--- a/python/moving.py	Wed Jun 03 16:00:46 2015 +0200
+++ b/python/moving.py	Fri Jun 05 02:25:30 2015 +0200
@@ -5,7 +5,7 @@
 from base import VideoFilenameAddable
 
 from math import sqrt, atan2, cos, sin
-from numpy import median, array, zeros, hypot, NaN, std
+from numpy import median, array, zeros, hypot, NaN, std, floor, float32
 from matplotlib.pyplot import plot
 from scipy.stats import scoreatpercentile
 from scipy.spatial.distance import cdist
@@ -78,7 +78,6 @@
         else:
             return None
 
-
 def unionIntervals(intervals):
     'returns the smallest interval containing all intervals'
     inter = intervals[0]
@@ -1129,8 +1128,9 @@
             print('Object {} has no features loaded.'.format(self.getNum()))
             return None
 
-    def getSpeeds(self):
-        return self.getVelocities().norm()
+    def getSpeeds(self, nInstantsIgnoredAtEnds = 0):
+        n = min(nInstantsIgnoredAtEnds, int(floor(self.length()/2.)))
+        return self.getVelocities().norm()[n:-n]
 
     def getSpeedIndicator(self):
         from indicators import SeverityIndicator
@@ -1346,23 +1346,20 @@
     ###
     # User Type Classification
     ###
-    def classifyUserTypeSpeedMotorized(self, threshold, aggregationFunc = median, ignoreNInstantsAtEnds = 0):
+    def classifyUserTypeSpeedMotorized(self, threshold, aggregationFunc = median, nInstantsIgnoredAtEnds = 0):
         '''Classifies slow and fast road users
         slow: non-motorized -> pedestrians
         fast: motorized -> cars
         
         aggregationFunc can be any function that can be applied to a vector of speeds, including percentile:
         aggregationFunc = lambda x: percentile(x, percentileFactor) # where percentileFactor is 85 for 85th percentile'''
-        if ignoreNInstantsAtEnds > 0:
-            speeds = self.getSpeeds()[ignoreNInstantsAtEnds:-ignoreNInstantsAtEnds]
-        else:
-            speeds = self.getSpeeds()
+        speeds = self.getSpeeds(nInstantsIgnoredAtEnds)
         if aggregationFunc(speeds) >= threshold:
             self.setUserType(userType2Num['car'])
         else:
             self.setUserType(userType2Num['pedestrian'])
 
-    def classifyUserTypeSpeed(self, speedProbabilities, aggregationFunc = median):
+    def classifyUserTypeSpeed(self, speedProbabilities, aggregationFunc = median, nInstantsIgnoredAtEnds = 0):
         '''Classifies road user per road user type
         speedProbabilities are functions return P(speed|class)
         in a dictionary indexed by user type names
@@ -1375,62 +1372,67 @@
         else:
         return x'''
         if not hasattr(self, 'aggregatedSpeed'):
-            self.aggregatedSpeed = aggregationFunc(self.getSpeeds())
+            self.aggregatedSpeed = aggregationFunc(self.getSpeeds(nInstantsIgnoredAtEnds))
         userTypeProbabilities = {}
         for userTypename in speedProbabilities:
             userTypeProbabilities[userType2Num[userTypename]] = speedProbabilities[userTypename](self.aggregatedSpeed)
         self.setUserType(utils.argmaxDict(userTypeProbabilities))
         return userTypeProbabilities
 
-    def initClassifyUserTypeHoGSVM(self, aggregationFunc = median):
+    def initClassifyUserTypeHoGSVM(self, aggregationFunc, pedBikeCarSVM, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), nInstantsIgnoredAtEnds = 0):
         '''Initializes the data structures for classification
 
-        TODO? compute speed for longest feature?
-        Skip beginning and end of feature for speed? Offer options instead of median'''
-        self.aggregatedSpeed = aggregationFunc(self.getSpeeds())
+        TODO? compute speed for longest feature?'''
+        self.aggregatedSpeed = aggregationFunc(self.getSpeeds(nInstantsIgnoredAtEnds))
+        if self.aggregatedSpeed < pedBikeSpeedTreshold or bikeCarSVM is None:
+            self.appearanceClassifier = pedBikeCarSVM
+        elif self.aggregatedSpeed < bikeCarSpeedThreshold:
+            self.appearanceClassifier = bikeCarSVM
+        else:
+            class CarClassifier:
+                def predict(self, hog):
+                    return userType2Num['car']
+            self.appearanceClassifier = CarClassifier()
+        
         self.userTypes = {}
 
-    def classifyUserTypeHoGSVMAtInstant(self, img, pedBikeCarSVM, instant, homography, width, height, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), px = 0.2, py = 0.2, pixelThreshold = 800):
+    def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px = 0.2, py = 0.2, minNPixels = 800):
         '''Extract the image box around the object and 
         applies the SVM model on it'''
-        croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = imageBox(img, self, instant, homography, width, height, px, py, pixelThreshold)
-        if len(croppedImg) > 0: # != []
-            hog = array([cvutils.HOG(croppedImg)], dtype = np.float32)
-            if self.aggregatedSpeed < pedBikeSpeedTreshold or bikeCarSVM is None:
-                self.userTypes[instant] = int(pedBikeCarSVM.predict(hog))
-            elif self.aggregatedSpeed < bikeCarSpeedTreshold:
-                self.userTypes[instant] = int(bikeCarSVM.predict(hog))
-            else:
-                self.userTypes[instant] = userType2Num['car']
+        croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = cvutils.imageBox(img, self, instant, homography, width, height, px, py, minNPixels)
+        if len(croppedImg) > 0:
+            hog = cvutils.HOG(croppedImg)#HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False)
+            self.userTypes[instant] = int(self.appearanceClassifier.predict(hog))
         else:
             self.userTypes[instant] = userType2Num['unknown']
 
-    def classifyUserTypeHoGSVM(self, images, pedBikeCarSVM, homography, width, height, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), speedProbabilities = None, aggregationFunc = median, px = 0.2, py = 0.2, pixelThreshold = 800):
+    def classifyUserTypeHoGSVM(self, pedBikeCarSVM = None, width = 0, height = 0, homography = None, images = None, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800):
         '''Agregates SVM detections in each image and returns probability
         (proportion of instants with classification in each category)
 
-        iamges is a dictionary of images indexed by instant
+        images is a dictionary of images indexed by instant
         With default parameters, the general (ped-bike-car) classifier will be used
-        TODO? consider all categories?'''
-        if not hasattr(self, aggregatedSpeed) or not hasattr(self, userTypes):
+        
+        Considered categories are the keys of speedProbabilities'''
+        if not hasattr(self, 'aggregatedSpeed') or not hasattr(self, 'userTypes'):
             print('Initilize the data structures for classification by HoG-SVM')
-            self.initClassifyUserTypeHoGSVM(aggregationFunc)
+            self.initClassifyUserTypeHoGSVM(aggregationFunc, pedBikeCarSVM, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, nInstantsIgnoredAtEnds)
 
-        if len(self.userTypes) != self.length(): # if classification has not been done previously
+        if len(self.userTypes) != self.length() and images is not None: # if classification has not been done previously
             for t in self.getTimeInterval():
                 if t not in self.userTypes:
-                    self.classifyUserTypeHoGSVMAtInstant(images[t], pedBikeCarSVM, t, homography, width, height, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, px, py, pixelThreshold)
+                    self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels)
         # compute P(Speed|Class)
-        if speedProbabilities is None: # equiprobable information from speed
+        if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed
             userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.}
         else:
             userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities}
         # result is P(Class|Appearance) x P(Speed|Class)
-        nInstantsUserType = {userType2Num[userTypename]: 0 for userTypename in userTypeProbabilities}# number of instants the object is classified as userTypename
+        nInstantsUserType = {userTypeNum: 0 for userTypeNum in userTypeProbabilities}# number of instants the object is classified as userTypename
         for t in self.userTypes:
-            nInstantsUserType[self.userTypes[t]] += 1
-        for userTypename in userTypeProbabilities:
-            userTypeProbabilities[userTypename] *= nInstantsUserType[userTypename]
+            nInstantsUserType[self.userTypes[t]] = nInstantsUserType.get(self.userTypes[t], 0) + 1
+        for userTypeNum in userTypeProbabilities:
+            userTypeProbabilities[userTypeNum] *= nInstantsUserType[userTypeNum]
         # class is the user type that maximizes usertype probabilities
         self.setUserType(utils.argmaxDict(userTypeProbabilities))