blakeblackshear.frigate/frigate/object_processing.py

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
        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():
            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):
            significant_update = True

        self.current_zones = current_zones
        return significant_update

    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
    ):
        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), 70])
        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, frame_manager):
        self.name = name
        self.config = config
        self.camera_config = config.cameras[name]
        self.frame_manager = frame_manager
        self.best_objects: Dict[str, TrackedObject] = {}
        self.object_counts = defaultdict(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 = 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
            if (
                frame_time - updated_obj.last_published > 5
                and updated_obj.last_updated > updated_obj.last_published
            ):
                # 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,
                    )
                    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,
                )

                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...")