Mercurial Hosting > traffic-intelligence

changeset 1247:439207b6c146

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bug corrected
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 12 Feb 2024 16:47:33 -0500
parents 2397de73770d
children c4c50678c856
files scripts/dltrack.py
diffstat 1 files changed, 46 insertions(+), 44 deletions(-) [+]
line wrap: on
line diff
--- a/scripts/dltrack.py	Fri Feb 09 17:47:33 2024 -0500
+++ b/scripts/dltrack.py	Mon Feb 12 16:47:33 2024 -0500
@@ -58,7 +58,8 @@
 else:
     lastFrameNum = inf
 
-# TODO use mask
+# TODO use mask, remove short objects, smooth
+
 # TODO add option to refine position with mask for vehicles, to save different positions
 # TODO work with optical flow (farneback or RAFT) https://pytorch.org/vision/main/models/raft.html
 
@@ -153,51 +154,52 @@
 
 costs = -np.array(costs)
 
+if costs.size > 0:
 # 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
-        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,:]
+    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
+            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,:]
 
-twIndices, matchingPedIndices = linear_sum_assignment(costs)
-for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
-    if -costs[twInd, pedInd] >= args.cyclistMatchingProportion:
-        tw = objects[twowheels[twInd]]
-        ped = objects[pedestrians[pedInd]]
-        mergeObjects(tw, ped)
-        del objects[pedestrians[pedInd]]
-        #TODO Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)
+    twIndices, matchingPedIndices = linear_sum_assignment(costs)
+    for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
+        if -costs[twInd, pedInd] >= args.cyclistMatchingProportion:
+            tw = objects[twowheels[twInd]]
+            ped = objects[pedestrians[pedInd]]
+            mergeObjects(tw, ped)
+            del objects[pedestrians[pedInd]]
+            #TODO Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)
 
 # interpolate and save image coordinates
 for num, obj in objects.items():