Parasitemia/ParasitemiaCore/KMedians.fs

   1 module ParasitemiaCore.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     {
  11         fg : Image<Gray, byte>
  12         median_bg : float
  13         median_fg : float
  14         d_fg : Image<Gray, float32> // Euclidean distances of the foreground to median_fg.
  15     }
  16
  17 let kmedians (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 mutable median_bg = (!max).[0] - ((!max).[0] - (!min).[0]) / 4.0
  29     let mutable median_fg = (!min).[0] + ((!max).[0] - (!min).[0]) / 4.0
  30     use mutable d_bg = new Image<Gray, float32>(img.Size)
  31     let mutable d_fg = new Image<Gray, float32>(img.Size)
  32     let mutable fg = new Image<Gray, byte>(img.Size)
  33
  34     for i = 1 to nbIteration do
  35         d_bg <- img.AbsDiff(Gray(median_bg))
  36         d_fg <- img.AbsDiff(Gray(median_fg))
  37
  38         CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
  39
  40         let bg_values = List<float>()
  41         let fg_values = List<float>()
  42
  43         for i = 0 to h - 1 do
  44             for j = 0 to w - 1 do
  45                 if fg.Data.[i, j, 0] > 0uy then
  46                     fg_values.Add(float img.Data.[i, j, 0])
  47                 else
  48                     bg_values.Add(float img.Data.[i, j, 0])
  49
  50         median_bg <- MathNet.Numerics.Statistics.Statistics.Median(bg_values)
  51         median_fg <- MathNet.Numerics.Statistics.Statistics.Median(fg_values)
  52
  53     { fg = fg; median_bg = median_bg; median_fg = median_fg; d_fg = d_fg }
  54
  55
  56
  57