add missing optional comment in docs

* Always show recording link even if recordings are currently disabled

* Fix test to consider all cameras to have recording link

.dockerignore

@@ -7,4 +7,5 @@ config/
 .git
 core
 *.mp4
-*.db
+*.db
+*.ts

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,56 +0,0 @@
----
-name: Bug report or Support request
-about: Bug report or Support request
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what your issue is.
-
-**Version of frigate**
-Output from `/api/version`
-
-**Config file**
-Include your full config file wrapped in triple back ticks.
-```yaml
-config here
-```
-
-**Frigate container logs**
-```
-Include relevant log output here
-```
-
-**Frigate stats**
-```json
-Output from frigate's /api/stats endpoint
-```
-
-**FFprobe from your camera**
-
-Run the following command and paste output below
-```
-ffprobe <stream_url>
-```
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Computer Hardware**
- - OS: [e.g. Ubuntu, Windows]
- - Install method: [e.g. Addon, Docker Compose, Docker Command]
- - Virtualization: [e.g. Proxmox, Virtualbox]
- - Coral Version: [e.g. USB, PCIe, None]
- - Network Setup: [e.g. Wired, WiFi]
-
-**Camera Info:**
- - Manufacturer: [e.g. Dahua]
- - Model: [e.g. IPC-HDW5231R-ZE]
- - Resolution: [e.g. 720p]
- - FPS: [e.g. 5]
-
-**Additional context**
-Add any other context about the problem here.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: enhancement
+assignees: ''
+
+---
+
+**Describe what you are trying to accomplish and why in non technical terms**
+I want to be able to ... so that I can ... 
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/support_request.yml

@@ -0,0 +1,107 @@
+name: Support Request
+description: Support for Frigate setup or configuration
+title: "[Support]: "
+labels: ["support", "triage"]
+assignees: []
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Describe the problem you are having
+    validations:
+      required: true
+  - type: input
+    id: version
+    attributes:
+      label: Version
+      description: Visible on the Debug page in the Web UI
+    validations:
+      required: true
+  - type: textarea
+    id: config
+    attributes:
+      label: Frigate config file
+      description: This will be automatically formatted into code, so no need for backticks.
+      render: yaml
+    validations:
+      required: true
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: ffprobe
+    attributes:
+      label: FFprobe output from your camera
+      description: Run `ffprobe <camera_url>` and provide output below
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: stats
+    attributes:
+      label: Frigate stats
+      description: Output from frigate's /api/stats endpoint
+      render: json
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
+  - type: dropdown
+    id: install-method
+    attributes:
+      label: Install method
+      options:
+        - HassOS Addon
+        - Docker Compose
+        - Docker CLI
+    validations:
+      required: true
+  - type: dropdown
+    id: coral
+    attributes:
+      label: Coral version
+      options:
+        - USB
+        - PCIe
+        - M.2
+        - Dev Board
+        - Other
+        - CPU (no coral)
+    validations:
+      required: true
+  - type: dropdown
+    id: network
+    attributes:
+      label: Network connection
+      options:
+        - Wired
+        - Wireless
+        - Mixed
+    validations:
+      required: true
+  - type: input
+    id: camera
+    attributes:
+      label: Camera make and model
+      description: Dahua, hikvision, amcrest, reolink, etc and model number
+    validations:
+      required: true
+  - type: textarea
+    id: other
+    attributes:
+      label: Any other information that may be helpful

.github/workflows/pull_request.yml

@@ -44,3 +44,27 @@ jobs:
       - name: Test
         run: npm run test
         working-directory: ./web
+        
+  docker_tests_on_aarch64:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v2
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v1
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+      - name: Build and run tests
+        run: make run_tests PLATFORM="linux/arm64/v8" ARCH="aarch64"
+
+  docker_tests_on_amd64:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v2
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v1
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+      - name: Build and run tests
+        run: make run_tests PLATFORM="linux/amd64" ARCH="amd64"

.github/workflows/push.yml

@@ -1,28 +0,0 @@
-name: On push
-
-on:
-  push:
-    branches:
-      - master
-      - release-0.8.0
-
-jobs:
-  deploy-docs:
-    name: Deploy docs
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ./docs
-    steps:
-      - uses: actions/checkout@master
-      - uses: actions/setup-node@master
-        with:
-          node-version: 12.x
-      - run: npm install
-      - name: Build docs
-        run: npm run build
-      - name: Deploy documentation
-        uses: peaceiris/actions-gh-pages@v3
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: ./docs/build

.gitignore

@@ -6,7 +6,9 @@ debug
 config/config.yml
 models
 *.mp4
+*.ts
 *.db
+*.csv
 frigate/version.py
 web/build
 web/node_modules

Makefile

@@ -3,7 +3,7 @@ default_target: amd64_frigate
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
 
 version:
-	echo "VERSION='0.9.0-$(COMMIT_HASH)'" > frigate/version.py
+	echo "VERSION='0.10.0-$(COMMIT_HASH)'" > frigate/version.py
 
 web:
 	docker build --tag frigate-web --file docker/Dockerfile.web web/
@@ -39,13 +39,13 @@ aarch64_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:1.0.3-aarch64 --file docker/Dockerfile.wheels .
 
 aarch64_ffmpeg:
-	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
+	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.3.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
 
 aarch64_frigate: version web
 	docker build --no-cache --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
 	docker build --no-cache --tag frigate --file docker/Dockerfile.aarch64 .
 
-armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
+aarch64_all: aarch64_wheels aarch64_ffmpeg aarch64_frigate
 
 armv7_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:1.0.3-armv7 --file docker/Dockerfile.wheels .
@@ -59,4 +59,16 @@ armv7_frigate: version web
 
 armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
 
-.PHONY: web
+run_tests:
+	# PLATFORM: linux/arm64/v8 linux/amd64 or linux/arm/v7
+	# ARCH: aarch64 amd64 or armv7
+	@cat docker/Dockerfile.base docker/Dockerfile.$(ARCH) > docker/Dockerfile.test
+	@sed -i "s/FROM frigate-web as web/#/g" docker/Dockerfile.test
+	@sed -i "s/COPY --from=web \/opt\/frigate\/build web\//#/g" docker/Dockerfile.test
+	@sed -i "s/FROM frigate-base/#/g" docker/Dockerfile.test
+	@echo "" >> docker/Dockerfile.test
+	@echo "RUN python3 -m unittest" >> docker/Dockerfile.test
+	@docker buildx build --platform=$(PLATFORM) --tag frigate-base --build-arg NGINX_VERSION=1.0.2 --build-arg FFMPEG_VERSION=1.0.0 --build-arg ARCH=$(ARCH) --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.test .
+	@rm docker/Dockerfile.test
+
+.PHONY: web run_tests

README.md

@@ -20,7 +20,7 @@ Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but
 
 ## Documentation
 
-View the documentation at https://blakeblackshear.github.io/frigate
+View the documentation at https://docs.frigate.video
 
 ## Donations
 

docker/Dockerfile.dev

@@ -22,3 +22,5 @@ RUN pip3 install pylint black
 # Install Node 14
 RUN curl -sL https://deb.nodesource.com/setup_14.x | bash - \
     && apt-get install -y nodejs
+
+RUN npm install -g npm@latest

docker/Dockerfile.ffmpeg.aarch64

@@ -9,7 +9,7 @@ WORKDIR     /tmp/workdir
 
 ENV     DEBIAN_FRONTEND=noninteractive
 RUN     apt-get -yqq update && \
-        apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
+        apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 xutils-dev && \
         apt-get autoremove -y && \
         apt-get clean -y
 
@@ -18,7 +18,7 @@ FROM base as build
 ENV     FFMPEG_VERSION=4.3.2 \
         AOM_VERSION=v1.0.0 \
         FDKAAC_VERSION=0.1.5 \
-        FREETYPE_VERSION=2.5.5 \
+        FREETYPE_VERSION=2.11.0 \
         FRIBIDI_VERSION=0.19.7 \
         KVAZAAR_VERSION=1.2.0 \
         LAME_VERSION=3.100 \
@@ -43,7 +43,7 @@ ENV     FFMPEG_VERSION=4.3.2 \
         LIBZMQ_VERSION=4.3.2 \
         SRC=/usr/local
 
-ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
+ARG         FREETYPE_SHA256SUM="a45c6b403413abd5706f3582f04c8339d26397c4304b78fa552f2215df64101f freetype-2.11.0.tar.gz"
 ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
 ARG         LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
 ARG         OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
@@ -392,6 +392,16 @@ RUN \
         make -j $(nproc) install && \
         rm -rf ${DIR}
 
+
+RUN \
+        DIR=/tmp/rkmpp && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        git clone https://github.com/rockchip-linux/libdrm-rockchip && git clone https://github.com/rockchip-linux/mpp && \
+        cd libdrm-rockchip && bash autogen.sh && ./configure && make && make install && \
+        cd ../mpp && cmake -DRKPLATFORM=ON -DHAVE_DRM=ON && make -j6 && make install && \
+        rm -rf ${DIR}
+
 ## ffmpeg https://ffmpeg.org/
 RUN  \
         DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
@@ -434,6 +444,8 @@ RUN \
         --enable-libkvazaar \
         --enable-libaom \
         --extra-libs=-lpthread \
+        --enable-rkmpp \
+        --enable-libdrm \
         # --enable-omx \
         # --enable-omx-rpi \
         # --enable-mmal \

docker/rootfs/usr/local/nginx/conf/nginx.conf

@@ -1,4 +1,5 @@
 daemon off;
+user root;
 worker_processes  1;
 
 error_log  /usr/local/nginx/logs/error.log warn;
@@ -57,7 +58,7 @@ http {
 
         # vod caches
         vod_metadata_cache metadata_cache 512m;
-        vod_mapping_cache mapping_cache 5m;
+        vod_mapping_cache mapping_cache 5m 10m;
 
         # gzip manifests
         gzip on;

docs/docs/configuration/advanced.md

@@ -1,50 +1,11 @@
 ---
 id: advanced
-title: Advanced
+title: Advanced Options
-sidebar_label: Advanced
+sidebar_label: Advanced Options
 ---
 
 ## 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 (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
-```
-
-### `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: 5x the frame rate)
-  max_disappeared: 25
-```
-
 ### `logger`
 
 Change the default log level for troubleshooting purposes.
@@ -72,12 +33,7 @@ Examples of available modules are:
 
 ### `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)
+This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
-
-```yaml
-environment_vars:
-  EXAMPLE_VAR: value
-```
 
 ### `database`
 
@@ -89,23 +45,7 @@ This may need to be in a custom location if network storage is used for the medi
 
 ```yaml
 database:
-  path: /media/frigate/frigate.db
+  path: /path/to/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`
@@ -116,11 +56,14 @@ The labelmap can be customized to your needs. A common reason to do this is to c
 
 ```yaml
 model:
-  # Required: height of the trained model
-  height: 320
-  # Required: width of the trained model
-  width: 320
-  # Optional: labelmap overrides
   labelmap:
-    7: car
+    2: vehicle
+    3: vehicle
+    5: vehicle
+    7: vehicle
+    15: animal
+    16: animal
+    17: animal
 ```
+
+Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.

docs/docs/configuration/camera_specific.md

@@ -0,0 +1,115 @@
+---
+id: camera_specific
+title: Camera Specific Configurations
+---
+
+### 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 -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 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v libx264 -an
+  rtmp: -c:v libx264 -an -f flv
+```
+
+### JPEG Stream Cameras
+
+Cameras using a live changing jpeg image will need input parameters as below
+
+```yaml
+input_args:
+- -r
+- 5 # << enter FPS here
+- -stream_loop
+- -1
+- -f
+- image2
+- -avoid_negative_ts
+- make_zero
+- -fflags
+- nobuffer
+- -flags
+- low_delay
+- -strict
+- experimental
+- -fflags
+- +genpts+discardcorrupt
+- -use_wallclock_as_timestamps
+- 1
+```
+
+Outputting the stream will have the same args and caveats as per [MJPEG Cameras](#mjpeg-cameras)
+
+### 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 -rw_timeout 5000000 -use_wallclock_as_timestamps 1 -f live_flv
+```
+
+### Reolink 410/520 (possibly others)
+
+According to [this discussion](https://github.com/blakeblackshear/frigate/issues/1713#issuecomment-932976305), the http video streams seem to be the most reliable for Reolink.
+
+```yaml
+cameras:
+  reolink:
+    ffmpeg:
+      hwaccel_args:
+      input_args:
+        - -avoid_negative_ts
+        - make_zero
+        - -fflags
+        - nobuffer+genpts+discardcorrupt
+        - -flags
+        - low_delay
+        - -strict
+        - experimental
+        - -analyzeduration
+        - 1000M
+        - -probesize
+        - 1000M
+        - -rw_timeout
+        - "5000000"
+      inputs:
+        - path: http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password
+          roles:
+            - record
+            - rtmp
+        - path: http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password
+          roles:
+            - detect
+    detect:
+      width: 896
+      height: 672
+      fps: 7
+```
+
+![Resolutions](/img/reolink-settings.png)
+
+### 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
+```
+
+### UDP Only Cameras
+
+If your cameras do not support TCP connections for RTSP, you can use UDP.
+
+```yaml
+ffmpeg:
+  input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport udp -stimeout 5000000 -use_wallclock_as_timestamps 1
+```

docs/docs/configuration/cameras.md

@@ -5,17 +5,15 @@ title: Cameras
 
 ## Setting Up Camera Inputs
 
-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.
+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.
 
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 
-| Role     | Description                                                                           |
+| Role     | Description                                                                                     |
-| -------- | ------------------------------------------------------------------------------------- |
+| -------- | ----------------------------------------------------------------------------------------------- |
-| `detect` | Main feed for object detection                                                        |
+| `detect` | Main feed for object detection                                                                  |
-| `record` | Saves segments of the video feed based on configuration settings. [docs](#recordings) |
+| `record` | Saves segments of the video feed based on configuration settings. [docs](/configuration/record) |
-| `rtmp`   | Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams)        |
+| `rtmp`   | Broadcast as an RTMP feed for other services to consume. [docs](/configuration/rtmp)            |
-
-### Example
 
 ```yaml
 mqtt:
@@ -34,524 +32,14 @@ cameras:
     detect:
       width: 1280
       height: 720
-      fps: 5
 ```
 
-`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.
+Additional cameras are simply added to the config under the `cameras` entry.
-
-## Masks & Zones
-
-### Masks
-
-Masks are used to ignore initial detection in areas of your camera's field of view.
-
-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 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.
-- **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.
-
-To create a poly mask:
-
-1. Visit the [web UI](/usage/web)
-1. Click the camera you wish to create a mask for
-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.yaml` 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"
-```
-
-![poly](/img/example-mask-poly.png)
-
-```yaml
-# 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
-```
-
-### 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, `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.
-
-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.
-
-```yaml
-# 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
-```
-
-## Objects
-
-For a list of available objects, see the [objects documentation](./objects.mdx).
-
-```yaml
-# 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
-```
-
-## Recordings
-
-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 in the event (default: shown below)
-    pre_capture: 5
-    # Optional: Number of seconds after the event to include in the event (default: shown below)
-    post_capture: 5
-    # Optional: Objects to save event for. (default: all tracked objects)
-    objects:
-      - person
-    # Optional: Restrict event to objects that entered any of the listed zones (default: no required zones)
-    required_zones: []
-    # Optional: Retention settings for event
-    retain:
-      # Required: Default retention days (default: shown below)
-      default: 10
-      # Optional: Per object retention days
-      objects:
-        person: 15
-```
-
-## 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:
-  # 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.
-
-## 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: 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)
-  #           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
 
 ```yaml
+mqtt: ...
 cameras:
-  # Required: name of the camera
+  back: ...
-  back:
+  front: ...
-    # Required: ffmpeg settings for the camera
+  side: ...
-    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:
-      # Required: 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: 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)
-    #           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"
 ```

docs/docs/configuration/detectors.md

@@ -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 +30,17 @@ detectors:
     device: usb:1
 ```
 
-Native Coral (Dev Board):
+### Native Coral (Dev Board)
+_warning: may have [compatibility issues](https://github.com/blakeblackshear/frigate/issues/1706) after `v0.9.x`_
 
 ```yaml
 detectors:
   coral:
     type: edgetpu
-    device: ''
+    device: ""
 ```
 
-Multiple PCIE/M.2 Corals:
+### Multiple PCIE/M.2 Corals
 
 ```yaml
 detectors:
@@ -51,7 +52,7 @@ detectors:
     device: pci:1
 ```
 
-Mixing Corals:
+### Mixing Corals
 
 ```yaml
 detectors:
@@ -63,12 +64,16 @@ 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.

docs/docs/configuration/hardware_acceleration.md

@@ -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.

docs/docs/configuration/index.md

@@ -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:
@@ -37,16 +40,16 @@ 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
   # Optional: password
-  # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
+  # NOTE: MQTT password can be specified with an environment variables that must begin with 'FRIGATE_'.
-  #       eg. password: '{FRIGATE_MQTT_PASSWORD}'
+  #       e.g. password: '{FRIGATE_MQTT_PASSWORD}'
   password: password
   # Optional: tls_ca_certs for enabling TLS using self-signed certs (default: None)
   tls_ca_certs: /path/to/ca.crt
@@ -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.
+# Optional: Database configuration
-
-```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
 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.
+# Optional: birdseye configuration
-
-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
 birdseye:
   # Optional: Enable birdseye view (default: shown below)
   enabled: True
@@ -232,4 +125,310 @@ birdseye:
   #   motion - cameras are included if motion was detected in the last 30 seconds
   #   continuous - all cameras are included always
   mode: objects
+
+# 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
+
+# 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
+  # Optional: Configuration for stationary object tracking
+  stationary:
+    # Optional: Frequency for running detection on stationary objects (default: shown below)
+    # When set to 0, object detection will never be run on stationary objects. If set to 10, it will be run on every 10th frame.
+    interval: 0
+    # Optional: Number of frames without a position change for an object to be considered stationary (default: 10x the frame rate or 10s)
+    threshold: 50
+    # Optional: Define a maximum number of frames for tracking a stationary object (default: not set, track forever)
+    # This can help with false positives for objects that should only be stationary for a limited amount of time.
+    # It can also be used to disable stationary object tracking. For example, you may want to set a value for person, but leave
+    # car at the default.
+    max_frames:
+      # Optional: Default for all object types (default: not set, track forever)
+      default: 3000
+      # Optional: Object specific values
+      objects:
+        person: 1000
+
+# 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: 30)
+  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
+  # make motion detection more sensitive to smaller moving objects.
+  # As a rule of thumb:
+  #  - 15 - high sensitivity
+  #  - 30 - medium sensitivity
+  #  - 50 - low sensitivity
+  contour_area: 30
+  # 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: 50)
+  # 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: 50
+  # 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)
+  # WARNING: Frigate does not currently support limiting recordings based
+  #          on available disk space automatically. If using recordings,
+  #          you must specify retention settings for a number of days that
+  #          will fit within the available disk space of your drive or Frigate
+  #          will crash.
+  enabled: False
+  # Optional: Number of minutes to wait between cleanup runs (default: shown below)
+  # This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
+  expire_interval: 60
+  # Optional: Retention settings for recording
+  retain:
+    # 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.
+    days: 0
+    # Optional: Mode for retention. Available options are: all, motion, and active_objects
+    #   all - save all recording segments regardless of activity
+    #   motion - save all recordings segments with any detected motion
+    #   active_objects - save all recording segments with active/moving objects
+    # NOTE: this mode only applies when the days setting above is greater than 0
+    mode: all
+  # 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: Mode for retention. (default: shown below)
+      #   all - save all recording segments for events regardless of activity
+      #   motion - save all recordings segments for events with any detected motion
+      #   active_objects - save all recording segments for event with active/moving objects
+      #
+      # NOTE: If the retain mode for the camera is more restrictive than the mode configured
+      #       here, the segments will already be gone by the time this mode is applied.
+      #       For example, if the camera retain mode is "motion", the segments without motion are
+      #       never stored, so setting the mode to "all" here won't bring them back.
+      mode: motion
+      # 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
+
+# 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
+
+# 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)
+  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
+
+# 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: path may include environment variables, which must begin with 'FRIGATE_' and 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: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
+        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: []
 ```

docs/docs/configuration/masks.md

@@ -0,0 +1,77 @@
+---
+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
+```
+
+![poly](/img/example-mask-poly-min.png)
+
+### Further Clarification
+
+This is a response to a [question posed on reddit](https://www.reddit.com/r/homeautomation/comments/ppxdve/replacing_my_doorbell_with_a_security_camera_a_6/hd876w4?utm_source=share&utm_medium=web2x&context=3):
+
+It is helpful to understand a bit about how Frigate uses motion detection and object detection together.
+
+First, Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.
+
+Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.
+
+After the area with motion is identified, Frigate creates a "region" (the green boxes in the debug viewer) to run object detection on. The models are trained on square images, so these regions are always squares. It adds a margin around the motion area in hopes of capturing a cropped view of the object moving that fills most of the image passed to object detection, but doesn't cut anything off. It also takes into consideration the location of the bounding box from the previous frame if it is tracking an object.
+
+After object detection runs, if there are detected objects that seem to be cut off, Frigate reframes the region and runs object detection again on the same frame to get a better look.
+
+All of this happens for each area of motion and tracked object.
+
+> Are you simply saying that INITIAL triggering of any kind of detection will only happen in un-masked areas, but that once this triggering happens, the masks become irrelevant and object detection takes precedence?
+
+Essentially, yes. I wouldn't describe it as object detection taking precedence though. The motion masks just prevent those areas from being counted as motion. Those masks do not modify the regions passed to object detection in any way, so you can absolutely detect objects in areas masked for motion.
+
+> If so, this is completely expected and intuitive behavior for me. Because obviously if a "foot" starts motion detection the camera should be able to check if it's an entire person before it fully crosses into the zone. The docs imply this is the behavior, so I also don't understand why this would be detrimental to object detection on the whole.
+
+When just a foot is triggering motion, Frigate will zoom in and look only at the foot. If that even qualifies as a person, it will determine the object is being cut off and look again and again until it zooms back out enough to find the whole person.
+
+It is also detrimental to how Frigate tracks a moving object. Motion nearby the bounding box from the previous frame is used to intelligently determine where the region should be in the next frame. With too much masking, tracking is hampered and if an object walks from an unmasked area into a fully masked area, they essentially disappear and will be picked up as a "new" object if they leave the masked area. This is important because Frigate uses the history of scores while tracking an object to determine if it is a false positive or not. It takes a minimum of 3 frames for Frigate to determine is the object type it thinks it is, and the median score must be greater than the threshold. If a person meets this threshold while on the sidewalk before they walk into your stoop, you will get an alert the instant they step a single foot into a zone.
+
+> I thought the main point of this feature was to cut down on CPU use when motion is happening in unnecessary areas.
+
+It is, but the definition of "unnecessary" varies. I want to ignore areas of motion that I know are definitely not being triggered by objects of interest. Timestamps, trees, sky, rooftops. I don't want to ignore motion from objects that I want to track and know where they go.
+
+> For me, giving my masks ANY padding results in a lot of people detection I'm not interested in. I live in the city and catch a lot of the sidewalk on my camera. People walk by my front door all the time and the margin between the sidewalk and actually walking onto my stoop is very thin, so I basically have everything but the exact contours of my stoop masked out. This results in very tidy detections but this info keeps throwing me off. Am I just overthinking it?
+
+This is what `required_zones` are for. You should define a zone (remember this is evaluated based on the bottom center of the bounding box) and make it required to save snapshots and clips (now events in 0.9.0). You can also use this in your conditions for a notification.
+
+> Maybe my specific situation just warrants this. I've just been having a hard time understanding the relevance of this information - it seems to be that it's exactly what would be expected when "masking out" an area of ANY image.
+
+That may be the case for you. Frigate will definitely work harder tracking people on the sidewalk to make sure it doesn't miss anyone who steps foot on your stoop. The trade off with the way you have it now is slower recognition of objects and potential misses. That may be acceptable based on your needs. Also, if your resolution is low enough on the detect stream, your regions may already be so big that they grab the entire object anyway.

docs/docs/configuration/nvdec.md

@@ -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
@@ -9,6 +9,8 @@ ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvi
 includes this support for amd64 platforms. An aarch64 for the Jetson, which
 also includes NVDEC may be added in the future.
 
+Some more detailed setup instructions are also available in [this issue](https://github.com/blakeblackshear/frigate/issues/1847#issuecomment-932076731).
+
 ## Docker setup
 
 ### Requirements
@@ -23,7 +25,7 @@ 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 +43,7 @@ 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 +59,9 @@ 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
 ```
@@ -96,15 +97,3 @@ processes:
 |    0   N/A  N/A     12827      C   ffmpeg                            417MiB |
 +-----------------------------------------------------------------------------+
 ```
-
-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
-```
-
-This setting, for example, allows Frigate to consume my 10-15fps camera streams on
-my relatively low powered Haswell machine with relatively low cpu usage.

docs/docs/configuration/objects.mdx

@@ -1,12 +1,15 @@
 ---
 id: objects
-title: Default available objects
+title: Objects
-sidebar_label: Available objects
 ---
 
 import labels from "../../../labelmap.txt";
 
-By default, Frigate includes the following object models from the Google Coral test data.
+Frigate includes the object models listed below from the Google Coral test data.
+
+Please note: 
+ - `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
+ - `person` is the only tracked object by default. See the [full configuration reference](https://docs.frigate.video/configuration/index#full-configuration-reference) for an example of expanding the list of tracked objects.
 
 <ul>
   {labels.split("\n").map((label) => (
@@ -22,4 +25,4 @@ 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.

docs/docs/configuration/optimizing.md

@@ -1,72 +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)
-**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.

docs/docs/configuration/record.md

@@ -0,0 +1,24 @@
+---
+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. 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.
+
+## What if I don't want 24/7 recordings?
+
+If you only used clips in previous versions with recordings disabled, you can use the following config to get the same behavior. This is also the default behavior when recordings are enabled.
+
+```yaml
+record:
+  enabled: True
+  events:
+    retain:
+      default: 10
+```
+
+This configuration will retain recording segments that overlap with events and have active tracked objects for 10 days. Because multiple events can reference the same recording segments, this avoids storing duplicate footage for overlapping events and reduces overall storage needs.
+
+When `retain_days` is set to `0`, segments will be deleted from the cache if no events are in progress.

docs/docs/configuration/rtmp.md

@@ -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. Some more information about it can be found [here](../faqs#audio-in-recordings). 

docs/docs/configuration/snapshots.md

@@ -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`.

docs/docs/configuration/zones.md

@@ -0,0 +1,40 @@
+---
+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. Presence in a zone is evaluated based on the bottom center of the bounding box for the object. It does not matter how much of the bounding box overlaps with the zone.
+
+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.
+
+### Restricting zones to specific objects
+
+Sometimes you want to limit a zone to specific object types to have more granular control of when events/snapshots are saved. The following example will limit one zone to person objects and the other to cars.
+
+```yaml
+camera:
+  record:
+    events:
+      required_zones:
+        - entire_yard
+        - front_yard_street
+  snapshots:
+    required_zones:
+      - entire_yard
+      - front_yard_street
+  zones:
+    entire_yard:
+      coordinates: ... (everywhere you want a person)
+      objects:
+        - person
+    front_yard_street:
+      coordinates: ... (just the street)
+      objects:
+        - car
+```
+
+Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. You will get events for person objects that enter anywhere in the yard, and events for cars only if they enter the street.

docs/docs/faqs.md

@@ -1,21 +1,40 @@
 ---
-id: troubleshooting
+id: faqs
-title: Troubleshooting and FAQ
+title: Frequently Asked Questions
 ---
 
+### Fatal Python error: Bus error
+
+This error message is due to a shm-size that is too small. Try updating your shm-size according to [this guide](/installation#calculating-required-shm-size).
+
 ### I am seeing a solid green image for my camera.
 
 A solid green image means that frigate has not received any frames from ffmpeg. Check the logs to see why ffmpeg is exiting and adjust your ffmpeg args accordingly.
 
-### How can I get sound or audio in my recordings?
+### How can I get sound or audio in my recordings? {#audio-in-recordings}
 
-By default, Frigate removes audio from recordings to reduce the likelihood of failing for invalid data. If you would like to include audio, you need to override the output args to remove `-an` for where you want to include audio. The recommended audio codec is `aac`. Not all audio codecs are supported by RTMP, so you may need to re-encode your audio with `-c:a aac`. The default ffmpeg args are shown [here](/frigate/configuration/index#ffmpeg).
+By default, Frigate removes audio from recordings to reduce the likelihood of failing for invalid data. If you would like to include audio, you need to override the output args to remove `-an` for where you want to include audio. The recommended audio codec is `aac`. Not all audio codecs are supported by RTMP, so you may need to re-encode your audio with `-c:a aac`. The default ffmpeg args are shown [here](/configuration/index/#full-configuration-reference).
+
+:::tip
+
+When using `-c:a aac`, do not forget to replace `-c copy` with `-c:v copy`. Example:
+
+```diff title="frigate.yml"
+ffmpeg:
+  output_args:
+-    record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
++    record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -c:a aac
+```
+
+This is needed because the `-c` flag (without `:a` or `:v`) applies for both audio and video, thus making it conflicting with `-c:a aac`.
+
+:::
 
 ### My mjpeg stream or snapshots look green and crazy
 
 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with vlc or another player. Also make sure you don't have the width and height values backwards.
 
-![mismatched-resolution](/img/mismatched-resolution.jpg)
+![mismatched-resolution](/img/mismatched-resolution-min.jpg)
 
 ### I can't view events or recordings in the Web UI.
 

docs/docs/guides/camera_setup.md

@@ -0,0 +1,47 @@
+---
+id: camera_setup
+title: Camera setup
+---
+
+Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but far less compatibility. Safari and Edge are the only browsers able to play H.265. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
+
+- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
+
+- **Recording**: This stream should be the resolution you wish to store for reference. Typically, this will be the highest resolution your camera supports. I recommend setting this feed to 15 fps.
+
+- **Stream Viewing**: This stream will be rebroadcast as is to Home Assistant for viewing with the stream component. Setting this resolution too high will use significant bandwidth when viewing streams in Home Assistant, and they may not load reliably over slower connections.
+
+### Choosing a detect resolution
+
+The ideal resolution for detection is one where the objects you want to detect fit inside the dimensions of the model used by Frigate (320x320). Frigate does not pass the entire camera frame to object detection. It will crop an area of motion from the full frame and look in that portion of the frame. If the area being inspected is larger than 320x320, Frigate must resize it before running object detection. Higher resolutions do not improve the detection accuracy because the additional detail is lost in the resize. Below you can see a reference for how large a 320x320 area is against common resolutions.
+
+Larger resolutions **do** improve performance if the objects are very small in the frame.
+
+![Resolutions](/img/resolutions-min.jpg)
+
+### Example Camera Configuration
+
+For the Dahua/Loryta 5442 camera, I use the following settings:
+
+**Main Stream (Recording)**
+
+- Encode Mode: H.264
+- Resolution: 2688\*1520
+- Frame Rate(FPS): 15
+- I Frame Interval: 30
+
+**Sub Stream 1 (RTMP)**
+
+- Enable: Sub Stream 1
+- Encode Mode: H.264
+- Resolution: 720\*576
+- Frame Rate: 10
+- I Frame Interval: 10
+
+**Sub Stream 2 (Detection)**
+
+- Enable: Sub Stream 2
+- Encode Mode: H.264
+- Resolution: 1280\*720
+- Frame Rate: 5
+- I Frame Interval: 5

docs/docs/guides/getting_started.md

@@ -0,0 +1,201 @@
+---
+id: getting_started
+title: Creating a config file
+---
+
+This guide walks through the steps to build a configuration file for Frigate. It assumes that you already have an environment setup as described in [Installation](/installation). You should also configure your cameras according to the [camera setup guide](/guides/camera_setup)
+
+### Step 1: Configure the MQTT server
+
+Frigate requires a functioning MQTT server. Start by adding the mqtt section at the top level in your config:
+
+```yaml
+mqtt:
+  host: <ip of your mqtt server>
+```
+
+If using the Mosquitto Addon in Home Assistant, a username and password is required. For example:
+
+```yaml
+mqtt:
+  host: <ip of your mqtt server>
+  user: <username>
+  password: <password>
+```
+
+Frigate supports many configuration options for mqtt. See the [configuration reference](/configuration/index#full-configuration-reference) for more info.
+
+### Step 2: Configure detectors
+
+By default, Frigate will use a single CPU detector. If you have a USB Coral, you will need to add a detectors section to your config.
+
+```yaml
+mqtt:
+  host: <ip of your mqtt server>
+
+detectors:
+  coral:
+    type: edgetpu
+    device: usb
+```
+
+More details on available detectors can be found [here](/configuration/detectors).
+
+### Step 3: Add a minimal camera configuration
+
+Now let's add the first camera:
+
+```yaml
+mqtt:
+  host: <ip of your mqtt server>
+
+detectors:
+  coral:
+    type: edgetpu
+    device: usb
+
+cameras:
+  camera_1: # <------ Name the camera
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.10.10:554/rtsp # <----- Update for your camera
+          roles:
+            - detect
+            - rtmp
+    rtmp:
+      enabled: False # <-- RTMP should be disabled if your stream is not H264
+    detect:
+      width: 1280 # <---- update for your camera's resolution
+      height: 720 # <---- update for your camera's resolution
+```
+
+### Step 4: Start Frigate
+
+At this point you should be able to start Frigate and see the the video feed in the UI.
+
+If you get a green image from the camera, this means ffmpeg was not able to get the video feed from your camera. Check the logs for error messages from ffmpeg. The default ffmpeg arguments are designed to work with H264 RTSP cameras that support TCP connections. If you do not have H264 cameras, make sure you have disabled RTMP. It is possible to enable it, but you must tell ffmpeg to re-encode the video with customized output args.
+
+FFmpeg arguments for other types of cameras can be found [here](/configuration/camera_specific).
+
+### Step 5: Configure hardware acceleration (optional)
+
+Now that you have a working camera configuration, you want to setup hardware acceleration to minimize the CPU required to decode your video streams. See the [hardware acceleration](/configuration/hardware_acceleration) config reference for examples applicable to your hardware.
+
+In order to best evaluate the performance impact of hardware acceleration, it is recommended to temporarily disable detection.
+
+```yaml
+mqtt: ...
+
+detectors: ...
+
+cameras:
+  camera_1:
+    ffmpeg: ...
+    detect:
+      enabled: False
+      ...
+```
+
+Here is an example configuration with hardware acceleration configured:
+
+```yaml
+mqtt: ...
+
+detectors: ...
+
+cameras:
+  camera_1:
+    ffmpeg:
+      inputs: ...
+      hwaccel_args: -c:v h264_v4l2m2m
+    detect: ...
+```
+
+### Step 6: Setup motion masks
+
+Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. To do this, navigate to the camera in the UI, select "Debug" at the top, and enable "Motion boxes" in the options below the video feed. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.
+
+Now that you know where you need to mask, use the "Mask & Zone creator" in the options pane to generate the coordinates needed for your config file. More information about masks can be found [here](/configuration/masks).
+
+:::caution
+
+Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get events and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
+
+:::
+
+Your configuration should look similar to this now.
+
+```yaml
+mqtt:
+  host: mqtt.local
+
+detectors:
+  coral:
+    type: edgetpu
+    device: usb
+
+cameras:
+  camera_1:
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.10.10:554/rtsp
+          roles:
+            - detect
+            - rtmp
+    detect:
+      width: 1280
+      height: 720
+    motion:
+      mask:
+        - 0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432
+```
+
+### Step 7: Enable recording (optional)
+
+To enable recording video, add the `record` role to a stream and enable it in the config.
+
+```yaml
+mqtt: ...
+
+detectors: ...
+
+cameras:
+  camera_1:
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.10.10:554/rtsp
+          roles:
+            - detect
+            - rtmp
+        - path: rtsp://10.0.10.10:554/high_res_stream # <----- Add high res stream
+          roles:
+            - record
+    detect: ...
+    record: # <----- Enable recording
+      enabled: True
+    motion: ...
+```
+
+If you don't have separate streams for detect and record, you would just add the record role to the list on the first input.
+
+By default, Frigate will retain video of all events for 10 days. The full set of options for recording can be found [here](/configuration/index#full-configuration-reference).
+
+### Step 8: Enable snapshots (optional)
+
+To enable snapshots of your events, just enable it in the config.
+
+```yaml
+mqtt: ...
+
+detectors: ...
+
+cameras:
+  camera_1: ...
+    detect: ...
+    record: ...
+    snapshots: # <----- Enable snapshots
+      enabled: True
+    motion: ...
+```
+
+By default, Frigate will retain snapshots of all events for 10 days. The full set of options for snapshots can be found [here](/configuration/index#full-configuration-reference).

docs/docs/guides/ha_notifications.md

@@ -0,0 +1,80 @@
+---
+id: ha_notifications
+title: Home Assistant notifications
+---
+
+The best way to get started with notifications for Frigate is to use the [Blueprint](https://community.home-assistant.io/t/frigate-mobile-app-notifications/311091). You can use the yaml generated from the Blueprint as a starting point and customize from there.
+
+It is generally recommended to trigger notifications based on the `frigate/events` mqtt topic. This provides the event_id needed to fetch [thumbnails/snapshots/clips](/integrations/home-assistant#notification-api) and other useful information to customize when and where you want to receive alerts. The data is published in the form of a change feed, which means you can reference the "previous state" of the object in the `before` section and the "current state" of the object in the `after` section. You can see an example [here](/integrations/mqtt#frigateevents).
+
+Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.
+
+```yaml
+automation:
+  - alias: Notify of events
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
+          data:
+            image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg?format=android'
+            tag: '{{trigger.payload_json["after"]["id"]}}'
+            when: '{{trigger.payload_json["after"]["start_time"]|int}}'
+```
+
+Note that iOS devices support live previews of cameras by adding a camera entity id to the message data.
+
+```yaml
+automation:
+  - alias: Security_Frigate_Notifications
+    description: ""
+    trigger:
+      - platform: mqtt
+        topic: frigate/events
+        payload: new
+        value_template: "{{ value_json.type }}"
+    action:
+      - service: notify.mobile_app_iphone
+        data:
+          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
+          data:
+            image: >-
+              https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg
+            tag: '{{trigger.payload_json["after"]["id"]}}'
+            when: '{{trigger.payload_json["after"]["start_time"]|int}}'
+            entity_id: camera.{{trigger.payload_json["after"]["camera"]}}
+    mode: single
+```
+
+## Conditions
+
+Conditions with the `before` and `after` values allow a high degree of customization for automations.
+
+When a person enters a zone named yard
+
+```yaml
+condition:
+  - "{{ trigger.payload_json['after']['label'] == 'person' }}"
+  - "{{ 'yard' in trigger.payload_json['after']['entered_zones'] }}"
+```
+
+When a person leaves a zone named yard
+
+```yaml
+condition:
+  - "{{ trigger.payload_json['after']['label'] == 'person' }}"
+  - "{{ 'yard' in trigger.payload_json['before']['current_zones'] }}"
+  - "{{ not 'yard' in trigger.payload_json['after']['current_zones'] }}"
+```
+
+Notify for dogs in the front with a high top score
+
+```yaml
+condition:
+  - "{{ trigger.payload_json['after']['label'] == 'dog' }}"
+  - "{{ trigger.payload_json['after']['camera'] == 'front' }}"
+  - "{{ trigger.payload_json['after']['top_score'] > 0.98 }}"
+```

docs/docs/guides/stationary_objects.md

@@ -0,0 +1,37 @@
+---
+id: stationary_objects
+title: Avoiding stationary objects
+---
+
+Many people use Frigate to detect cars entering their driveway, and they often run into an issue with repeated events of a parked car being repeatedly detected. This is because object tracking stops when motion ends and the event ends. Motion detection works by determining if a sufficient number of pixels have changed between frames. Shadows or other lighting changes will be detected as motion. This will often cause a new event for a parked car.
+
+You can use zones to restrict events and notifications to objects that have entered specific areas.
+
+:::caution
+
+It is not recommended to use masks to try and eliminate parked cars in your driveway. Masks are designed to prevent motion from triggering object detection and/or to indicate areas that are guaranteed false positives.
+
+Frigate is designed to track objects as they move and over-masking can prevent it from knowing that an object in the current frame is the same as the previous frame. You want Frigate to detect objects everywhere and configure your events and alerts to be based on the location of the object with zones.
+
+:::
+
+To only be notified of cars that enter your driveway from the street, you could create multiple zones that cover your driveway. For cars, you would only notify if `entered_zones` from the events MQTT topic has more than 1 zone.
+
+See [this example](/configuration/zones#restricting-zones-to-specific-objects) from the Zones documentation to see how to restrict zones to certain object types.
+
+![Driveway Zones](/img/driveway_zones-min.png)
+
+To limit snapshots and events, you can list the zone for the entrance of your driveway under `required_zones` in your configuration file. Example below.
+
+```yaml
+camera:
+  record:
+    events:
+      required_zones:
+        - zone_2
+  zones:
+    zone_1:
+      coordinates: ... (parking area)
+    zone_2:
+      coordinates: ... (entrance to driveway)
+```

docs/docs/hardware.md

@@ -7,23 +7,62 @@ title: Recommended hardware
 
 Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
 
-## Computer
+I recommend Dahua, Hikvision, and Amcrest in that order. Dahua edges out Hikvision because they are easier to find and order, not because they are better cameras. I personally use Dahua cameras because they are easier to purchase directly. In my experience Dahua and Hikvision both have multiple streams with configurable resolutions and frame rates and rock solid streams. They also both have models with large sensors well known for excellent image quality at night. Not all the models are equal. Larger sensors are better than higher resolutions; especially at night. Amcrest is the fallback recommendation because they are rebranded Dahuas. They are rebranding the lower end models with smaller sensors or less configuration options.
 
-| Name                    | Inference Speed | Notes                                                                                                                         |
+Many users have reported various issues with Reolink cameras, so I do not recommend them. If you are using Reolink, I suggest the [Reolink specific configuration](configuration/camera_specific#reolink-410520-possibly-others). Wifi cameras are also not recommended. Their streams are less reliable and cause connection loss and/or lost video data.
-| ----------------------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| Atomic Pi               | 16ms            | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
-| Intel NUC NUC7i3BNK     | 8-10ms          | Great performance. Can handle many cameras at 5fps depending on typical amounts of motion.                                    |
-| BMAX B2 Plus            | 10-12ms         | Good balance of performance and cost. Also capable of running many other services at the same time as frigate.                |
-| Minisforum GK41         | 9-10ms          | Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras.                                      |
-| Raspberry Pi 3B (32bit) | 60ms            | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.                                       |
-| Raspberry Pi 4 (32bit)  | 15-20ms         | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
-| Raspberry Pi 4 (64bit)  | 10-15ms         | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
 
-## Unraid
+Here are some of the camera's I recommend:
 
-Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
+- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
-To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
+- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
+- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
 
-| Name                                 | Inference Speed | Notes                                                                                                                                                                       |
+I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
-| ------------------------------------ | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+
-| [M2 Coral Edge TPU](http://coral.ai) | 6.2ms           | Install the Coral plugin from Unraid Community App Center [info here](https://forums.unraid.net/topic/98064-support-blakeblackshear-frigate/?do=findComment&comment=949789) |
+## Server
+
+My current favorite is the Odyssey X86 Blue J4125 because the Coral M.2 compatibility and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+
+| Name                                                                                                                             | Inference Speed | Coral Compatibility | Notes                                                                                                                         |
+| -------------------------------------------------------------------------------------------------------------------------------- | --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| <a href="https://amzn.to/3oH4BKi" target="_blank" rel="nofollow noopener sponsored">Odyssey X86 Blue J4125</a> (affiliate link)  | 9-10ms          | M.2 B+M             | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                     |
+| <a href="https://amzn.to/3oxEC8m" target="_blank" rel="nofollow noopener sponsored">Minisforum GK41</a> (affiliate link)         | 9-10ms          | USB                 | Great alternative to a NUC. Easily handles several 1080p cameras.                                                             |
+| <a href="https://amzn.to/3ixJFlb" target="_blank" rel="nofollow noopener sponsored">Minisforum GK50</a> (affiliate link)         | 9-10ms          | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                     |
+| <a href="https://amzn.to/3l7vCEI" target="_blank" rel="nofollow noopener sponsored">Intel NUC</a> (affiliate link)               | 8-10ms          | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion.             |
+| <a href="https://amzn.to/3a6TBh8" target="_blank" rel="nofollow noopener sponsored">BMAX B2 Plus</a> (affiliate link)            | 10-12ms         | USB                 | Good balance of performance and cost. Also capable of running many other services at the same time as frigate.                |
+| <a href="https://amzn.to/2YjpY9m" target="_blank" rel="nofollow noopener sponsored">Atomic Pi</a> (affiliate link)               | 16ms            | USB                 | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
+| <a href="https://amzn.to/2WIpwRU" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 3B (32bit)</a> (affiliate link) | 60ms            | USB                 | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.                                       |
+| <a href="https://amzn.to/2YhSGHH" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 4 (32bit)</a> (affiliate link)  | 15-20ms         | USB                 | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
+| <a href="https://amzn.to/2YhSGHH" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 4 (64bit)</a> (affiliate link)  | 10-15ms         | USB                 | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
+
+## Google Coral TPU
+
+It is strongly recommended to use a Google Coral. Frigate is designed around the expectation that a Coral is used to achieve very low inference speeds. 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
+
+The USB version is compatible with the widest variety of hardware and does not require a driver on the host machine. However, it does lack the automatic throttling features of the other versions.
+
+The PCIe and M.2 versions require installation of a driver on the host. Follow the instructions for your version from https://coral.ai
+
+A single Coral can handle many cameras and will be sufficient for the majority of users. You can calculate the maximum performance of your Coral based on the inference speed reported by Frigate. With an inference speed of 10, your Coral will top out at `1000/10=100`, or 100 frames per second. If your detection fps is regularly getting close to that, you should first consider tuning motion masks. If those are already properly configured, a second Coral may be needed.
+
+### What does Frigate use the CPU for and what does it use the Coral for? (ELI5 Version)
+
+This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
+
+CPU Usage: I am a CPU, Mendel is a Google Coral
+
+My buddy Mendel and I have been tasked with keeping the neighbor's red footed booby off my parent's yard. Now I'm really bad at identifying birds. It takes me forever, but my buddy Mendel is incredible at it.
+
+Mendel however, struggles at pretty much anything else. So we make an agreement. I wait till I see something that moves, and snap a picture of it for Mendel. I then show him the picture and he tells me what it is. Most of the time it isn't anything. But eventually I see some movement and Mendel tells me it is the Booby. Score!
+
+_What happens when I increase the resolution of my camera?_
+
+However we realize that there is a problem. There is still booby poop all over the yard. How could we miss that! I've been watching all day! My parents check the window and realize its dirty and a bit small to see the entire yard so they clean it and put a bigger one in there. Now there is so much more to see! However I now have a much bigger area to scan for movement and have to work a lot harder! Even my buddy Mendel has to work harder, as now the pictures have a lot more detail in them that he has to look at to see if it is our sneaky booby.
+
+Basically - When you increase the resolution and/or the frame rate of the stream there is now significantly more data for the CPU to parse. That takes additional computing power. The Google Coral is really good at doing object detection, but it doesn't have time to look everywhere all the time (especially when there are many windows to check). To balance it, Frigate uses the CPU to look for movement, then sends those frames to the Coral to do object detection. This allows the Coral to be available to a large number of cameras and not overload it.
+
+### Do hwaccel args help if I am using a Coral?
+
+YES! The Coral does not help with decoding video streams.
+
+Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://blog.video.ibm.com/streaming-video-tips/keyframes-interframe-video-compression/). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.

docs/docs/how-it-works.md

@@ -1,13 +0,0 @@
----
-id: how-it-works
-title: How Frigate Works
-sidebar_label: How it works
----
-
-Frigate is designed to minimize resource and maximize performance by only looking for objects when and where it is necessary
-
-![Diagram](/img/diagram.png)
-
-1. Look for Motion
-2. Calculate Detection Regions
-3. Run Object Detection

docs/docs/index.md

@@ -1,13 +1,12 @@
 ---
 id: index
-title: Frigate
+title: Introduction
-sidebar_label: Features
 slug: /
 ---
 
 A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
-Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
+Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but strongly recommended. CPU detection should only be used for testing purposes. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
 
 - Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
@@ -15,11 +14,12 @@ Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but
 - Uses a very low overhead motion detection to determine where to run object detection
 - Object detection with TensorFlow runs in separate processes for maximum FPS
 - Communicates over MQTT for easy integration into other systems
-- 24/7 recording
+- Recording with retention based on detected objects
 - Re-streaming via RTMP to reduce the number of connections to your camera
+- A dynamic combined camera view of all tracked cameras.
 
 ## Screenshots
 
-![Media Browser](/img/media_browser.png)
+![Media Browser](/img/media_browser-min.png)
 
-![Notification](/img/notification.png)
+![Notification](/img/notification-min.png)

docs/docs/installation.md

@@ -3,25 +3,105 @@ id: installation
 title: Installation
 ---
 
-Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). See instructions below for installing the HassOS addon.
+Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). Note that a Home Assistant Addon is **not** the same thing as the integration. The [integration](integrations/home-assistant) is required to integrate Frigate into Home Assistant.
 
-For Home Assistant users, there is also a [custom component (aka integration)](https://github.com/blakeblackshear/frigate-hass-integration). This custom component adds tighter integration with Home Assistant by automatically setting up camera entities, sensors, media browser for recordings, and a public API to simplify notifications.
+## Dependencies
 
-Note that HassOS Addons and custom components are different things. If you are already running Frigate with Docker directly, you do not need the Addon since the Addon would run another instance of Frigate.
+**MQTT broker** - Frigate requires an MQTT broker. If using Home Assistant, Frigate and Home Assistant must be connected to the same MQTT broker.
 
-## HassOS Addon
+## Preparing your hardware
 
-HassOS users can install via the addon repository. Frigate requires an MQTT server.
+### Operating System
 
-1. Navigate to Supervisor > Add-on Store > Repositories
+Frigate runs best with docker installed on bare metal debian-based distributions. For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer often introduces a sizable amount of overhead for communication with Coral devices, but [not in all circumstances](https://github.com/blakeblackshear/frigate/discussions/1837).
-2. Add https://github.com/blakeblackshear/frigate-hass-addons
+
-3. Setup your network configuration in the `Configuration` tab if deisred
+Windows is not officially supported, but some users have had success getting it to run under WSL or Virtualbox. Getting the GPU and/or Coral devices properly passed to Frigate may be difficult or impossible. Search previous discussions or issues for help.
-4. Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
+
-5. Start the addon container
+### Storage
-6. If you are using hardware acceleration for ffmpeg, you will need to disable "Protection mode"
+
+Frigate uses the following locations for read/write operations in the container. Docker volume mappings can be used to map these to any location on your host machine.
+
+:::caution
+
+Note that Frigate does not currently support limiting recordings based on available disk space automatically. If using recordings, you must specify retention settings for a number of days that will fit within the available disk space of your drive or Frigate will crash.
+
+:::
+
+- `/media/frigate/clips`: Used for snapshot storage. In the future, it will likely be renamed from `clips` to `snapshots`. The file structure here cannot be modified and isn't intended to be browsed or managed manually.
+- `/media/frigate/recordings`: Internal system storage for recording segments. The file structure here cannot be modified and isn't intended to be browsed or managed manually.
+- `/media/frigate/frigate.db`: Default location for the sqlite database. You will also see several files alongside this file while frigate is running. If moving the database location (often needed when using a network drive at `/media/frigate`), it is recommended to mount a volume with docker at `/db` and change the storage location of the database to `/db/frigate.db` in the config file.
+- `/tmp/cache`: Cache location for recording segments. Initial recordings are written here before being checked and converted to mp4 and moved to the recordings folder.
+- `/dev/shm`: It is not recommended to modify this directory or map it with docker. This is the location for raw decoded frames in shared memory and it's size is impacted by the `shm-size` calculations below.
+- `/config/config.yml`: Default location of the config file.
+
+#### Common docker compose storage configurations
+
+Writing to a local disk or external USB drive:
+
+```yaml
+version: "3.9"
+services:
+  frigate:
+    ...
+    volumes:
+      - /path/to/your/config.yml:/config/config.yml:ro
+      - /path/to/your/storage:/media/frigate
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+        target: /tmp/cache
+        tmpfs:
+          size: 1000000000
+    ...
+```
+
+Writing to a network drive with database on a local drive:
+
+```yaml
+version: "3.9"
+services:
+  frigate:
+    ...
+    volumes:
+      - /path/to/your/config.yml:/config/config.yml:ro
+      - /path/to/network/storage:/media/frigate
+      - /path/to/local/disk:/db
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+        target: /tmp/cache
+        tmpfs:
+          size: 1000000000
+    ...
+```
+
+frigate.yml
+
+```yaml
+database:
+  path: /db/frigate.db
+```
+
+### Calculating required shm-size
+
+Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is 64m.
+
+The default shm-size of 64m is fine for setups with 2 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size.
+
+You can calculate the necessary shm-size for each camera with the following formula using the resolution specified for detect:
+
+```
+(width * height * 1.5 * 9 + 270480)/1048576 = <shm size in mb>
+```
+
+The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
+
+### Raspberry Pi 3/4
+
+By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).
+
+Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
 
 ## Docker
 
+Running in Docker directly is the recommended install method.
+
 Make sure you choose the right image for your architecture:
 
 | Arch        | Image Name                                 |
@@ -41,13 +121,15 @@ services:
     privileged: true # this may not be necessary for all setups
     restart: unless-stopped
     image: blakeblackshear/frigate:<specify_version_tag>
+    shm_size: "64mb" # update for your cameras based on calculation above
     devices:
-      - /dev/bus/usb:/dev/bus/usb
+      - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
+      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
       - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
     volumes:
       - /etc/localtime:/etc/localtime:ro
-      - <path_to_config_file>:/config/config.yml:ro
+      - /path/to/your/config.yml:/config/config.yml:ro
-      - <path_to_directory_for_media>:/media/frigate
+      - /path/to/your/storage:/media/frigate
       - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
         target: /tmp/cache
         tmpfs:
@@ -68,8 +150,9 @@ docker run -d \
   --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
   --device /dev/bus/usb:/dev/bus/usb \
   --device /dev/dri/renderD128 \
-  -v <path_to_directory_for_media>:/media/frigate \
+  --shm-size=64m \
-  -v <path_to_config_file>:/config/config.yml:ro \
+  -v /path/to/your/storage:/media/frigate \
+  -v /path/to/your/config.yml:/config/config.yml:ro \
   -v /etc/localtime:/etc/localtime:ro \
   -e FRIGATE_RTSP_PASSWORD='password' \
   -p 5000:5000 \
@@ -77,48 +160,71 @@ docker run -d \
   blakeblackshear/frigate:<specify_version_tag>
 ```
 
-### Calculating shm-size
+## Home Assistant Operating System (HassOS)
 
-The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
+:::caution
 
-You can calculate the necessary shm-size for each camera with the following formula:
+Due to limitations in Home Assistant Operating System, utilizing external storage for recordings or snapshots requires [modifying udev rules manually](https://community.home-assistant.io/t/solved-mount-usb-drive-in-hassio-to-be-used-on-the-media-folder-with-udev-customization/258406/46).
 
-```
+:::
-(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
+
+:::tip
+
+If possible, it is recommended to run Frigate standalone in Docker and use [Frigate's Proxy Addon](https://github.com/blakeblackshear/frigate-hass-addons/blob/main/frigate_proxy/README.md).
+
+:::
+
+HassOS users can install via the addon repository.
+
+1. Navigate to Supervisor > Add-on Store > Repositories
+2. Add https://github.com/blakeblackshear/frigate-hass-addons
+3. Install your desired Frigate NVR Addon and navigate to it's page
+4. Setup your network configuration in the `Configuration` tab
+5. (not for proxy addon) Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
+6. Start the addon container
+7. (not for proxy addon) If you are using hardware acceleration for ffmpeg, you may need to disable "Protection mode"
+
+There are several versions of the addon available:
+
+| Addon Version                  | Description                                                |
+| ------------------------------ | ---------------------------------------------------------- |
+| Frigate NVR                    | Current release with protection mode on                    |
+| Frigate NVR (Full Access)      | Current release with the option to disable protection mode |
+| Frigate NVR Beta               | Beta release with protection mode on                       |
+| Frigate NVR Beta (Full Access) | Beta release with the option to disable protection mode    |
+
+## Home Assistant Supervised
+
+:::tip
+
+If possible, it is recommended to run Frigate standalone in Docker and use [Frigate's Proxy Addon](https://github.com/blakeblackshear/frigate-hass-addons/blob/main/frigate_proxy/README.md).
+
+:::
+
+When running Home Assistant with the [Supervised install method](https://github.com/home-assistant/supervised-installer), you can get the benefit of running the Addon along with the ability to customize the storage used by Frigate.
+
+In order to customize the storage location for Frigate, simply use `fstab` to mount the drive you want at `/usr/share/hassio/media`. Here is an example fstab entry:
+
+```shell
+UUID=1a65fec6-c25f-404a-b3d2-1f2fcf6095c8 /media/data ext4 defaults 0 0
+/media/data/homeassistant/media /usr/share/hassio/media none bind 0 0
 ```
 
-The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
+Then follow the instructions listed for [Home Assistant Operating System](#home-assistant-operating-system-hassos).
 
 ## Kubernetes
 
 Use the [helm chart](https://github.com/blakeblackshear/blakeshome-charts/tree/master/charts/frigate).
 
-## Virtualization
+## Unraid
 
-For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
+Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
+To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
 
-### Proxmox
+## Proxmox
 
-Some people have had success running Frigate in LXC directly with the following config:
+It is recommended to run Frigate in LXC for maximum performance. See [this discussion](https://github.com/blakeblackshear/frigate/discussions/1111) for more information.
 
-```
+## ESX
-arch: amd64
-cores: 2
-features: nesting=1
-hostname: FrigateLXC
-memory: 4096
-net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
-ostype: debian
-rootfs: local-lvm:vm-115-disk-0,size=12G
-swap: 512
-lxc.cgroup.devices.allow: c 189:385 rwm
-lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
-lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
-lxc.apparmor.profile: unconfined
-lxc.cgroup.devices.allow: a
-lxc.cap.drop:
-```
-
-### ESX
 
 For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).

docs/docs/integrations/api.md

@@ -192,6 +192,10 @@ Permanently deletes the event along with any clips/snapshots.
 
 Returns a thumbnail for the event id optimized for notifications. Works while the event is in progress and after completion. Passing `?format=android` will convert the thumbnail to 2:1 aspect ratio.
 
+### `GET /api/events/<id>/clip.mp4`
+
+Returns the clip for the event id. Works after the event has ended.
+
 ### `GET /api/events/<id>/snapshot.jpg`
 
 Returns the snapshot image for the event id. Works while the event is in progress and after completion.
@@ -206,10 +210,22 @@ Accepts the following query string parameters, but they are only applied when an
 | `crop`      | int  | Crop the snapshot to the (0 or 1)                 |
 | `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.    |
 
-### `/clips/<camera>-<id>.jpg`
+### `GET /clips/<camera>-<id>.jpg`
 
 JPG snapshot for the given camera and event id.
 
-### `/vod/<year>-<month>/<day>/<hour>/<camera>/master.m3u8`
+### `GET /vod/<year>-<month>/<day>/<hour>/<camera>/master.m3u8`
 
 HTTP Live Streaming Video on Demand URL for the specified hour and camera. Can be viewed in an application like VLC.
+
+### `GET /vod/event/<event-id>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
+
+### `GET /vod/event/<event-id>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
+
+### `GET /vod/<camera>/start/<start-timestamp>/end/<end-timestamp>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the camera with the specified time range. Can be viewed in an application like VLC.

docs/docs/integrations/home-assistant.md

@@ -0,0 +1,192 @@
+---
+id: home-assistant
+title: Home Assistant Integration
+---
+
+The best way to integrate with Home Assistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration).
+
+## Installation
+
+### Preparation
+
+The Frigate integration requires the `mqtt` integration to be installed and
+manually configured first.
+
+See the [MQTT integration
+documentation](https://www.home-assistant.io/integrations/mqtt/) for more
+details.
+
+### Integration installation
+
+Available via HACS as a default repository. To install:
+
+- Use [HACS](https://hacs.xyz/) to install the integration:
+
+```
+Home Assistant > HACS > Integrations > "Explore & Add Integrations" > Frigate
+```
+
+- Restart Home Assistant.
+- Then add/configure the integration:
+
+```
+Home Assistant > Configuration > Integrations > Add Integration > Frigate
+```
+
+Note: You will also need
+[media_source](https://www.home-assistant.io/integrations/media_source/) enabled
+in your Home Assistant configuration for the Media Browser to appear.
+
+### (Optional) Lovelace Card Installation
+
+To install the optional companion Lovelace card, please see the [separate
+installation instructions](https://github.com/dermotduffy/frigate-hass-card) for
+that card.
+
+## Configuration
+
+When configuring the integration, you will be asked for the `URL` of your frigate instance which is the URL you use to access Frigate in the browser. This may look like `http://<host>:5000/`. If you are using HassOS with the addon, the URL should be one of the following depending on which addon version you are using. Note that if you are using the Proxy Addon, you do NOT point the integration at the proxy URL. Just enter the URL used to access frigate directly from your network.
+
+| Addon Version                  | URL                                    |
+| ------------------------------ | -------------------------------------- |
+| Frigate NVR                    | `http://ccab4aaf-frigate:5000`         |
+| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`      |
+| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`    |
+| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000` |
+
+<a name="options"></a>
+
+## Options
+
+```
+Home Assistant > Configuration > Integrations > Frigate > Options
+```
+
+| Option            | Description                                                                                                                                                                                                                                                                                                                          |
+| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| RTMP URL Template | A [jinja2](https://jinja.palletsprojects.com/) template that is used to override the standard RTMP stream URL (e.g. for use with reverse proxies). This option is only shown to users who have [advanced mode](https://www.home-assistant.io/blog/2019/07/17/release-96/#advanced-mode) enabled. See [RTMP streams](#streams) below. |
+
+## Entities Provided
+
+| Platform        | Description                                                                       |
+| --------------- | --------------------------------------------------------------------------------- |
+| `camera`        | Live camera stream (requires RTMP), camera for image of the last detected object. |
+| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras.   |
+| `switch`        | Switch entities to toggle detection, recordings and snapshots.                    |
+| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                           |
+
+## Media Browser Support
+
+The integration provides:
+
+- Browsing event recordings with thumbnails
+- Browsing snapshots
+- Browsing recordings by month, day, camera, time
+
+This is accessible via "Media Browser" on the left menu panel in Home Assistant.
+
+<a name="api"></a>
+
+## Notification API
+
+Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
+
+To load a thumbnail for an event:
+
+```
+https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
+```
+
+To load a snapshot for an event:
+
+```
+https://HA_URL/api/frigate/notifications/<event-id>/snapshot.jpg
+```
+
+To load a video clip of an event:
+
+```
+https://HA_URL/api/frigate/notifications/<event-id>/clip.mp4
+```
+
+<a name="streams"></a>
+
+## RTMP stream
+
+In order for the live streams to function they need to be accessible on the RTMP
+port (default: `1935`) at `<frigatehost>:1935`. Home Assistant will directly
+connect to that streaming port when the live camera is viewed.
+
+#### RTMP URL Template
+
+For advanced usecases, this behavior can be changed with the [RTMP URL
+template](#options) option. When set, this string will override the default stream
+address that is derived from the default behavior described above. This option supports
+[jinja2 templates](https://jinja.palletsprojects.com/) and has the `camera` dict
+variables from [Frigate API](https://blakeblackshear.github.io/frigate/usage/api#apiconfig)
+available for the template. Note that no Home Assistant state is available to the
+template, only the camera dict from Frigate.
+
+This is potentially useful when Frigate is behind a reverse proxy, and/or when
+the default stream port is otherwise not accessible to Home Assistant (e.g.
+firewall rules).
+
+###### RTMP URL Template Examples
+
+Use a different port number:
+
+```
+rtmp://<frigate_host>:2000/live/front_door
+```
+
+Use the camera name in the stream URL:
+
+```
+rtmp://<frigate_host>:2000/live/{{ name }}
+```
+
+Use the camera name in the stream URL, converting it to lowercase first:
+
+```
+rtmp://<frigate_host>:2000/live/{{ name|lower }}
+```
+
+## Multiple Instance Support
+
+The Frigate integration seamlessly supports the use of multiple Frigate servers.
+
+### Requirements for Multiple Instances
+
+In order for multiple Frigate instances to function correctly, the
+`topic_prefix` and `client_id` parameters must be set differently per server.
+See [MQTT
+configuration](https://blakeblackshear.github.io/frigate/configuration/index#mqtt)
+for how to set these.
+
+#### API URLs
+
+When multiple Frigate instances are configured, [API](#api) URLs should include an
+identifier to tell Home Assistant which Frigate instance to refer to. The
+identifier used is the MQTT `client_id` paremeter included in the configuration,
+and is used like so:
+
+```
+https://HA_URL/api/frigate/<client-id>/notifications/<event-id>/thumbnail.jpg
+```
+
+```
+https://HA_URL/api/frigate/<client-id>/clips/front_door-1624599978.427826-976jaa.mp4
+```
+
+#### Default Treatment
+
+When a single Frigate instance is configured, the `client-id` parameter need not
+be specified in URLs/identifiers -- that single instance is assumed. When
+multiple Frigate instances are configured, the user **must** explicitly specify
+which server they are referring to.
+
+## FAQ
+
+#### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
+
+The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.

docs/docs/integrations/mqtt.md

@@ -36,7 +36,7 @@ Message published for each changed event. The first message is published when th
 
 ```json
 {
-  "type": "update", // new, update, end or clip_ready
+  "type": "update", // new, update, end
   "before": {
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
@@ -53,7 +53,12 @@ Message published for each changed event. The first message is published when th
     "region": [264, 450, 667, 853],
     "current_zones": ["driveway"],
     "entered_zones": ["yard", "driveway"],
-    "thumbnail": null
+    "thumbnail": null,
+    "has_snapshot": false,
+    "has_clip": false,
+    "stationary": false, // whether or not the object is considered stationary
+    "motionless_count": 0, // number of frames the object has been motionless
+    "position_changes": 2 // number of times the object has moved from a stationary position
   },
   "after": {
     "id": "1607123955.475377-mxklsc",
@@ -71,7 +76,12 @@ Message published for each changed event. The first message is published when th
     "region": [218, 440, 693, 915],
     "current_zones": ["yard", "driveway"],
     "entered_zones": ["yard", "driveway"],
-    "thumbnail": null
+    "thumbnail": null,
+    "has_snapshot": false,
+    "has_clip": false,
+    "stationary": false, // whether or not the object is considered stationary
+    "motionless_count": 0, // number of frames the object has been motionless
+    "position_changes": 2 // number of times the object has changed position
   }
 }
 ```

docs/docs/usage/home-assistant.md

@@ -1,281 +0,0 @@
----
-id: home-assistant
-title: Integration with Home Assistant
-sidebar_label: Home Assistant
----
-
-The best way to integrate with Home Assistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration).
-
-## Installation
-
-Available via HACS as a [custom repository](https://hacs.xyz/docs/faq/custom_repositories). To install:
-
-- Add the custom repository:
-
-```
-Home Assistant > HACS > Integrations > [...] > Custom Repositories
-```
-
-| Key            | Value                                                       |
-| -------------- | ----------------------------------------------------------- |
-| Repository URL | https://github.com/blakeblackshear/frigate-hass-integration |
-| Category       | Integration                                                 |
-
-- Use [HACS](https://hacs.xyz/) to install the integration:
-
-```
-Home Assistant > HACS > Integrations > "Explore & Add Integrations" > Frigate
-```
-
-- Restart Home Assistant.
-- Then add/configure the integration:
-
-```
-Home Assistant > Configuration > Integrations > Add Integration > Frigate
-```
-
-Note: You will also need
-[media_source](https://www.home-assistant.io/integrations/media_source/) enabled
-in your Home Assistant configuration for the Media Browser to appear.
-
-## Configuration
-
-When configuring the integration, you will be asked for the following parameters:
-
-| Variable | Description                                                                                                                                                                                                                                                                                                                                                                      |
-| -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| URL      | The `URL` of your frigate instance, the URL you use to access Frigate in the browser. This may look like `http://<host>:5000/`. If you are using HassOS with the addon, the URL should be `http://ccab4aaf-frigate:5000` (or `http://ccab4aaf-frigate-beta:5000` if your are using the beta version of the addon). Live streams required port 1935, see [RTMP streams](#streams) |
-
-<a name="options"></a>
-
-## Options
-
-```
-Home Assistant > Configuration > Integrations > Frigate > Options
-```
-
-| Option            | Description                                                                                                                                                                                                                                                                                                                          |
-| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| RTMP URL Template | A [jinja2](https://jinja.palletsprojects.com/) template that is used to override the standard RTMP stream URL (e.g. for use with reverse proxies). This option is only shown to users who have [advanced mode](https://www.home-assistant.io/blog/2019/07/17/release-96/#advanced-mode) enabled. See [RTMP streams](#streams) below. |
-
-## Entities Provided
-
-| Platform        | Description                                                                       |
-| --------------- | --------------------------------------------------------------------------------- |
-| `camera`        | Live camera stream (requires RTMP), camera for image of the last detected object. |
-| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras.   |
-| `switch`        | Switch entities to toggle detection, recordings and snapshots.                    |
-| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                           |
-
-## Media Browser Support
-
-The integration provides:
-
-- Rich UI with thumbnails for browsing event recordings
-- Rich UI for browsing 24/7 recordings by month, day, camera, time
-
-This is accessible via "Media Browser" on the left menu panel in Home Assistant.
-
-<a name="api"></a>
-
-## API
-
-- Notification API with public facing endpoints for images in notifications
-
-### Notifications
-
-Frigate publishes event information in the form of a change feed via MQTT. This
-allows lots of customization for notifications to meet your needs. Event changes
-are published with `before` and `after` information as shown
-[here](#frigateevents). Note that some people may not want to expose frigate to
-the web, so you can leverage the HA API that frigate custom_integration ties
-into (which is exposed to the web, and thus can be used for mobile notifications
-etc):
-
-To load an image taken by frigate from Home Assistants API see below:
-
-```
-https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
-```
-
-To load a video clip taken by frigate from Home Assistants API :
-
-```
-https://HA_URL/api/frigate/notifications/<event-id>/<camera>/clip.mp4
-```
-
-Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.
-
-```yaml
-automation:
-  - alias: Notify of events
-    trigger:
-      platform: mqtt
-      topic: frigate/events
-    action:
-      - service: notify.mobile_app_pixel_3
-        data_template:
-          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
-          data:
-            image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg?format=android'
-            tag: '{{trigger.payload_json["after"]["id"]}}'
-            when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-```yaml
-automation:
-  - alias: When a person enters a zone named yard
-    trigger:
-      platform: mqtt
-      topic: frigate/events
-    condition:
-      - "{{ trigger.payload_json['after']['label'] == 'person' }}"
-      - "{{ 'yard' in trigger.payload_json['after']['entered_zones'] }}"
-    action:
-      - service: notify.mobile_app_pixel_3
-        data_template:
-          message: "A {{trigger.payload_json['after']['label']}} has entered the yard."
-          data:
-            image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
-            tag: "{{trigger.payload_json['after']['id']}}"
-            when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-```yaml
-- alias: When a person leaves a zone named yard
-  trigger:
-    platform: mqtt
-    topic: frigate/events
-  condition:
-    - "{{ trigger.payload_json['after']['label'] == 'person' }}"
-    - "{{ 'yard' in trigger.payload_json['before']['current_zones'] }}"
-    - "{{ not 'yard' in trigger.payload_json['after']['current_zones'] }}"
-  action:
-    - service: notify.mobile_app_pixel_3
-      data_template:
-        message: "A {{trigger.payload_json['after']['label']}} has left the yard."
-        data:
-          image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
-          tag: "{{trigger.payload_json['after']['id']}}"
-          when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-```yaml
-- alias: Notify for dogs in the front with a high top score
-  trigger:
-    platform: mqtt
-    topic: frigate/events
-  condition:
-    - "{{ trigger.payload_json['after']['label'] == 'dog' }}"
-    - "{{ trigger.payload_json['after']['camera'] == 'front' }}"
-    - "{{ trigger.payload_json['after']['top_score'] > 0.98 }}"
-  action:
-    - service: notify.mobile_app_pixel_3
-      data_template:
-        message: "High confidence dog detection."
-        data:
-          image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
-          tag: "{{trigger.payload_json['after']['id']}}"
-          when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-If you are using telegram, you can fetch the image directly from Frigate:
-
-```yaml
-automation:
-  - alias: Notify of events
-    trigger:
-      platform: mqtt
-      topic: frigate/events
-    action:
-      - service: notify.telegram_full
-        data_template:
-          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
-          data:
-            photo:
-              # this url should work for addon users
-              - url: 'http://ccab4aaf-frigate:5000/api/events/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg'
-                caption: 'A {{trigger.payload_json["after"]["label"]}} was detected on {{ trigger.payload_json["after"]["camera"] }} camera'
-```
-
-<a name="streams"></a>
-
-## RTMP stream
-
-In order for the live streams to function they need to be accessible on the RTMP
-port (default: `1935`) at `<frigatehost>:1935`. Home Assistant will directly
-connect to that streaming port when the live camera is viewed.
-
-#### RTMP URL Template
-
-For advanced usecases, this behavior can be changed with the [RTMP URL
-template](#options) option. When set, this string will override the default stream
-address that is derived from the default behavior described above. This option supports
-[jinja2 templates](https://jinja.palletsprojects.com/) and has the `camera` dict
-variables from [Frigate API](https://blakeblackshear.github.io/frigate/usage/api#apiconfig)
-available for the template. Note that no Home Assistant state is available to the
-template, only the camera dict from Frigate.
-
-This is potentially useful when Frigate is behind a reverse proxy, and/or when
-the default stream port is otherwise not accessible to Home Assistant (e.g.
-firewall rules).
-
-###### RTMP URL Template Examples
-
-Use a different port number:
-
-```
-rtmp://<frigate_host>:2000/live/front_door
-```
-
-Use the camera name in the stream URL:
-
-```
-rtmp://<frigate_host>:2000/live/{{ name }}
-```
-
-Use the camera name in the stream URL, converting it to lowercase first:
-
-```
-rtmp://<frigate_host>:2000/live/{{ name|lower }}
-```
-
-## Multiple Instance Support
-
-The Frigate integration seamlessly supports the use of multiple Frigate servers.
-
-### Requirements for Multiple Instances
-
-In order for multiple Frigate instances to function correctly, the
-`topic_prefix` and `client_id` parameters must be set differently per server.
-See [MQTT
-configuration](https://blakeblackshear.github.io/frigate/configuration/index#mqtt)
-for how to set these.
-
-#### API URLs
-
-When multiple Frigate instances are configured, [API](#api) URLs should include an
-identifier to tell Home Assistant which Frigate instance to refer to. The
-identifier used is the MQTT `client_id` paremeter included in the configuration,
-and is used like so:
-
-```
-https://HA_URL/api/frigate/<client-id>/notifications/<event-id>/thumbnail.jpg
-```
-
-```
-https://HA_URL/api/frigate/<client-id>/clips/front_door-1624599978.427826-976jaa.mp4
-```
-
-#### Default Treatment
-
-When a single Frigate instance is configured, the `client-id` parameter need not
-be specified in URLs/identifiers -- that single instance is assumed. When
-multiple Frigate instances are configured, the user **must** explicitly specify
-which server they are referring to.
-
-## FAQ
-
-### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
-
-The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.

docs/docs/usage/howtos.md

@@ -1,11 +0,0 @@
----
-id: howtos
-title: Community Guides
-sidebar_label: Community Guides
----
-
-## Communitiy Guides/How-To's
-
-- Best Camera AI Person & Object Detection - How to Setup Frigate w/ Home Assistant - digiblurDIY [YouTube](https://youtu.be/V8vGdoYO6-Y) - [Article](https://www.digiblur.com/2021/05/how-to-setup-frigate-home-assistant.html)
-- Even More Free Local Object Detection with Home Assistant - Frigate Install - Everything Smart Home [YouTube](https://youtu.be/pqDCEZSVeRk)
-- Home Assistant Frigate integration for local image recognition - KPeyanski [YouTube](https://youtu.be/Q2UT78lFQpo) - [Article](https://peyanski.com/home-assistant-frigate-integration/)

docs/docs/usage/web.md

@@ -1,10 +0,0 @@
----
-id: web
-title: Web Interface
----
-
-Frigate comes bundled with a simple web ui that supports the following:
-
-- Show cameras
-- Browse events
-- Mask helper

docs/docusaurus.config.js

@@ -3,8 +3,8 @@ const path = require('path');
 module.exports = {
   title: 'Frigate',
   tagline: 'NVR With Realtime Object Detection for IP Cameras',
-  url: 'https://blakeblackshear.github.io',
+  url: 'https://docs.frigate.video',
-  baseUrl: '/frigate/',
+  baseUrl: '/',
   onBrokenLinks: 'throw',
   onBrokenMarkdownLinks: 'warn',
   favicon: 'img/favicon.ico',
@@ -29,6 +29,16 @@ module.exports = {
           label: 'Docs',
           position: 'left',
         },
+        {
+          href: 'https://frigate.video',
+          label: 'Website',
+          position: 'right',
+        },
+        {
+          href: 'https://demo.frigate.video',
+          label: 'Demo',
+          position: 'right',
+        },
         {
           href: 'https://github.com/blakeblackshear/frigate',
           label: 'GitHub',

docs/package.json

@@ -12,13 +12,13 @@
     "clear": "docusaurus clear"
   },
   "dependencies": {
-    "@docusaurus/core": "2.0.0-alpha.70",
+    "@docusaurus/core": "^2.0.0-beta.15",
-    "@docusaurus/preset-classic": "2.0.0-alpha.70",
+    "@docusaurus/preset-classic": "^2.0.0-beta.15",
-    "@mdx-js/react": "^1.6.21",
+    "@mdx-js/react": "^1.6.22",
     "clsx": "^1.1.1",
     "raw-loader": "^4.0.2",
-    "react": "^16.8.4",
+    "react": "^16.14.0",
-    "react-dom": "^16.8.4"
+    "react-dom": "^16.14.0"
   },
   "browserslist": {
     "production": [
@@ -31,5 +31,8 @@
       "last 1 firefox version",
       "last 1 safari version"
     ]
+  },
+  "devDependencies": {
+    "@types/react": "^16.14.0"
   }
 }

docs/sidebars.js

@@ -1,16 +1,34 @@
 module.exports = {
   docs: {
-    Frigate: ['index', 'how-it-works', 'hardware', 'installation', 'troubleshooting'],
+    Frigate: [
+      'index',
+      'hardware',
+      'installation',
+    ],
+    Guides: [
+      'guides/camera_setup',
+      'guides/getting_started',
+      'guides/false_positives',
+      'guides/ha_notifications',
+      'guides/stationary_objects',
+    ],
     Configuration: [
       'configuration/index',
-      'configuration/cameras',
-      'configuration/optimizing',
       'configuration/detectors',
-      'configuration/false_positives',
+      'configuration/cameras',
+      'configuration/masks',
+      'configuration/record',
+      'configuration/snapshots',
       'configuration/objects',
+      'configuration/rtmp',
+      'configuration/zones',
       'configuration/advanced',
+      'configuration/hardware_acceleration',
+      'configuration/nvdec',
+      'configuration/camera_specific',
     ],
-    Usage: ['usage/home-assistant', 'usage/web', 'usage/api', 'usage/mqtt'],
+    Integrations: ['integrations/home-assistant', 'integrations/api', 'integrations/mqtt'],
+    Troubleshooting: ['faqs'],
     Development: ['contributing'],
   },
 };

frigate/app.py

@@ -8,10 +8,12 @@ import threading
 from logging.handlers import QueueHandler
 from typing import Dict, List
 
+import traceback
 import yaml
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
 from playhouse.sqliteq import SqliteQueueDatabase
+from pydantic import ValidationError
 
 from frigate.config import DetectorTypeEnum, FrigateConfig
 from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
@@ -20,14 +22,14 @@ from frigate.events import EventCleanup, EventProcessor
 from frigate.http import create_app
 from frigate.log import log_process, root_configurer
 from frigate.models import Event, Recordings
-from frigate.mqtt import create_mqtt_client, MqttSocketRelay
+from frigate.mqtt import MqttSocketRelay, create_mqtt_client
 from frigate.object_processing import TrackedObjectProcessor
 from frigate.output import output_frames
 from frigate.record import RecordingCleanup, RecordingMaintainer
 from frigate.stats import StatsEmitter, stats_init
+from frigate.version import VERSION
 from frigate.video import capture_camera, track_camera
 from frigate.watchdog import FrigateWatchdog
-from frigate.zeroconf import broadcast_zeroconf
 
 logger = logging.getLogger(__name__)
 
@@ -66,6 +68,12 @@ class FrigateApp:
 
     def init_config(self):
         config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
+
+        # Check if we can use .yaml instead of .yml
+        config_file_yaml = config_file.replace(".yml", ".yaml")
+        if os.path.isfile(config_file_yaml):
+            config_file = config_file_yaml
+
         user_config = FrigateConfig.parse_file(config_file)
         self.config = user_config.runtime_config
 
@@ -85,29 +93,6 @@ class FrigateApp:
                 "frame_queue": mp.Queue(maxsize=2),
             }
 
-    def check_config(self):
-        for name, camera in self.config.cameras.items():
-            assigned_roles = list(
-                set([r for i in camera.ffmpeg.inputs for r in i.roles])
-            )
-            if not camera.record.enabled and "record" in assigned_roles:
-                logger.warning(
-                    f"Camera {name} has record assigned to an input, but record is not enabled."
-                )
-            elif camera.record.enabled and not "record" in assigned_roles:
-                logger.warning(
-                    f"Camera {name} has record enabled, but record is not assigned to an input."
-                )
-
-            if not camera.rtmp.enabled and "rtmp" in assigned_roles:
-                logger.warning(
-                    f"Camera {name} has rtmp assigned to an input, but rtmp is not enabled."
-                )
-            elif camera.rtmp.enabled and not "rtmp" in assigned_roles:
-                logger.warning(
-                    f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input."
-                )
-
     def set_log_levels(self):
         logging.getLogger().setLevel(self.config.logger.default.value.upper())
         for log, level in self.config.logger.logs.items():
@@ -127,6 +112,9 @@ class FrigateApp:
             maxsize=len(self.config.cameras.keys()) * 2
         )
 
+        # Queue for recordings info
+        self.recordings_info_queue = mp.Queue()
+
     def init_database(self):
         # Migrate DB location
         old_db_path = os.path.join(CLIPS_DIR, "frigate.db")
@@ -170,6 +158,7 @@ class FrigateApp:
         self.mqtt_relay.start()
 
     def start_detectors(self):
+        model_path = self.config.model.path
         model_shape = (self.config.model.height, self.config.model.width)
         for name in self.config.cameras.keys():
             self.detection_out_events[name] = mp.Event()
@@ -199,6 +188,7 @@ class FrigateApp:
                     name,
                     self.detection_queue,
                     self.detection_out_events,
+                    model_path,
                     model_shape,
                     "cpu",
                     detector.num_threads,
@@ -208,6 +198,7 @@ class FrigateApp:
                     name,
                     self.detection_queue,
                     self.detection_out_events,
+                    model_path,
                     model_shape,
                     detector.device,
                     detector.num_threads,
@@ -222,6 +213,7 @@ class FrigateApp:
             self.event_queue,
             self.event_processed_queue,
             self.video_output_queue,
+            self.recordings_info_queue,
             self.stop_event,
         )
         self.detected_frames_processor.start()
@@ -289,7 +281,9 @@ class FrigateApp:
         self.event_cleanup.start()
 
     def start_recording_maintainer(self):
-        self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
+        self.recording_maintainer = RecordingMaintainer(
+            self.config, self.recordings_info_queue, self.stop_event
+        )
         self.recording_maintainer.start()
 
     def start_recording_cleanup(self):
@@ -312,16 +306,29 @@ class FrigateApp:
 
     def start(self):
         self.init_logger()
+        logger.info(f"Starting Frigate ({VERSION})")
         try:
             try:
                 self.init_config()
             except Exception as e:
-                print(f"Error parsing config: {e}")
+                print("*************************************************************")
+                print("*************************************************************")
+                print("***    Your config file is not valid!                     ***")
+                print("***    Please check the docs at                           ***")
+                print("***    https://docs.frigate.video/configuration/index     ***")
+                print("*************************************************************")
+                print("*************************************************************")
+                print("***    Config Validation Errors                           ***")
+                print("*************************************************************")
+                print(e)
+                print(traceback.format_exc())
+                print("*************************************************************")
+                print("***    End Config Validation Errors                       ***")
+                print("*************************************************************")
                 self.log_process.terminate()
                 sys.exit(1)
             self.set_environment_vars()
             self.ensure_dirs()
-            self.check_config()
             self.set_log_levels()
             self.init_queues()
             self.init_database()

frigate/config.py

@@ -9,12 +9,11 @@ from typing import Dict, List, Optional, Tuple, Union
 import matplotlib.pyplot as plt
 import numpy as np
 import yaml
-from pydantic import BaseModel, Field, validator
+from pydantic import BaseModel, Extra, Field, validator
 from pydantic.fields import PrivateAttr
 
-from frigate.const import BASE_DIR, CACHE_DIR, RECORD_DIR
+from frigate.const import BASE_DIR, CACHE_DIR, YAML_EXT
-from frigate.edgetpu import load_labels
+from frigate.util import create_mask, deep_merge, load_labels
-from frigate.util import create_mask, deep_merge
 
 logger = logging.getLogger(__name__)
 
@@ -29,18 +28,23 @@ DEFAULT_TRACKED_OBJECTS = ["person"]
 DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
 
 
+class FrigateBaseModel(BaseModel):
+    class Config:
+        extra = Extra.forbid
+
+
 class DetectorTypeEnum(str, Enum):
     edgetpu = "edgetpu"
     cpu = "cpu"
 
 
-class DetectorConfig(BaseModel):
+class DetectorConfig(FrigateBaseModel):
     type: DetectorTypeEnum = Field(default=DetectorTypeEnum.cpu, title="Detector Type")
     device: str = Field(default="usb", title="Device Type")
     num_threads: int = Field(default=3, title="Number of detection threads")
 
 
-class MqttConfig(BaseModel):
+class MqttConfig(FrigateBaseModel):
     host: str = Field(title="MQTT Host")
     port: int = Field(default=1883, title="MQTT Port")
     topic_prefix: str = Field(default="frigate", title="MQTT Topic Prefix")
@@ -60,50 +64,68 @@ class MqttConfig(BaseModel):
         return v
 
 
-class RetainConfig(BaseModel):
+class RetainModeEnum(str, Enum):
-    default: int = Field(default=10, title="Default retention period.")
+    all = "all"
-    objects: Dict[str, int] = Field(
+    motion = "motion"
+    active_objects = "active_objects"
+
+
+class RetainConfig(FrigateBaseModel):
+    default: float = Field(default=10, title="Default retention period.")
+    mode: RetainModeEnum = Field(default=RetainModeEnum.motion, title="Retain mode.")
+    objects: Dict[str, float] = Field(
         default_factory=dict, title="Object retention period."
     )
 
 
-# DEPRECATED: Will eventually be removed
+class EventsConfig(FrigateBaseModel):
-class ClipsConfig(BaseModel):
+    max_seconds: int = Field(default=300, title="Maximum event duration.")
-    enabled: bool = Field(default=False, title="Save clips.")
+    pre_capture: int = Field(default=5, title="Seconds to retain before event starts.")
-    max_seconds: int = Field(default=300, title="Maximum clip duration.")
+    post_capture: int = Field(default=5, title="Seconds to retain after event ends.")
-    pre_capture: int = Field(default=5, title="Seconds to capture before event starts.")
-    post_capture: int = Field(default=5, title="Seconds to capture after event ends.")
     required_zones: List[str] = Field(
         default_factory=list,
-        title="List of required zones to be entered in order to save the clip.",
+        title="List of required zones to be entered in order to save the event.",
     )
     objects: Optional[List[str]] = Field(
-        title="List of objects to be detected in order to save the clip.",
+        title="List of objects to be detected in order to save the event.",
     )
     retain: RetainConfig = Field(
-        default_factory=RetainConfig, title="Clip retention settings."
+        default_factory=RetainConfig, title="Event retention settings."
     )
 
 
-class RecordConfig(BaseModel):
+class RecordRetainConfig(FrigateBaseModel):
+    days: float = Field(default=0, title="Default retention period.")
+    mode: RetainModeEnum = Field(default=RetainModeEnum.all, title="Retain mode.")
+
+
+class RecordConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable record on all cameras.")
-    retain_days: int = Field(default=0, title="Recording retention period in days.")
+    expire_interval: int = Field(
-    events: ClipsConfig = Field(
+        default=60,
-        default_factory=ClipsConfig, title="Event specific settings."
+        title="Number of minutes to wait between cleanup runs.",
+    )
+    # deprecated - to be removed in a future version
+    retain_days: Optional[float] = Field(title="Recording retention period in days.")
+    retain: RecordRetainConfig = Field(
+        default_factory=RecordRetainConfig, title="Record retention settings."
+    )
+    events: EventsConfig = Field(
+        default_factory=EventsConfig, title="Event specific settings."
     )
 
 
-class MotionConfig(BaseModel):
+class MotionConfig(FrigateBaseModel):
     threshold: int = Field(
         default=25,
         title="Motion detection threshold (1-255).",
         ge=1,
         le=255,
     )
-    contour_area: Optional[int] = Field(title="Contour Area")
+    contour_area: Optional[int] = Field(default=30, title="Contour Area")
     delta_alpha: float = Field(default=0.2, title="Delta Alpha")
     frame_alpha: float = Field(default=0.2, title="Frame Alpha")
-    frame_height: Optional[int] = Field(title="Frame Height")
+    frame_height: Optional[int] = Field(default=50, title="Frame Height")
     mask: Union[str, List[str]] = Field(
         default="", title="Coordinates polygon for the motion mask."
     )
@@ -116,15 +138,6 @@ class RuntimeMotionConfig(MotionConfig):
     def __init__(self, **config):
         frame_shape = config.get("frame_shape", (1, 1))
 
-        if "frame_height" not in config:
-            config["frame_height"] = max(frame_shape[0] // 6, 180)
-
-        if "contour_area" not in config:
-            frame_width = frame_shape[1] * config["frame_height"] / frame_shape[0]
-            config["contour_area"] = (
-                config["frame_height"] * frame_width * 0.00173611111
-            )
-
         mask = config.get("mask", "")
         config["raw_mask"] = mask
 
@@ -146,9 +159,33 @@ class RuntimeMotionConfig(MotionConfig):
 
     class Config:
         arbitrary_types_allowed = True
+        extra = Extra.ignore
 
 
-class DetectConfig(BaseModel):
+class StationaryMaxFramesConfig(FrigateBaseModel):
+    default: Optional[int] = Field(title="Default max frames.", ge=1)
+    objects: Dict[str, int] = Field(
+        default_factory=dict, title="Object specific max frames."
+    )
+
+
+class StationaryConfig(FrigateBaseModel):
+    interval: Optional[int] = Field(
+        default=0,
+        title="Frame interval for checking stationary objects.",
+        ge=0,
+    )
+    threshold: Optional[int] = Field(
+        title="Number of frames without a position change for an object to be considered stationary",
+        ge=1,
+    )
+    max_frames: StationaryMaxFramesConfig = Field(
+        default_factory=StationaryMaxFramesConfig,
+        title="Max frames for stationary objects.",
+    )
+
+
+class DetectConfig(FrigateBaseModel):
     height: int = Field(default=720, title="Height of the stream for the detect role.")
     width: int = Field(default=1280, title="Width of the stream for the detect role.")
     fps: int = Field(
@@ -158,9 +195,13 @@ class DetectConfig(BaseModel):
     max_disappeared: Optional[int] = Field(
         title="Maximum number of frames the object can dissapear before detection ends."
     )
+    stationary: StationaryConfig = Field(
+        default_factory=StationaryConfig,
+        title="Stationary objects config.",
+    )
 
 
-class FilterConfig(BaseModel):
+class FilterConfig(FrigateBaseModel):
     min_area: int = Field(
         default=0, title="Minimum area of bounding box for object to be counted."
     )
@@ -201,8 +242,10 @@ class RuntimeFilterConfig(FilterConfig):
 
     class Config:
         arbitrary_types_allowed = True
+        extra = Extra.ignore
 
 
+# this uses the base model because the color is an extra attribute
 class ZoneConfig(BaseModel):
     filters: Dict[str, FilterConfig] = Field(
         default_factory=dict, title="Zone filters."
@@ -244,7 +287,7 @@ class ZoneConfig(BaseModel):
             self._contour = np.array([])
 
 
-class ObjectConfig(BaseModel):
+class ObjectConfig(FrigateBaseModel):
     track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
     filters: Optional[Dict[str, FilterConfig]] = Field(title="Object filters.")
     mask: Union[str, List[str]] = Field(default="", title="Object mask.")
@@ -256,7 +299,7 @@ class BirdseyeModeEnum(str, Enum):
     continuous = "continuous"
 
 
-class BirdseyeConfig(BaseModel):
+class BirdseyeConfig(FrigateBaseModel):
     enabled: bool = Field(default=True, title="Enable birdseye view.")
     width: int = Field(default=1280, title="Birdseye width.")
     height: int = Field(default=720, title="Birdseye height.")
@@ -303,7 +346,7 @@ RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT = [
 ]
 
 
-class FfmpegOutputArgsConfig(BaseModel):
+class FfmpegOutputArgsConfig(FrigateBaseModel):
     detect: Union[str, List[str]] = Field(
         default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT,
         title="Detect role FFmpeg output arguments.",
@@ -318,7 +361,7 @@ class FfmpegOutputArgsConfig(BaseModel):
     )
 
 
-class FfmpegConfig(BaseModel):
+class FfmpegConfig(FrigateBaseModel):
     global_args: Union[str, List[str]] = Field(
         default=FFMPEG_GLOBAL_ARGS_DEFAULT, title="Global FFmpeg arguments."
     )
@@ -340,7 +383,7 @@ class CameraRoleEnum(str, Enum):
     detect = "detect"
 
 
-class CameraInput(BaseModel):
+class CameraInput(FrigateBaseModel):
     path: str = Field(title="Camera input path.")
     roles: List[CameraRoleEnum] = Field(title="Roles assigned to this input.")
     global_args: Union[str, List[str]] = Field(
@@ -371,7 +414,7 @@ class CameraFfmpegConfig(FfmpegConfig):
         return v
 
 
-class SnapshotsConfig(BaseModel):
+class SnapshotsConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Snapshots enabled.")
     clean_copy: bool = Field(
         default=True, title="Create a clean copy of the snapshot image."
@@ -399,22 +442,35 @@ class SnapshotsConfig(BaseModel):
     )
 
 
-class ColorConfig(BaseModel):
+class ColorConfig(FrigateBaseModel):
-    red: int = Field(default=255, le=0, ge=255, title="Red")
+    red: int = Field(default=255, ge=0, le=255, title="Red")
-    green: int = Field(default=255, le=0, ge=255, title="Green")
+    green: int = Field(default=255, ge=0, le=255, title="Green")
-    blue: int = Field(default=255, le=0, ge=255, title="Blue")
+    blue: int = Field(default=255, ge=0, le=255, title="Blue")
 
 
-class TimestampStyleConfig(BaseModel):
+class TimestampPositionEnum(str, Enum):
-    position: str = Field(default="tl", title="Timestamp position.")
+    tl = "tl"
+    tr = "tr"
+    bl = "bl"
+    br = "br"
+
+
+class TimestampEffectEnum(str, Enum):
+    solid = "solid"
+    shadow = "shadow"
+
+
+class TimestampStyleConfig(FrigateBaseModel):
+    position: TimestampPositionEnum = Field(
+        default=TimestampPositionEnum.tl, title="Timestamp position."
+    )
     format: str = Field(default=DEFAULT_TIME_FORMAT, title="Timestamp format.")
     color: ColorConfig = Field(default_factory=ColorConfig, title="Timestamp color.")
-    scale: float = Field(default=1.0, title="Timestamp scale.")
     thickness: int = Field(default=2, title="Timestamp thickness.")
-    effect: Optional[str] = Field(title="Timestamp effect.")
+    effect: Optional[TimestampEffectEnum] = Field(title="Timestamp effect.")
 
 
-class CameraMqttConfig(BaseModel):
+class CameraMqttConfig(FrigateBaseModel):
     enabled: bool = Field(default=True, title="Send image over MQTT.")
     timestamp: bool = Field(default=True, title="Add timestamp to MQTT image.")
     bounding_box: bool = Field(default=True, title="Add bounding box to MQTT image.")
@@ -432,17 +488,17 @@ class CameraMqttConfig(BaseModel):
     )
 
 
-class CameraRtmpConfig(BaseModel):
+class RtmpConfig(FrigateBaseModel):
     enabled: bool = Field(default=True, title="RTMP restreaming enabled.")
 
 
-class CameraLiveConfig(BaseModel):
+class CameraLiveConfig(FrigateBaseModel):
     height: int = Field(default=720, title="Live camera view height")
     quality: int = Field(default=8, ge=1, le=31, title="Live camera view quality")
 
 
-class CameraConfig(BaseModel):
+class CameraConfig(FrigateBaseModel):
-    name: Optional[str] = Field(title="Camera name.")
+    name: Optional[str] = Field(title="Camera name.", regex="^[a-zA-Z0-9_-]+$")
     ffmpeg: CameraFfmpegConfig = Field(title="FFmpeg configuration for the camera.")
     best_image_timeout: int = Field(
         default=60,
@@ -454,8 +510,8 @@ class CameraConfig(BaseModel):
     record: RecordConfig = Field(
         default_factory=RecordConfig, title="Record configuration."
     )
-    rtmp: CameraRtmpConfig = Field(
+    rtmp: RtmpConfig = Field(
-        default_factory=CameraRtmpConfig, title="RTMP restreaming configuration."
+        default_factory=RtmpConfig, title="RTMP restreaming configuration."
     )
     live: CameraLiveConfig = Field(
         default_factory=CameraLiveConfig, title="Live playback settings."
@@ -476,6 +532,7 @@ class CameraConfig(BaseModel):
     timestamp_style: TimestampStyleConfig = Field(
         default_factory=TimestampStyleConfig, title="Timestamp style configuration."
     )
+    _ffmpeg_cmds: List[Dict[str, List[str]]] = PrivateAttr()
 
     def __init__(self, **config):
         # Set zone colors
@@ -486,6 +543,10 @@ class CameraConfig(BaseModel):
                 for idx, (name, z) in enumerate(config["zones"].items())
             }
 
+        # add roles to the input if there is only one
+        if len(config["ffmpeg"]["inputs"]) == 1:
+            config["ffmpeg"]["inputs"][0]["roles"] = ["record", "rtmp", "detect"]
+
         super().__init__(**config)
 
     @property
@@ -498,6 +559,11 @@ class CameraConfig(BaseModel):
 
     @property
     def ffmpeg_cmds(self) -> List[Dict[str, List[str]]]:
+        return self._ffmpeg_cmds
+
+    def create_ffmpeg_cmds(self):
+        if "_ffmpeg_cmds" in self:
+            return
         ffmpeg_cmds = []
         for ffmpeg_input in self.ffmpeg.inputs:
             ffmpeg_cmd = self._get_ffmpeg_cmd(ffmpeg_input)
@@ -505,7 +571,7 @@ class CameraConfig(BaseModel):
                 continue
 
             ffmpeg_cmds.append({"roles": ffmpeg_input.roles, "cmd": ffmpeg_cmd})
-        return ffmpeg_cmds
+        self._ffmpeg_cmds = ffmpeg_cmds
 
     def _get_ffmpeg_cmd(self, ffmpeg_input: CameraInput):
         ffmpeg_output_args = []
@@ -541,6 +607,7 @@ class CameraConfig(BaseModel):
                 if isinstance(self.ffmpeg.output_args.record, list)
                 else self.ffmpeg.output_args.record.split(" ")
             )
+
             ffmpeg_output_args = (
                 record_args
                 + [f"{os.path.join(CACHE_DIR, self.name)}-%Y%m%d%H%M%S.mp4"]
@@ -577,13 +644,15 @@ class CameraConfig(BaseModel):
         return [part for part in cmd if part != ""]
 
 
-class DatabaseConfig(BaseModel):
+class DatabaseConfig(FrigateBaseModel):
     path: str = Field(
         default=os.path.join(BASE_DIR, "frigate.db"), title="Database path."
     )
 
 
-class ModelConfig(BaseModel):
+class ModelConfig(FrigateBaseModel):
+    path: Optional[str] = Field(title="Custom Object detection model path.")
+    labelmap_path: Optional[str] = Field(title="Label map for custom object detector.")
     width: int = Field(default=320, title="Object detection model input width.")
     height: int = Field(default=320, title="Object detection model input height.")
     labelmap: Dict[int, str] = Field(
@@ -597,14 +666,14 @@ class ModelConfig(BaseModel):
         return self._merged_labelmap
 
     @property
-    def colormap(self) -> Dict[int, tuple[int, int, int]]:
+    def colormap(self) -> Dict[int, Tuple[int, int, int]]:
         return self._colormap
 
     def __init__(self, **config):
         super().__init__(**config)
 
         self._merged_labelmap = {
-            **load_labels("/labelmap.txt"),
+            **load_labels(config.get("labelmap_path", "/labelmap.txt")),
             **config.get("labelmap", {}),
         }
 
@@ -623,7 +692,7 @@ class LogLevelEnum(str, Enum):
     critical = "critical"
 
 
-class LoggerConfig(BaseModel):
+class LoggerConfig(FrigateBaseModel):
     default: LogLevelEnum = Field(
         default=LogLevelEnum.info, title="Default logging level."
     )
@@ -632,7 +701,7 @@ class LoggerConfig(BaseModel):
     )
 
 
-class FrigateConfig(BaseModel):
+class FrigateConfig(FrigateBaseModel):
     mqtt: MqttConfig = Field(title="MQTT Configuration.")
     database: DatabaseConfig = Field(
         default_factory=DatabaseConfig, title="Database configuration."
@@ -656,6 +725,12 @@ class FrigateConfig(BaseModel):
     snapshots: SnapshotsConfig = Field(
         default_factory=SnapshotsConfig, title="Global snapshots configuration."
     )
+    live: CameraLiveConfig = Field(
+        default_factory=CameraLiveConfig, title="Global live configuration."
+    )
+    rtmp: RtmpConfig = Field(
+        default_factory=RtmpConfig, title="Global RTMP restreaming configuration."
+    )
     birdseye: BirdseyeConfig = Field(
         default_factory=BirdseyeConfig, title="Birdseye configuration."
     )
@@ -672,6 +747,10 @@ class FrigateConfig(BaseModel):
         default_factory=DetectConfig, title="Global object tracking configuration."
     )
     cameras: Dict[str, CameraConfig] = Field(title="Camera configuration.")
+    timestamp_style: TimestampStyleConfig = Field(
+        default_factory=TimestampStyleConfig,
+        title="Global timestamp style configuration.",
+    )
 
     @property
     def runtime_config(self) -> FrigateConfig:
@@ -687,10 +766,13 @@ class FrigateConfig(BaseModel):
             include={
                 "record": ...,
                 "snapshots": ...,
+                "live": ...,
+                "rtmp": ...,
                 "objects": ...,
                 "motion": ...,
                 "detect": ...,
                 "ffmpeg": ...,
+                "timestamp_style": ...,
             },
             exclude_unset=True,
         )
@@ -706,6 +788,11 @@ class FrigateConfig(BaseModel):
             if camera_config.detect.max_disappeared is None:
                 camera_config.detect.max_disappeared = max_disappeared
 
+            # Default stationary_threshold configuration
+            stationary_threshold = camera_config.detect.fps * 10
+            if camera_config.detect.stationary.threshold is None:
+                camera_config.detect.stationary.threshold = stationary_threshold
+
             # FFMPEG input substitution
             for input in camera_config.ffmpeg.inputs:
                 input.path = input.path.format(**FRIGATE_ENV_VARS)
@@ -753,6 +840,44 @@ class FrigateConfig(BaseModel):
                     **camera_config.motion.dict(exclude_unset=True),
                 )
 
+            # check runtime config
+            assigned_roles = list(
+                set([r for i in camera_config.ffmpeg.inputs for r in i.roles])
+            )
+            if camera_config.record.enabled and not "record" in assigned_roles:
+                raise ValueError(
+                    f"Camera {name} has record enabled, but record is not assigned to an input."
+                )
+
+            if camera_config.rtmp.enabled and not "rtmp" in assigned_roles:
+                raise ValueError(
+                    f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input."
+                )
+
+            # backwards compatibility for retain_days
+            if not camera_config.record.retain_days is None:
+                logger.warning(
+                    "The 'retain_days' config option has been DEPRECATED and will be removed in a future version. Please use the 'days' setting under 'retain'"
+                )
+                if camera_config.record.retain.days == 0:
+                    camera_config.record.retain.days = camera_config.record.retain_days
+
+            # warning if the higher level record mode is potentially more restrictive than the events
+            rank_map = {
+                RetainModeEnum.all: 0,
+                RetainModeEnum.motion: 1,
+                RetainModeEnum.active_objects: 2,
+            }
+            if (
+                camera_config.record.retain.days != 0
+                and rank_map[camera_config.record.retain.mode]
+                > rank_map[camera_config.record.events.retain.mode]
+            ):
+                logger.warning(
+                    f"{name}: Recording retention is configured for {camera_config.record.retain.mode} and event retention is configured for {camera_config.record.events.retain.mode}. The more restrictive retention policy will be applied."
+                )
+            # generage the ffmpeg commands
+            camera_config.create_ffmpeg_cmds()
             config.cameras[name] = camera_config
 
         return config
@@ -770,7 +895,7 @@ class FrigateConfig(BaseModel):
         with open(config_file) as f:
             raw_config = f.read()
 
-        if config_file.endswith(".yml"):
+        if config_file.endswith(YAML_EXT):
             config = yaml.safe_load(raw_config)
         elif config_file.endswith(".json"):
             config = json.loads(raw_config)

frigate/const.py

@@ -2,3 +2,4 @@ BASE_DIR = "/media/frigate"
 CLIPS_DIR = f"{BASE_DIR}/clips"
 RECORD_DIR = f"{BASE_DIR}/recordings"
 CACHE_DIR = "/tmp/cache"
+YAML_EXT = (".yaml", ".yml")

frigate/edgetpu.py

@@ -13,31 +13,11 @@ import tflite_runtime.interpreter as tflite
 from setproctitle import setproctitle
 from tflite_runtime.interpreter import load_delegate
 
-from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
+from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels
 
 logger = logging.getLogger(__name__)
 
 
-def load_labels(path, encoding="utf-8"):
-    """Loads labels from file (with or without index numbers).
-    Args:
-      path: path to label file.
-      encoding: label file encoding.
-    Returns:
-      Dictionary mapping indices to labels.
-    """
-    with open(path, "r", encoding=encoding) as f:
-        lines = f.readlines()
-        if not lines:
-            return {}
-
-        if lines[0].split(" ", maxsplit=1)[0].isdigit():
-            pairs = [line.split(" ", maxsplit=1) for line in lines]
-            return {int(index): label.strip() for index, label in pairs}
-        else:
-            return {index: line.strip() for index, line in enumerate(lines)}
-
-
 class ObjectDetector(ABC):
     @abstractmethod
     def detect(self, tensor_input, threshold=0.4):
@@ -45,7 +25,7 @@ class ObjectDetector(ABC):
 
 
 class LocalObjectDetector(ObjectDetector):
-    def __init__(self, tf_device=None, num_threads=3, labels=None):
+    def __init__(self, tf_device=None, model_path=None, num_threads=3, labels=None):
         self.fps = EventsPerSecond()
         if labels is None:
             self.labels = {}
@@ -64,7 +44,7 @@ class LocalObjectDetector(ObjectDetector):
                 edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
                 logger.info("TPU found")
                 self.interpreter = tflite.Interpreter(
-                    model_path="/edgetpu_model.tflite",
+                    model_path=model_path or "/edgetpu_model.tflite",
                     experimental_delegates=[edge_tpu_delegate],
                 )
             except ValueError:
@@ -77,7 +57,7 @@ class LocalObjectDetector(ObjectDetector):
                 "CPU detectors are not recommended and should only be used for testing or for trial purposes."
             )
             self.interpreter = tflite.Interpreter(
-                model_path="/cpu_model.tflite", num_threads=num_threads
+                model_path=model_path or "/cpu_model.tflite", num_threads=num_threads
             )
 
         self.interpreter.allocate_tensors()
@@ -133,6 +113,7 @@ def run_detector(
     out_events: Dict[str, mp.Event],
     avg_speed,
     start,
+    model_path,
     model_shape,
     tf_device,
     num_threads,
@@ -152,7 +133,9 @@ def run_detector(
     signal.signal(signal.SIGINT, receiveSignal)
 
     frame_manager = SharedMemoryFrameManager()
-    object_detector = LocalObjectDetector(tf_device=tf_device, num_threads=num_threads)
+    object_detector = LocalObjectDetector(
+        tf_device=tf_device, model_path=model_path, num_threads=num_threads
+    )
 
     outputs = {}
     for name in out_events.keys():
@@ -189,6 +172,7 @@ class EdgeTPUProcess:
         name,
         detection_queue,
         out_events,
+        model_path,
         model_shape,
         tf_device=None,
         num_threads=3,
@@ -199,6 +183,7 @@ class EdgeTPUProcess:
         self.avg_inference_speed = mp.Value("d", 0.01)
         self.detection_start = mp.Value("d", 0.0)
         self.detect_process = None
+        self.model_path = model_path
         self.model_shape = model_shape
         self.tf_device = tf_device
         self.num_threads = num_threads
@@ -226,6 +211,7 @@ class EdgeTPUProcess:
                 self.out_events,
                 self.avg_inference_speed,
                 self.detection_start,
+                self.model_path,
                 self.model_shape,
                 self.tf_device,
                 self.num_threads,

frigate/events.py

@@ -6,15 +6,25 @@ import threading
 import time
 from pathlib import Path
 
-from frigate.config import FrigateConfig, RecordConfig
-from frigate.const import CLIPS_DIR
-from frigate.models import Event, Recordings
-
 from peewee import fn
 
+from frigate.config import EventsConfig, FrigateConfig, RecordConfig
+from frigate.const import CLIPS_DIR
+from frigate.models import Event
+
 logger = logging.getLogger(__name__)
 
 
+def should_update_db(prev_event, current_event):
+    return (
+        prev_event["top_score"] != current_event["top_score"]
+        or prev_event["entered_zones"] != current_event["entered_zones"]
+        or prev_event["thumbnail"] != current_event["thumbnail"]
+        or prev_event["has_clip"] != current_event["has_clip"]
+        or prev_event["has_snapshot"] != current_event["has_snapshot"]
+    )
+
+
 class EventProcessor(threading.Thread):
     def __init__(
         self, config, camera_processes, event_queue, event_processed_queue, stop_event
@@ -29,41 +39,12 @@ class EventProcessor(threading.Thread):
         self.events_in_process = {}
         self.stop_event = stop_event
 
-    def should_create_clip(self, camera, event_data):
-        if event_data["false_positive"]:
-            return False
-
-        record_config: RecordConfig = self.config.cameras[camera].record
-
-        # Recording clips is disabled
-        if not record_config.enabled or (
-            record_config.retain_days == 0 and not record_config.events.enabled
-        ):
-            return False
-
-        # If there are required zones and there is no overlap
-        required_zones = record_config.events.required_zones
-        if len(required_zones) > 0 and not set(event_data["entered_zones"]) & set(
-            required_zones
-        ):
-            logger.debug(
-                f"Not creating clip for {event_data['id']} because it did not enter required zones"
-            )
-            return False
-
-        # If the required objects are not present
-        if (
-            record_config.events.objects is not None
-            and event_data["label"] not in record_config.events.objects
-        ):
-            logger.debug(
-                f"Not creating clip for {event_data['id']} because it did not contain required objects"
-            )
-            return False
-
-        return True
-
     def run(self):
+        # set an end_time on events without an end_time on startup
+        Event.update(end_time=Event.start_time + 30).where(
+            Event.end_time == None
+        ).execute()
+
         while not self.stop_event.is_set():
             try:
                 event_type, camera, event_data = self.event_queue.get(timeout=10)
@@ -72,32 +53,60 @@ class EventProcessor(threading.Thread):
 
             logger.debug(f"Event received: {event_type} {camera} {event_data['id']}")
 
+            event_config: EventsConfig = self.config.cameras[camera].record.events
+
             if event_type == "start":
                 self.events_in_process[event_data["id"]] = event_data
 
-            if event_type == "end":
+            elif event_type == "update" and should_update_db(
-                record_config: RecordConfig = self.config.cameras[camera].record
+                self.events_in_process[event_data["id"]], event_data
-
+            ):
-                has_clip = self.should_create_clip(camera, event_data)
+                self.events_in_process[event_data["id"]] = event_data
-
+                # TODO: this will generate a lot of db activity possibly
-                if has_clip or event_data["has_snapshot"]:
+                if event_data["has_clip"] or event_data["has_snapshot"]:
-                    Event.create(
+                    Event.replace(
                         id=event_data["id"],
                         label=event_data["label"],
                         camera=camera,
-                        start_time=event_data["start_time"],
+                        start_time=event_data["start_time"] - event_config.pre_capture,
-                        end_time=event_data["end_time"],
+                        end_time=None,
                         top_score=event_data["top_score"],
                         false_positive=event_data["false_positive"],
                         zones=list(event_data["entered_zones"]),
                         thumbnail=event_data["thumbnail"],
-                        has_clip=has_clip,
+                        region=event_data["region"],
+                        box=event_data["box"],
+                        area=event_data["area"],
+                        has_clip=event_data["has_clip"],
                         has_snapshot=event_data["has_snapshot"],
-                    )
+                    ).execute()
+
+            elif event_type == "end":
+                if event_data["has_clip"] or event_data["has_snapshot"]:
+                    Event.replace(
+                        id=event_data["id"],
+                        label=event_data["label"],
+                        camera=camera,
+                        start_time=event_data["start_time"] - event_config.pre_capture,
+                        end_time=event_data["end_time"] + event_config.post_capture,
+                        top_score=event_data["top_score"],
+                        false_positive=event_data["false_positive"],
+                        zones=list(event_data["entered_zones"]),
+                        thumbnail=event_data["thumbnail"],
+                        region=event_data["region"],
+                        box=event_data["box"],
+                        area=event_data["area"],
+                        has_clip=event_data["has_clip"],
+                        has_snapshot=event_data["has_snapshot"],
+                    ).execute()
 
                 del self.events_in_process[event_data["id"]]
-                self.event_processed_queue.put((event_data["id"], camera, has_clip))
+                self.event_processed_queue.put((event_data["id"], camera))
 
+        # set an end_time on events without an end_time before exiting
+        Event.update(end_time=datetime.datetime.now().timestamp()).where(
+            Event.end_time == None
+        ).execute()
         logger.info(f"Exiting event processor...")
 
 
@@ -225,12 +234,12 @@ class EventCleanup(threading.Thread):
         for event in duplicate_events:
             logger.debug(f"Removing duplicate: {event.id}")
             media_name = f"{event.camera}-{event.id}"
-            if event.has_snapshot:
+            media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
-                media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
+            media_path.unlink(missing_ok=True)
-                media_path.unlink(missing_ok=True)
+            media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png")
-            if event.has_clip:
+            media_path.unlink(missing_ok=True)
-                media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
+            media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
-                media_path.unlink(missing_ok=True)
+            media_path.unlink(missing_ok=True)
 
         (
             Event.delete()

frigate/http.py

@@ -1,6 +1,7 @@
 import base64
 from collections import OrderedDict
 from datetime import datetime, timedelta
+import copy
 import json
 import glob
 import logging
@@ -132,6 +133,8 @@ def delete_event(id):
     if event.has_snapshot:
         media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
         media.unlink(missing_ok=True)
+        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png")
+        media.unlink(missing_ok=True)
     if event.has_clip:
         media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
         media.unlink(missing_ok=True)
@@ -181,7 +184,7 @@ def event_thumbnail(id):
         thumbnail_bytes = jpg.tobytes()
 
     response = make_response(thumbnail_bytes)
-    response.headers["Content-Type"] = "image/jpg"
+    response.headers["Content-Type"] = "image/jpeg"
     return response
 
 
@@ -190,7 +193,7 @@ def event_snapshot(id):
     download = request.args.get("download", type=bool)
     jpg_bytes = None
     try:
-        event = Event.get(Event.id == id)
+        event = Event.get(Event.id == id, Event.end_time != None)
         if not event.has_snapshot:
             return "Snapshot not available", 404
         # read snapshot from disk
@@ -222,7 +225,7 @@ def event_snapshot(id):
         return "Event not found", 404
 
     response = make_response(jpg_bytes)
-    response.headers["Content-Type"] = "image/jpg"
+    response.headers["Content-Type"] = "image/jpeg"
     if download:
         response.headers[
             "Content-Disposition"
@@ -242,14 +245,14 @@ def event_clip(id):
     if not event.has_clip:
         return "Clip not available", 404
 
-    event_config = current_app.frigate_config.cameras[event.camera].record.events
-    start_ts = event.start_time - event_config.pre_capture
-    end_ts = event.end_time + event_config.post_capture
     file_name = f"{event.camera}-{id}.mp4"
     clip_path = os.path.join(CLIPS_DIR, file_name)
 
     if not os.path.isfile(clip_path):
-        return recording_clip(event.camera, start_ts, end_ts)
+        end_ts = (
+            datetime.now().timestamp() if event.end_time is None else event.end_time
+        )
+        return recording_clip(event.camera, event.start_time, end_ts)
 
     response = make_response()
     response.headers["Content-Description"] = "File Transfer"
@@ -324,7 +327,7 @@ def config():
     # add in the ffmpeg_cmds
     for camera_name, camera in current_app.frigate_config.cameras.items():
         camera_dict = config["cameras"][camera_name]
-        camera_dict["ffmpeg_cmds"] = camera.ffmpeg_cmds
+        camera_dict["ffmpeg_cmds"] = copy.deepcopy(camera.ffmpeg_cmds)
         for cmd in camera_dict["ffmpeg_cmds"]:
             cmd["cmd"] = " ".join(cmd["cmd"])
 
@@ -361,9 +364,16 @@ def best(camera_name, label):
 
         crop = bool(request.args.get("crop", 0, type=int))
         if crop:
-            box = best_object.get("box", (0, 0, 300, 300))
+            box_size = 300
+            box = best_object.get("box", (0, 0, box_size, box_size))
             region = calculate_region(
-                best_frame.shape, box[0], box[1], box[2], box[3], 1.1
+                best_frame.shape,
+                box[0],
+                box[1],
+                box[2],
+                box[3],
+                box_size,
+                multiplier=1.1,
             )
             best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
 
@@ -378,7 +388,7 @@ def best(camera_name, label):
             ".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
         )
         response = make_response(jpg.tobytes())
-        response.headers["Content-Type"] = "image/jpg"
+        response.headers["Content-Type"] = "image/jpeg"
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
@@ -440,7 +450,7 @@ def latest_frame(camera_name):
             ".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
         )
         response = make_response(jpg.tobytes())
-        response.headers["Content-Type"] = "image/jpg"
+        response.headers["Content-Type"] = "image/jpeg"
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
@@ -517,12 +527,17 @@ def recordings(camera_name):
         FROM C2
         WHERE cnt = 0
         )
+        SELECT id, label, camera, top_score, start_time, end_time
+        FROM event
+        WHERE camera = ? AND end_time IS NULL
+        UNION ALL
         SELECT MIN(id) as id, label, camera, MAX(top_score) as top_score, MIN(ts) AS start_time, max(ts) AS end_time
         FROM C3
         GROUP BY label, grpnum
         ORDER BY start_time;""",
         camera_name,
         camera_name,
+        camera_name,
     )
 
     event: Event
@@ -660,10 +675,15 @@ def vod_ts(camera, start_ts, end_ts):
         # Determine if we need to end the last clip early
         if recording.end_time > end_ts:
             duration -= int((recording.end_time - end_ts) * 1000)
-        clips.append(clip)
+
-        durations.append(duration)
+        if duration > 0:
+            clips.append(clip)
+            durations.append(duration)
+        else:
+            logger.warning(f"Recording clip is missing or empty: {recording.path}")
 
     if not clips:
+        logger.error("No recordings found for the requested time range")
         return "No recordings found.", 404
 
     hour_ago = datetime.now() - timedelta(hours=1)
@@ -692,20 +712,30 @@ def vod_event(id):
     try:
         event: Event = Event.get(Event.id == id)
     except DoesNotExist:
+        logger.error(f"Event not found: {id}")
         return "Event not found.", 404
 
     if not event.has_clip:
-        return "Clip not available", 404
+        logger.error(f"Event does not have recordings: {id}")
+        return "Recordings not available", 404
 
-    event_config = current_app.frigate_config.cameras[event.camera].record.events
-    start_ts = event.start_time - event_config.pre_capture
-    end_ts = event.end_time + event_config.post_capture
     clip_path = os.path.join(CLIPS_DIR, f"{event.camera}-{id}.mp4")
 
     if not os.path.isfile(clip_path):
-        return vod_ts(event.camera, start_ts, end_ts)
+        end_ts = (
+            datetime.now().timestamp() if event.end_time is None else event.end_time
+        )
+        vod_response = vod_ts(event.camera, event.start_time, end_ts)
+        # If the recordings are not found, set has_clip to false
+        if (
+            type(vod_response) == tuple
+            and len(vod_response) == 2
+            and vod_response[1] == 404
+        ):
+            Event.update(has_clip=False).where(Event.id == id).execute()
+        return vod_response
 
-    duration = int((end_ts - start_ts) * 1000)
+    duration = int((event.end_time - event.start_time) * 1000)
     return jsonify(
         {
             "cache": True,

frigate/models.py

@@ -15,6 +15,9 @@ class Event(Model):
     thumbnail = TextField()
     has_clip = BooleanField(default=True)
     has_snapshot = BooleanField(default=True)
+    region = JSONField()
+    box = JSONField()
+    area = IntegerField()
 
 
 class Recordings(Model):
@@ -24,3 +27,5 @@ class Recordings(Model):
     start_time = DateTimeField()
     end_time = DateTimeField()
     duration = FloatField()
+    motion = IntegerField(null=True)
+    objects = IntegerField(null=True)

frigate/motion.py

@@ -23,6 +23,7 @@ class MotionDetector:
             interpolation=cv2.INTER_LINEAR,
         )
         self.mask = np.where(resized_mask == [0])
+        self.save_images = False
 
     def detect(self, frame):
         motion_boxes = []
@@ -36,10 +37,15 @@ class MotionDetector:
             interpolation=cv2.INTER_LINEAR,
         )
 
-        # TODO: can I improve the contrast of the grayscale image here?
+        # Improve contrast
-
+        minval = np.percentile(resized_frame, 4)
-        # convert to grayscale
+        maxval = np.percentile(resized_frame, 96)
-        # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+        # don't adjust if the image is a single color
+        if minval < maxval:
+            resized_frame = np.clip(resized_frame, minval, maxval)
+            resized_frame = (
+                ((resized_frame - minval) / (maxval - minval)) * 255
+            ).astype(np.uint8)
 
         # mask frame
         resized_frame[self.mask] = [255]
@@ -49,6 +55,8 @@ class MotionDetector:
         if self.frame_counter < 30:
             self.frame_counter += 1
         else:
+            if self.save_images:
+                self.frame_counter += 1
             # compare to average
             frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
 
@@ -58,7 +66,6 @@ class MotionDetector:
             cv2.accumulateWeighted(frameDelta, self.avg_delta, self.config.delta_alpha)
 
             # compute the threshold image for the current frame
-            # TODO: threshold
             current_thresh = cv2.threshold(
                 frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY
             )[1]
@@ -75,8 +82,10 @@ class MotionDetector:
 
             # dilate the thresholded image to fill in holes, then find contours
             # on thresholded image
-            thresh = cv2.dilate(thresh, None, iterations=2)
+            thresh_dilated = cv2.dilate(thresh, None, iterations=2)
-            cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            cnts = cv2.findContours(
+                thresh_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+            )
             cnts = imutils.grab_contours(cnts)
 
             # loop over the contours
@@ -94,6 +103,35 @@ class MotionDetector:
                         )
                     )
 
+            if self.save_images:
+                thresh_dilated = cv2.cvtColor(thresh_dilated, cv2.COLOR_GRAY2BGR)
+                # print("--------")
+                # print(self.frame_counter)
+                for c in cnts:
+                    contour_area = cv2.contourArea(c)
+                    # print(contour_area)
+                    if contour_area > self.config.contour_area:
+                        x, y, w, h = cv2.boundingRect(c)
+                        cv2.rectangle(
+                            thresh_dilated,
+                            (x, y),
+                            (x + w, y + h),
+                            (0, 0, 255),
+                            2,
+                        )
+                # print("--------")
+                image_row_1 = cv2.hconcat(
+                    [
+                        cv2.cvtColor(frameDelta, cv2.COLOR_GRAY2BGR),
+                        cv2.cvtColor(avg_delta_image, cv2.COLOR_GRAY2BGR),
+                    ]
+                )
+                image_row_2 = cv2.hconcat(
+                    [cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR), thresh_dilated]
+                )
+                combined_image = cv2.vconcat([image_row_1, image_row_2])
+                cv2.imwrite(f"motion/motion-{self.frame_counter}.jpg", combined_image)
+
         if len(motion_boxes) > 0:
             self.motion_frame_count += 1
             if self.motion_frame_count >= 10:

frigate/mqtt.py

@@ -107,7 +107,7 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
                     + str(rc)
                 )
 
-        logger.info("MQTT connected")
+        logger.debug("MQTT connected")
         client.subscribe(f"{mqtt_config.topic_prefix}/#")
         client.publish(mqtt_config.topic_prefix + "/available", "online", retain=True)
 

frigate/object_processing.py

@@ -16,14 +16,14 @@ from typing import Callable, Dict
 import cv2
 import numpy as np
 
-from frigate.config import CameraConfig, FrigateConfig
+from frigate.config import CameraConfig, SnapshotsConfig, RecordConfig, FrigateConfig
 from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
-from frigate.edgetpu import load_labels
 from frigate.util import (
     SharedMemoryFrameManager,
     calculate_region,
     draw_box_with_label,
     draw_timestamp,
+    load_labels,
 )
 
 logger = logging.getLogger(__name__)
@@ -71,8 +71,10 @@ class TrackedObject:
         self.camera_config = camera_config
         self.frame_cache = frame_cache
         self.current_zones = []
-        self.entered_zones = set()
+        self.entered_zones = []
         self.false_positive = True
+        self.has_clip = False
+        self.has_snapshot = False
         self.top_score = self.computed_score = 0.0
         self.thumbnail_data = None
         self.last_updated = 0
@@ -99,14 +101,13 @@ class TrackedObject:
         return median(scores)
 
     def update(self, current_frame_time, obj_data):
-        significant_update = False
+        thumb_update = False
-        zone_change = False
+        significant_change = False
-        self.obj_data.update(obj_data)
         # if the object is not in the current frame, add a 0.0 to the score history
-        if self.obj_data["frame_time"] != current_frame_time:
+        if obj_data["frame_time"] != current_frame_time:
             self.score_history.append(0.0)
         else:
-            self.score_history.append(self.obj_data["score"])
+            self.score_history.append(obj_data["score"])
         # only keep the last 10 scores
         if len(self.score_history) > 10:
             self.score_history = self.score_history[-10:]
@@ -120,24 +121,24 @@ class TrackedObject:
         if not self.false_positive:
             # determine if this frame is a better thumbnail
             if self.thumbnail_data is None or is_better_thumbnail(
-                self.thumbnail_data, self.obj_data, self.camera_config.frame_shape
+                self.thumbnail_data, obj_data, self.camera_config.frame_shape
             ):
                 self.thumbnail_data = {
-                    "frame_time": self.obj_data["frame_time"],
+                    "frame_time": obj_data["frame_time"],
-                    "box": self.obj_data["box"],
+                    "box": obj_data["box"],
-                    "area": self.obj_data["area"],
+                    "area": obj_data["area"],
-                    "region": self.obj_data["region"],
+                    "region": obj_data["region"],
-                    "score": self.obj_data["score"],
+                    "score": obj_data["score"],
                 }
-                significant_update = True
+                thumb_update = True
 
         # check zones
         current_zones = []
-        bottom_center = (self.obj_data["centroid"][0], self.obj_data["box"][3])
+        bottom_center = (obj_data["centroid"][0], obj_data["box"][3])
         # check each zone
         for name, zone in self.camera_config.zones.items():
             # if the zone is not for this object type, skip
-            if len(zone.objects) > 0 and not self.obj_data["label"] in zone.objects:
+            if len(zone.objects) > 0 and not obj_data["label"] in zone.objects:
                 continue
             contour = zone.contour
             # check if the object is in the zone
@@ -145,14 +146,32 @@ class TrackedObject:
                 # if the object passed the filters once, dont apply again
                 if name in self.current_zones or not zone_filtered(self, zone.filters):
                     current_zones.append(name)
-                    self.entered_zones.add(name)
+                    if name not in self.entered_zones:
+                        self.entered_zones.append(name)
 
-        # if the zones changed, signal an update
+        if not self.false_positive:
-        if not self.false_positive and set(self.current_zones) != set(current_zones):
+            # if the zones changed, signal an update
-            zone_change = True
+            if set(self.current_zones) != set(current_zones):
+                significant_change = True
 
+            # if the position changed, signal an update
+            if self.obj_data["position_changes"] != obj_data["position_changes"]:
+                significant_change = True
+
+            # if the motionless_count reaches the stationary threshold
+            if (
+                self.obj_data["motionless_count"]
+                == self.camera_config.detect.stationary.threshold
+            ):
+                significant_change = True
+
+            # update at least once per minute
+            if self.obj_data["frame_time"] - self.previous["frame_time"] > 60:
+                significant_change = True
+
+        self.obj_data.update(obj_data)
         self.current_zones = current_zones
-        return (significant_update, zone_change)
+        return (thumb_update, significant_change)
 
     def to_dict(self, include_thumbnail: bool = False):
         snapshot_time = (
@@ -174,8 +193,14 @@ class TrackedObject:
             "box": self.obj_data["box"],
             "area": self.obj_data["area"],
             "region": self.obj_data["region"],
+            "stationary": self.obj_data["motionless_count"]
+            > self.camera_config.detect.stationary.threshold,
+            "motionless_count": self.obj_data["motionless_count"],
+            "position_changes": self.obj_data["position_changes"],
             "current_zones": self.current_zones.copy(),
-            "entered_zones": list(self.entered_zones).copy(),
+            "entered_zones": self.entered_zones.copy(),
+            "has_clip": self.has_clip,
+            "has_snapshot": self.has_snapshot,
         }
 
         if include_thumbnail:
@@ -258,8 +283,15 @@ class TrackedObject:
 
         if crop:
             box = self.thumbnail_data["box"]
+            box_size = 300
             region = calculate_region(
-                best_frame.shape, box[0], box[1], box[2], box[3], 1.1
+                best_frame.shape,
+                box[0],
+                box[1],
+                box[2],
+                box[3],
+                box_size,
+                multiplier=1.1,
             )
             best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
 
@@ -275,9 +307,8 @@ class TrackedObject:
                 self.thumbnail_data["frame_time"],
                 self.camera_config.timestamp_style.format,
                 font_effect=self.camera_config.timestamp_style.effect,
-                font_scale=self.camera_config.timestamp_style.scale,
                 font_thickness=self.camera_config.timestamp_style.thickness,
-                font_color=(color.red, color.green, color.blue),
+                font_color=(color.blue, color.green, color.red),
                 position=self.camera_config.timestamp_style.position,
             )
 
@@ -411,9 +442,8 @@ class CameraState:
                 frame_time,
                 self.camera_config.timestamp_style.format,
                 font_effect=self.camera_config.timestamp_style.effect,
-                font_scale=self.camera_config.timestamp_style.scale,
                 font_thickness=self.camera_config.timestamp_style.thickness,
-                font_color=(color.red, color.green, color.blue),
+                font_color=(color.blue, color.green, color.red),
                 position=self.camera_config.timestamp_style.position,
             )
 
@@ -454,11 +484,11 @@ class CameraState:
 
         for id in updated_ids:
             updated_obj = tracked_objects[id]
-            significant_update, zone_change = updated_obj.update(
+            thumb_update, significant_update = updated_obj.update(
                 frame_time, current_detections[id]
             )
 
-            if significant_update:
+            if thumb_update:
                 # ensure this frame is stored in the cache
                 if (
                     updated_obj.thumbnail_data["frame_time"] == frame_time
@@ -468,13 +498,13 @@ class CameraState:
 
                 updated_obj.last_updated = frame_time
 
-            # if it has been more than 5 seconds since the last publish
+            # if it has been more than 5 seconds since the last thumb update
             # and the last update is greater than the last publish or
-            # the object has changed zones
+            # the object has changed significantly
             if (
                 frame_time - updated_obj.last_published > 5
                 and updated_obj.last_updated > updated_obj.last_published
-            ) or zone_change:
+            ) or significant_update:
                 # call event handlers
                 for c in self.callbacks["update"]:
                     c(self.name, updated_obj, frame_time)
@@ -582,6 +612,7 @@ class TrackedObjectProcessor(threading.Thread):
         event_queue,
         event_processed_queue,
         video_output_queue,
+        recordings_info_queue,
         stop_event,
     ):
         threading.Thread.__init__(self)
@@ -593,6 +624,7 @@ class TrackedObjectProcessor(threading.Thread):
         self.event_queue = event_queue
         self.event_processed_queue = event_processed_queue
         self.video_output_queue = video_output_queue
+        self.recordings_info_queue = recordings_info_queue
         self.stop_event = stop_event
         self.camera_states: Dict[str, CameraState] = {}
         self.frame_manager = SharedMemoryFrameManager()
@@ -601,6 +633,8 @@ class TrackedObjectProcessor(threading.Thread):
             self.event_queue.put(("start", camera, obj.to_dict()))
 
         def update(camera, obj: TrackedObject, current_frame_time):
+            obj.has_snapshot = self.should_save_snapshot(camera, obj)
+            obj.has_clip = self.should_retain_recording(camera, obj)
             after = obj.to_dict()
             message = {
                 "before": obj.previous,
@@ -611,11 +645,51 @@ class TrackedObjectProcessor(threading.Thread):
                 f"{self.topic_prefix}/events", json.dumps(message), retain=False
             )
             obj.previous = after
+            self.event_queue.put(
+                ("update", camera, obj.to_dict(include_thumbnail=True))
+            )
 
         def end(camera, obj: TrackedObject, current_frame_time):
-            snapshot_config = self.config.cameras[camera].snapshots
+            # populate has_snapshot
-            event_data = obj.to_dict(include_thumbnail=True)
+            obj.has_snapshot = self.should_save_snapshot(camera, obj)
-            event_data["has_snapshot"] = False
+            obj.has_clip = self.should_retain_recording(camera, obj)
+
+            # write the snapshot to disk
+            if obj.has_snapshot:
+                snapshot_config: SnapshotsConfig = self.config.cameras[camera].snapshots
+                jpg_bytes = obj.get_jpg_bytes(
+                    timestamp=snapshot_config.timestamp,
+                    bounding_box=snapshot_config.bounding_box,
+                    crop=snapshot_config.crop,
+                    height=snapshot_config.height,
+                    quality=snapshot_config.quality,
+                )
+                if jpg_bytes is None:
+                    logger.warning(f"Unable to save snapshot for {obj.obj_data['id']}.")
+                else:
+                    with open(
+                        os.path.join(CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"),
+                        "wb",
+                    ) as j:
+                        j.write(jpg_bytes)
+
+                # write clean snapshot if enabled
+                if snapshot_config.clean_copy:
+                    png_bytes = obj.get_clean_png()
+                    if png_bytes is None:
+                        logger.warning(
+                            f"Unable to save clean snapshot for {obj.obj_data['id']}."
+                        )
+                    else:
+                        with open(
+                            os.path.join(
+                                CLIPS_DIR,
+                                f"{camera}-{obj.obj_data['id']}-clean.png",
+                            ),
+                            "wb",
+                        ) as p:
+                            p.write(png_bytes)
+
             if not obj.false_positive:
                 message = {
                     "before": obj.previous,
@@ -625,46 +699,8 @@ class TrackedObjectProcessor(threading.Thread):
                 self.client.publish(
                     f"{self.topic_prefix}/events", json.dumps(message), retain=False
                 )
-                # write snapshot to disk if enabled
-                if snapshot_config.enabled and self.should_save_snapshot(camera, obj):
-                    jpg_bytes = obj.get_jpg_bytes(
-                        timestamp=snapshot_config.timestamp,
-                        bounding_box=snapshot_config.bounding_box,
-                        crop=snapshot_config.crop,
-                        height=snapshot_config.height,
-                        quality=snapshot_config.quality,
-                    )
-                    if jpg_bytes is None:
-                        logger.warning(
-                            f"Unable to save snapshot for {obj.obj_data['id']}."
-                        )
-                    else:
-                        with open(
-                            os.path.join(
-                                CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"
-                            ),
-                            "wb",
-                        ) as j:
-                            j.write(jpg_bytes)
-                        event_data["has_snapshot"] = True
 
-                    # write clean snapshot if enabled
+            self.event_queue.put(("end", camera, obj.to_dict(include_thumbnail=True)))
-                    if snapshot_config.clean_copy:
-                        png_bytes = obj.get_clean_png()
-                        if png_bytes is None:
-                            logger.warning(
-                                f"Unable to save clean snapshot for {obj.obj_data['id']}."
-                            )
-                        else:
-                            with open(
-                                os.path.join(
-                                    CLIPS_DIR,
-                                    f"{camera}-{obj.obj_data['id']}-clean.png",
-                                ),
-                                "wb",
-                            ) as p:
-                                p.write(png_bytes)
-            self.event_queue.put(("end", camera, event_data))
 
         def snapshot(camera, obj: TrackedObject, current_frame_time):
             mqtt_config = self.config.cameras[camera].mqtt
@@ -713,9 +749,21 @@ class TrackedObjectProcessor(threading.Thread):
         self.zone_data = defaultdict(lambda: defaultdict(dict))
 
     def should_save_snapshot(self, camera, obj: TrackedObject):
+        if obj.false_positive:
+            return False
+
+        snapshot_config: SnapshotsConfig = self.config.cameras[camera].snapshots
+
+        if not snapshot_config.enabled:
+            return False
+
+        # object never changed position
+        if obj.obj_data["position_changes"] == 0:
+            return False
+
         # if there are required zones and there is no overlap
-        required_zones = self.config.cameras[camera].snapshots.required_zones
+        required_zones = snapshot_config.required_zones
-        if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
+        if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
             logger.debug(
                 f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
             )
@@ -723,10 +771,48 @@ class TrackedObjectProcessor(threading.Thread):
 
         return True
 
+    def should_retain_recording(self, camera, obj: TrackedObject):
+        if obj.false_positive:
+            return False
+
+        record_config: RecordConfig = self.config.cameras[camera].record
+
+        # Recording is disabled
+        if not record_config.enabled:
+            return False
+
+        # object never changed position
+        if obj.obj_data["position_changes"] == 0:
+            return False
+
+        # If there are required zones and there is no overlap
+        required_zones = record_config.events.required_zones
+        if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
+            logger.debug(
+                f"Not creating clip for {obj.obj_data['id']} because it did not enter required zones"
+            )
+            return False
+
+        # If the required objects are not present
+        if (
+            record_config.events.objects is not None
+            and obj.obj_data["label"] not in record_config.events.objects
+        ):
+            logger.debug(
+                f"Not creating clip for {obj.obj_data['id']} because it did not contain required objects"
+            )
+            return False
+
+        return True
+
     def should_mqtt_snapshot(self, camera, obj: TrackedObject):
+        # object never changed position
+        if obj.obj_data["position_changes"] == 0:
+            return False
+
         # if there are required zones and there is no overlap
         required_zones = self.config.cameras[camera].mqtt.required_zones
-        if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
+        if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
             logger.debug(
                 f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
             )
@@ -769,11 +855,26 @@ class TrackedObjectProcessor(threading.Thread):
                 frame_time, current_tracked_objects, motion_boxes, regions
             )
 
+            tracked_objects = [
+                o.to_dict() for o in camera_state.tracked_objects.values()
+            ]
+
             self.video_output_queue.put(
                 (
                     camera,
                     frame_time,
-                    current_tracked_objects,
+                    tracked_objects,
+                    motion_boxes,
+                    regions,
+                )
+            )
+
+            # send info on this frame to the recordings maintainer
+            self.recordings_info_queue.put(
+                (
+                    camera,
+                    frame_time,
+                    tracked_objects,
                     motion_boxes,
                     regions,
                 )
@@ -817,17 +918,7 @@ class TrackedObjectProcessor(threading.Thread):
 
             # cleanup event finished queue
             while not self.event_processed_queue.empty():
-                event_id, camera, clip_created = self.event_processed_queue.get()
+                event_id, camera = self.event_processed_queue.get()
-                if clip_created:
-                    obj = self.camera_states[camera].tracked_objects[event_id]
-                    message = {
-                        "before": obj.previous,
-                        "after": obj.to_dict(),
-                        "type": "clip_ready",
-                    }
-                    self.client.publish(
-                        f"{self.topic_prefix}/events", json.dumps(message), retain=False
-                    )
                 self.camera_states[camera].finished(event_id)
 
         logger.info(f"Exiting object processor...")

frigate/objects.py

@@ -13,31 +13,133 @@ import numpy as np
 from scipy.spatial import distance as dist
 
 from frigate.config import DetectConfig
-from frigate.util import draw_box_with_label
+from frigate.util import intersection_over_union
 
 
 class ObjectTracker:
     def __init__(self, config: DetectConfig):
         self.tracked_objects = {}
         self.disappeared = {}
+        self.positions = {}
         self.max_disappeared = config.max_disappeared
+        self.detect_config = config
 
     def register(self, index, obj):
         rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
         id = f"{obj['frame_time']}-{rand_id}"
         obj["id"] = id
         obj["start_time"] = obj["frame_time"]
+        obj["motionless_count"] = 0
+        obj["position_changes"] = 0
         self.tracked_objects[id] = obj
         self.disappeared[id] = 0
+        self.positions[id] = {
+            "xmins": [],
+            "ymins": [],
+            "xmaxs": [],
+            "ymaxs": [],
+            "xmin": 0,
+            "ymin": 0,
+            "xmax": self.detect_config.width,
+            "ymax": self.detect_config.height,
+        }
 
     def deregister(self, id):
         del self.tracked_objects[id]
         del self.disappeared[id]
 
+    # tracks the current position of the object based on the last N bounding boxes
+    # returns False if the object has moved outside its previous position
+    def update_position(self, id, box):
+        position = self.positions[id]
+        position_box = (
+            position["xmin"],
+            position["ymin"],
+            position["xmax"],
+            position["ymax"],
+        )
+
+        xmin, ymin, xmax, ymax = box
+
+        iou = intersection_over_union(position_box, box)
+
+        # if the iou drops below the threshold
+        # assume the object has moved to a new position and reset the computed box
+        if iou < 0.6:
+            self.positions[id] = {
+                "xmins": [xmin],
+                "ymins": [ymin],
+                "xmaxs": [xmax],
+                "ymaxs": [ymax],
+                "xmin": xmin,
+                "ymin": ymin,
+                "xmax": xmax,
+                "ymax": ymax,
+            }
+            return False
+
+        # if there are less than 10 entries for the position, add the bounding box
+        # and recompute the position box
+        if len(position["xmins"]) < 10:
+            position["xmins"].append(xmin)
+            position["ymins"].append(ymin)
+            position["xmaxs"].append(xmax)
+            position["ymaxs"].append(ymax)
+            # by using percentiles here, we hopefully remove outliers
+            position["xmin"] = np.percentile(position["xmins"], 15)
+            position["ymin"] = np.percentile(position["ymins"], 15)
+            position["xmax"] = np.percentile(position["xmaxs"], 85)
+            position["ymax"] = np.percentile(position["ymaxs"], 85)
+
+        return True
+
+    def is_expired(self, id):
+        obj = self.tracked_objects[id]
+        # get the max frames for this label type or the default
+        max_frames = self.detect_config.stationary.max_frames.objects.get(
+            obj["label"], self.detect_config.stationary.max_frames.default
+        )
+
+        # if there is no max_frames for this label type, continue
+        if max_frames is None:
+            return False
+
+        # if the object has exceeded the max_frames setting, deregister
+        if (
+            obj["motionless_count"] - self.detect_config.stationary.threshold
+            > max_frames
+        ):
+            print(f"expired: {obj['motionless_count']}")
+            return True
+
     def update(self, id, new_obj):
         self.disappeared[id] = 0
+        # update the motionless count if the object has not moved to a new position
+        if self.update_position(id, new_obj["box"]):
+            self.tracked_objects[id]["motionless_count"] += 1
+            if self.is_expired(id):
+                self.deregister(id)
+                return
+        else:
+            # register the first position change and then only increment if
+            # the object was previously stationary
+            if (
+                self.tracked_objects[id]["position_changes"] == 0
+                or self.tracked_objects[id]["motionless_count"]
+                >= self.detect_config.stationary.threshold
+            ):
+                self.tracked_objects[id]["position_changes"] += 1
+            self.tracked_objects[id]["motionless_count"] = 0
+
         self.tracked_objects[id].update(new_obj)
 
+    def update_frame_times(self, frame_time):
+        for id in list(self.tracked_objects.keys()):
+            self.tracked_objects[id]["frame_time"] = frame_time
+            self.tracked_objects[id]["motionless_count"] += 1
+            if self.is_expired(id):
+                self.deregister(id)
+
     def match_and_update(self, frame_time, new_objects):
         # group by name
         new_object_groups = defaultdict(lambda: [])

frigate/output.py

@@ -104,7 +104,7 @@ class BirdsEyeFrameManager:
         self.blank_frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]] = 16
 
         # find and copy the logo on the blank frame
-        logo_files = glob.glob("/opt/frigate/web/apple-touch-icon.*.png")
+        logo_files = glob.glob("/opt/frigate/frigate/birdseye.png")
         frigate_logo = None
         if len(logo_files) > 0:
             frigate_logo = cv2.imread(logo_files[0], cv2.IMREAD_UNCHANGED)
@@ -184,10 +184,7 @@ class BirdsEyeFrameManager:
         if self.mode == BirdseyeModeEnum.continuous:
             return True
 
-        if (
+        if self.mode == BirdseyeModeEnum.motion and motion_box_count > 0:
-            self.mode == BirdseyeModeEnum.motion
-            and object_box_count + motion_box_count > 0
-        ):
             return True
 
         if self.mode == BirdseyeModeEnum.objects and object_box_count > 0:
@@ -418,7 +415,7 @@ def output_frames(config: FrigateConfig, video_output_queue):
         ):
             if birdseye_manager.update(
                 camera,
-                len(current_tracked_objects),
+                len([o for o in current_tracked_objects if not o["stationary"]]),
                 len(motion_boxes),
                 frame_time,
                 frame,

frigate/record.py

@@ -1,21 +1,25 @@
 import datetime
 import itertools
 import logging
+import multiprocessing as mp
 import os
+import queue
 import random
 import shutil
 import string
 import subprocess as sp
 import threading
+import time
+from collections import defaultdict
 from pathlib import Path
 
 import psutil
+from peewee import JOIN, DoesNotExist
 
-from peewee import JOIN
+from frigate.config import RetainModeEnum, FrigateConfig
-
-from frigate.config import FrigateConfig
 from frigate.const import CACHE_DIR, RECORD_DIR
 from frigate.models import Event, Recordings
+from frigate.util import area
 
 logger = logging.getLogger(__name__)
 
@@ -39,20 +43,27 @@ def remove_empty_directories(directory):
 
 
 class RecordingMaintainer(threading.Thread):
-    def __init__(self, config: FrigateConfig, stop_event):
+    def __init__(
+        self, config: FrigateConfig, recordings_info_queue: mp.Queue, stop_event
+    ):
         threading.Thread.__init__(self)
         self.name = "recording_maint"
         self.config = config
+        self.recordings_info_queue = recordings_info_queue
         self.stop_event = stop_event
+        self.recordings_info = defaultdict(list)
+        self.end_time_cache = {}
 
     def move_files(self):
-        recordings = [
+        cache_files = sorted(
-            d
+            [
-            for d in os.listdir(CACHE_DIR)
+                d
-            if os.path.isfile(os.path.join(CACHE_DIR, d))
+                for d in os.listdir(CACHE_DIR)
-            and d.endswith(".mp4")
+                if os.path.isfile(os.path.join(CACHE_DIR, d))
-            and not d.startswith("clip_")
+                and d.endswith(".mp4")
-        ]
+                and not d.startswith("clip_")
+            ]
+        )
 
         files_in_use = []
         for process in psutil.process_iter():
@@ -67,7 +78,9 @@ class RecordingMaintainer(threading.Thread):
             except:
                 continue
 
-        for f in recordings:
+        # group recordings by camera
+        grouped_recordings = defaultdict(list)
+        for f in cache_files:
             # Skip files currently in use
             if f in files_in_use:
                 continue
@@ -77,45 +90,187 @@ class RecordingMaintainer(threading.Thread):
             camera, date = basename.rsplit("-", maxsplit=1)
             start_time = datetime.datetime.strptime(date, "%Y%m%d%H%M%S")
 
-            # Just delete files if recordings are turned off
+            grouped_recordings[camera].append(
-            if (
+                {
-                not camera in self.config.cameras
+                    "cache_path": cache_path,
-                or not self.config.cameras[camera].record.enabled
+                    "start_time": start_time,
-            ):
+                }
-                Path(cache_path).unlink(missing_ok=True)
-                continue
-
-            ffprobe_cmd = [
-                "ffprobe",
-                "-v",
-                "error",
-                "-show_entries",
-                "format=duration",
-                "-of",
-                "default=noprint_wrappers=1:nokey=1",
-                f"{cache_path}",
-            ]
-            p = sp.run(ffprobe_cmd, capture_output=True)
-            if p.returncode == 0:
-                duration = float(p.stdout.decode().strip())
-                end_time = start_time + datetime.timedelta(seconds=duration)
-            else:
-                logger.info(f"bad file: {f}")
-                Path(cache_path).unlink(missing_ok=True)
-                continue
-
-            directory = os.path.join(
-                RECORD_DIR, start_time.strftime("%Y-%m/%d/%H"), camera
             )
 
-            if not os.path.exists(directory):
+        # delete all cached files past the most recent 5
-                os.makedirs(directory)
+        keep_count = 5
+        for camera in grouped_recordings.keys():
+            if len(grouped_recordings[camera]) > keep_count:
+                to_remove = grouped_recordings[camera][:-keep_count]
+                for f in to_remove:
+                    Path(f["cache_path"]).unlink(missing_ok=True)
+                    self.end_time_cache.pop(f["cache_path"], None)
+                grouped_recordings[camera] = grouped_recordings[camera][-keep_count:]
 
-            file_name = f"{start_time.strftime('%M.%S.mp4')}"
+        for camera, recordings in grouped_recordings.items():
-            file_path = os.path.join(directory, file_name)
 
+            # clear out all the recording info for old frames
+            while (
+                len(self.recordings_info[camera]) > 0
+                and self.recordings_info[camera][0][0]
+                < recordings[0]["start_time"].timestamp()
+            ):
+                self.recordings_info[camera].pop(0)
+
+            # get all events with the end time after the start of the oldest cache file
+            # or with end_time None
+            events: Event = (
+                Event.select()
+                .where(
+                    Event.camera == camera,
+                    (Event.end_time == None)
+                    | (Event.end_time >= recordings[0]["start_time"].timestamp()),
+                    Event.has_clip,
+                )
+                .order_by(Event.start_time)
+            )
+            for r in recordings:
+                cache_path = r["cache_path"]
+                start_time = r["start_time"]
+
+                # Just delete files if recordings are turned off
+                if (
+                    not camera in self.config.cameras
+                    or not self.config.cameras[camera].record.enabled
+                ):
+                    Path(cache_path).unlink(missing_ok=True)
+                    self.end_time_cache.pop(cache_path, None)
+                    continue
+
+                if cache_path in self.end_time_cache:
+                    end_time, duration = self.end_time_cache[cache_path]
+                else:
+                    ffprobe_cmd = [
+                        "ffprobe",
+                        "-v",
+                        "error",
+                        "-show_entries",
+                        "format=duration",
+                        "-of",
+                        "default=noprint_wrappers=1:nokey=1",
+                        f"{cache_path}",
+                    ]
+                    p = sp.run(ffprobe_cmd, capture_output=True)
+                    if p.returncode == 0:
+                        duration = float(p.stdout.decode().strip())
+                        end_time = start_time + datetime.timedelta(seconds=duration)
+                        self.end_time_cache[cache_path] = (end_time, duration)
+                    else:
+                        logger.warning(f"Discarding a corrupt recording segment: {f}")
+                        Path(cache_path).unlink(missing_ok=True)
+                        continue
+
+                # if cached file's start_time is earlier than the retain days for the camera
+                if start_time <= (
+                    (
+                        datetime.datetime.now()
+                        - datetime.timedelta(
+                            days=self.config.cameras[camera].record.retain.days
+                        )
+                    )
+                ):
+                    # if the cached segment overlaps with the events:
+                    overlaps = False
+                    for event in events:
+                        # if the event starts in the future, stop checking events
+                        # and remove this segment
+                        if event.start_time > end_time.timestamp():
+                            overlaps = False
+                            Path(cache_path).unlink(missing_ok=True)
+                            self.end_time_cache.pop(cache_path, None)
+                            break
+
+                        # if the event is in progress or ends after the recording starts, keep it
+                        # and stop looking at events
+                        if (
+                            event.end_time is None
+                            or event.end_time >= start_time.timestamp()
+                        ):
+                            overlaps = True
+                            break
+
+                    if overlaps:
+                        record_mode = self.config.cameras[
+                            camera
+                        ].record.events.retain.mode
+                        # move from cache to recordings immediately
+                        self.store_segment(
+                            camera,
+                            start_time,
+                            end_time,
+                            duration,
+                            cache_path,
+                            record_mode,
+                        )
+                # else retain days includes this segment
+                else:
+                    record_mode = self.config.cameras[camera].record.retain.mode
+                    self.store_segment(
+                        camera, start_time, end_time, duration, cache_path, record_mode
+                    )
+
+    def segment_stats(self, camera, start_time, end_time):
+        active_count = 0
+        motion_count = 0
+        for frame in self.recordings_info[camera]:
+            # frame is after end time of segment
+            if frame[0] > end_time.timestamp():
+                break
+            # frame is before start time of segment
+            if frame[0] < start_time.timestamp():
+                continue
+
+            active_count += len(
+                [
+                    o
+                    for o in frame[1]
+                    if not o["false_positive"] and o["motionless_count"] == 0
+                ]
+            )
+
+            motion_count += sum([area(box) for box in frame[2]])
+
+        return (motion_count, active_count)
+
+    def store_segment(
+        self,
+        camera,
+        start_time,
+        end_time,
+        duration,
+        cache_path,
+        store_mode: RetainModeEnum,
+    ):
+        motion_count, active_count = self.segment_stats(camera, start_time, end_time)
+
+        # check if the segment shouldn't be stored
+        if (store_mode == RetainModeEnum.motion and motion_count == 0) or (
+            store_mode == RetainModeEnum.active_objects and active_count == 0
+        ):
+            Path(cache_path).unlink(missing_ok=True)
+            self.end_time_cache.pop(cache_path, None)
+            return
+
+        directory = os.path.join(RECORD_DIR, start_time.strftime("%Y-%m/%d/%H"), camera)
+
+        if not os.path.exists(directory):
+            os.makedirs(directory)
+
+        file_name = f"{start_time.strftime('%M.%S.mp4')}"
+        file_path = os.path.join(directory, file_name)
+
+        try:
+            start_frame = datetime.datetime.now().timestamp()
             # copy then delete is required when recordings are stored on some network drives
             shutil.copyfile(cache_path, file_path)
+            logger.debug(
+                f"Copied {file_path} in {datetime.datetime.now().timestamp()-start_frame} seconds."
+            )
             os.remove(cache_path)
 
             rand_id = "".join(
@@ -128,12 +283,56 @@ class RecordingMaintainer(threading.Thread):
                 start_time=start_time.timestamp(),
                 end_time=end_time.timestamp(),
                 duration=duration,
+                motion=motion_count,
+                # TODO: update this to store list of active objects at some point
+                objects=active_count,
             )
+        except Exception as e:
+            logger.error(f"Unable to store recording segment {cache_path}")
+            Path(cache_path).unlink(missing_ok=True)
+            logger.error(e)
+
+        # clear end_time cache
+        self.end_time_cache.pop(cache_path, None)
 
     def run(self):
         # Check for new files every 5 seconds
-        while not self.stop_event.wait(5):
+        wait_time = 5
-            self.move_files()
+        while not self.stop_event.wait(wait_time):
+            run_start = datetime.datetime.now().timestamp()
+
+            # empty the recordings info queue
+            while True:
+                try:
+                    (
+                        camera,
+                        frame_time,
+                        current_tracked_objects,
+                        motion_boxes,
+                        regions,
+                    ) = self.recordings_info_queue.get(False)
+
+                    if self.config.cameras[camera].record.enabled:
+                        self.recordings_info[camera].append(
+                            (
+                                frame_time,
+                                current_tracked_objects,
+                                motion_boxes,
+                                regions,
+                            )
+                        )
+                except queue.Empty:
+                    break
+
+            try:
+                self.move_files()
+            except Exception as e:
+                logger.error(
+                    "Error occurred when attempting to maintain recording cache"
+                )
+                logger.error(e)
+            duration = datetime.datetime.now().timestamp() - run_start
+            wait_time = max(0, 5 - duration)
 
         logger.info(f"Exiting recording maintenance...")
 
@@ -158,18 +357,22 @@ class RecordingCleanup(threading.Thread):
 
         logger.debug("Start deleted cameras.")
         # Handle deleted cameras
+        expire_days = self.config.record.retain.days
+        expire_before = (
+            datetime.datetime.now() - datetime.timedelta(days=expire_days)
+        ).timestamp()
         no_camera_recordings: Recordings = Recordings.select().where(
             Recordings.camera.not_in(list(self.config.cameras.keys())),
+            Recordings.end_time < expire_before,
         )
 
+        deleted_recordings = set()
         for recording in no_camera_recordings:
-            expire_days = self.config.record.retain_days
+            Path(recording.path).unlink(missing_ok=True)
-            expire_before = (
+            deleted_recordings.add(recording.id)
-                datetime.datetime.now() - datetime.timedelta(days=expire_days)
+
-            ).timestamp()
+        logger.debug(f"Expiring {len(deleted_recordings)} recordings")
-            if recording.end_time < expire_before:
+        Recordings.delete().where(Recordings.id << deleted_recordings).execute()
-                Path(recording.path).unlink(missing_ok=True)
-                Recordings.delete_by_id(recording.id)
         logger.debug("End deleted cameras.")
 
         logger.debug("Start all cameras.")
@@ -180,65 +383,83 @@ class RecordingCleanup(threading.Thread):
                 datetime.datetime.now()
                 - datetime.timedelta(seconds=config.record.events.max_seconds)
             ).timestamp()
-            expire_days = config.record.retain_days
+            expire_days = config.record.retain.days
             expire_before = (
                 datetime.datetime.now() - datetime.timedelta(days=expire_days)
             ).timestamp()
             expire_date = min(min_end, expire_before)
 
-            # Get recordings to remove
+            # Get recordings to check for expiration
-            recordings: Recordings = Recordings.select().where(
+            recordings: Recordings = (
-                Recordings.camera == camera,
+                Recordings.select()
-                Recordings.end_time < expire_date,
+                .where(
+                    Recordings.camera == camera,
+                    Recordings.end_time < expire_date,
+                )
+                .order_by(Recordings.start_time)
             )
 
-            for recording in recordings:
+            # Get all the events to check against
-                # See if there are any associated events
+            events: Event = (
-                events: Event = Event.select().where(
+                Event.select()
-                    Event.camera == recording.camera,
+                .where(
-                    (
+                    Event.camera == camera,
-                        Event.start_time.between(
+                    # need to ensure segments for all events starting
-                            recording.start_time, recording.end_time
+                    # before the expire date are included
-                        )
+                    Event.start_time < expire_date,
-                        | Event.end_time.between(
+                    Event.has_clip,
-                            recording.start_time, recording.end_time
-                        )
-                        | (
-                            (recording.start_time > Event.start_time)
-                            & (recording.end_time < Event.end_time)
-                        )
-                    ),
                 )
+                .order_by(Event.start_time)
+                .objects()
+            )
+
+            # loop over recordings and see if they overlap with any non-expired events
+            # TODO: expire segments based on segment stats according to config
+            event_start = 0
+            deleted_recordings = set()
+            for recording in recordings.objects().iterator():
                 keep = False
-                event_ids = set()
+                # Now look for a reason to keep this recording segment
+                for idx in range(event_start, len(events)):
+                    event = events[idx]
 
-                event: Event
+                    # if the event starts in the future, stop checking events
-                for event in events:
+                    # and let this recording segment expire
-                    event_ids.add(event.id)
+                    if event.start_time > recording.end_time:
-                    # Check event/label retention and keep the recording if within window
+                        keep = False
-                    expire_days_event = (
+                        break
-                        0
+
-                        if not config.record.events.enabled
+                    # if the event is in progress or ends after the recording starts, keep it
-                        else config.record.events.retain.objects.get(
+                    # and stop looking at events
-                            event.label, config.record.events.retain.default
+                    if event.end_time is None or event.end_time >= recording.start_time:
-                        )
-                    )
-                    expire_before_event = (
-                        datetime.datetime.now()
-                        - datetime.timedelta(days=expire_days_event)
-                    ).timestamp()
-                    if recording.end_time >= expire_before_event:
                         keep = True
+                        break
 
-                # Delete recordings outside of the retention window
+                    # if the event ends before this recording segment starts, skip
-                if not keep:
+                    # this event and check the next event for an overlap.
+                    # since the events and recordings are sorted, we can skip events
+                    # that end before the previous recording segment started on future segments
+                    if event.end_time < recording.start_time:
+                        event_start = idx
+
+                # Delete recordings outside of the retention window or based on the retention mode
+                if (
+                    not keep
+                    or (
+                        config.record.events.retain.mode == RetainModeEnum.motion
+                        and recording.motion == 0
+                    )
+                    or (
+                        config.record.events.retain.mode
+                        == RetainModeEnum.active_objects
+                        and recording.objects == 0
+                    )
+                ):
                     Path(recording.path).unlink(missing_ok=True)
-                    Recordings.delete_by_id(recording.id)
+                    deleted_recordings.add(recording.id)
-                    if event_ids:
+
-                        # Update associated events
+            logger.debug(f"Expiring {len(deleted_recordings)} recordings")
-                        Event.update(has_clip=False).where(
+            Recordings.delete().where(Recordings.id << deleted_recordings).execute()
-                            Event.id.in_(list(event_ids))
-                        ).execute()
 
             logger.debug(f"End camera: {camera}.")
 
@@ -250,30 +471,32 @@ class RecordingCleanup(threading.Thread):
 
         default_expire = (
             datetime.datetime.now().timestamp()
-            - SECONDS_IN_DAY * self.config.record.retain_days
+            - SECONDS_IN_DAY * self.config.record.retain.days
         )
         delete_before = {}
 
         for name, camera in self.config.cameras.items():
             delete_before[name] = (
                 datetime.datetime.now().timestamp()
-                - SECONDS_IN_DAY * camera.record.retain_days
+                - SECONDS_IN_DAY * camera.record.retain.days
             )
 
         # find all the recordings older than the oldest recording in the db
-        oldest_recording = (
+        try:
-            Recordings.select().order_by(Recordings.start_time.desc()).get()
+            oldest_recording = Recordings.select().order_by(Recordings.start_time).get()
-        )
 
-        oldest_timestamp = (
+            p = Path(oldest_recording.path)
-            oldest_recording.start_time
+            oldest_timestamp = p.stat().st_mtime - 1
-            if oldest_recording
+        except DoesNotExist:
-            else datetime.datetime.now().timestamp()
+            oldest_timestamp = datetime.datetime.now().timestamp()
-        )
+        except FileNotFoundError:
+            logger.warning(f"Unable to find file from recordings database: {p}")
+            Recordings.delete().where(Recordings.id == oldest_recording.id).execute()
+            return
 
         logger.debug(f"Oldest recording in the db: {oldest_timestamp}")
         process = sp.run(
-            ["find", RECORD_DIR, "-type", "f", "-newermt", f"@{oldest_timestamp}"],
+            ["find", RECORD_DIR, "-type", "f", "!", "-newermt", f"@{oldest_timestamp}"],
             capture_output=True,
             text=True,
         )
@@ -281,21 +504,52 @@ class RecordingCleanup(threading.Thread):
 
         for f in files_to_check:
             p = Path(f)
-            if p.stat().st_mtime < delete_before.get(p.parent.name, default_expire):
+            try:
-                p.unlink(missing_ok=True)
+                if p.stat().st_mtime < delete_before.get(p.parent.name, default_expire):
+                    p.unlink(missing_ok=True)
+            except FileNotFoundError:
+                logger.warning(f"Attempted to expire missing file: {f}")
 
         logger.debug("End expire files (legacy).")
 
+    def sync_recordings(self):
+        logger.debug("Start sync recordings.")
+
+        # get all recordings in the db
+        recordings: Recordings = Recordings.select()
+
+        # get all recordings files on disk
+        process = sp.run(
+            ["find", RECORD_DIR, "-type", "f"],
+            capture_output=True,
+            text=True,
+        )
+        files_on_disk = process.stdout.splitlines()
+
+        recordings_to_delete = []
+        for recording in recordings.objects().iterator():
+            if not recording.path in files_on_disk:
+                recordings_to_delete.append(recording.id)
+
+        logger.debug(
+            f"Deleting {len(recordings_to_delete)} recordings with missing files"
+        )
+        Recordings.delete().where(Recordings.id << recordings_to_delete).execute()
+
+        logger.debug("End sync recordings.")
+
     def run(self):
-        # Expire recordings every minute, clean directories every hour.
+        # on startup sync recordings with disk (disabled due to too much CPU usage)
-        for counter in itertools.cycle(range(60)):
+        # self.sync_recordings()
+
+        # Expire tmp clips every minute, recordings and clean directories every hour.
+        for counter in itertools.cycle(range(self.config.record.expire_interval)):
             if self.stop_event.wait(60):
                 logger.info(f"Exiting recording cleanup...")
                 break
-
-            self.expire_recordings()
             self.clean_tmp_clips()
 
             if counter == 0:
+                self.expire_recordings()
                 self.expire_files()
                 remove_empty_directories(RECORD_DIR)

frigate/stats.py

@@ -4,6 +4,7 @@ import threading
 import time
 import psutil
 import shutil
+import os
 
 from frigate.config import FrigateConfig
 from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
@@ -31,6 +32,28 @@ def get_fs_type(path):
     return fsType
 
 
+def read_temperature(path):
+    if os.path.isfile(path):
+        with open(path) as f:
+            line = f.readline().strip()
+            return int(line) / 1000
+    return None
+
+
+def get_temperatures():
+    temps = {}
+
+    # Get temperatures for all attached Corals
+    base = "/sys/class/apex/"
+    if os.path.isdir(base):
+        for apex in os.listdir(base):
+            temp = read_temperature(os.path.join(base, apex, "temp"))
+            if temp is not None:
+                temps[apex] = temp
+
+    return temps
+
+
 def stats_snapshot(stats_tracking):
     camera_metrics = stats_tracking["camera_metrics"]
     stats = {}
@@ -61,6 +84,7 @@ def stats_snapshot(stats_tracking):
         "uptime": (int(time.time()) - stats_tracking["started"]),
         "version": VERSION,
         "storage": {},
+        "temperatures": get_temperatures(),
     }
 
     for path in [RECORD_DIR, CLIPS_DIR, CACHE_DIR, "/dev/shm"]:

frigate/test/test_config.py

@@ -473,7 +473,7 @@ class TestConfig(unittest.TestCase):
                         "width": 1920,
                         "fps": 5,
                     },
-                    "record": {"events": {"enabled": True}},
+                    "record": {"events": {}},
                 }
             },
         }
@@ -572,7 +572,7 @@ class TestConfig(unittest.TestCase):
         assert config == frigate_config.dict(exclude_unset=True)
 
         runtime_config = frigate_config.runtime_config
-        assert runtime_config.cameras["back"].motion.frame_height >= 120
+        assert runtime_config.cameras["back"].motion.frame_height == 50
 
     def test_motion_contour_area_dynamic(self):
 
@@ -601,7 +601,7 @@ class TestConfig(unittest.TestCase):
         assert config == frigate_config.dict(exclude_unset=True)
 
         runtime_config = frigate_config.runtime_config
-        assert round(runtime_config.cameras["back"].motion.contour_area) == 99
+        assert round(runtime_config.cameras["back"].motion.contour_area) == 30
 
     def test_merge_labelmap(self):
 
@@ -702,7 +702,11 @@ class TestConfig(unittest.TestCase):
                         "inputs": [
                             {
                                 "path": "rtsp://10.0.0.1:554/video",
-                                "roles": ["detect", "clips"],
+                                "roles": ["detect"],
+                            },
+                            {
+                                "path": "rtsp://10.0.0.1:554/video2",
+                                "roles": ["clips"],
                             },
                         ]
                     },
@@ -717,6 +721,87 @@ class TestConfig(unittest.TestCase):
 
         self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
 
+    def test_fails_on_missing_role(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                            {
+                                "path": "rtsp://10.0.0.1:554/video2",
+                                "roles": ["record"],
+                            },
+                        ]
+                    },
+                    "detect": {
+                        "height": 1080,
+                        "width": 1920,
+                        "fps": 5,
+                    },
+                    "rtmp": {"enabled": True},
+                }
+            },
+        }
+
+        frigate_config = FrigateConfig(**config)
+        self.assertRaises(ValueError, lambda: frigate_config.runtime_config)
+
+    def test_works_on_missing_role_multiple_cams(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "rtmp": {"enabled": False},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                            {
+                                "path": "rtsp://10.0.0.1:554/video2",
+                                "roles": ["record"],
+                            },
+                        ]
+                    },
+                    "detect": {
+                        "height": 1080,
+                        "width": 1920,
+                        "fps": 5,
+                    },
+                },
+                "cam2": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                            {
+                                "path": "rtsp://10.0.0.1:554/video2",
+                                "roles": ["record"],
+                            },
+                        ]
+                    },
+                    "detect": {
+                        "height": 1080,
+                        "width": 1920,
+                        "fps": 5,
+                    },
+                },
+            },
+        }
+
+        frigate_config = FrigateConfig(**config)
+        runtime_config = frigate_config.runtime_config
+
     def test_global_detect(self):
 
         config = {
@@ -884,6 +969,305 @@ class TestConfig(unittest.TestCase):
         assert runtime_config.cameras["back"].snapshots.height == 150
         assert runtime_config.cameras["back"].snapshots.enabled
 
+    def test_global_rtmp(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "rtmp": {"enabled": True},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].rtmp.enabled
+
+    def test_default_rtmp(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    }
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].rtmp.enabled
+
+    def test_global_rtmp_merge(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "rtmp": {"enabled": False},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                    "rtmp": {
+                        "enabled": True,
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].rtmp.enabled
+
+    def test_global_rtmp_default(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "rtmp": {"enabled": False},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                            {
+                                "path": "rtsp://10.0.0.1:554/video2",
+                                "roles": ["record"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert not runtime_config.cameras["back"].rtmp.enabled
+
+    def test_global_live(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "live": {"quality": 4},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].live.quality == 4
+
+    def test_default_live(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    }
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].live.quality == 8
+
+    def test_global_live_merge(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "live": {"quality": 4, "height": 480},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                    "live": {
+                        "quality": 7,
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].live.quality == 7
+        assert runtime_config.cameras["back"].live.height == 480
+
+    def test_global_timestamp_style(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "timestamp_style": {"position": "bl"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].timestamp_style.position == "bl"
+
+    def test_default_timestamp_style(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    }
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].timestamp_style.position == "tl"
+
+    def test_global_timestamp_style_merge(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "rtmp": {"enabled": False},
+            "timestamp_style": {"position": "br", "thickness": 2},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                    "timestamp_style": {"position": "bl", "thickness": 4},
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].timestamp_style.position == "bl"
+        assert runtime_config.cameras["back"].timestamp_style.thickness == 4
+
+    def test_allow_retain_to_be_a_decimal(self):
+
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "snapshots": {"retain": {"default": 1.5}},
+            "cameras": {
+                "back": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+        frigate_config = FrigateConfig(**config)
+        assert config == frigate_config.dict(exclude_unset=True)
+
+        runtime_config = frigate_config.runtime_config
+        assert runtime_config.cameras["back"].snapshots.retain.default == 1.5
+
+    def test_fails_on_bad_camera_name(self):
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "snapshots": {"retain": {"default": 1.5}},
+            "cameras": {
+                "back camer#": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+
+        frigate_config = FrigateConfig(**config)
+
+        self.assertRaises(
+            ValidationError, lambda: frigate_config.runtime_config.cameras
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

frigate/test/test_reduce_boxes.py

@@ -0,0 +1,26 @@
+import numpy as np
+from unittest import TestCase, main
+from frigate.video import box_overlaps, reduce_boxes
+
+
+class TestBoxOverlaps(TestCase):
+    def test_overlap(self):
+        assert box_overlaps((100, 100, 200, 200), (50, 50, 150, 150))
+
+    def test_overlap_2(self):
+        assert box_overlaps((50, 50, 150, 150), (100, 100, 200, 200))
+
+    def test_no_overlap(self):
+        assert not box_overlaps((100, 100, 200, 200), (250, 250, 350, 350))
+
+
+class TestReduceBoxes(TestCase):
+    def test_cluster(self):
+        clusters = reduce_boxes(
+            [(144, 290, 221, 459), (225, 178, 426, 341), (343, 105, 584, 250)]
+        )
+        assert len(clusters) == 2
+
+
+if __name__ == "__main__":
+    main(verbosity=2)

frigate/util.py

@@ -18,6 +18,7 @@ import cv2
 import matplotlib.pyplot as plt
 import numpy as np
 import os
+import psutil
 
 logger = logging.getLogger(__name__)
 
@@ -51,18 +52,32 @@ def draw_timestamp(
     timestamp,
     timestamp_format,
     font_effect=None,
-    font_scale=1.0,
     font_thickness=2,
     font_color=(255, 255, 255),
     position="tl",
 ):
     time_to_show = datetime.datetime.fromtimestamp(timestamp).strftime(timestamp_format)
+
+    # calculate a dynamic font size
+    size = cv2.getTextSize(
+        time_to_show,
+        cv2.FONT_HERSHEY_SIMPLEX,
+        fontScale=1.0,
+        thickness=font_thickness,
+    )
+
+    text_width = size[0][0]
+    desired_size = max(150, 0.33 * frame.shape[1])
+    font_scale = desired_size / text_width
+
+    # calculate the actual size with the dynamic scale
     size = cv2.getTextSize(
         time_to_show,
         cv2.FONT_HERSHEY_SIMPLEX,
         fontScale=font_scale,
         thickness=font_thickness,
     )
+
     image_width = frame.shape[1]
     image_height = frame.shape[0]
     text_width = size[0][0]
@@ -174,12 +189,12 @@ def draw_box_with_label(
     )
 
 
-def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
+def calculate_region(frame_shape, xmin, ymin, xmax, ymax, model_size, multiplier=2):
     # size is the longest edge and divisible by 4
-    size = int(max(xmax - xmin, ymax - ymin) // 4 * 4 * multiplier)
+    size = int((max(xmax - xmin, ymax - ymin) * multiplier) // 4 * 4)
-    # dont go any smaller than 300
+    # dont go any smaller than the model_size
-    if size < 300:
+    if size < model_size:
-        size = 300
+        size = model_size
 
     # x_offset is midpoint of bounding box minus half the size
     x_offset = int((xmax - xmin) / 2.0 + xmin - size / 2.0)
@@ -520,7 +535,13 @@ def clipped(obj, frame_shape):
 
 
 def restart_frigate():
-    os.kill(os.getpid(), signal.SIGTERM)
+    proc = psutil.Process(1)
+    # if this is running via s6, sigterm pid 1
+    if proc.name() == "s6-svscan":
+        proc.terminate()
+    # otherwise, just try and exit frigate
+    else:
+        os.kill(os.getpid(), signal.SIGTERM)
 
 
 class EventsPerSecond:
@@ -546,6 +567,9 @@ class EventsPerSecond:
         # compute the (approximate) events in the last n seconds
         now = datetime.datetime.now().timestamp()
         seconds = min(now - self._start, last_n_seconds)
+        # avoid divide by zero
+        if seconds == 0:
+            seconds = 1
         return (
             len([t for t in self._timestamps if t > (now - last_n_seconds)]) / seconds
         )
@@ -581,6 +605,26 @@ def add_mask(mask, mask_img):
     cv2.fillPoly(mask_img, pts=[contour], color=(0))
 
 
+def load_labels(path, encoding="utf-8"):
+    """Loads labels from file (with or without index numbers).
+    Args:
+      path: path to label file.
+      encoding: label file encoding.
+    Returns:
+      Dictionary mapping indices to labels.
+    """
+    with open(path, "r", encoding=encoding) as f:
+        lines = f.readlines()
+        if not lines:
+            return {}
+
+        if lines[0].split(" ", maxsplit=1)[0].isdigit():
+            pairs = [line.split(" ", maxsplit=1) for line in lines]
+            return {int(index): label.strip() for index, label in pairs}
+        else:
+            return {index: line.strip() for index, line in enumerate(lines)}
+
+
 class FrameManager(ABC):
     @abstractmethod
     def create(self, name, size) -> AnyStr:

frigate/video.py

@@ -3,18 +3,19 @@ import itertools
 import logging
 import multiprocessing as mp
 import queue
-import subprocess as sp
+import random
 import signal
+import subprocess as sp
 import threading
 import time
 from collections import defaultdict
-from setproctitle import setproctitle
 from typing import Dict, List
 
-from cv2 import cv2
 import numpy as np
+from cv2 import cv2, reduce
+from setproctitle import setproctitle
 
-from frigate.config import CameraConfig
+from frigate.config import CameraConfig, DetectConfig
 from frigate.edgetpu import RemoteObjectDetector
 from frigate.log import LogPipe
 from frigate.motion import MotionDetector
@@ -23,8 +24,11 @@ from frigate.util import (
     EventsPerSecond,
     FrameManager,
     SharedMemoryFrameManager,
+    area,
     calculate_region,
     clipped,
+    intersection,
+    intersection_over_union,
     listen,
     yuv_region_2_rgb,
 )
@@ -149,10 +153,10 @@ def capture_frames(
         try:
             frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
         except Exception as e:
-            logger.info(f"{camera_name}: ffmpeg sent a broken frame. {e}")
+            logger.error(f"{camera_name}: Unable to read frames from ffmpeg process.")
 
             if ffmpeg_process.poll() != None:
-                logger.info(
+                logger.error(
                     f"{camera_name}: ffmpeg process is not running. exiting capture thread..."
                 )
                 frame_manager.delete(frame_name)
@@ -217,12 +221,11 @@ class CameraWatchdog(threading.Thread):
 
             if not self.capture_thread.is_alive():
                 self.logger.error(
-                    f"FFMPEG process crashed unexpectedly for {self.camera_name}."
+                    f"Ffmpeg process crashed unexpectedly for {self.camera_name}."
                 )
                 self.logger.error(
                     "The following ffmpeg logs include the last 100 lines prior to exit."
                 )
-                self.logger.error("You may have invalid args defined for this camera.")
                 self.logpipe.dump()
                 self.start_ffmpeg_detect()
             elif now - self.capture_thread.current_frame.value > 20:
@@ -364,6 +367,7 @@ def track_camera(
         frame_queue,
         frame_shape,
         model_shape,
+        config.detect,
         frame_manager,
         motion_detector,
         object_detector,
@@ -379,26 +383,36 @@ def track_camera(
     logger.info(f"{name}: exiting subprocess")
 
 
-def reduce_boxes(boxes):
+def box_overlaps(b1, b2):
-    if len(boxes) == 0:
+    if b1[2] < b2[0] or b1[0] > b2[2] or b1[1] > b2[3] or b1[3] < b2[1]:
-        return []
+        return False
-    reduced_boxes = cv2.groupRectangles(
+    return True
-        [list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2
+
-    )[0]
+
-    return [tuple(b) for b in reduced_boxes]
+def reduce_boxes(boxes, iou_threshold=0.0):
+    clusters = []
+
+    for box in boxes:
+        matched = 0
+        for cluster in clusters:
+            if intersection_over_union(box, cluster) > iou_threshold:
+                matched = 1
+                cluster[0] = min(cluster[0], box[0])
+                cluster[1] = min(cluster[1], box[1])
+                cluster[2] = max(cluster[2], box[2])
+                cluster[3] = max(cluster[3], box[3])
+
+        if not matched:
+            clusters.append(list(box))
+
+    return [tuple(c) for c in clusters]
 
 
-# modified from https://stackoverflow.com/a/40795835
 def intersects_any(box_a, boxes):
     for box in boxes:
-        if (
+        if box_overlaps(box_a, box):
-            box_a[2] < box[0]
+            return True
-            or box_a[0] > box[2]
+    return False
-            or box_a[1] > box[3]
-            or box_a[3] < box[1]
-        ):
-            continue
-        return True
 
 
 def detect(
@@ -434,6 +448,7 @@ def process_frames(
     frame_queue: mp.Queue,
     frame_shape,
     model_shape,
+    detect_config: DetectConfig,
     frame_manager: FrameManager,
     motion_detector: MotionDetector,
     object_detector: RemoteObjectDetector,
@@ -454,6 +469,8 @@ def process_frames(
     fps_tracker = EventsPerSecond()
     fps_tracker.start()
 
+    startup_scan_counter = 0
+
     while not stop_event.is_set():
         if exit_on_empty and frame_queue.empty():
             logger.info(f"Exiting track_objects...")
@@ -474,122 +491,219 @@ def process_frames(
             logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
             continue
 
-        if not detection_enabled.value:
-            fps.value = fps_tracker.eps()
-            object_tracker.match_and_update(frame_time, [])
-            detected_objects_queue.put(
-                (camera_name, frame_time, object_tracker.tracked_objects, [], [])
-            )
-            detection_fps.value = object_detector.fps.eps()
-            frame_manager.close(f"{camera_name}{frame_time}")
-            continue
-
         # look for motion
         motion_boxes = motion_detector.detect(frame)
 
-        # only get the tracked object boxes that intersect with motion
+        regions = []
-        tracked_object_boxes = [
-            obj["box"]
-            for obj in object_tracker.tracked_objects.values()
-            if intersects_any(obj["box"], motion_boxes)
-        ]
 
-        # combine motion boxes with known locations of existing objects
+        # if detection is disabled
-        combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
+        if not detection_enabled.value:
-
+            object_tracker.match_and_update(frame_time, [])
-        # compute regions
+        else:
-        regions = [
+            # get stationary object ids
-            calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
+            # check every Nth frame for stationary objects
-            for a in combined_boxes
+            # disappeared objects are not stationary
-        ]
+            # also check for overlapping motion boxes
-
+            stationary_object_ids = [
-        # combine overlapping regions
+                obj["id"]
-        combined_regions = reduce_boxes(regions)
+                for obj in object_tracker.tracked_objects.values()
-
+                # if there hasn't been motion for 10 frames
-        # re-compute regions
+                if obj["motionless_count"] >= 10
-        regions = [
+                # and it isn't due for a periodic check
-            calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
+                and (
-            for a in combined_regions
+                    detect_config.stationary.interval == 0
-        ]
+                    or obj["motionless_count"] % detect_config.stationary.interval != 0
-
-        # resize regions and detect
-        detections = []
-        for region in regions:
-            detections.extend(
-                detect(
-                    object_detector,
-                    frame,
-                    model_shape,
-                    region,
-                    objects_to_track,
-                    object_filters,
                 )
-            )
+                # and it hasn't disappeared
+                and object_tracker.disappeared[obj["id"]] == 0
+                # and it doesn't overlap with any current motion boxes
+                and not intersects_any(obj["box"], motion_boxes)
+            ]
 
-        #########
+            # get tracked object boxes that aren't stationary
-        # merge objects, check for clipped objects and look again up to 4 times
+            tracked_object_boxes = [
-        #########
+                obj["box"]
-        refining = True
+                for obj in object_tracker.tracked_objects.values()
-        refine_count = 0
+                if not obj["id"] in stationary_object_ids
-        while refining and refine_count < 4:
+            ]
-            refining = False
 
-            # group by name
+            # combine motion boxes with known locations of existing objects
-            detected_object_groups = defaultdict(lambda: [])
+            combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
-            for detection in detections:
-                detected_object_groups[detection[0]].append(detection)
 
-            selected_objects = []
+            region_min_size = max(model_shape[0], model_shape[1])
-            for group in detected_object_groups.values():
+            # compute regions
+            regions = [
+                calculate_region(
+                    frame_shape,
+                    a[0],
+                    a[1],
+                    a[2],
+                    a[3],
+                    region_min_size,
+                    multiplier=random.uniform(1.2, 1.5),
+                )
+                for a in combined_boxes
+            ]
 
-                # apply non-maxima suppression to suppress weak, overlapping bounding boxes
+            # consolidate regions with heavy overlap
-                boxes = [
+            regions = [
-                    (o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
+                calculate_region(
-                    for o in group
+                    frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
-                ]
+                )
-                confidences = [o[1] for o in group]
+                for a in reduce_boxes(regions, 0.4)
-                idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+            ]
 
-                for index in idxs:
+            # if starting up, get the next startup scan region
-                    obj = group[index[0]]
+            if startup_scan_counter < 9:
-                    if clipped(obj, frame_shape):
+                ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
-                        box = obj[2]
+                ymax = int(frame_shape[0] / 3 + ymin)
-                        # calculate a new region that will hopefully get the entire object
+                xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
-                        region = calculate_region(
+                xmax = int(frame_shape[1] / 3 + xmin)
-                            frame_shape, box[0], box[1], box[2], box[3]
+                regions.append(
-                        )
+                    calculate_region(
+                        frame_shape,
+                        xmin,
+                        ymin,
+                        xmax,
+                        ymax,
+                        region_min_size,
+                        multiplier=1.2,
+                    )
+                )
+                startup_scan_counter += 1
 
-                        regions.append(region)
+            # resize regions and detect
+            # seed with stationary objects
+            detections = [
+                (
+                    obj["label"],
+                    obj["score"],
+                    obj["box"],
+                    obj["area"],
+                    obj["region"],
+                )
+                for obj in object_tracker.tracked_objects.values()
+                if obj["id"] in stationary_object_ids
+            ]
 
-                        selected_objects.extend(
+            for region in regions:
-                            detect(
+                detections.extend(
-                                object_detector,
+                    detect(
-                                frame,
+                        object_detector,
-                                model_shape,
+                        frame,
-                                region,
+                        model_shape,
-                                objects_to_track,
+                        region,
-                                object_filters,
+                        objects_to_track,
+                        object_filters,
+                    )
+                )
+
+            #########
+            # merge objects, check for clipped objects and look again up to 4 times
+            #########
+            refining = len(regions) > 0
+            refine_count = 0
+            while refining and refine_count < 4:
+                refining = False
+
+                # group by name
+                detected_object_groups = defaultdict(lambda: [])
+                for detection in detections:
+                    detected_object_groups[detection[0]].append(detection)
+
+                selected_objects = []
+                for group in detected_object_groups.values():
+
+                    # apply non-maxima suppression to suppress weak, overlapping bounding boxes
+                    boxes = [
+                        (o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
+                        for o in group
+                    ]
+                    confidences = [o[1] for o in group]
+                    idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+                    for index in idxs:
+                        obj = group[index[0]]
+                        if clipped(obj, frame_shape):
+                            box = obj[2]
+                            # calculate a new region that will hopefully get the entire object
+                            region = calculate_region(
+                                frame_shape,
+                                box[0],
+                                box[1],
+                                box[2],
+                                box[3],
+                                region_min_size,
                             )
-                        )
 
-                        refining = True
+                            regions.append(region)
-                    else:
-                        selected_objects.append(obj)
-            # set the detections list to only include top, complete objects
-            # and new detections
-            detections = selected_objects
 
-            if refining:
+                            selected_objects.extend(
-                refine_count += 1
+                                detect(
+                                    object_detector,
+                                    frame,
+                                    model_shape,
+                                    region,
+                                    objects_to_track,
+                                    object_filters,
+                                )
+                            )
 
-        # Limit to the detections overlapping with motion areas
+                            refining = True
-        # to avoid picking up stationary background objects
+                        else:
-        detections_with_motion = [
+                            selected_objects.append(obj)
-            d for d in detections if intersects_any(d[2], motion_boxes)
+                # set the detections list to only include top, complete objects
-        ]
+                # and new detections
+                detections = selected_objects
 
-        # now that we have refined our detections, we need to track objects
+                if refining:
-        object_tracker.match_and_update(frame_time, detections_with_motion)
+                    refine_count += 1
+
+            ## drop detections that overlap too much
+            consolidated_detections = []
+
+            # if detection was run on this frame, consolidate
+            if len(regions) > 0:
+                # group by name
+                detected_object_groups = defaultdict(lambda: [])
+                for detection in detections:
+                    detected_object_groups[detection[0]].append(detection)
+
+                # loop over detections grouped by label
+                for group in detected_object_groups.values():
+                    # if the group only has 1 item, skip
+                    if len(group) == 1:
+                        consolidated_detections.append(group[0])
+                        continue
+
+                    # sort smallest to largest by area
+                    sorted_by_area = sorted(group, key=lambda g: g[3])
+
+                    for current_detection_idx in range(0, len(sorted_by_area)):
+                        current_detection = sorted_by_area[current_detection_idx][2]
+                        overlap = 0
+                        for to_check_idx in range(
+                            min(current_detection_idx + 1, len(sorted_by_area)),
+                            len(sorted_by_area),
+                        ):
+                            to_check = sorted_by_area[to_check_idx][2]
+                            # if 90% of smaller detection is inside of another detection, consolidate
+                            if (
+                                area(intersection(current_detection, to_check))
+                                / area(current_detection)
+                                > 0.9
+                            ):
+                                overlap = 1
+                                break
+                        if overlap == 0:
+                            consolidated_detections.append(
+                                sorted_by_area[current_detection_idx]
+                            )
+                # now that we have refined our detections, we need to track objects
+                object_tracker.match_and_update(frame_time, consolidated_detections)
+            # else, just update the frame times for the stationary objects
+            else:
+                object_tracker.update_frame_times(frame_time)
 
         # add to the queue if not full
         if detected_objects_queue.full():

frigate/watchdog.py

@@ -5,6 +5,10 @@ import time
 import os
 import signal
 
+from frigate.util import (
+    restart_frigate,
+)
+
 logger = logging.getLogger(__name__)
 
 
@@ -30,6 +34,6 @@ class FrigateWatchdog(threading.Thread):
                     detector.start_or_restart()
                 elif not detector.detect_process.is_alive():
                     logger.info("Detection appears to have stopped. Exiting frigate...")
-                    os.kill(os.getpid(), signal.SIGTERM)
+                    restart_frigate()
 
         logger.info(f"Exiting watchdog...")

migrations/003_create_recordings_table.py

@@ -28,17 +28,19 @@ SQL = pw.SQL
 
 
 def migrate(migrator, database, fake=False, **kwargs):
-    migrator.create_model(Recordings)
+    migrator.sql(
-
+        'CREATE TABLE IF NOT EXISTS "recordings" ("id" VARCHAR(30) NOT NULL PRIMARY KEY, "camera" VARCHAR(20) NOT NULL, "path" VARCHAR(255) NOT NULL, "start_time" DATETIME NOT NULL, "end_time" DATETIME NOT NULL, "duration" REAL NOT NULL)'
-    def add_index():
+    )
-        # First add the index here, because there is a bug in peewee_migrate
+    migrator.sql(
-        # when trying to create an multi-column index in the same migration
+        'CREATE INDEX IF NOT EXISTS "recordings_camera" ON "recordings" ("camera")'
-        # as the table: https://github.com/klen/peewee_migrate/issues/19
+    )
-        Recordings.add_index("start_time", "end_time")
+    migrator.sql(
-        Recordings.create_table()
+        'CREATE UNIQUE INDEX IF NOT EXISTS "recordings_path" ON "recordings" ("path")'
-
+    )
-    migrator.python(add_index)
+    migrator.sql(
+        'CREATE INDEX IF NOT EXISTS "recordings_start_time_end_time" ON "recordings" (start_time, end_time)'
+    )
 
 
 def rollback(migrator, database, fake=False, **kwargs):
-    migrator.remove_model(Recordings)
+    pass

migrations/004_add_bbox_region_area.py

@@ -0,0 +1,48 @@
+"""Peewee migrations -- 004_add_bbox_region_area.py.
+
+Some examples (model - class or model name)::
+
+    > Model = migrator.orm['model_name']            # Return model in current state by name
+
+    > migrator.sql(sql)                             # Run custom SQL
+    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.create_model(Model)                  # Create a model (could be used as decorator)
+    > migrator.remove_model(model, cascade=True)    # Remove a model
+    > migrator.add_fields(model, **fields)          # Add fields to a model
+    > migrator.change_fields(model, **fields)       # Change fields
+    > migrator.remove_fields(model, *field_names, cascade=True)
+    > migrator.rename_field(model, old_field_name, new_field_name)
+    > migrator.rename_table(model, new_table_name)
+    > migrator.add_index(model, *col_names, unique=False)
+    > migrator.drop_index(model, *col_names)
+    > migrator.add_not_null(model, *field_names)
+    > migrator.drop_not_null(model, *field_names)
+    > migrator.add_default(model, field_name, default)
+
+"""
+
+import datetime as dt
+import peewee as pw
+from playhouse.sqlite_ext import *
+from decimal import ROUND_HALF_EVEN
+from frigate.models import Event
+
+try:
+    import playhouse.postgres_ext as pw_pext
+except ImportError:
+    pass
+
+SQL = pw.SQL
+
+
+def migrate(migrator, database, fake=False, **kwargs):
+    migrator.add_fields(
+        Event,
+        region=JSONField(default=[]),
+        box=JSONField(default=[]),
+        area=pw.IntegerField(default=0),
+    )
+
+
+def rollback(migrator, database, fake=False, **kwargs):
+    migrator.remove_fields(Event, ["region", "box", "area"])

migrations/005_make_end_time_nullable.py

@@ -0,0 +1,43 @@
+"""Peewee migrations -- 004_add_bbox_region_area.py.
+
+Some examples (model - class or model name)::
+
+    > Model = migrator.orm['model_name']            # Return model in current state by name
+
+    > migrator.sql(sql)                             # Run custom SQL
+    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.create_model(Model)                  # Create a model (could be used as decorator)
+    > migrator.remove_model(model, cascade=True)    # Remove a model
+    > migrator.add_fields(model, **fields)          # Add fields to a model
+    > migrator.change_fields(model, **fields)       # Change fields
+    > migrator.remove_fields(model, *field_names, cascade=True)
+    > migrator.rename_field(model, old_field_name, new_field_name)
+    > migrator.rename_table(model, new_table_name)
+    > migrator.add_index(model, *col_names, unique=False)
+    > migrator.drop_index(model, *col_names)
+    > migrator.add_not_null(model, *field_names)
+    > migrator.drop_not_null(model, *field_names)
+    > migrator.add_default(model, field_name, default)
+
+"""
+
+import datetime as dt
+import peewee as pw
+from playhouse.sqlite_ext import *
+from decimal import ROUND_HALF_EVEN
+from frigate.models import Event
+
+try:
+    import playhouse.postgres_ext as pw_pext
+except ImportError:
+    pass
+
+SQL = pw.SQL
+
+
+def migrate(migrator, database, fake=False, **kwargs):
+    migrator.drop_not_null(Event, "end_time")
+
+
+def rollback(migrator, database, fake=False, **kwargs):
+    pass

migrations/006_add_motion_active_objects.py

@@ -0,0 +1,47 @@
+"""Peewee migrations -- 004_add_bbox_region_area.py.
+
+Some examples (model - class or model name)::
+
+    > Model = migrator.orm['model_name']            # Return model in current state by name
+
+    > migrator.sql(sql)                             # Run custom SQL
+    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.create_model(Model)                  # Create a model (could be used as decorator)
+    > migrator.remove_model(model, cascade=True)    # Remove a model
+    > migrator.add_fields(model, **fields)          # Add fields to a model
+    > migrator.change_fields(model, **fields)       # Change fields
+    > migrator.remove_fields(model, *field_names, cascade=True)
+    > migrator.rename_field(model, old_field_name, new_field_name)
+    > migrator.rename_table(model, new_table_name)
+    > migrator.add_index(model, *col_names, unique=False)
+    > migrator.drop_index(model, *col_names)
+    > migrator.add_not_null(model, *field_names)
+    > migrator.drop_not_null(model, *field_names)
+    > migrator.add_default(model, field_name, default)
+
+"""
+
+import datetime as dt
+import peewee as pw
+from playhouse.sqlite_ext import *
+from decimal import ROUND_HALF_EVEN
+from frigate.models import Recordings
+
+try:
+    import playhouse.postgres_ext as pw_pext
+except ImportError:
+    pass
+
+SQL = pw.SQL
+
+
+def migrate(migrator, database, fake=False, **kwargs):
+    migrator.add_fields(
+        Recordings,
+        objects=pw.IntegerField(null=True),
+        motion=pw.IntegerField(null=True),
+    )
+
+
+def rollback(migrator, database, fake=False, **kwargs):
+    migrator.remove_fields(Recordings, ["objects", "motion"])

process_clip.py

@@ -1,23 +1,26 @@
-import datetime
+import sys
+from typing_extensions import runtime
+
+sys.path.append("/lab/frigate")
+
 import json
 import logging
 import multiprocessing as mp
 import os
 import subprocess as sp
 import sys
-from unittest import TestCase, main
 
 import click
+import csv
 import cv2
 import numpy as np
 
-from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
+from frigate.config import FrigateConfig
 from frigate.edgetpu import LocalObjectDetector
 from frigate.motion import MotionDetector
 from frigate.object_processing import CameraState
 from frigate.objects import ObjectTracker
 from frigate.util import (
-    DictFrameManager,
     EventsPerSecond,
     SharedMemoryFrameManager,
     draw_box_with_label,
@@ -96,20 +99,22 @@ class ProcessClip:
         ffmpeg_process.wait()
         ffmpeg_process.communicate()
 
-    def process_frames(self, objects_to_track=["person"], object_filters={}):
+    def process_frames(
+        self, object_detector, objects_to_track=["person"], object_filters={}
+    ):
         mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
         mask[:] = 255
-        motion_detector = MotionDetector(
+        motion_detector = MotionDetector(self.frame_shape, self.camera_config.motion)
-            self.frame_shape, mask, self.camera_config.motion
+        motion_detector.save_images = False
-        )
 
-        object_detector = LocalObjectDetector(labels="/labelmap.txt")
         object_tracker = ObjectTracker(self.camera_config.detect)
         process_info = {
             "process_fps": mp.Value("d", 0.0),
             "detection_fps": mp.Value("d", 0.0),
             "detection_frame": mp.Value("d", 0.0),
         }
+
+        detection_enabled = mp.Value("d", 1)
         stop_event = mp.Event()
         model_shape = (self.config.model.height, self.config.model.width)
 
@@ -118,6 +123,7 @@ class ProcessClip:
             self.frame_queue,
             self.frame_shape,
             model_shape,
+            self.camera_config.detect,
             self.frame_manager,
             motion_detector,
             object_detector,
@@ -126,25 +132,16 @@ class ProcessClip:
             process_info,
             objects_to_track,
             object_filters,
-            mask,
+            detection_enabled,
             stop_event,
             exit_on_empty=True,
         )
 
-    def top_object(self, debug_path=None):
+    def stats(self, debug_path=None):
-        obj_detected = False
+        total_regions = 0
-        top_computed_score = 0.0
+        total_motion_boxes = 0
-
+        object_ids = set()
-        def handle_event(name, obj, frame_time):
+        total_frames = 0
-            nonlocal obj_detected
-            nonlocal top_computed_score
-            if obj.computed_score > top_computed_score:
-                top_computed_score = obj.computed_score
-            if not obj.false_positive:
-                obj_detected = True
-
-        self.camera_state.on("new", handle_event)
-        self.camera_state.on("update", handle_event)
 
         while not self.detected_objects_queue.empty():
             (
@@ -154,7 +151,8 @@ class ProcessClip:
                 motion_boxes,
                 regions,
             ) = self.detected_objects_queue.get()
-            if not debug_path is None:
+
+            if debug_path:
                 self.save_debug_frame(
                     debug_path, frame_time, current_tracked_objects.values()
                 )
@@ -162,10 +160,26 @@ class ProcessClip:
             self.camera_state.update(
                 frame_time, current_tracked_objects, motion_boxes, regions
             )
+            total_regions += len(regions)
+            total_motion_boxes += len(motion_boxes)
+            top_score = 0
+            for id, obj in self.camera_state.tracked_objects.items():
+                if not obj.false_positive:
+                    object_ids.add(id)
+                    if obj.top_score > top_score:
+                        top_score = obj.top_score
 
-        self.frame_manager.delete(self.camera_state.previous_frame_id)
+            total_frames += 1
 
-        return {"object_detected": obj_detected, "top_score": top_computed_score}
+            self.frame_manager.delete(self.camera_state.previous_frame_id)
+
+        return {
+            "total_regions": total_regions,
+            "total_motion_boxes": total_motion_boxes,
+            "true_positive_objects": len(object_ids),
+            "total_frames": total_frames,
+            "top_score": top_score,
+        }
 
     def save_debug_frame(self, debug_path, frame_time, tracked_objects):
         current_frame = cv2.cvtColor(
@@ -178,7 +192,6 @@ class ProcessClip:
         for obj in tracked_objects:
             thickness = 2
             color = (0, 0, 175)
-
             if obj["frame_time"] != frame_time:
                 thickness = 1
                 color = (255, 0, 0)
@@ -221,10 +234,9 @@ class ProcessClip:
 @click.command()
 @click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
 @click.option("-l", "--label", default="person", help="Label name to detect.")
-@click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
+@click.option("-o", "--output", default=None, help="File to save csv of data")
-@click.option("-s", "--scores", default=None, help="File to save csv of top scores")
 @click.option("--debug-path", default=None, help="Path to output frames for debugging.")
-def process(path, label, threshold, scores, debug_path):
+def process(path, label, output, debug_path):
     clips = []
     if os.path.isdir(path):
         files = os.listdir(path)
@@ -235,51 +247,79 @@ def process(path, label, threshold, scores, debug_path):
 
     json_config = {
         "mqtt": {"host": "mqtt"},
+        "detectors": {"coral": {"type": "edgetpu", "device": "usb"}},
         "cameras": {
             "camera": {
                 "ffmpeg": {
                     "inputs": [
                         {
                             "path": "path.mp4",
-                            "global_args": "",
+                            "global_args": "-hide_banner",
-                            "input_args": "",
+                            "input_args": "-loglevel info",
                             "roles": ["detect"],
                         }
                     ]
                 },
-                "height": 1920,
+                "rtmp": {"enabled": False},
-                "width": 1080,
+                "record": {"enabled": False},
             }
         },
     }
 
+    object_detector = LocalObjectDetector(labels="/labelmap.txt")
+
     results = []
     for c in clips:
         logger.info(c)
         frame_shape = get_frame_shape(c)
 
-        json_config["cameras"]["camera"]["height"] = frame_shape[0]
+        json_config["cameras"]["camera"]["detect"] = {
-        json_config["cameras"]["camera"]["width"] = frame_shape[1]
+            "height": frame_shape[0],
+            "width": frame_shape[1],
+        }
         json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
 
-        config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
+        frigate_config = FrigateConfig(**json_config)
+        runtime_config = frigate_config.runtime_config
+        runtime_config.cameras["camera"].create_ffmpeg_cmds()
 
-        process_clip = ProcessClip(c, frame_shape, config)
+        process_clip = ProcessClip(c, frame_shape, runtime_config)
         process_clip.load_frames()
-        process_clip.process_frames(objects_to_track=[label])
+        process_clip.process_frames(object_detector, objects_to_track=[label])
 
-        results.append((c, process_clip.top_object(debug_path)))
+        results.append((c, process_clip.stats(debug_path)))
 
-    if not scores is None:
+    positive_count = sum(
-        with open(scores, "w") as writer:
+        1 for result in results if result[1]["true_positive_objects"] > 0
-            for result in results:
+    )
-                writer.write(f"{result[0]},{result[1]['top_score']}\n")
-
-    positive_count = sum(1 for result in results if result[1]["object_detected"])
     print(
         f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
     )
 
+    if output:
+        # now we will open a file for writing
+        data_file = open(output, "w")
+
+        # create the csv writer object
+        csv_writer = csv.writer(data_file)
+
+        # Counter variable used for writing
+        # headers to the CSV file
+        count = 0
+
+        for result in results:
+            if count == 0:
+
+                # Writing headers of CSV file
+                header = ["file"] + list(result[1].keys())
+                csv_writer.writerow(header)
+                count += 1
+
+            # Writing data of CSV file
+            csv_writer.writerow([result[0]] + list(result[1].values()))
+
+        data_file.close()
+
 
 if __name__ == "__main__":
     process()

web/package.json

@@ -14,11 +14,11 @@
     "@cycjimmy/jsmpeg-player": "^5.0.1",
     "date-fns": "^2.21.3",
     "idb-keyval": "^5.0.2",
-    "immer": "^8.0.1",
+    "immer": "^9.0.6",
     "preact": "^10.5.9",
     "preact-async-route": "^2.2.1",
     "preact-router": "^3.2.1",
-    "video.js": "^7.13.0",
+    "video.js": "^7.15.4",
     "videojs-playlist": "^4.3.1",
     "videojs-seek-buttons": "^2.0.1"
   },

web/public/index.html

@@ -11,7 +11,8 @@
     <link rel="manifest" href="/site.webmanifest" />
     <link rel="mask-icon" href="/safari-pinned-tab.svg" color="#3b82f7" />
     <meta name="msapplication-TileColor" content="#3b82f7" />
-    <meta name="theme-color" content="#ff0000" />
+    <meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)" />
+    <meta name="theme-color" content="#111827" media="(prefers-color-scheme: dark)" />
   </head>
   <body>
     <div id="root" class="z-0"></div>

web/public/site.webmanifest

@@ -13,7 +13,7 @@
             "type": "image/png"
         }
     ],
-    "theme_color": "#ff0000",
+    "theme_color": "#ffffff",
-    "background_color": "#ff0000",
+    "background_color": "#ffffff",
     "display": "standalone"
 }

web/src/Sidebar.jsx

@@ -10,6 +10,7 @@ import NavigationDrawer, { Destination, Separator } from './components/Navigatio
 export default function Sidebar() {
   const { data: config } = useConfig();
   const cameras = useMemo(() => Object.entries(config.cameras), [config]);
+  const { birdseye } = config;
 
   return (
     <NavigationDrawer header={<Header />}>
@@ -49,7 +50,7 @@ export default function Sidebar() {
           ) : null
         }
       </Match>
-      <Destination href="/birdseye" text="Birdseye" />
+      {birdseye?.enabled ? <Destination href="/birdseye" text="Birdseye" /> : null}
       <Destination href="/events" text="Events" />
       <Destination href="/debug" text="Debug" />
       <Separator />
@@ -60,7 +61,7 @@ export default function Sidebar() {
           <Separator />
         </Fragment>
       ) : null}
-      <Destination className="self-end" href="https://blakeblackshear.github.io/frigate" text="Documentation" />
+      <Destination className="self-end" href="https://docs.frigate.video" text="Documentation" />
       <Destination className="self-end" href="https://github.com/blakeblackshear/frigate" text="GitHub" />
     </NavigationDrawer>
   );

web/src/api/__tests__/mqtt.test.jsx

@@ -121,12 +121,12 @@ describe('MqttProvider', () => {
       </MqttProvider>
     );
     await screen.findByTestId('data');
-    expect(screen.getByTestId('front/detect/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"ON"}');
+    expect(screen.getByTestId('front/detect/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"ON","retain":true}');
-    expect(screen.getByTestId('front/recordings/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF"}');
+    expect(screen.getByTestId('front/recordings/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF","retain":true}');
-    expect(screen.getByTestId('front/snapshots/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"ON"}');
+    expect(screen.getByTestId('front/snapshots/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"ON","retain":true}');
-    expect(screen.getByTestId('side/detect/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF"}');
+    expect(screen.getByTestId('side/detect/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF","retain":true}');
-    expect(screen.getByTestId('side/recordings/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF"}');
+    expect(screen.getByTestId('side/recordings/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF","retain":true}');
-    expect(screen.getByTestId('side/snapshots/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF"}');
+    expect(screen.getByTestId('side/snapshots/state')).toHaveTextContent('{"lastUpdate":123456,"payload":"OFF","retain":true}');
   });
 });
 

web/src/api/mqtt.jsx

@@ -42,9 +42,9 @@ export function MqttProvider({
   useEffect(() => {
     Object.keys(config.cameras).forEach((camera) => {
       const { name, record, detect, snapshots } = config.cameras[camera];
-      dispatch({ topic: `${name}/recordings/state`, payload: record.enabled ? 'ON' : 'OFF' });
+      dispatch({ topic: `${name}/recordings/state`, payload: record.enabled ? 'ON' : 'OFF', retain: true });
-      dispatch({ topic: `${name}/detect/state`, payload: detect.enabled ? 'ON' : 'OFF' });
+      dispatch({ topic: `${name}/detect/state`, payload: detect.enabled ? 'ON' : 'OFF', retain: true });
-      dispatch({ topic: `${name}/snapshots/state`, payload: snapshots.enabled ? 'ON' : 'OFF' });
+      dispatch({ topic: `${name}/snapshots/state`, payload: snapshots.enabled ? 'ON' : 'OFF', retain: true });
     });
   }, [config]);
 

web/src/components/AppBar.jsx

@@ -37,7 +37,8 @@ export default function AppBar({ title: Title, overflowRef, onOverflowClick }) {
 
   return (
     <div
-      className={`w-full border-b border-gray-200 dark:border-gray-700 flex items-center align-middle p-2  fixed left-0 right-0 z-10 bg-white dark:bg-gray-900 transform transition-all duration-200 ${
+      id="appbar"
+      className={`w-full border-b border-gray-200  dark:border-gray-700 flex items-center align-middle p-2  fixed left-0 right-0 z-10 bg-white dark:bg-gray-900 transform transition-all duration-200 ${
         !show ? '-translate-y-full' : 'translate-y-0'
       } ${!atZero ? 'shadow-sm' : ''}`}
       data-testid="appbar"

web/src/components/Calender.jsx

@@ -0,0 +1,329 @@
+import { h } from 'preact';
+import { useEffect, useState, useCallback, useMemo, useRef } from 'preact/hooks';
+import ArrowRight from '../icons/ArrowRight';
+import ArrowRightDouble from '../icons/ArrowRightDouble';
+
+const todayTimestamp = new Date().setHours(0, 0, 0, 0).valueOf();
+
+const Calender = ({ onChange, calenderRef, close }) => {
+  const keyRef = useRef([]);
+
+  const date = new Date();
+  const year = date.getFullYear();
+  const month = date.getMonth();
+
+  const daysMap = useMemo(() => ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'], []);
+  const monthMap = useMemo(
+    () => [
+      'January',
+      'February',
+      'March',
+      'April',
+      'May',
+      'June',
+      'July',
+      'August',
+      'September',
+      'October',
+      'November',
+      'December',
+    ],
+    []
+  );
+
+  const [state, setState] = useState({
+    getMonthDetails: [],
+    year,
+    month,
+    selectedDay: null,
+    timeRange: { before: null, after: null },
+    monthDetails: null,
+  });
+
+  const getNumberOfDays = useCallback((year, month) => {
+    return 40 - new Date(year, month, 40).getDate();
+  }, []);
+
+  const getDayDetails = useCallback(
+    (args) => {
+      const date = args.index - args.firstDay;
+      const day = args.index % 7;
+      let prevMonth = args.month - 1;
+      let prevYear = args.year;
+      if (prevMonth < 0) {
+        prevMonth = 11;
+        prevYear--;
+      }
+      const prevMonthNumberOfDays = getNumberOfDays(prevYear, prevMonth);
+      const _date = (date < 0 ? prevMonthNumberOfDays + date : date % args.numberOfDays) + 1;
+      const month = date < 0 ? -1 : date >= args.numberOfDays ? 1 : 0;
+      const timestamp = new Date(args.year, args.month, _date).getTime();
+      return {
+        date: _date,
+        day,
+        month,
+        timestamp,
+        dayString: daysMap[day],
+      };
+    },
+    [getNumberOfDays, daysMap]
+  );
+
+  const getMonthDetails = useCallback(
+    (year, month) => {
+      const firstDay = new Date(year, month).getDay();
+      const numberOfDays = getNumberOfDays(year, month);
+      const monthArray = [];
+      const rows = 6;
+      let currentDay = null;
+      let index = 0;
+      const cols = 7;
+
+      for (let row = 0; row < rows; row++) {
+        for (let col = 0; col < cols; col++) {
+          currentDay = getDayDetails({
+            index,
+            numberOfDays,
+            firstDay,
+            year,
+            month,
+          });
+          monthArray.push(currentDay);
+          index++;
+        }
+      }
+      return monthArray;
+    },
+    [getNumberOfDays, getDayDetails]
+  );
+
+  useEffect(() => {
+    setState((prev) => ({ ...prev, selectedDay: todayTimestamp, monthDetails: getMonthDetails(year, month) }));
+  }, [year, month, getMonthDetails]);
+
+  useEffect(() => {
+    // add refs for keyboard navigation
+    if (state.monthDetails) {
+      keyRef.current = keyRef.current.slice(0, state.monthDetails.length);
+    }
+    // set today date in focus for keyboard navigation
+    const todayDate = new Date(todayTimestamp).getDate();
+    keyRef.current.find((t) => t.tabIndex === todayDate)?.focus();
+  }, [state.monthDetails]);
+
+  const isCurrentDay = (day) => day.timestamp === todayTimestamp;
+
+  const isSelectedRange = useCallback(
+    (day) => {
+      if (!state.timeRange.after || !state.timeRange.before) return;
+
+      return day.timestamp < state.timeRange.before && day.timestamp >= state.timeRange.after;
+    },
+    [state.timeRange]
+  );
+
+  const isFirstDayInRange = useCallback(
+    (day) => {
+      if (isCurrentDay(day)) return;
+      return state.timeRange.after === day.timestamp;
+    },
+    [state.timeRange.after]
+  );
+
+  const isLastDayInRange = useCallback(
+    (day) => {
+      return state.timeRange.before === new Date(day.timestamp).setHours(24, 0, 0, 0);
+    },
+    [state.timeRange.before]
+  );
+
+  const getMonthStr = useCallback(
+    (month) => {
+      return monthMap[Math.max(Math.min(11, month), 0)] || 'Month';
+    },
+    [monthMap]
+  );
+
+  const onDateClick = (day) => {
+    const { before, after } = state.timeRange;
+    let timeRange = { before: null, after: null };
+
+    // user has selected a date < after, reset values
+    if (after === null || day.timestamp < after) {
+      timeRange = { before: new Date(day.timestamp).setHours(24, 0, 0, 0), after: day.timestamp };
+    }
+
+    // user has selected a date > after
+    if (after !== null && before !== new Date(day.timestamp).setHours(24, 0, 0, 0) && day.timestamp > after) {
+      timeRange = {
+        after,
+        before:
+          day.timestamp >= todayTimestamp
+            ? new Date(todayTimestamp).setHours(24, 0, 0, 0)
+            : new Date(day.timestamp).setHours(24, 0, 0, 0),
+      };
+    }
+
+    // reset values
+    if (before === new Date(day.timestamp).setHours(24, 0, 0, 0)) {
+      timeRange = { before: null, after: null };
+    }
+
+    setState((prev) => ({
+      ...prev,
+      timeRange,
+      selectedDay: day.timestamp,
+    }));
+
+    if (onChange) {
+      onChange(timeRange.after ? { before: timeRange.before / 1000, after: timeRange.after / 1000 } : ['all']);
+    }
+  };
+
+  const setYear = useCallback(
+    (offset) => {
+      const year = state.year + offset;
+      const month = state.month;
+      setState((prev) => {
+        return {
+          ...prev,
+          year,
+          monthDetails: getMonthDetails(year, month),
+        };
+      });
+    },
+    [state.year, state.month, getMonthDetails]
+  );
+
+  const setMonth = (offset) => {
+    let year = state.year;
+    let month = state.month + offset;
+    if (month === -1) {
+      month = 11;
+      year--;
+    } else if (month === 12) {
+      month = 0;
+      year++;
+    }
+    setState((prev) => {
+      return {
+        ...prev,
+        year,
+        month,
+        monthDetails: getMonthDetails(year, month),
+      };
+    });
+  };
+
+  const handleKeydown = (e, day, index) => {
+    if ((keyRef.current && e.key === 'Enter') || e.keyCode === 32) {
+      e.preventDefault();
+      day.month === 0 && onDateClick(day);
+    }
+    if (e.key === 'ArrowLeft') {
+      index > 0 && keyRef.current[index - 1].focus();
+    }
+    if (e.key === 'ArrowRight') {
+      index < 41 && keyRef.current[index + 1].focus();
+    }
+    if (e.key === 'ArrowUp') {
+      e.preventDefault();
+      index > 6 && keyRef.current[index - 7].focus();
+    }
+    if (e.key === 'ArrowDown') {
+      e.preventDefault();
+      index < 36 && keyRef.current[index + 7].focus();
+    }
+    if (e.key === 'Escape') {
+      close();
+    }
+  };
+
+  const renderCalendar = () => {
+    const days =
+      state.monthDetails &&
+      state.monthDetails.map((day, idx) => {
+        return (
+          <div
+            onClick={() => onDateClick(day)}
+            onkeydown={(e) => handleKeydown(e, day, idx)}
+            ref={(ref) => (keyRef.current[idx] = ref)}
+            tabIndex={day.month === 0 ? day.date : null}
+            className={`h-12 w-12 float-left flex flex-shrink justify-center items-center cursor-pointer ${
+              day.month !== 0 ? ' opacity-50 bg-gray-700 dark:bg-gray-700 pointer-events-none' : ''
+            }
+              ${isFirstDayInRange(day) ? ' rounded-l-xl ' : ''}
+              ${isSelectedRange(day) ? ' bg-blue-600 dark:hover:bg-blue-600' : ''}
+              ${isLastDayInRange(day) ? ' rounded-r-xl ' : ''}
+              ${isCurrentDay(day) && !isLastDayInRange(day) ? 'rounded-full bg-gray-100 dark:hover:bg-gray-100 ' : ''}`}
+            key={idx}
+          >
+            <div className="font-light">
+              <span className="text-gray-400">{day.date}</span>
+            </div>
+          </div>
+        );
+      });
+
+    return (
+      <div>
+        <div className="w-full flex justify-start flex-shrink">
+          {['SUN', 'MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT'].map((d, i) => (
+            <div key={i} className="w-12 text-xs font-light text-center">
+              {d}
+            </div>
+          ))}
+        </div>
+        <div className="w-full h-56">{days}</div>
+      </div>
+    );
+  };
+
+  return (
+    <div className="select-none w-96 flex flex-shrink" ref={calenderRef}>
+      <div className="py-4 px-6">
+        <div className="flex items-center">
+          <div className="w-1/6 relative flex justify-around">
+            <div
+              tabIndex={100}
+              className="flex justify-center items-center cursor-pointer absolute  -mt-4 text-center rounded-full w-10 h-10 bg-gray-500 hover:bg-gray-200 dark:hover:bg-gray-800"
+              onClick={() => setYear(-1)}
+            >
+              <ArrowRightDouble className="h-2/6 transform rotate-180 " />
+            </div>
+          </div>
+          <div className="w-1/6 relative flex justify-around ">
+            <div
+              tabIndex={101}
+              className="flex justify-center items-center cursor-pointer absolute  -mt-4 text-center rounded-full w-10 h-10 bg-gray-500 hover:bg-gray-200 dark:hover:bg-gray-800"
+              onClick={() => setMonth(-1)}
+            >
+              <ArrowRight className="h-2/6 transform rotate-180 red" />
+            </div>
+          </div>
+          <div className="w-1/3">
+            <div className="text-3xl text-center text-gray-200 font-extralight">{state.year}</div>
+            <div className="text-center text-gray-400 font-extralight">{getMonthStr(state.month)}</div>
+          </div>
+          <div className="w-1/6 relative flex justify-around ">
+            <div
+              tabIndex={102}
+              className="flex justify-center items-center cursor-pointer absolute  -mt-4 text-center rounded-full w-10 h-10 bg-gray-500 hover:bg-gray-200 dark:hover:bg-gray-800"
+              onClick={() => setMonth(1)}
+            >
+              <ArrowRight className="h-2/6" />
+            </div>
+          </div>
+          <div className="w-1/6 relative flex justify-around " tabIndex={104} onClick={() => setYear(1)}>
+            <div className="flex justify-center items-center cursor-pointer absolute  -mt-4 text-center rounded-full w-10 h-10 bg-gray-500 hover:bg-gray-200 dark:hover:bg-gray-800">
+              <ArrowRightDouble className="h-2/6" />
+            </div>
+          </div>
+        </div>
+        <div className="mt-3">{renderCalendar()}</div>
+      </div>
+    </div>
+  );
+};
+
+export default Calender;

web/src/components/DatePicker.jsx

@@ -0,0 +1,162 @@
+import { h } from 'preact';
+import { useCallback, useEffect, useState } from 'preact/hooks';
+
+export const DateFilterOptions = [
+  {
+    label: 'All',
+    value: ['all'],
+  },
+  {
+    label: 'Today',
+    value: {
+      //Before
+      before: new Date().setHours(24, 0, 0, 0) / 1000,
+      //After
+      after: new Date().setHours(0, 0, 0, 0) / 1000,
+    },
+  },
+  {
+    label: 'Yesterday',
+    value: {
+      //Before
+      before: new Date(new Date().setDate(new Date().getDate() - 1)).setHours(24, 0, 0, 0) / 1000,
+      //After
+      after: new Date(new Date().setDate(new Date().getDate() - 1)).setHours(0, 0, 0, 0) / 1000,
+    },
+  },
+  {
+    label: 'Last 7 Days',
+    value: {
+      //Before
+      before: new Date().setHours(24, 0, 0, 0) / 1000,
+      //After
+      after: new Date(new Date().setDate(new Date().getDate() - 7)).setHours(0, 0, 0, 0) / 1000,
+    },
+  },
+  {
+    label: 'This Month',
+    value: {
+      //Before
+      before: new Date().setHours(24, 0, 0, 0) / 1000,
+      //After
+      after: new Date(new Date().getFullYear(), new Date().getMonth(), 1).getTime() / 1000,
+    },
+  },
+  {
+    label: 'Last Month',
+    value: {
+      //Before
+      before: new Date(new Date().getFullYear(), new Date().getMonth(), 1).getTime() / 1000,
+      //After
+      after: new Date(new Date().getFullYear(), new Date().getMonth() - 1, 1).getTime() / 1000,
+    },
+  },
+  {
+    label: 'Custom Range',
+    value: 'custom_range',
+  },
+];
+
+export default function DatePicker({
+  helpText,
+  keyboardType = 'text',
+  inputRef,
+  label,
+  leadingIcon: LeadingIcon,
+  onBlur,
+  onChangeText,
+  onFocus,
+  readonly,
+  trailingIcon: TrailingIcon,
+  value: propValue = '',
+  ...props
+}) {
+  const [isFocused, setFocused] = useState(false);
+  const [value, setValue] = useState(propValue);
+
+  useEffect(() => {
+    if (propValue !== value) {
+      setValue(propValue);
+    }
+  }, [propValue, setValue, value]);
+
+  const handleFocus = useCallback(
+    (event) => {
+      setFocused(true);
+      onFocus && onFocus(event);
+    },
+    [onFocus]
+  );
+
+  const handleBlur = useCallback(
+    (event) => {
+      setFocused(false);
+      onBlur && onBlur(event);
+    },
+    [onBlur]
+  );
+
+  const handleChange = useCallback(
+    (event) => {
+      const { value } = event.target;
+      setValue(value);
+      onChangeText && onChangeText(value);
+    },
+    [onChangeText, setValue]
+  );
+
+  const onClick = (e) => {
+    props.onclick(e);
+  };
+  const labelMoved = isFocused || value !== '';
+
+  return (
+    <div className="w-full">
+      {props.children}
+      <div
+        className={`bg-gray-100 dark:bg-gray-700 rounded rounded-b-none border-gray-400 border-b p-1 pl-4 pr-3 ${
+          isFocused ? 'border-blue-500 dark:border-blue-500' : ''
+        }`}
+        ref={inputRef}
+      >
+        <label
+          className="flex space-x-2 items-center"
+          data-testid={`label-${label.toLowerCase().replace(/[^\w]+/g, '_')}`}
+        >
+          {LeadingIcon ? (
+            <div className="w-10 h-full">
+              <LeadingIcon />
+            </div>
+          ) : null}
+          <div className="relative w-full">
+            <input
+              className="h-6 mt-6 w-full bg-transparent focus:outline-none focus:ring-0"
+              type={keyboardType}
+              readOnly
+              onBlur={handleBlur}
+              onFocus={handleFocus}
+              onInput={handleChange}
+              tabIndex="0"
+              onClick={onClick}
+              value={propValue}
+              {...props}
+            />
+            <div
+              className={`absolute top-3 transition transform text-gray-600 dark:text-gray-400 ${
+                labelMoved ? 'text-xs -translate-y-2' : ''
+              } ${isFocused ? 'text-blue-500 dark:text-blue-500' : ''}`}
+            >
+              <p>{label}</p>
+            </div>
+          </div>
+          {TrailingIcon ? (
+            <div className="w-10 h-10">
+              <TrailingIcon />
+            </div>
+          ) : null}
+        </label>
+      </div>
+      {helpText ? <div className="text-xs pl-3 pt-1">{helpText}</div> : null}
+    </div>
+  );
+}

web/src/components/JSMpegPlayer.jsx

@@ -3,7 +3,7 @@ import { baseUrl } from '../api/baseUrl';
 import { useRef, useEffect } from 'preact/hooks';
 import JSMpeg from '@cycjimmy/jsmpeg-player';
 
-export default function JSMpegPlayer({ camera }) {
+export default function JSMpegPlayer({ camera, width, height }) {
   const playerRef = useRef();
   const url = `${baseUrl.replace(/^http/, 'ws')}/live/${camera}`
 
@@ -32,6 +32,6 @@ export default function JSMpegPlayer({ camera }) {
   }, [url]);
 
   return (
-    <div ref={playerRef} class="jsmpeg" />
+    <div ref={playerRef} class="jsmpeg" style={`max-height: ${height}px; max-width: ${width}px`} />
   );
 }

web/src/components/RecordingPlaylist.jsx

@@ -1,6 +1,14 @@
 import { h } from 'preact';
 import { useState } from 'preact/hooks';
-import { addSeconds, differenceInSeconds, fromUnixTime, format, parseISO, startOfHour } from 'date-fns';
+import {
+  differenceInSeconds,
+  fromUnixTime,
+  format,
+  parseISO,
+  startOfHour,
+  differenceInMinutes,
+  differenceInHours,
+} from 'date-fns';
 import ArrowDropdown from '../icons/ArrowDropdown';
 import ArrowDropup from '../icons/ArrowDropup';
 import Link from '../components/Link';
@@ -21,25 +29,31 @@ export default function RecordingPlaylist({ camera, recordings, selectedDate, se
         events={recording.events}
         selected={recording.date === selectedDate}
       >
-        {recording.recordings.slice().reverse().map((item, i) => (
+        {recording.recordings
-          <div className="mb-2 w-full">
+          .slice()
-            <div
+          .reverse()
-              className={`flex w-full text-md text-white px-8 py-2 mb-2 ${
+          .map((item, i) => (
-                i === 0 ? 'border-t border-white border-opacity-50' : ''
+            <div className="mb-2 w-full">
-              }`}
+              <div
-            >
+                className={`flex w-full text-md text-white px-8 py-2 mb-2 ${
-              <div className="flex-1">
+                  i === 0 ? 'border-t border-white border-opacity-50' : ''
-                <Link href={`/recording/${camera}/${recording.date}/${item.hour}`} type="text">
+                }`}
-                  {item.hour}:00
+              >
-                </Link>
+                <div className="flex-1">
+                  <Link href={`/recording/${camera}/${recording.date}/${item.hour}`} type="text">
+                    {item.hour}:00
+                  </Link>
+                </div>
+                <div className="flex-1 text-right">{item.events.length} Events</div>
               </div>
-              <div className="flex-1 text-right">{item.events.length} Events</div>
+              {item.events
+                .slice()
+                .reverse()
+                .map((event) => (
+                  <EventCard camera={camera} event={event} delay={item.delay} />
+                ))}
             </div>
-            {item.events.map((event) => (
+          ))}
-              <EventCard camera={camera} event={event} delay={item.delay} />
-            ))}
-          </div>
-        ))}
       </ExpandableList>
     );
   }
@@ -83,8 +97,17 @@ export function ExpandableList({ title, events = 0, children, selected = false }
 export function EventCard({ camera, event, delay }) {
   const apiHost = useApiHost();
   const start = fromUnixTime(event.start_time);
-  const end = fromUnixTime(event.end_time);
+  let duration = 'In Progress';
-  const duration = addSeconds(new Date(0), differenceInSeconds(end, start));
+  if (event.end_time) {
+    const end = fromUnixTime(event.end_time);
+    const hours = differenceInHours(end, start);
+    const minutes = differenceInMinutes(end, start) - hours * 60;
+    const seconds = differenceInSeconds(end, start) - hours * 60 - minutes * 60;
+    duration = '';
+    if (hours) duration += `${hours}h `;
+    if (minutes) duration += `${minutes}m `;
+    duration += `${seconds}s`;
+  }
   const position = differenceInSeconds(start, startOfHour(start));
   const offset = Object.entries(delay)
     .map(([p, d]) => (position > p ? d : 0))
@@ -102,7 +125,7 @@ export function EventCard({ camera, event, delay }) {
               <div className="flex-1">
                 <div className="text-2xl text-white leading-tight capitalize">{event.label}</div>
                 <div className="text-xs md:text-normal text-gray-300">Start: {format(start, 'HH:mm:ss')}</div>
-                <div className="text-xs md:text-normal text-gray-300">Duration: {format(duration, 'mm:ss')}</div>
+                <div className="text-xs md:text-normal text-gray-300">Duration: {duration}</div>
               </div>
               <div className="text-lg text-white text-right leading-tight">{(event.top_score * 100).toFixed(1)}%</div>
             </div>

web/src/components/RelativeModal.jsx

@@ -27,7 +27,7 @@ export default function RelativeModal({
 
   const handleKeydown = useCallback(
     (event) => {
-      const focusable = ref.current.querySelectorAll('[tabindex]');
+      const focusable = ref.current && ref.current.querySelectorAll('[tabindex]');
       if (event.key === 'Tab' && focusable.length) {
         if (event.shiftKey && document.activeElement === focusable[0]) {
           focusable[focusable.length - 1].focus();
@@ -69,14 +69,15 @@ export default function RelativeModal({
       let newTop = top;
       let newLeft = left;
 
-      // too far right
-      if (newLeft + width + WINDOW_PADDING >= windowWidth - WINDOW_PADDING) {
-        newLeft = windowWidth - width - WINDOW_PADDING;
-      }
       // too far left
-      else if (left < WINDOW_PADDING) {
+      if (left < WINDOW_PADDING) {
         newLeft = WINDOW_PADDING;
       }
+      // too far right
+      else if (newLeft + width + WINDOW_PADDING >= windowWidth - WINDOW_PADDING) {
+        newLeft = windowWidth - width - WINDOW_PADDING;
+      }
+
       // too close to bottom
       if (top + menuHeight > windowHeight - WINDOW_PADDING + window.scrollY) {
         newTop = WINDOW_PADDING;

web/src/components/Select.jsx

@@ -3,74 +3,27 @@ import ArrowDropdown from '../icons/ArrowDropdown';
 import ArrowDropup from '../icons/ArrowDropup';
 import Menu, { MenuItem } from './Menu';
 import TextField from './TextField';
+import DatePicker from './DatePicker';
+import Calender from './Calender';
 import { useCallback, useEffect, useMemo, useRef, useState } from 'preact/hooks';
 
-export default function Select({ label, onChange, options: inputOptions = [], selected: propSelected }) {
+export default function Select({
+  type,
+  label,
+  onChange,
+  paramName,
+  options: inputOptions = [],
+  selected: propSelected,
+}) {
   const options = useMemo(
     () =>
       typeof inputOptions[0] === 'string' ? inputOptions.map((opt) => ({ value: opt, label: opt })) : inputOptions,
     [inputOptions]
   );
+
   const [showMenu, setShowMenu] = useState(false);
-  const [selected, setSelected] = useState(
+  const [selected, setSelected] = useState();
-    Math.max(
+  const [datePickerValue, setDatePickerValue] = useState();
-      options.findIndex(({ value }) => value === propSelected),
-      0
-    )
-  );
-  const [focused, setFocused] = useState(null);
-
-  const ref = useRef(null);
-
-  const handleSelect = useCallback(
-    (value, label) => {
-      setSelected(options.findIndex((opt) => opt.value === value));
-      onChange && onChange(value, label);
-      setShowMenu(false);
-    },
-    [onChange, options]
-  );
-
-  const handleClick = useCallback(() => {
-    setShowMenu(true);
-  }, [setShowMenu]);
-
-  const handleKeydown = useCallback(
-    (event) => {
-      switch (event.key) {
-        case 'Enter': {
-          if (!showMenu) {
-            setShowMenu(true);
-            setFocused(selected);
-          } else {
-            setSelected(focused);
-            onChange && onChange(options[focused].value, options[focused].label);
-            setShowMenu(false);
-          }
-          break;
-        }
-
-        case 'ArrowDown': {
-          const newIndex = focused + 1;
-          newIndex < options.length && setFocused(newIndex);
-          break;
-        }
-
-        case 'ArrowUp': {
-          const newIndex = focused - 1;
-          newIndex > -1 && setFocused(newIndex);
-          break;
-        }
-
-        // no default
-      }
-    },
-    [onChange, options, showMenu, setShowMenu, setFocused, focused, selected]
-  );
-
-  const handleDismiss = useCallback(() => {
-    setShowMenu(false);
-  }, [setShowMenu]);
 
   // Reset the state if the prop value changes
   useEffect(() => {
@@ -85,25 +38,219 @@ export default function Select({ label, onChange, options: inputOptions = [], se
     // DO NOT include `selected`
   }, [options, propSelected]); // eslint-disable-line react-hooks/exhaustive-deps
 
-  return (
+  useEffect(() => {
-    <Fragment>
+    if (type === 'datepicker') {
-      <TextField
+      if ('after' && 'before' in propSelected) {
-        inputRef={ref}
+        if (!propSelected.before || !propSelected.after) return setDatePickerValue('all');
-        label={label}
+
-        onchange={onChange}
+        for (let i = 0; i < inputOptions.length; i++) {
-        onclick={handleClick}
+          if (
-        onkeydown={handleKeydown}
+            inputOptions[i].value &&
-        readonly
+            Object.entries(inputOptions[i].value).sort().toString() === Object.entries(propSelected).sort().toString()
-        trailingIcon={showMenu ? ArrowDropup : ArrowDropdown}
+          ) {
-        value={options[selected]?.label}
+            setDatePickerValue(inputOptions[i]?.label);
-      />
+            break;
-      {showMenu ? (
+          } else {
-        <Menu className="rounded-t-none" onDismiss={handleDismiss} relativeTo={ref} widthRelative>
+            setDatePickerValue(
-          {options.map(({ value, label }, i) => (
+              `${new Date(propSelected.after * 1000).toLocaleDateString()} -> ${new Date(
-            <MenuItem key={value} label={label} focus={focused === i} onSelect={handleSelect} value={value} />
+                propSelected.before * 1000 - 1
-          ))}
+              ).toLocaleDateString()}`
-        </Menu>
+            );
-      ) : null}
+          }
-    </Fragment>
+        }
+      }
+    }
+    if (type === 'dropdown') {
+      setSelected(
+        Math.max(
+          options.findIndex(({ value }) => Object.values(propSelected).includes(value)),
+          0
+        )
+      );
+    }
+  }, [type, options, inputOptions, propSelected, setSelected]);
+
+  const [focused, setFocused] = useState(null);
+  const [showCalender, setShowCalender] = useState(false);
+  const calenderRef = useRef(null);
+  const ref = useRef(null);
+
+  const handleSelect = useCallback(
+    (value) => {
+      setSelected(options.findIndex(({ value }) => Object.values(propSelected).includes(value)));
+      setShowMenu(false);
+
+      //show calender date range picker
+      if (value === 'custom_range') return setShowCalender(true);
+      onChange && onChange(value);
+    },
+    [onChange, options, propSelected, setSelected]
   );
+
+  const handleDateRange = useCallback(
+    (range) => {
+      onChange && onChange(range);
+      setShowMenu(false);
+    },
+    [onChange]
+  );
+
+  const handleClick = useCallback(() => {
+    setShowMenu(true);
+  }, [setShowMenu]);
+
+  const handleKeydownDatePicker = useCallback(
+    (event) => {
+      switch (event.key) {
+        case 'Enter': {
+          if (!showMenu) {
+            setShowMenu(true);
+            setFocused(selected);
+          } else {
+            setSelected(focused);
+            if (options[focused].value === 'custom_range') {
+              setShowMenu(false);
+              return setShowCalender(true);
+            }
+
+            onChange && onChange(options[focused].value);
+            setShowMenu(false);
+          }
+          break;
+        }
+
+        case 'ArrowDown': {
+          event.preventDefault();
+          const newIndex = focused + 1;
+          newIndex < options.length && setFocused(newIndex);
+          break;
+        }
+
+        case 'ArrowUp': {
+          event.preventDefault();
+          const newIndex = focused - 1;
+          newIndex > -1 && setFocused(newIndex);
+          break;
+        }
+
+        // no default
+      }
+    },
+    [onChange, options, showMenu, setShowMenu, setFocused, focused, selected]
+  );
+
+  const handleKeydown = useCallback(
+    (event) => {
+      switch (event.key) {
+        case 'Enter': {
+          if (!showMenu) {
+            setShowMenu(true);
+            setFocused(selected);
+          } else {
+            setSelected(focused);
+            onChange && onChange({ [paramName]: options[focused].value });
+            setShowMenu(false);
+          }
+          break;
+        }
+
+        case 'ArrowDown': {
+          event.preventDefault();
+          const newIndex = focused + 1;
+          newIndex < options.length && setFocused(newIndex);
+          break;
+        }
+
+        case 'ArrowUp': {
+          event.preventDefault();
+          const newIndex = focused - 1;
+          newIndex > -1 && setFocused(newIndex);
+          break;
+        }
+
+        // no default
+      }
+    },
+    [onChange, options, showMenu, setShowMenu, setFocused, focused, selected, paramName]
+  );
+
+  const handleDismiss = useCallback(() => {
+    setShowMenu(false);
+  }, [setShowMenu]);
+
+  const findDOMNodes = (component) => {
+    return (component && (component.base || (component.nodeType === 1 && component))) || null;
+  };
+
+  useEffect(() => {
+    const addBackDrop = (e) => {
+      if (showCalender && !findDOMNodes(calenderRef.current).contains(e.target)) {
+        setShowCalender(false);
+      }
+    };
+    window.addEventListener('click', addBackDrop);
+
+    return function cleanup() {
+      window.removeEventListener('click', addBackDrop);
+    };
+  }, [showCalender]);
+
+  switch (type) {
+    case 'datepicker':
+      return (
+        <Fragment>
+          <DatePicker
+            inputRef={ref}
+            label={label}
+            onchange={onChange}
+            onclick={handleClick}
+            onkeydown={handleKeydownDatePicker}
+            trailingIcon={showMenu ? ArrowDropup : ArrowDropdown}
+            value={datePickerValue}
+          />
+          {showCalender && (
+            <Menu className="rounded-t-none" onDismiss={handleDismiss} relativeTo={ref}>
+              <Calender onChange={handleDateRange} calenderRef={calenderRef} close={() => setShowCalender(false)} />
+            </Menu>
+          )}
+          {showMenu ? (
+            <Menu className="rounded-t-none" onDismiss={handleDismiss} relativeTo={ref} widthRelative>
+              {options.map(({ value, label }, i) => (
+                <MenuItem key={value} label={label} focus={focused === i} onSelect={handleSelect} value={value} />
+              ))}
+            </Menu>
+          ) : null}
+        </Fragment>
+      );
+
+    // case 'dropdown':
+    default:
+      return (
+        <Fragment>
+          <TextField
+            inputRef={ref}
+            label={label}
+            onchange={onChange}
+            onclick={handleClick}
+            onkeydown={handleKeydown}
+            readonly
+            trailingIcon={showMenu ? ArrowDropup : ArrowDropdown}
+            value={options[selected]?.label}
+          />
+          {showMenu ? (
+            <Menu className="rounded-t-none" onDismiss={handleDismiss} relativeTo={ref} widthRelative>
+              {options.map(({ value, label }, i) => (
+                <MenuItem
+                  key={value}
+                  label={label}
+                  focus={focused === i}
+                  onSelect={handleSelect}
+                  value={{ [paramName]: value }}
+                />
+              ))}
+            </Menu>
+          ) : null}
+        </Fragment>
+      );
+  }
 }

web/src/components/Table.jsx

@@ -14,9 +14,9 @@ export function Thead({ children, className, ...attrs }) {
   );
 }
 
-export function Tbody({ children, className, ...attrs }) {
+export function Tbody({ children, className, reference, ...attrs }) {
   return (
-    <tbody className={className} {...attrs}>
+    <tbody ref={reference} className={className} {...attrs}>
       {children}
     </tbody>
   );
@@ -30,9 +30,10 @@ export function Tfoot({ children, className = '', ...attrs }) {
   );
 }
 
-export function Tr({ children, className = '', ...attrs }) {
+export function Tr({ children, className = '', reference, ...attrs }) {
   return (
     <tr
+      ref={reference}
       className={`border-b border-gray-200 dark:border-gray-700 hover:bg-gray-100 dark:hover:bg-gray-800 ${className}`}
       {...attrs}
     >
@@ -49,9 +50,9 @@ export function Th({ children, className = '', colspan, ...attrs }) {
   );
 }
 
-export function Td({ children, className = '', colspan, ...attrs }) {
+export function Td({ children, className = '', reference, colspan, ...attrs }) {
   return (
-    <td className={`p-2 px-1 lg:p-4 ${className}`} colSpan={colspan} {...attrs}>
+    <td ref={reference} className={`p-2 px-1 lg:p-4 ${className}`} colSpan={colspan} {...attrs}>
       {children}
     </td>
   );

web/src/components/VideoPlayer.jsx

@@ -88,7 +88,7 @@ export default function VideoPlayer({ children, options, seekOptions = {}, onRea
 
   return (
     <div data-vjs-player>
-      <video ref={playerRef} className="video-js vjs-default-skin" controls playsinline />
+      <video ref={playerRef} className="small-player video-js vjs-default-skin" controls playsinline />
       {children}
     </div>
   );

web/src/components/__tests__/Select.test.jsx

@@ -5,21 +5,40 @@ import { fireEvent, render, screen } from '@testing-library/preact';
 describe('Select', () => {
   test('on focus, shows a menu', async () => {
     const handleChange = jest.fn();
-    render(<Select label="Tacos" onChange={handleChange} options={['tacos', 'burritos']} />);
+    render(
+      <Select
+        label="Tacos"
+        type="dropdown"
+        onChange={handleChange}
+        options={['all', 'tacos', 'burritos']}
+        paramName={['dinner']}
+        selected=""
+      />
+    );
 
     expect(screen.queryByRole('listbox')).not.toBeInTheDocument();
     fireEvent.click(screen.getByRole('textbox'));
     expect(screen.queryByRole('listbox')).toBeInTheDocument();
+    expect(screen.queryByRole('option', { name: 'all' })).toBeInTheDocument();
     expect(screen.queryByRole('option', { name: 'tacos' })).toBeInTheDocument();
     expect(screen.queryByRole('option', { name: 'burritos' })).toBeInTheDocument();
 
-    fireEvent.click(screen.queryByRole('option', { name: 'burritos' }));
+    fireEvent.click(screen.queryByRole('option', { name: 'tacos' }));
-    expect(handleChange).toHaveBeenCalledWith('burritos', 'burritos');
+    expect(handleChange).toHaveBeenCalledWith({ dinner: 'tacos' });
   });
 
   test('allows keyboard navigation', async () => {
     const handleChange = jest.fn();
-    render(<Select label="Tacos" onChange={handleChange} options={['tacos', 'burritos']} />);
+    render(
+      <Select
+        label="Tacos"
+        type="dropdown"
+        onChange={handleChange}
+        options={['tacos', 'burritos']}
+        paramName={['dinner']}
+        selected=""
+      />
+    );
 
     expect(screen.queryByRole('listbox')).not.toBeInTheDocument();
     const input = screen.getByRole('textbox');
@@ -29,6 +48,6 @@ describe('Select', () => {
 
     fireEvent.keyDown(input, { key: 'ArrowDown', code: 'ArrowDown' });
     fireEvent.keyDown(input, { key: 'Enter', code: 'Enter' });
-    expect(handleChange).toHaveBeenCalledWith('burritos', 'burritos');
+    expect(handleChange).toHaveBeenCalledWith({ dinner: 'burritos' });
   });
 });

web/src/hooks/useClickOutside.jsx

@@ -0,0 +1,22 @@
+import { useEffect, useRef } from 'preact/hooks';
+
+// https://stackoverflow.com/a/54292872/2693528
+export const useClickOutside = (callback) => {
+  const callbackRef = useRef(); // initialize mutable ref, which stores callback
+  const innerRef = useRef(); // returned to client, who marks "border" element
+
+  // update cb on each render, so second useEffect has access to current value
+  useEffect(() => {
+    callbackRef.current = callback;
+  });
+
+  useEffect(() => {
+    document.addEventListener('click', handleClick);
+    return () => document.removeEventListener('click', handleClick);
+    function handleClick(e) {
+      if (innerRef.current && callbackRef.current && !innerRef.current.contains(e.target)) callbackRef.current(e);
+    }
+  }, []);
+
+  return innerRef; // convenience for client (doesn't need to init ref himself)
+};