[Numpy-discussion] efficient 3d histogram creation

Chris Colbert sccolbert@gmail....
Wed May 6 18:39:18 CDT 2009


i just realized I don't need the line:

cdef int z = img.shape(2)

it's left over from tinkering. sorry. And i should probably convert the out
array to type float to handle large data sets.

Chris

On Wed, May 6, 2009 at 7:30 PM, <josef.pktd@gmail.com> wrote:

> On Wed, May 6, 2009 at 6:06 PM, Chris Colbert <sccolbert@gmail.com> wrote:
> > I decided to hold myself over until being able to take a hard look at the
> > numpy histogramdd code:
> >
> > Here is a quick thing a put together in cython. It's a 40x speedup over
> > histogramdd on Vista 32 using the minGW32 compiler. For a (480, 630, 3)
> > array, this executed in 0.005 seconds on my machine.
> >
> > This only works for arrays with uint8 data types having dimensions (x, y,
> 3)
> > (common image format). The return array is a (16, 16, 16) equal width bin
> > histogram of the input.
> >
> > If anyone wants the cython C-output, let me know and I will email it to
> you.
> >
> > If there is interest, I will extend this for different size bins and
> aliases
> > for different data types.
> >
> > Chris
> >
> > import numpy as np
> >
> > cimport numpy as np
> >
> > DTYPE = np.uint8
> > DTYPE32 = np.int
> >
> > ctypedef np.uint8_t DTYPE_t
> > ctypedef np.int_t DTYPE_t32
> >
> > def hist3d(np.ndarray[DTYPE_t, ndim=3] img):
> >     cdef int x = img.shape[0]
> >     cdef int y = img.shape[1]
> >     cdef int z = img.shape[2]
> >     cdef int addx
> >     cdef int addy
> >     cdef int addz
> >     cdef np.ndarray[DTYPE_t32, ndim=3] out = np.zeros([16, 16, 16],
> > dtype=DTYPE32)
> >     cdef int i, j, v0, v1, v2
> >
> >
> >     for i in range(x):
> >         for j in range(y):
> >             v0 = img[i, j, 0]
> >             v1 = img[i, j, 1]
> >             v2 = img[i, j, 2]
> >             addx = (v0 - (v0 % 16)) / 16
> >             addy = (v1 - (v1 % 16)) / 16
> >             addz = (v2 - (v2 % 16)) / 16
> >             out[addx, addy, addz] += 1
> >
> >     return out
> >
>
> Thanks for the example for using cython. Once I figure out what the
> types are, cython will look very convenient for loops, and pyximport
> takes care of the compiler.
>
> Josef
>
> import pyximport; pyximport.install()
> import hist_rgb    #name of .pyx files
>
> import numpy as np
> factors = np.random.randint(256,size=(480, 630, 3))
> h = hist_rgb.hist3d(factors.astype(np.uint8))
> print h[:,:,0]
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20090506/9fc598af/attachment.html 


More information about the Numpy-discussion mailing list