Number of bins in Entropy and Enhance_contrast for uint16

[Juan Nunez-Iglesias]

Hi Pål,

Actually, the rank filters are fast up to 12 bits, so if you can (manually)
compress your data to be in 0-2047 in a uint16 array, you might still get
good performance. Hope that helps!

Juan.

On Wed, Dec 10, 2014 at 11:17 PM, Pål Gunnar Ellingsen <paalge at gmail.com>
wrote:

> Hi
>
> Thank you for the quick answer.
> I agree that converting it to uint8 will speed it up by a lot, and I have
> also tried this.
> Though it also removes so much data from my 16 bit grayscale image, that
> the contrast I'm interesting in isn't there anymore.
> This is the reason why I think that changing the binning from 1000 to 100
> or even 50, without changing the data type would be a better choice.
>
> Kind regards
>
> Pål
>
>
>
> On Wednesday, 10 December 2014 11:10:48 UTC+1, Stefan van der Walt wrote:
>>
>> Hi Pål
>>
>> On 2014-12-10 12:05:19, Pål Gunnar Ellingsen <paa... at gmail.com> wrote:
>> > "Bitdepth of 15 may result in bad rank filter performance due to large
>> > number of bins".
>> > I'm wondering if it is possible to reduce the number of bins via an
>> option?
>> > I've tried to find such a keyword in the documentation and source code,
>> but
>> > I haven't been able to find it.
>>
>> The easiest is to change the image dtype by using, e.g.,
>>
>> from skimage import img_as_ubyte
>> image8 = img_as_ubyte(image16)
>>
>> The algorithm should run much faster on image8 than on image16.
>>
>> Regards
>> Stéfan
>>
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