hwaccel docs

Update attributes name to pull through the FPS

.dockerignore

@@ -1,5 +1,5 @@
 README.md
-diagram.png
+docs/
 .gitignore
 debug
 config/

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,55 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Version of frigate**
+What version are you using?
+
+**Config file**
+Include your full config file wrapped in back ticks.
+```
+config here
+```
+
+**Logs**
+```
+Include relevant log output here
+```
+
+**Frigate debug stats**
+```
+Output from frigate's /debug/stats endpoint
+```
+
+**FFprobe from your camera**
+
+Run the following command and paste output below
+```
+ffprobe <stream_url>
+```
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Computer Hardware**
+ - OS: [e.g. Ubuntu, Windows]
+ - Virtualization: [e.g. Proxmox, Virtualbox]
+ - Coral Version: [e.g. USB, PCIe, None]
+ - Network Setup: [e.g. Wired, WiFi]
+
+**Camera Info:**
+ - Manufacturer: [e.g. Dahua]
+ - Model: [e.g. IPC-HDW5231R-ZE]
+ - Resolution: [e.g. 720p]
+ - FPS: [e.g. 5]
+
+**Additional context**
+Add any other context about the problem here.

Dockerfile

@@ -1,57 +0,0 @@
-FROM ubuntu:18.04
-LABEL maintainer "blakeb@blakeshome.com"
-
-ENV DEBIAN_FRONTEND=noninteractive
-# Install packages for apt repo
-RUN apt -qq update && apt -qq install --no-install-recommends -y \
-    software-properties-common \
-    # apt-transport-https ca-certificates \
-    build-essential \
-    gnupg wget unzip tzdata \
-    # libcap-dev \
-    && add-apt-repository ppa:deadsnakes/ppa -y \
-    && apt -qq install --no-install-recommends -y \
-        python3.7 \
-        python3.7-dev \
-        python3-pip \
-        ffmpeg \
-        # VAAPI drivers for Intel hardware accel
-        libva-drm2 libva2 i965-va-driver vainfo \
-    && python3.7 -m pip install -U wheel setuptools \
-    && python3.7 -m pip install -U \
-        opencv-python-headless \
-        # python-prctl \
-        numpy \
-        imutils \
-        scipy \
-    && python3.7 -m pip install -U \
-        Flask \
-        paho-mqtt \
-        PyYAML \
-        matplotlib \
-        pyarrow \
-    && echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
-    && wget -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
-    && apt -qq update \
-    && echo "libedgetpu1-max libedgetpu/accepted-eula boolean true" | debconf-set-selections \
-    && apt -qq install --no-install-recommends -y \
-        libedgetpu1-max \
-    ## Tensorflow lite (python 3.7 only)
-    && wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && python3.7 -m pip install tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && rm tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && rm -rf /var/lib/apt/lists/* \
-    && (apt-get autoremove -y; apt-get autoclean -y)
-
-# get model and labels
-RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite -O /edgetpu_model.tflite --trust-server-names
-RUN wget -q https://dl.google.com/coral/canned_models/coco_labels.txt -O /labelmap.txt --trust-server-names
-RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite -O /cpu_model.tflite 
-
-
-WORKDIR /opt/frigate/
-ADD frigate frigate/
-COPY detect_objects.py .
-COPY benchmark.py .
-
-CMD ["python3.7", "-u", "detect_objects.py"]

Makefile

@@ -0,0 +1,37 @@
+default_target: amd64_frigate
+
+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 .
+
+amd64_frigate:
+	docker build --tag frigate-base --build-arg ARCH=amd64 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.amd64 .
+
+amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
+
+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 .
+
+aarch64_frigate:
+	docker build --tag frigate-base --build-arg ARCH=aarch64 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.aarch64 .
+
+armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
+
+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 .
+
+armv7_frigate:
+	docker build --tag frigate-base --build-arg ARCH=armv7 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.armv7 .
+
+armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate

README.md

@@ -1,71 +1,513 @@
-# Frigate - Realtime Object Detection for IP Cameras
+<p align="center">
+  <img width="40%" align="center" alt="logo" src="docs/frigate.png">
+</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.
 
-Use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/) is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
+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.
 
+- 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 a separate process
-- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
-- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
+- Object detection with TensorFlow runs in separate processes for maximum FPS
+- Communicates over MQTT for easy integration into other systems
 
-![Diagram](diagram.png)
+## Documentation
+- [How Frigate Works](docs/how-frigate-works.md)
+- [Recommended Hardware](#recommended-hardware)
+- [Installing](#installing)
+- [Configuration File](#configuration)
+- [Optimizing Performance](#optimizing-performance)
+- [Detectors](#detectors)
+- [Object Filters](#object-filters)
+- [Masks](#masks)
+- [Zones](#zones)
+- [Integration with HomeAssistant](#integration-with-homeassistant)
+- [MQTT Topics](#mqtt-topics)
+- [HTTP Endpoints](#http-endpoints)
+- [Custom Models](#custom-models)
+- [Troubleshooting](#troubleshooting)
 
-## Example video (from older version)
-You see multiple bounding boxes because it draws bounding boxes from all frames in the past 1 second where a person was detected. Not all of the bounding boxes were from the current frame.
-[![](http://img.youtube.com/vi/nqHbCtyo4dY/0.jpg)](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
+## Recommended Hardware
+|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.|
+|Intel NUC NUC7i3BNK|8-10ms|Great performance. Can handle many cameras at 5fps depending on typical amounts of motion.|
+|BMAX B2 Plus|10-12ms|Good balance of performance and cost. Also capable of running many other services at the same time as frigate.|
+|Minisforum GK41|9-10ms|Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras.|
+|Raspberry Pi 3B (32bit)|60ms|Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.|
+|Raspberry Pi 4 (32bit)|15-20ms|Can handle a small number of cameras. The 2GB version runs fine.|
+|Raspberry Pi 4 (64bit)|10-15ms|Can handle a small number of cameras. The 2GB version runs fine.|
 
-## Getting Started
-Run the container with
-```bash
-docker run --rm \
--name frigate \
---privileged \
---shm-size=512m \ # should work for a 2-3 cameras
--v /dev/bus/usb:/dev/bus/usb \
--v <path_to_config_dir>:/config:ro \
--v /etc/localtime:/etc/localtime:ro \
--p 5000:5000 \
--e FRIGATE_RTSP_PASSWORD='password' \
-blakeblackshear/frigate:stable
-```
+[Back to top](#documentation)
 
-Example docker-compose:
+## Installing
+
+### HassOS Addon
+HassOS users can install via the addon repository. Frigate requires that an MQTT server be running.
+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
+1. Start the addon container
+
+### Docker
+Make sure you choose the right image for your architecture:
+|Arch|Image Name|
+|-|-|
+|amd64|blakeblackshear/frigate:stable-amd64|
+|armv7|blakeblackshear/frigate:stable-armv7|
+|aarch64|blakeblackshear/frigate:stable-aarch64|
+
+It is recommended to run with docker-compose:
 ```yaml
   frigate:
     container_name: frigate
     restart: unless-stopped
     privileged: true
-    shm_size: '1g' # should work for 5-7 cameras
-    image: blakeblackshear/frigate:stable
+    image: blakeblackshear/frigate:stable-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
+        tmpfs:
+          size: 100000000
     ports:
       - "5000:5000"
     environment:
       FRIGATE_RTSP_PASSWORD: "password"
+    healthcheck:
+      test: ["CMD", "wget" , "-q", "-O-", "http://localhost:5000"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 3m
 ```
 
-A `config.yml` file must exist in the `config` directory. See example [here](config/config.example.yml) and device specific info can be found [here](docs/DEVICES.md).
+If you can't use docker compose, you can run the container with:
+```bash
+docker run --rm \
+--name frigate \
+--privileged \
+-v /dev/bus/usb:/dev/bus/usb \
+-v <path_to_config_dir>:/config:ro \
+-v /etc/localtime:/etc/localtime:ro \
+-p 5000:5000 \
+-e FRIGATE_RTSP_PASSWORD='password' \
+blakeblackshear/frigate:stable-amd64
+```
 
-## Recommended Hardware
-|Name|Inference Speed|Notes|
-|----|---------------|-----|
-|Atomic Pi|16ms|Best option for a dedicated low power board with a small number of cameras.|
-|Intel NUC NUC7i3BNK|8-10ms|Best possible performance. Can handle 7+ cameras at 5fps depending on typical amounts of motion.|
-|BMAX B2 Plus|10-12ms|Good balance of performance and cost. Also capable of running many other services at the same time as frigate.
+### Kubernetes
+Use the [helm chart](https://github.com/k8s-at-home/charts/tree/master/charts/frigate).
 
-ARM boards are not officially supported at the moment due to some python dependencies that require modification to work on ARM devices. The Raspberry Pi4 gets about 16ms inference speeds, but the hardware acceleration for ffmpeg does not work for converting yuv420 to rgb24. The Atomic Pi is x86 and much more efficient.
+### Virtualization
+For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
 
-Users have reported varying success in getting frigate to run in a VM. In some cases, the virtualization layer introduces a significant delay in communication with the Coral. If running virtualized in Proxmox, pass the USB card/interface to the virtual machine not the USB ID for faster inference speed.
+#### Proxmox
+Some people have had success running Frigate in LXC directly with the following config:
+```
+arch: amd64
+cores: 2
+features: nesting=1
+hostname: FrigateLXC
+memory: 4096
+net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
+ostype: debian
+rootfs: local-lvm:vm-115-disk-0,size=12G
+swap: 512
+lxc.cgroup.devices.allow: c 189:385 rwm
+lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
+lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
+lxc.apparmor.profile: unconfined
+lxc.cgroup.devices.allow: a
+lxc.cap.drop:
+```
+
+### Calculating shm-size
+The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
+
+You can calculate the necessary shm-size for each camera with the following formula:
+```
+(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
+```
+[Back to top](#documentation)
+
+## 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).
+
+```yaml
+# Optional: port for http server (default: shown below)
+web_port: 5000
+
+# Optional: detectors configuration
+# USB Coral devices will be auto detected with CPU fallback
+detectors:
+  # Required: name of the detector
+  coral:
+    # Required: type of the detector
+    # Valid values are 'edgetpu' (requires device property below) and 'cpu'.
+    type: edgetpu
+    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
+    device: usb
+
+# Required: mqtt configuration
+mqtt:
+  # Required: host name
+  host: mqtt.server.com
+  # Optional: port (default: shown below)
+  port: 1883
+  # Optional: topic prefix (default: shown below)
+  # WARNING: must be unique if you are running multiple instances
+  topic_prefix: frigate
+  # Optional: client id (default: shown below)
+  # WARNING: must be unique if you are running multiple instances
+  client_id: frigate
+  # Optional: user
+  user: mqtt_user
+  # Optional: password
+  # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}. 
+  #       eg. password: '{FRIGATE_MQTT_PASSWORD}'
+  password: password
+
+# Optional: Global configuration for saving clips
+save_clips:
+  # Optional: Maximum length of time to retain video during long events. (default: shown below)
+  # NOTE: If an object is being tracked for longer than this amount of time, the cache
+  #       will begin to expire and the resulting clip will be the last x seconds of the event.
+  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: Global ffmpeg args
+# "ffmpeg" + global_args + input_args + "-i" + input + output_args
+ffmpeg:
+  # Optional: global ffmpeg args (default: shown below)
+  global_args:
+    - -hide_banner
+    - -loglevel
+    - panic
+  # 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
+
+# Optional: Global object filters for all cameras.
+# NOTE: can be overridden at the camera level
+objects:
+  # Optional: list of objects to track from labelmap.txt (default: shown below)
+  track:
+    - person
+  # Optional: filters to reduce false positives for specific object types
+  filters:
+    person:
+      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
+      min_area: 5000
+      # Optional: maximum width*height of the bounding box for the detected object (default: max_int)
+      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
+
+# Required: configuration section for cameras
+cameras:
+  # Required: name of the camera
+  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
+      # Optional: camera specific global args (default: inherit)
+      global_args:
+      # Optional: camera specific hwaccel args (default: inherit)
+      hwaccel_args:
+      # Optional: camera specific input args (default: inherit)
+      input_args:
+      # Optional: camera specific output args (default: inherit)
+      output_args:
+    
+    # Optional: 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.
+    width: 1280
+    # Optional: desired fps for your camera
+    # 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
+    # NOTE: see docs for more detailed info on creating masks
+    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
+      # NOTE: This must be different than any camera names, but can match with another zone on another
+      #       camera.
+      front_steps:
+        # Required: List of x,y coordinates to define the polygon of the zone.
+        # NOTE: Coordinates can be generated at https://www.image-map.net/
+        coordinates: 545,1077,747,939,788,805
+        # Optional: Zone level object filters.
+        # NOTE: The global and camera filters are applied upstream.
+        filters:
+          person:
+            min_area: 5000
+            max_area: 100000
+            threshold: 0.8
+
+    # Optional: save clips configuration
+    # NOTE: This feature does not work if you have added "-vsync drop" in your input params. 
+    #       This will only work for camera feeds that can be copied into the mp4 container format without
+    #       encoding such as h264. It may not work for some types of streams.
+    save_clips:
+      # 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
+      # Optional: Objects to save clips for. (default: all tracked objects)
+      objects:
+        - person      
+
+    # Optional: Configuration for the snapshots in the debug view and mqtt
+    snapshots:
+      # Optional: print a timestamp on the snapshots (default: shown below)
+      show_timestamp: True
+      # Optional: draw zones on the debug mjpeg feed (default: shown below)
+      draw_zones: False
+      # Optional: draw bounding boxes on the mqtt snapshots (default: shown below)
+      draw_bounding_boxes: True
+
+    # Optional: Camera level object filters config. If defined, this is used instead of the global config.
+    objects:
+      track:
+        - person
+        - car
+      filters:
+        person:
+          min_area: 5000
+          max_area: 100000
+          min_score: 0.5
+          threshold: 0.85
+```
+[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.
+- **FPS**: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
+- **Hardware Acceleration**: Make sure you configure the `hwaccel_args` for your hardware. They provide a significant reduction in CPU usage if they are available.
+- **Masks**: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for `detection_fps` when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.
+
+### FFmpeg Hardware Acceleration
+Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.
+
+Raspberry Pi 3/4 (32-bit OS):
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -c:v
+    - h264_mmal
+```
+
+Raspberry Pi 3/4 (64-bit OS)
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -c:v
+    - h264_v4l2m2m
+```
+
+Intel-based CPUs (<10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -hwaccel
+    - vaapi
+    - -hwaccel_device
+    - /dev/dri/renderD128
+    - -hwaccel_output_format
+    - yuv420p
+```
+
+Intel-based CPUs (>=10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
+**Note:** You also need to set `LIBVA_DRIVER_NAME=iHD` as an environment variable on the container.
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -hwaccel
+    - vaapi
+    - -hwaccel_device
+    - /dev/dri/renderD128
+```
+
+[Back to top](#documentation)
+
+## Detectors
+By default Frigate will look for a USB Coral device and fall back to the CPU if it cannot be found. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
+
+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).
+
+Single USB Coral:
+```yaml
+detectors:
+  coral:
+    type: edgetpu
+    device: usb
+```
+
+Multiple USB Corals:
+```yaml
+detectors:
+  coral1:
+    type: edgetpu
+    device: usb:0
+  coral2:
+    type: edgetpu
+    device: usb:1
+```
+
+Mixing Corals:
+```yaml
+detectors:
+  coral_usb:
+    type: edgetpu
+    device: usb
+  coral_pci:
+    type: edgetpu
+    device: pci
+```
+
+CPU Detectors (not recommended):
+```yaml
+detectors:
+  cpu1:
+    type: cpu
+  cpu2:
+    type: cpu
+```
+[Back to top](#documentation)
+
+## Reducing False Positives
+Tune your object filters to adjust false positives: `min_area`, `max_area`, `min_score`, `threshold`.
+
+For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
+
+| Frame | Current Score | Score History | Computed Score | Detected Object |
+| --- | --- | --- | --- | --- |
+| 1 | 0.7 | 0.0, 0, 0.7 | 0.0 | No
+| 2 | 0.55 | 0.0, 0.7, 0.0 | 0.0 | No
+| 3 | 0.85 | 0.7, 0.0, 0.85 | 0.7 | No
+| 4 | 0.90 | 0.7, 0.85, 0.95, 0.90 | 0.875 | Yes
+| 5 | 0.88 | 0.7, 0.85, 0.95, 0.90, 0.88 | 0.88 | Yes
+| 6 | 0.95 | 0.7, 0.85, 0.95, 0.90, 0.88, 0.95 | 0.89 | Yes
+
+In frame 2, the score is below the `min_score` value, so frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
+
+[Back to top](#documentation)
+
+## Masks
+The following types of masks are supported:
+- `poly`: (Recommended) List of x,y points like zone configuration
+- `base64`: Base64 encoded image file
+- `image`: Image file in the `/config` directory
+
+`base64` and `image` masks must be the same aspect ratio and resolution as your camera. 
+
+The mask in the second image would limit motion detection on this camera to only the front yard and not the street. 
+
+<a href="docs/example-mask-check-point.png"><img src="docs/example-mask-check-point.png" height="300"></a>
+<a href="docs/example-mask.bmp"><img src="docs/example-mask.bmp" height="300"></a>
+<a href="docs/example-mask-overlay.png"><img src="docs/example-mask-overlay.png" height="300"></a>
+
+To create a poly mask:
+1. Download a camera snapshot image with the same resolution as the camera feed (`/<camera_name>/latest.jpg`).
+1. Upload the image to https://www.image-map.net/
+1. Select "shape" poly - start in the lowest left corner and place the first marker (point) and continue upwards and then to the right until the polygon shape covers the area that you want to mask out (ignore). 
+1. When you are finished with the polygon click "Show me the code!" and copy all coordinates (point), ie. `"0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"` 
+1. Adjust any -1 values to 0 and then add it all to the configuration (see the example configuration for correct indentation and placement)
+
+Example of a finished row corresponding to the below example image:
+```yaml
+mask: 'poly,0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432'
+```
+    
+<a href="docs/example-mask-poly.png"><img src="docs/example-mask-poly.png" height="300"></a>
+
+You can test your mask by temporarily configuring it as a [zone](#zones) and enabling`draw_zones` in your config.
+
+[Back to top](#documentation)
+
+## Zones
+Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
+
+During testing, `draw_zones` should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
+
+![Zone Example](docs/zone_example.jpg)
+
+[Back to top](#documentation)
+
+## 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.
+
+### 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.
+- `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)
 
 ## 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)
 
-Setup a the 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)
-
-```
+```yaml
 camera:
   - name: <camera_name> Last Person
     platform: mqtt
@@ -88,12 +530,13 @@ sensor:
     scan_interval: 5
     json_attributes:
       - <camera_name>
-      - coral
+      - detection_fps
+      - detectors
     value_template: 'OK'  
   - platform: template
     sensors:
       <camera_name>_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["<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"] }}'
@@ -101,11 +544,11 @@ sensor:
       <camera_name>_detection_fps: 
         value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["detection_fps"] }}'
         unit_of_measurement: 'FPS'
-      frigate_coral_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["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["coral"]["inference_speed"] }}' 
+        value_template: '{{ states.sensor.frigate_debug.attributes["detectors"]["coral"]["inference_speed"] }}' 
         unit_of_measurement: 'ms'
         
 automation:
@@ -128,41 +571,211 @@ automation:
               - url: http://<ip>:5000/<camera_name>/person/best.jpg
                 caption: A person was detected.        
 ```
-## Debugging Endpoint
 
-Keep in mind the MJPEG endpoint is for debugging only, but should not be used continuously as it will put additional load on the system. 
+[Back to top](#documentation)
 
-Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best snapshot for any object type with at `http://localhost:5000/<camera_name>/<object_name>/best.jpg`
+## HTTP Endpoints
+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`
 
-Debug info is available at `http://localhost:5000/debug/stats`
+### `/<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.
 
+Example parameters:
+- `h=300`: resizes the image to 300 pixes tall
+- `crop=1`: crops the image to the region of the detection rather than returning the entire image
 
-## Using a custom model
+### `/<camera_name>/latest.jpg[?h=300]`
+The most recent frame that frigate has finished processing. It is a full resolution image by default.
+
+Example parameters:
+- `h=300`: resizes the image to 300 pixes tall
+
+### `/debug/stats`
+Contains some granular debug info that can be used for sensors in HomeAssistant.
+
+Sample response:
+```jsonc
+{
+    /* Per Camera Stats */
+    "back": {
+        /***************
+        * Frames per second being consumed from your camera. If this is higher
+        * than it is supposed to be, you should set -r FPS in your input_args.
+        * camera_fps = process_fps + skipped_fps
+        ***************/
+        "camera_fps": 5.0,
+        /***************
+        * Number of times detection is run per second. This can be higher than
+        * your camera FPS because frigate often looks at the same frame multiple times
+        * or in multiple locations
+        ***************/
+        "detection_fps": 1.5,
+        /***************
+        * 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
+        },
+        /***************
+        * PID for the process that runs detection for this camera
+        ***************/
+        "pid": 34,
+        /***************
+        * Frames per second being processed by frigate.
+        ***************/
+        "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
+    },
+    /***************
+    * Sum of detection_fps across all cameras and detectors. 
+    * This should be the sum of all detection_fps values from cameras.
+    ***************/
+    "detection_fps": 5.0,
+    /* Detectors Stats */
+    "detectors": {
+      "coral": {
+          /***************
+          * Timestamp when object detection started. If this value stays non-zero and constant
+          * for a long time, that means the detection process is stuck.
+          ***************/
+          "detection_start": 0.0,
+          /***************
+          * Time spent running object detection in milliseconds.
+          ***************/
+          "inference_speed": 10.48,
+          /***************
+          * PID for the shared process that runs object detection on the Coral.
+          ***************/
+          "pid": 25321
+      }
+    }
+}
+```
+
+[Back to top](#documentation)
+
+## MQTT Topics
+These are the MQTT messages generated by Frigate. The default topic_prefix is `frigate`, but can be changed in the config file.
+
+### `frigate/available`
+Designed to be used as an availability topic with HomeAssistant. Possible message are:
+"online": published when frigate is running (on startup)
+"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.
+
+### `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
+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
+{
+    "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
+}
+```
+
+### `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
 Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
 - CPU Model: `/cpu_model.tflite`
 - EdgeTPU Model: `/edgetpu_model.tflite`
 - Labels: `/labelmap.txt`
 
-## Masks and limiting detection to a certain area
-You can create a *bitmap (bmp)* file the same aspect ratio as your camera feed to limit detection to certain areas. The mask works by looking at the bottom center of any bounding box (first image, red dot below) and comparing that to your mask. If that red dot falls on an area of your mask that is black, the detection (and motion) will be ignored. The mask in the second image would limit detection on this camera to only objects that are in the front yard and not the street. 
+### 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:
 
-<a href="docs/example-mask-check-point.png"><img src="docs/example-mask-check-point.png" height="300"></a>
-<a href="docs/example-mask.bmp"><img src="docs/example-mask.bmp" height="300"></a>
-<a href="docs/example-mask-overlay.png"><img src="docs/example-mask-overlay.png" height="300"></a>
+- Download the [COCO labelmap](https://dl.google.com/coral/canned_models/coco_labels.txt)
+- Modify the label names as desired. For example, change `7 truck` to `7 car`
+- Mount the new file at `/labelmap.txt` in the container with an additional volume
+  ```
+  -v ./config/labelmap.txt:/labelmap.txt
+  ```
 
-## Tips
-- Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed. Not as effective, but you can also modify the `take_frame` [configuration](config/config.example.yml) for each camera to only analyze every other frame, or every third frame, etc. 
-- Hard code the resolution of each camera in your config if you are having difficulty starting frigate or if the initial ffprobe for camerea resolution fails or returns incorrect info. Example:
+[Back to top](#documentation)
+
+## Troubleshooting
+
+### "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`:
+```yaml
+ffmpeg:
+  global_args:
+    - -hide_banner
+    - -loglevel
+    - info
 ```
-cameras:
-  back:
-    ffmpeg:
-      input: rtsp://<camera>
-    height: 1080
-    width: 1920
-```
-- Additional logging is available in the docker container - You can view the logs by running `docker logs -t frigate`
-- Object configuration - Tracked objects types, sizes and thresholds can be defined globally and/or on a per camera basis. The global and camera object configuration is *merged*. For example, if you defined tracking person, car, and truck globally but modified your backyard camera to only track person, the global config would merge making the effective list for the backyard camera still contain person, car and truck. If you want precise object tracking per camera, best practice to put a minimal list of objects at the global level and expand objects on a per camera basis. Object threshold and area configuration will be used first from the camera object config (if defined) and then from the global config.  See the [example config](config/config.example.yml) for more information. 
+
+### "On connect called"
+If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.
+
+[Back to top](#documentation)
 

benchmark.py

@@ -3,7 +3,7 @@ from statistics import mean
 import multiprocessing as mp
 import numpy as np
 import datetime
-from frigate.edgetpu import ObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
+from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
 
 my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
 labels = load_labels('/labelmap.txt')
@@ -11,7 +11,7 @@ labels = load_labels('/labelmap.txt')
 ######
 # Minimal same process runner
 ######
-# object_detector = ObjectDetector()
+# object_detector = LocalObjectDetector()
 # tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
 
 # start = datetime.datetime.now().timestamp()
@@ -37,11 +37,9 @@ labels = load_labels('/labelmap.txt')
 # print(f"Processed for {duration:.2f} seconds.")
 # print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 
-######
-# Separate process runner
-######
-def start(id, num_detections, detection_queue):
-  object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue)
+
+def start(id, num_detections, detection_queue, event):
+  object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
   start = datetime.datetime.now().timestamp()
 
   frame_times = []
@@ -51,23 +49,39 @@ def start(id, num_detections, detection_queue):
     frame_times.append(datetime.datetime.now().timestamp()-start_frame)
 
   duration = datetime.datetime.now().timestamp()-start
+  object_detector.cleanup()
   print(f"{id} - Processed for {duration:.2f} seconds.")
+  print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
   print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 
-edgetpu_process = EdgeTPUProcess()
+######
+# Separate process runner
+######
+# event = mp.Event()
+# detection_queue = mp.Queue()
+# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
 
-# start(1, 1000, edgetpu_process.detect_lock, edgetpu_process.detect_ready, edgetpu_process.frame_ready)
+# start(1, 1000, edgetpu_process.detection_queue, event)
+# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
 
 ####
 # Multiple camera processes
 ####
 camera_processes = []
+
+events = {}
 for x in range(0, 10):
-  camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue))
+  events[str(x)] = mp.Event()
+detection_queue = mp.Queue()
+edgetpu_process_1 = EdgeTPUProcess(detection_queue, events, 'usb:0')
+edgetpu_process_2 = EdgeTPUProcess(detection_queue, events, 'usb:1')
+
+for x in range(0, 10):
+  camera_process = mp.Process(target=start, args=(x, 300, detection_queue, events[str(x)]))
   camera_process.daemon = True
   camera_processes.append(camera_process)
 
-start = datetime.datetime.now().timestamp()
+start_time = datetime.datetime.now().timestamp()
 
 for p in camera_processes:
   p.start()
@@ -75,5 +89,5 @@ for p in camera_processes:
 for p in camera_processes:
   p.join()
 
-duration = datetime.datetime.now().timestamp()-start
+duration = datetime.datetime.now().timestamp()-start_time
 print(f"Total - Processed for {duration:.2f} seconds.")

config/config.example.yml

@@ -1,129 +0,0 @@
-web_port: 5000
-
-mqtt:
-  host: mqtt.server.com
-  topic_prefix: frigate
-  # client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
-  # user: username # Optional
-  #################
-  ## Environment variables that begin with 'FRIGATE_' may be referenced in {}.
-  ##   password: '{FRIGATE_MQTT_PASSWORD}'
-  #################
-  # password: password # Optional
-
-#################
-# Default ffmpeg args. Optional and can be overwritten per camera.
-# Should work with most RTSP cameras that send h264 video
-# Built from the properties below with:
-# "ffmpeg" + global_args + input_args + "-i" + input + output_args
-#################
-# ffmpeg:
-#   global_args:
-#     - -hide_banner
-#     - -loglevel
-#     - panic
-#   hwaccel_args: []
-#   input_args:
-#     - -avoid_negative_ts
-#     - make_zero
-#     - -fflags
-#     - nobuffer
-#     - -flags
-#     - low_delay
-#     - -strict
-#     - experimental
-#     - -fflags
-#     - +genpts+discardcorrupt
-#     - -vsync
-#     - drop
-#     - -rtsp_transport
-#     - tcp
-#     - -stimeout
-#     - '5000000'
-#     - -use_wallclock_as_timestamps
-#     - '1'
-#   output_args:
-#     - -f
-#     - rawvideo
-#     - -pix_fmt
-#     - rgb24
-
-####################
-# Global object configuration. Applies to all cameras
-# unless overridden at the camera levels.
-# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
-# All labels from the model are reported over MQTT. These values are used to filter out false positives.
-# min_area (optional): minimum width*height of the bounding box for the detected person
-# max_area (optional): maximum width*height of the bounding box for the detected person
-# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
-####################
-objects:
-  track:
-    - person
-    - car
-    - truck
-  filters:
-    person:
-      min_area: 5000
-      max_area: 100000
-      threshold: 0.5
-
-cameras:
-  back:
-    ffmpeg:
-      ################
-      # Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
-      # 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
-      #################
-      # These values will override default values for just this camera
-      #################
-      # global_args: []
-      # hwaccel_args: []
-      # input_args: []
-      # output_args: []
-    
-    ################
-    ## Optionally specify the resolution of the video feed. Frigate will try to auto detect if not specified
-    ################
-    # height: 1280
-    # width: 720
-
-    ################
-    ## Optional mask. Must be the same aspect ratio as your video feed.
-    ## 
-    ## The mask works by looking at the bottom center of the bounding box for the detected
-    ## person in the image. If that pixel in the mask is a black pixel, it ignores it as a
-    ## false positive. In my mask, the grass and driveway visible from my backdoor camera 
-    ## are white. The garage doors, sky, and trees (anywhere it would be impossible for a 
-    ## person to stand) are black.
-    ## 
-    ## Masked areas are also ignored for motion detection.
-    ################
-    # mask: back-mask.bmp
-
-    ################
-    # Allows you to limit the framerate within frigate for cameras that do not support
-    # custom framerates. A value of 1 tells frigate to look at every frame, 2 every 2nd frame, 
-    # 3 every 3rd frame, etc.
-    ################
-    take_frame: 1
-
-    ################
-    # Configuration for the snapshots in the debug view and mqtt
-    ################
-    snapshots:
-      show_timestamp: True
-
-    ################
-    # Camera level object config. This config is merged with the global config above.
-    ################
-    objects:
-      track:
-        - person
-      filters:
-        person:
-          min_area: 5000
-          max_area: 100000
-          threshold: 0.5

detect_objects.py

@@ -1,4 +1,6 @@
+import faulthandler; faulthandler.enable()
 import os
+import signal
 import sys
 import traceback
 import signal
@@ -7,6 +9,7 @@ import time
 import datetime
 import queue
 import yaml
+import json
 import threading
 import multiprocessing as mp
 import subprocess as sp
@@ -15,15 +18,30 @@ import logging
 from flask import Flask, Response, make_response, jsonify, request
 import paho.mqtt.client as mqtt
 
-from frigate.video import track_camera, get_ffmpeg_input, get_frame_shape, CameraCapture, start_or_restart_ffmpeg
+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_')}
 
-with open('/config/config.yml') as f:
-    CONFIG = yaml.safe_load(f)
+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)
@@ -47,37 +65,29 @@ FFMPEG_DEFAULT_CONFIG = {
          '-flags', 'low_delay',
          '-strict', 'experimental',
          '-fflags', '+genpts+discardcorrupt',
-         '-vsync', 'drop',
          '-rtsp_transport', 'tcp',
          '-stimeout', '5000000',
          '-use_wallclock_as_timestamps', '1']),
     'output_args': FFMPEG_CONFIG.get('output_args',
         ['-f', 'rawvideo',
-         '-pix_fmt', 'rgb24'])
+         '-pix_fmt', 'yuv420p'])
 }
 
 GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})
 
 WEB_PORT = CONFIG.get('web_port', 5000)
-DEBUG = (CONFIG.get('debug', '0') == '1')
+DETECTORS = CONFIG.get('detectors', {'coral': {'type': 'edgetpu', 'device': 'usb'}})
 
-def start_plasma_store():
-    plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
-    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
-    time.sleep(1)
-    rc = plasma_process.poll()
-    if rc is not None:
-        return None
-    return plasma_process
-
-class CameraWatchdog(threading.Thread):
-    def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, plasma_process):
+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.tflite_process = tflite_process
+        self.detectors = detectors
+        self.detection_queue = detection_queue
+        self.out_events = out_events
         self.tracked_objects_queue = tracked_objects_queue
-        self.plasma_process = plasma_process
+        self.stop_event = stop_event
 
     def run(self):
         time.sleep(10)
@@ -85,63 +95,41 @@ class CameraWatchdog(threading.Thread):
             # wait a bit before checking
             time.sleep(10)
 
-            now = datetime.datetime.now().timestamp()
-            
-            # check the plasma process
-            rc = self.plasma_process.poll()
-            if rc != None:
-                print(f"plasma_process exited unexpectedly with {rc}")
-                self.plasma_process = start_plasma_store()
+            if self.stop_event.is_set():
+                print(f"Exiting watchdog...")
+                break
 
-            # check the detection process
-            detection_start = self.tflite_process.detection_start.value
-            if (detection_start > 0.0 and 
-                now - detection_start > 10):
-                print("Detection appears to be stuck. Restarting detection process")
-                self.tflite_process.start_or_restart()
-            elif not self.tflite_process.detect_process.is_alive():
-                print("Detection appears to have stopped. Restarting detection process")
-                self.tflite_process.start_or_restart()
+            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[name], GLOBAL_OBJECT_CONFIG, camera_process['frame_queue'],
-                        camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue, 
-                        camera_process['process_fps'], camera_process['detection_fps'],
-                        camera_process['read_start'], camera_process['detection_frame']))
+                    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}")
-                
-                if not camera_process['capture_thread'].is_alive():
-                    frame_shape = camera_process['frame_shape']
-                    frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
-                    ffmpeg_process = start_or_restart_ffmpeg(camera_process['ffmpeg_cmd'], frame_size)
-                    camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, camera_process['frame_queue'], 
-                        camera_process['take_frame'], camera_process['camera_fps'], camera_process['detection_frame'])
-                    camera_capture.start()
-                    camera_process['ffmpeg_process'] = ffmpeg_process
-                    camera_process['capture_thread'] = camera_capture
-                elif now - camera_process['capture_thread'].current_frame > 5:
-                    print(f"No frames received from {name} in 5 seconds. Exiting ffmpeg...")
-                    ffmpeg_process = camera_process['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()
 
 def main():
+    stop_event = threading.Event()
     # connect to mqtt and setup last will
     def on_connect(client, userdata, flags, rc):
         print("On connect called")
@@ -164,24 +152,44 @@ def main():
     client.connect(MQTT_HOST, MQTT_PORT, 60)
     client.loop_start()
 
-    plasma_process = start_plasma_store()
-
     ##
     # Setup config defaults for cameras
     ##
     for name, config in CONFIG['cameras'].items():
         config['snapshots'] = {
-            'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True)
+            '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.SimpleQueue()
-    
-    # Start the shared tflite process
-    tflite_process = EdgeTPUProcess()
+    tracked_objects_queue = mp.Queue(maxsize=len(CONFIG['cameras'].keys())*2)
 
-    # start the camera processes
-    camera_processes = {}
+    # 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', {})
@@ -190,6 +198,27 @@ def main():
         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 +
@@ -198,53 +227,79 @@ def main():
                 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)
 
-        frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
-        take_frame = config.get('take_frame', 1)
-
-        detection_frame = mp.Value('d', 0.0)
-
-        ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size)
-        frame_queue = mp.SimpleQueue()
-        camera_fps = EventsPerSecond()
-        camera_fps.start()
-        camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, detection_frame)
-        camera_capture.start()
-
-        camera_processes[name] = {
-            'camera_fps': camera_fps,
-            'take_frame': take_frame,
+        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': detection_frame,
+            'detection_frame': mp.Value('d', 0.0),
             'read_start': mp.Value('d', 0.0),
-            'ffmpeg_process': ffmpeg_process,
-            'ffmpeg_cmd': ffmpeg_cmd,
-            'frame_queue': frame_queue,
-            'frame_shape': frame_shape,
-            'capture_thread': camera_capture
+            'frame_queue': mp.Queue(maxsize=2)
         }
 
-        camera_process = mp.Process(target=track_camera, args=(name, config, GLOBAL_OBJECT_CONFIG, frame_queue, frame_shape,
-            tflite_process.detection_queue, tracked_objects_queue, camera_processes[name]['process_fps'], 
-            camera_processes[name]['detection_fps'], 
-            camera_processes[name]['read_start'], camera_processes[name]['detection_frame']))
-        camera_process.daemon = True
-        camera_processes[name]['process'] = camera_process
+        # 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
+        }
 
-    for name, camera_process in camera_processes.items():
+        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}")
+        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)
+    object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
     object_processor.start()
     
-    camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process)
-    camera_watchdog.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__)
@@ -279,43 +334,51 @@ def main():
 
         total_detection_fps = 0
 
-        for name, camera_stats in camera_processes.items():
+        for name, camera_stats in camera_process_info.items():
             total_detection_fps += camera_stats['detection_fps'].value
-            capture_thread = camera_stats['capture_thread']
             stats[name] = {
-                'camera_fps': round(capture_thread.fps.eps(), 2),
+                'camera_fps': round(camera_stats['camera_fps'].value, 2),
                 'process_fps': round(camera_stats['process_fps'].value, 2),
-                'skipped_fps': round(capture_thread.skipped_fps.eps(), 2),
+                'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
                 'detection_fps': round(camera_stats['detection_fps'].value, 2),
-                'read_start': camera_stats['read_start'].value,
                 'pid': camera_stats['process'].pid,
-                'ffmpeg_pid': camera_stats['ffmpeg_process'].pid,
+                'capture_pid': camera_stats['capture_process'].pid,
                 'frame_info': {
-                    'read': capture_thread.current_frame,
                     'detect': camera_stats['detection_frame'].value,
                     'process': object_processor.camera_data[name]['current_frame_time']
                 }
             }
         
-        stats['coral'] = {
-            'fps': round(total_detection_fps, 2),
-            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
-            'detection_start': tflite_process.detection_start.value,
-            'pid': tflite_process.detect_process.pid
-        }
-
-        rc = camera_watchdog.plasma_process.poll()
-        stats['plasma_store_rc'] = rc
+        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_frame = object_processor.get_best(camera_name, label)
+            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)
-            best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
+            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'
@@ -333,19 +396,37 @@ def main():
                             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)
+            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)
-            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
 
             ret, jpg = cv2.imencode('.jpg', frame)
             yield (b'--frame\r\n'
@@ -354,8 +435,6 @@ def main():
     app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
 
     object_processor.join()
-    
-    plasma_process.terminate()
 
 if __name__ == '__main__':
     main()

docker/Dockerfile.aarch64

@@ -0,0 +1,22 @@
+FROM frigate-base
+LABEL maintainer "blakeb@blakeshome.com"
+
+ENV DEBIAN_FRONTEND=noninteractive
+# Install packages for apt repo
+RUN apt-get -qq update \
+    && apt-get -qq install --no-install-recommends -y \
+        # ffmpeg runtime dependencies
+        libgomp1 \
+        # runtime dependencies
+        libopenexr24 \
+        libgstreamer1.0-0 \
+        libgstreamer-plugins-base1.0-0 \
+        libopenblas-base \
+        libjpeg-turbo8 \
+        libpng16-16 \
+        libtiff5 \
+        libdc1394-22 \
+    ## Tensorflow lite
+    && pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp38-cp38-linux_aarch64.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)

docker/Dockerfile.amd64

@@ -0,0 +1,18 @@
+FROM frigate-base
+LABEL maintainer "blakeb@blakeshome.com"
+
+# By default, use the i965 driver
+ENV LIBVA_DRIVER_NAME=i965
+# Install packages for apt repo
+RUN apt-get -qq update \
+    && apt-get -qq install --no-install-recommends -y \
+        # ffmpeg dependencies
+        libgomp1 \
+        # VAAPI drivers for Intel hardware accel
+        libva-drm2 libva2 i965-va-driver vainfo intel-media-va-driver \
+        ## Tensorflow lite
+    && wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl \
+    && python3.8 -m pip install tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl \
+    && rm tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)

docker/Dockerfile.armv7

@@ -0,0 +1,24 @@
+FROM frigate-base
+LABEL maintainer "blakeb@blakeshome.com"
+
+ENV DEBIAN_FRONTEND=noninteractive
+# Install packages for apt repo
+RUN apt-get -qq update \
+    && apt-get -qq install --no-install-recommends -y \
+        # ffmpeg runtime dependencies
+        libgomp1 \
+        # runtime dependencies
+        libopenexr24 \
+        libgstreamer1.0-0 \
+        libgstreamer-plugins-base1.0-0 \
+        libopenblas-base \
+        libjpeg-turbo8 \
+        libpng16-16 \
+        libtiff5 \
+        libdc1394-22 \
+        libaom0 \
+        libx265-179 \
+    ## Tensorflow lite
+    && pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp38-cp38-linux_armv7l.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)

docker/Dockerfile.base

@@ -0,0 +1,44 @@
+ARG ARCH=amd64
+FROM blakeblackshear/frigate-wheels:${ARCH} as wheels
+FROM blakeblackshear/frigate-ffmpeg:${ARCH} as ffmpeg
+
+FROM ubuntu:20.04
+LABEL maintainer "blakeb@blakeshome.com"
+
+COPY --from=ffmpeg /usr/local /usr/local/
+
+COPY --from=wheels /wheels/. /wheels/
+
+ENV FLASK_ENV=development
+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 \
+    && apt-get -qq install --no-install-recommends -y \
+        python3-pip \
+    && pip3 install -U /wheels/*.whl \
+    && APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn apt-key adv --fetch-keys https://packages.cloud.google.com/apt/doc/apt-key.gpg \
+    && echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
+    && echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections \
+    && apt-get -qq update && apt-get -qq install --no-install-recommends -y \
+        libedgetpu1-max \
+    && 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 mkdir /cache /clips
+
+WORKDIR /opt/frigate/
+ADD frigate frigate/
+COPY detect_objects.py .
+COPY benchmark.py .
+COPY process_clip.py .
+
+CMD ["python3", "-u", "detect_objects.py"]

docker/Dockerfile.ffmpeg.aarch64

@@ -0,0 +1,533 @@
+# inspired by:
+# https://github.com/collelog/ffmpeg/blob/master/4.3.1-alpine-rpi4-arm64v8.Dockerfile
+# https://github.com/mmastrac/ffmpeg-omx-rpi-docker/blob/master/Dockerfile
+# https://github.com/jrottenberg/ffmpeg/pull/158/files
+# https://github.com/jrottenberg/ffmpeg/pull/239
+FROM        ubuntu:20.04 AS base
+
+WORKDIR     /tmp/workdir
+
+ENV     DEBIAN_FRONTEND=noninteractive
+RUN     apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
+        apt-get autoremove -y && \
+        apt-get clean -y
+
+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 \
+            XCBPROTO_VERSION=1.13 \
+            OGG_VERSION=1.3.2 \
+            OPENCOREAMR_VERSION=0.1.5 \
+            OPUS_VERSION=1.2 \
+            OPENJPEG_VERSION=2.1.2 \
+            THEORA_VERSION=1.1.1 \
+            VORBIS_VERSION=1.3.5 \
+            VPX_VERSION=1.8.0 \
+            WEBP_VERSION=1.0.2 \
+            X264_VERSION=20170226-2245-stable \
+            X265_VERSION=3.1.1 \
+            XAU_VERSION=1.0.9 \
+            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"
+
+
+ARG         LD_LIBRARY_PATH=/opt/ffmpeg/lib
+ARG         MAKEFLAGS="-j2"
+ARG         PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig"
+ARG         PREFIX=/opt/ffmpeg
+ARG         LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib"
+
+
+RUN      buildDeps="autoconf \
+                    automake \
+                    cmake \
+                    curl \
+                    bzip2 \
+                    libexpat1-dev \
+                    g++ \
+                    gcc \
+                    git \
+                    gperf \
+                    libtool \
+                    make \
+                    nasm \
+                    perl \
+                    pkg-config \
+                    python \
+                    libssl-dev \
+                    yasm \
+                    linux-headers-raspi2 \
+                    libomxil-bellagio-dev \
+                    zlib1g-dev" && \
+        apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ${buildDeps}
+## opencore-amr https://sourceforge.net/projects/opencore-amr/
+RUN \
+        DIR=/tmp/opencore-amr && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## x264 http://www.videolan.org/developers/x264.html
+RUN \
+        DIR=/tmp/x264 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
+        tar -jx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### x265 http://x265.org/
+RUN \
+        DIR=/tmp/x265 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz  | \
+        tar -zx && \
+        cd x265_${X265_VERSION}/build/linux && \
+        sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
+        sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
+        export CXXFLAGS="${CXXFLAGS} -fPIC" && \
+        ./multilib.sh && \
+        make -C 8bit install && \
+        rm -rf ${DIR}
+### libogg https://www.xiph.org/ogg/
+RUN \
+        DIR=/tmp/ogg && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
+        echo ${OGG_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libopus https://www.opus-codec.org/
+RUN \
+        DIR=/tmp/opus && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
+        echo ${OPUS_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
+        autoreconf -fiv && \
+        ./configure --prefix="${PREFIX}" --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libvorbis https://xiph.org/vorbis/
+RUN \
+        DIR=/tmp/vorbis && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
+        echo ${VORBIS_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libtheora http://www.theora.org/
+RUN \
+        DIR=/tmp/theora && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
+        echo ${THEORA_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
+        ./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libvpx https://www.webmproject.org/code/
+RUN \
+        DIR=/tmp/vpx && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
+        --disable-debug --disable-examples --disable-docs --disable-install-bins  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libwebp https://developers.google.com/speed/webp/
+RUN \
+        DIR=/tmp/vebp && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libmp3lame http://lame.sourceforge.net/
+RUN \
+        DIR=/tmp/lame && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### xvid https://www.xvid.com/
+RUN \
+        DIR=/tmp/xvid && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
+        echo ${XVID_SHA256SUM} | sha256sum --check && \
+        tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
+        cd xvidcore/build/generic && \
+        ./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### fdk-aac https://github.com/mstorsjo/fdk-aac
+RUN \
+        DIR=/tmp/fdk-aac && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        autoreconf -fiv && \
+        ./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## openjpeg https://github.com/uclouvain/openjpeg
+RUN \
+        DIR=/tmp/openjpeg && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        export CFLAGS="${CFLAGS} -DPNG_ARM_NEON_OPT=0" && \
+        cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## freetype https://www.freetype.org/
+RUN  \
+        DIR=/tmp/freetype && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
+        echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## libvstab https://github.com/georgmartius/vid.stab
+RUN  \
+        DIR=/tmp/vid.stab && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
+        echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check &&  \
+        tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
+        cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## fridibi https://www.fribidi.org/
+RUN  \
+        DIR=/tmp/fribidi && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
+        echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
+        sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
+        ./bootstrap --no-config --auto && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        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 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/aom && \
+        git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
+        cd ${DIR} ; \
+        rm -rf CMakeCache.txt CMakeFiles ; \
+        mkdir -p ./aom_build ; \
+        cd ./aom_build ; \
+        cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
+        make ; \
+        make install ; \
+        rm -rf ${DIR}
+
+## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
+RUN \
+        DIR=/tmp/xorg-macros && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/xproto && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libXau && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libpthread-stubs && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libxcb-proto && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
+        ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libxcb && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
+        ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make -j $(nproc) && \
+        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 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
+        echo ${LIBZMQ_SHA256SUM} | sha256sum --check && \
+        tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make check && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+## ffmpeg https://ffmpeg.org/
+RUN  \
+        DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
+        curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
+        tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
+
+RUN \
+        DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libass \
+        --enable-fontconfig \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libbluray \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${PREFIX}" \
+        --enable-libopenjpeg \
+        --enable-libkvazaar \
+        --enable-libaom \
+        --extra-libs=-lpthread \
+        # --enable-omx \
+        # --enable-omx-rpi \
+        # --enable-mmal \
+        --enable-v4l2_m2m \
+        --enable-neon \
+        --extra-cflags="-I${PREFIX}/include" \
+        --extra-ldflags="-L${PREFIX}/lib" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
+        make distclean && \
+        hash -r && \
+        cd tools && \
+        make qt-faststart && cp qt-faststart ${PREFIX}/bin/
+
+## cleanup
+RUN \
+        ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
+        for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
+        cp ${PREFIX}/bin/* /usr/local/bin/ && \
+        cp -r ${PREFIX}/share/ffmpeg /usr/local/share/ && \
+        LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
+        cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
+        mkdir -p /usr/local/lib/pkgconfig && \
+        for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
+          sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
+        done
+
+FROM        base AS release
+
+ENV         LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib:/usr/lib64:/lib:/lib64
+
+CMD         ["--help"]
+ENTRYPOINT  ["ffmpeg"]
+
+COPY --from=build /usr/local /usr/local/
+
+# Run ffmpeg with -c:v h264_v4l2m2m to enable HW accell for decoding on raspberry pi4 64-bit

docker/Dockerfile.ffmpeg.amd64

@@ -0,0 +1,526 @@
+# inspired by:
+# https://github.com/collelog/ffmpeg/blob/master/4.3.1-alpine-rpi4-arm64v8.Dockerfile
+# https://github.com/jrottenberg/ffmpeg/pull/158/files
+# https://github.com/jrottenberg/ffmpeg/pull/239
+FROM        ubuntu:20.04 AS base
+
+WORKDIR     /tmp/workdir
+
+ENV     DEBIAN_FRONTEND=noninteractive
+RUN     apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
+        apt-get autoremove -y && \
+        apt-get clean -y
+
+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 \
+            XCBPROTO_VERSION=1.13 \
+            OGG_VERSION=1.3.2 \
+            OPENCOREAMR_VERSION=0.1.5 \
+            OPUS_VERSION=1.2 \
+            OPENJPEG_VERSION=2.1.2 \
+            THEORA_VERSION=1.1.1 \
+            VORBIS_VERSION=1.3.5 \
+            VPX_VERSION=1.8.0 \
+            WEBP_VERSION=1.0.2 \
+            X264_VERSION=20170226-2245-stable \
+            X265_VERSION=3.1.1 \
+            XAU_VERSION=1.0.9 \
+            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"
+
+
+ARG         LD_LIBRARY_PATH=/opt/ffmpeg/lib
+ARG         MAKEFLAGS="-j2"
+ARG         PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig"
+ARG         PREFIX=/opt/ffmpeg
+ARG         LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib"
+
+
+RUN      buildDeps="autoconf \
+                    automake \
+                    cmake \
+                    curl \
+                    bzip2 \
+                    libexpat1-dev \
+                    g++ \
+                    gcc \
+                    git \
+                    gperf \
+                    libtool \
+                    make \
+                    nasm \
+                    perl \
+                    pkg-config \
+                    python \
+                    libssl-dev \
+                    yasm \
+                    libva-dev \
+                    zlib1g-dev" && \
+        apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ${buildDeps}
+## opencore-amr https://sourceforge.net/projects/opencore-amr/
+RUN \
+        DIR=/tmp/opencore-amr && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+## x264 http://www.videolan.org/developers/x264.html
+RUN \
+        DIR=/tmp/x264 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
+        tar -jx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### x265 http://x265.org/
+RUN \
+        DIR=/tmp/x265 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz  | \
+        tar -zx && \
+        cd x265_${X265_VERSION}/build/linux && \
+        sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
+        sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
+        ./multilib.sh && \
+        make -C 8bit install && \
+        rm -rf ${DIR}
+### libogg https://www.xiph.org/ogg/
+RUN \
+        DIR=/tmp/ogg && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
+        echo ${OGG_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### libopus https://www.opus-codec.org/
+RUN \
+        DIR=/tmp/opus && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
+        echo ${OPUS_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
+        autoreconf -fiv && \
+        ./configure --prefix="${PREFIX}" --enable-shared && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### libvorbis https://xiph.org/vorbis/
+RUN \
+        DIR=/tmp/vorbis && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
+        echo ${VORBIS_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### libtheora http://www.theora.org/
+RUN \
+        DIR=/tmp/theora && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
+        echo ${THEORA_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### libvpx https://www.webmproject.org/code/
+RUN \
+        DIR=/tmp/vpx && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
+        --disable-debug --disable-examples --disable-docs --disable-install-bins  && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### libwebp https://developers.google.com/speed/webp/
+RUN \
+        DIR=/tmp/vebp && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### libmp3lame http://lame.sourceforge.net/
+RUN \
+        DIR=/tmp/lame && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### xvid https://www.xvid.com/
+RUN \
+        DIR=/tmp/xvid && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
+        echo ${XVID_SHA256SUM} | sha256sum --check && \
+        tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
+        cd xvidcore/build/generic && \
+        ./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+### fdk-aac https://github.com/mstorsjo/fdk-aac
+RUN \
+        DIR=/tmp/fdk-aac && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        autoreconf -fiv && \
+        ./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+## openjpeg https://github.com/uclouvain/openjpeg
+RUN \
+        DIR=/tmp/openjpeg && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+## freetype https://www.freetype.org/
+RUN  \
+        DIR=/tmp/freetype && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
+        echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+## libvstab https://github.com/georgmartius/vid.stab
+RUN  \
+        DIR=/tmp/vid.stab && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
+        echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check &&  \
+        tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
+        cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+## fridibi https://www.fribidi.org/
+RUN  \
+        DIR=/tmp/fribidi && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
+        echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
+        sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
+        ./bootstrap --no-config --auto && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        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 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/aom && \
+        git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
+        cd ${DIR} ; \
+        rm -rf CMakeCache.txt CMakeFiles ; \
+        mkdir -p ./aom_build ; \
+        cd ./aom_build ; \
+        cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
+        make ; \
+        make install ; \
+        rm -rf ${DIR}
+
+## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
+RUN \
+        DIR=/tmp/xorg-macros && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/xproto && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libXau && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libpthread-stubs && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libxcb-proto && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
+        ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libxcb && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
+        ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make && \
+        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 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
+        echo ${LIBZMQ_SHA256SUM} | sha256sum --check && \
+        tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make && \
+        make check && \
+        make install && \
+        rm -rf ${DIR}
+
+## ffmpeg https://ffmpeg.org/
+RUN  \
+        DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
+        curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
+        tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
+
+
+
+RUN \
+        DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libass \
+        --enable-fontconfig \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libbluray \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${PREFIX}" \
+        --enable-libopenjpeg \
+        --enable-libkvazaar \
+        --enable-libaom \
+        --extra-libs=-lpthread \
+        --enable-vaapi \
+        --extra-cflags="-I${PREFIX}/include" \
+        --extra-ldflags="-L${PREFIX}/lib" && \
+        make && \
+        make install && \
+        make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
+        make distclean && \
+        hash -r && \
+        cd tools && \
+        make qt-faststart && cp qt-faststart ${PREFIX}/bin/
+
+## cleanup
+RUN \
+        ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
+        for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
+        cp ${PREFIX}/bin/* /usr/local/bin/ && \
+        cp -r ${PREFIX}/share/ffmpeg /usr/local/share/ && \
+        LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
+        cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
+        mkdir -p /usr/local/lib/pkgconfig && \
+        for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
+          sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
+        done
+
+FROM        base AS release
+
+ENV         LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib:/usr/lib64:/lib:/lib64
+
+CMD         ["--help"]
+ENTRYPOINT  ["ffmpeg"]
+
+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 && \
+	rm -rf /var/lib/apt/lists/*

docker/Dockerfile.ffmpeg.armv7

@@ -0,0 +1,549 @@
+# inspired by:
+# https://github.com/collelog/ffmpeg/blob/master/4.3.1-alpine-rpi4-arm64v8.Dockerfile
+# https://github.com/mmastrac/ffmpeg-omx-rpi-docker/blob/master/Dockerfile
+# https://github.com/jrottenberg/ffmpeg/pull/158/files
+# https://github.com/jrottenberg/ffmpeg/pull/239
+FROM        ubuntu:20.04 AS base
+
+WORKDIR     /tmp/workdir
+
+ENV     DEBIAN_FRONTEND=noninteractive
+RUN     apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
+        apt-get autoremove -y && \
+        apt-get clean -y
+
+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 \
+            XCBPROTO_VERSION=1.13 \
+            OGG_VERSION=1.3.2 \
+            OPENCOREAMR_VERSION=0.1.5 \
+            OPUS_VERSION=1.2 \
+            OPENJPEG_VERSION=2.1.2 \
+            THEORA_VERSION=1.1.1 \
+            VORBIS_VERSION=1.3.5 \
+            VPX_VERSION=1.8.0 \
+            WEBP_VERSION=1.0.2 \
+            X264_VERSION=20170226-2245-stable \
+            X265_VERSION=3.1.1 \
+            XAU_VERSION=1.0.9 \
+            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
+ARG         MAKEFLAGS="-j2"
+ARG         PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig:/opt/vc/lib/pkgconfig"
+ARG         PREFIX=/opt/ffmpeg
+ARG         LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib:/opt/vc/lib"
+
+
+RUN      buildDeps="autoconf \
+                    automake \
+                    cmake \
+                    curl \
+                    bzip2 \
+                    libexpat1-dev \
+                    g++ \
+                    gcc \
+                    git \
+                    gperf \
+                    libtool \
+                    make \
+                    nasm \
+                    perl \
+                    pkg-config \
+                    python \
+                    sudo \
+                    libssl-dev \
+                    yasm \
+                    linux-headers-raspi2 \
+                    libomxil-bellagio-dev \
+                    libx265-dev \
+                    libaom-dev \
+                    zlib1g-dev" && \
+        apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ${buildDeps}
+## opencore-amr https://sourceforge.net/projects/opencore-amr/
+RUN \
+        DIR=/tmp/opencore-amr && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## x264 http://www.videolan.org/developers/x264.html
+RUN \
+        DIR=/tmp/x264 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
+        tar -jx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+# ### x265 http://x265.org/
+# RUN \
+#         DIR=/tmp/x265 && \
+#         mkdir -p ${DIR} && \
+#         cd ${DIR} && \
+#         curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz  | \
+#         tar -zx && \
+#         cd x265_${X265_VERSION}/build/linux && \
+#         sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
+#         sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
+#         # export CXXFLAGS="${CXXFLAGS} -fPIC" && \
+#         ./multilib.sh && \
+#         make -C 8bit install && \
+#         rm -rf ${DIR}
+### libogg https://www.xiph.org/ogg/
+RUN \
+        DIR=/tmp/ogg && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
+        echo ${OGG_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libopus https://www.opus-codec.org/
+RUN \
+        DIR=/tmp/opus && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
+        echo ${OPUS_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
+        autoreconf -fiv && \
+        ./configure --prefix="${PREFIX}" --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libvorbis https://xiph.org/vorbis/
+RUN \
+        DIR=/tmp/vorbis && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
+        echo ${VORBIS_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libtheora http://www.theora.org/
+RUN \
+        DIR=/tmp/theora && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
+        echo ${THEORA_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
+        ./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libvpx https://www.webmproject.org/code/
+RUN \
+        DIR=/tmp/vpx && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
+        --disable-debug --disable-examples --disable-docs --disable-install-bins  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libwebp https://developers.google.com/speed/webp/
+RUN \
+        DIR=/tmp/vebp && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --enable-shared  && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### libmp3lame http://lame.sourceforge.net/
+RUN \
+        DIR=/tmp/lame && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        ./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### xvid https://www.xvid.com/
+RUN \
+        DIR=/tmp/xvid && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
+        echo ${XVID_SHA256SUM} | sha256sum --check && \
+        tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
+        cd xvidcore/build/generic && \
+        ./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+### fdk-aac https://github.com/mstorsjo/fdk-aac
+RUN \
+        DIR=/tmp/fdk-aac && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        autoreconf -fiv && \
+        ./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## openjpeg https://github.com/uclouvain/openjpeg
+RUN \
+        DIR=/tmp/openjpeg && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
+        tar -zx --strip-components=1 && \
+        export CFLAGS="${CFLAGS} -DPNG_ARM_NEON_OPT=0" && \
+        cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## freetype https://www.freetype.org/
+RUN  \
+        DIR=/tmp/freetype && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
+        echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## libvstab https://github.com/georgmartius/vid.stab
+RUN  \
+        DIR=/tmp/vid.stab && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
+        echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check &&  \
+        tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
+        cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+## fridibi https://www.fribidi.org/
+RUN  \
+        DIR=/tmp/fribidi && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
+        echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
+        tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
+        sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
+        ./bootstrap --no-config --auto && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        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 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+# RUN \
+#         DIR=/tmp/aom && \
+#         git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
+#         cd ${DIR} ; \
+#         rm -rf CMakeCache.txt CMakeFiles ; \
+#         mkdir -p ./aom_build ; \
+#         cd ./aom_build ; \
+#         cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
+#         make ; \
+#         make install ; \
+#         rm -rf ${DIR}
+
+## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
+RUN \
+        DIR=/tmp/xorg-macros && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/xproto && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
+        curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libXau && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
+        ./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libpthread-stubs && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
+        ./configure --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libxcb-proto && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
+        ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+RUN \
+        DIR=/tmp/libxcb && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
+        tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
+        ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
+        make -j $(nproc) && \
+        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 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
+        tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make -j $(nproc) && \
+        # make check && \
+        make -j $(nproc) install && \
+        rm -rf ${DIR}
+
+## userland https://github.com/raspberrypi/userland
+RUN \
+        DIR=/tmp/userland && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        git clone --depth 1 https://github.com/raspberrypi/userland.git . && \
+        ./buildme && \
+        rm -rf ${DIR}
+
+## ffmpeg https://ffmpeg.org/
+RUN  \
+        DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
+        curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
+        tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
+
+RUN \
+        DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libass \
+        --enable-fontconfig \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libbluray \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${PREFIX}" \
+        --enable-libopenjpeg \
+        --enable-libkvazaar \
+        --enable-libaom \
+        --extra-libs=-lpthread \
+        --enable-omx \
+        --enable-omx-rpi \
+        --enable-mmal \
+        --enable-v4l2_m2m \
+        --enable-neon \
+        --extra-cflags="-I${PREFIX}/include" \
+        --extra-ldflags="-L${PREFIX}/lib" && \
+        make -j $(nproc) && \
+        make -j $(nproc) install && \
+        make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
+        make distclean && \
+        hash -r && \
+        cd tools && \
+        make qt-faststart && cp qt-faststart ${PREFIX}/bin/
+
+## cleanup
+RUN \
+        ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
+        # copy userland lib too
+        ldd ${PREFIX}/bin/ffmpeg | grep opt/vc | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
+        for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
+        cp ${PREFIX}/bin/* /usr/local/bin/ && \
+        cp -r ${PREFIX}/share/ffmpeg /usr/local/share/ && \
+        LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
+        cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
+        mkdir -p /usr/local/lib/pkgconfig && \
+        for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
+          sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
+        done
+
+FROM        base AS release
+
+ENV         LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib:/usr/lib64:/lib:/lib64
+
+RUN \
+        apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends libx265-dev libaom-dev && \
+        apt-get autoremove -y && \
+        apt-get clean -y
+
+CMD         ["--help"]
+ENTRYPOINT  ["ffmpeg"]
+
+COPY --from=build /usr/local /usr/local/

docker/Dockerfile.wheels

@@ -0,0 +1,39 @@
+FROM ubuntu:20.04 as build
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -qq update \
+    && apt-get -qq install -y \
+    python3 \
+    python3-dev \
+    wget \
+    # opencv dependencies
+    build-essential cmake git pkg-config libgtk-3-dev \
+    libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
+    libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
+    gfortran openexr libatlas-base-dev libssl-dev\
+    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
+    libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
+    # scipy dependencies
+    gcc gfortran libopenblas-dev liblapack-dev cython
+
+RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
+    && python3 get-pip.py
+
+RUN pip3 install scikit-build
+
+RUN pip3 wheel --wheel-dir=/wheels \
+    opencv-python-headless \
+    numpy \
+    imutils \
+    scipy \
+    psutil \
+    Flask \
+    paho-mqtt \
+    PyYAML \
+    matplotlib \
+    click
+
+FROM scratch
+
+COPY --from=build /wheels /wheels

docker/Dockerfile.wheels.aarch64

@@ -0,0 +1,49 @@
+FROM ubuntu:20.04 as build
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -qq update \
+    && apt-get -qq install -y \
+    python3 \
+    python3-dev \
+    wget \
+    # opencv dependencies
+    build-essential cmake git pkg-config libgtk-3-dev \
+    libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
+    libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
+    gfortran openexr libatlas-base-dev libssl-dev\
+    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
+    libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
+    # scipy dependencies
+    gcc gfortran libopenblas-dev liblapack-dev cython
+
+RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
+    && python3 get-pip.py
+
+# need to build cmake from source because binary distribution is broken for arm64
+# https://github.com/scikit-build/cmake-python-distributions/issues/115
+# https://github.com/skvark/opencv-python/issues/366
+# https://github.com/scikit-build/cmake-python-distributions/issues/96#issuecomment-663062358
+RUN pip3 install scikit-build
+
+RUN git clone https://github.com/scikit-build/cmake-python-distributions.git \
+    && cd cmake-python-distributions/ \
+    && python3 setup.py bdist_wheel
+
+RUN pip3 install cmake-python-distributions/dist/*.whl
+
+RUN pip3 wheel --wheel-dir=/wheels \
+    opencv-python-headless \
+    numpy \
+    imutils \
+    scipy \
+    psutil \
+    Flask \
+    paho-mqtt \
+    PyYAML \
+    matplotlib \
+    click
+
+FROM scratch
+
+COPY --from=build /wheels /wheels

docs/DEVICES.md

@@ -1,74 +0,0 @@
-# Configuration Examples
-
-### Default (most RTSP cameras)
-This is the default ffmpeg command and should work with most RTSP cameras that send h264 video
-```yaml
-ffmpeg:
-  global_args:
-    - -hide_banner
-    - -loglevel
-    - panic
-  hwaccel_args: []
-  input_args:
-    - -avoid_negative_ts
-    - make_zero 
-    - -fflags
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -vsync
-    - drop
-    - -rtsp_transport
-    - tcp
-    - -stimeout
-    - '5000000' 
-    - -use_wallclock_as_timestamps
-    - '1'
-  output_args:
-    - -vf
-    - mpdecimate
-    - -f
-    - rawvideo
-    - -pix_fmt
-    - rgb24
-```
-
-### RTMP Cameras
-The input parameters need to be adjusted for RTMP cameras
-```yaml
-ffmpeg:
-input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -fflags
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -vsync
-    - drop
-    - -use_wallclock_as_timestamps
-    - '1'
-```
-
-
-### Hardware Acceleration
-
-Intel Quicksync
-```yaml
-ffmpeg:
-  hwaccel_args:
-    - -hwaccel
-    - vaapi
-    - -hwaccel_device
-    - /dev/dri/renderD128
-    - -hwaccel_output_format
-    - yuv420p
-```

docs/cameras.md

@@ -0,0 +1,21 @@
+# Camera Specific Configuration
+Frigate should work with most RTSP cameras and h264 feeds such as Dahua.
+
+## RTMP Cameras
+The input parameters need to be adjusted for RTMP cameras
+```yaml
+ffmpeg:
+input_args:
+    - -avoid_negative_ts
+    - make_zero
+    - -fflags
+    - nobuffer
+    - -flags
+    - low_delay
+    - -strict
+    - experimental
+    - -fflags
+    - +genpts+discardcorrupt
+    - -use_wallclock_as_timestamps
+    - '1'
+```

docs/how-frigate-works.md

@@ -0,0 +1,10 @@
+# How Frigate Works
+Frigate is designed to minimize resource and maximize performance by only looking for objects when and where it is necessary
+
+![Diagram](diagram.png)
+
+## 1. Look for Motion
+
+## 2. Calculate Detection Regions
+
+## 3. Run Object Detection

frigate/edgetpu.py

@@ -2,11 +2,14 @@ import os
 import datetime
 import hashlib
 import multiprocessing as mp
+import queue
+from multiprocessing.connection import Connection
+from abc import ABC, abstractmethod
+from typing import Dict
 import numpy as np
-import pyarrow.plasma as plasma
 import tflite_runtime.interpreter as tflite
 from tflite_runtime.interpreter import load_delegate
-from frigate.util import EventsPerSecond, listen
+from frigate.util import EventsPerSecond, listen, SharedMemoryFrameManager
 
 def load_labels(path, encoding='utf-8'):
   """Loads labels from file (with or without index numbers).
@@ -27,13 +30,32 @@ def load_labels(path, encoding='utf-8'):
     else:
         return {index: line.strip() for index, line in enumerate(lines)}
 
-class ObjectDetector():
-    def __init__(self):
+class ObjectDetector(ABC):
+    @abstractmethod
+    def detect(self, tensor_input, threshold = .4):
+        pass
+
+class LocalObjectDetector(ObjectDetector):
+    def __init__(self, tf_device=None, labels=None):
+        self.fps = EventsPerSecond()
+        if labels is None:
+            self.labels = {}
+        else:
+            self.labels = load_labels(labels)
+
+        device_config = {"device": "usb"}
+        if not tf_device is None:
+            device_config = {"device": tf_device}
+
         edge_tpu_delegate = None
-        try:
-            edge_tpu_delegate = load_delegate('libedgetpu.so.1.0')
-        except ValueError:
-            print("No EdgeTPU detected. Falling back to CPU.")
+
+        if tf_device != 'cpu':
+            try:
+                print(f"Attempting to load TPU as {device_config['device']}")
+                edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
+                print("TPU found")
+            except ValueError:
+                print("No EdgeTPU detected. Falling back to CPU.")
         
         if edge_tpu_delegate is None:
             self.interpreter = tflite.Interpreter(
@@ -48,6 +70,22 @@ class ObjectDetector():
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
     
+    def detect(self, tensor_input, threshold=.4):
+        detections = []
+
+        raw_detections = self.detect_raw(tensor_input)
+
+        for d in raw_detections:
+            if d[1] < threshold:
+                break
+            detections.append((
+                self.labels[int(d[0])],
+                float(d[1]),
+                (d[2], d[3], d[4], d[5])
+            ))
+        self.fps.update()
+        return detections
+
     def detect_raw(self, tensor_input):
         self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
         self.interpreter.invoke()
@@ -61,75 +99,91 @@ class ObjectDetector():
         
         return detections
 
-def run_detector(detection_queue, avg_speed, start):
+def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, start, tf_device):
     print(f"Starting detection process: {os.getpid()}")
     listen()
-    plasma_client = plasma.connect("/tmp/plasma")
-    object_detector = ObjectDetector()
+    frame_manager = SharedMemoryFrameManager()
+    object_detector = LocalObjectDetector(tf_device=tf_device)
 
+    outputs = {}
+    for name in out_events.keys():
+        out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
+        out_np = np.ndarray((20,6), dtype=np.float32, buffer=out_shm.buf)
+        outputs[name] = {
+            'shm': out_shm,
+            'np': out_np
+        }
+    
     while True:
-        object_id_str = detection_queue.get()
-        object_id_hash = hashlib.sha1(str.encode(object_id_str))
-        object_id = plasma.ObjectID(object_id_hash.digest())
-        object_id_out = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{object_id_str}")).digest())
-        input_frame = plasma_client.get(object_id, timeout_ms=0)
+        connection_id = detection_queue.get()
+        input_frame = frame_manager.get(connection_id, (1,300,300,3))
 
-        if input_frame is plasma.ObjectNotAvailable:
+        if input_frame is None:
             continue
 
-        # detect and put the output in the plasma store
+        # detect and send the output
         start.value = datetime.datetime.now().timestamp()
-        plasma_client.put(object_detector.detect_raw(input_frame), object_id_out)
+        detections = object_detector.detect_raw(input_frame)
         duration = datetime.datetime.now().timestamp()-start.value
+        outputs[connection_id]['np'][:] = detections[:]
+        out_events[connection_id].set()
         start.value = 0.0
 
         avg_speed.value = (avg_speed.value*9 + duration)/10
         
 class EdgeTPUProcess():
-    def __init__(self):
-        self.detection_queue = mp.SimpleQueue()
+    def __init__(self, detection_queue, out_events, tf_device=None):
+        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.tf_device = tf_device
         self.start_or_restart()
+    
+    def stop(self):
+        self.detect_process.terminate()
+        print("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...")
+            self.detect_process.kill()
+            self.detect_process.join()
 
     def start_or_restart(self):
         self.detection_start.value = 0.0
         if (not self.detect_process is None) and self.detect_process.is_alive():
-            self.detect_process.terminate()
-            print("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...")
-                self.detect_process.kill()
-                self.detect_process.join()
-        self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.avg_inference_speed, self.detection_start))
+            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.daemon = True
         self.detect_process.start()
 
 class RemoteObjectDetector():
-    def __init__(self, name, labels, detection_queue):
+    def __init__(self, name, labels, detection_queue, event):
         self.labels = load_labels(labels)
         self.name = name
         self.fps = EventsPerSecond()
-        self.plasma_client = plasma.connect("/tmp/plasma")
         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.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)
     
     def detect(self, tensor_input, threshold=.4):
         detections = []
 
-        now = f"{self.name}-{str(datetime.datetime.now().timestamp())}"
-        object_id_frame = plasma.ObjectID(hashlib.sha1(str.encode(now)).digest())
-        object_id_detections = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{now}")).digest())
-        self.plasma_client.put(tensor_input, object_id_frame)
-        self.detection_queue.put(now)
-        raw_detections = self.plasma_client.get(object_id_detections, timeout_ms=10000)
+        # copy input to shared memory
+        self.np_shm[:] = tensor_input[:]
+        self.event.clear()
+        self.detection_queue.put(self.name)
+        result = self.event.wait(timeout=10.0)
 
-        if raw_detections is plasma.ObjectNotAvailable:
-            self.plasma_client.delete([object_id_frame])
+        # if it timed out
+        if result is None:
             return detections
 
-        for d in raw_detections:
+        for d in self.out_np_shm:
             if d[1] < threshold:
                 break
             detections.append((
@@ -137,6 +191,9 @@ class RemoteObjectDetector():
                 float(d[1]),
                 (d[2], d[3], d[4], d[5])
             ))
-        self.plasma_client.delete([object_id_frame, object_id_detections])
         self.fps.update()
-        return detections
+        return detections
+    
+    def cleanup(self):
+        self.shm.unlink()
+        self.out_shm.unlink()

frigate/events.py

@@ -0,0 +1,174 @@
+import os
+import time
+import psutil
+import threading
+from collections import defaultdict
+import json
+import datetime
+import subprocess as sp
+import queue
+
+class EventProcessor(threading.Thread):
+    def __init__(self, config, camera_processes, cache_dir, clip_dir, event_queue, stop_event):
+        threading.Thread.__init__(self)
+        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.events_in_process = {}
+        self.stop_event = stop_event
+    
+    def refresh_cache(self):
+        cached_files = os.listdir(self.cache_dir)
+
+        files_in_use = []
+        for process_data in self.camera_processes.values():
+            try:
+                ffmpeg_process = psutil.Process(pid=process_data['ffmpeg_process'].pid)
+                flist = ffmpeg_process.open_files()
+                if flist:
+                    for nt in flist:
+                        if nt.path.startswith(self.cache_dir):
+                            files_in_use.append(nt.path.split('/')[-1])
+            except:
+                continue
+
+        for f in cached_files:
+            if f in files_in_use or f in self.cached_clips:
+                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(self.cache_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:
+                print(f"bad file: {f}")
+                os.remove(os.path.join(self.cache_dir,f))
+                continue
+
+            self.cached_clips[f] = {
+                'path': f,
+                'camera': camera,
+                'start_time': start_time.timestamp(),
+                'duration': duration
+            }
+
+        if len(self.events_in_process) > 0:
+            earliest_event = min(self.events_in_process.values(), key=lambda x:x['start_time'])['start_time']
+        else:
+            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)
+        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))
+
+    def create_clip(self, camera, event_data, pre_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']:
+            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_lines = []
+        for clip in sorted_clips:
+            # clip ends before playlist start time, skip
+            if clip['start_time']+clip['duration'] < playlist_start:
+                continue
+            # 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'])}'")
+            # 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'])}")
+            # if this is the ending clip, add an outpoint
+            if clip['start_time']+clip['duration'] > playlist_end:
+                playlist_lines.append(f"outpoint {int(playlist_end-clip['start_time'])}")
+
+        clip_name = f"{camera}-{event_data['id']}"
+        ffmpeg_cmd = [
+            'ffmpeg',
+            '-y',
+            '-protocol_whitelist',
+            'pipe,file',
+            '-f',
+            'concat',
+            '-safe',
+            '0',
+            '-i',
+            '-',
+            '-c',
+            'copy',
+            f"{os.path.join(self.clip_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)
+            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...")
+                break
+
+            try:
+                event_type, camera, event_data = self.event_queue.get(timeout=10)
+            except queue.Empty:
+                if not self.stop_event.is_set():
+                    self.refresh_cache()
+                continue
+
+            self.refresh_cache()
+
+            save_clips_config = self.config['cameras'][camera].get('save_clips', {})
+
+            # if save clips is not enabled for this camera, just continue
+            if not save_clips_config.get('enabled', False):
+                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 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))
+                del self.events_in_process[event_data['id']]
+
+                

frigate/motion.py

@@ -4,6 +4,7 @@ import numpy as np
 
 class MotionDetector():
     def __init__(self, frame_shape, mask, resize_factor=4):
+        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.avg_frame = np.zeros(self.motion_frame_size, np.float)
@@ -16,14 +17,16 @@ class MotionDetector():
     def detect(self, frame):
         motion_boxes = []
 
+        gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
+
         # resize frame
-        resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+        resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
 
         # convert to grayscale
-        gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+        # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
 
         # mask frame
-        gray[self.mask] = [255]
+        resized_frame[self.mask] = [255]
 
         # it takes ~30 frames to establish a baseline
         # dont bother looking for motion
@@ -31,7 +34,7 @@ class MotionDetector():
             self.frame_counter += 1
         else:
             # compare to average
-            frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg_frame))
+            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.
@@ -70,10 +73,10 @@ class MotionDetector():
             # 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(gray, self.avg_frame, 0.2)
+                cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
         else:
             # when no motion, just keep averaging the frames together
-            cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+            cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
             self.motion_frame_count = 0
 
         return motion_boxes

frigate/object_processing.py

@@ -5,13 +5,16 @@ import time
 import copy
 import cv2
 import threading
+import queue
+import copy
 import numpy as np
 from collections import Counter, defaultdict
 import itertools
-import pyarrow.plasma as plasma
 import matplotlib.pyplot as plt
-from frigate.util import draw_box_with_label, PlasmaManager
+from frigate.util import draw_box_with_label, SharedMemoryFrameManager
 from frigate.edgetpu import load_labels
+from typing import Callable, Dict
+from statistics import mean, median
 
 PATH_TO_LABELS = '/labelmap.txt'
 
@@ -22,13 +25,292 @@ COLOR_MAP = {}
 for key, val in LABELS.items():
     COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
 
-class TrackedObjectProcessor(threading.Thread):
-    def __init__(self, config, client, topic_prefix, tracked_objects_queue):
-        threading.Thread.__init__(self)
+def zone_filtered(obj, object_config):
+    object_name = obj['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']:
+            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']:
+            return True
+
+        # if the score is lower than the threshold, skip
+        if obj_settings.get('threshold', 0) > obj['computed_score']:
+            return True
+        
+    return False
+
+# Maintains the state of a camera
+class CameraState():
+    def __init__(self, name, config, frame_manager):
+        self.name = name
         self.config = config
+        self.frame_manager = frame_manager
+
+        self.best_objects = {}
+        self.object_status = defaultdict(lambda: 'OFF')
+        self.tracked_objects = {}
+        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_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):
+        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)
+        
+        frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
+        # draw on the frame
+        if draw:
+            # draw the bounding boxes on the frame
+            for obj in tracked_objects.values():
+                thickness = 2
+                color = COLOR_MAP[obj['label']]
+                
+                if obj['frame_time'] != frame_time:
+                    thickness = 1
+                    color = (255,0,0)
+
+                # 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)
+        
+        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 on(self, event_type: str, callback: Callable[[Dict], None]):
+        self.callbacks[event_type].append(callback)
+
+    def update(self, frame_time, tracked_objects):
+        self.current_frame_time = frame_time
+        # get the new frame and delete the old 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_ids = tracked_objects.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])
+
+            # call event handlers
+            for c in self.callbacks['start']:
+                c(self.name, tracked_objects[id])
+        
+        for id in updated_ids:
+            self.tracked_objects[id].update(tracked_objects[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:]
+
+            # 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])
+        
+        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
+
+        # 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']:
+                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
+                    for c in self.callbacks['snapshot']:
+                        c(self.name, self.best_objects[object_type])
+            else:
+                obj_copy['frame'] = np.copy(current_frame)
+                self.best_objects[object_type] = obj_copy
+                for c in self.callbacks['snapshot']:
+                    c(self.name, self.best_objects[object_type])
+        
+        # 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
+                
+        # 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
+                for c in self.callbacks['object_status']:
+                    c(self.name, obj_name, new_status)
+
+        # 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]
+        for obj_name in expired_objects:
+            self.object_status[obj_name] = 'OFF'
+            for c in self.callbacks['object_status']:
+                c(self.name, obj_name, 'OFF')
+            for c in self.callbacks['snapshot']:
+                c(self.name, self.best_objects[obj_name])
+        
+        with self.current_frame_lock:
+            self._current_frame = current_frame
+            if not self.previous_frame_id is None:
+                self.frame_manager.delete(self.previous_frame_id)
+            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):
+        threading.Thread.__init__(self)
+        self.camera_config = camera_config
         self.client = client
         self.topic_prefix = topic_prefix
         self.tracked_objects_queue = tracked_objects_queue
+        self.event_queue = event_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 update(camera, obj):
+            pass
+
+        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 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 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)
+            camera_state.on('start', start)
+            camera_state.on('update', update)
+            camera_state.on('end', end)
+            camera_state.on('snapshot', snapshot)
+            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')),
@@ -37,111 +319,75 @@ class TrackedObjectProcessor(threading.Thread):
             'current_frame_time': 0.0,
             'object_id': None
         })
-        self.plasma_client = PlasmaManager()
+        # {
+        #   'zone_name': {
+        #       'person': ['camera_1', 'camera_2']
+        #   }
+        # }
+        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}")
         
     def get_best(self, camera, label):
-        if label in self.camera_data[camera]['best_objects']:
-            return self.camera_data[camera]['best_objects'][label]['frame']
+        best_objects = self.camera_states[camera].best_objects
+        if label in best_objects:
+            return best_objects[label]
         else:
-            return None
+            return {}
     
-    def get_current_frame(self, camera):
-        return self.camera_data[camera]['current_frame']
+    def get_current_frame(self, camera, draw=False):
+        return self.camera_states[camera].get_current_frame(draw)
 
     def run(self):
         while True:
-            camera, frame_time, tracked_objects = self.tracked_objects_queue.get()
+            if self.stop_event.is_set():
+                print(f"Exiting object processor...")
+                break
 
-            config = self.config[camera]
-            best_objects = self.camera_data[camera]['best_objects']
-            current_object_status = self.camera_data[camera]['object_status']
-            self.camera_data[camera]['tracked_objects'] = tracked_objects
-            self.camera_data[camera]['current_frame_time'] = frame_time
+            try:
+                camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
+            except queue.Empty:
+                continue
 
-            ###
-            # Draw tracked objects on the frame
-            ###
-            current_frame = self.plasma_client.get(f"{camera}{frame_time}")
+            camera_state = self.camera_states[camera]
 
-            if not current_frame is plasma.ObjectNotAvailable:
-                # draw the bounding boxes on the frame
-                for obj in tracked_objects.values():
-                    thickness = 2
-                    color = COLOR_MAP[obj['label']]
-                    
-                    if obj['frame_time'] != frame_time:
-                        thickness = 1
-                        color = (255,0,0)
+            camera_state.update(frame_time, current_tracked_objects)
 
-                    # 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']
-                    cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
-                
-                if config['snapshots']['show_timestamp']:
-                    time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-                    cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
-
-                ###
-                # Set the current frame
-                ###
-                self.camera_data[camera]['current_frame'] = current_frame
-
-                # delete the previous frame from the plasma store and update the object id
-                if not self.camera_data[camera]['object_id'] is None:
-                    self.plasma_client.delete(self.camera_data[camera]['object_id'])
-                self.camera_data[camera]['object_id'] = f"{camera}{frame_time}"
-            
-            ###
-            # Maintain the highest scoring recent object and frame for each label
-            ###
-            for obj in tracked_objects.values():
-                # if the object wasn't seen on the current frame, skip it
-                if obj['frame_time'] != frame_time:
-                    continue
-                if obj['label'] in best_objects:
-                    now = datetime.datetime.now().timestamp()
-                    # if the object is a higher score than the current best score 
-                    # or the current object is more than 1 minute old, use the new object
-                    if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
-                        obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
-                        best_objects[obj['label']] = obj
-                else:
-                    obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
-                    best_objects[obj['label']] = obj
-
-            ###
-            # Report over MQTT
-            ###
-            # count objects with more than 2 entries in history by type
-            obj_counter = Counter()
-            for obj in tracked_objects.values():
-                if len(obj['history']) > 1:
-                    obj_counter[obj['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 != current_object_status[obj_name]:
-                    current_object_status[obj_name] = new_status
-                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
-                    # send the best snapshot over mqtt
-                    best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
-                    ret, jpg = cv2.imencode('.jpg', best_frame)
-                    if ret:
-                        jpg_bytes = jpg.tobytes()
-                        self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
-
-            # expire any objects that are ON and no longer detected
-            expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
-            for obj_name in expired_objects:
-                current_object_status[obj_name] = 'OFF'
-                self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
-                # send updated snapshot over mqtt
-                best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
-                ret, jpg = cv2.imencode('.jpg', best_frame)
-                if ret:
-                    jpg_bytes = jpg.tobytes()
-                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
+            # 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)

frigate/objects.py

@@ -5,6 +5,8 @@ import cv2
 import itertools
 import copy
 import numpy as np
+import random
+import string
 import multiprocessing as mp
 from collections import defaultdict
 from scipy.spatial import distance as dist
@@ -17,10 +19,11 @@ class ObjectTracker():
         self.max_disappeared = max_disappeared
 
     def register(self, index, obj):
-        id = f"{obj['frame_time']}-{index}"
+        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.add_history(obj)
         self.tracked_objects[id] = obj
         self.disappeared[id] = 0
 
@@ -31,22 +34,8 @@ class ObjectTracker():
     def update(self, id, new_obj):
         self.disappeared[id] = 0
         self.tracked_objects[id].update(new_obj)
-        self.add_history(self.tracked_objects[id])
         if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
             self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
-    
-    def add_history(self, obj):
-        entry = {
-            'score': obj['score'],
-            'box': obj['box'],
-            'region': obj['region'],
-            'centroid': obj['centroid'],
-            'frame_time': obj['frame_time']
-        }
-        if 'history' in obj:
-            obj['history'].append(entry)
-        else:
-            obj['history'] = [entry]
 
     def match_and_update(self, frame_time, new_objects):
         # group by name

frigate/util.py

@@ -1,3 +1,4 @@
+from abc import ABC, abstractmethod
 import datetime
 import time
 import signal
@@ -8,7 +9,8 @@ import cv2
 import threading
 import matplotlib.pyplot as plt
 import hashlib
-import pyarrow.plasma as plasma
+from multiprocessing import shared_memory
+from typing import AnyStr
 
 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:
@@ -43,6 +45,9 @@ def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thicknes
 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)
+    # 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])
@@ -65,6 +70,37 @@ def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
 
     return (x_offset, y_offset, x_offset+size, y_offset+size)
 
+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
+
+    x1 = region[0] 
+    y1 = region[1]
+
+    uv_x1 = x1//2
+    uv_y1 = y1//4
+
+    uv_width = size//2
+    uv_height = size//4
+
+    u_y_start = height
+    v_y_start = height + height//4
+    two_x_offset = width//2
+
+    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]
+
+    return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
+
 def intersection(box_a, box_b):
     return (
         max(box_a[0], box_b[0]),
@@ -122,12 +158,16 @@ class EventsPerSecond:
         self._start = datetime.datetime.now().timestamp()
 
     def update(self):
+        if self._start is None:
+            self.start()
         self._timestamps.append(datetime.datetime.now().timestamp())
         # truncate the list when it goes 100 over the max_size
         if len(self._timestamps) > self._max_events+100:
             self._timestamps = self._timestamps[(1-self._max_events):]
 
     def eps(self, last_n_seconds=10):
+        if self._start is None:
+            self.start()
 		# compute the (approximate) events in the last n seconds
         now = datetime.datetime.now().timestamp()
         seconds = min(now-self._start, last_n_seconds)
@@ -139,45 +179,66 @@ def print_stack(sig, frame):
 def listen():
     signal.signal(signal.SIGUSR1, print_stack)
 
-class PlasmaManager:
-    def __init__(self):
-        self.connect()
-    
-    def connect(self):
-        while True:
-            try:
-                self.plasma_client = plasma.connect("/tmp/plasma")
-                return
-            except:
-                print(f"TrackedObjectProcessor: unable to connect plasma client")
-                time.sleep(10)
+class FrameManager(ABC):
+    @abstractmethod
+    def create(self, name, size) -> AnyStr:
+        pass
 
+    @abstractmethod
     def get(self, name, timeout_ms=0):
-        object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
-        while True:
-            try:
-                return self.plasma_client.get(object_id, timeout_ms=timeout_ms)
-            except:
-                self.connect()
-                time.sleep(1)
+        pass
 
-    def put(self, name, obj):
-        object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
-        while True:
-            try:
-                self.plasma_client.put(obj, object_id)
-                return
-            except Exception as e:
-                print(f"Failed to put in plasma: {e}")
-                self.connect()
-                time.sleep(1)
+    @abstractmethod
+    def close(self, name):
+        pass
+
+    @abstractmethod
+    def delete(self, name):
+        pass
+
+class DictFrameManager(FrameManager):
+    def __init__(self):
+        self.frames = {}
+    
+    def create(self, name, size) -> AnyStr:
+        mem = bytearray(size)
+        self.frames[name] = mem
+        return mem
+    
+    def get(self, name, shape):
+        mem = self.frames[name]
+        return np.ndarray(shape, dtype=np.uint8, buffer=mem)
+    
+    def close(self, name):
+        pass
+    
+    def delete(self, name):
+        del self.frames[name]
+
+class SharedMemoryFrameManager(FrameManager):
+    def __init__(self):
+        self.shm_store = {}
+    
+    def create(self, name, size) -> AnyStr:
+        shm = shared_memory.SharedMemory(name=name, create=True, size=size)
+        self.shm_store[name] = shm
+        return shm.buf
+
+    def get(self, name, shape):
+        if name in self.shm_store:
+            shm = self.shm_store[name]
+        else:
+            shm = shared_memory.SharedMemory(name=name)
+            self.shm_store[name] = shm
+        return np.ndarray(shape, dtype=np.uint8, buffer=shm.buf)
+
+    def close(self, name):
+        if name in self.shm_store:
+            self.shm_store[name].close()
+            del self.shm_store[name]
 
     def delete(self, name):
-        object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
-        while True:
-            try:
-                self.plasma_client.delete([object_id])
-                return
-            except:
-                self.connect()
-                time.sleep(1)
+        if name in self.shm_store:
+            self.shm_store[name].close()
+            self.shm_store[name].unlink()
+            del self.shm_store[name]

frigate/video.py

@@ -5,15 +5,16 @@ import cv2
 import queue
 import threading
 import ctypes
-import pyarrow.plasma as plasma
 import multiprocessing as mp
 import subprocess as sp
 import numpy as np
 import copy
 import itertools
 import json
+import base64
+from typing import Dict, List
 from collections import defaultdict
-from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, PlasmaManager
+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 frigate.edgetpu import RemoteObjectDetector
 from frigate.motion import MotionDetector
@@ -52,7 +53,7 @@ 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)
 
-def filtered(obj, objects_to_track, object_filters, mask):
+def filtered(obj, objects_to_track, object_filters, mask=None):
     object_name = obj[0]
 
     if not object_name in objects_to_track:
@@ -71,8 +72,8 @@ def filtered(obj, objects_to_track, object_filters, mask):
         if obj_settings.get('max_area', 24000000) < obj[3]:
             return True
 
-        # if the score is lower than the threshold, skip
-        if obj_settings.get('threshold', 0) > obj[1]:
+        # if the score is lower than the min_score, skip
+        if obj_settings.get('min_score', 0) > obj[1]:
             return True
     
         # compute the coordinates of the object and make sure
@@ -81,13 +82,13 @@ def filtered(obj, objects_to_track, object_filters, mask):
         x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(mask[0])-1)
 
         # if the object is in a masked location, don't add it to detected objects
-        if mask[y_location][x_location] == [0]:
+        if (not mask is None) and (mask[y_location][x_location] == 0):
             return True
         
-        return False
+    return False
 
 def create_tensor_input(frame, region):
-    cropped_frame = frame[region[1]:region[3], region[0]:region[2]]
+    cropped_frame = yuv_region_2_rgb(frame, region)
 
     # Resize to 300x300 if needed
     if cropped_frame.shape != (300, 300, 3):
@@ -114,8 +115,104 @@ def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
     process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, 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_num = 0
+    frame_size = frame_shape[0] * frame_shape[1] * 3 // 2
+    frame_rate = EventsPerSecond()
+    skipped_eps = EventsPerSecond()
+    skipped_eps.start()
+    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.")
+
+          if ffmpeg_process.poll() != None:
+              print(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()
+            frame_manager.delete(frame_name)
+            continue
+
+        # close the frame
+        frame_manager.close(frame_name)
+
+        # add to the queue
+        frame_queue.put(current_frame.value)
+
+class CameraWatchdog(threading.Thread):
+    def __init__(self, name, config, frame_queue, camera_fps, stop_event):
+        threading.Thread.__init__(self)
+        self.name = name
+        self.config = config
+        self.capture_thread = None
+        self.ffmpeg_process = None
+        self.stop_event = stop_event
+        self.camera_fps = camera_fps
+        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
+
+    def run(self):
+        self.start_ffmpeg()
+        time.sleep(10)
+        while True:
+            if self.stop_event.is_set():
+                print(f"Exiting watchdog...")
+                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()
+                try:
+                    print("Waiting for ffmpeg to exit gracefully...")
+                    self.ffmpeg_process.communicate(timeout=30)
+                except sp.TimeoutExpired:
+                    print("FFmpeg didnt exit. Force killing...")
+                    self.ffmpeg_process.kill()
+                    self.ffmpeg_process.communicate()
+            
+            # 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.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()
+
 class CameraCapture(threading.Thread):
-    def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, detection_frame):
+    def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, stop_event):
         threading.Thread.__init__(self)
         self.name = name
         self.frame_shape = frame_shape
@@ -124,187 +221,155 @@ class CameraCapture(threading.Thread):
         self.take_frame = take_frame
         self.fps = fps
         self.skipped_fps = EventsPerSecond()
-        self.plasma_client = PlasmaManager()
+        self.frame_manager = SharedMemoryFrameManager()
         self.ffmpeg_process = ffmpeg_process
-        self.current_frame = 0
+        self.current_frame = mp.Value('d', 0.0)
         self.last_frame = 0
-        self.detection_frame = detection_frame
+        self.stop_event = stop_event
 
     def run(self):
-        frame_num = 0
         self.skipped_fps.start()
-        while True:
-            if self.ffmpeg_process.poll() != None:
-                print(f"{self.name}: ffmpeg process is not running. exiting capture thread...")
-                break
+        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)
 
-            frame_bytes = self.ffmpeg_process.stdout.read(self.frame_size)
-            self.current_frame = datetime.datetime.now().timestamp()
+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'], stop_event)
+    camera_watchdog.start()
+    camera_watchdog.join()
 
-            if len(frame_bytes) == 0:
-                print(f"{self.name}: ffmpeg didnt return a frame. something is wrong.")
-                continue
-
-            self.fps.update()
-
-            frame_num += 1
-            if (frame_num % self.take_frame) != 0:
-                self.skipped_fps.update()
-                continue
-
-            # if the detection process is more than 1 second behind, skip this frame
-            if self.detection_frame.value > 0.0 and (self.last_frame - self.detection_frame.value) > 1:
-                self.skipped_fps.update()
-                continue
-
-            # put the frame in the plasma store
-            self.plasma_client.put(f"{self.name}{self.current_frame}",
-                    np
-                        .frombuffer(frame_bytes, np.uint8)
-                        .reshape(self.frame_shape)
-                )
-            # add to the queue
-            self.frame_queue.put(self.current_frame)
-            self.last_frame = self.current_frame
-
-def track_camera(name, config, global_objects_config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame):
-    print(f"Starting process for {name}: {os.getpid()}")
+def track_camera(name, config, detection_queue, result_connection, detected_objects_queue, process_info, stop_event):
     listen()
 
-    detection_frame.value = 0.0
+    frame_queue = process_info['frame_queue']
+
+    frame_shape = config['frame_shape']
 
     # Merge the tracked object config with the global config
-    camera_objects_config = config.get('objects', {})    
-    # combine tracked objects lists
-    objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', []))
-    # merge object filters
-    global_object_filters = global_objects_config.get('filters', {})
-    camera_object_filters = camera_objects_config.get('filters', {})
-    objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys())
-    object_filters = {}
-    for obj in objects_with_config:
-        object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
-
-    frame = np.zeros(frame_shape, np.uint8)
+    camera_objects_config = config.get('objects', {})
+    objects_to_track = camera_objects_config.get('track', [])
+    object_filters = camera_objects_config.get('filters', {})
 
     # load in the mask for object detection
     if 'mask' in config:
-        mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE)
+        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:
-        mask = np.zeros((frame_shape[0], frame_shape[1], 1), np.uint8)
+    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)
+    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection)
 
     object_tracker = ObjectTracker(10)
 
-    plasma_client = PlasmaManager()
-    avg_wait = 0.0
+    frame_manager = SharedMemoryFrameManager()
+
+    process_frames(name, frame_queue, frame_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")
+
+def reduce_boxes(boxes):
+    if len(boxes) == 0:
+        return []
+    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)
+
+    detections = []
+    region_detections = object_detector.detect(tensor_input)
+    for d in region_detections:
+        box = d[2]
+        size = region[2]-region[0]
+        x_min = int((box[1] * size) + region[0])
+        y_min = int((box[0] * size) + region[1])
+        x_max = int((box[3] * size) + region[0])
+        y_max = int((box[2] * size) + region[1])
+        det = (d[0],
+            d[1],
+            (x_min, y_min, x_max, y_max),
+            (x_max-x_min)*(y_max-y_min),
+            region)
+        # apply object filters
+        if filtered(det, objects_to_track, object_filters, mask):
+            continue
+        detections.append(det)
+    return detections
+
+def process_frames(camera_name: str, frame_queue: mp.Queue, frame_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,
+    exit_on_empty: bool = False):
+    
+    fps = process_info['process_fps']
+    detection_fps = process_info['detection_fps']
+    current_frame_time = process_info['detection_frame']
+
     fps_tracker = EventsPerSecond()
     fps_tracker.start()
-    object_detector.fps.start()
-    while True:
-        read_start.value = datetime.datetime.now().timestamp()
-        frame_time = frame_queue.get()
-        duration = datetime.datetime.now().timestamp()-read_start.value
-        read_start.value = 0.0
-        avg_wait = (avg_wait*99+duration)/100
-        detection_frame.value = frame_time
-        
-        # Get frame from plasma store
-        frame = plasma_client.get(f"{name}{frame_time}")
 
-        if frame is plasma.ObjectNotAvailable:
+    while True:
+        if stop_event.is_set() or (exit_on_empty and frame_queue.empty()):
+                print(f"Exiting track_objects...")
+                break
+
+        try:
+            frame_time = frame_queue.get(True, 10)
+        except queue.Empty:
+            continue
+
+        current_frame_time.value = frame_time
+
+        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.")
             continue
 
-        fps_tracker.update()
-        fps.value = fps_tracker.eps()
-        detection_fps.value = object_detector.fps.eps()
-        
         # look for motion
         motion_boxes = motion_detector.detect(frame)
 
-        tracked_objects = object_tracker.tracked_objects.values()
+        tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values()]
 
-        # merge areas of motion that intersect with a known tracked object into a single area to look at
-        areas_of_interest = []
-        used_motion_boxes = []
-        for obj in tracked_objects:
-            x_min, y_min, x_max, y_max = obj['box']
-            for m_index, motion_box in enumerate(motion_boxes):
-                if intersection_over_union(motion_box, obj['box']) > .2:
-                    used_motion_boxes.append(m_index)
-                    x_min = min(obj['box'][0], motion_box[0])
-                    y_min = min(obj['box'][1], motion_box[1])
-                    x_max = max(obj['box'][2], motion_box[2])
-                    y_max = max(obj['box'][3], motion_box[3])
-            areas_of_interest.append((x_min, y_min, x_max, y_max))
-        unused_motion_boxes = set(range(0, len(motion_boxes))).difference(used_motion_boxes)
-        
-        # compute motion regions
-        motion_regions = [calculate_region(frame_shape, motion_boxes[i][0], motion_boxes[i][1], motion_boxes[i][2], motion_boxes[i][3], 1.2)
-            for i in unused_motion_boxes]
-        
-        # compute tracked object regions
-        object_regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
-            for a in areas_of_interest]
-        
-        # merge regions with high IOU
-        merged_regions = motion_regions+object_regions
-        while True:
-            max_iou = 0.0
-            max_indices = None
-            region_indices = range(len(merged_regions))
-            for a, b in itertools.combinations(region_indices, 2):
-                iou = intersection_over_union(merged_regions[a], merged_regions[b])
-                if iou > max_iou:
-                    max_iou = iou
-                    max_indices = (a, b)
-            if max_iou > 0.1:
-                a = merged_regions[max_indices[0]]
-                b = merged_regions[max_indices[1]]
-                merged_regions.append(calculate_region(frame_shape,
-                    min(a[0], b[0]),
-                    min(a[1], b[1]),
-                    max(a[2], b[2]),
-                    max(a[3], b[3]),
-                    1
-                ))
-                del merged_regions[max(max_indices[0], max_indices[1])]
-                del merged_regions[min(max_indices[0], max_indices[1])]
-            else:
-                break
+        # combine motion boxes with known locations of existing objects
+        combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
+
+        # compute regions
+        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
+            for a in combined_boxes]
+
+        # combine overlapping regions
+        combined_regions = reduce_boxes(regions)
+
+        # re-compute regions
+        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
+            for a in combined_regions]
 
         # resize regions and detect
         detections = []
-        for region in merged_regions:
-
-            tensor_input = create_tensor_input(frame, region)
-
-            region_detections = object_detector.detect(tensor_input)
-
-            for d in region_detections:
-                box = d[2]
-                size = region[2]-region[0]
-                x_min = int((box[1] * size) + region[0])
-                y_min = int((box[0] * size) + region[1])
-                x_max = int((box[3] * size) + region[0])
-                y_max = int((box[2] * size) + region[1])
-                det = (d[0],
-                    d[1],
-                    (x_min, y_min, x_max, y_max),
-                    (x_max-x_min)*(y_max-y_min),
-                    region)
-                if filtered(det, objects_to_track, object_filters, mask):
-                    continue
-                detections.append(det)
-
+        for region in regions:
+            detections.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+        
         #########
-        # merge objects, check for clipped objects and look again up to N times
+        # merge objects, check for clipped objects and look again up to 4 times
         #########
         refining = True
         refine_count = 0
@@ -334,40 +399,28 @@ def track_camera(name, config, global_objects_config, frame_queue, frame_shape,
                             box[0], box[1],
                             box[2], box[3])
                         
-                        tensor_input = create_tensor_input(frame, region)
-                        # run detection on new region
-                        refined_detections = object_detector.detect(tensor_input)
-                        for d in refined_detections:
-                            box = d[2]
-                            size = region[2]-region[0]
-                            x_min = int((box[1] * size) + region[0])
-                            y_min = int((box[0] * size) + region[1])
-                            x_max = int((box[3] * size) + region[0])
-                            y_max = int((box[2] * size) + region[1])
-                            det = (d[0],
-                                d[1],
-                                (x_min, y_min, x_max, y_max),
-                                (x_max-x_min)*(y_max-y_min),
-                                region)
-                            if filtered(det, objects_to_track, object_filters, mask):
-                                continue
-                            selected_objects.append(det)
+                        selected_objects.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
 
                         refining = True
                     else:
-                        selected_objects.append(obj)
-                
+                        selected_objects.append(obj)            
             # set the detections list to only include top, complete objects
             # and new detections
             detections = selected_objects
 
             if refining:
                 refine_count += 1
-        
+
         # now that we have refined our detections, we need to track objects
         object_tracker.match_and_update(frame_time, detections)
 
-        # add to the queue
-        detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects))
-
-    print(f"{name}: exiting subprocess")
+        # add to the queue if not full
+        if(detected_objects_queue.full()):
+          frame_manager.delete(f"{camera_name}{frame_time}")
+          continue
+        else:
+          fps_tracker.update()
+          fps.value = fps_tracker.eps()
+          detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects))
+          detection_fps.value = object_detector.fps.eps()
+          frame_manager.close(f"{camera_name}{frame_time}")

labelmap.txt

@@ -0,0 +1,80 @@
+0  person
+1  bicycle
+2  car
+3  motorcycle
+4  airplane
+5  bus
+6  train
+7  car
+8  boat
+9  traffic light
+10  fire hydrant
+12  stop sign
+13  parking meter
+14  bench
+15  bird
+16  cat
+17  dog
+18  horse
+19  sheep
+20  cow
+21  elephant
+22  bear
+23  zebra
+24  giraffe
+26  backpack
+27  umbrella
+30  handbag
+31  tie
+32  suitcase
+33  frisbee
+34  skis
+35  snowboard
+36  sports ball
+37  kite
+38  baseball bat
+39  baseball glove
+40  skateboard
+41  surfboard
+42  tennis racket
+43  bottle
+45  wine glass
+46  cup
+47  fork
+48  knife
+49  spoon
+50  bowl
+51  banana
+52  apple
+53  sandwich
+54  orange
+55  broccoli
+56  carrot
+57  hot dog
+58  pizza
+59  donut
+60  cake
+61  chair
+62  couch
+63  potted plant
+64  bed
+66  dining table
+69  toilet
+71  tv
+72  laptop
+73  mouse
+74  remote
+75  keyboard
+76  cell phone
+77  microwave
+78  oven
+79  toaster
+80  sink
+81  refrigerator
+83  book
+84  clock
+85  vase
+86  scissors
+87  teddy bear
+88  hair drier
+89  toothbrush

process_clip.py

@@ -0,0 +1,152 @@
+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()