--- a/scripts/learn-poi.py	Wed Jun 28 17:57:06 2017 -0400
+++ b/scripts/learn-poi.py	Wed Jun 28 23:43:52 2017 -0400
@@ -8,20 +8,21 @@
 
 import storage, ml
 
-parser = argparse.ArgumentParser(description='The program learns and displays Gaussians fit to beginnings and ends of object trajectories (based on Mohamed Gomaa Mohamed 2015 PhD). TODO: save the data')
+parser = argparse.ArgumentParser(description='The program learns and displays Gaussians fit to beginnings and ends of object trajectories (based on Mohamed Gomaa Mohamed 2015 PhD).')
 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object')
+parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to display', type = int)
 parser.add_argument('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int)
 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int)
 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.)
 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
-parser.add_argument('--assign', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
+parser.add_argument('--assign', dest = 'assign', help = 'display points of interests', action = 'store_true')
 
 args = parser.parse_args()
 
-objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType)
+objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects)
 
 beginnings = []
 ends = []
@@ -45,14 +46,15 @@
     model=gmm.fit(points)
     if not model.converged_:
         print('Warning: model for '+gmmType+' points did not converge')
+    if args.display or args.assign:
+        labels = model.predict(points)
     # plot
     if args.display:
         fig = plt.figure()
         if args.worldImageFilename is not None and args.unitsPerPixel is not None:
             img = plt.imread(args.worldImageFilename)
             plt.imshow(img)
-        labels = model.predict(points)
-        labels = ml.plotGMMClusters(model, labels, points, fig, nUnitsPerPixel = args.unitsPerPixel)
+        ml.plotGMMClusters(model, labels, points, fig, nUnitsPerPixel = args.unitsPerPixel)
         plt.axis('image')
         plt.title(gmmType)
         print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
@@ -60,7 +62,7 @@
     storage.savePOIs(args.databaseFilename, model, gmmType, gmmId)
     # save assignments
     if args.assign:
-        pass
+        pass # savePOIAssignments(
     gmmId += 1
 
 if args.display: