[datatable-help] Color extraction, quantification and analysis from image in R

[Pierrel]

Dear R-help Team,

I would like to `quantifying colors in an image`. 
I work on the iridescence of nacre (mother of pearl), and I want to
quantifying three colors (**red, yellow and green**) on this shell (for
example on the right picture on the link above).

<http://r.789695.n4.nabble.com/file/n4728690/xt7gE.jpg> 

So, I had test some packages (`imager`,  `ImageMagick`, `EBImage`...), but I
don't really find something that help me.

Well, I would like to make color quantification on R, with circles. The area
of ​​the primitive in pixel may be expressed as that of a circle of
equivalent surface area. The primitive is a contiguous area of neighboring
pixels of similar color. The center of the circle can be the anchor pixel. 
So, there is the equation which I think it's ok to do this:

    
> DeltaI = square root[(Ranchor - Ri)² - (Ganchor - Gi)² - (Banchor -Bi)²]

Where R,G and B are color components of a pixel, ranging from 0 to 255,
anchor is the anchor pixel and i is any pixel around the anchor pixel which
are the same equivalent color.

There is a image link to the expectation results (from Alçiçek & Balaban
2012):

<http://r.789695.n4.nabble.com/file/n4728690/BMgXt.png> 

So there is my (bootable worked) code, but I have really no idea how to
continue.. May be try to create a package ?

    library(png)
    nacre <-  readPNG("test.png")
    nacre

    dim(nacre)

    # show the full RGB image
    grid.raster(nacre)

    # show the 3 channels in separate images
    nacre.R = nacre
    nacre.G = nacre
    nacre.B = nacre

    # zero out the non-contributing channels for each image copy
    nacre.R[,,2:3] = 0
    nacre.G[,,1]=0
    nacre.G[,,3]=0
    nacre.B[,,1:2]=0

    # build the image grid
    img1 = rasterGrob(nacre.R)
    img2 = rasterGrob(nacre.G)
    img3 = rasterGrob(nacre.B)
    grid.arrange(img1, img2, img3, nrow=1)
    # Now let’s segment this image. First, we need to reshape the array into
a data frame with one row for each pixel and three columns for the RGB
channels:
  
  
    # reshape image into a data frame
    df = data.frame(
    red = matrix(nacre[,,1], ncol=1),
    green = matrix(nacre[,,2], ncol=1),
    blue = matrix(nacre[,,3], ncol=1)
      )
   

    ### compute the k-means clustering
    K = kmeans(df,4)
    df$label = K$cluster

    ### Replace the color of each pixel in the image with the mean 
    ### R,G, and B values of the cluster in which the pixel resides:

    # get the coloring
    colors = data.frame(
      label = 1:nrow(K$centers), 
      R = K$centers[,"red"],
      G = K$centers[,"green"],
      B = K$centers[,"blue"]
    )

    # merge color codes on to df
    df$order = 1:nrow(df)
    df = merge(df, colors)
    df = df[order(df$order),]
    df$order = NULL
  
    # get mean color channel values for each row of the df.
    R = matrix(df$R, nrow=dim(nacre)[1])
    G = matrix(df$G, nrow=dim(nacre)[1])
    B = matrix(df$B, nrow=dim(nacre)[1])

    # reconstitute the segmented image in the same shape as the input image
    nacre.segmented = array(dim=dim(nacre))
    nacre.segmented[,,1] = R
    nacre.segmented[,,2] = G
    nacre.segmented[,,3] = B

    # View the result
    grid.raster(nacre.segmented)

Someone have a track or any idea ?
Thanks for any help..


Link to the article:
http://s3.amazonaws.com/academia.edu.documents/41113722/tiger_prawn.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1486502777&Signature=ZcX1eV8nqS1%2BYRSgvJZyAURvCwo%3D&response-content-disposition=inline%3B%20filename%3DVisual_Attributes_of_Hot_Smoked_King_Sal.pdf




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