Thanks Stefan. That helps clarify some of the dtypes to me; however I still have a few confusions in regard to color data. I should have specified this more in my OP. I am trying to create a program where all color data is stored as RGB. This requires a validator that does flexible *to_rgb()* conversion. I want the users to have flexibility, so it should accept names like "aqua" as well as RGB tuples. I realize now that imshow() will do its own conversions, but still don't quite understand exactly what constraints I need to impose on users for all the various use cases. For example, if a user enters a single integer (say 239), is there a de-facto way to rgb-convert this? I've tried to exhause the scenarious below; any case with question marks is still unclear to me. INPUT TYPE INPUT EXAMPLE HANDLER DESIRED OUTPUT ----------------------------------------------------------------------------------------------------- hex string '#0FF000' ColorConverter.to_rgb() (.2, .4, .5) name string 'purple ' ColorConverter.to_rgb() (.1, .8, .3) < 1 float tuple ' (.5, .2, .4) PASS (.5, .2, .4)

1 float/int tuple (30, 28, 90) ???? ???? int 140 (Digital channel?) (140, 140, 140)??? float 39.5 (Error??) ???

I read on wiki that a RGB tuple with elements > 1 can be interpreted as a "Digital Channel", so perhaps just leave these as is. The tough cases for me are really when a user enters a single Int or Float. Of course, I could just raise an exception if there's no de-facto way to handle this... On Tue, Dec 31, 2013 at 6:15 AM, Stéfan van der Walt wrote:

Hi Adam

On Mon, 30 Dec 2013 22:37:43 -0800, Adam Hughes wrote:

...

I noticed recently that matplotlib.colors limits RGB values to a range (0 - 1), while in scikit image, RGB values can be much larger. For example:

*test = np.zeros( (500,500,3) )*

*test[:,:,0]=50* *test[:,:,1]=19* *test[:,:,2]=25*

*imshow(test); *

Produces a teal background. I was curious how the color teal is derived from this? I tried normalizing to 255 and and 50 but neither seemed to produce the same teal color.

Here's a write-up of the data-type and range representation that scikit-image uses:

http://scikit-image.org/docs/0.9.x/user_guide/data_types.html

When visualizing data with Matplotlib, note that data is normalized by default, so you have to specify "vmin" and "vmax" to correctly display your generated background.

Regards Stéfan

-- You received this message because you are subscribed to a topic in the Google Groups "scikit-image" group. To unsubscribe from this topic, visit https://groups.google.com/d/topic/scikit-image/a54ehbd1fLk/unsubscribe. To unsubscribe from this group and all of its topics, send an email to scikit-image+unsubscribe@googlegroups.com. For more options, visit https://groups.google.com/groups/opt_out.