mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
247 lines
9.3 KiB
Python
247 lines
9.3 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 intersection_over_union
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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.positions = {}
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self.max_disappeared = config.max_disappeared
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self.detect_config = config
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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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obj["motionless_count"] = 0
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obj["position_changes"] = 0
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self.tracked_objects[id] = obj
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self.disappeared[id] = 0
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self.positions[id] = {
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"xmins": [],
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"ymins": [],
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"xmaxs": [],
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"ymaxs": [],
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"xmin": 0,
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"ymin": 0,
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"xmax": self.detect_config.width,
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"ymax": self.detect_config.height,
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}
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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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# tracks the current position of the object based on the last N bounding boxes
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# returns False if the object has moved outside its previous position
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def update_position(self, id, box):
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position = self.positions[id]
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position_box = (
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position["xmin"],
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position["ymin"],
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position["xmax"],
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position["ymax"],
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)
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xmin, ymin, xmax, ymax = box
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iou = intersection_over_union(position_box, box)
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# if the iou drops below the threshold
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# assume the object has moved to a new position and reset the computed box
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if iou < 0.6:
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self.positions[id] = {
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"xmins": [xmin],
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"ymins": [ymin],
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"xmaxs": [xmax],
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"ymaxs": [ymax],
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"xmin": xmin,
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"ymin": ymin,
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"xmax": xmax,
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"ymax": ymax,
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}
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return False
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# if there are less than 10 entries for the position, add the bounding box
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# and recompute the position box
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if len(position["xmins"]) < 10:
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position["xmins"].append(xmin)
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position["ymins"].append(ymin)
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position["xmaxs"].append(xmax)
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position["ymaxs"].append(ymax)
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# by using percentiles here, we hopefully remove outliers
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position["xmin"] = np.percentile(position["xmins"], 15)
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position["ymin"] = np.percentile(position["ymins"], 15)
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position["xmax"] = np.percentile(position["xmaxs"], 85)
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position["ymax"] = np.percentile(position["ymaxs"], 85)
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return True
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def is_expired(self, id):
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obj = self.tracked_objects[id]
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# get the max frames for this label type or the default
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max_frames = self.detect_config.stationary.max_frames.objects.get(
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obj["label"], self.detect_config.stationary.max_frames.default
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)
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# if there is no max_frames for this label type, continue
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if max_frames is None:
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return False
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# if the object has exceeded the max_frames setting, deregister
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if (
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obj["motionless_count"] - self.detect_config.stationary.threshold
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> max_frames
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):
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return True
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return False
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def update(self, id, new_obj):
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self.disappeared[id] = 0
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# update the motionless count if the object has not moved to a new position
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if self.update_position(id, new_obj["box"]):
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self.tracked_objects[id]["motionless_count"] += 1
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if self.is_expired(id):
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self.deregister(id)
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return
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else:
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# register the first position change and then only increment if
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# the object was previously stationary
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if (
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self.tracked_objects[id]["position_changes"] == 0
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or self.tracked_objects[id]["motionless_count"]
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>= self.detect_config.stationary.threshold
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):
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self.tracked_objects[id]["position_changes"] += 1
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self.tracked_objects[id]["motionless_count"] = 0
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self.tracked_objects[id].update(new_obj)
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def update_frame_times(self, frame_time):
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for id in list(self.tracked_objects.keys()):
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self.tracked_objects[id]["frame_time"] = frame_time
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self.tracked_objects[id]["motionless_count"] += 1
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if self.is_expired(id):
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self.deregister(id)
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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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"ratio": obj[4],
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"region": obj[5],
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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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