X-Git-Url: http://git.euphorik.ch/?a=blobdiff_plain;f=Parasitemia%2FParasitemia%2FKMedians.fs;h=8a3fd2b2b77a90de156a72915c00ea33cc2f2e8f;hb=ca3f764fc93defb480bb9b1c34e9c41447cc07e0;hp=82e09cb25e6192286fc8ad5609c84e46458cae8e;hpb=e76da913cd58078ad2479357b2430ed62a6e0777;p=master-thesis.git

diff --git a/Parasitemia/Parasitemia/KMedians.fs b/Parasitemia/Parasitemia/KMedians.fs
index 82e09cb..8a3fd2b 100644
--- a/Parasitemia/Parasitemia/KMedians.fs
+++ b/Parasitemia/Parasitemia/KMedians.fs
@@ -10,7 +10,7 @@ type Result = {
     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
@@ -25,26 +25,27 @@ let kmedians (img: Image<Gray, float32>) (fgFactor: float) : Result =
 
     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
-        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)
+    for i in 1 .. nbIteration do
+        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 }