* pin genai review frames to the main stream
* retain previews as long as either stream has recordings
* watch sub stream recording health separately from main
* reject record_sub on the same input as record and document the role
* derive recording paths from the cache segment timestamp
Recording paths carry one second of resolution, but since sub stream recording start times are resolved to fractional wall clock, anchored to the cache file mtime and chained to the previous segment's end. A stream cutting segments faster than once a second resolves consecutive segments into the same second, so two rows collide on the unique path index and the batch insert fails. The cache segment name is unique per camera stream and second by construction because ffmpeg names segments with strftime, so the recording path is now built from that timestamp while the row keeps the resolved start time. This also restores the path semantics from before sub stream recording, when start times came straight from the cache filename.
Nothing derives times from recording paths: playback offsets, stream switching, and export all use the row's start time, which is unchanged, and the recordings sync matches files by exact path string.
* keep the rest of a recording batch when one row conflicts
* only publish record_sub status when a sub stream is configured
* don't shadow camera_cfg when publishing empty cache streams
* back off restarts when a recording stream goes stale
* give the shared sub stream grace on any capture thread reset
* include segment details in recording discard warnings
* Create frigate and go2rtc runtime users in the image
* Add single fix-ownership helper for volume permission migration
* Add init-usermod oneshot for PUID and PGID remapping
* Chown newly created runtime directories to the frigate user
* Run sentinel-guarded ownership sweep during prepare
* Add host-side volume permission migration script
* Guard log directory ownership for user-mode startup
* Fall back to plain s6-log when running without root
* Assert PUID remapping and sweep sentinel in CI smoke test
* Skip the ownership sweep in the devcontainer
* Pin FRIGATE_RUN_AS_ROOT in ownership tests
* Do not record the sweep as complete when a chown failed
* Validate PUID and PGID in the migration script
* Treat a failed ownership scan as an incomplete sweep
* Reject PUID and PGID of 0 during remapping
* Handle symlinks, dry runs, and sentinel write failures in the sweep
* Treat an absent sweep root as an incomplete sweep
* Verify s6-overlay downloads against pinned checksums
* Verify go2rtc download against pinned checksums
The v1.9.14 release publishes no checksums file, just the bare per-platform binaries, so these digests come from a one-time fetch rather than upstream. That pins the artifact against later substitution, which is the realistic threat for a version we stay on for months, but it does not verify the original download. The stage moves from `ADD --link` to a script because `ADD --checksum` can't express an architecture-dependent URL.
* Verify main image downloads against pinned checksums
Covers everything the main image downloads on the default path: tempio, the hailort runtime tarball and wheel, the six ffmpeg builds, the libedgetpu deb, and the thirteen Intel driver debs. The hailort tarball was streamed straight into `tar`, which can't be verified before extraction, so it downloads to `/tmp` first. The three ffmpeg blocks per arch collapse into one `install_ffmpeg` helper since they only differed by URL and install dir, and the Intel debs go through a `fetch_intel_deb` helper for the same reason.
The Intel debs are the ones that mattered most here. They're installed as root with `dpkg` on the default amd64 path and had no verification at all. compute-runtime publishes a `ww<week>.sum` asset with every release and npu-driver published `checksum.sha256` on v1.19.0, so those eight digests came from upstream rather than from us. intel-graphics-compiler and level-zero publish none, so those five and everything else here come from a one-time fetch, which pins the artifact against later substitution but doesn't verify the original download. The comment above the map says which is which and how to refresh them, since npu-driver has stopped publishing sums since v1.19.0 and that provenance won't survive the next bump.
Still unpinned: `get-pip.py`, which is a rolling URL where a digest would just break the build on pypa's next edit, and the per-variant artifacts for Axera, Synaptics, and Jetson. apt repositories are out of scope since apt already verifies signatures.
* Restrict generated TLS key permissions
OpenSSL 3.x already writes the key at 600 on its own, so this pins the guarantee rather than fixing an observed leak: the mode no longer depends on the openssl version or the umask the service happens to start with. Only the generated pair is touched. User-mounted certs take the other branch and are never chmod'd, which matters when they're mounted read-only.
* Add security headers and server_tokens off
Adds `X-Content-Type-Options: nosniff` and `Referrer-Policy: strict-origin-when-cross-origin`, and turns off nginx version disclosure.
No `X-Frame-Options` and no CSP `frame-ancestors`. HA's Webpage card and iframe panels frame Frigate's own address cross-origin, and either header would break them silently with nothing in Frigate's logs to explain it. Ingress is same-origin and would survive `SAMEORIGIN`, but Frigate can't tell the two apart from inside the container. `security_headers.conf` is a plain file in the image rather than a generated one, so anyone who does want framing restrictions can bind-mount it.
`add_header` doesn't inherit into a block that declares its own, so the include goes in per block, all nine of them, including the four nested static-asset locations that serve the JS bundles. Those are the ones nosniff actually matters for.
The run script now reads `get_nginx_settings.py` once into a variable instead of shelling out per template. That script imports the frigate config machinery, which is noticeable on an SBC.
Not fixed here: `listen.conf` is included at server level and carries `Strict-Transport-Security`, so those same nine blocks already drop HSTS under TLS today. Folding it into this file would change existing TLS behavior on nine paths, so it needs its own PR.
* Restrict go2rtc config file permissions
* Log failed login attempts with source address
Failed logins returned a bare 401 and left nothing behind, so credential stuffing was invisible unless you were already watching nginx access logs. Both failure branches now log a warning with the attempted username and the client address.
The address comes from `get_remote_addr()`, the same helper the login rate limiter keys on, so the two agree on who the client is and the trusted-proxy handling is consistent. Logging a raw `x-forwarded-for` instead would let an attacker forge the source address in the very log line meant to catch them.
The response is unchanged and identical either way. Which factor failed is only visible in the log, never to the client, and the password is never logged.
* Recommend least-privilege container options in install docs
The compose generator pushed `privileged: true` into every file it produced, no matter what hardware you picked, and it's the default tab on the install page so it's what most people copy. It now emits `security_opt: no-new-privileges:true` instead, and only adds `privileged: true` for hardware that actually needs it, with the reason inline. MemryX is the only one today, since it needs to reach the max-manager. Rockchip and Synaptics only want privileged during initial setup and their documented end state is device mappings, so neither gets it.
`no-new-privileges` merges into the same `security_opt` block as any device-specific entries, so Rockchip still gets its `apparmor=unconfined` and `systempaths=unconfined` without a duplicate key.
The static example now has `privileged` commented out, and there's a short section on the options worth adding, with a note that `cap_drop: ALL` breaks `telemetry.stats.network_bandwidth` since nethogs needs NET_ADMIN/NET_RAW.
* Add amd64 container smoke test to CI
Boots the built amd64 image against a minimal config and asserts the two security headers, that the Server header no longer carries a version, that no frame-ancestors is present, that nginx accepts its own config, and the two file modes. This is also the harness the rest of the hardening work extends.
The two negative assertions are written as `if grep; then exit 1; fi` rather than `! grep`. Bash exempts a negated command from `set -e`, so the `!` form would have passed even with the version and frame-ancestors both present, which is the opposite of what a regression net is for.
* improve keyframes messages
* don't pad the labelmap with unknown
`load_labels()` prefilled 91 `unknown` entries before reading the label file, so any model with fewer than 91 classes kept that padding in `merged_labelmap` and `unknown` showed up as a selectable object type in the objects settings UI. The padding only existed so `RemoteObjectDetector.detect` could index the labelmap without a KeyError, and it didn't even cover the empty-file case or Frigate+, which never had a prefill. Both lookups now skip class ids the labelmap doesn't name and warn once per id.
* add secrets.yaml and merge substitution sources by precedence
FRIGATE_ENV_VARS was built once at import from container env and /run/secrets, and the environment_vars validator overwrote it unconditionally, so the block beat the deployment and nothing could be re-read. Sources are now separate dicts merged lowest to highest (environment_vars, secrets.yaml, container env, credentials directory), re-read at the top of every parse, and a collision warns once naming the winner. An undefined {FRIGATE_*} raises a ValueError subclass so pydantic reports the field instead of a KeyError traceback.
* use the shared substitution namespace in go2rtc config
The generator rebuilt the namespace itself from os.environ and a hardcoded /run/secrets, so it never saw environment_vars or CREDENTIALS_DIRECTORY, and str.format made any stray brace fatal. It now installs the FRIGATE_ names from environment_vars and substitutes streams the same way every other field does.
* read the exec override from an import time snapshot
environment_vars is exported into os.environ, and is_go2rtc_arbitrary_exec_allowed read os.environ live, so the config file could enable exec sources. Snapshot the variable at import, which runs before any config is loaded.
* docs
* clarify docs
ffmpeg's stderr was piped but never read, so recording segments that generate more than 64 KB of ffmpeg warnings blocked ffmpeg mid-write, stranding the streaming thread and its anyio threadpool token for good. Enough of those and every sync endpoint stops responding until restart. The trigger is how noisy the segments are, not how long the clip is.
Send stderr to a temp file instead, and guarantee ffmpeg teardown and playlist cleanup on every exit path, including client disconnect.
Also fixes two bugs the deadlock hid: the failure branch was unreachable because returncode is None mid-loop, so the playlist file leaked and ffmpeg's logs were never reported. Playlist files now get a unique name so concurrent requests for one range cannot delete each other's input.
Extracts the terminate helper motion search already had into frigate/util/ffmpeg.py, now shared by both streaming call sites.
* add import/export for camera group layouts and streaming settings
Camera group layouts and per-camera streaming settings are stored in the browser's IndexedDB, so they are tied to a single browser on a single device. Users with more than one device have to rebuild every group layout and re-pick every camera's stream settings by hand, and clearing browser data loses the work.
Add a Backup & Restore card to Settings > UI Settings that exports these settings to a JSON file and imports that file on another device. Import shows a confirmation dialog with per-section counts, switches for layouts, streaming settings, and UI preferences, and warnings about camera groups or cameras in the file that are not on this server.
Server-side storage is deliberately avoided. These are per-device presentation settings: a layout arranged for a desktop is wrong on a tablet, and continuous full-resolution streams that are free on a wired LAN are not on a phone. An explicit file moves settings only when the user chooses to move them.
Implementation notes:
- web/src/utils/uiSettingsTransfer.ts owns a registry of transferable IndexedDB keys. Each entry records whether the key is user-namespaced, matching which persistence hook wrote it, plus a zod schema for its value.
- Only registry-known keys are ever written, and only when their value passes that schema. The file format deliberately lets unknown keys survive parsing, so this filter is what prevents a hand-edited file from writing arbitrary storage keys or out-of-range values.
- Export falls back to the legacy un-namespaced key, because the username migration runs lazily on first mount of each owning hook.
- Streaming settings merge per group rather than replacing the whole map, so groups configured only on the receiving device survive.
- Import writes storage and then reloads, because useUserPersistence reads a key only on mount and StreamingSettingsProvider would otherwise write its stale in-memory state back over the import.
- playbackBandwidthEstimate, frigate-search-history, and live-layout are excluded: the first two are measurements and user data rather than preferences, and live-layout's default is derived from the device.
* merge imported streaming settings per camera instead of per group
Allow audio classes to be grouped under a shared configured label.
Keep audio overrides separate from object labels and retain only the highest-scoring grouped detection.
Refs #23967
* refactor mqtt so that Frigate owns the transport lifecycle instead of delegating it to paho
* release the shutdown barrier on worker crash and replay retained publishes the broker never acked
* collapse in-flight retained values by topic and release the shutdown barrier from a finally
* replay the outage buffer before the publish queue so newer values are not reverted
* serve a segment startup ladder so seeks begin playing sooner
nginx-vod was handed one 10s segment per recording file, so every playlist start had to download and decode a full segment before the first frame. Declare real keyframe data per clip and let nginx cut short leading segments from it.
- add vod_bootstrap_segment_durations 1000/2000/4000 so each playlist starts with 1s/2s/4s segments before settling at 10s
- emit real clip-relative keyFrameDurations (plus firstKeyFrameOffset when nonzero) from the recording keyframe index; rows without an index keep the whole-clip declaration, the only safe cut without keyframe knowledge
- drop the manifest's segment_duration field, which was always inert: nginx-vod parses only camelCase segmentDuration
- rebuild the player source at the seek target, quantized to a 10s grid, so the ladder applies to every seek and seek URLs stay repeatable for nginx's mapping and response caches
- route the seek model, in-range checks, and the stale-report guard through the source window rather than the chunk range
- bridge repositioning seeks (>2s from the last played timestamp) through the preview player and hold the release anchor one commit, so neither path paints a stale frame
- clear a pending loading timer before replacing it; an orphaned timer escaped onPlaying's clearTimeout and flashed loading mid-playback
* keep recordings queries on their indexes
Several recordings queries degraded into full scans or large sorts on big databases: the planner ignored index order, or the query shape gave it nothing tight to seek on. Reshape them into bounded seeks and add the composite index the per-stream lookups need.
- index recordings on (camera, stream_type, start_time DESC) and drop the (camera, stream_type) index it supersedes
- walk the recordings summary day by day with EXISTS probes and per-camera MIN/MAX seeks, skipping ahead over empty gaps instead of bucketing every row for the requested cameras
- run the summary endpoint on the event loop rather than the threadpool
- bound the unavailable-recordings query by start_time per camera and merge the results in Python
- bound the expire query's start_time so it seeks the retention window instead of scanning a camera's whole history
- enumerate deleted cameras with one index seek each rather than a camera NOT IN (...) scan
- compute bandwidth with segment_size filtered in a CASE projection; as a WHERE predicate it baited the planner into the (camera, segment_size) index plus a full sort of the camera's history
- fall back to a 1000-segment window when the recent 100 are all zero-size, so an ingest glitch doesn't report zero bandwidth
- limit the needs_refresh count instead of counting every segment
- cover sub-only and sparse calendar days, midnight-spanning day attribution, multi-camera gap merging, deleted-camera expiry, and zero-size segment runs
* fix mypy
* add ptz controls to camera via wizard when onvif has already been probed
* i18n
* add e2e test
* backend add and remove subscriber
* tweaks
* turn on switch by default if pan and/or tilt capability is available
* fix test
* add sub stream recording with adaptive quality playback
Optionally record a second, lower bitrate stream alongside the main
recording stream via a `record_sub` input role and `record.sub` config block, with its own retention windows.
Recordings rows now carry the stream type plus the media details needed to serve both streams from one manifest: video codec, audio presence, audio codec and rate, and a record-time keyframe index.
Playback resolves coverage across both streams and merges them into a single VOD sequence, falling back to a discontinuity manifest with per-clip init segments when the media signatures differ. The player exposes a quality selector, and an auto governor picks the stream from stall time, bandwidth, codec support, and the save-data hint.
* fix tests and i18n
* Guard object processor queue handlers against unknown cameras
* Skip embeddings post processing for removed cameras
* End review segments for removed cameras
* Drop queued autotracker moves for removed cameras
* Release tracked event thumbnails when skipping a removed camera
* Add locked accessors for camera states
* Read camera states through the processor accessors
* Guard output and recording paths against cameras not yet known
* Resolve camera state once in ONVIF, notification, and transcription paths
* Add combined motion and object Birdseye mode
Add a motion_objects mode that keeps Birdseye active when motion is detected or a confirmed tracked object is present, including stationary objects.
Wire the mode through configuration, runtime commands, API schemas, documentation, and UI labels. Exclude false-positive trackers and add regression coverage for Birdseye activation and MQTT validation.
* Refactor Birdseye activity types as booleans
Replace combination-specific Birdseye modes with composable boolean activity types for motion, active objects, stationary objects, and continuous display.
Preserve legacy single-mode configuration and MQTT inputs, support canonical comma-separated MQTT combinations, and allow scalar YAML values to be replaced by nested settings through the config API.
* Preserve OpenVINO config translations
Regenerate the configuration translations with the OpenVINO detector schema available so the unrelated production detector labels remain intact.
* Preserve partial Birdseye mode overrides
Allow an empty activity selection with a canonical NONE MQTT state so partial camera and profile overrides can disable inherited flags without failing validation.
Add regression coverage for camera and profile inheritance, document the NONE contract, and keep the generated schema fixture scoped to Birdseye.
* Address Birdseye activity review feedback
Move scalar mode compatibility into the 0.18-1 config migration and reject empty activity selections instead of publishing a NONE state.
Pass activity signals through a frozen dataclass, preserve existing active-object tracker behavior, and require confirmed stationary objects. Revert the generic YAML mutation and cover migration, inheritance, MQTT, and activation regressions.
* Move Birdseye migration to 0.19
Use the 0.19-0 configuration revision for converting scalar Birdseye modes to composable activity flags, and update the migration regression coverage accordingly.
* Remove Birdseye migration test
Drop the dedicated config migration test as requested during review while retaining the 0.19-0 migration implementation.
* Make review user read status consistent with other APIs
* Validate URLs for web push endpoint
* Validate the role for a custom viewer, rate limit password changing
* Cleanup
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Co-authored-by: Abdollah Ashjaa <abdollah.ashjaa@gmail.com>
Co-authored-by: Amir reza Irani ali poor <amir1376irani@yahoo.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: حمید ملک محمدی <hmmftg@gmail.com>
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Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/fa/
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Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/fa/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/fa/
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Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/fa/
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Translation: Frigate NVR/Config - Cameras
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Co-authored-by: Fredrik B <fredrik@brannvall.nu>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Kristian Johansson <knmjohansson@gmail.com>
Co-authored-by: Mats Lojander <mats@lojander.com>
Co-authored-by: Samuel Åkesson <samuel.akesson@bolmso.se>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/audio/sv/
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Co-authored-by: Filippo-riccardo Franzin (filippo franzin) <filric01@gmail.com>
Co-authored-by: Gringo <ita.translations@tiscali.it>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Nton <arlatalpa@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-filter/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-replay/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/it/
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Translation: Frigate NVR/views-explore
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Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Klenner Martins Barros <klenne.al@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/pt_BR/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/pt_BR/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/pt_BR/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/components-dialog
Gemini 3.6 and newer reject role="function" on the function response
Content with 400 INVALID_ARGUMENT, breaking any chat query that triggers
a tool call. The tool call itself succeeds; only the hand-back to the
model fails, and because the error surfaces mid-stream the request still
returns HTTP 200, so it is easy to miss.
Google's function calling documentation specifies role="user" for
returning function results:
contents.append(response.candidates[0].content)
contents.append(types.Content(role="user", parts=[function_response_part]))
https://ai.google.dev/gemini-api/docs/generate-content/function-calling
Verified with my local setup.
* fix classification drawer closing instead of scrolling when list is long on mobile
* add qwen3.8 to genai docs
* add titles to more clearly separate model types
* subscribe to add in webpush
* add docs for detector cpu usage
* rebuild notification camera access when a camera is added at runtime
* document how frigate shows CPU usage metrics
* add faq about version key in config
sanitize_filename leaves ".." intact and collapses variants like "..:" and "..*" to "..", so filesystem paths built from face names, classification model/category names, image ids, and trigger data could escape their base directory. Route every such site through new frigate/util/path.py helpers (safe_join, sanitize_path_component, sanitize_contained_path), which reject traversal and verify containment.
Worst case was DELETE /classification/{name}, which rmtree'd /media/frigate and /config while returning 200.
Important to note that all affected endpoints already require admin permission, so this sould be considered hardening rather than fixing exploitable code.
<camera>/notifications/suspended arrives as a string over the live connection but as a number in the camera_activity snapshot, and the truthiness guard dropped the numeric 0, so a camera with notifications off rendered as active after a reload. Normalize to a string and derive isSuspended instead of storing it.
* fix(audio): correct sodeling typo to yodeling
Fixes a typo in audio-labelmap.txt where the yodeling class was
misspelled as "sodeling".
* fix(i18n): remove duplicate sodeling key in en audio.json
The en audio.json already contains a correct "yodeling" key. Remove
the duplicate/misspelled "sodeling" entry to avoid ambiguity.
* add host npu requirements to docs
* allow toggling live audio transcription via mqtt
* improve spacing consistency on mobile drawers
* fix clearing the region grid not surviving a restart
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Co-authored-by: GuoQing Liu <842607283@qq.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
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Translation: Frigate NVR/Config - Global
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Translation: Frigate NVR/views-settings
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Co-authored-by: Artem Vladimirov <artyomka71@mail.ru>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ru/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ru/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ru/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ru/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/views-settings
* update homekit docs
* update dictionary
* preserve function names in production builds
adds only 162kb gzipped/450k unzipped to the bundle
* margin tweak
* fix maximum update depth exceeded when dragging the timeline handlebar
Dragging the handlebar, especially quickly or with fast direction changes, could exceed React's nested update limit and unmount the whole app, leaving a blank screen. Motion search was worst affected.
The drag loop committed a new time into React state on every animation frame. Edge auto-scrolling mutates scrollTop each iteration, so the value always differed and React's same-value bail-out never engaged, letting the update chain run to the limit of 50. Pace those commits to one per 100ms and flush the pending value on release, so the drop position is still exact. The handlebar position and label are written to the DOM directly and remain at frame rate.
useUserInteraction dispatched state on every scroll and touchmove event; only commit on the leading edge.
Motion search also passed fresh array literals for the timeline's events, motion events and unavailable ranges, giving the segment memo and the drag effect new dependencies on every render. Both views also passed an inline arrow for onHandlebarDraggingChange, which is an effect dependency that calls setState.
* Verify motion search jobs belong to the requested camera
* Apply persisted profile and runtime overrides before workers start
Worker processes are handed a copy of the config when they start and only learn about later changes from the config_updater broadcast, which is plain ZMQ PUB/SUB with no queue, ack, or retained value, so a message published before a subscriber has connected is dropped and never re-sent. The persisted profile and the runtime camera toggles were restored only by that broadcast, at the very end of startup, so a worker that lost the race kept its yaml values for the rest of the session: audio detection kept running on a camera whose audio had been toggled off, even though /api/config, the UI, and the runtime state file all showed it disabled. Split both restores into a config half and a publish half. ProfileManager.restore_persisted_profile_to_config() and Dispatcher.reapply_runtime_state_to_config() now run right after init_profile_manager(), before the first worker starts, so every worker is handed a config that already carries both layers. ProfileManager.restore_persisted_profile() and Dispatcher.restore_runtime_state() still run at the end of startup: the recording, review, and embeddings processes start before the dispatcher exists, so the broadcast remains their only channel, and MQTT needs the retained switch states. Both config passes have to stay after init_profile_manager(), which snapshots the config as the no-profile base that deactivation resets to.
* End timeline drags on touchcancel
/auth grants anonymous admin to any request whose X-Server-Port matches networking.listen.internal, but it read that port off the live config while nginx binds its listeners once at container start and never reloads them, so any path that swaps the running config could move the trusted port without nginx moving with it. Saving networking.listen.internal equal to the external port applied immediately despite the restart-required warning, which handed unauthenticated admin to everything reaching the external port. Snapshot the port at app creation and compare against that instead, and reject a config whose two listeners share a port number, which nginx would refuse to start with anyway.
When a motion region filter is active, zoom each preview clip into the outer bounds of the selected cells instead of showing the full frame. Tiles take on the aspect ratio of the cropped region, clamped to avoid slivers when the selection is a single row or column, so the grid stays uniform. A "Crop to filter" switch in the preview settings turns this off and restores the previous 16:9 tiles. The transform is applied to a wrapper holding both the media and the dim overlay canvas so the motion heatmap stays registered to the pixels.
Fix the region filter grid, which mapped cells onto a hardcoded 16:9 box while the snapshot was letterboxed inside it with object-contain. Heatmap cells are indexed against the detect frame, so on a 4:3 camera every painted cell was off by up to 12.5% of the frame width, and the true left and right edges of the image could only be reached by painting the black bars. The grid box now takes the camera's detect aspect ratio, capped at 65dvh tall so 4:3 and portrait cameras do not overflow the dialog.
* update network requirements docs for keras weights download
* fix manual PTZ relative moves permanently stopping object detection
* document available camera set features and link profiles docs to the API
* fix stale stream name field when switching cameras
The live streams and known plates fields rendered the map key as an uncontrolled input, so switching cameras left the previous camera's stream name on screen and would rename the wrong key if that stale text was committed. Both now use a shared MapKeyInput that resyncs with the form data and commits per keystroke, except while the typed name belongs to another entry, so the section is marked modified without waiting for blur.
* improve display of gpu graphs in system metrics
* docs tweaks
* Only hide cameras with ui.dashboard disabled from the All Cameras dashboard
The settings camera selector and zone editor also filtered on ui.dashboard, so hiding a camera from the dashboard made its zones and masks uneditable in the UI (GH 23870). Drop those filters and correct the field title, help text, and reference docs to describe what the option actually does
* hide cameras with ui.review disabled from the Motion tab and the review summaries
The Motion tab built its own camera list that never checked ui.review, so a hidden camera still got a preview tile, and its motion and overlap queries fell back to every allowed camera. The review and recordings summaries had the same gap: they are aggregate day counts that can't be filtered client side, so a hidden camera kept contributing to the severity tab counts and calendar indicators while its items were absent from the list. Filter the motion camera list on ui.review and query all four endpoints with the visible camera list instead of letting the backend default to all, and skip the summary queries until the config resolves so the counts don't briefly render as zero.
* Scope every review page query to the cameras visible in review
The segments and the summary counts were derived from different camera sets: the list was fetched for all cameras and filtered client side, while the summaries were fetched for the visible cameras only when no explicit camera filter was set. A ?cameras= link can name a camera hidden from review, which left the count above zero with an empty list, pinning the new items to review popover open and making the auto refresh effect loop. Intersect an explicit camera selection with the visible list rather than trusting it, pass that to the segment and summary queries alike, and drop the now redundant client side filter, which the raw segments handed to the history view were bypassing anyway.
* fix watchdog process restarts reverting to the boot config
/api/config/set parses a new FrigateConfig and swaps the API and dispatcher onto it, but FrigateApp.config was never rebound, so the watchdog factories rebuilt a crashed process from the config as of startup. Fix is to read through a ConfigHolder that the swap updates.
* fix birdseye camera overrides being clobbered by a global mode change
A global birdseye save published only the global object, leaving the output process to infer which cameras were inheriting by comparing against the previous global mode. That cannot tell an inherited value from an explicit one that happens to match, so it overwrote the override until a restart. Publish the per-camera values the config parse already resolved instead.
* Reject non-finite numbers in GenAI review descriptions
A model returning NaN for confidence or potential_threat_level slipped through the model_construct fallback, which skips validation, and was written into the review segment's JSON data. NaN is not valid JSON, so every subsequent /review request failed with "Out of range float values are not JSON compliant", blanking the review page for any time range containing the poisoned row.
* restore fused DetectionOutput in the OpenVINO SSD model conversion
* fix rgb swap issue for face dataset testing script
* widen the logger name field in the per-process log level settings
* add details to timestamp error faq
* tweak genai docs
* tweak vector language
* Combine Qwen3.5 and Qwen3.6 listings
* fix openvino yolox detector crashing on every detection
The intermediate (N, 7) array in the yolox branch shadowed the pre-allocated (20, 6) detections buffer, so writing a detection into it raised "could not broadcast input array from shape (6,) into shape (7,)" on the first frame with anything above the confidence threshold. An empty frame also returned a (0, 7) array instead of the (20, 6) buffer.
Regressed in #13794, which renamed the intermediate from dets to detections as part of a cspell cleanup. Broken since 0.15.0.
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: lukasig <lukasig@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/audio/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ro/
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Translation: Frigate NVR/views-settings
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Priit Jõerüüt <jrthwlate@users.noreply.hosted.weblate.org>
Co-authored-by: Rasmus Kuusmann <rasmus.kuusmann@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/audio/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-replay/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/et/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/et/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/audio
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* fix calendars greying out the current day after midnight
The cutoff for disabling future days was computed with setHours(getHours() + 24, -1, 0, 0), which is not "24 hours from now" but tomorrow at the current hour minus one minute. Between 00:00 and 00:59 that lands back on today, and react-day-picker matches range matchers by calendar day, so today itself was disabled, leaving the export dialog's start time stuck on the previous day. TimezoneAwareCalendar also added the configured timezone's raw UTC offset instead of its difference from the browser's, widening the broken window to several hours in negative-offset zones and letting future days through in positive-offset ones. Derive the current date in the display timezone once, then build each cutoff in the space its calendar uses: ReviewActivityCalendar passes timeZone to react-day-picker so its day cells are TZDate and need a real instant, while TimezoneAwareCalendar is handed pre-shifted dates and needs a local one. Also corrects the today prop, which was off by the browser's offset, and the truthiness check that treated a configured timezone of UTC as unset.
* pin react-zoom-pan-pinch to 3.6.1
3.7.0 attaches a ResizeObserver to the transform wrapper and content unconditionally and clamps the pan position into the current bounds on every resize. The history player hides itself with display:none while scrubbing and while a new hour of recordings loads, so the observer measures it as 0x0, collapses the bounds to zero, and snaps a zoomed in view back to the top left corner. Zoom scale survives, only the position is lost.
That observer was only created for centerOnInit in 3.4.4 through 3.6.1 and 4.0.0 reverted it again, so 3.7.0 is the only affected release. The caret is what picked it up during the React 19 upgrade, so pin the version exactly.
Reported in #23807
* recreate review thumbnail directory before writing and log write failures
cleanup's remove_empty_directories() can rmdir an empty clips/review, after which thumbnail writes silently fail. Ensure the directory exists before both cv2.imwrite calls and check their return value
* add docs for add camera wizard
* Handle indefinite events when a segment needs to forcibly be ended for a ceamera
* update keyframe interval article link
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Recreate preview output directory before writing it
The preview directory is created once in PreviewRecorder.__init__, but
record cleanup's remove_empty_directories() can delete it again while it
is empty (e.g. a camera re-added after removal, or an hour with no
retained previews). FFMpegConverter then fails permanently with
"No such file or directory" and previews are silently lost with only
one ERROR log line per hour. Recreate the directory before invoking
ffmpeg so the hourly export self-heals.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Remove explanatory comments above the fix
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* resolve saved credential sentinel to the stored api_key in the GenAI probe
* add profile faq
* center the multi-camera export time range on the current playback position
* add faq about preview restart cache
* clarify exports bulk download
* Catch faces that become empty after cropping
* don't drop batched camera add/remove config updates
TrackedObjectProcessor drained all pending camera config updates at once but handled them in a mutually exclusive if/elif on enabled/add/remove, so only one topic was processed per drain. When an add arrived in the same batch as an enabled update, the add was skipped and the new camera never got a camera state. Adding a camera reliably produced that batch: config_set now re-applies runtime overrides, which republishes an enabled update for every previously toggled camera immediately before the add, in the same request. The dashboard and camera capture still saw the camera (the maintainer does not subscribe to enabled, so it got a clean add-only batch), but object_processing did not, and disabling the camera then crashed with a KeyError on the unguarded camera_states lookup.
Handle add and remove independently instead of as exclusive branches so a batched add is no longer dropped, and guard the remove lookup so a missing state is skipped rather than raising. Drop the enabled branch entirely: it only ever set prev_enabled when it was None, but prev_enabled is seeded to a bool at camera state creation and is never None (mypy flags the body as unreachable), and the actual enable/disable transition is already driven by the disabled-state loop from config.enabled.
* Don't stay on motion search page when user cancels flow
* fix notification test button being blocked by websocket auth
* fix overflowing model names in settings genai widget
* add note about auth debugging
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
/api/config/set and camera deletion re-parse yaml into a fresh FrigateConfig and swap it in, then re-layered the persisted runtime toggle overrides so a camera the user turned off wouldn't come back on. That re-layer ran apply_runtime_state, which replays each override through the command handlers, so every save re-published a ZMQ config update, a retained MQTT state message, and a runtime-state disk write for every camera with a stored toggle. All of it was redundant: the worker processes were never swapped and still hold the live toggle values, so only the in-process config object the API and dispatcher read was out of date. The extra traffic churned the retained MQTT topics, amplified disk writes, and co-drained enabled updates with other topics on the config socket.
Add Dispatcher.reapply_runtime_state_to_config, which corrects only the swapped-in config object, mirroring the field mutations and gates of the _on_*_command handlers with no ZMQ, MQTT, or disk writes. swap_runtime_config now calls it instead of apply_runtime_state; apply_runtime_state is unchanged and still used at startup, where the workers genuinely must be told.
* preserve runtime camera toggles across config saves
Runtime toggles (camera on/off, detect, recordings, snapshots, audio) mutate the in-memory config and persist an override to .runtime_state.json. /api/config/set re-parses yaml into a fresh FrigateConfig and swaps it in, re-applying the yaml and profile layers but dropping the runtime layer, so a camera turned off from the dashboard came back on when an unrelated camera was saved. The workers were never notified, so it only appeared to come back: the UI streamed go2rtc while ffmpeg stayed stopped.
Extract the startup replay into Dispatcher.apply_runtime_state() and call it from config_set after the swap, re-layering the overrides and republishing them so workers and the UI reconverge.
Remove the broad clear_runtime_state() from ProfileManager.update_config, which is only ever reached from config_set: with a profile active, every save wiped every camera's overrides from disk. The broad wipe stays in activate_profile, where a real profile switch does invalidate the steady state. Saves still clear the keys they rewrote via clear_runtime_state_for_yaml_keys, so yaml wins where the two disagree.
* sync runtime config on camera delete and prune its overrides
Deleting a camera re-parsed yaml into a fresh FrigateConfig but only rebound app.frigate_config and genai_manager, never dispatcher.config (nor profile_manager, stats_emitter, or the runtime overrides). The API and the dispatcher then drifted onto different config objects until the next config save re-synced them, so the API reported surviving cameras with their yaml enabled state while the dispatcher still acted on their real runtime state.
Extract the config swap that config_set already does into a shared swap_runtime_config helper and call it from both sites, so every collaborator is rebound and the surviving cameras' runtime toggles are re-layered. Also drop the deleted camera's persisted overrides via a new clear_camera so a camera later added under the same name does not inherit them.
* Cleanup llama.cpp and use api key when configured
* don't report auto-populated object and audio filters as camera overrides
* derive stale replay cameras from bounded directory listings to avoid scanning all clips at startup
* fix tests
* add -vaapi_device to the birdseye vaapi encode preset so hwupload can initialize on ffmpeg 8
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* resolve zone friendly names against the correct camera
* Improve handling of zone names in chat prompt
* show a numeric keyboard for numeric config form fields on mobile
* Specify english only for semantic search tool when model is JinaV1
* resolve export hwaccel args global value against the correct config path
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
The recognized_license_plate event filter passed attacker-controlled patterns to re.search on the single serialized SQLite queue thread, letting any authenticated user freeze the whole application with a catastrophic regex. This swaps stdlib re for the regex module with a per-evaluation timeout so a pathological pattern is aborted instead of stalling every database operation.
* Add logout endpoint to Nginx configuration to prevent logout from silently generating a new frigate_token cookie
* Change JWT cookie expiration to use max_age and have the appropriate expiration time based on JWT_SESSION_LENGTH
* ruff formatting
* Convert face crops to RGB before embedding
Face crops flow through the cv2 pipeline as BGR arrays, but
_process_image passes ndarrays to PIL without any channel conversion,
so the FaceNet and ArcFace embedders receive BGR input while both
models expect RGB. The error is symmetric between enrollment and
recognition so it partially cancels, but it still costs accuracy.
* Move BGR to RGB conversion into a shared helper
Deduplicate the channel swap from both _preprocess_inputs methods
into a BaseEmbedding._bgr_to_rgb static helper, as suggested in
review.
* add ability to edit enabled and save_attempts for classification models in the UI
* add state motion and interval configs to edit dialog
* fix preview playback rate for motion previews
* add docs note about environment vars and go2rtc
* update live view faq
* honor enabled flag for custom classification models
for both startup and dynamically, even though the UI doesn't currently have a way to toggle dynamically
* add test
* sort preview cameras in history by ui order
* sort cameras by UI order in various components for consistent display
* add no recordings faq
* fix link
* recording cache faq
* add link
* improve anchor naming in object detector docs
rather than #configuration-1, #configuration-2, etc
* use yaml instead of json for object detector docs
* fix anchor
* Pin ruff
* Add python upgrade fixes
This enables python upgrade checks in ruff to look for deprecated types and patterns. This namely fixes:
- usage of deprecated `Typing` which is now built in
- some specific exceptions which are caught and have new aliases
Some specific UP checks were also ignored as they are stylistic / unimportant and likely to cause bugs
* Remove async blocking calls
Use asyncio.to_thread on two remaining blocking calls to fix hanging event thread loop. Enable this specific rule to block it in the future.
* Use proper logging mechanism
* Correctly format logs
* Raise with context
When raising an exception include the from context to improve debugging
* Cleanup
* docs tweaks
* show reolink warning when using probe path in camera wizard
* note ffmpeg 8 default
* update links
* add faq about false positives
* tweak plus language
* Recommend OpenVINO uses YOLOv9 by default
* add mse/rtc live view faq
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Yechi Yang <yechiyang93@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/zh_Hans/
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Co-authored-by: Alberto-Audrix <alberto.suiwidjaya6@gmail.com>
Co-authored-by: Diazt Muhammad Firmansyah <diaztmuhammadfirmansyah@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Naufal F <fadhlurrahmannf0812@gmail.com>
Co-authored-by: Yeni Setiawan <yenisetiawan@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/id/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/id/
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Co-authored-by: Edoardo Sorrenti <ed.sorrenti@gmail.com>
Co-authored-by: Filippo-riccardo Franzin (filippo franzin) <filric01@gmail.com>
Co-authored-by: Gringo <ita.translations@tiscali.it>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/it/
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Translation: Frigate NVR/Config - Cameras
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Co-authored-by: Eduardo Pastor Fernández <123eduardoneko123@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
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Co-authored-by: A T <andrey.timchenko@gmail.com>
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Vitaliy Kreminskiy <vkrmk13@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/audio/uk/
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* fix per-camera notification MQTT topics never being registered
register notifications/set and notifications/suspend callbacks for each camera, and gate the global notifications topics on per-camera config as well as global (matching WebPushClient creation in app.py). Unregistered topics were silently dropped by paho since only registered callbacks receive messages.
* add tests
Deleting the last entry of a mapping or sequence via config/set orphaned ruamel's comment tokens, which were then emitted above a flow-style {} / [] at column 0, which is unparseable yaml that failed validation and silently rolled the change back. The fix is to clear the emptied collection's stale comment metadata (and the parent's entry for it) so the dump stays valid. Non-empty collections are left untouched so sibling comments are preserved. This covers both emptied maps and emptied lists.
* fix stale active object indicators on the live dashboard
The camera_activity/<camera> snapshot cache is only written when a client sends onConnect, and object "end" events only update the local state of mounted useCameraActivity hooks, never the cache. As a result, a hook that seeded from a stale cache or missed an "end" event while disconnected showed objects that had already left, with no path to correct itself short of a full page reload.
This change will re-request the snapshot on hook mount (collapsed to one onConnect per task across camera cards), and always re-notify camera_activity topics so hooks reconcile against their own local state instead of relying on snapshot-vs-snapshot comparison, and clear the payload dedup cache on reconnect and resync so byte-identical snapshots still apply.
* docs tweaks
* fix mqtt log message
* use consistent values for lpr debug frame filenames
with millisecond resolution
* apply object events through a functional updater to prevent lost updates
The events effect derived a new objects list from the value captured at render time and wrote the whole list back. When events arrived close
together, a run derived from a stale list erased a concurrent run's removal; the resurrected object then had no remaining "end" event to clear it, and the add branch could mint a duplicate entry that no splice could ever remove, leaving the live dashboard showing active objects the backend had already cleared, until a page reload.
The fix is to apply each event inside setObjects so it operates on the true current list exactly once. Unchanged results return the same reference so React bails out of re-rendering, and the label rewrite is hoisted so added objects get the sub_label/verified label directly instead of relying on the effect re-running against its own state update.
* Handle back seeking going to previous clip
* scope /recordings/unavailable query to the caller's allowed cameras
* listen for config updates in activity manager
* don't set search after awaited request
Intentionally do NOT setSearch() to mark the open event submitted. This runs after the awaited request, by which point the user may have closed the dialog; re-setting the parent's selected event would resurrect it and the force-open effect would reopen it (see #23599). The local "submitted" state covers the open card, and mutate() updates the events cache so the grid and any future open reflect the result.
* fix ruff
#23201 removed pathlib import but for some reason it's just now causing ruff to fail
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
Resolve conflicts in the export pipeline where dev's job-queue refactor
met master's chapter-metadata and security work.
- Unify chapter support under ChaptersEnum (none / recording_segments /
review_items); the realtime stream-copy export selects the per-segment
or per-review-item builder by the camera's configured mode. Thread
chapters through ExportRecordingsBody -> _build_export_job -> ExportJob
-> RecordingExporter.
- Keep master's creation_time/comment export metadata and fix a
video_path duplication the textual merge introduced in the preview
command.
- Move the chapters request field to ExportRecordingsBody (the single
export endpoint) where it is actually honored.
Restore security fixes the automatic merge would have reverted:
- frigate/util/services.py: restore the #23493 rename to the public
is_go2rtc_arbitrary_exec_allowed so create_config.py's dynamic-source
exec guard imports and runs (the merge otherwise left a broken import).
- Preserve the export image-path ".." traversal check inside
_sanitize_existing_image, applied to single/custom/batch exports.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* perf(track): avoid numpy reductions on tiny box lists in position smoothing
update_position runs per tracked object per frame. While a position has
fewer than 10 samples it calls np.percentile four times, and average_boxes
(per stationary object per frame) calls np.mean four times - all on lists of
at most 10 ints, where numpy's per-call dispatch/validation overhead
dominates the actual work.
Replace them with pure-Python equivalents:
- average_boxes: sum()/len() instead of np.mean (bit-identical output)
- interpolated_percentile(): linear-interpolated percentile matching
numpy.percentile (including its lerp branch at frac>=0.5) for the small
lists used here, in place of np.percentile
Measured in the release image (numpy 1.26.4) on a 10-element list:
np.percentile 18735 ns -> 191 ns/call (98x); np.mean-based average_boxes
7480 ns -> 591 ns (12.7x); ~74 us saved per object-frame in update_position.
A live py-spy --gil profile of a camera process_frames worker showed
np.percentile (update_position) and np.mean (average_boxes) among the top
Frigate-owned on-CPU frames.
Output is unchanged: added tests assert both helpers are bit-identical to
numpy over randomized small inputs.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Drop interpolated_percentile, keep only average_boxes
Per review: reimplementing np.percentile hurts readability and risks
divergence from numpy (e.g. numpy 2.x). Revert update_position to
np.percentile and remove the helper; keep only the average_boxes change
(sum()/len() instead of np.mean), which stays bit-identical.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two small per-object-per-frame improvements in the tracker hot path
(match_and_update), both bit-identical:
- get_stationary_threshold returned a freshly constructed StationaryThresholds
(a dataclass plus a list) on every call for any label not in the three
known lists - i.e. for common labels like person/dog. The default thresholds
are constant and never mutated, so return a shared module-level singleton,
as the other three cases already do.
- untracked_object_boxes membership used `box not in [list of boxes]` (O(n));
build a set of box tuples for O(1) membership. Boxes are hashable as tuples
and output is unchanged.
get_stationary_threshold appeared in a live py-spy --gil profile of a camera
process_frames worker. Adds tests for the threshold lookups.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
video/detect.py runs these for every frame:
- get_cluster_candidates: used_boxes was a list with `in` membership tests
inside the nested loop (O(n) per check). It is only ever membership-tested,
so switching it to a set (O(1)) leaves output unchanged.
- get_consolidated_object_detections: area(current_box) was recomputed on
every inner-loop iteration though it is loop-invariant; hoist it to one
call per outer detection.
Both are bit-identical (verified against the previous implementations over
randomized inputs). Measured in the release image, get_cluster_candidates on
a frame of 30 detection boxes: 59.2 us -> 42.1 us (1.4x); the gain scales
with the number of boxes per frame.
Adds a partition-invariant test (every box index lands in exactly one
cluster).
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A record-enabled camera whose record stream produces no cache segments
never appears in grouped_recordings, so the per-camera prune in
RecordingMaintainer.move_files() never runs for it. Its
object_recordings_info and audio_recordings_info buffers then grow
without bound until the recording process is OOM-killed (discussion
#23451).
Run a prune every move_files() cycle for cameras absent from
grouped_recordings, dropping entries older than the longest a segment
could still wait in cache before being matched
(MAX_SEGMENTS_IN_CACHE * MAX_SEGMENT_DURATION * 2). Cameras present in
grouped_recordings are left untouched and keep their existing prune.
Add a regression test asserting that an absent camera's stale entries
are dropped (recent ones kept) while a present camera's entries are
left intact.
Co-authored-by: John Pescatore <johnpescatore@claude.internal.johnpescatore.com>
* perf(util): use monotonic clock and bounded deque in EventsPerSecond
EventsPerSecond is updated on every captured frame, every detection and
every processed frame across all cameras and detectors. The previous
implementation derived timestamps from datetime.now().timestamp() (wall
clock), so an NTP or manual clock adjustment could skew the rolling-window
expiry; it also stored timestamps in a list and expired them with
del self._timestamps[0] (O(n) per removal) plus a periodic slice-copy to
cap growth.
Switch to time.monotonic() for the interval math (correct by construction
and immune to wall-clock jumps) and a collections.deque(maxlen=...) so
expiry is O(1) (popleft) and retention is bounded automatically. This
mirrors the deque-based expiry already used in video/ffmpeg.py and
watchdog.py. Observable output is unchanged.
Adds frigate/test/test_builtin.py covering rate calculation, window
expiry and the memory bound.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test: drop test_timestamps_are_memory_bounded
It only asserted that deque(maxlen=) caps length, which is stdlib behavior
rather than something this change needs to verify.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* display zone names consistently using friendly_name or raw id without transformation
* enforce camera-level access on go2rtc live stream websocket endpoints
* slightly darken bg-card
* change menu label
* move snapshot retain out of advanced fields
* add new ui options for collapsibles
* backend title and description
* remove unused snapshot retention field
* update reference config
* remove further references to snapshots retain.mode
* Implement tool call history keeping
* Refactor to match single message implementation
* Simplify data representation
* Cleanup chat page rendering
* Include system message to not break cache
* Formatting
* Update tests and update .gitignore
* update e2e mock data to remove deprecated fields
* remove scream audio label
scream was never mapped to anything in frigate's custom labelmap, yell is used everywhere
* document common audio labels
* deprecate ffmpeg 5
* language tweak
* add field message to recommend presets instead of manual hwaccel args
* add guidance to docs on choosing a detect fps
Currently translated at 100.0% (1272 of 1272 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (475 of 475 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (809 of 809 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (62 of 62 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (807 of 807 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (473 of 473 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (1268 of 1268 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (61 of 61 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 94.6% (1196 of 1263 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (1195 of 1195 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (239 of 239 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (100 of 100 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (1186 of 1186 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (23 of 23 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (1183 of 1183 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (54 of 54 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (1181 of 1181 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (54 of 54 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (811 of 811 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (53 of 53 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (238 of 238 strings)
Translated using Weblate (Chinese (Simplified Han script))
Currently translated at 100.0% (1176 of 1176 strings)
Co-authored-by: GuoQing Liu <842607283@qq.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-settings
Currently translated at 92.0% (46 of 50 strings)
Translated using Weblate (Swedish)
Currently translated at 94.0% (94 of 100 strings)
Translated using Weblate (Swedish)
Currently translated at 100.0% (74 of 74 strings)
Translated using Weblate (Swedish)
Currently translated at 50.7% (647 of 1276 strings)
Translated using Weblate (Swedish)
Currently translated at 54.4% (55 of 101 strings)
Translated using Weblate (Swedish)
Currently translated at 77.7% (136 of 175 strings)
Translated using Weblate (Swedish)
Currently translated at 54.4% (55 of 101 strings)
Translated using Weblate (Swedish)
Currently translated at 50.7% (647 of 1276 strings)
Translated using Weblate (Swedish)
Currently translated at 90.0% (54 of 60 strings)
Translated using Weblate (Swedish)
Currently translated at 93.0% (120 of 129 strings)
Translated using Weblate (Swedish)
Currently translated at 92.8% (222 of 239 strings)
Translated using Weblate (Swedish)
Currently translated at 94.4% (137 of 145 strings)
Translated using Weblate (Swedish)
Currently translated at 100.0% (74 of 74 strings)
Translated using Weblate (Swedish)
Currently translated at 100.0% (49 of 49 strings)
Translated using Weblate (Swedish)
Currently translated at 94.0% (94 of 100 strings)
Translated using Weblate (Swedish)
Currently translated at 90.0% (54 of 60 strings)
Translated using Weblate (Swedish)
Currently translated at 77.7% (136 of 175 strings)
Translated using Weblate (Swedish)
Currently translated at 50.7% (647 of 1276 strings)
Translated using Weblate (Swedish)
Currently translated at 100.0% (74 of 74 strings)
Translated using Weblate (Swedish)
Currently translated at 94.4% (137 of 145 strings)
Translated using Weblate (Swedish)
Currently translated at 93.0% (120 of 129 strings)
Translated using Weblate (Swedish)
Currently translated at 92.8% (222 of 239 strings)
Translated using Weblate (Swedish)
Currently translated at 91.2% (218 of 239 strings)
Co-authored-by: Douglas Stier <douglas.stier@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Mona Lisa <monalisa@users.noreply.hosted.weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-filter/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-search/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/sv/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/sv/
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-filter
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-search
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
Currently translated at 100.0% (1276 of 1276 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (50 of 50 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1272 of 1272 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (86 of 86 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (809 of 809 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (145 of 145 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (101 of 101 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (129 of 129 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (129 of 129 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (475 of 475 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (10 of 10 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (62 of 62 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (807 of 807 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (473 of 473 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1268 of 1268 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (61 of 61 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1263 of 1263 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1263 of 1263 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (239 of 239 strings)
Translated using Weblate (Spanish)
Currently translated at 99.2% (1253 of 1263 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (100 of 100 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1186 of 1186 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1183 of 1183 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (23 of 23 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1181 of 1181 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (54 of 54 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (238 of 238 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (1176 of 1176 strings)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Saninn Salas Diaz <saninnsalas@gmail.com>
Co-authored-by: ThatStella7922 <stella@thatstel.la>
Co-authored-by: jjavin <javiernovoa@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-configeditor/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/es/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-configeditor
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-settings
Currently translated at 55.3% (448 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 55.2% (447 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 75.3% (358 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 74.7% (355 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 54.8% (444 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 50.7% (241 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 41.7% (338 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (1276 of 1276 strings)
Translated using Weblate (Italian)
Currently translated at 34.1% (276 of 809 strings)
Translated using Weblate (Italian)
Currently translated at 37.0% (176 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (50 of 50 strings)
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Currently translated at 100.0% (64 of 64 strings)
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Currently translated at 100.0% (1272 of 1272 strings)
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Currently translated at 26.5% (126 of 475 strings)
Translated using Weblate (Italian)
Currently translated at 28.4% (230 of 809 strings)
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Currently translated at 94.6% (1204 of 1272 strings)
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Currently translated at 100.0% (62 of 62 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (100 of 100 strings)
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Currently translated at 100.0% (239 of 239 strings)
Translated using Weblate (Italian)
Currently translated at 26.4% (125 of 473 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (1195 of 1195 strings)
Translated using Weblate (Italian)
Currently translated at 28.3% (230 of 811 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Italian)
Currently translated at 26.2% (124 of 473 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Italian)
Currently translated at 28.2% (229 of 811 strings)
Translated using Weblate (Italian)
Currently translated at 28.1% (228 of 811 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (238 of 238 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (23 of 23 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (1183 of 1183 strings)
Translated using Weblate (Italian)
Currently translated at 100.0% (54 of 54 strings)
Translated using Weblate (Italian)
Currently translated at 26.0% (123 of 473 strings)
Co-authored-by: Filippo-riccardo Franzin (filippo franzin) <filric01@gmail.com>
Co-authored-by: Frank_ai <cyberpez.ai@gmail.com>
Co-authored-by: Gringo <ita.translations@tiscali.it>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/it/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/it/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-settings
Currently translated at 100.0% (1276 of 1276 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (50 of 50 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1272 of 1272 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (809 of 809 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (475 of 475 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (62 of 62 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (473 of 473 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1268 of 1268 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (807 of 807 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (61 of 61 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1263 of 1263 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (45 of 45 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1263 of 1263 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1195 of 1195 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (239 of 239 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (100 of 100 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1186 of 1186 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (811 of 811 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (145 of 145 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (23 of 23 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1183 of 1183 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (175 of 175 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1181 of 1181 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (238 of 238 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (54 of 54 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1176 of 1176 strings)
Co-authored-by: Eduardo Pastor Fernández <123eduardoneko123@gmail.com>
Co-authored-by: Gerard Ricart Castells <gerard.ricart@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-replay/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/ca/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
Currently translated at 100.0% (1276 of 1276 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (50 of 50 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (809 of 809 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1272 of 1272 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (175 of 175 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (101 of 101 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (475 of 475 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (62 of 62 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (473 of 473 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1268 of 1268 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (807 of 807 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1263 of 1263 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (239 of 239 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1263 of 1263 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1186 of 1186 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (100 of 100 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (23 of 23 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1183 of 1183 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (54 of 54 strings)
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: lukasig <lukasig@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/ro/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* refactor go2rtc docs
* clarify go2rtc language in live
* add export docs
* Move around config items to reflect reference config is now for advanced users
* Remove outdated ipv6 section
* Fix broken links
* live usage docs
* review usage docs
* history usage
* explore usage
* add usage sidebar and move related text to usage sections
* update links
* update live
* move exports to usage
* fix anchors
* Make starts of usage pages consistent
* refactor network config
* Adjustments for review
* Add AI details to history page
* describe alerts vs detections in review usage
* simplify
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* serialize OpenVINO inference per process to prevent concurrent-inference segfault
* clean up
* add max scaling meta to login page
* add more detect section field messages
* fix icon layout in settings field messages
* tweak edit icon color
* increase camera group icon size on mobile
add an animated slider when there is not enough space for all defined camera groups
* change desktop and mobile edit camera groups icon to pencil and add desktop tooltip
* apply safe area insets to mobile layout in PWA mode using viewport-fit=cover
* adaptively size bottom bar nav targets to 48px when they fit, else compact
icon size now targets the standardized 48×48px mobile touch target (Material Design 3 / Android 48dp bottom-nav minimum)
* 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
* refactor motion search
* cleanup dead code and tests
* tweaks
* fix multi-day seeking
* start playback a few seconds before the change so the motion is in view
* add ptz presets and default role widgets
* language tweaks
* fix width in triggers view
* tweak iOS PWA message in notifications settings
* deprecate ui.date_style and ui.time_style
these have been unused since date/time formatting has been pushed to i18n
* add config migrator to remove date_style and time_style
* remove date_style and time_style from reference config
* fix camera list scrolling in state classification wizard on mobile
* improve error parsing and increase skip default
* improve motion search layout to match tracking details
* implement draw and move mode on mobile
* update motion search docs
* language tweaks
* improve tips
* note actions menu
* improve visibility of blurred icon buttons
* add motion search to history actions menu and mobile drawer
* i18n
* use pure css for motion search dialog video
* defer profile restoration until subscribers are connected
* change order of features in mobile review settings drawer
Extends the custom URL validator to accept both rtsp:// and rtsps://, and updates the error message in all 25 translated locales to reflect both schemes. Also fixes a pre-existing typo in the Slovak translation (\"rtsp / \" → \"rtsp://\").
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* remove redundant per-view toasters in settings
* add variants to standardize dialog footer button layouts
* remove text-md
this class name compiles to nothing in tailwind. we used to add it to prevent iOS from zooming when focusing on an input, but that is now solved via the viewport meta in index.html
* make wizard footers consistent with dialog footers
* consistent destructive button style
remove text-white from individual buttons and add it to the variant
* stabilize chart options to stop ApexCharts updateOptions running on every stats tick
* constrain height of export dialog
* stop audio maintainer when deleting a camera
* run face register and recognize API handlers in threadpool
* add clone dialog
* i18n
* tweaks
* add to camera management pane
* add e2e test
* optional disable portal prop
* radio and checkbox tweaks
* tweak i18n
* add select all/select none
* fixes
* reset form only on open transition
* unselect all targets for existing camera
* fix test
* reorder sections for save and collapse to single put for new camera
* change source and allow cloning to multiple cameras
* tweak language
* fix overflowing text in save all popover
* tweaks
* fix per label object masks
* use grid for source and target
* language tweak
* resolve global record.export.hwaccel_args to fix phantom camera override
* auto-stop debug replay sessions after 12 hours
* docs tweaks
* add more tips to object classification docs
* tweak language
* Store hwaccel errors with timeout so it can retry
* Add error logs for Intel GPU stats
* add area
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
The zh-Hant translations are synced from Weblate (98% complete) but the
locale was never registered in the language selector, so users could not
select it. Register zh-Hant in supportedLanguageKeys, add its display
label, and map it to the zh-TW date-fns locale.
* add prop to disable id field
* disable id field when editing profile mask/zone
also, disable if the zone name already exists in required_zones or the base config is being edited and the id already exists on a profile
* add backend validation to reject profile-omly masks/zones
* add tests
* update docs
* tweak
* restructure camera enable/disable pane
* remove obsolete camera edit form
* change terminology to off/on instead of disabled/enabled
* docs
* move menu options and add current camera name badge
* docs
* tweaks
* filter motion review by allowed cameras
* filter alertCameras by allowed cameras so the recent alerts query for restricted roles doesn't reference cameras they can't access
* skip data streams in chapter exports to avoid ffmpeg segfault
* formatting
* restrict debug replay UI entry points to admin users
* Adjust default iGPU name when it can't be found
* Fix when model tries to request an invalid camera
* Improve prompt
* add collapsible main nav items in settings
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* add review padding to explore debug replay api calls
* add semantic search model size widget
disables model_size select with n/a text when an embeddings genai provider is selected
* regenerate zone contours and per-zone filter masks on detect resolution change
* treat null as a clear sentinel in buildOverrides so nullable field edits don't snap back
* extract replay config sheet to new component
* add validation and messages for detect settings
* unlink shm frames when camera is removed
* drop stale shm cache refs when cached segment is too small for requested shape
* skip new-object frame cache write when current_frame is unavailable
* add tests
* use setdefault when adding a new camera
Multiple subscribers in the same process each unpickle the ZMQ payload independently and would otherwise write divergent Python objects to the shared cameras dict — leaving long-lived references (e.g. CameraState.camera_config) pointing at a copy that subsequent in-place mutations like apply_section_update can never reach. setdefault collapses everyone onto the first writer's object so attribute mutations propagate to every consumer in this process.
* rebuild ffmpeg commands on detect update
Rebuild the cached ffmpeg cmd so the next process spawn picks up new resolution/fps. Running cameras keep their existing cmd (ffmpeg_cmds is only read at process startup); replay cameras are recycled by CameraMaintainer to pick up the rebuilt cmd
* drop stale shm cache refs when cached segment size doesn't match requested shape
The cached SharedMemoryFrameManager reference can point at a segment whose
size no longer matches the requested shape — the segment was unlinked and
recreated at a different size in a camera add/remove cycle. This catches
both a resolution increase (cached too small) and a decrease (cached too
large, pointing at an orphaned inode whose stale bytes would otherwise be
misinterpreted at the new shape, producing distorted/miscolored YUV frames).
After reopening, if the OS-level segment still doesn't match the requested
shape we're in a transient mid-recreate state — either the maintainer
hasn't allocated the new segment yet (size too small) or we opened a
pre-recycle segment (size too big). Either way, skip the frame and don't
cache the mismatched ref.
* recycle replay camera on detect update
* discard tracked-object state when detect resolution changes mid-session
When detect resolution changes mid-session every tracked object we hold
was localized against the old pixel grid. Their boxes no longer
correspond to anything in the new frame, and the `end` callback that
fires when their IDs disappear from the new detect process's detections
publishes those stale boxes to consumers (LPR, snapshot crop) that slice
the new frame and crash on empty arrays. Drop the tracked-object state
on a shape change so no stale boxes ever cross the CameraState boundary.
Belt-and-suspenders: also drop any incoming batch whose boxes exceed the
current detect resolution. These are in-flight queue entries from the
pre-recycle detect process that beat the new detect process to the
queue; processing them would re-introduce stale-resolution tracked
objects we just dropped above. The per-camera detect process clamps
legitimate boxes to detect.width-1 / detect.height-1, so any coord
beyond that is unambiguously stale.
* rebuild motion and object filter masks on detect resolution change
Apply the detect update first so frame_shape reflects the new resolution
before we rebuild dependents.
Motion's rasterized_mask is sized to frame_shape at construction. When
detect resolution changes we must rebuild RuntimeMotionConfig so the
mask matches the new frame size; otherwise consumers like the LPR
processor and motion detector hit a shape mismatch when they index
frames with the stale mask.
Same story for per-object filter masks — rebuild RuntimeFilterConfig at
the new frame_shape so the merged global+per-object masks they hold
match what they'll be indexed against.
* republish motion and objects on in-memory detect resize
A detect resolution change also invalidates the rasterized masks on
motion and per-object filters. apply_section_update has rebuilt them at
the new frame_shape; publish them too so other processes replace their
old values.
* add test
* frontend
* add refresh topic for camera maintainer recycle action
The maintainer's recycle branch is doing an action (recycle the camera)
in response to a section-level signal. Introduce a
CameraConfigUpdateEnum.refresh case as an explicit action signal — the
maintainer subscribes to refresh instead of detect, parallel with add
and remove. Publishers fire refresh alongside detect when a recycle is
needed; section-level subscribers keep their existing topic.
Since no main-process subscriber listens for detect anymore, the
refresh handler calls recreate_ffmpeg_cmds() explicitly so the shared
CameraConfig's ffmpeg_cmds is rebuilt before the new subprocesses
spawn.
* factor stale-resolution state drop into a CameraState method
* use monotonic clock for detector inference duration to prevent negative values from wall clock steps
* add ability to set camera's webui_url from camera management pane
* Gemini send thought signature
* Update docs
* copy face and lpr configs from source camera to replay camera
* add guard
* improve dummy camera docs
* remove version number
* fix stale field message after reverting a conditional form field
Routes field-level conditional messages through a dedicated React Context instead of merging them into uiSchema. RJSF's Form keeps state.uiSchema sticky across renders during processPendingChange (formData is updated, uiSchema is not), so a previously injected ui:messages array stays attached to a field even after the triggering condition flips back to false. Context propagation re-runs FieldTemplate directly on every provider value change, sidestepping that staleness.
* add semantic search field message to note that model_size is irrelevant when embeddings provider is selected
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* filter replay camera from camera selectors
* add face rec and lpr to replay configuration sheet
* add missing config topic subscriptions in embeddings maintainer
* pop replay camera from config object when stopping
* ensure motion masks from source camera are copied to replay
* stop polling debug_replay/status after live_ready
* use vod for constructing replay clips
* render orphaned filter entries as collapsibles instead of the Key/Value editor
* Symlink for various AI files
* change replay confg dialog to platform aware sheet
* change agents title
* fix test
* tweak collapsible
* remove camera ui section in settings
no point to having it anymore with profiles and camera management settings
* fix admin response cache leak to non-admin users via nginx proxy_cache
* add model fetcher endpoint for genai config ui
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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Co-authored-by: Edward Zhang <hsrzq@126.com>
Co-authored-by: GuoQing Liu <842607283@qq.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-settings
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: jjavin <javiernovoa@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-replay/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/es/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Bart Smeding <bartsmeding@gmail.com>
Co-authored-by: Björn Vanneste <info@nidhhoggr.net>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Hosted Weblate user 151476 <marijndekker3@gmail.com>
Co-authored-by: bb61523 <brambini@gmail.com>
Co-authored-by: soosterwaal <sebastiaan@bg-engineering.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-replay/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/nl/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-settings
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Co-authored-by: Eduardo Pastor Fernández <123eduardoneko123@gmail.com>
Co-authored-by: Gerard Ricart Castells <gerard.ricart@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/audio/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ca/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/audio
Translation: Frigate NVR/common
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-settings
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Ton Zabretooth <zabretooth@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-chat/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-motionsearch/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-replay/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/th/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/th/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/views-chat
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-motionSearch
Translation: Frigate NVR/views-replay
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* filter outbound ws broadcasts by per-recipient camera access
* fan out config updates to comms
* tests
* mypy
* allow viewers to use jobstate
* update agent instructions
* remove vitest
* Ensure runtime options are passed
* Add attribute info to prompt when configured
* Move GenAI plugins to dedicated directory
* Migrate prompts to dedicated folder
* Move chat prompts to prompts
* Implement reasoning traces in the UI
* Cleanup
* Make azure a subclass of openai
* Implement reasoning for other providers
* mypy
* Cleanup
* preserve user-set min_score on attribute filters instead of bumping any 0.5 value
use model_fields_set to distinguish "user explicitly set min_score" from "Pydantic applied the generic FilterConfig default of 0.5"
* add config test for attributes
* fix attributes frontend type
* add expanded hidden field context
* extend schema modification
* special case for attributes
* i18n for attributes
* handle dedicated lpr mode
* strip unrendered FilterConfig fields from attribute filter form data to fix validation errors
* start audio transcription post processor when enabled on any camera
* Fetch embed key whenever an error occurs in case the llama server was restarted
* mypy
* add tooltips for colored dots in settings menu
* add ability to reorder cameras from management pane
* add ability to reorder birdseye
* add reordering save text to camera management view
* Include NPU in latency performance hint
* Implement turbo for NPU on object detection
* hide order fields
* drop auto-derived field paths from camera value when unset globally
* use correct field type for export hwaccel args
* add debug replay to detail actions menu
* clarify debug replay in docs
* guard get_current_frame_time against missing camera state
* Implement debug reply from export
* Refactor debug replay to use sources for dynamic playback
* Mypy
* fix debug export replay source timestamp handling
* skip replay cameras in stats immediately
* broadcast debug replay state over ws and buffer pre-OPEN sends
- push debug replay session state over the job_state ws topic so the status bar reacts instantly to start/stop without polling
- fix child-effect-before-parent-effect race in WsProvider that silently dropped initial snapshot requests on cold load
* fix debug replay test hang
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* detector UI fixes
- derive detector and model from memo rather than using two drain useeffects
- sanitize save payload through sanitizeSectionData to prevent yaml validation issues
* increase display duration for restart required toasts
* mimic logic in detector section for save all button
also, increase toast duration for restart required toasts
* fixes and tweaks
- use section hidden fields for sanitization instead of duplicating code
- use parent hooks so save all, pending data, and the status dots work correctly
* add embedded mode to BaseSection so parents can host the save action
* add optional action slot to current Frigate+ model summary
* add w-full to action slot flex wrapper for explicit width contract
* i18n
* merged detectors and model settings view
* fix document title
* Embed detector form in merged settings view
* add detection model card with tabs and custom model embed
* add Frigate+ model selector with filter popover to merged page
* Add mismatch banner and gate save on detector and model compatibility
* Wire atomic save, restart toast, and undo on detectors and model page
* Clear child pending data on undo
* route merged detectors and model view in settings
* trim Frigate+ page to account-only and remove old detection model view
* basic e2e
* Fix unsaved-changes guard, custom path leak, and post-failure cache resync
* Rename to Detectors and model, float Modified badge, use ConfigMessageBanner for mismatch
* Hide Plus/Custom tabs when Frigate+ is not enabled
* Detect active Plus model via model.plus.id instead of path prefix
* Sync state back to snapshot when child form un-modifies and remount on undo
* Always require restart on save since model changes also need one
* Wrap Frigate+ model selector in SplitCardRow with label and description
* rename tab
* update docs
* sync top-level model with default detector's resolved model
when the user doesn't define a top-level `model:` block, `FrigateConfig.model` stayed at pydantic field defaults (320×320, /labelmap.txt) while the per-detector model picked up `DEFAULT_MODEL` for openvino on cpu (300×300, coco_91cl_bkgr.txt introduced in #23127), causing `RemoteObjectDetector` to fail with "buffer is too small for requested array" because the SHM was sized from the per-detector model but mapped using the top-level one. After the detector loop, copy the first detector's resolved model up to `self.model` so both sides agree on dimensions and labelmap
* revert to cpu detector by default
use openvino cpu for new configs only
* add defaults
* sync filter entries with track and listen labels
- Auto-populate `audio.filters` from `audio.listen` instead of the full audio labelmap, matching how `objects.filters` is keyed by `track` (no longer need to populate the full audio labelmap, which was added in #22630)
- Synthesize the matching filter entries in the settings form on load so each track/listen label shows its collapsible after a profile is selected, since the backend's auto-populate only runs at config init
* translate main label for lifecycle description with attribute
* reject restricted go2rtc stream sources when added via api
* add env var check function
* Support token streaming stats
* Propogate streaming token stats to chat calls
* Show token stats for each image
* Add settings to handle token stats and other options
* i18n
* Use select
* Improve mobile layout and spacing
On multi-GPU systems, OpenVINO enumerates devices as "GPU.0", "GPU.1",
etc. rather than a single "GPU". The exact string match in
is_openvino_gpu_npu_available() fails to recognize these suffixed device
names, causing enrichments (face recognition, semantic search) to
silently fall back to CPU-only inference via ONNXModelRunner instead of
using OpenVINOModelRunner on GPU.
Switch from exact match to prefix match so both single-GPU ("GPU") and
multi-GPU ("GPU.0", "GPU.1") device names are correctly detected, along
with any future suffixed variants for NPU and other accelerators.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* improve scroll handling for non-modal DropdownMenu in classification and face selection dialogs
* clean up
* fix incorrect key capitalization
* fix profile array overrides not replacing base arrays
don't use lodash merge(), it does positional merging and an empty source array doesn't override the destination, and shorter arrays leak destination elements through.
backend is unaffected, so the saved config and actual backend functionality was right
* only show audio debug tab when audio is enabled in config
* move apple_compatibility out of advanced
* remove retry_interval from UI
99% of users should never be changing this
* hide switch in optionalfieldwidget if editing a profile
* add override badges for cameras and profiles
collect shared functions into the config util and separate hooks
* Use new models endpoint info to determine modalities
* clarify language
* fix linter
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* restrict viewer access to logs, labels, and go2rtc stream list
* filter stats data for non admins
* track creator on vlm watch jobs and scope view/cancel to admin or creator
* add shortcut for admins in /stats
I have a very repeatable reproduction of an issue where most of my
cameras show a "No frames have been received, check error logs" image in
the UI, but restreaming in HomeAssistant is working flawlessly. The only
errors in the logs I saw were some like this:
`OSError: [Errno 121] Remote I/O error`.
Doing a bit more debugging, it looked like Frigate was failing to create
the thumbnail directory for a camera because it already existed. This
error was a clue as to the class of error. I was surprised to learn that
`os.path.exists` [silently suppresses errors from
os.stat and returns False](https://github.com/python/cpython/blob/main/Lib/genericpath.py#L22).
This makes for a plausible series of events: a transient stat call
fails, so Frigate takes the creation path, which gets upset that the
directory already exists.
I found a few other possible cases to fix but did not make an exhaustive
search. It seems that this `exist_ok` flag is used elsewhere within
Frigate so I thought it would be a good solution.
AI disclosure: I used AI to diagnose my issue and asked it to translate
its init-time patches to the container source into this repo. I verified
that its patches solved the problem I was facing. Its theory fits the
facts - I am using a distributed file system and I saw the error in my
logs. I checked the upstream Python code to verify the error suppression
behavior, and read the corresponding Frigate code. I did not use AI to
author this commit message/PR description; all diction and typos here are my own.
* add optional onClick to EmptyCard
* show EmptyCard in face rec when face library is empty
* add loading indicator
* add description to camera management pane
* Cleanup when use snapshot but can't load snapshot
* Migrate files
* fix birdseye color distortion when configured aspect ratio is unsupported
* Skip processing end for object descriptions
* don't crash if stats is null
* fix genai roles in migration
* frigate+ pane updates
- allow users to select a plus model from the select even when one was not previously loaded
- always show model summary card
- add model filter popover
- add restart button totast
* fix frigate+ pane layout and buttons to match other settings panes
* match button layout in go2rtc settings view
* make audio maintainer respond to dynamic config updates
* check correct zone name in publish state
* fix nested translation extraction for Optional dict and list fields
* mypy
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
get_relative_coordinates() previously logged
"Not applying mask due to invalid coordinates. X,Y is outside ..."
without naming the camera, so on a multi-camera setup the user had
to guess which one to fix.
Add an optional camera_name kwarg with default "" (no behavior
change for existing callers). The global object-mask path in
FrigateConfig.validate_config passes camera_name=camera_config.name
since it already has it in scope, so legacy configs with absolute
pixel coordinates now get an actionable log line:
Not applying mask due to invalid coordinates for camera back.
9000,9000 is outside of the detection resolution 800x400.
Use the editor in the UI to correct the mask.
Existing wording is preserved verbatim except for the inserted
" for camera <name>" segment. Runtime behavior is unchanged.
Co-authored-by: Claude <noreply@anthropic.com>
* Change order
* Improve title
* add loading spinner to exports
* Simplify JSON since not all providers see or use this the same
* Add fields to primary prompt
* Adjust centering for no overrides
* Use GenAI title for exports when available
* detect form-root objects by field path instead of schema identity
* add bosnian
* Strip v1 if included in url
* prevent fast clicks in video controls from selecting text
* Use title for metadata chapters
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
The literal string ``"Removed {count} empty directories"`` was passed
to ``logger.debug`` without an ``f`` prefix, so the ``{count}``
placeholder is emitted verbatim instead of being substituted. Convert
the call to an f-string so the count is logged.
* respect section hiddenFields when detecting config overrides
* change audio events to audio detection to match docs
* add field messages for object and review genai
* add more config messages
* more messages
* add guard to prevent race when adding camera dynamically
* fix duplicate websocket messages from zombie connection under react strict mode
detach ws event handlers before close() in WsProvider cleanup so a CONNECTING socket's deferred onclose can't schedule a reconnect after the next mount resets the unmounted guard, which was spawning a second live ws and duplicating every message
* fix double event publishes for stationary objects with attributes
* hide camera overrides badge from system sections
* show empty card on camera metrics page when no cameras are defined
* fix enabled camera state switch after adding via wizard
Cameras added mid-session have no WS state until the dispatcher publishes camera_activity (which only happens on a fresh onConnect). Fall back to the config's enabled value so the switch reflects reality immediately after the wizard closes.
* guard camera enabled access
console would throw errors after adding via camera wizard
* fix useOptimisticState dropping debounced setState under StrictMode
* use openvino on cpu as default model
- faster than tflite on cpu
- add to default generated config
* use an enum for model_size
the frontend will then render this as a select dropdown because of the changes in the json schema
* i18n
* sync object filter entries with tracked labels in camera config form
Filter sub-collapsibles in the camera Objects section are driven by `filters` dict keys, but profile merges and live track-switch edits don't add matching entries, so newly tracked labels (like from a profile override) had no collapsible. Synthesize default filter entries from `track` in the form data so every tracked label renders a collapsible; baseline data also gets the synthesized entries, so save payloads are unchanged.
* revalidate raw paths cache after config save so CameraPathWidget shows fresh credentials
* fix test
* restore masked ffmpeg credentials when persisting camera config
* formatting
* rebuild ffmpeg commands when enabling recording for the first time
Toggling record.enabled from the config UI updated the in-memory config but left ffmpeg running with its original command, so the record output args were never wired in and nothing landed in the cache for the maintainer to move. The record config update now rebuilds ffmpeg_cmds when enabled_in_config transitions, and the camera watchdog restarts ffmpeg on a false to true transition so the record output gets wired in. MQTT toggles, which only flip record.enabled at runtime, are unaffected and continue to work via the maintainer's drop/keep gate.
* keep record toggle switch in single camera view disabled until enabled in config
* fix override detection for sections unset in the global config
Override badges and the blue dot now compare against schema defaults for sections like motion that the API serializes as null when omitted from the global YAML, instead of treating any populated camera config as an override
* add support for config-aware patterns in section hiddenFields
Section configs can now declare dynamic hidden-field entries as functions of the loaded config; objects.ts uses this to hide auto-populated attribute filters (DHL, face, license_plate, etc.) from the form, save flow, and override popover when those labels aren't user-settable
* siimplify object filters handling
live updating was getting very messy. users will just need to save once they enable a new object in order to see filters for that object
* tweaks
* update docs for new detector default
* make genai provider required and add special case for UI
prevent validation errors from appearing on initial creation of genai provider by setting the first option in the select dropdown as default
* use continuous expire date when loading reviews for recording cleanup
* reset heatmap filter when motion preview camera changes
* Add note about speed zones unit when enabled
* don't display fps warning for dedicated LPR cameras
* language tweaks
* allow changing camera type from management UI
* i18n
* fix ollama tool calling failure when conversation contains multimodal content from live frame tool results
* fix mypy
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* fix(face_recognition): feed BGR (not RGB) to FaceDetectorYN in manual detection branch
Frigate's `requires_face_detection` branch in `FaceRealTimeProcessor.process_frame`
converts the YUV camera frame to RGB and passes it to `cv2.FaceDetectorYN`.
YuNet is trained on BGR — feeding it RGB silently degrades detection
confidence by ~10× on typical person crops, causing face_recognition to
emit no `sub_label` and produce no `train/` entries. There is no log signal
because the detector simply returns 0 faces; from outside the box it looks
like nobody is walking past any camera.
The same file already does the YUV→BGR conversion correctly in the
else-branch (was line 271, now line 285) — only the manual-detection
branch was missed.
## Reproduction
Verified in-pod against the running Frigate's models on identical
person crops (snapshot pulled from a real person event):
BGR (correct): cv2.FaceDetectorYN ← confidence 0.744 ✓
RGB (current): cv2.FaceDetectorYN ← confidence 0.047 ✗
The `score_threshold=0.5` set on `FaceDetectorYN.create()` filters anything
under 0.5 at the detector layer, so the RGB-degraded crops never reach
the user-configurable `detection_threshold`. Result: silent outage.
## Fix
Three changes in `frigate/data_processing/real_time/face.py`:
1. `cv2.COLOR_YUV2RGB_I420` → `cv2.COLOR_YUV2BGR_I420`
2. Variable rename `rgb` → `bgr` to match
3. Remove the now-redundant `cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)`
block — `face_frame` is already BGR after the upstream conversion change
Net diff: +6 / -7. Pure Python, no new dependencies.
## How a deployment confirms the fix
After this change, walking past a camera produces:
- `data.attributes` with a `face` entry on the person event (currently empty)
- New entries in `/api/faces` `train/` array (currently frozen)
- `sub_label` populated on subsequent person events for trained faces
Signed-off-by: Vinnie Esposito <vespo21@gmail.com>
* Cleanup comment
---------
Signed-off-by: Vinnie Esposito <vespo21@gmail.com>
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Update to ROCm 7.2.3
* Add inference time for 9060XT
* Update times
* Update hardware info for latest ROCm
* Add env vars to save kernels and miopen database
* re-enable face recognition for ROCm
* Update
* Save LLVM cache
* Rewrite intel GPU stats to use file descriptors instead of intel_gpu_top, leading to significantly better API for interaction and more accurate results
* Update tests
* Update docs
* Adjust approach
* Update strings
* use ReplayState enum
* extract shared ffmpeg progress helper
* make start call non-blocking with worker thread
* expose replay state on status endpoint and return 202 from start
* cancel in-flight ffmpeg when stop is called during preparation
* add replay i18n strings for preparing and error states
* show status in replay UI
* navigate immediately on 202 from debug replay menus and dialog
* remove unused
* simplify to use Job infrastructure
* tests
* cleanup and tweaks
* fetch schema
* update api spec
* formatting
* fix e2e test
* mypy
* clean up
* formatting
* fix
* fix test
* don't try to show camera image until status reports ready
* simplify loading logic
* fix race in latest_frame on debug replay shutdown
* remove toast when successfully stopping
it gets hidden almost immediately
- Add _auth_headers() helper to pass Bearer token when api_key is set
- Wire headers into all Ollama client instantiations (sync + async)
- Update docs with Ollama Cloud direct connection example and yaml config
* lpr fixes
- remove duplicate code
- fix min_area check for non frigate+ code path
- move log outside of non frigate+ code path
* only show chat link when a genai provider is configured with the chat role
* respect ui.timezone when generating fallback export names
* reapply radix pointer events fix to call sites that use navigate()
* formatting
* fall back to prior preview frame for short export thumbnails
* fix typing
* fix e2e test for chat navigation
* batch annotation offset to seek atomically and throttle slider drag
* add debug replay loading toast for explore actions
* Improve handling of webpush missing shortSummary
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* openvino log message and preview directory checks
* restrict config vars for viewer users
* recording timestamp fix
when startTime is exactly on an hour boundary, findIndex returns the first matching chunk, which is the previous hour's chunk (where before == startTime), instead of the correct chunk (where after == startTime)
the bug shows up when using the share timestamp feature and sharing a specific timestamp on the exact hour mark. when accessing the shared link, the timeline would jump to the incorrect hour
* use helper for chunked time range
* Adjustments to contributing docs
* tweak
* Improve wording
* tweak
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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Currently translated at 100.0% (790 of 790 strings)
Co-authored-by: GuoQing Liu <842607283@qq.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-settings
Currently translated at 16.6% (1 of 6 strings)
Translated using Weblate (Kannada)
Currently translated at 0.9% (1 of 101 strings)
Translated using Weblate (Kannada)
Currently translated at 4.0% (1 of 25 strings)
Translated using Weblate (Kannada)
Currently translated at 10.0% (1 of 10 strings)
Translated using Weblate (Kannada)
Currently translated at 0.8% (1 of 123 strings)
Translated using Weblate (Kannada)
Currently translated at 0.1% (1 of 1081 strings)
Translated using Weblate (Kannada)
Currently translated at 10.0% (1 of 10 strings)
Translated using Weblate (Kannada)
Currently translated at 50.0% (1 of 2 strings)
Translated using Weblate (Kannada)
Currently translated at 2.1% (1 of 47 strings)
Translated using Weblate (Kannada)
Currently translated at 0.5% (1 of 174 strings)
Translated using Weblate (Kannada)
Currently translated at 0.4% (1 of 236 strings)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Added translation using Weblate (Kannada)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Rakshit Chandrahasa <r211093@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-auth/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-icons/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-configeditor/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-recording/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/kn/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/kn/
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/common
Translation: Frigate NVR/components-auth
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-icons
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-configeditor
Translation: Frigate NVR/views-recording
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
Currently translated at 100.0% (99 of 99 strings)
Translated using Weblate (Spanish)
Currently translated at 57.4% (58 of 101 strings)
Translated using Weblate (Spanish)
Currently translated at 21.9% (103 of 469 strings)
Translated using Weblate (Spanish)
Currently translated at 70.3% (757 of 1076 strings)
Translated using Weblate (Spanish)
Currently translated at 31.3% (27 of 86 strings)
Translated using Weblate (Spanish)
Currently translated at 98.4% (127 of 129 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (25 of 25 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Spanish)
Currently translated at 20.5% (162 of 790 strings)
Translated using Weblate (Spanish)
Currently translated at 99.4% (173 of 174 strings)
Translated using Weblate (Spanish)
Currently translated at 95.9% (118 of 123 strings)
Translated using Weblate (Spanish)
Currently translated at 29.6% (24 of 81 strings)
Translated using Weblate (Spanish)
Currently translated at 67.6% (728 of 1076 strings)
Translated using Weblate (Spanish)
Currently translated at 92.7% (218 of 235 strings)
Translated using Weblate (Spanish)
Currently translated at 66.4% (715 of 1076 strings)
Translated using Weblate (Spanish)
Currently translated at 66.4% (714 of 1074 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (22 of 22 strings)
Translated using Weblate (Spanish)
Currently translated at 98.2% (57 of 58 strings)
Translated using Weblate (Spanish)
Currently translated at 100.0% (23 of 23 strings)
Translated using Weblate (Spanish)
Currently translated at 92.0% (23 of 25 strings)
Translated using Weblate (Spanish)
Currently translated at 10.2% (48 of 469 strings)
Translated using Weblate (Spanish)
Currently translated at 8.9% (71 of 790 strings)
Translated using Weblate (Spanish)
Currently translated at 99.4% (173 of 174 strings)
Translated using Weblate (Spanish)
Currently translated at 98.2% (171 of 174 strings)
Translated using Weblate (Spanish)
Currently translated at 97.1% (169 of 174 strings)
Translated using Weblate (Spanish)
Currently translated at 95.9% (167 of 174 strings)
Co-authored-by: Daniel G. <keybyte@gmail.com>
Co-authored-by: Francesc Domene <fdomenef@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Luis Enrique Barral <luisbarral22@hotmail.com>
Co-authored-by: NecrumBlacke4984a794e814493 <k_spin@hotmail.com>
Co-authored-by: Riker <alpha9@icloud.com>
Co-authored-by: ThatStella7922 <stella@thatstel.la>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/es/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
Currently translated at 18.6% (16 of 86 strings)
Translated using Weblate (Hungarian)
Currently translated at 7.6% (36 of 469 strings)
Translated using Weblate (Hungarian)
Currently translated at 80.0% (20 of 25 strings)
Translated using Weblate (Hungarian)
Currently translated at 5.9% (47 of 790 strings)
Translated using Weblate (Hungarian)
Currently translated at 86.3% (19 of 22 strings)
Translated using Weblate (Hungarian)
Currently translated at 74.7% (130 of 174 strings)
Translated using Weblate (Hungarian)
Currently translated at 4.1% (33 of 790 strings)
Translated using Weblate (Hungarian)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Hungarian)
Currently translated at 52.0% (13 of 25 strings)
Translated using Weblate (Hungarian)
Currently translated at 92.7% (218 of 235 strings)
Translated using Weblate (Hungarian)
Currently translated at 39.6% (427 of 1076 strings)
Translated using Weblate (Hungarian)
Currently translated at 6.1% (29 of 469 strings)
Translated using Weblate (Hungarian)
Currently translated at 59.0% (13 of 22 strings)
Translated using Weblate (Hungarian)
Currently translated at 66.1% (41 of 62 strings)
Translated using Weblate (Hungarian)
Currently translated at 87.8% (87 of 99 strings)
Translated using Weblate (Hungarian)
Currently translated at 5.5% (26 of 469 strings)
Translated using Weblate (Hungarian)
Currently translated at 54.5% (12 of 22 strings)
Translated using Weblate (Hungarian)
Currently translated at 37.9% (408 of 1076 strings)
Translated using Weblate (Hungarian)
Currently translated at 44.0% (11 of 25 strings)
Translated using Weblate (Hungarian)
Currently translated at 3.7% (30 of 790 strings)
Translated using Weblate (Hungarian)
Currently translated at 71.8% (125 of 174 strings)
Translated using Weblate (Hungarian)
Currently translated at 86.8% (86 of 99 strings)
Translated using Weblate (Hungarian)
Currently translated at 4.4% (21 of 469 strings)
Translated using Weblate (Hungarian)
Currently translated at 65.2% (15 of 23 strings)
Translated using Weblate (Hungarian)
Currently translated at 2.6% (21 of 790 strings)
Co-authored-by: Da4ndo <vrgdnl20@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: KecskeTech <teonyitas@gmail.com>
Co-authored-by: ZELO <zg1990@users.noreply.hosted.weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/hu/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/hu/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
Currently translated at 100.0% (236 of 236 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1081 of 1081 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1077 of 1077 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (101 of 101 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (64 of 64 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1077 of 1077 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (86 of 86 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (58 of 58 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (145 of 145 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (95 of 95 strings)
Translated using Weblate (Catalan)
Currently translated at 97.8% (93 of 95 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (95 of 95 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (81 of 81 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (145 of 145 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1076 of 1076 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (790 of 790 strings)
Translated using Weblate (Catalan)
Currently translated at 100.0% (1074 of 1074 strings)
Co-authored-by: Eduardo Pastor Fernández <123eduardoneko123@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: anton garcias <isaga.percompartir@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ca/
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-settings
Currently translated at 10.0% (79 of 790 strings)
Translated using Weblate (Japanese)
Currently translated at 63.4% (686 of 1081 strings)
Translated using Weblate (Japanese)
Currently translated at 63.4% (686 of 1081 strings)
Translated using Weblate (Japanese)
Currently translated at 80.1% (81 of 101 strings)
Translated using Weblate (Japanese)
Currently translated at 9.8% (46 of 469 strings)
Translated using Weblate (Japanese)
Currently translated at 96.5% (56 of 58 strings)
Translated using Weblate (Japanese)
Currently translated at 8.7% (41 of 469 strings)
Translated using Weblate (Japanese)
Currently translated at 70.3% (45 of 64 strings)
Translated using Weblate (Japanese)
Currently translated at 90.8% (158 of 174 strings)
Translated using Weblate (Japanese)
Currently translated at 76.2% (77 of 101 strings)
Translated using Weblate (Japanese)
Currently translated at 94.5% (122 of 129 strings)
Translated using Weblate (Japanese)
Currently translated at 100.0% (74 of 74 strings)
Translated using Weblate (Japanese)
Currently translated at 62.9% (681 of 1081 strings)
Translated using Weblate (Japanese)
Currently translated at 8.9% (71 of 790 strings)
Translated using Weblate (Japanese)
Currently translated at 6.1% (29 of 469 strings)
Translated using Weblate (Japanese)
Currently translated at 61.8% (669 of 1081 strings)
Translated using Weblate (Japanese)
Currently translated at 5.6% (45 of 790 strings)
Translated using Weblate (Japanese)
Currently translated at 92.3% (218 of 236 strings)
Translated using Weblate (Japanese)
Currently translated at 61.8% (669 of 1081 strings)
Translated using Weblate (Japanese)
Currently translated at 100.0% (25 of 25 strings)
Translated using Weblate (Japanese)
Currently translated at 68.3% (69 of 101 strings)
Translated using Weblate (Japanese)
Currently translated at 5.9% (28 of 469 strings)
Translated using Weblate (Japanese)
Currently translated at 100.0% (99 of 99 strings)
Translated using Weblate (Japanese)
Currently translated at 89.0% (155 of 174 strings)
Translated using Weblate (Japanese)
Currently translated at 67.1% (43 of 64 strings)
Translated using Weblate (Japanese)
Currently translated at 5.5% (44 of 790 strings)
Translated using Weblate (Japanese)
Currently translated at 93.7% (121 of 129 strings)
Translated using Weblate (Japanese)
Currently translated at 100.0% (22 of 22 strings)
Translated using Weblate (Japanese)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Japanese)
Currently translated at 61.0% (658 of 1077 strings)
Translated using Weblate (Japanese)
Currently translated at 62.3% (63 of 101 strings)
Translated using Weblate (Japanese)
Currently translated at 94.4% (137 of 145 strings)
Translated using Weblate (Japanese)
Currently translated at 92.3% (217 of 235 strings)
Translated using Weblate (Japanese)
Currently translated at 65.6% (42 of 64 strings)
Translated using Weblate (Japanese)
Currently translated at 98.8% (85 of 86 strings)
Translated using Weblate (Japanese)
Currently translated at 60.9% (656 of 1076 strings)
Translated using Weblate (Japanese)
Currently translated at 100.0% (74 of 74 strings)
Translated using Weblate (Japanese)
Currently translated at 93.0% (120 of 129 strings)
Translated using Weblate (Japanese)
Currently translated at 37.2% (32 of 86 strings)
Translated using Weblate (Japanese)
Currently translated at 37.2% (32 of 86 strings)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Yusuke, Hirota <hirota.yusuke@jp.fujitsu.com>
Co-authored-by: alpha <etc@alpha-line.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-filter/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ja/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/ja/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-filter
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
Currently translated at 100.0% (1081 of 1081 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (236 of 236 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1077 of 1077 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (26 of 26 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (101 of 101 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (64 of 64 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (86 of 86 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (62 of 62 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (95 of 95 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (81 of 81 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (145 of 145 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1076 of 1076 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (1074 of 1074 strings)
Translated using Weblate (Romanian)
Currently translated at 100.0% (790 of 790 strings)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: lukasig <lukasig@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-player/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ro/
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-player
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-settings
The idle heartbeat check in BirdsEyeOutputProcess.update() compares
time.monotonic() (seconds since an arbitrary point, typically boot)
against last_output_time which is set from datetime.datetime.now().timestamp()
(Unix epoch seconds).
These are completely different time bases. The subtraction produces a
large negative number, so the idle heartbeat condition can never be
satisfied. This means birdseye stops sending frames when all cameras
go idle, instead of continuing at the configured idle_heartbeat_fps.
Use datetime.datetime.now().timestamp() consistently for both the
heartbeat check and the output time tracking.
* Move openai specific workaround so it doesn't apply to other providers
* Fix gemini tool calling
* Improve efficiency of frame listing for previews
* debug replay fixes
- initial selection without changing the radio button in the dialog would select 1 hour (rather than 1 minute)
- use CLIPS_DIR instead of CACHE_DIR so that longer replay clips don't cause tmpfs cache overflows
* don't re-render the tracking details overlay on every video time tick
* change pinned to planned
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* ensure embeddings process restarts after maintainer thread crash
* add docs link to media sync settings
* fix color
Co-authored-by: Copilot <copilot@github.com>
* match link color with other sections
* ensure recording staleness threshold scales with segment_time
* docs tweak
* Fix llama.cpp media marker
* Fix gemini tools call
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* add ui to camera config update topics enum
* add mqtt to camera config update enum
* ensure cleanup runs when an event end skips post-processing
* end any in-progress audio events when audio detection is disabled
we already end in-progress audio events when we disable a camera, but this mirrors that logic for specifically disabling audio detection
* Improve GenAI metadata
* fix invalid recording segment topic being misrouted to the valid handler
* Add confidence default to avoid unnecessary field causing issues
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Reduce max frames per second to 1
* Use pydantic but don't fail if some constraints are not met.
* Adjust limits
* Adjust limits
* Cleanup
* add unsaved changes icon/popover to individual settings section
* allow changing camera friendly_name from camera management pane
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* Test for image token usage in llama.cpp so we can more appropriately decide how many frames to include
* Limit based on frames per second
* handle zone case sensitivity
* Improve formatting
* Add observations field so model can build CoT before outputting used fields
* ensure classification wizard dialog is scrollable on mobile too
* add chat and features group to mobile menu
Co-authored-by: Copilot <copilot@github.com>
* Set min length for summary too
* Don't use orange for review item
---------
Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* only send monitoring notifications to users with camera access
* check access to similarity search event id camera
* require admin role for storage usage endpoint
* check camera access for jsmpeg and birdseye cameras
* tests
* formatting
* use ffmpeg to probe rtsp urls instead of cv2
cv2 is faster (no subprocess launch) and will continue to be used for recording segments
* tweak faq
* change unsaved color to orange
avoids confusion with validation errors (red)
* don't use any variant of orange as a profile color
avoids confusion with unsaved changes
* more unsaved color tweaks
* fix: bump OpenVINO to 2025.4.x to resolve LXC container crash
* fix: replace openvino + onnxruntime with onnxruntime-openvino 1.24.*
onnxruntime-openvino 1.24.* bundles OpenVINO 2025.4.1, which fixes a
crash in constrained CPU environments (e.g. Proxmox LXC) where
lin_system_conf.cpp calls stoi("") on empty strings read from offline
CPU sysfs entries.
Consolidating to onnxruntime-openvino also ensures the OpenVINO runtime
and ONNX Runtime OpenVINO EP are always compatible versions.
* revert: restore onnxruntime, keep openvino bump
Reverting onnxruntime-openvino consolidation - onnxruntime is used with
multiple execution providers (CUDA, TensorRT, MIGraphX, CPU) and cannot
be replaced wholesale with the openvino-specific wheel.
* Bump radix-ui packages to align react-dismissable-layer version and fix nested overlay pointer-events bug
* remove workarounds for radix pointer events issues on dropdown and context menus
* remove disablePortal from popover
* remove modal on popovers
* remove workarounds in restart dialog
* keep onCloseAutoFocus for face, classification, and ptz
these are necessary to prevent tooltips from re-showing and from the arrow keys from reopening the ptz presets menu
* add tests
* apply annotation offset to frigate+ submission frame time
* fix broken docs links with hash fragments that resolve wrong on reload
* undo
* use recording snapshot for frigate+ frame submission from VideoControls
rather than a canvas grab/paint, which may not always align with an ffmpeg snapshot due to keyframes
* add more docs links
- display docs link for main sections on collapsible fields
* dialog button consistency
* Initial copy timestamp url implementation
* revise url format
* Implement share timestamp dialog
* Use translations
* Add comments
* Add validations to shared link
* Switch to searchEffect implementation
* Add missing accessibility related dialog description
* Change URL format to unix timestamps
* Remove unnecessary useEffect
* Remove duplicated dialog title
* Fixes/improvements based off PR review comments
* Add missing cancel button & separators to dialog
* Make share description clearer
* Bugfix: guard against showing toasts twice
Because this effect ends up running multiple times
* Clamp future timestamps to now
* Revert "Bugfix: guard against showing toasts twice"
This reverts commit 99fa5e1dee.
* Use normal separator
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* Fixes based off PR review comments
* Bugfix: Share dialog was not receiving the player timestamp after removing key that triggered remounts
* Defer `setRecording` and return true from hook for cleanup
* Remove timeout defer hack in favor of refactored hook
* Attempt to replay video muted on NotAllowedError
* Use separate persistent mute and temporary forced mute states
* Align cancel button with other dialogs
* Prevent wrapping on dialog title
* Remove extra "back" button on mobile drawer
* Fix back navigation when coming from direct shared timestamp links
* Use new timeformat hook
* Simplify dialog radio buttons
* Apply suggestions from code review
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* add log when probing detect stream on startup
when users don't explicitly set detect.width and detect.height, we probe for them. sometimes the probe hangs (camera doesn't support UDP, like some Reolinks), so this log message will make that clearer
* add faq about probing detect stream
* fix stuck activity ring when tracked object transitions to stationary
* drop cache segments past retain cutoff regardless of retention mode
* add maintainer test
* only link to profile settings in status bar for admin users
* use hasFullCameraAccess for group filtering
* add custom export args to record docs
* update recordings docs
* prevent review WS handler from poisoning SWR cache before initial fetch completes
* fix review page spinner not clearing when review item ends
* use last ended review item ID instead of counter
* use separate displayItems memo to overlay end_time updates without re-filtering reviewed items
* backend
* frontend + i18n
* tests + api spec
* tweak backend to use Job infrastructure for exports
* frontend tweaks and Job infrastructure
* tests
* tweaks
- add ability to remove from case
- change location of counts in case card
* add stale export reaper on startup
* fix toaster close button color
* improve add dialog
* formatting
* hide max_concurrent from camera config export settings
* remove border
* refactor batch endpoint for multiple review items
* frontend
* tests and fastapi spec
* fix deletion of in-progress exports in a case
* tweaks
- hide cases when filtering cameras that have no exports from those cameras
- remove description from case card
- use textarea instead of input for case description in add new case dialog
* add auth exceptions for exports
* add e2e test for deleting cases with exports
* refactor delete and case endpoints
allow bulk deleting and reassigning
* frontend
- bulk selection like Review
- gate admin-only actions
- consolidate dialogs
- spacing/padding tweaks
* i18n and tests
* update openapi spec
* tweaks
- add None to case selection list
- allow new case creation from single cam export dialog
* fix codeql
* fix i18n
* remove unused
* fix frontend tests
* fix video playback stutter when GenAI dialog is open in detail stream
Inline `onOpen` callback in DetailStream.tsx:522 creates a new function identity every render. GenAISummaryChip.tsx:98's useEffect depends on [open, onOpen], so it re-fires on every parent re-render while the dialog is open. Each fire calls onSeek -> setCurrentTime -> seekToTimestamp, creating a continuous re-render + seek loop
* add /profiles to EXEMPT_PATHS for non-admin users
* skip debug_replay/status poll for non-admin users
* use subquery for timeline lookup to avoid SQLite variable limit
* Add score fusion helpers for find_similar_objects chat tool
* Add candidate query builder for find_similar_objects chat tool
* register find_similar_objects chat tool definition
* implement _execute_find_similar_objects chat tool dispatcher
* Dispatch find_similar_objects in chat tool executor
* Teach chat system prompt when to use find_similar_objects
* Add i18n strings for find_similar_objects chat tool
* Add frontend extractor for find_similar_objects tool response
* Render anchor badge and similarity scores in chat results
* formatting
* filter similarity results in python, not sqlite-vec
* extract pure chat helpers to chat_util module
* Teach chat system prompt about attached_event marker
* Add parseAttachedEvent and prependAttachment helpers
* Add i18n strings for chat event attachments
* Add ChatAttachmentChip component
* Make chat thumbnails attach to composer on click
* Render attachment chip in user chat bubbles
* Add ChatQuickReplies pill row component
* Add ChatPaperclipButton with event picker popover
* Wire event attachments into chat composer and messages
* add ability to stop streaming
* tweak cursor to appear at the end of the same line of the streaming response
* use abort signal
* add tooltip
* display label and camera on attachment chip
* display area as proper percentage in debug view
* match replay objects list with debug view
* motion search fixes
- tweak progress bar to exclude heatmap and inactive segments
- show metrics immediately on search start
- fix preview frame loading race
- fix polygon missing after dialog remount
- don't try to drag the image when dragging vertex of polygon
* add activity indicator to storage metrics
* make sub label query for events API endpoints case insensitive
* fix mobile export crash by removing stale iOS non-modal drawer workaround
* Remove titlecase to avoid Gemma4 handling plain labels as proper nouns
* Improve titling:
* Make directions more clear
* Properly capitalize delivery services
* update dispatcher config reference on save
* subscribe to review topic so ReviewDescriptionProcessor knows genai is enabled
* auto-send ON genai review WS message when enabled_in_config transitions to true
* remove unused object level
* update docs to clarify pre/post capture settings
* add ui docs links
* improve known_plates field in settings UI
* only show save all when multiple sections are changed
or if the section being changed is not currently being viewed
* fix docs
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* basic e2e frontend test framework
* improve mock data generation and add test cases
* more cases
* add e2e tests to PR template
* don't generate mock data in PR CI
* satisfy codeql check
* fix flaky system page tab tests by guarding against crashes from incomplete mock stats
* reduce local test runs to 4 workers to match CI
* block ffmpeg args in custom exports for non-admin users only
* prune expired reconnect timestamps periodically in watchdog loop
reconnect timestamps were only pruned when a new reconnect
occurred. This meant a single reconnect would persist in the count indefinitely instead of expiring after 1 hour
* formatting
* refresh model dropdown after changing provider or base url
* decouple list_models from provider init
switching providers in the UI left an invalid model in the config, then _init_provider would fail and list_models would return an empty list, making it impossible to select a valid model
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Currently translated at 98.3% (120 of 122 strings)
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Currently translated at 100.0% (123 of 123 strings)
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Currently translated at 100.0% (10 of 10 strings)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: OverTheHillsAndFarAway <prosjektx@users.noreply.hosted.weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-configeditor/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/nb_NO/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/nb_NO/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-configeditor
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Anonymous <noreply@weblate.org>
Co-authored-by: GuoQing Liu <842607283@qq.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/zh_Hans/
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Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/zh_Hans/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Anonymous <noreply@weblate.org>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: N D <n.dubreuil@gmail.com>
Co-authored-by: Riton Du Boulon <henripl37@gmail.com>
Co-authored-by: alorente <gitmaster@passific.fr>
Co-authored-by: shdw <weblate@assez.biz>
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Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-icons/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/fr/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/fr/
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-icons
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Anonymous <noreply@weblate.org>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Marijn <168113859+Marijn0@users.noreply.github.com>
Co-authored-by: Mark Holtkamp <markholtkamp85@gmail.com>
Co-authored-by: Paul Bröerken <broerken@me.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/nl/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/nl/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Eduardo Pastor Fernández <123eduardoneko123@gmail.com>
Co-authored-by: Gerard Ricart Castells <gerard.ricart@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/ca/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: lukasig <lukasig@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/ro/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Currently translated at 72.8% (737 of 1011 strings)
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Currently translated at 70.2% (328 of 467 strings)
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Currently translated at 100.0% (231 of 231 strings)
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Currently translated at 58.8% (464 of 788 strings)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Ninja110292 <ninja110292@users.noreply.hosted.weblate.org>
Co-authored-by: PhillyMay <mein.alias@outlook.com>
Co-authored-by: Sebastian Sie <sebastian.neuplanitz@googlemail.com>
Co-authored-by: jmtatsch <julian@tatsch.it>
Co-authored-by: mvdberge <micha.vordemberge@christmann.info>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/de/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-facelibrary
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* add DictAsYamlField for genai provider and runtime options
* regenerate config translations
* chat tweaks
- add page title
- scroll if near bottom
- add tool call group that dynamically updates as tool calls are made
- add bouncing loading indicator and other UI polish
* tool call grouping
* Switch to a feature-based roles so it is easier to choose models for different tasks
* Fallback and try llama-swap format
* List models supported by provider
* Cleanup
* Add frontend
* Improve model loading
* Make it possible to update genai without restarting
* Cleanup
* Cleanup
* Mypy
* add ability to order subfields with dot notation
* put review genai enabled at the top of the genai subsection
* fix genai summary title truncation issue in detail stream
* add guards to reject missing sub commands
* mask/zone bugfixes
- fix websocket crash when creating a new mask or zone before a name is assigned
- fix deleted masks and zones not disappearing from the list until navigating away
- fix deleting profile override not reverting to the base mask in the list
- fix inertia defaulting to nan
* disable save button on invalid form state
* fix validation for speed estimation
* ensure polygon is closed before allowing save
* require all masks and zones to be on the base config
* clarify dialog message and tooltip when removing an override
* clarify docs
* set edgetpu for multi-instance
* improve error messages when mixing/matching detectors
* allow custom add button text via uiSchema
* clarify language in docs for configuring detectors via the UI
* scrub genai API keys and onvif credentials from config endpoint
* enforce camera access in thumbnail tracked-object fallback
The /events/{id}/thumbnail endpoint called require_camera_access when
loading persisted events but skipped the check in the tracked-object
fallback path for in-progress events. A restricted viewer could
retrieve thumbnails from cameras they should not have access to.
* block filter and attach flags in custom ffmpeg export args
The ffmpeg argument blocklist missed -filter_complex, -lavfi, -vf,
-af, -filter, and -attach. These flags can read arbitrary files via
source filters like movie= and amovie=, bypassing the existing -i
block. A user with camera access could exploit this through the
custom export endpoint.
* enforce camera access on VLM monitor endpoint
POST /vlm/monitor allowed any authenticated user to start VLM
monitoring on any camera without checking camera access. A viewer
restricted to specific cameras could monitor cameras they should
not have access to.
* enforce camera access in chat start_camera_watch tool
The start_camera_watch tool called via POST /chat/completion did not
validate camera access, allowing a restricted viewer to start VLM
monitoring on cameras outside their allowed set through the chat
interface.
* restrict review summary endpoint to admin role
* fix require_role call passing string instead of list
* fix section config uiSchema merge replacing base entries
mergeSectionConfig was replacing the entire base uiSchema when a
level override (global/camera) also defined one, causing base-level
ui:after/ui:before directives to be silently dropped. This broke
the SemanticSearchReindex button which was defined in base uiSchema.
* add generation script
a script to read yaml code blocks from docs markdown files and generate corresponding "Frigate UI" tab instructions based on the json schema, i18n, section configs (hidden fields), and nav mappings
* first pass
* components
* add to gitignore
* second pass
* fix broken anchors
* fixes
* clean up tabs
* version bump
* tweaks
* remove role mapping config from ui
* add validator for detect width and height
require both or neither
* coerce semantic search model string to enum
Built-in model names (jinav1, jinav2) get converted to the enum, genai provider names that don't match stay as plain strings and follow the existing validation path
* formatting
* add config messages to sections and fields
* add alert variants
* add messages to types
* add detect fps, review, and audio messages
* add a basic set of messages
* remove emptySelectionHintKey from switches widget
use the new messages framework and revert the changes made in #22664
* implement hook to return resolved "24hour" | "12hour" string
delegate to existing use24HourTime(), which correctly detects the browser's locale preference via Intl.DateTimeFormat
* update frontend to use use24HourTime(config) or useTimeFormat(config) instead of directly comparing config.ui.time_format
* embed cpu/mem stats into detectors, cameras, and processes
so history consumers don't need the full cpu_usages dict
* support dot-notation for nested keys
to avoid returning large objects when only specific subfields are needed
* fix setLastUpdated being called inside useMemo
this triggered a setState-during-render warning, so moved to a useEffect
* frontend types
* frontend
hide instead of unmount all graphs - re-rendering is much more expensive and disruptive than the amount of dom memory required
keep track of visited tabs to keep them mounted rather than re-mounting or mounting all tabs
add isActive prop to all charts to re-trigger animation when switching metrics tabs
fix chart data padding bug where the loop used number of series rather than number of data points
fix bug where only a shallow copy of the array was used for mutation
fix missing key prop causing console logs
* add isactive after rebase
* formatting
* skip None values in filtered output for dot notation
When mqtt.required_zones is configured, the initial mqtt snapshot on
object creation is always blocked because zone evaluation hasn't run
yet (entered_zones is empty). Later, the snapshot is only re-sent if
a better thumbnail is found, so if the first frame was already the
best capture the snapshot is silently lost.
Add a new_zone_entered flag to TrackedObject that triggers an mqtt
snapshot publish as soon as zone entry is confirmed, closing the gap
between object detection and zone evaluation.
Closesblakeblackshear/frigate#21027
* add review labels widget
* register widget and add to review section
* i18n
* add border to switches widget
* padding tweaks
* don't show audio labels if audio is not enabled
* add docs links
* ability to add custom labels to review
* add hint for empty selection in review labels and SwitchesWidget
* language consistency
* tweak language
* show validation errors in json response
* fix export hwaccel args field in UI
* increase annotation offset consts
* fix save button race conditions, add reset spinner, and fix enrichments profile leak
- Disable both Save and SaveAll buttons while either operation is in progress so users cannot trigger concurrent saves
- Show activity indicator on Reset to Default/Global button during the API call
- Enrichments panes (semantic search, genai, face recognition) now always show base config fields regardless of profile selection in the header dropdown
* fix genai additional_concerns validation error with textarea array widget
The additional_concerns field is list[str] in the backend but was using the textarea widget which produces a string value, causing validation errors.
Created a TextareaArrayWidget that converts between array (one item per line) and textarea display, and switched additional_concerns to use it
* populate and sort global audio filters for all audio labels
* add column labels in profiles view
* enforce a minimum value of 2 for min_initialized
* reuse widget and refactor for multiline
* fix
* change record copy preset to transcode audio to aac
subprocess.run() with preexec_fn forces Python to use fork() instead
of posix_spawn(). In Frigate's main process (75+ threads), fork()
creates a child that inherits locked mutexes from other threads. The
child may deadlocks e.g. on a pysqlite3 mutex before it can exec()
ffmpeg.
Replace preexec_fn=lower_priority (which calls os.nice(19)) with
prefixing the ffmpeg command with "nice -n 19", achieving the same
priority reduction without requiring preexec_fn. This allows Python
to use posix_spawn() which is safe in multithreaded processes.
Fixes both the primary export path and the CPU fallback retry path.
* Mark items as reviewed as a group with keyboard
* Improve handling of half model regions
* update viewport meta tag to prevent user scaling
fixes https://github.com/blakeblackshear/frigate/issues/22017
* add small animation to collapsible shadcn elements
* add proxy auth env var tests
* Improve search effect
* Fix mobile back navigation losing overlay state on classification page
* undo historyBack changes
* fix classification history navigation
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* add randomness to object classification
also ensure train_dir is fresh if user has regenerated examples
* frontend refresh button
* fix radix dropdown issue
* i18n
* mobile button spacing
* prevent console warning about div being descendant of p
* ensure consistent spacing
* add missing i18n keys
* i18n fixes
- add missing translations
- fix dot notation keys
* use plain string
* add missing key
* add i18next-cli commands for extraction and status
also add false positives removal for several keys
* add i18n key check step to PR workflow
* formatting
* fix genai settings ui
- add roles widget to select roles for genai providers
- add dropdown in semantic search to allow selection of embeddings genai provider
* tweak grouping to prioritize fieldOrder before groups
previously, groups were always rendered first. now fieldOrder is respected, and any fields in a group will cause the group and all the fields in that group to be rendered in order. this allows moving the enabled switches to the top of the section
* mobile tweaks
stack buttons, add more space on profiles pane, and move the overridden badge beneath the description
* language consistency
* prevent camera config sections from being regenerated for profiles
* conditionally import axengine module
to match other detectors
* i18n
* update vscode launch.json for new integrated browser
* formatting
* Use different association method
* Clarify
* Remove extra details from ollama schema
* Fix Gemini Chat
* Fix incorrect instructions
* Improve name handling
* Change order of information for llama.cpp
* Simplify prompt
* Fix formatting
* Add go2rtc settings section
- create separate settings section for all go2rtc streams
- extract credentials mask code into util
- create ffmpeg module utility
- i18n
* add camera config updater topic for live section
to support adding go2rtc streams after configuring a new one via the UI
* clean up
* tweak delete button color for consistency
* tweaks
* add CameraProfileConfig model for named config overrides
* add profiles field to CameraConfig
* add active_profile field to FrigateConfig
Runtime-only field excluded from YAML serialization, tracks which
profile is currently active.
* add ProfileManager for profile activation and persistence
Handles snapshotting base configs, applying profile overrides via
deep_merge + apply_section_update, publishing ZMQ updates, and
persisting active profile to /config/.active_profile.
* add profile API endpoints (GET /profiles, GET/PUT /profile)
* add MQTT and dispatcher integration for profiles
- Subscribe to frigate/profile/set MQTT topic
- Publish profile/state and profiles/available on connect
- Add _on_profile_command handler to dispatcher
- Broadcast active profile state on WebSocket connect
* wire ProfileManager into app startup and FastAPI
- Create ProfileManager after dispatcher init
- Restore persisted profile on startup
- Pass dispatcher and profile_manager to FastAPI app
* add tests for invalid profile values and keys
Tests that Pydantic rejects: invalid field values (fps: "not_a_number"),
unknown section keys (ffmpeg in profile), invalid nested values, and
invalid profiles in full config parsing.
* formatting
* fix CameraLiveConfig JSON serialization error on profile activation
refactor _publish_updates to only publish ZMQ updates for
sections that actually changed, not all sections on affected cameras.
* consolidate
* add enabled field to camera profiles for enabling/disabling cameras
* add zones support to camera profiles
* add frontend profile types, color utility, and config save support
* add profile state management and save preview support
* add profileName prop to BaseSection for profile-aware config editing
* add profile section dropdown and wire into camera settings pages
* add per-profile camera enable/disable to Camera Management view
* add profiles summary page with card-based layout and fix backend zone comparison bug
* add active profile badge to settings toolbar
* i18n
* add red dot for any pending changes including profiles
* profile support for mask and zone editor
* fix hidden field validation errors caused by lodash wildcard and schema gaps
lodash unset does not support wildcard (*) segments, so hidden fields like
filters.*.mask were never stripped from form data, leaving null raw_coordinates
that fail RJSF anyOf validation. Add unsetWithWildcard helper and also strip
hidden fields from the JSON schema itself as defense-in-depth.
* add face_recognition and lpr to profile-eligible sections
* move profile dropdown from section panes to settings header
* add profiles enable toggle and improve empty state
* formatting
* tweaks
* tweak colors and switch
* fix profile save diff, masksAndZones delete, and config sync
* ui tweaks
* ensure profile manager gets updated config
* rename profile settings to ui settings
* refactor profilesview and add dots/border colors when overridden
* implement an update_config method for profile manager
* fix mask deletion
* more unique colors
* add top-level profiles config section with friendly names
* implement profile friendly names and improve profile UI
- Add ProfileDefinitionConfig type and profiles field to FrigateConfig
- Use ProfilesApiResponse type with friendly_name support throughout
- Replace Record<string, unknown> with proper JsonObject/JsonValue types
- Add profile creation form matching zone pattern (Zod + NameAndIdFields)
- Add pencil icon for renaming profile friendly names in ProfilesView
- Move Profiles menu item to first under Camera Configuration
- Add activity indicators on save/rename/delete buttons
- Display friendly names in CameraManagementView profile selector
- Fix duplicate colored dots in management profile dropdown
- Fix i18n namespace for overridden base config tooltips
- Move profile override deletion from dropdown trash icon to footer
button with confirmation dialog, matching Reset to Global pattern
- Remove Add Profile from section header dropdown to prevent saving
camera overrides before top-level profile definition exists
- Clean up newProfiles state after API profile deletion
- Refresh profiles SWR cache after saving profile definitions
* remove profile badge in settings and add profiles to main menu
* use icon only on mobile
* change color order
* docs
* show activity indicator on trash icon while deleting a profile
* tweak language
* immediately create profiles on backend instead of deferring to Save All
* hide restart-required fields when editing a profile section
fields that require a restart cannot take effect via profile switching,
so they are merged into hiddenFields when profileName is set
* show active profile indicator in desktop status bar
* fix profile config inheritance bug where Pydantic defaults override base values
The /config API was dumping profile overrides with model_dump() which included
all Pydantic defaults. When the frontend merged these over
the camera's base config, explicitly-set base values were
lost. Now profile overrides are re-dumped with exclude_unset=True so only
user-specified fields are returned.
Also fixes the Save All path generating spurious deletion markers for
restart-required fields that are hidden during profile
editing but not excluded from the raw data sanitization in
prepareSectionSavePayload.
* docs tweaks
* docs tweak
* formatting
* formatting
* fix typing
* fix test pollution
test_maintainer was injecting MagicMock() into sys.modules["frigate.config.camera.updater"] at module load time and never restoring it. When the profile tests later imported CameraConfigUpdateEnum and CameraConfigUpdateTopic from that module, they got mock objects instead of the real dataclass/enum, so equality comparisons always failed
* remove
* fix settings showing profile-merged values when editing base config
When a profile is active, the in-memory config contains effective
(profile-merged) values. The settings UI was displaying these merged
values even when the "Base Config" view was selected.
Backend: snapshot pre-profile base configs in ProfileManager and expose
them via a `base_config` key in the /api/config camera response when a
profile is active. The top-level sections continue to reflect the
effective running config.
Frontend: read from `base_config` when available in BaseSection,
useConfigOverride, useAllCameraOverrides, and prepareSectionSavePayload.
Include formData labels in Object/Audio switches widgets so that labels
added only by a profile override remain visible when editing that profile.
* use rasterized_mask as field
makes it easier to exclude from the schema with exclude=True
prevents leaking of the field when using model_dump for profiles
* fix zones
- Fix zone colors not matching across profiles by falling back to base zone color when profile zone data lacks a color field
- Use base_config for base-layer values in masks/zones view so profile-merged values don't pollute the base config editing view
- Handle zones separately in profile manager snapshot/restore since ZoneConfig requires special serialization (color as private attr, contour generation)
- Inherit base zone color and generate contours for profile zone overrides in profile manager
* formatting
* don't require restart for camera enabled change for profiles
* publish camera state when changing profiles
* formatting
* remove available profiles from mqtt
* improve typing
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Co-authored-by: GuoQing Liu <842607283@qq.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: veberj.mark2c82ae088dda4760 <veberj.mark@gmail.com>
Co-authored-by: 郁闷的太子 <taiziccf@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/zh_Hans/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* fix: operator precedence bug in detection type check
The condition:
topic == DetectionTypeEnum.api.value or DetectionTypeEnum.lpr.value
evaluates as:
(topic == DetectionTypeEnum.api.value) or (DetectionTypeEnum.lpr.value)
Since DetectionTypeEnum.lpr.value is a non-empty string (truthy), the
second operand is always True regardless of topic. The intended check
is whether topic matches either enum value:
topic == DetectionTypeEnum.api.value or topic == DetectionTypeEnum.lpr.value
* fix: apply same or operator fix to review/maintainer.py
Same issue as record/maintainer.py — the condition was always true
because the bare enum value is truthy.
* style: ruff format record/maintainer.py
setVolumeStates was replacing the entire state object instead of
merging, so changing one camera's volume reset all others to default.
Uses the functional update pattern to preserve existing state, matching
how toggleAudio already works.
* fix: check HTTP response status before parsing JSON body
upload_image() calls r.json() before checking r.ok. If the server
returns an error response (401, 500, etc) with a non-JSON body,
this raises a confusing JSONDecodeError instead of the intended
'Unable to get signed urls' error message.
Move the r.ok check before the r.json() call.
* style: remove extra blank line for ruff
The name parameter was interpolated directly into the SQL query via
f-string, allowing SQL injection through crafted face name values.
Use a parameterized query with ? placeholder instead.
parse_preset_input() uses input[len(_user_agent_args) + 1] to find
the FPS placeholder, but preset-http-jpeg-generic does not include
_user_agent_args at the start of its list (only preset-http-mjpeg-generic
does). The FPS placeholder '{}' is at index 1, not index 3.
This means the detect_fps value overwrites '-1' (the stream_loop
argument) instead of the '{}' FPS placeholder, so the preset always
uses the literal string '{}' as the framerate.
When hwaccel_args is a list (not a preset string), the fallback in
parse_preset_hardware_acceleration_encode() calls
arg_map["default"].format(input, output) with only 2 positional args.
But PRESETS_HW_ACCEL_ENCODE_BIRDSEYE["default"] contains {0}, {1}, {2}
expecting ffmpeg_path as the first arg.
This causes IndexError: Replacement index 2 out of range for size 2
which crashes create_config.py on every go2rtc start, taking down
all camera streams.
Pass ffmpeg_path as the first argument to match the preset template.
In BirdsEyeFrameManager.__init__(), the numpy slice that copies the
custom logo (transparent_layer from custom.png alpha channel) onto
blank_frame has shape[0] and shape[1] swapped:
blank_frame[y:y+shape[1], x:x+shape[0]] = transparent_layer
shape[0] is rows (height) and shape[1] is cols (width), so the row
range needs shape[0] and the column range needs shape[1]:
blank_frame[y:y+shape[0], x:x+shape[1]] = transparent_layer
The bug is masked for square images where shape[0]==shape[1]. For
non-square images (e.g. 1920x1080), it produces:
ValueError: could not broadcast input array from shape (1080,1920)
into shape (1620,1080)
This silently kills the birdseye output process -- no frames are
written to the FIFO pipe, go2rtc exec ffmpeg times out, and the
birdseye restream shows a black screen with no errors in the UI.
In both expire_snapshots() and expire_clips(), the expired_events
query uses .iterator() for lazy evaluation, but the very next line
calls list(expired_events) inside an f-string for debug logging.
This consumes the entire iterator, so the subsequent for loop that
deletes media files from disk iterates over an exhausted iterator
and processes zero events.
Snapshots and clips for removed cameras are never deleted from disk,
causing gradual disk space exhaustion.
Materialize the iterator into a list before logging so both the
debug message and the cleanup loop use the same data.
The cosine similarity calculation is guarded by:
if not np.any(np.linalg.norm(velocities, axis=1))
This enters the block when ALL velocity norms are zero, then divides
by those zero norms. The condition should check that all norms are
non-zero before computing cosine similarity:
if np.all(np.linalg.norm(velocities, axis=1))
Also fixes debug log that shows average_velocity[0] for both x and y
velocity components (second should be average_velocity[1]).
The connect() function creates a WebSocket but never stores the
reference. The useEffect cleanup only closes the RTCPeerConnection
via pcRef, leaving the WebSocket open.
Each time the component re-renders with changed deps (camera switch,
playback toggle, microphone toggle), a new WebSocket is created
without closing the previous one. This leaks connections until the
browser garbage-collects them or the server times out.
Store the WebSocket in a ref and close it in the cleanup function.
cv2.imread with IMREAD_UNCHANGED loads the image as-is, but the code
unconditionally indexes channel 3 (birdseye_logo[:, :, 3]) assuming
RGBA format. This crashes with IndexError for:
- Grayscale PNGs (2D array, no channel dimension)
- RGB PNGs without alpha (3 channels, no index 3)
- Fully transparent PNGs saved as grayscale+alpha (2 channels)
Handle all image formats:
- 2D (grayscale): use directly as luminance
- 4+ channels (RGBA): extract alpha channel (existing behavior)
- 3 channels (RGB/BGR): convert to grayscale
Also fixes the shape[0]/shape[1] swap in the array slice that breaks
non-square images (related to #6802, #7863).
In BirdsEyeFrameManager.update(), the exception handler on line 756
resets self.active_cameras to [] (a list), but it is initialized as
set() and compared as a set throughout the rest of the code.
Since set() \!= [] evaluates to True even though both are empty, the
next call to update_frame() will incorrectly detect a layout change
and trigger an unnecessary frame rebuild after every exception.
escape_special_characters() returns a ValueError object instead of
raising it when the input path exceeds 1000 characters. The exception
object gets used as a string downstream instead of triggering error
handling.
When an existing tracked object's label or stationary status changes
(e.g. sub_label assignment from face recognition), the update handler
declares a new const newObjects that shadows the outer let newObjects.
The label and stationary mutations apply to the inner copy, but
handleSetObjects on line 148 reads the outer variable which was never
mutated. The update is silently discarded.
Remove the inner declaration so mutations apply to the outer variable
that gets passed to handleSetObjects.
gpu <= len(self._valid_gpus) should be gpu < len(self._valid_gpus).
The list is zero-indexed, so requesting gpu index equal to the list
length causes an IndexError. For example, with 2 valid GPUs (indices
0 and 1), requesting gpu=2 passes the check (2 <= 2) but
self._valid_gpus[2] is out of bounds.
* fix double scrollbar in debug replay
* always hide ffmpeg cpu warnings for replay cameras
* add slovenian
* fix motion previews on safari and ios
match the logic used in ScrubbablePreview for manually stepping currentTime at the correct rate
* prevent motion recalibration when opening motion tuner
* add shm frame lifetime calculation and update UI for shared memory metrics
* consistent sizing on activity indicator in save buttons
* fix offline overlay overflowing on mobile when in grid mode
asyncio.SubprocessError does not exist — Python's asyncio module has no
such class. The correct exception is subprocess.SubprocessError, which
is available via the existing `import subprocess as sp` alias already
present in this file.
The invalid exception reference causes the except clause to raise a
NameError rather than catching the intended exception.
* refactor websockets to remove react-tracked
react 19 removed useReducer eager bailout, which broke react-tracked.
react-tracked works by wrapping state in a JavaScript Proxy. When a component reads state.someField, the proxy records that access. On the next state update, it compares only the fields each component actually touched and skips re-renders if those fields are unchanged. Under the hood, this relies on useReducer — and in React 18, useReducer had an "eager bail-out" that short-circuited rendering when the new state was === to the old state. React 19 removed that optimization, so every dispatch now schedules a render regardless, and the proxy comparison runs too late to prevent it.
useSyncExternalStore is a React primitive (added in 18, stable in 19) designed for exactly this pattern: subscribing to an external store:
useSyncExternalStore(
subscribe, // (listener) => unsubscribe — called when the store changes
getSnapshot // () => value — returns the current value for this subscriber
)
React calls getSnapshot during render and compares the result with Object.is. If the value is the same reference, the component bails out — no re-render. The key difference from react-tracked is that this bail-out is built into React's reconciler, not bolted on via proxy tricks and useReducer.
The per-topic subscription model makes this efficient. Instead of one global store where every subscriber has to check if their fields changed, each useWs("some/topic", ...) call subscribes only to that topic's listener set. When a message arrives for front_door/detect/state, only components subscribed to that exact topic get their listener fired → React calls their getSnapshot → Object.is compares the value → bail-out if unchanged. Components watching back_yard/detect/state are never even notified.
* remove react-tracked and react-use-websocket
* refactor usePolygonStates to use ws topic subscription
* fix TimeAgo refresh interval always returning 1s due to unit mismatch (seconds vs milliseconds)
older events now correctly refresh every minute/hour instead of every second
* simplify
* clean up
* don't resend onconnect
* clean up
* remove patch
* Improve title to better capture activity
* Improve efficiency of prompt
* Use json format for llama.cpp
* Cleanup prompt
* Add output format for other LLMs
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Updated by "Squash Git commits" add-on in Weblate.
Added translation using Weblate (Spanish)
Added translation using Weblate (Spanish)
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Co-authored-by: Gerard Ricart Castells <gerard.ricart@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Jorge Sandi <jorensanbar+weblate@gmail.com>
Co-authored-by: Languages add-on <noreply-addon-languages@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/es/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/es/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Co-authored-by: Eduardo Pastor Fernández <123eduardoneko123@gmail.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Josh Hawkins <joshhawk2003@yahoo.com>
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Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/objects/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-classificationmodel/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/ca/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/ca/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/objects
Translation: Frigate NVR/views-classificationmodel
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Updated by "Squash Git commits" add-on in Weblate.
Added translation using Weblate (Romanian)
Added translation using Weblate (Romanian)
Added translation using Weblate (Romanian)
Added translation using Weblate (Romanian)
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Co-authored-by: Hosted Weblate <hosted@weblate.org>
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Co-authored-by: lukasig <lukasig@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-camera/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-filter/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-events/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-explore/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/ro/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-live/ro/
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Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/components-filter
Translation: Frigate NVR/views-events
Translation: Frigate NVR/views-explore
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-live
Translation: Frigate NVR/views-search
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
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Update translation files
Updated by "Squash Git commits" add-on in Weblate.
Added translation using Weblate (German)
Added translation using Weblate (German)
Added translation using Weblate (German)
Added translation using Weblate (German)
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Co-authored-by: Languages add-on <noreply-addon-languages@weblate.org>
Co-authored-by: Sebastian Sie <sebastian.neuplanitz@googlemail.com>
Co-authored-by: maz <matthi.hrbek@outlook.com>
Co-authored-by: redrekort <redrekort.wold@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/components-dialog/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-cameras/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-global/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-groups/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/config-validation/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-exports/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/de/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-system/de/
Translation: Frigate NVR/Config - Cameras
Translation: Frigate NVR/Config - Global
Translation: Frigate NVR/Config - Groups
Translation: Frigate NVR/Config - Validation
Translation: Frigate NVR/common
Translation: Frigate NVR/components-dialog
Translation: Frigate NVR/views-exports
Translation: Frigate NVR/views-settings
Translation: Frigate NVR/views-system
* optimize recordings/summary endpoint db query
replace strftime with integer arithmetic. increases speed by about 6x, especially noticeable for installs with long retention days
* optimize calendar rendering with Set lookups and remove unnecessary remount key
The old code built Date[] arrays with a TZDate object for every day in recording history (365+ timezone-aware date constructions). react-day-picker then did O(visible × history) date comparisons to match each of the displayed days against these arrays. Now we build Set<string> from the raw keys (zero date construction), and pass matcher functions that do O(1) Set.has() lookups. react-day-picker only calls these for visible days
* clean up
* enrichment updater and enum
* update_config stubs
* config updaters in enrichments
* update maintainer
* formatting
* simplify enrichment config updates to use single subscriber with topic-based routing
* add optional field widget
adds a switch to enable nullable fields like skip_motion_threshold
* config field updates
add skip_motion_threshold optional switch
add fps back to detect restart required
* don't use ternary operator when displaying motion previews
the main previews were being unnecessarily unmounted
* lazy mount motion preview clips to reduce DOM overhead
* Support GenAI for embeddings
* Add embed API support
* Add support for embedding via genai
* Basic docs
* undo
* Fix sending images
* Don't require download check
* Set model
* Handle emb correctly
* Clarification
* Cleanup
* Cleanup
* keep nav buttons visible
nav buttons would be hidden when closing and reopening dialog after selecting the tracking details pane
* better ux in tracking details
actually pause the video and seek when annotation offset changes to make it easier to visually line up the bounding box
* improve detail stream ux
* update dummy camera docs
* fix docs link
apply length and format filters to the clustered representative plate rather than individual OCR readings, so noisy variants still contribute to clustering even when they don't pass on their own
* face recognition dynamic config
* lpr dynamic config
* safe changes for birdseye dynamic config
* bird classification dynamic config
* always assign new config to stats emitter to make telemetry fields dynamic
* add wildcard support for camera config updates in config_set
* update restart required fields for global sections
* add test
* fix rebase issue
* collapsible settings sidebar
use the preexisting control available with shadcn's sidebar (cmd/ctrl-B) to give users more space to set masks/zones on smaller screens
* dynamic ffmpeg
* ensure previews dir exists
when ffmpeg processes restart, there's a brief window where the preview frame generation pipeline is torn down and restarted. before these changes, ffmpeg only restarted on crash/stall recovery or full Frigate restart. Now that ffmpeg restarts happen on-demand via config changes, there's a higher chance a frontend request hits the preview_mp4 or preview_gif endpoints during that brief restart window when the directory might not exist yet. The existing os.listdir() call would throw FileNotFoundError without a directory existence check. this fix just checks if the directory exists and returns 404 if not, exactly how preview_thumbnail already handles the same scenario a few lines below
* global ffmpeg section
* clean up
* tweak
* fix test
* fix useImageLoaded hook running on every render
* fix volume not applying for all cameras
* Fix maximum update depth exceeded errors on Review page
- use-overlay-state: use refs for location to keep setter identity
stable across renders, preventing cascading re-render loops when
effects depend on the setter. Add Object.is bail-out guard to skip
redundant navigate calls. Move setPersistedValue after bail-out to
avoid unnecessary IndexedDB writes.
* don't try to fetch previews when motion search dialog is open
* revert unneeded changes
re-rendering was caused by the overlay state hook, not this one
* filter dicts to only use id field in sync recordings
* fix ollama chat tool calling
handle dict arguments, streaming fallback, and message format
* pin setuptools<81 to ensure pkg_resources remains available
When ensure_torch_dependencies() installs torch/torchvision via pip, it can upgrade setuptools to >=81.0.0, which removed the pkg_resources module. rknn-toolkit2 depends on pkg_resources internally, so subsequent RKNN conversion fails with No module named 'pkg_resources'.
* remove unused RecoilRoot and fix implicit ref callback
Remove the vestigial recoil dependency (zero consumers) and convert
the implicit-return ref callback in SearchView to block form to
prevent React 19 interpreting it as a cleanup function.
* replace react-transition-group with framer-motion in Chip
Replace CSSTransition with framer-motion AnimatePresence + motion.div
for React 19 compatibility (react-transition-group uses findDOMNode).
framer-motion is already a project dependency.
* migrate react-grid-layout v1 to v2
- Replace WidthProvider(Responsive) HOC with useContainerWidth hook
- Update types: Layout (single item) → LayoutItem, Layout[] → Layout
- Replace isDraggable/isResizable/resizeHandles with dragConfig/resizeConfig
- Update EventCallback signature for v2 API
- Remove @types/react-grid-layout (v2 includes its own types)
* upgrade vaul, next-themes, framer-motion, react-zoom-pan-pinch
- vaul: ^0.9.1 → ^1.1.2
- next-themes: ^0.3.0 → ^0.4.6
- framer-motion: ^11.5.4 → ^12.35.0 (React 19 native support)
- react-zoom-pan-pinch: 3.4.4 → latest
* upgrade to React 19, react-konva v19, eslint-plugin-react-hooks v5
Core React 19 upgrade with all necessary type fixes:
- Update RefObject types to accept T | null (React 19 refs always nullable)
- Add JSX namespace imports (no longer global in React 19)
- Add initial values to useRef calls (required in React 19)
- Fix ReactElement.props unknown type in config-form components
- Fix IconWrapper interface to use HTMLAttributes instead of index signature
- Add monaco-editor as dev dependency for type declarations
- Upgrade react-konva to v19, eslint-plugin-react-hooks to v5
* upgrade typescript to 5.9.3
* modernize Context.Provider to React 19 shorthand
Replace <Context.Provider value={...}> with <Context value={...}>
across all project-owned context providers. External library contexts
(react-icons IconContext, radix TooltipPrimitive) left unchanged.
* add runtime patches for React 19 compatibility
- Patch @radix-ui/react-compose-refs@1.1.2: stabilize useComposedRefs
to prevent infinite render loops from unstable ref callbacks
https://github.com/radix-ui/primitives/issues/3799
- Patch @radix-ui/react-slot@1.2.4: use useComposedRefs hook in
SlotClone instead of inline composeRefs to prevent re-render cycles
https://github.com/radix-ui/primitives/pull/3804
- Patch react-use-websocket@4.8.1: remove flushSync wrappers that
cause "Maximum update depth exceeded" with React 19 auto-batching
https://github.com/facebook/react/issues/27613
- Add npm overrides to ensure single hoisted copies of compose-refs
and react-slot across all Radix packages
- Add postinstall script for patch-package
- Remove leftover react-transition-group dependency
* formatting
* use availableWidth instead of useContainerWidth for grid layout
The useContainerWidth hook from react-grid-layout v2 returns raw
container width without accounting for scrollbar width, causing the
grid to not fill the full available space. Use the existing
availableWidth value from useResizeObserver which already compensates
for scrollbar width, matching the working implementation.
* remove unused carousel component and fix React 19 peer deps
Remove embla-carousel-react and its unused Carousel UI component.
Upgrade sonner v1 → v2 for native React 19 support. Remove
@types/react-icons stub (react-icons bundles its own types).
These changes eliminate all peer dependency conflicts, so
npm install works without --legacy-peer-deps.
* fix React 19 infinite re-render loop on live dashboard
The "Maximum update depth exceeded" error was caused by two issues:
1. useDeferredStreamMetadata returned a new `{}` default on every render
when SWR data was undefined, creating an unstable reference that
triggered the useEffect in useCameraLiveMode on every render cycle.
Fixed by using a stable module-level EMPTY_METADATA constant.
2. useResizeObserver's rest parameter `...refs` created a new array on
every render, causing its useEffect to re-run and re-observe elements
continuously. Fixed by stabilizing refs with useRef and only
reconnecting the observer when actual DOM elements change.
* debug replay implementation
* fix masks after dev rebase
* fix squash merge issues
* fix
* fix
* fix
* no need to write debug replay camera to config
* camera and filter button and dropdown
* add filters
* add ability to edit motion and object config for debug replay
* add debug draw overlay to debug replay
* add guard to prevent crash when camera is no longer in camera_states
* fix overflow due to radix absolutely positioned elements
* increase number of messages
* ensure deep_merge replaces existing list values when override is true
* add back button
* add debug replay to explore and review menus
* clean up
* clean up
* update instructions to prevent exposing exception info
* fix typing
* refactor output logic
* refactor with helper function
* move init to function for consistency
Cameras that have `ui.dashboard = false` config are hidden from
the All Cameras "default" group, but their alerts still appear in the
top row. This hides the alerts as well.
One can still view the hidden cameras and their alerts by making a
custom camera group.
* migrator and runtime config changes
* component changes to use rasterized_mask
* frontend
* convert none to empty string for config save
* i18n
* update tests
* add enabled config to zones
* zones frontend
* i18n
* docs
* tweaks
* use dashed stroke to indicate disabled
* allow toggle from icon
* use filelock to ensure atomic config updates from endpoint
* enforce atomic config update in the frontend
* toggle via mqtt
* fix global object masks
* correctly handle global object masks in dispatcher
* ws hooks
* render masks and zones based on ws enabled state
* use enabled_in_config for zones and masks
* frontend for enabled_in_config
* tweaks
* i18n
* publish websocket on config save
* i18n tweaks
* pydantic title and description
* i18n generation
* tweaks
* fix typing
* use react-jsonschema-form for UI config
* don't use properties wrapper when generating config i18n json
* configure for full i18n support
* section fields
* add descriptions to all fields for i18n
* motion i18n
* fix nullable fields
* sanitize internal fields
* add switches widgets and use friendly names
* fix nullable schema entries
* ensure update_topic is added to api calls
this needs further backend implementation to work correctly
* add global sections, camera config overrides, and reset button
* i18n
* add reset logic to global config view
* tweaks
* fix sections and live validation
* fix validation for schema objects that can be null
* generic and custom per-field validation
* improve generic error validation messages
* remove show advanced fields switch
* tweaks
* use shadcn theme
* fix array field template
* i18n tweaks
* remove collapsible around root section
* deep merge schema for advanced fields
* add array field item template and fix ffmpeg section
* add missing i18n keys
* tweaks
* comment out api call for testing
* add config groups as a separate i18n namespace
* add descriptions to all pydantic fields
* make titles more concise
* new titles as i18n
* update i18n config generation script to use json schema
* tweaks
* tweaks
* rebase
* clean up
* form tweaks
* add wildcards and fix object filter fields
* add field template for additionalproperties schema objects
* improve typing
* add section description from schema and clarify global vs camera level descriptions
* separate and consolidate global and camera i18n namespaces
* clean up now obsolete namespaces
* tweaks
* refactor sections and overrides
* add ability to render components before and after fields
* fix titles
* chore(sections): remove legacy single-section components replaced by template
* refactor configs to use individual files with a template
* fix review description
* apply hidden fields after ui schema
* move util
* remove unused i18n
* clean up error messages
* fix fast refresh
* add custom validation and use it for ffmpeg input roles
* update nav tree
* remove unused
* re-add override and modified indicators
* mark pending changes and add confirmation dialog for resets
* fix red unsaved dot
* tweaks
* add docs links, readonly keys, and restart required per field
* add special case and comments for global motion section
* add section form special cases
* combine review sections
* tweaks
* add audio labels endpoint
* add audio label switches and input to filter list
* fix type
* remove key from config when resetting to default/global
* don't show description for new key/val fields
* tweaks
* spacing tweaks
* add activity indicator and scrollbar tweaks
* add docs to filter fields
* wording changes
* fix global ffmpeg section
* add review classification zones to review form
* add backend endpoint and frontend widget for ffmpeg presets and manual args
* improve wording
* hide descriptions for additional properties arrays
* add warning log about incorrectly nested model config
* spacing and language tweaks
* fix i18n keys
* networking section docs and description
* small wording tweaks
* add layout grid field
* refactor with shared utilities
* field order
* add individual detectors to schema
add detector titles and descriptions (docstrings in pydantic are used for descriptions) and add i18n keys to globals
* clean up detectors section and i18n
* don't save model config back to yaml when saving detectors
* add full detectors config to api model dump
works around the way we use detector plugins so we can have the full detector config for the frontend
* add restart button to toast when restart is required
* add ui option to remove inner cards
* fix buttons
* section tweaks
* don't zoom into text on mobile
* make buttons sticky at bottom of sections
* small tweaks
* highlight label of changed fields
* add null to enum list when unwrapping
* refactor to shared utils and add save all button
* add undo all button
* add RJSF to dictionary
* consolidate utils
* preserve form data when changing cameras
* add mono fonts
* add popover to show what fields will be saved
* fix mobile menu not re-rendering with unsaved dots
* tweaks
* fix logger and env vars config section saving
use escaped periods in keys to retain them in the config file (eg "frigate.embeddings")
* add timezone widget
* role map field with validation
* fix validation for model section
* add another hidden field
* add footer message for required restart
* use rjsf for notifications view
* fix config saving
* add replace rules field
* default column layout and add field sizing
* clean up field template
* refactor profile settings to match rjsf forms
* tweaks
* refactor frigate+ view and make tweaks to sections
* show frigate+ model info in detection model settings when using a frigate+ model
* update restartRequired for all fields
* fix restart fields
* tweaks and add ability enable disabled cameras
more backend changes required
* require restart when enabling camera that is disabled in config
* disable save when form is invalid
* refactor ffmpeg section for readability
* change label
* clean up camera inputs fields
* misc tweaks to ffmpeg section
- add raw paths endpoint to ensure credentials get saved
- restart required tooltip
* maintenance settings tweaks
* don't mutate with lodash
* fix description re-rendering for nullable object fields
* hide reindex field
* update rjsf
* add frigate+ description to settings pane
* disable save all when any section is invalid
* show translated field name in validation error pane
* clean up
* remove unused
* fix genai merge
* fix genai
* GenAI client manager
* Add config migration
* Convert to roles list
* Support getting client via manager
* Cleanup
* Fix import issues
* Set model in llama.cpp config
* Clenaup
* Use config update
* Clenaup
* Add new title and desc
The fallback to tensorflow was established back in 2023, because we could
not provide tflite-runtime downstream in nixpkgs.
By now we have ai-edge-litert available, which is the successor to the
tflite-runtime. It still provides the same entrypoints as tflite-runtime
and functionality has been verified in multiple deployments for the last
two weeks.
The psutil library reads the process commandline as by opening
/proc/pid/cmdline which returns a buffer that is larger than just the
program cmdline due to rounded memory allocation sizes.
That means that if the library does not detect a Null-terminated string
it keeps appending empty strings which add up as whitespaces when joined.
* - API created events will be alerts OR detections, depending on the event label, defaulting to alerts
- Indefinite API events will extend the recording segment until those events are ended
- API event start time is the actual start time, instead of having a pre-buffer of record.event_pre_capture
* Instead of checking for indefinite events on a camera before deciding if we should end the segment, only update last_detection_time and last_alert_time if frame_time is greater, which should have the same effect
* Add the ability to set a pre_capture number of seconds when creating a manual event via the API. Default behavior unchanged
* Remove unnecessary _publish_segment_start() call
* Formatting
* handle last_alert_time or last_detection_time being None when checking them against the frame_time
* comment manual_info["label"].split(": ")[0] for clarity
* improve jsmpeg player websocket handling
prevent websocket console messages from appearing when player is destroyed
* reformat files after ruff upgrade
* use latest preview frame for latest image when camera is offline
* remove frame extraction logic
* tests
* frontend
* add description to api endpoint
The original implementation did a full directory tree walk to find and remove
empty directories, so this implementation should remove the parents as well,
like the original did.
The previous empty directory cleanup did a full recursive directory
walk, which can be extremely slow. This new implementation only removes
directories which have a chance of being empty due to a recent file
deletion.
* generic job infrastructure
* types and dispatcher changes for jobs
* save data in memory only for completed jobs
* implement media sync job and endpoints
* change logs to debug
* websocket hook and types
* frontend
* i18n
* docs tweaks
* endpoint descriptions
* tweak docs
* Add rockchip temps
* Add support for GPU and NPU temperatures in the frontend
* Add support for Nvidia temperature
* Improve separation
* Adjust graph scaling
* added hwaccel_args to camera.record.export config struct
* populate camera.record.export.hwaccel_args with a cascade up to camera then global if 'auto'
* use new hwaccel args in export
* added documentation for camera-specific hwaccel export
* fix c/p error
* missed an import
* fleshed out the docs and comments a bit
* ruff lint
* separated out the tips in the doc
* fix documentation
* fix and simplify reference config doc
* Add Hailo temperature retrieval
* Refactor `get_hailo_temps()` to use ctxmanager
* Show Hailo temps in system UI
* Move hailo_platform import to get_hailo_temps
* Refactor temperatures calculations to use within detector block
* Adjust webUI to handle new location
---------
Co-authored-by: tigattack <10629864+tigattack@users.noreply.github.com>
* add camera connection quality metrics and indicator
* formatting
* move stall calcs to watchdog
* clean up
* change watchdog to 1s and separately track time for ffmpeg retry_interval
* implement status caching to reduce message volume
* Refactor export cards to match existing cards in other UI pages
* Show cases separately from exports
* Add proper filtering and display of cases
* Add ability to edit and select cases for exports
* Cleanup typing
* Hide if no unassigned
* Cleanup hiding logic
* fix scrolling
* Improve layout
* Update version
* Create scaffolding for case management (#21293)
* implement case management for export apis (#21295)
* refactor vainfo to search for first GPU (#21296)
use existing LibvaGpuSelector to pick appropritate libva device
* Case management UI (#21299)
* Refactor export cards to match existing cards in other UI pages
* Show cases separately from exports
* Add proper filtering and display of cases
* Add ability to edit and select cases for exports
* Cleanup typing
* Hide if no unassigned
* Cleanup hiding logic
* fix scrolling
* Improve layout
* Camera connection quality indicator (#21297)
* add camera connection quality metrics and indicator
* formatting
* move stall calcs to watchdog
* clean up
* change watchdog to 1s and separately track time for ffmpeg retry_interval
* implement status caching to reduce message volume
* Export filter UI (#21322)
* Get started on export filters
* implement basic filter
* Implement filtering and adjust api
* Improve filter handling
* Improve navigation
* Cleanup
* handle scrolling
* Refactor temperature reporting for detectors and implement Hailo temp reading (#21395)
* Add Hailo temperature retrieval
* Refactor `get_hailo_temps()` to use ctxmanager
* Show Hailo temps in system UI
* Move hailo_platform import to get_hailo_temps
* Refactor temperatures calculations to use within detector block
* Adjust webUI to handle new location
---------
Co-authored-by: tigattack <10629864+tigattack@users.noreply.github.com>
* Camera-specific hwaccel settings for timelapse exports (correct base) (#21386)
* added hwaccel_args to camera.record.export config struct
* populate camera.record.export.hwaccel_args with a cascade up to camera then global if 'auto'
* use new hwaccel args in export
* added documentation for camera-specific hwaccel export
* fix c/p error
* missed an import
* fleshed out the docs and comments a bit
* ruff lint
* separated out the tips in the doc
* fix documentation
* fix and simplify reference config doc
* Add support for GPU and NPU temperatures (#21495)
* Add rockchip temps
* Add support for GPU and NPU temperatures in the frontend
* Add support for Nvidia temperature
* Improve separation
* Adjust graph scaling
* Exports Improvements (#21521)
* Add images to case folder view
* Add ability to select case in export dialog
* Add to mobile review too
* Add API to handle deleting recordings (#21520)
* Add recording delete API
* Re-organize recordings apis
* Fix import
* Consolidate query types
* Add media sync API endpoint (#21526)
* add media cleanup functions
* add endpoint
* remove scheduled sync recordings from cleanup
* move to utils dir
* tweak import
* remove sync_recordings and add config migrator
* remove sync_recordings
* docs
* remove key
* clean up docs
* docs fix
* docs tweak
* Media sync API refactor and UI (#21542)
* generic job infrastructure
* types and dispatcher changes for jobs
* save data in memory only for completed jobs
* implement media sync job and endpoints
* change logs to debug
* websocket hook and types
* frontend
* i18n
* docs tweaks
* endpoint descriptions
* tweak docs
* use same logging pattern in sync_recordings as the other sync functions (#21625)
* Fix incorrect counting in sync_recordings (#21626)
* Update go2rtc to v1.9.13 (#21648)
Co-authored-by: Eugeny Tulupov <eugeny.tulupov@spirent.com>
* Refactor Time-Lapse Export (#21668)
* refactor time lapse creation to be a separate API call with ability to pass arbitrary ffmpeg args
* Add CPU fallback
* Optimize empty directory cleanup for recordings (#21695)
The previous empty directory cleanup did a full recursive directory
walk, which can be extremely slow. This new implementation only removes
directories which have a chance of being empty due to a recent file
deletion.
* Implement llama.cpp GenAI Provider (#21690)
* Implement llama.cpp GenAI Provider
* Add docs
* Update links
* Fix broken mqtt links
* Fix more broken anchors
* Remove parents in remove_empty_directories (#21726)
The original implementation did a full directory tree walk to find and remove
empty directories, so this implementation should remove the parents as well,
like the original did.
* Implement LLM Chat API with tool calling support (#21731)
* Implement initial tools definiton APIs
* Add initial chat completion API with tool support
* Implement other providers
* Cleanup
* Offline preview image (#21752)
* use latest preview frame for latest image when camera is offline
* remove frame extraction logic
* tests
* frontend
* add description to api endpoint
* Update to ROCm 7.2.0 (#21753)
* Update to ROCm 7.2.0
* ROCm now works properly with JinaV1
* Arcface has compilation error
* Add live context tool to LLM (#21754)
* Add live context tool
* Improve handling of images in request
* Improve prompt caching
* Add networking options for configuring listening ports (#21779)
* feat: add X-Frame-Time when returning snapshot (#21932)
Co-authored-by: Florent MORICONI <170678386+fmcloudconsulting@users.noreply.github.com>
* Improve jsmpeg player websocket handling (#21943)
* improve jsmpeg player websocket handling
prevent websocket console messages from appearing when player is destroyed
* reformat files after ruff upgrade
* Allow API Events to be Detections or Alerts, depending on the Event Label (#21923)
* - API created events will be alerts OR detections, depending on the event label, defaulting to alerts
- Indefinite API events will extend the recording segment until those events are ended
- API event start time is the actual start time, instead of having a pre-buffer of record.event_pre_capture
* Instead of checking for indefinite events on a camera before deciding if we should end the segment, only update last_detection_time and last_alert_time if frame_time is greater, which should have the same effect
* Add the ability to set a pre_capture number of seconds when creating a manual event via the API. Default behavior unchanged
* Remove unnecessary _publish_segment_start() call
* Formatting
* handle last_alert_time or last_detection_time being None when checking them against the frame_time
* comment manual_info["label"].split(": ")[0] for clarity
* ffmpeg Preview Segment Optimization for "high" and "very_high" (#21996)
* Introduce qmax parameter for ffmpeg preview encoding
Added PREVIEW_QMAX_PARAM to control ffmpeg encoding quality.
* formatting
* Fix spacing in qmax parameters for preview quality
* Adapt to new Gemini format
* Fix frame time access
* Remove exceptions
* Cleanup
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
Co-authored-by: tigattack <10629864+tigattack@users.noreply.github.com>
Co-authored-by: Andrew Roberts <adroberts@gmail.com>
Co-authored-by: Eugeny Tulupov <zhekka3@gmail.com>
Co-authored-by: Eugeny Tulupov <eugeny.tulupov@spirent.com>
Co-authored-by: John Shaw <1753078+johnshaw@users.noreply.github.com>
Co-authored-by: Eric Work <work.eric@gmail.com>
Co-authored-by: FL42 <46161216+fl42@users.noreply.github.com>
Co-authored-by: Florent MORICONI <170678386+fmcloudconsulting@users.noreply.github.com>
Co-authored-by: nulledy <254504350+nulledy@users.noreply.github.com>
2026-02-26 21:16:10 -07:00
2088 changed files with 250467 additions and 29718 deletions
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
description:Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.17.0-beta1)
placeholder:"0.17.0-beta1"
description:Visible on the System Metrics page in the Web UI. Please include the full version including the build identifier (eg. 0.18.0-beta1, 0.18.0-8b72c7a, etc.)
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
**If you are looking for support, start a new discussion and use a support category.**
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
Use this form to submit a reproducible bug in Frigate or Frigate's UI.
**⚠️ If you are running a beta version (0.17.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
**⚠️ If you are running a beta version (0.18.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
**If you are unsure if your issue is actually a bug or not, please submit a support request first.**
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
## Project Overview
Frigate NVR is a realtime object detection system for IP cameras that uses:
- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
- **Frontend**: React with TypeScript, Vite, TailwindCSS
- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
- **Focus**: Minimal resource usage with maximum performance
## Code Review Guidelines
When reviewing code, do NOT comment on:
- Missing imports - Static analysis tooling catches these
_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) and the [AI policy](https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md) before submitting a PR. Every PR must be read and submitted by a person, and PRs that appear to be unreviewed AI output will be closed without review._
## Proposed change
<!--
Thank you!
Describe what this pull request does and how it will benefit users of Frigate.
Please describe in detail any considerations, breaking changes, etc.
If you're introducing a new feature or significantly refactoring existing functionality,
we encourage you to start a discussion first. This helps ensure your idea aligns with
Frigate's development goals.
Describe what this pull request does and how it will benefit users of Frigate.
Please describe in detail any considerations, breaking changes, etc. that are
made in this pull request.
-->
## Type of change
- [ ] Dependency upgrade
@@ -25,6 +26,45 @@
- This PR fixes or closes issue: fixes #
- This PR is related to issue:
- Link to discussion with maintainers (**required** for any large or "planned" features):
## For new features
<!--
Every new feature adds scope that maintainers must test, maintain, and support long-term.
We try to be thoughtful about what we take on, and sometimes that means saying no to
good code if the feature isn't the right fit — or saying yes to something we weren't sure
about. These calls are sometimes subjective, and we won't always get them right. We're
happy to discuss and reconsider.
Linking to an existing feature request or discussion with community interest helps us
understand demand, but a great idea is a great idea even without a crowd behind it.
You can delete this section for bugfixes and non-feature changes.
-->
- [ ] There is an existing feature request or discussion with community interest for this change.
- Link:
## AI disclosure
<!--
We welcome contributions that use AI tools, but we need to understand your relationship
with the code you're submitting. See our AI usage policy in CONTRIBUTING.md for details.
Be honest — this won't disqualify your PR. Trust matters more than method.
-->
- [ ] No AI tools were used in this PR.
- [ ] AI tools were used in this PR. Details below:
'This PR was automatically closed because the description does not follow the [pull request template](https://github.com/blakeblackshear/frigate/blob/dev/.github/pull_request_template.md).',
'',
'**Issues found:**',
...errors.map((e) => `- ${e}`),
'',
'Please update your PR description to include all required sections from the template, then reopen this PR.',
'',
'> If you used an AI tool to generate this PR, please see our [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) for details.',
].join('\n');
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: prNumber,
body: message,
});
await github.rest.pulls.update({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: prNumber,
state: 'closed',
});
core.setFailed('PR description does not follow the template.');
This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
## Project Overview
Frigate NVR is a realtime object detection system for IP cameras that uses:
- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
- **Frontend**: React with TypeScript, Vite, TailwindCSS
- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
- **Focus**: Minimal resource usage with maximum performance
## Code Review Guidelines
When reviewing code, do NOT comment on:
- Missing imports - Static analysis tooling catches these
- Minor style inconsistencies already enforced by linters
## Python Backend Standards
### Python Requirements
- **Compatibility**: Python 3.13+
- **Language Features**: Use modern Python features:
- Pattern matching
- Type hints (comprehensive typing preferred)
- f-strings (preferred over `%` or `.format()`)
- Dataclasses
- Async/await patterns
### Code Quality Standards
- **Formatting**: Ruff (configured in `pyproject.toml`)
- **Linting**: Ruff with rules defined in project config
- **Type Checking**: Use type hints consistently
- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
- **Language**: American English for all code, comments, and documentation
- **Punctuation**: Do not use em dashes in documentation, comments, or strings; reword with standard punctuation (commas, colons, parentheses, or separate sentences)
### Logging Standards
- **Logger Pattern**: Use module-level logger
```python
import logging
logger = logging.getLogger(__name__)
```
- **Format Guidelines**:
- No periods at end of log messages
- No sensitive data (keys, tokens, passwords)
- Use lazy logging: `logger.debug("Message with %s", variable)`
- **Log Levels**:
- `debug`: Development and troubleshooting information
- `info`: Important runtime events (startup, shutdown, state changes)
- `warning`: Recoverable issues that should be addressed
- `error`: Errors that affect functionality but don't crash the app
- `exception`: Use in except blocks to include traceback
### Error Handling
- **Exception Types**: Choose most specific exception available
- **Try/Catch Best Practices**:
- Only wrap code that can throw exceptions
- Keep try blocks minimal - process data after the try/except
- Avoid bare exceptions except in background tasks
Bad pattern:
```python
try:
data = await device.get_data() # Can throw
# ❌ Don't process data inside try block
processed = data.get("value", 0) * 100
result = processed
except DeviceError:
logger.error("Failed to get data")
```
Good pattern:
```python
try:
data = await device.get_data() # Can throw
except DeviceError:
logger.error("Failed to get data")
return
# ✅ Process data outside try block
processed = data.get("value", 0) * 100
result = processed
```
### Async Programming
- **External I/O**: All external I/O operations must be async
- **Best Practices**:
- Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
- Avoid awaiting in loops - use `asyncio.gather()` instead
- No blocking calls in async functions
- Use `asyncio.create_task()` for background operations
- **Thread Safety**: Use proper synchronization for shared state
### Documentation Standards
- **Module Docstrings**: Concise descriptions at top of files
```python
"""Utilities for motion detection and analysis."""
```
- **Function Docstrings**: Required for public functions and methods
# Regenerate config translations from Pydantic models — outputs to
# web/public/locales/en/config/{global,cameras}.json. NEVER edit those
# JSON files by hand; change the Pydantic field title/description and
# re-run this script. (from repo root)
python3 generate_config_translations.py
# Extract i18n keys from source into the locale files after adding
# new t() calls. Use the :ci variant to verify the locale files are
# in sync with source (fails if extraction would change anything).
npm run i18n:extract
npm run i18n:extract:ci
```
### Docker Development
AI agents should never run these commands directly unless instructed.
```bash
# Build local image
make local
# Build debug image
make debug
```
## Common Patterns
### API Endpoint Pattern
```python
from fastapi import APIRouter, Request
from frigate.api.defs.tags import Tags
router = APIRouter(tags=[Tags.Events])
@router.get("/events")
async def get_events(request: Request, limit: int = 100):
"""Retrieve events from the database."""
# Implementation
```
After adding, changing, or removing an endpoint (or its auth dependency), regenerate the OpenAPI spec with `python3 generate_api_auth_spec.py` so `docs/static/frigate-api.yaml` stays in sync and the endpoint's auth requirement is documented. CI enforces this via the `--check` variant; never edit that file by hand.
logger.exception("Invalid parameters for API request")
return JSONResponse(
content={
"success": False,
"message": "Invalid request parameters",
},
)
```
## WebSocket Broadcasts
Outbound WebSocket broadcasts go through a per-recipient classifier in `frigate/comms/ws.py` that enforces camera-level access. **The classifier is fail-closed: any topic it doesn't recognize is dropped for every client.** New outbound topics must be classified there or they'll silently disappear.
## Project-Specific Conventions
### Configuration Files
- Main config: `config/config.yml`
### Directory Structure
- Backend code: `frigate/`
- Frontend code: `web/`
- Docker files: `docker/`
- Documentation: `docs/`
- Database migrations: `migrations/`
### Code Style Conformance
Always conform new and refactored code to the existing coding style in the project:
- Follow established patterns in similar files
- Match indentation and formatting of surrounding code
- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
- Maintain the same level of verbosity in comments and docstrings
## Additional Resources
- Documentation: https://docs.frigate.video
- Main Repository: https://github.com/blakeblackshear/frigate
- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration
- **Use AI tools if they help you.** We do too. This is about what you post, not which tools you use to write it.
- **A person has to read it and send it.** Don't wire a bot or an agent up to post on your behalf.
- **Write your posts yourself.** Your own words, the template filled in, and you answering maintainers rather than your assistant.
- **Don't paste an AI's guess at the cause as though it were a diagnosis.** Tell us what you actually observed.
- **Read your code before you submit it.** Disclose that AI was used, and be ready to explain every line.
- **If we misjudge something you wrote, just say so.** We'll take you at your word.
The rest of this document explains each of these, and why.
## Scope
AI tools are a reality of modern development and we're not opposed to their use. You are responsible for anything you submit, however it was produced, and we are responsible for anything we merge and release. We hold a high bar for both.
This policy applies everywhere this project is discussed: issues, discussions, pull requests, code reviews, and commit comments.
## Why this exists
Frigate is built and supported by a small group of maintainers and a community of volunteers who read every post and review every pull request. Nobody here is paid to do it, and time spent reading a post is time not spent fixing bugs or building features.
We're not opposed to AI tools. We use them too. But content generated by an AI and submitted without review costs a real person real time, and usually gives them less to work with than a few honest sentences would have. That is the problem this policy addresses.
## A person has to be in the loop
Every issue, discussion, comment, and pull request here must be read and submitted by a person. Using an AI tool to help you write is fine. Wiring one up to post on your behalf is not.
Specifically, do not:
- Connect a bot or agent to GitHub that opens issues, discussions, or pull requests without you reading them first
- Post output from a tool you have not read
- Use tooling to file bulk or drive-by contributions across the repository
We will close anything we believe was posted without a person reading it, and we may mark it as spam. Posts that skip the templates are the most common sign of this.
## Issues, discussions, and comments
We do not mind if you use AI tools to help you write. Do not have tools post unreviewed content on your behalf. We may hide any comment we believe to be unreviewed AI output.
Keep posts to what is needed to communicate your point. A long, confidently written, AI-padded post is harder to help with than a short direct one, not easier, and it is usually obvious.
**Describe your actual problem in your own words.** Tell us what you did, what you expected, and what actually happened. That is the information we need, and only you have it.
**Do not paste an AI's guess at the cause as though it were a diagnosis.** It is frequently wrong in ways that send everyone down the wrong path, and it buries the details that would have led to the real answer. We would rather see what you observed than what a model inferred.
**Fill in the template completely.** The templates ask for logs, config, version, and hardware because those are the things needed to help you. An AI cannot supply them for you, and a post missing them cannot be acted on.
**Answer maintainers yourself.** If we ask you a question, we are asking _you_, not your AI assistant. These are the spaces where we build trust and understanding with the community, and that only works if we're talking to each other. Using AI to fix your grammar or clarity is fine, but the substance has to be yours.
This applies to pull request descriptions and review replies as much as it does to bug reports and discussions.
### Quoting AI output
If you want to include something an AI told you, it must be:
- In a quote block, using `>`
- Disclosed as AI output, saying which tool it came from
- Accompanied by your own comment explaining why you think it is relevant
Keep the excerpt short. Do not paste long transcripts.
### Non-native English speakers
AI is genuinely useful for participating in a project that operates in English, and we would rather hear from you through a translation tool than not hear from you at all. Using AI to improve the grammar or clarity of something you wrote yourself is fine.
If you are translating your posts, make sure the translation says what you meant. Including your original text in a `<details>` block helps us verify the translation if something reads oddly, and keeps the thread readable.
## Code contributions
We need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
Because of the long-term maintenance burden every merged change creates, we require a human in the loop who understands the work the AI produced. Pull requests that appear to be unreviewed AI output will be closed without review.
### Requirements when AI is used
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
1.**Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest, this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
2.**Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
3.**Be prepared to explain every line of code you submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
4.**Check for an existing pull request addressing the same change.** If one exists, comment there and work with its author instead of opening a duplicate.
5.**It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
### Established contributors
Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption, it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
### What this means in practice
We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term, often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated, where the author can't explain the design, debug issues independently, or engage substantively in design discussions, doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
## Our use of AI
The Frigate documentation site has an "Ask AI" search that answers questions from the docs, and we may use AI tooling to help with triage and project management. Like any automated tooling, it is not always right.
If an AI tool leaves a comment on your contribution, treat it the way you would any other comment. If you think it is wrong, say so, and a brief explanation is enough. Maintainers always have the final say.
## Enforcement
Contributions and posts that do not follow this policy will be closed. Depending on the situation, maintainers may also:
- Hide or delete comments that appear to be unreviewed AI output
- Mark automated content as spam
- Close an issue, discussion, or pull request without further review
- Lock a conversation
- Temporarily or permanently block an account from participating in the project
Repeated violations may result in being blocked from contributing to Frigate.
### When we get it wrong
There is no reliable way to detect this, and we're not going to pretend otherwise. Whether something reads as unreviewed AI output is a judgment call, usually made quickly, by a volunteer with limited time and no way to know for certain. These calls are subjective and we won't always get them right.
If it happens to you, just say so. A short reply telling us you wrote it yourself is enough, and we'll take you at your word and pick the conversation back up. We would much rather occasionally reopen something we misjudged than treat everyone who posts here as a suspect.
We'd ask for some understanding in return. These calls get made quickly because the volume is real, and time spent second-guessing them is time not spent helping the person in the next thread.
## Attribution
Portions of this policy are adapted from the [Open Home Foundation AI Policy](https://developers.home-assistant.io/docs/ai_policy/).
Thank you for your interest in contributing to Frigate. This document covers the expectations and guidelines for contributions. Please read it before submitting a pull request.
All participation in this project, including pull requests, issues, and discussions, is covered by our [AI policy](AI_POLICY.md).
## Before you start
### Bugfixes
If you've found a bug and want to fix it, go for it. Link to the relevant issue in your PR if one exists, or describe the bug in the PR description.
### New features
A pull request is more than just code — it's a request for the maintainers to review, integrate, and support the change long-term. We're selective about what we take on, and prioritize changes that align with the project's direction and can be responsibly maintained in the long term.
**Large or highly-requested features** raise the bar even higher. Popularity signals demand, but it doesn't pre-approve any particular implementation. The bigger the change, the higher the long-term cost, and the more important it is that we're aligned on scope and approach before any code is written. A large PR that lands without prior discussion is unlikely to be merged as-is, no matter how well it's implemented.
Before writing code for a new feature:
1.**Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Feature requests tagged with "planned" are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
2.**Start a discussion or feature request first.** This helps ensure your idea aligns with Frigate's direction before you invest time building it. Community interest in a feature request helps us gauge demand, though a great idea is a great idea even without a crowd behind it.
## AI usage policy
AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting, and we need to hear from you rather than from your AI assistant.
**Read the [AI policy](AI_POLICY.md) before you open a pull request.** It is short, and it applies to everything you post here. The parts that most often catch people out:
- A person has to be in the loop. Don't wire a bot or agent up to open pull requests, issues, or discussions on your behalf.
- Disclose how AI was used. The PR template asks for this. Be honest, it won't automatically disqualify your PR.
- Review and test everything you submit, and be prepared to explain every line when asked.
- Don't use AI to write your PR description or your replies to maintainers.
Pull requests that appear to be unreviewed AI output will be closed without review.
## Pull request guidelines
### Before submitting
- **Search for existing PRs** to avoid duplicating effort.
- **Test your changes locally.** Your PR cannot be merged unless tests pass.
- **Format your code.** Run `ruff format frigate` for Python and `npm run prettier:write` from the `web/` directory for frontend changes.
- **Run the linter.** Run `ruff check frigate` for Python and `npm run lint` from `web/` for frontend.
- **One concern per PR.** Don't combine unrelated changes. A bugfix and a new feature should be separate PRs.
### What we look for in review
- **Does it work?** Tested locally, tests pass, no regressions.
- **Is it maintainable?** Clear code, appropriate complexity, good separation of concerns.
- **Does it fit?** Consistent with Frigate's architecture and design philosophy.
- **Is it scoped well?** Solves the stated problem without unnecessary additions.
### After submitting
- Be responsive to review feedback. We may ask for changes.
- Expect honest, direct feedback. We try to be respectful but we also try to be efficient.
- If your PR goes stale, rebase it on the latest `dev` branch.
## Coding standards
### Python (backend)
- **Python** — use modern language features (type hints, pattern matching, f-strings, dataclasses)
- **Formatting**: Ruff (configured in `pyproject.toml`)
- **Linting**: Ruff
- **Testing**: `python3 -u -m unittest`
- **Logging**: Use module-level `logger = logging.getLogger(__name__)` with lazy formatting
- **Async**: All external I/O must be async. No blocking calls in async functions.
- **Error handling**: Use specific exception types. Keep try blocks minimal.
- **Language**: American English for all code, comments, and documentation
### TypeScript/React (frontend)
- **Linting**: ESLint (`npm run lint` from `web/`)
- **Formatting**: Prettier (`npm run prettier:write` from `web/`)
- **i18n**: All user-facing strings must use `react-i18next`. Never hardcode display text in components. Add English strings to the appropriate files in `web/public/locales/en/`.
- **Components**: Use Radix UI/shadcn primitives and TailwindCSS with the `cn()` utility.
### Development commands
```bash
# Python
python3 -u -m unittest # Run all tests
python3 -u -m unittest frigate.test.test_ffmpeg_presets # Run specific test
ruff format frigate # Format
ruff check frigate # Lint
# Frontend (from web/ directory)
npm run build # Build
npm run lint # Lint
npm run lint:fix # Lint + fix
npm run prettier:write # Format
```
## Project structure
```
frigate/ # Python backend
api/ # FastAPI route handlers
config/ # Configuration parsing and validation
detectors/ # Object detection backends
events/ # Event management and storage
test/ # Backend tests
util/ # Shared utilities
web/ # React/TypeScript frontend
src/
api/ # API client functions
components/ # Reusable components
hooks/ # Custom React hooks
pages/ # Route components
types/ # TypeScript type definitions
views/ # Complex view components
docker/ # Docker build files
docs/ # Documentation site
migrations/ # Database migrations
```
## Translations
Frigate uses [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) for managing language translations. If you'd like to help translate Frigate into your language:
1. Visit the [Frigate project on Weblate](https://hosted.weblate.org/projects/frigate-nvr/).
2. Create an account or log in.
3. Browse the available languages and select the one you'd like to contribute to, or request a new language.
4. Translate strings directly in the Weblate interface — no code changes or pull requests needed.
Translation contributions through Weblate are automatically synced to the repository. Please do not submit pull requests for translation changes — use Weblate instead so that translations are properly tracked and coordinated.
## Resources
- [Documentation](https://docs.frigate.video)
- [Discussions, Support, and Bug Reports](https://github.com/blakeblackshear/frigate/discussions)
# Optional: module by module log level configuration
logs:
frigate.mqtt:error
```
Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
Examples of available modules are:
-`frigate.app`
-`frigate.mqtt`
-`frigate.object_detection.base`
-`detector.<detector_name>`
-`watchdog.<camera_name>`
-`ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
#### Go2RTC Logging
See [the go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#module-log) for logging configuration
```yaml
go2rtc:
streams:
# ...
log:
exec:trace
```
### `environment_vars`
This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS. Docker users should set environment variables in their `docker run` command (`-e FRIGATE_MQTT_PASSWORD=secret`) or `docker-compose.yml` file (`environment:` section) instead. Note that values set here are stored in plain text in your config file, so if the goal is to keep credentials out of your configuration, use Docker environment variables or Docker secrets instead.
Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
Example:
```yaml
environment_vars:
FRIGATE_MQTT_USER:my_mqtt_user
FRIGATE_MQTT_PASSWORD:my_mqtt_password
mqtt:
host:"{FRIGATE_MQTT_HOST}"
user:"{FRIGATE_MQTT_USER}"
password:"{FRIGATE_MQTT_PASSWORD}"
```
#### TensorFlow Thread Configuration
If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
```yaml
environment_vars:
TF_INTRA_OP_PARALLELISM_THREADS:"2"# Threads within operations (0 = use default)
TF_INTER_OP_PARALLELISM_THREADS:"2"# Threads between operations (0 = use default)
TF_DATASET_THREAD_POOL_SIZE:"2"# Data pipeline threads (0 = use default)
```
### `database`
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
This may need to be in a custom location if network storage is used for the media folder.
```yaml
database:
path:/path/to/frigate.db
```
### `model`
If using a custom model, the width and height will need to be specified.
Custom models may also require different input tensor formats. The colorspace conversion supports RGB, BGR, or YUV frames to be sent to the object detector. The input tensor shape parameter is an enumeration to match what specified by the model.
| Tensor Dimension | Description |
| :--------------: | -------------- |
| N | Batch Size |
| H | Model Height |
| W | Model Width |
| C | Color Channels |
| Available Input Tensor Shapes |
| :---------------------------: |
| "nhwc" |
| "nchw" |
```yaml
# Optional: model config
model:
path:/path/to/model
width:320
height:320
input_tensor:"nhwc"
input_pixel_format:"bgr"
```
#### `labelmap`
:::warning
If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../configuration/review.md#restricting-alerts-to-specific-labels) for more info.
:::
The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
```yaml
model:
labelmap:
2:vehicle
3:vehicle
5:vehicle
7:vehicle
15:animal
16:animal
17:animal
```
Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
:::warning
Some labels have special handling and modifications can disable functionality.
`person` objects are associated with `face` and `amazon`
`car` objects are associated with `license_plate`, `ups`, `fedex`, `amazon`
:::
## Network Configuration
Changes to Frigate's internal network configuration can be made by bind mounting nginx.conf into the container. For example:
IPv6 is disabled by default, to enable IPv6 listen.gotmpl needs to be bind mounted with IPv6 enabled. For example:
```
{{ if not .enabled }}
# intended for external traffic, protected by auth
listen 8971;
{{ else }}
# intended for external traffic, protected by auth
listen 8971 ssl;
# intended for internal traffic, not protected by auth
listen 5000;
```
becomes
```
{{ if not .enabled }}
# intended for external traffic, protected by auth
listen [::]:8971 ipv6only=off;
{{ else }}
# intended for external traffic, protected by auth
listen [::]:8971 ipv6only=off ssl;
# intended for internal traffic, not protected by auth
listen [::]:5000 ipv6only=off;
```
## Base path
By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.
### Set Base Path via HTTP Header
The preferred way to configure the base path is through the `X-Ingress-Path` HTTP header, which needs to be set to the desired base path in an upstream reverse proxy.
For example, in Nginx:
```
location /frigate {
proxy_set_header X-Ingress-Path /frigate;
proxy_pass http://frigate_backend;
}
```
### Set Base Path via Environment Variable
When it is not feasible to set the base path via a HTTP header, it can also be set via the `FRIGATE_BASE_PATH` environment variable in the Docker Compose file.
For example:
```
services:
frigate:
image: blakeblackshear/frigate:latest
environment:
- FRIGATE_BASE_PATH=/frigate
```
This can be used for example to access Frigate via a Tailscale agent (https), by simply forwarding all requests to the base path (http):
Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built `ffmpeg` and `ffprobe` binaries can be placed in `/config/custom-ffmpeg/bin` for Frigate to use.
To do this:
1. Download your ffmpeg build and uncompress it to the `/config/custom-ffmpeg` folder. Verify that both the `ffmpeg` and `ffprobe` binaries are located in `/config/custom-ffmpeg/bin`.
2. Update the `ffmpeg.path` in your Frigate config to `/config/custom-ffmpeg`.
3. Restart Frigate and the custom version will be used if the steps above were done correctly.
### Custom go2rtc version
Frigate currently includes go2rtc v1.9.10, there may be certain cases where you want to run a different version of go2rtc.
To do this:
1. Download the go2rtc build to the `/config` folder.
2. Rename the build to `go2rtc`.
3. Give `go2rtc` execute permission.
4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
## Validating your config.yml file updates
When frigate starts up, it checks whether your config file is valid, and if it is not, the process exits. To minimize interruptions when updating your config, you have three options -- you can edit the config via the WebUI which has built in validation, use the config API, or you can validate on the command line using the frigate docker container.
### Via API
Frigate can accept a new configuration file as JSON at the `/api/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
```bash
curl -X POST http://frigate_host:5000/api/config/save -d @config.json
```
if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
You can also validate your config at the command line by using the docker container itself. In CI/CD, you leverage the return code to determine if your config is valid, Frigate will return `1` if the config is invalid, or `0` if it's valid.
@@ -11,6 +11,8 @@ It is not recommended to copy this full configuration file. Only specify values
:::
Sections marked `# NOTE: Can be overridden at the camera level` can be set globally and then adjusted per camera. See [Global and Camera-Level Configuration](../config_overrides.md) for how that works.
```yaml
mqtt:
# Optional: Enable mqtt server (default: shown below)
@@ -54,17 +56,6 @@ mqtt:
# 2 = exactly once
qos: 0
# Optional: Detectors configuration. Defaults to a single CPU detector
detectors:
# Required: name of the detector
detector_name:
# Required: type of the detector
# Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
# Additional detector types can also be plugged in.
# Detectors may require additional configuration.
# Refer to the Detectors configuration page for more information.
type: cpu
# Optional: Database configuration
database:
# The path to store the SQLite DB (default: shown below)
@@ -75,11 +66,19 @@ tls:
# Optional: Enable TLS for port 8971 (default: shown below)
enabled: True
# Optional: IPv6 configuration
# Optional: Networking configuration
networking:
# Optional: Enable IPv6 on 5000, and 8971 if tls is configured (default: shown below)
# can also be set to `7.0` or `5.0` to specify one of the included versions
# can also be set to `8.0` or `5.0` to specify one of the included versions
# or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
path: "default"
# Optional: global ffmpeg args (default: shown below)
@@ -265,6 +294,8 @@ ffmpeg:
detect: -threads 2 -f rawvideo -pix_fmt yuv420p
# Optional: output args for record streams (default: shown below)
record: preset-record-generic
# Optional: output args for sub stream record streams (default: the record output args above)
# record_sub: preset-record-generic
# Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
# If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
# If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -284,6 +315,10 @@ detect:
width: 1280
# Optional: height of the frame for the input with the detect role (default: use native stream resolution)
height: 720
# Optional: the environment this camera looks at, which picks the model it runs on
# (default: the model with a scene of all)
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
scene: outdoor
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
fps: 5
@@ -320,7 +355,7 @@ 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 tracked object clip with a person walking from left to right.
# TIP: Imagine there is a tracked object clip with a person walking from left to right.
# If the tracked object lifecycle 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
@@ -339,7 +374,15 @@ objects:
# Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
# Checks based on the bottom center of the bounding box of the object.
# NOTE: This mask is COMBINED with the object type specific mask below
# Optional: module by module log level configuration
logs:
frigate.mqtt:error
```
</TabItem>
</ConfigTabs>
Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
Examples of available modules are:
-`frigate.app`
-`frigate.mqtt`
-`frigate.object_detection.base`
-`detector.<detector_name>`
-`watchdog.<camera_name>`
-`ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
#### Go2RTC Logging
See [the go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#module-log) for logging configuration
```yaml
go2rtc:
streams:
# ...
log:
exec:trace
```
### `environment_vars`
This section sets environment variables in the Frigate process for those unable to modify the environment of the container, like within Home Assistant OS. It's meant for process settings such as `LIBVA_DRIVER_NAME` or the TensorFlow thread counts below. Docker users should set environment variables in their `docker run` command (`-e LIBVA_DRIVER_NAME=i965`) or `docker-compose.yml` file (`environment:` section) instead. Values set here are stored in plain text in your config file, so credentials belong in `secrets.yaml`, Docker environment variables, or Docker secrets instead.
Names prefixed with `FRIGATE_` set here also take part in `{FRIGATE_VARIABLE_NAME}` substitution (see [below](#substitution-sources-and-precedence)), but `secrets.yaml` is the better home for them.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Environment variables" /> to add or edit environment variables.
| `TF_INTRA_OP_PARALLELISM_THREADS` | Threads within operations (`0` = use default) |
| `TF_INTER_OP_PARALLELISM_THREADS` | Threads between operations (`0` = use default) |
| `TF_DATASET_THREAD_POOL_SIZE` | Data pipeline threads (`0` = use default) |
</TabItem>
<TabItem value="yaml">
```yaml
environment_vars:
TF_INTRA_OP_PARALLELISM_THREADS:"2"# Threads within operations (0 = use default)
TF_INTER_OP_PARALLELISM_THREADS:"2"# Threads between operations (0 = use default)
TF_DATASET_THREAD_POOL_SIZE:"2"# Data pipeline threads (0 = use default)
```
</TabItem>
</ConfigTabs>
### `secrets.yaml`
A `secrets.yaml` file next to your `config.yml` is an additional source of `FRIGATE_` variables, for installs that can't set container environment variables or mount Docker secrets. It's a flat map of names to values, and it is never read or written by the Frigate UI:
```yaml
FRIGATE_CAM_USER:viewer
FRIGATE_CAM_PASS:"p@ss w0rd"
FRIGATE_MQTT_HOST:mqtt.internal.example
```
For Docker this is `/config/secrets.yaml` inside the container, so it lives in whatever host directory you mounted at `/config`. For the Home Assistant App it's `/addon_configs/<addon_directory>/secrets.yaml`, in the same folder as your `config.yml`; see [the App config directory](../config.md#accessing-app-config-dir) for the directory name for your variant.
Names must start with `FRIGATE_`, and nesting is not supported. `secrets.yaml` feeds `{FRIGATE_VARIABLE_NAME}` substitution, so the handful of variables Frigate reads straight from the process environment, such as `FRIGATE_JWT_SECRET`, still need a container environment variable or a Docker secret.
### Substitution sources and precedence
The same `{FRIGATE_VARIABLE_NAME}` placeholder resolves from four sources. When a name is defined in more than one, the higher one wins and a warning at startup names which source was used.
| Priority | Source | Where it's set | Who can use it |
| 1 (highest) | Docker secrets | Files in `/run/secrets`, or the directory named by `CREDENTIALS_DIRECTORY` | Docker, systemd |
| 2 | Container environment | `docker run -e`, the `environment:` section of `docker-compose.yml` | Docker |
| 3 | `secrets.yaml` | Next to `config.yml`, see above | Everyone, including the HA App |
| 4 (lowest) | `environment_vars` | The block in `config.yml` described above | Everyone, including the HA App |
For example, with this `secrets.yaml`:
```yaml
FRIGATE_MQTT_PASSWORD:from_secrets
```
and this `config.yml`:
```yaml
environment_vars:
FRIGATE_MQTT_PASSWORD:from_config
mqtt:
password:"{FRIGATE_MQTT_PASSWORD}"
```
the password resolves to `from_secrets`, and the log shows `FRIGATE_MQTT_PASSWORD is defined in more than one place, using the value from secrets.yaml`. Add `-e FRIGATE_MQTT_PASSWORD=from_env` to the container and it resolves to `from_env` instead.
Referencing a name that no source defines is a config validation error naming the field.
### `database`
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database if necessary.
This may need to be in a custom location if network storage is used for the media folder.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Database" />.
- Set **Database path** to the custom path for the Frigate database file (default: `/config/frigate.db`)
</TabItem>
<TabItem value="yaml">
```yaml
database:
path:/path/to/frigate.db
```
</TabItem>
</ConfigTabs>
### `model`
If using a custom model, the width and height will need to be specified.
Custom models may also require different input tensor formats. The colorspace conversion supports RGB, BGR, or YUV frames to be sent to the object detector. The input tensor shape parameter is an enumeration to match what specified by the model.
| Tensor Dimension | Description |
| :--------------: | -------------- |
| N | Batch Size |
| H | Model Height |
| W | Model Width |
| C | Color Channels |
| Available Input Tensor Shapes |
| :---------------------------: |
| "nhwc" |
| "nchw" |
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Detection models" /> and, on the model you want to change, open the **Custom Model** tab to configure the model path, dimensions, and input format.
| **Custom object detector model path** | Path to the custom model file |
| **Object detection model input width** | Model input width (default: 320) |
| **Object detection model input height** | Model input height (default: 320) |
| **Advanced > Model Input Tensor Shape** | Input tensor shape: `nhwc` or `nchw` |
| **Advanced > Model Input Pixel Color Format** | Pixel format: `rgb`, `bgr`, or `yuv` |
</TabItem>
<TabItem value="yaml">
```yaml
# Optional: model config
models:
- devices:
- openvino:GPU
path:/path/to/model
width:320
height:320
input_tensor:"nhwc"
input_pixel_format:"bgr"
```
</TabItem>
</ConfigTabs>
#### `labelmap`
:::warning
If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../review.md#restricting-alerts-to-specific-labels) for more info.
:::
The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
```yaml
models:
- labelmap:
2:vehicle
3:vehicle
5:vehicle
7:vehicle
15:animal
16:animal
17:animal
```
Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
:::warning
Some labels have special handling and modifications can disable functionality.
`person` objects are associated with `face` and `amazon`
`car` objects are associated with `license_plate`, `ups`, `fedex`, `amazon`
:::
## Network Configuration
Frigate exposes a few networking options. IPv6 and the listen ports are set in the `networking` configuration (or from the Settings UI); more advanced changes require [customizing the bundled Nginx configuration](#customizing-the-nginx-configuration).
### Enabling IPv6
By default Frigate listens on IPv4 only. To also listen on IPv6 (on port `5000`, and on `8971` when TLS is configured), enable it in the `networking` configuration.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Networking" /> and enable **IPv6**.
</TabItem>
<TabItem value="yaml">
```yaml
networking:
ipv6:
enabled:true
```
</TabItem>
</ConfigTabs>
### Listen on different ports
You can change the ports Nginx uses for listening. The internal port (unauthenticated) and external port (authenticated) can be changed independently. You can also specify an IP address using the format `ip:port` if you wish to bind the port to a specific interface. This may be useful for example to prevent exposing the internal port outside the container.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Networking" /> to configure the listen ports.
This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run``--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
:::
### Customizing the Nginx configuration
More advanced changes to Frigate's internal network configuration can be made by bind mounting your own `nginx.conf` into the container. For example:
By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.
### Set Base Path via HTTP Header
The preferred way to configure the base path is through the `X-Ingress-Path` HTTP header, which needs to be set to the desired base path in an upstream reverse proxy.
For example, in Nginx:
```
location /frigate {
proxy_set_header X-Ingress-Path /frigate;
proxy_pass http://frigate_backend;
}
```
### Set Base Path via Environment Variable
When it is not feasible to set the base path via a HTTP header, it can also be set via the `FRIGATE_BASE_PATH` environment variable in the Docker Compose file.
For example:
```
services:
frigate:
image: ghcr.io/blakeblackshear/frigate:stable
environment:
- FRIGATE_BASE_PATH=/frigate
```
This can be used for example to access Frigate via a Tailscale agent (https), by simply forwarding all requests to the base path (http):
Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built `ffmpeg` and `ffprobe` binaries can be placed in `/config/custom-ffmpeg/bin` for Frigate to use.
To do this:
1. Download your ffmpeg build and uncompress it to the `/config/custom-ffmpeg` folder. Verify that both the `ffmpeg` and `ffprobe` binaries are located in `/config/custom-ffmpeg/bin`.
2. Update the `ffmpeg.path` in your Frigate config to `/config/custom-ffmpeg`.
3. Restart Frigate and the custom version will be used if the steps above were done correctly.
### Custom go2rtc version
Frigate currently includes go2rtc v1.9.14, there may be certain cases where you want to run a different version of go2rtc.
To do this:
1. Download the go2rtc build to the `/config` folder.
2. Rename the build to `go2rtc`.
3. Give `go2rtc` execute permission.
4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
## Validating your config.yml file updates
When frigate starts up, it checks whether your config file is valid, and if it is not, the process exits. To minimize interruptions when updating your config, you have three options -- you can edit the config via the WebUI which has built in validation, use the config API, or you can validate on the command line using the frigate docker container.
### Via API
Frigate can accept a new configuration file as JSON at the `/api/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
```bash
curl -X POST http://frigate_host:5000/api/config/save -d @config.json
```
if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
You can also validate your config at the command line by using the docker container itself. In CI/CD, you leverage the return code to determine if your config is valid, Frigate will return `1` if the config is invalid, or `0` if it's valid.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Frigate provides a builtin audio detector which runs on the CPU. Compared to object detection in images, audio detection is a relatively lightweight operation so the only option is to run the detection on a CPU.
## Configuration
@@ -11,7 +15,17 @@ Audio events work by detecting a type of audio and creating an event, the event
### Enabling Audio Events
Audio events can be enabled for all cameras or only for specific cameras.
Audio events can be enabled globally or for specific cameras.
<ConfigTabs>
<TabItem value="ui">
**Global:** Navigate to <NavPath path="Settings > Global configuration > Audio events" /> and set **Enable audio detection** to on.
**Per-camera:** Navigate to <NavPath path="Settings > Camera configuration > Audio events" /> and set **Enable audio detection** to on for the desired camera.
</TabItem>
<TabItem value="yaml">
```yaml
@@ -26,6 +40,9 @@ cameras:
enabled:True# <- enable audio events for the front_camera
```
</TabItem>
</ConfigTabs>
If you are using multiple streams then you must set the `audio` role on the stream that is going to be used for audio detection, this can be any stream but the stream must have audio included.
:::note
@@ -34,6 +51,14 @@ The ffmpeg process for capturing audio will be a separate connection to the came
:::
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add an input with the `audio` role pointing to a stream that includes audio.
</TabItem>
<TabItem value="yaml">
```yaml
cameras:
front_camera:
@@ -48,9 +73,12 @@ cameras:
- detect
```
</TabItem>
</ConfigTabs>
### Configuring Minimum Volume
The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are is displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The [Debug view](/usage/live#the-single-camera-view) in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
:::tip
@@ -60,7 +88,18 @@ Volume is considered motion for recordings, this means when the `record -> retai
### Configuring Audio Events
The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`,`scream`,`speech`, and `yell` are enabled but these can be customized.
The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `speech`, and `yell` are enabled but these can be customized.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Audio events" />.
- Set **Enable audio detection** to on
- Set **Listen types** to include the audio types you want to detect
</TabItem>
<TabItem value="yaml">
```yaml
audio:
@@ -68,20 +107,130 @@ audio:
listen:
- bark
- fire_alarm
- scream
- speech
- yell
```
</TabItem>
</ConfigTabs>
#### Grouping Audio Labels
Related audio classes can be grouped under one label by mapping their numeric
class IDs to the same name. Add the grouped name to `listen` and use it for any
so each ID is one less than the displayed file line number.
Audio label mappings are separate from the object detector's `model.labelmap`.
### Common Audio Labels
The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
Some labels cover several related sounds: `yell` is triggered by shouting, yelling, children shouting, and screaming; `crying` covers baby cries, sobbing, and whimpering; and `speech` covers ordinary talking and conversation.
Frequently-heard labels like `speech` can generate a lot of events, and each event could save a snapshot and recording based on your configuration, so start with a focused set and expand from there. The defaults (`bark`, `fire_alarm`, `speech`, `yell`) plus a few of the safety labels above cover most needs. See the [full audio labelmap](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) or the Frigate UI for every available type.
:::
### Audio Transcription
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
:::info
Audio transcription requires a one-time internet connection to download the Whisper or Sherpa-ONNX model on first use. Once cached, transcription runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
:::
Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
#### Configuration
To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
To enable transcription, configure it globally and optionally disable for specific cameras. Audio detection must also be enabled as described above.
- Set **Transcription device** to the desired device
- Set **Model size** to the desired size
**Per-camera:** Navigate to <NavPath path="Settings > Camera configuration > Audio transcription" /> to enable or disable transcription for a specific camera.
</TabItem>
<TabItem value="yaml">
```yaml
audio_transcription:
@@ -100,6 +249,9 @@ cameras:
enabled:False
```
</TabItem>
</ConfigTabs>
:::note
Audio detection must be enabled and configured as described above in order to use audio transcription features.
@@ -128,7 +280,7 @@ The only field that is valid at the camera level is `enabled`.
#### Live transcription
The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing, or toggle it outside of the UI with the [`frigate/<camera_name>/audio_transcription/set`](/integrations/mqtt#frigatecamera_nameaudio_transcriptionset) MQTT topic or the HTTP API. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
Results can be error-prone due to a number of factors, including:
@@ -144,9 +296,9 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
#### Transcription and translation of `speech` audio events
Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for in your config. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
The transcribed/translated speech will appear in the description box in the Tracked Object Details pane. If Semantic Search is enabled, embeddings are generated for the transcription text and are fully searchable using the description search type.
@@ -162,16 +314,16 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
1. Why doesn't Frigate automatically transcribe all `speech` events?
Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That's a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
If you hear speech that’s actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button (the microphone icon) in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
2. Why don't you save live transcription text and use that for `speech` events?
There’s no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
There's no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.
Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That's why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
# Authentication
Frigate stores user information in its database. Password hashes are generated using industry standard PBKDF2-SHA256 with 600,000 iterations. Upon successful login, a JWT token is issued with an expiration date and set as a cookie. The cookie is refreshed as needed automatically. This JWT token can also be passed in the Authorization header as a bearer token.
@@ -22,13 +26,26 @@ On startup, an admin user and password are generated and printed in the logs. It
## Resetting admin password
In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.
In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Authentication" />.
- Set **Reset admin password** to on to reset the admin password and print it in the logs on next startup
</TabItem>
<TabItem value="yaml">
```yaml
auth:
reset_admin_password:true
```
</TabItem>
</ConfigTabs>
## Password guidance
Constructing secure passwords and managing them properly is important. Frigate requires a minimum length of 12 characters. For guidance on password standards see [NIST SP 800-63B](https://pages.nist.gov/800-63-3/sp800-63b.html). To learn what makes a password truly secure, read this [article](https://medium.com/peerio/how-to-build-a-billion-dollar-password-3d92568d9277).
@@ -47,7 +64,20 @@ Restarting Frigate will reset the rate limits.
If you are running Frigate behind a proxy, you will want to set `trusted_proxies` or these rate limits will apply to the upstream proxy IP address. This means that a brute force attack will rate limit login attempts from other devices and could temporarily lock you out of your instance. In order to ensure rate limits only apply to the actual IP address where the requests are coming from, you will need to list the upstream networks that you want to trust. These trusted proxies are checked against the `X-Forwarded-For` header when looking for the IP address where the request originated.
If you are running a reverse proxy in the same Docker Compose file as Frigate, here is an example of how your auth config might look:
If you are running a reverse proxy in the same Docker Compose file as Frigate, configure rate limiting and trusted proxies as follows:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Authentication" />.
| **Trusted proxies** | List of upstream network CIDRs to trust for `X-Forwarded-For` (e.g., `172.18.0.0/16` for internal Docker Compose network) |
</TabItem>
<TabItem value="yaml">
```yaml
auth:
@@ -56,9 +86,12 @@ auth:
- 172.18.0.0/16# <---- this is the subnet for the internal Docker Compose network
```
</TabItem>
</ConfigTabs>
## Session Length
The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required — otherwise, the user will need to log in again.
The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required. Otherwise, the user will need to log in again.
While the default provides a balance of security and convenience, you can customize this duration to suit your specific security requirements and user experience preferences. The session length is configured in seconds.
@@ -67,11 +100,24 @@ The default value of `86400` will expire the authentication session after 24 hou
-`0`: Setting the session length to 0 will require a user to log in every time they access the application or after a very short, immediate timeout.
-`604800`: Setting the session length to 604800 will require a user to log in if the token is not refreshed for 7 days.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Authentication" />.
- Set **Session length** to the duration in seconds before the authentication session expires (default: 86400 / 24 hours)
</TabItem>
<TabItem value="yaml">
```yaml
auth:
session_length:86400
```
</TabItem>
</ConfigTabs>
## JWT Token Secret
The JWT token secret needs to be kept secure. Anyone with this secret can generate valid JWT tokens to authenticate with Frigate. This should be a cryptographically random string of at least 64 characters.
@@ -95,11 +141,22 @@ Changing the secret will invalidate current tokens.
## Proxy configuration
Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth.
Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth. Frigate does not implement OIDC, SAML, or LDAP natively; as an NVR focused on recording and object detection, it relies on robust, battle-tested proxies to handle those protocols and passes the authenticated user and role through via headers (see below).
If you are leveraging the authentication of an upstream proxy, you likely want to disable Frigate's authentication as there is no correspondence between users in Frigate's database and users authenticated via the proxy. Optionally, if communication between the reverse proxy and Frigate is over an untrusted network, you should set an `auth_secret` in the `proxy` config and configure the proxy to send the secret value as a header named `X-Proxy-Secret`. Assuming this is an untrusted network, you will also want to [configure a real TLS certificate](tls.md) to ensure the traffic can't simply be sniffed to steal the secret.
Here is an example of how to disable Frigate's authentication and also ensure the requests come only from your known proxy.
To disable Frigate's authentication and ensure requests come only from your known proxy:
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > System > Authentication" />.
- Set **Enable authentication** to off
2. Navigate to <NavPath path="Settings > System > Proxy" />.
- Set **Proxy secret** to `<some random long string>`
</TabItem>
<TabItem value="yaml">
```yaml
auth:
@@ -109,6 +166,9 @@ proxy:
auth_secret:<some random long string>
```
</TabItem>
</ConfigTabs>
You can use the following code to generate a random secret.
If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Groups` values. Header names are not case sensitive. Multiple values can be included in the role header. Frigate expects that the character separating the roles is a comma, but this can be specified using the `separator` config entry.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Proxy" /> and configure the header mapping and separator settings.
| **Separator character** | Character separating multiple roles in the role header (default: comma). Authentik uses a pipe `\|`. |
| **Header mapping > User header** | Header name for the authenticated username (e.g., `x-forwarded-user`) |
| **Header mapping > Role header** | Header name for the authenticated role/groups (e.g., `x-forwarded-groups`) |
</TabItem>
<TabItem value="yaml">
```yaml
proxy:
...
@@ -128,19 +202,37 @@ proxy:
role:x-forwarded-groups
```
</TabItem>
</ConfigTabs>
Frigate supports `admin`, `viewer`, and custom roles (see below). When using port `8971`, Frigate validates these headers and subsequent requests use the headers `remote-user` and `remote-role` for authorization.
A default role can be provided. Any value in the mapped `role` header will override the default.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Proxy" /> and set the default role.
| **Default role** | Fallback role when no role header is present (e.g., `viewer`) |
</TabItem>
<TabItem value="yaml">
```yaml
proxy:
...
default_role:viewer
```
</TabItem>
</ConfigTabs>
## Role mapping
In some environments, upstream identity providers (OIDC, SAML, LDAP, etc.) do not pass a Frigate-compatible role directly, but instead pass one or more group claims. To handle this, Frigate supports a `role_map` that translates upstream group names into Frigate’s internal roles (`admin`, `viewer`, or custom).
In some environments, upstream identity providers (OIDC, SAML, LDAP, etc.) do not pass a Frigate-compatible role directly, but instead pass one or more group claims. To handle this, Frigate supports a `role_map` that translates upstream group names into Frigate's internal roles (`admin`, `viewer`, or custom). This is configurable via YAML in the configuration file:
```yaml
proxy:
@@ -170,12 +262,25 @@ In this example:
- Admin precedence: if the `admin` mapping matches, Frigate resolves the session to `admin` to avoid accidental downgrade when a user belongs to multiple groups (for example both `admin` and `viewer` groups).
:::note
If a user isn't getting the role you expect, enable debug logging to see exactly what headers Frigate is receiving from your proxy:
```yaml
logger:
default:info
logs:
frigate.api.auth:debug
```
:::
#### Port Considerations
**Authenticated Port (8971)**
- Header mapping is **fully supported**.
- The `remote-role` header determines the user’s privileges:
- The `remote-role` header determines the user's privileges:
- **admin** → Full access (user management, configuration changes).
- **viewer** → Read-only access.
- **Custom roles** → Read-only access limited to the cameras defined in `auth.roles[role]`.
@@ -232,6 +337,14 @@ The viewer role provides read-only access to all cameras in the UI and API. Cust
### Role Configuration Example
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Users > Roles" /> to define custom roles and assign which cameras each role can access.
</TabItem>
<TabItem value="yaml">
```yaml {11-16}
cameras:
front_door:
@@ -251,13 +364,16 @@ auth:
- side_yard
```
</TabItem>
</ConfigTabs>
If you want to provide access to all cameras to a specific user, just use the **viewer** role.
### Managing User Roles
1. Log in as an **admin** user via port `8971` (preferred), or unauthenticated via port `5000`.
2. Navigate to **Settings**.
3. In the **Users** section, edit a user’s role by selecting from available roles (admin, viewer, or custom).
3. In the **Users** section, edit a user's role by selecting from available roles (admin, viewer, or custom).
4. In the **Roles** section, add/edit/delete custom roles (select cameras via switches). Deleting a role auto-reassigns users to "viewer".
### Role Enforcement
@@ -277,7 +393,7 @@ To use role-based access control, you must connect to Frigate via the **authenti
1. Log in as an **admin** user via port `8971`.
2. Navigate to **Settings > Users**.
3. Edit a user’s role by selecting **admin** or **viewer**.
3. Edit a user's role by selecting **admin** or **viewer**.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
An ONVIF-capable, PTZ (pan-tilt-zoom) camera that supports relative movement within the field of view (FOV) can be configured to automatically track moving objects and keep them in the center of the frame.
@@ -29,12 +34,44 @@ A growing list of cameras and brands that have been reported by users to work wi
First, set up a PTZ preset in your camera's firmware and give it a name. If you're unsure how to do this, consult the documentation for your camera manufacturer's firmware. Some tutorials for common brands: [Amcrest](https://www.youtube.com/watch?v=lJlE9-krmrM), [Reolink](https://www.youtube.com/watch?v=VAnxHUY5i5w), [Dahua](https://www.youtube.com/watch?v=7sNbc5U-k54).
Edit your Frigate configuration file and enter the ONVIF parameters for your camera. Specify the object types to track, a required zone the object must enter to begin autotracking, and the camera preset name you configured in your camera's firmware to return to when tracking has ended. Optionally, specify a delay in seconds before Frigate returns the camera to the preset.
Configure the ONVIF connection and autotracking parameters for your camera. Specify the object types to track, a required zone the object must enter to begin autotracking, and the camera preset name you configured in your camera's firmware to return to when tracking has ended. Optionally, specify a delay in seconds before Frigate returns the camera to the preset.
An [ONVIF connection](cameras.md) is required for autotracking to function. Also, a [motion mask](masks.md) over your camera's timestamp and any overlay text is recommended to ensure they are completely excluded from scene change calculations when the camera is moving.
Note that `autotracking` is disabled by default but can be enabled in the configuration or by MQTT.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Camera configuration > ONVIF" /> for the desired camera.
| **ONVIF host** | Host of the camera being connected to. HTTP is assumed by default; prefix with `https://` for HTTPS. |
| **ONVIF port** | ONVIF port for device (default: 8000) |
| **ONVIF username** | Username for login. Some devices require admin to access ONVIF. |
| **ONVIF password** | Password for login |
| **Disable TLS verify** | Skip TLS verification and disable digest auth for ONVIF (default: false) |
| **ONVIF profile** | ONVIF media profile to use for PTZ control, matched by token or name. If not set, the first profile with valid PTZ configuration is selected automatically. |
| **Calibrate on start** | Calibrate the camera on startup by measuring PTZ motor speed (default: false) |
| **Zoom mode** | Zoom mode during autotracking: `disabled`, `absolute`, or `relative` (default: disabled) |
| **Zoom Factor** | Controls zoom behavior on tracked objects, between 0.1 and 0.75. Lower keeps more scene visible; higher zooms in more (default: 0.3) |
| **Tracked objects** | List of object types to track (default: person) |
| **Required Zones** | Zones an object must enter to begin autotracking |
| **Return Preset** | Name of ONVIF preset in camera firmware to return to when tracking ends (default: home) |
| **Return timeout** | Seconds to delay before returning to preset (default: 10) |
</TabItem>
<TabItem value="yaml">
```yaml
cameras:
ptzcamera:
@@ -52,6 +89,10 @@ cameras:
password:admin
# Optional: Skip TLS verification from the ONVIF server (default: shown below)
tls_insecure:False
# Optional: ONVIF media profile to use for PTZ control, matched by token or name. (default: shown below)
# If not set, the first profile with valid PTZ configuration is selected automatically.
# Use this when your camera has multiple ONVIF profiles and you need to select a specific one.
profile:None
# Optional: PTZ camera object autotracking. Keeps a moving object in
# the center of the frame by automatically moving the PTZ camera.
autotracking:
@@ -88,13 +129,16 @@ cameras:
movement_weights:[]
```
</TabItem>
</ConfigTabs>
## Calibration
PTZ motors operate at different speeds. Performing a calibration will direct Frigate to measure this speed over a variety of movements and use those measurements to better predict the amount of movement necessary to keep autotracked objects in the center of the frame.
Calibration is optional, but will greatly assist Frigate in autotracking objects that move across the camera's field of view more quickly.
To begin calibration, set the `calibrate_on_startup` for your camera to `True` and restart Frigate. Frigate will then make a series of small and large movements with your camera. Don't move the PTZ manually while calibration is in progress. Once complete, camera motion will stop and your config file will be automatically updated with a `movement_weights` parameter to be used in movement calculations. You should not modify this parameter manually.
To begin calibration, set `calibrate_on_startup` for your camera to `True` and restart Frigate. Frigate will then make a series of small and large movements with your camera. Don't move the PTZ manually while calibration is in progress. Once complete, camera motion will stop and your config file will be automatically updated with a `movement_weights` parameter to be used in movement calculations. You should not modify this parameter manually.
After calibration has ended, your PTZ will be moved to the preset specified by `return_preset`.
@@ -118,13 +162,13 @@ Every PTZ camera is different, so autotracking may not perform ideally in every
The object tracker in Frigate estimates the motion of the PTZ so that tracked objects are preserved when the camera moves. In most cases 5 fps is sufficient, but if you plan to track faster moving objects, you may want to increase this slightly. Higher frame rates (> 10fps) will only slow down Frigate and the motion estimator and may lead to dropped frames, especially if you are using experimental zooming.
A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's debug viewer for your camera to see a thicker colored box around the object currently being autotracked.
A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's [debug viewer](/usage/live#the-single-camera-view) for your camera to see a thicker colored box around the object currently being autotracked.
A full-frame zone in `required_zones` is not recommended, especially if you've calibrated your camera and there are `movement_weights` defined in the configuration file. Frigate will continue to autotrack an object that has entered one of the `required_zones`, even if it moves outside of that zone.
Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](index.md).
Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](advanced/reference.md).
## Zooming
@@ -144,30 +188,96 @@ In security and surveillance, it's common to use "spotter" cameras in combinatio
## Troubleshooting and FAQ
### The autotracker loses track of my object. Why?
### Camera Compatibility
<FaqItem id="which-ptz-camera-should-i-use-for-autotracking" question="Which PTZ camera should I use for autotracking?">
See the community-maintained list of [ONVIF PTZ camera recommendations](cameras.md#onvif-ptz-camera-recommendations) for cameras and brands reported to work (and not work) with autotracking. This is not an exhaustive list that is frequently updated, so other cameras not listed may also work well. Frigate's autotracking was developed with a Dahua SD1A404XB-GNR (now sold as the EmpireTech PTZ1A4M-4X-S2), and Dahua / EmpireTech PTZs are the most consistently reported as working well.
When comparing models:
- Verify ONVIF support first. See [Checking ONVIF camera support](#checking-onvif-camera-support) above.
- Favor a camera with a fast PTZ motor. Cameras with slow motors may fail [calibration](#calibration) and will struggle to keep up with objects that move across the field of view quickly.
</FaqItem>
<FaqItem id="does-autotracking-work-with-reolink-ptz-cameras" question="Does autotracking work with Reolink PTZ cameras?">
No. Reolink cameras (including the TrackMix series) lack the ONVIF FOV RelativeMove firmware support that Frigate's autotracker requires, so autotracking will not work with any current Reolink PTZ. Their video streams and basic PTZ controls still work in Frigate. If you want object tracking on a Reolink PTZ, you will need to use the tracking feature built into the camera's firmware, which is proprietary and operates independently of Frigate.
</FaqItem>
<FaqItem id="im-seeing-an-error-in-the-logs-that-my-camera-is-still-in-onvif-moving-status-what-does-this-mean" question={"I'm seeing an error in the logs that my camera \"is still in ONVIF 'MOVING' status.\" What does this mean?"}>
There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware, such as some Annke models. In rare cases the vendor may provide fixed firmware on request; for example, Annke has supplied firmware that resolves this for the CZ504 (see the [camera recommendations list](cameras.md#onvif-ptz-camera-recommendations)).
</FaqItem>
<FaqItem id="calibration-seems-to-have-completed-but-the-camera-is-not-actually-moving-to-track-my-object-why" question="Calibration seems to have completed, but the camera is not actually moving to track my object. Why?">
Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
</FaqItem>
### Calibration Issues
<FaqItem id="i-tried-calibrating-my-camera-but-the-logs-show-that-it-is-stuck-at-0-and-frigate-is-not-starting-up" question="I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.">
This is often caused by the same reason as the "MOVING" status error above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
</FaqItem>
<FaqItem id="frigate-reports-an-error-saying-that-calibration-has-failed-why" question="Frigate reports an error saying that calibration has failed. Why?">
Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner.
Some things to try:
- If your camera's firmware has a PTZ or motor speed setting, set it to the fastest available speed and calibrate again.
- Run without calibration: remove the `movement_weights` line from your config, set `calibrate_on_startup` to `False`, and restart.
If calibration consistently fails, this often means your camera's motor is too slow and autotracking will behave unpredictably or won't be able to keep up with moving objects.
</FaqItem>
<FaqItem id="autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction" question="Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?">
Frigate uses the `movement_weights` measured during calibration to predict how far the camera needs to move to keep an object centered, so inaccurate values produce movements that don't seem to make sense: overshooting, moving the opposite direction, or zooming in on an object's last known position and losing it entirely. This is almost always a calibration issue.
- Remove the `movement_weights` entry from your config and restart Frigate to run without calibration. If tracking improves, try recalibrating.
- Recalibrate several times. The `movement_weights` values should be close to each other after each run. If they vary significantly between runs, your camera may not be reporting its motor status reliably, and you may get better results without calibration.
- If you are using zooming, a high `zoom_factor` can cause the camera to zoom in too far and lose the object. Try a lower value.
Remember to recalibrate whenever you change your `return_preset`, change your camera's detect `fps`, or enable zooming after calibrating with it disabled.
</FaqItem>
### Tracking Behavior
<FaqItem id="the-autotracker-loses-track-of-my-object-why" question="The autotracker loses track of my object. Why?">
There are many reasons this could be the case. If you are using experimental zooming, your `zoom_factor` value might be too high, the object might be traveling too quickly, the scene might be too dark, there are not enough details in the scene (for example, a PTZ looking down on a driveway or other monotone background without a sufficient number of hard edges or corners), or the scene is otherwise less than optimal for Frigate to maintain tracking.
Your camera's shutter speed may also be set too low so that blurring occurs with motion. Check your camera's firmware to see if you can increase the shutter speed.
Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it.
Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it. If the camera consistently zooms in on the object and then loses it, see [Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?](#autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction) above.
### I'm seeing an error in the logs that my camera "is still in ONVIF 'MOVING' status." What does this mean?
</FaqItem>
There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware.
### I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.
This is often caused by the same reason as above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
### I'm seeing this error in the logs: "Autotracker: motion estimator couldn't get transformations". What does this mean?
<FaqItem id="im-seeing-this-error-in-the-logs-autotracker-motion-estimator-couldnt-get-transformations-what-does-this-mean" question={"I'm seeing this error in the logs: \"Autotracker: motion estimator couldn't get transformations\". What does this mean?"}>
To maintain object tracking during PTZ moves, Frigate tracks the motion of your camera based on the details of the frame. If you are seeing this message, it could mean that your `zoom_factor` may be set too high, the scene around your detected object does not have enough details (like hard edges or color variations), or your camera's shutter speed is too slow and motion blur is occurring. Try reducing `zoom_factor`, finding a way to alter the scene around your object, or changing your camera's shutter speed.
### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?
</FaqItem>
Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if thecamera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
<FaqItem id="why-does-object-detection-pause-briefly-when-the-camera-moves" question="Why does object detection pause briefly when the camera moves?">
### Frigate reports an error saying that calibration has failed. Why?
When the PTZ moves, the entire frame changes at once. Frigate's motion detection treats sudden scene-wide changes (like a lightning flash, an infrared mode switch, or a camera move) specially and pauses detection momentarily until the scene stabilizes. This is expected and normal, and detection resumes shortly after the camera stops moving. If detection does not resume once the camera is stationary, use the [debug view](/usage/live#the-single-camera-view) to see what is happening.
Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner. You can try running without calibration (just remove the `movement_weights` line from your config and restart), but if calibration fails, this often means that autotracking will behave unpredictably.
</FaqItem>
<FaqItem id="can-i-turn-autotracking-on-and-off-automatically" question="Can I turn autotracking on and off automatically?">
Yes. Autotracking can be toggled per camera at runtime over MQTT with the [`frigate/<camera_name>/ptz_autotracker/set`](../integrations/mqtt.md#frigatecamera_nameptz_autotrackerset) topic, and the [Home Assistant integration](../integrations/home-assistant.md) exposes a switch for it. This pairs well with the "spotter" camera automations described in [Usage applications](#usage-applications) above, for example only enabling autotracking at night or when nobody is home.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
:::info
Bird classification requires a one-time internet connection to download the classification model and label map from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
:::
## Minimum System Requirements
Bird classification runs a lightweight tflite model on the CPU, there are no significantly different system requirements than running Frigate itself.
@@ -15,7 +25,18 @@ The classification model used is the MobileNet INat Bird Classification, [availa
## Configuration
Bird classification is disabled by default, it must be enabled in your config file before it can be used. Bird classification is a global configuration setting.
Bird classification is disabled by default and must be enabled before it can be used. Bird classification is a global configuration setting.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > Object classification" />.
- Set **Bird classification config > Bird classification** to on
- Set **Bird classification config > Minimum score** to the desired confidence score (default: 0.9)
</TabItem>
<TabItem value="yaml">
```yaml
classification:
@@ -23,6 +44,9 @@ classification:
enabled:true
```
</TabItem>
</ConfigTabs>
## Advanced Configuration
Fine-tune bird classification with these optional parameters:
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
In addition to Frigate's Live camera dashboard, Birdseye allows a portable heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.
Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the "+" icon on the Live page, and choose "Birdseye" as one of the cameras.
Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the pencil icon in the sidebar on the Live page, and choose "Birdseye" as one of the cameras.
Birdseye can also be used in Home Assistant dashboards, cast to media devices, etc.
:::note
Each camera tile in Birdseye is composed from the frames of the stream assigned the `detect` role, so a camera's image quality in Birdseye matches its detect stream resolution rather than a higher-resolution recording stream. If a camera looks low quality in Birdseye, increasing the detect width and height (or assigning the `detect` role to a higher-resolution stream) is what affects it. See [setting up camera inputs](./cameras.md#setting-up-camera-inputs) for how roles are assigned.
:::
## Birdseye Behavior
### Birdseye Modes
### Birdseye Activity Types
Birdseye offers different modes to customize which cameras show under which circumstances.
Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be listed together.
- **continuous:** All cameras are always included
- **motion:** Cameras that have detected motion within the last 30 seconds are included
- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
- **continuous:** The camera is always included
- **motion:** The camera is included when motion was detected within the last 30 seconds
- **all_objects:** The camera is included when a tracked object is present, active or stationary
- **alerts:** The camera is included while an alert review item is in progress
- **detections:** The camera is included while a detection review item is in progress
`alerts` and `detections` follow the review item's own lifetime, so the camera is removed as soon as the review item ends. Which objects qualify for each is set in [review configuration](./review.md).
### Custom Birdseye Icon
@@ -22,28 +36,60 @@ A custom icon can be added to the birdseye background by providing a 180x180 ima
### Birdseye view override at camera level
If you want to include a camera in Birdseye view only for specific circumstances, or just don't include it at all, the Birdseye setting can be set at the camera level.
To include a camera in Birdseye view only for specific circumstances, or exclude it entirely, configure Birdseye at the camera level.
```yaml {8-10,12-14}
<ConfigTabs>
<TabItem value="ui">
**Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
| **Enable Birdseye** | Whether this camera appears in Birdseye view |
| **Activity types** | Conditions that determine when to show the camera |
</TabItem>
<TabItem value="yaml">
```yaml {10-12,15-16}
# Include all cameras by default in Birdseye view
birdseye:
enabled: True
mode: continuous
modes:
- continuous
cameras:
front:
# Only include the "front" camera in Birdseye view when objects are detected
# Only include the "front" camera in Birdseye view when an alert is in progress
birdseye:
mode: objects
modes:
- alerts
back:
# Exclude the "back" camera from Birdseye view
birdseye:
enabled: False
```
</TabItem>
</ConfigTabs>
### Birdseye Inactivity
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds, this can be configured:
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured, and applies to the `motion` and `all_objects` activity types only.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Birdseye" />.
| **Inactivity threshold** | Seconds of inactivity before a camera is hidden from Birdseye (default: 30) |
</TabItem>
<TabItem value="yaml">
```yaml
birdseye:
@@ -52,12 +98,28 @@ birdseye:
inactivity_threshold: 15
```
</TabItem>
</ConfigTabs>
## Birdseye Layout
### Birdseye Dimensions
The resolution and aspect ratio of birdseye can be configured. Resolution will increase the quality but does not affect the layout. Changing the aspect ratio of birdseye does affect how cameras are laid out.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Birdseye" />.
It is possible to override the order of cameras that are being shown in the Birdseye view.
The order needs to be set at the camera level.
It is possible to override the order of cameras that are being shown in the Birdseye view. The order is set at the camera level (when using YAML).
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Birdseye" /> and in the **Camera order** field, use the drag handle next to each camera name to control the display order.
</TabItem>
<TabItem value="yaml">
```yaml
# Include all cameras by default in Birdseye view
birdseye:
enabled: True
mode: continuous
modes:
- continuous
cameras:
front:
@@ -87,13 +160,26 @@ cameras:
order: 2
```
</TabItem>
</ConfigTabs>
_Note_: Cameras are sorted by default using their name to ensure a constant view inside Birdseye.
### Birdseye Cameras
It is possible to limit the number of cameras shown on birdseye at one time. When this is enabled, birdseye will show the cameras with most recent activity. There is a cooldown to ensure that cameras do not switch too frequently.
For example, this can be configured to only show the most recently active camera.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Birdseye" />.
| **Layout > Max cameras** | Maximum number of cameras shown at once (e.g., `1` for only the most active camera) |
</TabItem>
<TabItem value="yaml">
```yaml {3-4}
birdseye:
@@ -102,13 +188,31 @@ birdseye:
max_cameras: 1
```
</TabItem>
</ConfigTabs>
### Birdseye Scaling
By default birdseye tries to fit 2 cameras in each row and then double in size until a suitable layout is found. The scaling can be configured with a value between 1.0 and 5.0 depending on use case.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Birdseye" />.
import NavPath from "@site/src/components/NavPath";
:::note
This page makes use of presets of FFmpeg args. For more information on presets, see the [FFmpeg Presets](/configuration/ffmpeg_presets) page.
@@ -148,19 +150,34 @@ WEB Digest Algorithm - MD5
Reolink has many different camera models with inconsistently supported features and behavior. The below table shows a summary of various features and recommendations.
| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
| 5MP or lower | All | http-flv | Stream is h264 |
| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 |
| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
| 5MP or lower | All | http-flv | Stream is h264 |
| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 (Frigate's default) |
| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
Frigate works much better with newer reolink cameras that are setup with the below options:
Frigate works much better with newer Reolink cameras that are setup with the below options:
If available, recommended settings are:
- `On, fluency first` this sets the camera to CBR (constant bit rate)
- `Interframe Space 1x` this sets the iframe interval to the same as the frame rate
#### Setup via the Add Camera Wizard
The [Add Camera Wizard](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add a standard Reolink camera. Before starting, make sure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. The wizard uses the camera's HTTP API to determine its resolution and choose the recommended stream type from the table above.
1. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />.
2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.
3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower, or an RTSP stream for higher resolution cameras.
4. In the validation step, enable **Use stream compatibility mode** for http-flv streams when the wizard recommends it.
If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras where http-flv is recommended, the wizard will show a warning in the validation step.
The wizard covers standard single-camera setups. For two way talk, cameras connected through a Reolink NVR, or audio transcoding for WebRTC live view, configure the camera manually as shown below.
#### Manual configuration
According to [this discussion](https://github.com/blakeblackshear/frigate/issues/3235#issuecomment-1135876973), the http video streams seem to be the most reliable for Reolink.
Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
@@ -175,7 +192,7 @@ Reolink's latest cameras support two way audio via go2rtc and other applications
NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
Ensure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.
Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not possible to enable it in standalone mode.
:::
@@ -246,7 +264,7 @@ go2rtc:
- rtspx://192.168.1.1:7441/abcdefghijk
```
[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-rtsp)
[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-rtsp)
In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
@@ -269,7 +287,6 @@ Some community members have found better performance on Wyze cameras by using an
To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
- Preparation outside of Frigate:
- Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
- Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
- If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
## Adding a camera with the Add Camera Wizard
The Add Camera Wizard is the recommended way to add a camera. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />. The wizard connects to your camera, tests each stream, and writes the camera's configuration for you, including the [go2rtc](go2rtc.md) restream and the live view stream mapping, so a standard setup needs no hand-written YAML.
### Step 1: Name and connection
Enter a name for the camera along with its host or IP address and credentials, then choose how the wizard should find the camera's streams:
- **Probe camera** queries the camera over ONVIF (the ONVIF port is usually 80 or 8080) and asks it for its stream URLs. Some cameras use a separate ONVIF/service account rather than the device admin user, and some require **Use digest authentication** to be enabled.
- **Manual selection** builds a stream URL from a template for the camera brand you pick (Dahua/Amcrest/EmpireTech, Hikvision/Uniview/Annke, Ubiquiti, Reolink, Axis, TP-Link, or Foscam). Choose **Other** to enter a custom RTSP URL directly. Non-RTSP stream types must be [configured manually](#setting-up-camera-inputs).
The name you enter is lowercased and spaces become underscores. If the result still isn't a valid config key, the wizard generates a safe name and stores what you typed as `friendly_name`.
### Step 2: Probe or snapshot
In probe mode, the wizard reports what the camera returned (manufacturer, model, firmware, profile count, and whether PTZ, presets, and [autotracking](autotracking.md) are supported) along with the RTSP URLs it discovered. Test each candidate to see its resolution, frame rate, and codecs together with a snapshot, then select the one you want to use.
In manual mode, the wizard tests the templated URL and shows the same metadata and snapshot.
If no RTSP URLs are found, the credentials may be wrong or the camera may not support ONVIF. Go back and use manual selection instead.
### Step 3: Stream configuration
Assign [roles](#setting-up-camera-inputs) to the stream, and use **Add Another Stream** to add the camera's other streams, for example a substream for `detect` alongside the main stream for `record`. At least one stream must have the `detect` role before you can continue.
**Reduce connections to camera** routes that input through the go2rtc restream so Frigate and the live view share a single connection to the camera instead of each opening their own. See [restream](restream.md) for more detail.
### Step 4: Validation and testing
Connect each stream to get a live preview, an estimated bandwidth figure, and a list of validation results. The wizard checks for the most common misconfigurations, including:
- A detect resolution that is too high (increased resource usage) or too low for reliable detection, or one it could not probe at all
- A stream marked `record` whose audio codec is not AAC, or that has no audio at all
- A stream marked `audio` that carries no audio stream
- Using a restreamed input for the `record` role
- Brand-specific issues, such as an RTSP stream on a Reolink camera that should use http-flv, or a Dahua/Hikvision substream selected for `detect`
**Use stream compatibility mode** passes the stream through go2rtc's ffmpeg module. Enable it if a stream fails to load after several attempts. Note that this also prevents [two way talk](/configuration/live#two-way-talk) from being detected for that stream.
**Save New Camera** writes the configuration and starts the camera right away. No restart is required.
Other features, including [hardware acceleration](hardware_acceleration_video.md), [two way talk](/configuration/live#two-way-talk), and audio transcoding, is configured after the camera has been added. For camera model specific quirks, see the [camera specific](camera_specific.md) docs.
## Deleting a camera
Click **Delete Camera** in <NavPath path="Settings > Global configuration > Camera management" />, choose the camera, and confirm. Deleting a camera requires the `admin` role and cannot be undone.
:::warning
Deleting a camera permanently removes its recordings, tracked objects, and configuration. If you only want to stop processing a camera, set its state to **Off** or **Disabled** in <NavPath path="Settings > Global configuration > Camera management" /> instead. See [camera state](/configuration/live#camera-state).
:::
Deleting a camera removes:
- The camera's section of your config file, along with its entries in any [role](authentication.md#user-roles) camera list. A custom role left with no cameras is removed as well.
- Every database record for the camera: tracked objects, review items, recordings, previews, timeline entries, the saved region grid, and [triggers](semantic_search.md#triggers).
- Every media file for the camera: recordings, snapshots, thumbnails, and preview clips.
[Exports](/usage/exports) are kept by default, so saved footage survives the deletion of the camera it came from. Turn on **Also delete exports for this camera** in the confirmation step to remove those too.
The camera's processes are stopped and the change takes effect immediately, so no restart is required. If the resulting config cannot be parsed, Frigate restores the previous config and reports an error instead of leaving Frigate in a broken state.
Two things are not cleaned up for you:
- **go2rtc streams.** Frigate makes a best effort to stop a running [go2rtc](go2rtc.md) stream named after the camera, but stream entries in your config file remain and are recreated on the next restart. Remove them in <NavPath path="Settings > System > go2rtc streams" /> or in your config file.
- **Camera groups.** A deleted camera stays listed in any [camera group](#setting-up-camera-groups) that referenced it. The group skips the missing camera, so this is harmless, but you can edit the group to drop the stale entry.
## Setting Up Camera Inputs
Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
@@ -11,11 +83,33 @@ A camera is enabled by default but can be disabled by using `enabled: False`. Ca
Each role can only be assigned to one input per camera. The options for roles are as follows:
| **Camera inputs** | List of input stream definitions (paths and roles) for this camera. |
For each input you can choose its source: select **Restream (go2rtc)** to pick an existing [go2rtc stream](restream.md) from a dropdown (Frigate uses the `rtsp://127.0.0.1:8554/<stream>` path and `preset-rtsp-restream` input args for that input automatically), or **Manual input path** to type the stream URL directly.
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
| **Detect width** | Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution. |
| **Detect height** | Height (pixels) of frames used for the detect stream; leave empty to use the native stream resolution. |
</TabItem>
<TabItem value="yaml">
```yaml
mqtt:
@@ -36,7 +130,18 @@ cameras:
height:720# <- optional, by default Frigate tries to automatically detect resolution
```
Additional cameras are simply added to the config under the `cameras` entry.
</TabItem>
</ConfigTabs>
Additional cameras are simply added under the camera configuration section.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and use the [Add Camera Wizard](#adding-a-camera-with-the-add-camera-wizard) to configure each additional camera.
</TabItem>
<TabItem value="yaml">
```yaml
mqtt:...
@@ -46,6 +151,9 @@ cameras:
side:...
```
</TabItem>
</ConfigTabs>
:::note
If you only define one stream in your `inputs` and do not assign a `detect` role to it, Frigate will automatically assign it the `detect` role. Frigate will always decode a stream to support motion detection, Birdseye, the API image endpoints, and other features, even if you have disabled object detection with `enabled: False` in your config's `detect` section.
@@ -64,7 +172,19 @@ Not every PTZ supports ONVIF, which is the standard protocol Frigate uses to com
:::
Add the onvif section to your camera in your configuration file:
Configure the ONVIF connection for your camera to enable PTZ controls.
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Camera configuration > ONVIF" /> and select your camera.
- Set **ONVIF host** to your camera's IP address, e.g.: `10.0.10.10`
- Set **ONVIF port** to your camera's ONVIF port, e.g.: `8000`
- Set **ONVIF username** to your camera's ONVIF username, e.g.: `admin`
- Set **ONVIF password** to your camera's ONVIF password, e.g.: `password`
</TabItem>
<TabItem value="yaml">
```yaml {4-8}
cameras:
@@ -77,6 +197,9 @@ cameras:
password: password
```
</TabItem>
</ConfigTabs>
If the ONVIF connection is successful, PTZ controls will be available in the camera's WebUI.
:::note
@@ -91,6 +214,13 @@ If your ONVIF camera does not require authentication credentials, you may still
:::
If a camera connects but fails to authenticate, two optional fields can help:
- `tls_insecure`: Skips TLS certificate verification and sends the ONVIF password as plaintext (`PasswordText`) instead of a hashed digest (`PasswordDigest`). Some cameras reject the digest token and only accept plaintext. This weakens connection security, so only enable it on a trusted local network.
- `ignore_time_mismatch`: ONVIF authentication tokens include a timestamp, and a camera will reject the token if its clock differs too much from Frigate's. Enabling this makes Frigate compensate for the time offset so authentication can still succeed. Running NTP on both the camera and the Frigate host is the recommended fix; only use this in a "safe" environment, as it slightly weakens token validation.
If your camera has multiple ONVIF profiles, you can specify which one to use for PTZ control with the `profile` option, matched by token or name. When not set, Frigate selects the first profile with a valid PTZ configuration. Check the Frigate debug logs (`frigate.ptz.onvif: debug`) to see available profile names and tokens for your camera.
An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
## ONVIF PTZ camera recommendations
@@ -120,7 +250,7 @@ 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. |
@@ -128,13 +258,15 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
## Setting up camera groups
:::tip
Camera groups let you organize cameras together with a shared name and icon, making it easier to review and filter them. A default group for all cameras is always available.
It is recommended to set up camera groups using the UI.
<ConfigTabs>
<TabItem value="ui">
:::
On the Live dashboard, press the **pencil icon** in the main navigation to add a new camera group. Configure the group name, select which cameras to include, choose an icon, and set the display order.
Cameras can be grouped together and assigned a name and icon, this allows them to be reviewed and filtered together. There will always be the default group for all cameras.
</TabItem>
<TabItem value="yaml">
```yaml
camera_groups:
@@ -146,6 +278,9 @@ camera_groups:
order: 0
```
</TabItem>
</ConfigTabs>
## Two-Way Audio
See the guide [here](/configuration/live/#two-way-talk)
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach. It provides validation and a guided experience for all configuration options.
## Using the Settings UI
The Settings UI groups every configuration option into sections that are listed in the left-hand menu. Each section presents a guided form with validation, so you don't need to remember the structure of the YAML or look up option names by hand.
### Global vs. camera-level configuration
Settings are organized into two scopes:
- **Global configuration**: values under <NavPath path="Settings > Global configuration" /> apply to every camera by default. This is where you set the baseline behavior for object detection, recording, snapshots, motion, and so on.
- **Camera configuration**: values under <NavPath path="Settings > Camera configuration" /> apply to a single camera. Use the camera selector button at the top of these pages to choose which camera you are editing.
When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only: the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.<name>` takes precedence over the same value set at the top level. See [Global and Camera-Level Configuration](./config_overrides.md) for the full details, including how lists and maps are handled and which settings must be enabled globally first.
To undo an override and go back to inheriting from the parent scope, use the reset button at the bottom of the section:
- On a camera section, the button is labeled **Reset to Global** and restores the camera to the global value.
- On a global section, the button is labeled **Reset to Default** and restores Frigate's built-in default.
Resetting asks for confirmation and cannot be undone once applied.
### Saving changes and the Save All button
Edits are not applied until you save them. As soon as you change a value, the UI tracks it as a pending change:
- The edited section shows a **Modified** badge, and the changed fields are highlighted.
- A **You have unsaved changes** notice appears above the section's **Save** and **Undo** buttons. **Save** commits just that section; **Undo** discards its pending edits.
Because pending changes can span multiple sections (and multiple cameras), the header provides a **Save All** button that writes every pending change at once. Next to it, **Review pending changes** opens a summary that lists each pending edit with its scope (Global or a specific camera), the affected field, and the new value, so you can confirm exactly what will be written before committing. **Undo All** discards every pending change across all sections.
### Restart-required indicators
Most settings take effect immediately, but some require Frigate to restart before they apply. Fields that require a restart are marked with a small restart icon and a **Restart required** tooltip next to the field label.
When you save a change that touches one of these fields, Frigate confirms the save and reminds you that a restart is needed (for example, _"Settings saved successfully. Restart Frigate to apply your changes."_). The notification includes a one-click **Restart Frigate** action so you can apply the change right away, or you can continue editing and restart later.
### The colored dots in the camera configuration menu
When you are working under <NavPath path="Settings > Camera configuration" />, small colored dots can appear next to a section's name in the menu. They give you an at-a-glance summary of that section's state for the selected camera:
- **Blue dot**: this section **overrides the global configuration**. One or more values in the section have been set specifically for this camera and differ from the global defaults.
- **Profile-colored dot**: when you are viewing a [camera profile](./profiles.md), a dot in that profile's assigned color indicates the section is **overridden by that profile**. Each profile is given its own distinct color so you can tell at a glance which sections it changes.
- **Amber dot**: this section has **unsaved changes**. It appears alongside the **Modified** badge whenever you have pending edits in the section that haven't been saved yet.
Hover over any dot to see a tooltip describing what it means. Open a section to see exactly which fields are overridden: the section header indicates how many fields differ from the global (or base) configuration.
## Configuration File Location
For users who prefer to edit the YAML configuration file directly, it is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
- **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` (see [directory list](#accessing-app-config-dir))
- **All other installations:** Map to `/config/config.yml` inside the container
It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
A minimal starting configuration:
```yaml
mqtt:
enabled:False
cameras:
dummy_camera:# <--- this will be changed to your actual camera later
enabled:False
ffmpeg:
inputs:
- path:rtsp://127.0.0.1:554/rtsp
roles:
- detect
```
## Accessing the Home Assistant App configuration directory {#accessing-app-config-dir}
When running Frigate through the HA App, the Frigate `/config` directory is mapped to `/addon_configs/<addon_directory>` in the host, where `<addon_directory>` is specific to the variant of the Frigate App you are running.
**Whenever you see `/config` in the documentation, it refers to this directory.**
If for example you are running the standard App variant and use the [VS Code App](https://github.com/hassio-addons/addon-vscode) to browse your files, you can click _File_ > _Open folder..._ and navigate to `/addon_configs/ccab4aaf_frigate` to access the Frigate `/config` directory and edit the `config.yaml` file. You can also use the built-in config editor in the Frigate UI.
## VS Code Configuration Schema
VS Code supports JSON schemas for automatically validating configuration files. You can enable this feature by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the beginning of the configuration file. Replace `frigate_host` with the IP address or hostname of your Frigate server. If you're using both VS Code and Frigate as an App, you should use `ccab4aaf-frigate` instead. Make sure to expose the internal unauthenticated port `5000` when accessing the config from VS Code on another machine.
## Environment Variable Substitution
Frigate supports the use of environment variables starting with `FRIGATE_`**only** where specifically indicated in the [reference config](./advanced/reference.md). See [substitution sources and precedence](./advanced/system.md#substitution-sources-and-precedence) for where those values can come from, including `secrets.yaml`. For example, the following values can be replaced at runtime by using environment variables:
Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./advanced/reference.md) for detailed information about all config values.
### Raspberry Pi Home Assistant App with USB Coral
- Single camera with 720p, 5fps stream for detect
- MQTT connected to the Home Assistant Mosquitto App
- Hardware acceleration for decoding video
- USB Coral detector
- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
- Continue to keep all video if it qualified as an alert or detection for 30 days
- Save snapshots for 30 days
- Motion mask for the camera timestamp
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the MQTT connection to your Home Assistant Mosquitto broker
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
- MQTT disabled (not integrated with Home Assistant)
- VAAPI hardware acceleration for decoding video
- USB Coral detector
- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
- Continue to keep all video if it qualified as an alert or detection for 30 days
- Save snapshots for 30 days
- Motion mask for the camera timestamp
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > System > MQTT" /> and set **Enable MQTT** to off
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
### Home Assistant integrated Intel Mini PC with OpenVINO
- Single camera with 720p, 5fps stream for detect
- MQTT connected to same MQTT server as Home Assistant
- VAAPI hardware acceleration for decoding video
- OpenVINO detector
- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
- Continue to keep all video if it qualified as an alert or detection for 30 days
- Save snapshots for 30 days
- Motion mask for the camera timestamp
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the connection to your MQTT broker
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Intel GPU** from the **Hardware** dropdown
4. On the same model, open the **Custom Model** tab and configure the OpenVINO model path and settings
5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
6. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
7. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
8. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
</TabItem>
<TabItem value="yaml">
```yaml
mqtt:
host:192.168.X.X# <---- same mqtt broker that home assistant uses
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Most of Frigate's configuration can be set once for all cameras and then adjusted for individual cameras. The global value acts as the default for every camera, and any camera can override it.
This page explains how that inheritance works. For a tour of the Settings UI itself, see [Frigate Configuration](./config.md).
## The basics
Set a value globally and every camera uses it. Set the same value on a camera and that camera uses its own value instead.
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Global configuration > Object detection" /> and set **Detect FPS** to `5`. Every camera now detects at 5 fps.
2. Navigate to <NavPath path="Settings > Camera configuration > Object detection" />, select the `driveway` camera, and set **Detect FPS** to `10`.
The `driveway` camera now detects at 10 fps. Every other camera still uses the global value of 5.
</TabItem>
<TabItem value="yaml">
```yaml
detect:
fps:5# every camera detects at 5 fps
cameras:
front_door:
ffmpeg:...
driveway:
ffmpeg:...
detect:
fps:10# except this one
```
`front_door` inherits `fps: 5`, and `driveway` uses `10`.
</TabItem>
</ConfigTabs>
## Overrides apply per value, not per section
Overriding one value in a section does not detach the rest of that section. Everything you don't set on the camera still comes from the global configuration.
<ConfigTabs>
<TabItem value="ui">
If you set a camera's **Motion threshold** but leave **Contour area** alone, only the threshold is overridden. The contour area continues to follow <NavPath path="Settings > Global configuration > Motion detection" />, and changing it there still affects that camera.
Open a section to see which values are overridden: the section header indicates how many fields differ from the global configuration.
</TabItem>
<TabItem value="yaml">
```yaml
motion:
threshold:30
contour_area:10
cameras:
driveway:
motion:
threshold:40
```
The `driveway` camera ends up with `threshold: 40` and `contour_area: 10`. Only the value you wrote was overridden.
</TabItem>
</ConfigTabs>
## Returning a camera to the global value
<ConfigTabs>
<TabItem value="ui">
A camera section that has its own values shows an **Overridden** badge. To remove the override and go back to inheriting, use the **Reset to Global** button at the bottom of the section.
</TabItem>
<TabItem value="yaml">
Frigate treats a camera value as an override because it is written in the config file, not because it differs from the global value. Repeating the global value under a camera still creates an override:
```yaml
snapshots:
enabled:true
cameras:
driveway:
snapshots:
enabled:true# this is an override, even though it matches
```
If you later change the global `snapshots.enabled` to `false`, `driveway` keeps saving snapshots, because it has its own value. To make a camera follow the global value again, delete the key from the camera rather than setting it to match.
</TabItem>
</ConfigTabs>
## Lists replace, maps merge
This is the distinction that surprises people most.
**Lists are replaced entirely.** A camera's list does not add to the global list, it takes its place.
<ConfigTabs>
<TabItem value="ui">
The camera page shows the objects the camera is currently tracking, starting from the global list. Changing that selection under <NavPath path="Settings > Camera configuration > Objects" /> replaces the list for that camera, so make sure every object you want tracked is selected, not just the ones you are adding.
</TabItem>
<TabItem value="yaml">
```yaml
objects:
track:
- person
- car
cameras:
backyard:
objects:
track:
- dog# backyard tracks ONLY dog, not person or car
```
To track `dog` in addition to the global objects, list all of them on the camera.
</TabItem>
</ConfigTabs>
An empty list is a valid override, and is the normal way to opt a camera out of something:
```yaml
review:
alerts:
labels:
- person
cameras:
street:
review:
alerts:
labels:[]# this camera never creates alerts
```
**Maps are merged key by key.** A camera can add an entry without redeclaring the others.
<ConfigTabs>
<TabItem value="ui">
Adding a filter for one object under <NavPath path="Settings > Camera configuration > Objects" /> does not remove the filters inherited from <NavPath path="Settings > Global configuration > Objects" />. The camera keeps both.
</TabItem>
<TabItem value="yaml">
```yaml
objects:
filters:
person:
min_area:5000
cameras:
driveway:
objects:
filters:
car:
min_area:10000
```
The `driveway` camera ends up with both the `car` filter it defined and the `person` filter from the global configuration.
</TabItem>
</ConfigTabs>
## Which settings can be overridden
Most, but not all. The [full reference config](./advanced/reference.md) is the authoritative source: sections that support camera-level overrides are marked with the comment `# NOTE: Can be overridden at the camera level`. In the UI, a setting can be overridden if it appears under both <NavPath path="Settings > Global configuration" /> and <NavPath path="Settings > Camera configuration" />.
A few things worth knowing beyond that:
- Some sections are **global only** and have no camera-level equivalent, including `go2rtc`, `genai` providers, `classification`, `telemetry`, `camera_groups`, and `ui`.
- Some sections exist **only at the camera level**, such as `zones` and `onvif`.
- Some sections are **partially overridable**, meaning a camera accepts only a few of the keys available globally. `face_recognition`, `lpr`, and `audio_transcription` work this way, and the reference config notes which keys apply.
## Enrichments that must be enabled globally first
License plate recognition and face recognition are special: the global setting is not just a default, it is a switch that must be on before any camera can use the feature. Enabling one on a camera while it is disabled globally is a configuration error, and Frigate will refuse to start:
```
Camera driveway has lpr enabled but lpr is disabled at the global level of the config. You must enable lpr at the global level.
```
Enable the feature globally, then turn it off on the cameras that don't need it.
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Global configuration > License plate recognition" /> and enable **LPR**.
2. Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" />, select each camera that should not run LPR, and disable the **Enable LPR** toggle.
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
enabled:true
cameras:
driveway:
ffmpeg:...# inherits lpr, enabled
backyard:
ffmpeg:...
lpr:
enabled:false# opted out
```
</TabItem>
</ConfigTabs>
:::note
This applies only to `lpr` and `face_recognition`, because the global setting controls whether the supporting background process starts at all. Other features do not work this way. Audio transcription, for example, can be enabled on a single camera without being enabled globally.
:::
## Profiles
[Profiles](./profiles.md) add a further layer on top of everything described above. A profile is a named set of camera overrides that you can switch on and off while Frigate is running, for example to change detection and recording behavior when you leave the house.
Profiles are applied on top of a camera's already-resolved configuration, so a profile value wins over both the camera and the global value while that profile is active. Profiles cover a subset of the camera sections and do not modify your config file.
## Summary
- A camera inherits every value you don't set on it.
- Overriding one value does not detach the rest of the section.
- Writing a value on a camera overrides it, even if it matches the global value. Remove it to inherit again.
- Lists replace the global list. Maps merge into it.
- An empty list is an override, not an omission.
-`lpr` and `face_recognition` must be enabled globally before a camera can use them.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object. Classification results are visible in the Tracked Object Details pane in Explore, through the `frigate/tracked_object_details` MQTT topic, in Home Assistant sensors via the official Frigate integration, or through the event endpoints in the HTTP API.
:::info
Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
:::
## Minimum System Requirements
Object classification models are lightweight and run very fast on CPU.
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
Training the model does briefly use a high amount of system resources for about 1-3 minutes per training run. On lower-power devices, training may take longer.
A CPU with AVX + AVX2 instructions is required for training and inference.
@@ -27,7 +37,7 @@ For object classification:
### Classification Type
- **Sub label**:
- Applied to the object’s `sub_label` field.
- Applied to the object's `sub_label` field.
- Ideal for a single, more specific identity or type.
- Example: `cat` → `Leo`, `Charlie`, `None`.
@@ -55,7 +65,7 @@ This two-step verification prevents false positives by requiring consistent pred
### Sub label
- **Known pet vs unknown**: For `dog` objects, set sub label to your pet’s name (e.g., `buddy`) or `none` for others.
- **Known pet vs unknown**: For `dog` objects, set sub label to your pet's name (e.g., `buddy`) or `none` for others.
- **Mail truck vs normal car**: For `car`, classify as `mail_truck` vs `car` to filter important arrivals.
- **Delivery vs non-delivery person**: For `person`, classify `delivery` vs `visitor` based on uniform/props.
@@ -68,7 +78,27 @@ This two-step verification prevents false positives by requiring consistent pred
## Configuration
Object classification is configured as a custom classification model. Each model has its own name and settings. You must list which object labels should be classified.
Object classification is configured as a custom classification model. Each model has its own name and settings. Specify which object labels should be classified.
<ConfigTabs>
<TabItem value="ui">
Navigate to the **Classification** page from the main navigation sidebar, then click **Add Classification**.
| **Name** | A name for your classification model (e.g., `dog`) |
| **Type** | Select **Object** for object classification |
| **Object Label** | The object label to classify (e.g., `dog`, `person`, `car`) |
| **Classification Type** | Whether to assign results as a **Sub Label** or **Attribute** |
| **Classes** | The class names the model will learn to distinguish between |
The `threshold` (default: `0.8`) can be adjusted in the YAML configuration.
</TabItem>
<TabItem value="yaml">
```yaml
classification:
@@ -82,6 +112,9 @@ classification:
An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For object classification models, the default is 200.
</TabItem>
</ConfigTabs>
## Training the model
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
@@ -102,18 +135,47 @@ If examples for some of your classes do not appear in the grid, you can continue
### Improving the Model
:::tip Diversity matters far more than volume
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**
For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
:::
- **Start small and iterate**: Begin with a small, representative set of images per class. Models often begin working well with surprisingly few examples and improve naturally over time.
- **Favor hard examples**: When images appear in the Recent Classifications tab, prioritize images scoring below 90-100% or those captured under new lighting, weather, or distance conditions.
- **Avoid bulk training similar images**: Training large batches of images that already score 100% (or close) adds little new information and increases the risk of overfitting.
- **The wizard is just the starting point**: You don't need to find and label every class upfront. Missing classes will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
- **Data collection**: Use the model’s Recent Classification tab to gather balanced examples across times of day, weather, and distances.
- **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
- **Preprocessing**: Ensure examples reflect object crops similar to Frigate's boxes; keep the subject centered.
- **Crop size**: Aim for crops of at least 100×100 pixels (a 10,000 pixel area). Crops smaller than ~80×80 get stretched 3-7× by the model's 224×224 input resize and tend to collapse into a generic "blob" region of feature space where identity becomes unreliable. If most of your detections are small because the camera is far from the subject, consider repositioning the camera for closer crops.
- **Class balance**: Aim to keep your largest class within ~3× the count of your smallest. Beyond that, the model becomes biased toward the dominant class and tends to default borderline predictions to it (the "everything looks like Buddy" failure mode).
- **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
:::tip `none` works differently from named classes
Named classes work best with visually uniform examples. Every Buddy photo should look like Buddy. The `none` class needs the opposite: visual diversity across sizes, framings, and qualities, because at inference it has to absorb everything that isn't one of your named classes. Don't apply the same "only keep large, well-framed images" rule to `none` that you would to a named class. Mix in small crops, partial views, and false positives deliberately - otherwise the model has no signal for "small/ambiguous thing = not one of my known classes" and will force those crops into a named class by default.
:::
## Debugging Classification Models
To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.
Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Logging" />.
- Set **Logging level** to `debug`
- Set **Per-process log level > `frigate.data_processing.real_time.custom_classification`** to `debug` for verbose classification logging
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region. Classification results are available through the `frigate/<camera_name>/classification/<model_name>` MQTT topic and in Home Assistant sensors via the official Frigate integration.
:::info
Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
:::
## Minimum System Requirements
State classification models are lightweight and run very fast on CPU.
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
Training the model does briefly use a high amount of system resources for about 1-3 minutes per training run. On lower-power devices, training may take longer.
A CPU with AVX + AVX2 instructions is required for training and inference.
@@ -33,7 +43,25 @@ For state classification:
## Configuration
State classification is configured as a custom classification model. Each model has its own name and settings. You must provide at least one camera crop under `state_config.cameras`.
State classification is configured as a custom classification model. Each model has its own name and settings. Provide at least one camera crop under `state_config.cameras`.
<ConfigTabs>
<TabItem value="ui">
Navigate to the **Classification** page from the main navigation sidebar, select the **States** tab, then click **Add Classification**.
| **Name** | A name for your state classification model (e.g., `front_door`) |
| **Type** | Select **State** for state classification |
| **Classes** | The state names the model will learn to distinguish between (e.g., `open`, `closed`) |
After creating the model, the wizard will guide you through selecting the camera crop area and assigning training examples. The `threshold` (default: `0.8`), `motion`, and `interval` settings can be adjusted in the YAML configuration.
</TabItem>
<TabItem value="yaml">
```yaml
classification:
@@ -45,11 +73,18 @@ classification:
interval:10# also run every N seconds (optional)
cameras:
front:
crop:[0,180,220,400]
# [x1, y1, x2, y2] as decimals between 0 and 1, relative to the
# camera's detect resolution
crop:[0.0,0.25,0.3,0.85]
```
Crop coordinates are normalized: each value is a fraction of the camera's `detect` width or height, not a pixel value. Drawing the crop in the UI wizard writes these values for you.
An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
</TabItem>
</ConfigTabs>
## Training the model
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
@@ -70,10 +105,21 @@ Once some images are assigned, training will begin automatically.
### Improving the Model
:::tip Diversity matters far more than volume
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
:::
- **Start small and iterate**: Begin with a small, representative set of images per class. Models often begin working well with surprisingly few examples and improve naturally over time.
- **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
- **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
- **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
- **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
- **Favor hard examples**: When images appear in the Recent Classifications tab, prioritize images scoring below 90-100% or those captured under new conditions (e.g., first snow of the year, seasonal changes, objects temporarily in view, insects at night). These represent scenarios different from the default state and help prevent overfitting.
- **Avoid bulk training similar images**: Training large batches of images that already score 100% (or close) adds little new information and increases the risk of overfitting.
- **The wizard is just the starting point**: You don't need to find and label every state upfront. Missing states will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
## Debugging Classification Models
@@ -81,6 +127,17 @@ To troubleshoot issues with state classification models, enable debug logging to
Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > Logging" />.
- Set **Logging level** to `debug`
- Set **Per-process log level > `frigate.data_processing.real_time.custom_classification`** to `debug` for verbose classification logging
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
:::info
Face recognition requires a one-time internet connection to download detection and embedding models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
:::
## Model Requirements
### Face Detection
@@ -40,56 +51,115 @@ The `large` model is optimized for accuracy, an integrated or discrete GPU / NPU
## Configuration
Face recognition is disabled by default, face recognition must be enabled in the UI or in your config file before it can be used. Face recognition is a global configuration setting.
Face recognition is disabled by default and must be enabled before it can be used. Face recognition is a global configuration setting.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
- Set **Enable face recognition** to on
</TabItem>
<TabItem value="yaml">
```yaml
face_recognition:
enabled:true
```
</TabItem>
</ConfigTabs>
Like the other real-time processors in Frigate, face recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
## Advanced Configuration
Fine-tune face recognition with these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
Fine-tune face recognition with these optional parameters. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
### Detection
-`detection_threshold`: Face detection confidence score required before recognition runs:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
- **Detection threshold**: Face detection confidence score required before recognition runs. This field only applies to the standalone face detection model; `min_score` should be used to filter for models that have face detection built in.
- Default: `0.7`
- Note: This is field only applies to the standalone face detection model, `min_score` should be used to filter for models that have face detection built in.
-`min_area`: Defines the minimum size (in pixels) a face must be before recognition runs.
- Default: `500` pixels.
- Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
- **Minimum face area**: Minimum size (in pixels) a face must be before recognition runs. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
- Default: `750` pixels
</TabItem>
<TabItem value="yaml">
```yaml
face_recognition:
enabled:true
detection_threshold:0.7
min_area:750
```
</TabItem>
</ConfigTabs>
### Recognition
-`model_size`: Which model size to use, options are `small` or `large`
-`unknown_score`: Min score to mark a person as a potential match, matches at or below this will be marked as unknown.
- Default: `0.8`.
-`recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
- Default: `0.9`.
-`min_faces`: Min face recognitions for the sub label to be applied to the person object.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
-**Model size**: Which model size to use, options are `small` or `large`.
- **Unknown score threshold**: Min score to mark a person as a potential match; matches at or below this will be marked as unknown.
- Default: `0.8`
- **Recognition threshold**: Recognition confidence score required to add the face to the object as a sub label.
- Default: `0.9`
- **Minimum faces**: Min face recognitions for the sub label to be applied to the person object.
- Default: `1`
-`save_attempts`: Number of images of recognized faces to save for training.
- Default: `200`.
-`blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
- Default: `True`.
-`device`: Target a specific device to run the face recognition model on (multi-GPU installation).
- Default: `None`.
- Note: This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)
-**Saveattempts**: Number of images of recognized faces to save for training.
- Default: `200`
-**Blurconfidencefilter**: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
- Default: `True`
-**Device**: Target a specific device to run the face recognition model on (multi-GPU installation). This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/).
- Default: `None`
</TabItem>
<TabItem value="yaml">
```yaml
face_recognition:
enabled:true
model_size:small
unknown_score:0.8
recognition_threshold:0.9
min_faces:1
save_attempts:200
blur_confidence_filter:true
device:None
```
</TabItem>
</ConfigTabs>
## Usage
Follow these steps to begin:
1.**Enable face recognition** in your configuration file and restart Frigate.
1.**Enable face recognition** in your configuration and restart Frigate.
2.**Upload one face** using the **Add Face** button's wizard in the Face Library section of the Frigate UI. Read below for the best practices on expanding your training set.
3. When Frigate detects and attempts to recognize a face, it will appear in the **Train** tab of the Face Library, along with its associated recognition confidence.
4. From the **Train** tab, you can **assign the face** to a new or existing person to improve recognition accuracy for the future.
## Creating a Robust Training Set
:::tip
**The short version:** Start with a few clear, front-facing photos of each person. As faces are detected in the Recent Recognitions tab, train clear images that scored lower, adding variety (different angles, lighting, and expressions) slowly. Diversity matters far more than volume, and low-quality images hurt recognition more than they help.
For a step-by-step narrative of these best practices (and the same principles applied to state and object classification), see the [Frigate Tips: Best Practices for Training](https://github.com/blakeblackshear/frigate/discussions/21374) discussion.
:::
The number of images needed for a sufficient training set for face recognition varies depending on several factors:
- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
@@ -110,7 +180,7 @@ When choosing images to include in the face training set it is recommended to al
- If it is difficult to make out details in a persons face it will not be helpful in training.
- Avoid images with extreme under/over-exposure.
- Avoid blurry / pixelated images.
- Avoid training on infrared (gray-scale). The models are trained on color images and will be able to extract features from gray-scale images.
- Avoid training on infrared (gray-scale). The models are trained on color images and will not be able to extract features from gray-scale images.
- Using images of people wearing hats / sunglasses may confuse the model.
- Do not upload too many similar images at the same time, it is recommended to train no more than 4-6 similar images for each person to avoid over-fitting.
@@ -120,9 +190,27 @@ When choosing images to include in the face training set it is recommended to al
The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
Each face image is labeled with a name (or `Unknown`) along with the confidence score of that recognition attempt. Images are grouped by the person they were matched against, not by who they actually are, so a group labeled with a person's name can contain a crop that is really someone else but happened to score as a partial match. The name and score shown on each individual crop describe that single attempt.
Refer to the guidelines below for best practices on selecting images for training.
While each image can be used to train the system for a specific person, not all images are suitable for training. Refer to the guidelines below for best practices on selecting images for training.
### How Frigate Decides Who a Person Is
Recognition does not happen one frame at a time. While a `person` is in view, Frigate runs face recognition on many frames, not just a single frame. The final `sub_label` is decided from all of those attempts together, weighted by the area of each face (larger, closer faces count more), not from any single frame.
This has a few practical consequences:
- A handful of wrong guesses on blurry or distant frames usually do not change the result. If Frigate sees a person as "Tom, Tom, Sam, Tom, Tom," it will still conclude the person was Tom.
- The goal is not for every individual face crop to be correct. The goal is for each person to be recognized correctly overall, across all the faces captured while they were present.
- A single very high confidence match will not by itself assign a sub label. Recognition must be consistent. See [I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?](#i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned) below.
### Which Faces Are Worth Training?
Whether a face is worth training has little to do with what it was recognized as. A crop is a good training candidate when all of these are true:
- It did not already score high and correctly. Faces that are already recognized confidently add little and increase the risk of over-fitting.
- It is clear enough to be useful: not blurry, not heavily off-axis, not infrared (gray-scale). If it is hard for you to make out the face, it will not help the model.
- It adds something new: a different angle, lighting, expression, or distance than what you already have.
### Step 1 - Building a Strong Foundation
@@ -138,39 +226,81 @@ Once front-facing images are performing well, start choosing slightly off-angle
## FAQ
### How do I debug Face Recognition issues?
### Getting Recognition Working
<FaqItem id="how-do-i-debug-face-recognition-issues" question="How do I debug Face Recognition issues?">
Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
1. Enable debug logs to see exactly what Frigate is doing.
- Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
```yaml
logger:
default: info
logs:
# highlight-next-line
frigate.data_processing.real_time.face: debug
```
- These logs report where the pipeline stopped for each `person` object, such as no face being found within the person's bounding box, the detected face being smaller than `min_area`, or a face being recognized but scoring too low.
- If you see no face-related messages at all, also add `frigate.embeddings.maintainer: debug` to confirm that the face processor was created at startup and that `person` updates are reaching it.
2. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
If you are using a Frigate+ or `face` detecting model:
- Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
- Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
- You may need to adjust the `min_score` for the `face` object if faces are not being detected.
If you are **not** using a Frigate+ or `face` detecting model:
- Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
- You may need to lower your `detection_threshold` if faces are not being detected.
2. Any detected faces will then be _recognized_.
3. Any detected faces will then be _recognized_.
- Make sure you have trained at least one face per the recommendations above.
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
### Detection does not work well with blurry images?
</FaqItem>
Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
<FaqItem id="does-face-recognition-run-on-the-recording-stream" question="Does face recognition run on the recording stream?">
Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
</FaqItem>
### Improving Accuracy and Training
<FaqItem id="detection-does-not-work-well-with-blurry-images" question="Detection does not work well with blurry images?">
Accuracy is definitely going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
### Why can't I bulk upload photos?
</FaqItem>
<FaqItem id="can-i-train-faces-for-people-who-only-appear-at-night" question="Can I train faces for people who only appear at night?">
The embedding models are trained on color images, so gray-scale and infrared (IR) faces sit in a different feature distribution and are more easily confused with other people. Prefer color images, and avoid mixing gray-scale samples in early while you are building a foundation. If someone only ever appears at night, gray-scale training is acceptable, but keep those samples limited and as clear as possible, and add them only once color recognition is stable for your other people.
</FaqItem>
<FaqItem id="why-cant-i-bulk-upload-photos" question="Why can't I bulk upload photos?">
It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.
### Why can't I bulk reprocess faces?
</FaqItem>
<FaqItem id="why-cant-i-bulk-reprocess-faces" question="Why can't I bulk reprocess faces?">
Face embedding models work by breaking apart faces into different features. This means that when reprocessing an image, only images from a similar angle will have its score affected.
### Why do unknown people score similarly to known people?
</FaqItem>
<FaqItem id="why-do-unknown-people-score-similarly-to-known-people" question="Why do unknown people score similarly to known people?">
This can happen for a few different reasons, but this is usually an indicator that the training set needs to be improved. This is often related to over-fitting:
@@ -180,33 +310,54 @@ This can happen for a few different reasons, but this is usually an indicator th
Review your face collections and remove most of the unclear or low-quality images. Then, use the **Reprocess** button on each face in the **Train** tab to evaluate how the changes affect recognition scores.
Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower - ideally with different lighting, angles, and conditions—to help the model generalize more effectively.
Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower (ideally with different lighting, angles, and conditions) to help the model generalize more effectively.
### Frigate misidentified a face. Can I tell it that a face is "not" a specific person?
</FaqItem>
<FaqItem id="should-i-correct-a-face-that-was-recognized-as-the-wrong-person" question="Should I correct a face that was recognized as the wrong person?">
Only if it is a good image. Reassigning a face does add it to that person's training set, but two things are true at once:
- Reassigning a single misclassified frame has a small effect. The image is weighted against every other sample for that person, so correcting 1 frame out of 20 will not move recognition much. Occasional wrong guesses on poor frames are normal and do not need to be fixed.
- Reassigning a poor image (blurry, off-angle, low-resolution, gray-scale) can hurt more than the misidentification did, because low-quality samples degrade recognition for that whole person.
So the decision is about image quality, not about the wrong label. If the crop is clear, well-lit, and reasonably front-facing, and it scored low or was wrong, assigning it to the correct person is useful. If you can barely make out the face yourself, ignore it; do not train it just to correct the label.
If a person is repeatedly misidentified, do not keep reassigning the same frame. Instead, remove low-quality or misleading images and add a few high-quality samples to the correct person. See [Why do unknown people score similarly to known people?](#why-do-unknown-people-score-similarly-to-known-people) above.
</FaqItem>
<FaqItem id="frigate-misidentified-a-face-can-i-tell-it-that-a-face-is-not-a-specific-person" question={'Frigate misidentified a face. Can I tell it that a face is "not" a specific person?'}>
No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
For more guidance, refer to the section above on improving recognition accuracy.
### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
This also applies to a stranger who is repeatedly matched to a known person (for example, a delivery driver recognized as you). Do not create a profile for them and do not reassign their faces to yourself, as this pollutes your training set and makes recognition worse. Leave the detection as unknown and improve the known person's training set instead. Face recognition learns who someone is, not who they are not.
The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
</FaqItem>
### Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?
<FaqItem id="i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned" question="I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?">
Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
</FaqItem>
### Compatibility and Maintenance
<FaqItem id="can-i-use-other-face-recognition-software-like-doubletake-at-the-same-time-as-the-built-in-face-recognition" question="Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?">
No, using another face recognition service will interfere with Frigate's built in face recognition. When using double-take the sub_label feature must be disabled if the built in face recognition is also desired.
### Does face recognition run on the recording stream?
</FaqItem>
Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
### I get an unknown error when taking a photo directly with my iPhone
<FaqItem id="i-get-an-unknown-error-when-taking-a-photo-directly-with-my-iphone" question="I get an unknown error when taking a photo directly with my iPhone">
By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
### How can I delete the face database and start over?
</FaqItem>
<FaqItem id="how-can-i-delete-the-face-database-and-start-over" question="How can I delete the face database and start over?">
Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.
Some presets of FFmpeg args are provided by default to make the configuration easier. All presets can be seen in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
### Hwaccel Presets
Frigate ships with a set of FFmpeg presets to keep your configuration short and readable. Each preset expands to a longer list of FFmpeg arguments at runtime. You can see exactly what every preset expands to in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.
In the config file you reference a preset by its name (for example, `preset-vaapi`). In the UI, the same preset is shown with a friendly label (for example, **VAAPI (Intel/AMD GPU)**). Both refer to the same thing: the tables below list the config name alongside the label you'll see in the UI.
See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more info on how to setup hwaccel for your GPU / iGPU.
| preset-rpi-64-h264 | 64 bit Rpi with h264 stream | |
| preset-rpi-64-h265 | 64 bit Rpi with h265 stream | |
| preset-vaapi | Intel & AMD VAAPI | Check hwaccel docs to ensure correct driver is chosen |
| preset-intel-qsv-h264 | Intel QSV with h264 stream | If issues occur recommend using vaapi preset instead |
| preset-intel-qsv-h265 | Intel QSV with h265 stream | If issues occur recommend using vaapi preset instead |
| preset-nvidia | Nvidia GPU | |
| preset-jetson-h264 | Nvidia Jetson with h264 stream | |
| preset-jetson-h265 | Nvidia Jetson with h265 stream | |
| preset-rkmpp | Rockchip MPP | Use image with \*-rk suffix and privileged mode |
Hardware acceleration arguments tell FFmpeg to decode your camera's video stream on a GPU or integrated graphics chip instead of the CPU, which dramatically lowers CPU usage. Using a preset is highly recommended. Beyond replacing a long list of arguments, each preset also tells Frigate what hardware is available so it can offload additional work to the GPU, for example, encoding the Birdseye restream or scaling a stream whose resolution differs from the camera's native size.
See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md) for details on setting up hardware acceleration for your GPU / iGPU, then select the preset that matches your hardware.
| preset-rkmpp | Rockchip RKMPP | Rockchip MPP | Use an image with the `-rk` suffix and run in privileged mode |
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to the appropriate preset for your hardware.
2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and set **Hardware acceleration arguments** for that camera.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args:preset-vaapi
cameras:
front_door:
ffmpeg:
hwaccel_args:preset-nvidia
```
</TabItem>
</ConfigTabs>
### Input Args Presets
Input args presets help make the config more readable and handle use cases for different types of streams to ensure maximum compatibility.
Input arguments are passed to FFmpeg before your camera source and control how Frigate connects to and reads the stream: the transport protocol, timeouts, reconnection behavior, and how the stream is probed. The right input args ensure a reliable connection and maximum compatibility for each type of stream.
See [the cameraspecific docs](/configuration/camera_specific.md) for more info on non-standard cameras and recommendations for using them in Frigate.
See [the camera-specific docs](/configuration/camera_specific.md) for more on non-standard cameras and recommendations for using them in Frigate.
| preset-http-reolink | Reolink HTTP-FLV Stream | Only for reolink http, not when restreaming as rtsp |
| preset-rtmp-generic | RTMP Stream | |
| preset-rtsp-generic | RTSP Stream | This is the default when nothing is specified |
| preset-rtsp-restream | RTSP Stream from restream | Use for rtsp restream as source for frigate |
| preset-rtsp-restream-low-latency | RTSP Stream from restream | Use for rtsp restream as source for frigate to lower latency, may cause issues with some cameras |
| preset-rtsp-udp | RTSP Stream via UDP | Use when camera is UDP only |
| preset-rtsp-blue-iris | Blue Iris RTSP Stream | Use when consuming a stream from Blue Iris |
| preset-rtsp-generic | RTSP (Generic) | RTSP stream | The default when no input args are specified |
| preset-rtsp-restream | RTSP - Restream from go2rtc | RTSP stream from a restream | Use when a go2rtc restream is the source for Frigate |
| preset-rtsp-restream-low-latency | RTSP - Restream from go2rtc (Low Latency) | RTSP stream from a restream | Lowers latency for a go2rtc restream source; may cause issues with some cameras |
| preset-rtsp-udp | RTSP - UDP | RTSP stream over UDP | Use when the camera only supports UDP |
| preset-rtsp-blue-iris | RTSP - Blue Iris | Blue Iris RTSP stream | Use when consuming a stream from Blue Iris |
:::warning
It is important to be mindful of input args when using restream because you can have a mix of protocols. `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, when using a reolink cam with the rtsp restream as a source for record the preset-http-reolink will cause a crash. In this case presets will need to be set at the stream level. See the example below.
Be mindful of input arguments when restreaming, because you can end up with a mix of protocols. The`http` and `rtmp` presets cannot be used with `rtsp` streams. For example, using a Reolink camera with an RTSP restream as the recording source while `preset-http-reolink` is applied will cause a crash. In cases like this, set the preset at the stream level instead. See the example below.
:::
@@ -68,13 +96,15 @@ cameras:
### Output Args Presets
Output args presets help make the config more readable and handle use cases for different types of streams to ensure consistent recordings.
Output arguments are passed to FFmpeg after your camera source and control how recordings are written: which codecs are used and whether audio and video are copied as-is or re-encoded. The right output args ensure consistent, playable recordings for each type of stream.
| preset-record-generic | Record WITHOUT audio | If your camera doesn’t have audio, or if you don’t want to record audio, use this option |
| preset-record-generic-audio-copy | Record WITH original audio | Use this to enable audio in recordings |
| preset-record-generic-audio-aac | Record WITH transcoded aac audio | This is the default when no option is specified. Use it to transcode audio to AAC. If the source is already in AAC format, use preset-record-generic-audio-copy instead to avoid unnecessary re-encoding |
| preset-record-mjpeg | Record an mjpeg stream | Recommend restreaming mjpeg stream instead |
| preset-record-jpeg | Record live jpeg | Recommend restreaming live jpeg instead |
| preset-record-ubiquiti | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio |
| preset-record-generic | Record (Generic, no audio) | Record without audio | Use this if your camera has no audio, or if you don't want to record audio |
| preset-record-generic-audio-copy | Record (Generic + Copy Audio) | Record with the original audio | Use this to keep the camera's audio in recordings without re-encoding |
| preset-record-generic-audio-aac | Record (Generic + Audio to AAC) | Record with audio transcoded to AAC | The default when no output args are specified. Transcodes audio to AAC. If the source is already AAC, use `preset-record-generic-audio-copy` to avoid re-encoding |
| preset-record-mjpeg | Record - MJPEG Cameras | Record an MJPEG stream | Restreaming the MJPEG stream is recommended instead |
| preset-record-jpeg | Record - JPEG Cameras | Record a live JPEG | Restreaming the live JPEG is recommended instead |
| preset-record-ubiquiti | Record - Ubiquiti Cameras | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format |
These presets apply to the `record` output args. If [sub stream recording](/configuration/record#sub-stream-recording) is enabled, the same args are used for the `record_sub` role unless `output_args.record_sub` is set, which accepts the same presets and manual args.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
## Configuration
A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 5 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI-Compatible section below.
To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
`genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
- Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
- Set **Provider** to the service you are using (e.g., `ollama`)
- Set **Base URL**, **API key**, and **Model** as required by that provider
- Set **Roles** to the roles this provider should handle.
</TabItem>
<TabItem value="yaml">
```yaml
genai:
my_provider:# any name you like
provider:ollama
base_url:http://localhost:11434
model:qwen3-vl:4b
roles:
- descriptions
- embeddings
- chat
```
</TabItem>
</ConfigTabs>
The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
## Local Providers
Local providers run on your own hardware and keep all data processing private. These require a GPU or dedicated hardware for best performance.
:::warning
Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
Running Generative AI models on CPU is not recommended, as high inference times make using Generative AI impractical.
:::
[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
### Recommended Local Models
Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
#### Vision models
Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests).
You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. |
| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
| `gemma4` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. |
Most vision-language models are available as **instruct** models, which are fine-tuned to follow instructions and respond concisely to prompts. However, some models (such as certain Qwen-VL or minigpt variants) offer both **instruct** and **thinking** versions.
#### Embedding models
- **Instruct models** are always recommended for use with Frigate. These models generate direct, relevant, actionable descriptions that best fit Frigate's object and event summary use case.
- **Thinking models** are fine-tuned for more free-form, open-ended, and speculative outputs, which are typically not concise and may not provide the practical summaries Frigate expects. For this reason, Frigate does **not** recommend or support using thinking models.
The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, Frigate will always use instruct-style prompts and specifically disables thinking-mode behaviors to ensure concise, useful responses.
**Recommendation:**
Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider’s documentation or model library for guidance on the correct model variant to use.
### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). Note that Frigate will not automatically download the model you specify in your config, Ollama will try to download the model but it may take longer than the timeout, it is recommended to pull the model beforehand by running `ollama pull your_model` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
:::info
@@ -45,49 +83,218 @@ Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger s
:::
:::note
You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 24 GB to run the 33B models.
:::
### Model Types: Instruct vs Thinking
Vision-language models come in **instruct** variants (fine-tuned to follow instructions and respond concisely), **thinking** variants (fine-tuned for free-form, speculative reasoning), and **hybrid** variants that support both modes per request. Most modern vision-language models are hybrid.
Frigate manages reasoning per task automatically:
- **Description tasks** (object descriptions, review descriptions, review summaries) are synthesis-only and benefit from concise, direct output, so Frigate disables thinking for these calls when the model exposes a per-request toggle.
- **Chat** lets you toggle thinking on or off from the composer when the configured model supports it.
You can use a pure instruct, hybrid, or thinking-capable model with Frigate. No extra configuration is required to disable thinking for descriptions.
### llama.cpp
[llama.cpp](https://github.com/ggml-org/llama.cpp) is a C++ implementation of LLaMA that provides a high-performance inference server.
It is highly recommended to host the llama.cpp server on a machine with a discrete graphics card, or on an Apple silicon Mac for best performance.
#### Supported Models
You must use a vision capable model with Frigate. The llama.cpp server supports various vision models in GGUF format.
#### Configuration
All llama.cpp native options can be passed through `provider_options`, including `temperature`, `top_k`, `top_p`, `min_p`, `repeat_penalty`, `repeat_last_n`, `seed`, `grammar`, and more. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for a complete list of available parameters.
- Set **Base URL** to your llama.cpp server address (e.g., `http://localhost:8080`)
- Set **Model** to the name of your model
- Optionally, under **Provider Options**, set `context_size` to override the context size Frigate detects from the server
</TabItem>
<TabItem value="yaml">
```yaml
genai:
my_provider:
provider:llamacpp
base_url:http://localhost:8080
model:your-model-name
provider_options:
context_size:16000# Optional, overrides the context size reported by the server.
```
</TabItem>
</ConfigTabs>
Frigate queries the llama.cpp server for the model's context size at startup and logs it along with the other detected capabilities. If `context_size` is set in `provider_options`, that value is always used instead, even when the server reports its own.
### Ollama
[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests).
:::tip
If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. qwen3-VL supports vision and tools simultaneously in Ollama.
:::
The following models are recommended:
Note that Frigate will not automatically download the model you specify in your config. Ollama will try to download the model but it may take longer than the timeout, so it is recommended to pull the model beforehand by running `ollama pull your_model` on your Ollama server/Docker container. The model specified in Frigate's config must match the downloaded model tag.
| `Intern3.5VL` | Relatively fast with good vision comprehension |
| `gemma3` | Strong frame-to-frame understanding, slower inference times |
| `qwen2.5-vl` | Fast but capable model with good vision comprehension |
#### Configuration
:::note
<ConfigTabs>
<TabItem value="ui">
You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
- Set **Base URL** to your Ollama server address (e.g., `http://localhost:11434`)
- Set **Model** to the model tag (e.g., `qwen3-vl:4b`)
- Under **Provider Options**, set `keep_alive` (e.g., `-1`) and `options.num_ctx` to match your desired context size
:::
#### Ollama Cloud models
Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
### Configuration
</TabItem>
<TabItem value="yaml">
```yaml
genai:
provider:ollama
base_url:http://localhost:11434
model:qwen3-vl:4b
my_provider:
provider:ollama
base_url:http://localhost:11434
model:qwen3-vl:4b
provider_options:# other Ollama client options can be defined
keep_alive:-1
options:
num_ctx:8192# make sure the context matches other services that are using ollama
```
## Google Gemini
</TabItem>
</ConfigTabs>
### OpenAI-Compatible
Frigate supports any provider that implements the OpenAI API standard. This includes self-hosted solutions like [vLLM](https://docs.vllm.ai/), [LocalAI](https://localai.io/), and other OpenAI-compatible servers.
:::tip
For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
```yaml
genai:
my_provider:
provider:openai
base_url:http://your-llama-server
model:your-model-name
provider_options:
context_size:8192# Specify the configured context size
```
This ensures Frigate uses the correct context window size when generating prompts.
- Set **Base URL** to your server address (e.g., `http://your-server:port`)
- Set **API key** if required by your server
- Set **Model** to the model name
</TabItem>
<TabItem value="yaml">
```yaml
genai:
my_provider:
provider:openai
base_url:http://your-server:port
api_key:your-api-key# May not be required for local servers
model:your-model-name
```
</TabItem>
</ConfigTabs>
To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
## Cloud Providers
Cloud providers run on remote infrastructure and require an API key for authentication. These services handle all model inference on their servers.
:::info
Cloud Generative AI providers require an active internet connection to send images and prompts for processing. Local providers like llama.cpp and Ollama (with local models) do not require internet. See [Network Requirements](/frigate/network_requirements#generative-ai) for details.
:::
### Ollama Cloud
Ollama also supports [cloud models](https://ollama.com/cloud), where model inference is performed in the cloud. You can connect directly to Ollama Cloud by setting `base_url` to `https://ollama.com` and providing an API key. Alternatively, you can run Ollama locally and use a cloud model name so your local instance forwards requests to the cloud. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
- Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`) or `https://ollama.com` for direct cloud inference
- Set **API key** if required by your endpoint (e.g., when using `https://ollama.com`)
- Set **Model** to the cloud model name
</TabItem>
<TabItem value="yaml">
```yaml
genai:
my_provider:
provider:ollama
base_url:http://localhost:11434
model:cloud-model-name
```
or when using Ollama Cloud directly
```yaml
genai:
my_provider:
provider:ollama
base_url:https://ollama.com
model:cloud-model-name
api_key:your-api-key
```
</TabItem>
</ConfigTabs>
### Google Gemini
Google Gemini has a [free tier](https://ai.google.dev/pricing) for the API, however the limits may not be sufficient for standard Frigate usage. Choose a plan appropriate for your installation.
### Supported Models
#### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
### Get API Key
#### Get API Key
To start using Gemini, you must first get an API key from [Google AI Studio](https://aistudio.google.com).
@@ -96,52 +303,83 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
- Set **API key** to your Gemini API key (or use an environment variable such as `{FRIGATE_GEMINI_API_KEY}`)
- Set **Model** to the desired model (e.g., `gemini-2.5-flash`)
</TabItem>
<TabItem value="yaml">
```yaml
genai:
provider:gemini
api_key:"{FRIGATE_GEMINI_API_KEY}"
model:gemini-2.5-flash
my_provider:
provider:gemini
api_key:"{FRIGATE_GEMINI_API_KEY}"
model:gemini-2.5-flash
```
</TabItem>
</ConfigTabs>
:::note
To use a different Gemini-compatible API endpoint, set the `provider_options` with the `base_url` key to your provider's API URL. For example:
```yaml {4,5}
```yaml {5,6}
genai:
provider: gemini
...
provider_options:
base_url: https://...
my_provider:
provider: gemini
...
provider_options:
base_url: https://...
```
Other HTTP options are available, see the [python-genai documentation](https://github.com/googleapis/python-genai).
:::
## OpenAI
### OpenAI
OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.
OpenAI does not have a free tier for their API.
### Supported Models
#### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
### Get API Key
#### Get API Key
To start using OpenAI, you must first [create an API key](https://platform.openai.com/api-keys) and [configure billing](https://platform.openai.com/settings/organization/billing/overview).
- Set **API key** to your OpenAI API key (or use an environment variable such as `{FRIGATE_OPENAI_API_KEY}`)
- Set **Model** to the desired model (e.g., `gpt-4o`)
</TabItem>
<TabItem value="yaml">
```yaml
genai:
provider: openai
api_key: "{FRIGATE_OPENAI_API_KEY}"
model: gpt-4o
my_provider:
provider: openai
api_key: "{FRIGATE_OPENAI_API_KEY}"
model: gpt-4o
```
</TabItem>
</ConfigTabs>
:::note
To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
@@ -152,37 +390,133 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
```yaml {5,6}
```yaml {6,7}
genai:
provider: openai
base_url: http://your-llama-server
model: your-model-name
provider_options:
context_size: 8192 # Specify the configured context size
my_provider:
provider: openai
base_url: http://your-llama-server
model: your-model-name
provider_options:
context_size: 8192 # Specify the configured context size
```
This ensures Frigate uses the correct context window size when generating prompts.
:::
## Azure OpenAI
### Azure OpenAI
Microsoft offers several vision models through Azure OpenAI. A subscription is required.
### Supported Models
#### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
### Create Resource and Get API Key
#### Create Resource and Get API Key
To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
- Set **Base URL** to your Azure resource URL including the `api-version` parameter (e.g., `https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview`)
- Set **Model** to your deployed model name (e.g., `gpt-5-mini`)
- Set **API key** to your Azure OpenAI API key (or use an environment variable such as `{FRIGATE_OPENAI_API_KEY}`)
<FaqItem id="how-do-i-debug-genai-issues" question="How do I debug GenAI issues?">
Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
1. Confirm a provider is available and holds the `descriptions` role.
- Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in <NavPath path="Settings > Enrichments > Generative AI > Roles" /> (`genai.<provider>.roles`).
- A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
- In <NavPath path="Settings > Enrichments > Generative AI" />, use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
2. Confirm the feature you expect is actually enabled.
- Object descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Objects > GenAI object config > Enable GenAI" /> (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
- Review descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Review > GenAI config > Enable GenAI descriptions" /> (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
3. If object descriptions are never requested, check the filters that skip generation.
- <NavPath path="Settings > Global configuration > Objects > GenAI object config > GenAI objects" /> (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
- Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
- **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for <id>, file not found` and no description is generated.
- **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
5. Save the exact images and prompts that were sent to your provider.
- Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
- Review descriptions write `genai-requests/<review_id>/` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
- Review summary reports write `genai-requests/<start_ts>-<end_ts>/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
- Object descriptions write `genai-requests/<event_id>/` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
- Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
<ConfigTabs>
<TabItem value="ui">
For review descriptions, navigate to <NavPath path="Settings > Global configuration > Review" /> and set **GenAI config > Save thumbnails** to on.
For object descriptions, navigate to <NavPath path="Settings > Global configuration > Objects" />, expand **GenAI object config**, and set **Save thumbnails** to on.
</TabItem>
<TabItem value="yaml">
```yaml
review:
genai:
enabled: true
# highlight-next-line
debug_save_thumbnails: true
objects:
genai:
enabled: true
# highlight-next-line
debug_save_thumbnails: true
```
</TabItem>
</ConfigTabs>
6. Verify the prompt is what you think it is.
- Object description prompts are the ones you control directly. A camera-level <NavPath path="Settings > Camera configuration > Objects > GenAI object config > Caption prompt" /> (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
- Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are <NavPath path="Settings > Global configuration > Review > GenAI config > Activity context prompt" /> (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
- Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
- Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
- Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle, or can optionally be sent earlier after a number of significantly changed frames, for example in use in more real-time notifications. Descriptions can also be regenerated manually via the Frigate UI. Note that if you are manually entering a description for tracked objects prior to its end, this will be overwritten by the generated response.
@@ -11,13 +15,13 @@ By default, descriptions will be generated for all tracked objects and all zones
Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
Generative AI object descriptions can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
Generative AI object descriptions can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt#frigatecamera_nameobject_descriptionsset).
## Usage and Best Practices
Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
Frigate's thumbnail search excels at identifying specific details about tracked objects -- for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate's default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate's default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what's happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they're moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation's context.
## Custom Prompts
@@ -33,13 +37,25 @@ Prompts can use variable replacements `{label}`, `{sub_label}`, and `{camera}` t
:::
You are also able to define custom prompts in your configuration.
You can define custom prompts at the global level and per-object type. To configure custom prompts:
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Global configuration > Objects" />.
- Expand the **GenAI object config** section
- Set **Caption prompt** to your custom prompt text
- Under **Object prompts**, add entries keyed by object type (e.g., `person`, `car`) with custom prompts for each
</TabItem>
<TabItem value="yaml">
```yaml
genai:
provider:ollama
base_url:http://localhost:11434
model:qwen3-vl:8b-instruct
my_provider:
provider:ollama
base_url:http://localhost:11434
model:qwen3-vl:8b-instruct
objects:
genai:
@@ -49,7 +65,25 @@ objects:
car:"Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
```
Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
</TabItem>
</ConfigTabs>
Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera. To configure camera-level overrides:
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Camera configuration > Objects" /> for the desired camera.
- Expand the **GenAI object config** section
- Set **Enable GenAI** to on
- Set **Use snapshots** to on if desired
- Set **Caption prompt** to a camera-specific prompt
- Under **Object prompts**, add entries keyed by object type with camera-specific prompts
- Set **GenAI objects** to the list of object types that should receive descriptions (e.g., `person`, `cat`)
- Set **Required zones** to limit descriptions to objects in specific zones (e.g., `steps`)
</TabItem>
<TabItem value="yaml">
```yaml
cameras:
@@ -69,6 +103,9 @@ cameras:
- steps
```
</TabItem>
</ConfigTabs>
### Experiment with prompts
Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
@@ -76,3 +113,7 @@ Many providers also have a public facing chat interface for their models. Downlo
- OpenAI - [ChatGPT](https://chatgpt.com)
- Gemini - [Google AI Studio](https://aistudio.google.com)
If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Generative AI can be used to automatically generate structured summaries of review items. These summaries will show up in Frigate's native notifications as well as in the UI. Generative AI can also be used to take a collection of summaries over a period of time and provide a report, which may be useful to get a quick report of everything that happened while out for some amount of time.
Requests for a summary are requested automatically to your AI provider for alert review items when the activity has ended, they can also be optionally enabled for detections as well.
Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt#frigatecamera_namereview_descriptionsset).
## Review Summary Usage and Best Practices
@@ -28,6 +32,30 @@ This will show in multiple places in the UI to give additional context about eac
Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response.
To configure the activity context prompt:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Review" />.
- Set **GenAI config > Activity context prompt** to your custom activity context text
By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, you can configure Frigate to extract frames directly from recordings at a higher resolution:
By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, configure Frigate to extract frames directly from recordings at a higher resolution.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Review" />.
- Set **GenAI config > Enable GenAI descriptions** to on
- Set **GenAI config > Review image source** to `recordings` (default is `preview`)
</TabItem>
<TabItem value="yaml">
```yaml
review:
@@ -84,6 +123,9 @@ review:
image_source:recordings# Options: "preview" (default) or "recordings"
```
</TabItem>
</ConfigTabs>
When using `recordings`, frames are extracted at 480px height while maintaining the camera's original aspect ratio, providing better detail for the LLM while being mindful of context window size. This is particularly useful for scenarios where fine details matter, such as identifying license plates, reading text, or analyzing distant objects.
The number of frames sent to the LLM is dynamically calculated based on:
@@ -103,7 +145,17 @@ If recordings are not available for a given time period, the system will automat
### Additional Concerns
Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. These concerns can be configured so that the review summaries will make note of them if the activity requires additional review. For example:
Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. Configure these concerns so that review summaries will make note of them if the activity requires additional review.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Review" />.
- Set **GenAI config > Additional concerns** to a list of your concerns (e.g., `animals in the garden`)
</TabItem>
<TabItem value="yaml">
```yaml {4,5}
review:
@@ -113,9 +165,22 @@ review:
- animals in the garden
```
</TabItem>
</ConfigTabs>
### Preferred Language
By default, review summaries are generated in English. You can configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option:
By default, review summaries are generated in English. Configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Review" />.
- Set **GenAI config > Preferred language** to the desired language (e.g., `Spanish`)
</TabItem>
<TabItem value="yaml">
```yaml {4}
review:
@@ -124,6 +189,9 @@ review:
preferred_language: Spanish
```
</TabItem>
</ConfigTabs>
## Review Reports
Along with individual review item summaries, Generative AI can also produce a single report of review items from all cameras marked "suspicious" over a specified time period (for example, a daily summary of suspicious activity while you're on vacation).
@@ -133,3 +201,7 @@ Along with individual review item summaries, Generative AI can also produce a si
Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
## Troubleshooting
If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Frigate uses the bundled go2rtc to power a number of key features:
- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
- Live stream support for cameras in Home Assistant Integration
- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
:::tip[Most users no longer need to configure go2rtc by hand]
The [**camera setup wizard**](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add cameras. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, and the wizard probes your camera and writes its configuration for you, including the go2rtc restream and the live stream mapping, so go2rtc is set up automatically.
This guide is mainly useful if you are **upgrading from an older version and have existing cameras that don't yet use go2rtc**, or if you want to fine-tune a stream by hand (for example, to transcode a codec your browser can't play). The [go2rtc troubleshooting guide](/troubleshooting/go2rtc) applies regardless of how your cameras were added.
:::
## Adding a go2rtc stream manually
If you added your cameras with the wizard, go2rtc is already configured. You can skip straight to [troubleshooting](/troubleshooting/go2rtc). The steps below are for upgrading users with existing cameras that aren't using go2rtc yet, or for anyone who prefers to configure a stream by hand.
Configure go2rtc to connect to your camera by adding the stream you want to use for live view. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-streams), not just rtsp.
:::tip
For the best experience, set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
See [the live view docs](/configuration/live#setting-streams-for-live-ui) for more information.
:::
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and click **Add stream**. Give the stream a name (use the camera's name so Frigate can auto-map it - for example, if your camera's name is `back`, use `back` as the go2rtc stream name), then paste the camera's stream URL into the **Source** field. Save the section.
After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
### Next steps
1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
## Troubleshooting
If your stream won't play, has no audio, uses excessive CPU, or otherwise misbehaves, see the dedicated [go2rtc troubleshooting guide](/troubleshooting/go2rtc). It walks through how to isolate where the problem is and covers the most common issues: unsupported codecs, H.265/HEVC, audio, WebRTC and two-way talk, hardware-accelerated transcoding with FFmpeg 8, and camera-specific quirks.
## Homekit Configuration
To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://<frigate_host>:1984/editor.html`. Pairings are saved back to that file automatically.
See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.
import CommunityBadge from '@site/src/components/CommunityBadge';
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
# Video Decoding
@@ -14,8 +17,6 @@ Some types of hardware acceleration are detected and used automatically, but you
- Check the logs: A message will either say that hardware acceleration was automatically detected, or there will be a warning that no hardware acceleration was automatically detected
- If hardware acceleration is specified in the config, verification can be done by ensuring the logs are free from errors. There is no CPU fallback for hardware acceleration.
:::info
Frigate supports presets for optimal hardware accelerated video decoding:
**AMD**
@@ -46,29 +47,24 @@ Frigate supports presets for optimal hardware accelerated video decoding:
Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
:::
## Intel-based CPUs
Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.
The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.
@@ -78,111 +74,86 @@ See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/00
VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args:preset-vaapi
```
</TabItem>
</ConfigTabs>
### Via Quicksync
#### H.264 streams
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args:preset-intel-qsv-h264
```
</TabItem>
</ConfigTabs>
#### H.265 streams
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args:preset-intel-qsv-h265
```
### Configuring Intel GPU Stats in Docker
</TabItem>
</ConfigTabs>
Additional configuration is needed for the Docker container to be able to access the `intel_gpu_top` command for GPU stats. There are two options:
### Configuring Intel GPU Stats
1. Run the container as privileged.
2. Add the `CAP_PERFMON` capability (note: you might need to set the `perf_event_paranoid` low enough to allow access to the performance event system.)
Frigate reads Intel GPU utilization directly from the kernel's per-client DRM usage counters exposed at `/proc/<pid>/fdinfo/<fd>`. This requires:
#### Run as privileged
- Linux kernel **5.19 or newer** for the `i915` driver, or any release of the `xe` driver.
- Frigate running with permission to read other processes' fdinfo. Running as root inside the container (the default) satisfies this; non-root setups may need `CAP_SYS_PTRACE`.
This method works, but it gives more permissions to the container than are actually needed.
No `intel_gpu_top` binary, `CAP_PERFMON`, privileged mode, or `perf_event_paranoid` tuning is required.
##### Docker Compose - Privileged
#### Stats for SR-IOV or specific devices
```yaml
services:
frigate:
...
image:ghcr.io/blakeblackshear/frigate:stable
# highlight-next-line
privileged:true
```
##### Docker Run CLI - Privileged
```bash {4}
docker run -d \
--name frigate \
...
--privileged \
ghcr.io/blakeblackshear/frigate:stable
```
#### CAP_PERFMON
Only recent versions of Docker support the `CAP_PERFMON` capability. You can test to see if yours supports it by running: `docker run --cap-add=CAP_PERFMON hello-world`
##### Docker Compose - CAP_PERFMON
```yaml {5,6}
services:
frigate:
...
image: ghcr.io/blakeblackshear/frigate:stable
cap_add:
- CAP_PERFMON
```
##### Docker Run CLI - CAP_PERFMON
```bash {4}
docker run -d \
--name frigate \
...
--cap-add=CAP_PERFMON \
ghcr.io/blakeblackshear/frigate:stable
```
#### perf_event_paranoid
_Note: This setting must be changed for the entire system._
For more information on the various values across different distributions, see https://askubuntu.com/questions/1400874/what-does-perf-paranoia-level-four-do.
Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
#### Stats for SR-IOV or other devices
When using virtualized GPUs via SR-IOV, you need to specify the device path to use to gather stats from `intel_gpu_top`. This example may work for some systems using SR-IOV:
If the host has more than one Intel GPU (e.g. an iGPU plus a discrete GPU, or SR-IOV virtual functions), pin stats collection to a specific device by setting `intel_gpu_device` to either its PCI bus address or a DRM card/render-node path:
```yaml
telemetry:
stats:
intel_gpu_device: "sriov"
intel_gpu_device:"0000:00:02.0"
```
For other virtualized GPUs, try specifying the direct path to the device instead:
```yaml
telemetry:
stats:
intel_gpu_device: "drm:/dev/dri/card0"
intel_gpu_device:"/dev/dri/card1"
```
If you are passing in a device path, make sure you've passed the device through to the container.
When passing a device path, make sure the device is also passed through to the container.
## AMD-based CPUs
@@ -190,20 +161,31 @@ Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video
### Configuring Radeon Driver
You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
### Via VAAPI
VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args:preset-vaapi
```
</TabItem>
</ConfigTabs>
## NVIDIA GPUs
While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced/system#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
A more complete list of cards and their compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
@@ -244,11 +226,22 @@ docker run -d \
Using `preset-nvidia` ffmpeg will automatically select the necessary profile for the incoming video, and will log an error if the profile is not supported by your GPU.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args: preset-nvidia
```
</TabItem>
</ConfigTabs>
If everything is working correctly, you should see a significant improvement in performance.
Verify that hardware decoding is working by running `nvidia-smi`, which should show `ffmpeg`
processes:
@@ -296,6 +289,14 @@ These instructions were originally based on the [Jellyfin documentation](https:/
Ensure you increase the allocated RAM for your GPU to at least 128 (`raspi-config` > Performance Options > GPU Memory).
If you are using the HA App, you may need to use the full access variant and turn off _Protection mode_ for hardware acceleration.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
# if you want to decode a h264 stream
ffmpeg:
@@ -306,10 +307,14 @@ ffmpeg:
hwaccel_args: preset-rpi-64-h265
```
</TabItem>
</ConfigTabs>
:::note
If running Frigate through Docker, you either need to run in privileged mode or
map the `/dev/video*` devices to Frigate. With Docker Compose add:
If running Frigate through Docker, map the relevant `/dev/video*` devices into
the container. Running in privileged mode also works but grants far more access
than needed. With Docker Compose add:
```yaml {4-5}
services:
@@ -405,11 +410,22 @@ A list of supported codecs (you can use `ffmpeg -decoders | grep nvmpi` in the c
For example, for H264 video, you'll select `preset-jetson-h264`.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args: preset-jetson-h264
```
</TabItem>
</ConfigTabs>
If everything is working correctly, you should see a significant reduction in ffmpeg CPU load and power consumption.
Verify that hardware decoding is working by running `jtop` (`sudo pip3 install -U jetson-stats`), which should show
that NVDEC/NVDEC1 are in use.
@@ -424,13 +440,24 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
### Configuration
Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
Set the FFmpeg hwaccel preset to enable hardware video processing.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml
ffmpeg:
hwaccel_args: preset-rkmpp
```
</TabItem>
</ConfigTabs>
:::note
Make sure that your SoC supports hardware acceleration for your input stream. For example, if your camera streams with h265 encoding and a 4k resolution, your SoC must be able to de- and encode h265 with a 4k resolution or higher. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
@@ -451,7 +478,7 @@ Error marking filters as finished
Restarting ffmpeg...
```
you should try to uprade to FFmpeg 7. This can be done using this config option:
you should try to upgrade to FFmpeg 7. This can be done using this config option:
```yaml
ffmpeg:
@@ -480,7 +507,15 @@ Make sure to follow the [Synaptics specific installation instructions](/frigate/
### Configuration
Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
Set the FFmpeg hwaccel args to enable hardware video processing.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
</TabItem>
<TabItem value="yaml">
```yaml {2}
ffmpeg:
@@ -490,6 +525,9 @@ output_args:
record: preset-record-generic-audio-aac
```
</TabItem>
</ConfigTabs>
:::warning
Make sure that your SoC supports hardware acceleration for your input stream and your input stream is h264 encoding. For example, if your camera streams with h264 encoding, your SoC must be able to de- and encode with it. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
For Home Assistant App installations, the config file should be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](#accessing-app-config-dir).
For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.
It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
It is recommended to start with a minimal configuration and add to it as described in [this guide](../guides/getting_started.md) and use the built in configuration editor in Frigate's UI which supports validation.
```yaml
mqtt:
enabled:False
cameras:
dummy_camera:# <--- this will be changed to your actual camera later
enabled:False
ffmpeg:
inputs:
- path:rtsp://127.0.0.1:554/rtsp
roles:
- detect
```
## Accessing the Home Assistant App configuration directory {#accessing-app-config-dir}
When running Frigate through the HA App, the Frigate `/config` directory is mapped to `/addon_configs/<addon_directory>` in the host, where `<addon_directory>` is specific to the variant of the Frigate App you are running.
**Whenever you see `/config` in the documentation, it refers to this directory.**
If for example you are running the standard App variant and use the [VS Code App](https://github.com/hassio-addons/addon-vscode) to browse your files, you can click _File_ > _Open folder..._ and navigate to `/addon_configs/ccab4aaf_frigate` to access the Frigate `/config` directory and edit the `config.yaml` file. You can also use the built-in file editor in the Frigate UI to edit the configuration file.
## VS Code Configuration Schema
VS Code supports JSON schemas for automatically validating configuration files. You can enable this feature by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the beginning of the configuration file. Replace `frigate_host` with the IP address or hostname of your Frigate server. If you're using both VS Code and Frigate as an App, you should use `ccab4aaf-frigate` instead. Make sure to expose the internal unauthenticated port `5000` when accessing the config from VS Code on another machine.
## Environment Variable Substitution
Frigate supports the use of environment variables starting with `FRIGATE_`**only** where specifically indicated in the [reference config](./reference.md). For example, the following values can be replaced at runtime by using environment variables:
Here are some common starter configuration examples. Refer to the [reference config](./reference.md) for detailed information about all the config values.
### Raspberry Pi Home Assistant App with USB Coral
- Single camera with 720p, 5fps stream for detect
- MQTT connected to the Home Assistant Mosquitto App
- Hardware acceleration for decoding video
- USB Coral detector
- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
- Continue to keep all video if it qualified as an alert or detection for 30 days
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
:::info
License plate recognition requires a one-time internet connection to download OCR and detection models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
:::
When a plate is recognized, the details are:
- Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.
@@ -34,14 +45,35 @@ License plate recognition works by running AI models locally on your system. The
## Configuration
License plate recognition is disabled by default. Enable it in your config file:
License plate recognition is disabled by default and must be enabled before it can be used.
Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. You should disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras:
</TabItem>
</ConfigTabs>
Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. Disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
</TabItem>
<TabItem value="yaml">
```yaml {4,5}
cameras:
@@ -51,65 +83,144 @@ cameras:
enabled: False
```
</TabItem>
</ConfigTabs>
For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
## Advanced Configuration
Fine-tune the LPR feature using these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
Fine-tune the LPR feature using these optional parameters. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
- **Detection threshold**: License plate object detection confidence score required before recognition runs. This field only applies to the standalone license plate detection model; `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
- Default: `0.7`
- Note: This is field only applies to the standalone license plate detection model, `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
- **`min_area`**: Defines the minimum area (in pixels) a license plate must be before recognition runs.
- Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
- Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
- **`device`**: Device to use to run license plate detection _and_ recognition models.
- **Minimum plate area**: Minimum area (in pixels) a license plate must be before recognition runs. This is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
- Default: `1000` pixels
- **Device**: Device to use to run license plate detection _and_ recognition models. Auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
- Default: `None`
- This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
- **`model_size`**: The size of the model used to identify regions of text on plates.
- **Model size**: The size of the model used to identify regions of text on plates. The `small` model is fast and identifies groups of Latin and Chinese characters. The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. If your country or region does not use multi-line plates, you should use the `small` model.
- Default: `small`
- This can be `small` or `large`.
- The `small` model is fast and identifies groups of Latin and Chinese characters.
- The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the `small` model.
- If your country or region does not use multi-line plates, you should use the `small` model as performance is much better for single-line plates.
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
enabled: True
detection_threshold: 0.7
min_area: 1000
device: CPU
model_size: small
```
</TabItem>
</ConfigTabs>
### Recognition
- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
- Default: `0.9`.
- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label` to an object.
- Use this to filter out short, incomplete, or incorrect detections.
- **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
- `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7"
- `"^[A-Z]{2}[0-9]{2} [A-Z]{3}$"` matches plates like "AB12 XYZ" or "XY68 ABC"
- Websites like https://regex101.com/ can help test regular expressions for your plates.
- **Recognition threshold**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
- Default: `0.9`
- **Min plate length**: Minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label`. Use this to filter out short, incomplete, or incorrect detections.
- **Plate format regex**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded. Websites like https://regex101.com/ can help test regular expressions for your plates.
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
enabled: True
recognition_threshold: 0.9
min_plate_length: 4
format: "^[A-Z]{2}[0-9]{2} [A-Z]{3}$"
```
</TabItem>
</ConfigTabs>
### Matching
- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` and `motorcycle` objects when a recognized plate matches a known value.
- These labels appear in the UI, filters, and notifications.
- Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
- **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
- For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`.
- This parameter will _not_ operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`.
- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
- **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
enabled: True
match_distance: 1
known_plates:
Wife's Car:
- "ABC-1234"
Johnny:
- "J*N-*234"
```
</TabItem>
</ConfigTabs>
### Image Enhancement
- **`enhancement`**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. This preprocessing step can sometimes improve accuracy but may also have the opposite effect.
- **Enhancement level**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters. This setting is best adjusted at the camera level if running LPR on multiple cameras.
- Default: `0` (no enhancement)
- Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters, actually making them much harder for Frigate to recognize.
- This setting is best adjusted at the camera level if running LPR on multiple cameras.
- If Frigate is already recognizing plates correctly, leave this setting at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 5 and adjusting as needed. You should see how different enhancement levels affect your plates. Use the `debug_save_plates` configuration option (see below).
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
enabled: True
enhancement: 1
```
</TabItem>
</ConfigTabs>
If Frigate is already recognizing plates correctly, leave enhancement at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 3 and adjusting as needed. Use the `debug_save_plates` configuration option (see below) to see how different enhancement levels affect your plates.
### Normalization Rules
- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially and are applied _before_ the `format` regex, if specified. Each rule must have a `pattern` (which can be a string or a regex) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
<ConfigTabs>
<TabItem value="ui">
These rules must be defined at the global level of your `lpr` config.
- Any changes made by the rules are printed to the LPR debug log.
@@ -133,13 +249,50 @@ lpr:
### Debugging
- **`debug_save_plates`**: Set to `True` to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `<camera>/<event_id>`, and named based on the capture timestamp.
- These saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
- **Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
- **Save debug plates**: Set to on to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `<camera>/<event_id>`, and named based on the capture timestamp.
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
enabled: True
debug_save_plates: True
```
</TabItem>
</ConfigTabs>
The saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
**Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
## Configuration Examples
These configuration parameters are available at the global level of your config. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
These configuration parameters are available at the global level. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
| **Minimum plate area** | Set to `1500` to ignore plates with an area (length x width) smaller than 1500 pixels |
| **Min plate length** | Set to `4` to only recognize plates with 4 or more characters |
| **Known plates > Wife's Car** | `ABC-1234`, `ABC-I234` (accounts for potential confusion between the number one and capital letter I) |
| **Known plates > Johnny** | `J*N-*234` (matches JHN-1234 and JMN-I234; `*` matches any number of characters) |
| **Known plates > Sally** | `[S5]LL 1234` (matches both SLL 1234 and 5LL 1234) |
| **Known plates > Work Trucks** | `EMP-[0-9]{3}[A-Z]` (matches plates like EMP-123A, EMP-456Z) |
</TabItem>
<TabItem value="yaml">
```yaml
lpr:
@@ -158,28 +311,21 @@ lpr:
- "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
```
```yaml
lpr:
enabled: True
min_area: 4000 # Run recognition on larger plates only (4000 pixels represents a 63x63 pixel square in your image)
recognition_threshold: 0.85
format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers
match_distance: 1 # Allow one character variation in plate matching
replace_rules:
- pattern: "O"
replacement: "0" # Replace the letter O with the number 0 in every plate
known_plates:
Delivery Van:
- "RJ K5678"
- "UP A1234"
Supervisor:
- "MN D3163"
```
</TabItem>
</ConfigTabs>
:::note
If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
</TabItem>
<TabItem value="yaml">
```yaml
cameras:
side_yard:
@@ -188,13 +334,16 @@ cameras:
...
```
</TabItem>
</ConfigTabs>
:::
## Dedicated LPR Cameras
Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to capture license plates on distant vehicles, often with fine-tuned settings to capture plates at night.
To mark a camera as a dedicated LPR camera, add `type: "lpr"` the camera configuration.
To mark a camera as a dedicated LPR camera, set `type: "lpr"` in the camera configuration.
:::note
@@ -210,6 +359,55 @@ Users running a Frigate+ model (or any model that natively detects `license_plat
An example configuration for a dedicated LPR camera using a `license_plate`-detecting model:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
Navigate to <NavPath path="Settings > Camera configuration > Snapshots" />.
| Field | Description |
| -------------------- | ----------- |
| **Enable snapshots** | Set to on |
</TabItem>
<TabItem value="yaml">
```yaml
# LPR global configuration
lpr:
@@ -248,6 +446,9 @@ cameras:
- license_plate
```
</TabItem>
</ConfigTabs>
With this setup:
- License plates are treated as normal objects in Frigate.
@@ -259,10 +460,65 @@ With this setup:
### Using the Secondary LPR Pipeline (Without Frigate+)
If you are not running a Frigate+ model, you can use Frigate’s built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
If you are not running a Frigate+ model, you can use Frigate's built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
An example configuration for a dedicated LPR camera using the secondary pipeline:
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available and the correct Docker image is used). Set **Detection threshold** to `0.7` (change if necessary).
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for your dedicated LPR camera.
| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
| **Improve contrast** | Set to off |
Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
Navigate to <NavPath path="Settings > Camera configuration > Recording" />.
| **Detections config > Enable detections** | Set to on |
| **Detections config > Retain > Default** | Set to `7` days |
</TabItem>
<TabItem value="yaml">
```yaml
# LPR global configuration
lpr:
@@ -299,6 +555,9 @@ cameras:
default: 7
```
</TabItem>
</ConfigTabs>
With this setup:
- The standard object detection pipeline is bypassed. Any detected license plates on dedicated LPR cameras are treated similarly to manual events in Frigate. You must **not** specify `license_plate` as an object to track.
@@ -333,7 +592,9 @@ By selecting the appropriate configuration, users can optimize their dedicated L
## FAQ
### Why isn't my license plate being detected and recognized?
### Detection and Recognition
<FaqItem id="why-isnt-my-license-plate-being-detected-and-recognized" question="Why isn't my license plate being detected and recognized?">
Ensure that:
@@ -348,41 +609,70 @@ Recognized plates will show as object labels in the debug view and will appear i
If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.
### Can I run LPR without detecting `car` or `motorcycle` objects?
</FaqItem>
<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
### How can I improve detection accuracy?
</FaqItem>
<FaqItem id="how-can-i-improve-detection-accuracy" question="How can I improve detection accuracy?">
- Use high-quality cameras with good resolution.
- Adjust `detection_threshold` and `recognition_threshold` values.
- Define a `format` regex to filter out invalid detections.
### Does LPR work at night?
</FaqItem>
<FaqItem id="does-lpr-work-at-night" question="Does LPR work at night?">
Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.
### Can I limit LPR to specific zones?
</FaqItem>
<FaqItem id="can-i-limit-lpr-to-specific-zones" question="Can I limit LPR to specific zones?">
LPR, like other Frigate enrichments, runs at the camera level rather than the zone level. While you can't restrict LPR to specific zones directly, you can control when recognition runs by setting a `min_area` value to filter out smaller detections.
### How can I match known plates with minor variations?
</FaqItem>
<FaqItem id="how-can-i-match-known-plates-with-minor-variations" question="How can I match known plates with minor variations?">
Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`.
### How do I debug LPR issues?
</FaqItem>
### Performance and Troubleshooting
<FaqItem id="how-do-i-debug-lpr-issues" question="How do I debug LPR issues?">
Start with ["Why isn't my license plate being detected and recognized?"](#why-isnt-my-license-plate-being-detected-and-recognized). If you are still having issues, work through these steps.
1. Start with a simplified LPR config.
- Remove or comment out everything in your LPR config, including `min_area`, `min_plate_length`, `format`, `known_plates`, or `enhancement` values so that the only values left are `enabled` and `debug_save_plates`. This will run LPR with Frigate's default values.
2. Enable debug logs to see exactly what Frigate is doing.
- Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary. Restart Frigate after this change.
@@ -391,14 +681,14 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
If you are using a Frigate+ or `license_plate` detecting model:
- Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
- Watch the [Debug view](/usage/live#the-single-camera-view) to ensure that `license_plate` is being detected.
- View MQTT messages for `frigate/events` to verify detected plates.
- You may need to adjust your `min_score` and/or `threshold` for the `license_plate` object if your plates are not being detected.
@@ -407,21 +697,28 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
- You may need to adjust your `detection_threshold` if your plates are not being detected.
4. Ensure the characters on detected plates are being _recognized_.
- Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
- Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
### Will LPR slow down my system?
</FaqItem>
<FaqItem id="will-lpr-slow-down-my-system" question="Will LPR slow down my system?">
LPR's performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU or GPU for optimal results. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.
### I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?
</FaqItem>
<FaqItem id="i-am-seeing-a-yolov9-plate-detection-metric-in-enrichment-metrics-but-i-have-a-frigate-or-custom-model-that-detects-license_plate-why-is-the-yolov9-model-running" question={<>I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects <code>license_plate</code>. Why is the YOLOv9 model running?</>}>
The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
### It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?
</FaqItem>
<FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
@@ -429,6 +726,10 @@ If you are using a model that natively detects `license_plate`, add an _object m
If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
</FaqItem>
<FaqItem id="i-see-error-running--model-in-my-logs-or-my-inference-time-is-very-high-how-can-i-fix-this" question={'I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?'}>
This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
### Live View technologies
Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be [configured](/configuration/go2rtc).
The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
@@ -17,13 +22,19 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
| mse | native | native | yes (depends on audio codec) | yes | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured. |
| webrtc | native | native | yes (depends on audio codec) | yes | Requires extra configuration. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature. |
:::info
WebRTC may use an external STUN server for NAT traversal. MSE and HLS streaming do not require any internet access. See [Network Requirements](/frigate/network_requirements#webrtc-stun) for details.
:::
### Camera Settings Recommendations
If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
- Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
- Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://web.archive.org/web/20251213190836/https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
@@ -63,19 +74,36 @@ go2rtc:
### Setting Streams For Live UI
You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the`live -> streams` list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
:::note
Frigate's default dashboard ("All Cameras") will always use the first entry you've defined in `streams:` when playing live streams from your cameras.
Frigate's default dashboard ("All Cameras") will always use the first entry you've defined in streams when playing live streams from your cameras.
:::
Configure the `streams` option with a "friendly name" for your stream followed by the go2rtc stream name.
Configure a "friendly name" for your stream followed by the go2rtc stream name. Using Frigate's internal version of go2rtc is required to use this feature. You cannot specify paths in the streams configuration, only go2rtc stream names.
Using Frigate's internal version of go2rtc is required to use this feature. You cannot specify paths in the `streams` configuration, only go2rtc stream names.
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" /> and select your camera.
2. Under **Live stream names**, click **Add stream** to add a new entry.
3. In the **Stream name** field, enter a friendly name that will appear in the Live UI's stream dropdown (e.g., `Main Stream`).
4. In the **go2rtc stream** field, open the dropdown and select the go2rtc stream this name should map to (e.g., `test_cam`). The dropdown lists every stream configured under `go2rtc.streams`. If the go2rtc stream hasn't been created yet, you can type the name and choose **Use "..."** to save a custom value.
5. Repeat for each additional stream you want to expose (e.g., `Sub Stream` → `test_cam_sub`).
6. Use the trash icon on a row to remove a stream, then **Save** the section.
:::tip
Configure your go2rtc streams first under <NavPath path="Settings > System > go2rtc streams" /> so the dropdown is populated with valid options.
:::
</TabItem>
<TabItem value="yaml">
```yaml {3,6,8,25-29}
go2rtc:
@@ -109,6 +137,9 @@ cameras:
Special Stream: test_cam_another_sub
```
</TabItem>
</ConfigTabs>
### WebRTC extra configuration:
WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
@@ -165,7 +196,7 @@ services:
:::
See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-webrtc) for more information about this.
### Two way talk
@@ -185,7 +216,7 @@ To prevent go2rtc from blocking other applications from accessing your camera's
Frigate provides a dialog in the Camera Group Edit pane with several options for streaming on a camera group's dashboard. These settings are _per device_ and are saved in your device's local storage.
- Stream selection using the `live -> streams` configuration option (see _Setting Streams For Live UI_ above)
- Stream selection using the streams configuration option (see _Setting Streams For Live UI_ above)
- Streaming type:
- _No streaming_: Camera images will only update once per minute and no live streaming will occur.
- _Smart Streaming_ (default, recommended setting): Smart streaming will update your camera image once per minute when no detectable activity is occurring to conserve bandwidth and resources, since a static picture is the same as a streaming image with no motion or objects. When motion or objects are detected, the image seamlessly switches to a live stream.
@@ -203,19 +234,81 @@ Use a camera group if you want to change any of these settings from the defaults
:::
### Disabling cameras
### jsmpeg Stream Quality
Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.
The jsmpeg live view resolution and encoding quality can be adjusted globally or per camera. These settings only affect the jsmpeg player and do not apply when go2rtc is used for live view.
:::note
<ConfigTabs>
<TabItem value="ui">
Disabling a camera via the Frigate UI or MQTT is temporary and does not persist through restarts of Frigate.
Navigate to <NavPath path="Settings > Global configuration > Live playback" /> for global defaults, or <NavPath path="Settings > Camera configuration > Live playback" /> and select a camera for per-camera overrides.
| **Live height** | Height in pixels for the jsmpeg live stream; must be less than or equal to the detect stream height |
| **Live quality** | Encoding quality for the jsmpeg stream (1 = highest, 31 = lowest) |
For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
</TabItem>
<TabItem value="yaml">
Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
```yaml
# Global defaults
live:
height: 720
quality: 8
# Per-camera override
cameras:
front_door:
live:
height: 480
quality: 4
```
</TabItem>
</ConfigTabs>
### Camera state
Each camera has three possible states, surfaced as a status selector in **Settings → Global configuration → Camera management**:
- **On**: streams are processed normally. Object detection, recording, and Live view are active.
- **Off**: Frigate's ffmpeg processes are paused. Recording stops, object detection is paused, and the Live dashboard displays a blank image with a "Camera is off" message. The camera is still visible in the Live dashboard and its past review items, tracked objects, and historical footage remain accessible via the UI. The Off state persists across Frigate restarts via a `.runtime_state.json` file alongside `config.yml` (see [Runtime toggle persistence](#runtime-toggle-persistence)).
- **Disabled**: the change is saved to your configuration file (`enabled: False`). The camera stops immediately, Frigate stops ffmpeg processes, and all live and historical UI elements for the camera are no longer visible but remains retained on disk. The camera is still listed in **Settings → Global configuration → Camera management** so it can be re-enabled. **A restart of Frigate is required to bring a disabled camera back to On.**
#### Turning a camera on or off
Turning a camera off is temporary and does not require a restart. The available controls are:
- The power button in the single-camera Live view header
- The right-click context menu on a camera tile on the Live dashboard
- The Camera management settings pane (status set to **Off**)
- The mobile settings drawer on the single-camera Live view (admin users only)
- The [MQTT topic](/integrations/mqtt#frigatecamera_nameenabledset) `frigate/<camera_name>/enabled/set` with payload `ON` or `OFF`
- The Home Assistant integration via the [`camera.turn_on` / `camera.turn_off` actions](/integrations/home-assistant#camera-api)
#### Disabling a camera
Disabling a camera saves the change to your configuration file. Navigate to **Settings → Global configuration → Camera management** and set the camera's status to **Disabled**. Runtime processing stops immediately; the change persists across restarts.
Re-enabling a disabled camera requires a restart of Frigate so that the ffmpeg processes and other camera-scoped resources can be initialized. The UI will prompt you to restart when you switch a disabled camera back to On.
#### Restream behavior
For both Off and Disabled cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
#### Choosing Off versus Disabled
If you want a camera's historical data (review items, tracked objects, footage) to stay accessible in the UI while you stop processing, set the camera to **Off**. If you want the camera fully removed from the Live dashboard, review filters, and other UI surfaces, set it to **Disabled**. The Disabled state still keeps the camera in Camera management so it can be re-enabled later; if you want to remove all traces of a camera including its configuration, delete it via Camera management instead.
#### Runtime toggle persistence
The Live view toggles for **camera on/off**, **detect**, **recordings**, **snapshots**, and **audio detection** (along with the equivalent MQTT `/set` topics) write the new state to `.runtime_state.json` next to your `config.yml`. The file is replayed on Frigate startup so your last-known toggle states survive a restart. Two interactions worth knowing:
- **Settings UI saves win.** When you save a field through **Settings → Global configuration**, the matching entry is cleared from `.runtime_state.json` so the new value in your config file is the durable source.
- **Switching profiles clears all runtime overrides.** Activating or deactivating a [profile](/configuration/profiles) is treated as a deliberate state change, so the file is wiped to avoid stale overrides replaying on top of the new profile.
If you hand-edit `config.yml` while runtime overrides exist, the overrides will still replay on restart. Delete `.runtime_state.json` to reset to the YAML-defined defaults.
### Live player error messages
@@ -241,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
- **stalled**
- What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
- Possible console messages from the player code:
- `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
@@ -249,94 +342,155 @@ When your browser runs into problems playing back your camera streams, it will l
## Live view FAQ
1. **Why don't I have audio in my Live view?**
### Getting Live View Working
You must use go2rtc to hear audioin your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
<FaqItem id="why-dont-i-have-audio-in-my-live-view" question="Why don't I have audio in my Live view?">
Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?**
If the audio controls don't appear in the UI at all, verify that the Live view is actually using your go2rtc stream. If your go2rtc stream names don't match your Frigate camera name, you must map them with the `live -> streams` config (see [Setting Streams For Live UI](#setting-streams-for-live-ui) above); otherwise the UI falls back to the video-only jsmpeg player.
Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
</FaqItem>
If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
<FaqItem id="i-have-unmuted-some-cameras-on-my-dashboard-but-i-do-not-hear-sound-why" question="I have unmuted some cameras on my dashboard, but I do not hear sound. Why?">
Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
- Network issues (e.g., MSE or WebRTC network connection problems).
- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
To view browser console logs:
1. Open the Frigate Live View in your browser.
2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
4. Look for messages prefixed with the camera name.
</FaqItem>
These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera_settings_recommendations)).
- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see (WebRTC Extra Configuration)(#webrtc-extra-configuration)).
- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
<FaqItem id="my-live-view-shows-a-black-screen-or-doesnt-load-but-the-debug-view-works-why" question="My live view shows a black screen or doesn't load, but the debug view works. Why?">
3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
The debug view plays the `detect` stream processed by Frigate itself, while the Live view plays your go2rtc stream directly in the browser. If the debug view works but the Live view doesn't, your browser usually can't decode what the camera is sending, most often H.265 video or an incompatible audio track.
On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
Work through the [go2rtc troubleshooting guide](/troubleshooting/go2rtc#live-view-is-black-buffering-or-stuck-in-low-bandwidth-mode) to isolate the problem. Two fixes resolve the majority of cases:
4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?**
1. Restream through go2rtc's FFmpeg module by prefixing your source with `ffmpeg:`, for example `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream`.
2. If that doesn't help, transcode to compatible codecs: `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream#video=h264#audio=aac#hardware`.
This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
</FaqItem>
5. **How does "smart streaming" work?**
<FaqItem id="how-do-i-get-the-best-live-view-experience-in-home-assistant" question="How do I get the best live view experience in Home Assistant?">
Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
For a full-resolution, low-latency live view in Home Assistant dashboards, use the [Advanced Camera Card](https://card.camera) with the [go2rtc live provider](https://card.camera/#/configuration/cameras/live-provider?id=go2rtc), which streams directly from Frigate's bundled go2rtc. This also supports audio and [two-way talk](#two-way-talk) on capable cameras. See the [Home Assistant integration docs](/integrations/home-assistant) for setup.
This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
</FaqItem>
Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
### Streaming Behavior
This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
<FaqItem id="how-does-smart-streaming-work" question={'How does "smart streaming" work?'}>
6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
7. **My camera streams have lots of visual artifacts / distortion.**
Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
</FaqItem>
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
<FaqItem id="it-doesnt-seem-like-my-cameras-are-streaming-on-the-live-dashboard-why" question="It doesn't seem like my cameras are streaming on the Live dashboard. Why?">
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
Example: Resolutions from two streams
- Mismatched (may cause aspect ratio switching on the dashboard):
- Live/go2rtc stream: 1920x1080 (16:9)
- Detect stream: 640x352 (~1.82:1, not 16:9)
</FaqItem>
- Matched (prevents switching):
- Live/go2rtc stream: 1920x1080 (16:9)
- Detect stream: 640x360 (16:9)
<FaqItem id="frigate-shows-that-my-live-stream-is-in-low-bandwidth-mode-what-does-this-mean" question={'Frigate shows that my live stream is in "low bandwidth mode". What does this mean?'}>
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
```yaml
cameras:
front_door:
detect:
width: 640
height: 360 # set this to 360 instead of 352
ffmpeg:
inputs:
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
roles:
- record
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
roles:
- detect
```
When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
- Network issues (e.g., MSE or WebRTC network connection problems).
- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
To view browser console logs:
1. Open the Frigate Live View in your browser.
2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
4. Look for messages prefixed with the camera name.
These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
</FaqItem>
<FaqItem id="why-is-my-live-view-delayed-or-lagging-behind-real-time" question="Why is my live view delayed or lagging behind real time?">
A delay when a stream first starts is usually caused by your camera's I-frame (keyframe) interval. Playback cannot begin until a keyframe arrives, so an interval set higher than your camera's frame rate makes the stream take longer to start. Set the I-frame interval to match the frame rate (or "1x" on Reolink) per the [camera settings recommendations](#camera-settings-recommendations).
A stream that starts on time but falls further behind live is buffering, which is usually the browser struggling to decode too many high-resolution streams at once. Select a lower-bandwidth substream for your dashboards (see [Setting Streams For Live UI](#setting-streams-for-live-ui)), reduce the number of streams open at once, or improve the network connection between your browser and Frigate. Frigate's player automatically speeds up playback to catch up to live after buffering, and falls back to low bandwidth mode if it stalls for too long. The _Reset_ option forces a fresh connection at the live edge.
</FaqItem>
<FaqItem id="why-does-frigate-prefer-mse-over-webrtc-for-live-view" question="Why does Frigate prefer MSE over WebRTC for live view?">
Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
</FaqItem>
### Video Quality Issues
<FaqItem id="i-see-a-strange-diagonal-line-on-my-live-view-but-my-recordings-look-fine-how-can-i-fix-it" question="I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?">
This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
</FaqItem>
<FaqItem id="my-camera-streams-have-lots-of-visual-artifacts-or-distortion" question="My camera streams have lots of visual artifacts / distortion.">
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
</FaqItem>
<FaqItem id="why-does-my-camera-stream-switch-aspect-ratios-on-the-live-dashboard" question="Why does my camera stream switch aspect ratios on the Live dashboard?">
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
Example: Resolutions from two streams
- Mismatched (may cause aspect ratio switching on the dashboard):
- Live/go2rtc stream: 1920x1080 (16:9)
- Detect stream: 640x352 (~1.82:1, not 16:9)
- Matched (prevents switching):
- Live/go2rtc stream: 1920x1080 (16:9)
- Detect stream: 640x360 (16:9)
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
```yaml
cameras:
front_door:
detect:
width: 640
height: 360 # set this to 360 instead of 352
ffmpeg:
inputs:
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
roles:
- record
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
roles:
- detect
```
The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with `required_zones` is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
## Motion masks
Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the Debug feed (Settings --> Debug) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the [Debug view](/usage/live#the-single-camera-view) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
See [further clarification](#further-clarification) below on why you may not want to use a motion mask.
@@ -21,41 +25,79 @@ Object filter masks can be used to filter out stubborn false positives in fixed
## Which tool do I need?
| What you're trying to do | Recommended tool | How it works |
| Don't get alerts or recordings for activity in an area (e.g., the sidewalk in front of your house) | A [zone](zones.md) combined with `review.alerts.required_zones` (and/or `review.detections.required_zones`) | Frigate keeps detecting and tracking activity in the area, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
| What you're trying to do | Recommended tool | How it works |
| Only get alerts/detections for activity in the areas you care about, ignoring activity elsewhere (e.g., alert when someone enters your yard, but not when they walk past on the sidewalk) | A [zone](zones.md) combined with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) | Frigate keeps detecting and tracking activity everywhere in the frame, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
## Using the mask creator
To create a poly mask:
<ConfigTabs>
<TabItem value="ui">
1. Visit the Web UI
2. Click/tap the gear icon and open "Settings"
3. Select "Mask / zone editor"
4. At the top right, select the camera you wish to create a mask or zone for
5. Click the plus icon under the type of mask or zone you would like to create
6. Click on the camera's latest image to create the points for a masked area. Click the first point again to close the polygon.
7. When you've finished creating your mask, press Save.
Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select a camera. Use the mask editor to draw motion masks and object filter masks directly on the camera feed. Each mask can be given a friendly name and toggled on or off.
</TabItem>
<TabItem value="yaml">
Your config file will be updated with the relative coordinates of the mask/zone:
Both motion masks and object filter masks can be toggled on or off without removing them from the configuration. Disabled masks are completely ignored at runtime - they will not affect motion detection or object filtering. This is useful for temporarily disabling a mask during certain seasons or times of day without modifying the configuration.
### Further Clarification
This is a response to a [question posed on reddit](https://www.reddit.com/r/homeautomation/comments/ppxdve/replacing_my_doorbell_with_a_security_camera_a_6/hd876w4?utm_source=share&utm_medium=web2x&context=3):
@@ -97,10 +139,10 @@ That may be the case for you. Frigate will definitely work harder tracking peopl
## Common mistakes
**"I added a motion mask to ignore my driveway/sidewalk."**
A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to `review.alerts.required_zones`. Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
**"I added an object filter mask because I don't care about cars in my yard."**
Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with `required_zones`. If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
**"I masked everything except a thin strip on my stoop."**
Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use `required_zones` for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.
Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.
-`frigate_storage_total_bytes{storage=""}` - Storage total bytes
-`frigate_storage_used_bytes{storage=""}` - Storage used bytes
-`frigate_storage_mount_type{mount_type="", storage=""}` - Storage mount type info
These gauges report the operating system's figures for the whole filesystem (the same numbers as `df`), not Frigate's own recording footprint. For how this differs from the recordings usage shown in the UI, see [Understanding storage usage](/configuration/record#understanding-storage-usage).
### Service Metrics
-`frigate_service_uptime_seconds` - Uptime in seconds
-`frigate_service_last_updated_timestamp` - Stats recorded time (unix timestamp)
-`frigate_device_temperature{device=""}` - Device Temperature
### Event Metrics
-`frigate_camera_events{camera="", label=""}` - Count of camera events since exporter started
## Configuring Prometheus
@@ -48,10 +77,10 @@ To scrape metrics from Frigate, add the following to your Prometheus configurati
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
# Tuning Motion Detection
Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.
Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.
Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the [debug viewer](/usage/live#the-single-camera-view).
## The Goal
@@ -21,7 +25,7 @@ First, mask areas with regular motion not caused by the objects you want to dete
## Prepare For Testing
The easiest way to tune motion detection is to use the Frigate UI under Settings > Motion Tuner. This screen allows the changing of motion detection values live to easily see the immediate effect on what is detected as motion.
The recommended way to tune motion detection is to use the built-in Motion Tuner. Navigate to <NavPath path="Settings > Camera configuration > Motion tuner" /> and select the camera you want to tune. This screen lets you adjust motion detection values live and immediately see the effect on what is detected as motion, making it the fastest way to find optimal settings for each camera.
## Tuning Motion Detection During The Day
@@ -37,8 +41,21 @@ Remember that motion detection is just used to determine when object detection s
The threshold value dictates how much of a change in a pixels luminance is required to be considered motion.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> to set the threshold globally.
To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera, or use the <NavPath path="Settings > Camera configuration > Motion tuner" /> to adjust it live.
| **Motion threshold** | The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. 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. (default: 30) |
</TabItem>
<TabItem value="yaml">
```yaml
# default threshold value
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.
@@ -46,14 +63,30 @@ motion:
threshold:30
```
Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dogs blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
</TabItem>
</ConfigTabs>
Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dog blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
Watching the motion boxes in the debug view, increase the threshold until you only see motion that is visible to the eye. Once this is done, it is important to test and ensure that desired motion is still detected.
### Contour Area
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> to set the contour area globally.
To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera, or use the <NavPath path="Settings > Camera configuration > Motion tuner" /> to adjust it live.
| **Contour area** | Minimum size in pixels in the resized motion image that counts as 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: 10 = high sensitivity, 30 = medium sensitivity, 50 = low sensitivity. (default: 10) |
</TabItem>
<TabItem value="yaml">
```yaml
# default contour_area value
motion:
# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: shown below)
# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
@@ -65,6 +98,9 @@ motion:
contour_area:10
```
</TabItem>
</ConfigTabs>
Once the threshold calculation is run, the pixels that have changed are grouped together. The contour area value is used to decide which groups of changed pixels qualify as motion. Smaller values are more sensitive meaning people that are far away, small animals, etc. are more likely to be detected as motion, but it also means that small changes in shadows, leaves, etc. are detected as motion. Higher values are less sensitive meaning these things won't be detected as motion but with the risk that desired motion won't be detected until closer to the camera.
Watching the motion boxes in the debug view, adjust the contour area until there are no motion boxes smaller than the smallest you'd expect frigate to detect something moving.
@@ -81,27 +117,87 @@ However, if the preferred day settings do not work well at night it is recommend
## Tuning For Large Changes In Motion
### Lightning Threshold
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> and expand the advanced fields to find the lightning threshold setting.
To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera.
| **Lightning threshold** | The percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera. (default: 0.8) |
</TabItem>
<TabItem value="yaml">
```yaml
# default lightning_threshold:
motion:
# 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.
# Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching
# a doorbell camera.
# 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.
# Decreasing this value will make motion detection more likely
# to ignore large amounts of motion such as a person
# approaching a doorbell camera.
lightning_threshold:0.8
```
</TabItem>
</ConfigTabs>
Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. `lightning_threshold` defines the percentage of the image used to detect these substantial changes. Increasing this value makes motion detection more likely to treat large changes (like IR mode switches) as valid motion. Decreasing it makes motion detection more likely to ignore large amounts of motion, such as a person approaching a doorbell camera.
Note that `lightning_threshold` does **not** stop motion-based recordings from being saved. It only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.
:::warning
Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
Some cameras, like doorbell cameras, may have missed detections when someone walks directly in front of the camera and the `lightning_threshold` causes motion detection to recalibrate. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
:::
:::note
### Skip Motion On Large Scene Changes
Lightning threshold does not stop motion based recordings from being saved.
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> and expand the advanced fields to find the skip motion threshold setting.
To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera.
| **Skip motion threshold** | Fraction of the frame that must change in a single update before Frigate will completely ignore any motion in that frame. Values range between 0.0 and 1.0; leave unset (null) to disable. For example, setting this to 0.7 causes Frigate to skip reporting motion boxes when more than 70% of the image appears to change (e.g. during lightning storms, IR/color mode switches, or other sudden lighting events). |
</TabItem>
<TabItem value="yaml">
```yaml
motion:
# Optional: Fraction of the frame that must change in a single update
# before Frigate will completely ignore any motion in that frame.
# Values range between 0.0 and 1.0, leave unset (null) to disable.
# Setting this to 0.7 would cause Frigate to **skip** reporting
# motion boxes when more than 70% of the image appears to change
# (e.g. during lightning storms, IR/color mode switches, or other
# sudden lighting events).
skip_motion_threshold:0.7
```
</TabItem>
</ConfigTabs>
This option is handy when you want to prevent large transient changes from triggering recordings or object detection. It differs from `lightning_threshold` because it completely suppresses motion instead of just forcing a recalibration.
:::warning
When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise. They typically only take up a few megabytes and are quick to scan in the timeline UI.
:::
Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. This is done via the `lightning_threshold` configuration. It is defined as the percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera.
## Reviewing Detected Motion
To review what the detector picked up, or to search past recordings for motion in a specific region, see [Reviewing Motion](/usage/review#reviewing-motion) on the Review page.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
import FaqItem from "@site/src/components/FaqItem";
# Notifications
Frigate offers native notifications using the [WebPush Protocol](https://web.dev/articles/push-notifications-web-push-protocol) which uses the [VAPID spec](https://tools.ietf.org/html/draft-thomson-webpush-vapid) to deliver notifications to web apps using encryption.
:::info
Push notifications require internet access from the Frigate server to the browser vendor's push service (e.g., Google FCM, Mozilla autopush). See [Network Requirements](/frigate/network_requirements#push-notifications) for details.
:::
## Setting up Notifications
In order to use notifications the following requirements must be met:
- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
- Frigate must be accessed via a secure `https` connection while signed in as a Frigate user ([see the authorization docs](/configuration/authentication)).
- A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
- In order for notifications to be usable externally, Frigate must be accessible externally.
- For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
### Configuration
To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
Enable notifications and fill out the required fields.
Optionally, you can change the default cooldown period for notifications through the `cooldown` parameter in your config file. This parameter can also be overridden at the camera level.
Optionally, change the default cooldown period for notifications. The cooldown can also be overridden at the camera level.
Notifications will be prevented if either:
- The global cooldown period hasn't elapsed since any camera's last notification
- The camera-specific cooldown period hasn't elapsed for the specific camera
#### Global notifications
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Notifications > Notifications" />.
- Set **Email** to your email address
- Enable notifications for the desired cameras
</TabItem>
<TabItem value="yaml">
```yaml
notifications:
enabled:True
@@ -34,6 +57,21 @@ notifications:
cooldown:10# wait 10 seconds before sending another notification from any camera
```
</TabItem>
</ConfigTabs>
#### Per-camera notifications
<ConfigTabs>
<TabItem value="ui">
1. Navigate to <NavPath path="Settings > Camera configuration > Notifications" /> and select the desired camera.
- Set **Enable notifications** to on
- Set **Cooldown period** to the desired number of seconds to wait before sending another notification from this camera (e.g. `30`)
</TabItem>
<TabItem value="yaml">
```yaml
cameras:
doorbell:
@@ -43,9 +81,18 @@ cameras:
cooldown:30# wait 30 seconds before sending another notification from the doorbell camera
```
</TabItem>
</ConfigTabs>
### Registration
Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
Once notifications are enabled, press the `Register This Device` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
:::warning
Each registration is attached to the Frigate user account you are signed in as, so you must register over a secure connection to the authenticated port (`8971`). Reverse proxies and tunnels should point at port `8971`.
:::
## Supported Notifications
@@ -64,3 +111,62 @@ Different platforms handle notifications differently, some settings changes may
### Android
Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
## Notifications FAQ
<FaqItem id="how-do-i-debug-notifications-issues" question="How do I debug notifications issues?">
Push notifications involve Frigate, your browser, and your browser vendor's push service, so it helps to work from the server outward.
1. Enable debug logs for the push client by adding `frigate.comms.webpush: debug` to your `logger` configuration. Restart Frigate after this change.
```yaml
logger:
default: info
logs:
# highlight-next-line
frigate.comms.webpush: debug
```
These logs show exactly where a notification stopped, including:
- `Email must be provided for push notifications to be sent` means the global `email` field is empty and nothing will ever be sent.
- `Sending test notification` and `Sending push notification for <camera>, review ID <id>` mean Frigate handed the message off to the push service.
- `Skipping notification for <camera> - in global cooldown period` (or `camera-specific cooldown period`) means your [cooldown](#configuration) values suppressed it.
- `Notifications for <camera> are currently suspended` means notifications were suspended from <NavPath path="Settings > Notifications" /> or MQTT.
- `Notification endpoint expired for <user>, received 410` means that device's subscription is no longer valid and it must be re-registered.
- `Failed to send notification to <user> :: <status>` means the push service rejected the message. A `401` or `403` usually points at a VAPID or `email` problem, and a `5xx` is a problem on the push service's end.
- If you see no messages at all when an alert occurs, the notification was never queued. Confirm an actual **alert** was created (notifications are not sent for detections), and that notifications are enabled both globally and for that camera.
2. Verify the basics that most reports come down to:
- Frigate must be reached over `https` with a certificate your device trusts. Browsers silently refuse to register a service worker otherwise, and a self-signed certificate that is not installed as trusted on the device will fail.
- On iOS, notifications only work when Frigate has been installed to the Home Screen via **Share > Add to Home Screen** and opened from that icon. Safari and Chrome tabs cannot receive web push on iOS.
- Each device must be registered individually, and Frigate must be restarted after registering before anything can be sent, including test notifications.
- The Frigate server needs outbound internet access to the browser vendor's push service. See [Network Requirements](/frigate/network_requirements#push-notifications).
3. Test from the UI. Use the `Send a test notification` button in <NavPath path="Settings > Notifications" />. If the log shows `Sending test notification` but nothing arrives on the device, the problem is between the push service and your device rather than in Frigate.
4. Check the browser side on the device that is not receiving notifications:
- Confirm the site's notification permission is set to **Allow** in your browser or OS settings, and that a focus/do not disturb mode is not hiding them.
- In desktop browsers, open Developer Tools > Application > Service Workers and confirm `notifications-worker.js` is registered and activated. Unregistering it and registering the device again will rebuild a broken subscription.
- Check the browser console and your reverse proxy logs for failures loading `/notifications-worker.js` or errors on `/api/notifications/register`.
</FaqItem>
<FaqItem id="why-did-notifications-stop-arriving-after-working-for-a-while" question="Why did notifications stop arriving after working for a while?">
Push subscriptions are issued by the browser vendor and can be revoked, most often after a browser update, after clearing site data, or when a device has been offline for an extended period. When this happens the device still appears registered in Frigate, but the push service rejects the message. The debug logs will show `Notification endpoint expired` with a `404` or `410` status.
Unregister and re-register the affected device from <NavPath path="Settings > Notifications" />, then restart Frigate.
</FaqItem>
<FaqItem id="why-am-i-not-getting-notifications-for-one-specific-camera" question="Why am I not getting notifications for one specific camera?">
Work through these in order:
- Notifications are only sent for **alerts**. If the camera is producing detections instead, adjust the camera's `review > alerts > labels` so the objects you care about are classified as alerts.
- Confirm notifications are enabled for that camera in <NavPath path="Settings > Camera configuration > Notifications" />.
- Check the camera's `cooldown` value, and remember that the global cooldown applies across all cameras. A busy camera can consume the global cooldown and suppress a quieter one.
- If [authentication](/configuration/authentication) is enabled with roles, users only receive notifications for the cameras their role grants access to.
There are several types of object filters that can be used to reduce false positive rates.
import ConfigTabs from "@site/src/components/ConfigTabs";
import TabItem from "@theme/TabItem";
import NavPath from "@site/src/components/NavPath";
There are several types of object filters that can be used to reduce [false positive](/frigate/glossary#false-positive) rates.
## Object Scores
For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
For object filters, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
| Frame | Current Score | Score History | Computed Score | Detected Object |
@@ -20,13 +24,59 @@ For object filters in your configuration, any single detection below `min_score`
In frame 2, the score is below the `min_score` value, so Frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
The **top score** is the highest computed score the tracked object has ever reached during its lifetime. Because the computed score rises and falls as new frames come in, the top score can be thought of as the peak confidence Frigate had in the object. In Frigate's UI (such as the Tracking Details pane in Explore), you may see all three values:
- **Score**: the raw detector score for that single frame.
- **Computed Score**: the median of the most recent score history at that moment. This is the value compared against `threshold`.
- **Top Score**: the highest computed score reached so far for the tracked object.
### Minimum Score
Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid tracked objects to be lost or disjointed.
### Threshold
`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create a tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
## Configuring Object Scores
<ConfigTabs>
<TabItem value="ui">
Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set score filters globally.
| **Object filters > Person > Min Area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Max Area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box |
| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box |
To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
</TabItem>
<TabItem value="yaml">
```yaml
objects:
filters:
person:
min_area: 5000
max_area: 100000
min_ratio: 0.5
max_ratio: 2.0
```
To override at the camera level:
```yaml
cameras:
front_door:
objects:
filters:
person:
min_area: 5000
max_area: 100000
```
</TabItem>
</ConfigTabs>
## Other Tools
### Zones
[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
[Required zones](/configuration/zones.md#restricting-alerts-and-detections-to-specific-zones) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
### Object Masks
[Object Filter Masks](/configuration/masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape.
[Object Filter Masks](/configuration/masks#object-filter-masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape. Object filter masks can be configured in <NavPath path="Settings > Camera configuration > Masks / Zones" />.
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