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Author SHA1 Message Date
gwmullin
d309d18fbe
Merge c575fb223b into c25a522fcc 2026-06-02 13:03:24 -07:00
T13o
c25a522fcc
docs: fix spelling mistakes in documentation (#23380)
* docs: fix spelling mistakes in documentation

* docs: fix typos and revert incorrect dfine to define rename

* docs: fix typo in installation.md

---------

Co-authored-by: TheInfamousToTo <TheInfamousToTo@users.noreply.github.com>
2026-06-02 05:49:42 -06:00
Josh Hawkins
db9e64c598
replace motion activity resample apply/agg lambdas with vectorized max() and first() (#23383)
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2026-06-01 15:51:43 -06:00
Josh Hawkins
570e21340a
Miscellaneous fixes (#23373)
* republish MQTT switch states when a profile is activated or deactivated

* fix object mask default name when created from Explore tracking details

* tweak annotation offset max in UI

* optimize recordings/unavailable gap detection and drop empty motion activity buckets

* add tests
2026-06-01 13:55:52 -06:00
11 changed files with 198 additions and 30 deletions

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@ -179,7 +179,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
| Hikvision DS-2DE3A404IWG-E/W | ✅ | ✅ | |
| Reolink | ✅ | ❌ | |
| Speco O8P32X | ✅ | ❌ | |
| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable. |
| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatible. |
| Tapo | ✅ | ❌ | Many models supported, ONVIF Service Port: 2020 |
| Uniview IPC672LR-AX4DUPK | ✅ | ❌ | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands |
| Uniview IPC6612SR-X33-VG | ✅ | ✅ | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working. |

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@ -660,7 +660,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
#### RF-DETR
[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
:::warning

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@ -749,7 +749,7 @@ Failure to remap port 5000 on the host will result in the WebUI and all API endp
:::
Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native Swift app). The difference in inference speeds is negligible, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
@ -768,7 +768,7 @@ services:
- /path/to/your/recordings:/recordings
ports:
- "8971:8971"
# If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts
# If exposing on macOS map to a different host port like 5001 or any other port with no conflicts
# - "5001:5000" # Internal unauthenticated access. Expose carefully.
- "8554:8554" # RTSP feeds
extra_hosts:

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@ -299,22 +299,36 @@ async def no_recordings(
.iterator()
)
# Convert recordings to list of (start, end) tuples
# Convert recordings to list of (start, end) tuples, ordered by start_time
recordings = [(r["start_time"], r["end_time"]) for r in data]
# Merge overlapping/adjacent recordings into covered intervals. The query
# orders by start_time, so a single pass merges them
covered: list[tuple[float, float]] = []
for rec_start, rec_end in recordings:
if covered and rec_start <= covered[-1][1]:
covered[-1] = (covered[-1][0], max(covered[-1][1], rec_end))
else:
covered.append((rec_start, rec_end))
# Iterate through time segments and check if each has any recording
no_recording_segments = []
current = after
current_gap_start = None
idx = 0
covered_count = len(covered)
while current < before:
segment_end = min(current + scale, before)
# Check if this segment overlaps with any recording
has_recording = any(
rec_start < segment_end and rec_end > current
for rec_start, rec_end in recordings
)
# Advance past covered intervals that end before this segment begins;
# they cannot overlap this or any later segment.
while idx < covered_count and covered[idx][1] <= current:
idx += 1
# A covered interval overlaps the segment when it starts before the
# segment ends (its end is already known to be > current).
has_recording = idx < covered_count and covered[idx][0] < segment_end
if not has_recording:
# This segment has no recordings

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@ -632,9 +632,10 @@ def motion_activity(
if not filtered:
return JSONResponse(content=[])
camera_list = list(filtered)
clauses.append((Recordings.camera << camera_list))
else:
clauses.append((Recordings.camera << allowed_cameras))
camera_list = list(allowed_cameras)
clauses.append((Recordings.camera << camera_list))
data: list[Recordings] = (
Recordings.select(
@ -662,14 +663,12 @@ def motion_activity(
df.set_index(["start_time"], inplace=True)
# normalize data
motion = (
df["motion"]
.resample(f"{scale}s")
.apply(lambda x: max(x, key=abs, default=0.0))
.fillna(0.0)
.to_frame()
)
cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
motion = df["motion"].resample(f"{scale}s").max().fillna(0.0).to_frame()
if len(camera_list) == 1:
cameras = df["camera"].resample(f"{scale}s").first().fillna("")
else:
cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
df = motion.join(cameras)
length = df.shape[0]
@ -685,6 +684,11 @@ def motion_activity(
else:
df.iloc[i : i + chunk, 0] = 0.0
# Drop resample gap-fill buckets. The resample above emits a row for every
# {scale}s bucket spanning the range, and buckets with no recording get a
# motion of 0 (from fillna) and an empty camera (from joining an empty set).
df = df[df["camera"] != ""]
# change types for output
df.index = df.index.astype(int) // (10**9)
normalized = df.reset_index().to_dict("records")

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@ -5,7 +5,7 @@ import json
import logging
from datetime import datetime, timezone
from pathlib import Path
from typing import Optional
from typing import Any, Callable, Optional
from frigate.config.camera.updater import (
CameraConfigUpdateEnum,
@ -34,6 +34,45 @@ PROFILE_SECTION_UPDATES: dict[str, CameraConfigUpdateEnum] = {
"zones": CameraConfigUpdateEnum.zones,
}
# Retained MQTT switch topics per profile section, with a payload getter.
# Republished on profile change so MQTT/HA don't show a stale toggle.
SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
"audio": [("audio", lambda c: "ON" if c.audio.enabled else "OFF")],
"birdseye": [
("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
(
"birdseye_mode",
lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
),
],
"detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],
"motion": [
("motion", lambda c: "ON" if c.motion.enabled else "OFF"),
("improve_contrast", lambda c: "ON" if c.motion.improve_contrast else "OFF"),
("motion_threshold", lambda c: c.motion.threshold),
("motion_contour_area", lambda c: c.motion.contour_area),
],
"notifications": [
("notifications", lambda c: "ON" if c.notifications.enabled else "OFF"),
],
"objects": [
("object_descriptions", lambda c: "ON" if c.objects.genai.enabled else "OFF"),
],
"record": [("recordings", lambda c: "ON" if c.record.enabled else "OFF")],
"review": [
("review_alerts", lambda c: "ON" if c.review.alerts.enabled else "OFF"),
(
"review_detections",
lambda c: "ON" if c.review.detections.enabled else "OFF",
),
(
"review_descriptions",
lambda c: "ON" if c.review.genai.enabled else "OFF",
),
],
"snapshots": [("snapshots", lambda c: "ON" if c.snapshots.enabled else "OFF")],
}
PERSISTENCE_FILE = Path(CONFIG_DIR) / ".profiles"
@ -310,6 +349,15 @@ class ProfileManager:
settings,
)
# republish MQTT switch states
if self.dispatcher is not None:
for suffix, get_payload in SECTION_STATE_TOPICS.get(section, ()):
self.dispatcher.publish(
f"{cam_name}/{suffix}/state",
get_payload(cam_config),
retain=True,
)
def _persist_active_profile(self, profile_name: Optional[str]) -> None:
"""Persist the active profile state to disk as JSON."""
try:

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@ -403,3 +403,75 @@ class TestHttpMedia(BaseTestHttp):
assert len(summary) == 1
assert "2024-03-10" in summary
assert summary["2024-03-10"] is True
def test_recordings_unavailable_reports_gap_between_recordings(self):
"""A gap between two recordings is reported as an unavailable segment."""
with AuthTestClient(self.app) as client:
# Two recordings with a 20s gap (1010-1030) between them.
Recordings.insert(
id="rec_a",
path="/media/recordings/a.mp4",
camera="front_door",
start_time=1000,
end_time=1010,
duration=10,
motion=0,
).execute()
Recordings.insert(
id="rec_b",
path="/media/recordings/b.mp4",
camera="front_door",
start_time=1030,
end_time=1040,
duration=10,
motion=0,
).execute()
response = client.get(
"/recordings/unavailable",
params={
"after": 1000,
"before": 1040,
"scale": 5,
"cameras": "front_door",
},
)
assert response.status_code == 200
assert response.json() == [{"start_time": 1010, "end_time": 1030}]
def test_recordings_unavailable_merges_overlapping_recordings(self):
"""Overlapping recordings are merged so no false gap is reported."""
with AuthTestClient(self.app) as client:
# Overlapping recordings spanning the whole requested range.
Recordings.insert(
id="rec_a",
path="/media/recordings/a.mp4",
camera="front_door",
start_time=1000,
end_time=1020,
duration=20,
motion=0,
).execute()
Recordings.insert(
id="rec_b",
path="/media/recordings/b.mp4",
camera="front_door",
start_time=1010,
end_time=1030,
duration=20,
motion=0,
).execute()
response = client.get(
"/recordings/unavailable",
params={
"after": 1000,
"before": 1030,
"scale": 5,
"cameras": "front_door",
},
)
assert response.status_code == 200
assert response.json() == []

View File

@ -610,19 +610,16 @@ class TestHttpReview(BaseTestHttp):
response = client.get("/review/activity/motion", params=params)
assert response.status_code == 200
response_json = response.json()
assert len(response_json) == 61
# Only buckets with an actual recording are returned. Empty
# gap-fill buckets between the two recordings are dropped.
assert len(response_json) == 2
self.assertDictEqual(
{"motion": 50.5, "camera": "front_door", "start_time": now + 1},
response_json[0],
)
for item in response_json[1:-1]:
self.assertDictEqual(
{"motion": 0.0, "camera": "", "start_time": item["start_time"]},
item,
)
self.assertDictEqual(
{"motion": 100.0, "camera": "front_door", "start_time": one_m + 1},
response_json[len(response_json) - 1],
response_json[1],
)
####################################################################################################################

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@ -1,5 +1,6 @@
"""Tests for the profiles system."""
import copy
import json
import os
import unittest
@ -746,6 +747,36 @@ class TestProfileManager(unittest.TestCase):
manager.activate_profile(None)
dispatcher.clear_runtime_state.assert_called_once_with()
@patch.object(ProfileManager, "_persist_active_profile")
def test_profile_change_republishes_switch_states(self, mock_persist):
"""Profile changes republish MQTT switch states so HA stays in sync.
Regression: activating/deactivating a profile updated the in-memory
config (and Frigate's behavior) but left the retained MQTT state
topics stale, so external integrations like Home Assistant kept
showing the pre-profile toggle position.
"""
config_data = copy.deepcopy(self.config_data)
config_data["cameras"]["front"]["profiles"]["disarmed"]["review"] = {
"alerts": {"enabled": False},
}
config = FrigateConfig(**config_data)
dispatcher = MagicMock()
manager = ProfileManager(config, self.mock_updater, dispatcher)
# Activating disarmed turns alerts off -> MQTT state must follow
manager.activate_profile("disarmed")
dispatcher.publish.assert_any_call(
"front/review_alerts/state", "OFF", retain=True
)
# Deactivating restores the base (alerts on) -> MQTT state must follow
dispatcher.publish.reset_mock()
manager.activate_profile(None)
dispatcher.publish.assert_any_call(
"front/review_alerts/state", "ON", retain=True
)
@patch.object(ProfileManager, "_persist_active_profile")
def test_startup_replay_does_not_clear_runtime_state(self, mock_persist):
"""Startup callers pass clear_runtime_overrides=False to preserve state."""

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@ -12,7 +12,7 @@ export const JINA_EMBEDDING_MODELS = ["jinav1", "jinav2"] as const;
export const REDACTED_CREDENTIAL_SENTINEL = "__FRIGATE_SAVED_CREDENTIAL__";
export const ANNOTATION_OFFSET_MIN = -10000;
export const ANNOTATION_OFFSET_MAX = 5000;
export const ANNOTATION_OFFSET_MAX = 10000;
export const ANNOTATION_OFFSET_STEP = 50;
export const supportedLanguageKeys = [

View File

@ -53,6 +53,7 @@ export default function MasksAndZonesView({
const { data: config } = useSWR<FrigateConfig>("config");
const [allPolygons, setAllPolygons] = useState<Polygon[]>([]);
const [editingPolygons, setEditingPolygons] = useState<Polygon[]>([]);
const [polygonsInitialized, setPolygonsInitialized] = useState(false);
const [isLoading, setIsLoading] = useState(false);
const [loadingPolygonIndex, setLoadingPolygonIndex] = useState<
number | undefined
@ -609,6 +610,7 @@ export default function MasksAndZonesView({
...globalObjectMasks,
...objectMasks,
]);
setPolygonsInitialized(true);
// Don't overwrite editingPolygons during editing layout shifts
// from switching to the edit pane can trigger a resize which
// recalculates scaledWidth/scaledHeight and would discard the
@ -676,7 +678,7 @@ export default function MasksAndZonesView({
}, [currentEditingProfile]);
useSearchEffect("object_mask", (coordinates: string) => {
if (!scaledWidth || !scaledHeight || isLoading) {
if (!scaledWidth || !scaledHeight || isLoading || !polygonsInitialized) {
return false;
}
// convert box points string to points array