Parasitemia/Parasitemia/ImageAnalysis.fs

   1 module ImageAnalysis
   2
   3 open System
   4 open System.Drawing
   5
   6 open Emgu.CV
   7 open Emgu.CV.Structure
   8
   9 open Utils
  10 open ImgTools
  11 open Config
  12 open Types
  13
  14 (*type Result = {
  15     RBCPositions : Point list
  16     infectedRBCPositions : Point list
  17     img: Image<Bgr, byte>
  18 }*)
  19
  20 let doAnalysis (img: Image<Bgr, byte>) (config: Config) : Classifier.Cell list =
  21
  22     let imgFloat = img.Convert<Bgr, float32>()
  23     use scaledImg = if config.scale = 1.0 then imgFloat else imgFloat.Resize(config.scale, CvEnum.Inter.Area)
  24
  25     (*use scaledImg =
  26         if config.scale = 1.0
  27         then
  28             img
  29         else
  30             let m = new Mat()
  31             CvInvoke.Resize(img, m, Size(roundInt (float img.Size.Width * config.scale), roundInt (float img.Size.Height * config.scale)))
  32             m*)
  33
  34     use green = scaledImg.Item(1)
  35
  36     //use green = new Matrix<byte>(scaledImg.Size)
  37     //CvInvoke.MixChannels(scaledImg, green, [| 1; 0 |])
  38
  39     //let greenMatrix = new Matrix<byte>(green.Height, green.Width, green.DataPointer)
  40
  41     //let test = greenMatrix.[10, 10]
  42
  43     use filteredGreen = (gaussianFilter green config.doGSigma1) - config.doGLowFreqPercentageReduction * (gaussianFilter green config.doGSigma2)
  44
  45     use sobelKernel =
  46         new ConvolutionKernelF(array2D [[ 1.0f; 0.0f; -1.0f ]
  47                                         [ 2.0f; 0.0f; -2.0f ]
  48                                         [ 1.0f; 0.0f; -1.0f ]], Point(0, 0))
  49
  50     use xEdges = filteredGreen.Convolution(sobelKernel).Convert<Gray, float>()
  51     use yEdges = filteredGreen.Convolution(sobelKernel.Transpose()).Convert<Gray, float>()
  52
  53     let xEdgesData = xEdges.Data
  54     let yEdgesData = yEdges.Data
  55     for r in 0..xEdges.Rows-1 do
  56         xEdgesData.[r, 0, 0] <- 0.0
  57         xEdgesData.[r, xEdges.Cols-1, 0] <- 0.0
  58         yEdgesData.[r, 0, 0] <- 0.0
  59         yEdgesData.[r, xEdges.Cols-1, 0] <- 0.0
  60
  61     for c in 0..xEdges.Cols-1 do
  62         xEdgesData.[0, c, 0] <- 0.0
  63         xEdgesData.[xEdges.Rows-1, c, 0] <- 0.0
  64         yEdgesData.[0, c, 0] <- 0.0
  65         yEdgesData.[xEdges.Rows-1, c, 0] <- 0.0
  66
  67     use magnitudes = new Matrix<float>(xEdges.Size)
  68     CvInvoke.CartToPolar(xEdges, yEdges, magnitudes, new Mat()) // Compute the magnitudes (without angles).
  69
  70     let min = ref 0.0
  71     let minLocation = ref <| Point()
  72     let max = ref 0.0
  73     let maxLocation = ref <| Point()
  74     magnitudes.MinMax(min, max, minLocation, maxLocation)
  75
  76     use magnitudesByte = ((magnitudes / !max) * 255.0).Convert<byte>() // Otsu from OpenCV only support 'byte'.
  77     use edges = new Matrix<byte>(xEdges.Size)
  78     CvInvoke.Threshold(magnitudesByte, edges, 0.0, 1.0, CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.Binary) |> ignore
  79
  80     logTime "Finding edges" (fun() ->
  81         thin edges)
  82
  83     logTime "Removing small connected components" (fun () ->
  84         removeArea edges 12)
  85
  86     saveMat (edges * 255.0) "edges.png"
  87
  88
  89     let kmediansResults = KMedians.kmedians filteredGreen 1.0
  90
  91     let parasites = ParasitesMarker.find green filteredGreen kmediansResults config
  92
  93     saveImg parasites.darkStain "parasites_dark_stain.png"
  94     saveImg parasites.stain "parasites_stain.png"
  95     saveImg parasites.infection "parasites_infection.png"
  96
  97     let radiusRange = config.scale * 20.0, config.scale * 40.0
  98     let windowSize = roundInt (1.6 * (snd radiusRange))
  99     let factorNbPick = 1.5
 100     let ellipses = logTime "Finding ellipses" (fun () ->
 101         Ellipse.find edges xEdges yEdges radiusRange windowSize factorNbPick) |> List.filter (fun e -> not (e.CutAVericalLine 0.0) &&
 102                                                                                                        not (e.CutAVericalLine (float img.Width)) &&
 103                                                                                                        not (e.CutAnHorizontalLine 0.0) &&
 104                                                                                                        not (e.CutAnHorizontalLine (float img.Height)))
 105
 106     drawEllipses img ellipses (Bgr(0.0, 255.0, 255.0))
 107     //saveImg img "ellipses.png"
 108
 109     Classifier.findCells ellipses parasites kmediansResults.fg
 110
 111     //
 112
 113     (*use imageHSV = scaledImage.Convert<Hsv, uint8>()
 114     let H, S = match imageHSV.Split() with // Warning: H is from 0 to 179°.
 115                 | [| H; S; _|] -> H, S
 116                 | _ -> failwith "unable to split the HSV channels"
 117
 118     let hueShiftValue = 175
 119     // Modulo operator doesn't exist on matrix thus we have to apply a function to every pixels.
 120     let correctedH : Image<Gray, byte> = H.Convert(fun b _ _ ->
 121         (255 - int(b) * 255 / 179 + hueShiftValue) % 256 |> byte
 122     )
 123
 124     let correctedS : Image<Gray, byte> = S.Not()
 125
 126     let filteredH = correctedH.SmoothMedian(5)
 127     let filteredS = correctedS.SmoothMedian(5)*)
 128
 129     //let greenChannel = scaledImage.Item(1)
 130
 131     //let filteredImage = (gaussianFilter greenChannel config.doGSigma1) - config.doGLowFreqPercentageReduction * (gaussianFilter greenChannel config.doGSigma2)
 132
 133     // let filteredImage = greenChannel.ThresholdAdaptive(Gray(255.), CvEnum.AdaptiveThresholdType.GaussianC, CvEnum.ThresholdType.Binary, 61, Gray(5.0))
 134     // let thresholdedImage = filteredImage.CopyBlank()
 135
 136     // CvInvoke.Threshold(filteredImage, thresholdedImage, 0., 255., CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.BinaryInv) |> ignore
 137
 138     // filteredImage <|