Update coding style.

[master-thesis.git] / Parasitemia / ParasitemiaCore / ImgTools / ImgTools.fs

diff --git a/Parasitemia/ParasitemiaCore/ImgTools/ImgTools.fs b/Parasitemia/ParasitemiaCore/ImgTools/ImgTools.fs
index 0510bc8..fc89047 100644 (file)
--- a/Parasitemia/ParasitemiaCore/ImgTools/ImgTools.fs
+++ b/Parasitemia/ParasitemiaCore/ImgTools/ImgTools.fs
@@ -6,44 +6,51 @@ open System.Drawing
 open Emgu.CV
 open Emgu.CV.Structure
 
-let normalize (img: Image<Gray, float32>) (upperLimit: float) : Image<Gray, float32> =
+/// <summary>
+/// Normalize an image between 0 and 'upperLimit'.
+/// FIXME: use to many memory.
+/// </summary>
+/// <param name="img"></param>
+/// <param name="upperLimit"></param>
+let normalize (img : Image<Gray, float32>) (upperLimit : float) : Image<Gray, float32> =
     let min = ref [| 0.0 |]
-    let minLocation = ref <| [| Point() |]
+    let minLocation = ref <| [| Point () |]
     let max = ref [| 0.0 |]
-    let maxLocation = ref <| [| Point() |]
-    img.MinMax(min, max, minLocation, maxLocation)
+    let maxLocation = ref <| [| Point () |]
+    img.MinMax (min, max, minLocation, maxLocation)
     let normalized = (img - (!min).[0]) / ((!max).[0] - (!min).[0])
-    if upperLimit = 1.0
-    then normalized
-    else upperLimit * normalized
+    if upperLimit = 1.0 then
+        normalized
+    else
+        upperLimit * normalized
 
-let mergeChannels (img: Image<Bgr, float32>) (rgbWeights: float * float * float) : Image<Gray, float32> =
+let mergeChannels (img : Image<Bgr, float32>) (rgbWeights : float * float * float) : Image<Gray, float32> =
     match rgbWeights with
     | 1., 0., 0. -> img.[2]
     | 0., 1., 0. -> img.[1]
     | 0., 0., 1. -> img.[0]
     | redFactor, greenFactor, blueFactor ->
-        let result = new Image<Gray, float32>(img.Size)
-        CvInvoke.AddWeighted(result, 1., img.[2], redFactor, 0., result)
-        CvInvoke.AddWeighted(result, 1., img.[1], greenFactor, 0., result)
-        CvInvoke.AddWeighted(result, 1., img.[0], blueFactor, 0., result)
+        let result = new Image<Gray, float32> (img.Size)
+        CvInvoke.AddWeighted (result, 1., img.[2], redFactor, 0., result)
+        CvInvoke.AddWeighted (result, 1., img.[1], greenFactor, 0., result)
+        CvInvoke.AddWeighted (result, 1., img.[0], blueFactor, 0., result)
         result
 
-let mergeChannelsWithProjection (img: Image<Bgr, float32>) (v1r: float32, v1g: float32, v1b: float32) (v2r: float32, v2g: float32, v2b: float32) (upperLimit: float)  : Image<Gray, float32> =
+let mergeChannelsWithProjection (img : Image<Bgr, float32>) (v1r : float32, v1g : float32, v1b : float32) (v2r : float32, v2g : float32, v2b : float32) (upperLimit : float)  : Image<Gray, float32> =
     let vr, vg, vb = v2r - v1r, v2g - v1g, v2b - v1b
     let vMagnitude = sqrt (vr ** 2.f + vg ** 2.f + vb ** 2.f)
-    let project (r: float32) (g: float32) (b: float32) = ((r - v1r) * vr + (g - v1g) * vg + (b - v1b) * vb) / vMagnitude
-    let result = new Image<Gray, float32>(img.Size)
+    let project (r : float32) (g : float32) (b : float32) = ((r - v1r) * vr + (g - v1g) * vg + (b - v1b) * vb) / vMagnitude
+    let result = new Image<Gray, float32> (img.Size)
     // TODO: Essayer en bindant Data pour gagner du temps
-    for i in 0 .. img.Height - 1 do
-        for j in 0 .. img.Width - 1 do
+    for i = 0 to img.Height - 1 do
+        for j = 0 to img.Width - 1 do
             result.Data.[i, j, 0] <- project img.Data.[i, j, 2] img.Data.[i, j, 1] img.Data.[i, j, 0]
     normalize result upperLimit
 
 // Normalize image values between 0uy and 255uy.
-let normalizeAndConvert (img: Image<Gray, 'TDepth>) : Image<Gray, byte> =
-    (normalize (img.Convert<Gray, float32>()) 255.).Convert<Gray, byte>()
+let normalizeAndConvert (img : Image<Gray, 'TDepth>) : Image<Gray, byte> =
+    (normalize (img.Convert<Gray, float32> ()) 255.).Convert<Gray, byte> ()
 
 let gaussianFilter (img : Image<'TColor, 'TDepth>) (standardDeviation : float) : Image<'TColor, 'TDepth> =
     let size = 2 * int (ceil (4.0 * standardDeviation)) + 1
-    img.SmoothGaussian(size, size, standardDeviation, standardDeviation)
+    img.SmoothGaussian (size, size, standardDeviation, standardDeviation)