Merge b5a360be39 into e6601d50a6

* backend: endpoint and util funcs

* tests

* frontend and i18n

* update openapi spec

* add tip to docs

docs/docs/troubleshooting/recordings.md

@@ -121,6 +121,12 @@ 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

@@ -400,6 +400,35 @@ 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

@@ -34,11 +34,15 @@ 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
+from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
 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 ffprobe_stream, is_restricted_go2rtc_source
+from frigate.util.services import (
+    analyze_record_keyframes,
+    ffprobe_stream,
+    is_restricted_go2rtc_source,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -362,6 +366,48 @@ 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/output/birdseye.py

@@ -595,112 +595,92 @@ class BirdsEyeFrameManager:
     ) -> Optional[list[list[Any]]]:
         """Calculate the optimal layout for 2+ cameras."""
 
-        def map_layout(
-            camera_layout: list[list[Any]], row_height: int
-        ) -> tuple[int, int, Optional[list[list[Any]]]]:
-            """Map the calculated layout."""
-            candidate_layout = []
-            starting_x = 0
-            x = 0
-            max_width = 0
-            y = 0
+        def find_available_x(
+            current_x: int,
+            width: int,
+            reserved_ranges: list[tuple[int, int]],
+            max_width: int,
+        ) -> Optional[int]:
+            """Find the first horizontal slot that does not collide with reservations."""
+            x = current_x
 
-            for row in camera_layout:
-                final_row = []
-                max_width = max(max_width, x)
-                x = starting_x
-                for cameras in row:
-                    camera_dims = self.cameras[cameras[0]]["dimensions"].copy()
-                    camera_aspect = cameras[1]
+            for reserved_start, reserved_end in sorted(reserved_ranges):
+                if x >= reserved_end:
+                    continue
 
-                    if camera_dims[1] > camera_dims[0]:
-                        scaled_height = int(row_height * 2)
-                        scaled_width = int(scaled_height * camera_aspect)
-                        starting_x = scaled_width
-                    else:
-                        scaled_height = row_height
-                        scaled_width = int(scaled_height * camera_aspect)
+                if x + width <= reserved_start:
+                    return x
 
-                    # layout is too large
-                    if (
-                        x + scaled_width > self.canvas.width
-                        or y + scaled_height > self.canvas.height
-                    ):
-                        return x + scaled_width, y + scaled_height, None
+                x = max(x, reserved_end)
 
-                    final_row.append((cameras[0], (x, y, scaled_width, scaled_height)))
-                    x += scaled_width
+            if x + width <= max_width:
+                return x
 
-                y += row_height
-                candidate_layout.append(final_row)
-
-            if max_width == 0:
-                max_width = x
-
-            return max_width, y, candidate_layout
-
-        canvas_aspect_x, canvas_aspect_y = self.canvas.get_aspect(coefficient)
-        camera_layout: list[list[Any]] = []
-        camera_layout.append([])
-        starting_x = 0
-        x = starting_x
-        y = 0
-        y_i = 0
-        max_y = 0
-        for camera in cameras_to_add:
-            camera_dims = self.cameras[camera]["dimensions"].copy()
-            camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
-                camera, camera_dims[0], camera_dims[1]
-            )
-
-            if camera_dims[1] > camera_dims[0]:
-                portrait = True
-            else:
-                portrait = False
-
-            if (x + camera_aspect_x) <= canvas_aspect_x:
-                # insert if camera can fit on current row
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-
-                if portrait:
-                    starting_x = camera_aspect_x
-                else:
-                    max_y = max(
-                        max_y,
-                        camera_aspect_y,
-                    )
-
-                x += camera_aspect_x
-            else:
-                # move on to the next row and insert
-                y += max_y
-                y_i += 1
-                camera_layout.append([])
-                x = starting_x
-
-                if x + camera_aspect_x > canvas_aspect_x:
-                    return None
-
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-                x += camera_aspect_x
-
-        if y + max_y > canvas_aspect_y:
             return None
 
-        row_height = int(self.canvas.height / coefficient)
-        total_width, total_height, standard_candidate_layout = map_layout(
-            camera_layout, row_height
-        )
+        def map_layout(row_height: int) -> tuple[int, int, Optional[list[list[Any]]]]:
+            """Lay out cameras row by row while reserving portrait spans for the next row."""
+            candidate_layout: list[list[Any]] = []
+            reserved_ranges: dict[int, list[tuple[int, int]]] = {}
+            current_row: list[Any] = []
+            row_index = 0
+            row_y = 0
+            row_x = 0
+            max_width = 0
+            max_height = 0
+
+            for camera in cameras_to_add:
+                camera_dims = self.cameras[camera]["dimensions"].copy()
+                camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
+                    camera, camera_dims[0], camera_dims[1]
+                )
+                portrait = camera_dims[1] > camera_dims[0]
+                scaled_height = row_height * 2 if portrait else row_height
+                scaled_width = int(scaled_height * (camera_aspect_x / camera_aspect_y))
+
+                while True:
+                    x = find_available_x(
+                        row_x,
+                        scaled_width,
+                        reserved_ranges.get(row_index, []),
+                        self.canvas.width,
+                    )
+
+                    if x is not None and row_y + scaled_height <= self.canvas.height:
+                        current_row.append(
+                            (camera, (x, row_y, scaled_width, scaled_height))
+                        )
+                        row_x = x + scaled_width
+                        max_width = max(max_width, row_x)
+                        max_height = max(max_height, row_y + scaled_height)
+
+                        if portrait:
+                            reserved_ranges.setdefault(row_index + 1, []).append(
+                                (x, row_x)
+                            )
+
+                        break
+
+                    if current_row:
+                        candidate_layout.append(current_row)
+                        current_row = []
+
+                    row_index += 1
+                    row_y = row_index * row_height
+                    row_x = 0
+
+                    if row_y + scaled_height > self.canvas.height:
+                        overflow_width = max(max_width, scaled_width)
+                        overflow_height = row_y + scaled_height
+                        return overflow_width, overflow_height, None
+
+            if current_row:
+                candidate_layout.append(current_row)
+
+            return max_width, max_height, candidate_layout
+
+        row_height = max(1, int(self.canvas.height / coefficient))
+        total_width, total_height, standard_candidate_layout = map_layout(row_height)
 
         if not standard_candidate_layout:
             # if standard layout didn't work
@@ -709,9 +689,9 @@ class BirdsEyeFrameManager:
                 total_width / self.canvas.width,
                 total_height / self.canvas.height,
             )
-            row_height = int(row_height / scale_down_percent)
+            row_height = max(1, int(row_height / scale_down_percent))
             total_width, total_height, standard_candidate_layout = map_layout(
-                camera_layout, row_height
+                row_height
             )
 
             if not standard_candidate_layout:
@@ -725,8 +705,8 @@ class BirdsEyeFrameManager:
             1 / (total_width / self.canvas.width),
             1 / (total_height / self.canvas.height),
         )
-        row_height = int(row_height * scale_up_percent)
-        _, _, scaled_layout = map_layout(camera_layout, row_height)
+        row_height = max(1, int(row_height * scale_up_percent))
+        _, _, scaled_layout = map_layout(row_height)
 
         if scaled_layout:
             return scaled_layout

frigate/test/http_api/test_http_keyframe_analysis.py

@@ -0,0 +1,58 @@
+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_birdseye.py

@@ -1,11 +1,64 @@
-"""Test camera user and password cleanup."""
+"""Tests for Birdseye canvas sizing and layout behavior."""
 
 import unittest
+from multiprocessing import Event
 
-from frigate.output.birdseye import get_canvas_shape
+from frigate.config import FrigateConfig
+from frigate.output.birdseye import BirdsEyeFrameManager, get_canvas_shape
 
 
 class TestBirdseye(unittest.TestCase):
+    def _build_manager(
+        self, camera_dimensions: dict[str, tuple[int, int]]
+    ) -> BirdsEyeFrameManager:
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "birdseye": {"width": 1280, "height": 720},
+            "cameras": {},
+        }
+
+        for order, (camera, dimensions) in enumerate(
+            camera_dimensions.items(), start=1
+        ):
+            config["cameras"][camera] = {
+                "ffmpeg": {
+                    "inputs": [
+                        {
+                            "path": f"rtsp://10.0.0.1:554/{camera}",
+                            "roles": ["detect"],
+                        }
+                    ]
+                },
+                "detect": {
+                    "width": dimensions[0],
+                    "height": dimensions[1],
+                    "fps": 5,
+                },
+                "birdseye": {"order": order},
+            }
+
+        return BirdsEyeFrameManager(FrigateConfig(**config), Event())
+
+    def _assert_no_overlaps(
+        self, layout: list[list[tuple[str, tuple[int, int, int, int]]]]
+    ):
+        rectangles = [position for row in layout for _, position in row]
+
+        for index, rect in enumerate(rectangles):
+            x1, y1, width1, height1 = rect
+            for other in rectangles[index + 1 :]:
+                x2, y2, width2, height2 = other
+                overlap = (
+                    x1 < x2 + width2
+                    and x2 < x1 + width1
+                    and y1 < y2 + height2
+                    and y2 < y1 + height1
+                )
+                self.assertFalse(
+                    overlap,
+                    msg=f"Overlapping rectangles found: {rect} and {other}",
+                )
+
     def test_16x9(self):
         """Test 16x9 aspect ratio works as expected for birdseye."""
         width = 1280
@@ -45,3 +98,104 @@ class TestBirdseye(unittest.TestCase):
         canvas_width, canvas_height = get_canvas_shape(width, height)
         assert canvas_width == width  # width will be the same
         assert canvas_height != height
+
+    def test_portrait_camera_does_not_overlap_next_row(self):
+        """Portrait cameras should reserve their real horizontal position on the next row."""
+        manager = self._build_manager(
+            {
+                "cam_a": (1280, 720),
+                "cam_p": (360, 640),
+                "cam_b": (1280, 720),
+                "cam_c": (640, 480),
+            }
+        )
+
+        layout = manager.calculate_layout(["cam_a", "cam_p", "cam_b", "cam_c"], 3)
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+        cam_c = [
+            position for row in layout for camera, position in row if camera == "cam_c"
+        ][0]
+        self.assertEqual(cam_c[0], 0)
+
+    def test_portrait_reservation_only_applies_to_next_row(self):
+        """Portrait reservations should not push later rows after the span ends."""
+        manager = self._build_manager(
+            {
+                "cam_a": (1280, 720),
+                "cam_p": (360, 640),
+                "cam_b": (1280, 720),
+                "cam_c": (1280, 720),
+                "cam_d": (1280, 720),
+                "cam_e": (1280, 720),
+            }
+        )
+
+        layout = manager.calculate_layout(
+            ["cam_a", "cam_p", "cam_b", "cam_c", "cam_d", "cam_e"],
+            3,
+        )
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+        cam_e = [
+            position for row in layout for camera, position in row if camera == "cam_e"
+        ][0]
+        self.assertEqual(cam_e[0], 0)
+
+    def test_multiple_portraits_reserve_distinct_ranges(self):
+        """Multiple portrait cameras in one row should reserve separate spans below them."""
+        manager = self._build_manager(
+            {
+                "cam_a": (640, 480),
+                "cam_p1": (360, 640),
+                "cam_p2": (360, 640),
+                "cam_b": (640, 480),
+                "cam_c": (1280, 720),
+                "cam_d": (640, 480),
+            }
+        )
+
+        layout = manager.calculate_layout(
+            ["cam_a", "cam_p1", "cam_p2", "cam_b", "cam_c", "cam_d"],
+            4,
+        )
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+    def test_two_landscapes_then_portrait_then_two_landscapes(self):
+        """A portrait after two landscapes should reserve only its own tail span."""
+        manager = self._build_manager(
+            {
+                "cam_a": (1280, 720),
+                "cam_b": (1280, 720),
+                "cam_p": (360, 640),
+                "cam_c": (1280, 720),
+                "cam_d": (1280, 720),
+            }
+        )
+
+        layout = manager.calculate_layout(
+            ["cam_a", "cam_b", "cam_p", "cam_c", "cam_d"],
+            3,
+        )
+
+        self.assertIsNotNone(layout)
+        assert layout is not None
+        self._assert_no_overlaps(layout)
+
+        cam_c = [
+            position for row in layout for camera, position in row if camera == "cam_c"
+        ][0]
+        cam_d = [
+            position for row in layout for camera, position in row if camera == "cam_d"
+        ][0]
+        self.assertEqual(cam_c[0], 0)
+        self.assertEqual(cam_d[0], cam_c[0] + cam_c[2])

frigate/test/test_keyframe_analysis.py

@@ -0,0 +1,111 @@
+"""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

@@ -14,13 +14,16 @@ import urllib.parse
 from collections.abc import Mapping
 from multiprocessing.managers import ValueProxy
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import TYPE_CHECKING, 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__)
 
 
@@ -132,6 +135,24 @@ 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

@@ -879,6 +879,131 @@ 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_ffmpeg_arg_list
+from frigate.util.builtin import EventsPerSecond, get_record_segment_time
 from frigate.util.ffmpeg import start_or_restart_ffmpeg, stop_ffmpeg
 from frigate.util.image import (
     FrameManager,
@@ -34,23 +34,6 @@ 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],
@@ -185,7 +168,7 @@ 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

@@ -174,6 +174,21 @@
       "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/src/components/overlay/CameraInfoDialog.tsx

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

@@ -0,0 +1,193 @@
+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

@@ -135,3 +135,22 @@ 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 };
+};