((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
-
type ExtremumType =
| Maxima = 1
| Minima = 2
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
data2 <- tmp
-// FIXME: replace by a queue or stack.
-let pop (l: List<'a>) : 'a =
- let n = l.[l.Count - 1]
- l.RemoveAt(l.Count - 1)
- n
-
// Remove all 8-connected pixels with an area equal or greater than 'areaSize'.
// Modify 'mat' in place.
let removeArea (mat: Matrix<byte>) (areaSize: int) =
for j in 0..w-1 do
if data'.[i, j] = 1uy
then
- let neighborhood = List<(int*int)>()
- let neighborsToCheck = List<(int*int)>()
- neighborsToCheck.Add((i, j))
+ let neighborhood = List<Point>()
+ let neighborsToCheck = Stack<Point>()
+ neighborsToCheck.Push(Point(j, i))
data'.[i, j] <- 0uy
while neighborsToCheck.Count > 0 do
- let (ci, cj) = pop neighborsToCheck
- neighborhood.Add((ci, cj))
+ let n = neighborsToCheck.Pop()
+ neighborhood.Add(n)
for (ni, nj) in neighbors do
- let pi = ci + ni
- let pj = cj + nj
+ let pi = n.Y + ni
+ let pj = n.X + nj
if pi >= 0 && pi < h && pj >= 0 && pj < w && data'.[pi, pj] = 1uy
then
- neighborsToCheck.Add((pi, pj))
+ neighborsToCheck.Push(Point(pj, pi))
data'.[pi, pj] <- 0uy
if neighborhood.Count <= areaSize
then
- for (ni, nj) in neighborhood do
- data.[ni, nj] <- 0uy
+ for n in neighborhood do
+ data.[n.Y, n.X] <- 0uy
let connectedComponents (img: Image<Gray, byte>) (startPoints: List<Point>) : List<Point> =
let w = img.Width
let h = img.Height
let pointChecked = Points()
- let pointToCheck =
List<Point>(startPoints);
+ let pointToCheck =
Stack<Point>(startPoints);
let data = img.Data
while pointToCheck.Count > 0 do
- let next = pop pointToCheck
+ let next = pointToCheck.Pop()
pointChecked.Add(next) |> ignore
for ny in -1 .. 1 do
for nx in -1 .. 1 do
let p = Point(next.X + nx, next.Y + ny)
if p.X >= 0 && p.X < w && p.Y >= 0 && p.Y < h && data.[p.Y, p.X, 0] > 0uy && not (pointChecked.Contains p)
then
- pointToCheck.Add(p)
+ pointToCheck.Push(p)
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