Mercurial Hosting > traffic-intelligence

changeset 1271:b2f90cada58f

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
removed complex merging of bikes and peds, may result in more fragmentation
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 14 Jun 2024 15:56:01 -0400
parents 20a5e1292321
children 785c86013d2c 655a1646f0d5
files scripts/dltrack.py
diffstat 1 files changed, 45 insertions(+), 40 deletions(-) [+]
line wrap: on
line diff
--- a/scripts/dltrack.py	Mon Jun 10 16:44:19 2024 -0400
+++ b/scripts/dltrack.py	Fri Jun 14 15:56:01 2024 -0400
@@ -38,7 +38,7 @@
 parser.add_argument('--bike-prop', dest = 'bikeProportion', help = 'minimum proportion of time a person classified as bike or motorbike to be classified as cyclist', type = float, default = 0.2)
 parser.add_argument('--cyclist-iou', dest = 'cyclistIou', help = 'IoU threshold to associate a bike and ped bounding box', type = float, default = 0.15)
 parser.add_argument('--cyclist-match-prop', dest = 'cyclistMatchingProportion', help = 'minimum proportion of time a bike exists and is associated with a pedestrian to be merged as cyclist', type = float, default = 0.3)
-parser.add_argument('--max-temp-overal', dest = 'maxTemporalOverlap', help = 'maximum proportion of time to merge 2 bikes associated with same pedestrian', type = float, default = 0.05)
+#parser.add_argument('--max-temp-overal', dest = 'maxTemporalOverlap', help = 'maximum proportion of time to merge 2 bikes associated with same pedestrian', type = float, default = 0.05)
 
 args = parser.parse_args()
 params, videoFilename, databaseFilename, homography, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
@@ -65,8 +65,10 @@
     mask = None
 if params is not None:
     smoothingHalfWidth = params.smoothingHalfWidth
+    minObjectDuration = params.minFeatureTime
 else:
     smoothingHalfWidth = None
+    minObjectDuration = 3
 
 # TODO use mask, remove short objects, smooth
 
@@ -142,7 +144,7 @@
 # classification
 shortObjectNumbers = []
 for num, obj in objects.items():
-    if obj.length() < 3:
+    if obj.length() < minObjectDuration:
         shortObjectNumbers.append(num)
     else:
         obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed?
@@ -181,39 +183,39 @@
 # before matching, scan for pedestrians with good non-overlapping temporal match with different bikes
     for pedInd in range(costs.shape[1]):
         nMatchedBikes = (costs[:,pedInd] < -args.cyclistMatchingProportion).sum()
-        if nMatchedBikes == 0: # peds that have no bike matching: see if they have been classified as bikes sometimes
+        if nMatchedBikes == 0: # peds that have no bike matching: see if they have been classified as bikes sometimes (more than args.bikeProportion)
             userTypeStats = Counter(obj.userTypes)
             if (moving.userType2Num['cyclist'] in userTypeStats or (moving.userType2Num['motorcyclist'] in userTypeStats and moving.userType2Num['cyclist'] in userTypeStats and userTypeStats[moving.userType2Num['motorcyclist']]<=userTypeStats[moving.userType2Num['cyclist']])) and userTypeStats[moving.userType2Num['motorcyclist']]+userTypeStats[moving.userType2Num['cyclist']] > args.bikeProportion*userTypeStats.total(): # verif if not turning all motorbike into cyclists
                 obj.setUserType(moving.userType2Num['cyclist'])
-        elif nMatchedBikes > 1: # try to merge bikes first
-            twIndices = np.nonzero(costs[:,pedInd] < -args.cyclistMatchingProportion)[0]
-            # we have to compute temporal overlaps of all 2 wheels among themselves, then remove the ones with the most overlap (sum over column) one by one until there is little left
-            nTwoWheels = len(twIndices)
-            twTemporalOverlaps = np.zeros((nTwoWheels,nTwoWheels))
-            for i in range(nTwoWheels):
-                for j in range(i):
-                    twi = objects[twowheels[twIndices[i]]]
-                    twj = objects[twowheels[twIndices[j]]]
-                    twTemporalOverlaps[i,j] = len(set(twi.bboxes).intersection(set(twj.bboxes)))/max(len(twi.bboxes), len(twj.bboxes))
-                    #twTemporalOverlaps[j,i] = twTemporalOverlaps[i,j]
-            tw2merge = list(range(nTwoWheels))
-            while len(tw2merge)>0 and (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).max() >= 2:
-                i = (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).argmax()
-                del tw2merge[i]
-            twIndices = [twIndices[i] for i in tw2merge]
-            tw1 = objects[twowheels[twIndices[0]]]
-            twCost = costs[twIndices[0],:]*tw1.nBBoxes
-            nBBoxes = tw1.nBBoxes
-            for twInd in twIndices[1:]:
-                mergeObjects(tw1, objects[twowheels[twInd]])
-                twCost = twCost + costs[twInd,:]*objects[twowheels[twInd]].nBBoxes
-                nBBoxes += objects[twowheels[twInd]].nBBoxes
-            twIndicesToKeep = list(range(costs.shape[0]))
-            for twInd in twIndices[1:]:
-                twIndicesToKeep.remove(twInd)
-                del objects[twowheels[twInd]]
-            twowheels = [twowheels[i] for i in twIndicesToKeep]
-            costs = costs[twIndicesToKeep,:]
+        # elif nMatchedBikes > 1: # try to merge bikes first
+        #     twIndices = np.nonzero(costs[:,pedInd] < -args.cyclistMatchingProportion)[0]
+        #     # we have to compute temporal overlaps of all 2 wheels among themselves, then remove the ones with the most overlap (sum over column) one by one until there is little left
+        #     nTwoWheels = len(twIndices)
+        #     twTemporalOverlaps = np.zeros((nTwoWheels,nTwoWheels))
+        #     for i in range(nTwoWheels):
+        #         for j in range(i):
+        #             twi = objects[twowheels[twIndices[i]]]
+        #             twj = objects[twowheels[twIndices[j]]]
+        #             twTemporalOverlaps[i,j] = len(set(twi.bboxes).intersection(set(twj.bboxes)))/max(len(twi.bboxes), len(twj.bboxes))
+        #             #twTemporalOverlaps[j,i] = twTemporalOverlaps[i,j]
+        #     tw2merge = list(range(nTwoWheels))
+        #     while len(tw2merge)>0 and (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).max() >= 2:
+        #         i = (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).argmax()
+        #         del tw2merge[i]
+        #     twIndices = [twIndices[i] for i in tw2merge]
+        #     tw1 = objects[twowheels[twIndices[0]]]
+        #     twCost = costs[twIndices[0],:]*tw1.nBBoxes
+        #     nBBoxes = tw1.nBBoxes
+        #     for twInd in twIndices[1:]:
+        #         mergeObjects(tw1, objects[twowheels[twInd]])
+        #         twCost = twCost + costs[twInd,:]*objects[twowheels[twInd]].nBBoxes
+        #         nBBoxes += objects[twowheels[twInd]].nBBoxes
+        #     twIndicesToKeep = list(range(costs.shape[0]))
+        #     for twInd in twIndices[1:]:
+        #         twIndicesToKeep.remove(twInd)
+        #         del objects[twowheels[twInd]]
+        #     twowheels = [twowheels[i] for i in twIndicesToKeep]
+        #     costs = costs[twIndicesToKeep,:]
 
     twIndices, matchingPedIndices = linear_sum_assignment(costs)
     for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
@@ -222,8 +224,11 @@
             ped = objects[pedestrians[pedInd]]
             mergeObjects(tw, ped)
             del objects[pedestrians[pedInd]]
+            # link ped to each assigned bike, remove bike from cost (and ped is temporal match is high)
+            
             #TODO Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)
-
+    # TODO continue assigning if leftover bikes (if non temporally overlapping with existing bikes assigned to ped)
+            
 # interpolate and save image coordinates
 for num, obj in objects.items():
     for f in obj.getFeatures():
@@ -233,7 +238,7 @@
             f.positions = moving.Trajectory.fromPointList(list(f.tmpPositions.values()))
 if not args.notSavingImageCoordinates:
     storage.saveTrajectoriesToSqlite(utils.removeExtension(args.databaseFilename)+'-bb.sqlite', list(objects.values()), 'object')
-# project, smooth and save
+# project and smooth
 for num, obj in objects.items():
     features = obj.getFeatures()
     # possible to save bottom pedestrians? not consistent with other users
@@ -251,13 +256,13 @@
     #t = (moving.Trajectory.add(t1, t2)*0.5).asArray()
     projected = cvutils.imageToWorldProject(np.array(t).T, intrinsicCameraMatrix, distortionCoefficients, homography)
     featureNum = features[0].getNum()
-    obj.features=[moving.MovingObject(featureNum, obj.getTimeInterval(), moving.Trajectory(projected.tolist()))]
+    feature = moving.MovingObject(featureNum, obj.getTimeInterval(), moving.Trajectory(projected.tolist()))
+    if smoothingHalfWidth is not None: # smoothing
+        feature.smoothPositions(smoothingHalfWidth, replace = True)#f.positions = f.getPositions().filterMovingWindow(smoothingHalfWidth)
+    feature.computeVelocities()
+    obj.features=[feature]
     obj.featureNumbers = [featureNum]
-if smoothingHalfWidth is not None: # smoothing
-    for num, obj in objects.items():
-        for f in obj.getFeatures():
-            f.smoothPositions(smoothingHalfWidth, replace = True)#f.positions = f.getPositions().filterMovingWindow(smoothingHalfWidth)
-            f.computeVelocities()
+#saving
 storage.saveTrajectoriesToSqlite(args.databaseFilename, list(objects.values()), 'object')