First commit of the f# source code.

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

diff --git a/Parasitemia/Parasitemia/ImageAnalysis.fs b/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) : Result =
+
+    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)    
+    let threshold = CvInvoke.Threshold(magnitudesByte, edges, 0.0, 1.0, CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.Binary)        
+    thin edges    
+    removeArea edges 12
+    
+    saveMat (edges * 255.0) "edges.png" 
+
+    let radiusRange = config.scale * 20.0, config.scale * 40.0
+    let windowSize = roundInt (1.6 * (snd radiusRange))
+    let factorNbPick = 1.0;
+    let ellipses = Ellipse.find edges xEdges yEdges radiusRange windowSize factorNbPick
+        
+    drawEllipse img (List.head ellipses) (Bgr(0.0, 255.0, 255.0))
+    saveImg img "ellipses.png" 
+
+    { RBCPositions = []; infectedRBCPositions = []; img = img }
+
+    // 
+   
+    (*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 <| 
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