* Draw all the ellipses in debug mode.
let ellipsesWithNeigbors = ellipsesInside |> List.choose (fun e -> if e.Removed then None else Some (e, neighbors e))
- // 3) Remove ellipse with a lower percentage of foreground.
+ // 3) Remove ellipses with a lower percentage of foreground.
for e, neighbors in ellipsesWithNeigbors do
let minX, minY, maxX, maxY = ellipseWindow e
let mutable totalElement = 0
let mutable fgElement = 0
-
for y in minY .. maxY do
for x in minX .. maxX do
let yf, xf = float y, float x
if e.Contains xf yf && neighbors |> List.forall (fun (otherE, _, _) -> not <| otherE.Contains xf yf)
- then
- totalElement <- totalElement + 1
- if fg.Data.[y, x, 0] > 0uy
then
- fgElement <- fgElement + 1
+ totalElement <- totalElement + 1
+ if fg.Data.[y, x, 0] > 0uy
+ then
+ fgElement <- fgElement + 1
- if totalElement < config.minimumCellArea || (float fgElement) / (float totalElement) < config.percentageOfFgValidCell
+ if (float fgElement) / (float totalElement) < config.percentageOfFgValidCell
then
e.Removed <- true
- // 3) Define pixels associated to each ellipse and create the cells.
+ // 4) Remove ellipses with little area.
+ for e, neighbors in ellipsesWithNeigbors do
+ if not e.Removed
+ then
+ let minX, minY, maxX, maxY = ellipseWindow e
+
+ let mutable area = 0
+ for y in minY .. maxY do
+ for x in minX .. maxX do
+ let yf, xf = float y, float x
+ if fg.Data.[y, x, 0] > 0uy &&
+ e.Contains xf yf &&
+ neighbors |> List.forall (fun (otherE, _, _) -> otherE.Removed || not <| otherE.Contains xf yf)
+ then
+ area <- area + 1
+
+ if area < config.minimumCellArea
+ then
+ e.Removed <- true
+
+ // 5) Define pixels associated to each ellipse and create the cells.
// Return 'true' if the point 'p' is owned by e.
// The lines represents all intersections with other ellipses.
doGSigma2: float
doGLowFreqPercentageReduction: float
+ // Ellipse.
+ factorNbPick: float
+ factorWindowSize: float // factor of 'maxRBCSize'.
+
// Parasites detection.
darkStainLevel: float
open Emgu.CV.Structure
open Utils
+open Config
open MatchingEllipses
type private SearchExtremum = Minimum | Maximum
-
+
let private goldenSectionSearch (f: float -> float) (nbIter: int) (xmin: float) (xmax: float) (searchExtremum: SearchExtremum) : (float * float) =
let gr = 1.0 / 1.6180339887498948482
let mutable a = xmin
let mutable b = xmax
let mutable c = b - gr * (b - a)
let mutable d = a + gr * (b - a)
-
+
for i in 1 .. nbIter do
let mutable fc = f c
let mutable fd = f d
-
+
if searchExtremum = Maximum
then
let tmp = fc
fc <- fd
fd <- tmp
-
+
if fc < fd
then
b <- d
a <- c
c <- d
d <- a + gr * (b - a)
-
+
let x = (b + a) / 2.0
x, f x
// p3 as the referencial.
let p1x = p1x - p3x
let p1y = p1y - p3y
-
+
let p2x = p2x - p3x
let p2y = p2y - p3y
-
+
// Convert to polar coordinates.
let alpha1 = atan m1
let alpha2 = atan m2
-
+
let r1 = sqrt (p1x ** 2.0 + p1y ** 2.0)
let theta1 = atan2 p1y p1x
let r2 = sqrt (p2x**2.0 + p2y**2.0)
let theta2 = atan2 p2y p2x
- let valid =
- 4.0 * sin (alpha1 - theta1) * (-r1 * sin (alpha1 - theta1) + r2 * sin (alpha1 - theta2)) *
+ let valid =
+ 4.0 * 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) ** 2.0 * sin (theta1 - theta2) ** 2.0 < 0.0
-
+
if valid
then
- let r theta =
+ 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)) ** 2.0 - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.0)
-
+
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 0.0 Math.PI Maximum
let rTan = r thetaTan
-
+
let PTanx = rTan * cos thetaTan
let PTany = rTan * sin thetaTan
-
+
let d1a = tan alpha1
let d1b = -d1a * p1x + p1y
-
+
let d2a = tan alpha2
let d2b = -d2a * p2x + p2y
-
+
let d3a = -1.0 / tan thetaTan
let d3b = -d3a * PTanx + PTany
-
+
let Ux = -(d1b - d2b) / (d1a - d2a)
let Uy = -(d2a * d1b - d1a * d2b) / (d1a - d2a)
-
+
let Vx = -(d1b - d3b) / (d1a - d3a)
let Vy = -(d3a * d1b - d1a * d3b) / (d1a - d3a)
-
+
let Wx = p1x + (p2x - p1x) / 2.0
let Wy = p1y + (p2y - p1y) / 2.0
-
+
let Zx = p1x + (PTanx - p1x) / 2.0
let Zy = p1y + (PTany - p1y) / 2.0
-
+
let va = -(-Vy + Zy) / (Vx - Zx)
let vb = -(Zx * Vy - Vx * Zy) / (Vx - Zx)
-
+
let ua = -(-Uy + Wy) / (Ux - Wx)
let ub = -(Wx * Uy - Ux * Wy) / (Ux - Wx)
-
+
let cx = -(vb - ub) / (va - ua)
let cy = -(ua * vb - va * ub) / (va - ua)
-
+
let rc = sqrt (cx**2.0 + cy**2.0)
let psi = atan2 cy cx
-
- let rellipse theta =
+
+ let rellipse theta =
sqrt (
rc ** 2.0 + (r1 ** 2.0 * r2 ** 2.0 * (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)) ** 2.0 * sin (theta1 - theta2) ** 2.0) /
(sin (alpha1 - theta1) * sin (alpha2 - theta2) * (r1 * sin (theta - theta1) - r2 * sin (theta - theta2)) ** 2.0 - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.0) ** 2.0 -
(2.0 * 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)) ** 2.0 - r1 * r2 * sin (alpha1 - theta) * sin (alpha2 - theta) * sin (theta1 - theta2) ** 2.0))
-
- // We search for an interval [theta_a, theta_b] and assume the function is unimodal in this interval.
+
+ // 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 0.0 (Math.PI / 2.0) Maximum // Pi/2 and not pi because the period is Pi.
let r2eTheta, r2e = goldenSectionSearch rellipse accuracy_extremum_search_2 0.0 (Math.PI / 2.0) 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 < 0.0
then
- alpha <- alpha + Math.PI
-
+ alpha <- alpha + Math.PI
+
// Ride off the p3 referential.
let cx = cx + p3x
let cy = cy + p3y
if v1x > 0.0
then
rotation <- -1.0
- elif p1y < py
+ elif p1y < py
then
- if v1x < 0.0
+ if v1x < 0.0
then
rotation <- -1.0
elif p1x > px
if v1y < 0.0
then
rotation <- -1.0
- elif p1x < px
+ elif p1x < px
then
- if v1y > 0.0
+ if v1y > 0.0
then
rotation <- -1.0
rotation
let private areVectorsValid (p1x: float) (p1y: float) (p2x: float) (p2y: float) (v1x: float) (v1y: float) (v2x: float) (v2y: float) : (float * float) option =
let m1 = -v1x / v1y
let m2 = -v2x / v2y
-
+
let b1 = -m1 * p1x + p1y
let b2 = -m2 * p2x + p2y
let px = -((b1 - b2)/(m1 - m2))
let py = -((m2 * b1 - m1 * b2)/(m1 - m2))
-
+
let rot1 = vectorRotation p1x p1y v1x v1y px py
let rot2 = vectorRotation p2x p2y v2x v2y px py
-
+
if rot1 = rot2 || rot1 * atan2 (p1y - py) (p1x - px) + rot2 * atan2 (p2y - py) (p2x - px) <= 0.0
then
None
Some (m1, m2)
let find (edges: Matrix<byte>)
- (xDir: Image<Gray, float>)
- (yDir: Image<Gray, float>)
- (radiusRange: float * float)
- (windowSize: int)
- (factorNbPick: float) : Types.Ellipse list =
+ (xDir: Image<Gray, float>)
+ (yDir: Image<Gray, float>)
+ (config: Config) : MatchingEllipses =
+
+ let r1, r2 = config.scale * config.minRBCSize, config.scale * config.maxRBCSize
+ let windowSize = roundInt (config.factorWindowSize * r2)
+ let factorNbPick = config.factorNbPick
let increment = windowSize / 4
- let r1, r2 = radiusRange
let radiusTolerance = (r2 - r1) * 0.2
let minimumDistance = (r2 / 1.5) ** 2.0
let h = edges.Height
let w = edges.Width
-
+
let mutable last_i, last_j = Int32.MaxValue, Int32.MaxValue
let currentElements = List<(int * int)>()
let yDirData = yDir.Data
let rng = Random(42)
-
+
let ellipses = MatchingEllipses(r1)
-
- for window_i in -windowSize + increment .. increment .. h - increment do
- for window_j in -windowSize + increment .. increment .. w - increment do
-
+
+ for window_i in -windowSize + increment .. increment .. h - increment do
+ for window_j in -windowSize + increment .. increment .. w - increment do
+
let window_i_begin = if window_i < 0 then 0 else window_i
let window_i_end = if window_i + windowSize - 1 >= h then h - 1 else window_i + windowSize - 1
let window_j_begin = if window_j < 0 then 0 else window_j
match areVectorsValid p1xf p1yf p2xf p2yf -xDirData.[p1y, p1x, 0] -yDirData.[p1y, p1x, 0] -xDirData.[p2y, p2x, 0] -yDirData.[p2y, p2x, 0] with
| Some (m1, m2) ->
match ellipse p1xf p1yf m1 p2xf p2yf m2 p3xf p3yf with
- | Some e when e.Cx > 0.0 && e.Cx < (float w) - 1.0 && e.Cy > 0.0 && e.Cy < (float h) - 1.0 &&
+ | Some e when e.Cx > 0.0 && e.Cx < (float w) - 1.0 && e.Cy > 0.0 && e.Cy < (float h) - 1.0 &&
e.A >= r1 - radiusTolerance && e.A <= r2 + radiusTolerance && e.B >= r1 - radiusTolerance && e.B <= r2 + radiusTolerance ->
ellipses.Add e
| _ -> ()
| _ -> ()
currentElements.Clear()
-
- ellipses.Ellipses
+
+ ellipses
mat.CopyTo(img)
saveImg img filepath
-let drawLine (img: Image<'TColor, 'TDepth>) (color: 'TColor) (x0: int) (y0: int) (x1: int) (y1: int) =
- img.Draw(LineSegment2D(Point(x0, y0), Point(x1, y1)), color, 1);
+let drawLine (img: Image<'TColor, 'TDepth>) (color: 'TColor) (x0: int) (y0: int) (x1: int) (y1: int) (thickness: int) =
+ img.Draw(LineSegment2D(Point(x0, y0), Point(x1, y1)), color, thickness);
-let drawLineF (img: Image<'TColor, 'TDepth>) (color: 'TColor) (x0: float) (y0: float) (x1: float) (y1: float) =
- let x0, y0, x1, y1 = roundInt(x0), roundInt(y0), roundInt(x1), roundInt(y1)
- drawLine img color x0 y0 x1 y1
+let drawLineF (img: Image<'TColor, 'TDepth>) (color: 'TColor) (x0: float) (y0: float) (x1: float) (y1: float) (thickness: int) =
+ img.Draw(LineSegment2DF(PointF(float32 x0, float32 y0), PointF(float32 x1, float32 y1)), color, thickness, CvEnum.LineType.AntiAlias);
-let drawEllipse (img: Image<'TColor, 'TDepth>) (e: Types.Ellipse) (color: 'TColor) =
- let cosAlpha = cos e.Alpha
- let sinAlpha = sin e.Alpha
+let drawEllipse (img: Image<'TColor, 'TDepth>) (e: Types.Ellipse) (color: 'TColor) (alpha: float) =
- let mutable x0 = 0.0
- let mutable y0 = 0.0
- let mutable first_iteration = true
+ if alpha >= 1.0
+ then
+ img.Draw(Ellipse(PointF(float32 e.Cx, float32 e.Cy), SizeF(2. * e.B |> float32, 2. * e.A |> float32), float32 <| e.Alpha / Math.PI * 180.), color, 1, CvEnum.LineType.AntiAlias)
+ else
+ let windowPosX = e.Cx - e.A - 5.0
+ let gapX = windowPosX - (float (int windowPosX))
- let n = 40
- let thetaIncrement = 2.0 * Math.PI / (float n)
+ let windowPosY = e.Cy - e.A - 5.0
+ let gapY = windowPosY - (float (int windowPosY))
- for theta in 0.0 .. thetaIncrement .. 2.0 * Math.PI do
- let cosTheta = cos theta
- let sinTheta = sin theta
- let x = e.Cx + cosAlpha * e.A * cosTheta - sinAlpha * e.B * sinTheta
- let y = e.Cy + sinAlpha * e.A * cosTheta + cosAlpha * e.B * sinTheta
+ let roi = Rectangle(int windowPosX, int windowPosY, 2. * (e.A + 5.0) |> int, 2.* (e.A + 5.0) |> int)
- if not first_iteration
+ img.ROI <- roi
+ if roi = img.ROI // We do not display ellipses touching the edges (FIXME)
then
- drawLineF img color x0 y0 x y
- else
- first_iteration <- false
+ use i = new Image<'TColor, 'TDepth>(img.ROI.Size)
+ i.Draw(Ellipse(PointF(float32 <| (e.A + 5. + gapX) , float32 <| (e.A + 5. + gapY)), SizeF(2. * e.B |> float32, 2. * e.A |> float32), float32 <| e.Alpha / Math.PI * 180.), color, 1, CvEnum.LineType.AntiAlias)
+ CvInvoke.AddWeighted(img, 1.0, i, alpha, 0.0, img)
+ img.ROI <- Rectangle.Empty
- x0 <- x
- y0 <- y
-let drawEllipses (img: Image<'TColor, 'TDepth>) (ellipses: Types.Ellipse list) (color: 'TColor) =
- List.iter (fun e -> drawEllipse img e color) ellipses
+let drawEllipses (img: Image<'TColor, 'TDepth>) (ellipses: Types.Ellipse list) (color: 'TColor) (alpha: float) =
+ List.iter (fun e -> drawEllipse img e color alpha) ellipses
let rngCell = System.Random()
img.Data.[y + dy, x + dx, 1] <- if g + colorG > 255 then 255uy else byte (g + colorG)
img.Data.[y + dy, x + dx, 2] <- if r + colorR > 255 then 255uy else byte (r + colorR)
- let crossColor = match c.cellClass with
- | Types.HealthyRBC -> Bgr(255.0, 0.0, 0.0)
- | Types.InfectedRBC -> Bgr(0.0, 0.0, 255.0)
- | Types.Peculiar -> Bgr(0.0, 0.0, 0.0)
+ let crossColor, crossColor2 =
+ match c.cellClass with
+ | Types.HealthyRBC -> Bgr(255., 0., 0.), Bgr(255., 255., 255.)
+ | Types.InfectedRBC -> Bgr(0., 0., 255.), Bgr(120., 120., 255.)
+ | Types.Peculiar -> Bgr(0., 0., 0.), Bgr(80., 80., 80.)
+
+ drawLine img crossColor2 (c.center.X - 3) c.center.Y (c.center.X + 3) c.center.Y 2
+ drawLine img crossColor2 c.center.X (c.center.Y - 3) c.center.X (c.center.Y + 3) 2
+
+ drawLine img crossColor (c.center.X - 3) c.center.Y (c.center.X + 3) c.center.Y 1
+ drawLine img crossColor c.center.X (c.center.Y - 3) c.center.X (c.center.Y + 3) 1
- drawLine img crossColor (c.center.X - 3) c.center.Y (c.center.X + 3) c.center.Y
- drawLine img crossColor c.center.X (c.center.Y - 3) c.center.X (c.center.Y + 3)
let drawCells (img: Image<Bgr, byte>) (drawCellContent: bool) (cells: Types.Cell list) =
List.iter (fun c -> drawCell img drawCellContent c) cells
\ No newline at end of file
logTime "Finding edges" (fun() -> thin edges)
logTime "Removing small connected components from thinning" (fun () -> removeArea edges 12)
- (*
let kmediansResults = KMedians.kmedians filteredGreen 1.0
let parasites = ParasitesMarker.find green filteredGreen kmediansResults config
- let radiusRange = config.scale * config.minRBCSize, config.scale * config.maxRBCSize
- 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)
+ let allEllipses, ellipses = logTime "Finding ellipses" (fun () ->
+ let matchingEllipses = Ellipse.find edges xEdges yEdges config
+ matchingEllipses.Ellipses, matchingEllipses.PrunedEllipses )
let cells = logTime "Classifier" (fun () -> Classifier.findCells ellipses parasites kmediansResults.fg config)
- *)
- let cells = []
// Output pictures if debug flag is set.
match config.debug with
let buildFileName postfix = System.IO.Path.Combine(output, name + postfix)
saveMat (edges * 255.0) (buildFileName " - edges.png")
- (*saveImg parasites.darkStain (buildFileName " - parasites - dark stain.png")
+ saveImg parasites.darkStain (buildFileName " - parasites - dark stain.png")
saveImg parasites.stain (buildFileName " - parasites - stain.png")
saveImg parasites.infection (buildFileName " - parasites - infection.png")
+ let imgAllEllipses = img.Copy()
+ drawEllipses imgAllEllipses allEllipses (Bgr(0.0, 240.0, 240.0)) 0.1
+ saveImg imgAllEllipses (buildFileName " - ellipses - all.png")
+
let imgEllipses = img.Copy()
- drawEllipses imgEllipses ellipses (Bgr(0.0, 240.0, 240.0))
+ drawEllipses imgEllipses ellipses (Bgr(0.0, 240.0, 240.0)) 1.0
saveImg imgEllipses (buildFileName " - ellipses.png")
saveImg (kmediansResults.fg * 255.0) (buildFileName " - foreground.png")
let imgCells' = img.Copy()
drawCells imgCells' true cells
- saveImg imgCells' (buildFileName " - cells - full.png")*)
+ saveImg imgCells' (buildFileName " - cells - full.png")
| _ -> ()
cells
let matchingScoreThreshold1 = 0.6
-let matchingScoreThreshold2 = 1.0
+let matchingScoreThreshold2 = 1.
type private EllipseScoreFlaggedKd (matchingScore: float, e: Ellipse) =
let mutable matchingScore = matchingScore
member val Processed = false with get, set
member val Removed = false with get, set
- interface KdTree.I2DCoords with
+ interface KdTree.I2DCoords with
member this.X = this.Ellipse.Cx
member this.Y = this.Ellipse.Cy
-
+
type MatchingEllipses (radiusMin: float) =
let ellipses = List<EllipseScoreFlaggedKd>()
-
- member this.Add (e: Ellipse) =
+
+ member this.Add (e: Ellipse) =
ellipses.Add(EllipseScoreFlaggedKd(0.0, e))
member this.Ellipses : Ellipse list =
+ List.ofSeq ellipses |> List.map (fun e -> e.Ellipse)
+
+ member this.PrunedEllipses : Ellipse list =
if ellipses.Count = 0
then
[]
// 1) Create a kd-tree from the ellipses list.
let tree = KdTree.Tree.BuildTree (List.ofSeq ellipses)
- // 2) Compute the matching score of each ellipses.
+ // 2) Compute the matching score of each ellipses.
let windowSize = radiusMin
for e in ellipses do
e.Processed <- true
let areaE = e.Ellipse.Area
let window = { KdTree.minX = e.Ellipse.Cx - windowSize / 2.0
- KdTree.maxX = e.Ellipse.Cx + windowSize / 2.0
+ KdTree.maxX = e.Ellipse.Cx + windowSize / 2.0
KdTree.minY = e.Ellipse.Cy - windowSize / 2.0
KdTree.maxY = e.Ellipse.Cy + windowSize / 2.0 }
for other in tree.Search window do
// 3) Sort ellipses by their score.
ellipses.Sort(fun e1 e2 -> e2.MatchingScore.CompareTo(e1.MatchingScore))
- // 4) Remove ellipses wich have a low score.
+ // 4) Remove ellipses wich have a low score.
let i = ellipses.BinarySearch(EllipseScoreFlaggedKd(matchingScoreThreshold2, Ellipse(0.0, 0.0, 0.0, 0.0, 0.0)),
{ new IComparer<EllipseScoreFlaggedKd> with
member this.Compare(e1, e2) = e2.MatchingScore.CompareTo(e1.MatchingScore) }) |> abs
if not e.Removed
then
let window = { KdTree.minX = e.Ellipse.Cx - e.Ellipse.A
- KdTree.maxX = e.Ellipse.Cx + e.Ellipse.A
+ KdTree.maxX = e.Ellipse.Cx + e.Ellipse.A
KdTree.minY = e.Ellipse.Cy - e.Ellipse.A
KdTree.maxY = e.Ellipse.Cy + e.Ellipse.A }
for other in tree.Search window do
if not other.Removed && other.MatchingScore < e.MatchingScore
- then
+ then
if e.Ellipse.Contains other.Ellipse.Cx other.Ellipse.Cy
then
other.Removed <- true
List.ofSeq ellipses |> List.map (fun e -> e.Ellipse)
-
+
// * 'Infection' corresponds to the parasite. It shouldn't contain thrombocytes.
let find (green: Image<Gray, float32>) (filteredGreen: Image<Gray, float32>) (kmediansResult: KMedians.Result) (config: Config.Config) : Result =
- let green = ImgTools.gaussianFilter green 1.0
-
// We use the filtered image to find the dark stain.
let { KMedians.fg = fg; KMedians.median_bg = median_bg; KMedians.median_fg = median_fg; KMedians.d_fg = d_fg } = kmediansResult
let darkStain = d_fg.Cmp(median_bg * config.darkStainLevel, CvEnum.CmpType.GreaterThan)
darkStain._And(fg)
let fgFloat = (fg / 255.0).Convert<Gray, float32>()
- let greenWithoutBg = green.Copy()
+ use greenWithoutBg = ImgTools.gaussianFilter green 1.0
greenWithoutBg.SetValue(Gray(0.0), fg.Not())
let findSmears (sigma: float) (level: float) : Image<Gray, byte> =
- let greenWithoutBgSmoothed = ImgTools.gaussianFilter greenWithoutBg sigma
- let fgSmoothed = ImgTools.gaussianFilter fgFloat sigma
-
+ use greenWithoutBgSmoothed = ImgTools.gaussianFilter greenWithoutBg sigma
+ use fgSmoothed = ImgTools.gaussianFilter fgFloat sigma
let smears = (greenWithoutBg.Mul(fgSmoothed)).Cmp(greenWithoutBgSmoothed.Mul(level), CvEnum.CmpType.LessThan)
smears._And(fg)
smears
doGSigma2 = 20.
doGLowFreqPercentageReduction = 0.75
- darkStainLevel = 0.5
+ factorNbPick = 2.0
+ factorWindowSize = 1.6
+
+ darkStainLevel = 0.4 // Lower -> more sensitive.
stainSigma = 10.
stainLevel = 0.9
MaxDarkStainRatio = 0.1
- minimumCellArea = 600. * scale ** 2. |> int
+ minimumCellArea = 1200. * scale ** 2. |> int
maxOffcenter = 0.5 }
match mode with
projects / master-thesis.git / commitdiff