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group by label before tracking objects
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@ -251,6 +251,7 @@ class ObjectTracker(threading.Thread):
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def register(self, index, obj):
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id = f"{str(obj['frame_time'])}-{index}"
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obj['id'] = id
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self.tracked_objects[id] = obj
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self.disappeared[id] = 0
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@ -281,20 +282,30 @@ class ObjectTracker(threading.Thread):
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# to update
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return
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# compute centroids
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# group by name
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new_object_groups = defaultdict(lambda: [])
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for obj in new_objects:
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new_object_groups[obj['name']].append(obj)
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# track objects for each label type
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# TODO: this is going to miss deregistering objects that are not in the new groups
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for label, group in new_object_groups.items():
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current_objects = [o for o in self.tracked_objects.values() if o['name'] == label]
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current_ids = [o['id'] for o in current_objects]
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current_centroids = np.array([o['centroid'] for o in current_objects])
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# compute centroids
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for obj in group:
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centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
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centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
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obj['centroid'] = (centroid_x, centroid_y)
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if len(self.tracked_objects) == 0:
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for index, obj in enumerate(new_objects):
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if len(current_objects) == 0:
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for index, obj in enumerate(group):
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self.register(index, obj)
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return
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new_centroids = np.array([o['centroid'] for o in new_objects])
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current_ids = list(self.tracked_objects.keys())
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current_centroids = np.array([o['centroid'] for o in self.tracked_objects.values()])
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new_centroids = np.array([o['centroid'] for o in group])
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# compute the distance between each pair of tracked
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# centroids and new centroids, respectively -- our
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