document available camera set features and link profiles docs to the API

docs/docs/configuration/custom_classification/object_classification.md

@@ -11,7 +11,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 
 :::info
 
-Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -11,7 +11,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
 
 :::info
 
-Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/profiles.md

@@ -126,7 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other
 
 ## Activating Profiles
 
-Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
 
 In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
 

docs/docs/frigate/network_requirements.md

@@ -34,6 +34,12 @@ The following models are downloaded automatically the first time their associate
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
 | [Audio transcription](/configuration/advanced/system)                                         | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
 
+:::note
+
+The MobileNetV2 base weights are the one exception to the `/config/model_cache/` rule. They are also the only entry that is not downloaded when the feature is enabled: Frigate fetches them when a training run actually starts.
+
+:::
+
 ### Hardware-Specific Detector Models
 
 If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
@@ -75,7 +81,7 @@ If your Frigate instance has restricted internet access, you can point model dow
 | `HF_ENDPOINT`                       | `https://huggingface.co`            | Semantic search, Sherpa-ONNX, AXEngine models |
 | `GITHUB_ENDPOINT`                   | `https://github.com`                | Face recognition, LPR, RKNN models            |
 | `GITHUB_RAW_ENDPOINT`               | `https://raw.githubusercontent.com` | Bird classification                           |
-| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default)      | Custom classification training                |
+| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Unset (Keras uses its own default)  | Custom classification training                |
 
 ## Optional Cloud Services
 
@@ -147,9 +153,23 @@ When running as a Home Assistant App, the go2rtc startup script queries the loca
 To run Frigate in an air-gapped or offline environment:
 
 1. **Pre-download models**: Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
+2. **Pre-download the training base weights**: If you plan to train custom classification models, set `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` before training, then run one training job while online. Without this variable the base weights are cached outside `/config/` and are lost whenever the container is recreated, so a later training run will fail offline. If the machine never has internet access, copy the weights in manually as described below.
+3. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
+4. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
+5. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
+6. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, `GITHUB_RAW_ENDPOINT`, and `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` environment variables to point to local mirrors.
 
 After these steps, Frigate will operate with no outbound internet connections.
+
+### Manually Copying the Training Base Weights
+
+On a machine with internet access, download the weights:
+
+```bash
+curl -L -o mobilenet_v2_weights.h5 \
+  "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_224_no_top.h5"
+```
+
+Copy the file into your Frigate config volume as `/config/model_cache/MobileNet/mobilenet_v2_weights.h5`, keeping that exact filename, then set the environment variable `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` in your Docker compose file to the URL above and restart Frigate.
+
+The variable must be set even though the URL is never contacted. If it is unset, Frigate ignores the copied file and asks Keras to download the weights instead.

docs/static/frigate-api.yaml

@@ -693,6 +693,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: 
         camera_set_camera__camera_name__set__feature___sub_command__put
       parameters:
@@ -746,6 +783,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: camera_set_camera__camera_name__set__feature__put
       parameters:
         - name: camera_name

frigate/api/camera.py

@@ -1328,7 +1328,45 @@ def camera_set(
     body: CameraSetBody,
     sub_command: str | None = None,
 ):
-    """Set a camera feature state. Use camera_name='*' to target all cameras."""
+    """Set a camera feature state. Use camera_name='*' to target all cameras.
+
+    The value to set is sent in the request body as `{"value": "<value>"}`.
+
+    | Feature | Accepted values |
+    | --- | --- |
+    | `enabled` | `ON`, `OFF` |
+    | `detect` | `ON`, `OFF` |
+    | `motion` | `ON`, `OFF` |
+    | `recordings` | `ON`, `OFF` |
+    | `snapshots` | `ON`, `OFF` |
+    | `audio` | `ON`, `OFF` |
+    | `audio_transcription` | `ON`, `OFF` |
+    | `notifications` | `ON`, `OFF` |
+    | `review_alerts` | `ON`, `OFF` |
+    | `review_detections` | `ON`, `OFF` |
+    | `object_descriptions` | `ON`, `OFF` |
+    | `review_descriptions` | `ON`, `OFF` |
+    | `improve_contrast` | `ON`, `OFF` |
+    | `ptz_autotracker` | `ON`, `OFF` |
+    | `birdseye` | `ON`, `OFF` |
+    | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+    | `motion_contour_area` | integer |
+    | `motion_threshold` | integer |
+    | `motion_mask` | `ON`, `OFF` |
+    | `object_mask` | `ON`, `OFF` |
+    | `zone` | `ON`, `OFF` |
+    | `profile` | a profile name, or `none` to deactivate |
+
+    `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+    parameter to be set to the name of the mask or zone. All other features
+    reject a sub-command.
+
+    `profile` applies globally rather than per camera, so it requires
+    `camera_name` to be `*`.
+
+    These features map to the equivalent MQTT topics, which document the
+    behavior of each value in more detail.
+    """
     dispatcher = request.app.dispatcher
     frigate_config: FrigateConfig = request.app.frigate_config
 

frigate/ptz/onvif.py

@@ -625,14 +625,18 @@ class OnvifController:
             return
 
         self.cams[camera_name]["active"] = True
-        self.ptz_metrics[camera_name].motor_stopped.clear()
-        logger.debug(
-            f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
-        )
-        self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
-            camera_name
-        ].frame_time.value
-        self.ptz_metrics[camera_name].stop_time.value = 0
+
+        # only track start_time for autotracking
+        if self.ptz_metrics[camera_name].autotracker_enabled.value:
+            self.ptz_metrics[camera_name].motor_stopped.clear()
+            logger.debug(
+                f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
+            )
+            self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
+                camera_name
+            ].frame_time.value
+            self.ptz_metrics[camera_name].stop_time.value = 0
+
         move_request = self.cams[camera_name]["relative_move_request"]
 
         # function takes in -1 to 1 for pan and tilt, interpolate to the values of the camera.

frigate/test/test_ptz_onvif.py

@@ -1,4 +1,4 @@
-"""Tests for ONVIF init state that must not depend on the autotracking config.
+"""Tests for ONVIF state that must not depend on the autotracking config.
 
 Regression coverage for a camera that is initialized while autotracking is off and
 has it enabled later, which is the normal wizard flow: set the camera up first,
@@ -10,12 +10,17 @@ the tracking thread.
 
 The request objects are built from the locally parsed WSDL and cost no network, so
 they are always created and init=True now implies they exist.
+
+Also covers the inverse direction: the ptz movement timestamps must not be written
+for a camera that has autotracking off, because nothing clears them back out.
 """
 
 import unittest
 from unittest.mock import AsyncMock, MagicMock
 
+from frigate.camera import PTZMetrics
 from frigate.config import FrigateConfig
+from frigate.ptz.autotrack import ptz_moving_at_frame_time
 from frigate.ptz.onvif import OnvifController
 
 CAMERA = "ptz_cam"
@@ -97,6 +102,36 @@ def _make_controller(autotracking_enabled: bool) -> OnvifController:
     return controller
 
 
+def _make_move_controller(autotracking_enabled: bool) -> OnvifController:
+    """Build an already initialized controller for a camera that supports relative
+    FOV movement, with real metrics so the timestamp writes can be asserted on."""
+    config = _config(autotracking_enabled)
+    controller = OnvifController.__new__(OnvifController)
+    controller.config = config
+    controller.camera_configs = {CAMERA: config.cameras[CAMERA]}
+    controller.failed_cams = {}
+
+    ptz = MagicMock()
+    ptz.RelativeMove = AsyncMock()
+    controller.cams = {
+        CAMERA: {
+            "init": True,
+            "active": False,
+            "ptz": ptz,
+            "features": ["pt", "pt-r-fov"],
+            "relative_move_request": MagicMock(),
+            "relative_fov_range": {
+                "XRange": {"Min": -1.0, "Max": 1.0},
+                "YRange": {"Min": -1.0, "Max": 1.0},
+            },
+        }
+    }
+    controller.ptz_metrics = {
+        CAMERA: PTZMetrics(autotracker_enabled=autotracking_enabled)
+    }
+    return controller
+
+
 class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
     async def test_status_request_created_when_autotracking_disabled(self) -> None:
         # the wizard flow: onvif configured first, autotracking enabled later
@@ -143,5 +178,61 @@ class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
         ptz.GetStatus.assert_not_called()
 
 
+class TestManualRelativeMoveMetrics(unittest.IsolatedAsyncioTestCase):
+    """A manual move from the UI (click to move, drag to zoom) sends move_relative
+    for any camera that advertises pt-r-fov, autotracking or not."""
+
+    async def test_metrics_untouched_when_autotracking_disabled(self) -> None:
+        # only camera_maintenance polls get_camera_status, and only for autotracking
+        # cameras, so a manual move that starts the clock here is never stopped
+        controller = _make_move_controller(autotracking_enabled=False)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        controller.cams[CAMERA]["ptz"].RelativeMove.assert_awaited_once()
+        self.assertEqual(metrics.start_time.value, 0)
+        self.assertEqual(metrics.stop_time.value, 0)
+        self.assertTrue(metrics.motor_stopped.is_set())
+
+    async def test_detection_regions_not_suppressed_after_manual_move(self) -> None:
+        # the symptom of the bug: object detection stops entirely because motion
+        # boxes are never promoted to detection regions again
+        controller = _make_move_controller(autotracking_enabled=False)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        for later_frame_time in (1001.0, 1060.0, 4600.0):
+            with self.subTest(frame_time=later_frame_time):
+                self.assertFalse(
+                    ptz_moving_at_frame_time(
+                        later_frame_time,
+                        metrics.start_time.value,
+                        metrics.stop_time.value,
+                    )
+                )
+
+    async def test_metrics_written_when_autotracking_enabled(self) -> None:
+        # get_camera_status resets stop_time once the camera reports IDLE, so the
+        # autotracking path keeps its motion estimation timestamps
+        controller = _make_move_controller(autotracking_enabled=True)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        self.assertEqual(metrics.start_time.value, 1000.0)
+        self.assertEqual(metrics.stop_time.value, 0)
+        self.assertFalse(metrics.motor_stopped.is_set())
+        self.assertTrue(
+            ptz_moving_at_frame_time(
+                1001.0, metrics.start_time.value, metrics.stop_time.value
+            )
+        )
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/video/detect.py

@@ -358,12 +358,17 @@ def process_frames(
             ]
 
             # only add in the motion boxes when not calibrating and a ptz is not moving via autotracking
-            # ptz_moving_at_frame_time() always returns False for non-autotracking cameras
-            if not motion_detector.is_calibrating() and not ptz_moving_at_frame_time(
-                frame_time,
-                ptz_metrics.start_time.value,
-                ptz_metrics.stop_time.value,
-            ):
+            # the ptz timestamps are only maintained while autotracking is on, so gate
+            # on the metric rather than trusting them to be reset otherwise
+            ptz_moving = ptz_metrics.autotracker_enabled.value and (
+                ptz_moving_at_frame_time(
+                    frame_time,
+                    ptz_metrics.start_time.value,
+                    ptz_metrics.stop_time.value,
+                )
+            )
+
+            if not motion_detector.is_calibrating() and not ptz_moving:
                 # find motion boxes that are not inside tracked object regions
                 standalone_motion_boxes = [
                     b for b in motion_boxes if not inside_any(b, regions)