504172aa2909adf3767ba542a8357e2491fb5e79
[master-thesis.git] / 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 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 = 4
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 = 1 to 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 = 0 to h - 1 do
42 for j = 0 to 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