On 7 Feb., 23:10, Jeffrey Martin <360cit...@gmail.com> wrote:
> just to start things out in a simple way, couldn't we (well, not me :) > because I don't know how) just add a parameter to cpfind specifying how much > to reduce source images (not only halfsize or fullsize as we have now) A scaling factor would be nice indeed, but I reckon my pixels per degrees of field of view idea would be even more useful. I suppose we'll have to get Pablo's attention somehow ;-) > > I have made experiments in this direction using up- and downscaling of > > pto files. You can find a working prototype of my Python script here: > > >http://bazaar.launchpad.net/~kfj/+junk/script/view/head:/main/scale_p...<http://bazaar.launchpad.net/%7Ekfj/+junk/script/view/head:/main/scale...> > > > The idea is to run the process on smaller images and once the > > orientations are established, to replace the images with full scale > > versions and have all pto parameters that build on image coordinates > > rescaled. I wrote this because I wanted to work on the screen-sized > > images I carry with me on my laptop and apply the results to the full- > > scale images back home with the fat data corpus. It works, and > > surprisingly well. The scaled-down versions are usually a fair bit > > crisper than the full-sized images, so there is enough detail for the > > CPGs to work on - and since the feature detectors produce subpixel > > accuracy, the scaled-up pto often stitches without any need for > > further intervention - if you want you can run a global fine-tune on > > the CPs. > > this always worries me..... after optimizing something and you see it works, > optimizing again always has a risk that it will all go to hell again ;) but > maybe in real life this will not happen much....? My worries along these lines have greatly deminished ever since the advent of the undo feature. It goes to hell? so what - it can go back as well. > > The next idea, to look at the overlapping parts once the overlap has > > been roughly established, is also promising and heas been previously > > exploited, though I'm not entirely sure where the code is. Another > > interesting aspect along these lines is to warp the overlapping parts > > of two images to a common projection and run the CPGs on those warped > > partial images, to later retransform the CPs to original image > > coordinates. This has also been done, and I've experimented with it > > myself, but found the gain not so noteworthy as to make me want to > > investigate the matter more deeply - > > do you want some 2000 image panos to test it on? in that case the time > savings might be very significant. what I mean is, sure for panos containing > 4 or 10 images this just won't matter but for gigapixel images it might save > minutes or hours. My last comment was not about the downscaled images but about transforming overlapping parts of the images to a common projection. What I did can easily be explained in standard hugin terms: you choose rectilinear projection, rotate your panorama to put the center of the overlap into the center of the panorama, crop the panorama to the overlapping region and generate separate images instead of a panorama. The two resulting 'warped' images would, ideally, be very similar, so the CPGs should have an easy task of finding CPs. This is not about saving time - it's about getting better-distributed and more precise CPs. But, as I said, I didn't find the gains worth the effort. Thanks for the offer of your test data, anyway. I may make a python plugin to do the warped overlap extraction (it also makes for a nice acronym: WOE) once the plugin interface is established - but the outlined method above works just as well, only that the retransformation of the CPs from the warped images to original image coordinates takes some fiddling. Kay -- You received this message because you are subscribed to the Google Groups "Hugin and other free panoramic software" group. A list of frequently asked questions is available at: http://wiki.panotools.org/Hugin_FAQ To post to this group, send email to hugin-ptx@googlegroups.com To unsubscribe from this group, send email to hugin-ptx+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/hugin-ptx
