On Wed, Sep 29, 2010 at 8:25 AM, Gordon Wrigley <[email protected]> wrote: > Hi > First the disclaimer: This is my first numpy experience, so I have next to > no idea what I'm doing. > I've muddled through and managed to put together some code for my current > problem and now that I have it going I'd like to hear any comments people > may have on both my solution and other ways of approaching the problem. > I have two goals here, I'd like to make the process run faster and I'd like > to broaden my understanding of numpy as I can see from my brief use of it > that it is a remarkably powerful tool. > Now to the problem at hand. I find this difficult to explain but will try as > best I can. > The best word I have for the process is decimation. The input and output are > both 3 dimensional arrays of uint8's. The output is half the size of the > input along each dimension. Each cell [x,y,z] in the output corresponds to > the 2x2x2 block [2*x:2*x+2, 2*y:2*y+2, 2*z:2*z+2] in the input. The tricky > bit is in how the correspondence works. If all the cells in the input block > have the same value then the cell in the output block will also have that > value. Otherwise the output cell will have the value MIXED. > Here is my current solution, from my limited testing it seems to produce the > result I'm after. > def decimate(data_in): > in_x, in_y, in_z = data_in.shape > out_x = in_x / 2 > out_y = in_y / 2 > out_z = in_z / 2 > out_shape = out_x, out_y, out_z > out_size = product(out_shape) > # figure out which chunks are homogeneous > reshaped_array = data_in.reshape(out_x, 2, out_y, 2, out_z, > 2).transpose(0,2,4,1,3,5).reshape(out_x, out_y, out_z, 8) > min_array = numpy.amin(reshaped_array, axis=3) > max_array = numpy.amax(reshaped_array, axis=3) > equal_array = numpy.equal(min_array, max_array) > # select the actual value for the homogeneous chunks and MIXED for the > heterogeneous > decimated_array = data_in[::2,::2,::2] > mixed_array = numpy.tile(MIXED, out_size).reshape(out_shape) > data_out = numpy.where(equal_array, decimated_array, mixed_array)
data_out = numpy.where(equal_array, decimated_array, MIXED) should work I don't see anything else, unless there is something in scipy.ndimage. I have to remember your reshape trick for 3d. (I don't know how many temporary arrays this creates.) Josef > return data_out > For the curious this is will be used to build a voxel octtree for a 3d > graphics application. The final setup will be more complicated, this is the > minimum that will let me get up and running. > Regards > Gordon > P.S. congrats on numpy, it is a very impressive tool, I've only scraped the > surface and it's already impressed me several times over. > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
