open Types
open Utils
-
-let matchingScoreThreshold1 = 0.6
-let matchingScoreThreshold2 = 1.0
-
-type EllipseScore (matchingScore: float, e: Ellipse) =
+type private EllipseScoreFlaggedKd (matchingScore: float32, e: Ellipse) =
let mutable matchingScore = matchingScore
- member this.MatchingScore = matchingScore
member this.Ellipse = e
+
+ member this.MatchingScore = matchingScore
+
+ member this.AddMatchingScore (score: float32) =
+ matchingScore <- matchingScore + score
+
member val Processed = false with get, set
member val Removed = false with get, set
- member this.AddMatchingScore(score: float) =
- matchingScore <- matchingScore + score
-
- interface KdTree.I2DCoords with
- member this.X = e.Cx
- member this.Y = e.Cy
-
-type MatchingEllipses (radiusMin: float) =
- let ellipses = List<EllipseScore>()
-
- member this.Add (e: Ellipse) =
- ellipses.Add(EllipseScore(0.0, e))
+ interface KdTree.I2DCoords with
+ member this.X = this.Ellipse.Cx
+ member this.Y = this.Ellipse.Cy
+
+type MatchingEllipses (radius: float32) =
+ let ellipses = List<EllipseScoreFlaggedKd>()
+
+ // All ellipses with a score below this are removed.
+ let matchingScoreThreshold = 1.f
+
+ member this.Add (e: Ellipse) =
+ ellipses.Add(EllipseScoreFlaggedKd(0.f, e))
member this.Ellipses : Ellipse list =
- // 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.
- 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.minY = e.Ellipse.Cy - windowSize / 2.0
- KdTree.maxY = e.Ellipse.Cy + windowSize / 2.0 }
- for other in KdTree.Tree.search tree window do
- if not other.Processed
- then
- let areaOther = other.Ellipse.Area
- match EEOver.EEOverlapArea e.Ellipse other.Ellipse with
- | Some (commonArea, _, _) ->
- let matchingScore = 2.0 * commonArea / (areaE + areaOther)
- if matchingScore >= matchingScoreThreshold1
- then
- other.AddMatchingScore(matchingScore)
- e.AddMatchingScore(matchingScore)
- | _ -> ()
-
- // 3) Sort ellipses by their score.
- ellipses.Sort(fun e1 e2 -> e2.MatchingScore.CompareTo(e1.MatchingScore))
-
- // 4) Remove ellipses wich have a low score.
- let i = ellipses.BinarySearch(EllipseScore(matchingScoreThreshold2, Ellipse(0.0, 0.0, 0.0, 0.0, 0.0)),
- { new IComparer<EllipseScore> with
- member this.Compare(e1, e2) = e2.MatchingScore.CompareTo(e1.MatchingScore) }) |> abs
- let nbToRemove = ellipses.Count - i
- if nbToRemove > 0
+ List.ofSeq ellipses |> List.map (fun e -> e.Ellipse)
+
+ member this.PrunedEllipses : Ellipse list =
+ if ellipses.Count = 0
then
- for j in i .. nbToRemove - 1 do
- ellipses.[j].Removed <- true
- ellipses.RemoveRange(i, nbToRemove)
-
- // 5) Remove ellipses whose center is into an ellipse with a better score
- for e in ellipses do
- if not e.Removed
- then
- let window = { KdTree.minX = 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 KdTree.Tree.search tree window do
- if not other.Removed && other.MatchingScore < e.MatchingScore
- then
- if e.Ellipse.Contains other.Ellipse.Cx other.Ellipse.Cy
+ []
+ else
+ // 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.
+ let windowSize = radius / 2.f
+ for e in ellipses do
+ e.Processed <- true
+ let areaE = e.Ellipse.Area
+ let window = { KdTree.minX = e.Ellipse.Cx - windowSize / 2.f
+ KdTree.maxX = e.Ellipse.Cx + windowSize / 2.f
+ KdTree.minY = e.Ellipse.Cy - windowSize / 2.f
+ KdTree.maxY = e.Ellipse.Cy + windowSize / 2.f }
+ for other in tree.Search window do
+ if not other.Processed
+ then
+ let areaOther = other.Ellipse.Area
+ match EEOver.EEOverlapArea e.Ellipse other.Ellipse with
+ | Some (overlapArea, _, _) ->
+ let matchingScore = (2.f * overlapArea / (areaE + areaOther)) ** 20.f
+ if matchingScore <= 1.f // For approximation error.
+ then
+ other.AddMatchingScore(matchingScore)
+ e.AddMatchingScore(matchingScore)
+ | _ -> ()
+
+ // 3) Remove ellipses whose center is near the center of another ellipse with a better score.
+ for e in ellipses do
+ if e.MatchingScore < matchingScoreThreshold
+ then
+ e.Removed <- true
+ else
+ let window = { KdTree.minX = 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 && e.MatchingScore > other.MatchingScore &&
+ distanceTwoPoints (PointD(e.Ellipse.Cx, e.Ellipse.Cy)) (PointD(other.Ellipse.Cx, other.Ellipse.Cy)) < 0.3f * e.Ellipse.B
then
other.Removed <- true
- ellipses.RemoveAll(fun e -> e.Removed) |> ignore
-
- dprintfn "Number of ellipse: %A" ellipses.Count
-
- List.ofSeq ellipses |> List.map (fun e -> e.Ellipse)
-
-
-
+ ellipses
+ |> List.ofSeq
+ |> List.filter (fun e -> not e.Removed)
+ |> List.sortWith (fun e1 e2 -> e2.MatchingScore.CompareTo(e1.MatchingScore))
+ |> List.map (fun e -> e.Ellipse)
projects / master-thesis.git / blobdiff