Split apart video.py (#22631)

* add profile support and decouple relative move from autotracking

* add drag to zoom

* docs

* add profile selection to UI

* dynamically update onvif config

* ui tweak

* docs

* docs tweak

.github/pull_request_template.md

@@ -1,6 +1,7 @@
 _Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) before submitting a PR._
 
 ## Proposed change
+
 <!--
   Thank you!
 
@@ -25,6 +26,7 @@ _Please read the [contributing guidelines](https://github.com/blakeblackshear/fr
 
 - This PR fixes or closes issue: fixes #
 - This PR is related to issue:
+- Link to discussion with maintainers (**required** for large/pinned features):
 
 ## For new features
 

.github/workflows/pr_template_check.yml

@@ -0,0 +1,120 @@
+name: PR template check
+
+on:
+  pull_request_target:
+    types: [opened, edited]
+
+permissions:
+  pull-requests: write
+
+jobs:
+  check_template:
+    name: Validate PR description
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check PR description against template
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const maintainers = ['blakeblackshear', 'NickM-27', 'hawkeye217'];
+            const author = context.payload.pull_request.user.login;
+
+            if (maintainers.includes(author)) {
+              console.log(`Skipping template check for maintainer: ${author}`);
+              return;
+            }
+
+            const body = context.payload.pull_request.body || '';
+            const errors = [];
+
+            // Check that key template sections exist
+            const requiredSections = [
+              '## Proposed change',
+              '## Type of change',
+              '## AI disclosure',
+              '## Checklist',
+            ];
+
+            for (const section of requiredSections) {
+              if (!body.includes(section)) {
+                errors.push(`Missing section: **${section}**`);
+              }
+            }
+
+            // Check that "Proposed change" has content beyond the default HTML comment
+            const proposedChangeMatch = body.match(
+              /## Proposed change\s*(?:<!--[\s\S]*?-->\s*)?([\s\S]*?)(?=\n## )/
+            );
+            const proposedContent = proposedChangeMatch
+              ? proposedChangeMatch[1].trim()
+              : '';
+            if (!proposedContent) {
+              errors.push(
+                'The **Proposed change** section is empty. Please describe what this PR does.'
+              );
+            }
+
+            // Check that at least one "Type of change" checkbox is checked
+            const typeSection = body.match(
+              /## Type of change\s*([\s\S]*?)(?=\n## )/
+            );
+            if (typeSection && !/- \[x\]/i.test(typeSection[1])) {
+              errors.push(
+                'No **Type of change** selected. Please check at least one option.'
+              );
+            }
+
+            // Check that at least one AI disclosure checkbox is checked
+            const aiSection = body.match(
+              /## AI disclosure\s*([\s\S]*?)(?=\n## )/
+            );
+            if (aiSection && !/- \[x\]/i.test(aiSection[1])) {
+              errors.push(
+                'No **AI disclosure** option selected. Please indicate whether AI tools were used.'
+              );
+            }
+
+            // Check that at least one checklist item is checked
+            const checklistSection = body.match(
+              /## Checklist\s*([\s\S]*?)$/
+            );
+            if (checklistSection && !/- \[x\]/i.test(checklistSection[1])) {
+              errors.push(
+                'No **Checklist** items checked. Please review and check the items that apply.'
+              );
+            }
+
+            if (errors.length === 0) {
+              console.log('PR description passes template validation.');
+              return;
+            }
+
+            const prNumber = context.payload.pull_request.number;
+            const message = [
+              '## PR template validation failed',
+              '',
+              'This PR was automatically closed because the description does not follow the [pull request template](https://github.com/blakeblackshear/frigate/blob/dev/.github/pull_request_template.md).',
+              '',
+              '**Issues found:**',
+              ...errors.map((e) => `- ${e}`),
+              '',
+              'Please update your PR description to include all required sections from the template, then reopen this PR.',
+              '',
+              '> If you used an AI tool to generate this PR, please see our [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) for details.',
+            ].join('\n');
+
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: prNumber,
+              body: message,
+            });
+
+            await github.rest.pulls.update({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              pull_number: prNumber,
+              state: 'closed',
+            });
+
+            core.setFailed('PR description does not follow the template.');

docs/docs/configuration/autotracking.md

@@ -52,6 +52,10 @@ cameras:
       password: admin
       # Optional: Skip TLS verification from the ONVIF server (default: shown below)
       tls_insecure: False
+      # Optional: ONVIF media profile to use for PTZ control, matched by token or name. (default: shown below)
+      # If not set, the first profile with valid PTZ configuration is selected automatically.
+      # Use this when your camera has multiple ONVIF profiles and you need to select a specific one.
+      profile: None
       # Optional: PTZ camera object autotracking. Keeps a moving object in
       # the center of the frame by automatically moving the PTZ camera.
       autotracking:

docs/docs/configuration/cameras.md

@@ -91,6 +91,8 @@ If your ONVIF camera does not require authentication credentials, you may still
 
 :::
 
+If your camera has multiple ONVIF profiles, you can specify which one to use for PTZ control with the `profile` option, matched by token or name. When not set, Frigate selects the first profile with a valid PTZ configuration. Check the Frigate debug logs (`frigate.ptz.onvif: debug`) to see available profile names and tokens for your camera.
+
 An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
 
 ## ONVIF PTZ camera recommendations

docs/docs/configuration/reference.md

@@ -951,7 +951,7 @@ cameras:
     onvif:
       # Required: host of the camera being connected to.
       # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
-      # NOTE: ONVIF user, and password can be specified with environment variables or docker secrets
+      # NOTE: ONVIF host, user, and password can be specified with environment variables or docker secrets
       #       that must begin with 'FRIGATE_'. e.g. host: '{FRIGATE_ONVIF_USERNAME}'
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
@@ -966,6 +966,10 @@ cameras:
       # Optional: Ignores time synchronization mismatches between the camera and the server during authentication.
       # Using NTP on both ends is recommended and this should only be set to True in a "safe" environment due to the security risk it represents.
       ignore_time_mismatch: False
+      # Optional: ONVIF media profile to use for PTZ control, matched by token or name. (default: shown below)
+      # If not set, the first profile with valid PTZ configuration is selected automatically.
+      # Use this when your camera has multiple ONVIF profiles and you need to select a specific one.
+      profile: None
       # Optional: PTZ camera object autotracking. Keeps a moving object in
       # the center of the frame by automatically moving the PTZ camera.
       autotracking:

frigate/config/camera/onvif.py

@@ -117,6 +117,11 @@ class OnvifConfig(FrigateBaseModel):
         title="Disable TLS verify",
         description="Skip TLS verification and disable digest auth for ONVIF (unsafe; use in safe networks only).",
     )
+    profile: Optional[str] = Field(
+        default=None,
+        title="ONVIF profile",
+        description="Specific ONVIF media profile to use for PTZ control, matched by token or name. If not set, the first profile with valid PTZ configuration is selected automatically.",
+    )
     autotracking: PtzAutotrackConfig = Field(
         default_factory=PtzAutotrackConfig,
         title="Autotracking",

frigate/config/camera/updater.py

@@ -23,6 +23,7 @@ class CameraConfigUpdateEnum(str, Enum):
     notifications = "notifications"
     objects = "objects"
     object_genai = "object_genai"
+    onvif = "onvif"
     record = "record"
     remove = "remove"  # for removing a camera
     review = "review"
@@ -130,6 +131,8 @@ class CameraConfigUpdateSubscriber:
             config.lpr = updated_config
         elif update_type == CameraConfigUpdateEnum.snapshots:
             config.snapshots = updated_config
+        elif update_type == CameraConfigUpdateEnum.onvif:
+            config.onvif = updated_config
         elif update_type == CameraConfigUpdateEnum.zones:
             config.zones = updated_config
 

frigate/events/audio.py

@@ -37,8 +37,8 @@ from frigate.ffmpeg_presets import parse_preset_input
 from frigate.log import LogPipe, suppress_stderr_during
 from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
+from frigate.util.ffmpeg import start_or_restart_ffmpeg, stop_ffmpeg
 from frigate.util.process import FrigateProcess
-from frigate.video import start_or_restart_ffmpeg, stop_ffmpeg
 
 try:
     from tflite_runtime.interpreter import Interpreter

frigate/ptz/onvif.py

@@ -15,6 +15,10 @@ from zeep.exceptions import Fault, TransportError
 
 from frigate.camera import PTZMetrics
 from frigate.config import FrigateConfig, ZoomingModeEnum
+from frigate.config.camera.updater import (
+    CameraConfigUpdateEnum,
+    CameraConfigUpdateSubscriber,
+)
 from frigate.util.builtin import find_by_key
 
 logger = logging.getLogger(__name__)
@@ -65,7 +69,14 @@ class OnvifController:
                 self.camera_configs[cam_name] = cam
                 self.status_locks[cam_name] = asyncio.Lock()
 
+        self.config_subscriber = CameraConfigUpdateSubscriber(
+            self.config,
+            self.config.cameras,
+            [CameraConfigUpdateEnum.onvif],
+        )
+
         asyncio.run_coroutine_threadsafe(self._init_cameras(), self.loop)
+        asyncio.run_coroutine_threadsafe(self._poll_config_updates(), self.loop)
 
     def _run_event_loop(self) -> None:
         """Run the event loop in a separate thread."""
@@ -80,6 +91,52 @@ class OnvifController:
         for cam_name in self.camera_configs:
             await self._init_single_camera(cam_name)
 
+    async def _poll_config_updates(self) -> None:
+        """Poll for ONVIF config updates and re-initialize cameras as needed."""
+        while True:
+            await asyncio.sleep(1)
+            try:
+                updates = self.config_subscriber.check_for_updates()
+                for update_type, cameras in updates.items():
+                    if update_type == CameraConfigUpdateEnum.onvif.name:
+                        for cam_name in cameras:
+                            await self._reinit_camera(cam_name)
+            except Exception:
+                logger.error("Error checking for ONVIF config updates")
+
+    async def _close_camera(self, cam_name: str) -> None:
+        """Close the ONVIF client session for a camera."""
+        cam_state = self.cams.get(cam_name)
+        if cam_state and "onvif" in cam_state:
+            try:
+                await cam_state["onvif"].close()
+            except Exception:
+                logger.debug(f"Error closing ONVIF session for {cam_name}")
+
+    async def _reinit_camera(self, cam_name: str) -> None:
+        """Re-initialize a camera after config change."""
+        logger.info(f"Re-initializing ONVIF for {cam_name} due to config change")
+
+        # close existing session before re-init
+        await self._close_camera(cam_name)
+
+        cam = self.config.cameras.get(cam_name)
+        if not cam or not cam.onvif.host:
+            # ONVIF removed from config, clean up
+            self.cams.pop(cam_name, None)
+            self.camera_configs.pop(cam_name, None)
+            self.failed_cams.pop(cam_name, None)
+            return
+
+        # update stored config and reset state
+        self.camera_configs[cam_name] = cam
+        if cam_name not in self.status_locks:
+            self.status_locks[cam_name] = asyncio.Lock()
+        self.cams.pop(cam_name, None)
+        self.failed_cams.pop(cam_name, None)
+
+        await self._init_single_camera(cam_name)
+
     async def _init_single_camera(self, cam_name: str) -> bool:
         """Initialize a single camera by name.
 
@@ -118,6 +175,7 @@ class OnvifController:
                 "active": False,
                 "features": [],
                 "presets": {},
+                "profiles": [],
             }
             return True
         except (Fault, ONVIFError, TransportError, Exception) as e:
@@ -161,22 +219,60 @@ class OnvifController:
             )
             return False
 
+        # build list of valid PTZ profiles
+        valid_profiles = [
+            p
+            for p in profiles
+            if p.VideoEncoderConfiguration
+            and p.PTZConfiguration
+            and (
+                p.PTZConfiguration.DefaultContinuousPanTiltVelocitySpace is not None
+                or p.PTZConfiguration.DefaultContinuousZoomVelocitySpace is not None
+            )
+        ]
+
+        # store available profiles for API response and log for debugging
+        self.cams[camera_name]["profiles"] = [
+            {"name": getattr(p, "Name", None) or p.token, "token": p.token}
+            for p in valid_profiles
+        ]
+        for p in valid_profiles:
+            logger.debug(
+                "Onvif profile for %s: name='%s', token='%s'",
+                camera_name,
+                getattr(p, "Name", None),
+                p.token,
+            )
+
+        configured_profile = self.config.cameras[camera_name].onvif.profile
         profile = None
-        for _, onvif_profile in enumerate(profiles):
-            if (
-                onvif_profile.VideoEncoderConfiguration
-                and onvif_profile.PTZConfiguration
-                and (
-                    onvif_profile.PTZConfiguration.DefaultContinuousPanTiltVelocitySpace
-                    is not None
-                    or onvif_profile.PTZConfiguration.DefaultContinuousZoomVelocitySpace
-                    is not None
+
+        if configured_profile is not None:
+            # match by exact token first, then by name
+            for p in valid_profiles:
+                if p.token == configured_profile:
+                    profile = p
+                    break
+            if profile is None:
+                for p in valid_profiles:
+                    if getattr(p, "Name", None) == configured_profile:
+                        profile = p
+                        break
+            if profile is None:
+                available = [
+                    f"name='{getattr(p, 'Name', None)}', token='{p.token}'"
+                    for p in valid_profiles
+                ]
+                logger.error(
+                    "Onvif profile '%s' not found for camera %s. Available profiles: %s",
+                    configured_profile,
+                    camera_name,
+                    available,
                 )
-            ):
-                # use the first profile that has a valid ptz configuration
-                profile = onvif_profile
-                logger.debug(f"Selected Onvif profile for {camera_name}: {profile}")
-                break
+                return False
+        else:
+            # use the first profile that has a valid ptz configuration
+            profile = valid_profiles[0] if valid_profiles else None
 
         if profile is None:
             logger.error(
@@ -184,6 +280,8 @@ class OnvifController:
             )
             return False
 
+        logger.debug(f"Selected Onvif profile for {camera_name}: {profile}")
+
         # get the PTZ config for the profile
         try:
             configs = profile.PTZConfiguration
@@ -218,48 +316,92 @@ class OnvifController:
         move_request.ProfileToken = profile.token
         self.cams[camera_name]["move_request"] = move_request
 
-        # extra setup for autotracking cameras
-        if (
-            self.config.cameras[camera_name].onvif.autotracking.enabled_in_config
-            and self.config.cameras[camera_name].onvif.autotracking.enabled
-        ):
+        # get PTZ configuration options for feature detection and relative movement
+        ptz_config = None
+        fov_space_id = None
+
+        try:
             request = ptz.create_type("GetConfigurationOptions")
             request.ConfigurationToken = profile.PTZConfiguration.token
             ptz_config = await ptz.GetConfigurationOptions(request)
-            logger.debug(f"Onvif config for {camera_name}: {ptz_config}")
+            logger.debug(
+                f"Onvif PTZ configuration options for {camera_name}: {ptz_config}"
+            )
+        except (Fault, ONVIFError, TransportError, Exception) as e:
+            logger.debug(
+                f"Unable to get PTZ configuration options for {camera_name}: {e}"
+            )
+
+        # detect FOV translation space for relative movement
+        if ptz_config is not None:
+            try:
+                fov_space_id = next(
+                    (
+                        i
+                        for i, space in enumerate(
+                            ptz_config.Spaces.RelativePanTiltTranslationSpace
+                        )
+                        if "TranslationSpaceFov" in space["URI"]
+                    ),
+                    None,
+                )
+            except (AttributeError, TypeError):
+                fov_space_id = None
+
+        autotracking_config = self.config.cameras[camera_name].onvif.autotracking
+        autotracking_enabled = (
+            autotracking_config.enabled_in_config and autotracking_config.enabled
+        )
+
+        # autotracking-only: status request and service capabilities
+        if autotracking_enabled:
+            status_request = ptz.create_type("GetStatus")
+            status_request.ProfileToken = profile.token
+            self.cams[camera_name]["status_request"] = status_request
 
             service_capabilities_request = ptz.create_type("GetServiceCapabilities")
             self.cams[camera_name]["service_capabilities_request"] = (
                 service_capabilities_request
             )
 
-            fov_space_id = next(
-                (
-                    i
-                    for i, space in enumerate(
-                        ptz_config.Spaces.RelativePanTiltTranslationSpace
-                    )
-                    if "TranslationSpaceFov" in space["URI"]
-                ),
-                None,
-            )
-
-            # status request for autotracking and filling ptz-parameters
-            status_request = ptz.create_type("GetStatus")
-            status_request.ProfileToken = profile.token
-            self.cams[camera_name]["status_request"] = status_request
+        # setup relative move request when FOV relative movement is supported
+        if (
+            fov_space_id is not None
+            and configs.DefaultRelativePanTiltTranslationSpace is not None
+        ):
+            # one-off GetStatus to seed Translation field
+            status = None
             try:
-                status = await ptz.GetStatus(status_request)
-                logger.debug(f"Onvif status config for {camera_name}: {status}")
+                one_off_status_request = ptz.create_type("GetStatus")
+                one_off_status_request.ProfileToken = profile.token
+                status = await ptz.GetStatus(one_off_status_request)
+                logger.debug(f"Onvif status for {camera_name}: {status}")
             except Exception as e:
-                logger.warning(f"Unable to get status from camera: {camera_name}: {e}")
-                status = None
+                logger.warning(f"Unable to get status from camera {camera_name}: {e}")
 
-            # autotracking relative panning/tilting needs a relative zoom value set to 0
-            # if camera supports relative movement
+            rel_move_request = ptz.create_type("RelativeMove")
+            rel_move_request.ProfileToken = profile.token
+            logger.debug(f"{camera_name}: Relative move request: {rel_move_request}")
+
+            fov_uri = ptz_config["Spaces"]["RelativePanTiltTranslationSpace"][
+                fov_space_id
+            ]["URI"]
+
+            if rel_move_request.Translation is None:
+                if status is not None:
+                    # seed from current position
+                    rel_move_request.Translation = status.Position
+                    rel_move_request.Translation.PanTilt.space = fov_uri
+                else:
+                    # fallback: construct Translation explicitly
+                    rel_move_request.Translation = {
+                        "PanTilt": {"x": 0, "y": 0, "space": fov_uri}
+                    }
+
+            # configure zoom on relative move request
             if (
-                self.config.cameras[camera_name].onvif.autotracking.zooming
-                != ZoomingModeEnum.disabled
+                autotracking_enabled
+                and autotracking_config.zooming != ZoomingModeEnum.disabled
             ):
                 zoom_space_id = next(
                     (
@@ -271,60 +413,43 @@ class OnvifController:
                     ),
                     None,
                 )
-
-            # setup relative moving request for autotracking
-            move_request = ptz.create_type("RelativeMove")
-            move_request.ProfileToken = profile.token
-            logger.debug(f"{camera_name}: Relative move request: {move_request}")
-            if move_request.Translation is None and fov_space_id is not None:
-                move_request.Translation = status.Position
-                move_request.Translation.PanTilt.space = ptz_config["Spaces"][
-                    "RelativePanTiltTranslationSpace"
-                ][fov_space_id]["URI"]
-
-            # try setting relative zoom translation space
-            try:
-                if (
-                    self.config.cameras[camera_name].onvif.autotracking.zooming
-                    != ZoomingModeEnum.disabled
-                ):
+                try:
                     if zoom_space_id is not None:
-                        move_request.Translation.Zoom.space = ptz_config["Spaces"][
+                        rel_move_request.Translation.Zoom.space = ptz_config["Spaces"][
                             "RelativeZoomTranslationSpace"
                         ][zoom_space_id]["URI"]
-                else:
-                    if (
-                        move_request["Translation"] is not None
-                        and "Zoom" in move_request["Translation"]
-                    ):
-                        del move_request["Translation"]["Zoom"]
-                    if (
-                        move_request["Speed"] is not None
-                        and "Zoom" in move_request["Speed"]
-                    ):
-                        del move_request["Speed"]["Zoom"]
-                    logger.debug(
-                        f"{camera_name}: Relative move request after deleting zoom: {move_request}"
+                except Exception as e:
+                    autotracking_config.zooming = ZoomingModeEnum.disabled
+                    logger.warning(
+                        f"Disabling autotracking zooming for {camera_name}: Relative zoom not supported. Exception: {e}"
                     )
-            except Exception as e:
-                self.config.cameras[
-                    camera_name
-                ].onvif.autotracking.zooming = ZoomingModeEnum.disabled
-                logger.warning(
-                    f"Disabling autotracking zooming for {camera_name}: Relative zoom not supported. Exception: {e}"
+            else:
+                # remove zoom fields from relative move request
+                if (
+                    rel_move_request["Translation"] is not None
+                    and "Zoom" in rel_move_request["Translation"]
+                ):
+                    del rel_move_request["Translation"]["Zoom"]
+                if (
+                    rel_move_request["Speed"] is not None
+                    and "Zoom" in rel_move_request["Speed"]
+                ):
+                    del rel_move_request["Speed"]["Zoom"]
+                logger.debug(
+                    f"{camera_name}: Relative move request after deleting zoom: {rel_move_request}"
                 )
 
-            if move_request.Speed is None:
-                move_request.Speed = configs.DefaultPTZSpeed if configs else None
+            if rel_move_request.Speed is None:
+                rel_move_request.Speed = configs.DefaultPTZSpeed if configs else None
             logger.debug(
-                f"{camera_name}: Relative move request after setup: {move_request}"
+                f"{camera_name}: Relative move request after setup: {rel_move_request}"
             )
-            self.cams[camera_name]["relative_move_request"] = move_request
+            self.cams[camera_name]["relative_move_request"] = rel_move_request
 
-            # setup absolute moving request for autotracking zooming
-            move_request = ptz.create_type("AbsoluteMove")
-            move_request.ProfileToken = profile.token
-            self.cams[camera_name]["absolute_move_request"] = move_request
+        # setup absolute move request
+        abs_move_request = ptz.create_type("AbsoluteMove")
+        abs_move_request.ProfileToken = profile.token
+        self.cams[camera_name]["absolute_move_request"] = abs_move_request
 
         # setup existing presets
         try:
@@ -358,48 +483,48 @@ class OnvifController:
 
         if configs.DefaultRelativeZoomTranslationSpace:
             supported_features.append("zoom-r")
-            if (
-                self.config.cameras[camera_name].onvif.autotracking.enabled_in_config
-                and self.config.cameras[camera_name].onvif.autotracking.enabled
-            ):
+            if ptz_config is not None:
                 try:
-                    # get camera's zoom limits from onvif config
                     self.cams[camera_name]["relative_zoom_range"] = (
                         ptz_config.Spaces.RelativeZoomTranslationSpace[0]
                     )
                 except Exception as e:
-                    if (
-                        self.config.cameras[camera_name].onvif.autotracking.zooming
-                        == ZoomingModeEnum.relative
-                    ):
-                        self.config.cameras[
-                            camera_name
-                        ].onvif.autotracking.zooming = ZoomingModeEnum.disabled
+                    if autotracking_config.zooming == ZoomingModeEnum.relative:
+                        autotracking_config.zooming = ZoomingModeEnum.disabled
                         logger.warning(
                             f"Disabling autotracking zooming for {camera_name}: Relative zoom not supported. Exception: {e}"
                         )
 
         if configs.DefaultAbsoluteZoomPositionSpace:
             supported_features.append("zoom-a")
-            if (
-                self.config.cameras[camera_name].onvif.autotracking.enabled_in_config
-                and self.config.cameras[camera_name].onvif.autotracking.enabled
-            ):
+            if ptz_config is not None:
                 try:
-                    # get camera's zoom limits from onvif config
                     self.cams[camera_name]["absolute_zoom_range"] = (
                         ptz_config.Spaces.AbsoluteZoomPositionSpace[0]
                     )
                     self.cams[camera_name]["zoom_limits"] = configs.ZoomLimits
                 except Exception as e:
-                    if self.config.cameras[camera_name].onvif.autotracking.zooming:
-                        self.config.cameras[
-                            camera_name
-                        ].onvif.autotracking.zooming = ZoomingModeEnum.disabled
+                    if autotracking_config.zooming != ZoomingModeEnum.disabled:
+                        autotracking_config.zooming = ZoomingModeEnum.disabled
                         logger.warning(
                             f"Disabling autotracking zooming for {camera_name}: Absolute zoom not supported. Exception: {e}"
                         )
 
+        # disable autotracking zoom if required ranges are unavailable
+        if autotracking_config.zooming != ZoomingModeEnum.disabled:
+            if autotracking_config.zooming == ZoomingModeEnum.relative:
+                if "relative_zoom_range" not in self.cams[camera_name]:
+                    autotracking_config.zooming = ZoomingModeEnum.disabled
+                    logger.warning(
+                        f"Disabling autotracking zooming for {camera_name}: Relative zoom range unavailable"
+                    )
+            if autotracking_config.zooming == ZoomingModeEnum.absolute:
+                if "absolute_zoom_range" not in self.cams[camera_name]:
+                    autotracking_config.zooming = ZoomingModeEnum.disabled
+                    logger.warning(
+                        f"Disabling autotracking zooming for {camera_name}: Absolute zoom range unavailable"
+                    )
+
         if (
             self.cams[camera_name]["video_source_token"] is not None
             and imaging is not None
@@ -416,10 +541,9 @@ class OnvifController:
             except (Fault, ONVIFError, TransportError, Exception) as e:
                 logger.debug(f"Focus not supported for {camera_name}: {e}")
 
+        # detect FOV relative movement support
         if (
-            self.config.cameras[camera_name].onvif.autotracking.enabled_in_config
-            and self.config.cameras[camera_name].onvif.autotracking.enabled
-            and fov_space_id is not None
+            fov_space_id is not None
             and configs.DefaultRelativePanTiltTranslationSpace is not None
         ):
             supported_features.append("pt-r-fov")
@@ -548,11 +672,8 @@ class OnvifController:
         move_request.Translation.PanTilt.x = pan
         move_request.Translation.PanTilt.y = tilt
 
-        if (
-            "zoom-r" in self.cams[camera_name]["features"]
-            and self.config.cameras[camera_name].onvif.autotracking.zooming
-            == ZoomingModeEnum.relative
-        ):
+        # include zoom if requested and camera supports relative zoom
+        if zoom != 0 and "zoom-r" in self.cams[camera_name]["features"]:
             move_request.Speed = {
                 "PanTilt": {
                     "x": speed,
@@ -560,7 +681,7 @@ class OnvifController:
                 },
                 "Zoom": {"x": speed},
             }
-            move_request.Translation.Zoom.x = zoom
+            move_request["Translation"]["Zoom"] = {"x": zoom}
 
         await self.cams[camera_name]["ptz"].RelativeMove(move_request)
 
@@ -568,12 +689,8 @@ class OnvifController:
         move_request.Translation.PanTilt.x = 0
         move_request.Translation.PanTilt.y = 0
 
-        if (
-            "zoom-r" in self.cams[camera_name]["features"]
-            and self.config.cameras[camera_name].onvif.autotracking.zooming
-            == ZoomingModeEnum.relative
-        ):
-            move_request.Translation.Zoom.x = 0
+        if zoom != 0 and "zoom-r" in self.cams[camera_name]["features"]:
+            del move_request["Translation"]["Zoom"]
 
         self.cams[camera_name]["active"] = False
 
@@ -717,8 +834,18 @@ class OnvifController:
             elif command == OnvifCommandEnum.preset:
                 await self._move_to_preset(camera_name, param)
             elif command == OnvifCommandEnum.move_relative:
-                _, pan, tilt = param.split("_")
-                await self._move_relative(camera_name, float(pan), float(tilt), 0, 1)
+                parts = param.split("_")
+                if len(parts) == 3:
+                    _, pan, tilt = parts
+                    zoom = 0.0
+                elif len(parts) == 4:
+                    _, pan, tilt, zoom = parts
+                else:
+                    logger.error(f"Invalid move_relative params: {param}")
+                    return
+                await self._move_relative(
+                    camera_name, float(pan), float(tilt), float(zoom), 1
+                )
             elif command in (OnvifCommandEnum.zoom_in, OnvifCommandEnum.zoom_out):
                 await self._zoom(camera_name, command)
             elif command in (OnvifCommandEnum.focus_in, OnvifCommandEnum.focus_out):
@@ -773,6 +900,7 @@ class OnvifController:
                 "name": camera_name,
                 "features": self.cams[camera_name]["features"],
                 "presets": list(self.cams[camera_name]["presets"].keys()),
+                "profiles": self.cams[camera_name].get("profiles", []),
             }
 
         if camera_name not in self.cams.keys() and camera_name in self.config.cameras:
@@ -970,6 +1098,7 @@ class OnvifController:
             return
 
         logger.info("Exiting ONVIF controller...")
+        self.config_subscriber.stop()
 
         def stop_and_cleanup():
             try:

frigate/util/ffmpeg.py

@@ -0,0 +1,48 @@
+"""FFmpeg utility functions for managing ffmpeg processes."""
+
+import logging
+import subprocess as sp
+from typing import Any
+
+from frigate.log import LogPipe
+
+
+def stop_ffmpeg(ffmpeg_process: sp.Popen[Any], logger: logging.Logger):
+    logger.info("Terminating the existing ffmpeg process...")
+    ffmpeg_process.terminate()
+    try:
+        logger.info("Waiting for ffmpeg to exit gracefully...")
+        ffmpeg_process.communicate(timeout=30)
+        logger.info("FFmpeg has exited")
+    except sp.TimeoutExpired:
+        logger.info("FFmpeg didn't exit. Force killing...")
+        ffmpeg_process.kill()
+        ffmpeg_process.communicate()
+        logger.info("FFmpeg has been killed")
+    ffmpeg_process = None
+
+
+def start_or_restart_ffmpeg(
+    ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None
+) -> sp.Popen[Any]:
+    if ffmpeg_process is not None:
+        stop_ffmpeg(ffmpeg_process, logger)
+
+    if frame_size is None:
+        process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.DEVNULL,
+            stderr=logpipe,
+            stdin=sp.DEVNULL,
+            start_new_session=True,
+        )
+    else:
+        process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.PIPE,
+            stderr=logpipe,
+            stdin=sp.DEVNULL,
+            bufsize=frame_size * 10,
+            start_new_session=True,
+        )
+    return process

frigate/video/__init__.py

@@ -0,0 +1,2 @@
+from .detect import *  # noqa: F403
+from .ffmpeg import *  # noqa: F403

frigate/video/detect.py

@@ -0,0 +1,563 @@
+"""Manages camera object detection processes."""
+
+import logging
+import queue
+import time
+from datetime import datetime, timezone
+from multiprocessing import Queue
+from multiprocessing.synchronize import Event as MpEvent
+from typing import Any
+
+import cv2
+
+from frigate.camera import CameraMetrics, PTZMetrics
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.config import CameraConfig, DetectConfig, LoggerConfig, ModelConfig
+from frigate.config.camera.camera import CameraTypeEnum
+from frigate.config.camera.updater import (
+    CameraConfigUpdateEnum,
+    CameraConfigUpdateSubscriber,
+)
+from frigate.const import (
+    PROCESS_PRIORITY_HIGH,
+    REQUEST_REGION_GRID,
+)
+from frigate.motion import MotionDetector
+from frigate.motion.improved_motion import ImprovedMotionDetector
+from frigate.object_detection.base import RemoteObjectDetector
+from frigate.ptz.autotrack import ptz_moving_at_frame_time
+from frigate.track import ObjectTracker
+from frigate.track.norfair_tracker import NorfairTracker
+from frigate.track.tracked_object import TrackedObjectAttribute
+from frigate.util.builtin import EventsPerSecond
+from frigate.util.image import (
+    FrameManager,
+    SharedMemoryFrameManager,
+    draw_box_with_label,
+)
+from frigate.util.object import (
+    create_tensor_input,
+    get_cluster_candidates,
+    get_cluster_region,
+    get_cluster_region_from_grid,
+    get_min_region_size,
+    get_startup_regions,
+    inside_any,
+    intersects_any,
+    is_object_filtered,
+    reduce_detections,
+)
+from frigate.util.process import FrigateProcess
+from frigate.util.time import get_tomorrow_at_time
+
+logger = logging.getLogger(__name__)
+
+
+class CameraTracker(FrigateProcess):
+    def __init__(
+        self,
+        config: CameraConfig,
+        model_config: ModelConfig,
+        labelmap: dict[int, str],
+        detection_queue: Queue,
+        detected_objects_queue,
+        camera_metrics: CameraMetrics,
+        ptz_metrics: PTZMetrics,
+        region_grid: list[list[dict[str, Any]]],
+        stop_event: MpEvent,
+        log_config: LoggerConfig | None = None,
+    ) -> None:
+        super().__init__(
+            stop_event,
+            PROCESS_PRIORITY_HIGH,
+            name=f"frigate.process:{config.name}",
+            daemon=True,
+        )
+        self.config = config
+        self.model_config = model_config
+        self.labelmap = labelmap
+        self.detection_queue = detection_queue
+        self.detected_objects_queue = detected_objects_queue
+        self.camera_metrics = camera_metrics
+        self.ptz_metrics = ptz_metrics
+        self.region_grid = region_grid
+        self.log_config = log_config
+
+    def run(self) -> None:
+        self.pre_run_setup(self.log_config)
+        frame_queue = self.camera_metrics.frame_queue
+        frame_shape = self.config.frame_shape
+
+        motion_detector = ImprovedMotionDetector(
+            frame_shape,
+            self.config.motion,
+            self.config.detect.fps,
+            name=self.config.name,
+            ptz_metrics=self.ptz_metrics,
+        )
+        object_detector = RemoteObjectDetector(
+            self.config.name,
+            self.labelmap,
+            self.detection_queue,
+            self.model_config,
+            self.stop_event,
+        )
+
+        object_tracker = NorfairTracker(self.config, self.ptz_metrics)
+
+        frame_manager = SharedMemoryFrameManager()
+
+        # create communication for region grid updates
+        requestor = InterProcessRequestor()
+
+        process_frames(
+            requestor,
+            frame_queue,
+            frame_shape,
+            self.model_config,
+            self.config,
+            frame_manager,
+            motion_detector,
+            object_detector,
+            object_tracker,
+            self.detected_objects_queue,
+            self.camera_metrics,
+            self.stop_event,
+            self.ptz_metrics,
+            self.region_grid,
+        )
+
+        # empty the frame queue
+        logger.info(f"{self.config.name}: emptying frame queue")
+        while not frame_queue.empty():
+            (frame_name, _) = frame_queue.get(False)
+            frame_manager.delete(frame_name)
+
+        logger.info(f"{self.config.name}: exiting subprocess")
+
+
+def detect(
+    detect_config: DetectConfig,
+    object_detector,
+    frame,
+    model_config: ModelConfig,
+    region,
+    objects_to_track,
+    object_filters,
+):
+    tensor_input = create_tensor_input(frame, model_config, region)
+
+    detections = []
+    region_detections = object_detector.detect(tensor_input)
+    for d in region_detections:
+        box = d[2]
+        size = region[2] - region[0]
+        x_min = int(max(0, (box[1] * size) + region[0]))
+        y_min = int(max(0, (box[0] * size) + region[1]))
+        x_max = int(min(detect_config.width - 1, (box[3] * size) + region[0]))
+        y_max = int(min(detect_config.height - 1, (box[2] * size) + region[1]))
+
+        # ignore objects that were detected outside the frame
+        if (x_min >= detect_config.width - 1) or (y_min >= detect_config.height - 1):
+            continue
+
+        width = x_max - x_min
+        height = y_max - y_min
+        area = width * height
+        ratio = width / max(1, height)
+        det = (d[0], d[1], (x_min, y_min, x_max, y_max), area, ratio, region)
+        # apply object filters
+        if is_object_filtered(det, objects_to_track, object_filters):
+            continue
+        detections.append(det)
+    return detections
+
+
+def process_frames(
+    requestor: InterProcessRequestor,
+    frame_queue: Queue,
+    frame_shape: tuple[int, int],
+    model_config: ModelConfig,
+    camera_config: CameraConfig,
+    frame_manager: FrameManager,
+    motion_detector: MotionDetector,
+    object_detector: RemoteObjectDetector,
+    object_tracker: ObjectTracker,
+    detected_objects_queue: Queue,
+    camera_metrics: CameraMetrics,
+    stop_event: MpEvent,
+    ptz_metrics: PTZMetrics,
+    region_grid: list[list[dict[str, Any]]],
+    exit_on_empty: bool = False,
+):
+    next_region_update = get_tomorrow_at_time(2)
+    config_subscriber = CameraConfigUpdateSubscriber(
+        None,
+        {camera_config.name: camera_config},
+        [
+            CameraConfigUpdateEnum.detect,
+            CameraConfigUpdateEnum.enabled,
+            CameraConfigUpdateEnum.motion,
+            CameraConfigUpdateEnum.objects,
+        ],
+    )
+
+    fps_tracker = EventsPerSecond()
+    fps_tracker.start()
+
+    startup_scan = True
+    stationary_frame_counter = 0
+    camera_enabled = True
+
+    region_min_size = get_min_region_size(model_config)
+
+    attributes_map = model_config.attributes_map
+    all_attributes = model_config.all_attributes
+
+    # remove license_plate from attributes if this camera is a dedicated LPR cam
+    if camera_config.type == CameraTypeEnum.lpr:
+        modified_attributes_map = model_config.attributes_map.copy()
+
+        if (
+            "car" in modified_attributes_map
+            and "license_plate" in modified_attributes_map["car"]
+        ):
+            modified_attributes_map["car"] = [
+                attr
+                for attr in modified_attributes_map["car"]
+                if attr != "license_plate"
+            ]
+
+            attributes_map = modified_attributes_map
+
+        all_attributes = [
+            attr for attr in model_config.all_attributes if attr != "license_plate"
+        ]
+
+    while not stop_event.is_set():
+        updated_configs = config_subscriber.check_for_updates()
+
+        if "enabled" in updated_configs:
+            prev_enabled = camera_enabled
+            camera_enabled = camera_config.enabled
+
+        if "motion" in updated_configs:
+            motion_detector.config = camera_config.motion
+            motion_detector.update_mask()
+
+        if (
+            not camera_enabled
+            and prev_enabled != camera_enabled
+            and camera_metrics.frame_queue.empty()
+        ):
+            logger.debug(
+                f"Camera {camera_config.name} disabled, clearing tracked objects"
+            )
+            prev_enabled = camera_enabled
+
+            # Clear norfair's dictionaries
+            object_tracker.tracked_objects.clear()
+            object_tracker.disappeared.clear()
+            object_tracker.stationary_box_history.clear()
+            object_tracker.positions.clear()
+            object_tracker.track_id_map.clear()
+
+            # Clear internal norfair states
+            for trackers_by_type in object_tracker.trackers.values():
+                for tracker in trackers_by_type.values():
+                    tracker.tracked_objects = []
+            for tracker in object_tracker.default_tracker.values():
+                tracker.tracked_objects = []
+
+        if not camera_enabled:
+            time.sleep(0.1)
+            continue
+
+        if datetime.now().astimezone(timezone.utc) > next_region_update:
+            region_grid = requestor.send_data(REQUEST_REGION_GRID, camera_config.name)
+            next_region_update = get_tomorrow_at_time(2)
+
+        try:
+            if exit_on_empty:
+                frame_name, frame_time = frame_queue.get(False)
+            else:
+                frame_name, frame_time = frame_queue.get(True, 1)
+        except queue.Empty:
+            if exit_on_empty:
+                logger.info("Exiting track_objects...")
+                break
+            continue
+
+        camera_metrics.detection_frame.value = frame_time
+        ptz_metrics.frame_time.value = frame_time
+
+        frame = frame_manager.get(frame_name, (frame_shape[0] * 3 // 2, frame_shape[1]))
+
+        if frame is None:
+            logger.debug(
+                f"{camera_config.name}: frame {frame_time} is not in memory store."
+            )
+            continue
+
+        # look for motion if enabled
+        motion_boxes = motion_detector.detect(frame)
+
+        regions = []
+        consolidated_detections = []
+
+        # if detection is disabled
+        if not camera_config.detect.enabled:
+            object_tracker.match_and_update(frame_name, frame_time, [])
+        else:
+            # get stationary object ids
+            # check every Nth frame for stationary objects
+            # disappeared objects are not stationary
+            # also check for overlapping motion boxes
+            if stationary_frame_counter == camera_config.detect.stationary.interval:
+                stationary_frame_counter = 0
+                stationary_object_ids = []
+            else:
+                stationary_frame_counter += 1
+                stationary_object_ids = [
+                    obj["id"]
+                    for obj in object_tracker.tracked_objects.values()
+                    # if it has exceeded the stationary threshold
+                    if obj["motionless_count"]
+                    >= camera_config.detect.stationary.threshold
+                    # and it hasn't disappeared
+                    and object_tracker.disappeared[obj["id"]] == 0
+                    # and it doesn't overlap with any current motion boxes when not calibrating
+                    and not intersects_any(
+                        obj["box"],
+                        [] if motion_detector.is_calibrating() else motion_boxes,
+                    )
+                ]
+
+            # get tracked object boxes that aren't stationary
+            tracked_object_boxes = [
+                (
+                    # use existing object box for stationary objects
+                    obj["estimate"]
+                    if obj["motionless_count"]
+                    < camera_config.detect.stationary.threshold
+                    else obj["box"]
+                )
+                for obj in object_tracker.tracked_objects.values()
+                if obj["id"] not in stationary_object_ids
+            ]
+            object_boxes = tracked_object_boxes + object_tracker.untracked_object_boxes
+
+            # get consolidated regions for tracked objects
+            regions = [
+                get_cluster_region(
+                    frame_shape, region_min_size, candidate, object_boxes
+                )
+                for candidate in get_cluster_candidates(
+                    frame_shape, region_min_size, object_boxes
+                )
+            ]
+
+            # 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,
+            ):
+                # 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)
+                ]
+
+                if standalone_motion_boxes:
+                    motion_clusters = get_cluster_candidates(
+                        frame_shape,
+                        region_min_size,
+                        standalone_motion_boxes,
+                    )
+                    motion_regions = [
+                        get_cluster_region_from_grid(
+                            frame_shape,
+                            region_min_size,
+                            candidate,
+                            standalone_motion_boxes,
+                            region_grid,
+                        )
+                        for candidate in motion_clusters
+                    ]
+                    regions += motion_regions
+
+            # if starting up, get the next startup scan region
+            if startup_scan:
+                for region in get_startup_regions(
+                    frame_shape, region_min_size, region_grid
+                ):
+                    regions.append(region)
+                startup_scan = False
+
+            # resize regions and detect
+            # seed with stationary objects
+            detections = [
+                (
+                    obj["label"],
+                    obj["score"],
+                    obj["box"],
+                    obj["area"],
+                    obj["ratio"],
+                    obj["region"],
+                )
+                for obj in object_tracker.tracked_objects.values()
+                if obj["id"] in stationary_object_ids
+            ]
+
+            for region in regions:
+                detections.extend(
+                    detect(
+                        camera_config.detect,
+                        object_detector,
+                        frame,
+                        model_config,
+                        region,
+                        camera_config.objects.track,
+                        camera_config.objects.filters,
+                    )
+                )
+
+            consolidated_detections = reduce_detections(frame_shape, detections)
+
+            # if detection was run on this frame, consolidate
+            if len(regions) > 0:
+                tracked_detections = [
+                    d for d in consolidated_detections if d[0] not in all_attributes
+                ]
+                # now that we have refined our detections, we need to track objects
+                object_tracker.match_and_update(
+                    frame_name, frame_time, tracked_detections
+                )
+            # else, just update the frame times for the stationary objects
+            else:
+                object_tracker.update_frame_times(frame_name, frame_time)
+
+        # group the attribute detections based on what label they apply to
+        attribute_detections: dict[str, list[TrackedObjectAttribute]] = {}
+        for label, attribute_labels in attributes_map.items():
+            attribute_detections[label] = [
+                TrackedObjectAttribute(d)
+                for d in consolidated_detections
+                if d[0] in attribute_labels
+            ]
+
+        # build detections
+        detections = {}
+        for obj in object_tracker.tracked_objects.values():
+            detections[obj["id"]] = {**obj, "attributes": []}
+
+        # find the best object for each attribute to be assigned to
+        all_objects: list[dict[str, Any]] = object_tracker.tracked_objects.values()
+        for attributes in attribute_detections.values():
+            for attribute in attributes:
+                filtered_objects = filter(
+                    lambda o: attribute.label in attributes_map.get(o["label"], []),
+                    all_objects,
+                )
+                selected_object_id = attribute.find_best_object(filtered_objects)
+
+                if selected_object_id is not None:
+                    detections[selected_object_id]["attributes"].append(
+                        attribute.get_tracking_data()
+                    )
+
+        # debug object tracking
+        if False:
+            bgr_frame = cv2.cvtColor(
+                frame,
+                cv2.COLOR_YUV2BGR_I420,
+            )
+            object_tracker.debug_draw(bgr_frame, frame_time)
+            cv2.imwrite(
+                f"debug/frames/track-{'{:.6f}'.format(frame_time)}.jpg", bgr_frame
+            )
+        # debug
+        if False:
+            bgr_frame = cv2.cvtColor(
+                frame,
+                cv2.COLOR_YUV2BGR_I420,
+            )
+
+            for m_box in motion_boxes:
+                cv2.rectangle(
+                    bgr_frame,
+                    (m_box[0], m_box[1]),
+                    (m_box[2], m_box[3]),
+                    (0, 0, 255),
+                    2,
+                )
+
+            for b in tracked_object_boxes:
+                cv2.rectangle(
+                    bgr_frame,
+                    (b[0], b[1]),
+                    (b[2], b[3]),
+                    (255, 0, 0),
+                    2,
+                )
+
+            for obj in object_tracker.tracked_objects.values():
+                if obj["frame_time"] == frame_time:
+                    thickness = 2
+                    color = model_config.colormap.get(obj["label"], (255, 255, 255))
+                else:
+                    thickness = 1
+                    color = (255, 0, 0)
+
+                # draw the bounding boxes on the frame
+                box = obj["box"]
+
+                draw_box_with_label(
+                    bgr_frame,
+                    box[0],
+                    box[1],
+                    box[2],
+                    box[3],
+                    obj["label"],
+                    obj["id"],
+                    thickness=thickness,
+                    color=color,
+                )
+
+            for region in regions:
+                cv2.rectangle(
+                    bgr_frame,
+                    (region[0], region[1]),
+                    (region[2], region[3]),
+                    (0, 255, 0),
+                    2,
+                )
+
+            cv2.imwrite(
+                f"debug/frames/{camera_config.name}-{'{:.6f}'.format(frame_time)}.jpg",
+                bgr_frame,
+            )
+        # add to the queue if not full
+        if detected_objects_queue.full():
+            frame_manager.close(frame_name)
+            continue
+        else:
+            fps_tracker.update()
+            camera_metrics.process_fps.value = fps_tracker.eps()
+            detected_objects_queue.put(
+                (
+                    camera_config.name,
+                    frame_name,
+                    frame_time,
+                    detections,
+                    motion_boxes,
+                    regions,
+                )
+            )
+            camera_metrics.detection_fps.value = object_detector.fps.eps()
+            frame_manager.close(frame_name)
+
+    motion_detector.stop()
+    requestor.stop()
+    config_subscriber.stop()

frigate/video/ffmpeg.py

@@ -1,3 +1,5 @@
+"""Manages ffmpeg processes for camera frame capture."""
+
 import logging
 import queue
 import subprocess as sp
@@ -9,97 +11,30 @@ from multiprocessing import Queue, Value
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
 
-import cv2
-
-from frigate.camera import CameraMetrics, PTZMetrics
+from frigate.camera import CameraMetrics
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.comms.recordings_updater import (
     RecordingsDataSubscriber,
     RecordingsDataTypeEnum,
 )
-from frigate.config import CameraConfig, DetectConfig, LoggerConfig, ModelConfig
-from frigate.config.camera.camera import CameraTypeEnum
+from frigate.config import CameraConfig, LoggerConfig
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
-from frigate.const import (
-    PROCESS_PRIORITY_HIGH,
-    REQUEST_REGION_GRID,
-)
+from frigate.const import PROCESS_PRIORITY_HIGH
 from frigate.log import LogPipe
-from frigate.motion import MotionDetector
-from frigate.motion.improved_motion import ImprovedMotionDetector
-from frigate.object_detection.base import RemoteObjectDetector
-from frigate.ptz.autotrack import ptz_moving_at_frame_time
-from frigate.track import ObjectTracker
-from frigate.track.norfair_tracker import NorfairTracker
-from frigate.track.tracked_object import TrackedObjectAttribute
 from frigate.util.builtin import EventsPerSecond
+from frigate.util.ffmpeg import start_or_restart_ffmpeg, stop_ffmpeg
 from frigate.util.image import (
     FrameManager,
     SharedMemoryFrameManager,
-    draw_box_with_label,
-)
-from frigate.util.object import (
-    create_tensor_input,
-    get_cluster_candidates,
-    get_cluster_region,
-    get_cluster_region_from_grid,
-    get_min_region_size,
-    get_startup_regions,
-    inside_any,
-    intersects_any,
-    is_object_filtered,
-    reduce_detections,
 )
 from frigate.util.process import FrigateProcess
-from frigate.util.time import get_tomorrow_at_time
 
 logger = logging.getLogger(__name__)
 
 
-def stop_ffmpeg(ffmpeg_process: sp.Popen[Any], logger: logging.Logger):
-    logger.info("Terminating the existing ffmpeg process...")
-    ffmpeg_process.terminate()
-    try:
-        logger.info("Waiting for ffmpeg to exit gracefully...")
-        ffmpeg_process.communicate(timeout=30)
-        logger.info("FFmpeg has exited")
-    except sp.TimeoutExpired:
-        logger.info("FFmpeg didn't exit. Force killing...")
-        ffmpeg_process.kill()
-        ffmpeg_process.communicate()
-        logger.info("FFmpeg has been killed")
-    ffmpeg_process = None
-
-
-def start_or_restart_ffmpeg(
-    ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None
-) -> sp.Popen[Any]:
-    if ffmpeg_process is not None:
-        stop_ffmpeg(ffmpeg_process, logger)
-
-    if frame_size is None:
-        process = sp.Popen(
-            ffmpeg_cmd,
-            stdout=sp.DEVNULL,
-            stderr=logpipe,
-            stdin=sp.DEVNULL,
-            start_new_session=True,
-        )
-    else:
-        process = sp.Popen(
-            ffmpeg_cmd,
-            stdout=sp.PIPE,
-            stderr=logpipe,
-            stdin=sp.DEVNULL,
-            bufsize=frame_size * 10,
-            start_new_session=True,
-        )
-    return process
-
-
 def capture_frames(
     ffmpeg_process: sp.Popen[Any],
     config: CameraConfig,
@@ -708,513 +643,3 @@ class CameraCapture(FrigateProcess):
         )
         camera_watchdog.start()
         camera_watchdog.join()
-
-
-class CameraTracker(FrigateProcess):
-    def __init__(
-        self,
-        config: CameraConfig,
-        model_config: ModelConfig,
-        labelmap: dict[int, str],
-        detection_queue: Queue,
-        detected_objects_queue,
-        camera_metrics: CameraMetrics,
-        ptz_metrics: PTZMetrics,
-        region_grid: list[list[dict[str, Any]]],
-        stop_event: MpEvent,
-        log_config: LoggerConfig | None = None,
-    ) -> None:
-        super().__init__(
-            stop_event,
-            PROCESS_PRIORITY_HIGH,
-            name=f"frigate.process:{config.name}",
-            daemon=True,
-        )
-        self.config = config
-        self.model_config = model_config
-        self.labelmap = labelmap
-        self.detection_queue = detection_queue
-        self.detected_objects_queue = detected_objects_queue
-        self.camera_metrics = camera_metrics
-        self.ptz_metrics = ptz_metrics
-        self.region_grid = region_grid
-        self.log_config = log_config
-
-    def run(self) -> None:
-        self.pre_run_setup(self.log_config)
-        frame_queue = self.camera_metrics.frame_queue
-        frame_shape = self.config.frame_shape
-
-        motion_detector = ImprovedMotionDetector(
-            frame_shape,
-            self.config.motion,
-            self.config.detect.fps,
-            name=self.config.name,
-            ptz_metrics=self.ptz_metrics,
-        )
-        object_detector = RemoteObjectDetector(
-            self.config.name,
-            self.labelmap,
-            self.detection_queue,
-            self.model_config,
-            self.stop_event,
-        )
-
-        object_tracker = NorfairTracker(self.config, self.ptz_metrics)
-
-        frame_manager = SharedMemoryFrameManager()
-
-        # create communication for region grid updates
-        requestor = InterProcessRequestor()
-
-        process_frames(
-            requestor,
-            frame_queue,
-            frame_shape,
-            self.model_config,
-            self.config,
-            frame_manager,
-            motion_detector,
-            object_detector,
-            object_tracker,
-            self.detected_objects_queue,
-            self.camera_metrics,
-            self.stop_event,
-            self.ptz_metrics,
-            self.region_grid,
-        )
-
-        # empty the frame queue
-        logger.info(f"{self.config.name}: emptying frame queue")
-        while not frame_queue.empty():
-            (frame_name, _) = frame_queue.get(False)
-            frame_manager.delete(frame_name)
-
-        logger.info(f"{self.config.name}: exiting subprocess")
-
-
-def detect(
-    detect_config: DetectConfig,
-    object_detector,
-    frame,
-    model_config: ModelConfig,
-    region,
-    objects_to_track,
-    object_filters,
-):
-    tensor_input = create_tensor_input(frame, model_config, region)
-
-    detections = []
-    region_detections = object_detector.detect(tensor_input)
-    for d in region_detections:
-        box = d[2]
-        size = region[2] - region[0]
-        x_min = int(max(0, (box[1] * size) + region[0]))
-        y_min = int(max(0, (box[0] * size) + region[1]))
-        x_max = int(min(detect_config.width - 1, (box[3] * size) + region[0]))
-        y_max = int(min(detect_config.height - 1, (box[2] * size) + region[1]))
-
-        # ignore objects that were detected outside the frame
-        if (x_min >= detect_config.width - 1) or (y_min >= detect_config.height - 1):
-            continue
-
-        width = x_max - x_min
-        height = y_max - y_min
-        area = width * height
-        ratio = width / max(1, height)
-        det = (d[0], d[1], (x_min, y_min, x_max, y_max), area, ratio, region)
-        # apply object filters
-        if is_object_filtered(det, objects_to_track, object_filters):
-            continue
-        detections.append(det)
-    return detections
-
-
-def process_frames(
-    requestor: InterProcessRequestor,
-    frame_queue: Queue,
-    frame_shape: tuple[int, int],
-    model_config: ModelConfig,
-    camera_config: CameraConfig,
-    frame_manager: FrameManager,
-    motion_detector: MotionDetector,
-    object_detector: RemoteObjectDetector,
-    object_tracker: ObjectTracker,
-    detected_objects_queue: Queue,
-    camera_metrics: CameraMetrics,
-    stop_event: MpEvent,
-    ptz_metrics: PTZMetrics,
-    region_grid: list[list[dict[str, Any]]],
-    exit_on_empty: bool = False,
-):
-    next_region_update = get_tomorrow_at_time(2)
-    config_subscriber = CameraConfigUpdateSubscriber(
-        None,
-        {camera_config.name: camera_config},
-        [
-            CameraConfigUpdateEnum.detect,
-            CameraConfigUpdateEnum.enabled,
-            CameraConfigUpdateEnum.motion,
-            CameraConfigUpdateEnum.objects,
-        ],
-    )
-
-    fps_tracker = EventsPerSecond()
-    fps_tracker.start()
-
-    startup_scan = True
-    stationary_frame_counter = 0
-    camera_enabled = True
-
-    region_min_size = get_min_region_size(model_config)
-
-    attributes_map = model_config.attributes_map
-    all_attributes = model_config.all_attributes
-
-    # remove license_plate from attributes if this camera is a dedicated LPR cam
-    if camera_config.type == CameraTypeEnum.lpr:
-        modified_attributes_map = model_config.attributes_map.copy()
-
-        if (
-            "car" in modified_attributes_map
-            and "license_plate" in modified_attributes_map["car"]
-        ):
-            modified_attributes_map["car"] = [
-                attr
-                for attr in modified_attributes_map["car"]
-                if attr != "license_plate"
-            ]
-
-            attributes_map = modified_attributes_map
-
-        all_attributes = [
-            attr for attr in model_config.all_attributes if attr != "license_plate"
-        ]
-
-    while not stop_event.is_set():
-        updated_configs = config_subscriber.check_for_updates()
-
-        if "enabled" in updated_configs:
-            prev_enabled = camera_enabled
-            camera_enabled = camera_config.enabled
-
-        if "motion" in updated_configs:
-            motion_detector.config = camera_config.motion
-            motion_detector.update_mask()
-
-        if (
-            not camera_enabled
-            and prev_enabled != camera_enabled
-            and camera_metrics.frame_queue.empty()
-        ):
-            logger.debug(
-                f"Camera {camera_config.name} disabled, clearing tracked objects"
-            )
-            prev_enabled = camera_enabled
-
-            # Clear norfair's dictionaries
-            object_tracker.tracked_objects.clear()
-            object_tracker.disappeared.clear()
-            object_tracker.stationary_box_history.clear()
-            object_tracker.positions.clear()
-            object_tracker.track_id_map.clear()
-
-            # Clear internal norfair states
-            for trackers_by_type in object_tracker.trackers.values():
-                for tracker in trackers_by_type.values():
-                    tracker.tracked_objects = []
-            for tracker in object_tracker.default_tracker.values():
-                tracker.tracked_objects = []
-
-        if not camera_enabled:
-            time.sleep(0.1)
-            continue
-
-        if datetime.now().astimezone(timezone.utc) > next_region_update:
-            region_grid = requestor.send_data(REQUEST_REGION_GRID, camera_config.name)
-            next_region_update = get_tomorrow_at_time(2)
-
-        try:
-            if exit_on_empty:
-                frame_name, frame_time = frame_queue.get(False)
-            else:
-                frame_name, frame_time = frame_queue.get(True, 1)
-        except queue.Empty:
-            if exit_on_empty:
-                logger.info("Exiting track_objects...")
-                break
-            continue
-
-        camera_metrics.detection_frame.value = frame_time
-        ptz_metrics.frame_time.value = frame_time
-
-        frame = frame_manager.get(frame_name, (frame_shape[0] * 3 // 2, frame_shape[1]))
-
-        if frame is None:
-            logger.debug(
-                f"{camera_config.name}: frame {frame_time} is not in memory store."
-            )
-            continue
-
-        # look for motion if enabled
-        motion_boxes = motion_detector.detect(frame)
-
-        regions = []
-        consolidated_detections = []
-
-        # if detection is disabled
-        if not camera_config.detect.enabled:
-            object_tracker.match_and_update(frame_name, frame_time, [])
-        else:
-            # get stationary object ids
-            # check every Nth frame for stationary objects
-            # disappeared objects are not stationary
-            # also check for overlapping motion boxes
-            if stationary_frame_counter == camera_config.detect.stationary.interval:
-                stationary_frame_counter = 0
-                stationary_object_ids = []
-            else:
-                stationary_frame_counter += 1
-                stationary_object_ids = [
-                    obj["id"]
-                    for obj in object_tracker.tracked_objects.values()
-                    # if it has exceeded the stationary threshold
-                    if obj["motionless_count"]
-                    >= camera_config.detect.stationary.threshold
-                    # and it hasn't disappeared
-                    and object_tracker.disappeared[obj["id"]] == 0
-                    # and it doesn't overlap with any current motion boxes when not calibrating
-                    and not intersects_any(
-                        obj["box"],
-                        [] if motion_detector.is_calibrating() else motion_boxes,
-                    )
-                ]
-
-            # get tracked object boxes that aren't stationary
-            tracked_object_boxes = [
-                (
-                    # use existing object box for stationary objects
-                    obj["estimate"]
-                    if obj["motionless_count"]
-                    < camera_config.detect.stationary.threshold
-                    else obj["box"]
-                )
-                for obj in object_tracker.tracked_objects.values()
-                if obj["id"] not in stationary_object_ids
-            ]
-            object_boxes = tracked_object_boxes + object_tracker.untracked_object_boxes
-
-            # get consolidated regions for tracked objects
-            regions = [
-                get_cluster_region(
-                    frame_shape, region_min_size, candidate, object_boxes
-                )
-                for candidate in get_cluster_candidates(
-                    frame_shape, region_min_size, object_boxes
-                )
-            ]
-
-            # 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,
-            ):
-                # 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)
-                ]
-
-                if standalone_motion_boxes:
-                    motion_clusters = get_cluster_candidates(
-                        frame_shape,
-                        region_min_size,
-                        standalone_motion_boxes,
-                    )
-                    motion_regions = [
-                        get_cluster_region_from_grid(
-                            frame_shape,
-                            region_min_size,
-                            candidate,
-                            standalone_motion_boxes,
-                            region_grid,
-                        )
-                        for candidate in motion_clusters
-                    ]
-                    regions += motion_regions
-
-            # if starting up, get the next startup scan region
-            if startup_scan:
-                for region in get_startup_regions(
-                    frame_shape, region_min_size, region_grid
-                ):
-                    regions.append(region)
-                startup_scan = False
-
-            # resize regions and detect
-            # seed with stationary objects
-            detections = [
-                (
-                    obj["label"],
-                    obj["score"],
-                    obj["box"],
-                    obj["area"],
-                    obj["ratio"],
-                    obj["region"],
-                )
-                for obj in object_tracker.tracked_objects.values()
-                if obj["id"] in stationary_object_ids
-            ]
-
-            for region in regions:
-                detections.extend(
-                    detect(
-                        camera_config.detect,
-                        object_detector,
-                        frame,
-                        model_config,
-                        region,
-                        camera_config.objects.track,
-                        camera_config.objects.filters,
-                    )
-                )
-
-            consolidated_detections = reduce_detections(frame_shape, detections)
-
-            # if detection was run on this frame, consolidate
-            if len(regions) > 0:
-                tracked_detections = [
-                    d for d in consolidated_detections if d[0] not in all_attributes
-                ]
-                # now that we have refined our detections, we need to track objects
-                object_tracker.match_and_update(
-                    frame_name, frame_time, tracked_detections
-                )
-            # else, just update the frame times for the stationary objects
-            else:
-                object_tracker.update_frame_times(frame_name, frame_time)
-
-        # group the attribute detections based on what label they apply to
-        attribute_detections: dict[str, list[TrackedObjectAttribute]] = {}
-        for label, attribute_labels in attributes_map.items():
-            attribute_detections[label] = [
-                TrackedObjectAttribute(d)
-                for d in consolidated_detections
-                if d[0] in attribute_labels
-            ]
-
-        # build detections
-        detections = {}
-        for obj in object_tracker.tracked_objects.values():
-            detections[obj["id"]] = {**obj, "attributes": []}
-
-        # find the best object for each attribute to be assigned to
-        all_objects: list[dict[str, Any]] = object_tracker.tracked_objects.values()
-        for attributes in attribute_detections.values():
-            for attribute in attributes:
-                filtered_objects = filter(
-                    lambda o: attribute.label in attributes_map.get(o["label"], []),
-                    all_objects,
-                )
-                selected_object_id = attribute.find_best_object(filtered_objects)
-
-                if selected_object_id is not None:
-                    detections[selected_object_id]["attributes"].append(
-                        attribute.get_tracking_data()
-                    )
-
-        # debug object tracking
-        if False:
-            bgr_frame = cv2.cvtColor(
-                frame,
-                cv2.COLOR_YUV2BGR_I420,
-            )
-            object_tracker.debug_draw(bgr_frame, frame_time)
-            cv2.imwrite(
-                f"debug/frames/track-{'{:.6f}'.format(frame_time)}.jpg", bgr_frame
-            )
-        # debug
-        if False:
-            bgr_frame = cv2.cvtColor(
-                frame,
-                cv2.COLOR_YUV2BGR_I420,
-            )
-
-            for m_box in motion_boxes:
-                cv2.rectangle(
-                    bgr_frame,
-                    (m_box[0], m_box[1]),
-                    (m_box[2], m_box[3]),
-                    (0, 0, 255),
-                    2,
-                )
-
-            for b in tracked_object_boxes:
-                cv2.rectangle(
-                    bgr_frame,
-                    (b[0], b[1]),
-                    (b[2], b[3]),
-                    (255, 0, 0),
-                    2,
-                )
-
-            for obj in object_tracker.tracked_objects.values():
-                if obj["frame_time"] == frame_time:
-                    thickness = 2
-                    color = model_config.colormap.get(obj["label"], (255, 255, 255))
-                else:
-                    thickness = 1
-                    color = (255, 0, 0)
-
-                # draw the bounding boxes on the frame
-                box = obj["box"]
-
-                draw_box_with_label(
-                    bgr_frame,
-                    box[0],
-                    box[1],
-                    box[2],
-                    box[3],
-                    obj["label"],
-                    obj["id"],
-                    thickness=thickness,
-                    color=color,
-                )
-
-            for region in regions:
-                cv2.rectangle(
-                    bgr_frame,
-                    (region[0], region[1]),
-                    (region[2], region[3]),
-                    (0, 255, 0),
-                    2,
-                )
-
-            cv2.imwrite(
-                f"debug/frames/{camera_config.name}-{'{:.6f}'.format(frame_time)}.jpg",
-                bgr_frame,
-            )
-        # add to the queue if not full
-        if detected_objects_queue.full():
-            frame_manager.close(frame_name)
-            continue
-        else:
-            fps_tracker.update()
-            camera_metrics.process_fps.value = fps_tracker.eps()
-            detected_objects_queue.put(
-                (
-                    camera_config.name,
-                    frame_name,
-                    frame_time,
-                    detections,
-                    motion_boxes,
-                    regions,
-                )
-            )
-            camera_metrics.detection_fps.value = object_detector.fps.eps()
-            frame_manager.close(frame_name)
-
-    motion_detector.stop()
-    requestor.stop()
-    config_subscriber.stop()

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

@@ -787,6 +787,10 @@
       "label": "Disable TLS verify",
       "description": "Skip TLS verification and disable digest auth for ONVIF (unsafe; use in safe networks only)."
     },
+    "profile": {
+      "label": "ONVIF profile",
+      "description": "Specific ONVIF media profile to use for PTZ control, matched by token or name. If not set, the first profile with valid PTZ configuration is selected automatically."
+    },
     "autotracking": {
       "label": "Autotracking",
       "description": "Automatically track moving objects and keep them centered in the frame using PTZ camera movements.",

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

@@ -1536,6 +1536,10 @@
       "label": "Disable TLS verify",
       "description": "Skip TLS verification and disable digest auth for ONVIF (unsafe; use in safe networks only)."
     },
+    "profile": {
+      "label": "ONVIF profile",
+      "description": "Specific ONVIF media profile to use for PTZ control, matched by token or name. If not set, the first profile with valid PTZ configuration is selected automatically."
+    },
     "autotracking": {
       "label": "Autotracking",
       "description": "Automatically track moving objects and keep them centered in the frame using PTZ camera movements.",

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

@@ -17,6 +17,7 @@
       "clickMove": {
         "label": "Click in the frame to center the camera",
         "enable": "Enable click to move",
+        "enableWithZoom": "Enable click to move / drag to zoom",
         "disable": "Disable click to move"
       },
       "left": {

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

@@ -1573,5 +1573,9 @@
       "hardwareNone": "No hardware acceleration",
       "hardwareAuto": "Automatic hardware acceleration"
     }
+  },
+  "onvif": {
+    "profileAuto": "Auto",
+    "profileLoading": "Loading profiles..."
   }
 }

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

@@ -3,20 +3,12 @@ import type { SectionConfigOverrides } from "./types";
 const onvif: SectionConfigOverrides = {
   base: {
     sectionDocs: "/configuration/cameras#setting-up-camera-ptz-controls",
-    restartRequired: [
-      "host",
-      "port",
-      "user",
-      "password",
-      "tls_insecure",
-      "ignore_time_mismatch",
-      "autotracking.calibrate_on_startup",
-    ],
     fieldOrder: [
       "host",
       "port",
       "user",
       "password",
+      "profile",
       "tls_insecure",
       "ignore_time_mismatch",
       "autotracking",
@@ -27,10 +19,14 @@ const onvif: SectionConfigOverrides = {
     ],
     advancedFields: ["tls_insecure", "ignore_time_mismatch"],
     overrideFields: [],
+    restartRequired: ["autotracking.calibrate_on_startup"],
     uiSchema: {
       host: {
         "ui:options": { size: "sm" },
       },
+      profile: {
+        "ui:widget": "onvifProfile",
+      },
       autotracking: {
         required_zones: {
           "ui:widget": "zoneNames",

web/src/components/config-form/theme/frigateTheme.ts

@@ -29,6 +29,7 @@ import { TimezoneSelectWidget } from "./widgets/TimezoneSelectWidget";
 import { CameraPathWidget } from "./widgets/CameraPathWidget";
 import { OptionalFieldWidget } from "./widgets/OptionalFieldWidget";
 import { SemanticSearchModelWidget } from "./widgets/SemanticSearchModelWidget";
+import { OnvifProfileWidget } from "./widgets/OnvifProfileWidget";
 
 import { FieldTemplate } from "./templates/FieldTemplate";
 import { ObjectFieldTemplate } from "./templates/ObjectFieldTemplate";
@@ -79,6 +80,7 @@ export const frigateTheme: FrigateTheme = {
     timezoneSelect: TimezoneSelectWidget,
     optionalField: OptionalFieldWidget,
     semanticSearchModel: SemanticSearchModelWidget,
+    onvifProfile: OnvifProfileWidget,
   },
   templates: {
     FieldTemplate: FieldTemplate as React.ComponentType<FieldTemplateProps>,

web/src/components/config-form/theme/widgets/OnvifProfileWidget.tsx

@@ -0,0 +1,84 @@
+import type { WidgetProps } from "@rjsf/utils";
+import useSWR from "swr";
+import { useTranslation } from "react-i18next";
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from "@/components/ui/select";
+import type { ConfigFormContext } from "@/types/configForm";
+import type { CameraPtzInfo } from "@/types/ptz";
+import { getSizedFieldClassName } from "../utils";
+import ActivityIndicator from "@/components/indicators/activity-indicator";
+import { cn } from "@/lib/utils";
+
+const AUTO_VALUE = "__auto__";
+
+export function OnvifProfileWidget(props: WidgetProps) {
+  const { id, value, disabled, readonly, onChange, schema, options } = props;
+  const { t } = useTranslation(["views/settings"]);
+
+  const formContext = props.registry?.formContext as
+    | ConfigFormContext
+    | undefined;
+  const cameraName = formContext?.cameraName;
+  const isCameraLevel = formContext?.level === "camera";
+  const hasOnvifHost = !!formContext?.fullCameraConfig?.onvif?.host;
+
+  const { data: ptzInfo } = useSWR<CameraPtzInfo>(
+    isCameraLevel && cameraName && hasOnvifHost
+      ? `${cameraName}/ptz/info`
+      : null,
+    {
+      // ONVIF may not be initialized yet when the settings page loads,
+      // so retry until profiles become available
+      refreshInterval: (data) =>
+        data?.profiles && data.profiles.length > 0 ? 0 : 5000,
+    },
+  );
+
+  const profiles = ptzInfo?.profiles ?? [];
+  const fieldClassName = getSizedFieldClassName(options, "md");
+  const hasProfiles = profiles.length > 0;
+  const waiting = isCameraLevel && !!cameraName && hasOnvifHost && !hasProfiles;
+
+  const selected = value ?? AUTO_VALUE;
+
+  if (waiting) {
+    return (
+      <div className={cn("flex items-center gap-2", fieldClassName)}>
+        <ActivityIndicator className="size-4" />
+        <span className="text-sm text-muted-foreground">
+          {t("onvif.profileLoading")}
+        </span>
+      </div>
+    );
+  }
+
+  return (
+    <Select
+      value={String(selected)}
+      onValueChange={(val) => {
+        onChange(val === AUTO_VALUE ? null : val);
+      }}
+      disabled={disabled || readonly}
+    >
+      <SelectTrigger id={id} className={cn("text-left", fieldClassName)}>
+        <SelectValue placeholder={schema.title || "Select..."} />
+      </SelectTrigger>
+      <SelectContent>
+        <SelectItem value={AUTO_VALUE}>{t("onvif.profileAuto")}</SelectItem>
+        {profiles.map((p) => (
+          <SelectItem key={p.token} value={p.token}>
+            {p.name !== p.token ? `${p.name} (${p.token})` : p.token}
+          </SelectItem>
+        ))}
+        {!hasProfiles && value && value !== AUTO_VALUE && (
+          <SelectItem value={String(value)}>{String(value)}</SelectItem>
+        )}
+      </SelectContent>
+    </Select>
+  );
+}

web/src/components/overlay/PtzControlPanel.tsx

@@ -284,7 +284,9 @@ export default function PtzControlPanel({
             <p>
               {clickOverlay
                 ? t("ptz.move.clickMove.disable")
-                : t("ptz.move.clickMove.enable")}
+                : ptz?.features?.includes("zoom-r")
+                  ? t("ptz.move.clickMove.enableWithZoom")
+                  : t("ptz.move.clickMove.enable")}
             </p>
           </TooltipContent>
         </Tooltip>

web/src/types/ptz.ts

@@ -7,8 +7,14 @@ type PtzFeature =
   | "pt-r-fov"
   | "focus";
 
+export type OnvifProfile = {
+  name: string;
+  token: string;
+};
+
 export type CameraPtzInfo = {
   name: string;
   features: PtzFeature[];
   presets: string[];
+  profiles: OnvifProfile[];
 };

web/src/views/live/LiveCameraView.tsx

@@ -122,6 +122,11 @@ import {
   SnapshotResult,
 } from "@/utils/snapshotUtil";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
+import { Stage, Layer, Rect } from "react-konva";
+import type { KonvaEventObject } from "konva/lib/Node";
+
+/** Pixel threshold to distinguish drag from click. */
+const DRAG_MIN_PX = 15;
 
 type LiveCameraViewProps = {
   config?: FrigateConfig;
@@ -213,45 +218,112 @@ export default function LiveCameraView({
     };
   }, [audioTranscriptionState, sendTranscription]);
 
-  // click overlay for ptzs
+  // click-to-move / drag-to-zoom overlay for PTZ cameras
 
   const [clickOverlay, setClickOverlay] = useState(false);
   const clickOverlayRef = useRef<HTMLDivElement>(null);
   const { send: sendPtz } = usePtzCommand(camera.name);
 
-  const handleOverlayClick = useCallback(
-    (
-      e: React.MouseEvent<HTMLDivElement> | React.TouchEvent<HTMLDivElement>,
-    ) => {
-      if (!clickOverlay) {
-        return;
-      }
+  // drag rectangle state in stage-local coordinates
+  const [ptzRect, setPtzRect] = useState<{
+    x: number;
+    y: number;
+    width: number;
+    height: number;
+  } | null>(null);
+  const [isPtzDrawing, setIsPtzDrawing] = useState(false);
+  // raw origin to determine drag direction (not min/max corrected)
+  const ptzOriginRef = useRef<{ x: number; y: number } | null>(null);
 
-      let clientX;
-      let clientY;
-      if ("TouchEvent" in window && e.nativeEvent instanceof TouchEvent) {
-        clientX = e.nativeEvent.touches[0].clientX;
-        clientY = e.nativeEvent.touches[0].clientY;
-      } else if (e.nativeEvent instanceof MouseEvent) {
-        clientX = e.nativeEvent.clientX;
-        clientY = e.nativeEvent.clientY;
-      }
+  const [overlaySize] = useResizeObserver(clickOverlayRef);
 
-      if (clickOverlayRef.current && clientX && clientY) {
-        const rect = clickOverlayRef.current.getBoundingClientRect();
-
-        const normalizedX = (clientX - rect.left) / rect.width;
-        const normalizedY = (clientY - rect.top) / rect.height;
-
-        const pan = (normalizedX - 0.5) * 2;
-        const tilt = (0.5 - normalizedY) * 2;
-
-        sendPtz(`move_relative_${pan}_${tilt}`);
+  const onPtzStageDown = useCallback(
+    (e: KonvaEventObject<MouseEvent> | KonvaEventObject<TouchEvent>) => {
+      const pos = e.target.getStage()?.getPointerPosition();
+      if (pos) {
+        setIsPtzDrawing(true);
+        ptzOriginRef.current = { x: pos.x, y: pos.y };
+        setPtzRect({ x: pos.x, y: pos.y, width: 0, height: 0 });
       }
     },
-    [clickOverlayRef, clickOverlay, sendPtz],
+    [],
   );
 
+  const onPtzStageMove = useCallback(
+    (e: KonvaEventObject<MouseEvent> | KonvaEventObject<TouchEvent>) => {
+      if (!isPtzDrawing || !ptzRect) return;
+      const pos = e.target.getStage()?.getPointerPosition();
+      if (pos) {
+        setPtzRect({
+          ...ptzRect,
+          width: pos.x - ptzRect.x,
+          height: pos.y - ptzRect.y,
+        });
+      }
+    },
+    [isPtzDrawing, ptzRect],
+  );
+
+  const onPtzStageUp = useCallback(() => {
+    setIsPtzDrawing(false);
+
+    if (!ptzRect || !ptzOriginRef.current || overlaySize.width === 0) {
+      setPtzRect(null);
+      ptzOriginRef.current = null;
+      return;
+    }
+
+    const endX = ptzRect.x + ptzRect.width;
+    const endY = ptzRect.y + ptzRect.height;
+    const distX = Math.abs(ptzRect.width);
+    const distY = Math.abs(ptzRect.height);
+
+    if (distX < DRAG_MIN_PX && distY < DRAG_MIN_PX) {
+      // click — pan/tilt to point without zoom
+      const normX = endX / overlaySize.width;
+      const normY = endY / overlaySize.height;
+      const pan = (normX - 0.5) * 2;
+      const tilt = (0.5 - normY) * 2;
+      sendPtz(`move_relative_${pan}_${tilt}`);
+    } else {
+      // drag — pan/tilt to box center, zoom based on box size
+      const origin = ptzOriginRef.current;
+
+      const n0x = Math.min(origin.x, endX) / overlaySize.width;
+      const n0y = Math.min(origin.y, endY) / overlaySize.height;
+      const n1x = Math.max(origin.x, endX) / overlaySize.width;
+      const n1y = Math.max(origin.y, endY) / overlaySize.height;
+
+      let boxW = n1x - n0x;
+      let boxH = n1y - n0y;
+
+      // correct box to match camera aspect ratio so zoom is uniform
+      const frameAR = overlaySize.width / overlaySize.height;
+      const boxAR = boxW / boxH;
+      if (boxAR > frameAR) {
+        boxH = boxW / frameAR;
+      } else {
+        boxW = boxH * frameAR;
+      }
+
+      const centerX = (n0x + n1x) / 2;
+      const centerY = (n0y + n1y) / 2;
+      const pan = (centerX - 0.5) * 2;
+      const tilt = (0.5 - centerY) * 2;
+
+      // zoom magnitude from box size (small box = more zoom)
+      let zoom = Math.max(0.01, Math.min(1, Math.max(boxW, boxH)));
+      // drag direction: top-left → bottom-right = zoom in, reverse = zoom out
+      const zoomIn = endX > origin.x && endY > origin.y;
+      if (!zoomIn) zoom = -zoom;
+
+      sendPtz(`move_relative_${pan}_${tilt}_${zoom}`);
+    }
+
+    setPtzRect(null);
+    ptzOriginRef.current = null;
+  }, [ptzRect, overlaySize, sendPtz]);
+
   // pip state
 
   useEffect(() => {
@@ -440,7 +512,8 @@ export default function LiveCameraView({
     <TransformWrapper
       minScale={1.0}
       wheel={{ smoothStep: 0.005 }}
-      disabled={debug}
+      disabled={debug || clickOverlay}
+      panning={{ disabled: clickOverlay }}
     >
       <Toaster position="top-center" closeButton={true} />
       <div
@@ -634,13 +707,41 @@ export default function LiveCameraView({
               }}
             >
               <div
-                className={`flex flex-col items-center justify-center ${growClassName}`}
+                className={`relative flex flex-col items-center justify-center ${growClassName}`}
                 ref={clickOverlayRef}
-                onClick={handleOverlayClick}
                 style={{
                   aspectRatio: constrainedAspectRatio,
                 }}
               >
+                {clickOverlay && overlaySize.width > 0 && (
+                  <div className="absolute inset-0 z-40 cursor-crosshair">
+                    <Stage
+                      width={overlaySize.width}
+                      height={overlaySize.height}
+                      onMouseDown={onPtzStageDown}
+                      onMouseMove={onPtzStageMove}
+                      onMouseUp={onPtzStageUp}
+                      onTouchStart={onPtzStageDown}
+                      onTouchMove={onPtzStageMove}
+                      onTouchEnd={onPtzStageUp}
+                    >
+                      <Layer>
+                        {ptzRect && (
+                          <Rect
+                            x={ptzRect.x}
+                            y={ptzRect.y}
+                            width={ptzRect.width}
+                            height={ptzRect.height}
+                            stroke="white"
+                            strokeWidth={2}
+                            dash={[6, 4]}
+                            opacity={0.8}
+                          />
+                        )}
+                      </Layer>
+                    </Stage>
+                  </div>
+                )}
                 <LivePlayer
                   key={camera.name}
                   className={`${fullscreen ? "*:rounded-none" : ""}`}