--- a/python/moving.py	Fri Jun 10 15:44:08 2016 -0400
+++ b/python/moving.py	Fri Jun 10 17:07:36 2016 -0400
@@ -1542,17 +1542,20 @@
         
         self.userTypes = {}
 
-    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'''
+    def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock):
+        '''Extracts the image box around the object
+        (of square size max(width, height) of the box around the features, 
+        with an added px or py for width and height (around the box))
+        computes HOG on this cropped image (with parameters rescaleSize, orientations, pixelsPerCell, cellsPerBlock)
+        and applies the SVM model on it'''
         croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = cvutils.imageBox(img, self, instant, homography, width, height, px, py, minNPixels)
         if croppedImg is not None and len(croppedImg) > 0:
-            hog = cvutils.HOG(croppedImg)#HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False)
+            hog = cvutils.HOG(croppedImg, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize=False, normalize=False)
             self.userTypes[instant] = int(self.appearanceClassifier.predict(hog))
         else:
             self.userTypes[instant] = userType2Num['unknown']
 
-    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):
+    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, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2)):
         '''Agregates SVM detections in each image and returns probability
         (proportion of instants with classification in each category)
 
@@ -1567,7 +1570,7 @@
         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], t, homography, width, height, px, py, minNPixels)
+                    self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock)
         # compute P(Speed|Class)
         if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed
             userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.}