remove all references to degirum in frigate

the company ceased operations on 1 Aug 2026
Fix wrong box format passed to cv2.dnn.NMSBoxes (#23876 )
2026-08-01 16:42:18 +03:00 · 2026-08-01 07:32:45 -05:00 · 2026-07-31 08:57:23 -05:00 · 2026-07-30 17:20:41 -05:00
21 changed files with 369 additions and 390 deletions
@@ -79,7 +79,5 @@ sherpa-onnx==1.12.*
 faster-whisper==1.1.*
 librosa==0.11.*
 soundfile==0.13.*
 # DeGirum detector
 degirum == 0.16.*
 # Memory profiling
 memray == 1.15.*
@@ -1269,78 +1269,3 @@ axengine:
          input_dtype: int
          input_pixel_format: bgr
          labelmap_path: /labelmap/coco-80.txt
 degirumAiServer:
  title: DeGirum AI Server
  models:
    - key: ai-server-inference
      label: AI Server Inference
      recommended: true
      download: |-
        Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
        ```yaml
        degirum_detector:
          container_name: degirum
          image: degirum/aiserver:latest
          privileged: true
          ports:
            - "8778:8778"
        ```
        Set `location` to the server's service name, container name, or `host:port`.
      ui: |
        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
        | Field | Value |
        | --- | --- |
        | **Location** | `degirum` |
        | **Zoo** | `degirum/public` |
        | **Token** | your AI Hub token (optional for the public zoo) |
      yaml: |
        degirum_detector:
          type: degirum
          location: degirum
          zoo: degirum/public
          token: dg_example_token
 degirumLocal:
  title: DeGirum Local
  models:
    - key: local-inference
      label: Local Inference
      recommended: true
      download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
      ui: |
        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
        | Field | Value |
        | --- | --- |
        | **Location** | `@local` |
        | **Zoo** | `degirum/public` |
        | **Token** | your AI Hub token (optional for the public zoo) |
      yaml: |
        degirum_detector:
          type: degirum
          location: @local
          zoo: degirum/public
          token: dg_example_token
 degirumCloud:
  title: DeGirum AI Hub Cloud
  models:
    - key: ai-hub-cloud-inference
      label: AI Hub Cloud Inference
      recommended: true
      download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
      ui: |
        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
        | Field | Value |
        | --- | --- |
        | **Location** | `@cloud` |
        | **Zoo** | `degirum/public` |
        | **Token** | your AI Hub token (optional for the public zoo) |
      yaml: |
        degirum_detector:
          type: degirum
          location: @cloud
          zoo: degirum/public
          token: dg_example_token
@@ -981,7 +981,9 @@ cameras:
      # Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
      # By default the cameras are sorted alphabetically.
      order: 0
-      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
+      # Optional: Whether or not to show the camera on the default All Cameras live dashboard.
      # The camera is still available everywhere else, including camera groups and settings
      # (default: shown below)
      dashboard: True
      # Optional: Whether this camera is visible in review (the review page and its camera
      # filter, motion review, and the history view) (default: shown below)
@@ -334,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
 - **stalled**
  - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
  - Possible console messages from the player code:
    - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
@@ -24,7 +24,6 @@ Frigate supports multiple different detectors that work on different types of ha
 - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
 - <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 **AMD**
@@ -755,87 +754,6 @@ Explanation of the parameters:
  - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
 ## DeGirum
 DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
 ### Configuration {#configuration-degirum}
 #### AI Server Inference
 Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
 ```yaml
 degirum_detector:
  container_name: degirum
  image: degirum/aiserver:latest
  privileged: true
  ports:
    - "8778:8778"
 ```
 All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
 Once completed, configure the detector as follows:
 <ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
 Setting up a model in the `config.yml` is similar to setting up an AI server.
 You can set it to:
 - A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
 - A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
 - A path to some model.json.
 ```yaml
 model:
  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
  width: 300 # width is in the model name as the first number in the "int"x"int" section
  height: 300 # height is in the model name as the second number in the "int"x"int" section
  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 #### Local Inference
 It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
 1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
 2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
 3. Create a DeGirum detector in your configuration.
 <ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
 Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
 ```yaml
 model:
  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
  width: 300 # width is in the model name as the first number in the "int"x"int" section
  height: 300 # height is in the model name as the second number in the "int"x"int" section
  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 #### AI Hub Cloud Inference
 If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
 1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
 2. Get an access token.
 3. Create a DeGirum detector in your configuration.
 <ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
 Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
 ```yaml
 model:
  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
  width: 300 # width is in the model name as the first number in the "int"x"int" section
  height: 300 # height is in the model name as the second number in the "int"x"int" section
  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 ## AXERA
 Hardware accelerated object detection is supported on the following SoCs:
@@ -39,7 +39,7 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
 Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
-The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame.
+The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
 Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
@@ -13,8 +13,8 @@ class CameraUiConfig(FrigateBaseModel):
    )
    dashboard: bool = Field(
        default=True,
-        title="Show in UI",
+        title="Show on Live dashboard",
-        description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
+        description="Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings.",
    )
    review: bool = Field(
        default=True,
@@ -1,157 +0,0 @@
 import logging
 import queue
 from typing import Literal
 import numpy as np
 from pydantic import ConfigDict, Field
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
 logger = logging.getLogger(__name__)
 DETECTOR_KEY = "degirum"
 ### DETECTOR CONFIG ###
 class DGDetectorConfig(BaseDetectorConfig):
    """DeGirum detector for running models via DeGirum cloud or local inference services."""
    model_config = ConfigDict(
        title="DeGirum",
    )
    type: Literal[DETECTOR_KEY]
    location: str = Field(
        default=None,
        title="Inference Location",
        description="Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1').",
    )
    zoo: str = Field(
        default=None,
        title="Model Zoo",
        description="Path or URL to the DeGirum model zoo.",
    )
    token: str = Field(
        default=None,
        title="DeGirum Cloud Token",
        description="Token for DeGirum Cloud access.",
    )
 ### ACTUAL DETECTOR  ###
 class DGDetector(DetectionApi):
    type_key = DETECTOR_KEY
    def __init__(self, detector_config: DGDetectorConfig):
        try:
            import degirum as dg
        except ModuleNotFoundError:
            raise ImportError("Unable to import DeGirum detector.") from None
        self._queue = queue.Queue()
        self._zoo = dg.connect(
            detector_config.location, detector_config.zoo, detector_config.token
        )
        logger.debug(f"Models in zoo: {self._zoo.list_models()}")
        self.dg_model = self._zoo.load_model(
            detector_config.model.path,
        )
        # Setting input image format to raw reduces preprocessing time
        self.dg_model.input_image_format = "RAW"
        # Prioritize the most powerful hardware available
        self.select_best_device_type()
        # Frigate handles pre processing as long as these are all set
        input_shape = self.dg_model.input_shape[0]
        self.model_height = input_shape[1]
        self.model_width = input_shape[2]
        # Passing in dummy frame so initial connection latency happens in
        # init function and not during actual prediction
        frame = np.zeros(
            (detector_config.model.width, detector_config.model.height, 3),
            dtype=np.uint8,
        )
        # Pass in frame to overcome first frame latency
        self.dg_model(frame)
        self.prediction = self.prediction_generator()
    def select_best_device_type(self):
        """
        Helper function that selects fastest hardware available per model runtime
        """
        types = self.dg_model.supported_device_types
        device_map = {
            "OPENVINO": ["GPU", "NPU", "CPU"],
            "HAILORT": ["HAILO8L", "HAILO8"],
            "N2X": ["ORCA1", "CPU"],
            "ONNX": ["VITIS_NPU", "CPU"],
            "RKNN": ["RK3566", "RK3568", "RK3588"],
            "TENSORRT": ["DLA", "GPU", "DLA_ONLY"],
            "TFLITE": ["ARMNN", "EDGETPU", "CPU"],
        }
        runtime = types[0].split("/")[0]
        # Just create an array of format {runtime}/{hardware} for every hardware
        # in the value for appropriate key in device_map
        self.dg_model.device_type = [
            f"{runtime}/{hardware}" for hardware in device_map[runtime]
        ]
    def prediction_generator(self):
        """
        Generator for all incoming frames. By using this generator, we don't have to keep
        reconnecting our websocket on every "predict" call.
        """
        logger.debug("Prediction generator was called")
        with self.dg_model as model:
            while 1:
                logger.info(f"q size before calling get: {self._queue.qsize()}")
                data = self._queue.get(block=True)
                logger.info(f"q size after calling get: {self._queue.qsize()}")
                logger.debug(
                    f"Data we're passing into model predict: {data}, shape of data: {data.shape}"
                )
                result = model.predict(data)
                logger.debug(f"Prediction result: {result}")
                yield result
    def detect_raw(self, tensor_input):
        # Reshaping tensor to work with pysdk
        truncated_input = tensor_input.reshape(tensor_input.shape[1:])
        logger.debug(f"Detect raw was called for tensor input: {tensor_input}")
        # add tensor_input to input queue
        self._queue.put(truncated_input)
        logger.debug(f"Queue size after adding truncated input: {self._queue.qsize()}")
        # define empty detection result
        detections = np.zeros((20, 6), np.float32)
        # grab prediction
        res = next(self.prediction)
        # If we have an empty prediction, return immediately
        if len(res.results) == 0 or len(res.results[0]) == 0:
            return detections
        i = 0
        for result in res.results:
            if i >= 20:
                break
            detections[i] = [
                result["category_id"],
                float(result["score"]),
                result["bbox"][1] / self.model_height,
                result["bbox"][0] / self.model_width,
                result["bbox"][3] / self.model_height,
                result["bbox"][2] / self.model_width,
            ]
            i += 1
        logger.debug(f"Detections output: {detections}")
        return detections
@@ -9,6 +9,7 @@ from pydantic import ConfigDict, Field
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
 from frigate.util.model import xyxy_to_xywh_for_nms
 try:
    from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -297,7 +298,7 @@ class EdgeTpuTfl(DetectionApi):
            # until after filtering out redundant boxes
            # Shift the logit scores to be non-negative (required by cv2)
            indices = cv2.dnn.NMSBoxes(
-                bboxes=boxes_filtered_decoded,
+                bboxes=xyxy_to_xywh_for_nms(boxes_filtered_decoded),
                scores=max_scores_filtered_shiftedpositive,
                score_threshold=(
                    self.min_logit_value + self.logit_shift_to_positive_values
@@ -17,6 +17,7 @@ from frigate.detectors.detector_config import (
    ModelTypeEnum,
 )
 from frigate.util.file import FileLock
 from frigate.util.model import xyxy_to_xywh_for_nms
 logger = logging.getLogger(__name__)
@@ -581,7 +582,7 @@ class MemryXDetector(DetectionApi):
            # Convert coordinates to integers
            x_min, y_min, x_max, y_max = map(int, [x_min, y_min, x_max, y_max])
-            # Append valid detections [class_id, confidence, x, y, width, height]
+            # Append valid detections [class_id, confidence, x_min, y_min, x_max, y_max]
            detections.append([class_id, confidence, x_min, y_min, x_max, y_max])
        final_detections = np.zeros((20, 6), np.float32)
@@ -595,7 +596,7 @@ class MemryXDetector(DetectionApi):
        detections = np.array(detections, dtype=np.float32)
        # Apply Non-Maximum Suppression (NMS)
-        bboxes = detections[:, 2:6].tolist()  # (x_min, y_min, width, height)
+        bboxes = xyxy_to_xywh_for_nms(detections[:, 2:6])
        scores = detections[:, 1].tolist()  # Confidence scores
        indices = cv2.dnn.NMSBoxes(bboxes, scores, 0.45, 0.5)
@@ -12,7 +12,7 @@ from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
-from frigate.util.model import post_process_yolo
+from frigate.util.model import post_process_yolo, xyxy_to_xywh_for_nms
 from frigate.util.rknn_converter import auto_convert_model
 logger = logging.getLogger(__name__)
@@ -285,7 +285,7 @@ class Rknn(DetectionApi):
        # run nms
        indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes,
+            bboxes=xyxy_to_xywh_for_nms(boxes),
            scores=scores,
            score_threshold=0.4,
            nms_threshold=0.4,
@@ -0,0 +1,226 @@
 """Tests for detector post-processing NMS box format handling.
 cv2.dnn.NMSBoxes expects boxes as [x, y, width, height]. Passing corner
 coordinates [x1, y1, x2, y2] makes OpenCV treat x2/y2 as width/height,
 inflating every box toward the bottom-right by its distance from the origin.
 Two well separated objects far from the origin then appear to overlap and the
 lower scoring one is silently suppressed.
 The regression geometry used throughout: two boxes with zero true overlap,
 A = (393, 499, 484, 620) and B = (527, 499, 618, 620) in a 640x640 input
 (43 px gap). Misread as [x, y, w, h] their IoU is 0.465, above the 0.4 NMS
 threshold, so the buggy format drops the lower scoring box while correct
 conversion keeps both.
 """
 import math
 import unittest
 from queue import Queue
 import numpy as np
 from frigate.detectors.plugins.memryx import MemryXDetector
 from frigate.util.model import (
    post_process_dfine,
    post_process_rfdetr,
    post_process_yolo,
    post_process_yolox,
 )
 WIDTH = 640
 HEIGHT = 640
 # box A: xyxy (393, 499, 484, 620) as center format
 A_CX, A_CY, A_W, A_H = 438.5, 559.5, 91.0, 121.0
 # box B: xyxy (527, 499, 618, 620) as center format
 B_CX, B_CY, B_W, B_H = 572.5, 559.5, 91.0, 121.0
 # expected normalized output rows: [class_id, conf, y1, x1, y2, x2]
 A_ROW = [499 / 640, 393 / 640, 620 / 640, 484 / 640]
 B_ROW = [499 / 640, 527 / 640, 620 / 640, 618 / 640]
 def kept(detections: np.ndarray) -> np.ndarray:
    """Rows of the padded (20, 6) output that hold real detections."""
    return detections[detections[:, 1] > 0]
 class TestYoloNmsPostProcess(unittest.TestCase):
    def _single_output(self, rows: list[list[float]]) -> list[np.ndarray]:
        """Build a single-tensor YOLO output (1, attrs, anchors) from
        [cx, cy, w, h, class scores...] rows, padded with empty anchors."""
        anchors = np.zeros((10, len(rows[0])), dtype=np.float32)
        anchors[: len(rows)] = np.array(rows, dtype=np.float32)
        return [anchors.T[np.newaxis, ...]]
    def test_keeps_separated_objects_far_from_origin(self):
        output = self._single_output(
            [
                [A_CX, A_CY, A_W, A_H, 0.90, 0.0],
                [B_CX, B_CY, B_W, B_H, 0.0, 0.85],
            ]
        )
        detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
        self.assertEqual(len(detections), 2)
        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
    def test_still_suppresses_true_duplicates(self):
        # same object twice, shifted 4 px: true IoU 0.92, must dedupe to one
        output = self._single_output(
            [
                [A_CX, A_CY, A_W, A_H, 0.90, 0.0],
                [A_CX + 4, A_CY, A_W, A_H, 0.85, 0.0],
            ]
        )
        detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
        self.assertEqual(len(detections), 1)
        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
 class TestMultipartYoloPostProcess(unittest.TestCase):
    def _multipart_output(self) -> list[np.ndarray]:
        """Build a 3-scale anchor-based YOLO output containing boxes A and B,
        both decoded through anchor 0 of the stride-32 scale."""
        outputs = [
            np.zeros((1, 255, 80, 80), dtype=np.float32),
            np.zeros((1, 255, 40, 40), dtype=np.float32),
            np.zeros((1, 255, 20, 20), dtype=np.float32),
        ]
        stride, (anchor_w, anchor_h) = 32, (142, 110)
        for cx, cy, w, h, conf, class_channel in [
            (A_CX, A_CY, A_W, A_H, 0.95, 5),  # class 0
            (B_CX, B_CY, B_W, B_H, 0.90, 6),  # class 1
        ]:
            cell_x, cell_y = int(cx // stride), int(cy // stride)
            dx = (cx / stride - cell_x + 0.5) / 2
            dy = (cy / stride - cell_y + 0.5) / 2
            dw = math.sqrt(w / anchor_w) / 2
            dh = math.sqrt(h / anchor_h) / 2
            # anchor 0 occupies channels 0-84 of the 255 channel tensor
            outputs[2][0, 0:4, cell_y, cell_x] = [dx, dy, dw, dh]
            outputs[2][0, 4, cell_y, cell_x] = conf
            outputs[2][0, class_channel, cell_y, cell_x] = 1.0
        return outputs
    def test_keeps_separated_objects_far_from_origin(self):
        detections = kept(post_process_yolo(self._multipart_output(), WIDTH, HEIGHT))
        self.assertEqual(len(detections), 2)
        np.testing.assert_allclose(detections[0], [0, 0.95, *A_ROW], atol=2e-3)
        np.testing.assert_allclose(detections[1], [1, 0.90, *B_ROW], atol=2e-3)
    def test_empty_output_returns_no_detections(self):
        outputs = [
            np.zeros((1, 255, 80, 80), dtype=np.float32),
            np.zeros((1, 255, 40, 40), dtype=np.float32),
            np.zeros((1, 255, 20, 20), dtype=np.float32),
        ]
        detections = kept(post_process_yolo(outputs, WIDTH, HEIGHT))
        self.assertEqual(len(detections), 0)
 class TestYoloxPostProcess(unittest.TestCase):
    def test_keeps_separated_objects_far_from_origin(self):
        # with zero grids and unit strides the decode reduces to
        # cx = raw cx and w = exp(raw w)
        rows = np.zeros((10, 7), dtype=np.float32)
        rows[0] = [A_CX, A_CY, math.log(A_W), math.log(A_H), 1.0, 0.90, 0.0]
        rows[1] = [B_CX, B_CY, math.log(B_W), math.log(B_H), 1.0, 0.0, 0.85]
        predictions = rows[np.newaxis, ...]
        grids = np.zeros((1, 10, 2), dtype=np.float32)
        expanded_strides = np.ones((1, 10, 1), dtype=np.float32)
        detections = kept(
            post_process_yolox(predictions, WIDTH, HEIGHT, grids, expanded_strides)
        )
        self.assertEqual(len(detections), 2)
        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
 class TestDfinePostProcess(unittest.TestCase):
    def test_keeps_separated_objects_far_from_origin(self):
        # D-FINE emits absolute pixel xyxy boxes alongside labels and scores
        labels = np.zeros((1, 10), dtype=np.int64)
        labels[0, 1] = 1
        boxes = np.zeros((1, 10, 4), dtype=np.float32)
        boxes[0, 0] = [393, 499, 484, 620]
        boxes[0, 1] = [527, 499, 618, 620]
        scores = np.zeros((1, 10), dtype=np.float32)
        scores[0, 0] = 0.90
        scores[0, 1] = 0.85
        detections = kept(post_process_dfine([labels, boxes, scores], WIDTH, HEIGHT))
        self.assertEqual(len(detections), 2)
        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
 class TestRfdetrPostProcess(unittest.TestCase):
    def test_keeps_separated_objects_far_from_origin(self):
        # RF-DETR emits normalized center format boxes and class logits where
        # logit index 0 is the background class
        boxes = np.zeros((1, 10, 4), dtype=np.float32)
        boxes[0, 0] = [A_CX / WIDTH, A_CY / HEIGHT, A_W / WIDTH, A_H / HEIGHT]
        boxes[0, 1] = [B_CX / WIDTH, B_CY / HEIGHT, B_W / WIDTH, B_H / HEIGHT]
        # background heavy logits everywhere, then two confident objects
        logits = np.tile(np.array([10.0, 0.0, 0.0], dtype=np.float32), (1, 10, 1))
        logits[0, 0] = [0.0, 4.0, 0.0]  # class 0 after background offset
        logits[0, 1] = [0.0, 0.0, 3.5]  # class 1 after background offset
        detections = kept(post_process_rfdetr([boxes, logits]))
        conf_a = math.exp(4.0) / (math.exp(4.0) + 2)
        conf_b = math.exp(3.5) / (math.exp(3.5) + 2)
        self.assertEqual(len(detections), 2)
        np.testing.assert_allclose(detections[0], [0, conf_a, *A_ROW], atol=2e-3)
        np.testing.assert_allclose(detections[1], [1, conf_b, *B_ROW], atol=2e-3)
 class TestMemryxSsdlitePostProcess(unittest.TestCase):
    def test_keeps_separated_objects_far_from_origin(self):
        # the NMS math runs on the host CPU, so the real method is testable
        # without MemryX hardware; it only needs the model dimensions and
        # the output queue
        detector = object.__new__(MemryXDetector)
        detector.memx_model_width = WIDTH
        detector.memx_model_height = HEIGHT
        detector.output_queue = Queue()
        # this path uses a 0.5 NMS threshold, so use a tighter pair: zero
        # true overlap (10 px gap), IoU 0.69 when misread as [x, y, w, h]
        dets = np.zeros((1, 10, 5), dtype=np.float32)
        dets[0, 0] = [480, 500, 540, 620, 0.90]
        dets[0, 1] = [550, 500, 610, 620, 0.85]
        labels = np.zeros((1, 10), dtype=np.float32)
        labels[0, 1] = 1
        detector.post_process_ssdlite([dets, labels])
        detections = kept(detector.output_queue.get())
        self.assertEqual(len(detections), 2)
        np.testing.assert_allclose(
            detections[0],
            [0, 0.90, 500 / 640, 480 / 640, 620 / 640, 540 / 640],
            atol=2e-3,
        )
        np.testing.assert_allclose(
            detections[1],
            [1, 0.85, 500 / 640, 550 / 640, 620 / 640, 610 / 640],
            atol=2e-3,
        )
 if __name__ == "__main__":
    unittest.main()
@@ -16,6 +16,31 @@ logger = logging.getLogger(__name__)
 ### Post Processing
 def xyxy_to_xywh_for_nms(boxes: np.ndarray | list) -> np.ndarray:
    """Convert [x1, y1, x2, y2] boxes to the [x, y, width, height] format
    that cv2.dnn.NMSBoxes expects.
    Passing corner coordinates directly makes OpenCV treat x2/y2 as the box
    size, inflating every box toward the bottom-right by its distance from
    the origin, which suppresses valid detections near other objects.
    Args:
        boxes: Array-like of shape (N, 4) in corner format.
    Returns:
        Float32 array of shape (N, 4) in top-left plus size format.
    """
    boxes = np.asarray(boxes, dtype=np.float32)
    if boxes.size == 0:
        return np.zeros((0, 4), dtype=np.float32)
    xywh = boxes.copy()
    xywh[:, 2] -= xywh[:, 0]
    xywh[:, 3] -= xywh[:, 1]
    return xywh
 def post_process_dfine(
    tensor_output: np.ndarray, width: int, height: int
 ) -> np.ndarray:
@@ -25,7 +50,9 @@ def post_process_dfine(
    input_shape = np.array([height, width, height, width])
    boxes = np.divide(boxes, input_shape, dtype=np.float32)
-    indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
+    indices = cv2.dnn.NMSBoxes(
        xyxy_to_xywh_for_nms(boxes), scores, score_threshold=0.4, nms_threshold=0.4
    )
    detections = np.zeros((20, 6), np.float32)
    for i, (bbox, confidence, class_id) in enumerate(
@@ -78,7 +105,10 @@ def post_process_rfdetr(tensor_output: list[np.ndarray, np.ndarray]) -> np.ndarr
    # apply nms
    indices = cv2.dnn.NMSBoxes(
-        filtered_boxes, filtered_scores, score_threshold=0.4, nms_threshold=0.4
+        xyxy_to_xywh_for_nms(filtered_boxes),
        filtered_scores,
        score_threshold=0.4,
        nms_threshold=0.4,
    )
    detections = np.zeros((20, 6), np.float32)
@@ -159,7 +189,7 @@ def __post_process_multipart_yolo(
                    all_class_ids.append(class_id)
    indices = cv2.dnn.NMSBoxes(
-        bboxes=all_boxes,
+        bboxes=xyxy_to_xywh_for_nms(all_boxes),
        scores=all_scores,
        score_threshold=0.4,
        nms_threshold=0.4,
@@ -206,7 +236,9 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
    boxes = boxes_xyxy
    # run NMS
-    indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
+    indices = cv2.dnn.NMSBoxes(
        xyxy_to_xywh_for_nms(boxes), scores, score_threshold=0.4, nms_threshold=0.4
    )
    detections = np.zeros((20, 6), np.float32)
    for i, (bbox, confidence, class_id) in enumerate(
        zip(boxes[indices], scores[indices], class_ids[indices])
@@ -258,7 +290,7 @@ def post_process_yolox(
    scores = scores[np.arange(len(cls_inds)), cls_inds]
    indices = cv2.dnn.NMSBoxes(
-        boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4
+        xyxy_to_xywh_for_nms(boxes_xyxy), scores, score_threshold=0.4, nms_threshold=0.4
    )
    detections = np.zeros((20, 6), np.float32)
@@ -859,8 +859,8 @@
      "description": "Numeric order used to sort the camera in the UI (default dashboard and lists); larger numbers appear later."
    },
    "dashboard": {
-      "label": "Show in UI",
+      "label": "Show on Live dashboard",
-      "description": "Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again."
+      "description": "Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings."
    },
    "review": {
      "label": "Show in review",
@@ -357,22 +357,6 @@
        "description": "Optional API key for authenticated DeepStack services."
      }
    },
    "degirum": {
      "label": "DeGirum",
      "description": "DeGirum detector for running models via DeGirum cloud or local inference services.",
      "location": {
        "label": "Inference Location",
        "description": "Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1')."
      },
      "zoo": {
        "label": "Model Zoo",
        "description": "Path or URL to the DeGirum model zoo."
      },
      "token": {
        "label": "DeGirum Cloud Token",
        "description": "Token for DeGirum Cloud access."
      }
    },
    "edgetpu": {
      "label": "EdgeTPU",
      "description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
@@ -1543,8 +1527,8 @@
      "description": "Numeric order used to sort the camera in the UI (default dashboard and lists); larger numbers appear later."
    },
    "dashboard": {
-      "label": "Show in UI",
+      "label": "Show on Live dashboard",
-      "description": "Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again."
+      "description": "Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings."
    },
    "review": {
      "label": "Show in review",
@@ -499,7 +499,7 @@
        "webuiUrlHelp": "URL to visit the camera's web UI directly from the Debug view. Leave blank to disable the link.",
        "webuiUrlInvalid": "Must be a valid URL (e.g., https://example.com).",
        "dashboardLabel": "Show on Live dashboard",
-        "dashboardHelp": "Show this camera on the Live dashboard.",
+        "dashboardHelp": "Show this camera on the default All Cameras live dashboard. It remains available everywhere else, including camera groups.",
        "reviewLabel": "Show in Review",
        "reviewHelp": "Show this camera in Review, including the camera filter, motion review, and the history view."
      }
@@ -78,7 +78,7 @@ export default function ZoneEditPane({
    }
    return Object.values(config.cameras)
-      .filter((conf) => conf.ui.dashboard && conf.enabled_in_config)
+      .filter((conf) => conf.enabled_in_config)
      .sort((aConf, bConf) => aConf.ui.order - bConf.ui.order);
  }, [config]);
@@ -198,6 +198,19 @@ export default function Events() {
    return true;
  });
  const reviewCamerasParam = useMemo(() => {
    const selected: string | undefined = reviewSearchParams["cameras"];
    if (!selected) {
      return reviewCameras.join(",");
    }
    const selectedCameras = new Set(selected.split(","));
    return reviewCameras
      .filter((camera) => selectedCameras.has(camera))
      .join(",");
  }, [reviewCameras, reviewSearchParams]);
  useSearchEffect("labels", (labels: string) => {
    setReviewFilter({
      ...reviewFilter,
@@ -330,8 +343,12 @@ export default function Events() {
  }, []);
  const getKey = useCallback(() => {
    if (!timezone) {
      return null;
    }
    const params = {
-      cameras: reviewSearchParams["cameras"],
+      cameras: reviewCamerasParam,
      labels: reviewSearchParams["labels"],
      zones: reviewSearchParams["zones"],
      reviewed: null, // We want both reviewed and unreviewed items as we filter in the UI
@@ -339,7 +356,7 @@ export default function Events() {
      after: reviewSearchParams["after"] || last24Hours.after,
    };
    return ["review", params];
-  }, [reviewSearchParams, last24Hours]);
+  }, [reviewSearchParams, reviewCamerasParam, last24Hours, timezone]);
  const { data: reviews, mutate: updateSegments } = useSWR<ReviewSegment[]>(
    getKey,
@@ -361,10 +378,6 @@ export default function Events() {
    const motion: ReviewSegment[] = [];
    reviews?.forEach((segment) => {
      if (config?.cameras[segment.camera]?.ui?.review === false) {
        return;
      }
      all.push(segment);
      switch (segment.severity) {
@@ -386,7 +399,7 @@ export default function Events() {
      detection: detections,
      significant_motion: motion,
    };
-  }, [reviews, config?.cameras]);
+  }, [reviews]);
  // update review items in place when a review segment ends
  const reviewUpdate = useFrigateReviews();
@@ -450,15 +463,17 @@ export default function Events() {
  // review summary
  const { data: reviewSummary, mutate: updateSummary } = useSWR<ReviewSummary>(
-    [
+    timezone
-      "review/summary",
+      ? [
-      {
+          "review/summary",
-        timezone: timezone,
+          {
-        cameras: reviewSearchParams["cameras"] ?? null,
+            timezone: timezone,
-        labels: reviewSearchParams["labels"] ?? null,
+            cameras: reviewCamerasParam,
-        zones: reviewSearchParams["zones"] ?? null,
+            labels: reviewSearchParams["labels"] ?? null,
-      },
+            zones: reviewSearchParams["zones"] ?? null,
-    ],
+          },
        ]
      : null,
    {
      revalidateOnFocus: true,
      refreshInterval: 30000,
@@ -473,13 +488,17 @@ export default function Events() {
  // recordings summary
-  const { data: recordingsSummary } = useSWR<RecordingsSummary>([
+  const { data: recordingsSummary } = useSWR<RecordingsSummary>(
-    "recordings/summary",
+    timezone
-    {
+      ? [
-      timezone: timezone,
+          "recordings/summary",
-      cameras: reviewSearchParams["cameras"] ?? null,
+          {
-    },
+            timezone: timezone,
-  ]);
+            cameras: reviewCamerasParam,
          },
        ]
      : null,
  );
  // preview videos
  const previewTimes = useMemo(() => {
@@ -706,12 +706,7 @@ export default function Settings() {
    }
    return Object.values(config.cameras)
-      .filter(
+      .filter((conf) => conf.enabled_in_config && !isReplayCamera(conf.name))
        (conf) =>
          conf.ui.dashboard &&
          conf.enabled_in_config &&
          !isReplayCamera(conf.name),
      )
      .sort((aConf, bConf) => aConf.ui.order - bConf.ui.order);
  }, [config]);
@@ -1024,6 +1024,9 @@ function MotionReview({
      if (!allowedCameras.includes(cam.name)) {
        return false;
      }
      if (cam.ui?.review === false) {
        return false;
      }
      if (selectedCams && !selectedCams.includes(cam.name)) {
        return false;
      }
@@ -1033,6 +1036,11 @@ function MotionReview({
    return cameras.sort((a, b) => a.ui.order - b.ui.order);
  }, [config, filter, allowedCameras]);
  const reviewCamerasParam = useMemo(
    () => reviewCameras.map((cam) => cam.name).join(","),
    [reviewCameras],
  );
  const videoPlayersRef = useRef<{ [camera: string]: PreviewController }>({});
  // motion data
@@ -1052,7 +1060,7 @@ function MotionReview({
      before: alignedBefore,
      after: alignedAfter,
      scale: segmentDuration / 2,
-      cameras: filter?.cameras?.join(",") ?? null,
+      cameras: reviewCamerasParam,
    },
  ]);
@@ -1061,7 +1069,7 @@ function MotionReview({
    {
      before: alignedBefore,
      after: alignedAfter,
-      cameras: filter?.cameras?.join(",") ?? null,
+      cameras: reviewCamerasParam,
    },
  ]);
@@ -540,6 +540,36 @@ export default function GeneralMetrics({
    return Object.keys(series).length > 0 ? Object.values(series) : undefined;
  }, [statsHistory]);
  // Number of cards the hardware grid renders. Which ones appear depends on
  // the vendor, so the column count follows the count rather than assuming a
  // fixed set is present.
  const hardwareCardCount = useMemo(() => {
    if (!statsHistory[0]?.gpu_usages) {
      return 0;
    }
    const hasNpu = statsHistory[0].npu_usages != undefined;
    return (
      1 + // gpu usage always renders alongside gpu_usages
      (gpuMemSeries ? 1 : 0) +
      (gpuEncSeries?.length ? 1 : 0) +
      (gpuComputeSeries?.length ? 1 : 0) +
      (gpuDecSeries?.length ? 1 : 0) +
      (gpuTempSeries?.length ? 1 : 0) +
      (hasNpu ? 1 : 0) +
      (hasNpu && npuTempSeries?.length ? 1 : 0)
    );
  }, [
    statsHistory,
    gpuMemSeries,
    gpuEncSeries,
    gpuComputeSeries,
    gpuDecSeries,
    gpuTempSeries,
    npuTempSeries,
  ]);
  // other processes stats
  const hardwareType = useMemo(() => {
@@ -763,12 +793,9 @@ export default function GeneralMetrics({
            <div
              className={cn(
                "mt-4 grid grid-cols-1 gap-2 sm:grid-cols-2",
-                gpuTempSeries?.length && "md:grid-cols-3",
+                hardwareCardCount >= 3 && "lg:grid-cols-3",
-                (gpuEncSeries?.length || gpuComputeSeries?.length) &&
+                hardwareCardCount >= 4 && "xl:grid-cols-4",
-                  "xl:grid-cols-4",
+                hardwareCardCount >= 5 && "3xl:grid-cols-5",
                (gpuEncSeries?.length || gpuComputeSeries?.length) &&
                  gpuTempSeries?.length &&
                  "3xl:grid-cols-5",
              )}
            >
              {statsHistory[0]?.gpu_usages && (