Merge b5a360be39 into f3a352ef3f

* update e2e mock data to remove deprecated fields

* remove scream audio label

scream was never mapped to anything in frigate's custom labelmap, yell is used everywhere

* document common audio labels

* deprecate ffmpeg 5

* language tweak

* add field message to recommend presets instead of manual hwaccel args

* add guidance to docs on choosing a detect fps

docs/docs/configuration/advanced/reference.md

@@ -212,7 +212,6 @@ audio:
   listen:
     - bark
     - fire_alarm
-    - scream
     - speech
     - yell
   # Optional: Filters to configure detection.

docs/docs/configuration/audio_detectors.md

@@ -88,7 +88,7 @@ Volume is considered motion for recordings, this means when the `record -> retai
 
 ### Configuring Audio Events
 
-The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `scream`, `speech`, and `yell` are enabled but these can be customized.
+The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `speech`, and `yell` are enabled but these can be customized.
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -107,7 +107,6 @@ audio:
   listen:
     - bark
     - fire_alarm
-    - scream
     - speech
     - yell
 ```
@@ -115,6 +114,70 @@ audio:
 </TabItem>
 </ConfigTabs>
 
+### Common Audio Labels
+
+The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
+
+Some labels cover several related sounds: `yell` is triggered by shouting, yelling, children shouting, and screaming; `crying` covers baby cries, sobbing, and whimpering; and `speech` covers ordinary talking and conversation.
+
+**Safety and security**
+
+| Label            | Detects                            |
+| ---------------- | ---------------------------------- |
+| `yell`           | Shouting, yelling, screaming       |
+| `fire_alarm`     | Fire and smoke alarm sirens        |
+| `smoke_detector` | Smoke detector beeps               |
+| `alarm`          | General alarm sounds               |
+| `car_alarm`      | Car alarms                         |
+| `siren`          | Emergency vehicle and civil sirens |
+| `glass`          | Glass clinking                     |
+| `shatter`        | Breaking glass                     |
+| `breaking`       | Something breaking                 |
+| `gunshot`        | Gunshots                           |
+| `explosion`      | Explosions                         |
+
+**People and activity**
+
+| Label       | Detects                  |
+| ----------- | ------------------------ |
+| `speech`    | Talking and conversation |
+| `laughter`  | Laughing                 |
+| `crying`    | Baby crying and sobbing  |
+| `cough`     | Coughing                 |
+| `footsteps` | Footsteps and walking    |
+| `knock`     | Knocking on a door       |
+| `doorbell`  | Doorbell                 |
+| `ding-dong` | Doorbell chime           |
+
+**Pets and animals**
+
+| Label      | Detects          |
+| ---------- | ---------------- |
+| `bark`     | Dog barking      |
+| `dog`      | Other dog sounds |
+| `howl`     | Howling          |
+| `growling` | Growling         |
+| `meow`     | Cat meowing      |
+| `cat`      | Other cat sounds |
+| `hiss`     | Hissing          |
+
+**Vehicles and driveway**
+
+| Label             | Detects              |
+| ----------------- | -------------------- |
+| `car`             | Passing cars         |
+| `honk`            | Car horns            |
+| `truck`           | Trucks               |
+| `reversing_beeps` | Vehicle backup beeps |
+| `motorcycle`      | Motorcycles          |
+| `engine_starting` | Engines starting     |
+
+:::tip
+
+Frequently-heard labels like `speech` can generate a lot of events, and each event could save a snapshot and recording based on your configuration, so start with a focused set — the defaults (`bark`, `fire_alarm`, `speech`, `yell`) plus a few of the safety labels above cover most needs — and expand from there. See the [full audio labelmap](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) or the Frigate UI for every available type.
+
+:::
+
 ### Audio Transcription
 
 Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.

docs/docs/frigate/camera_setup.md

@@ -5,7 +5,7 @@ title: Camera setup
 
 Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
 
-- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
+- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The default frame rate of 5fps is correct for almost all cameras and rarely needs to be changed; see [Choosing a detect frame rate](#choosing-a-detect-frame-rate). Higher resolutions and frame rates will drive higher CPU usage on your server.
 
 - **Recording**: This stream should be the resolution you wish to store for reference. Typically, this will be the highest resolution your camera supports. I recommend setting this feed in your camera's firmware to 15 fps.
 
@@ -25,6 +25,44 @@ Larger resolutions **do** improve performance if the objects are very small in t
 
 ![Resolutions](/img/resolutions-min.jpg)
 
+### Choosing a detect frame rate
+
+`detect.fps` controls how many times per second Frigate runs object detection — it does **not** need to match your camera's frame rate. The default of **5** is correct for the vast majority of cameras.
+
+:::warning
+
+Most users who raise `detect.fps` above the default don't need to. Increasing it consumes more CPU/GPU (detection load scales directly with the frame rate) while providing **no benefit to tracking** once objects are already being followed smoothly. Leave it at **5** unless you have a specific scene that fails the test below, and confirm any change actually helps in the debug view.
+
+:::
+
+#### Why 5 is enough for almost everyone
+
+Frigate follows an object by matching its bounding box from one detection frame to the next, which requires the object to be detected often enough while it is on screen. At 5 fps this is satisfied in normal scenes: an object crossing a yard, porch, driveway, or walkway is in view for several seconds and produces ~15 or more detections, which is more than enough for a reliable track and a good snapshot. This includes fast subjects such as a running person or a bolting pet, which on a wide-angle view remain on screen for several seconds.
+
+A higher rate helps only when an object crosses the **entire frame in less than two seconds**, which is determined by camera framing rather than object speed - for example, a camera aimed down a street at fast cross-traffic. In those scenes 5 fps may produce too few detections to hold a track. Cameras covering normal approaches and open areas are unaffected.
+
+#### Checking whether a higher rate is needed
+
+Estimate how long an object is visible as it crosses the area of interest, aiming for roughly 8–10 detections during the pass:
+
+> **`detect.fps` ≈ 10 ÷ (seconds the object is in view)**
+
+Most objects — people walking or running, pets, and vehicles in a yard, driveway, or walkway — stay in view for two seconds or more, so the default of 5 fps is correct. Slowly try raising it to 10 (the recommended maximum) in increments only when objects routinely cross the entire frame in about a second, such as a camera aimed at a street or sidewalk with fast cross-traffic. Objects that transit in under a second cannot be tracked reliably at any practical rate, so reposition the camera instead.
+
+:::tip
+
+If the formula calls for more than 10, the fix is **camera placement, not frame rate**. Angle the camera so objects move toward it rather than across the view, or aim it where traffic slows. A higher `detect.fps` increases CPU load proportionally without producing more detections of a too-brief object.
+
+:::
+
+#### Verify in the debug view
+
+Confirm any change in the Debug view or Debug Replay. Watch a typical object cross the scene: if its bounding box follows it smoothly while visible, the rate is sufficient. A box that jumps erratically, drops out, or splits one object into multiple events indicates the rate should be increased one step.
+
+#### Dedicated LPR cameras
+
+A dedicated license plate recognition camera is the most common reason to use something higher than 5 fps: the camera is highly zoomed, the plate is small, and it moves at full vehicle speed, so it transits the frame quickly. However, the same ceiling applies: above 10 fps is unnecessary, and **placement matters most**: aim LPR cameras where vehicles slow down, such as gates, driveways, and parking entrances. A tight view of a fast through-road will not likely read plates reliably at any frame rate. See [License Plate Recognition](/configuration/license_plate_recognition) for details.
+
 ### Example Camera Configuration
 
 For the Dahua/Loryta 5442 camera, I use the following settings:

frigate/config/camera/audio.py

@@ -9,7 +9,7 @@ from ..base import FrigateBaseModel
 __all__ = ["AudioConfig", "AudioFilterConfig"]
 
 
-DEFAULT_LISTEN_AUDIO = ["bark", "fire_alarm", "scream", "speech", "yell"]
+DEFAULT_LISTEN_AUDIO = ["bark", "fire_alarm", "speech", "yell"]
 
 
 class AudioFilterConfig(FrigateBaseModel):
@@ -41,7 +41,7 @@ class AudioConfig(FrigateBaseModel):
     listen: list[str] = Field(
         default=DEFAULT_LISTEN_AUDIO,
         title="Listen types",
-        description="List of audio event types to detect (for example: bark, fire_alarm, scream, speech, yell).",
+        description="List of audio event types to detect (for example: bark, fire_alarm, speech, yell).",
     )
     filters: Optional[dict[str, AudioFilterConfig]] = Field(
         None,

frigate/config/camera/ffmpeg.py

@@ -49,7 +49,7 @@ class FfmpegConfig(FrigateBaseModel):
     path: str = Field(
         default="default",
         title="FFmpeg path",
-        description='Path to the FFmpeg binary to use or a version alias ("5.0" or "8.0").',
+        description='Path to the FFmpeg binary to use or a version alias ("7.0" or "8.0").',
     )
     global_args: Union[str, list[str]] = Field(
         default=FFMPEG_GLOBAL_ARGS_DEFAULT,

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/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])

web/e2e/fixtures/mock-data/cases.json

@@ -1 +1 @@
-[{"id": "case-001", "name": "Package Theft Investigation", "description": "Review of suspicious activity near the front porch", "created_at": 1775407931.3863528, "updated_at": 1775483531.3863528}]
+[{"id": "case-001", "name": "Package Theft Investigation", "description": "Review of suspicious activity near the front porch", "created_at": 1780597809.365581, "updated_at": 1780673409.365581}]

web/e2e/fixtures/mock-data/events.json

@@ -1 +1 @@
-[{"id": "event-person-001", "label": "person", "sub_label": null, "camera": "front_door", "start_time": 1775487131.3863528, "end_time": 1775487161.3863528, "false_positive": false, "zones": ["front_yard"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "abc123", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.92, "score": 0.92, "region": [0.1, 0.1, 0.5, 0.8], "box": [0.2, 0.15, 0.45, 0.75], "area": 0.18, "ratio": 0.6, "type": "object", "description": "A person walking toward the front door", "average_estimated_speed": 1.2, "velocity_angle": 45.0, "path_data": [[[0.2, 0.5], 0.0], [[0.3, 0.5], 1.0]]}}, {"id": "event-car-001", "label": "car", "sub_label": null, "camera": "backyard", "start_time": 1775483531.3863528, "end_time": 1775483576.3863528, "false_positive": false, "zones": ["driveway"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "def456", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.87, "score": 0.87, "region": [0.3, 0.2, 0.9, 0.7], "box": [0.35, 0.25, 0.85, 0.65], "area": 0.2, "ratio": 1.25, "type": "object", "description": "A car parked in the driveway", "average_estimated_speed": 0.0, "velocity_angle": 0.0, "path_data": []}}, {"id": "event-person-002", "label": "person", "sub_label": null, "camera": "garage", "start_time": 1775479931.3863528, "end_time": 1775479951.3863528, "false_positive": false, "zones": [], "thumbnail": null, "has_clip": false, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "ghi789", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.78, "score": 0.78, "region": [0.0, 0.0, 0.6, 0.9], "box": [0.1, 0.05, 0.5, 0.85], "area": 0.32, "ratio": 0.5, "type": "object", "description": null, "average_estimated_speed": 0.5, "velocity_angle": 90.0, "path_data": [[[0.1, 0.4], 0.0]]}}]
+[{"id": "event-person-001", "label": "person", "sub_label": null, "camera": "front_door", "start_time": 1780677009.365581, "end_time": 1780677039.365581, "false_positive": false, "zones": ["front_yard"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "abc123", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.92, "score": 0.92, "region": [0.1, 0.1, 0.5, 0.8], "box": [0.2, 0.15, 0.45, 0.75], "area": 0.18, "ratio": 0.6, "type": "object", "description": "A person walking toward the front door", "average_estimated_speed": 1.2, "velocity_angle": 45.0, "path_data": [[[0.2, 0.5], 0.0], [[0.3, 0.5], 1.0]]}}, {"id": "event-car-001", "label": "car", "sub_label": null, "camera": "backyard", "start_time": 1780673409.365581, "end_time": 1780673454.365581, "false_positive": false, "zones": ["driveway"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "def456", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.87, "score": 0.87, "region": [0.3, 0.2, 0.9, 0.7], "box": [0.35, 0.25, 0.85, 0.65], "area": 0.2, "ratio": 1.25, "type": "object", "description": "A car parked in the driveway", "average_estimated_speed": 0.0, "velocity_angle": 0.0, "path_data": []}}, {"id": "event-person-002", "label": "person", "sub_label": null, "camera": "garage", "start_time": 1780669809.365581, "end_time": 1780669829.365581, "false_positive": false, "zones": [], "thumbnail": null, "has_clip": false, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "ghi789", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.78, "score": 0.78, "region": [0.0, 0.0, 0.6, 0.9], "box": [0.1, 0.05, 0.5, 0.85], "area": 0.32, "ratio": 0.5, "type": "object", "description": null, "average_estimated_speed": 0.5, "velocity_angle": 90.0, "path_data": [[[0.1, 0.4], 0.0]]}}]

web/e2e/fixtures/mock-data/exports.json

@@ -1 +1 @@
-[{"id": "export-001", "camera": "front_door", "name": "Front Door - Person Alert", "date": 1775490731.3863528, "video_path": "/exports/export-001.mp4", "thumb_path": "/exports/export-001-thumb.jpg", "in_progress": false, "export_case_id": null}, {"id": "export-002", "camera": "backyard", "name": "Backyard - Car Detection", "date": 1775483531.3863528, "video_path": "/exports/export-002.mp4", "thumb_path": "/exports/export-002-thumb.jpg", "in_progress": false, "export_case_id": "case-001"}, {"id": "export-003", "camera": "garage", "name": "Garage - In Progress", "date": 1775492531.3863528, "video_path": "/exports/export-003.mp4", "thumb_path": "/exports/export-003-thumb.jpg", "in_progress": true, "export_case_id": null}]
+[{"id": "export-001", "camera": "front_door", "name": "Front Door - Person Alert", "date": 1780680609.365581, "video_path": "/exports/export-001.mp4", "thumb_path": "/exports/export-001-thumb.jpg", "in_progress": false, "export_case_id": null}, {"id": "export-002", "camera": "backyard", "name": "Backyard - Car Detection", "date": 1780673409.365581, "video_path": "/exports/export-002.mp4", "thumb_path": "/exports/export-002-thumb.jpg", "in_progress": false, "export_case_id": "case-001"}, {"id": "export-003", "camera": "garage", "name": "Garage - In Progress", "date": 1780682409.365581, "video_path": "/exports/export-003.mp4", "thumb_path": "/exports/export-003-thumb.jpg", "in_progress": true, "export_case_id": null}]

web/e2e/fixtures/mock-data/review-summary.json

@@ -1 +1 @@
-{"2026-04-06": {"day": "2026-04-06", "reviewed_alert": 1, "reviewed_detection": 0, "total_alert": 2, "total_detection": 2}, "2026-04-05": {"day": "2026-04-05", "reviewed_alert": 3, "reviewed_detection": 2, "total_alert": 3, "total_detection": 4}}
+{"2026-06-05": {"day": "2026-06-05", "reviewed_alert": 1, "reviewed_detection": 0, "total_alert": 2, "total_detection": 2}, "2026-06-04": {"day": "2026-06-04", "reviewed_alert": 3, "reviewed_detection": 2, "total_alert": 3, "total_detection": 4}}

web/e2e/fixtures/mock-data/reviews.json

@@ -1 +1 @@
-[{"id": "review-alert-001", "camera": "front_door", "start_time": "2026-04-06T09:52:11.386353", "end_time": "2026-04-06T09:52:41.386353", "has_been_reviewed": false, "severity": "alert", "thumb_path": "/clips/front_door/review-alert-001-thumb.jpg", "data": {"audio": [], "detections": ["person-abc123"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}, {"id": "review-alert-002", "camera": "backyard", "start_time": "2026-04-06T08:52:11.386353", "end_time": "2026-04-06T08:52:56.386353", "has_been_reviewed": true, "severity": "alert", "thumb_path": "/clips/backyard/review-alert-002-thumb.jpg", "data": {"audio": [], "detections": ["car-def456"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["driveway"]}}, {"id": "review-detect-001", "camera": "garage", "start_time": "2026-04-06T07:52:11.386353", "end_time": "2026-04-06T07:52:31.386353", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/garage/review-detect-001-thumb.jpg", "data": {"audio": [], "detections": ["person-ghi789"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": []}}, {"id": "review-detect-002", "camera": "front_door", "start_time": "2026-04-06T06:52:11.386353", "end_time": "2026-04-06T06:52:26.386353", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/front_door/review-detect-002-thumb.jpg", "data": {"audio": [], "detections": ["car-jkl012"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}]
+[{"id": "review-alert-001", "camera": "front_door", "start_time": "2026-06-05T11:30:09.365581", "end_time": "2026-06-05T11:30:39.365581", "has_been_reviewed": false, "severity": "alert", "thumb_path": "/clips/front_door/review-alert-001-thumb.jpg", "data": {"audio": [], "detections": ["person-abc123"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}, {"id": "review-alert-002", "camera": "backyard", "start_time": "2026-06-05T10:30:09.365581", "end_time": "2026-06-05T10:30:54.365581", "has_been_reviewed": true, "severity": "alert", "thumb_path": "/clips/backyard/review-alert-002-thumb.jpg", "data": {"audio": [], "detections": ["car-def456"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["driveway"]}}, {"id": "review-detect-001", "camera": "garage", "start_time": "2026-06-05T09:30:09.365581", "end_time": "2026-06-05T09:30:29.365581", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/garage/review-detect-001-thumb.jpg", "data": {"audio": [], "detections": ["person-ghi789"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": []}}, {"id": "review-detect-002", "camera": "front_door", "start_time": "2026-06-05T08:30:09.365581", "end_time": "2026-06-05T08:30:24.365581", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/front_door/review-detect-002-thumb.jpg", "data": {"audio": [], "detections": ["car-jkl012"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}]

web/public/locales/en/config/cameras.json

@@ -29,7 +29,7 @@
     },
     "listen": {
       "label": "Listen types",
-      "description": "List of audio event types to detect (for example: bark, fire_alarm, scream, speech, yell)."
+      "description": "List of audio event types to detect (for example: bark, fire_alarm, speech, yell)."
     },
     "filters": {
       "label": "Audio filters",
@@ -156,7 +156,7 @@
     "description": "FFmpeg settings including binary path, args, hwaccel options, and per-role output args.",
     "path": {
       "label": "FFmpeg path",
-      "description": "Path to the FFmpeg binary to use or a version alias (\"5.0\" or \"7.0\")."
+      "description": "Path to the FFmpeg binary to use or a version alias (\"7.0\" or \"8.0\")."
     },
     "global_args": {
       "label": "FFmpeg global arguments",

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

@@ -547,7 +547,7 @@
     },
     "listen": {
       "label": "Listen types",
-      "description": "List of audio event types to detect (for example: bark, fire_alarm, scream, speech, yell)."
+      "description": "List of audio event types to detect (for example: bark, fire_alarm, speech, yell)."
     },
     "filters": {
       "label": "Audio filters",
@@ -683,7 +683,7 @@
     "description": "FFmpeg settings including binary path, args, hwaccel options, and per-role output args.",
     "path": {
       "label": "FFmpeg path",
-      "description": "Path to the FFmpeg binary to use or a version alias (\"5.0\" or \"7.0\")."
+      "description": "Path to the FFmpeg binary to use or a version alias (\"7.0\" or \"8.0\")."
     },
     "global_args": {
       "label": "FFmpeg global arguments",

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

@@ -1914,6 +1914,9 @@
       "resolutionHigh": "This detect resolution is higher than recommended and may cause increased resource usage without improving detection accuracy. A detect resolution at or below 1080p is recommended for most cameras.",
       "globalResolutionMultipleCameras": "A global detect resolution is set while multiple cameras are configured. Unless all cameras share the same resolution and aspect ratio, the detect width and height should be defined per camera to match each camera's native aspect ratio."
     },
+    "ffmpeg": {
+      "hwaccelManualNotRecommended": "Manual hardware acceleration arguments are not recommended. Unless a specific requirement exists, select the preset that matches your hardware."
+    },
     "objects": {
       "genaiNoDescriptionsProvider": "You must configure a GenAI provider with the 'descriptions' role for descriptions to be generated."
     },

web/src/components/config-form/section-configs/ffmpeg.ts

@@ -22,6 +22,27 @@ const ffmpegArgsWidget = (
 const ffmpeg: SectionConfigOverrides = {
   base: {
     sectionDocs: "/configuration/ffmpeg_presets",
+    fieldMessages: [
+      {
+        key: "hwaccel-manual-not-recommended",
+        field: "hwaccel_args",
+        position: "after",
+        messageKey: "configMessages.ffmpeg.hwaccelManualNotRecommended",
+        severity: "warning",
+        condition: (ctx) => {
+          // Manual mode is active when hwaccel_args is an explicit args list
+          // or a non-preset string
+          const value = ctx.formData?.hwaccel_args;
+          if (Array.isArray(value)) {
+            return value.length > 0;
+          }
+          if (typeof value === "string") {
+            return !value.startsWith("preset-");
+          }
+          return false;
+        },
+      },
+    ],
     fieldDocs: {
       hwaccel_args: "/configuration/ffmpeg_presets#hwaccel-presets",
       "inputs.hwaccel_args": "/configuration/ffmpeg_presets#hwaccel-presets",

web/src/components/config-form/theme/templates/FieldTemplate.tsx

@@ -386,11 +386,14 @@ export function FieldTemplate(props: FieldTemplateProps) {
   const beforeContent = renderCustom(beforeSpec);
   const afterContent = renderCustom(afterSpec);
 
-  // Read field-level conditional messages from FieldMessagesContext
+  // Read field-level conditional messages from FieldMessagesContext.
+  // For multi-schema fields (anyOf/oneOf), FieldTemplate renders twice for
+  // the same path (wrapper + inner branch); skip the wrapper pass so the
+  // message isn't shown twice, mirroring how labels/descriptions dedupe.
   const fieldPathStr = pathSegments.join(".");
-  const fieldMessageSpecs = allFieldMessages.filter(
-    (m) => m.field === fieldPathStr,
-  );
+  const fieldMessageSpecs = isMultiSchemaWrapper
+    ? []
+    : allFieldMessages.filter((m) => m.field === fieldPathStr);
   const beforeMessages = fieldMessageSpecs.filter(
     (m) => (m.position ?? "before") === "before",
   );