X-Git-Url: http://git.euphorik.ch/?a=blobdiff_plain;f=Parasitemia%2FParasitemiaCore%2FKMedians.fs;h=bae7a248d4ed5046f2dc6f0c0f72e66eb4368a89;hb=b87b35b922551f122228df1fd9c530bbb807935a;hp=5819a669ebea52ef18e897fb5cae6f2e57d653e5;hpb=4bfa3cbdc6145e6944f02e24829ab2ef3a851ac1;p=master-thesis.git

diff --git a/Parasitemia/ParasitemiaCore/KMedians.fs b/Parasitemia/ParasitemiaCore/KMedians.fs
index 5819a66..bae7a24 100644
--- a/Parasitemia/ParasitemiaCore/KMedians.fs
+++ b/Parasitemia/ParasitemiaCore/KMedians.fs
@@ -6,13 +6,15 @@ 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 =
+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
@@ -29,7 +31,7 @@ let kmedians (img: Image<Gray, float32>) : Result =
     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))
 
@@ -38,11 +40,12 @@ let kmedians (img: Image<Gray, float32>) : Result =
         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)