install i965-va-driver-shaders (#5451)

* Add ability to GPU device to be automatically detected when multiple exist

* Add logging info

* Fix access

* Fix

* Formatting

* Fix path of device

* Use log error instead of raise

* Remove log which could apply to other caess

* Set default value

* rework logic and support auto gpu selection for encoding gpu as well

Dockerfile

@@ -27,7 +27,7 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
 FROM wget AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
-RUN wget -qO go2rtc "https://github.com/AlexxIT/go2rtc/releases/download/v1.1.0/go2rtc_linux_${TARGETARCH}" \
+RUN wget -qO go2rtc "https://github.com/AlexxIT/go2rtc/releases/download/v1.1.2/go2rtc_linux_${TARGETARCH}" \
     && chmod +x go2rtc
 
 

docker/install_deps.sh

@@ -64,6 +64,9 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
         intel-opencl-icd \
         mesa-va-drivers libva-drm2 intel-media-va-driver-non-free i965-va-driver libmfx1 radeontop intel-gpu-tools
+    # something about this dependency requires it to be installed in a separate call rather than in the line above
+    apt-get -qq install --no-install-recommends --no-install-suggests -y \
+        i965-va-driver-shaders
     rm -f /etc/apt/sources.list.d/debian-testing.list
 fi
 

docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -35,7 +35,7 @@ function get_ip_and_port_from_supervisor() {
             jq --exit-status --raw-output '.data.network["8555/tcp"]'
     ) && [[ "${webrtc_port}" =~ ${port_regex} ]]; then
         webrtc_port="${BASH_REMATCH[1]}"
-        echo "[INFO] Got WebRTC port from supervisor: ${ip_address}" >&2
+        echo "[INFO] Got WebRTC port from supervisor: ${webrtc_port}" >&2
     else
         echo "[WARN] Failed to get WebRTC port from supervisor" >&2
         return 0

docker/rootfs/usr/local/go2rtc/create_config.py

@@ -44,6 +44,14 @@ if not go2rtc_config.get("webrtc", {}).get("candidates", []):
 else:
     print("[INFO] Not injecting WebRTC candidates into go2rtc config as it has been set manually", file=sys.stderr)
     
+# sets default RTSP response to be equivalent to ?video=h264,h265&audio=aac
+# this means user does not need to specify audio codec when using restream
+# as source for frigate and the integration supports HLS playback
+if go2rtc_config.get("rtsp") is None:
+    go2rtc_config["rtsp"] = {"default_query": "mp4"}
+elif go2rtc_config["rtsp"].get("default_query") is None:
+    go2rtc_config["rtsp"]["default_query"] = "mp4"
+
 # need to replace ffmpeg command when using ffmpeg4
 if not os.path.exists(BTBN_PATH):
     if go2rtc_config.get("ffmpeg") is None:

docs/docs/configuration/detectors.md

@@ -101,7 +101,7 @@ The OpenVINO device to be used is specified using the `"device"` attribute accor
 
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. A supported Intel platform is required to use the `GPU` device with OpenVINO. The `MYRIAD` device may be run on any platform, including Arm devices. For detailed system requirements, see [OpenVINO System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
 
-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.
+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 model.
 
 ```yaml
 detectors:
@@ -119,6 +119,25 @@ model:
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 ```
 
+This detector also supports YOLOx models, and has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. Frigate does not come with `yolox_tiny` model, you will need to follow [OpenVINO documentation](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) to provide your own model to Frigate. There is currently no support for other types of YOLO models (YOLOv3, YOLOv4, etc...). Below is an example of how `yolox_tiny` and other yolox variants can be used in Frigate:
+
+```yaml
+detectors:
+  ov:
+    type: openvino
+    device: AUTO
+    model:
+      path: /path/to/yolox_tiny.xml
+
+model:
+  width: 416
+  height: 416
+  input_tensor: nchw
+  input_pixel_format: bgr
+  model_type: yolox
+  labelmap_path: /path/to/coco_80cl.txt
+```
+
 ### Intel NCS2 VPU and Myriad X Setup
 
 Intel produces a neural net inference accelleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for accelleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.

docs/docs/configuration/ffmpeg_presets.md

@@ -29,13 +29,14 @@ Input args presets help make the config more readable and handle use cases for d
 See [the camera specific docs](/configuration/camera_specific.md) for more info on non-standard cameras and recommendations for using them in Frigate.
 
 | Preset                           | Usage                     | Other Notes                                                                                      |
-| ------------------------- | ------------------------- | --------------------------------------------------- |
+| -------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------ |
 | preset-http-jpeg-generic         | HTTP Live Jpeg            | Recommend restreaming live jpeg instead                                                          |
 | preset-http-mjpeg-generic        | HTTP Mjpeg Stream         | Recommend restreaming mjpeg stream instead                                                       |
 | preset-http-reolink              | Reolink HTTP-FLV Stream   | Only for reolink http, not when restreaming as rtsp                                              |
 | preset-rtmp-generic              | RTMP Stream               |                                                                                                  |
 | preset-rtsp-generic              | RTSP Stream               | This is the default when nothing is specified                                                    |
-| preset-rtsp-restream      | RTSP Stream from restream | Use when using rtsp restream as source              |
+| preset-rtsp-restream             | RTSP Stream from restream | Use for rtsp restream as source for frigate                                                      |
+| preset-rtsp-restream-low-latency | RTSP Stream from restream | Use for rtsp restream as source for frigate to lower latency, may cause issues with some cameras |
 | preset-rtsp-udp                  | RTSP Stream via UDP       | Use when camera is UDP only                                                                      |
 | preset-rtsp-blue-iris            | Blue Iris RTSP Stream     | Use when consuming a stream from Blue Iris                                                       |
 
@@ -46,21 +47,22 @@ It is important to be mindful of input args when using restream because you can
 :::
 
 ```yaml
+go2rtc:
+  streams:
+    reolink_cam: http://192.168.0.139/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=admin&password=password
+
 cameras:
   reolink_cam:
     ffmpeg:
       inputs:
-        - path: http://192.168.0.139/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=admin&password={FRIGATE_CAM_PASSWORD}
+        - path: http://192.168.0.139/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=admin&password=password
           input_args: preset-http-reolink
           roles:
             - detect
-        - path: rtsp://192.168.0.10:8554/garage
+        - path: rtsp://127.0.0.1:8554/reolink_cam
           input_args: preset-rtsp-generic
           roles:
             - record
-        - path: http://192.168.0.139/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=admin&password={FRIGATE_CAM_PASSWORD}
-          roles:
-            - restream
 ```
 
 ### Output Args Presets

docs/docs/configuration/hardware_acceleration.md

@@ -15,23 +15,39 @@ ffmpeg:
   hwaccel_args: preset-rpi-64-h264
 ```
 
-### Intel-based CPUs (<10th Generation) via Quicksync
+### Intel-based CPUs (<10th Generation) via VAAPI
+
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams. VAAPI is recommended for all generations of Intel-based CPUs if QSV does not work.
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
+
 **NOTICE**: With some of the processors, like the J4125, the default driver `iHD` doesn't seem to work correctly for hardware acceleration. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the frigate.yml for HA OS users](advanced.md#environment_vars).
 
 ### Intel-based CPUs (>=10th Generation) via Quicksync
 
+QSV must be set specifically based on the video encoding of the stream.
+
+#### H.264 streams
+
 ```yaml
 ffmpeg:
   hwaccel_args: preset-intel-qsv-h264
 ```
 
+#### H.265 streams
+
+```yaml
+ffmpeg:
+  hwaccel_args: preset-intel-qsv-h265
+```
+
 ### AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
 
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+
 **Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
 
 ```yaml

docs/docs/configuration/index.md

@@ -105,6 +105,9 @@ model:
   # Optional: Object detection model input tensor format
   # Valid values are nhwc or nchw (default: shown below)
   input_tensor: nhwc
+  # Optional: Object detection model type, currently only used with the OpenVINO detector
+  # Valid values are ssd or yolox (default: shown below)
+  model_type: ssd
   # Optional: Label name modifications. These are merged into the standard labelmap.
   labelmap:
     2: vehicle
@@ -350,7 +353,7 @@ rtmp:
   enabled: False
 
 # Optional: Restream configuration
-# Uses https://github.com/AlexxIT/go2rtc (v1.0.1)
+# Uses https://github.com/AlexxIT/go2rtc (v1.1.2)
 go2rtc:
 
 # Optional: jsmpeg stream configuration for WebUI

docs/docs/configuration/live.md

@@ -50,11 +50,11 @@ cameras:
       output_args:
         record: preset-record-generic-audio-copy
       inputs:
-        - path: rtsp://127.0.0.1:8554/test_cam?video=copy&audio=aac # <--- the name here must match the name of the camera in restream
+        - path: rtsp://127.0.0.1:8554/test_cam # <--- the name here must match the name of the camera in restream
           input_args: preset-rtsp-restream
           roles:
             - record
-        - path: rtsp://127.0.0.1:8554/test_cam_sub?video=copy # <--- the name here must match the name of the camera_sub in restream
+        - path: rtsp://127.0.0.1:8554/test_cam_sub # <--- the name here must match the name of the camera_sub in restream
           input_args: preset-rtsp-restream
           roles:
             - detect

docs/docs/configuration/restream.md

@@ -9,6 +9,12 @@ Frigate can restream your video feed as an RTSP feed for other applications such
 
 Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc#configuration) for more advanced configurations and features.
 
+:::note
+
+You can access the go2rtc webUI at `http://frigate_ip:5000/live/webrtc` which can be helpful to debug as well as provide useful information about your camera streams.
+
+:::
+
 ### Birdseye Restream
 
 Birdseye RTSP restream can be enabled at `birdseye -> restream` and accessed at `rtsp://<frigate_host>:8554/birdseye`. Enabling the restream will cause birdseye to run 24/7 which may increase CPU usage somewhat.
@@ -56,7 +62,7 @@ cameras:
       output_args:
         record: preset-record-generic-audio-copy
       inputs:
-        - path: rtsp://127.0.0.1:8554/rtsp_cam?video=copy&audio=aac # <--- the name here must match the name of the camera in restream
+        - path: rtsp://127.0.0.1:8554/rtsp_cam # <--- the name here must match the name of the camera in restream
           input_args: preset-rtsp-restream
           roles:
             - record
@@ -66,7 +72,7 @@ cameras:
       output_args:
         record: preset-record-generic-audio-copy
       inputs:
-        - path: rtsp://127.0.0.1:8554/http_cam?video=copy&audio=aac # <--- the name here must match the name of the camera in restream
+        - path: rtsp://127.0.0.1:8554/http_cam # <--- the name here must match the name of the camera in restream
           input_args: preset-rtsp-restream
           roles:
             - record
@@ -99,11 +105,11 @@ cameras:
       output_args:
         record: preset-record-generic-audio-copy
       inputs:
-        - path: rtsp://127.0.0.1:8554/rtsp_cam?video=copy&audio=aac # <--- the name here must match the name of the camera in restream
+        - path: rtsp://127.0.0.1:8554/rtsp_cam # <--- the name here must match the name of the camera in restream
           input_args: preset-rtsp-restream
           roles:
             - record
-        - path: rtsp://127.0.0.1:8554/rtsp_cam_sub?video=copy&audio=aac # <--- the name here must match the name of the camera_sub in restream
+        - path: rtsp://127.0.0.1:8554/rtsp_cam_sub # <--- the name here must match the name of the camera_sub in restream
           input_args: preset-rtsp-restream
           roles:
             - detect
@@ -112,11 +118,11 @@ cameras:
       output_args:
         record: preset-record-generic-audio-copy
       inputs:
-        - path: rtsp://127.0.0.1:8554/http_cam?video=copy&audio=aac # <--- the name here must match the name of the camera in restream
+        - path: rtsp://127.0.0.1:8554/http_cam # <--- the name here must match the name of the camera in restream
           input_args: preset-rtsp-restream
           roles:
             - record
-        - path: rtsp://127.0.0.1:8554/http_cam_sub?video=copy&audio=aac # <--- the name here must match the name of the camera_sub in restream
+        - path: rtsp://127.0.0.1:8554/http_cam_sub # <--- the name here must match the name of the camera_sub in restream
           input_args: preset-rtsp-restream
           roles:
             - detect

docs/docs/frigate/camera_setup.md

@@ -3,7 +3,7 @@ id: camera_setup
 title: Camera setup
 ---
 
-Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but far less compatibility. Safari and Edge are the only browsers able to play H.265. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
+Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
 
 - **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
 

docs/docs/frigate/hardware.md

@@ -58,11 +58,20 @@ More information is available [in the detector docs](/configuration/detectors#op
 Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known examples are below:
 
 | Name                 | Inference Speed | Notes                                                                 |
-| ------------------- | --------------- | --------------------------------------------------------------------- |
+| -------------------- | --------------- | --------------------------------------------------------------------- |
-| Intel Celeron J4105 | ~ 25 ms         | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel Celeron N4020 | 50 - 200 ms     | Inference speeds on CPU were ~ 800 ms, greatly depends on other loads |
 | Intel NCS2 VPU       | 60 - 65 ms      | May vary based on host device                                         |
+| Intel Celeron J4105  | ~ 25 ms         | Inference speeds on CPU were 150 - 200 ms                             |
+| Intel Celeron N3060  | 130 - 150 ms    | Inference speeds on CPU were ~ 550 ms                                 |
+| Intel Celeron N3205U | ~ 120 ms        | Inference speeds on CPU were ~ 380 ms                                 |
+| Intel Celeron N4020  | 50 - 200 ms     | Inference speeds on CPU were ~ 800 ms, greatly depends on other loads |
+| Intel i3 6100T       | 15 - 35 ms      | Inference speeds on CPU were 60 - 120 ms                              |
+| Intel i3 8100        | ~ 15 ms         | Inference speeds on CPU were ~ 65 ms                                  |
+| Intel i5 4590        | ~ 20 ms         | Inference speeds on CPU were ~ 230 ms                                 |
+| Intel i5 6500        | ~ 15 ms         | Inference speeds on CPU were ~ 150 ms                                 |
+| Intel i5 7200u       | 15 - 25 ms      | Inference speeds on CPU were ~ 150 ms                                 |
+| Intel i5 7500        | ~ 15 ms         | Inference speeds on CPU were ~ 260 ms                                 |
 | Intel i5 1135G7      | 10 - 15 ms      |                                                                       |
+| Intel i5 12600K      | ~ 15 ms         | Inference speeds on CPU were ~ 35 ms                                  |
 
 ### TensorRT
 

docs/docs/guides/configuring_go2rtc.md

@@ -23,7 +23,7 @@ The easiest live view to get working is MSE. After adding this to the config, re
 
 ### What if my video doesn't play?
 
-If you are unable to see your video feed, first check the go2rtc logs in the Frigate UI under Logs in the sidebar. If go2rtc is having difficulty connecting to your camera, you should see some error messages in the log. If you do not see any errors, then video codec of the stream may not be supported in your browser. If your camera stream is set to H265, try switching to H264. You can see more information about [video codec compatibility](https://github.com/AlexxIT/go2rtc#codecs-madness) in the go2rtc documentation. If you are not able to switch your camera settings from H265 to H264 or your stream is a different format such as MJPEG, you can use go2rtc to re-encode the video using the [FFmpeg parameters](https://github.com/AlexxIT/go2rtc#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view. Here is an example of a config that will re-encode the stream to H264 without hardware acceleration:
+If you are unable to see your video feed, first check the go2rtc logs in the Frigate UI under Logs in the sidebar. If go2rtc is having difficulty connecting to your camera, you should see some error messages in the log. If you do not see any errors, then the video codec of the stream may not be supported in your browser. If your camera stream is set to H265, try switching to H264. You can see more information about [video codec compatibility](https://github.com/AlexxIT/go2rtc#codecs-madness) in the go2rtc documentation. If you are not able to switch your camera settings from H265 to H264 or your stream is a different format such as MJPEG, you can use go2rtc to re-encode the video using the [FFmpeg parameters](https://github.com/AlexxIT/go2rtc#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view. Here is an example of a config that will re-encode the stream to H264 without hardware acceleration:
 
 ```yaml
 go2rtc:
@@ -33,6 +33,15 @@ go2rtc:
       - "ffmpeg:back#video=h264"
 ```
 
+Some camera streams may need to use the ffmpeg module in go2rtc. This has the downside of slower startup times, but has compatibility with more stream types.
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+```
+
 If you can see the video but do not have audio, this is most likely because your camera's audio stream is not AAC. If possible, update your camera's audio settings to AAC. If your cameras do not support AAC audio, you will need to tell go2rtc to re-encode the audio to AAC on demand if you want audio. This will use additional CPU and add some latency. To add AAC audio on demand, you can update your go2rtc config as follows:
 
 ```yaml
@@ -53,6 +62,15 @@ go2rtc:
       - "ffmpeg:back#video=h264#audio=aac"
 ```
 
+When using the ffmpeg module, you would add AAC audio like this:
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac"
+```
+
 ## Next steps
 
 1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).

frigate/app.py

@@ -183,8 +183,7 @@ class FrigateApp:
         if self.config.mqtt.enabled:
             comms.append(MqttClient(self.config))
 
-        self.ws_client = WebSocketClient(self.config)
+        comms.append(WebSocketClient(self.config))
-        comms.append(self.ws_client)
         self.dispatcher = Dispatcher(self.config, self.camera_metrics, comms)
 
     def start_detectors(self) -> None:
@@ -417,7 +416,17 @@ class FrigateApp:
         logger.info(f"Stopping...")
         self.stop_event.set()
 
-        self.ws_client.stop()
+        for detector in self.detectors.values():
+            detector.stop()
+
+        # Empty the detection queue and set the events for all requests
+        while not self.detection_queue.empty():
+            connection_id = self.detection_queue.get(timeout=1)
+            self.detection_out_events[connection_id].set()
+        self.detection_queue.close()
+        self.detection_queue.join_thread()
+
+        self.dispatcher.stop()
         self.detected_frames_processor.join()
         self.event_processor.join()
         self.event_cleanup.join()
@@ -427,10 +436,20 @@ class FrigateApp:
         self.frigate_watchdog.join()
         self.db.stop()
 
-        for detector in self.detectors.values():
-            detector.stop()
-
         while len(self.detection_shms) > 0:
             shm = self.detection_shms.pop()
             shm.close()
             shm.unlink()
+
+        for queue in [
+            self.event_queue,
+            self.event_processed_queue,
+            self.video_output_queue,
+            self.detected_frames_queue,
+            self.recordings_info_queue,
+            self.log_queue,
+        ]:
+            while not queue.empty():
+                queue.get_nowait()
+            queue.close()
+            queue.join_thread()

frigate/comms/dispatcher.py

@@ -27,6 +27,11 @@ class Communicator(ABC):
         """Pass receiver so communicators can pass commands."""
         pass
 
+    @abstractmethod
+    def stop(self) -> None:
+        """Stop the communicator."""
+        pass
+
 
 class Dispatcher:
     """Handle communication between Frigate and communicators."""
@@ -72,6 +77,10 @@ class Dispatcher:
         for comm in self.comms:
             comm.publish(topic, payload, retain)
 
+    def stop(self) -> None:
+        for comm in self.comms:
+            comm.stop()
+
     def _on_detect_command(self, camera_name: str, payload: str) -> None:
         """Callback for detect topic."""
         detect_settings = self.config.cameras[camera_name].detect

frigate/comms/mqtt.py

@@ -35,6 +35,9 @@ class MqttClient(Communicator):  # type: ignore[misc]
             f"{self.mqtt_config.topic_prefix}/{topic}", payload, retain=retain
         )
 
+    def stop(self) -> None:
+        self.client.disconnect()
+
     def _set_initial_topics(self) -> None:
         """Set initial state topics."""
         for camera_name, camera in self.config.cameras.items():

frigate/comms/ws.py

@@ -95,3 +95,4 @@ class WebSocketClient(Communicator):  # type: ignore[misc]
         self.websocket_server.manager.join()
         self.websocket_server.shutdown()
         self.websocket_thread.join()
+        logger.info("Exiting websocket client...")

frigate/detectors/detector_config.py

@@ -23,6 +23,11 @@ class InputTensorEnum(str, Enum):
     nhwc = "nhwc"
 
 
+class ModelTypeEnum(str, Enum):
+    ssd = "ssd"
+    yolox = "yolox"
+
+
 class ModelConfig(BaseModel):
     path: Optional[str] = Field(title="Custom Object detection model path.")
     labelmap_path: Optional[str] = Field(title="Label map for custom object detector.")
@@ -37,6 +42,9 @@ class ModelConfig(BaseModel):
     input_pixel_format: PixelFormatEnum = Field(
         default=PixelFormatEnum.rgb, title="Model Input Pixel Color Format"
     )
+    model_type: ModelTypeEnum = Field(
+        default=ModelTypeEnum.ssd, title="Object Detection Model Type"
+    )
     _merged_labelmap: Optional[Dict[int, str]] = PrivateAttr()
     _colormap: Dict[int, Tuple[int, int, int]] = PrivateAttr()
 

frigate/detectors/plugins/openvino.py

@@ -3,7 +3,7 @@ import numpy as np
 import openvino.runtime as ov
 
 from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
 from typing import Literal
 from pydantic import Extra, Field
 
@@ -24,12 +24,18 @@ class OvDetector(DetectionApi):
     def __init__(self, detector_config: OvDetectorConfig):
         self.ov_core = ov.Core()
         self.ov_model = self.ov_core.read_model(detector_config.model.path)
+        self.ov_model_type = detector_config.model.model_type
+
+        self.h = detector_config.model.height
+        self.w = detector_config.model.width
 
         self.interpreter = self.ov_core.compile_model(
             model=self.ov_model, device_name=detector_config.device
         )
+
         logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
         self.output_indexes = 0
+
         while True:
             try:
                 tensor_shape = self.interpreter.output(self.output_indexes).shape
@@ -38,12 +44,34 @@ class OvDetector(DetectionApi):
             except:
                 logger.info(f"Model has {self.output_indexes} Output Tensors")
                 break
+        if self.ov_model_type == ModelTypeEnum.yolox:
+            self.num_classes = tensor_shape[2] - 5
+            logger.info(f"YOLOX model has {self.num_classes} classes")
+            self.set_strides_grids()
+
+    def set_strides_grids(self):
+        grids = []
+        expanded_strides = []
+
+        strides = [8, 16, 32]
+
+        hsizes = [self.h // stride for stride in strides]
+        wsizes = [self.w // stride for stride in strides]
+
+        for hsize, wsize, stride in zip(hsizes, wsizes, strides):
+            xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
+            grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+            grids.append(grid)
+            shape = grid.shape[:2]
+            expanded_strides.append(np.full((*shape, 1), stride))
+        self.grids = np.concatenate(grids, 1)
+        self.expanded_strides = np.concatenate(expanded_strides, 1)
 
     def detect_raw(self, tensor_input):
-
         infer_request = self.interpreter.create_infer_request()
         infer_request.infer([tensor_input])
 
+        if self.ov_model_type == ModelTypeEnum.ssd:
             results = infer_request.get_output_tensor()
 
             detections = np.zeros((20, 6), np.float32)
@@ -62,5 +90,46 @@ class OvDetector(DetectionApi):
                     object_detected[5],  # x_max
                 ]
                 i += 1
-
+            return detections
+        elif self.ov_model_type == ModelTypeEnum.yolox:
+            out_tensor = infer_request.get_output_tensor()
+            # [x, y, h, w, box_score, class_no_1, ..., class_no_80],
+            results = out_tensor.data
+            results[..., :2] = (results[..., :2] + self.grids) * self.expanded_strides
+            results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides
+            image_pred = results[0, ...]
+
+            class_conf = np.max(
+                image_pred[:, 5 : 5 + self.num_classes], axis=1, keepdims=True
+            )
+            class_pred = np.argmax(image_pred[:, 5 : 5 + self.num_classes], axis=1)
+            class_pred = np.expand_dims(class_pred, axis=1)
+
+            conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
+            # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
+            dets = np.concatenate((image_pred[:, :5], class_conf, class_pred), axis=1)
+            dets = dets[conf_mask]
+
+            ordered = dets[dets[:, 5].argsort()[::-1]][:20]
+
+            detections = np.zeros((20, 6), np.float32)
+            i = 0
+
+            for object_detected in ordered:
+                if i < 20:
+                    detections[i] = [
+                        object_detected[6],  # Label ID
+                        object_detected[5],  # Confidence
+                        (object_detected[1] - (object_detected[3] / 2))
+                        / self.h,  # y_min
+                        (object_detected[0] - (object_detected[2] / 2))
+                        / self.w,  # x_min
+                        (object_detected[1] + (object_detected[3] / 2))
+                        / self.h,  # y_max
+                        (object_detected[0] + (object_detected[2] / 2))
+                        / self.w,  # x_max
+                    ]
+                    i += 1
+                else:
+                    break
             return detections

frigate/events.py

@@ -67,7 +67,7 @@ class EventProcessor(threading.Thread):
 
         while not self.stop_event.is_set():
             try:
-                event_type, camera, event_data = self.event_queue.get(timeout=10)
+                event_type, camera, event_data = self.event_queue.get(timeout=1)
             except queue.Empty:
                 continue
 

frigate/ffmpeg_presets.py

@@ -1,14 +1,52 @@
 """Handles inserting and maintaining ffmpeg presets."""
 
+import logging
 import os
 
 from typing import Any
 
 from frigate.version import VERSION
 from frigate.const import BTBN_PATH
+from frigate.util import vainfo_hwaccel
+
+
+logger = logging.getLogger(__name__)
+
+
+class LibvaGpuSelector:
+    "Automatically selects the correct libva GPU."
+
+    _selected_gpu = None
+
+    def get_selected_gpu(self) -> str:
+        """Get selected libva GPU."""
+        if not os.path.exists("/dev/dri"):
+            return ""
+
+        if self._selected_gpu:
+            return self._selected_gpu
+
+        devices = list(filter(lambda d: d.startswith("render"), os.listdir("/dev/dri")))
+
+        if len(devices) < 2:
+            self._selected_gpu = "/dev/dri/renderD128"
+            return self._selected_gpu
+
+        for device in devices:
+            check = vainfo_hwaccel(device_name=device)
+
+            logger.debug(f"{device} return vainfo status code: {check.returncode}")
+
+            if check.returncode == 0:
+                self._selected_gpu = f"/dev/dri/{device}"
+                return self._selected_gpu
+
+        return ""
+
 
 TIMEOUT_PARAM = "-timeout" if os.path.exists(BTBN_PATH) else "-stimeout"
 
+_gpu_selector = LibvaGpuSelector()
 _user_agent_args = [
     "-user_agent",
     f"FFmpeg Frigate/{VERSION}",
@@ -23,7 +61,7 @@ PRESETS_HW_ACCEL_DECODE = {
         "-hwaccel",
         "vaapi",
         "-hwaccel_device",
-        "/dev/dri/renderD128",
+        _gpu_selector.get_selected_gpu(),
         "-hwaccel_output_format",
         "vaapi",
     ],
@@ -31,7 +69,7 @@ PRESETS_HW_ACCEL_DECODE = {
         "-hwaccel",
         "qsv",
         "-qsv_device",
-        "/dev/dri/renderD128",
+        _gpu_selector.get_selected_gpu(),
         "-hwaccel_output_format",
         "qsv",
         "-c:v",
@@ -43,7 +81,7 @@ PRESETS_HW_ACCEL_DECODE = {
         "-hwaccel",
         "qsv",
         "-qsv_device",
-        "/dev/dri/renderD128",
+        _gpu_selector.get_selected_gpu(),
         "-hwaccel_output_format",
         "qsv",
         "-c:v",
@@ -95,6 +133,7 @@ PRESETS_HW_ACCEL_SCALE = {
 PRESETS_HW_ACCEL_ENCODE = {
     "preset-rpi-32-h264": "ffmpeg -hide_banner {0} -c:v h264_v4l2m2m -g 50 -bf 0 {1}",
     "preset-rpi-64-h264": "ffmpeg -hide_banner {0} -c:v h264_v4l2m2m -g 50 -bf 0 {1}",
+    "preset-vaapi": "ffmpeg -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {2} {0} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {1}",
     "preset-intel-qsv-h264": "ffmpeg -hide_banner {0} -c:v h264_qsv -g 50 -bf 0 -profile:v high -level:v 4.1 -async_depth:v 1 {1}",
     "preset-intel-qsv-h265": "ffmpeg -hide_banner {0} -c:v h264_qsv -g 50 -bf 0 -profile:v high -level:v 4.1 -async_depth:v 1 {1}",
     "preset-nvidia-h264": "ffmpeg -hide_banner {0} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {1}",
@@ -142,6 +181,7 @@ def parse_preset_hardware_acceleration_encode(arg: Any, input: str, output: str)
     return PRESETS_HW_ACCEL_ENCODE.get(arg, PRESETS_HW_ACCEL_ENCODE["default"]).format(
         input,
         output,
+        _gpu_selector.get_selected_gpu(),
     )
 
 
@@ -231,6 +271,13 @@ PRESETS_INPUT = {
         "1",
     ],
     "preset-rtsp-restream": _user_agent_args
+    + [
+        "-rtsp_transport",
+        "tcp",
+        TIMEOUT_PARAM,
+        "5000000",
+    ],
+    "preset-rtsp-restream-low-latency": _user_agent_args
     + [
         "-rtsp_transport",
         "tcp",

frigate/log.py

@@ -2,11 +2,16 @@
 import logging
 import threading
 import os
+import signal
 import queue
+import multiprocessing as mp
 from multiprocessing.queues import Queue
 from logging import handlers
+from typing import Optional
+from types import FrameType
 from setproctitle import setproctitle
-from typing import Deque
+from typing import Deque, Optional
+from types import FrameType
 from collections import deque
 
 from frigate.util import clean_camera_user_pass
@@ -34,10 +39,21 @@ def log_process(log_queue: Queue) -> None:
     threading.current_thread().name = f"logger"
     setproctitle("frigate.logger")
     listener_configurer()
+
+    stop_event = mp.Event()
+
+    def receiveSignal(signalNumber: int, frame: Optional[FrameType]) -> None:
+        stop_event.set()
+
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
     while True:
         try:
-            record = log_queue.get(timeout=5)
+            record = log_queue.get(timeout=1)
         except (queue.Empty, KeyboardInterrupt):
+            if stop_event.is_set():
+                break
             continue
         logger = logging.getLogger(record.name)
         logger.handle(record)

frigate/object_detection.py

@@ -88,6 +88,7 @@ def run_detector(
     stop_event = mp.Event()
 
     def receiveSignal(signalNumber, frame):
+        logger.info("Signal to exit detection process...")
         stop_event.set()
 
     signal.signal(signal.SIGTERM, receiveSignal)
@@ -104,7 +105,7 @@ def run_detector(
 
     while not stop_event.is_set():
         try:
-            connection_id = detection_queue.get(timeout=5)
+            connection_id = detection_queue.get(timeout=1)
         except queue.Empty:
             continue
         input_frame = frame_manager.get(
@@ -125,6 +126,8 @@ def run_detector(
 
         avg_speed.value = (avg_speed.value * 9 + duration) / 10
 
+    logger.info("Exited detection process...")
+
 
 class ObjectDetectProcess:
     def __init__(
@@ -144,6 +147,9 @@ class ObjectDetectProcess:
         self.start_or_restart()
 
     def stop(self):
+        # if the process has already exited on its own, just return
+        if self.detect_process and self.detect_process.exitcode:
+            return
         self.detect_process.terminate()
         logging.info("Waiting for detection process to exit gracefully...")
         self.detect_process.join(timeout=30)
@@ -151,6 +157,7 @@ class ObjectDetectProcess:
             logging.info("Detection process didnt exit. Force killing...")
             self.detect_process.kill()
             self.detect_process.join()
+        logging.info("Detection process has exited...")
 
     def start_or_restart(self):
         self.detection_start.value = 0.0
@@ -173,12 +180,13 @@ class ObjectDetectProcess:
 
 
 class RemoteObjectDetector:
-    def __init__(self, name, labels, detection_queue, event, model_config):
+    def __init__(self, name, labels, detection_queue, event, model_config, stop_event):
         self.labels = labels
         self.name = name
         self.fps = EventsPerSecond()
         self.detection_queue = detection_queue
         self.event = event
+        self.stop_event = stop_event
         self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
         self.np_shm = np.ndarray(
             (1, model_config.height, model_config.width, 3),
@@ -193,11 +201,14 @@ class RemoteObjectDetector:
     def detect(self, tensor_input, threshold=0.4):
         detections = []
 
+        if self.stop_event.is_set():
+            return detections
+
         # copy input to shared memory
         self.np_shm[:] = tensor_input[:]
         self.event.clear()
         self.detection_queue.put(self.name)
-        result = self.event.wait(timeout=10.0)
+        result = self.event.wait(timeout=5.0)
 
         # if it timed out
         if result is None:

frigate/object_processing.py

@@ -901,7 +901,7 @@ class TrackedObjectProcessor(threading.Thread):
                     current_tracked_objects,
                     motion_boxes,
                     regions,
-                ) = self.tracked_objects_queue.get(True, 10)
+                ) = self.tracked_objects_queue.get(True, 1)
             except queue.Empty:
                 continue
 

frigate/output.py

@@ -109,14 +109,15 @@ class FFMpegConverter:
 
 
 class BroadcastThread(threading.Thread):
-    def __init__(self, camera, converter, websocket_server):
+    def __init__(self, camera, converter, websocket_server, stop_event):
         super(BroadcastThread, self).__init__()
         self.camera = camera
         self.converter = converter
         self.websocket_server = websocket_server
+        self.stop_event = stop_event
 
     def run(self):
-        while True:
+        while not self.stop_event.is_set():
             buf = self.converter.read(65536)
             if buf:
                 manager = self.websocket_server.manager
@@ -426,7 +427,7 @@ def output_frames(config: FrigateConfig, video_output_queue):
             cam_config.live.quality,
         )
         broadcasters[camera] = BroadcastThread(
-            camera, converters[camera], websocket_server
+            camera, converters[camera], websocket_server, stop_event
         )
 
     if config.birdseye.enabled:
@@ -439,7 +440,7 @@ def output_frames(config: FrigateConfig, video_output_queue):
             config.birdseye.restream,
         )
         broadcasters["birdseye"] = BroadcastThread(
-            "birdseye", converters["birdseye"], websocket_server
+            "birdseye", converters["birdseye"], websocket_server, stop_event
         )
 
     websocket_thread.start()
@@ -463,7 +464,7 @@ def output_frames(config: FrigateConfig, video_output_queue):
                 current_tracked_objects,
                 motion_boxes,
                 regions,
-            ) = video_output_queue.get(True, 10)
+            ) = video_output_queue.get(True, 1)
         except queue.Empty:
             continue
 

frigate/stats.py

@@ -283,8 +283,10 @@ class StatsEmitter(threading.Thread):
     def run(self) -> None:
         time.sleep(10)
         while not self.stop_event.wait(self.config.mqtt.stats_interval):
+            logger.debug("Starting stats collection")
             stats = stats_snapshot(
                 self.config, self.stats_tracking, self.hwaccel_errors
             )
             self.dispatcher.publish("stats", json.dumps(stats), retain=False)
-        logger.info(f"Exiting watchdog...")
+            logger.debug("Finished stats collection")
+        logger.info(f"Exiting stats emitter...")

frigate/util.py

@@ -14,7 +14,7 @@ from abc import ABC, abstractmethod
 from collections import Counter
 from collections.abc import Mapping
 from multiprocessing import shared_memory
-from typing import Any, AnyStr, Tuple
+from typing import Any, AnyStr, Optional, Tuple
 
 import cv2
 import numpy as np
@@ -926,6 +926,17 @@ def get_nvidia_gpu_stats() -> dict[str, str]:
         "--format=csv",
     ]
 
+    if (
+        "CUDA_VISIBLE_DEVICES" in os.environ
+        and os.environ["CUDA_VISIBLE_DEVICES"].isdigit()
+    ):
+        nvidia_smi_command.extend(["--id", os.environ["CUDA_VISIBLE_DEVICES"]])
+    elif (
+        "NVIDIA_VISIBLE_DEVICES" in os.environ
+        and os.environ["NVIDIA_VISIBLE_DEVICES"].isdigit()
+    ):
+        nvidia_smi_command.extend(["--id", os.environ["NVIDIA_VISIBLE_DEVICES"]])
+
     p = sp.run(
         nvidia_smi_command,
         encoding="ascii",
@@ -965,9 +976,13 @@ def ffprobe_stream(path: str) -> sp.CompletedProcess:
     return sp.run(ffprobe_cmd, capture_output=True)
 
 
-def vainfo_hwaccel() -> sp.CompletedProcess:
+def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
     """Run vainfo."""
-    ffprobe_cmd = ["vainfo"]
+    ffprobe_cmd = (
+        ["vainfo"]
+        if not device_name
+        else ["vainfo", "--display", "drm", "--device", f"/dev/dri/{device_name}"]
+    )
     return sp.run(ffprobe_cmd, capture_output=True)
 
 

frigate/video.py

@@ -160,6 +160,7 @@ def capture_frames(
     fps: mp.Value,
     skipped_fps: mp.Value,
     current_frame: mp.Value,
+    stop_event: mp.Event,
 ):
 
     frame_size = frame_shape[0] * frame_shape[1]
@@ -177,6 +178,9 @@ def capture_frames(
         try:
             frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
         except Exception as e:
+            # shutdown has been initiated
+            if stop_event.is_set():
+                break
             logger.error(f"{camera_name}: Unable to read frames from ffmpeg process.")
 
             if ffmpeg_process.poll() != None:
@@ -340,6 +344,7 @@ class CameraWatchdog(threading.Thread):
             self.frame_shape,
             self.frame_queue,
             self.camera_fps,
+            self.stop_event,
         )
         self.capture_thread.start()
 
@@ -368,13 +373,16 @@ class CameraWatchdog(threading.Thread):
 
 
 class CameraCapture(threading.Thread):
-    def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
+    def __init__(
+        self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps, stop_event
+    ):
         threading.Thread.__init__(self)
         self.name = f"capture:{camera_name}"
         self.camera_name = camera_name
         self.frame_shape = frame_shape
         self.frame_queue = frame_queue
         self.fps = fps
+        self.stop_event = stop_event
         self.skipped_fps = EventsPerSecond()
         self.frame_manager = SharedMemoryFrameManager()
         self.ffmpeg_process = ffmpeg_process
@@ -392,6 +400,7 @@ class CameraCapture(threading.Thread):
             self.fps,
             self.skipped_fps,
             self.current_frame,
+            self.stop_event,
         )
 
 
@@ -404,6 +413,9 @@ def capture_camera(name, config: CameraConfig, process_info):
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
+    threading.current_thread().name = f"capture:{name}"
+    setproctitle(f"frigate.capture:{name}")
+
     frame_queue = process_info["frame_queue"]
     camera_watchdog = CameraWatchdog(
         name,
@@ -458,7 +470,7 @@ def track_camera(
         motion_contour_area,
     )
     object_detector = RemoteObjectDetector(
-        name, labelmap, detection_queue, result_connection, model_config
+        name, labelmap, detection_queue, result_connection, model_config, stop_event
     )
 
     object_tracker = ObjectTracker(config.detect)
@@ -598,7 +610,7 @@ def process_frames(
             break
 
         try:
-            frame_time = frame_queue.get(True, 10)
+            frame_time = frame_queue.get(True, 1)
         except queue.Empty:
             continue
 
@@ -784,6 +796,7 @@ def process_frames(
                             refining = True
                         else:
                             selected_objects.append(obj)
+
                 # set the detections list to only include top, complete objects
                 # and new detections
                 detections = selected_objects

web/src/components/CameraImage.jsx

@@ -11,7 +11,15 @@ export default function CameraImage({ camera, onload, searchParams = '', stretch
   const [hasLoaded, setHasLoaded] = useState(false);
   const containerRef = useRef(null);
   const canvasRef = useRef(null);
-  const [{ width: availableWidth }] = useResizeObserver(containerRef);
+  const [{ width: containerWidth }] = useResizeObserver(containerRef);
+
+  // Add scrollbar width (when visible) to the available observer width to eliminate screen juddering.
+  // https://github.com/blakeblackshear/frigate/issues/1657
+  let scrollBarWidth = 0;
+  if (window.innerWidth && document.body.offsetWidth) {
+    scrollBarWidth = window.innerWidth - document.body.offsetWidth;
+  }
+  const availableWidth = scrollBarWidth ? containerWidth + scrollBarWidth : containerWidth;
 
   const { name } = config ? config.cameras[camera] : '';
   const enabled = config ? config.cameras[camera].enabled : 'True';
@@ -22,7 +30,11 @@ export default function CameraImage({ camera, onload, searchParams = '', stretch
     const scaledHeight = Math.floor(availableWidth / aspectRatio);
     return stretch ? scaledHeight : Math.min(scaledHeight, height);
   }, [availableWidth, aspectRatio, height, stretch]);
-  const scaledWidth = useMemo(() => Math.ceil(scaledHeight * aspectRatio), [scaledHeight, aspectRatio]);
+  const scaledWidth = useMemo(() => Math.ceil(scaledHeight * aspectRatio - scrollBarWidth), [
+    scaledHeight,
+    aspectRatio,
+    scrollBarWidth,
+  ]);
 
   const img = useMemo(() => new Image(), []);
   img.onload = useCallback(

web/src/routes/Logs.jsx

@@ -4,6 +4,7 @@ import { useCallback, useEffect, useState } from 'preact/hooks';
 import ButtonsTabbed from '../components/ButtonsTabbed';
 import useSWR from 'swr';
 import Button from '../components/Button';
+import copy from 'copy-to-clipboard';
 
 export default function Logs() {
   const [logService, setLogService] = useState('frigate');
@@ -14,10 +15,7 @@ export default function Logs() {
   const { data: nginxLogs } = useSWR('logs/nginx');
 
   const handleCopyLogs = useCallback(() => {
-    async function copy() {
+    copy(logs);
-      await window.navigator.clipboard.writeText(logs);
-    }
-    copy();
   }, [logs]);
 
   useEffect(() => {

web/src/routes/System.jsx

@@ -187,8 +187,9 @@ export default function System() {
                   <div className="p-2">
                     {gpu_usages[gpu]['gpu'] == -1 ? (
                       <div className="p-4">
-                        There was an error getting usage stats. Either your GPU does not support this or Frigate does
+                        There was an error getting usage stats. This does not mean hardware acceleration is not working.
-                        not have proper access.
+                        Either your GPU does not support this or Frigate does not have proper access to get statistics.
+                        This is expected for the Home Assistant addon.
                       </div>
                     ) : (
                       <Table className="w-full">