[SciPy-User] Help optimizing an algorithm
Chris Weisiger
cweisiger@msg.ucsf....
Wed Jan 30 11:47:37 CST 2013
Right, I should have clarified that g is different for each pixel. It looks
like scipy.interpolate.interp1d ought to do exactly what I want, though
I'll have to handle the bounds conditions (where the input data is outside
the range of the interpolation function that interp1d generates) myself.
Thanks for the help!
-Chris
On Wed, Jan 30, 2013 at 9:38 AM, <josef.pktd@gmail.com> wrote:
> On Wed, Jan 30, 2013 at 12:29 PM, Chris Weisiger <cweisiger@msg.ucsf.edu>
> wrote:
> > We have a camera at our lab that has a nonlinear (but monotonic)
> response to
> > light. I'm attempting to linearize the data output by the camera. I'm
> doing
> > this by sampling the response curve of the camera, generating a linear
> fit
> > of the sample, and mapping new data to the linear fit by way of the
> sample.
> > In other words, we have the following functions:
> >
> > f(x): the response curve of the camera (maps photon intensity to reported
> > counts by the camera)
> > g(x): an approximation of f(x), composed of line segments
> > h(x): a linear fit of g(x)
> >
> > We get a new pixel value Y in -- this is counts reported by the camera.
> We
> > invert g() to get the approximate photon intensity for that many counts.
> And
> > then we plug that photon intensity into the linear fit.
> >
> > Right now I believe I have a working algorithm, but it's very slow
> (which in
> > turn makes testing for validity slow), largely because inverting g()
> > involves iterating over each datapoint in the approximation to find the
> two
> > that bracket Y so that I can linearly interpolate between them. Having to
> > iterate over every pixel in the image in Python isn't doing me any favors
> > either; we typically deal with 528x512 images so that's 270k iterations
> per
> > image.
> >
> > If anyone has any suggestions for optimizations I could make, I'd love to
> > hear them. My current algorithm can be seen here:
> > http://pastebin.com/mwaxWHGy
>
>
> np.searchsorted or scipy.interp1d
>
> If g is the same for all pixels, then there is no loop necessary and
> can be done fully vectorized
>
> Josef
>
> >
> > -Chris
> >
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> >
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