mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
472 lines
18 KiB
Python
472 lines
18 KiB
Python
"""Maintains state of camera."""
|
|
|
|
import datetime
|
|
import logging
|
|
import os
|
|
import threading
|
|
from collections import defaultdict
|
|
from typing import Callable
|
|
|
|
import cv2
|
|
import numpy as np
|
|
|
|
from frigate.config import (
|
|
FrigateConfig,
|
|
ZoomingModeEnum,
|
|
)
|
|
from frigate.const import CLIPS_DIR, THUMB_DIR
|
|
from frigate.ptz.autotrack import PtzAutoTrackerThread
|
|
from frigate.track.tracked_object import TrackedObject
|
|
from frigate.util.image import (
|
|
SharedMemoryFrameManager,
|
|
draw_box_with_label,
|
|
draw_timestamp,
|
|
is_better_thumbnail,
|
|
is_label_printable,
|
|
)
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class CameraState:
|
|
def __init__(
|
|
self,
|
|
name,
|
|
config: FrigateConfig,
|
|
frame_manager: SharedMemoryFrameManager,
|
|
ptz_autotracker_thread: PtzAutoTrackerThread,
|
|
):
|
|
self.name = name
|
|
self.config = config
|
|
self.camera_config = config.cameras[name]
|
|
self.frame_manager = frame_manager
|
|
self.best_objects: dict[str, TrackedObject] = {}
|
|
self.tracked_objects: dict[str, TrackedObject] = {}
|
|
self.frame_cache = {}
|
|
self.zone_objects = defaultdict(list)
|
|
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.motion_boxes = []
|
|
self.regions = []
|
|
self.previous_frame_id = None
|
|
self.callbacks = defaultdict(list)
|
|
self.ptz_autotracker_thread = ptz_autotracker_thread
|
|
self.prev_enabled = self.camera_config.enabled
|
|
|
|
def get_current_frame(self, draw_options={}):
|
|
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()}
|
|
motion_boxes = self.motion_boxes.copy()
|
|
regions = self.regions.copy()
|
|
|
|
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
|
|
# draw on the frame
|
|
if draw_options.get("mask"):
|
|
mask_overlay = np.where(self.camera_config.motion.mask == [0])
|
|
frame_copy[mask_overlay] = [0, 0, 0]
|
|
|
|
if draw_options.get("bounding_boxes"):
|
|
# draw the bounding boxes on the frame
|
|
for obj in tracked_objects.values():
|
|
if obj["frame_time"] == frame_time:
|
|
if obj["stationary"]:
|
|
color = (220, 220, 220)
|
|
thickness = 1
|
|
else:
|
|
thickness = 2
|
|
color = self.config.model.colormap[obj["label"]]
|
|
else:
|
|
thickness = 1
|
|
color = (255, 0, 0)
|
|
|
|
# draw thicker box around ptz autotracked object
|
|
if (
|
|
self.camera_config.onvif.autotracking.enabled
|
|
and self.ptz_autotracker_thread.ptz_autotracker.autotracker_init[
|
|
self.name
|
|
]
|
|
and self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
|
|
self.name
|
|
]
|
|
is not None
|
|
and obj["id"]
|
|
== self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
|
|
self.name
|
|
].obj_data["id"]
|
|
and obj["frame_time"] == frame_time
|
|
):
|
|
thickness = 5
|
|
color = self.config.model.colormap[obj["label"]]
|
|
|
|
# debug autotracking zooming - show the zoom factor box
|
|
if (
|
|
self.camera_config.onvif.autotracking.zooming
|
|
!= ZoomingModeEnum.disabled
|
|
):
|
|
max_target_box = self.ptz_autotracker_thread.ptz_autotracker.tracked_object_metrics[
|
|
self.name
|
|
]["max_target_box"]
|
|
side_length = max_target_box * (
|
|
max(
|
|
self.camera_config.detect.width,
|
|
self.camera_config.detect.height,
|
|
)
|
|
)
|
|
|
|
centroid_x = (obj["box"][0] + obj["box"][2]) // 2
|
|
centroid_y = (obj["box"][1] + obj["box"][3]) // 2
|
|
top_left = (
|
|
int(centroid_x - side_length // 2),
|
|
int(centroid_y - side_length // 2),
|
|
)
|
|
bottom_right = (
|
|
int(centroid_x + side_length // 2),
|
|
int(centroid_y + side_length // 2),
|
|
)
|
|
cv2.rectangle(
|
|
frame_copy,
|
|
top_left,
|
|
bottom_right,
|
|
(255, 255, 0),
|
|
2,
|
|
)
|
|
|
|
# draw the bounding boxes on the frame
|
|
box = obj["box"]
|
|
text = (
|
|
obj["sub_label"][0]
|
|
if (
|
|
obj.get("sub_label") and is_label_printable(obj["sub_label"][0])
|
|
)
|
|
else obj.get("recognized_license_plate", [None])[0]
|
|
if (
|
|
obj.get("recognized_license_plate")
|
|
and obj["recognized_license_plate"][0]
|
|
)
|
|
else obj["label"]
|
|
)
|
|
draw_box_with_label(
|
|
frame_copy,
|
|
box[0],
|
|
box[1],
|
|
box[2],
|
|
box[3],
|
|
text,
|
|
f"{obj['score']:.0%} {int(obj['area'])}"
|
|
+ (
|
|
f" {float(obj['current_estimated_speed']):.1f}"
|
|
if obj["current_estimated_speed"] != 0
|
|
else ""
|
|
),
|
|
thickness=thickness,
|
|
color=color,
|
|
)
|
|
|
|
# draw any attributes
|
|
for attribute in obj["current_attributes"]:
|
|
box = attribute["box"]
|
|
draw_box_with_label(
|
|
frame_copy,
|
|
box[0],
|
|
box[1],
|
|
box[2],
|
|
box[3],
|
|
attribute["label"],
|
|
f"{attribute['score']:.0%}",
|
|
thickness=thickness,
|
|
color=color,
|
|
)
|
|
|
|
if draw_options.get("regions"):
|
|
for region in regions:
|
|
cv2.rectangle(
|
|
frame_copy,
|
|
(region[0], region[1]),
|
|
(region[2], region[3]),
|
|
(0, 255, 0),
|
|
2,
|
|
)
|
|
|
|
if draw_options.get("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)
|
|
|
|
if draw_options.get("motion_boxes"):
|
|
for m_box in motion_boxes:
|
|
cv2.rectangle(
|
|
frame_copy,
|
|
(m_box[0], m_box[1]),
|
|
(m_box[2], m_box[3]),
|
|
(0, 0, 255),
|
|
2,
|
|
)
|
|
|
|
if draw_options.get("timestamp"):
|
|
color = self.camera_config.timestamp_style.color
|
|
draw_timestamp(
|
|
frame_copy,
|
|
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,
|
|
)
|
|
|
|
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_name: str,
|
|
frame_time: float,
|
|
current_detections: dict[str, dict[str, any]],
|
|
motion_boxes: list[tuple[int, int, int, int]],
|
|
regions: list[tuple[int, int, int, int]],
|
|
):
|
|
current_frame = self.frame_manager.get(
|
|
frame_name, self.camera_config.frame_shape_yuv
|
|
)
|
|
|
|
tracked_objects = self.tracked_objects.copy()
|
|
current_ids = set(current_detections.keys())
|
|
previous_ids = set(tracked_objects.keys())
|
|
removed_ids = previous_ids.difference(current_ids)
|
|
new_ids = current_ids.difference(previous_ids)
|
|
updated_ids = current_ids.intersection(previous_ids)
|
|
|
|
for id in new_ids:
|
|
new_obj = tracked_objects[id] = TrackedObject(
|
|
self.config.model,
|
|
self.camera_config,
|
|
self.config.ui,
|
|
self.frame_cache,
|
|
current_detections[id],
|
|
)
|
|
|
|
# call event handlers
|
|
for c in self.callbacks["start"]:
|
|
c(self.name, new_obj, frame_name)
|
|
|
|
for id in updated_ids:
|
|
updated_obj = tracked_objects[id]
|
|
thumb_update, significant_update, autotracker_update = updated_obj.update(
|
|
frame_time, current_detections[id], current_frame is not None
|
|
)
|
|
|
|
if autotracker_update or significant_update:
|
|
for c in self.callbacks["autotrack"]:
|
|
c(self.name, updated_obj, frame_name)
|
|
|
|
if thumb_update and current_frame is not None:
|
|
# ensure this frame is stored in the cache
|
|
if (
|
|
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)
|
|
|
|
updated_obj.last_updated = frame_time
|
|
|
|
# if it has been more than 5 seconds since the last thumb update
|
|
# and the last update is greater than the last publish or
|
|
# the object has changed significantly
|
|
if (
|
|
frame_time - updated_obj.last_published > 5
|
|
and updated_obj.last_updated > updated_obj.last_published
|
|
) or significant_update:
|
|
# call event handlers
|
|
for c in self.callbacks["update"]:
|
|
c(self.name, updated_obj, frame_name)
|
|
updated_obj.last_published = frame_time
|
|
|
|
for id in removed_ids:
|
|
# publish events to mqtt
|
|
removed_obj = tracked_objects[id]
|
|
if "end_time" not 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_name)
|
|
|
|
# TODO: can i switch to looking this up and only changing when an event ends?
|
|
# maintain best objects
|
|
camera_activity: dict[str, list[any]] = {
|
|
"motion": len(motion_boxes) > 0,
|
|
"objects": [],
|
|
}
|
|
|
|
for obj in tracked_objects.values():
|
|
object_type = obj.obj_data["label"]
|
|
active = obj.is_active()
|
|
|
|
if not obj.false_positive:
|
|
label = object_type
|
|
sub_label = None
|
|
|
|
if obj.obj_data.get("sub_label"):
|
|
if (
|
|
obj.obj_data.get("sub_label")[0]
|
|
in self.config.model.all_attributes
|
|
):
|
|
label = obj.obj_data["sub_label"][0]
|
|
else:
|
|
label = f"{object_type}-verified"
|
|
sub_label = obj.obj_data["sub_label"][0]
|
|
|
|
camera_activity["objects"].append(
|
|
{
|
|
"id": obj.obj_data["id"],
|
|
"label": label,
|
|
"stationary": not active,
|
|
"area": obj.obj_data["area"],
|
|
"ratio": obj.obj_data["ratio"],
|
|
"score": obj.obj_data["score"],
|
|
"sub_label": sub_label,
|
|
"current_zones": obj.current_zones,
|
|
}
|
|
)
|
|
|
|
# if we don't have access to the current frame or
|
|
# if the object's thumbnail is not from the current frame, skip
|
|
if (
|
|
current_frame is None
|
|
or obj.thumbnail_data is None
|
|
or obj.false_positive
|
|
or obj.thumbnail_data["frame_time"] != 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(
|
|
object_type,
|
|
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_name)
|
|
else:
|
|
self.best_objects[object_type] = obj
|
|
for c in self.callbacks["snapshot"]:
|
|
c(self.name, self.best_objects[object_type], frame_name)
|
|
|
|
for c in self.callbacks["camera_activity"]:
|
|
c(self.name, camera_activity)
|
|
|
|
# cleanup thumbnail frame cache
|
|
current_thumb_frames = {
|
|
obj.thumbnail_data["frame_time"]
|
|
for obj in tracked_objects.values()
|
|
if not obj.false_positive and obj.thumbnail_data is not None
|
|
}
|
|
current_best_frames = {
|
|
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 t not in current_thumb_frames and t not in current_best_frames
|
|
]
|
|
for t in thumb_frames_to_delete:
|
|
del self.frame_cache[t]
|
|
|
|
with self.current_frame_lock:
|
|
self.tracked_objects = tracked_objects
|
|
self.motion_boxes = motion_boxes
|
|
self.regions = regions
|
|
|
|
if current_frame is not None:
|
|
self.current_frame_time = frame_time
|
|
self._current_frame = np.copy(current_frame)
|
|
|
|
if self.previous_frame_id is not None:
|
|
self.frame_manager.close(self.previous_frame_id)
|
|
|
|
self.previous_frame_id = frame_name
|
|
|
|
def save_manual_event_image(
|
|
self,
|
|
frame: np.ndarray | None,
|
|
event_id: str,
|
|
label: str,
|
|
draw: dict[str, list[dict]],
|
|
) -> None:
|
|
img_frame = frame if frame is not None else self.get_current_frame()
|
|
|
|
# write clean snapshot if enabled
|
|
if self.camera_config.snapshots.clean_copy:
|
|
ret, png = cv2.imencode(".png", img_frame)
|
|
|
|
if ret:
|
|
with open(
|
|
os.path.join(
|
|
CLIPS_DIR,
|
|
f"{self.camera_config.name}-{event_id}-clean.png",
|
|
),
|
|
"wb",
|
|
) as p:
|
|
p.write(png.tobytes())
|
|
|
|
# write jpg snapshot with optional annotations
|
|
if draw.get("boxes") and isinstance(draw.get("boxes"), list):
|
|
for box in draw.get("boxes"):
|
|
x = int(box["box"][0] * self.camera_config.detect.width)
|
|
y = int(box["box"][1] * self.camera_config.detect.height)
|
|
width = int(box["box"][2] * self.camera_config.detect.width)
|
|
height = int(box["box"][3] * self.camera_config.detect.height)
|
|
|
|
draw_box_with_label(
|
|
img_frame,
|
|
x,
|
|
y,
|
|
x + width,
|
|
y + height,
|
|
label,
|
|
f"{box.get('score', '-')}% {int(width * height)}",
|
|
thickness=2,
|
|
color=box.get("color", (255, 0, 0)),
|
|
)
|
|
|
|
ret, jpg = cv2.imencode(".jpg", img_frame)
|
|
with open(
|
|
os.path.join(CLIPS_DIR, f"{self.camera_config.name}-{event_id}.jpg"),
|
|
"wb",
|
|
) as j:
|
|
j.write(jpg.tobytes())
|
|
|
|
# create thumbnail with max height of 175 and save
|
|
width = int(175 * img_frame.shape[1] / img_frame.shape[0])
|
|
thumb = cv2.resize(img_frame, dsize=(width, 175), interpolation=cv2.INTER_AREA)
|
|
thumb_path = os.path.join(THUMB_DIR, self.camera_config.name)
|
|
os.makedirs(thumb_path, exist_ok=True)
|
|
cv2.imwrite(os.path.join(thumb_path, f"{event_id}.webp"), thumb)
|
|
|
|
def shutdown(self) -> None:
|
|
for obj in self.tracked_objects.values():
|
|
if not obj.obj_data.get("end_time"):
|
|
obj.write_thumbnail_to_disk()
|