[Numpy-discussion] fast grayscale conversion

[Alex Flint]

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 <efiring at hawaii.edu> wrote:

> 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), dtype=numpy.uint8)
> >
> > In [17]: timeit numpy.dot(a.astype(float), numpy.ones(3)/3.)
> > 10 loops, best of 3: 116 ms per loop
> >
> > In [18]: timeit a.astype(float).sum(axis=-1)/3.
> > 10 loops, best of 3: 85.3 ms per loop
> >
> > In [19]: timeit a.astype(float)
> > 10 loops, best of 3: 23.3 ms per loop
> >
> >
>
> On my slower machine (older laptop, core2 duo), you can speed it up more:
>
> In [3]: timeit a.astype(float).sum(axis=-1)/3.0
> 1 loops, best of 3: 235 ms per loop
>
> In [5]: timeit b = a.astype(float).sum(axis=-1); b /= 3.0
> 1 loops, best of 3: 181 ms per loop
>
> In [7]: timeit b = a.astype(np.float32).sum(axis=-1); b /= 3.0
> 10 loops, best of 3: 148 ms per loop
>
> If you really want float64, it is still faster to do the first operation
> with single precision:
>
> In [8]: timeit b = a.astype(np.float32).sum(axis=-1).astype(np.float64);
> b /= 3.0
> 10 loops, best of 3: 163 ms per loop
>
> Eric
>
>
> >
> >
> > On Jun 20, 2011, at 4:15 PM, Alex Flint wrote:
> >
> >> 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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