mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-23 19:11:14 +01:00
80f8256422
Use `np.unique` to determine the correct set of row/col pairs to iterate over when doing the object matching without needing to track which rows or columns have already been seen. Add to some of the accompanying documentation to clarify this algorithm. Also fix what looks to be an erroneous early return, and change this to a continue.
143 lines
5.5 KiB
Python
143 lines
5.5 KiB
Python
import copy
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import datetime
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import itertools
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import multiprocessing as mp
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import random
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import string
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import threading
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import time
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from collections import defaultdict
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import cv2
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import numpy as np
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from scipy.spatial import distance as dist
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from frigate.config import DetectConfig
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from frigate.util import draw_box_with_label
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class ObjectTracker:
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def __init__(self, config: DetectConfig):
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self.tracked_objects = {}
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self.disappeared = {}
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self.max_disappeared = config.max_disappeared
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def register(self, index, obj):
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rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
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id = f"{obj['frame_time']}-{rand_id}"
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obj["id"] = id
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obj["start_time"] = obj["frame_time"]
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self.tracked_objects[id] = obj
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self.disappeared[id] = 0
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def deregister(self, id):
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del self.tracked_objects[id]
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del self.disappeared[id]
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def update(self, id, new_obj):
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self.disappeared[id] = 0
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self.tracked_objects[id].update(new_obj)
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def match_and_update(self, frame_time, new_objects):
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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[0]].append(
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{
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"label": obj[0],
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"score": obj[1],
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"box": obj[2],
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"area": obj[3],
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"region": obj[4],
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"frame_time": frame_time,
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}
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)
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# update any tracked objects with labels that are not
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# seen in the current objects and deregister if needed
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for obj in list(self.tracked_objects.values()):
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if not obj["label"] in new_object_groups:
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if self.disappeared[obj["id"]] >= self.max_disappeared:
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self.deregister(obj["id"])
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else:
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self.disappeared[obj["id"]] += 1
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if len(new_objects) == 0:
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return
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# track objects for each label type
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for label, group in new_object_groups.items():
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current_objects = [
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o for o in self.tracked_objects.values() if o["label"] == label
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]
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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 of new objects
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for obj in group:
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centroid_x = int((obj["box"][0] + obj["box"][2]) / 2.0)
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centroid_y = int((obj["box"][1] + obj["box"][3]) / 2.0)
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obj["centroid"] = (centroid_x, centroid_y)
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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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continue
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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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# goal will be to match each current centroid to a new
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# object centroid
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D = dist.cdist(current_centroids, new_centroids)
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# in order to perform this matching we must (1) find the smallest
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# value in each row (i.e. the distance from each current object to
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# the closest new object) and then (2) sort the row indexes based
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# on their minimum values so that the row with the smallest
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# distance (the best match) is at the *front* of the index list
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rows = D.min(axis=1).argsort()
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# next, we determine which new object each existing object matched
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# against, and apply the same sorting as was applied previously
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cols = D.argmin(axis=1)[rows]
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# many current objects may register with each new object, so only
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# match the closest ones. unique returns the indices of the first
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# occurrences of each value, and because the rows are sorted by
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# distance, this will be index of the closest match
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_, index = np.unique(cols, return_index=True)
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rows = rows[index]
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cols = cols[index]
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# loop over the combination of the (row, column) index tuples
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for row, col in zip(rows, cols):
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# grab the object ID for the current row, set its new centroid,
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# and reset the disappeared counter
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objectID = current_ids[row]
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self.update(objectID, group[col])
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# compute the row and column indices we have NOT yet examined
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unusedRows = set(range(D.shape[0])).difference(rows)
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unusedCols = set(range(D.shape[1])).difference(cols)
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# in the event that the number of object centroids is
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# equal or greater than the number of input centroids
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# we need to check and see if some of these objects have
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# potentially disappeared
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if D.shape[0] >= D.shape[1]:
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for row in unusedRows:
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id = current_ids[row]
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if self.disappeared[id] >= self.max_disappeared:
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self.deregister(id)
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else:
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self.disappeared[id] += 1
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# if the number of input centroids is greater
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# than the number of existing object centroids we need to
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# register each new input centroid as a trackable object
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else:
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for col in unusedCols:
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self.register(col, group[col])
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