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 =
+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 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 = 1 to nbIteration do
d_bg <- img.AbsDiff(Gray(median_bg))
d_fg <- img.AbsDiff(Gray(median_fg))
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])
+ for i = 0 to h - 1 do
+ for j = 0 to 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)