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 contr
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 wrote:
> On 06/20/2011 10:41 AM, Zachary Pincus wrote:
> > You could try:
> > src_mono =
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), dt
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 numpy.dot(a.astype(floa
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
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