module ParasitemiaCore.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 = 1 to 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 = 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

    { fg = fg; median_bg = median_bg; median_fg = median_fg; d_fg = d_fg }