mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-30 19:09:13 +01:00
837 lines
31 KiB
Python
837 lines
31 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,
|
|
draw_timestamp,
|
|
calculate_region,
|
|
)
|
|
|
|
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"] + 0.05:
|
|
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.last_updated = 0
|
|
self.last_published = 0
|
|
self.frame = None
|
|
self.previous = self.to_dict()
|
|
|
|
# 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
|
|
return self.computed_score < threshold
|
|
|
|
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):
|
|
significant_update = False
|
|
zone_change = False
|
|
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"],
|
|
}
|
|
significant_update = True
|
|
|
|
# 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():
|
|
# if the zone is not for this object type, skip
|
|
if len(zone.objects) > 0 and not self.obj_data["label"] in zone.objects:
|
|
continue
|
|
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)
|
|
|
|
# if the zones changed, signal an update
|
|
if not self.false_positive and set(self.current_zones) != set(current_zones):
|
|
zone_change = True
|
|
|
|
self.current_zones = current_zones
|
|
return (significant_update, zone_change)
|
|
|
|
def to_dict(self, include_thumbnail: bool = False):
|
|
snapshot_time = (
|
|
self.thumbnail_data["frame_time"]
|
|
if not self.thumbnail_data is None
|
|
else 0.0
|
|
)
|
|
event = {
|
|
"id": self.obj_data["id"],
|
|
"camera": self.camera,
|
|
"frame_time": self.obj_data["frame_time"],
|
|
"snapshot_time": snapshot_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(),
|
|
}
|
|
|
|
if include_thumbnail:
|
|
event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8")
|
|
|
|
return event
|
|
|
|
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 = COLOR_MAP[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,
|
|
)
|
|
|
|
if crop:
|
|
box = self.thumbnail_data["box"]
|
|
region = calculate_region(
|
|
best_frame.shape, box[0], box[1], box[2], box[3], 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_scale=self.camera_config.timestamp_style.scale,
|
|
font_thickness=self.camera_config.timestamp_style.thickness,
|
|
font_color=(color.red, color.green, color.blue),
|
|
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
|
|
|
|
return False
|
|
|
|
|
|
# Maintains the state of a camera
|
|
class CameraState:
|
|
def __init__(
|
|
self, name, config: FrigateConfig, frame_manager: SharedMemoryFrameManager
|
|
):
|
|
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(int)
|
|
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)
|
|
|
|
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("bounding_boxes"):
|
|
# draw the bounding boxes on the frame
|
|
for obj in tracked_objects.values():
|
|
if obj["frame_time"] == frame_time:
|
|
thickness = 2
|
|
color = COLOR_MAP[obj["label"]]
|
|
else:
|
|
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"{obj['score']:.0%} {int(obj['area'])}",
|
|
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("mask"):
|
|
mask_overlay = np.where(self.camera_config.motion.mask == [0])
|
|
frame_copy[mask_overlay] = [0, 0, 0]
|
|
|
|
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_scale=self.camera_config.timestamp_style.scale,
|
|
font_thickness=self.camera_config.timestamp_style.thickness,
|
|
font_color=(color.red, color.green, color.blue),
|
|
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_time, current_detections, motion_boxes, regions):
|
|
# 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
|
|
)
|
|
|
|
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.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 = tracked_objects[id]
|
|
significant_update, zone_change = updated_obj.update(
|
|
frame_time, current_detections[id]
|
|
)
|
|
|
|
if significant_update:
|
|
# 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 publish
|
|
# and the last update is greater than the last publish or
|
|
# the object has changed zones
|
|
if (
|
|
frame_time - updated_obj.last_published > 5
|
|
and updated_obj.last_updated > updated_obj.last_published
|
|
) or zone_change:
|
|
# call event handlers
|
|
for c in self.callbacks["update"]:
|
|
c(self.name, updated_obj, frame_time)
|
|
updated_obj.last_published = frame_time
|
|
|
|
for id in removed_ids:
|
|
# publish events to mqtt
|
|
removed_obj = 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?
|
|
# maintain best objects
|
|
for obj in 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"] != 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(
|
|
obj.obj_data["label"]
|
|
for obj in tracked_objects.values()
|
|
if not obj.false_positive
|
|
)
|
|
|
|
# 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 obj_name not 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 = {
|
|
obj.thumbnail_data["frame_time"]
|
|
for obj in tracked_objects.values()
|
|
if not obj.false_positive
|
|
}
|
|
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.current_frame_time = frame_time
|
|
self.motion_boxes = motion_boxes
|
|
self.regions = regions
|
|
self._current_frame = current_frame
|
|
if self.previous_frame_id is not None:
|
|
self.frame_manager.close(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,
|
|
video_output_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.video_output_queue = video_output_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):
|
|
after = obj.to_dict()
|
|
message = {
|
|
"before": obj.previous,
|
|
"after": after,
|
|
"type": "new" if obj.previous["false_positive"] else "update",
|
|
}
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/events", json.dumps(message), retain=False
|
|
)
|
|
obj.previous = after
|
|
|
|
def end(camera, obj: TrackedObject, current_frame_time):
|
|
snapshot_config = self.config.cameras[camera].snapshots
|
|
event_data = obj.to_dict(include_thumbnail=True)
|
|
event_data["has_snapshot"] = False
|
|
if not obj.false_positive:
|
|
message = {
|
|
"before": obj.previous,
|
|
"after": obj.to_dict(),
|
|
"type": "end",
|
|
}
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/events", json.dumps(message), retain=False
|
|
)
|
|
# write snapshot to disk if enabled
|
|
if snapshot_config.enabled and self.should_save_snapshot(camera, obj):
|
|
jpg_bytes = obj.get_jpg_bytes(
|
|
timestamp=snapshot_config.timestamp,
|
|
bounding_box=snapshot_config.bounding_box,
|
|
crop=snapshot_config.crop,
|
|
height=snapshot_config.height,
|
|
quality=snapshot_config.quality,
|
|
)
|
|
if jpg_bytes is None:
|
|
logger.warning(
|
|
f"Unable to save snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
with open(
|
|
os.path.join(
|
|
CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"
|
|
),
|
|
"wb",
|
|
) as j:
|
|
j.write(jpg_bytes)
|
|
event_data["has_snapshot"] = True
|
|
|
|
# write clean snapshot if enabled
|
|
if snapshot_config.clean_copy:
|
|
png_bytes = obj.get_clean_png()
|
|
if png_bytes is None:
|
|
logger.warning(
|
|
f"Unable to save clean snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
with open(
|
|
os.path.join(
|
|
CLIPS_DIR,
|
|
f"{camera}-{obj.obj_data['id']}-clean.png",
|
|
),
|
|
"wb",
|
|
) as p:
|
|
p.write(png_bytes)
|
|
self.event_queue.put(("end", camera, event_data))
|
|
|
|
def snapshot(camera, obj: TrackedObject, current_frame_time):
|
|
mqtt_config = self.config.cameras[camera].mqtt
|
|
if mqtt_config.enabled and self.should_mqtt_snapshot(camera, obj):
|
|
jpg_bytes = obj.get_jpg_bytes(
|
|
timestamp=mqtt_config.timestamp,
|
|
bounding_box=mqtt_config.bounding_box,
|
|
crop=mqtt_config.crop,
|
|
height=mqtt_config.height,
|
|
quality=mqtt_config.quality,
|
|
)
|
|
|
|
if jpg_bytes is None:
|
|
logger.warning(
|
|
f"Unable to send mqtt snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/{camera}/{obj.obj_data['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.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(dict))
|
|
|
|
def should_save_snapshot(self, camera, obj: TrackedObject):
|
|
# if there are required zones and there is no overlap
|
|
required_zones = self.config.cameras[camera].snapshots.required_zones
|
|
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
|
|
logger.debug(
|
|
f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
|
|
)
|
|
return False
|
|
|
|
return True
|
|
|
|
def should_mqtt_snapshot(self, camera, obj: TrackedObject):
|
|
# if there are required zones and there is no overlap
|
|
required_zones = self.config.cameras[camera].mqtt.required_zones
|
|
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
|
|
logger.debug(
|
|
f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
|
|
)
|
|
return False
|
|
|
|
return True
|
|
|
|
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.thumbnail_data.copy()
|
|
best["frame"] = camera_state.frame_cache.get(
|
|
best_obj.thumbnail_data["frame_time"]
|
|
)
|
|
return best
|
|
else:
|
|
return {}
|
|
|
|
def get_current_frame(self, camera, draw_options={}):
|
|
return self.camera_states[camera].get_current_frame(draw_options)
|
|
|
|
def run(self):
|
|
while not self.stop_event.is_set():
|
|
try:
|
|
(
|
|
camera,
|
|
frame_time,
|
|
current_tracked_objects,
|
|
motion_boxes,
|
|
regions,
|
|
) = 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, motion_boxes, regions
|
|
)
|
|
|
|
self.video_output_queue.put(
|
|
(
|
|
camera,
|
|
frame_time,
|
|
current_tracked_objects,
|
|
motion_boxes,
|
|
regions,
|
|
)
|
|
)
|
|
|
|
# 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(
|
|
obj.obj_data["label"]
|
|
for obj in camera_state.tracked_objects.values()
|
|
if zone in obj.current_zones and not obj.false_positive
|
|
)
|
|
|
|
# update counts and publish status
|
|
for label in set(self.zone_data[zone].keys()) | set(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, clip_created = self.event_processed_queue.get()
|
|
if clip_created:
|
|
obj = self.camera_states[camera].tracked_objects[event_id]
|
|
message = {
|
|
"before": obj.previous,
|
|
"after": obj.to_dict(),
|
|
"type": "clip_ready",
|
|
}
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/events", json.dumps(message), retain=False
|
|
)
|
|
self.camera_states[camera].finished(event_id)
|
|
|
|
logger.info(f"Exiting object processor...")
|