+ tree.Search (searchRegion e)
+ // We only keep the ellipses touching 'e'.
+ |> List.choose (fun otherE ->
+ if e <> otherE
+ then
+ match EEOver.EEOverlapArea e otherE with
+ | Some (_, px, _) when px.Length > 2 ->
+ otherE.Removed <- true
+ None
+ | Some (area, px, py) when area > 0.f && px.Length = 2 ->
+ Some (otherE, PointD(px.[0], py.[0]), PointD(px.[1], py.[1]))
+ | _ ->
+ None
+ else
+ 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 touching the edges.
+ for e in ellipses do
+ if e.isOutside w_f h_f then e.Removed <- true
+
+ // 3) Remove ellipses with a high standard deviation (high contrast).
+ let imgData = img.Data
+ let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(seq {
+ for y in 0 .. h - 1 do
+ for x in 0 .. w - 1 do
+ yield float imgData.[y, x, 0] })
+
+ for e in ellipses do
+ if not e.Removed
+ then
+ let shrinkedE = e.Scale 0.9f
+ let minX, minY, maxX, maxY = ellipseWindow shrinkedE
+
+ 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 shrinkedE.Contains (float32 x) (float32 y)
+ then
+ yield float imgData.[y, x, 0] })
+
+ if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then
+ e.Removed <- true
+
+(*
+ let imgData = img.Data
+ let stdDeviations = [
+ for e in ellipses do
+ if not e.Removed
+ then
+ let shrinkedE = e.Scale 0.9f
+ let minX, minY, maxX, maxY = ellipseWindow shrinkedE
+
+ let stdDeviation = float32 <| 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 shrinkedE.Contains (float32 x) (float32 y)
+ then
+ yield float imgData.[y, x, 0] })
+
+ e.StdDeviation <- stdDeviation
+ yield stdDeviation ]
+
+ // We use Otsu and eliminate some cells only if the curve may be bimodal.
+ // See https://en.wikipedia.org/wiki/Multimodal_distribution#Bimodality_coefficient
+ let skewness, kurtosis = MathNet.Numerics.Statistics.Statistics.PopulationSkewnessKurtosis (stdDeviations |> List.map float)
+ let n = float stdDeviations.Length
+ let bimodalityCoefficient = (skewness ** 2. + 1.) / (kurtosis + 3. * (n - 1.) ** 2. / ((n - 2.) * (n - 3.)))
+
+ if bimodalityCoefficient > 5. / 9.
+ then
+ let hist = ImgTools.histogram stdDeviations 200
+ let thresh, _, _ = ImgTools.otsu hist
+ for e in ellipses do
+ if not e.Removed && e.StdDeviation > thresh
+ then e.Removed <- true
+*)