--- a/python/utils.py	Fri Jun 05 17:13:28 2015 +0200
+++ b/python/utils.py	Tue Jun 09 17:29:03 2015 +0200
@@ -624,7 +624,11 @@
 
 class LCSS(object):
     '''Class that keeps the LCSS parameters
-    and puts together the various computations'''
+    and puts together the various computations
+
+    the methods with names starting with _ are not to be shadowed
+    in child classes, who will shadow the other methods, 
+    ie compute and computeXX methods'''
     def __init__(self, similarityFunc, delta = float('inf'), aligned = False, lengthFunc = min):
         self.similarityFunc = similarityFunc
         self.aligned = aligned

--- a/scripts/classify-objects.py	Fri Jun 05 17:13:28 2015 +0200
+++ b/scripts/classify-objects.py	Tue Jun 09 17:29:03 2015 +0200
@@ -12,19 +12,24 @@
 
 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
+parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
+parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true')
 parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display', type = float, default = 50.)
-#parser.add_argument('-u', dest = 'undistort', help = 'undistort the video (because features have been extracted that way)', action = 'store_true')
-#parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int)
-#parser.add_argument('--last-frame', dest = 'lastFrameNum', help = 'number of last frame number to save (for image saving, no display is made)', type = int)
-# parser.add_argument('--min-speed-equiprobable', dest = 'minSpeedEquiprobable', help = 'speed value below which all classes are equiprobable (distributions give odd values there) (km/h)', type = float, default = 3.33)
-# parser.add_argument('--speed-aggregation', dest = 'speedAggregationMethod', help = 'method to aggregate road user speed', type = str, choices = ['median', 'mean', 'quantile'], default = 'median')
-# parser.add_argument('--speed-aggregation-quantile', dest = 'speedAggregationQuantile', help = 'quantile for the speed aggregation, if quantile is chosen', type = int, default = 50)
 
 args = parser.parse_args()
 params = storage.ProcessParameters(args.configFilename)
 
+if args.videoFilename is not None:
+    videoFilename = args.videoFilename
+else:
+    videoFilename = params.videoFilename
+if args.databaseFilename is not None:
+    databaseFilename = args.databaseFilename
+else:
+    databaseFilename = params.databaseFilename
+
 params.convertToFrames(3.6)
 if params.homography is not None:
     invHomography = np.linalg.inv(params.homography)
@@ -61,15 +66,15 @@
     plt.show()
     sys.exit()
 
-objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object', args.nObjects, withFeatures = True)
-#features = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'feature')
+objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
+#features = storage.loadTrajectoriesFromSqlite(databaseFilename, 'feature')
 intervals = []
 for obj in objects:
     #obj.setFeatures(features)
     intervals.append(obj.getTimeInterval())
 timeInterval = moving.unionIntervals(intervals)
 
-capture = cv2.VideoCapture(params.videoFilename)
+capture = cv2.VideoCapture(videoFilename)
 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
 
@@ -108,4 +113,4 @@
         obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
         pastObjects.append(obj)
     print('Saving user types')
-    storage.setRoadUserTypes(params.databaseFilename, pastObjects)
+    storage.setRoadUserTypes(databaseFilename, pastObjects)