On 15 Feb., 04:36, Pablo d'Angelo <pablo.dang...@web.de> wrote:
> Am 13.02.2011 16:25, schrieb kfj:

> > I am currently toying with this mechanism for use in another demo
> > plugin, but I want to throw in rewarping of the parts of the images
> > that correspond to the ROIs to a common projection to make them
> > geometrically as similar as possible, thus improving CPG performance
> > especially with fisheye images and avoiding warp-related CPG problems
> > - some sort of high-end matching which would produce very good
> > quality, well-distributed CPs, particularly for applications like lens
> > calibration.
>
> Yes, something like that would be very nice. Or one could try Least
> squares matching. (estimates local affine transformation as part of the
> matching process, is typically accurate to ~ 0.1 pixel). However, it
> needs good initialisations (1-2 pixels error, good estimate for the
> initial affine transform).
>
I'm getting ahead slowly with the code for my plugin, because the
precise masking out of the non-overlapping parts isn't trivial. Sadly
the code for masks in Mask.cpp and Mask.h is totally undocumented. My
initial attempt was this:

If I have an overlap of two images, I sample one image's boundaries,
transform that to the other image's coordinates and use it as a mask
for the other image. This would be fine if I could use what's outside
of the image as a mask as exclude mask, but the obvious flag in
Masks.cpp ( Mask.setInverted() ) doesn't seem to do what I want, the
Mask is forever just the inside of the Polygon.

So I'm now writing python code for the masks, which is cleaner anyway
than my quick-and-dirty approach (it's even fun) - but it' sad that
the Mask code isn't a bit more user-friendly.

Once I get it to run I'll post again.

Kay

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