Use float32 to reduce memory footprint.

[master-thesis.git] / Parasitemia / Parasitemia / KMedians.fs

diff --git a/Parasitemia/Parasitemia/KMedians.fs b/Parasitemia/Parasitemia/KMedians.fs
index 1822c75..f7f2e54 100644 (file)
--- a/Parasitemia/Parasitemia/KMedians.fs
+++ b/Parasitemia/Parasitemia/KMedians.fs
@@ -10,9 +10,9 @@ 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 kmedians (img: Image<Gray, float32>) : Result =
     let nbIteration = 3
     let w = img.Width
     let h = img.Height
@@ -30,21 +30,22 @@ let kmedians (img: Image<Gray, float32>) (fgFactor: float) : Result =
     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)
+        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 }