Parasitemia/Parasitemia/KMedians.fs

   1 module KMedians
   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     fg: Image<Gray, byte>
  11     median_bg: float
  12     median_fg: float
  13     d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to median_fg.
  14
  15 let kmedians (img: Image<Gray, float32>) : Result =
  16     let nbIteration = 3
  17     let w = img.Width
  18     let h = img.Height
  19
  20     let min = ref [| 0.0 |]
  21     let minLocation = ref <| [| Point() |]
  22     let max = ref [| 0.0 |]
  23     let maxLocation = ref <| [| Point() |]
  24     img.MinMax(min, max, minLocation, maxLocation)
  25
  26     let mutable median_bg = (!max).[0] - ((!max).[0] - (!min).[0]) / 4.0
  27     let mutable median_fg = (!min).[0] + ((!max).[0] - (!min).[0]) / 4.0
  28     use mutable d_bg = new Image<Gray, float32>(img.Size)
  29     let mutable d_fg = new Image<Gray, float32>(img.Size)
  30     let mutable fg = new Image<Gray, byte>(img.Size)
  31
  32     for i in 1 .. nbIteration do
  33         d_bg <- img.AbsDiff(Gray(median_bg))
  34         d_fg <- img.AbsDiff(Gray(median_fg))
  35
  36         CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
  37
  38         let bg_values = List<float>()
  39         let fg_values = List<float>()
  40
  41         for i in 0 .. h - 1 do
  42             for j in 0 .. w - 1 do
  43                 if fg.Data.[i, j, 0] > 0uy
  44                 then fg_values.Add(float img.Data.[i, j, 0])
  45                 else bg_values.Add(float img.Data.[i, j, 0])
  46
  47         median_bg <- MathNet.Numerics.Statistics.Statistics.Median(bg_values)
  48         median_fg <- MathNet.Numerics.Statistics.Statistics.Median(fg_values)
  49
  50     { fg = fg; median_bg = median_bg; median_fg = median_fg; d_fg = d_fg }
  51
  52
  53
  54