Use float32 to reduce memory footprint.

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

diff --git a/Parasitemia/Parasitemia/KMeans.fs b/Parasitemia/Parasitemia/KMeans.fs
index 7c18120..15651ae 100644 (file)
--- a/Parasitemia/Parasitemia/KMeans.fs
+++ b/Parasitemia/Parasitemia/KMeans.fs
@@ -8,11 +8,11 @@ open Emgu.CV.Structure
 
 type Result = {
     fg: Image<Gray, byte>
-    mean_bg: float
-    mean_fg: float
+    mean_bg: float32
+    mean_fg: float32
     d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to mean_fg.
 
-let kmeans (img: Image<Gray, float32>) (fgFactor: float) : Result =
+let kmeans (img: Image<Gray, float32>) : Result =
     let nbIteration = 3
     let w = img.Width
     let h = img.Height
@@ -23,35 +23,47 @@ let kmeans (img: Image<Gray, float32>) (fgFactor: float) : Result =
     let maxLocation = ref <| [| Point() |]
     img.MinMax(min, max, minLocation, maxLocation)
 
-    let mutable mean_bg = (!max).[0] - ((!max).[0] - (!min).[0]) / 4.0
-    let mutable mean_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)
+    let minf = float32 (!min).[0]
+    let maxf = float32 (!max).[0]
+
+    let mutable mean_bg = maxf - (maxf - minf) / 4.f
+    let mutable mean_fg = minf + (maxf - minf) / 4.f
+    use mutable d_bg : Image<Gray, float32> = null
+    let mutable d_fg : Image<Gray, float32> = null
+    let fg = new Image<Gray, byte>(img.Size)
+
+    let imgData = img.Data
+    let fgData = fg.Data
 
     for i in 1 .. nbIteration do
-        d_bg <- img.AbsDiff(Gray(mean_bg))
-        d_fg <- img.AbsDiff(Gray(mean_fg))
+        if d_bg <> null
+        then
+            d_bg.Dispose()
+            d_fg.Dispose()
+
+        // EmGu doesn't import the in-place version of 'AbsDiff' so we have to create two images for each iteration.
+        d_bg <- img.AbsDiff(Gray(float mean_bg))
+        d_fg <- img.AbsDiff(Gray(float mean_fg))
 
         CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
 
-        let mutable bg_total = 0.0
+        let mutable bg_total = 0.f
         let mutable bg_nb = 0
 
-        let mutable fg_total = 0.0
+        let mutable fg_total = 0.f
         let mutable fg_nb = 0
 
         for i in 0 .. h - 1 do
             for j in 0 .. w - 1 do
-                if fg.Data.[i, j, 0] > 0uy
+                if fgData.[i, j, 0] > 0uy
                 then
-                    fg_total <- fg_total + float img.Data.[i, j, 0]
+                    fg_total <- fg_total + imgData.[i, j, 0]
                     fg_nb <- fg_nb + 1
                 else
-                    bg_total <- bg_total + float img.Data.[i, j, 0]
+                    bg_total <- bg_total + imgData.[i, j, 0]
                     bg_nb <- bg_nb + 1
 
-        mean_bg <- bg_total / float bg_nb
-        mean_fg <- fg_total / float fg_nb
+        mean_bg <- bg_total / float32 bg_nb
+        mean_fg <- fg_total / float32 fg_nb
 
     { fg = fg; mean_bg = mean_bg; mean_fg = mean_fg; d_fg = d_fg }
\ No newline at end of file