3 open System.Collections.Generic
13 d_fg
: Image<Gray, float32
> } // Euclidean distances of the foreground to median_fg.
15 let kmedians (img
: Image<Gray, float32
>) (fgFactor
: float) : Result =
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)
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)
32 for i
in 1 .. nbIteration do
33 CvInvoke.Pow(img
- median_bg, 2.0, d_bg)
34 CvInvoke.Pow(img
- median_fg, 2.0, d_fg)
35 CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
37 let bg_values = List<float>()
38 let fg_values = List<float>()
40 for i
in 0 .. h - 1 do
41 for j
in 0 .. w - 1 do
42 if fg.Data.[i
, j
, 0] > 0uy
43 then fg_values.Add(float img.Data.[i
, j
, 0])
44 else bg_values.Add(float img.Data.[i
, j
, 0])
46 median_bg <- MathNet.Numerics.Statistics.Statistics.Median(bg_values)
47 median_fg <- MathNet.Numerics.Statistics.Statistics.Median(fg_values)
49 CvInvoke.Sqrt(d_fg, d_fg)
51 { fg = fg; median_bg = median_bg; median_fg = median_fg; d_fg = d_fg }