Merge 0f3dd097ec into 4171efcd79

* Reduce max frames per second to 1

* Use pydantic but don't fail if some constraints are not met.

* Adjust limits

* Adjust limits

* Cleanup

* add unsaved changes icon/popover to individual settings section

* allow changing camera friendly_name from camera management pane

---------

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

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 -qO- "https://github.com/${HAILORT_GIT_REPO}/releases/download/v${HAILORT_VERSION}/hailort-debian12-${TARGETARCH}.tar.gz" | tar -C / -xzf -
-wget -P /wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"
+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/review_descriptions.py

@@ -40,7 +40,7 @@ logger = logging.getLogger(__name__)
 RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
 MIN_RECORDING_DURATION = 10
 MAX_IMAGE_TOKENS = 24000
-MAX_FRAMES_PER_SECOND = 2
+MAX_FRAMES_PER_SECOND = 1
 
 
 class ReviewDescriptionProcessor(PostProcessorApi):

frigate/data_processing/post/types.py

@@ -1,25 +1,48 @@
-from pydantic import BaseModel, ConfigDict, Field
+from typing import Annotated
+
+from pydantic import BaseModel, ConfigDict, Field, StringConstraints
+
+ObservationItem = Annotated[str, StringConstraints(min_length=20, max_length=160)]
 
 
 class ReviewMetadata(BaseModel):
     model_config = ConfigDict(extra="ignore", protected_namespaces=())
 
-    observations: list[str] = Field(
+    observations: list[ObservationItem] = Field(
-        default_factory=list,
+        ...,
-        description="Chronological list of significant observations from the frames, written before the scene narrative is composed.",
+        min_length=3,
+        max_length=15,
+        description=(
+            "Enumerate the significant observations across all frames, in "
+            "chronological order, BEFORE composing the scene narrative. "
+            "Include the very start of the activity — for example, a vehicle "
+            "entering the frame or pulling into the driveway — even if it "
+            "lasts only a few frames and the rest of the clip is dominated "
+            "by a longer activity. Include each arrival, departure, motion "
+            "event, object handled, and notable change in position or state. "
+            "Each item is a single concrete fact written as a complete "
+            "sentence. Do not summarize, interpret, or assign meaning here — "
+            "that belongs in the scene field."
+        ),
     )
     title: str = Field(
-        description="A short title characterizing what took place and where, under 10 words."
+        max_length=80,
+        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=150,
+        max_length=600,
+        description="A chronological narrative of what happens from start to finish, drawing directly from the items in observations.",
     )
     shortSummary: str = Field(
-        description="A brief 2-sentence summary of the scene, suitable for notifications."
+        min_length=70,
+        max_length=100,
+        description="A brief 2-sentence summary of the scene, suitable for notifications.",
     )
     confidence: float = Field(
         ge=0.0,
-        description="Confidence in the analysis, from 0 to 1.",
+        le=1.0,
+        description="Confidence in the analysis as a decimal between 0.0 and 1.0, where 0.0 means no confidence and 1.0 means complete confidence. Express ONLY as a decimal.",
     )
     potential_threat_level: int = Field(
         ge=0,

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/genai/__init__.py

@@ -2,6 +2,7 @@
 
 import datetime
 import importlib
+import json
 import logging
 import os
 import re
@@ -9,6 +10,7 @@ from typing import Any, Callable, Optional
 
 import numpy as np
 from playhouse.shortcuts import model_to_dict
+from pydantic import ValidationError
 
 from frigate.config import CameraConfig, GenAIConfig, GenAIProviderEnum
 from frigate.const import CLIPS_DIR
@@ -151,50 +153,6 @@ Each line represents a detection state, not necessarily unique individuals. The
             if "other_concerns" in schema.get("required", []):
                 schema["required"].remove("other_concerns")
 
-        # Length hints injected into the schema as suggestions to the model
-        # (enforced by grammar-based providers like llama.cpp) but kept off the
-        # Pydantic model so a non-compliant response does not fail validation.
-        length_hints = {
-            "scene": {"minLength": 120, "maxLength": 600},
-            "shortSummary": {"minLength": 70, "maxLength": 100},
-        }
-        for field, hints in length_hints.items():
-            prop = schema.get("properties", {}).get(field)
-            if prop is not None:
-                prop.update(hints)
-
-        # observations is a chain-of-thought-by-schema field: forcing the model
-        # to enumerate concrete facts before writing scene/title surfaces details
-        # the narrative would otherwise gloss past (e.g. brief vehicle arrivals
-        # overshadowed by a longer activity). The minItems floor scales with
-        # event duration so longer clips get more observations.
-        observations_prop = schema.get("properties", {}).get("observations")
-        if observations_prop is not None:
-            duration_seconds = float(review_data.get("duration") or 0)
-            min_observations = max(3, round(duration_seconds / 5))
-            max_observations = min_observations + 8
-            observations_prop["description"] = (
-                "Enumerate the significant observations across all frames, in "
-                "chronological order, BEFORE composing the scene narrative. "
-                "Include the very start of the activity — for example, a "
-                "vehicle entering the frame or pulling into the driveway — "
-                "even if it lasts only a few frames and the rest of the clip "
-                "is dominated by a longer activity. Include each arrival, "
-                "departure, motion event, object handled, and notable change "
-                "in position or state. Each item is a single concrete fact "
-                "written as a complete sentence (e.g., 'A blue sedan turns "
-                "from the street into the driveway', 'Nick exits the driver "
-                "side carrying a plant pot'). Do not summarize, interpret, or "
-                "assign meaning here — that belongs in the scene field."
-            )
-            observations_prop["minItems"] = min_observations
-            observations_prop["maxItems"] = max_observations
-            observations_prop["items"] = {"type": "string", "minLength": 20}
-
-            required = schema.setdefault("required", [])
-            if "observations" not in required:
-                required.append("observations")
-
         # OpenAI strict mode requires additionalProperties: false on all objects
         schema["additionalProperties"] = False
 
@@ -225,7 +183,35 @@ Each line represents a detection state, not necessarily unique individuals. The
 
             try:
                 metadata = ReviewMetadata.model_validate_json(clean_json)
+            except ValidationError as ve:
+                # Constraint violations (length, item count, ranges) are logged
+                # at debug and the response is kept anyway — a slightly
+                # off-spec answer is still usable, and dropping the whole
+                # response loses the narrative content the model produced.
+                for err in ve.errors():
+                    loc = ".".join(str(p) for p in err["loc"]) or "<root>"
+                    logger.debug(
+                        "Review metadata soft validation: %s — %s (input: %r)",
+                        loc,
+                        err["msg"],
+                        err.get("input"),
+                    )
+                try:
+                    raw = json.loads(clean_json)
+                except json.JSONDecodeError as je:
+                    logger.error("Failed to parse review description JSON: %s", je)
+                    return None
+                # observations is required on the model; fill an empty default
+                # if the response omitted it so attribute access stays safe.
+                raw.setdefault("observations", [])
+                metadata = ReviewMetadata.model_construct(**raw)
+            except Exception as e:
+                logger.error(
+                    f"Failed to parse review description as the response did not match expected format. {e}"
+                )
+                return None
 
+            try:
                 # Normalize confidence if model returned a percentage (e.g. 85 instead of 0.85)
                 if metadata.confidence > 1.0:
                     metadata.confidence = min(metadata.confidence / 100.0, 1.0)
@@ -238,10 +224,7 @@ Each line represents a detection state, not necessarily unique individuals. The
                 metadata.time = review_data["start"]
                 return metadata
             except Exception as e:
-                # rarely LLMs can fail to follow directions on output format
+                logger.error(f"Failed to post-process review metadata: {e}")
-                logger.warning(
-                    f"Failed to parse review description as the response did not match expected format. {e}"
-                )
                 return None
         else:
             logger.debug(

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

@@ -457,7 +457,13 @@
       "enableDesc": "Temporarily disable an enabled camera until Frigate restarts. Disabling a camera completely stops Frigate's processing of this camera's streams. Detection, recording, and debugging will be unavailable.<br /> <em>Note: This does not disable go2rtc restreams.</em>",
       "disableLabel": "Disabled cameras",
       "disableDesc": "Enable a camera that is currently not visible in the UI and disabled in the configuration. A restart of Frigate is required after enabling.",
-      "enableSuccess": "Enabled {{cameraName}} in configuration. Restart Frigate to apply the changes."
+      "enableSuccess": "Enabled {{cameraName}} in configuration. Restart Frigate to apply the changes.",
+      "friendlyName": {
+        "edit": "Edit camera display name",
+        "title": "Edit Display Name",
+        "description": "Set the friendly name shown for this camera throughout the Frigate UI. Leave blank to use the camera ID.",
+        "rename": "Rename"
+      }
     },
     "cameraConfig": {
       "add": "Add Camera",

web/src/components/config-form/sections/BaseSection.tsx

@@ -65,10 +65,14 @@ import {
   globalCameraDefaultSections,
   buildOverrides,
   buildConfigDataForPath,
+  flattenOverrides,
   getBaseCameraSectionValue,
   sanitizeSectionData as sharedSanitizeSectionData,
   requiresRestartForOverrides as sharedRequiresRestartForOverrides,
 } from "@/utils/configUtil";
+import SaveAllPreviewPopover, {
+  type SaveAllPreviewItem,
+} from "@/components/overlay/detail/SaveAllPreviewPopover";
 import RestartDialog from "@/components/overlay/dialog/RestartDialog";
 import { useRestart } from "@/api/ws";
 import type {
@@ -913,6 +917,34 @@ export function ConfigSection({
     );
   }, [sectionConfig?.renderers, sectionPath, cameraName, setPendingData]);
 
+  // Build a flat list of pending field changes for this section only.
+  // Mirrors the global Save All preview but scoped to the current section so
+  // users can inspect what will be saved without leaving the section.
+  const sectionPreviewItems = useMemo<SaveAllPreviewItem[]>(() => {
+    if (!hasChanges) return [];
+    if (!effectiveOverrides || typeof effectiveOverrides !== "object") {
+      return [];
+    }
+    const flattened = flattenOverrides(effectiveOverrides as JsonValue);
+    return flattened.map(({ path, value }) => ({
+      scope: effectiveLevel,
+      cameraName,
+      profileName: profileName
+        ? (profileFriendlyName ?? profileName)
+        : undefined,
+      fieldPath: path ? `${sectionPath}.${path}` : sectionPath,
+      value,
+    }));
+  }, [
+    hasChanges,
+    effectiveOverrides,
+    effectiveLevel,
+    cameraName,
+    profileName,
+    profileFriendlyName,
+    sectionPath,
+  ]);
+
   if (!modifiedSchema) {
     return null;
   }
@@ -1018,6 +1050,12 @@ export function ConfigSection({
                   defaultValue: "You have unsaved changes",
                 })}
               </span>
+              <SaveAllPreviewPopover
+                items={sectionPreviewItems}
+                className="h-7 w-7"
+                align="start"
+                side="top"
+              />
             </div>
           )}
           <div className="flex w-full flex-col gap-2 sm:flex-row sm:items-center md:w-auto">

web/src/components/overlay/dialog/TextEntryDialog.tsx

@@ -1,3 +1,4 @@
+import ActivityIndicator from "@/components/indicators/activity-indicator";
 import TextEntry from "@/components/input/TextEntry";
 import { Button } from "@/components/ui/button";
 import {
@@ -19,7 +20,9 @@ type TextEntryDialogProps = {
   setOpen: (open: boolean) => void;
   onSave: (text: string) => void;
   defaultValue?: string;
+  placeholder?: string;
   allowEmpty?: boolean;
+  isSaving?: boolean;
   regexPattern?: RegExp;
   regexErrorMessage?: string;
   forbiddenPattern?: RegExp;
@@ -33,7 +36,9 @@ export default function TextEntryDialog({
   setOpen,
   onSave,
   defaultValue = "",
+  placeholder,
   allowEmpty = false,
+  isSaving = false,
   regexPattern,
   regexErrorMessage,
   forbiddenPattern,
@@ -50,6 +55,7 @@ export default function TextEntryDialog({
         </DialogHeader>
         <TextEntry
           defaultValue={defaultValue}
+          placeholder={placeholder}
           allowEmpty={allowEmpty}
           onSave={onSave}
           regexPattern={regexPattern}
@@ -58,11 +64,22 @@ export default function TextEntryDialog({
           forbiddenErrorMessage={forbiddenErrorMessage}
         >
           <DialogFooter className={cn("pt-4", isMobile && "gap-2")}>
-            <Button type="button" onClick={() => setOpen(false)}>
+            <Button
+              type="button"
+              disabled={isSaving}
+              onClick={() => setOpen(false)}
+            >
               {t("button.cancel")}
             </Button>
-            <Button variant="select" type="submit">
+            <Button variant="select" type="submit" disabled={isSaving}>
-              {t("button.save")}
+              {isSaving ? (
+                <div className="flex flex-row items-center gap-2">
+                  <ActivityIndicator className="size-4" />
+                  <span>{t("button.saving")}</span>
+                </div>
+              ) : (
+                t("button.save")
+              )}
             </Button>
           </DialogFooter>
         </TextEntry>

web/src/pages/Settings.tsx

@@ -28,11 +28,7 @@ import useOptimisticState from "@/hooks/use-optimistic-state";
 import { isMobile } from "react-device-detect";
 import { FaVideo } from "react-icons/fa";
 import { CameraConfig, FrigateConfig } from "@/types/frigateConfig";
-import type {
+import type { ConfigSectionData, JsonObject } from "@/types/configForm";
-  ConfigSectionData,
-  JsonObject,
-  JsonValue,
-} from "@/types/configForm";
 import useSWR from "swr";
 import FilterSwitch from "@/components/filter/FilterSwitch";
 import { ZoneMaskFilterButton } from "@/components/filter/ZoneMaskFilter";
@@ -93,6 +89,7 @@ import { mutate } from "swr";
 import { RJSFSchema } from "@rjsf/utils";
 import {
   buildConfigDataForPath,
+  flattenOverrides,
   parseProfileFromSectionPath,
   prepareSectionSavePayload,
   PROFILE_ELIGIBLE_SECTIONS,
@@ -190,25 +187,6 @@ const parsePendingDataKey = (pendingDataKey: string) => {
   };
 };
 
-const flattenOverrides = (
-  value: JsonValue | undefined,
-  path: string[] = [],
-): Array<{ path: string; value: JsonValue }> => {
-  if (value === undefined) return [];
-  if (value === null || typeof value !== "object" || Array.isArray(value)) {
-    return [{ path: path.join("."), value }];
-  }
-
-  const entries = Object.entries(value);
-  if (entries.length === 0) {
-    return [{ path: path.join("."), value: {} }];
-  }
-
-  return entries.flatMap(([key, entryValue]) =>
-    flattenOverrides(entryValue, [...path, key]),
-  );
-};
-
 const createSectionPage = (
   sectionKey: string,
   level: "global" | "camera",

web/src/utils/configUtil.ts

@@ -219,6 +219,32 @@ export function buildOverrides(
   return current;
 }
 
+// ---------------------------------------------------------------------------
+// flattenOverrides — turn an overrides object into a list of leaf paths
+// ---------------------------------------------------------------------------
+
+// Walks a nested overrides value and produces a flat list of `{ path, value }`
+// entries, one per leaf.  Used by save/preview UIs to enumerate the individual
+// fields that will be changed.
+export function flattenOverrides(
+  value: JsonValue | undefined,
+  path: string[] = [],
+): Array<{ path: string; value: JsonValue }> {
+  if (value === undefined) return [];
+  if (value === null || typeof value !== "object" || Array.isArray(value)) {
+    return [{ path: path.join("."), value }];
+  }
+
+  const entries = Object.entries(value);
+  if (entries.length === 0) {
+    return [{ path: path.join("."), value: {} }];
+  }
+
+  return entries.flatMap(([key, entryValue]) =>
+    flattenOverrides(entryValue, [...path, key]),
+  );
+}
+
 // ---------------------------------------------------------------------------
 // sanitizeSectionData — normalize config values and strip hidden fields
 // ---------------------------------------------------------------------------

web/src/views/settings/CameraManagementView.tsx

@@ -14,7 +14,7 @@ import { useTranslation } from "react-i18next";
 import CameraEditForm from "@/components/settings/CameraEditForm";
 import CameraWizardDialog from "@/components/settings/CameraWizardDialog";
 import DeleteCameraDialog from "@/components/overlay/dialog/DeleteCameraDialog";
-import { LuPlus, LuTrash2 } from "react-icons/lu";
+import { LuPencil, LuPlus, LuTrash2 } from "react-icons/lu";
 import { IoMdArrowRoundBack } from "react-icons/io";
 import { isDesktop } from "react-device-detect";
 import { CameraNameLabel } from "@/components/camera/FriendlyNameLabel";
@@ -26,6 +26,12 @@ import axios from "axios";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
 import RestartDialog from "@/components/overlay/dialog/RestartDialog";
 import RestartRequiredIndicator from "@/components/indicators/RestartRequiredIndicator";
+import TextEntryDialog from "@/components/overlay/dialog/TextEntryDialog";
+import {
+  Tooltip,
+  TooltipContent,
+  TooltipTrigger,
+} from "@/components/ui/tooltip";
 import type { ProfileState } from "@/types/profile";
 import { getProfileColor } from "@/utils/profileColors";
 import { cn } from "@/lib/utils";
@@ -161,7 +167,13 @@ export default function CameraManagementView({
                             key={camera}
                             className="flex flex-row items-center justify-between"
                           >
-                            <CameraNameLabel camera={camera} />
+                            <div className="flex items-center gap-1">
+                              <CameraNameLabel camera={camera} />
+                              <CameraFriendlyNameEditor
+                                cameraName={camera}
+                                onConfigChanged={updateConfig}
+                              />
+                            </div>
                             <CameraEnableSwitch cameraName={camera} />
                           </div>
                         ))}
@@ -297,6 +309,103 @@ function CameraEnableSwitch({ cameraName }: CameraEnableSwitchProps) {
   );
 }
 
+type CameraFriendlyNameEditorProps = {
+  cameraName: string;
+  onConfigChanged: () => Promise<unknown>;
+};
+
+function CameraFriendlyNameEditor({
+  cameraName,
+  onConfigChanged,
+}: CameraFriendlyNameEditorProps) {
+  const { t } = useTranslation(["views/settings", "common"]);
+  const { data: config } = useSWR<FrigateConfig>("config");
+  const [open, setOpen] = useState(false);
+  const [isSaving, setIsSaving] = useState(false);
+
+  const currentFriendlyName = config?.cameras?.[cameraName]?.friendly_name;
+
+  const onSave = useCallback(
+    async (text: string) => {
+      if (isSaving) return;
+      setIsSaving(true);
+
+      try {
+        await axios.put("config/set", {
+          requires_restart: 0,
+          config_data: {
+            cameras: {
+              [cameraName]: {
+                friendly_name: text.trim() || null,
+              },
+            },
+          },
+        });
+
+        await onConfigChanged();
+        setOpen(false);
+
+        toast.success(t("toast.save.success", { ns: "common" }), {
+          position: "top-center",
+        });
+      } catch (error) {
+        const errorMessage =
+          axios.isAxiosError(error) &&
+          (error.response?.data?.message || error.response?.data?.detail)
+            ? error.response?.data?.message || error.response?.data?.detail
+            : t("toast.save.error.noMessage", { ns: "common" });
+
+        toast.error(
+          t("toast.save.error.title", { errorMessage, ns: "common" }),
+          { position: "top-center" },
+        );
+      } finally {
+        setIsSaving(false);
+      }
+    },
+    [cameraName, isSaving, onConfigChanged, t],
+  );
+
+  const renameLabel = t("cameraManagement.streams.friendlyName.rename", {
+    ns: "views/settings",
+  });
+
+  return (
+    <>
+      <Tooltip>
+        <TooltipTrigger asChild>
+          <Button
+            variant="ghost"
+            size="icon"
+            className="size-7"
+            aria-label={renameLabel}
+            onClick={() => setOpen(true)}
+            disabled={isSaving}
+          >
+            <LuPencil className="size-3.5" />
+          </Button>
+        </TooltipTrigger>
+        <TooltipContent>{renameLabel}</TooltipContent>
+      </Tooltip>
+      <TextEntryDialog
+        open={open}
+        setOpen={setOpen}
+        title={t("cameraManagement.streams.friendlyName.title", {
+          ns: "views/settings",
+        })}
+        description={t("cameraManagement.streams.friendlyName.description", {
+          ns: "views/settings",
+        })}
+        defaultValue={currentFriendlyName ?? ""}
+        placeholder={currentFriendlyName ? undefined : cameraName}
+        allowEmpty
+        isSaving={isSaving}
+        onSave={onSave}
+      />
+    </>
+  );
+}
+
 type CameraConfigEnableSwitchProps = {
   cameraName: string;
   setRestartDialogOpen: React.Dispatch<React.SetStateAction<boolean>>;