docs: move docs to docusaurus

docs/docs/configuration/advanced.md

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+---
+id: advanced
+title: Advanced
+sidebar_label: Advanced
+---
+
+## Advanced configuration
+
+### `motion`
+
+Global motion detection config. These may also be defined at the camera level.
+
+```yaml
+motion:
+  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
+  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
+  # The value should be between 1 and 255.
+  threshold: 25
+  # Optional: Minimum size in pixels in the resized motion image that counts as motion
+  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
+  # moving objects.
+  contour_area: 100
+  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
+  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
+  # Too low and a fast moving person wont be detected as motion.
+  delta_alpha: 0.2
+  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
+  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
+  # Low values will cause things like moving shadows to be detected as motion for longer.
+  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
+  frame_alpha: 0.2
+  # Optional: Height of the resized motion frame  (default: 1/6th of the original frame height)
+  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense of higher CPU usage.
+  # Lower values result in less CPU, but small changes may not register as motion.
+  frame_height: 180
+```
+
+### `detect`
+
+Global object detection settings. These may also be defined at the camera level.
+
+```yaml
+detect:
+  # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: double the frame rate)
+  max_disappeared: 10
+```
+
+### `logger`
+
+Change the default log level for troubleshooting purposes.
+
+```yaml
+logger:
+  # Optional: default log level (default: shown below)
+  default: info
+  # Optional: module by module log level configuration
+  logs:
+    frigate.mqtt: error
+```
+
+Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
+
+Examples of available modules are:
+
+- `frigate.app`
+- `frigate.mqtt`
+- `frigate.edgetpu`
+- `frigate.zeroconf`
+- `detector.<detector_name>`
+- `watchdog.<camera_name>`
+- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
+
+### `environment_vars`
+
+This section can be used to set environment variables for those unable to modify the environment of the container (ie. within Hass.io)
+
+```yaml
+environment_vars:
+  EXAMPLE_VAR: value
+```
+
+### `database`
+
+Event and clip information is managed in a sqlite database at `/media/frigate/clips/frigate.db`. If that database is deleted, clips will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within HomeAssistant.
+
+If you are storing your clips on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
+
+This may need to be in a custom location if network storage is used for clips.
+
+```yaml
+database:
+  path: /media/frigate/clips/frigate.db
+```
+
+### `detectors`
+
+```yaml
+detectors:
+  # Required: name of the detector
+  coral:
+    # Required: type of the detector
+    # Valid values are 'edgetpu' (requires device property below) and 'cpu'. type: edgetpu
+    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
+    device: usb
+    # Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
+    # This value is only used for CPU types
+    num_threads: 3
+```
+
+### `model`
+
+```yaml
+model:
+  # Required: height of the trained model
+  height: 320
+  # Required: width of the trained model
+  width: 320
+```
+
+## Custom Models
+
+Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
+
+- CPU Model: `/cpu_model.tflite`
+- EdgeTPU Model: `/edgetpu_model.tflite`
+- Labels: `/labelmap.txt`
+
+You also need to update the model width/height in the config if they differ from the defaults.
+
+### Customizing the Labelmap
+
+The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. You must retain the same number of labels, but you can change the names. To change:
+
+- Download the [COCO labelmap](https://dl.google.com/coral/canned_models/coco_labels.txt)
+- Modify the label names as desired. For example, change `7 truck` to `7 car`
+- Mount the new file at `/labelmap.txt` in the container with an additional volume
+  ```
+  -v ./config/labelmap.txt:/labelmap.txt
+  ```