mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-26 19:06:11 +01:00
244 lines
9.2 KiB
Python
244 lines
9.2 KiB
Python
import copy
|
|
import datetime
|
|
import itertools
|
|
import multiprocessing as mp
|
|
import random
|
|
import string
|
|
import threading
|
|
import time
|
|
from collections import defaultdict
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from scipy.spatial import distance as dist
|
|
|
|
from frigate.config import DetectConfig
|
|
from frigate.util import intersection_over_union
|
|
|
|
|
|
class ObjectTracker:
|
|
def __init__(self, config: DetectConfig):
|
|
self.tracked_objects = {}
|
|
self.disappeared = {}
|
|
self.positions = {}
|
|
self.max_disappeared = config.max_disappeared
|
|
self.detect_config = config
|
|
|
|
def register(self, index, obj):
|
|
rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
|
|
id = f"{obj['frame_time']}-{rand_id}"
|
|
obj["id"] = id
|
|
obj["start_time"] = obj["frame_time"]
|
|
obj["motionless_count"] = 0
|
|
obj["position_changes"] = 0
|
|
self.tracked_objects[id] = obj
|
|
self.disappeared[id] = 0
|
|
self.positions[id] = {
|
|
"xmins": [],
|
|
"ymins": [],
|
|
"xmaxs": [],
|
|
"ymaxs": [],
|
|
"xmin": 0,
|
|
"ymin": 0,
|
|
"xmax": self.detect_config.width,
|
|
"ymax": self.detect_config.height,
|
|
}
|
|
|
|
def deregister(self, id):
|
|
del self.tracked_objects[id]
|
|
del self.disappeared[id]
|
|
|
|
# tracks the current position of the object based on the last N bounding boxes
|
|
# returns False if the object has moved outside its previous position
|
|
def update_position(self, id, box):
|
|
position = self.positions[id]
|
|
position_box = (
|
|
position["xmin"],
|
|
position["ymin"],
|
|
position["xmax"],
|
|
position["ymax"],
|
|
)
|
|
|
|
xmin, ymin, xmax, ymax = box
|
|
|
|
iou = intersection_over_union(position_box, box)
|
|
|
|
# if the iou drops below the threshold
|
|
# assume the object has moved to a new position and reset the computed box
|
|
if iou < 0.6:
|
|
self.positions[id] = {
|
|
"xmins": [xmin],
|
|
"ymins": [ymin],
|
|
"xmaxs": [xmax],
|
|
"ymaxs": [ymax],
|
|
"xmin": xmin,
|
|
"ymin": ymin,
|
|
"xmax": xmax,
|
|
"ymax": ymax,
|
|
}
|
|
return False
|
|
|
|
# if there are less than 10 entries for the position, add the bounding box
|
|
# and recompute the position box
|
|
if len(position["xmins"]) < 10:
|
|
position["xmins"].append(xmin)
|
|
position["ymins"].append(ymin)
|
|
position["xmaxs"].append(xmax)
|
|
position["ymaxs"].append(ymax)
|
|
# by using percentiles here, we hopefully remove outliers
|
|
position["xmin"] = np.percentile(position["xmins"], 15)
|
|
position["ymin"] = np.percentile(position["ymins"], 15)
|
|
position["xmax"] = np.percentile(position["xmaxs"], 85)
|
|
position["ymax"] = np.percentile(position["ymaxs"], 85)
|
|
|
|
return True
|
|
|
|
def is_expired(self, id):
|
|
obj = self.tracked_objects[id]
|
|
# get the max frames for this label type or the default
|
|
max_frames = self.detect_config.stationary.max_frames.objects.get(
|
|
obj["label"], self.detect_config.stationary.max_frames.default
|
|
)
|
|
|
|
# if there is no max_frames for this label type, continue
|
|
if max_frames is None:
|
|
return False
|
|
|
|
# if the object has exceeded the max_frames setting, deregister
|
|
if (
|
|
obj["motionless_count"] - self.detect_config.stationary.threshold
|
|
> max_frames
|
|
):
|
|
return True
|
|
|
|
def update(self, id, new_obj):
|
|
self.disappeared[id] = 0
|
|
# update the motionless count if the object has not moved to a new position
|
|
if self.update_position(id, new_obj["box"]):
|
|
self.tracked_objects[id]["motionless_count"] += 1
|
|
if self.is_expired(id):
|
|
self.deregister(id)
|
|
return
|
|
else:
|
|
# register the first position change and then only increment if
|
|
# the object was previously stationary
|
|
if (
|
|
self.tracked_objects[id]["position_changes"] == 0
|
|
or self.tracked_objects[id]["motionless_count"]
|
|
>= self.detect_config.stationary.threshold
|
|
):
|
|
self.tracked_objects[id]["position_changes"] += 1
|
|
self.tracked_objects[id]["motionless_count"] = 0
|
|
|
|
self.tracked_objects[id].update(new_obj)
|
|
|
|
def update_frame_times(self, frame_time):
|
|
for id in list(self.tracked_objects.keys()):
|
|
self.tracked_objects[id]["frame_time"] = frame_time
|
|
self.tracked_objects[id]["motionless_count"] += 1
|
|
if self.is_expired(id):
|
|
self.deregister(id)
|
|
|
|
def match_and_update(self, frame_time, new_objects):
|
|
# group by name
|
|
new_object_groups = defaultdict(lambda: [])
|
|
for obj in new_objects:
|
|
new_object_groups[obj[0]].append(
|
|
{
|
|
"label": obj[0],
|
|
"score": obj[1],
|
|
"box": obj[2],
|
|
"area": obj[3],
|
|
"region": obj[4],
|
|
"frame_time": frame_time,
|
|
}
|
|
)
|
|
|
|
# update any tracked objects with labels that are not
|
|
# seen in the current objects and deregister if needed
|
|
for obj in list(self.tracked_objects.values()):
|
|
if not obj["label"] in new_object_groups:
|
|
if self.disappeared[obj["id"]] >= self.max_disappeared:
|
|
self.deregister(obj["id"])
|
|
else:
|
|
self.disappeared[obj["id"]] += 1
|
|
|
|
if len(new_objects) == 0:
|
|
return
|
|
|
|
# track objects for each label type
|
|
for label, group in new_object_groups.items():
|
|
current_objects = [
|
|
o for o in self.tracked_objects.values() if o["label"] == label
|
|
]
|
|
current_ids = [o["id"] for o in current_objects]
|
|
current_centroids = np.array([o["centroid"] for o in current_objects])
|
|
|
|
# compute centroids of new objects
|
|
for obj in group:
|
|
centroid_x = int((obj["box"][0] + obj["box"][2]) / 2.0)
|
|
centroid_y = int((obj["box"][1] + obj["box"][3]) / 2.0)
|
|
obj["centroid"] = (centroid_x, centroid_y)
|
|
|
|
if len(current_objects) == 0:
|
|
for index, obj in enumerate(group):
|
|
self.register(index, obj)
|
|
continue
|
|
|
|
new_centroids = np.array([o["centroid"] for o in group])
|
|
|
|
# compute the distance between each pair of tracked
|
|
# centroids and new centroids, respectively -- our
|
|
# goal will be to match each current centroid to a new
|
|
# object centroid
|
|
D = dist.cdist(current_centroids, new_centroids)
|
|
|
|
# in order to perform this matching we must (1) find the smallest
|
|
# value in each row (i.e. the distance from each current object to
|
|
# the closest new object) and then (2) sort the row indexes based
|
|
# on their minimum values so that the row with the smallest
|
|
# distance (the best match) is at the *front* of the index list
|
|
rows = D.min(axis=1).argsort()
|
|
|
|
# next, we determine which new object each existing object matched
|
|
# against, and apply the same sorting as was applied previously
|
|
cols = D.argmin(axis=1)[rows]
|
|
|
|
# many current objects may register with each new object, so only
|
|
# match the closest ones. unique returns the indices of the first
|
|
# occurrences of each value, and because the rows are sorted by
|
|
# distance, this will be index of the closest match
|
|
_, index = np.unique(cols, return_index=True)
|
|
rows = rows[index]
|
|
cols = cols[index]
|
|
|
|
# loop over the combination of the (row, column) index tuples
|
|
for row, col in zip(rows, cols):
|
|
# grab the object ID for the current row, set its new centroid,
|
|
# and reset the disappeared counter
|
|
objectID = current_ids[row]
|
|
self.update(objectID, group[col])
|
|
|
|
# compute the row and column indices we have NOT yet examined
|
|
unusedRows = set(range(D.shape[0])).difference(rows)
|
|
unusedCols = set(range(D.shape[1])).difference(cols)
|
|
|
|
# in the event that the number of object centroids is
|
|
# equal or greater than the number of input centroids
|
|
# we need to check and see if some of these objects have
|
|
# potentially disappeared
|
|
if D.shape[0] >= D.shape[1]:
|
|
for row in unusedRows:
|
|
id = current_ids[row]
|
|
|
|
if self.disappeared[id] >= self.max_disappeared:
|
|
self.deregister(id)
|
|
else:
|
|
self.disappeared[id] += 1
|
|
# if the number of input centroids is greater
|
|
# than the number of existing object centroids we need to
|
|
# register each new input centroid as a trackable object
|
|
else:
|
|
for col in unusedCols:
|
|
self.register(col, group[col])
|