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 kmedians (img: Image<Gray, float32>) : Result =
+ let nbIteration = 4
let w = img.Width
let h = img.Height
let mutable fg = new Image<Gray, byte>(img.Size)
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)
+ d_bg <- img.AbsDiff(Gray(median_bg))
+ d_fg <- img.AbsDiff(Gray(median_fg))
- 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 })
+ CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
- 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 })
+ let bg_values = List<float>()
+ let fg_values = List<float>()
- CvInvoke.Sqrt(d_fg, d_fg)
+ 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 }