open Emgu.CV
open Emgu.CV.Structure
+
type Result = {
fg: Image<Gray, byte>
- mean_bg: float
- mean_fg: float
+ mean_bg: float32
+ mean_fg: float32
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 kmeans (img: Image<Gray, float32>) : Result =
+ let nbIteration = 4
let w = img.Width
let h = img.Height
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)
+ let minf = float32 (!min).[0]
+ let maxf = float32 (!max).[0]
+
+ let mutable mean_bg = maxf - (maxf - minf) / 4.f
+ let mutable mean_fg = minf + (maxf - minf) / 4.f
+ use mutable d_bg : Image<Gray, float32> = null
+ let mutable d_fg : Image<Gray, float32> = null
+ let fg = new Image<Gray, byte>(img.Size)
+
+ let imgData = img.Data
+ let fgData = fg.Data
for i in 1 .. nbIteration do
- d_bg <- img.AbsDiff(Gray(mean_bg))
- d_fg <- img.AbsDiff(Gray(mean_fg))
+ match d_bg with
+ | null -> ()
+ | _ ->
+ d_bg.Dispose()
+ d_fg.Dispose()
+
+ // EmGu doesn't import the in-place version of 'AbsDiff' so we have to create two images for each iteration.
+ d_bg <- img.AbsDiff(Gray(float mean_bg))
+ d_fg <- img.AbsDiff(Gray(float mean_fg))
CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
- let mutable bg_total = 0.0
+ let mutable bg_total = 0.f
let mutable bg_nb = 0
- let mutable fg_total = 0.0
+ let mutable fg_total = 0.f
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
+ if fgData.[i, j, 0] > 0uy
then
- fg_total <- fg_total + float img.Data.[i, j, 0]
+ fg_total <- fg_total + imgData.[i, j, 0]
fg_nb <- fg_nb + 1
else
- bg_total <- bg_total + float img.Data.[i, j, 0]
+ bg_total <- bg_total + imgData.[i, j, 0]
bg_nb <- bg_nb + 1
- mean_bg <- bg_total / float bg_nb
- mean_fg <- fg_total / float fg_nb
+ mean_bg <- bg_total / float32 bg_nb
+ mean_fg <- fg_total / float32 fg_nb
{ fg = fg; mean_bg = mean_bg; mean_fg = mean_fg; d_fg = d_fg }
\ No newline at end of file