Repeated warping about imagetransformations.jl HOT 6 OPEN

Is there a way to calculate the transformation once, and then apply it multiple times to each of the frames?

Calculating the transformation won't be the major performance hotspot, so what benefits does it give? BTW, there's also an in-place version imresize!.

from imagetransformations.jl.

I'm sure you are correct, but that depends on the number of frames I'm processing. But perhaps the number of frames needed for this to start making a difference is in the thousands -- I don't know how much of the time goes to the interpolation versus calculating the transform.

More importantly, it would allow me to use the viewed versions of the warp functions. In my case I'm looking for the next location of the tracked animal only in the vicinity of the last know location, so there's no need to scale the whole image, just the region of interest.

And the reason I don't simply build my own transformation with CoordinateTransformations.jl is due to VideoIO.jl's requirement that the dimensions of a saved frame be a multiple of 2. So I need some way of guaranteeing that scaled image has pair height and width. That is straightforward with imresize.

from imagetransformations.jl.

Maybe you can instead build a lookup table of coordinates and calling warp!, and see how that improves the performance. Something like #64 (comment) but backed by a pre-computed lookup table, with the core coordinate computation reduced to getindex.

To call warp! we current need to build our own extrapolation, for the simplest bilinear case with zero fill value we can do etp = extrapolate(interpolate(img, BSpline(Linear())), zero(eltype(img)))

from imagetransformations.jl.

I might not follow you exactly, but to be clear this is the kind of transform I'm using:

scale = 4
sar = 4/3
scaleh = scale * sar
scalew = scale
tform = LinearMap(SDiagonal(scaleh, scalew))

pretty standard stuff. I'm just shrinking the image by a factor of 4 and fixing its aspect ratio. So no need to use a novel lookup table...?

I think I need to either get my hands on the transform imresize is using, or be able to produce a LinearMap that guarantees the resulting image has dimensions that are multiples of 2.

from imagetransformations.jl.

Building a transformation map won't take much time, it is applying map(i-> tform(SVector(i.I)), CartesianIndices(img)) that takes time, so if you want to save the computation, you can pre-calculate lookup_table = map(i-> tform(SVector(i.I)), CartesianIndices(img)) and then define a function to directly query from this table instead of computing it for every frame.

from imagetransformations.jl.

The linear coordinate transform that imresize uses is defined this way:

from imagetransformations.jl.