--- a/scripts/learn-motion-patterns.py	Mon Jun 20 10:56:41 2016 -0400
+++ b/scripts/learn-motion-patterns.py	Tue Jun 21 17:08:07 2016 -0400
@@ -11,6 +11,7 @@
 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
 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 = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
+parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 3)
 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
@@ -34,7 +35,7 @@
     features = []
     for o in objects:
         tmp = utils.sortByLength(o.getFeatures(), reverse = True)
-        features += tmp[:min(len(tmp), 3)]
+        features += tmp[:min(len(tmp), args.maxNObjectFeatures)]
     objects = features
 
 trajectories = [o.getPositions().asArray().T for o in objects]
@@ -50,8 +51,10 @@
 
 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize) # this line can be called again without reinitializing similarities
 
+print(ml.computeClusterSizes(labels, prototypeIndices, -1))
+
 if args.display:
-    from matplotlib.pyplot import figure
+    from matplotlib.pyplot import figure, show
     figure()
     for i,o in enumerate(objects):
         if i not in prototypeIndices:
@@ -61,5 +64,6 @@
                 o.plot(utils.colors[labels[i]])
     for i in prototypeIndices:
             objects[i].plot(utils.colors[i]+'o')
+    show()
 
 # TODO store the prototypes (if features, easy, if objects, info must be stored about the type)

--- a/scripts/learn-poi.py	Mon Jun 20 10:56:41 2016 -0400
+++ b/scripts/learn-poi.py	Tue Jun 21 17:08:07 2016 -0400
@@ -16,6 +16,7 @@
 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
 
 args = parser.parse_args()
 
@@ -44,18 +45,22 @@
     if not model.converged_:
         print('Warning: model for '+gmmType+' points did not converge')
     # plot
-    fig = plt.figure()
-    if args.worldImageFilename is not None and args.unitsPerPixel is not None:
-        img = plt.imread(args.worldImageFilename)
-        plt.imshow(img)
-    labels = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel)
-    plt.axis('image')
-    plt.title(gmmType)
-    print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
+    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 = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel)
+        plt.axis('image')
+        plt.title(gmmType)
+        print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
     # save
     storage.savePOIs(args.databaseFilename, model, gmmType, gmmId)
     gmmId += 1
-                     
+
+if args.display:
+    plt.show()
+
 # fig = plt.figure()
 # if args.worldImageFilename is not None and args.pixelsPerUnit is not None:
 #     img = plt.imread(args.worldImageFilename)