--- a/trafficintelligence/moving.py	Mon Feb 05 14:14:14 2024 -0500
+++ b/trafficintelligence/moving.py	Mon Feb 05 17:06:01 2024 -0500
@@ -305,6 +305,9 @@
         projected = cvutils.homographyProject(array([[self.x], [self.y]]), homography)
         return Point(projected[0], projected[1])
 
+    def inRectangle(self, xmin, xmax, ymin, ymax):
+        return (xmin <= p.x <= xmax) and (ymin <= p.y <= ymax)
+    
     def inPolygon(self, polygon):
         '''Indicates if the point x, y is inside the polygon
         (array of Nx2 coordinates of the polygon vertices)
@@ -2051,12 +2054,12 @@
         self.setUserType(utils.argmaxDict(userTypeProbabilities))
         return userTypeProbabilities
 
-    def initClassifyUserTypeHoGSVM(self, aggregationFunc, pedBikeCarSVM, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), nInstantsIgnoredAtEnds = 0, homography = None, intrinsicCameraMatrix = None, distortionCoefficients = None):
+    def initClassifyUserTypeHoGSVM(self, aggregationFunc, pedBikeCarSVM, bikeCarSVM = None, pedBikeSpeedThreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), nInstantsIgnoredAtEnds = 0, homography = None, intrinsicCameraMatrix = None, distortionCoefficients = None):
         '''Initializes the data structures for classification
 
         TODO? compute speed for longest feature?'''
         self.aggregatedSpeed = aggregationFunc(self.getSpeeds(nInstantsIgnoredAtEnds))
-        if self.aggregatedSpeed < pedBikeSpeedTreshold or bikeCarSVM is None:
+        if self.aggregatedSpeed < pedBikeSpeedThreshold or bikeCarSVM is None:
             self.appearanceClassifier = pedBikeCarSVM
         elif self.aggregatedSpeed < bikeCarSpeedThreshold:
             self.appearanceClassifier = bikeCarSVM
@@ -2082,7 +2085,24 @@
         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, maxPercentUnknown = 0.5, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2)):
+    def classifyUserTypeYoloAtInstant(self, img, instant, width, height, px, py, minNPixels, bboxes):
+        '''Finds the user type based on where the feature fall in detected bboxes'''
+        userTypes = []
+        if self.hasFeatures():
+            for f in self.getFeatures():
+                if f.existsAtInstant(frameNum):
+                    p = f.getPositionAtInstant(frameNum)
+                    for box in bboxes:
+                        if box.id is not None:
+                            xyxy = box.xyxy[0].tolist()
+                            if p.inRectangle(xyxy[0], xyxy[1], xyxy[2], xyxy[3]):
+                                userTypes.append(moving.coco2Types[int(box.cls.item())])
+        if len(userTypes) > 0:
+            pass
+        else:
+            self.userTypes[instant] = userType2Num['unknown']
+
+    def classifyUserTypeHoGSVM(self, minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, maxPercentUnknown = 0.5):
         '''Agregates SVM detections in each image and returns probability
         (proportion of instants with classification in each category)
 
@@ -2090,14 +2110,14 @@
         With default parameters, the general (ped-bike-car) classifier will be used
 
         Considered categories are the keys of speedProbabilities'''
-        if not hasattr(self, 'aggregatedSpeed') or not hasattr(self, 'userTypes'):
-            print('Initializing the data structures for classification by HoG-SVM')
-            self.initClassifyUserTypeHoGSVM(aggregationFunc, pedBikeCarSVM, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, nInstantsIgnoredAtEnds)
+        #if not hasattr(self, 'aggregatedSpeed') or not hasattr(self, 'userTypes'):
+        #    print('Initializing the data structures for classification by HoG-SVM')
+        #    self.initClassifyUserTypeHoGSVM(aggregationFunc, pedBikeCarSVM, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, nInstantsIgnoredAtEnds)
 
-        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, rescaleSize, orientations, pixelsPerCell, cellsPerBlock)
+        # 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, 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.}