[SciPy-user] histogram equalization using scipy.interpolate.splrep
David Huard
david.huard@gmail....
Thu Aug 2 10:06:27 CDT 2007
Sebastian,
2007/8/2, Sebastian Haase <haase@msg.ucsf.edu>:
>
>
> a1 = sa I assume.
Yes. Missed that one.
What is aa ?\
Sorry, that's what happen with crappy notation. Here is a clearer version.
flattened_a = a.flatten()
sorted_a = numpy.sort(flattened_a)
cdf = sorted_a.cumsum()/sorted_a[-1] # This is the cumulative distribution
function [0,1]
cdf = cdf*(max-min)+min # Scale the CDF to whatever scale you're using
(eg. [0,256])
# Apply the scaling to the original image
y = numpy.interp(flattened_a, sorted_a, cdf).
# Reshape to the original form.
new_a = y.reshape (a.shape)
The only concern I have is that this might eat lots of memory and time
> for large images. Sometimes I have 3D data with multiple 100MB in
> size.
Do you want the final image to have the same resolution ? If you want to
optimize the equalization process,
you could do
y = numpy.interp(flattened_a, bins, cdf).
hist, bins = numpy.histogram(flattened_a, nbins, normed=True)
cdf = hist.cumsum()
There were posts a while back with C implementation of histogram that is
quite fast even for large matrices.
For example, when the image's dtype is float or float32, I was hoping
> that a "coarser" histogram would suffice.
> Maybe, since you know "interp", you might know of another function,
> that can lookup values from a function defined by a (sparse) sequence
> of value pairs.
> How about looking up multiple values at one (I think this is called
> vectorizing !?) ?
Not sure what you mean, here. My 2c: get it to work then worry about
optimization...
Cheers,
David
Thanks again,
> Sebastian
>
>
>
>
>
>
> >
> > 2007/7/31, Sebastian Haase <haase@msg.ucsf.edu>:
> > >
> > > Hi,
> > > I'm trying to implement an efficiant numpy / scipy based
> > > implementation of an image analysis procedure called "histogram
> > > equalization".
> > > http://en.wikipedia.org/wiki/Histogram_equalization
> > >
> > > (I don't want to use the Python Imaging Library !)
> > >
> > > For that I calculate the intesity histogram of my 2D or 3D images
> > > (Let's call this 1D array `h`). Then I calculate `hh=
> > > h.astype(N.float).cumsum()`
> > >
> > > Now I need to create a new 2D / 3D image replacing each pixel value
> > > with the corresponding "look-up value" in `hh`.
> > > The means essentially doing something like
> > >
> > > `a2 = hh[a]` -- if `a` was the original image.
> > > Of course this syntax does not work, because
> > > a) the array index lookup probably is not possible using 2D/3D indices
> > > like this - right !?
> > > and
> > > b) my histogram is just sampling / approximating all the actually
> > > occuring values in `a`. In other words: the histogram is based on
> > > creating bins, and nearby pixel values in `a` are then counted into
> > > same bin. Consequently there is no simple "array index lookup" to
> > > get the needed value out of the `h` array. Instead one needs to
> > > interpolate.
> > >
> > > This is were I thought the scipy.interpolate package might come in
> handy
> > ;-)
> > > In fact, if `a` was a 1D "image" I think this would work:
> > > >>> rep = scipy.interpolate.splrep(x,y) # x,y being the
> > > horizontal,count axis in my histogram
> > > >>> aa = scipy.interpolate.splev(a,rep)
> > >
> > > But for a.ndim being 2 I get:
> > > Traceback (most recent call last):
> > > File "<input>", line 1, in ?
> > > File "C:\PrWinN\scipy\interpolate\fitpack.py", line
> > 443, in splev
> > > y,ier=_fitpack._spl_(x,der,t,c,k)
> > > ValueError: object too deep for desired array
> > >
> > > Using N.vectorize(lambda x: scipy.interpolate.splev(x,rep))
> > > works but is slow.
> > >
> > > I there a fast vectorized version of some interpolation already in
> > > scipy.interpolate ?
> > > Am I missing something ?
> > >
> > > Thanks,
> > > Sebastian Haase
> > > _______________________________________________
> > > SciPy-user mailing list
> > > SciPy-user@scipy.org
> > > http://projects.scipy.org/mailman/listinfo/scipy-user
> > >
> >
> >
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