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fix double event publishes for stationary objects with attributes
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@ -330,7 +330,12 @@ class TrackedObject:
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if self.obj_data["position_changes"] != obj_data["position_changes"]:
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significant_change = True
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if self.obj_data["attributes"] != obj_data["attributes"]:
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# disappearance of a per-frame attribute can be caused by detection
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# skipping the object on a frame (stationary objects on non-interval
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# frames), so only flag when a new attribute label appears
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prev_labels = {a["label"] for a in self.obj_data["attributes"]}
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curr_labels = {a["label"] for a in obj_data["attributes"]}
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if curr_labels - prev_labels:
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significant_change = True
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# if the state changed between stationary and active
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@ -438,34 +438,32 @@ def process_frames(
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else:
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object_tracker.update_frame_times(frame_name, frame_time)
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# group the attribute detections based on what label they apply to
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attribute_detections: dict[str, list[TrackedObjectAttribute]] = {}
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for label, attribute_labels in attributes_map.items():
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attribute_detections[label] = [
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TrackedObjectAttribute(d)
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for d in consolidated_detections
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if d[0] in attribute_labels
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]
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# build detections
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detections = {}
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for obj in object_tracker.tracked_objects.values():
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detections[obj["id"]] = {**obj, "attributes": []}
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# find the best object for each attribute to be assigned to
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# assign each detected attribute to the best matching object.
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# iterate consolidated_detections once so attributes that appear under
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# multiple parent labels in attributes_map (e.g. license_plate is in
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# both "car" and "motorcycle") are not appended more than once
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all_objects: list[dict[str, Any]] = object_tracker.tracked_objects.values()
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for attributes in attribute_detections.values():
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for attribute in attributes:
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filtered_objects = filter(
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lambda o: attribute.label in attributes_map.get(o["label"], []),
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all_objects,
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)
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selected_object_id = attribute.find_best_object(filtered_objects)
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detected_attributes = [
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TrackedObjectAttribute(d)
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for d in consolidated_detections
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if d[0] in all_attributes
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]
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for attribute in detected_attributes:
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filtered_objects = filter(
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lambda o: attribute.label in attributes_map.get(o["label"], []),
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all_objects,
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)
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selected_object_id = attribute.find_best_object(filtered_objects)
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if selected_object_id is not None:
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detections[selected_object_id]["attributes"].append(
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attribute.get_tracking_data()
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)
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if selected_object_id is not None:
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detections[selected_object_id]["attributes"].append(
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attribute.get_tracking_data()
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)
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# debug object tracking
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if False:
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