[SciPy-User] Help optimizing an algorithm

josef.pktd@gmai... josef.pktd@gmai...
Wed Jan 30 11:38:26 CST 2013

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


> -Chris
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