ensembleseg : use mean, new cell efficiency

matlab/+Ensemble/+Draw/recapScore.m

+% score the cells in nest
+time=1
+iNest=71
+
+q=fetch(conn,['select * from tblEnsemble where time=' num2str(time) ' and iNest='...
+    num2str(iNest)]);
+[im]=DenoiseCache.readImages(time,CONSTANTS,segParams,[]);
+
+cx=[];
+for i=1:length(q.time)
+    nc=Ensemble.makeEnsembleCell(q(i,:));    
+    cx=[cx,nc];
+end
+
+cx=Ensemble.setCellEfficiency(cx,im,segParams);
+
+% find rp
+cx=Read.setCellsIdxPts(cx,CONSTANTS);
+for i=1:length(cx)
+    L=0*im;
+    L(cx(i).idxPts)=1;
+    rp=regionprops(L,'area','convexarea','circularity');
+    cx(i).area=rp.Area;
+    cx(i).ConvexArea=rp.ConvexArea;
+    cx(i).convDef=rp.Area/rp.ConvexArea;
+    cx(i).circularity=rp.Circularity;   
+end

matlab/+Ensemble/processEnsemble.m

@@ -155,17 +155,18 @@ for j=1:length(nestedCells{ic})
     % question of combining function -- min,max,mean,etc. relates to
     % cluster structure function. empirically chose min.
     % and on Dec 13 2019 tried max and mean as well. chose mean for now..:)
-    scoreJ(j)=max([nestedCells{ic}(cellsJ{j}).score]);
+%     scoreJ(j)=max([nestedCells{ic}(cellsJ{j}).score]);
+    scoreJ(j)=mean([nestedCells{ic}(cellsJ{j}).score]);
     nestedCells{ic}(j).nest.score=scoreJ(j);
 end
 % ensemble vote on k -- score+=#k/total#
-nx=[nestedCells{ic}.nest];
-lx=cellfun(@length,{nx.segList});
-for j=1:length(scoreJ)
-    k=length(nestedCells{ic}(j).nest.segList);
-    pk=length(find(lx==k))/length(lx);
-    scoreJ(j)=scoreJ(j)+pk;
-end
+% nx=[nestedCells{ic}.nest];
+% lx=cellfun(@length,{nx.segList});
+% for j=1:length(scoreJ)
+%     k=length(nestedCells{ic}(j).nest.segList);
+%     pk=length(find(lx==k))/length(lx);
+%     scoreJ(j)=scoreJ(j)+pk;
+% end
 % now pick the best of the combi nations
 [mm,idx]=max(scoreJ);
 newCells=nestedCells{ic}(cellsJ{idx});

matlab/+Ensemble/setCellEfficiency.m

@@ -48,7 +48,7 @@ for i=1:length(Cells)
         imBackgroundEff=1;
     else
         % high number means cell pixels are brighter than background
-        imBackgroundEff=mean(im(idxPts)-T(idxPts));
+        imBackgroundEff=mean(im(idxPts)-T(idxPts))/mean(im(bw)-T(bw));
     end
     
     % how dark on average the surface neighborhood is (1 is good)