At the moment I'm using numpy.dot to convert a WxHx3 RGB image to a
grayscale image:
src_mono = np.dot(src_rgb.astype(np.float), np.ones(3)/3.);
This seems quite slow though (several seconds for a 3 megapixel image) - is
there a more specialized routine better suited to this?
Cheers,
Alex
You could try:
src_mono = src_rgb.astype(float).sum(axis=-1) / 3.
But that speed does seem slow. Here are the relevant timings on my machine (a
recent MacBook Pro) for a 3.1-megapixel-size array:
In [16]: a = numpy.empty((2048, 1536, 3), dtype=numpy.uint8)
In [17]: timeit
On 06/20/2011 10:41 AM, Zachary Pincus wrote:
You could try:
src_mono = src_rgb.astype(float).sum(axis=-1) / 3.
But that speed does seem slow. Here are the relevant timings on my machine (a
recent MacBook Pro) for a 3.1-megapixel-size array:
In [16]: a = numpy.empty((2048, 1536, 3),
Thanks, that's helpful. I'm now getting comparable times on a different
machine, it must be something else slowing down my machine more generally,
not just numpy.
On Mon, Jun 20, 2011 at 5:11 PM, Eric Firing efir...@hawaii.edu wrote:
On 06/20/2011 10:41 AM, Zachary Pincus wrote:
You could
Alex Flint wrote:
Thanks, that's helpful. I'm now getting comparable times on a different
machine, it must be something else slowing down my machine more
generally, not just numpy.
you also might want to get a bit fancier than simply scaling linearly
R,G, and B don't necessarily all
