Parasitemia/ParasitemiaCore/KMeans.fs

   1 module ParasitemiaCore.KMeans
   2
   3 open System.Collections.Generic
   4 open System.Drawing
   5
   6 open Emgu.CV
   7 open Emgu.CV.Structure
   8
   9 type Result =
  10     {
  11         fg : Image<Gray, byte>
  12         mean_bg : float32
  13         mean_fg : float32
  14         d_fg : Image<Gray, float32> // Euclidean distances of the foreground to mean_fg.
  15     }
  16
  17 let kmeans (img : Image<Gray, float32>) : Result =
  18     let nbIteration = 4
  19     let w = img.Width
  20     let h = img.Height
  21
  22     let min = ref [| 0.0 |]
  23     let minLocation = ref <| [| Point () |]
  24     let max = ref [| 0.0 |]
  25     let maxLocation = ref <| [| Point () |]
  26     img.MinMax (min, max, minLocation, maxLocation)
  27
  28     let minf = float32 (!min).[0]
  29     let maxf = float32 (!max).[0]
  30
  31     let mutable mean_bg = maxf - (maxf - minf) / 4.f
  32     let mutable mean_fg = minf + (maxf - minf) / 4.f
  33     use mutable d_bg : Image<Gray, float32> = null
  34     let mutable d_fg : Image<Gray, float32> = null
  35     let fg = new Image<Gray, byte> (img.Size)
  36
  37     let imgData = img.Data
  38     let fgData = fg.Data
  39
  40     for i = 1 to nbIteration do
  41         match d_bg with
  42         | null -> ()
  43         | _ ->
  44             d_bg.Dispose ()
  45             d_fg.Dispose ()
  46
  47         // EmGu doesn't import the in-place version of 'AbsDiff' so we have to create two images for each iteration.
  48         d_bg <- img.AbsDiff (Gray (float mean_bg))
  49         d_fg <- img.AbsDiff (Gray (float mean_fg))
  50
  51         CvInvoke.Compare (d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
  52
  53         let mutable bg_total = 0.f
  54         let mutable bg_nb = 0
  55
  56         let mutable fg_total = 0.f
  57         let mutable fg_nb = 0
  58
  59         for i = 0 to h - 1 do
  60             for j = 0 to w - 1 do
  61                 if fgData.[i, j, 0] > 0uy then
  62                     fg_total <- fg_total + imgData.[i, j, 0]
  63                     fg_nb <- fg_nb + 1
  64                 else
  65                     bg_total <- bg_total + imgData.[i, j, 0]
  66                     bg_nb <- bg_nb + 1
  67
  68         mean_bg <- bg_total / float32 bg_nb
  69         mean_fg <- fg_total / float32 fg_nb
  70
  71     { fg = fg; mean_bg = mean_bg; mean_fg = mean_fg; d_fg = d_fg }