mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-30 19:09:13 +01:00
448 lines
16 KiB
Python
448 lines
16 KiB
Python
|
"""Object attribute."""
|
||
|
|
|
||
|
|
import base64
|
||
|
|
import logging
|
||
|
|
from collections import defaultdict
|
||
|
|
from statistics import median
|
||
|
|
|
||
|
|
import cv2
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from frigate.config import (
|
||
|
|
CameraConfig,
|
||
|
|
ModelConfig,
|
||
|
|
)
|
||
|
|
from frigate.util.image import (
|
||
|
|
area,
|
||
|
|
calculate_region,
|
||
|
|
draw_box_with_label,
|
||
|
|
draw_timestamp,
|
||
|
|
is_better_thumbnail,
|
||
|
|
)
|
||
|
|
from frigate.util.object import box_inside
|
||
|
|
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class TrackedObject:
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
model_config: ModelConfig,
|
||
|
|
camera_config: CameraConfig,
|
||
|
|
frame_cache,
|
||
|
|
obj_data: dict[str, any],
|
||
|
|
):
|
||
|
|
# set the score history then remove as it is not part of object state
|
||
|
|
self.score_history = obj_data["score_history"]
|
||
|
|
del obj_data["score_history"]
|
||
|
|
|
||
|
|
self.obj_data = obj_data
|
||
|
|
self.colormap = model_config.colormap
|
||
|
|
self.logos = model_config.all_attribute_logos
|
||
|
|
self.camera_config = camera_config
|
||
|
|
self.frame_cache = frame_cache
|
||
|
|
self.zone_presence: dict[str, int] = {}
|
||
|
|
self.zone_loitering: dict[str, int] = {}
|
||
|
|
self.current_zones = []
|
||
|
|
self.entered_zones = []
|
||
|
|
self.attributes = defaultdict(float)
|
||
|
|
self.false_positive = True
|
||
|
|
self.has_clip = False
|
||
|
|
self.has_snapshot = False
|
||
|
|
self.top_score = self.computed_score = 0.0
|
||
|
|
self.thumbnail_data = None
|
||
|
|
self.last_updated = 0
|
||
|
|
self.last_published = 0
|
||
|
|
self.frame = None
|
||
|
|
self.active = True
|
||
|
|
self.pending_loitering = False
|
||
|
|
self.previous = self.to_dict()
|
||
|
|
|
||
|
|
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
|
||
|
|
return self.computed_score < threshold
|
||
|
|
|
||
|
|
def compute_score(self):
|
||
|
|
"""get median of scores for object."""
|
||
|
|
return median(self.score_history)
|
||
|
|
|
||
|
|
def update(self, current_frame_time: float, obj_data, has_valid_frame: bool):
|
||
|
|
thumb_update = False
|
||
|
|
significant_change = False
|
||
|
|
autotracker_update = False
|
||
|
|
# if the object is not in the current frame, add a 0.0 to the score history
|
||
|
|
if obj_data["frame_time"] != current_frame_time:
|
||
|
|
self.score_history.append(0.0)
|
||
|
|
else:
|
||
|
|
self.score_history.append(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()
|
||
|
|
self.active = self.is_active()
|
||
|
|
|
||
|
|
if not self.false_positive and has_valid_frame:
|
||
|
|
# determine if this frame is a better thumbnail
|
||
|
|
if self.thumbnail_data is None or is_better_thumbnail(
|
||
|
|
self.obj_data["label"],
|
||
|
|
self.thumbnail_data,
|
||
|
|
obj_data,
|
||
|
|
self.camera_config.frame_shape,
|
||
|
|
):
|
||
|
|
self.thumbnail_data = {
|
||
|
|
"frame_time": current_frame_time,
|
||
|
|
"box": obj_data["box"],
|
||
|
|
"area": obj_data["area"],
|
||
|
|
"region": obj_data["region"],
|
||
|
|
"score": obj_data["score"],
|
||
|
|
"attributes": obj_data["attributes"],
|
||
|
|
}
|
||
|
|
thumb_update = True
|
||
|
|
|
||
|
|
# check zones
|
||
|
|
current_zones = []
|
||
|
|
bottom_center = (obj_data["centroid"][0], obj_data["box"][3])
|
||
|
|
in_loitering_zone = False
|
||
|
|
|
||
|
|
# check each zone
|
||
|
|
for name, zone in self.camera_config.zones.items():
|
||
|
|
# if the zone is not for this object type, skip
|
||
|
|
if len(zone.objects) > 0 and obj_data["label"] not in zone.objects:
|
||
|
|
continue
|
||
|
|
contour = zone.contour
|
||
|
|
zone_score = self.zone_presence.get(name, 0) + 1
|
||
|
|
# 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):
|
||
|
|
# an object is only considered present in a zone if it has a zone inertia of 3+
|
||
|
|
if zone_score >= zone.inertia:
|
||
|
|
# if the zone has loitering time, update loitering status
|
||
|
|
if zone.loitering_time > 0:
|
||
|
|
in_loitering_zone = True
|
||
|
|
|
||
|
|
loitering_score = self.zone_loitering.get(name, 0) + 1
|
||
|
|
|
||
|
|
# loitering time is configured as seconds, convert to count of frames
|
||
|
|
if loitering_score >= (
|
||
|
|
self.camera_config.zones[name].loitering_time
|
||
|
|
* self.camera_config.detect.fps
|
||
|
|
):
|
||
|
|
current_zones.append(name)
|
||
|
|
|
||
|
|
if name not in self.entered_zones:
|
||
|
|
self.entered_zones.append(name)
|
||
|
|
else:
|
||
|
|
self.zone_loitering[name] = loitering_score
|
||
|
|
else:
|
||
|
|
self.zone_presence[name] = zone_score
|
||
|
|
else:
|
||
|
|
# once an object has a zone inertia of 3+ it is not checked anymore
|
||
|
|
if 0 < zone_score < zone.inertia:
|
||
|
|
self.zone_presence[name] = zone_score - 1
|
||
|
|
|
||
|
|
# update loitering status
|
||
|
|
self.pending_loitering = in_loitering_zone
|
||
|
|
|
||
|
|
# maintain attributes
|
||
|
|
for attr in obj_data["attributes"]:
|
||
|
|
if self.attributes[attr["label"]] < attr["score"]:
|
||
|
|
self.attributes[attr["label"]] = attr["score"]
|
||
|
|
|
||
|
|
# populate the sub_label for object with highest scoring logo
|
||
|
|
if self.obj_data["label"] in ["car", "package", "person"]:
|
||
|
|
recognized_logos = {
|
||
|
|
k: self.attributes[k] for k in self.logos if k in self.attributes
|
||
|
|
}
|
||
|
|
if len(recognized_logos) > 0:
|
||
|
|
max_logo = max(recognized_logos, key=recognized_logos.get)
|
||
|
|
|
||
|
|
# don't overwrite sub label if it is already set
|
||
|
|
if (
|
||
|
|
self.obj_data.get("sub_label") is None
|
||
|
|
or self.obj_data["sub_label"][0] == max_logo
|
||
|
|
):
|
||
|
|
self.obj_data["sub_label"] = (max_logo, recognized_logos[max_logo])
|
||
|
|
|
||
|
|
# check for significant change
|
||
|
|
if not self.false_positive:
|
||
|
|
# if the zones changed, signal an update
|
||
|
|
if set(self.current_zones) != set(current_zones):
|
||
|
|
significant_change = True
|
||
|
|
|
||
|
|
# if the position changed, signal an update
|
||
|
|
if self.obj_data["position_changes"] != obj_data["position_changes"]:
|
||
|
|
significant_change = True
|
||
|
|
|
||
|
|
if self.obj_data["attributes"] != obj_data["attributes"]:
|
||
|
|
significant_change = True
|
||
|
|
|
||
|
|
# if the state changed between stationary and active
|
||
|
|
if self.previous["active"] != self.active:
|
||
|
|
significant_change = True
|
||
|
|
|
||
|
|
# update at least once per minute
|
||
|
|
if self.obj_data["frame_time"] - self.previous["frame_time"] > 60:
|
||
|
|
significant_change = True
|
||
|
|
|
||
|
|
# update autotrack at most 3 objects per second
|
||
|
|
if self.obj_data["frame_time"] - self.previous["frame_time"] >= (1 / 3):
|
||
|
|
autotracker_update = True
|
||
|
|
|
||
|
|
self.obj_data.update(obj_data)
|
||
|
|
self.current_zones = current_zones
|
||
|
|
return (thumb_update, significant_change, autotracker_update)
|
||
|
|
|
||
|
|
def to_dict(self, include_thumbnail: bool = False):
|
||
|
|
event = {
|
||
|
|
"id": self.obj_data["id"],
|
||
|
|
"camera": self.camera_config.name,
|
||
|
|
"frame_time": self.obj_data["frame_time"],
|
||
|
|
"snapshot": self.thumbnail_data,
|
||
|
|
"label": self.obj_data["label"],
|
||
|
|
"sub_label": self.obj_data.get("sub_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"],
|
||
|
|
"ratio": self.obj_data["ratio"],
|
||
|
|
"region": self.obj_data["region"],
|
||
|
|
"active": self.active,
|
||
|
|
"stationary": not self.active,
|
||
|
|
"motionless_count": self.obj_data["motionless_count"],
|
||
|
|
"position_changes": self.obj_data["position_changes"],
|
||
|
|
"current_zones": self.current_zones.copy(),
|
||
|
|
"entered_zones": self.entered_zones.copy(),
|
||
|
|
"has_clip": self.has_clip,
|
||
|
|
"has_snapshot": self.has_snapshot,
|
||
|
|
"attributes": self.attributes,
|
||
|
|
"current_attributes": self.obj_data["attributes"],
|
||
|
|
"pending_loitering": self.pending_loitering,
|
||
|
|
}
|
||
|
|
|
||
|
|
if include_thumbnail:
|
||
|
|
event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8")
|
||
|
|
|
||
|
|
return event
|
||
|
|
|
||
|
|
def is_active(self):
|
||
|
|
return not self.is_stationary()
|
||
|
|
|
||
|
|
def is_stationary(self):
|
||
|
|
return (
|
||
|
|
self.obj_data["motionless_count"]
|
||
|
|
> self.camera_config.detect.stationary.threshold
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_thumbnail(self):
|
||
|
|
if (
|
||
|
|
self.thumbnail_data is None
|
||
|
|
or self.thumbnail_data["frame_time"] not in self.frame_cache
|
||
|
|
):
|
||
|
|
ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
|
||
|
|
|
||
|
|
jpg_bytes = self.get_jpg_bytes(
|
||
|
|
timestamp=False, bounding_box=False, crop=True, height=175
|
||
|
|
)
|
||
|
|
|
||
|
|
if jpg_bytes:
|
||
|
|
return jpg_bytes
|
||
|
|
else:
|
||
|
|
ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
|
||
|
|
return jpg.tobytes()
|
||
|
|
|
||
|
|
def get_clean_png(self):
|
||
|
|
if self.thumbnail_data is None:
|
||
|
|
return None
|
||
|
|
|
||
|
|
try:
|
||
|
|
best_frame = cv2.cvtColor(
|
||
|
|
self.frame_cache[self.thumbnail_data["frame_time"]],
|
||
|
|
cv2.COLOR_YUV2BGR_I420,
|
||
|
|
)
|
||
|
|
except KeyError:
|
||
|
|
logger.warning(
|
||
|
|
f"Unable to create clean png because frame {self.thumbnail_data['frame_time']} is not in the cache"
|
||
|
|
)
|
||
|
|
return None
|
||
|
|
|
||
|
|
ret, png = cv2.imencode(".png", best_frame)
|
||
|
|
if ret:
|
||
|
|
return png.tobytes()
|
||
|
|
else:
|
||
|
|
return None
|
||
|
|
|
||
|
|
def get_jpg_bytes(
|
||
|
|
self, timestamp=False, bounding_box=False, crop=False, height=None, quality=70
|
||
|
|
):
|
||
|
|
if self.thumbnail_data is None:
|
||
|
|
return None
|
||
|
|
|
||
|
|
try:
|
||
|
|
best_frame = cv2.cvtColor(
|
||
|
|
self.frame_cache[self.thumbnail_data["frame_time"]],
|
||
|
|
cv2.COLOR_YUV2BGR_I420,
|
||
|
|
)
|
||
|
|
except KeyError:
|
||
|
|
logger.warning(
|
||
|
|
f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache"
|
||
|
|
)
|
||
|
|
return None
|
||
|
|
|
||
|
|
if bounding_box:
|
||
|
|
thickness = 2
|
||
|
|
color = self.colormap[self.obj_data["label"]]
|
||
|
|
|
||
|
|
# draw the bounding boxes on the frame
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
|
||
|
|
# draw any attributes
|
||
|
|
for attribute in self.thumbnail_data["attributes"]:
|
||
|
|
box = attribute["box"]
|
||
|
|
draw_box_with_label(
|
||
|
|
best_frame,
|
||
|
|
box[0],
|
||
|
|
box[1],
|
||
|
|
box[2],
|
||
|
|
box[3],
|
||
|
|
attribute["label"],
|
||
|
|
f"{attribute['score']:.0%}",
|
||
|
|
thickness=thickness,
|
||
|
|
color=color,
|
||
|
|
)
|
||
|
|
|
||
|
|
if crop:
|
||
|
|
box = self.thumbnail_data["box"]
|
||
|
|
box_size = 300
|
||
|
|
region = calculate_region(
|
||
|
|
best_frame.shape,
|
||
|
|
box[0],
|
||
|
|
box[1],
|
||
|
|
box[2],
|
||
|
|
box[3],
|
||
|
|
box_size,
|
||
|
|
multiplier=1.1,
|
||
|
|
)
|
||
|
|
best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
|
||
|
|
|
||
|
|
if 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 timestamp:
|
||
|
|
color = self.camera_config.timestamp_style.color
|
||
|
|
draw_timestamp(
|
||
|
|
best_frame,
|
||
|
|
self.thumbnail_data["frame_time"],
|
||
|
|
self.camera_config.timestamp_style.format,
|
||
|
|
font_effect=self.camera_config.timestamp_style.effect,
|
||
|
|
font_thickness=self.camera_config.timestamp_style.thickness,
|
||
|
|
font_color=(color.blue, color.green, color.red),
|
||
|
|
position=self.camera_config.timestamp_style.position,
|
||
|
|
)
|
||
|
|
|
||
|
|
ret, jpg = cv2.imencode(
|
||
|
|
".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), quality]
|
||
|
|
)
|
||
|
|
if ret:
|
||
|
|
return jpg.tobytes()
|
||
|
|
else:
|
||
|
|
return None
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
# if the object is not proportionally wide enough
|
||
|
|
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]]) -> str:
|
||
|
|
"""Find the best attribute for each object and return its ID."""
|
||
|
|
best_object_area = None
|
||
|
|
best_object_id = 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"]
|
||
|
|
elif object_area < best_object_area:
|
||
|
|
best_object_area = object_area
|
||
|
|
best_object_id = obj["id"]
|
||
|
|
|
||
|
|
return best_object_id
|