Merge 0f3dd097ec into 819e8de172

* revert modal changes from #22963

* add test and lint

Makefile

@@ -21,6 +21,13 @@ local: version
 		--tag frigate:latest \
 		--load
 
+localh10: version
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+	    --build-arg HAILORT_VERSION=5.1.1 \
+		--build-arg HAILORT_GIT_REPO=mathieu-d/hailort \
+		--tag frigate:latest \
+		--load
+
 debug: version
 	docker buildx build --target=frigate --file docker/main/Dockerfile . \
 	    --build-arg DEBUG=true \

docker-compose.yml

@@ -12,6 +12,11 @@ services:
     build:
       context: .
       dockerfile: docker/main/Dockerfile
+      # Use args to specify hailort version and location
+      # args:
+      #   HAILORT_VERSION: "5.1.1"
+      #   HAILORT_GIT_REPO: "mathieu-d/hailort"
+
       # Use target devcontainer-trt for TensorRT dev
       target: devcontainer
       cache_from:
@@ -29,6 +34,7 @@ services:
     # devices:
       # - /dev/bus/usb:/dev/bus/usb # Uncomment for Google Coral USB
       # - /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware
+
     volumes:
       - .:/workspace/frigate:cached
       - ./web/dist:/opt/frigate/web:cached

docker/hailo10h/user_installation.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+# Update package list and install hailo driver version 5.1.1 for Hailo-10H
+sudo apt update
+sudo apt install -y hailo-h10-all=5.1.1
+
+

docker/main/Dockerfile

@@ -157,6 +157,8 @@ FROM base AS wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
 ARG DEBUG=false
+ARG HAILORT_VERSION=4.21.0
+ARG HAILORT_GIT_REPO=frigate-nvr/hailort
 
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \

docker/main/install_hailort.sh

@@ -2,13 +2,11 @@
 
 set -euxo pipefail
 
-hailo_version="4.21.0"
-
 if [[ "${TARGETARCH}" == "amd64" ]]; then
     arch="x86_64"
 elif [[ "${TARGETARCH}" == "arm64" ]]; then
     arch="aarch64"
 fi
 
-wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-debian12-${TARGETARCH}.tar.gz" | tar -C / -xzf -
-wget -P /wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"
+wget -qO- "https://github.com/${HAILORT_GIT_REPO}/releases/download/v${HAILORT_VERSION}/hailort-debian12-${TARGETARCH}.tar.gz" | tar -C / -xzf -
+wget -P /wheels/ "https://github.com/${HAILORT_GIT_REPO}/releases/download/v${HAILORT_VERSION}/hailort-${HAILORT_VERSION}-cp311-cp311-linux_${arch}.whl"

frigate/data_processing/post/types.py

@@ -8,7 +8,9 @@ class ReviewMetadata(BaseModel):
         description="A short title characterizing what took place and where, under 10 words."
     )
     scene: str = Field(
-        description="A chronological narrative of what happens from start to finish."
+        min_length=120,
+        max_length=600,
+        description="A chronological narrative of what happens from start to finish.",
     )
     shortSummary: str = Field(
         description="A brief 2-sentence summary of the scene, suitable for notifications."

frigate/detectors/plugins/hailo10h.py

@@ -0,0 +1,415 @@
+import logging
+import os
+import subprocess
+import threading
+import urllib.request
+from functools import partial
+from typing import Dict, List, Optional, Tuple
+
+import cv2
+import numpy as np
+from pydantic import ConfigDict, Field
+from typing_extensions import Literal
+
+from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import (
+    BaseDetectorConfig,
+)
+from frigate.object_detection.util import RequestStore, ResponseStore
+
+logger = logging.getLogger(__name__)
+
+
+# ----------------- Utility Functions ----------------- #
+
+
+def preprocess_tensor(image: np.ndarray, model_w: int, model_h: int) -> np.ndarray:
+    """
+    Resize an image with unchanged aspect ratio using padding.
+    Assumes input image shape is (H, W, 3).
+    """
+    if image.ndim == 4 and image.shape[0] == 1:
+        image = image[0]
+
+    h, w = image.shape[:2]
+    scale = min(model_w / w, model_h / h)
+    new_w, new_h = int(w * scale), int(h * scale)
+    resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
+    padded_image = np.full((model_h, model_w, 3), 114, dtype=image.dtype)
+    x_offset = (model_w - new_w) // 2
+    y_offset = (model_h - new_h) // 2
+    padded_image[y_offset : y_offset + new_h, x_offset : x_offset + new_w] = (
+        resized_image
+    )
+    return padded_image
+
+
+# ----------------- Global Constants ----------------- #
+DETECTOR_KEY = "hailo10h"
+ARCH = None
+H10H_DEFAULT_MODEL = "yolov6n.hef"
+H10H_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v5.2.0/hailo10h/yolov6n.hef"
+
+
+def detect_hailo_arch():
+    try:
+        result = subprocess.run(
+            ["hailortcli", "fw-control", "identify"], capture_output=True, text=True
+        )
+        if result.returncode != 0:
+            logger.error(f"Inference error: {result.stderr}")
+            return None
+        for line in result.stdout.split("\n"):
+            if "Device Architecture" in line:
+                if "HAILO10H" in line:
+                    return "hailo10h"
+        logger.error("Inference error: Could not determine Hailo architecture.")
+        return None
+    except Exception as e:
+        logger.error(f"Inference error: {e}")
+        return None
+
+
+# ----------------- HailoAsyncInference Class ----------------- #
+class HailoAsyncInference:
+    def __init__(
+        self,
+        hef_path: str,
+        input_store: RequestStore,
+        output_store: ResponseStore,
+        batch_size: int = 1,
+        input_type: Optional[str] = None,
+        output_type: Optional[Dict[str, str]] = None,
+        send_original_frame: bool = False,
+    ) -> None:
+        # when importing hailo it activates the driver
+        # which leaves processes running even though it may not be used.
+        try:
+            from hailo_platform import (
+                HEF,
+                FormatType,
+                HailoSchedulingAlgorithm,
+                VDevice,
+            )
+        except ModuleNotFoundError:
+            pass
+
+        self.input_store = input_store
+        self.output_store = output_store
+
+        params = VDevice.create_params()
+        params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
+
+        self.hef = HEF(hef_path)
+        self.target = VDevice(params)
+        self.infer_model = self.target.create_infer_model(hef_path)
+        self.infer_model.set_batch_size(batch_size)
+
+        if input_type is not None:
+            self.infer_model.input().set_format_type(getattr(FormatType, input_type))
+
+        if output_type is not None:
+            for output_name, output_type in output_type.items():
+                self.infer_model.output(output_name).set_format_type(
+                    getattr(FormatType, output_type)
+                )
+
+        self.output_type = output_type
+        self.send_original_frame = send_original_frame
+
+    def callback(
+        self,
+        completion_info,
+        bindings_list: List,
+        input_batch: List,
+        request_ids: List[int],
+    ):
+        if completion_info.exception:
+            logger.error(f"Inference error: {completion_info.exception}")
+        else:
+            for i, bindings in enumerate(bindings_list):
+                if len(bindings._output_names) == 1:
+                    result = bindings.output().get_buffer()
+                else:
+                    result = {
+                        name: np.expand_dims(bindings.output(name).get_buffer(), axis=0)
+                        for name in bindings._output_names
+                    }
+                self.output_store.put(request_ids[i], (input_batch[i], result))
+
+    def _create_bindings(self, configured_infer_model) -> object:
+        if self.output_type is None:
+            output_buffers = {
+                output_info.name: np.empty(
+                    self.infer_model.output(output_info.name).shape,
+                    dtype=getattr(
+                        np, str(output_info.format.type).split(".")[1].lower()
+                    ),
+                )
+                for output_info in self.hef.get_output_vstream_infos()
+            }
+        else:
+            output_buffers = {
+                name: np.empty(
+                    self.infer_model.output(name).shape,
+                    dtype=getattr(np, self.output_type[name].lower()),
+                )
+                for name in self.output_type
+            }
+        return configured_infer_model.create_bindings(output_buffers=output_buffers)
+
+    def get_input_shape(self) -> Tuple[int, ...]:
+        return self.hef.get_input_vstream_infos()[0].shape
+
+    def run(self) -> None:
+        job = None
+        with self.infer_model.configure() as configured_infer_model:
+            while True:
+                batch_data = self.input_store.get()
+
+                if batch_data is None:
+                    break
+
+                request_id, frame_data = batch_data
+                preprocessed_batch = [frame_data]
+                request_ids = [request_id]
+                input_batch = preprocessed_batch  # non-send_original_frame mode
+
+                bindings_list = []
+                for frame in preprocessed_batch:
+                    bindings = self._create_bindings(configured_infer_model)
+                    bindings.input().set_buffer(np.array(frame))
+                    bindings_list.append(bindings)
+                configured_infer_model.wait_for_async_ready(timeout_ms=10000)
+                job = configured_infer_model.run_async(
+                    bindings_list,
+                    partial(
+                        self.callback,
+                        input_batch=input_batch,
+                        request_ids=request_ids,
+                        bindings_list=bindings_list,
+                    ),
+                )
+
+            if job is not None:
+                job.wait(100)
+
+
+# ----------------- HailoDetector Class ----------------- #
+class HailoDetector(DetectionApi):
+    type_key = DETECTOR_KEY
+
+    def __init__(self, detector_config: "HailoDetectorConfig"):
+        global ARCH
+        ARCH = detect_hailo_arch()
+        self.cache_dir = MODEL_CACHE_DIR
+        self.device_type = detector_config.device
+        self.model_height = (
+            detector_config.model.height
+            if hasattr(detector_config.model, "height")
+            else None
+        )
+        self.model_width = (
+            detector_config.model.width
+            if hasattr(detector_config.model, "width")
+            else None
+        )
+        self.model_type = (
+            detector_config.model.model_type
+            if hasattr(detector_config.model, "model_type")
+            else None
+        )
+        self.tensor_format = (
+            detector_config.model.input_tensor
+            if hasattr(detector_config.model, "input_tensor")
+            else None
+        )
+        self.pixel_format = (
+            detector_config.model.input_pixel_format
+            if hasattr(detector_config.model, "input_pixel_format")
+            else None
+        )
+        self.input_dtype = (
+            detector_config.model.input_dtype
+            if hasattr(detector_config.model, "input_dtype")
+            else None
+        )
+        self.output_type = "FLOAT32"
+        self.set_path_and_url(detector_config.model.path)
+        self.working_model_path = self.check_and_prepare()
+
+        self.batch_size = 1
+        self.input_store = RequestStore()
+        self.response_store = ResponseStore()
+
+        try:
+            logger.debug(f"[INIT] Loading HEF model from {self.working_model_path}")
+            self.inference_engine = HailoAsyncInference(
+                self.working_model_path,
+                self.input_store,
+                self.response_store,
+                self.batch_size,
+            )
+            self.input_shape = self.inference_engine.get_input_shape()
+            logger.debug(f"[INIT] Model input shape: {self.input_shape}")
+            self.inference_thread = threading.Thread(
+                target=self.inference_engine.run, daemon=True
+            )
+            self.inference_thread.start()
+        except Exception as e:
+            logger.error(f"[INIT] Failed to initialize HailoAsyncInference: {e}")
+            raise
+
+    def set_path_and_url(self, path: str = None):
+        if not path:
+            self.model_path = None
+            self.url = None
+            return
+        if self.is_url(path):
+            self.url = path
+            self.model_path = None
+        else:
+            self.model_path = path
+            self.url = None
+
+    def is_url(self, url: str) -> bool:
+        return (
+            url.startswith("http://")
+            or url.startswith("https://")
+            or url.startswith("www.")
+        )
+
+    @staticmethod
+    def extract_model_name(path: str = None, url: str = None) -> str:
+        if path and path.endswith(".hef"):
+            return os.path.basename(path)
+        elif url and url.endswith(".hef"):
+            return os.path.basename(url)
+        else:
+            return H10H_DEFAULT_MODEL
+
+    @staticmethod
+    def download_model(url: str, destination: str):
+        if not url.endswith(".hef"):
+            raise ValueError("Invalid model URL. Only .hef files are supported.")
+        try:
+            urllib.request.urlretrieve(url, destination)
+            logger.debug(f"Downloaded model to {destination}")
+        except Exception as e:
+            raise RuntimeError(f"Failed to download model from {url}: {str(e)}")
+
+    def check_and_prepare(self) -> str:
+        if not os.path.exists(self.cache_dir):
+            os.makedirs(self.cache_dir)
+        model_name = self.extract_model_name(self.model_path, self.url)
+        cached_model_path = os.path.join(self.cache_dir, model_name)
+        if not self.model_path and not self.url:
+            if os.path.exists(cached_model_path):
+                logger.debug(f"Model found in cache: {cached_model_path}")
+                return cached_model_path
+            else:
+                logger.debug(f"Downloading default model: {model_name}")
+                self.download_model(H10H_DEFAULT_URL, cached_model_path)
+
+        elif self.url:
+            logger.debug(f"Downloading model from URL: {self.url}")
+            self.download_model(self.url, cached_model_path)
+        elif self.model_path:
+            if os.path.exists(self.model_path):
+                logger.debug(f"Using existing model at: {self.model_path}")
+                return self.model_path
+            else:
+                raise FileNotFoundError(f"Model file not found at: {self.model_path}")
+        return cached_model_path
+
+    def detect_raw(self, tensor_input):
+        tensor_input = self.preprocess(tensor_input)
+
+        if isinstance(tensor_input, np.ndarray) and len(tensor_input.shape) == 3:
+            tensor_input = np.expand_dims(tensor_input, axis=0)
+
+        request_id = self.input_store.put(tensor_input)
+
+        try:
+            _, infer_results = self.response_store.get(request_id, timeout=1.0)
+        except TimeoutError:
+            logger.error(
+                f"Timeout waiting for inference results for request {request_id}"
+            )
+
+            if not self.inference_thread.is_alive():
+                raise RuntimeError(
+                    "HailoRT inference thread has stopped, restart required."
+                )
+
+            return np.zeros((20, 6), dtype=np.float32)
+
+        if isinstance(infer_results, list) and len(infer_results) == 1:
+            infer_results = infer_results[0]
+
+        threshold = 0.4
+        all_detections = []
+        for class_id, detection_set in enumerate(infer_results):
+            if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
+                continue
+            for det in detection_set:
+                if det.shape[0] < 5:
+                    continue
+                score = float(det[4])
+                if score < threshold:
+                    continue
+                all_detections.append([class_id, score, det[0], det[1], det[2], det[3]])
+
+        if len(all_detections) == 0:
+            detections_array = np.zeros((20, 6), dtype=np.float32)
+        else:
+            detections_array = np.array(all_detections, dtype=np.float32)
+            if detections_array.shape[0] > 20:
+                detections_array = detections_array[:20, :]
+            elif detections_array.shape[0] < 20:
+                pad = np.zeros((20 - detections_array.shape[0], 6), dtype=np.float32)
+                detections_array = np.vstack((detections_array, pad))
+
+        return detections_array
+
+    def preprocess(self, image):
+        if isinstance(image, np.ndarray):
+            processed = preprocess_tensor(
+                image, self.input_shape[1], self.input_shape[0]
+            )
+            return np.expand_dims(processed, axis=0)
+        else:
+            raise ValueError("Unsupported image format for preprocessing")
+
+    def close(self):
+        """Properly shuts down the inference engine and releases the VDevice."""
+        logger.debug("[CLOSE] Closing HailoDetector")
+        try:
+            if hasattr(self, "inference_engine"):
+                if hasattr(self.inference_engine, "target"):
+                    self.inference_engine.target.release()
+                logger.debug("Hailo VDevice released successfully")
+        except Exception as e:
+            logger.error(f"Failed to close Hailo device: {e}")
+            raise
+
+    def __del__(self):
+        """Destructor to ensure cleanup when the object is deleted."""
+        self.close()
+
+
+# ----------------- HailoDetectorConfig Class ----------------- #
+class HailoDetectorConfig(BaseDetectorConfig):
+    """Hailo10H detector using HEF models and the HailoRT SDK for inference on Hailo hardware."""
+
+    model_config = ConfigDict(
+        title="Hailo-10H",
+    )
+
+    type: Literal[DETECTOR_KEY]
+    device: str = Field(
+        default="PCIe",
+        title="Device Type",
+        description="The device to use for Hailo inference (e.g. 'PCIe', 'M.2').",
+    )

frigate/stats/util.py

@@ -123,6 +123,15 @@ def get_detector_temperature(
             if index < len(hailo_device_names):
                 device_name = hailo_device_names[index]
                 return hailo_temps[device_name]
+    elif detector_type == "hailo10h":
+        # Get temperatures for Hailo devices
+        hailo_temps = get_hailo_temps()
+        if hailo_temps:
+            hailo_device_names = sorted(hailo_temps.keys())
+            index = detector_index_by_type.get("hailo10h", 0)
+            if index < len(hailo_device_names):
+                device_name = hailo_device_names[index]
+                return hailo_temps[device_name]
     elif detector_type == "rknn":
         # Rockchip temperatures are handled by the GPU / NPU stats
         # as there are not detector specific temperatures

web/e2e/specs/export.spec.ts

@@ -1,4 +1,114 @@
 import { test, expect } from "../fixtures/frigate-test";
+import {
+  expectBodyInteractive,
+  waitForBodyInteractive,
+} from "../helpers/overlay-interaction";
+
+test.describe("Export Page - Delete race @high", () => {
+  // Empirical guard for radix-ui/primitives#3445: when a modal DropdownMenu
+  // opens an AlertDialog and the AlertDialog's confirm action causes the
+  // parent's optimistic cache update to unmount the card, we want to know
+  // whether the deduped react-dismissable-layer (1.1.11) handles the
+  // pointer-events stack cleanup or whether `modal={false}` is still
+  // required on the DropdownMenu. The classic "canonical" pattern, distinct
+  // from the FaceSelectionDialog auto-unmount race already covered by
+  // face-library.spec.ts.
+  test("deleting an export via dropdown→alert→confirm leaves body interactive", async ({
+    frigateApp,
+  }) => {
+    if (frigateApp.isMobile) {
+      test.skip();
+      return;
+    }
+
+    const initialExports = [
+      {
+        id: "export-race-001",
+        camera: "front_door",
+        name: "Race - Test Export",
+        date: 1775490731.3863528,
+        video_path: "/exports/export-race-001.mp4",
+        thumb_path: "/exports/export-race-001-thumb.jpg",
+        in_progress: false,
+        export_case_id: null,
+      },
+    ];
+    let deleted = false;
+
+    await frigateApp.installDefaults({
+      exports: initialExports,
+    });
+
+    // Flip /api/export to empty after the delete POST is observed so the
+    // page's SWR mutate sees the export gone.
+    await frigateApp.page.route("**/api/export**", async (route) => {
+      const payload = deleted ? [] : initialExports;
+      await route.fulfill({ json: payload });
+    });
+    await frigateApp.page.route("**/api/exports/delete", async (route) => {
+      deleted = true;
+      const delayMs = Number(
+        (globalThis as { process?: { env?: Record<string, string> } }).process
+          ?.env?.DELETE_DELAY_MS ?? "100",
+      );
+      if (delayMs > 0) {
+        await new Promise((resolve) => setTimeout(resolve, delayMs));
+      }
+      await route.fulfill({ json: { success: true } });
+    });
+
+    await frigateApp.goto("/export");
+    await expect(frigateApp.page.getByText("Race - Test Export")).toBeVisible({
+      timeout: 5_000,
+    });
+
+    // Open the kebab menu on the export card. The kebab uses the
+    // (misleading) aria-label "Edit name" from ExportCard's source — it
+    // wraps the FiMoreVertical icon. There is exactly one such button on
+    // the page once we have a single export rendered.
+    const kebab = frigateApp.page
+      .getByRole("button", { name: /edit name/i })
+      .first();
+    await expect(kebab).toBeVisible({ timeout: 5_000 });
+    await kebab.click();
+
+    const menu = frigateApp.page
+      .locator('[role="menu"], [data-radix-menu-content]')
+      .first();
+    await expect(menu).toBeVisible({ timeout: 3_000 });
+
+    // Delete Export
+    await menu
+      .getByRole("menuitem", { name: /delete export/i })
+      .first()
+      .click();
+
+    // AlertDialog at page level. The confirm button's accessible name is
+    // "Delete Export" (its aria-label), the visible text is just "Delete".
+    const confirm = frigateApp.page.getByRole("alertdialog");
+    await expect(confirm).toBeVisible({ timeout: 3_000 });
+    await confirm
+      .getByRole("button", { name: /^delete export$/i })
+      .first()
+      .click();
+
+    // The card optimistically disappears, the dialog closes, and body
+    // pointer-events must come unstuck.
+    await expect(
+      frigateApp.page.getByText("Race - Test Export"),
+    ).not.toBeVisible({ timeout: 5_000 });
+    await waitForBodyInteractive(frigateApp.page, 5_000);
+    await expectBodyInteractive(frigateApp.page);
+
+    // Sanity: another page-level button still responds.
+    const newCase = frigateApp.page.getByRole("button", { name: /new case/i });
+    await expect(newCase).toBeVisible({ timeout: 3_000 });
+    await newCase.click();
+    await expect(
+      frigateApp.page.getByRole("dialog").filter({ hasText: /create case/i }),
+    ).toBeVisible({ timeout: 3_000 });
+  });
+});
 
 test.describe("Export Page - Overview @high", () => {
   test("renders uncategorized exports and case cards from mock data", async ({

web/e2e/specs/face-library.spec.ts

@@ -358,6 +358,158 @@ test.describe("FaceSelectionDialog @high", () => {
     await frigateApp.page.keyboard.press("Escape");
     await expect(menu).not.toBeVisible({ timeout: 3_000 });
   });
+
+  test("classifying the last image in a group leaves body interactive", async ({
+    frigateApp,
+  }) => {
+    // Regression guard for the stuck body pointer-events bug when the
+    // last image in a grouped-recognition detail Dialog is classified.
+    // Tracked upstream at radix-ui/primitives#3445.
+    //
+    // Root cause: when the user clicks a FaceSelectionDialog menu item,
+    // the modal DropdownMenu enters its exit animation (Radix's Presence
+    // keeps it in the DOM with data-state="closed" until animationend).
+    // While that is in flight the classify axios resolves, SWR removes
+    // the image from /api/faces, the parent's map no longer renders the
+    // grouped card, and React unmounts the subtree — including the still-
+    // animating DropdownMenu's Presence container. DismissableLayer's
+    // shared modal-layer stack can't reconcile the interrupted exit, so
+    // the `body { pointer-events: none }` entry it put on mount is never
+    // popped and the rest of the UI becomes unclickable.
+    //
+    // The fix is `modal={false}` on the FaceSelectionDialog's
+    // DropdownMenu (desktop path only). With modal=false the DropdownMenu
+    // never puts an entry on DismissableLayer's body-pointer-events stack
+    // in the first place, so there's nothing to leak when its Presence is
+    // torn down mid-animation. The Radix-community-documented workaround
+    // for #3445.
+    //
+    // The bug only reproduces when the mock resolves fast enough that
+    // the parent unmounts before the dropdown's exit animation finishes.
+    // Measured window via a 3x sweep on the pre-fix build: 0–200 ms
+    // triggers it; 300 ms+ no longer reproduces. Production LAN networks
+    // sit comfortably inside the bad window, while `npm run dev` seems
+    // to mask it via React StrictMode's double-effect scheduling.
+    const EVENT_ID = "1775487131.3863528-race";
+    const initialFaces = withGroupedTrainingAttempt(basicFacesMock(), {
+      eventId: EVENT_ID,
+      attempts: [
+        { timestamp: 1775487131.3863528, label: "unknown", score: 0.95 },
+      ],
+    });
+
+    let classified = false;
+
+    await frigateApp.installDefaults({
+      faces: initialFaces,
+      events: [
+        {
+          id: EVENT_ID,
+          label: "person",
+          sub_label: null,
+          camera: "front_door",
+          start_time: 1775487131.3863528,
+          end_time: 1775487161.3863528,
+          false_positive: false,
+          zones: ["front_yard"],
+          thumbnail: null,
+          has_clip: true,
+          has_snapshot: true,
+          retain_indefinitely: false,
+          plus_id: null,
+          model_hash: "abc123",
+          detector_type: "cpu",
+          model_type: "ssd",
+          data: {
+            top_score: 0.92,
+            score: 0.92,
+            region: [0.1, 0.1, 0.5, 0.8],
+            box: [0.2, 0.15, 0.45, 0.75],
+            area: 0.18,
+            ratio: 0.6,
+            type: "object",
+            path_data: [],
+          },
+        },
+      ],
+    });
+
+    // Re-route /api/faces to flip to the "train empty" payload once the
+    // classify POST has been received. Registered AFTER installDefaults so
+    // Playwright's LIFO route matching hits this handler first.
+    await frigateApp.page.route("**/api/faces", async (route) => {
+      const payload = classified ? basicFacesMock() : initialFaces;
+      await route.fulfill({ json: payload });
+    });
+
+    // Hold the classify POST briefly. The race opens when the parent
+    // unmounts before the dropdown's exit animation finishes (~200ms
+    // in Radix). 100ms keeps us comfortably inside that window and
+    // reliably triggered the bug in a 3x sweep across 0/50/100/200ms
+    // on the pre-fix build. CLASSIFY_DELAY_MS overrides for local sweeps.
+    const delayMs = Number(
+      (globalThis as { process?: { env?: Record<string, string> } }).process
+        ?.env?.CLASSIFY_DELAY_MS ?? "100",
+    );
+    await frigateApp.page.route(
+      "**/api/faces/train/*/classify",
+      async (route) => {
+        classified = true;
+        if (delayMs > 0) {
+          await new Promise((resolve) => setTimeout(resolve, delayMs));
+        }
+        await route.fulfill({ json: { success: true } });
+      },
+    );
+
+    await frigateApp.goto("/faces");
+
+    // Open the grouped detail Dialog.
+    const groupedImage = frigateApp.page
+      .locator('img[src*="clips/faces/train/"]')
+      .first();
+    await expect(groupedImage).toBeVisible({ timeout: 5_000 });
+    await groupedImage.locator("xpath=..").click();
+    const dialog = frigateApp.page
+      .getByRole("dialog")
+      .filter({
+        has: frigateApp.page.locator('img[src*="clips/faces/train/"]'),
+      })
+      .first();
+    await expect(dialog).toBeVisible({ timeout: 5_000 });
+
+    // Single attempt → single `+` trigger.
+    const triggers = dialog.locator('[aria-haspopup="menu"]');
+    await expect(triggers).toHaveCount(1);
+    await triggers.first().click();
+
+    const menu = frigateApp.page
+      .locator('[role="menu"], [data-radix-menu-content]')
+      .first();
+    await expect(menu).toBeVisible({ timeout: 5_000 });
+    await menu.getByRole("menuitem", { name: /^alice$/i }).click();
+
+    // The Dialog must leave the tree cleanly, and body must recover.
+    await expect(dialog).not.toBeVisible({ timeout: 5_000 });
+
+    // Give Radix's exit animation + cleanup a comfortable margin on top of
+    // the ~300ms simulated network delay.
+    await waitForBodyInteractive(frigateApp.page, 5_000);
+    await expectBodyInteractive(frigateApp.page);
+
+    // User-visible confirmation: click something outside the dialog
+    // and assert it actually responds.
+    const librarySelector = frigateApp.page
+      .getByRole("button")
+      .filter({ hasText: /\(\d+\)/ })
+      .first();
+    await librarySelector.click();
+    await expect(
+      frigateApp.page
+        .locator('[role="menu"], [data-radix-menu-content]')
+        .first(),
+    ).toBeVisible({ timeout: 3_000 });
+  });
 });
 
 test.describe("Face Library — mobile @high @mobile", () => {

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

@@ -397,6 +397,14 @@
         "description": "The device to use for Hailo inference (e.g. 'PCIe', 'M.2')."
       }
     },
+    "hailo10h": {
+      "label": "Hailo-10H",
+      "description": "Hailo-10H detector using HEF models and the HailoRT SDK for inference on Hailo hardware.",
+      "device": {
+        "label": "Device Type",
+        "description": "The device to use for Hailo inference (e.g. 'PCIe', 'M.2')."
+      }
+    },   
     "memryx": {
       "label": "MemryX",
       "description": "MemryX MX3 detector that runs compiled DFP models on MemryX accelerators.",

web/src/components/overlay/ClassificationSelectionDialog.tsx

@@ -124,7 +124,7 @@ export default function ClassificationSelectionDialog({
       />
 
       <Tooltip>
-        <Selector>
+        <Selector {...(isDesktop ? { modal: false } : {})}>
           <SelectorTrigger asChild>
             <TooltipTrigger asChild={isChildButton}>{children}</TooltipTrigger>
           </SelectorTrigger>

web/src/components/overlay/FaceSelectionDialog.tsx

@@ -85,7 +85,7 @@ export default function FaceSelectionDialog({
       )}
 
       <Tooltip>
-        <Selector>
+        <Selector {...(isDesktop ? { modal: false } : {})}>
           <SelectorTrigger asChild>
             <TooltipTrigger asChild={isChildButton}>{children}</TooltipTrigger>
           </SelectorTrigger>