X-Git-Url: http://git.euphorik.ch/?a=blobdiff_plain;ds=sidebyside;f=Parasitemia%2FParasitemiaCore%2FImgTools%2FEdges.fs;h=df5858ddc2493f0c294a6999524577e1c323cf42;hb=2e3cd07dd099944059ef5e7a7f5ef57ffe3d677b;hp=b174ee9d060ed08ad4541b3e45112465cbe8a943;hpb=3f8b0d281b3058faf23dbd0363de440bd04c6574;p=master-thesis.git

diff --git a/Parasitemia/ParasitemiaCore/ImgTools/Edges.fs b/Parasitemia/ParasitemiaCore/ImgTools/Edges.fs
index b174ee9..df5858d 100644
--- a/Parasitemia/ParasitemiaCore/ImgTools/Edges.fs
+++ b/Parasitemia/ParasitemiaCore/ImgTools/Edges.fs
@@ -11,6 +11,10 @@ open Const
 open Histogram
 open Otsu
 
+// Sensibilities of the hysteresis search.
+let sensibilityHigh = 0.1f
+let sensibilityLow = 0.0f
+
 /// <summary>
 /// Find edges of an image by using the Canny approach.
 /// The thresholds are automatically defined with otsu on gradient magnitudes.
@@ -21,9 +25,9 @@ let find (img: Image<Gray, float32>) : Matrix<byte> * Matrix<float32> * Matrix<f
     let h = img.Height
 
     use sobelKernel =
-        new Matrix<float32>(array2D [[ 1.0f; 0.0f; -1.0f ]
-                                     [ 2.0f; 0.0f; -2.0f ]
-                                     [ 1.0f; 0.0f; -1.0f ]])
+        new Matrix<float32>(array2D [[ -1.0f; 0.0f; 1.0f ]
+                                     [ -2.0f; 0.0f; 2.0f ]
+                                     [ -1.0f; 0.0f; 1.0f ]])
 
     let xGradient = new Matrix<float32>(img.Size)
     let yGradient = new Matrix<float32>(img.Size)
@@ -32,11 +36,9 @@ let find (img: Image<Gray, float32>) : Matrix<byte> * Matrix<float32> * Matrix<f
 
     use magnitudes = new Matrix<float32>(xGradient.Size)
     use angles = new Matrix<float32>(xGradient.Size)
-    CvInvoke.CartToPolar(xGradient, yGradient, magnitudes, angles) // Compute the magnitudes and angles.
+    CvInvoke.CartToPolar(xGradient, yGradient, magnitudes, angles) // Compute the magnitudes and angles. The angles are between 0 and 2 * pi.
 
     let thresholdHigh, thresholdLow =
-        let sensibilityHigh = 0.1f
-        let sensibilityLow = 0.0f
         let threshold, _, _ = otsu (histogramMat magnitudes 300)
         threshold + (sensibilityHigh * threshold), threshold - (sensibilityLow * threshold)
 
@@ -49,16 +51,8 @@ let find (img: Image<Gray, float32>) : Matrix<byte> * Matrix<float32> * Matrix<f
     let xGradientData = xGradient.Data
     let yGradientData = yGradient.Data
 
-    for i in 0 .. h - 1 do
-        nmsData.[i, 0] <- 0uy
-        nmsData.[i, w - 1] <- 0uy
-
-    for j in 0 .. w - 1 do
-        nmsData.[0, j] <- 0uy
-        nmsData.[h - 1, j] <- 0uy
-
-    for i in 1 .. h - 2 do
-        for j in 1 .. w - 2 do
+    for i = 1 to h - 2 do
+        for j = 1 to w - 2 do
             let vx = xGradientData.[i, j]
             let vy = yGradientData.[i, j]
             if vx <> 0.f || vy <> 0.f
@@ -98,8 +92,8 @@ let find (img: Image<Gray, float32>) : Matrix<byte> * Matrix<float32> * Matrix<f
 
     // Hysteresis thresholding.
     let toVisit = Stack<Point>()
-    for i in 0 .. h - 1 do
-        for j in 0 .. w - 1 do
+    for i = 0 to h - 1 do
+        for j = 0 to w - 1 do
             if nmsData.[i, j] = 1uy && magnitudesData.[i, j] >= thresholdHigh
             then
                 nmsData.[i, j] <- 0uy
@@ -107,8 +101,8 @@ let find (img: Image<Gray, float32>) : Matrix<byte> * Matrix<float32> * Matrix<f
                 while toVisit.Count > 0 do
                     let p = toVisit.Pop()
                     edgesData.[p.Y, p.X] <- 1uy
-                    for i' in -1 .. 1  do
-                        for j' in -1 .. 1 do
+                    for i' = -1 to 1  do
+                        for j' = -1 to 1 do
                             if i' <> 0 || j' <> 0
                             then
                                 let ni = p.Y + i'