mirror of
https://github.com/blakeblackshear/frigate.git
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82f8694464
* backend * frontend * docs * fix topic name and initial websocket state * update reference config * fix mqtt docs * fix initial topics * don't apply max severity when alerts/detections are disabled * fix ws merge * tweaks
566 lines
21 KiB
Python
566 lines
21 KiB
Python
"""Object attribute."""
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import base64
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import logging
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from collections import defaultdict
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from statistics import median
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from typing import Optional
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import cv2
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import numpy as np
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from frigate.config import (
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CameraConfig,
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ModelConfig,
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UIConfig,
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)
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from frigate.review.types import SeverityEnum
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from frigate.util.image import (
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area,
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calculate_region,
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draw_box_with_label,
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draw_timestamp,
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is_better_thumbnail,
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)
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from frigate.util.object import box_inside
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from frigate.util.velocity import calculate_real_world_speed
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logger = logging.getLogger(__name__)
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class TrackedObject:
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def __init__(
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self,
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model_config: ModelConfig,
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camera_config: CameraConfig,
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ui_config: UIConfig,
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frame_cache,
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obj_data: dict[str, any],
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):
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# set the score history then remove as it is not part of object state
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self.score_history = obj_data["score_history"]
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del obj_data["score_history"]
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self.obj_data = obj_data
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self.colormap = model_config.colormap
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self.logos = model_config.all_attribute_logos
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self.camera_config = camera_config
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self.ui_config = ui_config
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self.frame_cache = frame_cache
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self.zone_presence: dict[str, int] = {}
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self.zone_loitering: dict[str, int] = {}
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self.current_zones = []
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self.entered_zones = []
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self.attributes = defaultdict(float)
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self.false_positive = True
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self.has_clip = False
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self.has_snapshot = False
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self.top_score = self.computed_score = 0.0
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self.thumbnail_data = None
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self.last_updated = 0
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self.last_published = 0
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self.frame = None
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self.active = True
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self.pending_loitering = False
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self.speed_history = []
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self.current_estimated_speed = 0
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self.average_estimated_speed = 0
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self.velocity_angle = 0
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self.previous = self.to_dict()
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@property
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def max_severity(self) -> Optional[str]:
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review_config = self.camera_config.review
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if (
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self.camera_config.review.alerts.enabled
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and self.obj_data["label"] in review_config.alerts.labels
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and (
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not review_config.alerts.required_zones
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or set(self.entered_zones) & set(review_config.alerts.required_zones)
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)
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):
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return SeverityEnum.alert
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if (
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self.camera_config.review.detections.enabled
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and (
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not review_config.detections.labels
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or self.obj_data["label"] in review_config.detections.labels
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)
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and (
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not review_config.detections.required_zones
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or set(self.entered_zones)
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& set(review_config.detections.required_zones)
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)
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):
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return SeverityEnum.detection
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return None
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def _is_false_positive(self):
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# once a true positive, always a true positive
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if not self.false_positive:
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return False
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threshold = self.camera_config.objects.filters[self.obj_data["label"]].threshold
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return self.computed_score < threshold
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def compute_score(self):
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"""get median of scores for object."""
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return median(self.score_history)
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def update(self, current_frame_time: float, obj_data, has_valid_frame: bool):
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thumb_update = False
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significant_change = False
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autotracker_update = False
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# if the object is not in the current frame, add a 0.0 to the score history
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if obj_data["frame_time"] != current_frame_time:
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self.score_history.append(0.0)
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else:
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self.score_history.append(obj_data["score"])
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# only keep the last 10 scores
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if len(self.score_history) > 10:
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self.score_history = self.score_history[-10:]
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# calculate if this is a false positive
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self.computed_score = self.compute_score()
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if self.computed_score > self.top_score:
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self.top_score = self.computed_score
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self.false_positive = self._is_false_positive()
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self.active = self.is_active()
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if not self.false_positive and has_valid_frame:
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# determine if this frame is a better thumbnail
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if self.thumbnail_data is None or is_better_thumbnail(
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self.obj_data["label"],
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self.thumbnail_data,
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obj_data,
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self.camera_config.frame_shape,
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):
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self.thumbnail_data = {
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"frame_time": current_frame_time,
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"box": obj_data["box"],
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"area": obj_data["area"],
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"region": obj_data["region"],
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"score": obj_data["score"],
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"attributes": obj_data["attributes"],
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"current_estimated_speed": self.current_estimated_speed,
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"velocity_angle": self.velocity_angle,
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}
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thumb_update = True
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# check zones
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current_zones = []
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bottom_center = (obj_data["centroid"][0], obj_data["box"][3])
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in_loitering_zone = False
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in_speed_zone = False
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# check each zone
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for name, zone in self.camera_config.zones.items():
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# if the zone is not for this object type, skip
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if len(zone.objects) > 0 and obj_data["label"] not in zone.objects:
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continue
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contour = zone.contour
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zone_score = self.zone_presence.get(name, 0) + 1
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# check if the object is in the zone
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if cv2.pointPolygonTest(contour, bottom_center, False) >= 0:
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# if the object passed the filters once, dont apply again
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if name in self.current_zones or not zone_filtered(self, zone.filters):
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# Calculate speed first if this is a speed zone
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if (
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zone.distances
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and obj_data["frame_time"] == current_frame_time
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and self.active
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):
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speed_magnitude, self.velocity_angle = (
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calculate_real_world_speed(
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zone.contour,
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zone.distances,
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self.obj_data["estimate_velocity"],
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bottom_center,
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self.camera_config.detect.fps,
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)
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)
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if self.ui_config.unit_system == "metric":
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self.current_estimated_speed = (
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speed_magnitude * 3.6
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) # m/s to km/h
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else:
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self.current_estimated_speed = (
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speed_magnitude * 0.681818
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) # ft/s to mph
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self.speed_history.append(self.current_estimated_speed)
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if len(self.speed_history) > 10:
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self.speed_history = self.speed_history[-10:]
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self.average_estimated_speed = sum(self.speed_history) / len(
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self.speed_history
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)
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# we've exceeded the speed threshold on the zone
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# or we don't have a speed threshold set
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if (
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zone.speed_threshold is None
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or self.average_estimated_speed > zone.speed_threshold
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):
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in_speed_zone = True
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logger.debug(
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f"Camera: {self.camera_config.name}, tracked object ID: {self.obj_data['id']}, "
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f"zone: {name}, pixel velocity: {str(tuple(np.round(self.obj_data['estimate_velocity']).flatten().astype(int)))}, "
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f"speed magnitude: {speed_magnitude}, velocity angle: {self.velocity_angle}, "
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f"estimated speed: {self.current_estimated_speed:.1f}, "
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f"average speed: {self.average_estimated_speed:.1f}, "
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f"length: {len(self.speed_history)}"
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)
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# Check zone entry conditions - for speed zones, require both inertia and speed
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if zone_score >= zone.inertia:
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if zone.distances and not in_speed_zone:
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continue # Skip zone entry for speed zones until speed threshold met
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# if the zone has loitering time, update loitering status
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if zone.loitering_time > 0:
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in_loitering_zone = True
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loitering_score = self.zone_loitering.get(name, 0) + 1
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# loitering time is configured as seconds, convert to count of frames
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if loitering_score >= (
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self.camera_config.zones[name].loitering_time
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* self.camera_config.detect.fps
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):
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current_zones.append(name)
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if name not in self.entered_zones:
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self.entered_zones.append(name)
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else:
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self.zone_loitering[name] = loitering_score
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else:
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self.zone_presence[name] = zone_score
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else:
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# once an object has a zone inertia of 3+ it is not checked anymore
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if 0 < zone_score < zone.inertia:
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self.zone_presence[name] = zone_score - 1
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# Reset speed if not in speed zone
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if zone.distances and name not in current_zones:
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self.current_estimated_speed = 0
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# update loitering status
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self.pending_loitering = in_loitering_zone
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# maintain attributes
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for attr in obj_data["attributes"]:
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if self.attributes[attr["label"]] < attr["score"]:
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self.attributes[attr["label"]] = attr["score"]
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# populate the sub_label for object with highest scoring logo
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if self.obj_data["label"] in ["car", "package", "person"]:
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recognized_logos = {
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k: self.attributes[k] for k in self.logos if k in self.attributes
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}
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if len(recognized_logos) > 0:
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max_logo = max(recognized_logos, key=recognized_logos.get)
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# don't overwrite sub label if it is already set
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if (
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self.obj_data.get("sub_label") is None
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or self.obj_data["sub_label"][0] == max_logo
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):
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self.obj_data["sub_label"] = (max_logo, recognized_logos[max_logo])
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# check for significant change
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if not self.false_positive:
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# if the zones changed, signal an update
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if set(self.current_zones) != set(current_zones):
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significant_change = True
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# if the position changed, signal an update
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if self.obj_data["position_changes"] != obj_data["position_changes"]:
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significant_change = True
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if self.obj_data["attributes"] != obj_data["attributes"]:
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significant_change = True
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# if the state changed between stationary and active
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if self.previous["active"] != self.active:
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significant_change = True
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# update at least once per minute
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if self.obj_data["frame_time"] - self.previous["frame_time"] > 60:
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significant_change = True
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# update autotrack at most 3 objects per second
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if self.obj_data["frame_time"] - self.previous["frame_time"] >= (1 / 3):
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autotracker_update = True
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self.obj_data.update(obj_data)
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self.current_zones = current_zones
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return (thumb_update, significant_change, autotracker_update)
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def to_dict(self, include_thumbnail: bool = False):
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event = {
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"id": self.obj_data["id"],
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"camera": self.camera_config.name,
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"frame_time": self.obj_data["frame_time"],
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"snapshot": self.thumbnail_data,
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"label": self.obj_data["label"],
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"sub_label": self.obj_data.get("sub_label"),
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"top_score": self.top_score,
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"false_positive": self.false_positive,
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"start_time": self.obj_data["start_time"],
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"end_time": self.obj_data.get("end_time", None),
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"score": self.obj_data["score"],
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"box": self.obj_data["box"],
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"area": self.obj_data["area"],
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"ratio": self.obj_data["ratio"],
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"region": self.obj_data["region"],
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"active": self.active,
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"stationary": not self.active,
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"motionless_count": self.obj_data["motionless_count"],
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"position_changes": self.obj_data["position_changes"],
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"current_zones": self.current_zones.copy(),
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"entered_zones": self.entered_zones.copy(),
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"has_clip": self.has_clip,
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"has_snapshot": self.has_snapshot,
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"attributes": self.attributes,
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"current_attributes": self.obj_data["attributes"],
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"pending_loitering": self.pending_loitering,
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"max_severity": self.max_severity,
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"current_estimated_speed": self.current_estimated_speed,
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"average_estimated_speed": self.average_estimated_speed,
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"velocity_angle": self.velocity_angle,
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}
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if include_thumbnail:
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event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8")
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return event
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def is_active(self):
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return not self.is_stationary()
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def is_stationary(self):
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return (
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self.obj_data["motionless_count"]
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> self.camera_config.detect.stationary.threshold
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)
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def get_thumbnail(self):
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if (
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self.thumbnail_data is None
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or self.thumbnail_data["frame_time"] not in self.frame_cache
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):
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ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
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jpg_bytes = self.get_jpg_bytes(
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timestamp=False, bounding_box=False, crop=True, height=175
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)
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if jpg_bytes:
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return jpg_bytes
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else:
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ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
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return jpg.tobytes()
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def get_clean_png(self):
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if self.thumbnail_data is None:
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return None
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try:
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best_frame = cv2.cvtColor(
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self.frame_cache[self.thumbnail_data["frame_time"]],
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cv2.COLOR_YUV2BGR_I420,
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)
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except KeyError:
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logger.warning(
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f"Unable to create clean png because frame {self.thumbnail_data['frame_time']} is not in the cache"
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)
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return None
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ret, png = cv2.imencode(".png", best_frame)
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if ret:
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return png.tobytes()
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else:
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return None
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def get_jpg_bytes(
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self, timestamp=False, bounding_box=False, crop=False, height=None, quality=70
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):
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if self.thumbnail_data is None:
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return None
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try:
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best_frame = cv2.cvtColor(
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self.frame_cache[self.thumbnail_data["frame_time"]],
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cv2.COLOR_YUV2BGR_I420,
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)
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except KeyError:
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logger.warning(
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f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache"
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)
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return None
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if bounding_box:
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thickness = 2
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color = self.colormap[self.obj_data["label"]]
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# draw the bounding boxes on the frame
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box = self.thumbnail_data["box"]
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draw_box_with_label(
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best_frame,
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box[0],
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box[1],
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box[2],
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box[3],
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self.obj_data["label"],
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f"{int(self.thumbnail_data['score'] * 100)}% {int(self.thumbnail_data['area'])}"
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+ (
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f" {self.thumbnail_data['current_estimated_speed']:.1f}"
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if self.thumbnail_data["current_estimated_speed"] != 0
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else ""
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),
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thickness=thickness,
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color=color,
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)
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# draw any attributes
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for attribute in self.thumbnail_data["attributes"]:
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box = attribute["box"]
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draw_box_with_label(
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best_frame,
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box[0],
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box[1],
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box[2],
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box[3],
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attribute["label"],
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f"{attribute['score']:.0%}",
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thickness=thickness,
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color=color,
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)
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if crop:
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box = self.thumbnail_data["box"]
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box_size = 300
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region = calculate_region(
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best_frame.shape,
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box[0],
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box[1],
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box[2],
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box[3],
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box_size,
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multiplier=1.1,
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)
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best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
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if height:
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width = int(height * best_frame.shape[1] / best_frame.shape[0])
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best_frame = cv2.resize(
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best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
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)
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if timestamp:
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color = self.camera_config.timestamp_style.color
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draw_timestamp(
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best_frame,
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self.thumbnail_data["frame_time"],
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self.camera_config.timestamp_style.format,
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font_effect=self.camera_config.timestamp_style.effect,
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font_thickness=self.camera_config.timestamp_style.thickness,
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font_color=(color.blue, color.green, color.red),
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position=self.camera_config.timestamp_style.position,
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)
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ret, jpg = cv2.imencode(
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".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), quality]
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)
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if ret:
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return jpg.tobytes()
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else:
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return None
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|
|
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def zone_filtered(obj: TrackedObject, object_config):
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object_name = obj.obj_data["label"]
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|
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if object_name in object_config:
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obj_settings = object_config[object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.min_area > obj.obj_data["area"]:
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return True
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|
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.max_area < obj.obj_data["area"]:
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return True
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|
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# if the score is lower than the threshold, skip
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if obj_settings.threshold > obj.computed_score:
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return True
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|
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# if the object is not proportionally wide enough
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|
if obj_settings.min_ratio > obj.obj_data["ratio"]:
|
|
return True
|
|
|
|
# if the object is proportionally too wide
|
|
if obj_settings.max_ratio < obj.obj_data["ratio"]:
|
|
return True
|
|
|
|
return False
|
|
|
|
|
|
class TrackedObjectAttribute:
|
|
def __init__(self, raw_data: tuple) -> None:
|
|
self.label = raw_data[0]
|
|
self.score = raw_data[1]
|
|
self.box = raw_data[2]
|
|
self.area = raw_data[3]
|
|
self.ratio = raw_data[4]
|
|
self.region = raw_data[5]
|
|
|
|
def get_tracking_data(self) -> dict[str, any]:
|
|
"""Return data saved to the object."""
|
|
return {
|
|
"label": self.label,
|
|
"score": self.score,
|
|
"box": self.box,
|
|
}
|
|
|
|
def find_best_object(self, objects: list[dict[str, any]]) -> Optional[str]:
|
|
"""Find the best attribute for each object and return its ID."""
|
|
best_object_area = None
|
|
best_object_id = None
|
|
best_object_label = None
|
|
|
|
for obj in objects:
|
|
if not box_inside(obj["box"], self.box):
|
|
continue
|
|
|
|
object_area = area(obj["box"])
|
|
|
|
# if multiple objects have the same attribute then they
|
|
# are overlapping, it is most likely that the smaller object
|
|
# is the one with the attribute
|
|
if best_object_area is None:
|
|
best_object_area = object_area
|
|
best_object_id = obj["id"]
|
|
best_object_label = obj["label"]
|
|
else:
|
|
if best_object_label == "car" and obj["label"] == "car":
|
|
# if multiple cars are overlapping with the same label then the label will not be assigned
|
|
return None
|
|
elif object_area < best_object_area:
|
|
# if a car and person are overlapping then assign the label to the smaller object (which should be the person)
|
|
best_object_area = object_area
|
|
best_object_id = obj["id"]
|
|
best_object_label = obj["label"]
|
|
|
|
return best_object_id
|