X-Git-Url: http://git.euphorik.ch/?p=master-thesis.git;a=blobdiff_plain;f=Parasitemia%2FParasitemia%2FImageAnalysis.fs;fp=Parasitemia%2FParasitemia%2FImageAnalysis.fs;h=0000000000000000000000000000000000000000;hp=feb427dff9567383629c46fb359efdd333883835;hb=e76da913cd58078ad2479357b2430ed62a6e0777;hpb=d9a6e072ecf299db691c05bb559a71265f812ba3 diff --git a/Parasitemia/Parasitemia/ImageAnalysis.fs b/Parasitemia/Parasitemia/ImageAnalysis.fs deleted file mode 100644 index feb427d..0000000 --- a/Parasitemia/Parasitemia/ImageAnalysis.fs +++ /dev/null @@ -1,138 +0,0 @@ -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 -}*) - -let doAnalysis (img: Image) (config: Config) : Classifier.Cell list = - - let imgFloat = img.Convert() - 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(scaledImg.Size) - //CvInvoke.MixChannels(scaledImg, green, [| 1; 0 |]) - - //let greenMatrix = new Matrix(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() - use yEdges = filteredGreen.Convolution(sobelKernel.Transpose()).Convert() - - 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(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() // Otsu from OpenCV only support 'byte'. - use edges = new Matrix(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() - 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 = H.Convert(fun b _ _ -> - (255 - int(b) * 255 / 179 + hueShiftValue) % 256 |> byte - ) - - let correctedS : Image = 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