mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
278 lines
14 KiB
Python
278 lines
14 KiB
Python
import json
|
|
import hashlib
|
|
import datetime
|
|
import time
|
|
import copy
|
|
import cv2
|
|
import threading
|
|
import queue
|
|
import numpy as np
|
|
from collections import Counter, defaultdict
|
|
import itertools
|
|
import pyarrow.plasma as plasma
|
|
import matplotlib.pyplot as plt
|
|
from frigate.util import draw_box_with_label, PlasmaFrameManager
|
|
from frigate.edgetpu import load_labels
|
|
|
|
PATH_TO_LABELS = '/labelmap.txt'
|
|
|
|
LABELS = load_labels(PATH_TO_LABELS)
|
|
cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
|
|
|
|
COLOR_MAP = {}
|
|
for key, val in LABELS.items():
|
|
COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
|
|
|
|
def filter_false_positives(event):
|
|
if len(event['history']) < 2:
|
|
return True
|
|
return False
|
|
|
|
def zone_filtered(obj, object_config):
|
|
object_name = obj['label']
|
|
object_filters = object_config.get('filters', {})
|
|
|
|
if object_name in object_filters:
|
|
obj_settings = object_filters[object_name]
|
|
|
|
# if the min area is larger than the
|
|
# detected object, don't add it to detected objects
|
|
if obj_settings.get('min_area',-1) > obj['area']:
|
|
return True
|
|
|
|
# if the detected object is larger than the
|
|
# max area, don't add it to detected objects
|
|
if obj_settings.get('max_area', 24000000) < obj['area']:
|
|
return True
|
|
|
|
# if the score is lower than the threshold, skip
|
|
if obj_settings.get('threshold', 0) > obj['score']:
|
|
return True
|
|
|
|
return False
|
|
|
|
class TrackedObjectProcessor(threading.Thread):
|
|
def __init__(self, camera_config, zone_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
|
|
threading.Thread.__init__(self)
|
|
self.camera_config = camera_config
|
|
self.zone_config = zone_config
|
|
self.client = client
|
|
self.topic_prefix = topic_prefix
|
|
self.tracked_objects_queue = tracked_objects_queue
|
|
self.event_queue = event_queue
|
|
self.stop_event = stop_event
|
|
self.camera_data = defaultdict(lambda: {
|
|
'best_objects': {},
|
|
'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
|
|
'tracked_objects': {},
|
|
'current_frame': np.zeros((720,1280,3), np.uint8),
|
|
'current_frame_time': 0.0,
|
|
'object_id': None
|
|
})
|
|
self.zone_data = defaultdict(lambda: {
|
|
'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
|
|
'contours': {}
|
|
})
|
|
|
|
# create zone contours
|
|
for name, config in zone_config.items():
|
|
for camera, camera_zone_config in config.items():
|
|
coordinates = camera_zone_config['coordinates']
|
|
if isinstance(coordinates, list):
|
|
self.zone_data[name]['contours'][camera] = np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
|
|
elif isinstance(coordinates, str):
|
|
points = coordinates.split(',')
|
|
self.zone_data[name]['contours'][camera] = np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
|
|
else:
|
|
print(f"Unable to parse zone coordinates for {name} - {camera}")
|
|
|
|
# set colors for zones
|
|
colors = plt.cm.get_cmap('tab10', len(self.zone_data.keys()))
|
|
for i, zone in enumerate(self.zone_data.values()):
|
|
zone['color'] = tuple(int(round(255 * c)) for c in colors(i)[:3])
|
|
|
|
self.plasma_client = PlasmaFrameManager(self.stop_event)
|
|
|
|
def get_best(self, camera, label):
|
|
if label in self.camera_data[camera]['best_objects']:
|
|
return self.camera_data[camera]['best_objects'][label]['frame']
|
|
else:
|
|
return None
|
|
|
|
def get_current_frame(self, camera):
|
|
return self.camera_data[camera]['current_frame']
|
|
|
|
def run(self):
|
|
while True:
|
|
if self.stop_event.is_set():
|
|
print(f"Exiting object processor...")
|
|
break
|
|
|
|
try:
|
|
camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
|
|
except queue.Empty:
|
|
continue
|
|
|
|
camera_config = self.camera_config[camera]
|
|
best_objects = self.camera_data[camera]['best_objects']
|
|
current_object_status = self.camera_data[camera]['object_status']
|
|
tracked_objects = self.camera_data[camera]['tracked_objects']
|
|
|
|
current_ids = current_tracked_objects.keys()
|
|
previous_ids = tracked_objects.keys()
|
|
removed_ids = list(set(previous_ids).difference(current_ids))
|
|
new_ids = list(set(current_ids).difference(previous_ids))
|
|
updated_ids = list(set(current_ids).intersection(previous_ids))
|
|
|
|
for id in new_ids:
|
|
# only register the object here if we are sure it isnt a false positive
|
|
if not filter_false_positives(current_tracked_objects[id]):
|
|
tracked_objects[id] = current_tracked_objects[id]
|
|
# publish events to mqtt
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(tracked_objects[id]), retain=False)
|
|
self.event_queue.put(('start', camera, tracked_objects[id]))
|
|
|
|
for id in updated_ids:
|
|
tracked_objects[id] = current_tracked_objects[id]
|
|
|
|
for id in removed_ids:
|
|
# publish events to mqtt
|
|
tracked_objects[id]['end_time'] = frame_time
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(tracked_objects[id]), retain=False)
|
|
self.event_queue.put(('end', camera, tracked_objects[id]))
|
|
del tracked_objects[id]
|
|
|
|
self.camera_data[camera]['current_frame_time'] = frame_time
|
|
|
|
# build a dict of objects in each zone for current camera
|
|
current_objects_in_zones = defaultdict(lambda: [])
|
|
for obj in tracked_objects.values():
|
|
bottom_center = (obj['centroid'][0], obj['box'][3])
|
|
# check each zone
|
|
for name, zone in self.zone_data.items():
|
|
current_contour = zone['contours'].get(camera, None)
|
|
# if the current camera does not have a contour for this zone, skip
|
|
if current_contour is None:
|
|
continue
|
|
# check if the object is in the zone and not filtered
|
|
if (cv2.pointPolygonTest(current_contour, bottom_center, False) >= 0
|
|
and not zone_filtered(obj, self.zone_config[name][camera])):
|
|
current_objects_in_zones[name].append(obj['label'])
|
|
|
|
###
|
|
# Draw tracked objects on the frame
|
|
###
|
|
current_frame = self.plasma_client.get(f"{camera}{frame_time}")
|
|
|
|
if not current_frame is plasma.ObjectNotAvailable:
|
|
# draw the bounding boxes on the frame
|
|
for obj in tracked_objects.values():
|
|
thickness = 2
|
|
color = COLOR_MAP[obj['label']]
|
|
|
|
if obj['frame_time'] != frame_time:
|
|
thickness = 1
|
|
color = (255,0,0)
|
|
|
|
# draw the bounding boxes on the frame
|
|
box = obj['box']
|
|
draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
|
|
# draw the regions on the frame
|
|
region = obj['region']
|
|
cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
|
|
|
|
if camera_config['snapshots']['show_timestamp']:
|
|
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
|
|
cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
|
|
|
|
if camera_config['snapshots']['draw_zones']:
|
|
for name, zone in self.zone_data.items():
|
|
thickness = 2 if len(current_objects_in_zones[name]) == 0 else 8
|
|
if camera in zone['contours']:
|
|
cv2.drawContours(current_frame, [zone['contours'][camera]], -1, zone['color'], thickness)
|
|
|
|
###
|
|
# Set the current frame
|
|
###
|
|
self.camera_data[camera]['current_frame'] = current_frame
|
|
|
|
# delete the previous frame from the plasma store and update the object id
|
|
if not self.camera_data[camera]['object_id'] is None:
|
|
self.plasma_client.delete(self.camera_data[camera]['object_id'])
|
|
self.camera_data[camera]['object_id'] = f"{camera}{frame_time}"
|
|
|
|
###
|
|
# Maintain the highest scoring recent object and frame for each label
|
|
###
|
|
for obj in tracked_objects.values():
|
|
# if the object wasn't seen on the current frame, skip it
|
|
if obj['frame_time'] != frame_time:
|
|
continue
|
|
if obj['label'] in 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'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
|
|
obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
|
|
best_objects[obj['label']] = obj
|
|
# send updated snapshot over mqtt
|
|
best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_RGB2BGR)
|
|
ret, jpg = cv2.imencode('.jpg', best_frame)
|
|
if ret:
|
|
jpg_bytes = jpg.tobytes()
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", jpg_bytes, retain=True)
|
|
else:
|
|
obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
|
|
best_objects[obj['label']] = obj
|
|
|
|
###
|
|
# Report over MQTT
|
|
###
|
|
|
|
# get the zones that are relevant for this camera
|
|
relevant_zones = [zone for zone, config in self.zone_config.items() if camera in config]
|
|
for zone in relevant_zones:
|
|
# create the set of labels in the current frame and previously reported
|
|
labels_for_zone = set(current_objects_in_zones[zone] + list(self.zone_data[zone]['object_status'][camera].keys()))
|
|
# for each label
|
|
for label in labels_for_zone:
|
|
# compute the current 'ON' vs 'OFF' status by checking if any camera sees the object in the zone
|
|
previous_state = any([c[label] == 'ON' for c in self.zone_data[zone]['object_status'].values()])
|
|
self.zone_data[zone]['object_status'][camera][label] = 'ON' if label in current_objects_in_zones[zone] else 'OFF'
|
|
new_state = any([c[label] == 'ON' for c in self.zone_data[zone]['object_status'].values()])
|
|
# if the value is changing, send over MQTT
|
|
if previous_state == False and new_state == True:
|
|
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'ON', retain=False)
|
|
elif previous_state == True and new_state == False:
|
|
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'OFF', retain=False)
|
|
|
|
# count by type
|
|
obj_counter = Counter()
|
|
for obj in tracked_objects.values():
|
|
obj_counter[obj['label']] += 1
|
|
|
|
# report on detected objects
|
|
for obj_name, count in obj_counter.items():
|
|
new_status = 'ON' if count > 0 else 'OFF'
|
|
if new_status != current_object_status[obj_name]:
|
|
current_object_status[obj_name] = new_status
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
|
|
# send the best snapshot over mqtt
|
|
best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
|
|
ret, jpg = cv2.imencode('.jpg', best_frame)
|
|
if ret:
|
|
jpg_bytes = jpg.tobytes()
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
|
|
|
|
# expire any objects that are ON and no longer detected
|
|
expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
|
|
for obj_name in expired_objects:
|
|
current_object_status[obj_name] = 'OFF'
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
|
|
# send updated snapshot over mqtt
|
|
best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
|
|
ret, jpg = cv2.imencode('.jpg', best_frame)
|
|
if ret:
|
|
jpg_bytes = jpg.tobytes()
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
|