* Add otsu method in ImgTools.
if e.isOutside w_f h_f then e.Removed <- true
// 3) Remove ellipses with a high standard deviation (high contrast).
-
- // CvInvoke.Normalize(img, img, 0.0, 255.0, CvEnum.NormType.MinMax) // Not needed.
let imgData = img.Data
- let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.StandardDeviation(seq {
+ let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(seq {
for y in 0 .. h - 1 do
for x in 0 .. w - 1 do
yield float imgData.[y, x, 0] })
if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then
e.Removed <- true
+(*
+ let imgData = img.Data
+ let stdDeviations = [
+ for e in ellipses do
+ if not e.Removed
+ then
+ let shrinkedE = e.Scale 0.9f
+ let minX, minY, maxX, maxY = ellipseWindow shrinkedE
+
+ let stdDeviation = float32 <| MathNet.Numerics.Statistics.Statistics.StandardDeviation (seq {
+ for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
+ for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
+ if shrinkedE.Contains (float32 x) (float32 y)
+ then
+ yield float imgData.[y, x, 0] })
+
+ e.StdDeviation <- stdDeviation
+ yield stdDeviation ]
+
+ // We use Otsu and eliminate some cells only if the curve may be bimodal.
+ // See https://en.wikipedia.org/wiki/Multimodal_distribution#Bimodality_coefficient
+ let skewness, kurtosis = MathNet.Numerics.Statistics.Statistics.PopulationSkewnessKurtosis (stdDeviations |> List.map float)
+ let n = float stdDeviations.Length
+ let bimodalityCoefficient = (skewness ** 2. + 1.) / (kurtosis + 3. * (n - 1.) ** 2. / ((n - 2.) * (n - 3.)))
+
+ if bimodalityCoefficient > 5. / 9.
+ then
+ let hist = ImgTools.histogram stdDeviations 200
+ let thresh, _, _ = ImgTools.otsu hist
+ for e in ellipses do
+ if not e.Removed && e.StdDeviation > thresh
+ then e.Removed <- true
+*)
+
// 4) Remove ellipses with little area.
let minArea = config.RBCMinArea
for e, neighbors in ellipsesWithNeigbors do
| DebugOn of string // Output directory.
type Parameters = {
- scale: float
-
initialAreaOpen: int
minRbcRadius: float32
type private SearchExtremum = Minimum | Maximum
-let private goldenSectionSearch (f: float32 -> float32) (nbIter: int) (xmin: float32) (xmax: float32) (searchExtremum: SearchExtremum) : (float32 * float32) =
- let gr = 1.f / 1.6180339887498948482f
+let private goldenSectionSearch (f: float -> float) (nbIter: int) (xmin: float) (xmax: float) (searchExtremum: SearchExtremum) : (float * float) =
+ let gr = 1. / 1.6180339887498948482
let mutable a = xmin
let mutable b = xmax
let mutable c = b - gr * (b - a)
c <- d
d <- a + gr * (b - a)
- let x = (b + a) / 2.f
+ let x = (b + a) / 2.
x, f x
// Ellipse.A is always equal or greater than Ellipse.B.
// Ellipse.Alpha is between 0 and Pi.
-let ellipse (p1x: float32) (p1y: float32) (m1: float32) (p2x: float32) (p2y: float32) (m2: float32) (p3x: float32) (p3y: float32) : Types.Ellipse option =
- let accuracy_extremum_search_1 = 8 // 3
- let accuracy_extremum_search_2 = 8 // 4
+let ellipse (p1x: float) (p1y: float) (m1: float) (p2x: float) (p2y: float) (m2: float) (p3x: float) (p3y: float) : Types.Ellipse option =
+ let accuracy_extremum_search_1 = 10 // 3
+ let accuracy_extremum_search_2 = 10 // 4
// p3 as the referencial.
let p1x = p1x - p3x
let alpha1 = atan m1
let alpha2 = atan m2
- let r1 = sqrt (p1x ** 2.f + p1y ** 2.f)
+ let r1 = sqrt (p1x ** 2. + p1y ** 2.)
let theta1 = atan2 p1y p1x
- let r2 = sqrt (p2x ** 2.f + p2y ** 2.f)
+ let r2 = sqrt (p2x ** 2. + p2y ** 2.)
let theta2 = atan2 p2y p2x
let valid =
- 4.f * sin (alpha1 - theta1) * (-r1 * sin (alpha1 - theta1) + r2 * sin (alpha1 - theta2)) *
+ 4. * sin (alpha1 - theta1) * (-r1 * sin (alpha1 - theta1) + r2 * sin (alpha1 - theta2)) *
sin (alpha2 - theta2) * (-r1 * sin (alpha2 - theta1) + r2 * sin (alpha2 - theta2)) +
- r1 * r2 * sin (alpha1 - alpha2) ** 2.f * sin (theta1 - theta2) ** 2.f < 0.f
+ r1 * r2 * sin (alpha1 - alpha2) ** 2. * sin (theta1 - theta2) ** 2. < 0.
if valid
then
let r theta =
(r1 * r2 * (r1 * (cos (alpha2 + theta - theta1 - theta2) - cos (alpha2 - theta) * cos (theta1 - theta2)) * sin (alpha1 - theta1) + r2 * (-cos (alpha1 + theta - theta1 - theta2) + cos (alpha1 - theta) * cos (theta1 - theta2)) * sin (alpha2 - theta2)) * sin (theta1 - theta2)) /
- (sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2.f - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.f)
+ (sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2. - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.)
let rabs = r >> abs
// We search for an interval [theta_a, theta_b] and assume the function is unimodal in this interval.
- let thetaTan, _ = goldenSectionSearch rabs accuracy_extremum_search_1 0.f PI Maximum
+ let thetaTan, _ = goldenSectionSearch rabs accuracy_extremum_search_1 0. Math.PI Maximum
let rTan = r thetaTan
let PTanx = rTan * cos thetaTan
let d2a = tan alpha2
let d2b = -d2a * p2x + p2y
- let d3a = -1.f / tan thetaTan
+ let d3a = -1. / tan thetaTan
let d3b = -d3a * PTanx + PTany
let Ux = -(d1b - d2b) / (d1a - d2a)
let Vx = -(d1b - d3b) / (d1a - d3a)
let Vy = -(d3a * d1b - d1a * d3b) / (d1a - d3a)
- let Wx = p1x + (p2x - p1x) / 2.f
- let Wy = p1y + (p2y - p1y) / 2.f
+ let Wx = p1x + (p2x - p1x) / 2.
+ let Wy = p1y + (p2y - p1y) / 2.
- let Zx = p1x + (PTanx - p1x) / 2.f
- let Zy = p1y + (PTany - p1y) / 2.f
+ let Zx = p1x + (PTanx - p1x) / 2.
+ let Zy = p1y + (PTany - p1y) / 2.
let va = -(-Vy + Zy) / (Vx - Zx)
let vb = -(Zx * Vy - Vx * Zy) / (Vx - Zx)
let cx = -(vb - ub) / (va - ua)
let cy = -(ua * vb - va * ub) / (va - ua)
- let rc = sqrt (cx ** 2.f + cy ** 2.f)
+ let rc = sqrt (cx ** 2. + cy ** 2.)
let psi = atan2 cy cx
let rellipse theta =
sqrt (
- rc ** 2.f + (r1 ** 2.f * r2 ** 2.f * (r1 * (cos (alpha2 + theta - theta1 - theta2) - cos (alpha2 - theta) * cos (theta1 - theta2)) * sin (alpha1 - theta1) + r2 * (-cos (alpha1 + theta - theta1 - theta2) + cos (alpha1 - theta) * cos (theta1 - theta2)) * sin (alpha2 - theta2)) ** 2.f * sin (theta1 - theta2) ** 2.f) /
- (sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2.f - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.f) ** 2.f -
- (2.f * r1 * r2 * rc * cos (theta - psi) * (r1 * (cos (alpha2 + theta - theta1 - theta2) - cos (alpha2 - theta) * cos (theta1 - theta2)) * sin (alpha1 - theta1) + r2 * (-cos (alpha1 + theta - theta1 - theta2) + cos (alpha1 - theta) * cos (theta1 - theta2)) * sin (alpha2 - theta2)) * sin (theta1 - theta2)) /
- (sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2.f - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.f))
+ rc ** 2. + (r1 ** 2. * r2 ** 2. * (r1 * (cos (alpha2 + theta - theta1 - theta2) - cos (alpha2 - theta) * cos (theta1 - theta2)) * sin (alpha1 - theta1) + r2 * (-cos (alpha1 + theta - theta1 - theta2) + cos (alpha1 - theta) * cos (theta1 - theta2)) * sin (alpha2 - theta2)) ** 2. * sin (theta1 - theta2) ** 2.) /
+ (sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2. - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.) ** 2. -
+ (2. * r1 * r2 * rc * cos (theta - psi) * (r1 * (cos (alpha2 + theta - theta1 - theta2) - cos (alpha2 - theta) * cos (theta1 - theta2)) * sin (alpha1 - theta1) + r2 * (-cos (alpha1 + theta - theta1 - theta2) + cos (alpha1 - theta) * cos (theta1 - theta2)) * sin (alpha2 - theta2)) * sin (theta1 - theta2)) /
+ (sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2. - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.))
// We search for an interval [theta_a, theta_b] and assume the function is unimodal in this interval.
- let r1eTheta, r1e = goldenSectionSearch rellipse accuracy_extremum_search_2 0.f (PI / 2.f) Maximum // Pi/2 and not pi because the period is Pi.
- let r2eTheta, r2e = goldenSectionSearch rellipse accuracy_extremum_search_2 0.f (PI / 2.f) Minimum
+ let r1eTheta, r1e = goldenSectionSearch rellipse accuracy_extremum_search_2 0. (Math.PI / 2.) Maximum // Pi/2 and not pi because the period is Pi.
+ let r2eTheta, r2e = goldenSectionSearch rellipse accuracy_extremum_search_2 0. (Math.PI / 2.) Minimum
let rr1e = r r1eTheta
let r1ex = rr1e * cos r1eTheta
let r1ey = rr1e * sin r1eTheta
let mutable alpha = atan ((r1ey - cy) / (r1ex - cx))
- if alpha < 0.f
+ if alpha < 0.
then
- alpha <- alpha + PI
+ alpha <- alpha + Math.PI
// Ride off the p3 referential.
let cx = cx + p3x
let cy = cy + p3y
- Some (Types.Ellipse(cx, cy, r1e, r2e, alpha))
+ Some (Types.Ellipse(float32 cx, float32 cy, float32 r1e, float32 r2e, float32 alpha))
else
None
let radiusTolerance = (r2 - r1) * 0.2f
- let squaredMinimumDistance = (r2 / 1.5f) ** 2.f
- let squaredDistance x1 y1 x2 y2 = (x1 - x2) ** 2.f + (y1 - y2) ** 2.f
+ let squaredMinimumDistance = (float r2 / 1.5) ** 2.
+ let inline squaredDistance x1 y1 x2 y2 = (x1 - x2) ** 2. + (y1 - y2) ** 2.
let h = edges.Height
let w = edges.Width
if p1 <> p2 && p1 <> p3 && p2 <> p3
then
nbOfPicks <- nbOfPicks - 1
- let p1yf, p1xf = float32 p1.Y, float32 p1.X
- let p2yf, p2xf = float32 p2.Y, float32 p2.X
- let p3yf, p3xf = float32 p3.Y, float32 p3.X
+ let p1yf, p1xf = float p1.Y, float p1.X
+ let p2yf, p2xf = float p2.Y, float p2.X
+ let p3yf, p3xf = float p3.Y, float p3.X
if squaredDistance p1xf p1yf p2xf p2yf >= squaredMinimumDistance &&
squaredDistance p1xf p1yf p3xf p3yf >= squaredMinimumDistance &&
squaredDistance p2xf p2yf p3xf p3yf >= squaredMinimumDistance
then
- match areVectorsValid p1xf p1yf p2xf p2yf -xDirData.[p1.Y, p1.X, 0] -yDirData.[p1.Y, p1.X, 0] -xDirData.[p2.Y, p2.X, 0] -yDirData.[p2.Y, p2.X, 0] with
+ match areVectorsValid (float32 p1xf) (float32 p1yf) (float32 p2xf) (float32 p2yf) -xDirData.[p1.Y, p1.X, 0] -yDirData.[p1.Y, p1.X, 0] -xDirData.[p2.Y, p2.X, 0] -yDirData.[p2.Y, p2.X, 0] with
| Some (m1, m2) ->
- match ellipse p1xf p1yf m1 p2xf p2yf m2 p3xf p3yf with
+ match ellipse p1xf p1yf (float m1) p2xf p2yf (float m2) p3xf p3yf with
| Some e when e.Cx > 0.f && e.Cx < w_f - 1.f && e.Cy > 0.f && e.Cy < h_f - 1.f &&
e.A >= r1 - radiusTolerance && e.A <= r2 + radiusTolerance && e.B >= r1 - radiusTolerance && e.B <= r2 + radiusTolerance ->
ellipses.Add e
saveImg img filepath
+type Histogram = { data: int[]; total: int; sum: int; min: float32; max: float32 }
+
+let histogramImg (img: Image<Gray, float32>) (nbSamples: int) : Histogram =
+ let imgData = img.Data
+
+ let min, max =
+ let min = ref [| 0.0 |]
+ let minLocation = ref <| [| Point() |]
+ let max = ref [| 0.0 |]
+ let maxLocation = ref <| [| Point() |]
+ img.MinMax(min, max, minLocation, maxLocation)
+ float32 (!min).[0], float32 (!max).[0]
+
+ let bin (x: float32) : int =
+ let p = int ((x - min) / (max - min) * float32 nbSamples)
+ if p >= nbSamples then nbSamples - 1 else p
+
+ let data = Array.zeroCreate nbSamples
+
+ for i in 0 .. img.Height - 1 do
+ for j in 0 .. img.Width - 1 do
+ let p = bin imgData.[i, j, 0]
+ data.[p] <- data.[p] + 1
+
+ { data = data; total = img.Height * img.Width; sum = Array.sum data; min = min; max = max }
+
+let histogramMat (mat: Matrix<float32>) (nbSamples: int) : Histogram =
+ let matData = mat.Data
+
+ let min, max =
+ let min = ref 0.0
+ let minLocation = ref <| Point()
+ let max = ref 0.0
+ let maxLocation = ref <| Point()
+ mat.MinMax(min, max, minLocation, maxLocation)
+ float32 !min, float32 !max
+
+ let bin (x: float32) : int =
+ let p = int ((x - min) / (max - min) * float32 nbSamples)
+ if p >= nbSamples then nbSamples - 1 else p
+
+ let data = Array.zeroCreate nbSamples
+
+ for i in 0 .. mat.Height - 1 do
+ for j in 0 .. mat.Width - 1 do
+ let p = bin matData.[i, j]
+ data.[p] <- data.[p] + 1
+
+ { data = data; total = mat.Height * mat.Width; sum = Array.sum data; min = min; max = max }
+
+let histogram (values: float32 seq) (nbSamples: int) : Histogram =
+ let mutable min = Single.MaxValue
+ let mutable max = Single.MinValue
+ let mutable n = 0
+
+ for v in values do
+ n <- n + 1
+ if v < min then min <- v
+ if v > max then max <- v
+
+ let bin (x: float32) : int =
+ let p = int ((x - min) / (max - min) * float32 nbSamples)
+ if p >= nbSamples then nbSamples - 1 else p
+
+ let data = Array.zeroCreate nbSamples
+
+ for v in values do
+ let p = bin v
+ data.[p] <- data.[p] + 1
+
+ { data = data; total = n; sum = Array.sum data; min = min; max = max }
+
+let otsu (hist: Histogram) : float32 * float32 * float32 =
+ let mutable sumB = 0
+ let mutable wB = 0
+ let mutable maximum = 0.0
+ let mutable level = 0
+ let sum = hist.data |> Array.mapi (fun i v -> i * v) |> Array.sum |> float
+
+ for i in 0 .. hist.data.Length - 1 do
+ wB <- wB + hist.data.[i]
+ if wB <> 0
+ then
+ let wF = hist.total - wB
+ if wF <> 0
+ then
+ sumB <- sumB + i * hist.data.[i]
+ let mB = (float sumB) / (float wB)
+ let mF = (sum - float sumB) / (float wF)
+ let between = (float wB) * (float wF) * (mB - mF) ** 2.;
+ if between >= maximum
+ then
+ level <- i
+ maximum <- between
+
+ let mean1 =
+ let mutable sum = 0
+ let mutable nb = 0
+ for i in 0 .. level - 1 do
+ sum <- sum + i * hist.data.[i]
+ nb <- nb + hist.data.[i]
+ (sum + level * hist.data.[level] / 2) / (nb + hist.data.[level] / 2)
+
+ let mean2 =
+ let mutable sum = 0
+ let mutable nb = 0
+ for i in level + 1 .. hist.data.Length - 1 do
+ sum <- sum + i * hist.data.[i]
+ nb <- nb + hist.data.[i]
+ (sum + level * hist.data.[level] / 2) / (nb + hist.data.[level] / 2)
+
+ let toFloat l =
+ float32 l / float32 hist.data.Length * (hist.max - hist.min) + hist.min
+
+ toFloat level, toFloat mean1, toFloat mean2
+
+
let suppressMConnections (img: Matrix<byte>) =
let w = img.Width
let h = img.Height
let thresholdHigh, thresholdLow =
let sensibilityHigh = 0.1f
- let sensibilityLow = 0.1f
+ let sensibilityLow = 0.0f
use magnitudesByte = magnitudes.Convert<byte>()
let threshold = float32 <| CvInvoke.Threshold(magnitudesByte, magnitudesByte, 0.0, 1.0, CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.Binary)
+ let threshold, _, _ = otsu (histogramMat magnitudes 300)
+
threshold + (sensibilityHigh * threshold), threshold - (sensibilityLow * threshold)
// Non-maximum suppression.
open Emgu.CV
open Emgu.CV.Structure
+
type Result = {
fg: Image<Gray, byte>
mean_bg: float32
d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to mean_fg.
let kmeans (img: Image<Gray, float32>) : Result =
- let nbIteration = 3
+ let nbIteration = 4
let w = img.Width
let h = img.Height
d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to median_fg.
let kmedians (img: Image<Gray, float32>) : Result =
- let nbIteration = 3
+ let nbIteration = 4
let w = img.Width
let h = img.Height
let doAnalysis (img: Image<Bgr, byte>) (name: string) (config: Config) : Cell list =
- let scaledImg = if config.Parameters.scale = 1.0 then img else img.Resize(config.Parameters.scale, CvEnum.Inter.Area)
-
- use green = scaledImg.Item(1)
+ use green = img.Item(1)
let greenFloat = green.Convert<Gray, float32>()
let filteredGreen = gaussianFilter greenFloat (float config.Parameters.preFilterSigma)
drawEllipses imgAllEllipses allEllipses (Bgr(0.0, 240.0, 240.0)) 0.05
saveImg imgAllEllipses (buildFileName " - ellipses - all.png")
- let imgEllipses = img.Copy()
+ let imgEllipses = filteredGreenWhitoutStain.Convert<Bgr, byte>()
drawEllipses imgEllipses ellipses (Bgr(0.0, 240.0, 240.0)) 1.0
saveImg imgEllipses (buildFileName " - ellipses.png")
saveImg green (buildFileName " - green.png")
- use blue = scaledImg.Item(0)
+ use blue = img.Item(0)
saveImg blue (buildFileName " - blue.png")
- use red = scaledImg.Item(2)
+ use red = img.Item(2)
saveImg red (buildFileName " - red.png")
| _ -> ()
// * 'Stain' corresponds to the stain around the parasites.
// * 'Infection' corresponds to the parasite. It shouldn't contain thrombocytes.
let findMa (green: Image<Gray, float32>) (filteredGreen: Image<Gray, float32>) (config: Config.Config) : Result * Image<Gray, byte> * Image<Gray, byte> =
-
// We use the filtered image to find the dark stain.
let kmediansResults = logTime "Finding fg/bg (k-medians)" (fun () -> KMedians.kmedians filteredGreen)
let { KMedians.fg = fg; KMedians.median_bg = median_bg; KMedians.median_fg = median_fg; KMedians.d_fg = d_fg } = kmediansResults
// * 'Stain' corresponds to the stain around the parasites.
// * 'Infection' corresponds to the parasite. It shouldn't contain thrombocytes.
let find (filteredGreen: Image<Gray, float32>) (config: Config.Config) : Result * Image<Gray, float32> =
-
use filteredGreenWithoutInfection = filteredGreen.Copy()
ImgTools.areaCloseF filteredGreenWithoutInfection (int config.InfectionArea)
ImgTools.areaCloseF filteredGreenWithoutStain (int config.StainArea)
// We use the filtered image to find the dark stain.
+ let _, mean_fg, mean_bg =
+ let hist = ImgTools.histogramImg filteredGreenWithoutInfection 300
+ ImgTools.otsu hist
- // With K-Means.
- let kmeansResults = logTime "Finding fg/bg (k-means)" (fun () -> KMeans.kmeans (filteredGreenWithoutInfection))
- let { KMeans.mean_bg = value_bg; KMeans.mean_fg = value_fg; KMeans.d_fg = d_fg } = kmeansResults
-
- // With K-Medians.
- (* let kmediansResults = logTime "Finding fg/bg (k-medians)" (fun () -> KMedians.kmedians (filteredGreenWithoutInfection)) // FIXME: avoid converting this again in MainAnalysis
- let { KMedians.median_bg = value_bg; KMedians.median_fg = value_fg; KMedians.d_fg = d_fg } = kmediansResults *)
-
- let darkStain = d_fg.Cmp((float value_bg) * config.Parameters.darkStainLevel, CvEnum.CmpType.GreaterThan)
- darkStain._And(filteredGreenWithoutInfection.Cmp(float value_fg, CvEnum.CmpType.LessThan))
+ let darkStain = filteredGreenWithoutInfection.Cmp(-(float mean_bg) * config.Parameters.darkStainLevel + (float mean_fg), CvEnum.CmpType.LessThan)
let marker (img: Image<Gray, float32>) (closed: Image<Gray, float32>) (level: float) : Image<Gray, byte> =
- let diff = img.Copy() // closed - (img * level)
+ let diff = img.Copy()
diff._Mul(level)
CvInvoke.Subtract(closed, diff, diff)
diff._ThresholdBinary(Gray(0.0), Gray(255.))
| mode, debug ->
let config =
Config(
- { scale = 1.
-
+ {
initialAreaOpen = 2000
minRbcRadius = -0.32f
factorNbPick = 1.0
- darkStainLevel = 0.22 // Lower -> more sensitive. 0.3. Careful about illumination on the borders.
+ darkStainLevel = 0.25 // Lower -> more sensitive. 0.3. Careful about illumination on the borders.
maxDarkStainRatio = 0.1 // 10 %
infectionArea = 0.012f // 1.2 %
projects / master-thesis.git / commitdiff