fg: Image<Gray, byte>
median_bg: float
median_fg: float
- d_fg: Image<Gray, float32> } // Distances to median_fg.
+ d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to median_fg.
let kmedians (img: Image<Gray, float32>) (fgFactor: float) : Result =
let nbIteration = 3
let mutable median_bg = (!max).[0] - ((!max).[0] - (!min).[0]) / 4.0
let mutable median_fg = (!min).[0] + ((!max).[0] - (!min).[0]) / 4.0
- let mutable d_bg = new Image<Gray, float32>(img.Size)
+ 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
+ for i in 1 .. nbIteration do
CvInvoke.Pow(img - median_bg, 2.0, d_bg)
CvInvoke.Pow(img - median_fg, 2.0, d_fg)
- fg <- (d_fg * fgFactor).Cmp(d_bg, CvEnum.CmpType.LessThan)
+ CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
- median_fg <- MathNet.Numerics.Statistics.Statistics.Median(seq {
- for i in 0 .. h - 1 do
- for j in 0 .. w - 1 do
- if fg.Data.[i, j, 0] > 0uy then yield img.Data.[i, j, 0] |> float })
+ let bg_values = List<float>()
+ let fg_values = List<float>()
- median_bg <- MathNet.Numerics.Statistics.Statistics.Median(seq {
- for i in 0 .. h - 1 do
- for j in 0 .. w - 1 do
- if fg.Data.[i, j, 0] = 0uy then yield img.Data.[i, j, 0] |> 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)
CvInvoke.Sqrt(d_fg, d_fg)