Hi, Also try NumExpr (http://www.scipy.org/SciPyPackages/NumExpr): "The scipy.sandbox.numexpr package supplies routines for the fast evaluation of array expressions elementwise by using a vector-based virtual machine. "
However, this page is probably a little out of date. Regards Bruce On Jan 21, 2008 4:17 PM, Arnar Flatberg <[EMAIL PROTECTED]> wrote: > Hi Theodore > > Probably not the fastest, but a full example of how you may vectorize your > loop. > Ran just one test, and that speeded up the original code. > > Example: > ---------------- > > from numpy import empty_like > > def vectorized_rgb2hsv(im): > "im is a (m, n, 3) array.""" > im = im/255. > out = empty_like(im) > im_max = im.max(-1) > delta = im.ptp(-1) > s = delta/im_max > s[delta==0] = 0 > index = im[:,:,0] == im_max # red is max > out[index, 0] = (im[index, 1] - im[index, 2] ) / delta[index] > index = im[:,:,1] == im_max # green is max > out[index, 0] = 2 + (im[index, 2] - im[index, 0] ) / delta[index] > index = im[:,:,2] == im_max # blue is max > out[index, 0] = 4 + (im[index, 0] - im[index, 1] ) / delta[index] > out[:,:,0] = (out[:,:,0]/6.0) % 1.0 > out[:,:,1] = s > out[:,:,2] = im_max > out = (255.*out).astype(uint8) > return out > > > Timings (shape: 1202, 800, 3): > ----------------- > code in first post: > %prun a = rgb2hsv.rgb2hsv(s) > 1923207 function calls in 18.423 CPU seconds > > removing loop: > %prun b = rgb2hsv.vectorized_rgb2hsv(s) > 8 function calls in 4.630 CPU seconds > > > Arnar > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
