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imagesc: how to set NaN as white color

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Dario le 18 Déc 2017
I need to set NaN values as white color using imagesc. I attach the matrix which is a 1200x1200 matrix with different values equal to NaN.

Réponses (4)

William Thielicke
William Thielicke le 6 Oct 2021
Modifié(e) : Walter Roberson le 8 Oct 2021
As this is still the first hit when searching on Google...
The best solution IMHO:
Setting data that is NaN to transparent
set(h, 'AlphaData', ~isnan(img_data))
  2 commentaires
jon erickson
jon erickson le 8 Oct 2021
agree - this is a really clever, quick, easy solution that works in vast majority of use-cases.
Walter Roberson
Walter Roberson le 8 Oct 2021
image objects do not (or historically did not) accept logical data, so you had to convert to double,
set(h, 'AlphaData', 1-isnan(img_data))

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Adam le 18 Déc 2017
Modifié(e) : Adam le 7 Juin 2019
doc colormap
allows you to define whatever colourmap you want. You can tag a white point on the front of e.g. a Parula colourmap of any size you want, e.g.
cmap = [0 0 0; parula(6)];
In order to make sure only NaNs take the white value though you would likely have to do some messing around with caxis. It isn't easy to define a colourmap for many values where you want precisely one to have one specific colour on the colourmap, unless you map it to true RGB, in which case you can do whatever you want.
  5 commentaires
Sophia Salazar
Sophia Salazar le 7 Juin 2019
I think you're right. I ended up using jet(200) so that a smaller portion of the cmap is white, I think this works. Thanks!
Adam le 7 Juin 2019
Your data will be mapped linearly to your colourmap, so the bigger the colourmap the smaller the range of values mapped into each bin. If you have a 256-element colourmap then the bottom 1/256 ( 0.39% ) of values will be mapped to the first bin of the colourmap. Nans will also get mapped here, whatever size your colourmap and range your data.
This is why I said "It isn't easy to define a colourmap for many values where you want precisely one to have one specific colour on the colourmap". If you are dealing with a uint8 image it is quite easy as you can have 1 bin per pixel value and just add an extra bin for the NaNs.
As also mentioned, you can play around with the caxis limits to achieve the right effect, but it all depends on your data.
a = rand( 10 );
a(3,4) = NaN;
figure; imagesc( a );
colourmap( [0 0 0; parula(256)] )
If you do only that then quite likely you will have multiple black pixels instead of just 1 (this is random data though so it depends on the particular randon numbers you get. In my run I ended up with 4 black squares)
Now you can try:
caxis( [-0.001 1] )
and some or all of the non-NaN values will now take on a colour from the true colourmap instead of white. But if you have a very small value you may need to go for more like
caxis( [-0.01 1] )
caxis( [-0.1 1] )
by trial and error (or it can be mathematically calculated, I just couldn't be bothered)

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Image Analyst
Image Analyst le 7 Juin 2019
Have you tried this solution:
yourImage(isnan(yourImage)) = 255;

Emmanuel Atoleya Atindama
Emmanuel Atoleya Atindama le 10 Fév 2021
First, you need to know the scale value of the color. Example white = 1, black = 0.
p = data_array(:,:,:);
p(isnan(data_array))=1; % this assigns white to all the nan values, while all other values maintain their original color
figure; imshow(p)
Say you want to assign white to all nan values and black to the known values.
p = zeros(size(data_array));
p(isnan(data_array))=1; % this assigns white to all the nan values, and black to all other values
figure; imshow(p)


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