+ tree.Search (searchRegion e)
+ // We only keep the ellipses touching 'e'.
+ |> List.choose (fun otherE ->
+ match EEOver.EEOverlapArea e otherE with
+ | Some (_, px, _) when px.Length > 2 ->
+ otherE.Removed <- true
+ None
+ | Some (area, px, py) when area > 0.0 && px.Length = 2 ->
+ Some (otherE, PointD(px.[0], py.[0]), PointD(px.[1], py.[1]))
+ | _ ->
+ None )
+ else
+ []
+
+ // We reverse the list to get the lower score ellipses first.
+ let ellipsesWithNeigbors = ellipses |> List.map (fun e -> e, neighbors e) |> List.rev
+
+ // 2) Remove ellipses with a high standard deviation (high contrast).
+
+ // CvInvoke.Normalize(img, img, 0.0, 255.0, CvEnum.NormType.MinMax) // Not needed.
+
+ let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.StandardDeviation(seq {
+ for y in 0 .. h - 1 do
+ for x in 0 .. w - 1 do
+ yield img.Data.[y, x, 0] |> float })
+
+ for e in ellipses do
+ let minX, minY, maxX, maxY = ellipseWindow e
+
+ let stdDeviation = 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 e.Contains (float x) (float y)
+ then
+ yield float img.Data.[y, x, 0] })
+
+ if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then