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initial implementation of zones

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Blake Blackshear 2020-07-25 07:44:07 -05:00
parent 3a1f1c946b
commit 69f5249788
3 changed files with 127 additions and 8 deletions
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36
config/config.example.yml
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@ -68,6 +68,41 @@ objects:
max_area: 100000
threshold: 0.5
zones:
#################
# Name of the zone
################
front_steps:
cameras:
front_door:
####################
# For each camera, a list of x,y coordinates to define the polygon of the zone.
# Can also be a comma separated string of all x,y coordinates combined.
# The same zone can exist across multiple cameras if they have overlapping FOVs.
# An object is determined to be in the zone based on whether or not the bottom center
# of it's bounding box is within the polygon. The polygon must have at least 3 points.
# Coordinates can be generated at https://www.image-map.net/
####################
coordinates:
- 545,1077
- 747,939
- 788,805
################
# Zone level object filters. These are applied in addition to the global and camera filters
# and should be more restrictive than the global and camera filters. The global and camera
# filters are applied upstream.
################
filters:
person:
min_area: 5000
max_area: 100000
threshold: 0.5
driveway:
cameras:
front_door:
coordinates: 545,1077,747,939,788,805
yard:
cameras:
back:
ffmpeg:
@ -137,6 +172,7 @@ cameras:
################
snapshots:
show_timestamp: True
draw_zones: False
################
# Camera level object config. This config is merged with the global config above.

5
detect_objects.py
View File

@ -171,7 +171,8 @@ def main():
##
for name, config in CONFIG['cameras'].items():
config['snapshots'] = {
'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True)
'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True),
'draw_zones': config.get('snapshots', {}).get('draw_zones', False)
}
# Queue for cameras to push tracked objects to
@ -265,7 +266,7 @@ def main():
event_processor = EventProcessor(CONFIG['cameras'], camera_processes, '/cache', '/clips', event_queue)
event_processor.start()
object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue)
object_processor = TrackedObjectProcessor(CONFIG['cameras'], CONFIG.get('zones', {}), client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue)
object_processor.start()
camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process)

92
frigate/object_processing.py
View File

@ -27,10 +27,34 @@ def filter_false_positives(event):
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, config, client, topic_prefix, tracked_objects_queue, event_queue):
def __init__(self, camera_config, zone_config, client, topic_prefix, tracked_objects_queue, event_queue):
threading.Thread.__init__(self)
self.config = config
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
@ -43,6 +67,28 @@ class TrackedObjectProcessor(threading.Thread):
'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 = PlasmaManager()
def get_best(self, camera, label):
@ -58,7 +104,7 @@ class TrackedObjectProcessor(threading.Thread):
while True:
camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get()
config = self.config[camera]
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']
@ -89,6 +135,17 @@ class TrackedObjectProcessor(threading.Thread):
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():
# check each camera with a contour for the zone
for camera, contour in zone['contours'].items():
if cv2.pointPolygonTest(contour, bottom_center, False) >= 0 and not zone_filtered(obj, self.zone_config[name][camera].get('filters', {})):
current_objects_in_zones[name].append(obj['label'])
###
# Draw tracked objects on the frame
###
@ -111,10 +168,16 @@ class TrackedObjectProcessor(threading.Thread):
region = obj['region']
cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
if config['snapshots']['show_timestamp']:
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
###
@ -152,7 +215,26 @@ class TrackedObjectProcessor(threading.Thread):
###
# Report over MQTT
###
# count objects by type
# 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 each zone
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([camera[label] == 'ON' for camera 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([camera[label] == 'ON' for camera 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