--- a/python/cvutils.py	Tue Jul 11 17:56:23 2017 -0400
+++ b/python/cvutils.py	Wed Jul 12 01:24:31 2017 -0400
@@ -257,16 +257,16 @@
             print('Video capture for {} failed'.format(videoFilename))
             return None
         
-    def imageBoxSize(obj, frameNum, homography, width, height, px = 0.2, py = 0.2):
+    def imageBoxSize(obj, frameNum, width, height, px = 0.2, py = 0.2):
         'Computes the bounding box size of object at frameNum'
         x = []
         y = []
         if obj.hasFeatures():
             for f in obj.getFeatures():
                 if f.existsAtInstant(frameNum):
-                    projectedPosition = f.getPositionAtInstant(frameNum).project(homography)
-                    x.append(projectedPosition.x)
-                    y.append(projectedPosition.y)
+                    projectedPosition = f.projectPositions[:, frameNum-f.getFirstInstant()]
+                    x.append(projectedPosition[0])
+                    y.append(projectedPosition[1])
         xmin = min(x)
         xmax = max(x)
         ymin = min(y)
@@ -280,9 +280,9 @@
         xCropMax = int(min(width - 1, .5 * (xmin + xmax + a)))
         return yCropMin, yCropMax, xCropMin, xCropMax
         
-    def imageBox(img, obj, frameNum, homography, width, height, px = 0.2, py = 0.2, minNPixels = 800):
+    def imageBox(img, obj, frameNum, width, height, px = 0.2, py = 0.2, minNPixels = 800):
         'Computes the bounding box of object at frameNum'
-        yCropMin, yCropMax, xCropMin, xCropMax = imageBoxSize(obj, frameNum, homography, width, height, px, py)
+        yCropMin, yCropMax, xCropMin, xCropMax = imageBoxSize(obj, frameNum, width, height, px, py)
         if yCropMax != yCropMin and xCropMax != xCropMin and (yCropMax - yCropMin) * (xCropMax - xCropMin) > minNPixels:
             return img[yCropMin : yCropMax, xCropMin : xCropMax]
         else:
@@ -518,19 +518,23 @@
             out.write('{0} '.format(cvmat[i,j]))
         out.write('\n')
 
-def projectArray(homography, points):
+def projectArray(homography, points, intrinsicCameraMatrix = None, distortionCoefficients = None):
     '''Returns the coordinates of the projected points through homography
     (format: array 2xN points)'''
-    if points.shape[0] != 2:
+    if points.shape[0] not in [2, 3]:
         raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1]))
 
-    if (homography is not None) and homography.size>0:
-        #alternatively, on could use cv2.convertpointstohomogeneous and other conversion to/from homogeneous coordinates
-        augmentedPoints = append(points,[[1]*points.shape[1]], 0)
+    augmentedPoints = append(points,[[1]*points.shape[1]], 0)
+    if homography is not None and homography.size>0:
         prod = dot(homography, augmentedPoints)
-        return prod[0:2]/prod[2]
+        projected = prod/prod[2]
+        projected[3,:] = 0
     else:
-        return points
+        projected = augmentedPoints
+
+    if intrinsicCameraMatrix is not None and distortionCoefficients is not None:
+        projected = cv2.projectPoints(projected, None, None, intrinsicCameraMatrix, distortionCoefficients)
+    return projected
 
 def project(homography, p):
     '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1]

--- a/python/moving.py	Tue Jul 11 17:56:23 2017 -0400
+++ b/python/moving.py	Wed Jul 12 01:24:31 2017 -0400
@@ -551,7 +551,7 @@
     speedOrientation is the other encoding of velocity, (speed, orientation)
     speedOrientation and control are NormAngle'''
     predictedSpeedTheta = speedOrientation+control
-    if maxSpeed:
+    if maxSpeed is not None:
          predictedSpeedTheta.norm = min(predictedSpeedTheta.norm, maxSpeed)
     predictedPosition = position+predictedSpeedTheta.getPoint()
     return predictedPosition, predictedSpeedTheta
@@ -1561,7 +1561,7 @@
         self.setUserType(utils.argmaxDict(userTypeProbabilities))
         return userTypeProbabilities
 
-    def initClassifyUserTypeHoGSVM(self, aggregationFunc, pedBikeCarSVM, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), nInstantsIgnoredAtEnds = 0):
+    def initClassifyUserTypeHoGSVM(self, aggregationFunc, pedBikeCarSVM, bikeCarSVM = None, pedBikeSpeedTreshold = 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?'''
@@ -1575,7 +1575,10 @@
                 def predict(self, hog):
                     return userType2Num['car']
             self.appearanceClassifier = CarClassifier()
-        
+        # project feature positions
+        if self.hasFeatures():
+            for f in self.getFeatures():
+                f.projectedPositions = cvutils.projectArray(homography, f.getPositions().asArray(), intrinsicCameraMatrix, distortionCoefficients)
         self.userTypes = {}
 
     def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm):

--- a/python/prediction.py	Tue Jul 11 17:56:23 2017 -0400
+++ b/python/prediction.py	Wed Jul 12 01:24:31 2017 -0400
@@ -51,7 +51,7 @@
     def getControl(self):
         return self.control
         
-def findNearestParams(initialPosition,prototypeTrajectory):
+def findNearestParams(initialPosition, prototypeTrajectory):
     ''' nearest parameters are the index of minDistance and the orientation  '''
     distances=[]
     for position in prototypeTrajectory.positions:
@@ -71,7 +71,7 @@
     (applying a constant ratio equal 
     to the ratio of the user instantaneous speed and the trajectory closest speed)'''
 
-    def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.):
+    def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = False, probability = 1.):
         self.prototypeTrajectory = prototypeTrajectory
         self.constantSpeed = constantSpeed
         self.probability = probability

--- a/scripts/classify-objects.py	Tue Jul 11 17:56:23 2017 -0400
+++ b/scripts/classify-objects.py	Wed Jul 12 01:24:31 2017 -0400
@@ -75,8 +75,12 @@
 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
 
 if undistort: # setup undistortion
-    [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
-    height, width = map1.shape
+#     [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
+#     height, width = map1.shape
+    newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication)))
+    newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True)
+else:
+    newCameraMatrix = None
 
 pastObjects = []
 currentObjects = []
@@ -92,11 +96,11 @@
         if ret:
             if frameNum%50 == 0:
                 print('frame number: {}'.format(frameNum))
-            if undistort:
-                img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)            
+            #if undistort:
+            #    img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)            
             for obj in objects:
                 if obj.getFirstInstant() <= frameNum: # if images are skipped
-                    obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds)
+                    obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, newCameraMatrix, distortionCoefficients)
                     currentObjects.append(obj)
                     objects.remove(obj)
 
@@ -106,7 +110,7 @@
                     pastObjects.append(obj)
                     currentObjects.remove(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)
+                    obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
         frameNum += 1
     
     for obj in currentObjects:

--- a/scripts/safety-analysis.py	Tue Jul 11 17:56:23 2017 -0400
+++ b/scripts/safety-analysis.py	Wed Jul 12 01:24:31 2017 -0400
@@ -71,7 +71,7 @@
     for inter in interactions:
         inter.computePET(params.collisionDistance)
     
-storage.saveIndicators(params.databaseFilename, interactions)
+storage.saveIndicatorsToSqlite(params.databaseFilename, interactions)
 
 if args.displayCollisionPoints:
     plt.figure()