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docs updates
This commit is contained in:
Blake Blackshear
@@ -1,50 +1,11 @@
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---
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id: advanced
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title: Advanced
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sidebar_label: Advanced
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title: Advanced Options
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sidebar_label: Advanced Options
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---
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## Advanced configuration
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### `motion`
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Global motion detection config. These may also be defined at the camera level.
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```yaml
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motion:
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# Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
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# Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
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# The value should be between 1 and 255.
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threshold: 25
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# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: ~0.17% of the motion frame area)
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# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
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# moving objects.
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contour_area: 100
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# Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
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# Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
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# Too low and a fast moving person wont be detected as motion.
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delta_alpha: 0.2
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# Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
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# Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
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# Low values will cause things like moving shadows to be detected as motion for longer.
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# https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
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frame_alpha: 0.2
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# Optional: Height of the resized motion frame (default: 1/6th of the original frame height, but no less than 180)
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# This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense of higher CPU usage.
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# Lower values result in less CPU, but small changes may not register as motion.
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frame_height: 180
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```
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### `detect`
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Global object detection settings. These may also be defined at the camera level.
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```yaml
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detect:
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# Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
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max_disappeared: 25
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```
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### `logger`
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Change the default log level for troubleshooting purposes.
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@@ -72,12 +33,7 @@ Examples of available modules are:
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### `environment_vars`
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This section can be used to set environment variables for those unable to modify the environment of the container (ie. within Hass.io)
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```yaml
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environment_vars:
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EXAMPLE_VAR: value
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```
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This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
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### `database`
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@@ -87,40 +43,8 @@ If you are storing your database on a network share (SMB, NFS, etc), you may get
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This may need to be in a custom location if network storage is used for the media folder.
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```yaml
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database:
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path: /media/frigate/frigate.db
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```
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### `detectors`
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```yaml
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detectors:
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# Required: name of the detector
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coral:
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# Required: type of the detector
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# Valid values are 'edgetpu' (requires device property below) and 'cpu'.
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type: edgetpu
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# Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
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device: usb
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# Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
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# This value is only used for CPU types
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num_threads: 3
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```
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### `model`
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If using a custom model, the width and height will need to be specified.
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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. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
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```yaml
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model:
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# Required: height of the trained model
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height: 320
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# Required: width of the trained model
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width: 320
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# Optional: labelmap overrides
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labelmap:
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7: car
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```
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@@ -0,0 +1,82 @@
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---
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id: camera_specific
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title: Camera Specific Configurations
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---
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### MJPEG Cameras
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The input and output parameters need to be adjusted for MJPEG cameras
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```yaml
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input_args:
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- -avoid_negative_ts
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- make_zero
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- -fflags
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- nobuffer
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- -flags
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- low_delay
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- -strict
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- experimental
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- -fflags
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- +genpts+discardcorrupt
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- -r
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- "3" # <---- adjust depending on your desired frame rate from the mjpeg image
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- -use_wallclock_as_timestamps
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- "1"
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```
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Note that mjpeg cameras require encoding the video into h264 for recording, and rtmp roles. This will use significantly more CPU than if the cameras supported h264 feeds directly.
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```yaml
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output_args:
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record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v libx264 -an
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rtmp: -c:v libx264 -an -f flv
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```
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### RTMP Cameras (Reolink 410/520 and possibly others)
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The input parameters need to be adjusted for RTMP cameras
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```yaml
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ffmpeg:
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input_args:
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- -avoid_negative_ts
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- make_zero
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- -fflags
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- nobuffer
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- -flags
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- low_delay
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- -strict
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- experimental
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- -fflags
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- +genpts+discardcorrupt
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- -rw_timeout
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- "5000000"
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- -use_wallclock_as_timestamps
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- "1"
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- -f
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- live_flv
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```
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### Blue Iris RTSP Cameras
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You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
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```yaml
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ffmpeg:
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input_args:
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- -avoid_negative_ts
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- make_zero
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- -flags
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- low_delay
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- -strict
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- experimental
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- -fflags
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- +genpts+discardcorrupt
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- -rtsp_transport
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- tcp
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- -stimeout
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- "5000000"
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- -use_wallclock_as_timestamps
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- "1"
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```
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@@ -5,17 +5,15 @@ title: Cameras
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## Setting Up Camera Inputs
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Up to 4 inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
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Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
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Each role can only be assigned to one input per camera. The options for roles are as follows:
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| Role | Description |
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| -------- | ------------------------------------------------------------------------------------- |
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| `detect` | Main feed for object detection |
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| `record` | Saves segments of the video feed based on configuration settings. [docs](#recordings) |
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| `rtmp` | Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams) |
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### Example
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| Role | Description |
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| -------- | ----------------------------------------------------------------------------------------------- |
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| `detect` | Main feed for object detection |
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| `record` | Saves segments of the video feed based on configuration settings. [docs](/configuration/record) |
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| `rtmp` | Broadcast as an RTMP feed for other services to consume. [docs](/configuration/rtmp) |
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```yaml
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mqtt:
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@@ -34,526 +32,14 @@ cameras:
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detect:
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width: 1280
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height: 720
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fps: 5
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```
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`width`, `height`, and `fps` are only used for the `detect` role. Other streams are passed through, so there is no need to specify the resolution.
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## Masks & Zones
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### Masks
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Masks are used to ignore initial detection in areas of your camera's field of view.
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There are two types of masks available:
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- **Motion masks**: Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the video feed with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. Over masking will make it more difficult for objects to be tracked. To see this effect, create a mask, and then watch the video feed with `Motion Boxes` enabled again.
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- **Object filter masks**: Object filter masks are used to filter out false positives for a given object type. These should be used to filter any areas where it is not possible for an object of that type to be. The bottom center of the detected object's bounding box is evaluated against the mask. If it is in a masked area, it is assumed to be a false positive. For example, you may want to mask out rooftops, walls, the sky, treetops for people. For cars, masking locations other than the street or your driveway will tell frigate that anything in your yard is a false positive.
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To create a poly mask:
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1. Visit the [web UI](/usage/web)
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1. Click the camera you wish to create a mask for
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1. Click "Mask & Zone creator"
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1. Click "Add" on the type of mask or zone you would like to create
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1. Click on the camera's latest image to create a masked area. The yaml representation will be updated in real-time
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1. When you've finished creating your mask, click "Copy" and paste the contents into your `config.yaml` file and restart Frigate
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Example of a finished row corresponding to the below example image:
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```yaml
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motion:
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mask: "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
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```
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```yaml
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# Optional: camera level motion config
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motion:
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# Optional: motion mask
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# NOTE: see docs for more detailed info on creating masks
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mask: 0,900,1080,900,1080,1920,0,1920
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```
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### Zones
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Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
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During testing, `draw_zones` should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
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To create a zone, follow the same steps above for a "Motion mask", but use the section of the web UI for creating a zone instead.
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```yaml
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# Optional: zones for this camera
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zones:
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# Required: name of the zone
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# NOTE: This must be different than any camera names, but can match with another zone on another
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# camera.
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front_steps:
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# Required: List of x,y coordinates to define the polygon of the zone.
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# NOTE: Coordinates can be generated at https://www.image-map.net/
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coordinates: 545,1077,747,939,788,805
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# Optional: List of objects that can trigger this zone (default: all tracked objects)
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objects:
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- person
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# Optional: Zone level object filters.
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# NOTE: The global and camera filters are applied upstream.
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filters:
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person:
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min_area: 5000
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max_area: 100000
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threshold: 0.7
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```
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## Objects
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For a list of available objects, see the [objects documentation](./objects.mdx).
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```yaml
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# Optional: Camera level object filters config.
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objects:
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track:
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- person
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- car
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# Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
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# Checks based on the bottom center of the bounding box of the object.
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# NOTE: This mask is COMBINED with the object type specific mask below
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mask: 0,0,1000,0,1000,200,0,200
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filters:
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person:
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min_area: 5000
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max_area: 100000
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min_score: 0.5
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threshold: 0.7
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# Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
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# Checks based on the bottom center of the bounding box of the object
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mask: 0,0,1000,0,1000,200,0,200
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```
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## Recordings
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24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in Home Assistant's media browser. Each camera supports a configurable retention policy in the config.
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Exported clips are also created off of these recordings. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
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These recordings will not be playable in the web UI or in Home Assistant's media browser unless your camera sends video as h264.
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:::caution
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Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
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:::
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|
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```yaml
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record:
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# Optional: Enable recording (default: shown below)
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enabled: False
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# Optional: Number of days to retain (default: shown below)
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retain_days: 0
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# Optional: Event recording settings
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events:
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# Optional: Enable event recording retention settings (default: shown below)
|
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enabled: False
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# Optional: Maximum length of time to retain video during long events. (default: shown below)
|
||||
# NOTE: If an object is being tracked for longer than this amount of time, the cache
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# will begin to expire and the resulting clip will be the last x seconds of the event unless retain_days under record is > 0.
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max_seconds: 300
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# Optional: Number of seconds before the event to include in the event (default: shown below)
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pre_capture: 5
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# Optional: Number of seconds after the event to include in the event (default: shown below)
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post_capture: 5
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# Optional: Objects to save event for. (default: all tracked objects)
|
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objects:
|
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- person
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# Optional: Restrict event to objects that entered any of the listed zones (default: no required zones)
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required_zones: []
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# Optional: Retention settings for event
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retain:
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# Required: Default retention days (default: shown below)
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default: 10
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# Optional: Per object retention days
|
||||
objects:
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person: 15
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```
|
||||
|
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## Snapshots
|
||||
|
||||
Frigate can save a snapshot image to `/media/frigate/clips` for each event named as `<camera>-<id>.jpg`.
|
||||
|
||||
```yaml
|
||||
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
|
||||
snapshots:
|
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# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
|
||||
# This value can be set via MQTT and will be updated in startup based on retained value
|
||||
enabled: False
|
||||
# Optional: Enable writing a clean copy png snapshot to /media/frigate/clips (default: shown below)
|
||||
# Only works if snapshots are enabled. This image is intended to be used for training purposes.
|
||||
clean_copy: True
|
||||
# Optional: print a timestamp on the snapshots (default: shown below)
|
||||
timestamp: False
|
||||
# Optional: draw bounding box on the snapshots (default: shown below)
|
||||
bounding_box: False
|
||||
# Optional: crop the snapshot (default: shown below)
|
||||
crop: False
|
||||
# Optional: height to resize the snapshot to (default: original size)
|
||||
height: 175
|
||||
# Optional: jpeg encode quality (default: shown below)
|
||||
quality: 70
|
||||
# Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
# Optional: Camera override for retention settings (default: global values)
|
||||
retain:
|
||||
# Required: Default retention days (default: shown below)
|
||||
default: 10
|
||||
# Optional: Per object retention days
|
||||
objects:
|
||||
person: 15
|
||||
```
|
||||
|
||||
## RTMP streams
|
||||
|
||||
Frigate can re-stream your video feed as a RTMP feed for other applications such as Home Assistant to utilize it at `rtmp://<frigate_host>/live/<camera_name>`. Port 1935 must be open. This allows you to use a video feed for detection in frigate and Home Assistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
|
||||
|
||||
Some video feeds are not compatible with RTMP. If you are experiencing issues, check to make sure your camera feed is h264 with AAC audio. If your camera doesn't support a compatible format for RTMP, you can use the ffmpeg args to re-encode it on the fly at the expense of increased CPU utilization.
|
||||
|
||||
```yaml
|
||||
rtmp:
|
||||
# Optional: Enable the RTMP stream (default: True)
|
||||
enabled: True
|
||||
```
|
||||
|
||||
## Timestamp style configuration
|
||||
|
||||
For the debug view and snapshots it is possible to embed a timestamp in the feed. In some instances the default position obstructs important space, visibility or contrast is too low because of color or the datetime format does not match ones desire.
|
||||
|
||||
```yaml
|
||||
# Optional: in-feed timestamp style configuration
|
||||
timestamp_style:
|
||||
# Optional: Position of the timestamp (default: shown below)
|
||||
# "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
|
||||
position: "tl"
|
||||
# Optional: Format specifier conform to the Python package "datetime" (default: shown below)
|
||||
# Additional Examples:
|
||||
# german: "%d.%m.%Y %H:%M:%S"
|
||||
format: "%m/%d/%Y %H:%M:%S"
|
||||
# Optional: Color of font
|
||||
color:
|
||||
# All Required when color is specified (default: shown below)
|
||||
red: 255
|
||||
green: 255
|
||||
blue: 255
|
||||
# Optional: Line thickness of font (default: shown below)
|
||||
thickness: 2
|
||||
# Optional: Effect of lettering (default: shown below)
|
||||
# None (No effect),
|
||||
# "solid" (solid background in inverse color of font)
|
||||
# "shadow" (shadow for font)
|
||||
effect: None
|
||||
```
|
||||
|
||||
## Full example
|
||||
|
||||
The following is a full example of all of the options together for a camera configuration
|
||||
Additional cameras are simply added to the config under the `cameras` entry.
|
||||
|
||||
```yaml
|
||||
mqtt: ...
|
||||
cameras:
|
||||
# Required: name of the camera
|
||||
back:
|
||||
# Required: ffmpeg settings for the camera
|
||||
ffmpeg:
|
||||
# Required: A list of input streams for the camera. See documentation for more information.
|
||||
inputs:
|
||||
# Required: the path to the stream
|
||||
# NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
|
||||
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
|
||||
# Required: list of roles for this stream. valid values are: detect,record,rtmp
|
||||
# NOTICE: In addition to assigning the record, and rtmp roles,
|
||||
# they must also be enabled in the camera config.
|
||||
roles:
|
||||
- detect
|
||||
- rtmp
|
||||
# Optional: stream specific global args (default: inherit)
|
||||
global_args:
|
||||
# Optional: stream specific hwaccel args (default: inherit)
|
||||
hwaccel_args:
|
||||
# Optional: stream specific input args (default: inherit)
|
||||
input_args:
|
||||
# Optional: camera specific global args (default: inherit)
|
||||
global_args:
|
||||
# Optional: camera specific hwaccel args (default: inherit)
|
||||
hwaccel_args:
|
||||
# Optional: camera specific input args (default: inherit)
|
||||
input_args:
|
||||
# Optional: camera specific output args (default: inherit)
|
||||
output_args:
|
||||
|
||||
# Required: Camera level detect settings
|
||||
detect:
|
||||
# Optional: width of the frame for the input with the detect role (default: shown below)
|
||||
width: 1280
|
||||
# Optional: height of the frame for the input with the detect role (default: shown below)
|
||||
height: 720
|
||||
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
|
||||
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
|
||||
fps: 5
|
||||
# Optional: enables detection for the camera (default: True)
|
||||
# This value can be set via MQTT and will be updated in startup based on retained value
|
||||
enabled: True
|
||||
# Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
|
||||
max_disappeared: 25
|
||||
|
||||
# Optional: camera level motion config
|
||||
motion:
|
||||
# Optional: motion mask
|
||||
# NOTE: see docs for more detailed info on creating masks
|
||||
mask: 0,900,1080,900,1080,1920,0,1920
|
||||
|
||||
# Optional: timeout for highest scoring image before allowing it
|
||||
# to be replaced by a newer image. (default: shown below)
|
||||
best_image_timeout: 60
|
||||
|
||||
# Optional: zones for this camera
|
||||
zones:
|
||||
# Required: name of the zone
|
||||
# NOTE: This must be different than any camera names, but can match with another zone on another
|
||||
# camera.
|
||||
front_steps:
|
||||
# Required: List of x,y coordinates to define the polygon of the zone.
|
||||
# NOTE: Coordinates can be generated at https://www.image-map.net/
|
||||
coordinates: 545,1077,747,939,788,805
|
||||
# Optional: List of objects that can trigger this zone (default: all tracked objects)
|
||||
objects:
|
||||
- person
|
||||
# Optional: Zone level object filters.
|
||||
# NOTE: The global and camera filters are applied upstream.
|
||||
filters:
|
||||
person:
|
||||
min_area: 5000
|
||||
max_area: 100000
|
||||
threshold: 0.7
|
||||
|
||||
# Optional: 24/7 recording configuration
|
||||
record:
|
||||
# Optional: Enable recording (default: global setting)
|
||||
enabled: False
|
||||
# Optional: Number of days to retain (default: global setting)
|
||||
retain_days: 30
|
||||
# Optional: Event recording settings
|
||||
events:
|
||||
# Required: enables event recordings for the camera (default: shown below)
|
||||
# This value can be set via MQTT and will be updated in startup based on retained value
|
||||
enabled: False
|
||||
# Optional: Number of seconds before the event to include (default: shown below)
|
||||
pre_capture: 5
|
||||
# Optional: Number of seconds after the event to include (default: shown below)
|
||||
post_capture: 5
|
||||
# Optional: Objects to save events for. (default: all tracked objects)
|
||||
objects:
|
||||
- person
|
||||
# Optional: Restrict events to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
# Optional: Camera override for retention settings (default: global values)
|
||||
retain:
|
||||
# Required: Default retention days (default: shown below)
|
||||
default: 10
|
||||
# Optional: Per object retention days
|
||||
objects:
|
||||
person: 15
|
||||
|
||||
# Optional: RTMP re-stream configuration
|
||||
rtmp:
|
||||
# Optional: Enable the RTMP stream (default: True)
|
||||
enabled: True
|
||||
|
||||
# Optional: Live stream configuration for WebUI
|
||||
live:
|
||||
# Optional: Set the height of the live stream. (default: 720)
|
||||
# This must be less than or equal to the height of the detect stream. Lower resolutions
|
||||
# reduce bandwidth required for viewing the live stream. Width is computed to match known aspect ratio.
|
||||
height: 720
|
||||
# Optional: Set the encode quality of the live stream (default: shown below)
|
||||
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
|
||||
quality: 8
|
||||
|
||||
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
|
||||
snapshots:
|
||||
# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
|
||||
# This value can be set via MQTT and will be updated in startup based on retained value
|
||||
enabled: False
|
||||
# Optional: print a timestamp on the snapshots (default: shown below)
|
||||
timestamp: False
|
||||
# Optional: draw bounding box on the snapshots (default: shown below)
|
||||
bounding_box: False
|
||||
# Optional: crop the snapshot (default: shown below)
|
||||
crop: False
|
||||
# Optional: height to resize the snapshot to (default: original size)
|
||||
height: 175
|
||||
# Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
# Optional: Camera override for retention settings (default: global values)
|
||||
retain:
|
||||
# Required: Default retention days (default: shown below)
|
||||
default: 10
|
||||
# Optional: Per object retention days
|
||||
objects:
|
||||
person: 15
|
||||
|
||||
# Optional: Configuration for the jpg snapshots published via MQTT
|
||||
mqtt:
|
||||
# Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
|
||||
# NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
|
||||
# All other messages will still be published.
|
||||
enabled: True
|
||||
# Optional: print a timestamp on the snapshots (default: shown below)
|
||||
timestamp: True
|
||||
# Optional: draw bounding box on the snapshots (default: shown below)
|
||||
bounding_box: True
|
||||
# Optional: crop the snapshot (default: shown below)
|
||||
crop: True
|
||||
# Optional: height to resize the snapshot to (default: shown below)
|
||||
height: 270
|
||||
# Optional: jpeg encode quality (default: shown below)
|
||||
quality: 70
|
||||
# Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
|
||||
# Optional: Camera level object filters config.
|
||||
objects:
|
||||
track:
|
||||
- person
|
||||
- car
|
||||
# Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
|
||||
# Checks based on the bottom center of the bounding box of the object.
|
||||
# NOTE: This mask is COMBINED with the object type specific mask below
|
||||
mask: 0,0,1000,0,1000,200,0,200
|
||||
filters:
|
||||
person:
|
||||
min_area: 5000
|
||||
max_area: 100000
|
||||
min_score: 0.5
|
||||
threshold: 0.7
|
||||
# Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
|
||||
# Checks based on the bottom center of the bounding box of the object
|
||||
mask: 0,0,1000,0,1000,200,0,200
|
||||
|
||||
# Optional: In-feed timestamp style configuration
|
||||
timestamp_style:
|
||||
# Optional: Position of the timestamp (default: shown below)
|
||||
# "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
|
||||
position: "tl"
|
||||
# Optional: Format specifier conform to the Python package "datetime" (default: shown below)
|
||||
# Additional Examples:
|
||||
# german: "%d.%m.%Y %H:%M:%S"
|
||||
format: "%m/%d/%Y %H:%M:%S"
|
||||
# Optional: Color of font
|
||||
color:
|
||||
# All Required when color is specified (default: shown below)
|
||||
red: 255
|
||||
green: 255
|
||||
blue: 255
|
||||
# Optional: Line thickness of font (default: shown below)
|
||||
thickness: 2
|
||||
# Optional: Effect of lettering (default: shown below)
|
||||
# None (No effect),
|
||||
# "solid" (solid background in inverse color of font)
|
||||
# "shadow" (shadow for font)
|
||||
effect: None
|
||||
```
|
||||
|
||||
## Camera specific configuration
|
||||
|
||||
### MJPEG Cameras
|
||||
|
||||
The input and output parameters need to be adjusted for MJPEG cameras
|
||||
|
||||
```yaml
|
||||
input_args:
|
||||
- -avoid_negative_ts
|
||||
- make_zero
|
||||
- -fflags
|
||||
- nobuffer
|
||||
- -flags
|
||||
- low_delay
|
||||
- -strict
|
||||
- experimental
|
||||
- -fflags
|
||||
- +genpts+discardcorrupt
|
||||
- -r
|
||||
- "3" # <---- adjust depending on your desired frame rate from the mjpeg image
|
||||
- -use_wallclock_as_timestamps
|
||||
- "1"
|
||||
```
|
||||
|
||||
Note that mjpeg cameras require encoding the video into h264 for recording, and rtmp roles. This will use significantly more CPU than if the cameras supported h264 feeds directly.
|
||||
|
||||
```yaml
|
||||
output_args:
|
||||
record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v libx264 -an
|
||||
rtmp: -c:v libx264 -an -f flv
|
||||
```
|
||||
|
||||
### RTMP Cameras
|
||||
|
||||
The input parameters need to be adjusted for RTMP cameras
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
input_args:
|
||||
- -avoid_negative_ts
|
||||
- make_zero
|
||||
- -fflags
|
||||
- nobuffer
|
||||
- -flags
|
||||
- low_delay
|
||||
- -strict
|
||||
- experimental
|
||||
- -fflags
|
||||
- +genpts+discardcorrupt
|
||||
- -use_wallclock_as_timestamps
|
||||
- "1"
|
||||
```
|
||||
|
||||
### Reolink 410/520 (possibly others)
|
||||
|
||||
Several users have reported success with the rtmp video from Reolink cameras.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
input_args:
|
||||
- -avoid_negative_ts
|
||||
- make_zero
|
||||
- -fflags
|
||||
- nobuffer
|
||||
- -flags
|
||||
- low_delay
|
||||
- -strict
|
||||
- experimental
|
||||
- -fflags
|
||||
- +genpts+discardcorrupt
|
||||
- -rw_timeout
|
||||
- "5000000"
|
||||
- -use_wallclock_as_timestamps
|
||||
- "1"
|
||||
```
|
||||
|
||||
### Blue Iris RTSP Cameras
|
||||
|
||||
You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
input_args:
|
||||
- -avoid_negative_ts
|
||||
- make_zero
|
||||
- -flags
|
||||
- low_delay
|
||||
- -strict
|
||||
- experimental
|
||||
- -fflags
|
||||
- +genpts+discardcorrupt
|
||||
- -rtsp_transport
|
||||
- tcp
|
||||
- -stimeout
|
||||
- "5000000"
|
||||
- -use_wallclock_as_timestamps
|
||||
- "1"
|
||||
back: ...
|
||||
front: ...
|
||||
side: ...
|
||||
```
|
||||
|
||||
@@ -3,13 +3,13 @@ id: detectors
|
||||
title: Detectors
|
||||
---
|
||||
|
||||
The default config will look for a USB Coral device. If you do not have a Coral, you will need to configure a CPU detector. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
|
||||
By default, Frigate will use a single CPU detector. If you have a Coral, you will need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
|
||||
|
||||
Frigate supports `edgetpu` and `cpu` as detector types. The device value should be specified according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api).
|
||||
|
||||
**Note**: There is no support for Nvidia GPUs to perform object detection with tensorflow. It can be used for ffmpeg decoding, but not object detection.
|
||||
|
||||
Single USB Coral:
|
||||
### Single USB Coral
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
@@ -18,7 +18,7 @@ detectors:
|
||||
device: usb
|
||||
```
|
||||
|
||||
Multiple USB Corals:
|
||||
### Multiple USB Corals
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
@@ -30,16 +30,16 @@ detectors:
|
||||
device: usb:1
|
||||
```
|
||||
|
||||
Native Coral (Dev Board):
|
||||
### Native Coral (Dev Board)
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: ''
|
||||
device: ""
|
||||
```
|
||||
|
||||
Multiple PCIE/M.2 Corals:
|
||||
### Multiple PCIE/M.2 Corals
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
@@ -51,7 +51,7 @@ detectors:
|
||||
device: pci:1
|
||||
```
|
||||
|
||||
Mixing Corals:
|
||||
### Mixing Corals
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
@@ -63,12 +63,16 @@ detectors:
|
||||
device: pci
|
||||
```
|
||||
|
||||
CPU Detectors (not recommended):
|
||||
### CPU Detectors (not recommended)
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
cpu1:
|
||||
type: cpu
|
||||
num_threads: 3
|
||||
cpu2:
|
||||
type: cpu
|
||||
num_threads: 3
|
||||
```
|
||||
|
||||
When using CPU detectors, you can add a CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
---
|
||||
id: false_positives
|
||||
title: Reducing false positives
|
||||
---
|
||||
|
||||
Tune your object filters to adjust false positives: `min_area`, `max_area`, `min_score`, `threshold`.
|
||||
|
||||
For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
|
||||
|
||||
| Frame | Current Score | Score History | Computed Score | Detected Object |
|
||||
| ----- | ------------- | --------------------------------- | -------------- | --------------- |
|
||||
| 1 | 0.7 | 0.0, 0, 0.7 | 0.0 | No |
|
||||
| 2 | 0.55 | 0.0, 0.7, 0.0 | 0.0 | No |
|
||||
| 3 | 0.85 | 0.7, 0.0, 0.85 | 0.7 | No |
|
||||
| 4 | 0.90 | 0.7, 0.85, 0.95, 0.90 | 0.875 | Yes |
|
||||
| 5 | 0.88 | 0.7, 0.85, 0.95, 0.90, 0.88 | 0.88 | Yes |
|
||||
| 6 | 0.95 | 0.7, 0.85, 0.95, 0.90, 0.88, 0.95 | 0.89 | Yes |
|
||||
|
||||
In frame 2, the score is below the `min_score` value, so frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
|
||||
@@ -0,0 +1,70 @@
|
||||
---
|
||||
id: hardware_acceleration
|
||||
title: Hardware Acceleration
|
||||
---
|
||||
|
||||
It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
|
||||
|
||||
### Raspberry Pi 3/4 (32-bit OS)
|
||||
|
||||
Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory).
|
||||
**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -c:v
|
||||
- h264_mmal
|
||||
```
|
||||
|
||||
### Raspberry Pi 3/4 (64-bit OS)
|
||||
|
||||
**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -c:v
|
||||
- h264_v4l2m2m
|
||||
```
|
||||
|
||||
### Intel-based CPUs (<10th Generation) via Quicksync
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -hwaccel
|
||||
- vaapi
|
||||
- -hwaccel_device
|
||||
- /dev/dri/renderD128
|
||||
- -hwaccel_output_format
|
||||
- yuv420p
|
||||
```
|
||||
|
||||
### Intel-based CPUs (>=10th Generation) via Quicksync
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -hwaccel
|
||||
- qsv
|
||||
- -qsv_device
|
||||
- /dev/dri/renderD128
|
||||
```
|
||||
|
||||
### AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
|
||||
|
||||
**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -hwaccel
|
||||
- vaapi
|
||||
- -hwaccel_device
|
||||
- /dev/dri/renderD128
|
||||
```
|
||||
|
||||
### NVIDIA GPU
|
||||
|
||||
NVIDIA GPU based decoding via NVDEC is supported, but requires special configuration. See the [NVIDIA NVDEC documentation](/configuration/nvdec) for more details.
|
||||
+265
-154
@@ -1,13 +1,13 @@
|
||||
---
|
||||
id: index
|
||||
title: Configuration
|
||||
title: Configuration File
|
||||
---
|
||||
|
||||
For HassOS installations, the default location for the config file is `/config/frigate.yml`.
|
||||
For Home Assistant Addon installations, the config file needs to be in the root of your Home Assistant config directory (same location as `configuration.yaml`) and named `frigate.yml`.
|
||||
|
||||
For all other installations, the default location for the config file is '/config/config.yml'. This can be overridden with the `CONFIG_FILE` environment variable. Camera specific ffmpeg parameters are documented [here](cameras.md).
|
||||
For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.
|
||||
|
||||
It is recommended to start with a minimal configuration and add to it:
|
||||
It is recommended to start with a minimal configuration and add to it as described in [this guide](/guides/getting_started):
|
||||
|
||||
```yaml
|
||||
mqtt:
|
||||
@@ -23,12 +23,15 @@ cameras:
|
||||
detect:
|
||||
width: 1280
|
||||
height: 720
|
||||
fps: 5
|
||||
```
|
||||
|
||||
## Required
|
||||
### Full configuration reference:
|
||||
|
||||
## `mqtt`
|
||||
:::caution
|
||||
|
||||
It is not recommended to copy this full configuration file. Only specify values that are different from the defaults. Configuration options and default values may change in future versions.
|
||||
|
||||
:::
|
||||
|
||||
```yaml
|
||||
mqtt:
|
||||
@@ -37,10 +40,10 @@ mqtt:
|
||||
# Optional: port (default: shown below)
|
||||
port: 1883
|
||||
# Optional: topic prefix (default: shown below)
|
||||
# WARNING: must be unique if you are running multiple instances
|
||||
# NOTE: must be unique if you are running multiple instances
|
||||
topic_prefix: frigate
|
||||
# Optional: client id (default: shown below)
|
||||
# WARNING: must be unique if you are running multiple instances
|
||||
# NOTE: must be unique if you are running multiple instances
|
||||
client_id: frigate
|
||||
# Optional: user
|
||||
user: mqtt_user
|
||||
@@ -61,59 +64,39 @@ mqtt:
|
||||
tls_insecure: false
|
||||
# Optional: interval in seconds for publishing stats (default: shown below)
|
||||
stats_interval: 60
|
||||
```
|
||||
|
||||
## `cameras`
|
||||
# Optional: Detectors configuration. Defaults to a single CPU detector
|
||||
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
|
||||
|
||||
Each of your cameras must be configured. The following is the minimum required to register a camera in Frigate. Check the [camera configuration page](cameras.md) for a complete list of options.
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
# Name of your camera
|
||||
front_door:
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
|
||||
roles:
|
||||
- detect
|
||||
- rtmp
|
||||
detect:
|
||||
width: 1280
|
||||
height: 720
|
||||
fps: 5
|
||||
```
|
||||
|
||||
## Optional
|
||||
|
||||
### `database`
|
||||
|
||||
```yaml
|
||||
# Optional: Database configuration
|
||||
database:
|
||||
# The path to store the SQLite DB (default: shown below)
|
||||
path: /media/frigate/frigate.db
|
||||
```
|
||||
|
||||
### `model`
|
||||
|
||||
```yaml
|
||||
# Optional: model modifications
|
||||
model:
|
||||
# Optional: path to the model (default: automatic based on detector)
|
||||
path: /edgetpu_model.tflite
|
||||
# Optional: path to the labelmap (default: shown below)
|
||||
labelmap_path: /labelmap.txt
|
||||
# Required: Object detection model input width (default: shown below)
|
||||
width: 320
|
||||
# Required: Object detection model input height (default: shown below)
|
||||
height: 320
|
||||
# Optional: Label name modifications
|
||||
# Optional: Label name modifications. These are merged into the standard labelmap.
|
||||
labelmap:
|
||||
2: vehicle # previously "car"
|
||||
```
|
||||
2: vehicle
|
||||
|
||||
### `detectors`
|
||||
|
||||
Check the [detectors configuration page](detectors.md) for a complete list of options.
|
||||
|
||||
### `logger`
|
||||
|
||||
```yaml
|
||||
# Optional: logger verbosity settings
|
||||
logger:
|
||||
# Optional: Default log verbosity (default: shown below)
|
||||
@@ -121,102 +104,12 @@ logger:
|
||||
# Optional: Component specific logger overrides
|
||||
logs:
|
||||
frigate.event: debug
|
||||
```
|
||||
|
||||
### `record`
|
||||
# Optional: set environment variables
|
||||
environment_vars:
|
||||
EXAMPLE_VAR: value
|
||||
|
||||
Can be overridden at the camera level. 24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in Home Assistant's media browser. Each camera supports a configurable retention policy in the config.
|
||||
|
||||
Exported clips are also created off of these recordings. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
|
||||
|
||||
These recordings will not be playable in the web UI or in Home Assistant's media browser unless your camera sends video as h264.
|
||||
|
||||
:::caution
|
||||
Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
|
||||
:::
|
||||
|
||||
```yaml
|
||||
record:
|
||||
# Optional: Enable recording (default: shown below)
|
||||
enabled: False
|
||||
# Optional: Number of days to retain (default: shown below)
|
||||
retain_days: 0
|
||||
# Optional: Event recording settings
|
||||
events:
|
||||
# Optional: Enable event recording retention settings (default: shown below)
|
||||
enabled: False
|
||||
# Optional: Maximum length of time to retain video during long events. (default: shown below)
|
||||
# NOTE: If an object is being tracked for longer than this amount of time, the cache
|
||||
# will begin to expire and the resulting clip will be the last x seconds of the event unless retain_days under record is > 0.
|
||||
max_seconds: 300
|
||||
# Optional: Number of seconds before the event to include (default: shown below)
|
||||
pre_capture: 5
|
||||
# Optional: Number of seconds after the event to include (default: shown below)
|
||||
post_capture: 5
|
||||
# Optional: Objects to save recordings for. (default: all tracked objects)
|
||||
objects:
|
||||
- person
|
||||
# Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
# Optional: Retention settings for events
|
||||
retain:
|
||||
# Required: Default retention days (default: shown below)
|
||||
default: 10
|
||||
# Optional: Per object retention days
|
||||
objects:
|
||||
person: 15
|
||||
```
|
||||
|
||||
### `ffmpeg`
|
||||
|
||||
Can be overridden at the camera level.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
# Optional: global ffmpeg args (default: shown below)
|
||||
global_args: -hide_banner -loglevel warning
|
||||
# Optional: global hwaccel args (default: shown below)
|
||||
# NOTE: See hardware acceleration docs for your specific device
|
||||
hwaccel_args: []
|
||||
# Optional: global input args (default: shown below)
|
||||
input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
|
||||
# Optional: global output args
|
||||
output_args:
|
||||
# Optional: output args for detect streams (default: shown below)
|
||||
detect: -f rawvideo -pix_fmt yuv420p
|
||||
# Optional: output args for record streams (default: shown below)
|
||||
record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
|
||||
# Optional: output args for rtmp streams (default: shown below)
|
||||
rtmp: -c copy -f flv
|
||||
```
|
||||
|
||||
### `objects`
|
||||
|
||||
Can be overridden at the camera level. For a list of available objects, see the [objects documentation](./objects.mdx).
|
||||
|
||||
```yaml
|
||||
objects:
|
||||
# Optional: list of objects to track from labelmap.txt (default: shown below)
|
||||
track:
|
||||
- person
|
||||
# Optional: filters to reduce false positives for specific object types
|
||||
filters:
|
||||
person:
|
||||
# Optional: minimum width*height of the bounding box for the detected object (default: 0)
|
||||
min_area: 5000
|
||||
# Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
|
||||
max_area: 100000
|
||||
# Optional: minimum score for the object to initiate tracking (default: shown below)
|
||||
min_score: 0.5
|
||||
# Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
|
||||
threshold: 0.7
|
||||
```
|
||||
|
||||
### `birdseye`
|
||||
|
||||
A dynamic combined camera view of all tracked cameras. This is optimized for minimal bandwidth and server resource utilization. Encoding is only performed when actively viewing the video feed, and only active (defined by the mode) cameras are included in the view.
|
||||
|
||||
```yaml
|
||||
# Optional: birdseye configuration
|
||||
birdseye:
|
||||
# Optional: Enable birdseye view (default: shown below)
|
||||
enabled: True
|
||||
@@ -232,24 +125,169 @@ birdseye:
|
||||
# motion - cameras are included if motion was detected in the last 30 seconds
|
||||
# continuous - all cameras are included always
|
||||
mode: objects
|
||||
```
|
||||
|
||||
### `rtmp`
|
||||
# Optional: ffmpeg configuration
|
||||
ffmpeg:
|
||||
# Optional: global ffmpeg args (default: shown below)
|
||||
global_args: -hide_banner -loglevel warning
|
||||
# Optional: global hwaccel args (default: shown below)
|
||||
# NOTE: See hardware acceleration docs for your specific device
|
||||
hwaccel_args: []
|
||||
# Optional: global input args (default: shown below)
|
||||
input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
|
||||
# Optional: global output args
|
||||
output_args:
|
||||
# Optional: output args for detect streams (default: shown below)
|
||||
detect: -f rawvideo -pix_fmt yuv420p
|
||||
# Optional: output args for record streams (default: shown below)
|
||||
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
|
||||
# Optional: output args for rtmp streams (default: shown below)
|
||||
rtmp: -c copy -f flv
|
||||
|
||||
Can be overridden at the camera level. See the [cameras configuration page](cameras.md) for more information about RTMP streaming.
|
||||
# Optional: Detect configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
detect:
|
||||
# Optional: width of the frame for the input with the detect role (default: shown below)
|
||||
width: 1280
|
||||
# Optional: height of the frame for the input with the detect role (default: shown below)
|
||||
height: 720
|
||||
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
|
||||
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
|
||||
fps: 5
|
||||
# Optional: enables detection for the camera (default: True)
|
||||
# This value can be set via MQTT and will be updated in startup based on retained value
|
||||
enabled: True
|
||||
# Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
|
||||
max_disappeared: 25
|
||||
|
||||
```yaml
|
||||
# Optional: Object configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
objects:
|
||||
# Optional: list of objects to track from labelmap.txt (default: shown below)
|
||||
track:
|
||||
- person
|
||||
# Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
|
||||
# Checks based on the bottom center of the bounding box of the object.
|
||||
# NOTE: This mask is COMBINED with the object type specific mask below
|
||||
mask: 0,0,1000,0,1000,200,0,200
|
||||
# Optional: filters to reduce false positives for specific object types
|
||||
filters:
|
||||
person:
|
||||
# Optional: minimum width*height of the bounding box for the detected object (default: 0)
|
||||
min_area: 5000
|
||||
# Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
|
||||
max_area: 100000
|
||||
# Optional: minimum score for the object to initiate tracking (default: shown below)
|
||||
min_score: 0.5
|
||||
# Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
|
||||
threshold: 0.7
|
||||
# Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
|
||||
# Checks based on the bottom center of the bounding box of the object
|
||||
mask: 0,0,1000,0,1000,200,0,200
|
||||
|
||||
# Optional: Motion configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
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 (default: ~0.17% of the motion frame area)
|
||||
# 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, but no less than 180)
|
||||
# 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
|
||||
# Optional: motion mask
|
||||
# NOTE: see docs for more detailed info on creating masks
|
||||
mask: 0,900,1080,900,1080,1920,0,1920
|
||||
|
||||
# Optional: Record configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
record:
|
||||
# Optional: Enable recording (default: shown below)
|
||||
enabled: False
|
||||
# Optional: Number of days to retain recordings regardless of events (default: shown below)
|
||||
# NOTE: This should be set to 0 and retention should be defined in events section below
|
||||
# if you only want to retain recordings of events.
|
||||
retain_days: 0
|
||||
# Optional: Event recording settings
|
||||
events:
|
||||
# Optional: Maximum length of time to retain video during long events. (default: shown below)
|
||||
# NOTE: If an object is being tracked for longer than this amount of time, the retained recordings
|
||||
# will be the last x seconds of the event unless retain_days under record is > 0.
|
||||
max_seconds: 300
|
||||
# Optional: Number of seconds before the event to include (default: shown below)
|
||||
pre_capture: 5
|
||||
# Optional: Number of seconds after the event to include (default: shown below)
|
||||
post_capture: 5
|
||||
# Optional: Objects to save recordings for. (default: all tracked objects)
|
||||
objects:
|
||||
- person
|
||||
# Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
# Optional: Retention settings for recordings of events
|
||||
retain:
|
||||
# Required: Default retention days (default: shown below)
|
||||
default: 10
|
||||
# Optional: Per object retention days
|
||||
objects:
|
||||
person: 15
|
||||
|
||||
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
|
||||
# NOTE: Can be overridden at the camera level
|
||||
snapshots:
|
||||
# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
|
||||
# This value can be set via MQTT and will be updated in startup based on retained value
|
||||
enabled: False
|
||||
# Optional: print a timestamp on the snapshots (default: shown below)
|
||||
timestamp: False
|
||||
# Optional: draw bounding box on the snapshots (default: shown below)
|
||||
bounding_box: False
|
||||
# Optional: crop the snapshot (default: shown below)
|
||||
crop: False
|
||||
# Optional: height to resize the snapshot to (default: original size)
|
||||
height: 175
|
||||
# Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
# Optional: Camera override for retention settings (default: global values)
|
||||
retain:
|
||||
# Required: Default retention days (default: shown below)
|
||||
default: 10
|
||||
# Optional: Per object retention days
|
||||
objects:
|
||||
person: 15
|
||||
|
||||
# Optional: RTMP configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
rtmp:
|
||||
# Optional: Enable the RTMP stream (default: True)
|
||||
enabled: True
|
||||
```
|
||||
|
||||
## `timestamp_style`
|
||||
# Optional: Live stream configuration for WebUI
|
||||
# NOTE: Can be overridden at the camera level
|
||||
live:
|
||||
# Optional: Set the height of the live stream. (default: 720)
|
||||
# This must be less than or equal to the height of the detect stream. Lower resolutions
|
||||
# reduce bandwidth required for viewing the live stream. Width is computed to match known aspect ratio.
|
||||
height: 720
|
||||
# Optional: Set the encode quality of the live stream (default: shown below)
|
||||
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
|
||||
quality: 8
|
||||
|
||||
Can be overridden at the camera level. See the [cameras configuration page](cameras.md) for more information about timestamp styling.
|
||||
|
||||
```yaml
|
||||
# Optional: in-feed timestamp style configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
timestamp_style:
|
||||
# Optional: Position of the timestamp (default: shown below)
|
||||
# "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
|
||||
@@ -264,8 +302,6 @@ timestamp_style:
|
||||
red: 255
|
||||
green: 255
|
||||
blue: 255
|
||||
# Optional: Scale factor for font (default: shown below)
|
||||
scale: 1.0
|
||||
# Optional: Line thickness of font (default: shown below)
|
||||
thickness: 2
|
||||
# Optional: Effect of lettering (default: shown below)
|
||||
@@ -273,4 +309,79 @@ timestamp_style:
|
||||
# "solid" (solid background in inverse color of font)
|
||||
# "shadow" (shadow for font)
|
||||
effect: None
|
||||
```
|
||||
|
||||
# Required
|
||||
cameras:
|
||||
# Required: name of the camera
|
||||
back:
|
||||
# Required: ffmpeg settings for the camera
|
||||
ffmpeg:
|
||||
# Required: A list of input streams for the camera. See documentation for more information.
|
||||
inputs:
|
||||
# Required: the path to the stream
|
||||
# NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
|
||||
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
|
||||
# Required: list of roles for this stream. valid values are: detect,record,rtmp
|
||||
# NOTICE: In addition to assigning the record, and rtmp roles,
|
||||
# they must also be enabled in the camera config.
|
||||
roles:
|
||||
- detect
|
||||
- rtmp
|
||||
# Optional: stream specific global args (default: inherit)
|
||||
# global_args:
|
||||
# Optional: stream specific hwaccel args (default: inherit)
|
||||
# hwaccel_args:
|
||||
# Optional: stream specific input args (default: inherit)
|
||||
# input_args:
|
||||
# Optional: camera specific global args (default: inherit)
|
||||
# global_args:
|
||||
# Optional: camera specific hwaccel args (default: inherit)
|
||||
# hwaccel_args:
|
||||
# Optional: camera specific input args (default: inherit)
|
||||
# input_args:
|
||||
# Optional: camera specific output args (default: inherit)
|
||||
# output_args:
|
||||
|
||||
# Optional: timeout for highest scoring image before allowing it
|
||||
# to be replaced by a newer image. (default: shown below)
|
||||
best_image_timeout: 60
|
||||
|
||||
# Optional: zones for this camera
|
||||
zones:
|
||||
# Required: name of the zone
|
||||
# NOTE: This must be different than any camera names, but can match with another zone on another
|
||||
# camera.
|
||||
front_steps:
|
||||
# Required: List of x,y coordinates to define the polygon of the zone.
|
||||
# NOTE: Coordinates can be generated at https://www.image-map.net/
|
||||
coordinates: 545,1077,747,939,788,805
|
||||
# Optional: List of objects that can trigger this zone (default: all tracked objects)
|
||||
objects:
|
||||
- person
|
||||
# Optional: Zone level object filters.
|
||||
# NOTE: The global and camera filters are applied upstream.
|
||||
filters:
|
||||
person:
|
||||
min_area: 5000
|
||||
max_area: 100000
|
||||
threshold: 0.7
|
||||
|
||||
# Optional: Configuration for the jpg snapshots published via MQTT
|
||||
mqtt:
|
||||
# Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
|
||||
# NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
|
||||
# All other messages will still be published.
|
||||
enabled: True
|
||||
# Optional: print a timestamp on the snapshots (default: shown below)
|
||||
timestamp: True
|
||||
# Optional: draw bounding box on the snapshots (default: shown below)
|
||||
bounding_box: True
|
||||
# Optional: crop the snapshot (default: shown below)
|
||||
crop: True
|
||||
# Optional: height to resize the snapshot to (default: shown below)
|
||||
height: 270
|
||||
# Optional: jpeg encode quality (default: shown below)
|
||||
quality: 70
|
||||
# Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
|
||||
required_zones: []
|
||||
```
|
||||
|
||||
@@ -0,0 +1,39 @@
|
||||
---
|
||||
id: masks
|
||||
title: Masks
|
||||
---
|
||||
|
||||
There are two types of masks available:
|
||||
|
||||
**Motion masks**: Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the debug feed with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. Over masking will make it more difficult for objects to be tracked. To see this effect, create a mask, and then watch the video feed with `Motion Boxes` enabled again.
|
||||
|
||||
**Object filter masks**: Object filter masks are used to filter out false positives for a given object type based on location. These should be used to filter any areas where it is not possible for an object of that type to be. The bottom center of the detected object's bounding box is evaluated against the mask. If it is in a masked area, it is assumed to be a false positive. For example, you may want to mask out rooftops, walls, the sky, treetops for people. For cars, masking locations other than the street or your driveway will tell frigate that anything in your yard is a false positive.
|
||||
|
||||
To create a poly mask:
|
||||
|
||||
1. Visit the Web UI
|
||||
1. Click the camera you wish to create a mask for
|
||||
1. Select "Debug" at the top
|
||||
1. Expand the "Options" below the video feed
|
||||
1. Click "Mask & Zone creator"
|
||||
1. Click "Add" on the type of mask or zone you would like to create
|
||||
1. Click on the camera's latest image to create a masked area. The yaml representation will be updated in real-time
|
||||
1. When you've finished creating your mask, click "Copy" and paste the contents into your config file and restart Frigate
|
||||
|
||||
Example of a finished row corresponding to the below example image:
|
||||
|
||||
```yaml
|
||||
motion:
|
||||
mask: "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
|
||||
```
|
||||
|
||||
Multiple masks can be listed.
|
||||
|
||||
```yaml
|
||||
motion:
|
||||
mask:
|
||||
- 458,1346,336,973,317,869,375,866,432
|
||||
- 0,461,3,0,1919,0,1919,843,1699,492,1344
|
||||
```
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: nvdec
|
||||
title: nVidia hardware decoder
|
||||
title: NVIDIA hardware decoder
|
||||
---
|
||||
|
||||
Certain nvidia cards include a hardware decoder, which can greatly improve the
|
||||
@@ -23,7 +23,7 @@ In order to pass NVDEC, the docker engine must be set to `nvidia` and the enviro
|
||||
|
||||
In a docker compose file, these lines need to be set:
|
||||
|
||||
```
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
...
|
||||
@@ -41,7 +41,7 @@ The decoder you choose will depend on the input video.
|
||||
|
||||
A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
|
||||
|
||||
```
|
||||
```shell
|
||||
V..... h263_cuvid Nvidia CUVID H263 decoder (codec h263)
|
||||
V..... h264_cuvid Nvidia CUVID H264 decoder (codec h264)
|
||||
V..... hevc_cuvid Nvidia CUVID HEVC decoder (codec hevc)
|
||||
@@ -57,10 +57,9 @@ A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the c
|
||||
For example, for H265 video (hevc), you'll select `hevc_cuvid`. Add
|
||||
`-c:v hevc_cuvid` to your ffmpeg input arguments:
|
||||
|
||||
```
|
||||
```yaml
|
||||
ffmpeg:
|
||||
input_args:
|
||||
...
|
||||
input_args: ...
|
||||
- -c:v
|
||||
- hevc_cuvid
|
||||
```
|
||||
@@ -100,10 +99,10 @@ processes:
|
||||
To further improve performance, you can set ffmpeg to skip frames in the output,
|
||||
using the fps filter:
|
||||
|
||||
```
|
||||
output_args:
|
||||
- -filter:v
|
||||
- fps=fps=5
|
||||
```yaml
|
||||
output_args:
|
||||
- -filter:v
|
||||
- fps=fps=5
|
||||
```
|
||||
|
||||
This setting, for example, allows Frigate to consume my 10-15fps camera streams on
|
||||
|
||||
@@ -1,12 +1,11 @@
|
||||
---
|
||||
id: objects
|
||||
title: Default available objects
|
||||
sidebar_label: Available objects
|
||||
title: Objects
|
||||
---
|
||||
|
||||
import labels from "../../../labelmap.txt";
|
||||
|
||||
By default, Frigate includes the following object models from the Google Coral test data.
|
||||
By default, Frigate includes the following object models from the Google Coral test data. Note that `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
|
||||
|
||||
<ul>
|
||||
{labels.split("\n").map((label) => (
|
||||
@@ -22,4 +21,4 @@ Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use yo
|
||||
- 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.
|
||||
You also need to update the [model config](/configuration/advanced#model) if they differ from the defaults.
|
||||
|
||||
@@ -1,73 +0,0 @@
|
||||
---
|
||||
id: optimizing
|
||||
title: Optimizing performance
|
||||
---
|
||||
|
||||
- **Google Coral**: It is strongly recommended to use a Google Coral, Frigate will no longer fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
|
||||
- **Resolution**: For the `detect` input, choose a camera resolution where the smallest object you want to detect barely fits inside a 300x300px square. The model used by Frigate is trained on 300x300px images, so you will get worse performance and no improvement in accuracy by using a larger resolution since Frigate resizes the area where it is looking for objects to 300x300 anyway.
|
||||
- **FPS**: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
|
||||
- **Hardware Acceleration**: Make sure you configure the `hwaccel_args` for your hardware. They provide a significant reduction in CPU usage if they are available.
|
||||
- **Masks**: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for `detection_fps` when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.
|
||||
|
||||
### FFmpeg Hardware Acceleration
|
||||
|
||||
Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.
|
||||
|
||||
Raspberry Pi 3/4 (32-bit OS)
|
||||
Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory).
|
||||
**NOTICE**: If you are using the addon, ensure you turn off `Protection mode` for hardware acceleration.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -c:v
|
||||
- h264_mmal
|
||||
```
|
||||
|
||||
Raspberry Pi 3/4 (64-bit OS)
|
||||
**NOTICE**: If you are using the addon, ensure you turn off `Protection mode` for hardware acceleration.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -c:v
|
||||
- h264_v4l2m2m
|
||||
```
|
||||
|
||||
Intel-based CPUs (<10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -hwaccel
|
||||
- vaapi
|
||||
- -hwaccel_device
|
||||
- /dev/dri/renderD128
|
||||
- -hwaccel_output_format
|
||||
- yuv420p
|
||||
```
|
||||
|
||||
Intel-based CPUs (>=10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -hwaccel
|
||||
- qsv
|
||||
- -qsv_device
|
||||
- /dev/dri/renderD128
|
||||
```
|
||||
|
||||
AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
|
||||
**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
hwaccel_args:
|
||||
- -hwaccel
|
||||
- vaapi
|
||||
- -hwaccel_device
|
||||
- /dev/dri/renderD128
|
||||
```
|
||||
|
||||
Nvidia GPU based decoding via NVDEC is supported, but requires special configuration. See the [nvidia NVDEC documentation](/configuration/nvdec) for more details.
|
||||
@@ -0,0 +1,10 @@
|
||||
---
|
||||
id: record
|
||||
title: Recording
|
||||
---
|
||||
|
||||
Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config.
|
||||
|
||||
Exported clips are also created off of these recordings. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
|
||||
|
||||
H265 recordings can be viewed in Edge and Safari only. All other browsers require recordings to be encoded with H264.
|
||||
@@ -0,0 +1,8 @@
|
||||
---
|
||||
id: rtmp
|
||||
title: RTMP
|
||||
---
|
||||
|
||||
Frigate can re-stream your video feed as a RTMP feed for other applications such as Home Assistant to utilize it at `rtmp://<frigate_host>/live/<camera_name>`. Port 1935 must be open. This allows you to use a video feed for detection in frigate and Home Assistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
|
||||
|
||||
Some video feeds are not compatible with RTMP. If you are experiencing issues, check to make sure your camera feed is h264 with AAC audio. If your camera doesn't support a compatible format for RTMP, you can use the ffmpeg args to re-encode it on the fly at the expense of increased CPU utilization.
|
||||
@@ -0,0 +1,6 @@
|
||||
---
|
||||
id: snapshots
|
||||
title: Snapshots
|
||||
---
|
||||
|
||||
Frigate can save a snapshot image to `/media/frigate/clips` for each event named as `<camera>-<id>.jpg`.
|
||||
@@ -0,0 +1,10 @@
|
||||
---
|
||||
id: zones
|
||||
title: Zones
|
||||
---
|
||||
|
||||
Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
|
||||
|
||||
During testing, enable the Zones option for the debug feed so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
|
||||
|
||||
To create a zone, follow [the steps for a "Motion mask"](/configuration/masks), but use the section of the web UI for creating a zone instead.
|
||||
Reference in New Issue
Block a user