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

diff --git a/Parasitemia/Parasitemia/ImageAnalysis.fs b/Parasitemia/Parasitemia/ImageAnalysis.fs
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--- a/Parasitemia/Parasitemia/ImageAnalysis.fs
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-module ImageAnalysis
-
-open System
-open System.Drawing
-
-open Emgu.CV
-open Emgu.CV.Structure
-
-open Utils
-open ImgTools
-open Config
-open Types
-
-(*type Result = {
-    RBCPositions : Point list
-    infectedRBCPositions : Point list
-    img: Image<Bgr, byte>
-}*)
-
-let doAnalysis (img: Image<Bgr, byte>) (config: Config) : Classifier.Cell list =
-
-    let imgFloat = img.Convert<Bgr, float32>()
-    use scaledImg = if config.scale = 1.0 then imgFloat else imgFloat.Resize(config.scale, CvEnum.Inter.Area)
-
-    (*use scaledImg = 
-        if config.scale = 1.0
-        then
-            img
-        else
-            let m = new Mat()
-            CvInvoke.Resize(img, m, Size(roundInt (float img.Size.Width * config.scale), roundInt (float img.Size.Height * config.scale)))
-            m*)
-
-    use green = scaledImg.Item(1)
-
-    //use green = new Matrix<byte>(scaledImg.Size)
-    //CvInvoke.MixChannels(scaledImg, green, [| 1; 0 |])
-
-    //let greenMatrix = new Matrix<byte>(green.Height, green.Width, green.DataPointer)
-    
-    //let test = greenMatrix.[10, 10]
-
-    use filteredGreen = (gaussianFilter green config.doGSigma1) - config.doGLowFreqPercentageReduction * (gaussianFilter green config.doGSigma2)
-    
-    use sobelKernel = 
-        new ConvolutionKernelF(array2D [[ 1.0f; 0.0f; -1.0f ]
-                                        [ 2.0f; 0.0f; -2.0f ]
-                                        [ 1.0f; 0.0f; -1.0f ]], Point(0, 0))
-
-    use xEdges = filteredGreen.Convolution(sobelKernel).Convert<Gray, float>()
-    use yEdges = filteredGreen.Convolution(sobelKernel.Transpose()).Convert<Gray, float>()
-            
-    let xEdgesData = xEdges.Data
-    let yEdgesData = yEdges.Data
-    for r in 0..xEdges.Rows-1 do
-        xEdgesData.[r, 0, 0] <- 0.0
-        xEdgesData.[r, xEdges.Cols-1, 0] <- 0.0
-        yEdgesData.[r, 0, 0] <- 0.0
-        yEdgesData.[r, xEdges.Cols-1, 0] <- 0.0
-    
-    for c in 0..xEdges.Cols-1 do
-        xEdgesData.[0, c, 0] <- 0.0
-        xEdgesData.[xEdges.Rows-1, c, 0] <- 0.0
-        yEdgesData.[0, c, 0] <- 0.0
-        yEdgesData.[xEdges.Rows-1, c, 0] <- 0.0
-        
-    use magnitudes = new Matrix<float>(xEdges.Size)    
-    CvInvoke.CartToPolar(xEdges, yEdges, magnitudes, new Mat()) // Compute the magnitudes (without angles).
-    
-    let min = ref 0.0
-    let minLocation = ref <| Point()
-    let max = ref 0.0
-    let maxLocation = ref <| Point()
-    magnitudes.MinMax(min, max, minLocation, maxLocation)
-
-    use magnitudesByte = ((magnitudes / !max) * 255.0).Convert<byte>() // Otsu from OpenCV only support 'byte'.
-    use edges = new Matrix<byte>(xEdges.Size)    
-    CvInvoke.Threshold(magnitudesByte, edges, 0.0, 1.0, CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.Binary) |> ignore
-        
-    logTime "Finding edges" (fun() ->
-        thin edges)
-
-    logTime "Removing small connected components" (fun () -> 
-        removeArea edges 12)
-    
-    saveMat (edges * 255.0) "edges.png" 
-
-
-    let kmediansResults = KMedians.kmedians filteredGreen 1.0
-
-    let parasites = ParasitesMarker.find green filteredGreen kmediansResults config
-    
-    saveImg parasites.darkStain "parasites_dark_stain.png"
-    saveImg parasites.stain "parasites_stain.png"
-    saveImg parasites.infection "parasites_infection.png"
-
-    let radiusRange = config.scale * 20.0, config.scale * 40.0
-    let windowSize = roundInt (1.6 * (snd radiusRange))
-    let factorNbPick = 1.5
-    let ellipses = logTime "Finding ellipses" (fun () ->
-        Ellipse.find edges xEdges yEdges radiusRange windowSize factorNbPick) |> List.filter (fun e -> not (e.CutAVericalLine 0.0) &&
-                                                                                                       not (e.CutAVericalLine (float img.Width)) &&
-                                                                                                       not (e.CutAnHorizontalLine 0.0) &&
-                                                                                                       not (e.CutAnHorizontalLine (float img.Height)))
-                
-    drawEllipses img ellipses (Bgr(0.0, 255.0, 255.0))
-    //saveImg img "ellipses.png"
-
-    Classifier.findCells ellipses parasites kmediansResults.fg
-
-    // 
-   
-    (*use imageHSV = scaledImage.Convert<Hsv, uint8>()
-    let H, S = match imageHSV.Split() with // Warning: H is from 0 to 179°.
-                | [| H; S; _|] -> H, S
-                | _ -> failwith "unable to split the HSV channels"                         
-   
-    let hueShiftValue = 175
-    // Modulo operator doesn't exist on matrix thus we have to apply a function to every pixels.
-    let correctedH : Image<Gray, byte> = H.Convert(fun b _ _ ->
-        (255 - int(b) * 255 / 179 + hueShiftValue) % 256 |> byte
-    )
-   
-    let correctedS : Image<Gray, byte> = S.Not()
-   
-    let filteredH = correctedH.SmoothMedian(5)
-    let filteredS = correctedS.SmoothMedian(5)*)
-
-    //let greenChannel = scaledImage.Item(1)
-
-    //let filteredImage = (gaussianFilter greenChannel config.doGSigma1) - config.doGLowFreqPercentageReduction * (gaussianFilter greenChannel config.doGSigma2)
-    
-    // let filteredImage = greenChannel.ThresholdAdaptive(Gray(255.), CvEnum.AdaptiveThresholdType.GaussianC, CvEnum.ThresholdType.Binary, 61, Gray(5.0))
-    // let thresholdedImage = filteredImage.CopyBlank()
-
-    // CvInvoke.Threshold(filteredImage, thresholdedImage, 0., 255., CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.BinaryInv) |> ignore
-
-    // filteredImage <| 
\ No newline at end of file