i'll take a look at them over the next few days and see what i can hack out.
Chris On Mon, May 4, 2009 at 3:18 PM, David Huard <david.hu...@gmail.com> wrote: > > > On Mon, May 4, 2009 at 7:00 AM, <josef.p...@gmail.com> wrote: > >> On Mon, May 4, 2009 at 12:31 AM, Chris Colbert <sccolb...@gmail.com> >> wrote: >> > this actually sort of worked. Thanks for putting me on the right track. >> > >> > Here is what I ended up with. >> > >> > this is what I ended up with: >> > >> > def hist3d(imgarray): >> > histarray = N.zeros((16, 16, 16)) >> > temp = imgarray.copy() >> > bins = N.arange(0, 257, 16) >> > histarray = N.histogramdd((temp[:,:,0].ravel(), temp[:,:,1].ravel(), >> > temp[:,:,2].ravel()), bins=(bins, bins, bins))[0] >> > return histarray >> > >> > this creates a 3d histogram of rgb image values in the range 0,255 using >> 16 >> > bins per component color. >> > >> > on a 640x480 image, it executes in 0.3 seconds vs 4.5 seconds for a for >> > loop. >> > >> > not quite framerate, but good enough for prototyping. >> > >> >> I don't think your copy to temp is necessary, and use reshape(-1,3) as >> in the example of Stefan, which will avoid copying the array 3 times. >> >> If you need to gain some more speed, then rewriting histogramdd and >> removing some of the unnecessary checks and calculations looks >> possible. > > > Indeed, the strategy used in the histogram function is faster than the one > used in the histogramdd case, so porting one to the other should speed > things up. > > David > > >> >> Josef >> _______________________________________________ >> Numpy-discussion mailing list >> Numpy-discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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