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fix multiple object type tracking

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This commit is contained in:
Blake Blackshear 2020-01-11 13:22:56 -06:00
parent d87f4407a0
commit 2aada930e3
3 changed files with 61 additions and 82 deletions
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80
frigate/objects.py
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@ -4,7 +4,9 @@ import threading
import cv2
import prctl
import itertools
import copy
import numpy as np
import multiprocessing as mp
from collections import defaultdict
from scipy.spatial import distance as dist
from frigate.util import draw_box_with_label, LABELS, compute_intersection_rectangle, compute_intersection_over_union, calculate_region
@ -25,6 +27,14 @@ class ObjectCleaner(threading.Thread):
if not frame_time in self.camera.frame_cache:
del self.camera.detected_objects[frame_time]
with self.camera.object_tracker.tracked_objects_lock:
now = datetime.datetime.now().timestamp()
for id, obj in list(self.camera.object_tracker.tracked_objects.items()):
# if the object is more than 10 seconds old
# and not in the most recent frame, deregister
if (now - obj['frame_time']) > 10 and self.camera.object_tracker.most_recent_frame_time > obj['frame_time']:
self.camera.object_tracker.deregister(id)
class DetectedObjectsProcessor(threading.Thread):
def __init__(self, camera):
threading.Thread.__init__(self)
@ -222,15 +232,17 @@ class ObjectTracker(threading.Thread):
threading.Thread.__init__(self)
self.camera = camera
self.tracked_objects = {}
self.disappeared = {}
self.max_disappeared = max_disappeared
self.tracked_objects_lock = mp.Lock()
self.most_recent_frame_time = None
def run(self):
prctl.set_name(self.__class__.__name__)
while True:
frame_time = self.camera.refined_frame_queue.get()
with self.tracked_objects_lock:
self.match_and_update(self.camera.detected_objects[frame_time])
self.camera.frame_output_queue.put(frame_time)
self.most_recent_frame_time = frame_time
self.camera.frame_output_queue.put((frame_time, copy.deepcopy(self.tracked_objects)))
if len(self.tracked_objects) > 0:
with self.camera.objects_tracked:
self.camera.objects_tracked.notify_all()
@ -241,10 +253,8 @@ class ObjectTracker(threading.Thread):
obj['top_score'] = obj['score']
self.add_history(obj)
self.tracked_objects[id] = obj
self.disappeared[id] = 0
def deregister(self, id):
del self.disappeared[id]
del self.tracked_objects[id]
def update(self, id, new_obj):
@ -267,22 +277,7 @@ class ObjectTracker(threading.Thread):
obj['history'] = [entry]
def match_and_update(self, new_objects):
# check to see if the list of input bounding box rectangles
# is empty
if len(new_objects) == 0:
# loop over any existing tracked objects and mark them
# as disappeared
for objectID in list(self.disappeared.keys()):
self.disappeared[objectID] += 1
# if we have reached a maximum number of consecutive
# frames where a given object has been marked as
# missing, deregister it
if self.disappeared[objectID] > self.max_disappeared:
self.deregister(objectID)
# return early as there are no centroids or tracking info
# to update
return
# group by name
@ -291,13 +286,12 @@ class ObjectTracker(threading.Thread):
new_object_groups[obj['name']].append(obj)
# track objects for each label type
# TODO: this is going to miss deregistering objects that are not in the new groups
for label, group in new_object_groups.items():
current_objects = [o for o in self.tracked_objects.values() if o['name'] == label]
current_ids = [o['id'] for o in current_objects]
current_centroids = np.array([o['centroid'] for o in current_objects])
# compute centroids
# compute centroids of new objects
for obj in group:
centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
@ -339,7 +333,6 @@ class ObjectTracker(threading.Thread):
for (row, col) in zip(rows, cols):
# if we have already examined either the row or
# column value before, ignore it
# val
if row in usedRows or col in usedCols:
continue
@ -347,43 +340,22 @@ class ObjectTracker(threading.Thread):
# set its new centroid, and reset the disappeared
# counter
objectID = current_ids[row]
self.update(objectID, new_objects[col])
self.disappeared[objectID] = 0
self.update(objectID, group[col])
# indicate that we have examined each of the row and
# column indexes, respectively
usedRows.add(row)
usedCols.add(col)
# compute both the row and column index we have NOT yet
# examined
unusedRows = set(range(0, D.shape[0])).difference(usedRows)
# compute the column index we have NOT yet examined
unusedCols = set(range(0, D.shape[1])).difference(usedCols)
# 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]:
# loop over the unused row indexes
for row in unusedRows:
# grab the object ID for the corresponding row
# index and increment the disappeared counter
objectID = current_ids[row]
self.disappeared[objectID] += 1
# check to see if the number of consecutive
# frames the object has been marked "disappeared"
# for warrants deregistering the object
if self.disappeared[objectID] > self.max_disappeared:
self.deregister(objectID)
# otherwise, if the number of input centroids is greater
# 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:
# if D.shape[0] < D.shape[1]:
for col in unusedCols:
self.register(col, new_objects[col])
self.register(col, group[col])
# Maintains the frame and object with the highest score
class BestFrames(threading.Thread):
@ -400,18 +372,18 @@ class BestFrames(threading.Thread):
with self.camera.objects_tracked:
self.camera.objects_tracked.wait()
# make a copy of detected objects
detected_objects = list(self.camera.object_tracker.tracked_objects.values()).copy()
# make a copy of tracked objects
tracked_objects = list(self.camera.object_tracker.tracked_objects.values())
for obj in detected_objects:
for obj in tracked_objects:
if obj['name'] in self.best_objects:
now = datetime.datetime.now().timestamp()
# if the object is a higher score than the current best score
# or the current object is more than 1 minute old, use the new object
if obj['score'] > self.best_objects[obj['name']]['score'] or (now - self.best_objects[obj['name']]['frame_time']) > 60:
self.best_objects[obj['name']] = obj
self.best_objects[obj['name']] = copy.deepcopy(obj)
else:
self.best_objects[obj['name']] = obj
self.best_objects[obj['name']] = copy.deepcopy(obj)
for name, obj in self.best_objects.items():
if obj['frame_time'] in self.camera.frame_cache:

5
frigate/util.py
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@ -75,12 +75,13 @@ def compute_intersection_over_union(box_a, box_b):
def tonumpyarray(mp_arr):
return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info):
def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None):
if color is None:
color = COLOR_MAP[label]
display_text = "{}: {}".format(label, info)
cv2.rectangle(frame, (x_min, y_min),
(x_max, y_max),
color, 2)
color, thickness)
font_scale = 0.5
font = cv2.FONT_HERSHEY_SIMPLEX
# get the width and height of the text box

50
frigate/video.py
View File

@ -9,6 +9,7 @@ import multiprocessing as mp
import subprocess as sp
import numpy as np
import prctl
import copy
import itertools
from collections import defaultdict
from frigate.util import tonumpyarray, LABELS, draw_box_with_label, calculate_region, EventsPerSecond
@ -64,7 +65,7 @@ class CameraWatchdog(threading.Thread):
# wait a bit before checking
time.sleep(10)
if (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 300:
if self.camera.frame_time.value != 0.0 and (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 300:
print("last frame is more than 5 minutes old, restarting camera capture...")
self.camera.start_or_restart_capture()
time.sleep(5)
@ -116,12 +117,12 @@ class VideoWriter(threading.Thread):
def run(self):
prctl.set_name(self.__class__.__name__)
while True:
frame_time = self.camera.frame_output_queue.get()
if len(self.camera.object_tracker.tracked_objects) == 0:
continue
f = open(f"/debug/{self.camera.name}-{str(frame_time)}.jpg", 'wb')
f.write(self.camera.frame_with_objects(frame_time))
f.close()
(frame_time, tracked_objects) = self.camera.frame_output_queue.get()
# if len(self.camera.object_tracker.tracked_objects) == 0:
# continue
# f = open(f"/debug/output/{self.camera.name}-{str(format(frame_time, '.8f'))}.jpg", 'wb')
# f.write(self.camera.frame_with_objects(frame_time, tracked_objects))
# f.close()
class Camera:
def __init__(self, name, ffmpeg_config, global_objects_config, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
@ -195,6 +196,14 @@ class Camera:
for obj in objects_with_config:
self.object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
# start a thread to track objects
self.object_tracker = ObjectTracker(self, 10)
self.object_tracker.start()
# start a thread to write tracked frames to disk
self.video_writer = VideoWriter(self)
self.video_writer.start()
# start a thread to queue resize requests for regions
self.region_requester = RegionRequester(self)
self.region_requester.start()
@ -222,14 +231,6 @@ class Camera:
self.region_refiner.start()
self.dynamic_region_fps.start()
# start a thread to track objects
self.object_tracker = ObjectTracker(self, 10)
self.object_tracker.start()
# start a thread to write tracked frames to disk
self.video_writer = VideoWriter(self)
self.video_writer.start()
# start a thread to publish object scores
mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self)
mqtt_publisher.start()
@ -312,8 +313,9 @@ class Camera:
'dynamic_regions_per_sec': self.dynamic_region_fps.eps()
}
def frame_with_objects(self, frame_time):
def frame_with_objects(self, frame_time, tracked_objects=None):
frame = self.frame_cache[frame_time].copy()
detected_objects = self.detected_objects[frame_time].copy()
for region in self.regions:
color = (255,255,255)
@ -322,12 +324,16 @@ class Camera:
color, 2)
# draw the bounding boxes on the screen
for id, obj in list(self.object_tracker.tracked_objects.items()):
# for obj in detected_objects[frame_time]:
cv2.rectangle(frame, (obj['region']['xmin'], obj['region']['ymin']),
(obj['region']['xmax'], obj['region']['ymax']),
(0,255,0), 1)
draw_box_with_label(frame, obj['box']['xmin'], obj['box']['ymin'], obj['box']['xmax'], obj['box']['ymax'], obj['name'], f"{int(obj['score']*100)}% {obj['area']} {id}")
if tracked_objects is None:
tracked_objects = copy.deepcopy(self.object_tracker.tracked_objects)
for obj in detected_objects:
draw_box_with_label(frame, obj['box']['xmin'], obj['box']['ymin'], obj['box']['xmax'], obj['box']['ymax'], obj['name'], f"{int(obj['score']*100)}% {obj['area']}", thickness=3)
for id, obj in tracked_objects.items():
color = (0, 255,0) if obj['frame_time'] == frame_time else (255, 0, 0)
draw_box_with_label(frame, obj['box']['xmin'], obj['box']['ymin'], obj['box']['xmax'], obj['box']['ymax'], obj['name'], f"{int(obj['score']*100)}% {obj['area']} {id}", color=color, thickness=1)
# print a timestamp
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")