Parasitemia/ParasitemiaCore/ImgTools/ImgTools.fs

   1 module ParasitemiaCore.ImgTools
   2
   3 open System
   4 open System.Drawing
   5
   6 open Emgu.CV
   7 open Emgu.CV.Structure
   8
   9 /// <summary>
  10 /// Normalize an image between 0 and 'upperLimit'.
  11 /// FIXME: use to many memory.
  12 /// </summary>
  13 /// <param name="img"></param>
  14 /// <param name="upperLimit"></param>
  15 let normalize (img: Image<Gray, float32>) (upperLimit: float) : Image<Gray, float32> =
  16     let min = ref [| 0.0 |]
  17     let minLocation = ref <| [| Point() |]
  18     let max = ref [| 0.0 |]
  19     let maxLocation = ref <| [| Point() |]
  20     img.MinMax(min, max, minLocation, maxLocation)
  21     let normalized = (img - (!min).[0]) / ((!max).[0] - (!min).[0])
  22     if upperLimit = 1.0
  23     then normalized
  24     else upperLimit * normalized
  25
  26 let mergeChannels (img: Image<Bgr, float32>) (rgbWeights: float * float * float) : Image<Gray, float32> =
  27     match rgbWeights with
  28     | 1., 0., 0. -> img.[2]
  29     | 0., 1., 0. -> img.[1]
  30     | 0., 0., 1. -> img.[0]
  31     | redFactor, greenFactor, blueFactor ->
  32         let result = new Image<Gray, float32>(img.Size)
  33         CvInvoke.AddWeighted(result, 1., img.[2], redFactor, 0., result)
  34         CvInvoke.AddWeighted(result, 1., img.[1], greenFactor, 0., result)
  35         CvInvoke.AddWeighted(result, 1., img.[0], blueFactor, 0., result)
  36         result
  37
  38 let mergeChannelsWithProjection (img: Image<Bgr, float32>) (v1r: float32, v1g: float32, v1b: float32) (v2r: float32, v2g: float32, v2b: float32) (upperLimit: float)  : Image<Gray, float32> =
  39     let vr, vg, vb = v2r - v1r, v2g - v1g, v2b - v1b
  40     let vMagnitude = sqrt (vr ** 2.f + vg ** 2.f + vb ** 2.f)
  41     let project (r: float32) (g: float32) (b: float32) = ((r - v1r) * vr + (g - v1g) * vg + (b - v1b) * vb) / vMagnitude
  42     let result = new Image<Gray, float32>(img.Size)
  43     // TODO: Essayer en bindant Data pour gagner du temps
  44     for i = 0 to img.Height - 1 do
  45         for j = 0 to img.Width - 1 do
  46             result.Data.[i, j, 0] <- project img.Data.[i, j, 2] img.Data.[i, j, 1] img.Data.[i, j, 0]
  47     normalize result upperLimit
  48
  49 // Normalize image values between 0uy and 255uy.
  50 let normalizeAndConvert (img: Image<Gray, 'TDepth>) : Image<Gray, byte> =
  51     (normalize (img.Convert<Gray, float32>()) 255.).Convert<Gray, byte>()
  52
  53 let gaussianFilter (img : Image<'TColor, 'TDepth>) (standardDeviation : float) : Image<'TColor, 'TDepth> =
  54     let size = 2 * int (ceil (4.0 * standardDeviation)) + 1
  55     img.SmoothGaussian(size, size, standardDeviation, standardDeviation)