When plotting a set of lines, you may want to distinguish them by color. By default, Matlab chooses a small set of colors and cycles among them, and so if you have more than a few lines there will be confusion about which line is which. To fix this problem, one would want to be able to pick a much larger set of distinct colors, where the number of colors equals or exceeds the number of lines you want to plot. Because our ability to distinguish among colors has limits, one should choose these colors to be "maximally perceptually distinguishable."
This function generates a set of colors which are distinguishable by reference to the "Lab" color space, which more closely matches human color perception than RGB. Given an initial large list of possible colors, it iteratively chooses the entry in the list that is farthest (in Lab space) from all previously-chosen entries.
Tim Holy (2020). Generate maximally perceptually-distinct colors (https://www.mathworks.com/matlabcentral/fileexchange/29702-generate-maximally-perceptually-distinct-colors), MATLAB Central File Exchange. Retrieved .
Works very well.
Thanks a lot.
Never mind to my question below, it was user error (I saved it to an adjacent folder).
I'm getting an error with R2019a:
Undefined function 'distinguishable_colors' for input arguments of type 'double'.
Error in PN_model (line 87)
colors = distinguishable_colors(9);
The function description does not mention anything about a specific type of input number, does anyone have any suggestions? I've tried single(9) and int8(9), and they both receive a similar error that the function does not take those as input.
getting an error with R2020a
Undefined function 'makecform' for input arguments of type 'char'.
Error in distinguishable_colors (line 93)
C = makecform('srgb2lab');
Even without the image processing Toolbox this function is very useful! Simply download the colorspace-transformation function (Link below)! Which is, by the way, also mentioned in the given example...
better than its counterparts you can find on internet
This is great. Thanks.
Awesome thank you
Great work! Thanks a lot for sharing
So easy to use. thanks very much for sharing this.
Exactly what I was looking for! Thank you!
My plots and I THANK YOU!!
It's wonderful and more useful than jet. Thanks~
Awesome tool. Thanks.
Thanks, it works perfectly!
Thanks, very useful!
Don't have image processing toolbox. Used the alternative MEX colorspace transformation (got it via File Exchange).
Really handy, and worked first time.
Thank you very much!
I have ported this algorithm to Python and wrote another program to take the distinguishable_colors output and a label adjacency matrix to group colors as part of the mindboggle.info brain image software project:
Really usefull for making a clear graph!
Useless, really, as it requires the image processing toolbox AND/OR few other scripts from file exchange.
nice and easy!!
A really, really useful function. Thank you.
Uses "makecform", which belongs to image processing => usless for those who do not have it. USing the alternative from commens requires some extra effort. Pity.
Awesome function. Very useful.
Great function. Any plans to add in colorblind-friendly functionality?
Awesome piece of code....this helped me a lot in building intuition in a different problem I am working on..thanks a lot...
See comments from 2010 for a workaround that doesn't require the Image Processing Toolbox.
Requires the image processing toolbox.
Looks like it's the same idea. Thanks, I didn't know about that work.
How does your submission relate to Glasbey LUT?
Glasbey, Chris, et al. "Colour displays for categorical images." Color Research & Application 32.4 (2007): 304-309.
Thanks! Learned quite a bit from your code.
Very cool idea! One cautionary comment: I'm one of the 5 to 10% of scientists who is mildly colorblind, and I cannot distinguish between colors 3 and 6 in the distinguishable_colors image shown above.
Thanks for this! And thanks to the below commenter who suggested replacing the ~ on line 115.
I love this function! Thank you.
Well done !
Thank you for writinig this, it does generate an interesting set of colors that do appear to be contrasting. Would it be possible to generate one, or more, "key" colors that are highly contasted with the rest. I have about 30 lines on a figure, along with the average. While I did make the average wider, it is difficult to determine the color for the "key" line(s) using this routine.
Thanks. Suggestion: Add these lines at the beginning
if nargin < 1
(taken from jet.m) in order to allow something like
Very neat. The usage in combination with Colorspace Transformations is especially elegant.
Change [~,index] to [tmp,index] on line 115 to work on pre-2010 versions of Matlab.
@Robert Daly: sorry I didn't see your question earlier. It's an interesting application.
I'm no expert at these matters, but I imagine you could convert to LMS color space and then set one or more of the components to zero. You'd probably then want to convert back to Lab to judge perceptual distinguishability.
Alternatively (and perhaps more guaranteed to be accurate), you could use this lookup table:
and then convert the modified RGB values to Lab.
If you implement this, I'd be curious to see what the final result looks like.
Is there a colour space function that I could use with this function to find colours that are perceptually-distinct to a person with a colour vision deficiency (colour blind)? In the example I have in mind colours that are only different by the amount of red in them such as blue and purple look the same (Protanopia).
I've downloaded the 'colorspace' function from FEX and now it works. Great job!
This is an extremely useful function, but it's unusable for me because I do not have the image processing toolbox :-( Any workaround?
Excellent idea. I have one suggestion. Often times, I want to avoid not just a single background color but a set of them. For example both black and white when I have black text on white background already.
This works very well. If you are using an older version of MatLab, you will need to modify line 98
[~,index] = max(mindist2);
as the ~ operator will produce an error. Simply change it to junk for example.
Thanks for the feedback and suggestions, everyone. Perhaps even more important than the issue of depending on the Image Processing Toolbox, you helped me realize that there's no particular reason to restrict this to Lab colorspace; one can easily envision scenarios where the user would like to be able to have some control over what is considered "perceptually similar." So I have uploaded a new version that allows the user to supply his/her own function that converts RGB colors into whatever space "similarity" is to be judged in. Of course, one effect is that this will allow you to use the "colorspace" function.
Even though I do not have image processing toolbox either, I agree this is an excellent idea and well developed code.
We should be able to use the colorspace FEX by replacing this section of code
C = makecform('srgb2lab');
lab = applycform(rgb,C);
bglab = applycform(bg,C);
with this one:
From the description is seems to be one of the functions that I have been looking for many times, and the excellent rating from Brett only wettens my appetite. Alas, I do not have the image processing toolbox. Would it be possible to instead use this FEX contribution:
Very nicely done...well written, solid, useful code.
As suggested by Il, I added the ability to avoid multiple background colors.
I have added the option for the user to supply a function handle to any desired colorspace conversion function. One application is to use the file exchange's "colorspace" set of tools. In this case, you no longer need the image processing toolbox.
Inspired: Maximally Distinct Color Generator, Youden's plot, Explore Experimental Data, Colorize Document Bar, CubeHelix Colormap Generator: Beautiful and Versatile!, ColorBrewer: Attractive and Distinctive Colormaps, Intuitive RGB color values from XKCD, rgbmap color maps, MatPlotLib Perceptually Uniform Colormaps, Feedback Control of Many Differential-Drive Robots with Uniform Control Inputs, TACTICS Toolbox, Simulate Control of Magnetized Tetrahymena Pyriformis Cells, plot spread points (beeswarm plot), justinblaber/image_match