mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-26 19:06:11 +01:00
508 lines
22 KiB
Python
508 lines
22 KiB
Python
import copy
|
|
import base64
|
|
import datetime
|
|
import hashlib
|
|
import itertools
|
|
import json
|
|
import logging
|
|
import os
|
|
import queue
|
|
import threading
|
|
import time
|
|
from collections import Counter, defaultdict
|
|
from statistics import mean, median
|
|
from typing import Callable, Dict
|
|
|
|
import cv2
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
from frigate.config import FrigateConfig, CameraConfig
|
|
from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
|
|
from frigate.edgetpu import load_labels
|
|
from frigate.util import SharedMemoryFrameManager, draw_box_with_label
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
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 on_edge(box, frame_shape):
|
|
if (
|
|
box[0] == 0 or
|
|
box[1] == 0 or
|
|
box[2] == frame_shape[1]-1 or
|
|
box[3] == frame_shape[0]-1
|
|
):
|
|
return True
|
|
|
|
def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
|
|
# larger is better
|
|
# cutoff images are less ideal, but they should also be smaller?
|
|
# better scores are obviously better too
|
|
|
|
# if the new_thumb is on an edge, and the current thumb is not
|
|
if on_edge(new_obj['box'], frame_shape) and not on_edge(current_thumb['box'], frame_shape):
|
|
return False
|
|
|
|
# if the score is better by more than 5%
|
|
if new_obj['score'] > current_thumb['score']+.05:
|
|
return True
|
|
|
|
# if the area is 10% larger
|
|
if new_obj['area'] > current_thumb['area']*1.1:
|
|
return True
|
|
|
|
return False
|
|
|
|
class TrackedObject():
|
|
def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
|
|
self.obj_data = obj_data
|
|
self.camera = camera
|
|
self.camera_config = camera_config
|
|
self.frame_cache = frame_cache
|
|
self.current_zones = []
|
|
self.entered_zones = set()
|
|
self.false_positive = True
|
|
self.top_score = self.computed_score = 0.0
|
|
self.thumbnail_data = None
|
|
self.frame = None
|
|
self.previous = None
|
|
self._snapshot_jpg_time = 0
|
|
ret, jpg = cv2.imencode('.jpg', np.zeros((300,300,3), np.uint8))
|
|
self._snapshot_jpg = jpg.tobytes()
|
|
|
|
# start the score history
|
|
self.score_history = [self.obj_data['score']]
|
|
|
|
def _is_false_positive(self):
|
|
# once a true positive, always a true positive
|
|
if not self.false_positive:
|
|
return False
|
|
|
|
threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
|
|
if self.computed_score < threshold:
|
|
return True
|
|
return False
|
|
|
|
def compute_score(self):
|
|
scores = self.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 update(self, current_frame_time, obj_data):
|
|
previous = self.to_dict()
|
|
self.obj_data.update(obj_data)
|
|
# if the object is not in the current frame, add a 0.0 to the score history
|
|
if self.obj_data['frame_time'] != current_frame_time:
|
|
self.score_history.append(0.0)
|
|
else:
|
|
self.score_history.append(self.obj_data['score'])
|
|
# only keep the last 10 scores
|
|
if len(self.score_history) > 10:
|
|
self.score_history = self.score_history[-10:]
|
|
|
|
# calculate if this is a false positive
|
|
self.computed_score = self.compute_score()
|
|
if self.computed_score > self.top_score:
|
|
self.top_score = self.computed_score
|
|
self.false_positive = self._is_false_positive()
|
|
|
|
if not self.false_positive:
|
|
# determine if this frame is a better thumbnail
|
|
if (
|
|
self.thumbnail_data is None
|
|
or is_better_thumbnail(self.thumbnail_data, self.obj_data, self.camera_config.frame_shape)
|
|
):
|
|
self.thumbnail_data = {
|
|
'frame_time': self.obj_data['frame_time'],
|
|
'box': self.obj_data['box'],
|
|
'area': self.obj_data['area'],
|
|
'region': self.obj_data['region'],
|
|
'score': self.obj_data['score']
|
|
}
|
|
self.previous = previous
|
|
|
|
# check zones
|
|
current_zones = []
|
|
bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
|
|
# check each zone
|
|
for name, zone in self.camera_config.zones.items():
|
|
contour = zone.contour
|
|
# check if the object is in the zone
|
|
if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
|
|
# if the object passed the filters once, dont apply again
|
|
if name in self.current_zones or not zone_filtered(self, zone.filters):
|
|
current_zones.append(name)
|
|
self.entered_zones.add(name)
|
|
|
|
self.current_zones = current_zones
|
|
|
|
def to_dict(self, include_thumbnail: bool = False):
|
|
return {
|
|
'id': self.obj_data['id'],
|
|
'camera': self.camera,
|
|
'frame_time': self.obj_data['frame_time'],
|
|
'label': self.obj_data['label'],
|
|
'top_score': self.top_score,
|
|
'false_positive': self.false_positive,
|
|
'start_time': self.obj_data['start_time'],
|
|
'end_time': self.obj_data.get('end_time', None),
|
|
'score': self.obj_data['score'],
|
|
'box': self.obj_data['box'],
|
|
'area': self.obj_data['area'],
|
|
'region': self.obj_data['region'],
|
|
'current_zones': self.current_zones.copy(),
|
|
'entered_zones': list(self.entered_zones).copy(),
|
|
'thumbnail': base64.b64encode(self.get_jpg_bytes()).decode('utf-8') if include_thumbnail else None
|
|
}
|
|
|
|
def get_jpg_bytes(self):
|
|
if self.thumbnail_data is None or self._snapshot_jpg_time == self.thumbnail_data['frame_time']:
|
|
return self._snapshot_jpg
|
|
|
|
if not self.thumbnail_data['frame_time'] in self.frame_cache:
|
|
logger.error(f"Unable to create thumbnail for {self.obj_data['id']}")
|
|
logger.error(f"Looking for frame_time of {self.thumbnail_data['frame_time']}")
|
|
logger.error(f"Thumbnail frames: {','.join([str(k) for k in self.frame_cache.keys()])}")
|
|
return self._snapshot_jpg
|
|
|
|
# TODO: crop first to avoid converting the entire frame?
|
|
snapshot_config = self.camera_config.snapshots
|
|
best_frame = cv2.cvtColor(self.frame_cache[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
|
|
|
|
if snapshot_config.draw_bounding_boxes:
|
|
thickness = 2
|
|
color = COLOR_MAP[self.obj_data['label']]
|
|
box = self.thumbnail_data['box']
|
|
draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], self.obj_data['label'],
|
|
f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color)
|
|
|
|
if snapshot_config.crop_to_region:
|
|
region = self.thumbnail_data['region']
|
|
best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
|
|
|
|
if snapshot_config.height:
|
|
height = snapshot_config.height
|
|
width = int(height*best_frame.shape[1]/best_frame.shape[0])
|
|
best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
|
|
|
|
if snapshot_config.show_timestamp:
|
|
time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
|
|
size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
|
|
text_width = size[0][0]
|
|
desired_size = max(150, 0.33*best_frame.shape[1])
|
|
font_scale = desired_size/text_width
|
|
cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX,
|
|
fontScale=font_scale, color=(255, 255, 255), thickness=2)
|
|
|
|
ret, jpg = cv2.imencode('.jpg', best_frame)
|
|
if ret:
|
|
self._snapshot_jpg = jpg.tobytes()
|
|
|
|
return self._snapshot_jpg
|
|
|
|
def zone_filtered(obj: TrackedObject, object_config):
|
|
object_name = obj.obj_data['label']
|
|
|
|
if object_name in object_config:
|
|
obj_settings = object_config[object_name]
|
|
|
|
# if the min area is larger than the
|
|
# detected object, don't add it to detected objects
|
|
if obj_settings.min_area > obj.obj_data['area']:
|
|
return True
|
|
|
|
# if the detected object is larger than the
|
|
# max area, don't add it to detected objects
|
|
if obj_settings.max_area < obj.obj_data['area']:
|
|
return True
|
|
|
|
# if the score is lower than the threshold, skip
|
|
if obj_settings.threshold > 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.camera_config = config.cameras[name]
|
|
self.frame_manager = frame_manager
|
|
self.best_objects: Dict[str, TrackedObject] = {}
|
|
self.object_counts = defaultdict(lambda: 0)
|
|
self.tracked_objects: Dict[str, TrackedObject] = {}
|
|
self.frame_cache = {}
|
|
self.zone_objects = defaultdict(lambda: [])
|
|
self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
|
|
self.current_frame_lock = threading.Lock()
|
|
self.current_frame_time = 0.0
|
|
self.previous_frame_id = None
|
|
self.callbacks = defaultdict(lambda: [])
|
|
|
|
def get_current_frame(self, draw=False):
|
|
with self.current_frame_lock:
|
|
frame_copy = np.copy(self._current_frame)
|
|
frame_time = self.current_frame_time
|
|
tracked_objects = {k: v.to_dict() for k,v in self.tracked_objects.items()}
|
|
|
|
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
|
|
# draw on the frame
|
|
if draw:
|
|
# 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(frame_copy, 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(frame_copy, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
|
|
|
|
if self.camera_config.snapshots.show_timestamp:
|
|
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
|
|
cv2.putText(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
|
|
|
|
if self.camera_config.snapshots.draw_zones:
|
|
for name, zone in self.camera_config.zones.items():
|
|
thickness = 8 if any([name in obj['current_zones'] for obj in tracked_objects.values()]) else 2
|
|
cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
|
|
|
|
return frame_copy
|
|
|
|
def finished(self, obj_id):
|
|
del self.tracked_objects[obj_id]
|
|
|
|
def on(self, event_type: str, callback: Callable[[Dict], None]):
|
|
self.callbacks[event_type].append(callback)
|
|
|
|
def update(self, frame_time, current_detections):
|
|
self.current_frame_time = frame_time
|
|
# get the new frame
|
|
frame_id = f"{self.name}{frame_time}"
|
|
current_frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape_yuv)
|
|
|
|
current_ids = current_detections.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:
|
|
new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.frame_cache, current_detections[id])
|
|
|
|
# call event handlers
|
|
for c in self.callbacks['start']:
|
|
c(self.name, new_obj, frame_time)
|
|
|
|
for id in updated_ids:
|
|
updated_obj = self.tracked_objects[id]
|
|
updated_obj.update(frame_time, current_detections[id])
|
|
|
|
if (not updated_obj.false_positive
|
|
and updated_obj.thumbnail_data['frame_time'] == frame_time
|
|
and frame_time not in self.frame_cache):
|
|
self.frame_cache[frame_time] = np.copy(current_frame)
|
|
|
|
# call event handlers
|
|
for c in self.callbacks['update']:
|
|
c(self.name, updated_obj, frame_time)
|
|
|
|
for id in removed_ids:
|
|
# publish events to mqtt
|
|
removed_obj = self.tracked_objects[id]
|
|
if not 'end_time' in removed_obj.obj_data:
|
|
removed_obj.obj_data['end_time'] = frame_time
|
|
for c in self.callbacks['end']:
|
|
c(self.name, removed_obj, frame_time)
|
|
|
|
# TODO: can i switch to looking this up and only changing when an event ends?
|
|
# maybe make an api endpoint that pulls the thumbnail from the file system?
|
|
# maintain best objects
|
|
for obj in self.tracked_objects.values():
|
|
object_type = obj.obj_data['label']
|
|
# if the object's thumbnail is not from the current frame
|
|
if obj.false_positive or obj.thumbnail_data['frame_time'] != self.current_frame_time:
|
|
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 older than desired, use the new object
|
|
if (is_better_thumbnail(current_best.thumbnail_data, obj.thumbnail_data, self.camera_config.frame_shape)
|
|
or (now - current_best.thumbnail_data['frame_time']) > self.camera_config.best_image_timeout):
|
|
self.best_objects[object_type] = obj
|
|
for c in self.callbacks['snapshot']:
|
|
c(self.name, self.best_objects[object_type], frame_time)
|
|
else:
|
|
self.best_objects[object_type] = obj
|
|
for c in self.callbacks['snapshot']:
|
|
c(self.name, self.best_objects[object_type], frame_time)
|
|
|
|
# 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.obj_data['label']] += 1
|
|
|
|
# report on detected objects
|
|
for obj_name, count in obj_counter.items():
|
|
if count != self.object_counts[obj_name]:
|
|
self.object_counts[obj_name] = count
|
|
for c in self.callbacks['object_status']:
|
|
c(self.name, obj_name, count)
|
|
|
|
# expire any objects that are >0 and no longer detected
|
|
expired_objects = [obj_name for obj_name, count in self.object_counts.items() if count > 0 and not obj_name in obj_counter]
|
|
for obj_name in expired_objects:
|
|
self.object_counts[obj_name] = 0
|
|
for c in self.callbacks['object_status']:
|
|
c(self.name, obj_name, 0)
|
|
for c in self.callbacks['snapshot']:
|
|
c(self.name, self.best_objects[obj_name], frame_time)
|
|
|
|
# cleanup thumbnail frame cache
|
|
current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj.false_positive])
|
|
current_best_frames = set([obj.thumbnail_data['frame_time'] for obj in self.best_objects.values()])
|
|
thumb_frames_to_delete = [t for t in self.frame_cache.keys() if not t in current_thumb_frames and not t in current_best_frames]
|
|
for t in thumb_frames_to_delete:
|
|
del self.frame_cache[t]
|
|
|
|
with self.current_frame_lock:
|
|
self._current_frame = current_frame
|
|
if not self.previous_frame_id is None:
|
|
self.frame_manager.delete(self.previous_frame_id)
|
|
self.previous_frame_id = frame_id
|
|
|
|
class TrackedObjectProcessor(threading.Thread):
|
|
def __init__(self, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, event_processed_queue, stop_event):
|
|
threading.Thread.__init__(self)
|
|
self.name = "detected_frames_processor"
|
|
self.config = config
|
|
self.client = client
|
|
self.topic_prefix = topic_prefix
|
|
self.tracked_objects_queue = tracked_objects_queue
|
|
self.event_queue = event_queue
|
|
self.event_processed_queue = event_processed_queue
|
|
self.stop_event = stop_event
|
|
self.camera_states: Dict[str, CameraState] = {}
|
|
self.frame_manager = SharedMemoryFrameManager()
|
|
|
|
def start(camera, obj: TrackedObject, current_frame_time):
|
|
self.event_queue.put(('start', camera, obj.to_dict()))
|
|
|
|
def update(camera, obj: TrackedObject, current_frame_time):
|
|
if not obj.thumbnail_data is None and obj.thumbnail_data['frame_time'] == current_frame_time:
|
|
message = { 'before': obj.previous, 'after': obj.to_dict() }
|
|
self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
|
|
|
|
def end(camera, obj: TrackedObject, current_frame_time):
|
|
if not obj.false_positive:
|
|
message = { 'before': obj.previous, 'after': obj.to_dict() }
|
|
self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
|
|
if self.config.cameras[camera].save_clips.enabled and not obj.false_positive:
|
|
thumbnail_file_name = f"{camera}-{obj.obj_data['id']}.jpg"
|
|
with open(os.path.join(CLIPS_DIR, thumbnail_file_name), 'wb') as f:
|
|
f.write(obj.get_jpg_bytes())
|
|
self.event_queue.put(('end', camera, obj.to_dict(include_thumbnail=True)))
|
|
|
|
def snapshot(camera, obj: TrackedObject, current_frame_time):
|
|
self.client.publish(f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot", obj.get_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.config.cameras.keys():
|
|
camera_state = CameraState(camera, self.config, self.frame_manager)
|
|
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
|
|
|
|
# {
|
|
# 'zone_name': {
|
|
# 'person': {
|
|
# 'camera_1': 2,
|
|
# 'camera_2': 1
|
|
# }
|
|
# }
|
|
# }
|
|
self.zone_data = defaultdict(lambda: defaultdict(lambda: {}))
|
|
|
|
def get_best(self, camera, label):
|
|
# TODO: need a lock here
|
|
camera_state = self.camera_states[camera]
|
|
if label in camera_state.best_objects:
|
|
best_obj = camera_state.best_objects[label]
|
|
best = best_obj.to_dict()
|
|
best['frame'] = camera_state.frame_cache[best_obj.thumbnail_data['frame_time']]
|
|
return best
|
|
else:
|
|
return {}
|
|
|
|
def get_current_frame(self, camera, draw=False):
|
|
return self.camera_states[camera].get_current_frame(draw)
|
|
|
|
def run(self):
|
|
while True:
|
|
if self.stop_event.is_set():
|
|
logger.info(f"Exiting object processor...")
|
|
break
|
|
|
|
try:
|
|
camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
|
|
except queue.Empty:
|
|
continue
|
|
|
|
camera_state = self.camera_states[camera]
|
|
|
|
camera_state.update(frame_time, current_tracked_objects)
|
|
|
|
# update zone counts for each label
|
|
# for each zone in the current camera
|
|
for zone in self.config.cameras[camera].zones.keys():
|
|
# count labels for the camera in the zone
|
|
obj_counter = Counter()
|
|
for obj in camera_state.tracked_objects.values():
|
|
if zone in obj.current_zones and not obj.false_positive:
|
|
obj_counter[obj.obj_data['label']] += 1
|
|
|
|
# update counts and publish status
|
|
for label in set(list(self.zone_data[zone].keys()) + list(obj_counter.keys())):
|
|
# if we have previously published a count for this zone/label
|
|
zone_label = self.zone_data[zone][label]
|
|
if camera in zone_label:
|
|
current_count = sum(zone_label.values())
|
|
zone_label[camera] = obj_counter[label] if label in obj_counter else 0
|
|
new_count = sum(zone_label.values())
|
|
if new_count != current_count:
|
|
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", new_count, retain=False)
|
|
# if this is a new zone/label combo for this camera
|
|
else:
|
|
if label in obj_counter:
|
|
zone_label[camera] = obj_counter[label]
|
|
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", obj_counter[label], retain=False)
|
|
|
|
# cleanup event finished queue
|
|
while not self.event_processed_queue.empty():
|
|
event_id, camera = self.event_processed_queue.get()
|
|
self.camera_states[camera].finished(event_id)
|