--- a/scripts/classify-objects.py	Wed Jun 28 15:36:25 2017 -0400
+++ b/scripts/classify-objects.py	Wed Jun 28 16:46:45 2017 -0400
@@ -15,6 +15,7 @@
 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('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', action = 'store_true')
 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 (km/h)', type = float, default = 50.)
 
@@ -66,6 +67,8 @@
 
 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
+if args.startFrame0:
+    timeInterval.first = 0
 
 capture = cv2.VideoCapture(videoFilename)
 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
@@ -80,7 +83,8 @@
 if capture.isOpened():
     ret = True
     frameNum = timeInterval.first
-    capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
+    if not args.startFrame0:
+        capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
     lastFrameNum = timeInterval.last
 
     while ret and frameNum <= lastFrameNum:
@@ -91,13 +95,13 @@
             if undistort:
                 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)            
             for obj in objects:
-                if obj.getFirstInstant() == frameNum:
+                if obj.getFirstInstant() >= frameNum: # if images are skipped
                     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:
+                if obj.getLastInstant() <= frameNum:  # if images are skipped
                     obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
                     pastObjects.append(obj)
                     currentObjects.remove(obj)

--- a/scripts/extract-appearance-images.py	Wed Jun 28 15:36:25 2017 -0400
+++ b/scripts/extract-appearance-images.py	Wed Jun 28 16:46:45 2017 -0400
@@ -15,6 +15,7 @@
 parser.add_argument('--delimiter', dest = 'classificationAnnotationFilenameDelimiter', help = 'delimiter for the fields in the correct classification file', default= ' ')
 parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
+parser.add_argument('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', action = 'store_true')
 parser.add_argument('-o', dest = 'overlap', help = 'maximum intersection over union of the features nFramesStep apart to save image', type = float, default = 0.2)
 parser.add_argument('--extract-all', dest = 'extractAllObjectImages', help = 'extracts the images for all objects, well classified or not (otherwise, extracts only for the misclassified)', action = 'store_true')
 parser.add_argument('--prefix', dest = 'imagePrefix', help = 'image prefix', default = 'img')
@@ -40,6 +41,8 @@
     if obj.getNum() in annotatedObjectNumbers:
         obj.setUserType(classificationAnnotations.loc[obj.getNum(), 'road_user_type'])
 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
+if args.startFrame0:
+    timeInterval.first = 0
 
 for userType in classificationAnnotations['road_user_type'].unique():
     if not os.path.exists(args.directoryName+os.sep+moving.userTypeNames[userType]):
@@ -56,7 +59,8 @@
 if capture.isOpened():
     ret = True
     frameNum = timeInterval.first
-    capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
+    if not args.startFrame0:
+        capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
     lastFrameNum = timeInterval.last
     while ret and frameNum <= timeInterval.last:
         ret, img = capture.read()