Merge remote-tracking branch 'upstream/dev' into dev

benchmark.py

@@ -61,7 +61,7 @@ def start(id, num_detections, detection_queue, event):
     object_detector.cleanup()
     print(f"{id} - Processed for {duration:.2f} seconds.")
     print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
-    print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+    print(f"{id} - Average frame processing time: {mean(frame_times) * 1000:.2f}ms")
 
 
 ######

docs/docs/configuration/camera_specific.md

@@ -156,7 +156,9 @@ cameras:
 
 #### Reolink Doorbell
 
-The reolink doorbell supports 2-way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
+The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
+
+Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
 
 ```yaml
 go2rtc:

docs/docs/configuration/index.md

@@ -203,14 +203,13 @@ detectors:
   ov:
     type: openvino
     device: AUTO
-    model:
-      path: /openvino-model/ssdlite_mobilenet_v2.xml
 
 model:
   width: 300
   height: 300
   input_tensor: nhwc
   input_pixel_format: bgr
+  path: /openvino-model/ssdlite_mobilenet_v2.xml
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 
 record:

docs/docs/configuration/live.md

@@ -29,7 +29,7 @@ The default video and audio codec on your camera may not always be compatible wi
 
 ### Audio Support
 
-MSE Requires AAC audio, WebRTC requires PCMU/PCMA, or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
+MSE Requires PCMA/PCMU or AAC audio, WebRTC requires PCMA/PCMU or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
 
 ```yaml
 go2rtc:
@@ -138,3 +138,13 @@ services:
 :::
 
 See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
+
+### Two way talk
+
+For devices that support two way talk, Frigate can be configured to use the feature from the camera's Live view in the Web UI. You should:
+
+- Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
+- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
+- For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
+
+To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)

docs/docs/configuration/object_detectors.md

@@ -144,7 +144,7 @@ detectors:
 
 #### SSDLite MobileNet v2
 
-An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model. 
+An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
 
 Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
 
@@ -506,11 +506,12 @@ detectors:
   cpu1:
     type: cpu
     num_threads: 3
-    model:
-      path: "/custom_model.tflite"
   cpu2:
     type: cpu
     num_threads: 3
+
+model:
+  path: "/custom_model.tflite"
 ```
 
 When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
@@ -637,8 +638,6 @@ detectors:
   hailo8l:
     type: hailo8l
     device: PCIe
-    model:
-      path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
 
 model:
   width: 300
@@ -646,4 +645,5 @@ model:
   input_tensor: nhwc
   input_pixel_format: bgr
   model_type: ssd
+  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
 ```

docs/docs/configuration/reference.md

@@ -52,7 +52,7 @@ detectors:
   # Required: name of the detector
   detector_name:
     # Required: type of the detector
-    # Frigate provided types include 'cpu', 'edgetpu', 'openvino' and 'tensorrt' (default: shown below)
+    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
     # Additional detector types can also be plugged in.
     # Detectors may require additional configuration.
     # Refer to the Detectors configuration page for more information.

docs/docs/frigate/installation.md

@@ -305,8 +305,15 @@ To install make sure you have the [community app plugin here](https://forums.unr
 
 ## Proxmox
 
-It is recommended to run Frigate in LXC, rather than in a VM, for maximum performance. The setup can be complex so be prepared to read the Proxmox and LXC documentation. Suggestions include:
+[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers.
 
+:::warning
+
+If you choose to run Frigate via LXC in Proxmox the setup can be complex so be prepared to read the Proxmox and LXC documentation, Frigate does not officially support running inside of an LXC.
+
+:::
+
+ Suggestions include:
 - For Intel-based hardware acceleration, to allow access to the `/dev/dri/renderD128` device with major number 226 and minor number 128, add the following lines to the `/etc/pve/lxc/<id>.conf` LXC configuration:
   - `lxc.cgroup2.devices.allow: c 226:128 rwm`
   - `lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file`

docs/docs/troubleshooting/recordings.md

@@ -3,7 +3,15 @@ id: recordings
 title: Troubleshooting Recordings
 ---
 
-### WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
+## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?
+
+You'll want to:
+
+- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
+- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
+- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+
+## I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
 
 This error can be caused by a number of different issues. The first step in troubleshooting is to enable debug logging for recording. This will enable logging showing how long it takes for recordings to be moved from RAM cache to the disk.
 
@@ -40,6 +48,7 @@ On linux, some helpful tools/commands in diagnosing would be:
 On modern linux kernels, the system will utilize some swap if enabled. Setting vm.swappiness=1 no longer means that the kernel will only swap in order to avoid OOM. To prevent any swapping inside a container, set allocations memory and memory+swap to be the same and disable swapping by setting the following docker/podman run parameters:
 
 **Compose example**
+
 ```yaml
 version: "3.9"
 services:
@@ -54,6 +63,7 @@ services:
 ```
 
 **Run command example**
+
 ```
 --memory=<MAXRAM> --memory-swap=<MAXSWAP> --memory-swappiness=0
 ```

frigate/comms/webpush.py

@@ -151,7 +151,7 @@ class WebPushClient(Communicator):  # type: ignore[misc]
         camera: str = payload["after"]["camera"]
         title = f"{', '.join(sorted_objects).replace('_', ' ').title()}{' was' if state == 'end' else ''} detected in {', '.join(payload['after']['data']['zones']).replace('_', ' ').title()}"
         message = f"Detected on {camera.replace('_', ' ').title()}"
-        image = f'{payload["after"]["thumb_path"].replace("/media/frigate", "")}'
+        image = f"{payload['after']['thumb_path'].replace('/media/frigate', '')}"
 
         # if event is ongoing open to live view otherwise open to recordings view
         direct_url = f"/review?id={reviewId}" if state == "end" else f"/#{camera}"

frigate/config/camera/zone.py

@@ -85,7 +85,7 @@ class ZoneConfig(BaseModel):
             if explicit:
                 self.coordinates = ",".join(
                     [
-                        f'{round(int(p.split(",")[0]) / frame_shape[1], 3)},{round(int(p.split(",")[1]) / frame_shape[0], 3)}'
+                        f"{round(int(p.split(',')[0]) / frame_shape[1], 3)},{round(int(p.split(',')[1]) / frame_shape[0], 3)}"
                         for p in coordinates
                     ]
                 )

frigate/config/config.py

@@ -594,35 +594,27 @@ class FrigateConfig(FrigateBaseModel):
                 if isinstance(detector, dict)
                 else detector.model_dump(warnings="none")
             )
-            detector_config: DetectorConfig = adapter.validate_python(model_dict)
+            detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)
-            if detector_config.model is None:
-                detector_config.model = self.model.model_copy()
-            else:
-                path = detector_config.model.path
-                detector_config.model = self.model.model_copy()
-                detector_config.model.path = path
 
-                if "path" not in model_dict or len(model_dict.keys()) > 1:
+            # users should not set model themselves
-                    logger.warning(
+            if detector_config.model:
-                        "Customizing more than a detector model path is unsupported."
+                detector_config.model = None
-                    )
 
-            merged_model = deep_merge(
+            model_config = self.model.model_dump(exclude_unset=True, warnings="none")
-                detector_config.model.model_dump(exclude_unset=True, warnings="none"),
-                self.model.model_dump(exclude_unset=True, warnings="none"),
-            )
 
-            if "path" not in merged_model:
+            if detector_config.model_path:
+                model_config["path"] = detector_config.model_path
+
+            if "path" not in model_config:
                 if detector_config.type == "cpu":
-                    merged_model["path"] = "/cpu_model.tflite"
+                    model_config["path"] = "/cpu_model.tflite"
                 elif detector_config.type == "edgetpu":
-                    merged_model["path"] = "/edgetpu_model.tflite"
+                    model_config["path"] = "/edgetpu_model.tflite"
 
-            detector_config.model = ModelConfig.model_validate(merged_model)
+            model = ModelConfig.model_validate(model_config)
-            detector_config.model.check_and_load_plus_model(
+            model.check_and_load_plus_model(self.plus_api, detector_config.type)
-                self.plus_api, detector_config.type
+            model.compute_model_hash()
-            )
+            detector_config.model = model
-            detector_config.model.compute_model_hash()
             self.detectors[key] = detector_config
 
         return self

frigate/detectors/detector_config.py

@@ -194,6 +194,9 @@ class BaseDetectorConfig(BaseModel):
     model: Optional[ModelConfig] = Field(
         default=None, title="Detector specific model configuration."
     )
+    model_path: Optional[str] = Field(
+        default=None, title="Detector specific model path."
+    )
     model_config = ConfigDict(
         extra="allow", arbitrary_types_allowed=True, protected_namespaces=()
     )

frigate/detectors/plugins/tensorrt.py

@@ -219,19 +219,19 @@ class TensorRtDetector(DetectionApi):
         ]
 
     def __init__(self, detector_config: TensorRTDetectorConfig):
-        assert (
+        assert TRT_SUPPORT, (
-            TRT_SUPPORT
+            f"TensorRT libraries not found, {DETECTOR_KEY} detector not present"
-        ), f"TensorRT libraries not found, {DETECTOR_KEY} detector not present"
+        )
 
         (cuda_err,) = cuda.cuInit(0)
-        assert (
+        assert cuda_err == cuda.CUresult.CUDA_SUCCESS, (
-            cuda_err == cuda.CUresult.CUDA_SUCCESS
+            f"Failed to initialize cuda {cuda_err}"
-        ), f"Failed to initialize cuda {cuda_err}"
+        )
         err, dev_count = cuda.cuDeviceGetCount()
         logger.debug(f"Num Available Devices: {dev_count}")
-        assert (
+        assert detector_config.device < dev_count, (
-            detector_config.device < dev_count
+            f"Invalid TensorRT Device Config. Device {detector_config.device} Invalid."
-        ), f"Invalid TensorRT Device Config. Device {detector_config.device} Invalid."
+        )
         err, self.cu_ctx = cuda.cuCtxCreate(
             cuda.CUctx_flags.CU_CTX_MAP_HOST, detector_config.device
         )

frigate/ffmpeg_presets.py

@@ -71,8 +71,8 @@ PRESETS_HW_ACCEL_DECODE = {
     "preset-rpi-64-h264": "-c:v:1 h264_v4l2m2m",
     "preset-rpi-64-h265": "-c:v:1 hevc_v4l2m2m",
     FFMPEG_HWACCEL_VAAPI: f"-hwaccel_flags allow_profile_mismatch -hwaccel vaapi -hwaccel_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format vaapi",
-    "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v h264_qsv",
+    "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v h264_qsv -bsf:v dump_extra",  # https://trac.ffmpeg.org/ticket/9766#comment:17
-    "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v hevc_qsv",
+    "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v hevc_qsv -bsf:v dump_extra",  # https://trac.ffmpeg.org/ticket/9766#comment:17
     FFMPEG_HWACCEL_NVIDIA: "-hwaccel cuda -hwaccel_output_format cuda",
     "preset-jetson-h264": "-c:v h264_nvmpi -resize {1}x{2}",
     "preset-jetson-h265": "-c:v hevc_nvmpi -resize {1}x{2}",

frigate/plus.py

@@ -68,11 +68,13 @@ class PlusApi:
             or self._token_data["expires"] - datetime.datetime.now().timestamp() < 60
         ):
             if self.key is None:
-                raise Exception("Plus API not activated")
+                raise Exception(
+                    "Plus API key not set. See https://docs.frigate.video/integrations/plus#set-your-api-key"
+                )
             parts = self.key.split(":")
             r = requests.get(f"{self.host}/v1/auth/token", auth=(parts[0], parts[1]))
             if not r.ok:
-                raise Exception("Unable to refresh API token")
+                raise Exception(f"Unable to refresh API token: {r.text}")
             self._token_data = r.json()
 
     def _get_authorization_header(self) -> dict:
@@ -116,15 +118,6 @@ class PlusApi:
             logger.error(f"Failed to upload original: {r.status_code} {r.text}")
             raise Exception(r.text)
 
-        # resize and submit annotate
-        files = {"file": get_jpg_bytes(image, 640, 70)}
-        data = presigned_urls["annotate"]["fields"]
-        data["content-type"] = "image/jpeg"
-        r = requests.post(presigned_urls["annotate"]["url"], files=files, data=data)
-        if not r.ok:
-            logger.error(f"Failed to upload annotate: {r.status_code} {r.text}")
-            raise Exception(r.text)
-
         # resize and submit thumbnail
         files = {"file": get_jpg_bytes(image, 200, 70)}
         data = presigned_urls["thumbnail"]["fields"]

frigate/ptz/autotrack.py

@@ -135,7 +135,7 @@ class PtzMotionEstimator:
 
             try:
                 logger.debug(
-                    f"{camera}: Motion estimator transformation: {self.coord_transformations.rel_to_abs([[0,0]])}"
+                    f"{camera}: Motion estimator transformation: {self.coord_transformations.rel_to_abs([[0, 0]])}"
                 )
             except Exception:
                 pass
@@ -471,7 +471,7 @@ class PtzAutoTracker:
                 self.onvif.get_camera_status(camera)
 
             logger.info(
-                f"Calibration for {camera} in progress: {round((step/num_steps)*100)}% complete"
+                f"Calibration for {camera} in progress: {round((step / num_steps) * 100)}% complete"
             )
 
         self.calibrating[camera] = False
@@ -690,7 +690,7 @@ class PtzAutoTracker:
                             f"{camera}: Predicted movement time: {self._predict_movement_time(camera, pan, tilt)}"
                         )
                         logger.debug(
-                            f"{camera}: Actual movement time: {self.ptz_metrics[camera].stop_time.value-self.ptz_metrics[camera].start_time.value}"
+                            f"{camera}: Actual movement time: {self.ptz_metrics[camera].stop_time.value - self.ptz_metrics[camera].start_time.value}"
                         )
 
                     # save metrics for better estimate calculations
@@ -983,10 +983,10 @@ class PtzAutoTracker:
             logger.debug(f"{camera}: Zoom test: at max zoom: {at_max_zoom}")
             logger.debug(f"{camera}: Zoom test: at min zoom: {at_min_zoom}")
             logger.debug(
-                f'{camera}: Zoom test: zoom in hysteresis limit: {zoom_in_hysteresis} value: {AUTOTRACKING_ZOOM_IN_HYSTERESIS} original: {self.tracked_object_metrics[camera]["original_target_box"]} max: {self.tracked_object_metrics[camera]["max_target_box"]} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]["target_box"]}'
+                f"{camera}: Zoom test: zoom in hysteresis limit: {zoom_in_hysteresis} value: {AUTOTRACKING_ZOOM_IN_HYSTERESIS} original: {self.tracked_object_metrics[camera]['original_target_box']} max: {self.tracked_object_metrics[camera]['max_target_box']} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]['target_box']}"
             )
             logger.debug(
-                f'{camera}: Zoom test: zoom out hysteresis limit: {zoom_out_hysteresis} value: {AUTOTRACKING_ZOOM_OUT_HYSTERESIS} original: {self.tracked_object_metrics[camera]["original_target_box"]} max: {self.tracked_object_metrics[camera]["max_target_box"]} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]["target_box"]}'
+                f"{camera}: Zoom test: zoom out hysteresis limit: {zoom_out_hysteresis} value: {AUTOTRACKING_ZOOM_OUT_HYSTERESIS} original: {self.tracked_object_metrics[camera]['original_target_box']} max: {self.tracked_object_metrics[camera]['max_target_box']} target: {calculated_target_box if calculated_target_box else self.tracked_object_metrics[camera]['target_box']}"
             )
 
         # Zoom in conditions (and)
@@ -1069,7 +1069,7 @@ class PtzAutoTracker:
                 pan = ((centroid_x / camera_width) - 0.5) * 2
                 tilt = (0.5 - (centroid_y / camera_height)) * 2
 
-            logger.debug(f'{camera}: Original box: {obj.obj_data["box"]}')
+            logger.debug(f"{camera}: Original box: {obj.obj_data['box']}")
             logger.debug(f"{camera}: Predicted box: {tuple(predicted_box)}")
             logger.debug(
                 f"{camera}: Velocity: {tuple(np.round(average_velocity).flatten().astype(int))}"
@@ -1179,7 +1179,7 @@ class PtzAutoTracker:
                     )
                     zoom = (ratio - 1) / (ratio + 1)
                     logger.debug(
-                        f'{camera}: limit: {self.tracked_object_metrics[camera]["max_target_box"]}, ratio: {ratio} zoom calculation: {zoom}'
+                        f"{camera}: limit: {self.tracked_object_metrics[camera]['max_target_box']}, ratio: {ratio} zoom calculation: {zoom}"
                     )
                     if not result:
                         # zoom out with special condition if zooming out because of velocity, edges, etc.

frigate/record/maintainer.py

@@ -449,7 +449,7 @@ class RecordingMaintainer(threading.Thread):
                     return None
                 else:
                     logger.debug(
-                        f"Copied {file_path} in {datetime.datetime.now().timestamp()-start_frame} seconds."
+                        f"Copied {file_path} in {datetime.datetime.now().timestamp() - start_frame} seconds."
                     )
 
                 try:

frigate/review/maintainer.py

@@ -256,7 +256,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                 elif object["sub_label"][0] in self.config.model.all_attributes:
                     segment.detections[object["id"]] = object["sub_label"][0]
                 else:
-                    segment.detections[object["id"]] = f'{object["label"]}-verified'
+                    segment.detections[object["id"]] = f"{object['label']}-verified"
                     segment.sub_labels[object["id"]] = object["sub_label"][0]
 
                 # if object is alert label
@@ -352,7 +352,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                 elif object["sub_label"][0] in self.config.model.all_attributes:
                     detections[object["id"]] = object["sub_label"][0]
                 else:
-                    detections[object["id"]] = f'{object["label"]}-verified'
+                    detections[object["id"]] = f"{object['label']}-verified"
                     sub_labels[object["id"]] = object["sub_label"][0]
 
                 # if object is alert label
@@ -527,7 +527,9 @@ class ReviewSegmentMaintainer(threading.Thread):
 
                         if event_id in self.indefinite_events[camera]:
                             self.indefinite_events[camera].pop(event_id)
-                            current_segment.last_update = manual_info["end_time"]
+
+                            if len(self.indefinite_events[camera]) == 0:
+                                current_segment.last_update = manual_info["end_time"]
                         else:
                             logger.error(
                                 f"Event with ID {event_id} has a set duration and can not be ended manually."

frigate/service_manager/multiprocessing.py

@@ -72,8 +72,7 @@ class BaseServiceProcess(Service, ABC):
                 running = False
             except TimeoutError:
                 self.manager.logger.warning(
-                    f"{self.name} is still running after "
+                    f"{self.name} is still running after {timeout} seconds. Killing."
-                    f"{timeout} seconds. Killing."
                 )
 
         if running:

frigate/test/test_config.py

@@ -75,11 +75,11 @@ class TestConfig(unittest.TestCase):
             "detectors": {
                 "cpu": {
                     "type": "cpu",
-                    "model": {"path": "/cpu_model.tflite"},
+                    "model_path": "/cpu_model.tflite",
                 },
                 "edgetpu": {
                     "type": "edgetpu",
-                    "model": {"path": "/edgetpu_model.tflite"},
+                    "model_path": "/edgetpu_model.tflite",
                 },
                 "openvino": {
                     "type": "openvino",

frigate/track/tracked_object.py

@@ -339,7 +339,7 @@ class TrackedObject:
                 box[2],
                 box[3],
                 self.obj_data["label"],
-                f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}",
+                f"{int(self.thumbnail_data['score'] * 100)}% {int(self.thumbnail_data['area'])}",
                 thickness=thickness,
                 color=color,
             )

frigate/util/config.py

@@ -13,7 +13,7 @@ from frigate.util.services import get_video_properties
 
 logger = logging.getLogger(__name__)
 
-CURRENT_CONFIG_VERSION = "0.15-0"
+CURRENT_CONFIG_VERSION = "0.15-1"
 DEFAULT_CONFIG_FILE = "/config/config.yml"
 
 
@@ -77,6 +77,13 @@ def migrate_frigate_config(config_file: str):
             yaml.dump(new_config, f)
         previous_version = "0.15-0"
 
+    if previous_version < "0.15-1":
+        logger.info(f"Migrating frigate config from {previous_version} to 0.15-1...")
+        new_config = migrate_015_1(config)
+        with open(config_file, "w") as f:
+            yaml.dump(new_config, f)
+        previous_version = "0.15-1"
+
     logger.info("Finished frigate config migration...")
 
 
@@ -267,6 +274,21 @@ def migrate_015_0(config: dict[str, dict[str, any]]) -> dict[str, dict[str, any]
     return new_config
 
 
+def migrate_015_1(config: dict[str, dict[str, any]]) -> dict[str, dict[str, any]]:
+    """Handle migrating frigate config to 0.15-1"""
+    new_config = config.copy()
+
+    for detector, detector_config in config.get("detectors", {}).items():
+        path = detector_config.get("model", {}).get("path")
+
+        if path:
+            new_config["detectors"][detector]["model_path"] = path
+            del new_config["detectors"][detector]["model"]
+
+    new_config["version"] = "0.15-1"
+    return new_config
+
+
 def get_relative_coordinates(
     mask: Optional[Union[str, list]], frame_shape: tuple[int, int]
 ) -> Union[str, list]:
@@ -292,7 +314,7 @@ def get_relative_coordinates(
                             continue
 
                         rel_points.append(
-                            f"{round(x / frame_shape[1], 3)},{round(y  / frame_shape[0], 3)}"
+                            f"{round(x / frame_shape[1], 3)},{round(y / frame_shape[0], 3)}"
                         )
 
                     relative_masks.append(",".join(rel_points))
@@ -315,7 +337,7 @@ def get_relative_coordinates(
                     return []
 
                 rel_points.append(
-                    f"{round(x / frame_shape[1], 3)},{round(y  / frame_shape[0], 3)}"
+                    f"{round(x / frame_shape[1], 3)},{round(y / frame_shape[0], 3)}"
                 )
 
             mask = ",".join(rel_points)

frigate/util/services.py

@@ -390,12 +390,22 @@ def try_get_info(f, h, default="N/A"):
 
 
 def get_nvidia_gpu_stats() -> dict[int, dict]:
+    names: dict[str, int] = {}
     results = {}
     try:
         nvml.nvmlInit()
         deviceCount = nvml.nvmlDeviceGetCount()
         for i in range(deviceCount):
             handle = nvml.nvmlDeviceGetHandleByIndex(i)
+            gpu_name = nvml.nvmlDeviceGetName(handle)
+
+            # handle case where user has multiple of same GPU
+            if gpu_name in names:
+                names[gpu_name] += 1
+                gpu_name += f" ({names.get(gpu_name)})"
+            else:
+                names[gpu_name] = 1
+
             meminfo = try_get_info(nvml.nvmlDeviceGetMemoryInfo, handle)
             util = try_get_info(nvml.nvmlDeviceGetUtilizationRates, handle)
             enc = try_get_info(nvml.nvmlDeviceGetEncoderUtilization, handle)
@@ -423,7 +433,7 @@ def get_nvidia_gpu_stats() -> dict[int, dict]:
                 dec_util = -1
 
             results[i] = {
-                "name": nvml.nvmlDeviceGetName(handle),
+                "name": gpu_name,
                 "gpu": gpu_util,
                 "mem": gpu_mem_util,
                 "enc": enc_util,

process_clip.py

@@ -208,7 +208,7 @@ class ProcessClip:
                 box[2],
                 box[3],
                 obj["id"],
-                f"{int(obj['score']*100)}% {int(obj['area'])}",
+                f"{int(obj['score'] * 100)}% {int(obj['area'])}",
                 thickness=thickness,
                 color=color,
             )
@@ -227,7 +227,7 @@ class ProcessClip:
             )
 
         cv2.imwrite(
-            f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
+            f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time * 1000000)}.jpg",
             current_frame,
         )
 
@@ -290,7 +290,7 @@ def process(path, label, output, debug_path):
         1 for result in results if result[1]["true_positive_objects"] > 0
     )
     print(
-        f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
+        f"Objects were detected in {positive_count}/{len(results)}({positive_count / len(results) * 100:.2f}%) clip(s)."
     )
 
     if output: