Merge branch 'dev' of github.com:blakeblackshear/frigate into audio-events

Dockerfile

@@ -129,7 +129,9 @@ RUN apt-get -qq update \
     libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
     # scipy dependencies
-    gcc gfortran libopenblas-dev liblapack-dev && \
+    gcc gfortran libopenblas-dev liblapack-dev \
+    # faster-fifo dependencies
+    g++ cython3 && \
     rm -rf /var/lib/apt/lists/*
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \

benchmark_motion.py

@@ -1,14 +1,13 @@
 import datetime
 import multiprocessing as mp
 import os
-from statistics import mean
 
 import cv2
 import numpy as np
 
 from frigate.config import MotionConfig
-from frigate.motion.frigate_motion import FrigateMotionDetector
 from frigate.motion.improved_motion import ImprovedMotionDetector
+from frigate.util import create_mask
 
 # get info on the video
 # cap = cv2.VideoCapture("debug/front_cam_2023_05_23_08_41__2023_05_23_08_43.mp4")
@@ -20,84 +19,85 @@ height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 fps = cap.get(cv2.CAP_PROP_FPS)
 frame_shape = (height, width, 3)
 
+mask = create_mask(
+    (height, width),
+    [],
+)
+
 # create the motion config
-motion_config = MotionConfig()
+motion_config_1 = MotionConfig()
-motion_config.mask = np.zeros((height, width), np.uint8)
+motion_config_1.mask = np.zeros((height, width), np.uint8)
-motion_config.mask[:] = 255
+motion_config_1.mask[:] = mask
-motion_config.improve_contrast = 1
+# motion_config_1.improve_contrast = 1
-motion_config.frame_alpha = 0.02
+# motion_config_1.frame_height = 150
-motion_config.threshold = 40
+# motion_config_1.frame_alpha = 0.02
-motion_config.contour_area = 15
+# motion_config_1.threshold = 30
+# motion_config_1.contour_area = 10
+
+motion_config_2 = MotionConfig()
+motion_config_2.mask = np.zeros((height, width), np.uint8)
+motion_config_2.mask[:] = mask
+# motion_config_2.improve_contrast = 1
+# motion_config_2.frame_height = 150
+# motion_config_2.frame_alpha = 0.01
+# motion_config_2.threshold = 20
+# motion_config.contour_area = 10
 save_images = True
 
-# create motion detectors
+improved_motion_detector_1 = ImprovedMotionDetector(
-frigate_motion_detector = FrigateMotionDetector(
     frame_shape=frame_shape,
-    config=motion_config,
+    config=motion_config_1,
     fps=fps,
-    improve_contrast=mp.Value("i", motion_config.improve_contrast),
+    improve_contrast=mp.Value("i", motion_config_1.improve_contrast),
-    threshold=mp.Value("i", motion_config.threshold),
+    threshold=mp.Value("i", motion_config_1.threshold),
-    contour_area=mp.Value("i", motion_config.contour_area),
+    contour_area=mp.Value("i", motion_config_1.contour_area),
+    name="default",
+    clipLimit=2.0,
+    tileGridSize=(8, 8),
 )
-frigate_motion_detector.save_images = save_images
+improved_motion_detector_1.save_images = save_images
 
-improved_motion_detector = ImprovedMotionDetector(
+improved_motion_detector_2 = ImprovedMotionDetector(
     frame_shape=frame_shape,
-    config=motion_config,
+    config=motion_config_2,
     fps=fps,
-    improve_contrast=mp.Value("i", motion_config.improve_contrast),
+    improve_contrast=mp.Value("i", motion_config_2.improve_contrast),
-    threshold=mp.Value("i", motion_config.threshold),
+    threshold=mp.Value("i", motion_config_2.threshold),
-    contour_area=mp.Value("i", motion_config.contour_area),
+    contour_area=mp.Value("i", motion_config_2.contour_area),
+    name="compare",
 )
-improved_motion_detector.save_images = save_images
+improved_motion_detector_2.save_images = save_images
 
 # read and process frames
-frame_times = {"frigate": [], "improved": []}
 ret, frame = cap.read()
 frame_counter = 1
 while ret:
     yuv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV_I420)
 
     start_frame = datetime.datetime.now().timestamp()
-    frigate_motion_detector.detect(yuv_frame)
+    improved_motion_detector_1.detect(yuv_frame)
-    frame_times["frigate"].append(datetime.datetime.now().timestamp() - start_frame)
 
     start_frame = datetime.datetime.now().timestamp()
-    improved_motion_detector.detect(yuv_frame)
+    improved_motion_detector_2.detect(yuv_frame)
-    frame_times["improved"].append(datetime.datetime.now().timestamp() - start_frame)
 
-    frigate_frame = f"debug/frames/frigate-{frame_counter}.jpg"
+    default_frame = f"debug/frames/default-{frame_counter}.jpg"
-    improved_frame = f"debug/frames/improved-{frame_counter}.jpg"
+    compare_frame = f"debug/frames/compare-{frame_counter}.jpg"
-    if os.path.exists(frigate_frame) and os.path.exists(improved_frame):
+    if os.path.exists(default_frame) and os.path.exists(compare_frame):
-        image_row_1 = cv2.hconcat(
+        images = [
-            [
+            cv2.imread(default_frame),
-                cv2.imread(frigate_frame),
+            cv2.imread(compare_frame),
-                cv2.imread(improved_frame),
+        ]
-            ]
-        )
-
-        image_row_2 = cv2.resize(
-            frame,
-            dsize=(
-                frigate_motion_detector.motion_frame_size[1] * 2,
-                frigate_motion_detector.motion_frame_size[0] * 2,
-            ),
-            interpolation=cv2.INTER_LINEAR,
-        )
 
         cv2.imwrite(
             f"debug/frames/all-{frame_counter}.jpg",
-            cv2.vconcat([image_row_1, image_row_2]),
+            cv2.vconcat(images)
+            if frame_shape[0] > frame_shape[1]
+            else cv2.hconcat(images),
         )
-        os.unlink(frigate_frame)
+        os.unlink(default_frame)
-        os.unlink(improved_frame)
+        os.unlink(compare_frame)
     frame_counter += 1
 
     ret, frame = cap.read()
 
 cap.release()
-
-print("Frigate Motion Detector")
-print(f"Average frame processing time: {mean(frame_times['frigate'])*1000:.2f}ms")
-print("Improved Motion Detector")
-print(f"Average frame processing time: {mean(frame_times['improved'])*1000:.2f}ms")

docs/docs/configuration/index.md

@@ -230,7 +230,7 @@ detect:
   # especially when using separate streams for detect and record.
   # Use this setting to make the timeline bounding boxes more closely align
   # with the recording. The value can be positive or negative.
-  # TIP: Imagine there is an event clip with a person walking from left to right. 
+  # TIP: Imagine there is an event clip with a person walking from left to right.
   #      If the event timeline bounding box is consistently to the left of the person
   #      then the value should be decreased. Similarly, if a person is walking from
   #      left to right and the bounding box is consistently ahead of the person
@@ -275,7 +275,7 @@ motion:
   # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
   # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
   # The value should be between 1 and 255.
-  threshold: 40
+  threshold: 20
   # Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection
   #           needs to recalibrate. (default: shown below)
   # Increasing this value will make motion detection more likely to consider lightning or ir mode changes as valid motion.
@@ -286,19 +286,19 @@ motion:
   # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
   # make motion detection more sensitive to smaller moving objects.
   # As a rule of thumb:
-  #  - 15 - high sensitivity
+  #  - 10 - high sensitivity
   #  - 30 - medium sensitivity
   #  - 50 - low sensitivity
-  contour_area: 15
+  contour_area: 10
   # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
   # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
   # Low values will cause things like moving shadows to be detected as motion for longer.
   # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
-  frame_alpha: 0.02
+  frame_alpha: 0.01
   # Optional: Height of the resized motion frame  (default: 50)
   # Higher values will result in more granular motion detection at the expense of higher CPU usage.
   # Lower values result in less CPU, but small changes may not register as motion.
-  frame_height: 50
+  frame_height: 100
   # Optional: motion mask
   # NOTE: see docs for more detailed info on creating masks
   mask: 0,900,1080,900,1080,1920,0,1920

frigate/app.py

@@ -6,12 +6,12 @@ import shutil
 import signal
 import sys
 import traceback
-from multiprocessing.queues import Queue
 from multiprocessing.synchronize import Event as MpEvent
 from types import FrameType
 from typing import Optional
 
 import psutil
+from faster_fifo import Queue
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
 from playhouse.sqliteq import SqliteQueueDatabase

frigate/config.py

@@ -188,7 +188,7 @@ class RecordConfig(FrigateBaseModel):
 
 class MotionConfig(FrigateBaseModel):
     threshold: int = Field(
-        default=40,
+        default=20,
         title="Motion detection threshold (1-255).",
         ge=1,
         le=255,
@@ -197,10 +197,10 @@ class MotionConfig(FrigateBaseModel):
         default=0.8, title="Lightning detection threshold (0.3-1.0).", ge=0.3, le=1.0
     )
     improve_contrast: bool = Field(default=True, title="Improve Contrast")
-    contour_area: Optional[int] = Field(default=15, title="Contour Area")
+    contour_area: Optional[int] = Field(default=10, title="Contour Area")
     delta_alpha: float = Field(default=0.2, title="Delta Alpha")
-    frame_alpha: float = Field(default=0.02, title="Frame Alpha")
+    frame_alpha: float = Field(default=0.01, title="Frame Alpha")
-    frame_height: Optional[int] = Field(default=50, title="Frame Height")
+    frame_height: Optional[int] = Field(default=100, title="Frame Height")
     mask: Union[str, List[str]] = Field(
         default="", title="Coordinates polygon for the motion mask."
     )

frigate/const.py

@@ -13,6 +13,16 @@ PLUS_ENV_VAR = "PLUS_API_KEY"
 PLUS_API_HOST = "https://api.frigate.video"
 BTBN_PATH = "/usr/lib/btbn-ffmpeg"
 
+# Attributes
+
+ATTRIBUTE_LABEL_MAP = {
+    "person": ["face", "amazon"],
+    "car": ["ups", "fedex", "amazon", "license_plate"],
+}
+ALL_ATTRIBUTE_LABELS = [
+    item for sublist in ATTRIBUTE_LABEL_MAP.values() for item in sublist
+]
+
 # Audio Consts
 
 AUDIO_DURATION = 0.975

frigate/events/external.py

@@ -6,10 +6,10 @@ import logging
 import os
 import random
 import string
-from multiprocessing.queues import Queue
 from typing import Optional
 
 import cv2
+from faster_fifo import Queue
 
 from frigate.config import CameraConfig, FrigateConfig
 from frigate.const import CLIPS_DIR

frigate/events/maintainer.py

@@ -3,10 +3,11 @@ import logging
 import queue
 import threading
 from enum import Enum
-from multiprocessing.queues import Queue
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Dict
 
+from faster_fifo import Queue
+
 from frigate.config import EventsConfig, FrigateConfig
 from frigate.models import Event
 from frigate.types import CameraMetricsTypes

frigate/http.py

@@ -410,6 +410,24 @@ def set_sub_label(id):
     )
 
 
+@bp.route("/labels")
+def get_labels():
+    camera = request.args.get("camera", type=str, default="")
+
+    try:
+        if camera:
+            events = Event.select(Event.label).where(Event.camera == camera).distinct()
+        else:
+            events = Event.select(Event.label).distinct()
+    except Exception as e:
+        return jsonify(
+            {"success": False, "message": f"Failed to get labels: {e}"}, "404"
+        )
+
+    labels = sorted([e.label for e in events])
+    return jsonify(labels)
+
+
 @bp.route("/sub_labels")
 def get_sub_labels():
     split_joined = request.args.get("split_joined", type=int)

frigate/log.py

@@ -7,10 +7,10 @@ import signal
 import threading
 from collections import deque
 from logging import handlers
-from multiprocessing.queues import Queue
 from types import FrameType
 from typing import Deque, Optional
 
+from faster_fifo import Queue
 from setproctitle import setproctitle
 
 from frigate.util import clean_camera_user_pass

frigate/motion/improved_motion.py

@@ -15,7 +15,11 @@ class ImprovedMotionDetector(MotionDetector):
         improve_contrast,
         threshold,
         contour_area,
+        clipLimit=2.0,
+        tileGridSize=(2, 2),
+        name="improved",
     ):
+        self.name = name
         self.config = config
         self.frame_shape = frame_shape
         self.resize_factor = frame_shape[0] / config.frame_height
@@ -38,6 +42,7 @@ class ImprovedMotionDetector(MotionDetector):
         self.improve_contrast = improve_contrast
         self.threshold = threshold
         self.contour_area = contour_area
+        self.clahe = cv2.createCLAHE(clipLimit=clipLimit, tileGridSize=tileGridSize)
 
     def detect(self, frame):
         motion_boxes = []
@@ -51,11 +56,20 @@ class ImprovedMotionDetector(MotionDetector):
             interpolation=cv2.INTER_LINEAR,
         )
 
+        if self.save_images:
+            resized_saved = resized_frame.copy()
+
         resized_frame = cv2.GaussianBlur(resized_frame, (3, 3), cv2.BORDER_DEFAULT)
 
+        if self.save_images:
+            blurred_saved = resized_frame.copy()
+
         # Improve contrast
         if self.improve_contrast.value:
-            resized_frame = cv2.equalizeHist(resized_frame)
+            resized_frame = self.clahe.apply(resized_frame)
+
+        if self.save_images:
+            contrasted_saved = resized_frame.copy()
 
         # mask frame
         resized_frame[self.mask] = [255]
@@ -118,8 +132,19 @@ class ImprovedMotionDetector(MotionDetector):
                     (0, 0, 255),
                     2,
                 )
+            frames = [
+                cv2.cvtColor(resized_saved, cv2.COLOR_GRAY2BGR),
+                cv2.cvtColor(blurred_saved, cv2.COLOR_GRAY2BGR),
+                cv2.cvtColor(contrasted_saved, cv2.COLOR_GRAY2BGR),
+                cv2.cvtColor(frameDelta, cv2.COLOR_GRAY2BGR),
+                cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR),
+                thresh_dilated,
+            ]
             cv2.imwrite(
-                f"debug/frames/improved-{self.frame_counter}.jpg", thresh_dilated
+                f"debug/frames/{self.name}-{self.frame_counter}.jpg",
+                cv2.hconcat(frames)
+                if self.frame_shape[0] > self.frame_shape[1]
+                else cv2.vconcat(frames),
             )
 
         if len(motion_boxes) > 0:

frigate/test/test_config.py

@@ -730,7 +730,7 @@ class TestConfig(unittest.TestCase):
         assert config == frigate_config.dict(exclude_unset=True)
 
         runtime_config = frigate_config.runtime_config()
-        assert runtime_config.cameras["back"].motion.frame_height == 50
+        assert runtime_config.cameras["back"].motion.frame_height == 100
 
     def test_motion_contour_area_dynamic(self):
         config = {
@@ -758,7 +758,7 @@ class TestConfig(unittest.TestCase):
         assert config == frigate_config.dict(exclude_unset=True)
 
         runtime_config = frigate_config.runtime_config()
-        assert round(runtime_config.cameras["back"].motion.contour_area) == 15
+        assert round(runtime_config.cameras["back"].motion.contour_area) == 10
 
     def test_merge_labelmap(self):
         config = {

frigate/timeline.py

@@ -3,9 +3,10 @@
 import logging
 import queue
 import threading
-from multiprocessing.queues import Queue
 from multiprocessing.synchronize import Event as MpEvent
 
+from faster_fifo import Queue
+
 from frigate.config import FrigateConfig
 from frigate.events.maintainer import EventTypeEnum
 from frigate.models import Timeline

frigate/types.py

@@ -1,8 +1,9 @@
 from multiprocessing.context import Process
-from multiprocessing.queues import Queue
 from multiprocessing.sharedctypes import Synchronized
 from typing import Optional, TypedDict
 
+from faster_fifo import Queue
+
 from frigate.object_detection import ObjectDetectProcess
 
 

frigate/util.py

@@ -650,34 +650,42 @@ def restart_frigate():
 
 
 class EventsPerSecond:
-    def __init__(self, max_events=1000):
+    def __init__(self, max_events=1000, last_n_seconds=10):
         self._start = None
         self._max_events = max_events
+        self._last_n_seconds = last_n_seconds
         self._timestamps = []
 
     def start(self):
         self._start = datetime.datetime.now().timestamp()
 
     def update(self):
+        now = datetime.datetime.now().timestamp()
         if self._start is None:
-            self.start()
+            self._start = now
-        self._timestamps.append(datetime.datetime.now().timestamp())
+        self._timestamps.append(now)
         # truncate the list when it goes 100 over the max_size
         if len(self._timestamps) > self._max_events + 100:
             self._timestamps = self._timestamps[(1 - self._max_events) :]
+        self.expire_timestamps(now)
 
-    def eps(self, last_n_seconds=10):
+    def eps(self):
-        if self._start is None:
-            self.start()
-        # compute the (approximate) events in the last n seconds
         now = datetime.datetime.now().timestamp()
-        seconds = min(now - self._start, last_n_seconds)
+        if self._start is None:
+            self._start = now
+        # compute the (approximate) events in the last n seconds
+        self.expire_timestamps(now)
+        seconds = min(now - self._start, self._last_n_seconds)
         # avoid divide by zero
         if seconds == 0:
             seconds = 1
-        return (
+        return len(self._timestamps) / seconds
-            len([t for t in self._timestamps if t > (now - last_n_seconds)]) / seconds
+
-        )
+    # remove aged out timestamps
+    def expire_timestamps(self, now):
+        threshold = now - self._last_n_seconds
+        while self._timestamps and self._timestamps[0] < threshold:
+            del self._timestamps[0]
 
 
 def print_stack(sig, frame):

frigate/video.py

@@ -15,7 +15,7 @@ import numpy as np
 from setproctitle import setproctitle
 
 from frigate.config import CameraConfig, DetectConfig
-from frigate.const import CACHE_DIR
+from frigate.const import ALL_ATTRIBUTE_LABELS, ATTRIBUTE_LABEL_MAP, CACHE_DIR
 from frigate.detectors.detector_config import PixelFormatEnum
 from frigate.log import LogPipe
 from frigate.motion import MotionDetector
@@ -172,7 +172,7 @@ def capture_frames(
     skipped_eps.start()
     while True:
         fps.value = frame_rate.eps()
-        skipped_eps.eps()
+        skipped_fps.value = skipped_eps.eps()
 
         current_frame.value = datetime.datetime.now().timestamp()
         frame_name = f"{camera_name}{current_frame.value}"
@@ -215,6 +215,7 @@ class CameraWatchdog(threading.Thread):
         config: CameraConfig,
         frame_queue,
         camera_fps,
+        skipped_fps,
         ffmpeg_pid,
         stop_event,
     ):
@@ -227,6 +228,7 @@ class CameraWatchdog(threading.Thread):
         self.logpipe = LogPipe(f"ffmpeg.{self.camera_name}.detect")
         self.ffmpeg_other_processes: list[dict[str, any]] = []
         self.camera_fps = camera_fps
+        self.skipped_fps = skipped_fps
         self.ffmpeg_pid = ffmpeg_pid
         self.frame_queue = frame_queue
         self.frame_shape = self.config.frame_shape_yuv
@@ -346,6 +348,7 @@ class CameraWatchdog(threading.Thread):
             self.frame_shape,
             self.frame_queue,
             self.camera_fps,
+            self.skipped_fps,
             self.stop_event,
         )
         self.capture_thread.start()
@@ -376,7 +379,14 @@ class CameraWatchdog(threading.Thread):
 
 class CameraCapture(threading.Thread):
     def __init__(
-        self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps, stop_event
+        self,
+        camera_name,
+        ffmpeg_process,
+        frame_shape,
+        frame_queue,
+        fps,
+        skipped_fps,
+        stop_event,
     ):
         threading.Thread.__init__(self)
         self.name = f"capture:{camera_name}"
@@ -385,14 +395,13 @@ class CameraCapture(threading.Thread):
         self.frame_queue = frame_queue
         self.fps = fps
         self.stop_event = stop_event
-        self.skipped_fps = EventsPerSecond()
+        self.skipped_fps = skipped_fps
         self.frame_manager = SharedMemoryFrameManager()
         self.ffmpeg_process = ffmpeg_process
         self.current_frame = mp.Value("d", 0.0)
         self.last_frame = 0
 
     def run(self):
-        self.skipped_fps.start()
         capture_frames(
             self.ffmpeg_process,
             self.camera_name,
@@ -424,6 +433,7 @@ def capture_camera(name, config: CameraConfig, process_info):
         config,
         frame_queue,
         process_info["camera_fps"],
+        process_info["skipped_fps"],
         process_info["ffmpeg_pid"],
         stop_event,
     )
@@ -723,14 +733,6 @@ def process_frames(
     stop_event,
     exit_on_empty: bool = False,
 ):
-    # attribute labels are not tracked and are not assigned regions
-    attribute_label_map = {
-        "person": ["face", "amazon"],
-        "car": ["ups", "fedex", "amazon", "license_plate"],
-    }
-    all_attribute_labels = [
-        item for sublist in attribute_label_map.values() for item in sublist
-    ]
     fps = process_info["process_fps"]
     detection_fps = process_info["detection_fps"]
     current_frame_time = process_info["detection_frame"]
@@ -906,7 +908,7 @@ def process_frames(
                 tracked_detections = [
                     d
                     for d in consolidated_detections
-                    if d[0] not in all_attribute_labels
+                    if d[0] not in ALL_ATTRIBUTE_LABELS
                 ]
                 # now that we have refined our detections, we need to track objects
                 object_tracker.match_and_update(frame_time, tracked_detections)
@@ -916,7 +918,7 @@ def process_frames(
 
         # group the attribute detections based on what label they apply to
         attribute_detections = {}
-        for label, attribute_labels in attribute_label_map.items():
+        for label, attribute_labels in ATTRIBUTE_LABEL_MAP.items():
             attribute_detections[label] = [
                 d for d in consolidated_detections if d[0] in attribute_labels
             ]

requirements-wheels.txt

@@ -1,5 +1,6 @@
 click == 8.1.*
 Flask == 2.3.*
+faster-fifo == 1.4.*
 imutils == 0.5.*
 matplotlib == 3.7.*
 mypy == 0.942

web/src/components/Tooltip.jsx

@@ -29,7 +29,7 @@ export default function Tooltip({ relativeTo, text }) {
       let newLeft = left - Math.round(tipWidth / 2);
       // too far right
       if (newLeft + tipWidth + TIP_SPACE > windowWidth - window.scrollX) {
-        newLeft = left - tipWidth - TIP_SPACE;
+        newLeft = Math.max(0, left - tipWidth - TIP_SPACE);
         newTop = top - Math.round(tipHeight / 2);
       }
       // too far left

web/src/routes/Camera.jsx

@@ -22,6 +22,7 @@ const emptyObject = Object.freeze({});
 
 export default function Camera({ camera }) {
   const { data: config } = useSWR('config');
+  const { data: trackedLabels } = useSWR(['labels', { camera }]);
   const apiHost = useApiHost();
   const [showSettings, setShowSettings] = useState(false);
   const [viewMode, setViewMode] = useState('live');
@@ -121,7 +122,9 @@ export default function Camera({ camera }) {
             <div className="max-w-5xl">
               <video-stream
                 mode="mse"
-                src={new URL(`${baseUrl.replace(/^http/, 'ws')}live/webrtc/api/ws?src=${cameraConfig.live.stream_name}`)}
+                src={
+                  new URL(`${baseUrl.replace(/^http/, 'ws')}live/webrtc/api/ws?src=${cameraConfig.live.stream_name}`)
+                }
               />
             </div>
           </Fragment>
@@ -203,7 +206,7 @@ export default function Camera({ camera }) {
       <div className="space-y-4">
         <Heading size="sm">Tracked objects</Heading>
         <div className="flex flex-wrap justify-start">
-          {cameraConfig.objects.track.map((objectType) => (
+          {(trackedLabels || []).map((objectType) => (
             <Card
               className="mb-4 mr-4"
               key={objectType}

web/src/routes/Events.jsx

@@ -106,6 +106,7 @@ export default function Events({ path, ...props }) {
 
   const { data: config } = useSWR('config');
 
+  const { data: allLabels } = useSWR(['labels']);
   const { data: allSubLabels } = useSWR(['sub_labels', { split_joined: 1 }]);
 
   const filterValues = useMemo(
@@ -120,15 +121,10 @@ export default function Events({ path, ...props }) {
           .filter((value, i, self) => self.indexOf(value) === i),
         'None',
       ],
-      labels: Object.values(config?.cameras || {})
+      labels: Object.values(allLabels || {}),
-        .reduce((memo, camera) => {
-          memo = memo.concat(camera?.objects?.track || []);
-          return memo;
-        }, config?.objects?.track || [])
-        .filter((value, i, self) => self.indexOf(value) === i),
       sub_labels: (allSubLabels || []).length > 0 ? [...Object.values(allSubLabels), 'None'] : [],
     }),
-    [config, allSubLabels]
+    [config, allLabels, allSubLabels]
   );
 
   const onSave = async (e, eventId, save) => {