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refactor and reduce false positives

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Blake Blackshear 2020-09-07 12:17:42 -05:00
parent ea4ecae27c
commit acb75fa02d
10 changed files with 539 additions and 230 deletions
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3
Dockerfile
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@ -31,6 +31,7 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
PyYAML \
matplotlib \
pyarrow \
click \
&& echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
&& wget -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
&& apt -qq update \
@ -46,7 +47,7 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
# get model and labels
RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite -O /edgetpu_model.tflite --trust-server-names
RUN wget -q https://dl.google.com/coral/canned_models/coco_labels.txt -O /labelmap.txt --trust-server-names
COPY labelmap.txt /labelmap.txt
RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite -O /cpu_model.tflite

14
README.md
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@ -342,5 +342,17 @@ cameras:
width: 1920
```
- Additional logging is available in the docker container - You can view the logs by running `docker logs -t frigate`
- Object configuration - Tracked objects types, sizes and thresholds can be defined globally and/or on a per camera basis. The global and camera object configuration is *merged*. For example, if you defined tracking person, car, and truck globally but modified your backyard camera to only track person, the global config would merge making the effective list for the backyard camera still contain person, car and truck. If you want precise object tracking per camera, best practice to put a minimal list of objects at the global level and expand objects on a per camera basis. Object threshold and area configuration will be used first from the camera object config (if defined) and then from the global config. See the [example config](config/config.example.yml) for more information.
- Object configuration - Tracked objects types, sizes and thresholds can be defined globally and/or on a per camera basis. The global and camera object configuration is *merged*. For example, if you defined tracking person, car, and truck globally but modified your backyard camera to only track person, the global config would merge making the effective list for the backyard camera still contain person, car and truck. If you want precise object tracking per camera, best practice to put a minimal list of objects at the global level and expand objects on a per camera basis. Object threshold and area configuration will be used first from the camera object config (if defined) and then from the global config. See the [example config](config/config.example.yml) for more information.
## Troubleshooting
### "ffmpeg didnt return a frame. something is wrong"
Turn on logging for the camera by overriding the global_args and setting the log level to `info`:
```yaml
ffmpeg:
global_args:
- -hide_banner
- -loglevel
- panic
```

13
config/config.example.yml
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@ -53,9 +53,10 @@ mqtt:
# unless overridden at the camera levels.
# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
# All labels from the model are reported over MQTT. These values are used to filter out false positives.
# min_area (optional): minimum width*height of the bounding box for the detected person
# max_area (optional): maximum width*height of the bounding box for the detected person
# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
# min_area (optional): minimum width*height of the bounding box for the detected object
# max_area (optional): maximum width*height of the bounding box for the detected object
# min_score (optional): minimum score for the object
# threshold (optional): The minimum decimal percentage for tracked object's computed score to considered a true positive
####################
objects:
track:
@ -66,7 +67,8 @@ objects:
person:
min_area: 5000
max_area: 100000
threshold: 0.8
min_score: 0.5
threshold: 0.85
zones:
#################
@ -184,4 +186,5 @@ cameras:
person:
min_area: 5000
max_area: 100000
threshold: 0.8
min_score: 0.5
threshold: 0.85

25
detect_objects.py
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@ -120,7 +120,7 @@ class CameraWatchdog(threading.Thread):
process = mp.Process(target=track_camera, args=(name, self.config[name], GLOBAL_OBJECT_CONFIG, camera_process['frame_queue'],
camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue,
camera_process['process_fps'], camera_process['detection_fps'],
camera_process['read_start'], camera_process['detection_frame']))
camera_process['read_start'], camera_process['detection_frame'], self.stop_event))
process.daemon = True
camera_process['process'] = process
process.start()
@ -135,7 +135,7 @@ class CameraWatchdog(threading.Thread):
camera_capture.start()
camera_process['ffmpeg_process'] = ffmpeg_process
camera_process['capture_thread'] = camera_capture
elif now - camera_process['capture_thread'].current_frame > 5:
elif now - camera_process['capture_thread'].current_frame.value > 5:
print(f"No frames received from {name} in 5 seconds. Exiting ffmpeg...")
ffmpeg_process = camera_process['ffmpeg_process']
ffmpeg_process.terminate()
@ -181,6 +181,7 @@ def main():
'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True),
'draw_zones': config.get('snapshots', {}).get('draw_zones', False)
}
config['zones'] = {}
# Queue for cameras to push tracked objects to
tracked_objects_queue = mp.Queue()
@ -259,7 +260,23 @@ def main():
'capture_thread': camera_capture
}
camera_process = mp.Process(target=track_camera, args=(name, config, GLOBAL_OBJECT_CONFIG, frame_queue, frame_shape,
# merge global object config into camera object config
camera_objects_config = config.get('objects', {})
# get objects to track for camera
objects_to_track = camera_objects_config.get('track', GLOBAL_OBJECT_CONFIG.get('track', ['person']))
# merge object filters
global_object_filters = GLOBAL_OBJECT_CONFIG.get('filters', {})
camera_object_filters = camera_objects_config.get('filters', {})
objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys())
object_filters = {}
for obj in objects_with_config:
object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
config['objects'] = {
'track': objects_to_track,
'filters': object_filters
}
camera_process = mp.Process(target=track_camera, args=(name, config, frame_queue, frame_shape,
tflite_process.detection_queue, tracked_objects_queue, camera_processes[name]['process_fps'],
camera_processes[name]['detection_fps'],
camera_processes[name]['read_start'], camera_processes[name]['detection_frame'], stop_event))
@ -340,7 +357,7 @@ def main():
'pid': camera_stats['process'].pid,
'ffmpeg_pid': camera_stats['ffmpeg_process'].pid,
'frame_info': {
'read': capture_thread.current_frame,
'read': capture_thread.current_frame.value,
'detect': camera_stats['detection_frame'].value,
'process': object_processor.camera_data[name]['current_frame_time']
}

431
frigate/object_processing.py
View File

@ -13,6 +13,8 @@ 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
from typing import Callable, Dict
from statistics import mean, median
PATH_TO_LABELS = '/labelmap.txt'
@ -23,11 +25,6 @@ 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', {})
@ -46,11 +43,186 @@ def zone_filtered(obj, object_config):
return True
# if the score is lower than the threshold, skip
if obj_settings.get('threshold', 0) > obj['score']:
if obj_settings.get('threshold', 0) > obj['computed_score']:
return True
return False
# Maintains the state of a camera
class CameraState():
def __init__(self, name, config, frame_manager):
self.name = name
self.config = config
self.frame_manager = frame_manager
self.best_objects = {}
self.object_status = defaultdict(lambda: 'OFF')
self.tracked_objects = {}
self.zone_objects = defaultdict(lambda: [])
self.current_frame = np.zeros((720,1280,3), np.uint8)
self.current_frame_time = 0.0
self.previous_frame_id = None
self.callbacks = defaultdict(lambda: [])
def false_positive(self, obj):
threshold = self.config['objects'].get('filters', {}).get(obj['label'], {}).get('threshold', 0.85)
if obj['computed_score'] < threshold:
return True
return False
def compute_score(self, obj):
scores = obj['score_history'][:]
# pad with zeros if you dont have at least 3 scores
if len(scores) < 3:
scores += [0.0]*(3 - len(scores))
return median(scores)
def on(self, event_type: str, callback: Callable[[Dict], None]):
self.callbacks[event_type].append(callback)
def update(self, frame_time, tracked_objects):
self.current_frame_time = frame_time
# get the new frame and delete the old frame
frame_id = f"{self.name}{frame_time}"
self.current_frame = self.frame_manager.get(frame_id)
if not self.previous_frame_id is None:
self.frame_manager.delete(self.previous_frame_id)
self.previous_frame_id = frame_id
current_ids = tracked_objects.keys()
previous_ids = self.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:
self.tracked_objects[id] = tracked_objects[id]
self.tracked_objects[id]['zones'] = []
# start the score history
self.tracked_objects[id]['score_history'] = [self.tracked_objects[id]['score']]
# calculate if this is a false positive
self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
# call event handlers
for c in self.callbacks['start']:
c(self.name, tracked_objects[id])
for id in updated_ids:
self.tracked_objects[id].update(tracked_objects[id])
# if the object is not in the current frame, add a 0.0 to the score history
if self.tracked_objects[id]['frame_time'] != self.current_frame_time:
self.tracked_objects[id]['score_history'].append(0.0)
else:
self.tracked_objects[id]['score_history'].append(self.tracked_objects[id]['score'])
# only keep the last 10 scores
if len(self.tracked_objects[id]['score_history']) > 10:
self.tracked_objects[id]['score_history'] = self.tracked_objects[id]['score_history'][-10:]
# calculate if this is a false positive
self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
# call event handlers
for c in self.callbacks['update']:
c(self.name, self.tracked_objects[id])
for id in removed_ids:
# publish events to mqtt
self.tracked_objects[id]['end_time'] = frame_time
for c in self.callbacks['end']:
c(self.name, self.tracked_objects[id])
del self.tracked_objects[id]
# check to see if the objects are in any zones
for obj in self.tracked_objects.values():
current_zones = []
bottom_center = (obj['centroid'][0], obj['box'][3])
# check each zone
for name, zone in self.config['zones'].items():
contour = zone['contour']
# check if the object is in the zone and not filtered
if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0
and not zone_filtered(obj, zone.get('filters', {}))):
current_zones.append(name)
obj['zones'] = current_zones
# draw on the frame
if not self.current_frame is None:
# draw the bounding boxes on the frame
for obj in self.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(self.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(self.current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
if self.config['snapshots']['show_timestamp']:
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
cv2.putText(self.current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
if self.config['snapshots']['draw_zones']:
for name, zone in self.config['zones'].items():
thickness = 8 if any([name in obj['zones'] for obj in self.tracked_objects.values()]) else 2
cv2.drawContours(self.current_frame, [zone['contour']], -1, zone['color'], thickness)
# maintain best objects
for obj in self.tracked_objects.values():
object_type = obj['label']
# if the object wasn't seen on the current frame, skip it
if obj['frame_time'] != self.current_frame_time or obj['false_positive']:
continue
if object_type in self.best_objects:
current_best = self.best_objects[object_type]
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'] > current_best['score'] or (now - current_best['frame_time']) > 60:
obj['frame'] = np.copy(self.current_frame)
self.best_objects[object_type] = obj
for c in self.callbacks['snapshot']:
c(self.name, self.best_objects[object_type])
else:
obj['frame'] = np.copy(self.current_frame)
self.best_objects[object_type] = obj
for c in self.callbacks['snapshot']:
c(self.name, self.best_objects[object_type])
# update overall camera state for each object type
obj_counter = Counter()
for obj in self.tracked_objects.values():
if not obj['false_positive']:
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 != self.object_status[obj_name]:
self.object_status[obj_name] = new_status
for c in self.callbacks['object_status']:
c(self.name, obj_name, new_status)
# expire any objects that are ON and no longer detected
expired_objects = [obj_name for obj_name, status in self.object_status.items() if status == 'ON' and not obj_name in obj_counter]
for obj_name in expired_objects:
self.object_status[obj_name] = 'OFF'
for c in self.callbacks['object_status']:
c(self.name, obj_name, 'OFF')
for c in self.callbacks['snapshot']:
c(self.name, self.best_objects[object_type])
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)
@ -61,6 +233,40 @@ class TrackedObjectProcessor(threading.Thread):
self.tracked_objects_queue = tracked_objects_queue
self.event_queue = event_queue
self.stop_event = stop_event
self.camera_states: Dict[str, CameraState] = {}
self.plasma_client = PlasmaFrameManager(self.stop_event)
def start(camera, obj):
# publish events to mqtt
self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps({x: obj[x] for x in obj if x not in ['frame']}), retain=False)
self.event_queue.put(('start', camera, obj))
def update(camera, obj):
pass
def end(camera, obj):
self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps({x: obj[x] for x in obj if x not in ['frame']}), retain=False)
self.event_queue.put(('end', camera, obj))
def snapshot(camera, obj):
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)
def object_status(camera, object_name, status):
self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
for camera in self.camera_config.keys():
camera_state = CameraState(camera, self.camera_config[camera], self.plasma_client)
camera_state.on('start', start)
camera_state.on('update', update)
camera_state.on('end', end)
camera_state.on('snapshot', snapshot)
camera_state.on('object_status', object_status)
self.camera_states[camera] = camera_state
self.camera_data = defaultdict(lambda: {
'best_objects': {},
'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
@ -69,38 +275,43 @@ 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': {}
})
# {
# 'zone_name': {
# 'person': ['camera_1', 'camera_2']
# }
# }
self.zone_data = defaultdict(lambda: defaultdict(lambda: set()))
# set colors for zones
zone_colors = {}
colors = plt.cm.get_cmap('tab10', len(self.zone_config.keys()))
for i, zone in enumerate(self.zone_config.keys()):
zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
# create zone contours
for name, config in zone_config.items():
for zone_name, config in zone_config.items():
for camera, camera_zone_config in config.items():
camera_zone = {}
camera_zone['color'] = zone_colors[zone_name]
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])
camera_zone['contour'] = 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)])
camera_zone['contour'] = 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)
print(f"Unable to parse zone coordinates for {zone_name} - {camera}")
self.camera_config[camera]['zones'][zone_name] = camera_zone
def get_best(self, camera, label):
if label in self.camera_data[camera]['best_objects']:
return self.camera_data[camera]['best_objects'][label]['frame']
best_objects = self.camera_states[camera].best_objects
if label in best_objects:
return best_objects[label]['frame']
else:
return None
def get_current_frame(self, camera):
return self.camera_data[camera]['current_frame']
return self.camera_states[camera].current_frame
def run(self):
while True:
@ -113,165 +324,27 @@ class TrackedObjectProcessor(threading.Thread):
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']
camera_state = self.camera_states[camera]
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))
camera_state.update(frame_time, current_tracked_objects)
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()])
# update zone status for each label
for zone in camera_state.config['zones'].keys():
# get labels for current camera and all labels in current zone
labels_for_camera = set([obj['label'] for obj in camera_state.tracked_objects.values() if zone in obj['zones']])
labels_to_check = labels_for_camera | set(self.zone_data[zone].keys())
# for each label in zone
for label in labels_to_check:
camera_list = self.zone_data[zone][label]
# remove or add the camera to the list for the current label
previous_state = len(camera_list) > 0
if label in labels_for_camera:
camera_list.add(camera_state.name)
elif camera_state.name in camera_list:
camera_list.remove(camera_state.name)
new_state = len(camera_list) > 0
# 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)

18
frigate/objects.py
View File

@ -24,7 +24,6 @@ class ObjectTracker():
obj['id'] = id
obj['start_time'] = obj['frame_time']
obj['top_score'] = obj['score']
self.add_history(obj)
self.tracked_objects[id] = obj
self.disappeared[id] = 0
@ -35,25 +34,8 @@ class ObjectTracker():
def update(self, id, new_obj):
self.disappeared[id] = 0
self.tracked_objects[id].update(new_obj)
self.add_history(self.tracked_objects[id])
if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
def add_history(self, obj):
entry = {
'score': obj['score'],
'box': obj['box'],
'region': obj['region'],
'centroid': obj['centroid'],
'frame_time': obj['frame_time']
}
if 'history' in obj:
obj['history'].append(entry)
# only maintain the last 20 in history
if len(obj['history']) > 20:
obj['history'] = obj['history'][-20:]
else:
obj['history'] = [entry]
def match_and_update(self, frame_time, new_objects):
# group by name

3
frigate/util.py
View File

@ -44,6 +44,9 @@ def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thicknes
def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
# size is larger than longest edge
size = int(max(xmax-xmin, ymax-ymin)*multiplier)
# dont go any smaller than 300
if size < 300:
size = 300
# if the size is too big to fit in the frame
if size > min(frame_shape[0], frame_shape[1]):
size = min(frame_shape[0], frame_shape[1])

37
frigate/video.py
View File

@ -73,8 +73,8 @@ def filtered(obj, objects_to_track, object_filters, mask=None):
if obj_settings.get('max_area', 24000000) < obj[3]:
return True
# if the score is lower than the threshold, skip
if obj_settings.get('threshold', 0) > obj[1]:
# if the score is lower than the min_score, skip
if obj_settings.get('min_score', 0) > obj[1]:
return True
# compute the coordinates of the object and make sure
@ -83,10 +83,10 @@ def filtered(obj, objects_to_track, object_filters, mask=None):
x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(mask[0])-1)
# if the object is in a masked location, don't add it to detected objects
if mask != None and mask[y_location][x_location] == [0]:
if (not mask is None) and (mask[y_location][x_location][0] == 0):
return True
return False
return False
def create_tensor_input(frame, region):
cropped_frame = frame[region[1]:region[3], region[0]:region[2]]
@ -118,7 +118,7 @@ def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager,
frame_queue, take_frame: int, fps:EventsPerSecond, skipped_fps: EventsPerSecond,
stop_event: mp.Event, detection_frame: mp.Value):
stop_event: mp.Event, detection_frame: mp.Value, current_frame: mp.Value):
frame_num = 0
last_frame = 0
@ -130,7 +130,7 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
break
frame_bytes = ffmpeg_process.stdout.read(frame_size)
current_frame = datetime.datetime.now().timestamp()
current_frame.value = datetime.datetime.now().timestamp()
if len(frame_bytes) == 0:
print(f"{camera_name}: ffmpeg didnt return a frame. something is wrong.")
@ -154,14 +154,14 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
continue
# put the frame in the frame manager
frame_manager.put(f"{camera_name}{current_frame}",
frame_manager.put(f"{camera_name}{current_frame.value}",
np
.frombuffer(frame_bytes, np.uint8)
.reshape(frame_shape)
)
# add to the queue
frame_queue.put(current_frame)
last_frame = current_frame
frame_queue.put(current_frame.value)
last_frame = current_frame.value
class CameraCapture(threading.Thread):
def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, detection_frame, stop_event):
@ -175,7 +175,7 @@ class CameraCapture(threading.Thread):
self.skipped_fps = EventsPerSecond()
self.plasma_client = PlasmaFrameManager(stop_event)
self.ffmpeg_process = ffmpeg_process
self.current_frame = 0
self.current_frame = mp.Value('d', 0.0)
self.last_frame = 0
self.detection_frame = detection_frame
self.stop_event = stop_event
@ -183,25 +183,18 @@ class CameraCapture(threading.Thread):
def run(self):
self.skipped_fps.start()
capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.plasma_client, self.frame_queue, self.take_frame,
self.fps, self.skipped_fps, self.stop_event, self.detection_frame)
self.fps, self.skipped_fps, self.stop_event, self.detection_frame, self.current_frame)
def track_camera(name, config, global_objects_config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame, stop_event):
def track_camera(name, config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame, stop_event):
print(f"Starting process for {name}: {os.getpid()}")
listen()
detection_frame.value = 0.0
# Merge the tracked object config with the global config
camera_objects_config = config.get('objects', {})
# combine tracked objects lists
objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', []))
# merge object filters
global_object_filters = global_objects_config.get('filters', {})
camera_object_filters = camera_objects_config.get('filters', {})
objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys())
object_filters = {}
for obj in objects_with_config:
object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
camera_objects_config = config.get('objects', {})
objects_to_track = camera_objects_config.get('track', [])
object_filters = camera_objects_config.get('filters', {})
# load in the mask for object detection
if 'mask' in config:

80
labelmap.txt Normal file
View File

@ -0,0 +1,80 @@
0 person
1 bicycle
2 car
3 motorcycle
4 airplane
5 bus
6 train
7 car
8 boat
9 traffic light
10 fire hydrant
12 stop sign
13 parking meter
14 bench
15 bird
16 cat
17 dog
18 horse
19 sheep
20 cow
21 elephant
22 bear
23 zebra
24 giraffe
26 backpack
27 umbrella
30 handbag
31 tie
32 suitcase
33 frisbee
34 skis
35 snowboard
36 sports ball
37 kite
38 baseball bat
39 baseball glove
40 skateboard
41 surfboard
42 tennis racket
43 bottle
45 wine glass
46 cup
47 fork
48 knife
49 spoon
50 bowl
51 banana
52 apple
53 sandwich
54 orange
55 broccoli
56 carrot
57 hot dog
58 pizza
59 donut
60 cake
61 chair
62 couch
63 potted plant
64 bed
66 dining table
69 toilet
71 tv
72 laptop
73 mouse
74 remote
75 keyboard
76 cell phone
77 microwave
78 oven
79 toaster
80 sink
81 refrigerator
83 book
84 clock
85 vase
86 scissors
87 teddy bear
88 hair drier
89 toothbrush

145
process_clip.py Normal file
View File

@ -0,0 +1,145 @@
import sys
import click
import os
import datetime
from unittest import TestCase, main
from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames, get_frame_shape
from frigate.util import DictFrameManager, EventsPerSecond, draw_box_with_label
from frigate.motion import MotionDetector
from frigate.edgetpu import LocalObjectDetector
from frigate.objects import ObjectTracker
import multiprocessing as mp
import numpy as np
import cv2
from frigate.object_processing import COLOR_MAP, CameraState
class ProcessClip():
def __init__(self, clip_path, frame_shape, config):
self.clip_path = clip_path
self.frame_shape = frame_shape
self.camera_name = 'camera'
self.frame_manager = DictFrameManager()
self.frame_queue = mp.Queue()
self.detected_objects_queue = mp.Queue()
self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
def load_frames(self):
fps = EventsPerSecond()
skipped_fps = EventsPerSecond()
stop_event = mp.Event()
detection_frame = mp.Value('d', datetime.datetime.now().timestamp()+100000)
current_frame = mp.Value('d', 0.0)
ffmpeg_cmd = f"ffmpeg -hide_banner -loglevel panic -i {self.clip_path} -f rawvideo -pix_fmt rgb24 pipe:".split(" ")
ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.frame_shape[0]*self.frame_shape[1]*self.frame_shape[2])
capture_frames(ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue, 1, fps, skipped_fps, stop_event, detection_frame, current_frame)
ffmpeg_process.wait()
ffmpeg_process.communicate()
def process_frames(self, objects_to_track=['person'], object_filters={}):
mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
mask[:] = 255
motion_detector = MotionDetector(self.frame_shape, mask)
object_detector = LocalObjectDetector(labels='/labelmap.txt')
object_tracker = ObjectTracker(10)
process_fps = EventsPerSecond()
current_frame = mp.Value('d', 0.0)
stop_event = mp.Event()
process_frames(self.camera_name, self.frame_queue, self.frame_shape, self.frame_manager, motion_detector, object_detector, object_tracker, self.detected_objects_queue,
process_fps, current_frame, objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
def objects_found(self, debug_path=None):
obj_detected = False
top_computed_score = 0.0
def handle_event(name, obj):
nonlocal obj_detected
nonlocal top_computed_score
if obj['computed_score'] > top_computed_score:
top_computed_score = obj['computed_score']
if not obj['false_positive']:
obj_detected = True
self.camera_state.on('new', handle_event)
self.camera_state.on('update', handle_event)
while(not self.detected_objects_queue.empty()):
camera_name, frame_time, current_tracked_objects = self.detected_objects_queue.get()
if not debug_path is None:
self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
self.camera_state.update(frame_time, current_tracked_objects)
return {
'object_detected': obj_detected,
'top_score': top_computed_score
}
def save_debug_frame(self, debug_path, frame_time, tracked_objects):
current_frame = self.frame_manager.get(f"{self.camera_name}{frame_time}")
# draw the bounding boxes on the frame
for obj in tracked_objects:
thickness = 2
color = (0,0,175)
if obj['frame_time'] != frame_time:
thickness = 1
color = (255,0,0)
else:
color = (255,255,0)
# 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']
draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR))
@click.command()
@click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
@click.option("-l", "--label", default='person', help="Label name to detect.")
@click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
@click.option("--debug-path", default=None, help="Path to output frames for debugging.")
def process(path, label, threshold, debug_path):
clips = []
if os.path.isdir(path):
files = os.listdir(path)
files.sort()
clips = [os.path.join(path, file) for file in files]
elif os.path.isfile(path):
clips.append(path)
config = {
'snapshots': {
'show_timestamp': False,
'draw_zones': False
},
'zones': {},
'objects': {
'track': [label],
'filters': {
'person': {
'threshold': threshold
}
}
}
}
results = []
for c in clips:
frame_shape = get_frame_shape(c)
process_clip = ProcessClip(c, frame_shape, config)
process_clip.load_frames()
process_clip.process_frames(objects_to_track=config['objects']['track'])
results.append((c, process_clip.objects_found(debug_path)))
for result in results:
print(f"{result[0]}: {result[1]}")
positive_count = sum(1 for result in results if result[1]['object_detected'])
print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")
if __name__ == '__main__':
process()