[SciPy-user] histogram equalization using scipy.interpolate.splrep
Sebastian Haase
haase@msg.ucsf....
Tue Jul 31 16:58:19 CDT 2007
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
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