On Wed, May 6, 2009 at 6:06 PM, Chris Colbert <sccolb...@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