> > ...where the image data is loaded into a numpy array > (1600x1200x3)...
One comment: that is a big array, too big for the cache memory. I know that in these cases it makes a difference how many times the slices of the array are loaded and unloaded from RAM onto cache. One issue is that a 2D array l[i,j] is actually a 1D array: either l[i,j]=l[i*N+j] or l[i,j]=l[j*N+i] I don't know which in python/numpy. In the first case, when you fix i and change j, you get consecutive positions in the underlying 1D array, and they all belong to the same slice, which is loaded onto cache very fast as a block. If you do the opposite, you are making jumps to distant positions in the array (not a slice), and performance suffers. In gimp, for example, the underlying array is split into 'tiles', which are 64x64 pixel regions that can be loaded/unloaded as a block. And that's about all I know on the issue. So it ma -- http://mail.python.org/mailman/listinfo/python-list
