Merge 6a6c5b7389 into f3a352ef3f

Currently translated at 100.0% (50 of 50 strings)

Translated using Weblate (Chinese (Simplified Han script))

Currently translated at 100.0% (1276 of 1276 strings)

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/
Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-settings/zh_Hans/
Translation: Frigate NVR/components-camera
Translation: Frigate NVR/views-settings

docs/docs/frigate/glossary.md

@@ -5,40 +5,20 @@ title: Glossary
 
 The glossary explains terms commonly used in Frigate's documentation.
 
-## Alert
-
-The higher-priority of the two [review item](#review-item) severities, the other being a [detection](#detection). By default a review item is an alert when it involves a `person` or `car`; the qualifying [labels](#label) and [zones](#zone) can be configured. [See the review docs for more info](/configuration/review)
-
-## Attribute
-
-A property detected on an [object](#object) that exists alongside its [label](#label). Unlike a [sub label](#sub-label), an object can carry several attributes at once. Some attributes come directly from the object detection [model](#model) — for example `face`, `license_plate`, or delivery carrier logos such as `amazon`, `ups`, and `fedex` — while others come from a [custom object classification model](/configuration/custom_classification/object_classification) configured with the `attribute` type. Attributes are visible in the Tracked Object Details pane in Explore, in `frigate/events` MQTT messages, and through the HTTP API.
-
 ## Bounding Box
 
-A box returned by the object detection [model](#model) that outlines a detected [object](#object) in the frame. In the Debug view, bounding boxes are colored by object [label](#label).
+A box returned from the object detection model that outlines an object in the frame. These have multiple colors depending on object type in the debug live view.
 
 ### Bounding Box Colors
 
 - At startup different colors will be assigned to each object label
 - A dark blue thin line indicates that object is not detected at this current point in time
 - A gray thin line indicates that object is detected as being stationary
-- A thick line indicates that object is the subject of autotracking (when enabled)
-
-## Class
-
-The categories a classification [model](#model) is trained to distinguish between. Each class is a distinct visual category the model predicts, plus a `none` class for inputs that don't fit any category. For example, a custom object classification model for `person` objects might use the classes `delivery_person`, `resident`, and `none`. The predicted class is applied to the [object](#object) as either a [sub label](#sub-label) or an [attribute](#attribute), depending on the model's configuration. [See the object classification docs for more info](/configuration/custom_classification/object_classification)
-
-## Detection
-
-The lower-priority of the two [review item](#review-item) severities, the other being an [alert](#alert). By default, any review item that does not qualify as an alert is a detection; the qualifying [labels](#label) and [zones](#zone) can be configured. Despite the name, a detection is a category of review item — not the same as the object detection performed by the [model](#model). [See the review docs for more info](/configuration/review)
+- A thick line indicates that object is the subject of autotracking (when enabled).
 
 ## False Positive
 
-An incorrect result from the object detection [model](#model), where it assigns the wrong [label](#label) to something in the frame — for example a dog identified as a person, or a chair identified as a dog. A person correctly identified in an area you want to ignore is not a false positive.
-
-## Label
-
-The type assigned to a detected [object](#object) by the object detection [model](#model), drawn from the model's labelmap — for example `person`, `car`, or `dog`. Frigate tracks `person` by default; additional labels are tracked by adding them to the objects configuration. [See the available objects docs for the full list](/configuration/objects)
+An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.
 
 ## Mask
 
@@ -46,56 +26,44 @@ There are two types of masks in Frigate. [See the mask docs for more info](/conf
 
 ### Motion Mask
 
-A motion mask stops [motion](#motion) in the masked area from triggering object detection. It does not stop an object from being detected when object detection runs because of motion in a nearby area. Use motion masks for parts of the frame that change constantly but never contain objects you care about — camera timestamps, the sky, the tops of trees, and so on.
+Motion masks prevent detection of [motion](#motion) in masked areas from triggering Frigate to run object detection, but do not prevent objects from being detected if object detection runs due to motion in nearby areas. For example: camera timestamps, skies, the tops of trees, etc.
 
 ### Object Mask
 
-An object filter mask drops any [bounding box](#bounding-box) whose bottom center falls inside the masked area (overlap elsewhere doesn't matter). The object is forced to be treated as a [false positive](#false-positive) and ignored.
+Object filter masks drop any bounding boxes where the bottom center (overlap doesn't matter) is in the masked area. It forces them to be considered a [false positive](#false-positive) so that they are ignored.
 
 ## Min Score
 
-The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
-
-## Model
-
-A machine learning model that Frigate uses to detect or classify objects. The object detection model locates [objects](#object) in each frame and returns their [labels](#label) and [bounding boxes](#bounding-box). Additional enrichment models run on tracked objects to add detail: face recognition, license plate recognition, bird classification, custom object and state classification, and the embedding models used for semantic search. [See the object detectors docs for more info](/configuration/object_detectors)
+The lowest score that an object can be detected with during tracking, any detection with a lower score will be assumed to be a false positive
 
 ## Motion
 
-A change in pixels between the current camera frame and previous frames. When many nearby pixels change together, they are grouped and shown as a red motion box in the debug live view. [See the motion detection docs for more info](/configuration/motion_detection)
-
-## Object
-
-Something Frigate can detect and follow in a camera frame, identified by its [label](#label) (for example a person or a car). The object types Frigate watches for are set in the `objects` configuration. Once an object is detected and followed across frames it becomes a [tracked object](#tracked-object-event-in-previous-versions), which may also carry a [sub label](#sub-label) and [attributes](#attribute). [See the available objects docs for more info](/configuration/objects)
+When pixels in the current camera frame are different than previous frames. When many nearby pixels are different in the current frame they grouped together and indicated with a red motion box in the live debug view. [See the motion detection docs for more info](/configuration/motion_detection)
 
 ## Region
 
-A portion of the camera frame sent to the object detection [model](#model). Regions are selected because of [motion](#motion), active objects, or occasionally to recheck stationary objects, and are shown as green boxes in the debug live view.
+A portion of the camera frame that is sent to object detection, regions can be sent due to motion, active objects, or occasionally for stationary objects. These are represented by green boxes in the debug live view.
 
 ## Review Item
 
-A period of time during which one or more [tracked objects](#tracked-object-event-in-previous-versions) were active, grouped together for review. Each review item is categorized as either an [alert](#alert) or a [detection](#detection). [See the review docs for more info](/configuration/review)
+A review item is a time period where any number of events/tracked objects were active. [See the review docs for more info](/configuration/review)
 
 ## Snapshot Score
 
-The object's score at the specific moment the snapshot was captured.
-
-## Sub Label
-
-A more specific identity assigned to a [tracked object](#tracked-object-event-in-previous-versions) in addition to its [label](#label). A `person` may get the name of a recognized face, a `car` may get the name of a known license plate, and a `bird` may get its species. An object can have only one sub label at a time. Sub labels are produced by face recognition, license plate recognition, bird classification, custom object classification configured with the `sub label` type, and semantic search triggers.
+The score shown in a snapshot is the score of that object at that specific moment in time.
 
 ## Threshold
 
-The median score an object must reach to be considered a true positive.
+The threshold is the median score that an object must reach in order to be considered a true positive.
 
 ## Top Score
 
-The highest median score an object reached over its lifetime.
+The top score for an object is the highest median score for an object.
 
 ## Tracked Object ("event" in previous versions)
 
-An [object](#object) followed from the moment it enters the frame until it leaves, including any time it stays still. A tracked object is saved once it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording.
+The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
 
 ## Zone
 
-A user-defined area of interest within the camera frame. Zones can be used for notifications and to limit where Frigate creates a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
+Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)

docs/docs/troubleshooting/recordings.md

@@ -121,12 +121,6 @@ If segments are only ~1 second instead of ~10 seconds, the camera is sending cor
 - **Changing codec, bitrate, or resolution mid-stream** — Any encoding changes during an active stream can cause unpredictable segment splitting.
 - **Camera firmware bugs** — Check for firmware updates from your camera manufacturer.
 
-:::tip
-
-You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera Probe Info** dialog (the info icon on the System → Metrics → Cameras page) and check the **Keyframe analysis** section. It probes the record stream and flags sparse or variable keyframes, which is what smart/"+" codecs (H.264+/H.265+) and long keyframe intervals produce.
-
-:::
-
 ### Step 4: Check for a stuck detector
 
 If the detect stream is not processing frames, segments will accumulate. Common causes:

docs/static/frigate-api.yaml

@@ -400,35 +400,6 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
-  /keyframe_analysis:
-    get:
-      tags:
-        - Camera
-      summary: Keyframe Analysis
-      description: >-
-        Probe a camera's record stream and classify its keyframe spacing.
-        Detects smart/+ codecs and long/variable GOPs that degrade recording.
-      operationId: keyframe_analysis_keyframe_analysis_get
-      parameters:
-        - name: camera
-          in: query
-          required: false
-          schema:
-            type: string
-            default: ""
-            title: Camera
-      responses:
-        "200":
-          description: Successful Response
-          content:
-            application/json:
-              schema: {}
-        "422":
-          description: Validation Error
-          content:
-            application/json:
-              schema:
-                $ref: "#/components/schemas/HTTPValidationError"
   /ffprobe/snapshot:
     get:
       tags:

frigate/api/camera.py

@@ -34,15 +34,11 @@ from frigate.config.camera.updater import (
 )
 from frigate.config.env import substitute_frigate_vars
 from frigate.models import User
-from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
+from frigate.util.builtin import clean_camera_user_pass
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 from frigate.util.config import find_config_file
 from frigate.util.image import run_ffmpeg_snapshot
-from frigate.util.services import (
-    analyze_record_keyframes,
-    ffprobe_stream,
-    is_restricted_go2rtc_source,
-)
+from frigate.util.services import ffprobe_stream, is_restricted_go2rtc_source
 
 logger = logging.getLogger(__name__)
 
@@ -366,48 +362,6 @@ def ffprobe(request: Request, paths: str = "", detailed: bool = False):
     return JSONResponse(content=output)
 
 
-@router.get("/keyframe_analysis", dependencies=[Depends(require_role(["admin"]))])
-async def keyframe_analysis(request: Request, camera: str = ""):
-    """Probe a camera's record stream and classify its keyframe spacing.
-
-    Detects smart/+ codecs and long/variable GOPs that degrade recording.
-    """
-    config: FrigateConfig = request.app.frigate_config
-
-    if camera not in config.cameras:
-        return JSONResponse(
-            content={"success": False, "message": f"{camera} is not a valid camera."},
-            status_code=404,
-        )
-
-    camera_config = config.cameras[camera]
-
-    if not camera_config.enabled:
-        return JSONResponse(
-            content={"success": False, "message": f"{camera} is not enabled."},
-            status_code=404,
-        )
-
-    # keyframe spacing only matters when this camera is recording
-    if not camera_config.record.enabled:
-        return JSONResponse(content={"severity": "record_disabled"})
-
-    # recording guarantees an input carries the record role; its index matches
-    # the "Stream N" numbering the ffprobe endpoint surfaces (same input order)
-    record_index, record_input = next(
-        (idx, i)
-        for idx, i in enumerate(camera_config.ffmpeg.inputs)
-        if "record" in i.roles
-    )
-
-    segment_time = get_record_segment_time(camera_config)
-    result = await analyze_record_keyframes(
-        config.ffmpeg, record_input.path, segment_time
-    )
-    result["stream_index"] = record_index
-    return JSONResponse(content=result)
-
-
 @router.get("/ffprobe/snapshot", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe_snapshot(request: Request, url: str = "", timeout: int = 10):
     """Get a snapshot from a stream URL using ffmpeg."""

frigate/config/camera/updater.py

@@ -73,12 +73,7 @@ class CameraConfigUpdateSubscriber:
 
         base_topic = "config/cameras"
 
-        # global subscribers must hear every camera; only narrow per-camera workers
-        is_global_subscriber = (
-            CameraConfigUpdateEnum.add in self.topics
-            or CameraConfigUpdateEnum.remove in self.topics
-        )
-        if not is_global_subscriber and len(self.camera_configs) == 1:
+        if len(self.camera_configs) == 1:
             base_topic += f"/{list(self.camera_configs.keys())[0]}"
 
         self.subscriber = ConfigSubscriber(

frigate/test/http_api/test_http_keyframe_analysis.py

@@ -1,58 +0,0 @@
-from unittest.mock import AsyncMock, patch
-
-from frigate.models import Event, Recordings, ReviewSegment
-from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
-
-
-class TestHttpKeyframeAnalysis(BaseTestHttp):
-    def setUp(self):
-        super().setUp([Event, Recordings, ReviewSegment])
-
-    def test_invalid_camera_returns_404(self):
-        app = super().create_app()
-        with AuthTestClient(app) as client:
-            response = client.get("/keyframe_analysis?camera=does_not_exist")
-            assert response.status_code == 404
-
-    def test_record_disabled_returns_neutral(self):
-        # default minimal_config has recording disabled
-        app = super().create_app()
-        with AuthTestClient(app) as client:
-            response = client.get("/keyframe_analysis?camera=front_door")
-            assert response.status_code == 200
-            assert response.json()["severity"] == "record_disabled"
-
-    def test_probes_record_input_and_returns_severity(self):
-        self.minimal_config["cameras"]["front_door"]["ffmpeg"]["inputs"] = [
-            {
-                "path": "rtsp://10.0.0.1:554/record",
-                "roles": ["detect", "record"],
-            }
-        ]
-        self.minimal_config["cameras"]["front_door"]["record"] = {"enabled": True}
-        app = super().create_app()
-
-        canned = {
-            "severity": "ok",
-            "keyframe_count": 5,
-            "max_gap": 1.0,
-            "mean_gap": 1.0,
-            "min_gap": 1.0,
-            "segment_time": 10,
-            "duration_observed": 4.0,
-            "thresholds": {"warning": 4.0, "error": 10},
-        }
-
-        with patch(
-            "frigate.api.camera.analyze_record_keyframes",
-            AsyncMock(return_value=canned),
-        ) as mock_probe:
-            with AuthTestClient(app) as client:
-                response = client.get("/keyframe_analysis?camera=front_door")
-
-        assert response.status_code == 200
-        assert response.json()["severity"] == "ok"
-        # index matches the input carrying the record role ("Stream 1")
-        assert response.json()["stream_index"] == 0
-        # the record-role input path was probed
-        assert mock_probe.await_args.args[1] == "rtsp://10.0.0.1:554/record"

frigate/test/test_keyframe_analysis.py

@@ -1,111 +0,0 @@
-"""Tests for keyframe-spacing analysis used to detect smart/+ codecs."""
-
-import asyncio
-import unittest
-from unittest.mock import AsyncMock, MagicMock, patch
-
-from frigate.util.services import (
-    analyze_record_keyframes,
-    classify_keyframe_gaps,
-    parse_keyframe_packets,
-)
-
-
-class TestClassifyKeyframeGaps(unittest.TestCase):
-    def test_ok_when_gaps_small(self):
-        # keyframes every ~1s
-        pts = [0.0, 1.0, 2.0, 3.0, 4.0]
-        result = classify_keyframe_gaps(pts, segment_time=10)
-        self.assertEqual(result["severity"], "ok")
-        self.assertEqual(result["max_gap"], 1.0)
-        self.assertEqual(result["keyframe_count"], 5)
-        self.assertEqual(result["thresholds"], {"warning": 4.0, "error": 10})
-
-    def test_warning_when_gap_exceeds_four_seconds(self):
-        pts = [0.0, 1.0, 6.5]  # 5.5s gap
-        result = classify_keyframe_gaps(pts, segment_time=10)
-        self.assertEqual(result["severity"], "warning")
-        self.assertEqual(result["max_gap"], 5.5)
-
-    def test_error_when_gap_exceeds_segment_time(self):
-        pts = [0.0, 12.0]  # 12s gap > 10s segment
-        result = classify_keyframe_gaps(pts, segment_time=10)
-        self.assertEqual(result["severity"], "error")
-
-    def test_error_threshold_tracks_segment_time(self):
-        pts = [0.0, 6.0]  # 6s gap, segment_time=5 -> error
-        result = classify_keyframe_gaps(pts, segment_time=5)
-        self.assertEqual(result["severity"], "error")
-
-    def test_unknown_with_single_keyframe(self):
-        result = classify_keyframe_gaps([1.0], segment_time=10)
-        self.assertEqual(result["severity"], "unknown")
-        self.assertIsNone(result["max_gap"])
-        self.assertEqual(result["keyframe_count"], 1)
-
-    def test_unknown_with_no_keyframes(self):
-        result = classify_keyframe_gaps([], segment_time=10)
-        self.assertEqual(result["severity"], "unknown")
-        self.assertEqual(result["keyframe_count"], 0)
-
-
-class TestParseKeyframePackets(unittest.TestCase):
-    def test_extracts_keyframe_pts_and_max(self):
-        output = "0.000000,K__\n0.033333,___\n1.000000,K__\n1.500000,___\n"
-        keyframe_pts, max_pts = parse_keyframe_packets(output)
-        self.assertEqual(keyframe_pts, [0.0, 1.0])
-        self.assertEqual(max_pts, 1.5)
-
-    def test_skips_unparseable_and_empty_lines(self):
-        output = "N/A,K__\n\n2.0,K__\nbad line\n"
-        keyframe_pts, max_pts = parse_keyframe_packets(output)
-        self.assertEqual(keyframe_pts, [2.0])
-        self.assertEqual(max_pts, 2.0)
-
-    def test_empty_output(self):
-        keyframe_pts, max_pts = parse_keyframe_packets("")
-        self.assertEqual(keyframe_pts, [])
-        self.assertIsNone(max_pts)
-
-
-class TestAnalyzeRecordKeyframes(unittest.IsolatedAsyncioTestCase):
-    async def test_merges_duration_and_classification(self):
-        csv = b"0.0,K__\n1.0,___\n6.0,K__\n7.0,___\n"
-        proc = MagicMock()
-        proc.communicate = AsyncMock(return_value=(csv, b""))
-        ffmpeg = MagicMock()
-        ffmpeg.ffprobe_path = "/usr/bin/ffprobe"
-
-        with patch(
-            "frigate.util.services.asyncio.create_subprocess_exec",
-            AsyncMock(return_value=proc),
-        ):
-            result = await analyze_record_keyframes(
-                ffmpeg, "rtsp://cam/stream", segment_time=10
-            )
-
-        self.assertEqual(result["severity"], "warning")  # 6s gap > 4s
-        self.assertEqual(result["max_gap"], 6.0)
-        self.assertEqual(result["duration_observed"], 7.0)
-
-    async def test_timeout_returns_unknown(self):
-        proc = MagicMock()
-        proc.communicate = AsyncMock(side_effect=asyncio.TimeoutError())
-        proc.kill = MagicMock()
-        ffmpeg = MagicMock()
-        ffmpeg.ffprobe_path = "/usr/bin/ffprobe"
-
-        with patch(
-            "frigate.util.services.asyncio.create_subprocess_exec",
-            AsyncMock(return_value=proc),
-        ):
-            result = await analyze_record_keyframes(
-                ffmpeg, "rtsp://cam/stream", segment_time=10
-            )
-
-        self.assertEqual(result["severity"], "unknown")
-        proc.kill.assert_called_once()
-
-
-if __name__ == "__main__":
-    unittest.main()

frigate/util/builtin.py

@@ -14,16 +14,13 @@ import urllib.parse
 from collections.abc import Mapping
 from multiprocessing.managers import ValueProxy
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import numpy as np
 from ruamel.yaml import YAML
 
 from frigate.const import REGEX_HTTP_CAMERA_USER_PASS, REGEX_RTSP_CAMERA_USER_PASS
 
-if TYPE_CHECKING:
-    from frigate.config import CameraConfig
-
 logger = logging.getLogger(__name__)
 
 
@@ -135,24 +132,6 @@ def get_ffmpeg_arg_list(arg: Any) -> list:
     return arg if isinstance(arg, list) else shlex.split(arg)
 
 
-# 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 load_labels(
     path: Optional[str], encoding="utf-8", prefill=91, indexed: bool | None = None
 ):

frigate/util/services.py

@@ -879,131 +879,6 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
     return result
 
 
-KEYFRAME_PROBE_WINDOW_SECONDS = 20
-KEYFRAME_GAP_WARNING_SECONDS = 4.0
-
-
-def parse_keyframe_packets(output: str) -> Tuple[List[float], Optional[float]]:
-    """Parse ffprobe CSV `pts_time,flags` output.
-
-    Returns the presentation timestamps of keyframes (flags containing "K")
-    and the maximum timestamp observed across all packets.
-    """
-    keyframe_pts: List[float] = []
-    max_pts: Optional[float] = None
-
-    for line in output.splitlines():
-        parts = line.split(",")
-        if len(parts) < 2:
-            continue
-        try:
-            pts = float(parts[0])
-        except ValueError:
-            continue
-        if max_pts is None or pts > max_pts:
-            max_pts = pts
-        if "K" in parts[1]:
-            keyframe_pts.append(pts)
-
-    return keyframe_pts, max_pts
-
-
-def classify_keyframe_gaps(
-    keyframe_pts: List[float], segment_time: int
-) -> dict[str, Any]:
-    """Classify keyframe spacing for recording suitability.
-
-    A camera using a smart/+ codec or a long/variable GOP produces large or
-    irregular gaps between keyframes, which breaks time-based recording
-    segmentation. Severity:
-      - "unknown" when fewer than two keyframes were observed
-      - "error" when the longest gap exceeds the record segment length
-      - "warning" when the longest gap exceeds the warning threshold
-      - "ok" otherwise
-    """
-    thresholds = {
-        "warning": KEYFRAME_GAP_WARNING_SECONDS,
-        "error": segment_time,
-    }
-
-    if len(keyframe_pts) < 2:
-        return {
-            "keyframe_count": len(keyframe_pts),
-            "max_gap": None,
-            "mean_gap": None,
-            "min_gap": None,
-            "segment_time": segment_time,
-            "severity": "unknown",
-            "thresholds": thresholds,
-        }
-
-    gaps = [b - a for a, b in zip(keyframe_pts, keyframe_pts[1:])]
-    max_gap = max(gaps)
-
-    if max_gap > segment_time:
-        severity = "error"
-    elif max_gap > KEYFRAME_GAP_WARNING_SECONDS:
-        severity = "warning"
-    else:
-        severity = "ok"
-
-    return {
-        "keyframe_count": len(keyframe_pts),
-        "max_gap": round(max_gap, 2),
-        "mean_gap": round(sum(gaps) / len(gaps), 2),
-        "min_gap": round(min(gaps), 2),
-        "segment_time": segment_time,
-        "severity": severity,
-        "thresholds": thresholds,
-    }
-
-
-async def analyze_record_keyframes(
-    ffmpeg, url: str, segment_time: int, window: int = KEYFRAME_PROBE_WINDOW_SECONDS
-) -> dict[str, Any]:
-    """Probe a stream for ~`window` seconds and classify its keyframe spacing.
-
-    Reads video packet flags via ffprobe to find keyframes, then measures the
-    gaps between them. On timeout or failure returns an "unknown" result rather
-    than a false all-clear.
-    """
-    clean_url = escape_special_characters(url)
-    cmd = [
-        ffmpeg.ffprobe_path,
-        "-v",
-        "error",
-        "-select_streams",
-        "v:0",
-        "-read_intervals",
-        f"%+{window}",
-        "-show_entries",
-        "packet=pts_time,flags",
-        "-of",
-        "csv=p=0",
-        clean_url,
-    ]
-
-    try:
-        proc = await asyncio.create_subprocess_exec(
-            *cmd,
-            stdout=asyncio.subprocess.PIPE,
-            stderr=asyncio.subprocess.PIPE,
-        )
-        stdout, _ = await asyncio.wait_for(proc.communicate(), timeout=window + 15)
-    except asyncio.TimeoutError:
-        logger.warning("Keyframe probe timed out for record stream")
-        proc.kill()
-        return classify_keyframe_gaps([], segment_time)
-    except OSError as err:
-        logger.error("Keyframe probe failed: %s", err)
-        return classify_keyframe_gaps([], segment_time)
-
-    keyframe_pts, max_pts = parse_keyframe_packets(stdout.decode("utf-8", "replace"))
-    result = classify_keyframe_gaps(keyframe_pts, segment_time)
-    result["duration_observed"] = round(max_pts, 2) if max_pts is not None else None
-    return result
-
-
 def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
     """Run vainfo."""
     if not device_name:

frigate/video/ffmpeg.py

@@ -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, get_record_segment_time
+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],
@@ -168,7 +185,7 @@ class CameraWatchdog(threading.Thread):
         # `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)
+        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)

web/public/locales/en/views/system.json

@@ -174,21 +174,6 @@
       "error": "Error: {{error}}",
       "tips": {
         "title": "Camera Probe Info"
-      },
-      "keyframes": {
-        "title": "Keyframe analysis",
-        "analyzing": "Analyzing keyframes... {{seconds}} seconds remaining",
-        "stillAnalyzing": "Still analyzing keyframes...",
-        "recordStream": "Record stream:",
-        "keyframeCount": "Keyframes observed:",
-        "observedDuration": "Observed duration:",
-        "gap": "Keyframe gap (min / avg / max):",
-        "segmentLength": "Recording segment length:",
-        "ok": "Keyframes every ~{{seconds}}s, good for recording and playback.",
-        "warning": "Sparse or variable keyframes (longest gap ~{{seconds}}s), likely a smart codec (H.264+/H.265+), this is not recommended.",
-        "error": "Keyframe gap (~{{seconds}}s) exceeds the recording segment length ({{segmentTime}}s). Some segments may have no keyframe, which breaks playback. Disable the smart/+ codec on the camera or shorten its keyframe interval.",
-        "unknown": "Couldn't determine keyframe spacing.",
-        "recordDisabled": "Recording is disabled for this camera."
       }
     },
     "framesAndDetections": "Frames / Detections",

web/public/locales/id/components/camera.json

@@ -55,10 +55,8 @@
             },
             "continuousStreaming": {
               "desc": {
-                "title": "Kamera akan selalu live stream selama muncul di dashboard, meskipun tidak ada aktivitas yang terdeteksi.",
-                "warning": "Streaming Terus Menerus dapat menyebabkan penggunaan bandwidth yang tinggi dan masalah performa. Gunakan dengan hati-hati."
-              },
-              "label": "Streaming Terus Menerus"
+                "title": "Kamera akan selalu live stream selama muncul di dashboard, meskipun tidak ada aktivitas yang terdeteksi."
+              }
             }
           }
         },
@@ -84,7 +82,6 @@
     "motion": "Gerakan",
     "regions": "Wilayah",
     "timestamp": "Timestamp",
-    "mask": "Masker",
-    "paths": "Path"
+    "mask": "Masker"
   }
 }

web/public/locales/id/components/dialog.json

@@ -52,10 +52,7 @@
     "export": "Ekspor",
     "selectOrExport": "Pilih atau Ekspor",
     "toast": {
-      "success": "Berhasil memulai ekspor. Lihat file pada halaman ekspor.",
-      "queued": "Ekspor diantrekan. Lihat progres di halaman ekspor.",
-      "view": "Melihat",
-      "batchSuccess_other": "{{count}} Ekspor dimulai. Membuka kasusnya sekarang."
+      "success": "Berhasil memulai ekspor. Lihat file pada halaman ekspor."
     },
     "case": {
       "newCaseOption": "Membuat Kasus Baru",
@@ -77,24 +74,14 @@
       "noCameras": "Tidak ada Kamera Tersedia",
       "detectionCount_other": "{{count}} Objek terlacak",
       "nameLabel": "Nama Ekspor",
-      "exportButton_other": "Ekspor {{count}} kamera",
-      "cameraSelectionHelp": "Kamera dengan objek yang dilacak dalam rentang waktu ini telah dipilih sebelumnya",
-      "queueingButton": "Mengantrekan ekspor..."
+      "exportButton_other": "Ekspor {{count}} kamera"
     },
     "multi": {
-      "title_other": "Ekspor {{count}} Ulasan",
+      "title_other": "",
       "toast": {
-        "startedNoCase_other": "{{count}} Ekspor Dimulai.",
-        "started_other": "{{count}} ekspor dimulai. Membuka kasusnya sekarang.",
-        "partial": "Memulai {{successful}} dari {{total}} ekspor. Gagal: {{failedItems}}",
-        "failed": "Gagal memulai {{total}} ekspor. Gagal: {{failedItems}}"
-      },
-      "description": "Ekspor setiap ulasan yang dipilih. Semua Ekspor akan dikelompokkan dalam satu kasus.",
-      "descriptionNoCase": "Ekspor setiap ulasan yang dipilih.",
-      "exportButton_other": "Ekspor {{count}} ulasan",
-      "exportingButton": "Mengekspor..."
-    },
-    "queueing": "Mengantrekan Ekspor..."
+        "startedNoCase_other": "{{count}} Ekspor Dimulai."
+      }
+    }
   },
   "search": {
     "saveSearch": {

web/public/locales/it/config/global.json

@@ -179,17 +179,11 @@
                 "label": "Numero di processi di rilevamento",
                 "description": "Il numero di processi utilizzati per l'inferenza basata sulla CPU."
             },
-            "description": "Rilevatore CPU TFLite che esegue modelli TensorFlow Lite sulla CPU di sistema senza accelerazione hardware. Sconsigliato.",
-            "label": "CPU"
+            "description": "Rilevatore CPU TFLite che esegue modelli TensorFlow Lite sulla CPU di sistema senza accelerazione hardware. Sconsigliato."
         },
         "label": "Dispositivo di rilevamento",
         "hailo8l": {
-            "description": "Rilevatore Hailo-8/Hailo-8L che utilizza modelli HEF e l'SDK HailoRT per l'inferenza sul dispositivo Hailo.",
-            "label": "Hailo-8/Hailo-8L",
-            "device": {
-                "label": "Tipo di dispositivo",
-                "description": "Il dispositivo da utilizzare per l'inferenza Hailo (ad es. 'PCIe', 'M.2')."
-            }
+            "description": "Rilevatore Hailo-8/Hailo-8L che utilizza modelli HEF e l'SDK HailoRT per l'inferenza sul dispositivo Hailo."
         },
         "openvino": {
             "description": "Rilevatore OpenVINO per CPU AMD e Intel, GPU Intel e dispositivo Intel VPU."
@@ -224,88 +218,7 @@
                 "description": "Altezza del tensore di input del modello in pixel."
             },
             "labelmap": {
-                "label": "Personalizzazione labelmap",
-                "description": "Sostituzioni o voci di rimappatura da unire alla labelmap standard."
-            },
-            "attributes_map": {
-                "label": "Mappa delle etichette oggetto con le relative etichette di attributo",
-                "description": "Mappatura dalle etichette oggetto alle etichette di attributo utilizzata per allegare metadati (ad esempio 'car' -> ['license_plate'])."
-            },
-            "input_tensor": {
-                "label": "Forma del tensore di input del modello",
-                "description": "Formato del tensore richiesto dal modello: 'nhwc' o 'nchw'."
-            },
-            "input_pixel_format": {
-                "label": "Formato colore dei pixel di input del modello",
-                "description": "Spazio colore dei pixel richiesto dal modello: 'rgb', 'bgr' o 'yuv'."
-            },
-            "input_dtype": {
-                "label": "Tipo di dati di input del modello (Dtype)",
-                "description": "Tipo di dati del tensore di input del modello (ad esempio 'float32')."
-            },
-            "model_type": {
-                "label": "Tipo di modello di rilevamento oggetti",
-                "description": "Tipo di architettura del modello del rilevatore (ssd, yolox, yolonas) utilizzato da alcuni rilevatori per l'ottimizzazione."
-            }
-        },
-        "model_path": {
-            "label": "Percorso del modello specifico del rilevatore",
-            "description": "Percorso del file binario del modello del rilevatore, se richiesto dal rilevatore scelto."
-        },
-        "axengine": {
-            "label": "NPU AXEngine",
-            "description": "Rilevatore NPU AXERA AX650N/AX8850N che esegue file .axmodel compilati tramite il runtime AXEngine."
-        },
-        "deepstack": {
-            "label": "DeepStack",
-            "description": "Rilevatore DeepStack/CodeProject.AI che invia immagini a una API HTTP DeepStack remota per l'inferenza. Non consigliato.",
-            "api_url": {
-                "label": "URL API DeepStack",
-                "description": "L'URL delle API DeepStack."
-            },
-            "api_timeout": {
-                "label": "Timeout API DeepStack (in secondi)",
-                "description": "Tempo massimo consentito per una richiesta alle API DeepStack."
-            },
-            "api_key": {
-                "label": "Chiave API DeepStack (se richiesta)",
-                "description": "Chiave API opzionale per i servizi DeepStack autenticati."
-            }
-        },
-        "degirum": {
-            "label": "DeGirum",
-            "description": "Rilevatore DeGirum per l'esecuzione di modelli tramite il cloud DeGirum o servizi di inferenza locali.",
-            "zoo": {
-                "label": "Model Zoo",
-                "description": "Percorso o URL del model zoo di DeGirum."
-            },
-            "token": {
-                "label": "Token Cloud DeGirum",
-                "description": "Token per l'accesso al Cloud DeGirum."
-            }
-        },
-        "edgetpu": {
-            "label": "EdgeTPU",
-            "description": "Rilevatore EdgeTPU che esegue modelli TensorFlow Lite compilati per Coral EdgeTPU utilizzando il delegate EdgeTPU.",
-            "device": {
-                "label": "Tipo di dispositivo",
-                "description": "Il dispositivo da utilizzare per l'inferenza EdgeTPU (ad es. 'usb', 'pci')."
-            }
-        },
-        "memryx": {
-            "label": "MemryX",
-            "description": "Rilevatore MemryX MX3 che esegue modelli DFP compilati su acceleratori MemryX.",
-            "device": {
-                "label": "Percorso del dispositivo",
-                "description": "Il dispositivo da utilizzare per l'inferenza MemryX (ad es. 'PCIe')."
-            }
-        },
-        "onnx": {
-            "label": "ONNX",
-            "description": "Rilevatore ONNX per l'esecuzione di modelli ONNX; utilizzerà i backend di accelerazione disponibili (CUDA/ROCm/OpenVINO) quando presenti.",
-            "device": {
-                "label": "Tipo di dispositivo",
-                "description": "Il dispositivo da utilizzare per l'inferenza ONNX (ad es. 'AUTO', 'CPU', 'GPU')."
+                "label": "Personalizzazione labelmap"
             }
         }
     },

web/src/components/overlay/CameraInfoDialog.tsx

@@ -7,8 +7,7 @@ import {
   DialogTitle,
 } from "../ui/dialog";
 import ActivityIndicator from "../indicators/activity-indicator";
-import KeyframeAnalysisSection from "./KeyframeAnalysisSection";
-import { Ffprobe, KeyframeAnalysis } from "@/types/stats";
+import { Ffprobe } from "@/types/stats";
 import { Button } from "../ui/button";
 import copy from "copy-to-clipboard";
 import { CameraConfig } from "@/types/frigateConfig";
@@ -31,7 +30,6 @@ export default function CameraInfoDialog({
 }: CameraInfoDialogProps) {
   const { t } = useTranslation(["views/system"]);
   const [ffprobeInfo, setFfprobeInfo] = useState<Ffprobe[]>();
-  const [keyframeInfo, setKeyframeInfo] = useState<KeyframeAnalysis>();
 
   useEffect(() => {
     axios
@@ -69,12 +67,7 @@ export default function CameraInfoDialog({
   }, []);
 
   const onCopyFfprobe = async () => {
-    copy(
-      JSON.stringify({
-        ffprobe: ffprobeInfo,
-        keyframe_analysis: keyframeInfo,
-      }),
-    );
+    copy(JSON.stringify(ffprobeInfo));
     toast.success(t("cameras.toast.success.copyToClipboard"));
   };
 
@@ -103,7 +96,7 @@ export default function CameraInfoDialog({
             <Trans ns="views/system">cameras.info.streamDataFromFFPROBE</Trans>
           </DialogDescription>
 
-          <div className="mb-2 p-4 text-sm">
+          <div className="mb-2 p-4">
             {ffprobeInfo ? (
               <div>
                 {ffprobeInfo.map((stream, idx) => (
@@ -191,10 +184,6 @@ export default function CameraInfoDialog({
                     )}
                   </div>
                 ))}
-                <KeyframeAnalysisSection
-                  cameraName={camera.name}
-                  onResult={setKeyframeInfo}
-                />
               </div>
             ) : (
               <div className="flex flex-col items-center">

web/src/components/overlay/KeyframeAnalysisSection.tsx

@@ -1,193 +0,0 @@
-import { useEffect, useMemo, useState } from "react";
-import { useTranslation } from "react-i18next";
-import axios from "axios";
-import { FaCircleCheck, FaTriangleExclamation } from "react-icons/fa6";
-import { LuX } from "react-icons/lu";
-import ActivityIndicator from "../indicators/activity-indicator";
-import { KeyframeAnalysis } from "@/types/stats";
-
-const PROBE_WINDOW_SECONDS = 20;
-
-type KeyframeAnalysisSectionProps = {
-  cameraName: string;
-  onResult?: (analysis: KeyframeAnalysis) => void;
-};
-
-export default function KeyframeAnalysisSection({
-  cameraName,
-  onResult,
-}: KeyframeAnalysisSectionProps) {
-  const { t } = useTranslation(["views/system"]);
-  const [analysis, setAnalysis] = useState<KeyframeAnalysis>();
-  const [failed, setFailed] = useState(false);
-  const [secondsRemaining, setSecondsRemaining] =
-    useState(PROBE_WINDOW_SECONDS);
-
-  // fire the probe once on mount
-  useEffect(() => {
-    let active = true;
-    axios
-      .get("keyframe_analysis", { params: { camera: cameraName } })
-      .then((res) => {
-        if (active) {
-          setAnalysis(res.data);
-          onResult?.(res.data);
-        }
-      })
-      .catch(() => {
-        if (active) {
-          setFailed(true);
-        }
-      });
-    return () => {
-      active = false;
-    };
-    // re-probing only depends on the camera; onResult is a stable setter
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [cameraName]);
-
-  // countdown while waiting for the probe to return
-  useEffect(() => {
-    if (analysis || failed) {
-      return;
-    }
-    const interval = setInterval(() => {
-      setSecondsRemaining((s) => (s > 0 ? s - 1 : 0));
-    }, 1000);
-    return () => clearInterval(interval);
-  }, [analysis, failed]);
-
-  const content = useMemo(() => {
-    if (failed) {
-      return <Row icon="unknown">{t("cameras.info.keyframes.unknown")}</Row>;
-    }
-
-    if (!analysis) {
-      return (
-        <div className="flex items-center gap-2 text-muted-foreground">
-          <ActivityIndicator className="size-4" />
-          <span>
-            {secondsRemaining > 0
-              ? t("cameras.info.keyframes.analyzing", {
-                  seconds: secondsRemaining,
-                })
-              : t("cameras.info.keyframes.stillAnalyzing")}
-          </span>
-        </div>
-      );
-    }
-
-    let summary;
-    switch (analysis.severity) {
-      case "ok":
-        summary = (
-          <Row icon="ok">
-            {t("cameras.info.keyframes.ok", { seconds: analysis.mean_gap })}
-          </Row>
-        );
-        break;
-      case "warning":
-        summary = (
-          <Row icon="warning">
-            {t("cameras.info.keyframes.warning", { seconds: analysis.max_gap })}
-          </Row>
-        );
-        break;
-      case "error":
-        summary = (
-          <Row icon="error">
-            {t("cameras.info.keyframes.error", {
-              seconds: analysis.max_gap,
-              segmentTime: analysis.segment_time,
-            })}
-          </Row>
-        );
-        break;
-      case "record_disabled":
-        summary = (
-          <Row icon="unknown">{t("cameras.info.keyframes.recordDisabled")}</Row>
-        );
-        break;
-      default:
-        summary = (
-          <Row icon="unknown">{t("cameras.info.keyframes.unknown")}</Row>
-        );
-    }
-
-    // gap statistics are only meaningful once at least two keyframes were seen
-    const hasStats = analysis.max_gap != null;
-    const hasDetails = hasStats || analysis.stream_index != null;
-
-    return (
-      <div className="text-muted-foreground">
-        {analysis.stream_index != null && (
-          <div>
-            {t("cameras.info.keyframes.recordStream")}{" "}
-            <span className="text-primary">
-              {t("cameras.info.stream", { idx: analysis.stream_index + 1 })}
-            </span>
-          </div>
-        )}
-        {hasStats && (
-          <div>
-            <div>
-              {t("cameras.info.keyframes.keyframeCount")}{" "}
-              <span className="text-primary">{analysis.keyframe_count}</span>
-            </div>
-            <div>
-              {t("cameras.info.keyframes.observedDuration")}{" "}
-              <span className="text-primary">
-                {analysis.duration_observed}s
-              </span>
-            </div>
-            <div>
-              {t("cameras.info.keyframes.gap")}{" "}
-              <span className="text-primary">
-                {analysis.min_gap}s / {analysis.mean_gap}s / {analysis.max_gap}s
-              </span>
-            </div>
-            <div>
-              {t("cameras.info.keyframes.segmentLength")}{" "}
-              <span className="text-primary">{analysis.segment_time}s</span>
-            </div>
-          </div>
-        )}
-        <div className={hasDetails ? "mt-3" : undefined}>{summary}</div>
-      </div>
-    );
-  }, [analysis, failed, secondsRemaining, t]);
-
-  return (
-    <div className="mb-5">
-      <div className="mb-1 rounded-md bg-secondary p-2 text-lg text-primary">
-        {t("cameras.info.keyframes.title")}
-      </div>
-      <div className="ml-2">{content}</div>
-    </div>
-  );
-}
-
-type RowProps = {
-  icon: "ok" | "warning" | "error" | "unknown";
-  children: React.ReactNode;
-};
-
-function Row({ icon, children }: RowProps) {
-  return (
-    <div className="flex items-start gap-2">
-      {icon === "ok" && (
-        <FaCircleCheck className="mt-0.5 size-4 flex-shrink-0 text-success" />
-      )}
-      {icon === "warning" && (
-        <FaTriangleExclamation className="mt-0.5 size-4 flex-shrink-0 text-yellow-500" />
-      )}
-      {icon === "error" && (
-        <LuX className="mt-0.5 size-4 flex-shrink-0 text-danger" />
-      )}
-      {icon === "unknown" && (
-        <FaTriangleExclamation className="mt-0.5 size-4 flex-shrink-0 text-muted-foreground" />
-      )}
-      <span className="text-primary">{children}</span>
-    </div>
-  );
-}

web/src/types/stats.ts

@@ -135,22 +135,3 @@ export type Ffprobe = {
     }[];
   };
 };
-
-export type KeyframeSeverity =
-  | "ok"
-  | "warning"
-  | "error"
-  | "unknown"
-  | "record_disabled";
-
-export type KeyframeAnalysis = {
-  severity: KeyframeSeverity;
-  stream_index?: number;
-  keyframe_count?: number;
-  max_gap?: number | null;
-  mean_gap?: number | null;
-  min_gap?: number | null;
-  duration_observed?: number | null;
-  segment_time?: number;
-  thresholds?: { warning: number; error: number };
-};