Image's pixels GPU colour classes splitting

Question

How can I split in color classes, the pixels of an image that I load in Mathematica?

I would an output like this:

<Class1Name> 50% 
<Class2Name> 30% 
<Class3Name> 20%

The percentages refere to the amount of pixels of the image that are into that colour class.

The definition of the classes would be something like this: <Class1Name> = All pixels that are into a RGB range of colours (for example from a dark green to a light green)

I have tried a code like this:

Image[{List@@@DominantColors[<imagefile>, n]}, ImageSize -> 300]

But I don't know how to split the output in classes. By encreasing the value of $n$ I will have a better precision right? I would love also a plot of the normal distribution of the pixels, where in the $x$ axis lie the names of the classes and on the $y$ axis a normalized scale that go from 0 to 1. So the plot should look similar to this:

If is possible I would execute the computation of all this on the GPU or on both the CPU and GPU togheter, I would like to chose between an OpenCL and CUDA version of the code and if is not possible I would have a code optimized to perform the computation on all cores an threads of my CPU.

P.S. I have used in the examples only three classes but actually I would like to choose the number of classes that I prefer every time that I run the code.

Answer 1

One function you might find useful is PixelValuePositions. You can take an image img and find all the pixel positions where (say) Green occurs by

PixelValuePositions[img, Green, 0.1]

You could count how many there are by

Length[PixelValuePositions[img, Green, 0.1]]

and then calculate the percentages you wish. The third argument specifies how close to the given color a pixel must be in order to be counted. For example, the color

Green
RGBColor[0, 1, 0]

so the 0.1 means any pixel within 0.1 of {0,1,0}. You can see what these colors look like by choosing a bunch of them -- here you can plot 10 disks all within 0.1 color range of Green.

Table[Graphics[{RGBColor[#[[1]], 1 - #[[2]], #[[3]]] &@
    RandomVariate[UniformDistribution[{0, 0.1}], 3], Disk[]}], {i, 10}]

Answer 2

I'm not sure if you plan to pre-define the colour classes or extract them from the image, but here is one way to do that latter.

I use ColorQuantize to reduce the image to a palette of $n$ colours, then simply Tally the pixel values in the quantized image. The bar chart shows the fraction of pixels for each colour.

img = ExampleData[{"TestImage", "JellyBeans2"}];

n = 10;

q = ColorQuantize[img, n];
{colors, counts} = Transpose[Tally[ImageData[q] ~Flatten~ 1] ~SortBy~ Last];
BarChart[Normalize[counts, Total], ChartStyle -> RGBColor /@ colors] ~Legended~ img