--- /dev/null
+module KMeans
+
+open System.Collections.Generic
+open System.Drawing
+
+open Emgu.CV
+open Emgu.CV.Structure
+
+type Result = {
+ fg: Image<Gray, byte>
+ mean_bg: float
+ mean_fg: float
+ d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to mean_fg.
+
+let kmeans (img: Image<Gray, float32>) (fgFactor: float) : Result =
+ let nbIteration = 3
+ let w = img.Width
+ let h = img.Height
+
+ let min = ref [| 0.0 |]
+ let minLocation = ref <| [| Point() |]
+ let max = ref [| 0.0 |]
+ let maxLocation = ref <| [| Point() |]
+ img.MinMax(min, max, minLocation, maxLocation)
+
+ let mutable mean_bg = (!max).[0] - ((!max).[0] - (!min).[0]) / 4.0
+ let mutable mean_fg = (!min).[0] + ((!max).[0] - (!min).[0]) / 4.0
+ use mutable d_bg = new Image<Gray, float32>(img.Size)
+ let mutable d_fg = new Image<Gray, float32>(img.Size)
+ let mutable fg = new Image<Gray, byte>(img.Size)
+
+ for i in 1 .. nbIteration do
+ d_bg <- img.AbsDiff(Gray(mean_bg))
+ d_fg <- img.AbsDiff(Gray(mean_fg))
+
+ CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
+
+ let mutable bg_total = 0.0
+ let mutable bg_nb = 0
+
+ let mutable fg_total = 0.0
+ let mutable fg_nb = 0
+
+ for i in 0 .. h - 1 do
+ for j in 0 .. w - 1 do
+ if fg.Data.[i, j, 0] > 0uy
+ then
+ fg_total <- fg_total + float img.Data.[i, j, 0]
+ fg_nb <- fg_nb + 1
+ else
+ bg_total <- bg_total + float img.Data.[i, j, 0]
+ bg_nb <- bg_nb + 1
+
+ mean_bg <- bg_total / float bg_nb
+ mean_fg <- fg_total / float fg_nb
+
+ { fg = fg; mean_bg = mean_bg; mean_fg = mean_fg; d_fg = d_fg }
\ No newline at end of file