First off, thanks Joe Your code is what I was looking for - AND, it sounds like - the thing to do, unless one wanted to embark in the kind of regression Matthias suggests. Second, Stéfan, sorry for the confusion about the rainbowbot, that was an aside, I happened to post on both of them at the same time, but really the Jupiter idea was more because I was playing with image size reduction via conversion to intensity and component reduction via PCA, and was wondering how one could compare the quality of the result with the input image, but in color as opposed to in grayscale. Matthias, I think it would be fun to work together on the rainbowbot idea, and then to send those 'viridize' tweets. I'll send you an email with some ideas. Cheers, Matteo On Thu, Jan 28, 2016 at 3:33 PM, Stéfan van der Walt wrote:

On Thu, Jan 28, 2016 at 1:29 PM, Joe Kington wrote:

...

I was the one Matteo mentioned on the slack group. At first I thought Matteo was referring to exactly what you described: Take a colormapped single-band image and unmap/remap the color palette.

In one of the threads here, I read

"""

My idea would be to make it into a web app called 'rainbowbot' which would automatically detect bad colormaps either form online images or user uploaded images, and then provide them with tools to either equalize the colormaps or replace with a perceptual version with same hue range, or.....

It is in here

https://github.com/mycarta/rainbowbot I am open to suggestions, and offers to collaborate. """

Otherwise, I guess the problem is as simple as:

image = color.rgb2gray(io.imread('image.jpg')) plt.imshow(image, cmap='viridis')

If it is about turning false color maps into true color maps, that would be hard, given the many mappings out there.

Stéfan

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