I have a image of 1000 x 1000 pixels of which I have made 5 x 5 partitions. After that I am looking for a way to count number of color pixels in each partition. Thanks in advance for help.
ImagePartition[I,200]//Grid
I got the following partitions:
img = RemoveAlphaChannel@Import["https://i.sstatic.net/nmMA6.png"];
parts = ImagePartition[img, 200];
(* see the documentation here: https://wolfram.com/xid/0enzd2s6c-6u9yga *)
countColors[img_] := Module[{count = 0},
ImageScan[If[Mean[#] != 1, count++] &, img];
Return[count]
]
Map[countColors, parts, {2}] //Grid
16306 7574 24259 7669 16459
18185 25874 19051 26063 18207
22254 20128 17908 20212 22234
18035 25878 19046 26033 18066
16216 7063 24078 7175 16327
For colour frequencies per part, this will get you a matrix of associations that count the commonest colours appearing in each. An association contains colour name $\rightarrow$ frequency pairs. I've not used DominantColors because it does clustering and may return inexact colours not present in the image. Instead I've used the 32 most common colours including white as the basis for counting:
cols = TakeLargestBy[Tally[Flatten[ImageData[img], 1]], Last, 32];
NearestColorName = ResourceFunction["NearestColorName"];
tallycols[img_] := Association[
(RGBColor[#] -> Count[ImageData[img], #, 2]) & /@ cols[[All, 1]]
]
partcols = Map[tallycols, parts, {2}];
You can then have a look at partcols[[2, 3]] for example:
If you need to name them you should use NearestColorName, use ColorData, or use a custom mapping as in an answer to a previous question of yours.
countColors here from Mean[#] != 1 to something else like for example to search for red pixels: # == {1.,0.,0.}
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HowManyNonWhite[i_Image] := (Times @@ ImageDimensions[i]) -
Count[ImageData[RemoveAlphaChannel[i]], {1., 1., 1.}, {2}]
Map[HowManyNonWhite,ImagePartition[image,200],{2}]
Where image is the original image.
I would suggest this:
img = Import["https://i.sstatic.net/nmMA6.png"];
parts = ImagePartition[img, 200];
countWhitePixels[im_] := Length@PixelValuePositions[im, White, 0]
200^2 - Map[countWhitePixels, parts, {2}] // MatrixForm
A threshold for "white" sometimes is useful. Here I've set it rather high, and the counts do not change with a threshold higher than 0.9999. Compare with @flinty and @SHuisman, which produce slightly higher counts, to determine which method might be most appropriate.
img = Import["https://i.sstatic.net/nmMA6.png"];
parts = ImagePartition[RemoveAlphaChannel[img], 200]
Map[Total[ImageData@Binarize[#, {0., 0.9999}], 2] &, parts, {2}]
(* {{16297, 7561, 24258, 7639, 16448}, {18182, 25858, 19048, 26049, 18197}, {22249, 20105, 17885, 20182, 22225}, {18029, 25870, 19037, 26014, 18055}, {16208, 7050, 24071, 7146, 16317}} *)
The DominantColors function can be used to count the number of pixels of each color. However, this also includes the count for white pixels
imgParts = ImagePartition[i, 200];
Grid[
DominantColors[#, All, {"Color", "Count"}] & /@ imgParts,
Frame -> All
]
DominantColors because it does clustering. I've found that if you look at the white colour for instance and do FullForm on it, you'll see it is a very close white but RGBColor[0.9999603021318101`, 0.9999708034308904`, 0.9999640700153131`] is not a value that appears in the ImageData.
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DominantColors uses a clustering algorithm. From the documentation, you can see that the various methods that you can tell Mathematica to use are KMeans, KMedoids, MedianCut, MinVariance, and Octree. By default, Mathematica picks the most suitable method to generate color clusters. For the last part of your question, can you explain what sort of computations are you trying to do?
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Ias a variable name. $\endgroup$