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Author SHA1 Message Date
mathieu-d
802493ce6c
Merge 0f3dd097ec into 01a7ec1060 2026-05-01 04:05:36 +10:00
Nicolas Mowen
01a7ec1060
Miscellaneous fixes (#23044)
* 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>
2026-04-30 12:53:34 -05:00
Josh Hawkins
95b5b89ed9
Miscellaneous fixes (#23032)
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* 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>
2026-04-29 16:20:19 -06:00
19 changed files with 191 additions and 67 deletions

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@ -26,7 +26,7 @@ _Please read the [contributing guidelines](https://github.com/blakeblackshear/fr
- This PR fixes or closes issue: fixes #
- This PR is related to issue:
- Link to discussion with maintainers (**required** for large/pinned features):
- Link to discussion with maintainers (**required** for any large or "planned" features):
## For new features

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@ -19,8 +19,8 @@ jobs:
days-before-stale: 30
days-before-close: 3
exempt-draft-pr: true
exempt-issue-labels: "pinned,security"
exempt-pr-labels: "pinned,security,dependencies"
exempt-issue-labels: "planned,security"
exempt-pr-labels: "planned,security,dependencies"
operations-per-run: 120
- name: Print outputs
env:

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@ -12,7 +12,7 @@ If you've found a bug and want to fix it, go for it. Link to the relevant issue
Every new feature adds scope that the maintainers must test, maintain, and support long-term. 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. Pinned feature requests 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.
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.
3. **Be open to "no".** 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 for the project. These calls are sometimes subjective, and we won't always get them right. We're happy to discuss and reconsider.

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@ -171,7 +171,7 @@ 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.

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@ -1368,12 +1368,17 @@ def preview_gif(
file_start = f"preview_{camera_name}-"
start_file = f"{file_start}{start_ts}.{PREVIEW_FRAME_TYPE}"
end_file = f"{file_start}{end_ts}.{PREVIEW_FRAME_TYPE}"
camera_files = [
entry.name
for entry in os.scandir(preview_dir)
if entry.name.startswith(file_start)
]
camera_files.sort()
selected_previews = []
for file in sorted(os.listdir(preview_dir)):
if not file.startswith(file_start):
continue
for file in camera_files:
if file < start_file:
continue
@ -1550,12 +1555,17 @@ def preview_mp4(
file_start = f"preview_{camera_name}-"
start_file = f"{file_start}{start_ts}.{PREVIEW_FRAME_TYPE}"
end_file = f"{file_start}{end_ts}.{PREVIEW_FRAME_TYPE}"
camera_files = [
entry.name
for entry in os.scandir(preview_dir)
if entry.name.startswith(file_start)
]
camera_files.sort()
selected_previews = []
for file in sorted(os.listdir(preview_dir)):
if not file.startswith(file_start):
continue
for file in camera_files:
if file < start_file:
continue

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@ -148,12 +148,17 @@ def get_preview_frames_from_cache(camera_name: str, start_ts: float, end_ts: flo
file_start = f"preview_{camera_name}-"
start_file = f"{file_start}{start_ts}.{PREVIEW_FRAME_TYPE}"
end_file = f"{file_start}{end_ts}.{PREVIEW_FRAME_TYPE}"
camera_files = [
entry.name
for entry in os.scandir(preview_dir)
if entry.name.startswith(file_start)
]
camera_files.sort()
selected_previews = []
for file in sorted(os.listdir(preview_dir)):
if not file.startswith(file_start):
continue
for file in camera_files:
if file < start_file:
continue

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@ -15,7 +15,7 @@ TRIGGER_DIR = f"{CLIPS_DIR}/triggers"
BIRDSEYE_PIPE = "/tmp/cache/birdseye"
CACHE_DIR = "/tmp/cache"
REPLAY_CAMERA_PREFIX = "_replay_"
REPLAY_DIR = os.path.join(CACHE_DIR, "replay")
REPLAY_DIR = os.path.join(CLIPS_DIR, "replay")
PLUS_ENV_VAR = "PLUS_API_KEY"
PLUS_API_HOST = "https://api.frigate.video"

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@ -366,12 +366,17 @@ class ReviewDescriptionProcessor(PostProcessorApi):
file_start = f"preview_{camera}-"
start_file = f"{file_start}{start_time}.webp"
end_file = f"{file_start}{end_time}.webp"
camera_files = [
entry.name
for entry in os.scandir(preview_dir)
if entry.name.startswith(file_start)
]
camera_files.sort()
all_frames: list[str] = []
for file in sorted(os.listdir(preview_dir)):
if not file.startswith(file_start):
continue
for file in camera_files:
if file < start_file:
if len(all_frames):
all_frames[0] = os.path.join(preview_dir, file)

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@ -4,6 +4,7 @@ import base64
import json
import logging
import os
import sys
import threading
from json.decoder import JSONDecodeError
from multiprocessing.synchronize import Event as MpEvent
@ -52,6 +53,14 @@ class EmbeddingProcess(FrigateProcess):
self.stop_event,
)
maintainer.start()
maintainer.join()
# If the maintainer thread exited but no shutdown was requested, it
# crashed. Surface as a non-zero exit so the watchdog restarts us
# instead of treating the silent thread death as a clean shutdown.
if not self.stop_event.is_set():
logger.error("Embeddings maintainer thread exited unexpectedly")
sys.exit(1)
class EmbeddingsContext:

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@ -153,9 +153,6 @@ Each line represents a detection state, not necessarily unique individuals. The
if "other_concerns" in schema.get("required", []):
schema["required"].remove("other_concerns")
# OpenAI strict mode requires additionalProperties: false on all objects
schema["additionalProperties"] = False
response_format = {
"type": "json_schema",
"json_schema": {

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@ -136,22 +136,44 @@ class GeminiClient(GenAIClient):
)
)
elif role == "assistant":
gemini_messages.append(
types.Content(
role="model", parts=[types.Part.from_text(text=content)]
)
)
parts: list[types.Part] = []
if content:
parts.append(types.Part.from_text(text=content))
for tc in msg.get("tool_calls") or []:
func = tc.get("function") or {}
tc_name = func.get("name") or ""
tc_args: Any = func.get("arguments")
if isinstance(tc_args, str):
try:
tc_args = json.loads(tc_args)
except (json.JSONDecodeError, TypeError):
tc_args = {}
if not isinstance(tc_args, dict):
tc_args = {}
if tc_name:
parts.append(
types.Part.from_function_call(
name=tc_name, args=tc_args
)
)
if not parts:
parts.append(types.Part.from_text(text=" "))
gemini_messages.append(types.Content(role="model", parts=parts))
elif role == "tool":
# Handle tool response
function_response = {
"name": msg.get("name", ""),
"response": content,
}
response_payload = (
content if isinstance(content, dict) else {"result": content}
)
gemini_messages.append(
types.Content(
role="function",
parts=[
types.Part.from_function_response(function_response) # type: ignore[misc,call-arg,arg-type]
types.Part.from_function_response(
name=msg.get("name")
or msg.get("tool_call_id")
or "",
response=response_payload,
)
],
)
)
@ -343,22 +365,44 @@ class GeminiClient(GenAIClient):
)
)
elif role == "assistant":
gemini_messages.append(
types.Content(
role="model", parts=[types.Part.from_text(text=content)]
)
)
parts: list[types.Part] = []
if content:
parts.append(types.Part.from_text(text=content))
for tc in msg.get("tool_calls") or []:
func = tc.get("function") or {}
tc_name = func.get("name") or ""
tc_args: Any = func.get("arguments")
if isinstance(tc_args, str):
try:
tc_args = json.loads(tc_args)
except (json.JSONDecodeError, TypeError):
tc_args = {}
if not isinstance(tc_args, dict):
tc_args = {}
if tc_name:
parts.append(
types.Part.from_function_call(
name=tc_name, args=tc_args
)
)
if not parts:
parts.append(types.Part.from_text(text=" "))
gemini_messages.append(types.Content(role="model", parts=parts))
elif role == "tool":
# Handle tool response
function_response = {
"name": msg.get("name", ""),
"response": content,
}
response_payload = (
content if isinstance(content, dict) else {"result": content}
)
gemini_messages.append(
types.Content(
role="function",
parts=[
types.Part.from_function_response(function_response) # type: ignore[misc,call-arg,arg-type]
types.Part.from_function_response(
name=msg.get("name")
or msg.get("tool_call_id")
or "",
response=response_payload,
)
],
)
)

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@ -44,6 +44,7 @@ class LlamaCppClient(GenAIClient):
_supports_tools: bool
_image_token_cache: dict[tuple[int, int], int]
_text_baseline_tokens: int | None
_media_marker: str
def _init_provider(self) -> str | None:
"""Initialize the client and query model metadata from the server."""
@ -56,6 +57,7 @@ class LlamaCppClient(GenAIClient):
self._supports_tools = False
self._image_token_cache = {}
self._text_baseline_tokens = None
self._media_marker = "<__media__>"
base_url = (
self.genai_config.base_url.rstrip("/")
@ -141,6 +143,13 @@ class LlamaCppClient(GenAIClient):
chat_caps = props.get("chat_template_caps", {})
self._supports_tools = chat_caps.get("supports_tools", False)
# Media marker for multimodal embeddings; the server randomizes this
# per startup unless LLAMA_MEDIA_MARKER is set, so we must read it
# from /props rather than hardcoding "<__media__>".
media_marker = props.get("media_marker")
if isinstance(media_marker, str) and media_marker:
self._media_marker = media_marker
logger.info(
"llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s",
configured_model,
@ -465,10 +474,11 @@ class LlamaCppClient(GenAIClient):
jpeg_bytes = _to_jpeg(img)
to_encode = jpeg_bytes if jpeg_bytes is not None else img
encoded = base64.b64encode(to_encode).decode("utf-8")
# prompt_string must contain <__media__> placeholder for image tokenization
# prompt_string must contain the server's media marker placeholder.
# The marker is randomized per server startup (read from /props).
content.append(
{
"prompt_string": "<__media__>\n",
"prompt_string": f"{self._media_marker}\n",
"multimodal_data": [encoded], # type: ignore[dict-item]
}
)

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@ -73,8 +73,17 @@ class OpenAIClient(GenAIClient):
**self.genai_config.runtime_options,
}
if response_format:
# OpenAI strict mode requires additionalProperties: false on the schema
if response_format.get("type") == "json_schema" and response_format.get(
"json_schema", {}
).get("strict"):
schema = response_format.get("json_schema", {}).get("schema")
if isinstance(schema, dict):
schema["additionalProperties"] = False
request_params["response_format"] = response_format
result = self.provider.chat.completions.create(**request_params)
if (
result is not None
and hasattr(result, "choices")

View File

@ -24,7 +24,7 @@ from frigate.config.camera.updater import (
)
from frigate.const import PROCESS_PRIORITY_HIGH
from frigate.log import LogPipe
from frigate.util.builtin import EventsPerSecond
from frigate.util.builtin import EventsPerSecond, get_ffmpeg_arg_list
from frigate.util.ffmpeg import start_or_restart_ffmpeg, stop_ffmpeg
from frigate.util.image import (
FrameManager,
@ -34,6 +34,23 @@ from frigate.util.process import FrigateProcess
logger = logging.getLogger(__name__)
# all built-in record presets use this segment_time
DEFAULT_RECORD_SEGMENT_TIME = 10
def _get_record_segment_time(config: CameraConfig) -> int:
"""Extract -segment_time from the camera's record output args."""
record_args = get_ffmpeg_arg_list(config.ffmpeg.output_args.record)
if record_args and record_args[0].startswith("preset"):
return DEFAULT_RECORD_SEGMENT_TIME
try:
idx = record_args.index("-segment_time")
return int(record_args[idx + 1])
except (ValueError, IndexError):
return DEFAULT_RECORD_SEGMENT_TIME
def capture_frames(
ffmpeg_process: sp.Popen[Any],
@ -164,6 +181,12 @@ class CameraWatchdog(threading.Thread):
self.latest_cache_segment_time: float = 0
self.record_enable_time: datetime | None = None
# `valid` segments are published with the segment's start time, so the
# gap between consecutive publishes can reach 2 * segment_time. Pad the
# staleness threshold so it's never tighter than that worst case.
segment_time = _get_record_segment_time(self.config)
self.record_stale_threshold = max(120, 2 * segment_time + 30)
# Stall tracking (based on last processed frame)
self._stall_timestamps: deque[float] = deque()
self._stall_active: bool = False
@ -413,16 +436,17 @@ class CameraWatchdog(threading.Thread):
# ensure segments are still being created and that they have valid video data
# Skip checks during grace period to allow segments to start being created
stale_window = timedelta(seconds=self.record_stale_threshold)
cache_stale = not in_grace_period and now_utc > (
latest_cache_dt + timedelta(seconds=120)
latest_cache_dt + stale_window
)
valid_stale = not in_grace_period and now_utc > (
latest_valid_dt + timedelta(seconds=120)
latest_valid_dt + stale_window
)
invalid_stale_condition = (
self.latest_invalid_segment_time > 0
and not in_grace_period
and now_utc > (latest_invalid_dt + timedelta(seconds=120))
and now_utc > (latest_invalid_dt + stale_window)
and self.latest_valid_segment_time
<= self.latest_invalid_segment_time
)
@ -439,7 +463,7 @@ class CameraWatchdog(threading.Thread):
)
self.logger.error(
f"{reason} for {self.config.name} in the last 120s. Restarting the ffmpeg record process..."
f"{reason} for {self.config.name} in the last {self.record_stale_threshold}s. Restarting the ffmpeg record process..."
)
p["process"] = start_or_restart_ffmpeg(
p["cmd"],

View File

@ -28,6 +28,7 @@ class MonitoredProcess:
restart_timestamps: deque[float] = field(
default_factory=lambda: deque(maxlen=MAX_RESTARTS)
)
clean_exit_logged: bool = False
def is_restarting_too_fast(self, now: float) -> bool:
while (
@ -72,7 +73,9 @@ class FrigateWatchdog(threading.Thread):
exitcode = entry.process.exitcode
if exitcode == 0:
logger.info("Process %s exited cleanly, not restarting", entry.name)
if not entry.clean_exit_logged:
logger.info("Process %s exited cleanly, not restarting", entry.name)
entry.clean_exit_logged = True
return
logger.warning(

View File

@ -391,10 +391,8 @@ export default function MobileReviewSettingsDrawer({
className="flex w-full items-center justify-center gap-2"
aria-label={t("title", { ns: "views/replay" })}
onClick={() => {
const now = new Date(latestTime * 1000);
now.setHours(now.getHours() - 1);
setDebugReplayRange({
after: now.getTime() / 1000,
after: latestTime - 60,
before: latestTime,
});
setSelectedReplayOption("1");
@ -541,11 +539,9 @@ export default function MobileReviewSettingsDrawer({
return;
}
const hours = parseInt(option);
const minutes = parseInt(option, 10);
const end = latestTime;
const now = new Date(end * 1000);
now.setHours(now.getHours() - hours);
setDebugReplayRange({ after: now.getTime() / 1000, before: end });
setDebugReplayRange({ after: end - minutes * 60, before: end });
};
content = (

View File

@ -396,7 +396,6 @@ export default function HlsVideoPlayer({
}}
>
<ObjectTrackOverlay
key={`overlay-${currentTime}`}
camera={camera}
showBoundingBoxes={!isPlaying}
currentTime={currentTime}

View File

@ -728,10 +728,8 @@ export function RecordingView({
setShareTimestampOpen(true);
}}
onDebugReplayClick={() => {
const now = new Date(timeRange.before * 1000);
now.setHours(now.getHours() - 1);
setDebugReplayRange({
after: now.getTime() / 1000,
after: timeRange.before - 60,
before: timeRange.before,
});
setDebugReplayMode("select");

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@ -10,13 +10,16 @@ import axios from "axios";
import { toast } from "sonner";
import { useJobStatus } from "@/api/ws";
import { Switch } from "@/components/ui/switch";
import { LuCheck, LuX } from "react-icons/lu";
import { LuCheck, LuExternalLink, LuX } from "react-icons/lu";
import { cn } from "@/lib/utils";
import { formatUnixTimestampToDateTime } from "@/utils/dateUtil";
import { MediaSyncResults, MediaSyncStats } from "@/types/ws";
import { useDocDomain } from "@/hooks/use-doc-domain";
import { Link } from "react-router-dom";
export default function MediaSyncSettingsView() {
const { t } = useTranslation("views/settings");
const { getLocaleDocUrl } = useDocDomain();
const [selectedMediaTypes, setSelectedMediaTypes] = useState<string[]>([
"all",
]);
@ -109,13 +112,25 @@ export default function MediaSyncSettingsView() {
<Heading as="h4" className="mb-2 hidden md:block">
{t("maintenance.sync.title")}
</Heading>
<div className="max-w-6xl">
<div className="mb-5 mt-2 flex max-w-5xl flex-col gap-2 text-sm text-muted-foreground">
<p>{t("maintenance.sync.desc")}</p>
<div className="flex items-center text-primary-variant">
<Link
to={getLocaleDocUrl(
"configuration/record#syncing-media-files-with-disk",
)}
target="_blank"
rel="noopener noreferrer"
className="inline"
>
{t("readTheDocumentation", { ns: "common" })}
<LuExternalLink className="ml-2 inline-flex size-3" />
</Link>
</div>
</div>
</div>
<div className="space-y-6">
{/* Media Types Selection */}
<div>