--- a/scripts/classify-objects.py	Thu Jun 22 12:02:34 2017 -0400
+++ b/scripts/classify-objects.py	Thu Jun 22 16:57:34 2017 -0400
@@ -20,7 +20,6 @@
 
 args = parser.parse_args()
 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
-
 classifierParams = storage.ClassifierParameters(params.classifierFilename)
 classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame
 
@@ -66,18 +65,14 @@
     sys.exit()
 
 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.TimeInterval.unionIntervals(intervals)
+timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
 
 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))
 
 pastObjects = []
+currentObjects = []
 if undistort: # setup undistortion
     [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
 if capture.isOpened():
@@ -92,26 +87,23 @@
             if frameNum%50 == 0:
                 print('frame number: {}'.format(frameNum))
             if undistort:
-                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
-            currentObjects = []
+                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)            
             for obj in objects:
-                inter = obj.getTimeInterval()
-                if inter.contains(frameNum):
-                    if inter.first == frameNum:
-                        obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
-                        currentObjects.append(obj)
-                    elif inter.last == frameNum:
-                        obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
-                        pastObjects.append(obj)
-                    else:
-                        obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
-                        currentObjects.append(obj)
+                if obj.getFirstInstant() == frameNum:
+                    obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
+                    currentObjects.append(obj)
+                    objects.remove(obj)
+
+            for obj in currentObjects:
+                if obj.getLastInstant() == frameNum:
+                    obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
+                    pastObjects.append(obj)
+                    currentObjects.remove(obj)
                 else:
-                    currentObjects.append(obj)
-            objects = currentObjects
+                    obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
         frameNum += 1
     
-    for obj in objects:
+    for obj in currentObjects:
         obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
         pastObjects.append(obj)
     print('Saving user types')