Recordings - fix expire_file

ref: https://github.com/blakeblackshear/frigate/issues/373

.dockerignore

@@ -3,4 +3,5 @@ docs/
 .gitignore
 debug
 config/
-*.pyc
+*.pyc
+.git

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,6 +1,6 @@
 ---
-name: Bug report
-about: Create a report to help us improve
+name: Bug report or Support request
+about: ''
 title: ''
 labels: ''
 assignees: ''
@@ -8,10 +8,10 @@ assignees: ''
 ---
 
 **Describe the bug**
-A clear and concise description of what the bug is.
+A clear and concise description of what your issue is.
 
 **Version of frigate**
-What version are you using?
+Output from `/version`
 
 **Config file**
 Include your full config file wrapped in triple back ticks.
@@ -19,14 +19,14 @@ Include your full config file wrapped in triple back ticks.
 config here
 ```
 
-**Logs**
+**Frigate container logs**
 ```
 Include relevant log output here
 ```
 
-**Frigate debug stats**
-```
-Output from frigate's /debug/stats endpoint
+**Frigate stats**
+```json
+Output from frigate's /stats endpoint
 ```
 
 **FFprobe from your camera**
@@ -41,6 +41,7 @@ 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]

.gitignore

@@ -1,4 +1,8 @@
 *.pyc 
 debug
 .vscode
-config/config.yml
+config/config.yml
+models
+*.mp4
+*.db
+frigate/version.py

Makefile

@@ -1,13 +1,18 @@
 default_target: amd64_frigate
 
+COMMIT_HASH := $(shell git log -1 --pretty=format:"%h")
+
+version:
+	echo "VERSION='0.8.0-$(COMMIT_HASH)'" > frigate/version.py
+
 amd64_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:amd64 --file docker/Dockerfile.wheels .
 
 amd64_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:amd64 --file docker/Dockerfile.ffmpeg.amd64 .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.1.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
 
-amd64_frigate:
-	docker build --tag frigate-base --build-arg ARCH=amd64 --file docker/Dockerfile.base .
+amd64_frigate: version
+	docker build --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --file docker/Dockerfile.base .
 	docker build --tag frigate --file docker/Dockerfile.amd64 .
 
 amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
@@ -16,10 +21,10 @@ amd64nvidia_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:amd64nvidia --file docker/Dockerfile.wheels .
 
 amd64nvidia_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
 
-amd64nvidia_frigate:
-	docker build --tag frigate-base --build-arg ARCH=amd64nvidia --file docker/Dockerfile.base .
+amd64nvidia_frigate: version
+	docker build --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --file docker/Dockerfile.base .
 	docker build --tag frigate --file docker/Dockerfile.amd64nvidia .
 
 amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
@@ -28,10 +33,10 @@ aarch64_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:aarch64 --file docker/Dockerfile.wheels.aarch64 .
 
 aarch64_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
 
-aarch64_frigate:
-	docker build --tag frigate-base --build-arg ARCH=aarch64 --file docker/Dockerfile.base .
+aarch64_frigate: version
+	docker build --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --file docker/Dockerfile.base .
 	docker build --tag frigate --file docker/Dockerfile.aarch64 .
 
 armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
@@ -40,10 +45,10 @@ armv7_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:armv7 --file docker/Dockerfile.wheels .
 
 armv7_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:armv7 --file docker/Dockerfile.ffmpeg.armv7 .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
 
-armv7_frigate:
-	docker build --tag frigate-base --build-arg ARCH=armv7 --file docker/Dockerfile.base .
+armv7_frigate: version
+	docker build --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --file docker/Dockerfile.base .
 	docker build --tag frigate --file docker/Dockerfile.armv7 .
 
 armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate

README.md

@@ -3,26 +3,39 @@
 </p>
 
 # Frigate - NVR With Realtime Object Detection for IP Cameras
-Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
+A complete and local NVR designed for HomeAssistant 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.
 
+- Tight integration with HomeAssistant 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
 - Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
 - 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
+- Re-streaming via RTMP to reduce the number of connections to your camera
+
+## Screenshots
+<div>
+<a href="docs/media_browser.png"><img src="docs/media_browser.png" height=400></a>
+<a href="docs/notification.png"><img src="docs/notification.png" height=400></a>
+</div>
 
 ## Documentation
 - [How Frigate Works](docs/how-frigate-works.md)
 - [Recommended Hardware](#recommended-hardware)
 - [Installing](#installing)
 - [Configuration File](#configuration)
+- [Setting Up Camera Inputs](#setting-up-camera-inputs)
 - [Optimizing Performance](#optimizing-performance)
 - [Detectors](#detectors)
 - [Object Filters](#object-filters)
 - [Masks](#masks)
 - [Zones](#zones)
+- [Recording Clips (save_clips)](#recording-clips)
+- [24/7 Recordings (record)](#247-recordings)
+- [RTMP Streams (rtmp)](#rtmp-streams)
 - [Integration with HomeAssistant](#integration-with-homeassistant)
 - [MQTT Topics](#mqtt-topics)
 - [HTTP Endpoints](#http-endpoints)
@@ -30,6 +43,11 @@ Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but
 - [Troubleshooting](#troubleshooting)
 
 ## Recommended Hardware
+
+### Cameras
+Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and HomeAssistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, clips, and recordings without re-encoding.
+
+### Computer
 |Name|Inference Speed|Notes|
 |----|---------------|-----|
 |Atomic Pi|16ms|Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding.|
@@ -43,9 +61,14 @@ Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but
 [Back to top](#documentation)
 
 ## Installing
+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.
+
+For HomeAssistant users, there is also a [custom component (aka integration)](https://github.com/blakeblackshear/frigate-hass-integration). This custom component adds tighter integration with HomeAssistant by automatically setting up camera entities, sensors, media browser for clips and recordings, and a public API to simplify notifications.
+
+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.
 
 ### HassOS Addon
-HassOS users can install via the addon repository. Frigate requires that an MQTT server be running.
+HassOS users can install via the addon repository. Frigate requires an MQTT server.
 1. Navigate to Supervisor > Add-on Store > Repositories
 1. Add https://github.com/blakeblackshear/frigate-hass-addons
 1. Setup your configuration in the `Configuration` tab
@@ -56,48 +79,51 @@ Make sure you choose the right image for your architecture:
 |Arch|Image Name|
 |-|-|
 |amd64|blakeblackshear/frigate:stable-amd64|
+|amd64nvidia|blakeblackshear/frigate:stable-amd64nvidia|
 |armv7|blakeblackshear/frigate:stable-armv7|
 |aarch64|blakeblackshear/frigate:stable-aarch64|
 
 It is recommended to run with docker-compose:
 ```yaml
+version: "3.6"
+services:
   frigate:
     container_name: frigate
     restart: unless-stopped
     privileged: true
-    image: blakeblackshear/frigate:stable-amd64
+    image: blakeblackshear/frigate:0.8.0-beta2-amd64
     volumes:
       - /dev/bus/usb:/dev/bus/usb
       - /etc/localtime:/etc/localtime:ro
       - <path_to_config>:/config
-      - <path_to_directory_for_clips>:/clips      
-      - type: tmpfs # 1GB of memory, reduces SSD/SD Card wear
-        target: /cache
+      - <path_to_directory_for_clips>:/media/frigate/clips      
+      - <path_to_directory_for_recordings>:/media/frigate/recordings      
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+        target: /tmp/cache
         tmpfs:
           size: 100000000
     ports:
       - "5000:5000"
+      - "1935:1935" # RTMP feeds
     environment:
       FRIGATE_RTSP_PASSWORD: "password"
-    healthcheck:
-      test: ["CMD", "wget" , "-q", "-O-", "http://localhost:5000"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 3m
 ```
 
-If you can't use docker compose, you can run the container with:
+If you can't use docker compose, you can run the container with something similar to this:
 ```bash
 docker run --rm \
 --name frigate \
 --privileged \
+--mount type=tmpfs,target=/tmp/cache,tmpfs-size=100000000 \
 -v /dev/bus/usb:/dev/bus/usb \
--v <path_to_config_dir>:/config:ro \
+-v <path_to_directory_for_clips>:/media/frigate/clips \
+-v <path_to_directory_for_recordings>:/media/frigate/recordings \
+-v <path_to_config>:/config:ro \
 -v /etc/localtime:/etc/localtime:ro \
--p 5000:5000 \
 -e FRIGATE_RTSP_PASSWORD='password' \
-blakeblackshear/frigate:stable-amd64
+-p 5000:5000 \
+-p 1935:1935 \
+blakeblackshear/frigate:0.8.0-beta2-amd64
 ```
 
 ### Kubernetes
@@ -138,9 +164,37 @@ You can calculate the necessary shm-size for each camera with the following form
 ## Configuration
 HassOS users can manage their configuration directly in the addon Configuration tab. For 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](docs/cameras.md).
 
+It is recommended to start with a minimal configuration and add to it:
 ```yaml
-# Optional: port for http server (default: shown below)
-web_port: 5000
+mqtt:
+  host: mqtt.server.com
+cameras:
+  back:
+    ffmpeg:
+      inputs:
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          roles:
+            - detect
+            - rtmp
+    width: 1280
+    height: 720
+    fps: 5
+```
+Here are all the configuration options:
+```yaml
+# Optional: Logging configuration
+logger:
+  # Optional: default log level (default: shown below)
+  default: info
+  # Optional: module by module log level configuration
+  logs:
+    frigate.mqtt: error
+
+# Optional: database configuration
+database:
+  # Optional: database path
+  # This may need to be in a custom location if network storage is used for clips
+  path: /media/frigate/clips/frigate.db
 
 # Optional: detectors configuration
 # USB Coral devices will be auto detected with CPU fallback
@@ -152,6 +206,16 @@ detectors:
     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
+
+# Optional: model configuration
+model:
+  # Required: height of the trained model
+  height: 320
+  # Required: width of the trained model
+  width: 320
 
 # Required: mqtt configuration
 mqtt:
@@ -178,47 +242,39 @@ save_clips:
   # 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.
   max_seconds: 300
-  # Optional: Location to save event clips. (default: shown below)
-  clips_dir: /clips
-  # Optional: Location to save cache files for creating clips. (default: shown below)
-  # NOTE: To reduce wear on SSDs and SD cards, use a tmpfs volume.
-  cache_dir: /cache
+  # Optional: size of tmpfs mount to create for cache files (default: not set)
+  # mount -t tmpfs -o size={tmpfs_cache_size} tmpfs /tmp/cache
+  # Notice: If you have mounted a tmpfs volume through docker, this value should not be set in your config
+  tmpfs_cache_size: 256m
+  # Optional: Retention settings for clips (default: shown below)
+  retain:
+    # Required: Default retention days (default: shown below)
+    default: 10
+    # Optional: Per object retention days
+    objects:
+      person: 15
 
 # Optional: Global ffmpeg args
+# Args may be provided as a string or an array
 # "ffmpeg" + global_args + input_args + "-i" + input + output_args
 ffmpeg:
   # Optional: global ffmpeg args (default: shown below)
-  global_args:
-    - -hide_banner
-    - -loglevel
-    - panic
+  global_args: -hide_banner -loglevel fatal
   # 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
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -rtsp_transport
-    - tcp
-    - -stimeout
-    - '5000000'
-    - -use_wallclock_as_timestamps
-    - '1'
-  # Optional: global output args (default: shown below)
-  output_args:
-    - -f
-    - rawvideo
-    - -pix_fmt
-    - yuv420p
+  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 clips streams (default: shown below)
+    clips: -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: Global object filters for all cameras.
 # NOTE: can be overridden at the camera level
@@ -231,12 +287,44 @@ objects:
     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: max_int)
+      # 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: 0.85
+      threshold: 0.7
+
+# Optional: Global motion detection config. These may also be defined at the camera level.
+# ADVANCED: Most users will not need to set these values in their config
+motion:
+  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
+  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
+  # The value should be between 1 and 255.
+  threshold: 25
+  # Optional: Minimum size in pixels in the resized motion image that counts as motion
+  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
+  # moving objects.
+  contour_area: 100
+  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
+  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
+  # Too low and a fast moving person wont be detected as motion.
+  delta_alpha: 0.2
+  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
+  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
+  # Low values will cause things like moving shadows to be detected as motion for longer.
+  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
+  frame_alpha: 0.2
+  # Optional: Height of the resized motion frame  (default: 1/6th of the original frame height)
+  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense of higher CPU usage.
+  # Lower values result in less CPU, but small changes may not register as motion.
+  frame_height: 180
+
+# Optional: Global detecttion settings. These may also be defined at the camera level.
+# ADVANCED: Most users will not need to set these values in their config
+detect:
+  # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: double the frame rate)
+  max_disappeared: 10
+
 
 # Required: configuration section for cameras
 cameras:
@@ -244,9 +332,24 @@ cameras:
   back:
     # Required: ffmpeg settings for the camera
     ffmpeg:
-      # Required: Source passed to ffmpeg after the -i parameter.
-      # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
-      input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+      # 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,clips,rtmp
+          # NOTICE: In addition to assigning the record, clips, 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)
@@ -256,32 +359,24 @@ cameras:
       # Optional: camera specific output args (default: inherit)
       output_args:
     
-    # Optional: height of the frame
-    # NOTE: Recommended to set this value, but frigate will attempt to autodetect.
-    height: 720
-    # Optional: width of the frame
-    # NOTE: Recommended to set this value, but frigate will attempt to autodetect.
+    # Required: width of the frame for the input with the detect role
     width: 1280
-    # Optional: desired fps for your camera
+    # Required: height of the frame for the input with the detect role
+    height: 720
+    # Optional: desired fps for your camera for the input with the detect role
     # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
     #       Frigate will attempt to autodetect if not specified.
     fps: 5
 
-    # Optional: motion mask
+    # Optional: list of motion masks
     # NOTE: see docs for more detailed info on creating masks
-    mask: poly,0,900,1080,900,1080,1920,0,1920
+    mask: 
+      - poly,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: camera specific mqtt settings
-    mqtt:
-      # Optional: crop the camera frame to the detection region of the object (default: False)
-      crop_to_region: True
-      # Optional: resize the image before publishing over mqtt
-      snapshot_height: 300
-
     # Optional: zones for this camera
     zones:
       # Required: name of the zone
@@ -297,7 +392,7 @@ cameras:
           person:
             min_area: 5000
             max_area: 100000
-            threshold: 0.8
+            threshold: 0.7
 
     # Optional: save clips configuration
     # NOTE: This feature does not work if you have added "-vsync drop" in your input params. 
@@ -307,10 +402,31 @@ cameras:
       # Required: enables clips for the camera (default: shown below)
       enabled: False
       # Optional: Number of seconds before the event to include in the clips (default: shown below)
-      pre_capture: 30
+      pre_capture: 5
+      # Optional: Number of seconds after the event to include in the clips (default: shown below)
+      post_capture: 5
       # Optional: Objects to save clips for. (default: all tracked objects)
       objects:
-        - person      
+        - person
+      # 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: 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: RTMP re-stream configuration
+    rtmp:
+      # Required: Enable the live stream (default: True)
+      enabled: True
 
     # Optional: Configuration for the snapshots in the debug view and mqtt
     snapshots:
@@ -320,6 +436,11 @@ cameras:
       draw_zones: False
       # Optional: draw bounding boxes on the mqtt snapshots (default: shown below)
       draw_bounding_boxes: True
+      # Optional: crop the snapshot to the detection region (default: shown below)
+      crop_to_region: True
+      # Optional: height to resize the snapshot to (default: shown below)
+      # NOTE: 175px is optimized for thumbnails in the homeassistant media browser
+      height: 175
 
     # Optional: Camera level object filters config. If defined, this is used instead of the global config.
     objects:
@@ -331,13 +452,48 @@ cameras:
           min_area: 5000
           max_area: 100000
           min_score: 0.5
-          threshold: 0.85
+          threshold: 0.7
 ```
 [Back to top](#documentation)
 
+## 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 clips 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|
+|----|-----|
+|`detect`|Main feed for object detection|
+|`clips`|Clips of events from objects detected in the `detect` feed. [docs](#recording-clips)|
+|`record`|Saves 60 second segments of the video feed. [docs](#247-recordings)|
+|`rtmp`|Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams)|
+
+Example:
+```yaml
+mqtt:
+  host: mqtt.server.com
+cameras:
+  back:
+    ffmpeg:
+      inputs:
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          roles:
+            - detect
+            - rtmp
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
+          roles:
+            - clips
+            - record
+    width: 1280
+    height: 720
+    fps: 5
+```
+
+
+[Back to top](#documentation)
+
 ## Optimizing Performance
-- **Google Coral**: It is strongly recommended to use a Google Coral, but Frigate will 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.
-- **Resolution**: 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.
+- **Google Coral**: It is strongly recommended to use a Google Coral, but Frigate will 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.
@@ -345,7 +501,8 @@ cameras:
 ### 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):
+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:
@@ -354,6 +511,7 @@ ffmpeg:
 ```
 
 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:
@@ -374,7 +532,17 @@ ffmpeg:
 ```
 
 Intel-based CPUs (>=10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
-**Note:** You also need to set `LIBVA_DRIVER_NAME=iHD` as an environment variable on the container.
+```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:
@@ -393,6 +561,8 @@ By default Frigate will look for a USB Coral device and fall back to the CPU if
 
 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:
 ```yaml
 detectors:
@@ -495,84 +665,77 @@ During testing, `draw_zones` should be set in the config to draw the zone on the
 ## Recording Clips
 **Note**: 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.
 
-Frigate can save video clips without any CPU overhead for encoding by simply copying the stream directly with FFmpeg. It leverages FFmpeg's segment functionality to maintain a cache of video for each camera. The cache files are written to disk at `cache_dir` and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to the `clips_dir` directory along with a json file containing the current information about the tracked object.
+Frigate can save video clips without any CPU overhead for encoding by simply copying the stream directly with FFmpeg. It leverages FFmpeg's segment functionality to maintain a cache of video for each camera. The cache files are written to disk at `/tmp/cache` and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to `/media/frigate/clips`. Clips are retained according to the retention settings defined on the config for each object type.
+
+### Database
+Event and clip information is managed in a sqlite database at `/media/frigate/clips/frigate.db`. If that database is deleted, clips will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within HomeAssistant.
+
+If you are storing your clips on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
 
 ### Global Configuration Options
 - `max_seconds`: This limits the size of the cache when an object is being tracked. If an object is stationary and being tracked for a long time, the cache files will expire and this value will be the maximum clip length for the *end* of the event. For example, if this is set to 300 seconds and an object is being tracked for 600 seconds, the clip will end up being the last 300 seconds. Defaults to 300 seconds.
 
 ### Per-camera Configuration Options
-- `pre_capture`: Defines how much time should be included in the clip prior to the beginning of the event. Defaults to 30 seconds.
+- `pre_capture`: Defines how much time should be included in the clip prior to the beginning of the event. Defaults to 5 seconds.
+- `post_capture`: Defines how much time should be included in the clip after the end of the event. Defaults to 5 seconds.
 - `objects`: List of object types to save clips for. Object types here must be listed for tracking at the camera or global configuration. Defaults to all tracked objects.
 
+
+[Back to top](#documentation)
+
+## 24/7 Recordings
+**Note**: 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.
+
+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 HomeAssistant's media browser. Each camera supports a configurable retention policy in the config.
+
+[Back to top](#documentation)
+
+## RTMP Streams
+Frigate can re-stream your video feed as a RTMP feed for other applications such as HomeAssistant 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 HomeAssistant 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.
+
 [Back to top](#documentation)
 
 ## Integration with HomeAssistant
-Setup a camera, binary_sensor, sensor and optionally automation as shown for each camera you define in frigate. Replace <camera_name> with the camera name as defined in the frigate `config.yml` (The `frigate_coral_fps` and `frigate_coral_inference` sensors only need to be defined once)
+The best way to integrate with HomeAssistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration). When configuring the integration, you will be asked for the `Host` of your frigate instance. This value should be the url you use to access Frigate in the browser and will look like `http://<host>:5000/`. If you are using HassOS with the addon, the host should be `http://ccab4aaf-frigate:5000` (or `http://ccab4aaf-frigate-beta:5000` if your are using the beta version of the addon). HomeAssistant needs access to port 5000 (api) and 1935 (rtmp) for all features. The integration will setup the following entities within HomeAssistant:
 
+Sensors:
+- Stats to monitor frigate performance
+- Object counts for all zones and cameras
+
+Cameras:
+- Cameras for image of the last detected object for each camera
+- Camera entities with stream support (requires RTMP)
+
+Media Browser:
+- Rich UI with thumbnails for browsing event clips
+- Rich UI for browsing 24/7 recordings by month, day, camera, time
+
+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).
+
+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
-camera:
-  - name: <camera_name> Last Person
-    platform: mqtt
-    topic: frigate/<camera_name>/person/snapshot
-  - name: <camera_name> Last Car
-    platform: mqtt
-    topic: frigate/<camera_name>/car/snapshot
-
-binary_sensor:
-  - name: <camera_name> Person
-    platform: mqtt
-    state_topic: "frigate/<camera_name>/person"
-    device_class: motion
-    availability_topic: "frigate/available"
-
-sensor:
-  - platform: rest
-    name: Frigate Debug
-    resource: http://localhost:5000/debug/stats
-    scan_interval: 5
-    json_attributes:
-      - <camera_name>
-      - detection_fps
-      - detectors
-    value_template: 'OK'  
-  - platform: template
-    sensors:
-      <camera_name>_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["camera_fps"] }}'
-        unit_of_measurement: 'FPS'
-      <camera_name>_skipped_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["skipped_fps"] }}'
-        unit_of_measurement: 'FPS'
-      <camera_name>_detection_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["detection_fps"] }}'
-        unit_of_measurement: 'FPS'
-      frigate_detection_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["detection_fps"] }}'
-        unit_of_measurement: 'FPS'
-      frigate_coral_inference:
-        value_template: '{{ states.sensor.frigate_debug.attributes["detectors"]["coral"]["inference_speed"] }}' 
-        unit_of_measurement: 'ms'
-        
 automation:
-  - alias: Alert me if a person is detected while armed away
-    trigger: 
-      platform: state
-      entity_id: binary_sensor.camera_person
-      from: 'off'
-      to: 'on'
-    condition:
-      - condition: state
-        entity_id: alarm_control_panel.home_alarm
-        state: armed_away
+  - alias: Notify of events
+    trigger:
+      platform: mqtt
+      topic: frigate/events
     action:
-      - service: notify.user_telegram
-        data:
-          message: "A person was detected."
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
           data:
-            photo:
-              - url: http://<ip>:5000/<camera_name>/person/best.jpg
-                caption: A person was detected.        
+            image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}.jpg?format=android'
+            tag: '{{trigger.payload_json["after"]["id"]}}'
 ```
+Note that the image url has `?format=android`. This adjusts the aspect ratio to be idea for android notifications. For iOS optimized snapshots, no format parameter needs to be passed.
+
+You can find some additional examples for notifications [here](docs/notification-examples.md).
 
 [Back to top](#documentation)
 
@@ -582,7 +745,19 @@ A web server is available on port 5000 with the following endpoints.
 ### `/<camera_name>`
 An mjpeg stream for debugging. Keep in mind the mjpeg endpoint is for debugging only and will put additional load on the system when in use. 
 
-You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `http://localhost:5000/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/back?fps=10` or both with `?fps=10&h=1000`
+Accepts the following query string parameters:
+|param|Type|Description|
+|----|-----|--|
+|`fps`|int|Frame rate|
+|`h`|int|Height in pixels|
+|`bbox`|int|Show bounding boxes for detected objects (0 or 1)|
+|`timestamp`|int|Print the timestamp in the upper left (0 or 1)|
+|`zones`|int|Draw the zones on the image (0 or 1)|
+|`mask`|int|Overlay the mask on the image (0 or 1)|
+|`motion`|int|Draw blue boxes for areas with detected motion (0 or 1)|
+|`regions`|int|Draw green boxes for areas where object detection was run (0 or 1)|
+
+You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `http://localhost:5000/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/back?fps=10` or both with `?fps=10&h=1000`.
 
 ### `/<camera_name>/<object_name>/best.jpg[?h=300&crop=1]`
 The best snapshot for any object type. It is a full resolution image by default.
@@ -594,10 +769,21 @@ Example parameters:
 ### `/<camera_name>/latest.jpg[?h=300]`
 The most recent frame that frigate has finished processing. It is a full resolution image by default.
 
+Accepts the following query string parameters:
+|param|Type|Description|
+|----|-----|--|
+|`h`|int|Height in pixels|
+|`bbox`|int|Show bounding boxes for detected objects (0 or 1)|
+|`timestamp`|int|Print the timestamp in the upper left (0 or 1)|
+|`zones`|int|Draw the zones on the image (0 or 1)|
+|`mask`|int|Overlay the mask on the image (0 or 1)|
+|`motion`|int|Draw blue boxes for areas with detected motion (0 or 1)|
+|`regions`|int|Draw green boxes for areas where object detection was run (0 or 1)|
+
 Example parameters:
 - `h=300`: resizes the image to 300 pixes tall
 
-### `/debug/stats`
+### `/stats`
 Contains some granular debug info that can be used for sensors in HomeAssistant.
 
 Sample response:
@@ -620,25 +806,7 @@ Sample response:
         /***************
         * PID for the ffmpeg process that consumes this camera
         ***************/
-        "ffmpeg_pid": 27,
-        /***************
-        * Timestamps of frames in various parts of processing
-        ***************/
-        "frame_info": {
-            /***************
-            * Timestamp of the frame frigate is running object detection on.
-            ***************/
-            "detect": 1596994991.91426,
-            /***************
-            * Timestamp of the frame frigate is processing detected objects on.
-            * This is where MQTT messages are sent, zones are checked, etc.
-            ***************/
-            "process": 1596994991.91426,
-            /***************
-            * Timestamp of the frame frigate last read from ffmpeg.
-            ***************/
-            "read": 1596994991.91426
-        },
+        "capture_pid": 27,
         /***************
         * PID for the process that runs detection for this camera
         ***************/
@@ -648,11 +816,6 @@ Sample response:
         ***************/
         "process_fps": 5.1,
         /***************
-        * Timestamp when the detection process started looking for a frame. If this value stays constant
-        * for a long time, that means there aren't any frames in the frame queue.
-        ***************/
-        "read_start": 1596994991.943814,
-        /***************
         * Frames per second skip for processing by frigate.
         ***************/
         "skipped_fps": 0.0
@@ -683,6 +846,34 @@ Sample response:
 }
 ```
 
+### `/config`
+A json representation of your configuration
+
+### `/version`
+Version info
+
+### `/events`
+Events from the database. Accepts the following query string parameters:
+|param|Type|Description|
+|----|-----|--|
+|`before`|int|Epoch time|
+|`after`|int|Epoch time|
+|`camera`|str|Camera name|
+|`label`|str|Label name|
+|`zone`|str|Zone name|
+|`limit`|int|Limit the number of events returned|
+
+### `/events/summary`
+Returns summary data for events in the database. Used by the HomeAssistant integration.
+
+### `/events/<id>`
+Returns data for a single event.
+### `/events/<id>/snapshot.jpg`
+Returns a snapshot 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.
+
+### `/clips/<camera>-<id>.mp4`
+Video clip for the given camera and event id.
+
 [Back to top](#documentation)
 
 ## MQTT Topics
@@ -694,7 +885,10 @@ Designed to be used as an availability topic with HomeAssistant. Possible messag
 "offline": published right before frigate stops
 
 ### `frigate/<camera_name>/<object_name>`
-Publishes `ON` or `OFF` and is designed to be used a as a binary sensor in HomeAssistant for whether or not that object type is detected.
+Publishes the count of objects for the camera for use as a sensor in HomeAssistant.
+
+### `frigate/<zone_name>/<object_name>`
+Publishes the count of objects for the zone for use as a sensor in HomeAssistant.
 
 ### `frigate/<camera_name>/<object_name>/snapshot`
 Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image
@@ -702,48 +896,79 @@ is published again.
 
 The height and crop of snapshots can be configured in the config.
 
-### `frigate/<camera_name>/events/start`
-Message published at the start of any tracked object. JSON looks as follows:
-```json
+### `frigate/events`
+Message published for each changed event. The first message is published when the tracked object is no longer marked as a false_positive. When frigate finds a better snapshot of the tracked object or when a zone change occurs, it will publish a message with the same id. When the event ends, a final message is published with `end_time` set.
+
+```jsonc
 {
-    "label": "person",
-    "score": 0.87890625,
-    "box": [
-        95,
-        155,
-        581,
-        1182
-    ],
-    "area": 499122,
-    "region": [
-        0,
-        132,
-        1080,
-        1212
-    ],
-    "frame_time": 1600208805.60284,
-    "centroid": [
-        338,
-        668
-    ],
-    "id": "1600208805.60284-k1l43p",
-    "start_time": 1600208805.60284,
-    "top_score": 0.87890625,
-    "zones": [],
-    "score_history": [
-        0.87890625
-    ],
-    "computed_score": 0.0,
-    "false_positive": true
+    "before": {
+        "id": "1607123955.475377-mxklsc",
+        "camera": "front_door",
+        "frame_time": 1607123961.837752,
+        "label": "person",
+        "top_score": 0.958984375,
+        "false_positive": false,
+        "start_time": 1607123955.475377,
+        "end_time": null,
+        "score": 0.7890625,
+        "box": [
+            424,
+            500,
+            536,
+            712
+        ],
+        "area": 23744,
+        "region": [
+            264,
+            450,
+            667,
+            853
+        ],
+        "current_zones": [
+            "driveway"
+        ],
+        "entered_zones": [
+            "yard",
+            "driveway"
+        ],
+        "thumbnail": null
+    },
+    "after": {
+        "id": "1607123955.475377-mxklsc",
+        "camera": "front_door",
+        "frame_time": 1607123962.082975,
+        "label": "person",
+        "top_score": 0.958984375,
+        "false_positive": false,
+        "start_time": 1607123955.475377,
+        "end_time": null,
+        "score": 0.87890625,
+        "box": [
+            432,
+            496,
+            544,
+            854
+        ],
+        "area": 40096,
+        "region": [
+            218,
+            440,
+            693,
+            915
+        ],
+        "current_zones": [
+            "yard",
+            "driveway"
+        ],
+        "entered_zones": [
+            "yard",
+            "driveway"
+        ],
+        "thumbnail": null
+    }
 }
 ```
 
-### `frigate/<camera_name>/events/end`
-Same as `frigate/<camera_name>/events/start`, but with an `end_time` property as well.
-
-### `frigate/<zone_name>/<object_name>`
-Publishes `ON` when the object enters the zone and `OFF` when the object disappears or exits the zone. Designed to be used a as a binary sensor in HomeAssistant for whether or not that object type is detected in the zone.
-
 [Back to top](#documentation)
 
 ## Custom Models
@@ -752,6 +977,8 @@ 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.
+
 ### Customizing the Labelmap
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. You must retain the same number of labels, but you can change the names. To change:
 
@@ -764,16 +991,28 @@ The labelmap can be customized to your needs. A common reason to do this is to c
 
 [Back to top](#documentation)
 
+## Logging
+Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
+
+Examples of available modules are:
+- `frigate.app`
+- `frigate.mqtt`
+- `frigate.edgetpu`
+- `frigate.zeroconf`
+- `detector.<detector_name>`
+- `watchdog.<camera_name>`
+- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
+
 ## Troubleshooting
 
+### "[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5639eeb6e140] moov atom not found"
+These messages in the logs are expected in certain situations. Frigate checks the integrity of the video cache before assembling clips. Occasionally these cached files will be invalid and cleaned up automatically.
+
 ### "ffmpeg didnt return a frame. something is wrong"
-Turn on logging for the camera by overriding the global_args and setting the log level to `info`:
+Turn on logging for the ffmpeg process by overriding the global_args and setting the log level to `info` (the default is `fatal`). Note that all ffmpeg logs show up in the Frigate logs as `ERROR` level. This does not mean they are actually errors.
 ```yaml
 ffmpeg:
-  global_args:
-    - -hide_banner
-    - -loglevel
-    - info
+  global_args: -hide_banner -loglevel info
 ```
 
 ### "On connect called"

detect_objects.py

@@ -1,441 +0,0 @@
-import faulthandler; faulthandler.enable()
-import os
-import signal
-import sys
-import traceback
-import signal
-import cv2
-import time
-import datetime
-import queue
-import yaml
-import json
-import threading
-import multiprocessing as mp
-import subprocess as sp
-import numpy as np
-import logging
-from flask import Flask, Response, make_response, jsonify, request
-import paho.mqtt.client as mqtt
-
-from frigate.video import capture_camera, track_camera, get_ffmpeg_input, get_frame_shape, CameraCapture, start_or_restart_ffmpeg
-from frigate.object_processing import TrackedObjectProcessor
-from frigate.events import EventProcessor
-from frigate.util import EventsPerSecond
-from frigate.edgetpu import EdgeTPUProcess
-
-FRIGATE_VARS = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
-
-CONFIG_FILE = os.environ.get('CONFIG_FILE', '/config/config.yml')
-
-if CONFIG_FILE.endswith(".yml"):
-    with open(CONFIG_FILE) as f:
-        CONFIG = yaml.safe_load(f)
-elif CONFIG_FILE.endswith(".json"):
-    with open(CONFIG_FILE) as f:
-        CONFIG = json.load(f)
-
-CACHE_DIR = CONFIG.get('save_clips', {}).get('cache_dir', '/cache')
-CLIPS_DIR = CONFIG.get('save_clips', {}).get('clips_dir', '/clips')
-
-if not os.path.exists(CACHE_DIR) and not os.path.islink(CACHE_DIR):
-    os.makedirs(CACHE_DIR)
-if not os.path.exists(CLIPS_DIR) and not os.path.islink(CLIPS_DIR):
-    os.makedirs(CLIPS_DIR)
-
-MQTT_HOST = CONFIG['mqtt']['host']
-MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
-MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
-MQTT_USER = CONFIG.get('mqtt', {}).get('user')
-MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
-if not MQTT_PASS is None:
-    MQTT_PASS = MQTT_PASS.format(**FRIGATE_VARS)
-MQTT_CLIENT_ID = CONFIG.get('mqtt', {}).get('client_id', 'frigate')
-
-# Set the default FFmpeg config
-FFMPEG_CONFIG = CONFIG.get('ffmpeg', {})
-FFMPEG_DEFAULT_CONFIG = {
-    'global_args': FFMPEG_CONFIG.get('global_args', 
-        ['-hide_banner','-loglevel','panic']),
-    'hwaccel_args': FFMPEG_CONFIG.get('hwaccel_args',
-        []),
-    'input_args': FFMPEG_CONFIG.get('input_args',
-        ['-avoid_negative_ts', 'make_zero',
-         '-fflags', 'nobuffer',
-         '-flags', 'low_delay',
-         '-strict', 'experimental',
-         '-fflags', '+genpts+discardcorrupt',
-         '-rtsp_transport', 'tcp',
-         '-stimeout', '5000000',
-         '-use_wallclock_as_timestamps', '1']),
-    'output_args': FFMPEG_CONFIG.get('output_args',
-        ['-f', 'rawvideo',
-         '-pix_fmt', 'yuv420p'])
-}
-
-GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})
-
-WEB_PORT = CONFIG.get('web_port', 5000)
-DETECTORS = CONFIG.get('detectors', {'coral': {'type': 'edgetpu', 'device': 'usb'}})
-
-class FrigateWatchdog(threading.Thread):
-    def __init__(self, camera_processes, config, detectors, detection_queue, out_events, tracked_objects_queue, stop_event):
-        threading.Thread.__init__(self)
-        self.camera_processes = camera_processes
-        self.config = config
-        self.detectors = detectors
-        self.detection_queue = detection_queue
-        self.out_events = out_events
-        self.tracked_objects_queue = tracked_objects_queue
-        self.stop_event = stop_event
-
-    def run(self):
-        time.sleep(10)
-        while True:
-            # wait a bit before checking
-            time.sleep(10)
-
-            if self.stop_event.is_set():
-                print(f"Exiting watchdog...")
-                break
-
-            now = datetime.datetime.now().timestamp()
-
-            # check the detection processes
-            for detector in self.detectors.values():
-                detection_start = detector.detection_start.value
-                if (detection_start > 0.0 and 
-                    now - detection_start > 10):
-                    print("Detection appears to be stuck. Restarting detection process")
-                    detector.start_or_restart()
-                elif not detector.detect_process.is_alive():
-                    print("Detection appears to have stopped. Restarting detection process")
-                    detector.start_or_restart()
-
-            # check the camera processes
-            for name, camera_process in self.camera_processes.items():
-                process = camera_process['process']
-                if not process.is_alive():
-                    print(f"Track process for {name} is not alive. Starting again...")
-                    camera_process['camera_fps'].value = 0.0
-                    camera_process['process_fps'].value = 0.0
-                    camera_process['detection_fps'].value = 0.0
-                    camera_process['read_start'].value = 0.0
-                    process = mp.Process(target=track_camera, args=(name, self.config,
-                        self.detection_queue, self.out_events[name], self.tracked_objects_queue, camera_process, self.stop_event))
-                    process.daemon = True
-                    camera_process['process'] = process
-                    process.start()
-                    print(f"Track process started for {name}: {process.pid}")
-
-def main():
-    stop_event = threading.Event()
-    # connect to mqtt and setup last will
-    def on_connect(client, userdata, flags, rc):
-        print("On connect called")
-        if rc != 0:
-            if rc == 3:
-                print ("MQTT Server unavailable")
-            elif rc == 4:
-                print ("MQTT Bad username or password")
-            elif rc == 5:
-                print ("MQTT Not authorized")
-            else:
-                print ("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
-        # publish a message to signal that the service is running
-        client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
-    client = mqtt.Client(client_id=MQTT_CLIENT_ID)
-    client.on_connect = on_connect
-    client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
-    if not MQTT_USER is None:
-        client.username_pw_set(MQTT_USER, password=MQTT_PASS)
-    client.connect(MQTT_HOST, MQTT_PORT, 60)
-    client.loop_start()
-
-    ##
-    # Setup config defaults for cameras
-    ##
-    for name, config in CONFIG['cameras'].items():
-        config['snapshots'] = {
-            'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True),
-            'draw_zones': config.get('snapshots', {}).get('draw_zones', False),
-            'draw_bounding_boxes': config.get('snapshots', {}).get('draw_bounding_boxes', True)
-        }
-        config['zones'] = config.get('zones', {})
-
-    # Queue for cameras to push tracked objects to
-    tracked_objects_queue = mp.Queue(maxsize=len(CONFIG['cameras'].keys())*2)
-
-    # Queue for clip processing
-    event_queue = mp.Queue()
-
-    # create the detection pipes and shms
-    out_events = {}
-    camera_shms = []
-    for name in CONFIG['cameras'].keys():
-        out_events[name] = mp.Event()
-        shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=300*300*3)
-        shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
-        camera_shms.append(shm_in)
-        camera_shms.append(shm_out)
-
-    detection_queue = mp.Queue()
-
-    detectors = {}
-    for name, detector in DETECTORS.items():
-        if detector['type'] == 'cpu':
-            detectors[name] = EdgeTPUProcess(detection_queue, out_events=out_events, tf_device='cpu')
-        if detector['type'] == 'edgetpu':
-            detectors[name] = EdgeTPUProcess(detection_queue, out_events=out_events, tf_device=detector['device'])
-
-    # create the camera processes
-    camera_process_info = {}
-    for name, config in CONFIG['cameras'].items():
-        # Merge the ffmpeg config with the global config
-        ffmpeg = config.get('ffmpeg', {})
-        ffmpeg_input = get_ffmpeg_input(ffmpeg['input'])
-        ffmpeg_global_args = ffmpeg.get('global_args', FFMPEG_DEFAULT_CONFIG['global_args'])
-        ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', FFMPEG_DEFAULT_CONFIG['hwaccel_args'])
-        ffmpeg_input_args = ffmpeg.get('input_args', FFMPEG_DEFAULT_CONFIG['input_args'])
-        ffmpeg_output_args = ffmpeg.get('output_args', FFMPEG_DEFAULT_CONFIG['output_args'])
-        if not config.get('fps') is None:
-            ffmpeg_output_args = ["-r", str(config.get('fps'))] + ffmpeg_output_args
-        if config.get('save_clips', {}).get('enabled', False):
-            ffmpeg_output_args = [
-                "-f",
-                "segment",
-                "-segment_time",
-                "10",
-                "-segment_format",
-                "mp4",
-                "-reset_timestamps",
-                "1",
-                "-strftime",
-                "1",
-                "-c",
-                "copy",
-                "-an",
-                "-map",
-                "0",
-                f"{os.path.join(CACHE_DIR, name)}-%Y%m%d%H%M%S.mp4"
-            ] + ffmpeg_output_args
-        ffmpeg_cmd = (['ffmpeg'] +
-                ffmpeg_global_args +
-                ffmpeg_hwaccel_args +
-                ffmpeg_input_args +
-                ['-i', ffmpeg_input] +
-                ffmpeg_output_args +
-                ['pipe:'])
-        
-        config['ffmpeg_cmd'] = ffmpeg_cmd
-        
-        if 'width' in config and 'height' in config:
-            frame_shape = (config['height'], config['width'], 3)
-        else:
-            frame_shape = get_frame_shape(ffmpeg_input)
-        
-        config['frame_shape'] = frame_shape
-        config['take_frame'] = config.get('take_frame', 1)
-
-        camera_process_info[name] = {
-            'camera_fps': mp.Value('d', 0.0),
-            'skipped_fps': mp.Value('d', 0.0),
-            'process_fps': mp.Value('d', 0.0),
-            'detection_fps': mp.Value('d', 0.0),
-            'detection_frame': mp.Value('d', 0.0),
-            'read_start': mp.Value('d', 0.0),
-            'ffmpeg_pid': mp.Value('i', 0),
-            'frame_queue': mp.Queue(maxsize=2)
-        }
-
-        # merge global object config into camera object config
-        camera_objects_config = config.get('objects', {})
-        # get objects to track for camera
-        objects_to_track = camera_objects_config.get('track', GLOBAL_OBJECT_CONFIG.get('track', ['person']))
-        # get object filters
-        object_filters = camera_objects_config.get('filters', GLOBAL_OBJECT_CONFIG.get('filters', {}))
-        config['objects'] = {
-            'track': objects_to_track,
-            'filters': object_filters
-        }
-
-        capture_process = mp.Process(target=capture_camera, args=(name, config,
-            camera_process_info[name], stop_event))
-        capture_process.daemon = True
-        camera_process_info[name]['capture_process'] = capture_process
-
-        camera_process = mp.Process(target=track_camera, args=(name, config,
-            detection_queue, out_events[name], tracked_objects_queue, camera_process_info[name], stop_event))
-        camera_process.daemon = True
-        camera_process_info[name]['process'] = camera_process
-
-    # start the camera_processes
-    for name, camera_process in camera_process_info.items():
-        camera_process['capture_process'].start()
-        print(f"Camera capture process started for {name}: {camera_process['capture_process'].pid}")
-        camera_process['process'].start()
-        print(f"Camera process started for {name}: {camera_process['process'].pid}")
-
-    event_processor = EventProcessor(CONFIG, camera_process_info, CACHE_DIR, CLIPS_DIR, event_queue, stop_event)
-    event_processor.start()
-    
-    object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
-    object_processor.start()
-    
-    frigate_watchdog = FrigateWatchdog(camera_process_info, CONFIG['cameras'], detectors, detection_queue, out_events, tracked_objects_queue, stop_event)
-    frigate_watchdog.start()
-
-    def receiveSignal(signalNumber, frame):
-        print('Received:', signalNumber)
-        stop_event.set()
-        event_processor.join()
-        object_processor.join()
-        frigate_watchdog.join()
-
-        for detector in detectors.values():
-            detector.stop()
-        for shm in camera_shms:
-            shm.close()
-            shm.unlink()
-        sys.exit()
-    
-    signal.signal(signal.SIGTERM, receiveSignal)
-    signal.signal(signal.SIGINT, receiveSignal)
-
-    # create a flask app that encodes frames a mjpeg on demand
-    app = Flask(__name__)
-    log = logging.getLogger('werkzeug')
-    log.setLevel(logging.ERROR)
-
-    @app.route('/')
-    def ishealthy():
-        # return a healh
-        return "Frigate is running. Alive and healthy!"
-
-    @app.route('/debug/stack')
-    def processor_stack():
-        frame = sys._current_frames().get(object_processor.ident, None)
-        if frame:
-            return "<br>".join(traceback.format_stack(frame)), 200
-        else:
-            return "no frame found", 200
-
-    @app.route('/debug/print_stack')
-    def print_stack():
-        pid = int(request.args.get('pid', 0))
-        if pid == 0:
-            return "missing pid", 200
-        else:
-            os.kill(pid, signal.SIGUSR1)
-            return "check logs", 200
-
-    @app.route('/debug/stats')
-    def stats():
-        stats = {}
-
-        total_detection_fps = 0
-
-        for name, camera_stats in camera_process_info.items():
-            total_detection_fps += camera_stats['detection_fps'].value
-            stats[name] = {
-                'camera_fps': round(camera_stats['camera_fps'].value, 2),
-                'process_fps': round(camera_stats['process_fps'].value, 2),
-                'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
-                'detection_fps': round(camera_stats['detection_fps'].value, 2),
-                'pid': camera_stats['process'].pid,
-                'capture_pid': camera_stats['capture_process'].pid,
-                'frame_info': {
-                    'detect': camera_stats['detection_frame'].value,
-                    'process': object_processor.camera_data[name]['current_frame_time']
-                }
-            }
-        
-        stats['detectors'] = {}
-        for name, detector in detectors.items():
-            stats['detectors'][name] = {
-                'inference_speed': round(detector.avg_inference_speed.value*1000, 2),
-                'detection_start': detector.detection_start.value,
-                'pid': detector.detect_process.pid
-            }
-        stats['detection_fps'] = round(total_detection_fps, 2)
-
-        return jsonify(stats)
-
-    @app.route('/<camera_name>/<label>/best.jpg')
-    def best(camera_name, label):
-        if camera_name in CONFIG['cameras']:
-            best_object = object_processor.get_best(camera_name, label)
-            best_frame = best_object.get('frame')
-            if best_frame is None:
-                best_frame = np.zeros((720,1280,3), np.uint8)
-            else:
-                best_frame = cv2.cvtColor(best_frame, cv2.COLOR_YUV2BGR_I420)
-            
-            crop = bool(request.args.get('crop', 0, type=int))
-            if crop:
-                region = best_object.get('region', [0,0,300,300])
-                best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
-            
-            height = int(request.args.get('h', str(best_frame.shape[0])))
-            width = int(height*best_frame.shape[1]/best_frame.shape[0])
-
-            best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
-            ret, jpg = cv2.imencode('.jpg', best_frame)
-            response = make_response(jpg.tobytes())
-            response.headers['Content-Type'] = 'image/jpg'
-            return response
-        else:
-            return "Camera named {} not found".format(camera_name), 404
-
-    @app.route('/<camera_name>')
-    def mjpeg_feed(camera_name):
-        fps = int(request.args.get('fps', '3'))
-        height = int(request.args.get('h', '360'))
-        if camera_name in CONFIG['cameras']:
-            # return a multipart response
-            return Response(imagestream(camera_name, fps, height),
-                            mimetype='multipart/x-mixed-replace; boundary=frame')
-        else:
-            return "Camera named {} not found".format(camera_name), 404
-    
-    @app.route('/<camera_name>/latest.jpg')
-    def latest_frame(camera_name):
-        if camera_name in CONFIG['cameras']:
-            # max out at specified FPS
-            frame = object_processor.get_current_frame(camera_name)
-            if frame is None:
-                frame = np.zeros((720,1280,3), np.uint8)
-
-            height = int(request.args.get('h', str(frame.shape[0])))
-            width = int(height*frame.shape[1]/frame.shape[0])
-
-            frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
-
-            ret, jpg = cv2.imencode('.jpg', frame)
-            response = make_response(jpg.tobytes())
-            response.headers['Content-Type'] = 'image/jpg'
-            return response
-        else:
-            return "Camera named {} not found".format(camera_name), 404
-            
-    def imagestream(camera_name, fps, height):
-        while True:
-            # max out at specified FPS
-            time.sleep(1/fps)
-            frame = object_processor.get_current_frame(camera_name, draw=True)
-            if frame is None:
-                frame = np.zeros((height,int(height*16/9),3), np.uint8)
-
-            width = int(height*frame.shape[1]/frame.shape[0])
-            frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
-
-            ret, jpg = cv2.imencode('.jpg', frame)
-            yield (b'--frame\r\n'
-                b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
-
-    app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
-
-    object_processor.join()
-
-if __name__ == '__main__':
-    main()

docker/Dockerfile.amd64

@@ -9,7 +9,7 @@ RUN apt-get -qq update \
         # ffmpeg dependencies
         libgomp1 \
         # VAAPI drivers for Intel hardware accel
-        libva-drm2 libva2 i965-va-driver vainfo intel-media-va-driver \
+        libva-drm2 libva2 libmfx1 i965-va-driver vainfo intel-media-va-driver mesa-va-drivers \
         ## Tensorflow lite
     && wget -q https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
     && python3.8 -m pip install tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \

docker/Dockerfile.base

@@ -1,6 +1,7 @@
 ARG ARCH=amd64
+ARG FFMPEG_VERSION
 FROM blakeblackshear/frigate-wheels:${ARCH} as wheels
-FROM blakeblackshear/frigate-ffmpeg:${ARCH} as ffmpeg
+FROM blakeblackshear/frigate-ffmpeg:${FFMPEG_VERSION}-${ARCH} as ffmpeg
 
 FROM ubuntu:20.04
 LABEL maintainer "blakeb@blakeshome.com"
@@ -10,12 +11,13 @@ COPY --from=ffmpeg /usr/local /usr/local/
 COPY --from=wheels /wheels/. /wheels/
 
 ENV FLASK_ENV=development
+# ENV FONTCONFIG_PATH=/etc/fonts
 ENV DEBIAN_FRONTEND=noninteractive
 # Install packages for apt repo
 RUN apt-get -qq update \
     && apt-get upgrade -y \
     && apt-get -qq install --no-install-recommends -y \
-    gnupg wget unzip tzdata \
+    gnupg wget unzip tzdata nginx libnginx-mod-rtmp \
     && apt-get -qq install --no-install-recommends -y \
         python3-pip \
     && pip3 install -U /wheels/*.whl \
@@ -27,18 +29,25 @@ RUN apt-get -qq update \
     && rm -rf /var/lib/apt/lists/* /wheels \
     && (apt-get autoremove -y; apt-get autoclean -y)
 
-# get model and labels
-ARG MODEL_REFS=7064b94dd5b996189242320359dbab8b52c94a84
-COPY labelmap.txt /labelmap.txt
-RUN wget -q https://github.com/google-coral/edgetpu/raw/$MODEL_REFS/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite -O /edgetpu_model.tflite
-RUN wget -q https://github.com/google-coral/edgetpu/raw/$MODEL_REFS/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite -O /cpu_model.tflite
+RUN pip3 install \
+    peewee \
+    zeroconf \
+    voluptuous
 
-RUN mkdir /cache /clips
+COPY nginx/nginx.conf /etc/nginx/nginx.conf
+
+# get model and labels
+COPY labelmap.txt /labelmap.txt
+RUN wget -q https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite -O /edgetpu_model.tflite
+RUN wget -q https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess.tflite -O /cpu_model.tflite
 
 WORKDIR /opt/frigate/
 ADD frigate frigate/
-COPY detect_objects.py .
-COPY benchmark.py .
-COPY process_clip.py .
 
-CMD ["python3", "-u", "detect_objects.py"]
+COPY run.sh /run.sh
+RUN chmod +x /run.sh
+
+EXPOSE 5000
+EXPOSE 1935
+
+CMD ["/run.sh"]

docker/Dockerfile.ffmpeg.aarch64

@@ -18,12 +18,10 @@ FROM base as build
 ENV         FFMPEG_VERSION=4.3.1 \
             AOM_VERSION=v1.0.0 \
             FDKAAC_VERSION=0.1.5 \
-            FONTCONFIG_VERSION=2.12.4 \
             FREETYPE_VERSION=2.5.5 \
             FRIBIDI_VERSION=0.19.7 \
             KVAZAAR_VERSION=1.2.0 \
             LAME_VERSION=3.100 \
-            LIBASS_VERSION=0.13.7 \
             LIBPTHREAD_STUBS_VERSION=0.4 \
             LIBVIDSTAB_VERSION=1.1.0 \
             LIBXCB_VERSION=1.13.1 \
@@ -42,22 +40,17 @@ ENV         FFMPEG_VERSION=4.3.1 \
             XORG_MACROS_VERSION=1.19.2 \
             XPROTO_VERSION=7.0.31 \
             XVID_VERSION=1.3.4 \
-            LIBXML2_VERSION=2.9.10 \
-            LIBBLURAY_VERSION=1.1.2 \
             LIBZMQ_VERSION=4.3.2 \
             SRC=/usr/local
 
 ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
 ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
-ARG         LIBASS_SHA256SUM="8fadf294bf701300d4605e6f1d92929304187fca4b8d8a47889315526adbafd7 0.13.7.tar.gz"
 ARG         LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
 ARG         OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
 ARG         OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
 ARG         THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
 ARG         VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
 ARG         XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
-ARG         LIBXML2_SHA256SUM="f07dab13bf42d2b8db80620cce7419b3b87827cc937c8bb20fe13b8571ee9501  libxml2-v2.9.10.tar.gz"
-ARG         LIBBLURAY_SHA256SUM="a3dd452239b100dc9da0d01b30e1692693e2a332a7d29917bf84bb10ea7c0b42 libbluray-1.1.2.tar.bz2"
 ARG         LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
 
 
@@ -287,30 +280,7 @@ RUN  \
         make -j1 && \
         make -j $(nproc) install && \
         rm -rf ${DIR}
-## fontconfig https://www.freedesktop.org/wiki/Software/fontconfig/
-RUN  \
-        DIR=/tmp/fontconfig && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://www.freedesktop.org/software/fontconfig/release/fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        tar -jx --strip-components=1 -f fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
-## libass https://github.com/libass/libass
-RUN  \
-        DIR=/tmp/libass && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://github.com/libass/libass/archive/${LIBASS_VERSION}.tar.gz && \
-        echo ${LIBASS_SHA256SUM} | sha256sum --check && \
-        tar -zx --strip-components=1 -f ${LIBASS_VERSION}.tar.gz && \
-        ./autogen.sh && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
+
 ## kvazaar https://github.com/ultravideo/kvazaar
 RUN \
         DIR=/tmp/kvazaar && \
@@ -407,32 +377,6 @@ RUN \
         make -j $(nproc) install && \
         rm -rf ${DIR}
 
-## libxml2 - for libbluray
-RUN \
-        DIR=/tmp/libxml2 && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://gitlab.gnome.org/GNOME/libxml2/-/archive/v${LIBXML2_VERSION}/libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        echo ${LIBXML2_SHA256SUM} | sha256sum --check && \
-        tar -xz --strip-components=1 -f libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        ./autogen.sh --prefix="${PREFIX}" --with-ftp=no --with-http=no --with-python=no && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
-
-## libbluray - Requires libxml, freetype, and fontconfig
-RUN \
-        DIR=/tmp/libbluray && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://download.videolan.org/pub/videolan/libbluray/${LIBBLURAY_VERSION}/libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        echo ${LIBBLURAY_SHA256SUM} | sha256sum --check && \
-        tar -jx --strip-components=1 -f libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-examples --disable-bdjava-jar --disable-static --enable-shared && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
-
 ## libzmq https://github.com/zeromq/libzmq/
 RUN \
         DIR=/tmp/libzmq && \
@@ -465,8 +409,6 @@ RUN \
         --enable-libopencore-amrnb \
         --enable-libopencore-amrwb \
         --enable-gpl \
-        --enable-libass \
-        --enable-fontconfig \
         --enable-libfreetype \
         --enable-libvidstab \
         --enable-libmp3lame \
@@ -485,7 +427,6 @@ RUN \
         --enable-postproc \
         --enable-small \
         --enable-version3 \
-        --enable-libbluray \
         --enable-libzmq \
         --extra-libs=-ldl \
         --prefix="${PREFIX}" \

docker/Dockerfile.ffmpeg.amd64

@@ -17,12 +17,10 @@ FROM base as build
 ENV         FFMPEG_VERSION=4.3.1 \
             AOM_VERSION=v1.0.0 \
             FDKAAC_VERSION=0.1.5 \
-            FONTCONFIG_VERSION=2.12.4 \
             FREETYPE_VERSION=2.5.5 \
             FRIBIDI_VERSION=0.19.7 \
             KVAZAAR_VERSION=1.2.0 \
             LAME_VERSION=3.100 \
-            LIBASS_VERSION=0.13.7 \
             LIBPTHREAD_STUBS_VERSION=0.4 \
             LIBVIDSTAB_VERSION=1.1.0 \
             LIBXCB_VERSION=1.13.1 \
@@ -41,22 +39,17 @@ ENV         FFMPEG_VERSION=4.3.1 \
             XORG_MACROS_VERSION=1.19.2 \
             XPROTO_VERSION=7.0.31 \
             XVID_VERSION=1.3.4 \
-            LIBXML2_VERSION=2.9.10 \
-            LIBBLURAY_VERSION=1.1.2 \
             LIBZMQ_VERSION=4.3.2 \
             SRC=/usr/local
 
 ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
 ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
-ARG         LIBASS_SHA256SUM="8fadf294bf701300d4605e6f1d92929304187fca4b8d8a47889315526adbafd7 0.13.7.tar.gz"
 ARG         LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
 ARG         OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
 ARG         OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
 ARG         THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
 ARG         VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
 ARG         XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
-ARG         LIBXML2_SHA256SUM="f07dab13bf42d2b8db80620cce7419b3b87827cc937c8bb20fe13b8571ee9501  libxml2-v2.9.10.tar.gz"
-ARG         LIBBLURAY_SHA256SUM="a3dd452239b100dc9da0d01b30e1692693e2a332a7d29917bf84bb10ea7c0b42 libbluray-1.1.2.tar.bz2"
 ARG         LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
 
 
@@ -86,6 +79,7 @@ RUN      buildDeps="autoconf \
                     libssl-dev \
                     yasm \
                     libva-dev \
+                    libmfx-dev \
                     zlib1g-dev" && \
         apt-get -yqq update && \
         apt-get install -yq --no-install-recommends ${buildDeps}
@@ -281,30 +275,6 @@ RUN  \
         make -j1 && \
         make install && \
         rm -rf ${DIR}
-## fontconfig https://www.freedesktop.org/wiki/Software/fontconfig/
-RUN  \
-        DIR=/tmp/fontconfig && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://www.freedesktop.org/software/fontconfig/release/fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        tar -jx --strip-components=1 -f fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
-## libass https://github.com/libass/libass
-RUN  \
-        DIR=/tmp/libass && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://github.com/libass/libass/archive/${LIBASS_VERSION}.tar.gz && \
-        echo ${LIBASS_SHA256SUM} | sha256sum --check && \
-        tar -zx --strip-components=1 -f ${LIBASS_VERSION}.tar.gz && \
-        ./autogen.sh && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
 ## kvazaar https://github.com/ultravideo/kvazaar
 RUN \
         DIR=/tmp/kvazaar && \
@@ -399,32 +369,6 @@ RUN \
         make install && \
         rm -rf ${DIR}
 
-## libxml2 - for libbluray
-RUN \
-        DIR=/tmp/libxml2 && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://gitlab.gnome.org/GNOME/libxml2/-/archive/v${LIBXML2_VERSION}/libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        echo ${LIBXML2_SHA256SUM} | sha256sum --check && \
-        tar -xz --strip-components=1 -f libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        ./autogen.sh --prefix="${PREFIX}" --with-ftp=no --with-http=no --with-python=no && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
-
-## libbluray - Requires libxml, freetype, and fontconfig
-RUN \
-        DIR=/tmp/libbluray && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://download.videolan.org/pub/videolan/libbluray/${LIBBLURAY_VERSION}/libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        echo ${LIBBLURAY_SHA256SUM} | sha256sum --check && \
-        tar -jx --strip-components=1 -f libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-examples --disable-bdjava-jar --disable-static --enable-shared && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
-
 ## libzmq https://github.com/zeromq/libzmq/
 RUN \
         DIR=/tmp/libzmq && \
@@ -459,10 +403,9 @@ RUN \
         --enable-libopencore-amrnb \
         --enable-libopencore-amrwb \
         --enable-gpl \
-        --enable-libass \
-        --enable-fontconfig \
         --enable-libfreetype \
         --enable-libvidstab \
+        --enable-libmfx \
         --enable-libmp3lame \
         --enable-libopus \
         --enable-libtheora \
@@ -479,7 +422,6 @@ RUN \
         --enable-postproc \
         --enable-small \
         --enable-version3 \
-        --enable-libbluray \
         --enable-libzmq \
         --extra-libs=-ldl \
         --prefix="${PREFIX}" \
@@ -522,5 +464,5 @@ COPY --from=build /usr/local /usr/local/
 
 RUN \
 	apt-get update -y && \
-	apt-get install -y --no-install-recommends libva-drm2 libva2 i965-va-driver && \
+	apt-get install -y --no-install-recommends libva-drm2 libva2 i965-va-driver mesa-va-drivers && \
 	rm -rf /var/lib/apt/lists/*

docker/Dockerfile.ffmpeg.amd64nvidia

@@ -40,12 +40,10 @@ ENV        NVIDIA_HEADERS_VERSION=9.1.23.1
 ENV         FFMPEG_VERSION=4.3.1 \
             AOM_VERSION=v1.0.0 \
             FDKAAC_VERSION=0.1.5 \
-            FONTCONFIG_VERSION=2.12.4 \
             FREETYPE_VERSION=2.5.5 \
             FRIBIDI_VERSION=0.19.7 \
             KVAZAAR_VERSION=1.2.0 \
             LAME_VERSION=3.100 \
-            LIBASS_VERSION=0.13.7 \
             LIBPTHREAD_STUBS_VERSION=0.4 \
             LIBVIDSTAB_VERSION=1.1.0 \
             LIBXCB_VERSION=1.13.1 \
@@ -64,8 +62,6 @@ ENV         FFMPEG_VERSION=4.3.1 \
             XORG_MACROS_VERSION=1.19.2 \
             XPROTO_VERSION=7.0.31 \
             XVID_VERSION=1.3.4 \
-            LIBXML2_VERSION=2.9.10 \
-            LIBBLURAY_VERSION=1.1.2 \
             LIBZMQ_VERSION=4.3.2 \
             LIBSRT_VERSION=1.4.1 \
             LIBARIBB24_VERSION=1.0.3 \
@@ -74,15 +70,12 @@ ENV         FFMPEG_VERSION=4.3.1 \
 
 ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
 ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
-ARG         LIBASS_SHA256SUM="8fadf294bf701300d4605e6f1d92929304187fca4b8d8a47889315526adbafd7 0.13.7.tar.gz"
 ARG         LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
 ARG         OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
 ARG         OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
 ARG         THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
 ARG         VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
 ARG         XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
-ARG         LIBXML2_SHA256SUM="f07dab13bf42d2b8db80620cce7419b3b87827cc937c8bb20fe13b8571ee9501  libxml2-v2.9.10.tar.gz"
-ARG         LIBBLURAY_SHA256SUM="a3dd452239b100dc9da0d01b30e1692693e2a332a7d29917bf84bb10ea7c0b42 libbluray-1.1.2.tar.bz2"
 ARG         LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
 ARG         LIBARIBB24_SHA256SUM="f61560738926e57f9173510389634d8c06cabedfa857db4b28fb7704707ff128 v1.0.3.tar.gz"
 
@@ -317,30 +310,6 @@ RUN  \
         make -j1 && \
         make install && \
         rm -rf ${DIR}
-## fontconfig https://www.freedesktop.org/wiki/Software/fontconfig/
-RUN  \
-        DIR=/tmp/fontconfig && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://www.freedesktop.org/software/fontconfig/release/fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        tar -jx --strip-components=1 -f fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
-## libass https://github.com/libass/libass
-RUN  \
-        DIR=/tmp/libass && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://github.com/libass/libass/archive/${LIBASS_VERSION}.tar.gz && \
-        echo ${LIBASS_SHA256SUM} | sha256sum --check && \
-        tar -zx --strip-components=1 -f ${LIBASS_VERSION}.tar.gz && \
-        ./autogen.sh && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
 ## kvazaar https://github.com/ultravideo/kvazaar
 RUN \
         DIR=/tmp/kvazaar && \
@@ -435,32 +404,6 @@ RUN \
         make install && \
         rm -rf ${DIR}
 
-## libxml2 - for libbluray
-RUN \
-        DIR=/tmp/libxml2 && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://gitlab.gnome.org/GNOME/libxml2/-/archive/v${LIBXML2_VERSION}/libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        echo ${LIBXML2_SHA256SUM} | sha256sum --check && \
-        tar -xz --strip-components=1 -f libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        ./autogen.sh --prefix="${PREFIX}" --with-ftp=no --with-http=no --with-python=no && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
-
-## libbluray - Requires libxml, freetype, and fontconfig
-RUN \
-        DIR=/tmp/libbluray && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://download.videolan.org/pub/videolan/libbluray/${LIBBLURAY_VERSION}/libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        echo ${LIBBLURAY_SHA256SUM} | sha256sum --check && \
-        tar -jx --strip-components=1 -f libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-examples --disable-bdjava-jar --disable-static --enable-shared && \
-        make && \
-        make install && \
-        rm -rf ${DIR}
-
 ## libzmq https://github.com/zeromq/libzmq/
 RUN \
         DIR=/tmp/libzmq && \
@@ -533,8 +476,6 @@ RUN \
         --enable-libopencore-amrnb \
         --enable-libopencore-amrwb \
         --enable-gpl \
-        --enable-libass \
-        --enable-fontconfig \
         --enable-libfreetype \
         --enable-libvidstab \
         --enable-libmp3lame \
@@ -553,7 +494,6 @@ RUN \
         --enable-postproc \
         --enable-small \
         --enable-version3 \
-        --enable-libbluray \
         --enable-libzmq \
         --extra-libs=-ldl \
         --prefix="${PREFIX}" \
@@ -593,7 +533,6 @@ RUN \
 
 
 FROM        runtime-base AS release
-MAINTAINER  Julien Rottenberg <julien@rottenberg.info>
 
 ENV         LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64
 

docker/Dockerfile.ffmpeg.armv7

@@ -18,12 +18,10 @@ FROM base as build
 ENV         FFMPEG_VERSION=4.3.1 \
             AOM_VERSION=v1.0.0 \
             FDKAAC_VERSION=0.1.5 \
-            FONTCONFIG_VERSION=2.12.4 \
             FREETYPE_VERSION=2.5.5 \
             FRIBIDI_VERSION=0.19.7 \
             KVAZAAR_VERSION=1.2.0 \
             LAME_VERSION=3.100 \
-            LIBASS_VERSION=0.13.7 \
             LIBPTHREAD_STUBS_VERSION=0.4 \
             LIBVIDSTAB_VERSION=1.1.0 \
             LIBXCB_VERSION=1.13.1 \
@@ -42,22 +40,17 @@ ENV         FFMPEG_VERSION=4.3.1 \
             XORG_MACROS_VERSION=1.19.2 \
             XPROTO_VERSION=7.0.31 \
             XVID_VERSION=1.3.4 \
-            LIBXML2_VERSION=2.9.10 \
-            LIBBLURAY_VERSION=1.1.2 \
             LIBZMQ_VERSION=4.3.3 \
             SRC=/usr/local
 
 ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
 ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
-ARG         LIBASS_SHA256SUM="8fadf294bf701300d4605e6f1d92929304187fca4b8d8a47889315526adbafd7 0.13.7.tar.gz"
 ARG         LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
 ARG         OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
 ARG         OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
 ARG         THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
 ARG         VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
 ARG         XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
-ARG         LIBXML2_SHA256SUM="f07dab13bf42d2b8db80620cce7419b3b87827cc937c8bb20fe13b8571ee9501  libxml2-v2.9.10.tar.gz"
-ARG         LIBBLURAY_SHA256SUM="a3dd452239b100dc9da0d01b30e1692693e2a332a7d29917bf84bb10ea7c0b42 libbluray-1.1.2.tar.bz2"
 
 
 ARG         LD_LIBRARY_PATH=/opt/ffmpeg/lib
@@ -289,30 +282,7 @@ RUN  \
         make -j1 && \
         make -j $(nproc) install && \
         rm -rf ${DIR}
-## fontconfig https://www.freedesktop.org/wiki/Software/fontconfig/
-RUN  \
-        DIR=/tmp/fontconfig && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://www.freedesktop.org/software/fontconfig/release/fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        tar -jx --strip-components=1 -f fontconfig-${FONTCONFIG_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
-## libass https://github.com/libass/libass
-RUN  \
-        DIR=/tmp/libass && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://github.com/libass/libass/archive/${LIBASS_VERSION}.tar.gz && \
-        echo ${LIBASS_SHA256SUM} | sha256sum --check && \
-        tar -zx --strip-components=1 -f ${LIBASS_VERSION}.tar.gz && \
-        ./autogen.sh && \
-        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
+
 ## kvazaar https://github.com/ultravideo/kvazaar
 RUN \
         DIR=/tmp/kvazaar && \
@@ -409,32 +379,6 @@ RUN \
         make -j $(nproc) install && \
         rm -rf ${DIR}
 
-## libxml2 - for libbluray
-RUN \
-        DIR=/tmp/libxml2 && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://gitlab.gnome.org/GNOME/libxml2/-/archive/v${LIBXML2_VERSION}/libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        echo ${LIBXML2_SHA256SUM} | sha256sum --check && \
-        tar -xz --strip-components=1 -f libxml2-v${LIBXML2_VERSION}.tar.gz && \
-        ./autogen.sh --prefix="${PREFIX}" --with-ftp=no --with-http=no --with-python=no && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
-
-## libbluray - Requires libxml, freetype, and fontconfig
-RUN \
-        DIR=/tmp/libbluray && \
-        mkdir -p ${DIR} && \
-        cd ${DIR} && \
-        curl -sLO https://download.videolan.org/pub/videolan/libbluray/${LIBBLURAY_VERSION}/libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        echo ${LIBBLURAY_SHA256SUM} | sha256sum --check && \
-        tar -jx --strip-components=1 -f libbluray-${LIBBLURAY_VERSION}.tar.bz2 && \
-        ./configure --prefix="${PREFIX}" --disable-examples --disable-bdjava-jar --disable-static --enable-shared && \
-        make -j $(nproc) && \
-        make -j $(nproc) install && \
-        rm -rf ${DIR}
-
 ## libzmq https://github.com/zeromq/libzmq/
 RUN \
         DIR=/tmp/libzmq && \
@@ -475,8 +419,6 @@ RUN \
         --enable-libopencore-amrnb \
         --enable-libopencore-amrwb \
         --enable-gpl \
-        --enable-libass \
-        --enable-fontconfig \
         --enable-libfreetype \
         --enable-libvidstab \
         --enable-libmp3lame \
@@ -495,7 +437,6 @@ RUN \
         --enable-postproc \
         --enable-small \
         --enable-version3 \
-        --enable-libbluray \
         --enable-libzmq \
         --extra-libs=-ldl \
         --prefix="${PREFIX}" \

docs/notification-examples.md

@@ -0,0 +1,71 @@
+# Notification examples
+
+Here are some examples of notifications for the HomeAssistant android companion app:
+```yaml
+automation:
+
+  - alias: When a person enters a zone named yard
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ 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']}}.jpg"
+            tag: "{{trigger.payload_json['after']['id']}}"
+
+  - alias: When a person leaves a zone named yard
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ 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']}}.jpg"
+            tag: "{{trigger.payload_json['after']['id']}}"
+
+  - alias: Notify for dogs in the front with a high top score
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ 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']}}.jpg"
+            tag: "{{trigger.payload_json['after']['id']}}"
+```
+
+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/events/{{trigger.payload_json["after"]["id"]}}/snapshot.jpg'
+                caption : 'A {{trigger.payload_json["after"]["label"]}} was detected on {{ trigger.payload_json["after"]["camera"] }} camera'
+```

frigate/__main__.py

@@ -0,0 +1,15 @@
+import faulthandler; faulthandler.enable()
+import sys
+import threading
+
+threading.current_thread().name = "frigate"
+
+from frigate.app import FrigateApp
+
+cli = sys.modules['flask.cli']
+cli.show_server_banner = lambda *x: None
+
+if __name__ == '__main__':
+    frigate_app = FrigateApp()
+
+    frigate_app.start()

frigate/app.py

@@ -0,0 +1,235 @@
+import json
+import logging
+import multiprocessing as mp
+import os
+from logging.handlers import QueueHandler
+from typing import Dict, List
+import sys
+import signal
+
+import yaml
+from playhouse.sqlite_ext import SqliteExtDatabase
+
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.edgetpu import EdgeTPUProcess
+from frigate.events import EventProcessor, EventCleanup
+from frigate.http import create_app
+from frigate.log import log_process, root_configurer
+from frigate.models import Event
+from frigate.mqtt import create_mqtt_client
+from frigate.object_processing import TrackedObjectProcessor
+from frigate.record import RecordingMaintainer
+from frigate.video import capture_camera, track_camera
+from frigate.watchdog import FrigateWatchdog
+from frigate.zeroconf import broadcast_zeroconf
+
+logger = logging.getLogger(__name__)
+
+class FrigateApp():
+    def __init__(self):
+        self.stop_event = mp.Event()
+        self.config: FrigateConfig = None
+        self.detection_queue = mp.Queue()
+        self.detectors: Dict[str, EdgeTPUProcess] = {}
+        self.detection_out_events: Dict[str, mp.Event] = {}
+        self.detection_shms: List[mp.shared_memory.SharedMemory] = []
+        self.log_queue = mp.Queue()
+        self.camera_metrics = {}
+
+    def ensure_dirs(self):
+        for d in [RECORD_DIR, CLIPS_DIR, CACHE_DIR]:
+            if not os.path.exists(d) and not os.path.islink(d):
+                logger.info(f"Creating directory: {d}")
+                os.makedirs(d)
+            else:
+                logger.debug(f"Skipping directory: {d}")
+
+        tmpfs_size = self.config.save_clips.tmpfs_cache_size
+        if tmpfs_size:
+             logger.info(f"Creating tmpfs of size {tmpfs_size}")
+             rc = os.system(f"mount -t tmpfs -o size={tmpfs_size} tmpfs {CACHE_DIR}")
+             if rc != 0:
+                 logger.error(f"Failed to create tmpfs, error code: {rc}")
+    
+    def init_logger(self):
+        self.log_process = mp.Process(target=log_process, args=(self.log_queue,), name='log_process')
+        self.log_process.daemon = True
+        self.log_process.start()
+        root_configurer(self.log_queue)
+    
+    def init_config(self):
+        config_file = os.environ.get('CONFIG_FILE', '/config/config.yml')
+        self.config = FrigateConfig(config_file=config_file)
+
+        for camera_name in self.config.cameras.keys():
+            # create camera_metrics
+            self.camera_metrics[camera_name] = {
+                'camera_fps': mp.Value('d', 0.0),
+                'skipped_fps': mp.Value('d', 0.0),
+                'process_fps': mp.Value('d', 0.0),
+                'detection_fps': mp.Value('d', 0.0),
+                'detection_frame': mp.Value('d', 0.0),
+                'read_start': mp.Value('d', 0.0),
+                'ffmpeg_pid': mp.Value('i', 0),
+                '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.save_clips.enabled and 'clips' in assigned_roles:
+                logger.warning(f"Camera {name} has clips assigned to an input, but save_clips is not enabled.")
+            elif camera.save_clips.enabled and not 'clips' in assigned_roles:
+                logger.warning(f"Camera {name} has save_clips enabled, but clips is not assigned to an input.")
+
+            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)
+        for log, level in self.config.logger.logs.items():
+            logging.getLogger(log).setLevel(level)
+        
+        if not 'werkzeug' in self.config.logger.logs:
+            logging.getLogger('werkzeug').setLevel('ERROR')
+
+    def init_queues(self):
+        # Queues for clip processing
+        self.event_queue = mp.Queue()
+        self.event_processed_queue = mp.Queue()
+
+        # Queue for cameras to push tracked objects to
+        self.detected_frames_queue = mp.Queue(maxsize=len(self.config.cameras.keys())*2)
+
+    def init_database(self):
+        self.db = SqliteExtDatabase(self.config.database.path)
+        models = [Event]
+        self.db.bind(models)
+        self.db.create_tables(models, safe=True)
+
+    def init_web_server(self):
+        self.flask_app = create_app(self.config, self.db, self.camera_metrics, self.detectors, self.detected_frames_processor)
+
+    def init_mqtt(self):
+        self.mqtt_client = create_mqtt_client(self.config.mqtt)
+
+    def start_detectors(self):
+        model_shape = (self.config.model.height, self.config.model.width)
+        for name in self.config.cameras.keys():
+            self.detection_out_events[name] = mp.Event()
+            shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=self.config.model.height*self.config.model.width*3)
+            shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
+            self.detection_shms.append(shm_in)
+            self.detection_shms.append(shm_out)
+
+        for name, detector in self.config.detectors.items():
+            if detector.type == 'cpu':
+                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, 'cpu', detector.num_threads)
+            if detector.type == 'edgetpu':
+                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, detector.device, detector.num_threads)
+
+    def start_detected_frames_processor(self):
+        self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix, 
+            self.detected_frames_queue, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.detected_frames_processor.start()
+
+    def start_camera_processors(self):
+        model_shape = (self.config.model.height, self.config.model.width)
+        for name, config in self.config.cameras.items():
+            camera_process = mp.Process(target=track_camera, name=f"camera_processor:{name}", args=(name, config, model_shape,
+                self.detection_queue, self.detection_out_events[name], self.detected_frames_queue, 
+                self.camera_metrics[name]))
+            camera_process.daemon = True
+            self.camera_metrics[name]['process'] = camera_process
+            camera_process.start()
+            logger.info(f"Camera processor started for {name}: {camera_process.pid}")
+
+    def start_camera_capture_processes(self):
+        for name, config in self.config.cameras.items():
+            capture_process = mp.Process(target=capture_camera, name=f"camera_capture:{name}", args=(name, config,
+                self.camera_metrics[name]))
+            capture_process.daemon = True
+            self.camera_metrics[name]['capture_process'] = capture_process
+            capture_process.start()
+            logger.info(f"Capture process started for {name}: {capture_process.pid}")
+    
+    def start_event_processor(self):
+        self.event_processor = EventProcessor(self.config, self.camera_metrics, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.event_processor.start()
+    
+    def start_event_cleanup(self):
+        self.event_cleanup = EventCleanup(self.config, self.stop_event)
+        self.event_cleanup.start()
+    
+    def start_recording_maintainer(self):
+        self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
+        self.recording_maintainer.start()
+
+    def start_watchdog(self):
+        self.frigate_watchdog = FrigateWatchdog(self.detectors, self.stop_event)
+        self.frigate_watchdog.start()
+
+    def start(self):
+        self.init_logger()
+        try:
+            try:
+                self.init_config()
+            except Exception as e:
+                logger.error(f"Error parsing config: {e}")
+                self.log_process.terminate()
+                sys.exit(1)
+            self.ensure_dirs()
+            self.check_config()
+            self.set_log_levels()
+            self.init_queues()
+            self.init_database()
+            self.init_mqtt()
+        except Exception as e:
+            print(e)
+            self.log_process.terminate()
+            sys.exit(1)
+        self.start_detectors()
+        self.start_detected_frames_processor()
+        self.start_camera_processors()
+        self.start_camera_capture_processes()
+        self.init_web_server()
+        self.start_event_processor()
+        self.start_event_cleanup()
+        self.start_recording_maintainer()
+        self.start_watchdog()
+        # self.zeroconf = broadcast_zeroconf(self.config.mqtt.client_id)
+
+        def receiveSignal(signalNumber, frame):
+            self.stop()
+            sys.exit()
+        
+        signal.signal(signal.SIGTERM, receiveSignal)
+
+        self.flask_app.run(host='127.0.0.1', port=5001, debug=False)
+        self.stop()
+    
+    def stop(self):
+        logger.info(f"Stopping...")
+        self.stop_event.set()
+
+        self.detected_frames_processor.join()
+        self.event_processor.join()
+        self.event_cleanup.join()
+        self.recording_maintainer.join()
+        self.frigate_watchdog.join()
+
+        for detector in self.detectors.values():
+            detector.stop()
+
+        while len(self.detection_shms) > 0:
+            shm = self.detection_shms.pop()
+            shm.close()
+            shm.unlink()

frigate/config.py

@@ -0,0 +1,968 @@
+import base64
+import json
+import logging
+import os
+from typing import Dict
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import voluptuous as vol
+import yaml
+
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+
+logger = logging.getLogger(__name__)
+
+DETECTORS_SCHEMA = vol.Schema(
+    {
+        vol.Required(str): {
+            vol.Required('type', default='edgetpu'): vol.In(['cpu', 'edgetpu']), 
+            vol.Optional('device', default='usb'): str,
+            vol.Optional('num_threads', default=3): int
+        }
+    }
+)
+
+DEFAULT_DETECTORS = {
+    'coral': {
+        'type': 'edgetpu',
+        'device': 'usb'
+    }
+}
+
+MQTT_SCHEMA = vol.Schema(
+    {
+        vol.Required('host'): str,
+        vol.Optional('port', default=1883): int,
+        vol.Optional('topic_prefix', default='frigate'): str,
+        vol.Optional('client_id', default='frigate'): str,
+        'user': str,
+        'password': str
+    }
+)
+
+SAVE_CLIPS_RETAIN_SCHEMA = vol.Schema(
+    {
+        vol.Required('default',default=10): int,
+        'objects': {
+            str: int
+        }
+    }
+)
+
+SAVE_CLIPS_SCHEMA = vol.Schema(
+    {
+        vol.Optional('max_seconds', default=300): int,
+        'tmpfs_cache_size': str,
+        vol.Optional('retain', default={}): SAVE_CLIPS_RETAIN_SCHEMA
+    }
+)
+
+FFMPEG_GLOBAL_ARGS_DEFAULT = ['-hide_banner','-loglevel','fatal']
+FFMPEG_INPUT_ARGS_DEFAULT = ['-avoid_negative_ts', 'make_zero',
+    '-fflags', '+genpts+discardcorrupt',
+    '-rtsp_transport', 'tcp',
+    '-stimeout', '5000000',
+    '-use_wallclock_as_timestamps', '1']
+DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT = ['-f', 'rawvideo',
+    '-pix_fmt', 'yuv420p']
+RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-c", "copy", "-f", "flv"]
+SAVE_CLIPS_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "segment", "-segment_time",
+    "10", "-segment_format", "mp4", "-reset_timestamps", "1", "-strftime",
+    "1", "-c", "copy", "-an"]
+RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "segment", "-segment_time",
+    "60", "-segment_format", "mp4", "-reset_timestamps", "1", "-strftime",
+    "1", "-c", "copy", "-an"]
+
+GLOBAL_FFMPEG_SCHEMA = vol.Schema(
+    { 
+        vol.Optional('global_args', default=FFMPEG_GLOBAL_ARGS_DEFAULT):  vol.Any(str, [str]),
+        vol.Optional('hwaccel_args', default=[]): vol.Any(str, [str]),
+        vol.Optional('input_args', default=FFMPEG_INPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+        vol.Optional('output_args', default={}): {
+            vol.Optional('detect', default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('record', default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('clips', default=SAVE_CLIPS_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('rtmp', default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+        }
+    }
+)
+
+MOTION_SCHEMA = vol.Schema(
+    {
+        'threshold': vol.Range(min=1, max=255),
+        'contour_area': int,
+        'delta_alpha': float,
+        'frame_alpha': float,
+        'frame_height': int
+    }
+)
+
+DETECT_SCHEMA = vol.Schema(
+    {
+        'max_disappeared': int
+    }
+)
+
+FILTER_SCHEMA = vol.Schema(
+    { 
+        str: {
+                vol.Optional('min_area', default=0): int,
+                vol.Optional('max_area', default=24000000): int,
+                vol.Optional('threshold', default=0.7): float
+            }
+    }
+)
+
+def filters_for_all_tracked_objects(object_config):
+    for tracked_object in object_config.get('track', ['person']):
+        if not 'filters' in object_config:
+            object_config['filters'] = {}
+        if not tracked_object in object_config['filters']:
+            object_config['filters'][tracked_object] = {}
+    return object_config
+
+OBJECTS_SCHEMA = vol.Schema(vol.All(filters_for_all_tracked_objects,
+    {
+        vol.Optional('track', default=['person']): [str],
+        vol.Optional('filters', default = {}): FILTER_SCHEMA.extend({ str: {vol.Optional('min_score', default=0.5): float}})
+    }
+))
+
+def each_role_used_once(inputs):
+    roles = [role for i in inputs for role in i['roles']]
+    roles_set = set(roles)
+    if len(roles) > len(roles_set):
+        raise ValueError
+    return inputs
+
+CAMERA_FFMPEG_SCHEMA = vol.Schema(
+    { 
+        vol.Required('inputs'): vol.All([{
+            vol.Required('path'): str,
+            vol.Required('roles'): ['detect', 'clips', 'record', 'rtmp'],
+            'global_args':  vol.Any(str, [str]),
+            'hwaccel_args': vol.Any(str, [str]),
+            'input_args': vol.Any(str, [str]),
+        }], vol.Msg(each_role_used_once, msg="Each input role may only be used once")), 
+        'output_args': {
+            vol.Optional('detect', default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('record', default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('clips', default=SAVE_CLIPS_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('rtmp', default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+        }
+    }
+)
+
+def ensure_zones_and_cameras_have_different_names(cameras):
+    zones = [zone for camera in cameras.values() for zone in camera['zones'].keys()]
+    for zone in zones:
+        if zone in cameras.keys():
+            raise ValueError
+    return cameras
+
+CAMERAS_SCHEMA = vol.Schema(vol.All(
+    {
+        str: {
+            vol.Required('ffmpeg'): CAMERA_FFMPEG_SCHEMA,
+            vol.Required('height'): int,
+            vol.Required('width'): int,
+            'fps': int,
+            'mask': vol.Any(str, [str]),
+            vol.Optional('best_image_timeout', default=60): int,
+            vol.Optional('zones', default={}):  {
+                str: {
+                    vol.Required('coordinates'): vol.Any(str, [str]),
+                    vol.Optional('filters', default={}): FILTER_SCHEMA
+                }
+            },
+            vol.Optional('save_clips', default={}): {
+                vol.Optional('enabled', default=False): bool,
+                vol.Optional('pre_capture', default=5): int,
+                vol.Optional('post_capture', default=5): int,
+                'objects': [str],
+                vol.Optional('retain', default={}): SAVE_CLIPS_RETAIN_SCHEMA,
+            },
+            vol.Optional('record', default={}): {
+                'enabled': bool,
+                'retain_days': int,
+            },
+            vol.Optional('rtmp', default={}): {
+                vol.Required('enabled', default=True): bool,
+            },
+            vol.Optional('snapshots', default={}): {
+                vol.Optional('show_timestamp', default=True): bool,
+                vol.Optional('draw_zones', default=False): bool,
+                vol.Optional('draw_bounding_boxes', default=True): bool,
+                vol.Optional('crop_to_region', default=True): bool,
+                vol.Optional('height', default=175): int
+            },
+            'objects': OBJECTS_SCHEMA,
+            vol.Optional('motion', default={}): MOTION_SCHEMA,
+            vol.Optional('detect', default={}): DETECT_SCHEMA
+        }
+    }, vol.Msg(ensure_zones_and_cameras_have_different_names, msg='Zones cannot share names with cameras'))
+)
+
+FRIGATE_CONFIG_SCHEMA = vol.Schema(
+    {
+        vol.Optional('database', default={}): {
+            vol.Optional('path', default=os.path.join(CLIPS_DIR, 'frigate.db')): str
+        },
+        vol.Optional('model', default={'width': 320, 'height': 320}): {
+            vol.Required('width'): int,
+            vol.Required('height'): int
+        },
+        vol.Optional('detectors', default=DEFAULT_DETECTORS): DETECTORS_SCHEMA,
+        'mqtt': MQTT_SCHEMA,
+        vol.Optional('logger', default={'default': 'info', 'logs': {}}): {
+            vol.Optional('default', default='info'): vol.In(['info', 'debug', 'warning', 'error', 'critical']),
+            vol.Optional('logs', default={}): {str: vol.In(['info', 'debug', 'warning', 'error', 'critical']) }
+        },
+        vol.Optional('save_clips', default={}): SAVE_CLIPS_SCHEMA,
+        vol.Optional('record', default={}): {
+            vol.Optional('enabled', default=False): bool,
+            vol.Optional('retain_days', default=30): int,
+        },
+        vol.Optional('ffmpeg', default={}): GLOBAL_FFMPEG_SCHEMA,
+        vol.Optional('objects', default={}): OBJECTS_SCHEMA,
+        vol.Optional('motion', default={}): MOTION_SCHEMA,
+        vol.Optional('detect', default={}): DETECT_SCHEMA,
+        vol.Required('cameras', default={}): CAMERAS_SCHEMA
+    }
+)
+
+class DatabaseConfig():
+    def __init__(self, config):
+        self._path = config['path']
+
+    @property
+    def path(self):
+        return self._path
+    
+    def to_dict(self):
+        return {
+            'path': self.path
+        }
+
+class ModelConfig():
+    def __init__(self, config):
+        self._width = config['width']
+        self._height = config['height']
+    
+    @property
+    def width(self):
+        return self._width
+    
+    @property
+    def height(self):
+        return self._height
+    
+    def to_dict(self):
+        return {
+            'width': self.width,
+            'height': self.height
+        }
+
+class DetectorConfig():
+    def __init__(self, config):
+        self._type = config['type']
+        self._device = config['device']
+        self._num_threads = config['num_threads']
+    
+    @property
+    def type(self):
+        return self._type
+    
+    @property
+    def device(self):
+        return self._device
+    
+    @property
+    def num_threads(self):
+        return self._num_threads
+    
+    def to_dict(self):
+        return {
+            'type': self.type,
+            'device': self.device,
+            'num_threads': self.num_threads
+        }
+
+class LoggerConfig():
+    def __init__(self, config):
+        self._default = config['default'].upper()
+        self._logs = {k: v.upper() for k, v in config['logs'].items()}
+    
+    @property
+    def default(self):
+        return self._default
+    
+    @property
+    def logs(self):
+        return self._logs
+    
+    def to_dict(self):
+        return {
+            'default': self.default,
+            'logs': self.logs
+        }
+
+class MqttConfig():
+    def __init__(self, config):
+        self._host = config['host']
+        self._port = config['port']
+        self._topic_prefix = config['topic_prefix']
+        self._client_id = config['client_id']
+        self._user = config.get('user')
+        self._password = config.get('password')
+    
+    @property
+    def host(self):
+        return self._host
+    
+    @property
+    def port(self):
+        return self._port
+    
+    @property
+    def topic_prefix(self):
+        return self._topic_prefix
+    
+    @property
+    def client_id(self):
+        return self._client_id
+    
+    @property
+    def user(self):
+        return self._user
+    
+    @property
+    def password(self):
+        return self._password
+
+    def to_dict(self):
+        return {
+            'host': self.host,
+            'port': self.port,
+            'topic_prefix': self.topic_prefix,
+            'client_id': self.client_id,
+            'user': self.user
+        }
+
+class CameraInput():
+    def __init__(self, global_config, ffmpeg_input):
+        self._path = ffmpeg_input['path']
+        self._roles = ffmpeg_input['roles']
+        self._global_args = ffmpeg_input.get('global_args', global_config['global_args'])
+        self._hwaccel_args = ffmpeg_input.get('hwaccel_args', global_config['hwaccel_args'])
+        self._input_args = ffmpeg_input.get('input_args', global_config['input_args'])
+    
+    @property
+    def path(self):
+        return self._path
+    
+    @property
+    def roles(self):
+        return self._roles
+    
+    @property
+    def global_args(self):
+        return self._global_args if isinstance(self._global_args, list) else self._global_args.split(' ')
+    
+    @property
+    def hwaccel_args(self):
+        return self._hwaccel_args if isinstance(self._hwaccel_args, list) else self._hwaccel_args.split(' ')
+
+    @property
+    def input_args(self):
+        return self._input_args if isinstance(self._input_args, list) else self._input_args.split(' ')
+
+class CameraFfmpegConfig():
+    def __init__(self, global_config, config):
+        self._inputs = [CameraInput(global_config, i) for i in config['inputs']]
+        self._output_args = config.get('output_args', global_config['output_args'])
+    
+    @property
+    def inputs(self):
+        return self._inputs
+    
+    @property
+    def output_args(self):
+        return {k: v if isinstance(v, list) else v.split(' ') for k, v in self._output_args.items()}
+
+class SaveClipsRetainConfig():
+    def __init__(self, global_config, config):
+        self._default = config.get('default', global_config.get('default'))
+        self._objects = config.get('objects', global_config.get('objects', {}))
+    
+    @property
+    def default(self):
+        return self._default
+    
+    @property
+    def objects(self):
+        return self._objects
+    
+    def to_dict(self):
+        return {
+            'default': self.default,
+            'objects': self.objects
+        }
+
+class SaveClipsConfig():
+    def __init__(self, config):
+        self._max_seconds = config['max_seconds']
+        self._tmpfs_cache_size = config.get('tmpfs_cache_size', '').strip()
+        self._retain = SaveClipsRetainConfig(config['retain'], config['retain'])
+    
+    @property
+    def max_seconds(self):
+        return self._max_seconds
+    
+    @property
+    def tmpfs_cache_size(self):
+        return self._tmpfs_cache_size
+
+    @property
+    def retain(self):
+        return self._retain
+    
+    def to_dict(self):
+        return {
+            'max_seconds': self.max_seconds,
+            'tmpfs_cache_size': self.tmpfs_cache_size,
+            'retain': self.retain.to_dict()
+        }
+
+class RecordConfig():
+    def __init__(self, global_config, config):
+        self._enabled = config.get('enabled', global_config['enabled'])
+        self._retain_days = config.get('retain_days', global_config['retain_days'])
+
+    @property
+    def enabled(self):
+        return self._enabled
+    
+    @property
+    def retain_days(self):
+        return self._retain_days
+    
+    def to_dict(self):
+        return {
+            'enabled': self.enabled,
+            'retain_days': self.retain_days,
+        }
+
+class FilterConfig():
+    def __init__(self, config):
+        self._min_area = config['min_area']
+        self._max_area = config['max_area']
+        self._threshold = config['threshold']
+        self._min_score = config.get('min_score')
+    
+    @property
+    def min_area(self):
+        return self._min_area
+
+    @property
+    def max_area(self):
+        return self._max_area
+
+    @property
+    def threshold(self):
+        return self._threshold
+    
+    @property
+    def min_score(self):
+        return self._min_score
+    
+    def to_dict(self):
+        return {
+            'min_area': self.min_area,
+            'max_area': self.max_area,
+            'threshold': self.threshold,
+            'min_score': self.min_score
+        }
+
+class ObjectConfig():
+    def __init__(self, global_config, config):
+        self._track = config.get('track', global_config['track'])
+        if 'filters' in config:
+            self._filters = { name: FilterConfig(c) for name, c in config['filters'].items() }
+        else:
+            self._filters = { name: FilterConfig(c) for name, c in global_config['filters'].items() }
+    
+    @property
+    def track(self):
+        return self._track
+    
+    @property
+    def filters(self) -> Dict[str, FilterConfig]:
+        return self._filters
+
+    def to_dict(self):
+        return {
+            'track': self.track,
+            'filters': { k: f.to_dict() for k, f in self.filters.items() }
+        }
+
+class CameraSnapshotsConfig():
+    def __init__(self, config):
+        self._show_timestamp = config['show_timestamp']
+        self._draw_zones = config['draw_zones']
+        self._draw_bounding_boxes = config['draw_bounding_boxes']
+        self._crop_to_region = config['crop_to_region']
+        self._height = config.get('height')
+    
+    @property
+    def show_timestamp(self):
+        return self._show_timestamp
+    
+    @property
+    def draw_zones(self):
+        return self._draw_zones
+
+    @property
+    def draw_bounding_boxes(self):
+        return self._draw_bounding_boxes
+
+    @property
+    def crop_to_region(self):
+        return self._crop_to_region
+
+    @property
+    def height(self):
+        return self._height
+    
+    def to_dict(self):
+        return {
+            'show_timestamp': self.show_timestamp,
+            'draw_zones': self.draw_zones,
+            'draw_bounding_boxes': self.draw_bounding_boxes,
+            'crop_to_region': self.crop_to_region,
+            'height': self.height
+        }
+
+class CameraSaveClipsConfig():
+    def __init__(self, global_config, config):
+        self._enabled = config['enabled']
+        self._pre_capture = config['pre_capture']
+        self._post_capture = config['post_capture']
+        self._objects = config.get('objects', global_config['objects']['track'])
+        self._retain = SaveClipsRetainConfig(global_config['save_clips']['retain'], config['retain'])
+    
+    @property
+    def enabled(self):
+        return self._enabled
+    
+    @property
+    def pre_capture(self):
+        return self._pre_capture
+    
+    @property
+    def post_capture(self):
+        return self._post_capture
+
+    @property
+    def objects(self):
+        return self._objects
+    
+    @property
+    def retain(self):
+        return self._retain
+    
+    def to_dict(self):
+        return {
+            'enabled': self.enabled,
+            'pre_capture': self.pre_capture,
+            'post_capture': self.post_capture,
+            'objects': self.objects,
+            'retain': self.retain.to_dict()
+        }
+
+class CameraRtmpConfig():
+    def __init__(self, global_config, config):
+        self._enabled = config['enabled']
+    
+    @property
+    def enabled(self):
+        return self._enabled
+    
+    def to_dict(self):
+        return {
+            'enabled': self.enabled,
+        }
+
+class MotionConfig():
+    def __init__(self, global_config, config, camera_height: int):
+        self._threshold = config.get('threshold', global_config.get('threshold', 25))
+        self._contour_area = config.get('contour_area', global_config.get('contour_area', 100))
+        self._delta_alpha = config.get('delta_alpha', global_config.get('delta_alpha', 0.2))
+        self._frame_alpha = config.get('frame_alpha', global_config.get('frame_alpha', 0.2))
+        self._frame_height = config.get('frame_height', global_config.get('frame_height', camera_height//6))
+    
+    @property
+    def threshold(self):
+        return self._threshold
+
+    @property
+    def contour_area(self):
+        return self._contour_area
+
+    @property
+    def delta_alpha(self):
+        return self._delta_alpha
+
+    @property
+    def frame_alpha(self):
+        return self._frame_alpha
+
+    @property
+    def frame_height(self):
+        return self._frame_height
+    
+    def to_dict(self):
+        return {
+            'threshold': self.threshold,
+            'contour_area': self.contour_area,
+            'delta_alpha': self.delta_alpha,
+            'frame_alpha': self.frame_alpha,
+            'frame_height': self.frame_height,
+        }
+
+
+
+class DetectConfig():
+    def __init__(self, global_config, config, camera_fps):
+        self._max_disappeared = config.get('max_disappeared', global_config.get('max_disappeared', camera_fps*2))
+    
+    @property
+    def max_disappeared(self):
+        return self._max_disappeared
+    
+    def to_dict(self):
+        return {
+            'max_disappeared': self._max_disappeared,
+        }
+
+class ZoneConfig():
+    def __init__(self, name, config):
+        self._coordinates = config['coordinates']
+        self._filters = { name: FilterConfig(c) for name, c in config['filters'].items() }
+
+        if isinstance(self._coordinates, list):
+            self._contour =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in self._coordinates])
+        elif isinstance(self._coordinates, str):
+            points = self._coordinates.split(',')
+            self._contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+        else:
+            print(f"Unable to parse zone coordinates for {name}")
+            self._contour = np.array([])
+        
+        self._color = (0,0,0)
+    
+    @property
+    def coordinates(self):
+        return self._coordinates
+    
+    @property
+    def contour(self):
+        return self._contour
+    
+    @contour.setter
+    def contour(self, val):
+        self._contour = val
+    
+    @property
+    def color(self):
+        return self._color
+    
+    @color.setter
+    def color(self, val):
+        self._color = val
+    
+    @property
+    def filters(self):
+        return self._filters
+    
+    def to_dict(self):
+        return {
+            'filters': {k: f.to_dict() for k, f in self.filters.items()}
+        }
+
+class CameraConfig():
+    def __init__(self, name, config, global_config):
+        self._name = name
+        self._ffmpeg = CameraFfmpegConfig(global_config['ffmpeg'], config['ffmpeg'])
+        self._height = config.get('height')
+        self._width = config.get('width')
+        self._frame_shape = (self._height, self._width)
+        self._frame_shape_yuv = (self._frame_shape[0]*3//2, self._frame_shape[1])
+        self._fps = config.get('fps')
+        self._mask = self._create_mask(config.get('mask'))
+        self._best_image_timeout = config['best_image_timeout']
+        self._zones = { name: ZoneConfig(name, z) for name, z in config['zones'].items() }
+        self._save_clips = CameraSaveClipsConfig(global_config, config['save_clips'])
+        self._record = RecordConfig(global_config['record'], config['record'])
+        self._rtmp = CameraRtmpConfig(global_config, config['rtmp'])
+        self._snapshots = CameraSnapshotsConfig(config['snapshots'])
+        self._objects = ObjectConfig(global_config['objects'], config.get('objects', {}))
+        self._motion = MotionConfig(global_config['motion'], config['motion'], self._height)
+        self._detect = DetectConfig(global_config['detect'], config['detect'], config.get('fps', 5))
+
+        self._ffmpeg_cmds = []
+        for ffmpeg_input in self._ffmpeg.inputs:
+            ffmpeg_cmd = self._get_ffmpeg_cmd(ffmpeg_input)
+            if ffmpeg_cmd is None:
+                continue
+
+            self._ffmpeg_cmds.append({
+                'roles': ffmpeg_input.roles,
+                'cmd': ffmpeg_cmd
+            })
+
+
+        self._set_zone_colors(self._zones)
+
+    def _create_mask(self, mask):
+        mask_img = np.zeros(self.frame_shape, np.uint8)
+        mask_img[:] = 255
+
+        if isinstance(mask, list):
+            for m in mask:
+                self._add_mask(m, mask_img)
+
+        elif isinstance(mask, str):
+            self._add_mask(mask, mask_img)
+        
+        return mask_img
+    
+    def _add_mask(self, mask, mask_img):
+        if mask.startswith('poly,'):
+            points = mask.split(',')[1:]
+            contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+            cv2.fillPoly(mask_img, pts=[contour], color=(0))
+        else:
+            mask_file = cv2.imread(f"/config/{mask}", cv2.IMREAD_GRAYSCALE)
+            if mask_file is None or mask_file.size == 0:
+                logger.warning(f"Could not read mask file {mask}")
+            else:
+                mask_img[np.where(mask_file==[0])] = [0]
+    
+    def _get_ffmpeg_cmd(self, ffmpeg_input):
+        ffmpeg_output_args = []
+        if 'detect' in ffmpeg_input.roles:
+            ffmpeg_output_args = self.ffmpeg.output_args['detect'] + ffmpeg_output_args + ['pipe:']
+            if self.fps:
+                ffmpeg_output_args = ["-r", str(self.fps)] + ffmpeg_output_args
+        if 'rtmp' in ffmpeg_input.roles and self.rtmp.enabled:
+            ffmpeg_output_args = self.ffmpeg.output_args['rtmp'] + [
+                f"rtmp://127.0.0.1/live/{self.name}"
+            ] + ffmpeg_output_args
+        if 'clips' in ffmpeg_input.roles and self.save_clips.enabled:
+            ffmpeg_output_args = self.ffmpeg.output_args['clips'] + [
+                f"{os.path.join(CACHE_DIR, self.name)}-%Y%m%d%H%M%S.mp4"
+            ] + ffmpeg_output_args
+        if 'record' in ffmpeg_input.roles and self.record.enabled:
+            ffmpeg_output_args = self.ffmpeg.output_args['record'] + [
+                f"{os.path.join(RECORD_DIR, self.name)}-%Y%m%d%H%M%S.mp4"
+            ] + ffmpeg_output_args
+        
+        # if there arent any outputs enabled for this input
+        if len(ffmpeg_output_args) == 0:
+            return None
+
+        cmd = (['ffmpeg'] +
+                ffmpeg_input.global_args +
+                ffmpeg_input.hwaccel_args +
+                ffmpeg_input.input_args +
+                ['-i', ffmpeg_input.path] +
+                ffmpeg_output_args)
+        
+        return [part for part in cmd if part != '']
+    
+    def _set_zone_colors(self, zones: Dict[str, ZoneConfig]):
+        # set colors for zones
+        all_zone_names = zones.keys()
+        zone_colors = {}
+        colors = plt.cm.get_cmap('tab10', len(all_zone_names))
+        for i, zone in enumerate(all_zone_names):
+            zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
+        
+        for name, zone in zones.items():
+            zone.color = zone_colors[name]
+    
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def ffmpeg(self):
+        return self._ffmpeg
+    
+    @property
+    def height(self):
+        return self._height
+    
+    @property
+    def width(self):
+        return self._width
+    
+    @property
+    def fps(self):
+        return self._fps
+    
+    @property
+    def mask(self):
+        return self._mask
+    
+    @property
+    def best_image_timeout(self):
+        return self._best_image_timeout
+    
+    @property
+    def zones(self)-> Dict[str, ZoneConfig]:
+        return self._zones
+    
+    @property
+    def save_clips(self):
+        return self._save_clips
+    
+    @property
+    def record(self):
+        return self._record
+    
+    @property
+    def rtmp(self):
+        return self._rtmp
+    
+    @property
+    def snapshots(self):
+        return self._snapshots
+    
+    @property
+    def objects(self):
+        return self._objects
+    
+    @property
+    def motion(self):
+        return self._motion
+    
+    @property
+    def detect(self):
+        return self._detect
+
+    @property
+    def frame_shape(self):
+        return self._frame_shape
+
+    @property
+    def frame_shape_yuv(self):
+        return self._frame_shape_yuv
+
+    @property
+    def ffmpeg_cmds(self):
+        return self._ffmpeg_cmds
+
+    def to_dict(self):
+        return {
+            'name': self.name,
+            'height': self.height,
+            'width': self.width,
+            'fps': self.fps,
+            'best_image_timeout': self.best_image_timeout,
+            'zones': {k: z.to_dict() for k, z in self.zones.items()},
+            'save_clips': self.save_clips.to_dict(),
+            'record': self.record.to_dict(),
+            'rtmp': self.rtmp.to_dict(),
+            'snapshots': self.snapshots.to_dict(),
+            'objects': self.objects.to_dict(),
+            'motion': self.motion.to_dict(),
+            'detect': self.detect.to_dict(),
+            'frame_shape': self.frame_shape,
+            'ffmpeg_cmds': [{'roles': c['roles'], 'cmd': ' '.join(c['cmd'])} for c in self.ffmpeg_cmds],
+        }
+
+
+class FrigateConfig():
+    def __init__(self, config_file=None, config=None):
+        if config is None and config_file is None:
+            raise ValueError('config or config_file must be defined')
+        elif not config_file is None:
+            config = self._load_file(config_file)
+        
+        config = FRIGATE_CONFIG_SCHEMA(config)
+
+        config = self._sub_env_vars(config)
+
+        self._database = DatabaseConfig(config['database'])
+        self._model = ModelConfig(config['model'])
+        self._detectors = { name: DetectorConfig(d) for name, d in config['detectors'].items() }
+        self._mqtt = MqttConfig(config['mqtt'])
+        self._save_clips = SaveClipsConfig(config['save_clips'])
+        self._cameras = { name: CameraConfig(name, c, config) for name, c in config['cameras'].items() }
+        self._logger = LoggerConfig(config['logger'])
+
+    def _sub_env_vars(self, config):
+        frigate_env_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
+
+        if 'password' in config['mqtt']:
+            config['mqtt']['password'] = config['mqtt']['password'].format(**frigate_env_vars) 
+        
+        for camera in config['cameras'].values():
+            for i in camera['ffmpeg']['inputs']:
+                i['path'] = i['path'].format(**frigate_env_vars)
+        
+        return config
+
+    def _load_file(self, config_file):
+        with open(config_file) as f:
+            raw_config = f.read()
+        
+        if config_file.endswith(".yml"):    
+            config = yaml.safe_load(raw_config)
+        elif config_file.endswith(".json"):
+            config = json.loads(raw_config)
+        
+        return config
+    
+    def to_dict(self):
+        return {
+            'database': self.database.to_dict(),
+            'model': self.model.to_dict(),
+            'detectors': {k: d.to_dict() for k, d in self.detectors.items()},
+            'mqtt': self.mqtt.to_dict(),
+            'save_clips': self.save_clips.to_dict(),
+            'cameras': {k: c.to_dict() for k, c in self.cameras.items()},
+            'logger': self.logger.to_dict()
+        }
+    
+    @property
+    def database(self):
+        return self._database
+    
+    @property
+    def model(self):
+        return self._model
+    
+    @property
+    def detectors(self) -> Dict[str, DetectorConfig]:
+        return self._detectors
+    
+    @property
+    def logger(self):
+        return self._logger
+
+    @property
+    def mqtt(self):
+        return self._mqtt
+    
+    @property
+    def save_clips(self):
+        return self._save_clips
+
+    @property
+    def cameras(self) -> Dict[str, CameraConfig]:
+        return self._cameras

frigate/const.py

@@ -0,0 +1,3 @@
+CLIPS_DIR = '/media/frigate/clips'
+RECORD_DIR = '/media/frigate/recordings'
+CACHE_DIR = '/tmp/cache'

frigate/edgetpu.py

@@ -1,15 +1,22 @@
-import os
 import datetime
 import hashlib
+import logging
 import multiprocessing as mp
+import os
 import queue
-from multiprocessing.connection import Connection
+import threading
+import signal
 from abc import ABC, abstractmethod
+from multiprocessing.connection import Connection
 from typing import Dict
+
 import numpy as np
 import tflite_runtime.interpreter as tflite
 from tflite_runtime.interpreter import load_delegate
-from frigate.util import EventsPerSecond, listen, SharedMemoryFrameManager
+
+from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
+
+logger = logging.getLogger(__name__)
 
 def load_labels(path, encoding='utf-8'):
   """Loads labels from file (with or without index numbers).
@@ -36,7 +43,7 @@ class ObjectDetector(ABC):
         pass
 
 class LocalObjectDetector(ObjectDetector):
-    def __init__(self, tf_device=None, labels=None):
+    def __init__(self, tf_device=None, num_threads=3, labels=None):
         self.fps = EventsPerSecond()
         if labels is None:
             self.labels = {}
@@ -51,15 +58,15 @@ class LocalObjectDetector(ObjectDetector):
 
         if tf_device != 'cpu':
             try:
-                print(f"Attempting to load TPU as {device_config['device']}")
+                logger.info(f"Attempting to load TPU as {device_config['device']}")
                 edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
-                print("TPU found")
+                logger.info("TPU found")
             except ValueError:
-                print("No EdgeTPU detected. Falling back to CPU.")
+                logger.info("No EdgeTPU detected. Falling back to CPU.")
         
         if edge_tpu_delegate is None:
             self.interpreter = tflite.Interpreter(
-                model_path='/cpu_model.tflite')
+                model_path='/cpu_model.tflite', num_threads=num_threads)
         else:
             self.interpreter = tflite.Interpreter(
                 model_path='/edgetpu_model.tflite',
@@ -99,11 +106,21 @@ class LocalObjectDetector(ObjectDetector):
         
         return detections
 
-def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, start, tf_device):
-    print(f"Starting detection process: {os.getpid()}")
+def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.Event], avg_speed, start, model_shape, tf_device, num_threads):
+    threading.current_thread().name = f"detector:{name}"
+    logger = logging.getLogger(f"detector.{name}")
+    logger.info(f"Starting detection process: {os.getpid()}")
     listen()
+
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
+    
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
     frame_manager = SharedMemoryFrameManager()
-    object_detector = LocalObjectDetector(tf_device=tf_device)
+    object_detector = LocalObjectDetector(tf_device=tf_device, num_threads=num_threads)
 
     outputs = {}
     for name in out_events.keys():
@@ -115,8 +132,14 @@ def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, st
         }
     
     while True:
-        connection_id = detection_queue.get()
-        input_frame = frame_manager.get(connection_id, (1,300,300,3))
+        if stop_event.is_set():
+            break
+
+        try:
+            connection_id = detection_queue.get(timeout=5)
+        except queue.Empty:
+            continue
+        input_frame = frame_manager.get(connection_id, (1,model_shape[0],model_shape[1],3))
 
         if input_frame is None:
             continue
@@ -132,21 +155,24 @@ def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, st
         avg_speed.value = (avg_speed.value*9 + duration)/10
         
 class EdgeTPUProcess():
-    def __init__(self, detection_queue, out_events, tf_device=None):
+    def __init__(self, name, detection_queue, out_events, model_shape, tf_device=None, num_threads=3):
+        self.name = name
         self.out_events = out_events
         self.detection_queue = detection_queue
         self.avg_inference_speed = mp.Value('d', 0.01)
         self.detection_start = mp.Value('d', 0.0)
         self.detect_process = None
+        self.model_shape = model_shape
         self.tf_device = tf_device
+        self.num_threads = num_threads
         self.start_or_restart()
     
     def stop(self):
         self.detect_process.terminate()
-        print("Waiting for detection process to exit gracefully...")
+        logging.info("Waiting for detection process to exit gracefully...")
         self.detect_process.join(timeout=30)
         if self.detect_process.exitcode is None:
-            print("Detection process didnt exit. Force killing...")
+            logging.info("Detection process didnt exit. Force killing...")
             self.detect_process.kill()
             self.detect_process.join()
 
@@ -154,19 +180,19 @@ class EdgeTPUProcess():
         self.detection_start.value = 0.0
         if (not self.detect_process is None) and self.detect_process.is_alive():
             self.stop()
-        self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.tf_device))
+        self.detect_process = mp.Process(target=run_detector, name=f"detector:{self.name}", args=(self.name, self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.model_shape, self.tf_device, self.num_threads))
         self.detect_process.daemon = True
         self.detect_process.start()
 
 class RemoteObjectDetector():
-    def __init__(self, name, labels, detection_queue, event):
+    def __init__(self, name, labels, detection_queue, event, model_shape):
         self.labels = load_labels(labels)
         self.name = name
         self.fps = EventsPerSecond()
         self.detection_queue = detection_queue
         self.event = event
         self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
-        self.np_shm = np.ndarray((1,300,300,3), dtype=np.uint8, buffer=self.shm.buf)
+        self.np_shm = np.ndarray((1,model_shape[0],model_shape[1],3), dtype=np.uint8, buffer=self.shm.buf)
         self.out_shm = mp.shared_memory.SharedMemory(name=f"out-{self.name}", create=False)
         self.out_np_shm = np.ndarray((20,6), dtype=np.float32, buffer=self.out_shm.buf)
     
@@ -196,4 +222,4 @@ class RemoteObjectDetector():
     
     def cleanup(self):
         self.shm.unlink()
-        self.out_shm.unlink()
+        self.out_shm.unlink()

frigate/events.py

@@ -1,36 +1,49 @@
-import os
-import time
-import psutil
-import threading
-from collections import defaultdict
-import json
 import datetime
-import subprocess as sp
+import json
+import logging
+import os
 import queue
+import subprocess as sp
+import threading
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import psutil
+
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.models import Event
+
+from peewee import fn
+
+logger = logging.getLogger(__name__)
 
 class EventProcessor(threading.Thread):
-    def __init__(self, config, camera_processes, cache_dir, clip_dir, event_queue, stop_event):
+    def __init__(self, config, camera_processes, event_queue, event_processed_queue, stop_event):
         threading.Thread.__init__(self)
+        self.name = 'event_processor'
         self.config = config
         self.camera_processes = camera_processes
-        self.cache_dir = cache_dir
-        self.clip_dir = clip_dir
         self.cached_clips = {}
         self.event_queue = event_queue
+        self.event_processed_queue = event_processed_queue
         self.events_in_process = {}
         self.stop_event = stop_event
     
     def refresh_cache(self):
-        cached_files = os.listdir(self.cache_dir)
+        cached_files = os.listdir(CACHE_DIR)
 
         files_in_use = []
-        for process_data in self.camera_processes.values():
+        for process in psutil.process_iter():
             try:
-                ffmpeg_process = psutil.Process(pid=process_data['ffmpeg_pid'].value)
-                flist = ffmpeg_process.open_files()
+                if process.name() != 'ffmpeg':
+                    continue
+
+                flist = process.open_files()
                 if flist:
                     for nt in flist:
-                        if nt.path.startswith(self.cache_dir):
+                        if nt.path.startswith(CACHE_DIR):
                             files_in_use.append(nt.path.split('/')[-1])
             except:
                 continue
@@ -50,7 +63,7 @@ class EventProcessor(threading.Thread):
                 'format=duration',
                 '-of',
                 'default=noprint_wrappers=1:nokey=1',
-                f"{os.path.join(self.cache_dir,f)}"
+                f"{os.path.join(CACHE_DIR,f)}"
             ])
             p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
             (output, err) = p.communicate()
@@ -58,8 +71,8 @@ class EventProcessor(threading.Thread):
             if p_status == 0:
                 duration = float(output.decode('utf-8').strip())
             else:
-                print(f"bad file: {f}")
-                os.remove(os.path.join(self.cache_dir,f))
+                logger.info(f"bad file: {f}")
+                os.remove(os.path.join(CACHE_DIR,f))
                 continue
 
             self.cached_clips[f] = {
@@ -75,27 +88,27 @@ class EventProcessor(threading.Thread):
             earliest_event = datetime.datetime.now().timestamp()
 
         # if the earliest event exceeds the max seconds, cap it
-        max_seconds = self.config.get('save_clips', {}).get('max_seconds', 300)
+        max_seconds = self.config.save_clips.max_seconds
         if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
             earliest_event = datetime.datetime.now().timestamp()-max_seconds
         
         for f, data in list(self.cached_clips.items()):
             if earliest_event-90 > data['start_time']+data['duration']:
                 del self.cached_clips[f]
-                os.remove(os.path.join(self.cache_dir,f))
+                os.remove(os.path.join(CACHE_DIR,f))
 
-    def create_clip(self, camera, event_data, pre_capture):
+    def create_clip(self, camera, event_data, pre_capture, post_capture):
         # get all clips from the camera with the event sorted
         sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
 
-        while sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']:
+        while len(sorted_clips) == 0 or sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']+post_capture:
             time.sleep(5)
             self.refresh_cache()
             # get all clips from the camera with the event sorted
             sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
         
         playlist_start = event_data['start_time']-pre_capture
-        playlist_end = event_data['end_time']+5
+        playlist_end = event_data['end_time']+post_capture
         playlist_lines = []
         for clip in sorted_clips:
             # clip ends before playlist start time, skip
@@ -104,7 +117,7 @@ class EventProcessor(threading.Thread):
             # clip starts after playlist ends, finish
             if clip['start_time'] > playlist_end:
                 break
-            playlist_lines.append(f"file '{os.path.join(self.cache_dir,clip['path'])}'")
+            playlist_lines.append(f"file '{os.path.join(CACHE_DIR,clip['path'])}'")
             # if this is the starting clip, add an inpoint
             if clip['start_time'] < playlist_start:
                 playlist_lines.append(f"inpoint {int(playlist_start-clip['start_time'])}")
@@ -126,21 +139,20 @@ class EventProcessor(threading.Thread):
             '-',
             '-c',
             'copy',
-            f"{os.path.join(self.clip_dir, clip_name)}.mp4"
+            '-movflags',
+            '+faststart',
+            f"{os.path.join(CLIPS_DIR, clip_name)}.mp4"
         ]
 
         p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
         if p.returncode != 0:
-            print(p.stderr)
+            logger.error(p.stderr)
             return
-        
-        with open(f"{os.path.join(self.clip_dir, clip_name)}.json", 'w') as outfile:
-            json.dump(event_data, outfile)
 
     def run(self):
         while True:
             if self.stop_event.is_set():
-                print(f"Exiting event processor...")
+                logger.info(f"Exiting event processor...")
                 break
 
             try:
@@ -152,23 +164,126 @@ class EventProcessor(threading.Thread):
 
             self.refresh_cache()
 
-            save_clips_config = self.config['cameras'][camera].get('save_clips', {})
+            save_clips_config = self.config.cameras[camera].save_clips
 
             # if save clips is not enabled for this camera, just continue
-            if not save_clips_config.get('enabled', False):
+            if not save_clips_config.enabled:
+                if event_type == 'end':
+                    self.event_processed_queue.put((event_data['id'], camera))
                 continue
 
             # if specific objects are listed for this camera, only save clips for them
-            if 'objects' in save_clips_config:
-                if not event_data['label'] in save_clips_config['objects']:
-                    continue
+            if not event_data['label'] in save_clips_config.objects:
+                if event_type == 'end':
+                    self.event_processed_queue.put((event_data['id'], camera))
+                continue
 
             if event_type == 'start':
                 self.events_in_process[event_data['id']] = event_data
 
             if event_type == 'end':
                 if len(self.cached_clips) > 0 and not event_data['false_positive']:
-                    self.create_clip(camera, event_data, save_clips_config.get('pre_capture', 30))
+                    self.create_clip(camera, event_data, save_clips_config.pre_capture, save_clips_config.post_capture)
+                    Event.create(
+                        id=event_data['id'],
+                        label=event_data['label'],
+                        camera=camera,
+                        start_time=event_data['start_time'],
+                        end_time=event_data['end_time'],
+                        top_score=event_data['top_score'],
+                        false_positive=event_data['false_positive'],
+                        zones=list(event_data['entered_zones']),
+                        thumbnail=event_data['thumbnail']
+                    )
                 del self.events_in_process[event_data['id']]
+                self.event_processed_queue.put((event_data['id'], camera))
 
-                
+class EventCleanup(threading.Thread):
+    def __init__(self, config: FrigateConfig, stop_event):
+        threading.Thread.__init__(self)
+        self.name = 'event_cleanup'
+        self.config = config
+        self.stop_event = stop_event
+
+    def run(self):
+        counter = 0
+        while(True):
+            if self.stop_event.is_set():
+                logger.info(f"Exiting event cleanup...")
+                break
+
+            # only expire events every 10 minutes, but check for stop events every 10 seconds
+            time.sleep(10)
+            counter = counter + 1
+            if counter < 60:
+                continue
+            counter = 0
+
+            camera_keys = list(self.config.cameras.keys())
+
+            # Expire events from unlisted cameras based on the global config
+            retain_config = self.config.save_clips.retain
+            
+            distinct_labels = (Event.select(Event.label)
+                        .where(Event.camera.not_in(camera_keys))
+                        .distinct())
+            
+            # loop over object types in db
+            for l in distinct_labels:
+                # get expiration time for this label
+                expire_days = retain_config.objects.get(l.label, retain_config.default)
+                expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+                # grab all events after specific time
+                expired_events = (
+                    Event.select()
+                        .where(Event.camera.not_in(camera_keys), 
+                            Event.start_time < expire_after, 
+                            Event.label == l.label)
+                )
+                # delete the grabbed clips from disk
+                for event in expired_events:
+                    clip_name = f"{event.camera}-{event.id}"
+                    clip = Path(f"{os.path.join(CLIPS_DIR, clip_name)}.mp4")
+                    clip.unlink(missing_ok=True)
+                # delete the event for this type from the db
+                delete_query = (
+                    Event.delete()
+                        .where(Event.camera.not_in(camera_keys), 
+                            Event.start_time < expire_after, 
+                            Event.label == l.label)
+                )
+                delete_query.execute()
+
+            # Expire events from cameras based on the camera config
+            for name, camera in self.config.cameras.items():
+                retain_config = camera.save_clips.retain
+                # get distinct objects in database for this camera
+                distinct_labels = (Event.select(Event.label)
+                        .where(Event.camera == name)
+                        .distinct())
+
+                # loop over object types in db
+                for l in distinct_labels:
+                    # get expiration time for this label
+                    expire_days = retain_config.objects.get(l.label, retain_config.default)
+                    expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+                    # grab all events after specific time
+                    expired_events = (
+                        Event.select()
+                            .where(Event.camera == name, 
+                                Event.start_time < expire_after, 
+                                Event.label == l.label)
+                    )
+                    # delete the grabbed clips from disk
+                    for event in expired_events:
+                        clip_name = f"{event.camera}-{event.id}"
+                        clip = Path(f"{os.path.join(CLIPS_DIR, clip_name)}.mp4")
+                        clip.unlink(missing_ok=True)
+                    # delete the event for this type from the db
+                    delete_query = (
+                        Event.delete()
+                            .where( Event.camera == name, 
+                                Event.start_time < expire_after, 
+                                Event.label == l.label)
+                    )
+                    delete_query.execute()

frigate/http.py

@@ -0,0 +1,269 @@
+import base64
+import datetime
+import logging
+import os
+import time
+from functools import reduce
+
+import cv2
+import numpy as np
+from flask import (Blueprint, Flask, Response, current_app, jsonify,
+                   make_response, request)
+from peewee import SqliteDatabase, operator, fn, DoesNotExist
+from playhouse.shortcuts import model_to_dict
+
+from frigate.models import Event
+from frigate.util import calculate_region
+from frigate.version import VERSION
+
+logger = logging.getLogger(__name__)
+
+bp = Blueprint('frigate', __name__)
+
+def create_app(frigate_config, database: SqliteDatabase, camera_metrics, detectors, detected_frames_processor):
+    app = Flask(__name__)
+
+    @app.before_request
+    def _db_connect():
+        database.connect()
+
+    @app.teardown_request
+    def _db_close(exc):
+        if not database.is_closed():
+            database.close()
+
+    app.frigate_config = frigate_config
+    app.camera_metrics = camera_metrics
+    app.detectors = detectors
+    app.detected_frames_processor = detected_frames_processor
+    
+    app.register_blueprint(bp)
+
+    return app
+
+@bp.route('/')
+def is_healthy():
+    return "Frigate is running. Alive and healthy!"
+
+@bp.route('/events/summary')
+def events_summary():
+    groups = (
+        Event
+            .select(
+                Event.camera, 
+                Event.label, 
+                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')).alias('day'), 
+                Event.zones,
+                fn.COUNT(Event.id).alias('count')
+            )
+            .group_by(
+                Event.camera, 
+                Event.label, 
+                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')),
+                Event.zones
+            )
+        )
+
+    return jsonify([e for e in groups.dicts()])
+
+@bp.route('/events/<id>')
+def event(id):
+    try:
+        return model_to_dict(Event.get(Event.id == id))
+    except DoesNotExist:
+        return "Event not found", 404
+
+@bp.route('/events/<id>/snapshot.jpg')
+def event_snapshot(id):
+    format = request.args.get('format', 'ios')
+    thumbnail_bytes = None
+    try:
+        event = Event.get(Event.id == id)
+        thumbnail_bytes = base64.b64decode(event.thumbnail)
+    except DoesNotExist:
+        # see if the object is currently being tracked
+        try:
+            for camera_state in current_app.detected_frames_processor.camera_states.values():
+                if id in camera_state.tracked_objects:
+                    tracked_obj = camera_state.tracked_objects.get(id)
+                    if not tracked_obj is None:
+                        thumbnail_bytes = tracked_obj.get_jpg_bytes()
+        except:
+            return "Event not found", 404
+    
+    if thumbnail_bytes is None:
+        return "Event not found", 404
+    
+    # android notifications prefer a 2:1 ratio
+    if format == 'android':
+        jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
+        img = cv2.imdecode(jpg_as_np, flags=1)
+        thumbnail = cv2.copyMakeBorder(img, 0, 0, int(img.shape[1]*0.5), int(img.shape[1]*0.5), cv2.BORDER_CONSTANT, (0,0,0))
+        ret, jpg = cv2.imencode('.jpg', thumbnail)    
+        thumbnail_bytes = jpg.tobytes()
+    
+    response = make_response(thumbnail_bytes)
+    response.headers['Content-Type'] = 'image/jpg'
+    return response
+
+@bp.route('/events')
+def events():
+    limit = request.args.get('limit', 100)
+    camera = request.args.get('camera')
+    label = request.args.get('label')
+    zone = request.args.get('zone')
+    after = request.args.get('after', type=int)
+    before = request.args.get('before', type=int)
+
+    clauses = []
+    
+    if camera:
+        clauses.append((Event.camera == camera))
+    
+    if label:
+        clauses.append((Event.label == label))
+    
+    if zone:
+        clauses.append((Event.zones.cast('text') % f"*\"{zone}\"*"))
+    
+    if after:
+        clauses.append((Event.start_time >= after))
+    
+    if before:
+        clauses.append((Event.start_time <= before))
+
+    if len(clauses) == 0:
+        clauses.append((1 == 1))
+
+    events =    (Event.select()
+                .where(reduce(operator.and_, clauses))
+                .order_by(Event.start_time.desc())
+                .limit(limit))
+
+    return jsonify([model_to_dict(e) for e in events])
+
+@bp.route('/config')
+def config():
+    return jsonify(current_app.frigate_config.to_dict())
+
+@bp.route('/version')
+def version():
+    return VERSION
+
+@bp.route('/stats')
+def stats():
+    camera_metrics = current_app.camera_metrics
+    stats = {}
+
+    total_detection_fps = 0
+
+    for name, camera_stats in camera_metrics.items():
+        total_detection_fps += camera_stats['detection_fps'].value
+        stats[name] = {
+            'camera_fps': round(camera_stats['camera_fps'].value, 2),
+            'process_fps': round(camera_stats['process_fps'].value, 2),
+            'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
+            'detection_fps': round(camera_stats['detection_fps'].value, 2),
+            'pid': camera_stats['process'].pid,
+            'capture_pid': camera_stats['capture_process'].pid
+        }
+    
+    stats['detectors'] = {}
+    for name, detector in current_app.detectors.items():
+        stats['detectors'][name] = {
+            'inference_speed': round(detector.avg_inference_speed.value*1000, 2),
+            'detection_start': detector.detection_start.value,
+            'pid': detector.detect_process.pid
+        }
+    stats['detection_fps'] = round(total_detection_fps, 2)
+
+    return jsonify(stats)
+
+@bp.route('/<camera_name>/<label>/best.jpg')
+def best(camera_name, label):
+    if camera_name in current_app.frigate_config.cameras:
+        best_object = current_app.detected_frames_processor.get_best(camera_name, label)
+        best_frame = best_object.get('frame')
+        if best_frame is None:
+            best_frame = np.zeros((720,1280,3), np.uint8)
+        else:
+            best_frame = cv2.cvtColor(best_frame, cv2.COLOR_YUV2BGR_I420)
+        
+        crop = bool(request.args.get('crop', 0, type=int))
+        if crop:
+            box = best_object.get('box', (0,0,300,300))
+            region = calculate_region(best_frame.shape, box[0], box[1], box[2], box[3], 1.1)
+            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+        
+        height = int(request.args.get('h', str(best_frame.shape[0])))
+        width = int(height*best_frame.shape[1]/best_frame.shape[0])
+
+        best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+        ret, jpg = cv2.imencode('.jpg', best_frame)
+        response = make_response(jpg.tobytes())
+        response.headers['Content-Type'] = 'image/jpg'
+        return response
+    else:
+        return "Camera named {} not found".format(camera_name), 404
+
+@bp.route('/<camera_name>')
+def mjpeg_feed(camera_name):
+    fps = int(request.args.get('fps', '3'))
+    height = int(request.args.get('h', '360'))
+    draw_options = {
+        'bounding_boxes': request.args.get('bbox', type=int),
+        'timestamp': request.args.get('timestamp', type=int),
+        'zones': request.args.get('zones', type=int),
+        'mask': request.args.get('mask', type=int),
+        'motion_boxes': request.args.get('motion', type=int),
+        'regions': request.args.get('regions', type=int),
+    }
+    if camera_name in current_app.frigate_config.cameras:
+        # return a multipart response
+        return Response(imagestream(current_app.detected_frames_processor, camera_name, fps, height, draw_options),
+                        mimetype='multipart/x-mixed-replace; boundary=frame')
+    else:
+        return "Camera named {} not found".format(camera_name), 404
+
+@bp.route('/<camera_name>/latest.jpg')
+def latest_frame(camera_name):
+    draw_options = {
+        'bounding_boxes': request.args.get('bbox', type=int),
+        'timestamp': request.args.get('timestamp', type=int),
+        'zones': request.args.get('zones', type=int),
+        'mask': request.args.get('mask', type=int),
+        'motion_boxes': request.args.get('motion', type=int),
+        'regions': request.args.get('regions', type=int),
+    }
+    if camera_name in current_app.frigate_config.cameras:
+        # max out at specified FPS
+        frame = current_app.detected_frames_processor.get_current_frame(camera_name, draw_options)
+        if frame is None:
+            frame = np.zeros((720,1280,3), np.uint8)
+
+        height = int(request.args.get('h', str(frame.shape[0])))
+        width = int(height*frame.shape[1]/frame.shape[0])
+
+        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+
+        ret, jpg = cv2.imencode('.jpg', frame)
+        response = make_response(jpg.tobytes())
+        response.headers['Content-Type'] = 'image/jpg'
+        return response
+    else:
+        return "Camera named {} not found".format(camera_name), 404
+        
+def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
+    while True:
+        # max out at specified FPS
+        time.sleep(1/fps)
+        frame = detected_frames_processor.get_current_frame(camera_name, draw_options)
+        if frame is None:
+            frame = np.zeros((height,int(height*16/9),3), np.uint8)
+
+        width = int(height*frame.shape[1]/frame.shape[0])
+        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
+
+        ret, jpg = cv2.imencode('.jpg', frame)
+        yield (b'--frame\r\n'
+            b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')

frigate/log.py

@@ -0,0 +1,75 @@
+# adapted from https://medium.com/@jonathonbao/python3-logging-with-multiprocessing-f51f460b8778
+import logging
+import threading
+import os
+import signal
+import queue
+import multiprocessing as mp
+from logging import handlers
+
+
+def listener_configurer():
+    root = logging.getLogger()
+    console_handler = logging.StreamHandler()
+    formatter = logging.Formatter('%(name)-30s %(levelname)-8s: %(message)s')
+    console_handler.setFormatter(formatter)
+    root.addHandler(console_handler)
+    root.setLevel(logging.INFO)
+
+def root_configurer(queue):
+    h = handlers.QueueHandler(queue)
+    root = logging.getLogger()
+    root.addHandler(h)
+    root.setLevel(logging.INFO)
+
+def log_process(log_queue):
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
+    
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
+    threading.current_thread().name = f"logger"
+    listener_configurer()
+    while True:
+        if stop_event.is_set() and log_queue.empty():
+            break
+        try:
+            record = log_queue.get(timeout=5)
+        except queue.Empty:
+            continue
+        logger = logging.getLogger(record.name)
+        logger.handle(record)
+
+# based on https://codereview.stackexchange.com/a/17959
+class LogPipe(threading.Thread):
+    def __init__(self, log_name, level):
+        """Setup the object with a logger and a loglevel
+        and start the thread
+        """
+        threading.Thread.__init__(self)
+        self.daemon = False
+        self.logger = logging.getLogger(log_name)
+        self.level = level
+        self.fdRead, self.fdWrite = os.pipe()
+        self.pipeReader = os.fdopen(self.fdRead)
+        self.start()
+
+    def fileno(self):
+        """Return the write file descriptor of the pipe
+        """
+        return self.fdWrite
+
+    def run(self):
+        """Run the thread, logging everything.
+        """
+        for line in iter(self.pipeReader.readline, ''):
+            self.logger.log(self.level, line.strip('\n'))
+
+        self.pipeReader.close()
+
+    def close(self):
+        """Close the write end of the pipe.
+        """
+        os.close(self.fdWrite)

frigate/models.py

@@ -0,0 +1,14 @@
+from peewee import *
+from playhouse.sqlite_ext import *
+
+
+class Event(Model):
+    id = CharField(null=False, primary_key=True, max_length=30)
+    label = CharField(index=True, max_length=20)
+    camera = CharField(index=True, max_length=20)
+    start_time = DateTimeField()
+    end_time = DateTimeField()
+    top_score = FloatField()
+    false_positive = BooleanField()
+    zones = JSONField()
+    thumbnail = TextField()

frigate/motion.py

@@ -1,12 +1,15 @@
 import cv2
 import imutils
 import numpy as np
+from frigate.config import MotionConfig
+
 
 class MotionDetector():
-    def __init__(self, frame_shape, mask, resize_factor=4):
+    def __init__(self, frame_shape, mask, config: MotionConfig):
+        self.config = config
         self.frame_shape = frame_shape
-        self.resize_factor = resize_factor
-        self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
+        self.resize_factor = frame_shape[0]/config.frame_height
+        self.motion_frame_size = (config.frame_height, config.frame_height*frame_shape[1]//frame_shape[0])
         self.avg_frame = np.zeros(self.motion_frame_size, np.float)
         self.avg_delta = np.zeros(self.motion_frame_size, np.float)
         self.motion_frame_count = 0
@@ -22,6 +25,8 @@ class MotionDetector():
         # resize frame
         resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
 
+        # TODO: can I improve the contrast of the grayscale image here?
+
         # convert to grayscale
         # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
 
@@ -37,22 +42,21 @@ class MotionDetector():
             frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
 
             # compute the average delta over the past few frames
-            # the alpha value can be modified to configure how sensitive the motion detection is.
             # 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
-            # this also assumes that a person is in the same location across more than a single frame
-            cv2.accumulateWeighted(frameDelta, self.avg_delta, 0.2)
+            cv2.accumulateWeighted(frameDelta, self.avg_delta, self.config.delta_alpha)
 
             # compute the threshold image for the current frame
-            current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
+            # TODO: threshold
+            current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
 
             # black out everything in the avg_delta where there isnt motion in the current frame
             avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
-            avg_delta_image[np.where(current_thresh==[0])] = [0]
+            avg_delta_image = cv2.bitwise_and(avg_delta_image, current_thresh)
 
             # then look for deltas above the threshold, but only in areas where there is a delta
             # in the current frame. this prevents deltas from previous frames from being included
-            thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
+            thresh = cv2.threshold(avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
 
             # dilate the thresholded image to fill in holes, then find contours
             # on thresholded image
@@ -64,19 +68,18 @@ class MotionDetector():
             for c in cnts:
                 # if the contour is big enough, count it as motion
                 contour_area = cv2.contourArea(c)
-                if contour_area > 100:
+                if contour_area > self.config.contour_area:
                     x, y, w, h = cv2.boundingRect(c)
-                    motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
+                    motion_boxes.append((int(x*self.resize_factor), int(y*self.resize_factor), int((x+w)*self.resize_factor), int((y+h)*self.resize_factor)))
         
         if len(motion_boxes) > 0:
             self.motion_frame_count += 1
-            # TODO: this really depends on FPS
             if self.motion_frame_count >= 10:
-                # only average in the current frame if the difference persists for at least 3 frames
-                cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
+                # only average in the current frame if the difference persists for a bit
+                cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
         else:
             # when no motion, just keep averaging the frames together
-            cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
+            cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
             self.motion_frame_count = 0
 
-        return motion_boxes
+        return motion_boxes

frigate/mqtt.py

@@ -0,0 +1,36 @@
+import logging
+import threading
+
+import paho.mqtt.client as mqtt
+
+from frigate.config import MqttConfig
+
+logger = logging.getLogger(__name__)
+
+def create_mqtt_client(config: MqttConfig):
+    client = mqtt.Client(client_id=config.client_id)
+    def on_connect(client, userdata, flags, rc):
+        threading.current_thread().name = "mqtt"
+        if rc != 0:
+            if rc == 3:
+                logger.error("MQTT Server unavailable")
+            elif rc == 4:
+                logger.error("MQTT Bad username or password")
+            elif rc == 5:
+                logger.error("MQTT Not authorized")
+            else:
+                logger.error("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
+            
+        logger.info("MQTT connected")
+        client.publish(config.topic_prefix+'/available', 'online', retain=True)       
+    client.on_connect = on_connect
+    client.will_set(config.topic_prefix+'/available', payload='offline', qos=1, retain=True)
+    if not config.user is None:
+        client.username_pw_set(config.user, password=config.password)
+    try:
+        client.connect(config.host, config.port, 60)
+    except Exception as e:
+        logger.error(f"Unable to connect to MQTT server: {e}")
+        raise
+    client.loop_start()
+    return client

frigate/object_processing.py

@@ -1,20 +1,28 @@
-import json
-import hashlib
+import copy
+import base64
 import datetime
-import time
-import copy
-import cv2
-import threading
-import queue
-import copy
-import numpy as np
-from collections import Counter, defaultdict
+import hashlib
 import itertools
-import matplotlib.pyplot as plt
-from frigate.util import draw_box_with_label, SharedMemoryFrameManager
-from frigate.edgetpu import load_labels
-from typing import Callable, Dict
+import json
+import logging
+import os
+import queue
+import threading
+import time
+from collections import Counter, defaultdict
 from statistics import mean, median
+from typing import Callable, Dict
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+from frigate.config import FrigateConfig, CameraConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.edgetpu import load_labels
+from frigate.util import SharedMemoryFrameManager, draw_box_with_label, calculate_region
+
+logger = logging.getLogger(__name__)
 
 PATH_TO_LABELS = '/labelmap.txt'
 
@@ -25,24 +33,207 @@ COLOR_MAP = {}
 for key, val in LABELS.items():
     COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
 
-def zone_filtered(obj, object_config):
-    object_name = obj['label']
+def on_edge(box, frame_shape):
+    if (
+        box[0] == 0 or
+        box[1] == 0 or
+        box[2] == frame_shape[1]-1 or
+        box[3] == frame_shape[0]-1
+    ):
+        return True
+
+def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
+    # larger is better
+    # cutoff images are less ideal, but they should also be smaller?
+    # better scores are obviously better too
+
+    # if the new_thumb is on an edge, and the current thumb is not
+    if on_edge(new_obj['box'], frame_shape) and not on_edge(current_thumb['box'], frame_shape):
+        return False
+
+    # if the score is better by more than 5%
+    if new_obj['score'] > current_thumb['score']+.05:
+        return True
+    
+    # if the area is 10% larger
+    if new_obj['area'] > current_thumb['area']*1.1:
+        return True
+    
+    return False
+
+class TrackedObject():
+    def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
+        self.obj_data = obj_data
+        self.camera = camera
+        self.camera_config = camera_config
+        self.frame_cache = frame_cache
+        self.current_zones = []
+        self.entered_zones = set()
+        self.false_positive = True
+        self.top_score = self.computed_score = 0.0
+        self.thumbnail_data = None
+        self.frame = None
+        self.previous = self.to_dict()
+        self._snapshot_jpg_time = 0
+        ret, jpg = cv2.imencode('.jpg', np.zeros((300,300,3), np.uint8))
+        self._snapshot_jpg = jpg.tobytes()
+
+        # start the score history
+        self.score_history = [self.obj_data['score']]
+
+    def _is_false_positive(self):
+        # once a true positive, always a true positive
+        if not self.false_positive:
+            return False
+
+        threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
+        if self.computed_score < threshold:
+            return True
+        return False
+
+    def compute_score(self):
+        scores = self.score_history[:]
+        # pad with zeros if you dont have at least 3 scores
+        if len(scores) < 3:
+            scores += [0.0]*(3 - len(scores))
+        return median(scores)
+    
+    def update(self, current_frame_time, obj_data):
+        significant_update = 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:
+            self.score_history.append(0.0)
+        else:
+            self.score_history.append(self.obj_data['score'])
+        # only keep the last 10 scores
+        if len(self.score_history) > 10:
+            self.score_history = self.score_history[-10:]
+
+        # calculate if this is a false positive
+        self.computed_score = self.compute_score()
+        if self.computed_score > self.top_score:
+            self.top_score = self.computed_score
+        self.false_positive = self._is_false_positive()
+
+        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 = {
+                    'frame_time': self.obj_data['frame_time'],
+                    'box': self.obj_data['box'],
+                    'area': self.obj_data['area'],
+                    'region': self.obj_data['region'],
+                    'score': self.obj_data['score']
+                }
+                significant_update = True
+        
+        # check zones
+        current_zones = []
+        bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
+        # check each zone
+        for name, zone in self.camera_config.zones.items():
+            contour = zone.contour
+            # check if the object is in the zone
+            if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
+                # 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 the zones changed, signal an update
+        if not self.false_positive and set(self.current_zones) != set(current_zones):
+            significant_update = True
+
+        self.current_zones = current_zones
+        return significant_update
+    
+    def to_dict(self, include_thumbnail: bool = False):
+        return {
+            'id': self.obj_data['id'],
+            'camera': self.camera,
+            'frame_time': self.obj_data['frame_time'],
+            'label': self.obj_data['label'],
+            'top_score': self.top_score,
+            'false_positive': self.false_positive,
+            'start_time': self.obj_data['start_time'],
+            'end_time': self.obj_data.get('end_time', None),
+            'score': self.obj_data['score'],
+            'box': self.obj_data['box'],
+            'area': self.obj_data['area'],
+            'region': self.obj_data['region'],
+            'current_zones': self.current_zones.copy(),
+            'entered_zones': list(self.entered_zones).copy(),
+            'thumbnail': base64.b64encode(self.get_jpg_bytes()).decode('utf-8') if include_thumbnail else None
+        }
+    
+    def get_jpg_bytes(self):
+        if self.thumbnail_data is None or self._snapshot_jpg_time == self.thumbnail_data['frame_time']:
+            return self._snapshot_jpg
+        
+        if not self.thumbnail_data['frame_time'] in self.frame_cache:
+            logger.error(f"Unable to create thumbnail for {self.obj_data['id']}")
+            logger.error(f"Looking for frame_time of {self.thumbnail_data['frame_time']}")
+            logger.error(f"Thumbnail frames: {','.join([str(k) for k in self.frame_cache.keys()])}")
+            return self._snapshot_jpg
+
+        # TODO: crop first to avoid converting the entire frame?
+        snapshot_config = self.camera_config.snapshots
+        best_frame = cv2.cvtColor(self.frame_cache[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
+
+        if snapshot_config.draw_bounding_boxes:
+            thickness = 2
+            color = COLOR_MAP[self.obj_data['label']]
+            box = self.thumbnail_data['box']
+            draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], self.obj_data['label'], 
+                f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color)
+            
+        if snapshot_config.crop_to_region:
+            box = self.thumbnail_data['box']
+            region = calculate_region(best_frame.shape, box[0], box[1], box[2], box[3], 1.1)
+            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+
+        if snapshot_config.height: 
+            height = snapshot_config.height
+            width = int(height*best_frame.shape[1]/best_frame.shape[0])
+            best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+        
+        if snapshot_config.show_timestamp:
+            time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
+            size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
+            text_width = size[0][0]
+            desired_size = max(150, 0.33*best_frame.shape[1])
+            font_scale = desired_size/text_width
+            cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX, 
+                fontScale=font_scale, color=(255, 255, 255), thickness=2)
+
+        ret, jpg = cv2.imencode('.jpg', best_frame)
+        if ret:
+            self._snapshot_jpg = jpg.tobytes()
+        
+        return self._snapshot_jpg
+
+def zone_filtered(obj: TrackedObject, object_config):
+    object_name = obj.obj_data['label']
 
     if object_name in object_config:
         obj_settings = object_config[object_name]
 
         # if the min area is larger than the
         # detected object, don't add it to detected objects
-        if obj_settings.get('min_area',-1) > obj['area']:
+        if obj_settings.min_area > obj.obj_data['area']:
             return True
         
         # if the detected object is larger than the
         # max area, don't add it to detected objects
-        if obj_settings.get('max_area', 24000000) < obj['area']:
+        if obj_settings.max_area < obj.obj_data['area']:
             return True
 
         # if the score is lower than the threshold, skip
-        if obj_settings.get('threshold', 0) > obj['computed_score']:
+        if obj_settings.threshold > obj.computed_score:
             return True
         
     return False
@@ -52,27 +243,30 @@ class CameraState():
     def __init__(self, name, config, frame_manager):
         self.name = name
         self.config = config
+        self.camera_config = config.cameras[name]
         self.frame_manager = frame_manager
-
-        self.best_objects = {}
-        self.object_status = defaultdict(lambda: 'OFF')
-        self.tracked_objects = {}
+        self.best_objects: Dict[str, TrackedObject] = {}
+        self.object_counts = defaultdict(lambda: 0)
+        self.tracked_objects: Dict[str, TrackedObject] = {}
+        self.frame_cache = {}
         self.zone_objects = defaultdict(lambda: [])
-        self._current_frame = np.zeros((self.config['frame_shape'][0]*3//2, self.config['frame_shape'][1]), np.uint8)
+        self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
         self.current_frame_lock = threading.Lock()
         self.current_frame_time = 0.0
         self.previous_frame_id = None
         self.callbacks = defaultdict(lambda: [])
 
-    def get_current_frame(self, draw=False):
+    def get_current_frame(self, draw_options={}):
         with self.current_frame_lock:
             frame_copy = np.copy(self._current_frame)
             frame_time = self.current_frame_time
-            tracked_objects = copy.deepcopy(self.tracked_objects)
+            tracked_objects = {k: v.to_dict() for k,v in self.tracked_objects.items()}
+            motion_boxes = self.motion_boxes.copy()
+            regions = self.regions.copy()
         
         frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
         # draw on the frame
-        if draw:
+        if draw_options.get('bounding_boxes'):
             # draw the bounding boxes on the frame
             for obj in tracked_objects.values():
                 thickness = 2
@@ -85,155 +279,128 @@ class CameraState():
                 # draw the bounding boxes on the frame
                 box = obj['box']
                 draw_box_with_label(frame_copy, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
-                # draw the regions on the frame
-                region = obj['region']
-                cv2.rectangle(frame_copy, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
-            
-            if self.config['snapshots']['show_timestamp']:
-                time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-                cv2.putText(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
-
-            if self.config['snapshots']['draw_zones']:
-                for name, zone in self.config['zones'].items():
-                    thickness = 8 if any([name in obj['zones'] for obj in tracked_objects.values()]) else 2
-                    cv2.drawContours(frame_copy, [zone['contour']], -1, zone['color'], thickness)
         
+        if draw_options.get('regions'):
+            for region in regions:
+                cv2.rectangle(frame_copy, (region[0], region[1]), (region[2], region[3]), (0,255,0), 2)
+        
+        if draw_options.get('timestamp'):
+            time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
+            cv2.putText(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
+
+        if draw_options.get('zones'):
+            for name, zone in self.camera_config.zones.items():
+                thickness = 8 if any([name in obj['current_zones'] for obj in tracked_objects.values()]) else 2
+                cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
+        
+        if draw_options.get('mask'):
+            mask_overlay = np.where(self.camera_config.mask==[0])
+            frame_copy[mask_overlay] = [0,0,0]
+        
+        if draw_options.get('motion_boxes'):
+            for m_box in motion_boxes:
+                cv2.rectangle(frame_copy, (m_box[0], m_box[1]), (m_box[2], m_box[3]), (0,0,255), 2)
+
         return frame_copy
 
-    def false_positive(self, obj):
-        # once a true positive, always a true positive
-        if not obj.get('false_positive', True):
-            return False
-
-        threshold = self.config['objects'].get('filters', {}).get(obj['label'], {}).get('threshold', 0.85)
-        if obj['computed_score'] < threshold:
-            return True
-        return False
-
-    def compute_score(self, obj):
-        scores = obj['score_history'][:]
-        # pad with zeros if you dont have at least 3 scores
-        if len(scores) < 3:
-            scores += [0.0]*(3 - len(scores))
-        return median(scores)
+    def finished(self, obj_id):
+        del self.tracked_objects[obj_id]
 
     def on(self, event_type: str, callback: Callable[[Dict], None]):
         self.callbacks[event_type].append(callback)
 
-    def update(self, frame_time, tracked_objects):
+    def update(self, frame_time, current_detections, motion_boxes, regions):
         self.current_frame_time = frame_time
-        # get the new frame and delete the old frame
+        self.motion_boxes = motion_boxes
+        self.regions = regions
+        # get the new frame
         frame_id = f"{self.name}{frame_time}"
-        current_frame = self.frame_manager.get(frame_id, (self.config['frame_shape'][0]*3//2, self.config['frame_shape'][1]))
+        current_frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape_yuv)
 
-        current_ids = tracked_objects.keys()
+        current_ids = current_detections.keys()
         previous_ids = self.tracked_objects.keys()
         removed_ids = list(set(previous_ids).difference(current_ids))
         new_ids = list(set(current_ids).difference(previous_ids))
         updated_ids = list(set(current_ids).intersection(previous_ids))
 
         for id in new_ids:
-            self.tracked_objects[id] = tracked_objects[id]
-            self.tracked_objects[id]['zones'] = []
-
-            # start the score history
-            self.tracked_objects[id]['score_history'] = [self.tracked_objects[id]['score']]
-
-            # calculate if this is a false positive
-            self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
-            self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
+            new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.frame_cache, current_detections[id])
 
             # call event handlers
             for c in self.callbacks['start']:
-                c(self.name, tracked_objects[id])
+                c(self.name, new_obj, frame_time)
         
         for id in updated_ids:
-            self.tracked_objects[id].update(tracked_objects[id])
+            updated_obj = self.tracked_objects[id]
+            significant_update = updated_obj.update(frame_time, current_detections[id])
 
-            # if the object is not in the current frame, add a 0.0 to the score history
-            if self.tracked_objects[id]['frame_time'] != self.current_frame_time:
-                self.tracked_objects[id]['score_history'].append(0.0)
-            else:
-                self.tracked_objects[id]['score_history'].append(self.tracked_objects[id]['score'])
-            # only keep the last 10 scores
-            if len(self.tracked_objects[id]['score_history']) > 10:
-                self.tracked_objects[id]['score_history'] = self.tracked_objects[id]['score_history'][-10:]
+            if significant_update:
+                # ensure this frame is stored in the cache
+                if updated_obj.thumbnail_data['frame_time'] == frame_time and frame_time not in self.frame_cache:
+                    self.frame_cache[frame_time] = np.copy(current_frame)
 
-            # calculate if this is a false positive
-            self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
-            self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
-
-            # call event handlers
-            for c in self.callbacks['update']:
-                c(self.name, self.tracked_objects[id])
+                # call event handlers
+                for c in self.callbacks['update']:
+                    c(self.name, updated_obj, frame_time)
         
         for id in removed_ids:
             # publish events to mqtt
-            self.tracked_objects[id]['end_time'] = frame_time
-            for c in self.callbacks['end']:
-                c(self.name, self.tracked_objects[id])
-            del self.tracked_objects[id]
-
-        # check to see if the objects are in any zones
-        for obj in self.tracked_objects.values():
-            current_zones = []
-            bottom_center = (obj['centroid'][0], obj['box'][3])
-            # check each zone
-            for name, zone in self.config['zones'].items():
-                contour = zone['contour']
-                # check if the object is in the zone
-                if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
-                    # if the object passed the filters once, dont apply again
-                    if name in obj.get('zones', []) or not zone_filtered(obj, zone.get('filters', {})):
-                        current_zones.append(name)
-                    
-            obj['zones'] = current_zones
+            removed_obj = self.tracked_objects[id]
+            if not 'end_time' in removed_obj.obj_data:
+                removed_obj.obj_data['end_time'] = frame_time
+                for c in self.callbacks['end']:
+                    c(self.name, removed_obj, frame_time)
 
+        # TODO: can i switch to looking this up and only changing when an event ends?
         # maintain best objects
         for obj in self.tracked_objects.values():
-            object_type = obj['label']
-            # if the object wasn't seen on the current frame, skip it
-            if obj['frame_time'] != self.current_frame_time or obj['false_positive']:
+            object_type = obj.obj_data['label']
+            # if the object's thumbnail is not from the current frame
+            if obj.false_positive or obj.thumbnail_data['frame_time'] != self.current_frame_time:
                 continue
-            obj_copy = copy.deepcopy(obj)
             if object_type in self.best_objects:
                 current_best = self.best_objects[object_type]
                 now = datetime.datetime.now().timestamp()
                 # if the object is a higher score than the current best score 
                 # or the current object is older than desired, use the new object
-                if obj_copy['score'] > current_best['score'] or (now - current_best['frame_time']) > self.config.get('best_image_timeout', 60):
-                    obj_copy['frame'] = np.copy(current_frame)
-                    self.best_objects[object_type] = obj_copy
+                if (is_better_thumbnail(current_best.thumbnail_data, obj.thumbnail_data, self.camera_config.frame_shape) 
+                    or (now - current_best.thumbnail_data['frame_time']) > self.camera_config.best_image_timeout):
+                    self.best_objects[object_type] = obj
                     for c in self.callbacks['snapshot']:
-                        c(self.name, self.best_objects[object_type])
+                        c(self.name, self.best_objects[object_type], frame_time)
             else:
-                obj_copy['frame'] = np.copy(current_frame)
-                self.best_objects[object_type] = obj_copy
+                self.best_objects[object_type] = obj
                 for c in self.callbacks['snapshot']:
-                    c(self.name, self.best_objects[object_type])
+                    c(self.name, self.best_objects[object_type], frame_time)
         
         # update overall camera state for each object type
         obj_counter = Counter()
         for obj in self.tracked_objects.values():
-            if not obj['false_positive']:
-                obj_counter[obj['label']] += 1
+            if not obj.false_positive:
+                obj_counter[obj.obj_data['label']] += 1
                 
         # report on detected objects
         for obj_name, count in obj_counter.items():
-            new_status = 'ON' if count > 0 else 'OFF'
-            if new_status != self.object_status[obj_name]:
-                self.object_status[obj_name] = new_status
+            if count != self.object_counts[obj_name]:
+                self.object_counts[obj_name] = count
                 for c in self.callbacks['object_status']:
-                    c(self.name, obj_name, new_status)
+                    c(self.name, obj_name, count)
 
-        # expire any objects that are ON and no longer detected
-        expired_objects = [obj_name for obj_name, status in self.object_status.items() if status == 'ON' and not obj_name in obj_counter]
+        # expire any objects that are >0 and no longer detected
+        expired_objects = [obj_name for obj_name, count in self.object_counts.items() if count > 0 and not obj_name in obj_counter]
         for obj_name in expired_objects:
-            self.object_status[obj_name] = 'OFF'
+            self.object_counts[obj_name] = 0
             for c in self.callbacks['object_status']:
-                c(self.name, obj_name, 'OFF')
+                c(self.name, obj_name, 0)
             for c in self.callbacks['snapshot']:
-                c(self.name, self.best_objects[obj_name])
+                c(self.name, self.best_objects[obj_name], frame_time)
+        
+        # cleanup thumbnail frame cache
+        current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj.false_positive])
+        current_best_frames = set([obj.thumbnail_data['frame_time'] for obj in self.best_objects.values()])
+        thumb_frames_to_delete = [t for t in self.frame_cache.keys() if not t in current_thumb_frames and not t in current_best_frames]
+        for t in thumb_frames_to_delete:
+            del self.frame_cache[t]
         
         with self.current_frame_lock:
             self._current_frame = current_frame
@@ -242,68 +409,42 @@ class CameraState():
             self.previous_frame_id = frame_id
 
 class TrackedObjectProcessor(threading.Thread):
-    def __init__(self, camera_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
+    def __init__(self, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, event_processed_queue, stop_event):
         threading.Thread.__init__(self)
-        self.camera_config = camera_config
+        self.name = "detected_frames_processor"
+        self.config = config
         self.client = client
         self.topic_prefix = topic_prefix
         self.tracked_objects_queue = tracked_objects_queue
         self.event_queue = event_queue
+        self.event_processed_queue = event_processed_queue
         self.stop_event = stop_event
         self.camera_states: Dict[str, CameraState] = {}
         self.frame_manager = SharedMemoryFrameManager()
 
-        def start(camera, obj):
-            # publish events to mqtt
-            self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(obj), retain=False)
-            self.event_queue.put(('start', camera, obj))
+        def start(camera, obj: TrackedObject, current_frame_time):
+            self.event_queue.put(('start', camera, obj.to_dict()))
 
-        def update(camera, obj):
-            pass
+        def update(camera, obj: TrackedObject, current_frame_time):
+            after = obj.to_dict()
+            message = { 'before': obj.previous, 'after': after }
+            self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
+            obj.previous = after
 
-        def end(camera, obj):
-            self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(obj), retain=False)
-            self.event_queue.put(('end', camera, obj))
+        def end(camera, obj: TrackedObject, current_frame_time):
+            if not obj.false_positive:
+                message = { 'before': obj.previous, 'after': obj.to_dict() }
+                self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
+            self.event_queue.put(('end', camera, obj.to_dict(include_thumbnail=True)))
         
-        def snapshot(camera, obj):
-            if not 'frame' in obj:
-                return
-            
-            best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_YUV2BGR_I420)
-            if self.camera_config[camera]['snapshots']['draw_bounding_boxes']:
-                thickness = 2
-                color = COLOR_MAP[obj['label']]
-                box = obj['box']
-                draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
-                
-            mqtt_config = self.camera_config[camera].get('mqtt', {'crop_to_region': False})
-            if mqtt_config.get('crop_to_region'):
-                region = obj['region']
-                best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
-            if 'snapshot_height' in mqtt_config: 
-                height = int(mqtt_config['snapshot_height'])
-                width = int(height*best_frame.shape[1]/best_frame.shape[0])
-                best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
-            
-            if self.camera_config[camera]['snapshots']['show_timestamp']:
-                time_to_show = datetime.datetime.fromtimestamp(obj['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
-                size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
-                text_width = size[0][0]
-                text_height = size[0][1]
-                desired_size = max(200, 0.33*best_frame.shape[1])
-                font_scale = desired_size/text_width
-                cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=(255, 255, 255), thickness=2)
-
-            ret, jpg = cv2.imencode('.jpg', best_frame)
-            if ret:
-                jpg_bytes = jpg.tobytes()
-                self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", jpg_bytes, retain=True)
+        def snapshot(camera, obj: TrackedObject, current_frame_time):
+            self.client.publish(f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot", obj.get_jpg_bytes(), retain=True)
         
         def object_status(camera, object_name, status):
             self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
 
-        for camera in self.camera_config.keys():
-            camera_state = CameraState(camera, self.camera_config[camera], self.frame_manager)
+        for camera in self.config.cameras.keys():
+            camera_state = CameraState(camera, self.config, self.frame_manager)
             camera_state.on('start', start)
             camera_state.on('update', update)
             camera_state.on('end', end)
@@ -311,83 +452,71 @@ class TrackedObjectProcessor(threading.Thread):
             camera_state.on('object_status', object_status)
             self.camera_states[camera] = camera_state
 
-        self.camera_data = defaultdict(lambda: {
-            'best_objects': {},
-            'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
-            'tracked_objects': {},
-            'current_frame': np.zeros((720,1280,3), np.uint8),
-            'current_frame_time': 0.0,
-            'object_id': None
-        })
         # {
         #   'zone_name': {
-        #       'person': ['camera_1', 'camera_2']
+        #       'person': {
+        #           'camera_1': 2,
+        #           'camera_2': 1
+        #       }
         #   }
         # }
-        self.zone_data = defaultdict(lambda: defaultdict(lambda: set()))
-
-        # set colors for zones
-        all_zone_names = set([zone for config in self.camera_config.values() for zone in config['zones'].keys()])
-        zone_colors = {}
-        colors = plt.cm.get_cmap('tab10', len(all_zone_names))
-        for i, zone in enumerate(all_zone_names):
-            zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
-
-        # create zone contours
-        for camera_config in self.camera_config.values():
-            for zone_name, zone_config in camera_config['zones'].items():
-                zone_config['color'] = zone_colors[zone_name]
-                coordinates = zone_config['coordinates']
-                if isinstance(coordinates, list):
-                    zone_config['contour'] =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
-                elif isinstance(coordinates, str):
-                    points = coordinates.split(',')
-                    zone_config['contour'] =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
-                else:
-                    print(f"Unable to parse zone coordinates for {zone_name} - {camera}")
+        self.zone_data = defaultdict(lambda: defaultdict(lambda: {}))
         
     def get_best(self, camera, label):
-        best_objects = self.camera_states[camera].best_objects
-        if label in best_objects:
-            return best_objects[label]
+        # TODO: need a lock here
+        camera_state = self.camera_states[camera]
+        if label in camera_state.best_objects:
+            best_obj = camera_state.best_objects[label]
+            best = best_obj.thumbnail_data.copy()
+            best['frame'] = camera_state.frame_cache.get(best_obj.thumbnail_data['frame_time'])
+            return best
         else:
             return {}
     
-    def get_current_frame(self, camera, draw=False):
-        return self.camera_states[camera].get_current_frame(draw)
+    def get_current_frame(self, camera, draw_options={}):
+        return self.camera_states[camera].get_current_frame(draw_options)
 
     def run(self):
         while True:
             if self.stop_event.is_set():
-                print(f"Exiting object processor...")
+                logger.info(f"Exiting object processor...")
                 break
 
             try:
-                camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
+                camera, frame_time, current_tracked_objects, motion_boxes, regions = self.tracked_objects_queue.get(True, 10)
             except queue.Empty:
                 continue
 
             camera_state = self.camera_states[camera]
 
-            camera_state.update(frame_time, current_tracked_objects)
+            camera_state.update(frame_time, current_tracked_objects, motion_boxes, regions)
 
-            # update zone status for each label
-            for zone in camera_state.config['zones'].keys():
-                # get labels for current camera and all labels in current zone
-                labels_for_camera = set([obj['label'] for obj in camera_state.tracked_objects.values() if zone in obj['zones'] and not obj['false_positive']])
-                labels_to_check = labels_for_camera | set(self.zone_data[zone].keys())
-                # for each label in zone
-                for label in labels_to_check:
-                    camera_list = self.zone_data[zone][label]
-                    # remove or add the camera to the list for the current label
-                    previous_state = len(camera_list) > 0
-                    if label in labels_for_camera:
-                        camera_list.add(camera_state.name)
-                    elif camera_state.name in camera_list:
-                        camera_list.remove(camera_state.name)
-                    new_state = len(camera_list) > 0
-                    # if the value is changing, send over MQTT
-                    if previous_state == False and new_state == True:
-                        self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'ON', retain=False)
-                    elif previous_state == True and new_state == False:
-                        self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'OFF', retain=False)
+            # update zone counts for each label
+            # for each zone in the current camera
+            for zone in self.config.cameras[camera].zones.keys():
+                # count labels for the camera in the zone
+                obj_counter = Counter()
+                for obj in camera_state.tracked_objects.values():
+                    if zone in obj.current_zones and not obj.false_positive:
+                        obj_counter[obj.obj_data['label']] += 1
+                
+                # update counts and publish status
+                for label in set(list(self.zone_data[zone].keys()) + list(obj_counter.keys())):
+                    # if we have previously published a count for this zone/label
+                    zone_label = self.zone_data[zone][label]
+                    if camera in zone_label:
+                        current_count = sum(zone_label.values())
+                        zone_label[camera] = obj_counter[label] if label in obj_counter else 0
+                        new_count = sum(zone_label.values())
+                        if new_count != current_count:
+                            self.client.publish(f"{self.topic_prefix}/{zone}/{label}", new_count, retain=False)
+                    # if this is a new zone/label combo for this camera
+                    else:
+                        if label in obj_counter:
+                            zone_label[camera] = obj_counter[label]
+                            self.client.publish(f"{self.topic_prefix}/{zone}/{label}", obj_counter[label], retain=False)
+
+            # cleanup event finished queue
+            while not self.event_processed_queue.empty():
+                event_id, camera = self.event_processed_queue.get()
+                self.camera_states[camera].finished(event_id)

frigate/objects.py

@@ -1,29 +1,32 @@
-import time
-import datetime
-import threading
-import cv2
-import itertools
 import copy
-import numpy as np
+import datetime
+import itertools
+import multiprocessing as mp
 import random
 import string
-import multiprocessing as mp
+import threading
+import time
 from collections import defaultdict
+
+import cv2
+import numpy as np
 from scipy.spatial import distance as dist
-from frigate.util import draw_box_with_label, calculate_region
+
+from frigate.config import DetectConfig
+from frigate.util import draw_box_with_label
+
 
 class ObjectTracker():
-    def __init__(self, max_disappeared):
+    def __init__(self, config: DetectConfig):
         self.tracked_objects = {}
         self.disappeared = {}
-        self.max_disappeared = max_disappeared
+        self.max_disappeared = config.max_disappeared
 
     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['top_score'] = obj['score']
         self.tracked_objects[id] = obj
         self.disappeared[id] = 0
 
@@ -34,8 +37,6 @@ class ObjectTracker():
     def update(self, id, new_obj):
         self.disappeared[id] = 0
         self.tracked_objects[id].update(new_obj)
-        if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
-            self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
 
     def match_and_update(self, frame_time, new_objects):
         # group by name

frigate/process_clip.py

@@ -0,0 +1,208 @@
+import datetime
+import json
+import logging
+import multiprocessing as mp
+import os
+import subprocess as sp
+import sys
+from unittest import TestCase, main
+
+import click
+import cv2
+import numpy as np
+
+from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
+from frigate.edgetpu import LocalObjectDetector
+from frigate.motion import MotionDetector
+from frigate.object_processing import COLOR_MAP, CameraState
+from frigate.objects import ObjectTracker
+from frigate.util import (DictFrameManager, EventsPerSecond,
+                          SharedMemoryFrameManager, draw_box_with_label)
+from frigate.video import (capture_frames, process_frames,
+                           start_or_restart_ffmpeg)
+
+logging.basicConfig()
+logging.root.setLevel(logging.DEBUG)
+
+logger = logging.getLogger(__name__)
+
+def get_frame_shape(source):
+    ffprobe_cmd = " ".join([
+        'ffprobe',
+        '-v',
+        'panic',
+        '-show_error',
+        '-show_streams',
+        '-of',
+        'json',
+        '"'+source+'"'
+    ])
+    p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+    (output, err) = p.communicate()
+    p_status = p.wait()
+    info = json.loads(output)
+
+    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
+
+    if video_info['height'] != 0 and video_info['width'] != 0:
+        return (video_info['height'], video_info['width'], 3)
+    
+    # fallback to using opencv if ffprobe didnt succeed
+    video = cv2.VideoCapture(source)
+    ret, frame = video.read()
+    frame_shape = frame.shape
+    video.release()
+    return frame_shape
+
+class ProcessClip():
+    def __init__(self, clip_path, frame_shape, config: FrigateConfig):
+        self.clip_path = clip_path
+        self.camera_name = 'camera'
+        self.config = config
+        self.camera_config = self.config.cameras['camera']
+        self.frame_shape = self.camera_config.frame_shape
+        self.ffmpeg_cmd = [c['cmd'] for c in self.camera_config.ffmpeg_cmds if 'detect' in c['roles']][0]
+        self.frame_manager = SharedMemoryFrameManager()
+        self.frame_queue = mp.Queue()
+        self.detected_objects_queue = mp.Queue()
+        self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
+
+    def load_frames(self):
+        fps = EventsPerSecond()
+        skipped_fps = EventsPerSecond()
+        current_frame = mp.Value('d', 0.0)
+        frame_size = self.camera_config.frame_shape_yuv[0] * self.camera_config.frame_shape_yuv[1]
+        ffmpeg_process = start_or_restart_ffmpeg(self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size)
+        capture_frames(ffmpeg_process, self.camera_name, self.camera_config.frame_shape_yuv, self.frame_manager, 
+            self.frame_queue, fps, skipped_fps, current_frame)
+        ffmpeg_process.wait()
+        ffmpeg_process.communicate()
+    
+    def process_frames(self, 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(self.frame_shape, mask, self.camera_config.motion)
+
+        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)
+        }
+        stop_event = mp.Event()
+        model_shape = (self.config.model.height, self.config.model.width)
+
+        process_frames(self.camera_name, self.frame_queue, self.frame_shape, model_shape, 
+            self.frame_manager, motion_detector, object_detector, object_tracker, 
+            self.detected_objects_queue, process_info, 
+            objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
+    
+    def top_object(self, debug_path=None):
+        obj_detected = False
+        top_computed_score = 0.0
+        def handle_event(name, obj, frame_time):
+            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()):
+            camera_name, frame_time, current_tracked_objects, motion_boxes, regions = self.detected_objects_queue.get()
+            if not debug_path is None:
+                self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
+
+            self.camera_state.update(frame_time, current_tracked_objects, motion_boxes, regions)
+        
+        self.frame_manager.delete(self.camera_state.previous_frame_id)
+        
+        return {
+            'object_detected': obj_detected,
+            'top_score': top_computed_score
+        }
+    
+    def save_debug_frame(self, debug_path, frame_time, tracked_objects):
+        current_frame = cv2.cvtColor(self.frame_manager.get(f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv), cv2.COLOR_YUV2BGR_I420)
+        # draw the bounding boxes on the frame
+        for obj in tracked_objects:
+            thickness = 2
+            color = (0,0,175)
+
+            if obj['frame_time'] != frame_time:
+                thickness = 1
+                color = (255,0,0)
+            else:
+                color = (255,255,0)
+
+            # draw the bounding boxes on the frame
+            box = obj['box']
+            draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['id'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
+            # draw the regions on the frame
+            region = obj['region']
+            draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
+        
+        cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", current_frame)
+
+@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("-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):
+    clips = []
+    if os.path.isdir(path):
+        files = os.listdir(path)
+        files.sort()
+        clips = [os.path.join(path, file) for file in files]
+    elif os.path.isfile(path):  
+        clips.append(path)
+
+    json_config = {
+        'mqtt': {
+            'host': 'mqtt'
+        },
+        'cameras': {
+            'camera': {
+                'ffmpeg': {
+                    'inputs': [
+                        { 'path': 'path.mp4', 'global_args': '', 'input_args': '', 'roles': ['detect'] }
+                    ]
+                },
+                'height': 1920,
+                'width': 1080
+            }
+        }
+    }
+
+    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']['width'] = frame_shape[1]
+        json_config['cameras']['camera']['ffmpeg']['inputs'][0]['path'] = c
+
+        config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
+
+        process_clip = ProcessClip(c, frame_shape, config)
+        process_clip.load_frames()
+        process_clip.process_frames(objects_to_track=[label])
+
+        results.append((c, process_clip.top_object(debug_path)))
+
+    if not scores is None:
+        with open(scores, 'w') as writer:
+            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 __name__ == '__main__':
+    process()

frigate/record.py

@@ -0,0 +1,125 @@
+import datetime
+import json
+import logging
+import os
+import queue
+import subprocess as sp
+import threading
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import psutil
+
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+
+logger = logging.getLogger(__name__)
+
+SECONDS_IN_DAY = 60 * 60 * 24
+
+def remove_empty_directories(directory):
+  # list all directories recursively and sort them by path,
+  # longest first
+  paths = sorted(
+      [x[0] for x in os.walk(RECORD_DIR)],
+      key=lambda p: len(str(p)),
+      reverse=True,
+  )
+  for path in paths:
+      # don't delete the parent
+      if path == RECORD_DIR:
+          continue
+      if len(os.listdir(path)) == 0:
+          os.rmdir(path)
+
+class RecordingMaintainer(threading.Thread):
+    def __init__(self, config: FrigateConfig, stop_event):
+        threading.Thread.__init__(self)
+        self.name = 'recording_maint'
+        self.config = config
+        self.stop_event = stop_event
+
+    def move_files(self):
+        recordings = [d for d in os.listdir(RECORD_DIR) if os.path.isfile(os.path.join(RECORD_DIR, d)) and d.endswith(".mp4")]
+
+        files_in_use = []
+        for process in psutil.process_iter():
+            if process.name() != 'ffmpeg':
+                continue
+            try:
+                flist = process.open_files()
+                if flist:
+                    for nt in flist:
+                        if nt.path.startswith(RECORD_DIR):
+                            files_in_use.append(nt.path.split('/')[-1])
+            except:
+                continue
+
+        for f in recordings:
+            if f in files_in_use:
+                continue
+
+            camera = '-'.join(f.split('-')[:-1])
+            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
+        
+            ffprobe_cmd = " ".join([
+                'ffprobe',
+                '-v',
+                'error',
+                '-show_entries',
+                'format=duration',
+                '-of',
+                'default=noprint_wrappers=1:nokey=1',
+                f"{os.path.join(RECORD_DIR,f)}"
+            ])
+            p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+            (output, err) = p.communicate()
+            p_status = p.wait()
+            if p_status == 0:
+                duration = float(output.decode('utf-8').strip())
+            else:
+                logger.info(f"bad file: {f}")
+                os.remove(os.path.join(RECORD_DIR,f))
+                continue
+
+            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')}"
+
+            os.rename(os.path.join(RECORD_DIR,f), os.path.join(directory,file_name))
+
+    def expire_files(self):
+        delete_before = {}
+        for name, camera in self.config.cameras.items():
+            delete_before[name] = datetime.datetime.now().timestamp() - SECONDS_IN_DAY*camera.record.retain_days
+
+        for p in Path('/media/frigate/recordings').rglob("*.mp4"):
+            if not p.parent.name in delete_before:
+                continue
+            if p.stat().st_mtime < delete_before[p.parent.name]:
+                p.unlink(missing_ok=True)
+
+    def run(self):
+        counter = 0
+        self.expire_files()
+        while(True):
+            if self.stop_event.is_set():
+                logger.info(f"Exiting recording maintenance...")
+                break
+
+            # only expire events every 10 minutes, but check for new files every 10 seconds
+            time.sleep(10)
+            counter = counter + 1
+            if counter > 60:
+                self.expire_files()
+                remove_empty_directories(RECORD_DIR)
+                counter = 0
+
+            self.move_files()
+
+
+   

frigate/test/test_config.py

@@ -0,0 +1,344 @@
+import json
+from unittest import TestCase, main
+import voluptuous as vol
+from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
+
+class TestConfig(TestCase):
+    def setUp(self):
+        self.minimal = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+    def test_empty(self):
+        FRIGATE_CONFIG_SCHEMA({})
+
+    def test_minimal(self):
+        FRIGATE_CONFIG_SCHEMA(self.minimal)
+    
+    def test_config_class(self):
+        FrigateConfig(config=self.minimal)
+    
+    def test_inherit_tracked_objects(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.track)
+    
+    def test_override_tracked_objects(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'objects': {
+                        'track': ['cat']
+                    }
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('cat' in frigate_config.cameras['back'].objects.track)
+    
+    def test_default_object_filters(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.filters)
+    
+    def test_inherit_object_filters(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog'],
+                'filters': {
+                    'dog': {
+                        'threshold': 0.7
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.filters)
+        assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
+    
+    def test_override_object_filters(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'objects': {
+                        'track': ['person', 'dog'],
+                        'filters': {
+                            'dog': {
+                                'threshold': 0.7
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.filters)
+        assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
+    
+    def test_ffmpeg_params(self):
+        config = {
+            'ffmpeg': {
+                'input_args': ['-re']
+            },
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'objects': {
+                        'track': ['person', 'dog'],
+                        'filters': {
+                            'dog': {
+                                'threshold': 0.7
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
+    
+    def test_inherit_save_clips_retention(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert(frigate_config.cameras['back'].save_clips.retain.objects['person'] == 30)
+    
+    def test_roles_listed_twice_throws_error(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] },
+                            { 'path': 'rtsp://10.0.0.1:554/video2', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
+    
+    def test_zone_matching_camera_name_throws_error(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'zones': {
+                        'back': {
+                            'coordinates': '1,1,1,1,1,1'
+                        }
+                    }
+                }
+            }
+        }
+        self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
+    
+    def test_save_clips_should_default_to_global_objects(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'save_clips': {
+                        'enabled': True
+                    }
+                }
+            }
+        }
+        config = FrigateConfig(config=config)
+        assert(len(config.cameras['back'].save_clips.objects) == 2)
+        assert('dog' in config.cameras['back'].save_clips.objects)
+        assert('person' in config.cameras['back'].save_clips.objects)
+    
+    def test_role_assigned_but_not_enabled(self):
+        json_config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect', 'rtmp'] },
+                            { 'path': 'rtsp://10.0.0.1:554/clips', 'roles': ['clips'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+
+        config = FrigateConfig(config=json_config)
+        ffmpeg_cmds = config.cameras['back'].ffmpeg_cmds
+        assert(len(ffmpeg_cmds) == 1)
+        assert(not 'clips' in ffmpeg_cmds[0]['roles'])
+
+
+if __name__ == '__main__':
+    main(verbosity=2)

frigate/test/test_yuv_region_2_rgb.py

@@ -0,0 +1,39 @@
+import cv2
+import numpy as np
+from unittest import TestCase, main
+from frigate.util import yuv_region_2_rgb
+
+class TestYuvRegion2RGB(TestCase):
+    def setUp(self):
+        self.bgr_frame = np.zeros((100, 200, 3), np.uint8)
+        self.bgr_frame[:] = (0, 0, 255)
+        self.bgr_frame[5:55, 5:55] = (255,0,0)
+        # cv2.imwrite(f"bgr_frame.jpg", self.bgr_frame)
+        self.yuv_frame = cv2.cvtColor(self.bgr_frame, cv2.COLOR_BGR2YUV_I420)
+
+    def test_crop_yuv(self):
+        cropped = yuv_region_2_rgb(self.yuv_frame, (10,10,50,50))
+        # ensure the upper left pixel is blue
+        assert(np.all(cropped[0, 0] == [0, 0, 255]))
+
+    def test_crop_yuv_out_of_bounds(self):
+        cropped = yuv_region_2_rgb(self.yuv_frame, (0,0,200,200))
+        # cv2.imwrite(f"cropped.jpg", cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
+        # ensure the upper left pixel is red
+        # the yuv conversion has some noise
+        assert(np.all(cropped[0, 0] == [255, 1, 0]))
+        # ensure the bottom right is black
+        assert(np.all(cropped[199, 199] == [0, 0, 0]))
+
+    def test_crop_yuv_portrait(self):
+        bgr_frame = np.zeros((1920, 1080, 3), np.uint8)
+        bgr_frame[:] = (0, 0, 255)
+        bgr_frame[5:55, 5:55] = (255,0,0)
+        # cv2.imwrite(f"bgr_frame.jpg", self.bgr_frame)
+        yuv_frame = cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2YUV_I420)
+
+        cropped = yuv_region_2_rgb(yuv_frame, (0, 852, 648, 1500))
+        # cv2.imwrite(f"cropped.jpg", cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
+
+if __name__ == '__main__':
+    main(verbosity=2)

frigate/util.py

@@ -1,17 +1,21 @@
-from abc import ABC, abstractmethod
-import datetime
-import time
-import signal
-import traceback
 import collections
-import numpy as np
-import cv2
-import threading
-import matplotlib.pyplot as plt
+import datetime
 import hashlib
+import json
+import signal
+import subprocess as sp
+import threading
+import time
+import traceback
+from abc import ABC, abstractmethod
 from multiprocessing import shared_memory
 from typing import AnyStr
 
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+
 def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
     if color is None:
         color = (0,0,255)
@@ -43,14 +47,11 @@ def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thicknes
     cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
 
 def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):    
-    # size is larger than longest edge
-    size = int(max(xmax-xmin, ymax-ymin)*multiplier)
+    # size is the longest edge and divisible by 4
+    size = int(max(xmax-xmin, ymax-ymin)//4*4*multiplier)
     # dont go any smaller than 300
     if size < 300:
         size = 300
-    # if the size is too big to fit in the frame
-    if size > min(frame_shape[0], frame_shape[1]):
-        size = min(frame_shape[0], frame_shape[1])
 
     # x_offset is midpoint of bounding box minus half the size
     x_offset = int((xmax-xmin)/2.0+xmin-size/2.0)
@@ -58,48 +59,156 @@ def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
     if x_offset < 0:
         x_offset = 0
     elif x_offset > (frame_shape[1]-size):
-        x_offset = (frame_shape[1]-size)
+        x_offset = max(0, (frame_shape[1]-size))
 
     # y_offset is midpoint of bounding box minus half the size
     y_offset = int((ymax-ymin)/2.0+ymin-size/2.0)
-    # if outside the image
+    # # if outside the image
     if y_offset < 0:
         y_offset = 0
     elif y_offset > (frame_shape[0]-size):
-        y_offset = (frame_shape[0]-size)
+        y_offset = max(0, (frame_shape[0]-size))
 
     return (x_offset, y_offset, x_offset+size, y_offset+size)
 
+def get_yuv_crop(frame_shape, crop):
+    # crop should be (x1,y1,x2,y2)
+    frame_height = frame_shape[0]//3*2
+    frame_width = frame_shape[1]
+
+    # compute the width/height of the uv channels
+    uv_width = frame_width//2 # width of the uv channels
+    uv_height = frame_height//4 # height of the uv channels
+
+    # compute the offset for upper left corner of the uv channels
+    uv_x_offset = crop[0]//2 # x offset of the uv channels
+    uv_y_offset = crop[1]//4 # y offset of the uv channels
+
+    # compute the width/height of the uv crops
+    uv_crop_width  = (crop[2] - crop[0])//2 # width of the cropped uv channels
+    uv_crop_height = (crop[3] - crop[1])//4 # height of the cropped uv channels
+
+    # ensure crop dimensions are multiples of 2 and 4
+    y = (
+        crop[0],
+        crop[1],
+        crop[0]      + uv_crop_width*2,
+        crop[1]      + uv_crop_height*4
+    )
+
+    u1 = (
+        0            + uv_x_offset,
+        frame_height + uv_y_offset,
+        0            + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_y_offset  +  uv_crop_height
+    )
+
+    u2 = (
+        uv_width     + uv_x_offset,
+        frame_height + uv_y_offset,
+        uv_width     + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_y_offset  +  uv_crop_height
+    )
+
+    v1 = (
+        0            + uv_x_offset,
+        frame_height + uv_height    +  uv_y_offset,
+        0            + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_height    +  uv_y_offset  +  uv_crop_height
+    )
+
+    v2 = (
+        uv_width     + uv_x_offset,
+        frame_height + uv_height    +  uv_y_offset,
+        uv_width     + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_height    +  uv_y_offset + uv_crop_height
+    )
+
+    return y, u1, u2, v1, v2
+
 def yuv_region_2_rgb(frame, region):
-    height = frame.shape[0]//3*2
-    width = frame.shape[1]
-    # make sure the size is a multiple of 4
-    size = (region[3] - region[1])//4*4
+    try:
+        height = frame.shape[0]//3*2
+        width = frame.shape[1]
 
-    x1 = region[0] 
-    y1 = region[1]
+        # get the crop box if the region extends beyond the frame
+        crop_x1 = max(0, region[0])
+        crop_y1 = max(0, region[1])
+        # ensure these are a multiple of 4
+        crop_x2 = min(width,  region[2])
+        crop_y2 = min(height, region[3])
+        crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
 
-    uv_x1 = x1//2
-    uv_y1 = y1//4
+        y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
 
-    uv_width = size//2
-    uv_height = size//4
+        # if the region starts outside the frame, indent the start point in the cropped frame
+        y_channel_x_offset = abs(min(0, region[0]))
+        y_channel_y_offset = abs(min(0, region[1]))
 
-    u_y_start = height
-    v_y_start = height + height//4
-    two_x_offset = width//2
+        uv_channel_x_offset = y_channel_x_offset//2
+        uv_channel_y_offset = y_channel_y_offset//4
 
-    yuv_cropped_frame = np.zeros((size+size//2, size), np.uint8)
-    # y channel
-    yuv_cropped_frame[0:size, 0:size] = frame[y1:y1+size, x1:x1+size]
-    # u channel
-    yuv_cropped_frame[size:size+uv_height, 0:uv_width] = frame[uv_y1+u_y_start:uv_y1+u_y_start+uv_height, uv_x1:uv_x1+uv_width]
-    yuv_cropped_frame[size:size+uv_height, uv_width:size] = frame[uv_y1+u_y_start:uv_y1+u_y_start+uv_height, uv_x1+two_x_offset:uv_x1+two_x_offset+uv_width]
-    # v channel
-    yuv_cropped_frame[size+uv_height:size+uv_height*2, 0:uv_width] = frame[uv_y1+v_y_start:uv_y1+v_y_start+uv_height, uv_x1:uv_x1+uv_width]
-    yuv_cropped_frame[size+uv_height:size+uv_height*2, uv_width:size] = frame[uv_y1+v_y_start:uv_y1+v_y_start+uv_height, uv_x1+two_x_offset:uv_x1+two_x_offset+uv_width]
+        # create the yuv region frame
+        # make sure the size is a multiple of 4
+        size = (region[3] - region[1])//4*4
+        yuv_cropped_frame = np.zeros((size+size//2, size), np.uint8)
+        # fill in black
+        yuv_cropped_frame[:] = 128
+        yuv_cropped_frame[0:size,0:size] = 16
 
-    return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
+        # copy the y channel
+        yuv_cropped_frame[
+                y_channel_y_offset:y_channel_y_offset + y[3] - y[1],
+                y_channel_x_offset:y_channel_x_offset + y[2] - y[0]
+            ] = frame[
+                y[1]:y[3], 
+                y[0]:y[2]
+            ]
+
+        uv_crop_width = u1[2] - u1[0]
+        uv_crop_height = u1[3] - u1[1]
+
+        # copy u1
+        yuv_cropped_frame[
+                size + uv_channel_y_offset:size + uv_channel_y_offset + uv_crop_height,
+                0    + uv_channel_x_offset:0    + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                u1[1]:u1[3], 
+                u1[0]:u1[2]
+            ]
+
+        # copy u2
+        yuv_cropped_frame[
+                size    + uv_channel_y_offset:size    + uv_channel_y_offset + uv_crop_height,
+                size//2 + uv_channel_x_offset:size//2 + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                u2[1]:u2[3], 
+                u2[0]:u2[2]
+            ]
+
+        # copy v1
+        yuv_cropped_frame[
+                size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
+                0            + uv_channel_x_offset:0            + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                v1[1]:v1[3], 
+                v1[0]:v1[2]
+            ]
+
+        # copy v2
+        yuv_cropped_frame[
+                size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
+                size//2      + uv_channel_x_offset:size//2      + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                v2[1]:v2[3], 
+                v2[0]:v2[2]
+            ]
+
+        return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
+    except:
+        print(f"frame.shape: {frame.shape}")
+        print(f"region: {region}")
+        raise
 
 def intersection(box_a, box_b):
     return (
@@ -241,4 +350,4 @@ class SharedMemoryFrameManager(FrameManager):
         if name in self.shm_store:
             self.shm_store[name].close()
             self.shm_store[name].unlink()
-            del self.shm_store[name]
+            del self.shm_store[name]

frigate/video.py

@@ -1,57 +1,34 @@
-import os
-import time
-import datetime
-import cv2
-import queue
-import threading
-import ctypes
-import multiprocessing as mp
-import subprocess as sp
-import numpy as np
+import base64
 import copy
+import ctypes
+import datetime
 import itertools
 import json
-import base64
-from typing import Dict, List
+import logging
+import multiprocessing as mp
+import os
+import queue
+import subprocess as sp
+import signal
+import threading
+import time
 from collections import defaultdict
-from frigate.util import draw_box_with_label, yuv_region_2_rgb, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, FrameManager, SharedMemoryFrameManager
-from frigate.objects import ObjectTracker
+from typing import Dict, List
+
+import cv2
+import numpy as np
+
+from frigate.config import CameraConfig
 from frigate.edgetpu import RemoteObjectDetector
+from frigate.log import LogPipe
 from frigate.motion import MotionDetector
+from frigate.objects import ObjectTracker
+from frigate.util import (EventsPerSecond, FrameManager,
+                          SharedMemoryFrameManager, area, calculate_region,
+                          clipped, draw_box_with_label, intersection,
+                          intersection_over_union, listen, yuv_region_2_rgb)
 
-def get_frame_shape(source):
-    ffprobe_cmd = " ".join([
-        'ffprobe',
-        '-v',
-        'panic',
-        '-show_error',
-        '-show_streams',
-        '-of',
-        'json',
-        '"'+source+'"'
-    ])
-    print(ffprobe_cmd)
-    p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
-    (output, err) = p.communicate()
-    p_status = p.wait()
-    info = json.loads(output)
-    print(info)
-
-    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
-
-    if video_info['height'] != 0 and video_info['width'] != 0:
-        return (video_info['height'], video_info['width'], 3)
-    
-    # fallback to using opencv if ffprobe didnt succeed
-    video = cv2.VideoCapture(source)
-    ret, frame = video.read()
-    frame_shape = frame.shape
-    video.release()
-    return frame_shape
-
-def get_ffmpeg_input(ffmpeg_input):
-    frigate_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
-    return ffmpeg_input.format(**frigate_vars)
+logger = logging.getLogger(__name__)
 
 def filtered(obj, objects_to_track, object_filters, mask=None):
     object_name = obj[0]
@@ -64,16 +41,16 @@ def filtered(obj, objects_to_track, object_filters, mask=None):
 
         # if the min area is larger than the
         # detected object, don't add it to detected objects
-        if obj_settings.get('min_area',-1) > obj[3]:
+        if obj_settings.min_area > obj[3]:
             return True
         
         # if the detected object is larger than the
         # max area, don't add it to detected objects
-        if obj_settings.get('max_area', 24000000) < obj[3]:
+        if obj_settings.max_area < obj[3]:
             return True
 
         # if the score is lower than the min_score, skip
-        if obj_settings.get('min_score', 0) > obj[1]:
+        if obj_settings.min_score > obj[1]:
             return True
     
         # compute the coordinates of the object and make sure
@@ -87,40 +64,42 @@ def filtered(obj, objects_to_track, object_filters, mask=None):
         
     return False
 
-def create_tensor_input(frame, region):
+def create_tensor_input(frame, model_shape, region):
     cropped_frame = yuv_region_2_rgb(frame, region)
 
     # Resize to 300x300 if needed
-    if cropped_frame.shape != (300, 300, 3):
-        cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
+    if cropped_frame.shape != (model_shape[0], model_shape[1], 3):
+        cropped_frame = cv2.resize(cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR)
     
-    # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
+    # Expand dimensions since the model expects images to have shape: [1, height, width, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
-def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
-    if not ffmpeg_process is None:
-        print("Terminating the existing ffmpeg process...")
-        ffmpeg_process.terminate()
-        try:
-            print("Waiting for ffmpeg to exit gracefully...")
-            ffmpeg_process.communicate(timeout=30)
-        except sp.TimeoutExpired:
-            print("FFmpeg didnt exit. Force killing...")
-            ffmpeg_process.kill()
-            ffmpeg_process.communicate()
-        ffmpeg_process = None
+def stop_ffmpeg(ffmpeg_process, logger):
+    logger.info("Terminating the existing ffmpeg process...")
+    ffmpeg_process.terminate()
+    try:
+        logger.info("Waiting for ffmpeg to exit gracefully...")
+        ffmpeg_process.communicate(timeout=30)
+    except sp.TimeoutExpired:
+        logger.info("FFmpeg didnt exit. Force killing...")
+        ffmpeg_process.kill()
+        ffmpeg_process.communicate()
+    ffmpeg_process = None
 
-    print("Creating ffmpeg process...")
-    print(" ".join(ffmpeg_cmd))
-    process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
+def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None):
+    if not ffmpeg_process is None:
+        stop_ffmpeg(ffmpeg_process, logger)
+
+    if frame_size is None:
+        process = sp.Popen(ffmpeg_cmd, stdout = sp.DEVNULL, stderr=logpipe, stdin = sp.DEVNULL, start_new_session=True)
+    else:
+        process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stderr=logpipe, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
     return process
 
 def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, 
-    frame_queue, take_frame: int, fps:mp.Value, skipped_fps: mp.Value, 
-    stop_event: mp.Event, current_frame: mp.Value):
+    frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value):
 
-    frame_num = 0
-    frame_size = frame_shape[0] * frame_shape[1] * 3 // 2
+    frame_size = frame_shape[0] * frame_shape[1]
     frame_rate = EventsPerSecond()
     frame_rate.start()
     skipped_eps = EventsPerSecond()
@@ -128,33 +107,23 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
     while True:
         fps.value = frame_rate.eps()
         skipped_fps = skipped_eps.eps()
-        if stop_event.is_set():
-            print(f"{camera_name}: stop event set. exiting capture thread...")
-            break
 
         current_frame.value = datetime.datetime.now().timestamp()
         frame_name = f"{camera_name}{current_frame.value}"
         frame_buffer = frame_manager.create(frame_name, frame_size)
         try:
-          frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
-        except:
-          print(f"{camera_name}: ffmpeg sent a broken frame. something is wrong.")
+            frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
+        except Exception as e:
+            logger.info(f"{camera_name}: ffmpeg sent a broken frame. {e}")
 
-          if ffmpeg_process.poll() != None:
-              print(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
-              frame_manager.delete(frame_name)
-              break
-          
-          continue
+            if ffmpeg_process.poll() != None:
+                logger.info(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
+                frame_manager.delete(frame_name)
+                break
+            continue
 
         frame_rate.update()
 
-        frame_num += 1
-        if (frame_num % take_frame) != 0:
-            skipped_eps.update()
-            frame_manager.delete(frame_name)
-            continue
-
         # if the queue is full, skip this frame
         if frame_queue.full():
             skipped_eps.update()
@@ -168,123 +137,138 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
         frame_queue.put(current_frame.value)
 
 class CameraWatchdog(threading.Thread):
-    def __init__(self, name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
+    def __init__(self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
         threading.Thread.__init__(self)
-        self.name = name
+        self.logger = logging.getLogger(f"watchdog.{camera_name}")
+        self.camera_name = camera_name
         self.config = config
         self.capture_thread = None
-        self.ffmpeg_process = None
-        self.stop_event = stop_event
+        self.ffmpeg_detect_process = None
+        self.logpipe = LogPipe(f"ffmpeg.{self.camera_name}.detect", logging.ERROR)
+        self.ffmpeg_other_processes = []
         self.camera_fps = camera_fps
         self.ffmpeg_pid = ffmpeg_pid
         self.frame_queue = frame_queue
-        self.frame_shape = self.config['frame_shape']
-        self.frame_size = self.frame_shape[0] * self.frame_shape[1] * 3 // 2
+        self.frame_shape = self.config.frame_shape_yuv
+        self.frame_size = self.frame_shape[0] * self.frame_shape[1]
+        self.stop_event = stop_event
 
     def run(self):
-        self.start_ffmpeg()
+        self.start_ffmpeg_detect()
+
+        for c in self.config.ffmpeg_cmds:
+            if 'detect' in c['roles']:
+                continue
+            logpipe = LogPipe(f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}", logging.ERROR)
+            self.ffmpeg_other_processes.append({
+                'cmd': c['cmd'],
+                'logpipe': logpipe,
+                'process': start_or_restart_ffmpeg(c['cmd'], self.logger, logpipe)
+            })
+        
         time.sleep(10)
         while True:
             if self.stop_event.is_set():
-                print(f"Exiting watchdog...")
+                stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
+                for p in self.ffmpeg_other_processes:
+                    stop_ffmpeg(p['process'], self.logger)
+                    p['logpipe'].close()
+                self.logpipe.close()
                 break
 
             now = datetime.datetime.now().timestamp()
 
             if not self.capture_thread.is_alive():
-                self.start_ffmpeg()
-            elif now - self.capture_thread.current_frame.value > 5:
-                print(f"No frames received from {self.name} in 5 seconds. Exiting ffmpeg...")
-                self.ffmpeg_process.terminate()
+                self.start_ffmpeg_detect()
+            elif now - self.capture_thread.current_frame.value > 20:
+                self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...")
+                self.ffmpeg_detect_process.terminate()
                 try:
-                    print("Waiting for ffmpeg to exit gracefully...")
-                    self.ffmpeg_process.communicate(timeout=30)
+                    self.logger.info("Waiting for ffmpeg to exit gracefully...")
+                    self.ffmpeg_detect_process.communicate(timeout=30)
                 except sp.TimeoutExpired:
-                    print("FFmpeg didnt exit. Force killing...")
-                    self.ffmpeg_process.kill()
-                    self.ffmpeg_process.communicate()
+                    self.logger.info("FFmpeg didnt exit. Force killing...")
+                    self.ffmpeg_detect_process.kill()
+                    self.ffmpeg_detect_process.communicate()
+            
+            for p in self.ffmpeg_other_processes:
+                poll = p['process'].poll()
+                if poll == None:
+                    continue
+                p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process'])
             
             # wait a bit before checking again
             time.sleep(10)
     
-    def start_ffmpeg(self):
-      self.ffmpeg_process = start_or_restart_ffmpeg(self.config['ffmpeg_cmd'], self.frame_size)
-      self.ffmpeg_pid.value = self.ffmpeg_process.pid
-      self.capture_thread = CameraCapture(self.name, self.ffmpeg_process, self.frame_shape, self.frame_queue, 
-          self.config['take_frame'], self.camera_fps, self.stop_event)
-      self.capture_thread.start()
+    def start_ffmpeg_detect(self):
+        ffmpeg_cmd = [c['cmd'] for c in self.config.ffmpeg_cmds if 'detect' in c['roles']][0]
+        self.ffmpeg_detect_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.logger, self.logpipe, self.frame_size)
+        self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid
+        self.capture_thread = CameraCapture(self.camera_name, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue, 
+            self.camera_fps)
+        self.capture_thread.start()
 
 class CameraCapture(threading.Thread):
-    def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, stop_event):
+    def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
         threading.Thread.__init__(self)
-        self.name = name
+        self.name = f"capture:{camera_name}"
+        self.camera_name = camera_name
         self.frame_shape = frame_shape
-        self.frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
         self.frame_queue = frame_queue
-        self.take_frame = take_frame
         self.fps = fps
         self.skipped_fps = EventsPerSecond()
         self.frame_manager = SharedMemoryFrameManager()
         self.ffmpeg_process = ffmpeg_process
         self.current_frame = mp.Value('d', 0.0)
         self.last_frame = 0
-        self.stop_event = stop_event
 
     def run(self):
         self.skipped_fps.start()
-        capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.frame_manager, self.frame_queue, self.take_frame,
-            self.fps, self.skipped_fps, self.stop_event, self.current_frame)
+        capture_frames(self.ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue,
+            self.fps, self.skipped_fps, self.current_frame)
+
+def capture_camera(name, config: CameraConfig, process_info):
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
+    
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
 
-def capture_camera(name, config, process_info, stop_event):
     frame_queue = process_info['frame_queue']
     camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'], stop_event)
     camera_watchdog.start()
     camera_watchdog.join()
 
-def track_camera(name, config, detection_queue, result_connection, detected_objects_queue, process_info, stop_event):
+def track_camera(name, config: CameraConfig, model_shape, detection_queue, result_connection, detected_objects_queue, process_info):
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
+    
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
+    threading.current_thread().name = f"process:{name}"
     listen()
 
     frame_queue = process_info['frame_queue']
 
-    frame_shape = config['frame_shape']
+    frame_shape = config.frame_shape
+    objects_to_track = config.objects.track
+    object_filters = config.objects.filters
+    mask = config.mask
 
-    # Merge the tracked object config with the global config
-    camera_objects_config = config.get('objects', {})
-    objects_to_track = camera_objects_config.get('track', [])
-    object_filters = camera_objects_config.get('filters', {})
+    motion_detector = MotionDetector(frame_shape, mask, config.motion)
+    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection, model_shape)
 
-    # load in the mask for object detection
-    if 'mask' in config:
-        if config['mask'].startswith('base64,'):
-            img = base64.b64decode(config['mask'][7:]) 
-            npimg = np.fromstring(img, dtype=np.uint8)
-            mask = cv2.imdecode(npimg, cv2.IMREAD_GRAYSCALE)
-        elif config['mask'].startswith('poly,'):
-            points = config['mask'].split(',')[1:]
-            contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
-            mask = np.zeros((frame_shape[0], frame_shape[1]), np.uint8)
-            mask[:] = 255
-            cv2.fillPoly(mask, pts=[contour], color=(0))
-        else:
-            mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE)
-    else:
-        mask = None
-
-    if mask is None or mask.size == 0:
-        mask = np.zeros((frame_shape[0], frame_shape[1]), np.uint8)
-        mask[:] = 255
-
-    motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
-    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection)
-
-    object_tracker = ObjectTracker(10)
+    object_tracker = ObjectTracker(config.detect)
 
     frame_manager = SharedMemoryFrameManager()
 
-    process_frames(name, frame_queue, frame_shape, frame_manager, motion_detector, object_detector,
+    process_frames(name, frame_queue, frame_shape, model_shape, frame_manager, motion_detector, object_detector,
         object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, mask, stop_event)
 
-    print(f"{name}: exiting subprocess")
+    logger.info(f"{name}: exiting subprocess")
 
 def reduce_boxes(boxes):
     if len(boxes) == 0:
@@ -292,8 +276,8 @@ def reduce_boxes(boxes):
     reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0]
     return [tuple(b) for b in reduced_boxes]
 
-def detect(object_detector, frame, region, objects_to_track, object_filters, mask):
-    tensor_input = create_tensor_input(frame, region)
+def detect(object_detector, frame, model_shape, region, objects_to_track, object_filters, mask):
+    tensor_input = create_tensor_input(frame, model_shape, region)
 
     detections = []
     region_detections = object_detector.detect(tensor_input)
@@ -315,11 +299,11 @@ def detect(object_detector, frame, region, objects_to_track, object_filters, mas
         detections.append(det)
     return detections
 
-def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, 
+def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_shape,
     frame_manager: FrameManager, motion_detector: MotionDetector, 
     object_detector: RemoteObjectDetector, object_tracker: ObjectTracker,
     detected_objects_queue: mp.Queue, process_info: Dict,
-    objects_to_track: List[str], object_filters: Dict, mask, stop_event: mp.Event,
+    objects_to_track: List[str], object_filters, mask, stop_event,
     exit_on_empty: bool = False):
     
     fps = process_info['process_fps']
@@ -330,9 +314,12 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
     fps_tracker.start()
 
     while True:
-        if stop_event.is_set() or (exit_on_empty and frame_queue.empty()):
-                print(f"Exiting track_objects...")
-                break
+        if stop_event.is_set():
+            break
+
+        if exit_on_empty and frame_queue.empty():
+            logger.info(f"Exiting track_objects...")
+            break
 
         try:
             frame_time = frame_queue.get(True, 10)
@@ -344,7 +331,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
         frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
 
         if frame is None:
-            print(f"{camera_name}: frame {frame_time} is not in memory store.")
+            logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
             continue
 
         # look for motion
@@ -369,7 +356,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
         # resize regions and detect
         detections = []
         for region in regions:
-            detections.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+            detections.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters, mask))
         
         #########
         # merge objects, check for clipped objects and look again up to 4 times
@@ -401,8 +388,10 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
                         region = calculate_region(frame_shape, 
                             box[0], box[1],
                             box[2], box[3])
+
+                        regions.append(region)
                         
-                        selected_objects.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+                        selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters, mask))
 
                         refining = True
                     else:
@@ -424,6 +413,6 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
         else:
           fps_tracker.update()
           fps.value = fps_tracker.eps()
-          detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects))
+          detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects, motion_boxes, regions))
           detection_fps.value = object_detector.fps.eps()
           frame_manager.close(f"{camera_name}{frame_time}")

frigate/watchdog.py

@@ -0,0 +1,36 @@
+import datetime
+import logging
+import threading
+import time
+
+logger = logging.getLogger(__name__)
+
+class FrigateWatchdog(threading.Thread):
+    def __init__(self, detectors, stop_event):
+        threading.Thread.__init__(self)
+        self.name = 'frigate_watchdog'
+        self.detectors = detectors
+        self.stop_event = stop_event
+
+    def run(self):
+        time.sleep(10)
+        while True:
+            # wait a bit before checking
+            time.sleep(10)
+
+            if self.stop_event.is_set():
+                logger.info(f"Exiting watchdog...")
+                break
+
+            now = datetime.datetime.now().timestamp()
+
+            # check the detection processes
+            for detector in self.detectors.values():
+                detection_start = detector.detection_start.value
+                if (detection_start > 0.0 and 
+                    now - detection_start > 10):
+                    logger.info("Detection appears to be stuck. Restarting detection process")
+                    detector.start_or_restart()
+                elif not detector.detect_process.is_alive():
+                    logger.info("Detection appears to have stopped. Restarting detection process")
+                    detector.start_or_restart()

frigate/zeroconf.py

@@ -0,0 +1,58 @@
+import logging
+import socket
+
+from zeroconf import (
+    ServiceInfo,
+    NonUniqueNameException,
+    InterfaceChoice,
+    IPVersion,
+    Zeroconf,
+)
+
+logger = logging.getLogger(__name__)
+
+ZEROCONF_TYPE = "_frigate._tcp.local."
+
+# Taken from: http://stackoverflow.com/a/11735897
+def get_local_ip() -> str:
+    """Try to determine the local IP address of the machine."""
+    try:
+        sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+
+        # Use Google Public DNS server to determine own IP
+        sock.connect(("8.8.8.8", 80))
+
+        return sock.getsockname()[0]  # type: ignore
+    except OSError:
+        try:
+            return socket.gethostbyname(socket.gethostname())
+        except socket.gaierror:
+            return "127.0.0.1"
+    finally:
+        sock.close()
+
+def broadcast_zeroconf(frigate_id):
+    zeroconf = Zeroconf(interfaces=InterfaceChoice.Default, ip_version=IPVersion.V4Only)
+
+    host_ip = get_local_ip()
+
+    try:
+        host_ip_pton = socket.inet_pton(socket.AF_INET, host_ip)
+    except OSError:
+        host_ip_pton = socket.inet_pton(socket.AF_INET6, host_ip)
+
+    info = ServiceInfo(
+        ZEROCONF_TYPE,
+        name=f"{frigate_id}.{ZEROCONF_TYPE}",
+        addresses=[host_ip_pton],
+        port=5000,
+    )
+
+    logger.info("Starting Zeroconf broadcast")
+    try:
+        zeroconf.register_service(info)
+    except NonUniqueNameException:
+        logger.error(
+            "Frigate instance with identical name present in the local network"
+        )
+    return zeroconf

nginx/nginx.conf

@@ -0,0 +1,122 @@
+worker_processes  1;
+
+error_log  /var/log/nginx/error.log warn;
+pid        /var/run/nginx.pid;
+
+load_module "modules/ngx_rtmp_module.so";
+
+events {
+    worker_connections  1024;
+}
+
+http {
+    include       /etc/nginx/mime.types;
+    default_type  application/octet-stream;
+
+    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
+                        '$status $body_bytes_sent "$http_referer" '
+                        '"$http_user_agent" "$http_x_forwarded_for"';
+
+    access_log  /var/log/nginx/access.log  main;
+
+    sendfile        on;
+
+    keepalive_timeout  65;
+
+    upstream frigate_api {
+      server localhost:5001;
+      keepalive 1024;
+    }
+
+    server {
+        listen 5000;
+
+        location /stream/ {
+            add_header 'Cache-Control' 'no-cache';
+            add_header 'Access-Control-Allow-Origin' "$http_origin" always;
+            add_header 'Access-Control-Allow-Credentials' 'true';
+            add_header 'Access-Control-Expose-Headers' 'Content-Length';
+            if ($request_method = 'OPTIONS') {
+                add_header 'Access-Control-Allow-Origin' "$http_origin";
+                add_header 'Access-Control-Max-Age' 1728000;
+                add_header 'Content-Type' 'text/plain charset=UTF-8';
+                add_header 'Content-Length' 0;
+                return 204;
+            }
+
+            types {
+                application/dash+xml mpd;
+                application/vnd.apple.mpegurl m3u8;
+                video/mp2t ts;
+                image/jpeg jpg;
+            }
+
+            root /tmp;
+        }
+
+        location /clips/ {
+            add_header 'Access-Control-Allow-Origin' "$http_origin" always;
+            add_header 'Access-Control-Allow-Credentials' 'true';
+            add_header 'Access-Control-Expose-Headers' 'Content-Length';
+            if ($request_method = 'OPTIONS') {
+                add_header 'Access-Control-Allow-Origin' "$http_origin";
+                add_header 'Access-Control-Max-Age' 1728000;
+                add_header 'Content-Type' 'text/plain charset=UTF-8';
+                add_header 'Content-Length' 0;
+                return 204;
+            }
+
+            types {
+                video/mp4 mp4;
+                image/jpeg jpg;
+            }
+
+            autoindex on;
+            root /media/frigate;
+        }
+
+        location /recordings/ {
+            add_header 'Access-Control-Allow-Origin' "$http_origin" always;
+            add_header 'Access-Control-Allow-Credentials' 'true';
+            add_header 'Access-Control-Expose-Headers' 'Content-Length';
+            if ($request_method = 'OPTIONS') {
+                add_header 'Access-Control-Allow-Origin' "$http_origin";
+                add_header 'Access-Control-Max-Age' 1728000;
+                add_header 'Content-Type' 'text/plain charset=UTF-8';
+                add_header 'Content-Length' 0;
+                return 204;
+            }
+
+            types {
+                video/mp4 mp4;
+            }
+
+            autoindex on;
+            autoindex_format json;
+            root /media/frigate;
+        }
+
+        location / {
+            proxy_pass http://frigate_api/;
+            proxy_pass_request_headers on;
+            proxy_set_header Host $host;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+        }
+    }
+}
+
+rtmp {
+    server {
+        listen 1935;
+        chunk_size 4096;
+        allow publish 127.0.0.1;
+        deny publish all;
+        allow play all;
+        application live {
+            live on;
+            record off;
+            meta copy;
+        }
+    }
+}

process_clip.py

@@ -1,152 +0,0 @@
-import sys
-import click
-import os
-import datetime
-from unittest import TestCase, main
-from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames, get_frame_shape
-from frigate.util import DictFrameManager, SharedMemoryFrameManager, EventsPerSecond, draw_box_with_label
-from frigate.motion import MotionDetector
-from frigate.edgetpu import LocalObjectDetector
-from frigate.objects import ObjectTracker
-import multiprocessing as mp
-import numpy as np
-import cv2
-from frigate.object_processing import COLOR_MAP, CameraState
-
-class ProcessClip():
-    def __init__(self, clip_path, frame_shape, config):
-        self.clip_path = clip_path
-        self.frame_shape = frame_shape
-        self.camera_name = 'camera'
-        self.frame_manager = DictFrameManager()
-        # self.frame_manager = SharedMemoryFrameManager()
-        self.frame_queue = mp.Queue()
-        self.detected_objects_queue = mp.Queue()
-        self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
-
-    def load_frames(self):
-        fps = EventsPerSecond()
-        skipped_fps = EventsPerSecond()
-        stop_event = mp.Event()
-        detection_frame = mp.Value('d', datetime.datetime.now().timestamp()+100000)
-        current_frame = mp.Value('d', 0.0)
-        ffmpeg_cmd = f"ffmpeg -hide_banner -loglevel panic -i {self.clip_path} -f rawvideo -pix_fmt rgb24 pipe:".split(" ")
-        ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.frame_shape[0]*self.frame_shape[1]*self.frame_shape[2])
-        capture_frames(ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue, 1, fps, skipped_fps, stop_event, detection_frame, current_frame)
-        ffmpeg_process.wait()
-        ffmpeg_process.communicate()
-    
-    def process_frames(self, 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(self.frame_shape, mask)
-
-        object_detector = LocalObjectDetector(labels='/labelmap.txt')
-        object_tracker = ObjectTracker(10)
-        process_fps = mp.Value('d', 0.0)
-        detection_fps = mp.Value('d', 0.0)
-        current_frame = mp.Value('d', 0.0)
-        stop_event = mp.Event()
-
-        process_frames(self.camera_name, self.frame_queue, self.frame_shape, self.frame_manager, motion_detector, object_detector, object_tracker, self.detected_objects_queue, 
-            process_fps, detection_fps, current_frame, objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
-    
-    def objects_found(self, debug_path=None):
-        obj_detected = False
-        top_computed_score = 0.0
-        def handle_event(name, obj):
-            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()):
-            camera_name, frame_time, current_tracked_objects = self.detected_objects_queue.get()
-            if not debug_path is None:
-                self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
-
-            self.camera_state.update(frame_time, current_tracked_objects)
-            for obj in self.camera_state.tracked_objects.values():
-                print(f"{frame_time}: {obj['id']} - {obj['computed_score']} - {obj['score_history']}")
-        
-        self.frame_manager.delete(self.camera_state.previous_frame_id)
-        
-        return {
-            'object_detected': obj_detected,
-            'top_score': top_computed_score
-        }
-    
-    def save_debug_frame(self, debug_path, frame_time, tracked_objects):
-        current_frame = self.frame_manager.get(f"{self.camera_name}{frame_time}", self.frame_shape)
-        # draw the bounding boxes on the frame
-        for obj in tracked_objects:
-            thickness = 2
-            color = (0,0,175)
-
-            if obj['frame_time'] != frame_time:
-                thickness = 1
-                color = (255,0,0)
-            else:
-                color = (255,255,0)
-
-            # draw the bounding boxes on the frame
-            box = obj['box']
-            draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
-            # draw the regions on the frame
-            region = obj['region']
-            draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
-        
-        cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR))
-
-@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("--debug-path", default=None, help="Path to output frames for debugging.")
-def process(path, label, threshold, debug_path):
-    clips = []
-    if os.path.isdir(path):
-        files = os.listdir(path)
-        files.sort()
-        clips = [os.path.join(path, file) for file in files]
-    elif os.path.isfile(path):  
-        clips.append(path)
-
-    config = {
-        'snapshots': {
-            'show_timestamp': False, 
-            'draw_zones': False
-        },
-        'zones': {},
-        'objects': {
-            'track': [label],
-            'filters': {
-                'person': {
-                    'threshold': threshold
-                }
-            }
-        }
-    }
-
-    results = []
-    for c in clips:
-        frame_shape = get_frame_shape(c)
-        config['frame_shape'] = frame_shape
-        process_clip = ProcessClip(c, frame_shape, config)
-        process_clip.load_frames()
-        process_clip.process_frames(objects_to_track=config['objects']['track'])
-
-        results.append((c, process_clip.objects_found(debug_path)))
-
-    for result in results:
-        print(f"{result[0]}: {result[1]}")
-    
-    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 __name__ == '__main__':
-    process()

run.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+service nginx start
+exec python3 -u -m frigate