| Re: color value interpolation from colorbar [message #64218 is a reply to message #64128] |
Thu, 04 December 2008 17:00  |
Jeremy Bailin
Messages: 618
Registered: April 2008
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Senior Member |
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On Dec 4, 4:35 pm, Paolo <pgri...@gmail.com> wrote:
> So your first step is to "digitize" the color bar:
>
> Create 3 arrays of r,g,b with the colors as a function of pixel size
> and an array x of the pixel index number.
>
> Then, show us a plot of r(x), g(x), b(x)
>
> Ciao,
> Paolo
>
> j.coe...@gmail.com wrote:
>> Thanks. The red-to-yellow colorbar is already on the image -- I don't
>> create it. The map of gradient values to the 3D colorspace would have
>> to be determined from this colorbar, which also has errors in it
>> (garbage in, as Dr. Fanning says.) I suppose this would be an
>> irregular grid of gradient values within the 3D colorspace.
>
>> Basically, there's a color-coded image with a colorbar *on* the
>> image. The colors, even those in the bar, often have errors because
>> they were digitized from analog tape recordings. Still, it is
>> possible to eyeball the images to determine color levels. I want the
>> computer to do something more quantitative than the eyeball analysis.
>
>
How many points do you have on the colour bar? If it's sampled finely
enough, it might be okay to assume a straight line through RGB space
between them. Then you could calculate the minimum distance of any
desired colour from each of those interpolated segments in RGB space
to find the closest segment, and then use its component in the
direction of the segment.
That's only going to work if the input isn't too garbagy - you'll need
points on the colour bar that sample the gradiant quite finely, and
the colours on the image can't be too distant in RGB space from the
locus that the colour bar defines. But if those conditions are met,
this might work.
I'm imagining something like this. You have cbar_r, cbar_g and cbar_b,
each of which contain nbar points and represent data values cbar_data.
To find the best data value for a pixel with colour image_r, image_g
and image_b (untested and unwieldy - can easily be vectorized to work
on the image at once, but I'm too hungry):
; find the closest colourbar point
cbardist2 = (cbar_r-image_r)^2 + (cbar_g-image_g)^2 + (cbar_b-image_b)
^2
cbarmin = min(cbardist2, segment1)
case segment1 of
0: segment2=1
nbar-1: segment2=nbar-2
else: segment2 = (cbardist2[segment1-1] gt cbardist2[segment1+1) ?
(segment1+1) $
: (segment1-1)
endcase
; find component along the segment
from1toi = [image_r-cbar_r[segment1],image_g-cbar_b[segment1],image_b-
cbar_b[segment1]]
from1to2 = [cbar_r[segment2]-cbar_r[segment1],cbar_g[segment2]-cbar_g
[segment1], $
cbar_b[segment2]-cbar_b[segment1]]
projcomp = total(from1toi * from1to2) / (norm(from1to2) * norm
(from1toi))
; interpolate data value
imagedata = cbar_data[segment1]*(1.-projcomp) + cbar_data[segment2]
*projcomp
-Jeremy.
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