((img - (!min).[0]) / ((!max).[0] - (!min).[0]) * 255.0).Convert<Gray, byte>()
+let saveImg (img: Image<'TColor, 'TDepth>) (filepath: string) =
+ img.Save(filepath)
+
+
+let saveMat (mat: Matrix<'TDepth>) (filepath: string) =
+ use img = new Image<Gray, 'TDeph>(mat.Size)
+ mat.CopyTo(img)
+ saveImg img filepath
+
+
+let suppressMConnections (img: Matrix<byte>) =
+ let w = img.Width
+ let h = img.Height
+ for i in 1 .. h - 2 do
+ for j in 1 .. w - 2 do
+ if img.[i, j] > 0uy && img.Data.[i + 1, j] > 0uy && (img.Data.[i, j - 1] > 0uy && img.Data.[i - 1, j + 1] = 0uy || img.Data.[i, j + 1] > 0uy && img.Data.[i - 1, j - 1] = 0uy) then
+ img.[i, j] <- 0uy
+ for i in 1 .. h - 2 do
+ for j in 1 .. w - 2 do
+ if img.[i, j] > 0uy && img.Data.[i - 1, j] > 0uy && (img.Data.[i, j - 1] > 0uy && img.Data.[i + 1, j + 1] = 0uy || img.Data.[i, j + 1] > 0uy && img.Data.[i + 1, j - 1] = 0uy) then
+ img.[i, j] <- 0uy
+
+let findEdges (img: Image<Gray, float32>) : Matrix<byte> * Image<Gray, float> * Image<Gray, float> =
+ let w = img.Width
+ let h = img.Height
+
+ use sobelKernel =
+ new ConvolutionKernelF(array2D [[ 1.0f; 0.0f; -1.0f ]
+ [ 2.0f; 0.0f; -2.0f ]
+ [ 1.0f; 0.0f; -1.0f ]], Point(1, 1))
+
+ let xGradient = img.Convolution(sobelKernel).Convert<Gray, float>()
+ let yGradient = img.Convolution(sobelKernel.Transpose()).Convert<Gray, float>()
+
+ let xGradientData = xGradient.Data
+ let yGradientData = yGradient.Data
+ for r in 0 .. h - 1 do
+ xGradientData.[r, 0, 0] <- 0.0
+ xGradientData.[r, w - 1, 0] <- 0.0
+ yGradientData.[r, 0, 0] <- 0.0
+ yGradientData.[r, w - 1, 0] <- 0.0
+
+ for c in 0 .. w - 1 do
+ xGradientData.[0, c, 0] <- 0.0
+ xGradientData.[h - 1, c, 0] <- 0.0
+ yGradientData.[0, c, 0] <- 0.0
+ yGradientData.[h - 1, c, 0] <- 0.0
+
+ use magnitudes = new Matrix<float>(xGradient.Size)
+ CvInvoke.CartToPolar(xGradient, yGradient, magnitudes, new Mat()) // Compute the magnitudes (without angles).
+
+ let thresholdHigh, thresholdLow =
+ let sensibility = 0.1
+ use magnitudesByte = magnitudes.Convert<byte>()
+ let threshold = CvInvoke.Threshold(magnitudesByte, magnitudesByte, 0.0, 1.0, CvEnum.ThresholdType.Otsu ||| CvEnum.ThresholdType.Binary)
+ threshold + (sensibility * threshold), threshold - (sensibility * threshold)
+
+ // Non-maximum suppression.
+ use nms = new Matrix<byte>(xGradient.Size)
+ nms.SetValue(1.0)
+
+ for i in 0 .. h - 1 do
+ nms.Data.[i, 0] <- 0uy
+ nms.Data.[i, w - 1] <- 0uy
+
+ for j in 0 .. w - 1 do
+ nms.Data.[0, j] <- 0uy
+ nms.Data.[h - 1, j] <- 0uy
+
+ for i in 1 .. h - 2 do
+ for j in 1 .. w - 2 do
+ let vx = xGradient.Data.[i, j, 0]
+ let vy = yGradient.Data.[i, j, 0]
+ let angle =
+ let a = atan2 vy vx
+ if a < 0.0 then 2. * Math.PI + a else a
+
+ let mNeigbors (sign: int) : float =
+ if angle < Math.PI / 8. || angle >= 15.0 * Math.PI / 8. then magnitudes.Data.[i, j + sign]
+ elif angle < 3.0 * Math.PI / 8. then magnitudes.Data.[i + sign, j + sign]
+ elif angle < 5.0 * Math.PI / 8. then magnitudes.Data.[i + sign, j]
+ elif angle < 7.0 * Math.PI / 8. then magnitudes.Data.[i + sign, j - sign]
+ elif angle < 9.0 * Math.PI / 8. then magnitudes.Data.[i, j - sign]
+ elif angle < 11.0 * Math.PI / 8. then magnitudes.Data.[i - sign, j - sign]
+ elif angle < 13.0 * Math.PI / 8. then magnitudes.Data.[i - sign, j]
+ else magnitudes.Data.[i - sign, j + sign]
+
+ let m = magnitudes.Data.[i, j]
+ if m < mNeigbors 1 || m < mNeigbors -1 || m < thresholdLow then
+ nms.Data.[i, j] <- 0uy
+
+ // suppressMConnections nms // It's not usefull for the rest of the process (ellipse detection).
+
+ let edges = new Matrix<byte>(xGradient.Size)
+
+ // Histeresis thresholding.
+ let toVisit = Stack<Point>()
+ for i in 0 .. h - 1 do
+ for j in 0 .. w - 1 do
+ if nms.Data.[i, j] = 1uy && magnitudes.Data.[i, j] >= thresholdHigh then
+ nms.Data.[i, j] <- 0uy
+ toVisit.Push(Point(j, i))
+ while toVisit.Count > 0 do
+ let p = toVisit.Pop()
+ edges.Data.[p.Y, p.X] <- 1uy
+ for i' in -1 .. 1 do
+ for j' in -1 .. 1 do
+ if i' <> 0 || j' <> 0 then
+ let ni = p.Y + i'
+ let nj = p.X + j'
+ if ni >= 0 && ni < h && nj >= 0 && nj < w && nms.Data.[ni, nj] = 1uy then
+ nms.Data.[ni, nj] <- 0uy
+ toVisit.Push(Point(nj, ni))
+
+
+ edges, xGradient, yGradient
+
+
let gaussianFilter (img : Image<'TColor, 'TDepth>) (standardDeviation : float) : Image<'TColor, 'TDepth> =
let size = 2 * int (ceil (4.0 * standardDeviation)) + 1
img.SmoothGaussian(size, size, standardDeviation, standardDeviation)
for p in points do
img.Data.[p.Y, p.X, 0] <- intensity
-let findMaxima (img: Image<Gray, byte>) : IEnumerable<Points> =
- use suppress = new Image<Gray, byte>(img.Size)
+type ExtremumType =
+ | Maxima = 1
+ | Minima = 2
+
+let findExtremum (img: Image<Gray, byte>) (extremumType: ExtremumType) : IEnumerable<Points> =
let w = img.Width
let h = img.Height
+ let se = [| -1, 0; 0, -1; 1, 0; 0, 1 |]
let imgData = img.Data
- let suppressData = suppress.Data
+ let suppress: bool[,] = Array2D.zeroCreate h w
let result = List<List<Point>>()
while betterLevelToCheck.Count > 0 do
let p = betterLevelToCheck.Pop()
- if suppressData.[p.Y, p.X, 0] = 0uy
+ if not suppress.[p.Y, p.X]
then
- suppressData.[p.Y, p.X, 0] <- 1uy
+ suppress.[p.Y, p.X] <- true
sameLevelToCheck.Push(p)
let current = List<Point>()
let p' = sameLevelToCheck.Pop()
let currentLevel = imgData.[p'.Y, p'.X, 0]
current.Add(p') |> ignore
- for i in -1 .. 1 do
- for j in -1 .. 1 do
- if i <> 0 || j <> 0
+ for i, j in se do
+ let ni = i + p'.Y
+ let nj = j + p'.X
+ if ni >= 0 && ni < h && nj >= 0 && nj < w
+ then
+ let level = imgData.[ni, nj, 0]
+ let notSuppressed = not suppress.[ni, nj]
+
+ if level = currentLevel && notSuppressed
+ then
+ suppress.[ni, nj] <- true
+ sameLevelToCheck.Push(Point(nj, ni))
+ elif if extremumType = ExtremumType.Maxima then level > currentLevel else level < currentLevel
then
- let ni = i + p'.Y
- let nj = j + p'.X
- if ni >= 0 && ni < h && nj >= 0 && nj < w
+ betterExists <- true
+ if notSuppressed
then
- let level = imgData.[ni, nj, 0]
- let notSuppressed = suppressData.[ni, nj, 0] = 0uy
-
- if level = currentLevel && notSuppressed
- then
- suppressData.[ni, nj, 0] <- 1uy
- sameLevelToCheck.Push(Point(nj, ni))
- elif level > currentLevel
- then
- betterExists <- true
- if notSuppressed
- then
- betterLevelToCheck.Push(Point(nj, ni))
+ betterLevelToCheck.Push(Point(nj, ni))
if not betterExists
then
for j in 0 .. w - 1 do
let maxima = flood (Point(j, i))
if maxima.Count > 0
- then result.AddRange(maxima)
+ then
+ result.AddRange(maxima)
result.Select(fun l -> Points(l))
+let findMaxima (img: Image<Gray, byte>) : IEnumerable<Points> =
+ findExtremum img ExtremumType.Maxima
+
+let findMinima (img: Image<Gray, byte>) : IEnumerable<Points> =
+ findExtremum img ExtremumType.Minima
+
+
type PriorityQueue () =
let size = 256
- let q: Points[] = Array.init size (fun i -> Points()) // TODO: Check performance with an HasSet
+ let q: Points[] = Array.init size (fun i -> Points())
let mutable highest = -1 // Value of the first elements of 'q'.
let mutable lowest = size
- member this.Next () : byte * Point =
+ member this.NextMax () : byte * Point =
if this.IsEmpty
then
invalidOp "Queue is empty"
value, next
+ member this.NextMin () : byte * Point =
+ if this.IsEmpty
+ then
+ invalidOp "Queue is empty"
+ else
+ let l = q.[lowest + 1]
+ let next = l.First()
+ l.Remove(next) |> ignore
+ let value = byte (lowest + 1)
+
+ if l.Count = 0
+ then
+ lowest <- lowest + 1
+ while lowest < highest && q.[lowest + 1].Count = 0 do
+ lowest <- lowest + 1
+ if highest = lowest
+ then
+ highest <- -1
+ lowest <- size
+
+ value, next
+
member this.Max =
highest |> byte
+ member this.Min =
+ lowest + 1 |> byte
+
member this.Add (value: byte) (p: Point) =
let vi = int value
lowest <- size
-type AreaState =
+type private AreaState =
| Removed = 1
| Unprocessed = 2
| Validated = 3
+type private AreaOperation =
+ | Opening = 1
+ | Closing = 2
+
[<AllowNullLiteral>]
-type Area (elements: Points) =
+type private Area (elements: Points) =
member this.Elements = elements
member val Intensity = None with get, set
member val State = AreaState.Unprocessed with get, set
-let areaOpen (img: Image<Gray, byte>) (area: int) =
+let private areaOperation (img: Image<Gray, byte>) (area: int) (op: AreaOperation) =
let w = img.Width
let h = img.Height
let imgData = img.Data
+ let se = [| -1, 0; 0, -1; 1, 0; 0, 1 |]
- let areas = List<Area>(findMaxima img |> Seq.map Area)
+ let areas = List<Area>((if op = AreaOperation.Opening then findMaxima img else findMinima img) |> Seq.map Area)
let pixels: Area[,] = Array2D.create h w null
for m in areas do
let addEdgeToQueue (elements: Points) =
for p in elements do
- for i in -1 .. 1 do
- for j in -1 .. 1 do
- if i <> 0 || j <> 0
- then
- let ni = i + p.Y
- let nj = j + p.X
- let p' = Point(nj, ni)
- if ni >= 0 && ni < h && nj >= 0 && nj < w && not (elements.Contains(p'))
- then
- queue.Add (imgData.[ni, nj, 0]) p'
+ for i, j in se do
+ let ni = i + p.Y
+ let nj = j + p.X
+ let p' = Point(nj, ni)
+ if ni >= 0 && ni < h && nj >= 0 && nj < w && not (elements.Contains(p'))
+ then
+ queue.Add (imgData.[ni, nj, 0]) p'
// Reverse order is quicker.
for i in areas.Count - 1 .. -1 .. 0 do
queue.Clear()
addEdgeToQueue m.Elements
- let mutable intensity = queue.Max
+ let mutable intensity = if op = AreaOperation.Opening then queue.Max else queue.Min
let nextElements = Points()
let mutable stop = false
while not stop do
- let intensity', p = queue.Next ()
+ let intensity', p = if op = AreaOperation.Opening then queue.NextMax () else queue.NextMin ()
let mutable merged = false
if intensity' = intensity // The intensity doesn't change.
else
nextElements.Add(p) |> ignore
- elif intensity' < intensity
+ elif if op = AreaOperation.Opening then intensity' < intensity else intensity' > intensity
then
m.Elements.UnionWith(nextElements)
for e in nextElements do
nextElements.Clear()
nextElements.Add(p) |> ignore
- else // i' > i
+ else
let m' = pixels.[p.Y, p.X]
if m' <> null
then
queue.Remove imgData.[e.Y, e.X, 0] e
addEdgeToQueue m'.Elements
m.Elements.UnionWith(m'.Elements)
- let intensityMax = queue.Max
+ let intensityMax = if op = AreaOperation.Opening then queue.Max else queue.Min
if intensityMax <> intensity
then
intensity <- intensityMax
if not stop && not merged
then
- for i in -1 .. 1 do
- for j in -1 .. 1 do
- if i <> 0 || j <> 0
- then
- let ni = i + p.Y
- let nj = j + p.X
- let p' = Point(nj, ni)
- if ni < 0 || ni >= h || nj < 0 || nj >= w
- then
- m.State <- AreaState.Validated
- m.Intensity <- Some (intensity)
- stop <- true
- elif not (m.Elements.Contains(p')) && not (nextElements.Contains(p'))
- then
- queue.Add (imgData.[ni, nj, 0]) p'
+ for i, j in se do
+ let ni = i + p.Y
+ let nj = j + p.X
+ let p' = Point(nj, ni)
+ if ni < 0 || ni >= h || nj < 0 || nj >= w
+ then
+ m.State <- AreaState.Validated
+ m.Intensity <- Some (intensity)
+ stop <- true
+ elif not (m.Elements.Contains(p')) && not (nextElements.Contains(p'))
+ then
+ queue.Add (imgData.[ni, nj, 0]) p'
if queue.IsEmpty
then
()
+let areaOpen (img: Image<Gray, byte>) (area: int) =
+ areaOperation img area AreaOperation.Opening
+
+let areaClose (img: Image<Gray, byte>) (area: int) =
+ areaOperation img area AreaOperation.Closing
+
+// A simpler algorithm than 'areaOpen' but slower.
+let areaOpen2 (img: Image<Gray, byte>) (area: int) =
+ let w = img.Width
+ let h = img.Height
+ let imgData = img.Data
+ let se = [| -1, 0; 0, -1; 1, 0; 0, 1 |]
+
+ let histogram = Array.zeroCreate 256
+ for i in 0 .. h - 1 do
+ for j in 0 .. w - 1 do
+ let v = imgData.[i, j, 0] |> int
+ histogram.[v] <- histogram.[v] + 1
+
+ let flooded : bool[,] = Array2D.zeroCreate h w
+
+ let pointsChecked = HashSet<Point>()
+ let pointsToCheck = Stack<Point>()
+
+ for level in 255 .. -1 .. 0 do
+ let mutable n = histogram.[level]
+ if n > 0
+ then
+ for i in 0 .. h - 1 do
+ for j in 0 .. w - 1 do
+ if not flooded.[i, j] && imgData.[i, j, 0] = byte level
+ then
+ let mutable maxNeighborValue = 0uy
+ pointsChecked.Clear()
+ pointsToCheck.Clear()
+ pointsToCheck.Push(Point(j, i))
+
+ while pointsToCheck.Count > 0 do
+ let next = pointsToCheck.Pop()
+ pointsChecked.Add(next) |> ignore
+ flooded.[next.Y, next.X] <- true
+
+ for nx, ny in se do
+ let p = Point(next.X + nx, next.Y + ny)
+ if p.X >= 0 && p.X < w && p.Y >= 0 && p.Y < h
+ then
+ let v = imgData.[p.Y, p.X, 0]
+ if v = byte level
+ then
+ if not (pointsChecked.Contains(p))
+ then
+ pointsToCheck.Push(p)
+ elif v > maxNeighborValue
+ then
+ maxNeighborValue <- v
+
+ if int maxNeighborValue < level && pointsChecked.Count <= area
+ then
+ for p in pointsChecked do
+ imgData.[p.Y, p.X, 0] <- maxNeighborValue
+
+
// Zhang and Suen algorithm.
// Modify 'mat' in place.
let thin (mat: Matrix<byte>) =
List<Point>(pointChecked)
-let saveImg (img: Image<'TColor, 'TDepth>) (filepath: string) =
- img.Save(filepath)
-
-
-let saveMat (mat: Matrix<'TDepth>) (filepath: string) =
- use img = new Image<Gray, 'TDeph>(mat.Size)
- mat.CopyTo(img)
- saveImg img filepath
-
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) (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) (alpha: float) =
if alpha >= 1.0
projects / master-thesis.git / blobdiff