[Numpy-discussion] fast grayscale conversion
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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] fast grayscale conversion
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 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] fast grayscale conversion
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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] fast grayscale conversion
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 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] fast grayscale conversion
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 contribute equally to our sense of whiteness For instance, PIL uses: When from a colour image to black and white, the library uses the ITU-R 601-2 luma transform: L = R * 299/1000 + G * 587/1000 + B * 114/1000 which would be easy enough to do with numpy. -Chris On Mon, Jun 20, 2011 at 5:11 PM, Eric Firing efir...@hawaii.edu mailto:efir...@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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org mailto:NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org mailto:NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org mailto:NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
