+++ /dev/null
-module KMedians
-
-open System.Collections.Generic
-open System.Drawing
-
-open Emgu.CV
-open Emgu.CV.Structure
-
-type Result = {
- fg: Image<Gray, byte>
- median_bg: float
- median_fg: float
- d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to median_fg.
-
-let kmedians (img: Image<Gray, float32>) : Result =
- let nbIteration = 4
- 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 median_bg = (!max).[0] - ((!max).[0] - (!min).[0]) / 4.0
- let mutable median_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(median_bg))
- d_fg <- img.AbsDiff(Gray(median_fg))
-
- CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
-
- let bg_values = List<float>()
- let fg_values = List<float>()
-
- for i in 0 .. h - 1 do
- for j in 0 .. w - 1 do
- if fg.Data.[i, j, 0] > 0uy
- then fg_values.Add(float img.Data.[i, j, 0])
- else bg_values.Add(float img.Data.[i, j, 0])
-
- median_bg <- MathNet.Numerics.Statistics.Statistics.Median(bg_values)
- median_fg <- MathNet.Numerics.Statistics.Statistics.Median(fg_values)
-
- { fg = fg; median_bg = median_bg; median_fg = median_fg; d_fg = d_fg }
-
-
-
-