.fovea_scale = 0.177 |
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fovea_scale vs self.scale?
| self.scale = 1.0 | ||
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| def new_image_(image): |
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now static, rename to read_image?
| center = self.retina.getInputSize()[0] / 2 | ||
| M = self.retina.getInputSize()[0] * self.scale | ||
| if self.parvo_enc is not None: | ||
| parvo = cv2.logPolar(parvo, |
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should we apply the logPolar only in the Channel encoder? Bcs that one expects its data in that form.
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WIP PR, I made some cleanups,improvements to the PR, but broke scaling/logpolar.
What I'd like to do is use Retina's
useLogSampling, but imho that unfortunatelyOur current approach is neither ideal,
we have to crop to ROI
I'd like to re-center the image to given center (& fov. diameter)
What is the difference for
self.scaleandself.scale_fovea?Follow up to #691