Parasitemia/Parasitemia/KMeans.fs

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