tweak screenshots

This package will add support for the iGPU of AMD Ryzen and presumably a few more AMD cards.
See details of the package here: https://packages.ubuntu.com/focal/mesa-va-drivers
It also adds support for the open source Nvidia Nouveau driver according to https://wiki.debian.org/HardwareVideoAcceleration

.dockerignore

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

.github/FUNDING.yml

@@ -1 +1 @@
-ko_fi: blakeblackshear
+github: blakeblackshear

.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 triple back ticks.
+```yaml
+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.

.gitignore

@@ -1,2 +1,4 @@
 *.pyc 
 debug
+.vscode
+config/config.yml

Dockerfile

@@ -1,109 +0,0 @@
-FROM ubuntu:18.04
-
-ARG DEVICE
-
-# Install packages for apt repo
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
-    apt-transport-https \
-    ca-certificates \
-    curl \
-    wget \
-    gnupg-agent \
-    dirmngr \
-    software-properties-common \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY scripts/install_odroid_repo.sh .
-
-RUN if [ "$DEVICE" = "odroid" ]; then \
-      sh /install_odroid_repo.sh; \
-    fi
-
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
- python3 \ 
- # OpenCV dependencies
- ffmpeg \
- build-essential \
- cmake \
- unzip \
- pkg-config \
- libjpeg-dev \
- libpng-dev \
- libtiff-dev \
- libavcodec-dev \
- libavformat-dev \
- libswscale-dev \
- libv4l-dev \
- libxvidcore-dev \
- libx264-dev \
- libgtk-3-dev \
- libatlas-base-dev \
- gfortran \
- python3-dev \
- # Coral USB Python API Dependencies
- libusb-1.0-0 \
- python3-pip \
- python3-pil \
- python3-numpy \
- libc++1 \
- libc++abi1 \
- libunwind8 \
- libgcc1 \
- # VAAPI drivers for Intel hardware accel
- libva-drm2 libva2 i965-va-driver vainfo \
- && rm -rf /var/lib/apt/lists/* 
-
-# Install core packages 
-RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
-RUN  pip install -U pip \
- numpy \
- Flask \
- paho-mqtt \
- PyYAML
-
-# Download & build OpenCV
-# TODO: use multistage build to reduce image size: 
-#   https://medium.com/@denismakogon/pain-and-gain-running-opencv-application-with-golang-and-docker-on-alpine-3-7-435aa11c7aec
-#   https://www.merixstudio.com/blog/docker-multi-stage-builds-python-development/
-RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
-RUN cd /usr/local/src/ \
- && unzip 4.0.1.zip \
- && rm 4.0.1.zip \
- && cd /usr/local/src/opencv-4.0.1/ \
- && mkdir build \
- && cd /usr/local/src/opencv-4.0.1/build \ 
- && cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
- && make -j4 \
- && make install \
- && ldconfig \
- && rm -rf /usr/local/src/opencv-4.0.1
-
-# Download and install EdgeTPU libraries for Coral
-RUN wget https://dl.google.com/coral/edgetpu_api/edgetpu_api_latest.tar.gz -O edgetpu_api.tar.gz --trust-server-names \
-  && tar xzf edgetpu_api.tar.gz
-
-COPY scripts/install_edgetpu_api.sh edgetpu_api/install.sh
-
-RUN cd edgetpu_api \
-  && /bin/bash install.sh
-
-# Copy a python 3.6 version
-RUN cd /usr/local/lib/python3.6/dist-packages/edgetpu/swig/ \
-  && ln -s _edgetpu_cpp_wrapper.cpython-35m-arm-linux-gnueabihf.so _edgetpu_cpp_wrapper.cpython-36m-arm-linux-gnueabihf.so
-
-# symlink the model and labels
-RUN wget https://dl.google.com/coral/canned_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite -O mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite --trust-server-names
-RUN wget https://dl.google.com/coral/canned_models/coco_labels.txt -O coco_labels.txt --trust-server-names
-RUN ln -s mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
-RUN ln -s /coco_labels.txt /label_map.pbtext
-
-# Minimize image size 
-RUN (apt-get autoremove -y; \
-     apt-get autoclean -y)
-
-WORKDIR /opt/frigate/
-ADD frigate frigate/
-COPY detect_objects.py .
-COPY benchmark.py .
-
-CMD ["python3", "-u", "detect_objects.py"]

LICENSE

@@ -1,661 +1,21 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
-                       Version 3, 19 November 2007
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+The MIT License
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

@@ -0,0 +1,49 @@
+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:1.0.0-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
+
+amd64nvidia_wheels:
+	docker build --tag blakeblackshear/frigate-wheels:amd64nvidia --file docker/Dockerfile.wheels .
+
+amd64nvidia_ffmpeg:
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
+
+amd64nvidia_frigate:
+	docker build --tag frigate-base --build-arg ARCH=amd64nvidia --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.amd64nvidia .
+
+amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
+
+aarch64_wheels:
+	docker build --tag blakeblackshear/frigate-wheels:aarch64 --file docker/Dockerfile.wheels.aarch64 .
+
+aarch64_ffmpeg:
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
+
+aarch64_frigate:
+	docker build --tag frigate-base --build-arg ARCH=aarch64 --file docker/Dockerfile.base .
+	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:1.0.0-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

@@ -1,120 +1,912 @@
-<a href='https://ko-fi.com/P5P7XGO9' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://az743702.vo.msecnd.net/cdn/kofi4.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
+<p align="center">
+  <img align="center" alt="logo" src="docs/frigate.png">
+</p>
 
-# Frigate - Realtime Object Detection for RTSP Cameras
-**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
+# Frigate - NVR With Realtime Object Detection for IP Cameras
+A complete and local NVR designed for HomeAssistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
-Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP cameras. Designed for integration with HomeAssistant or others via MQTT.
+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.
 
-- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
-- Allows you to define specific regions (squares) in the image to look for objects
-- No motion detection (for now)
-- Object detection with Tensorflow runs in a separate thread
-- 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
+- Tight integration with HomeAssistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
+- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
+- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
+- Uses a very low overhead motion detection to determine where to run object detection
+- Object detection with TensorFlow runs in separate processes for maximum FPS
+- Communicates over MQTT for easy integration into other systems
+- 24/7 recording
+- Re-streaming via RTMP to reduce the number of connections to your camera
 
-![Diagram](diagram.png)
+## Screenshots
+<div>
+<a href="docs/media_browser.png"><img src="docs/media_browser.png" height=400></a>
+<a href="docs/notification.png"><img src="docs/notification.png" height=400></a>
+</div>
 
-## 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")
+## Documentation
+- [How Frigate Works](docs/how-frigate-works.md)
+- [Recommended Hardware](#recommended-hardware)
+- [Installing](#installing)
+- [Configuration File](#configuration)
+- [Setting Up Camera Inputs](#setting-up-camera-inputs)
+- [Optimizing Performance](#optimizing-performance)
+- [Detectors](#detectors)
+- [Object Filters](#object-filters)
+- [Masks](#masks)
+- [Zones](#zones)
+- [Recording Clips](#recording-clips)
+- [24/7 Recordings](#247-recordings)
+- [RTMP Streams](#rtmp-streams)
+- [Integration with HomeAssistant](#integration-with-homeassistant)
+- [MQTT Topics](#mqtt-topics)
+- [HTTP Endpoints](#http-endpoints)
+- [Custom Models](#custom-models)
+- [Troubleshooting](#troubleshooting)
 
-## Getting Started
-Build the container with
-```
-docker build -t 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.|
 
-The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
+[Back to top](#documentation)
 
-Run the container with
-```
-docker run --rm \
---privileged \
--v /dev/bus/usb:/dev/bus/usb \
--v <path_to_config_dir>:/config:ro \
--p 5000:5000 \
--e RTSP_PASSWORD='password' \
-frigate:latest
-```
+## Installing
 
-Example docker-compose:
-```
+### 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
-    image: frigate:latest
+    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>:/media/frigate/clips      
+      - <path_to_directory_for_recordings>:/media/frigate/recordings      
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+        target: /tmp/cache
+        tmpfs:
+          size: 100000000
     ports:
       - "5000:5000"
+      - "1935:1935" # RTMP feeds
     environment:
-      RTSP_PASSWORD: "password"
+      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.yml).
+If you can't use docker compose, you can run the container with something similar to this:
+```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
+```
 
-Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
+### Kubernetes
+Use the [helm chart](https://github.com/k8s-at-home/charts/tree/master/charts/frigate).
+
+### 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.
+
+#### 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).
+
+It is recommended to start with a minimal configuration and add to it:
+```yaml
+mqtt:
+  host: mqtt.server.com
+cameras:
+  back:
+    ffmpeg:
+      inputs:
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          roles:
+            - detect
+            - rtmp
+    height: 720
+    width: 1280
+    fps: 5
+```
+Here are all the configuration options:
+```yaml
+# Optional: Logging configuration
+logger:
+  # Optional: default log level (default: shown below)
+  default: info
+  # Optional: module by module log level configuration
+  logs:
+    frigate.mqtt: error
+
+# Optional: 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: Retention settings for clips (default: shown below)
+  retain:
+    # Required: Default retention days (default: shown below)
+    default: 10
+    # Optional: Per object retention days
+    objects:
+      person: 15
+
+# Optional: Global ffmpeg args
+# Args may be provided as a string or an array
+# "ffmpeg" + global_args + input_args + "-i" + input + output_args
+ffmpeg:
+  # Optional: global ffmpeg args (default: shown below)
+  global_args: -hide_banner -loglevel fatal
+  # Optional: global hwaccel args (default: shown below)
+  # NOTE: See hardware acceleration docs for your specific device
+  hwaccel_args: []
+  # Optional: global input args (default: shown below)
+  input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
+  # Optional: global output args
+  output_args: 
+    # Optional: output args for detect streams (default: shown below)
+    detect: -f rawvideo -pix_fmt yuv420p
+    # Optional: output args for record streams (default: shown below)
+    record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
+    # Optional: output args for clips streams (default: shown below)
+    clips: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
+    # Optional: output args for rtmp streams (default: shown below)
+    rtmp: -c copy -f flv
+
+# Optional: Global object filters for all cameras.
+# NOTE: can be overridden at the camera level
+objects:
+  # Optional: list of objects to track from labelmap.txt (default: shown below)
+  track:
+    - person
+  # Optional: filters to reduce false positives for specific object types
+  filters:
+    person:
+      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
+      min_area: 5000
+      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
+      max_area: 100000
+      # Optional: minimum score for the object to initiate tracking (default: shown below)
+      min_score: 0.5
+      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
+      threshold: 0.85
+
+# Required: configuration section for cameras
+cameras:
+  # Required: name of the camera
+  back:
+    # Required: ffmpeg settings for the camera
+    ffmpeg:
+      # Required: A list of input streams for the camera. See documentation for more information.
+      inputs: 
+          # Required: the path to the stream
+          # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          # Required: list of roles for this stream. valid values are: detect,record,clips,rtmp
+          roles:
+            - detect
+            - rtmp
+          # Optional: stream specific global args (default: inherit)
+          global_args:
+          # Optional: stream specific hwaccel args (default: inherit)
+          hwaccel_args:
+          # Optional: stream specific input args (default: inherit)
+          input_args:
+        
+      # Optional: camera specific global args (default: inherit)
+      global_args:
+      # Optional: camera specific hwaccel args (default: inherit)
+      hwaccel_args:
+      # Optional: camera specific input args (default: inherit)
+      input_args:
+      # Optional: camera specific output args (default: inherit)
+      output_args:
+    
+    # Required: height of the frame
+    # NOTE: Recommended to set this value, but frigate will attempt to autodetect.
+    height: 720
+    # Required: 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: 175
+
+    # 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: Camera override for retention settings (default: global values)
+      retain:
+        # Required: Default retention days (default: shown below)
+        default: 10
+        # Optional: Per object retention days
+        objects:
+          person: 15  
+
+    # Optional: 24/7 recording configuration
+    record:
+      # Optional: Enable recording (default: global setting)
+      enabled: False
+      # Optional: Number of days to retain (default: global setting)
+      retain_days: 30
+    
+    # Optional: RTMP re-stream configuration
+    rtmp:
+      # Required: Enable the live stream (default: True)
+      enabled: True
+
+    # Optional: Configuration for the snapshots in the debug view and mqtt
+    snapshots:
+      # 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: crop the snapshot to the detection region (default: shown below)
+      crop_to_region: True
+      # Optional: height to resize the snapshot to (default: shown below)
+      # NOTE: 175px is optimized for thumbnails in the homeassistant media browser
+      height: 175
+
+    # Optional: Camera level object filters config. If defined, this is used instead of the global config.
+    objects:
+      track:
+        - person
+        - car
+      filters:
+        person:
+          min_area: 5000
+          max_area: 100000
+          min_score: 0.5
+          threshold: 0.85
+```
+[Back to top](#documentation)
+
+## Setting Up Camera Inputs
+Up to 4 inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create clips from a higher resolution stream, or vice versa.
+
+Each role can only be assigned to one input per camera. The options for roles are as follows:
+|Role|Description|
+|----|-----|
+|`detect`|Main feed for object detection|
+|`clips`|Clips of events from objects detected in the `detect` feed. [docs](#recording-clips)|
+|`record`|Saves 60 second segments of the video feed. [docs](#247-recordings)|
+|`rtmp`|Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams)|
+
+Example:
+```yaml
+mqtt:
+  host: mqtt.server.com
+cameras:
+  back:
+    ffmpeg:
+      inputs:
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          roles:
+            - detect
+            - rtmp
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
+          roles:
+            - clips
+            - record
+    height: 720
+    width: 1280
+    fps: 5
+```
+
+
+[Back to top](#documentation)
+
+## Optimizing Performance
+- **Google Coral**: It is strongly recommended to use a Google Coral, but Frigate will fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU.
+- **Resolution**: For the `detect` input, choose a camera resolution where the smallest object you want to detect barely fits inside a 300x300px square. The model used by Frigate is trained on 300x300px images, so you will get worse performance and no improvement in accuracy by using a larger resolution since Frigate resizes the area where it is looking for objects to 300x300 anyway.
+- **FPS**: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
+- **Hardware Acceleration**: Make sure you configure the `hwaccel_args` for your hardware. They provide a significant reduction in CPU usage if they are available.
+- **Masks**: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for `detection_fps` when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.
+
+### FFmpeg Hardware Acceleration
+Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.
+
+Raspberry Pi 3/4 (32-bit OS):
+```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
+```
+
+Nvidia GPU based decoding via NVDEC is supported, but requires special configuration. See the [nvidia NVDEC documentation](docs/nvdec.md) for more details.
+
+[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. Zones are visible on the [MJPEG feed](#camera_name).
+
+[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. Zones are visible on the [MJPEG feed](#camera_name).
+
+![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 `/tmp/cache` and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to `/media/frigate/clips`. Clips are retained according to the retention settings defined on the config for each object type.
+
+### Database
+Event and clip information is managed in a sqlite database at `/media/frigate/clips/frigate.db`. If that database is deleted, clips will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within HomeAssistant.
+
+### 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)
+
+## 24/7 Recordings
+**Note**: Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
+
+24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in HomeAssistant's media browser. Each camera supports a configurable retention policy in the config.
+
+[Back to top](#documentation)
+
+## RTMP Streams
+Frigate can re-stream your video feed as a RTMP feed for other applications such as HomeAssistant to utilize it. This allows you to use a video feed for detection in frigate and HomeAssistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
+
+[Back to top](#documentation)
 
 ## Integration with HomeAssistant
-```
-camera:
-  - name: Camera Last Person
-    platform: mqtt
-    topic: frigate/<camera_name>/snapshot
+The best way to integrate with HomeAssistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration). When configuring the integration, you will be asked for the `Host` of your frigate instance. This value should be the url you use to access Frigate in the browser and will look like `http://<host>:5000/`. If you are using HassOS with the addon, the host should be `http://ccab4aaf-frigate:5000`. HomeAssistant needs access to port 5000 (api) and 1935 (rtmp) for all features. The integration will setup the following entities within HomeAssistant:
 
-binary_sensor:
-  - name: Camera Person
-    platform: mqtt
-    state_topic: "frigate/<camera_name>/objects"
-    value_template: '{{ value_json.person }}'
-    device_class: motion
-    availability_topic: "frigate/available"
+Sensors:
+- Stats to monitor frigate performance
+- Object counts for all zones and cameras
 
+Cameras:
+- Cameras for image of the last detected object for each camera
+- Camera entities with stream support (requires RTMP)
+
+Media Browser:
+- Rich UI with thumbnails for browsing event clips
+- Rich UI for browsing 24/7 recordings by month, day, camera, time
+
+API:
+- Notification API with public facing endpoints for images in notifications
+
+### Notifications
+Frigate publishes event information in the form of a change feed via MQTT. This allows lots of customization for notifications to meet your needs. Event changes are published with `before` and `after` information as shown [here](#frigateevents).
+
+Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.
+```yaml
 automation:
-  - alias: Alert me if a person is detected while armed away
+  - alias: Notify of events
     trigger:
-      platform: state
-      entity_id: binary_sensor.camera_person
-      from: 'off'
-      to: 'on'
-    condition:
-      - condition: state
-        entity_id: alarm_control_panel.home_alarm
-        state: armed_away
+      platform: mqtt
+      topic: frigate/events
     action:
-      - service: notify.user_telegram
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
           data:
-          message: "A person was detected."
-          data:
-            photo:
-              - url: http://<ip>:5000/<camera_name>/best_person.jpg
-                caption: A person was detected.
+            image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}.jpg?format=android'
+            tag: '{{trigger.payload_json["after"]["id"]}}'
+```
+Note that the image url has `?format=android`. This adjusts the aspect ratio to be idea for android notifications. For iOS optimized snapshots, no format parameter needs to be passed.
+
+You can find some additional examples for notifications [here](docs/notification-examples.md).
+
+[Back to top](#documentation)
+
+## 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`
+
+### `/<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
+
+### `/<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
+
+### `/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
+        ***************/
+        "capture_pid": 27,
+        /***************
+        * PID for the process that runs detection for this camera
+        ***************/
+        "pid": 34,
+        /***************
+        * Frames per second being processed by frigate.
+        ***************/
+        "process_fps": 5.1,
+        /***************
+        * 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
+      }
+    }
+}
 ```
 
-## Tips
-- Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
+### `/config`
+A json representation of your configuration
+
+### `/events`
+Events from the database. Accepts the following query string parameters:
+|param|Type|Description|
+|----|-----|--|
+|`before`|int|Epoch time|
+|`after`|int|Epoch time|
+|`camera`|str|Camera name|
+|`label`|str|Label name|
+|`zone`|str|Zone name|
+|`limit`|int|Limit the number of events returned|
+
+### `/events/summary`
+Returns summary data for events in the database. Used by the HomeAssistant integration.
+
+### `/events/<id>`
+Returns data for a single event.
+### `/events/<id>/snapshot.jpg`
+Returns a snapshot for the event id optimized for notifications. Works while the event is in progress and after completion. Passing `?format=android` will convert the thumbnail to 2:1 aspect ratio.
+
+[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 the count of objects for the camera for use as a sensor in HomeAssistant.
+
+### `frigate/<zone_name>/<object_name>`
+Publishes the count of objects for the zone for use as a sensor in HomeAssistant.
+
+### `frigate/<camera_name>/<object_name>/snapshot`
+Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image
+is published again.
+
+The height and crop of snapshots can be configured in the config.
+
+### `frigate/events`
+Message published for each changed event:
+```json
+{
+    "before": {
+        "id": "1607123955.475377-mxklsc",
+        "camera": "front_door",
+        "frame_time": 1607123961.837752,
+        "label": "person",
+        "top_score": 0.958984375,
+        "false_positive": false,
+        "start_time": 1607123955.475377,
+        "end_time": null,
+        "score": 0.7890625,
+        "box": [
+            424,
+            500,
+            536,
+            712
+        ],
+        "area": 23744,
+        "region": [
+            264,
+            450,
+            667,
+            853
+        ],
+        "current_zones": [
+            "driveway"
+        ],
+        "entered_zones": [
+            "yard",
+            "driveway"
+        ],
+        "thumbnail": null
+    },
+    "after": {
+        "id": "1607123955.475377-mxklsc",
+        "camera": "front_door",
+        "frame_time": 1607123962.082975,
+        "label": "person",
+        "top_score": 0.958984375,
+        "false_positive": false,
+        "start_time": 1607123955.475377,
+        "end_time": null,
+        "score": 0.87890625,
+        "box": [
+            432,
+            496,
+            544,
+            854
+        ],
+        "area": 40096,
+        "region": [
+            218,
+            440,
+            693,
+            915
+        ],
+        "current_zones": [
+            "yard",
+            "driveway"
+        ],
+        "entered_zones": [
+            "yard",
+            "driveway"
+        ],
+        "thumbnail": null
+    }
+}
+```
+
+[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`
+
+### Customizing the Labelmap
+The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. You must retain the same number of labels, but you can change the names. To change:
+
+- Download the [COCO labelmap](https://dl.google.com/coral/canned_models/coco_labels.txt)
+- Modify the label names as desired. For example, change `7 truck` to `7 car`
+- Mount the new file at `/labelmap.txt` in the container with an additional volume
+  ```
+  -v ./config/labelmap.txt:/labelmap.txt
+  ```
+
+[Back to top](#documentation)
+
+## Logging
+Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
+
+Examples of available modules are:
+- `frigate.app`
+- `frigate.mqtt`
+- `frigate.edgetpu`
+- `frigate.zeroconf`
+- `detector.<detector_name>`
+- `watchdog.<camera_name>`
+- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
+
+## Troubleshooting
+
+### "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
+```
+
+### "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)
 
-## Future improvements
-- [x] Remove motion detection for now
-- [x] Try running object detection in a thread rather than a process
-- [x] Implement min person size again
-- [x] Switch to a config file
-- [x] Handle multiple cameras in the same container
-- [ ] Attempt to figure out coral symlinking
-- [ ] Add object list to config with min scores for mqtt
-- [ ] Move mjpeg encoding to a separate process
-- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
-- [ ] See if motion detection is even worth running
-- [ ] Scan for people across entire image rather than specfic regions
-- [ ] Dynamically resize detection area and follow people
-- [ ] Add ability to turn detection on and off via MQTT
-- [ ] Output movie clips of people for notifications, etc.
-- [ ] Integrate with homeassistant push camera
-- [ ] Merge bounding boxes that span multiple regions
-- [ ] Implement mode to save labeled objects for training
-- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
-- [ ] Look into GPU accelerated decoding of RTSP stream
-- [ ] Send video over a socket and use JSMPEG
-- [x] Look into neural compute stick

benchmark.py

@@ -1,20 +1,93 @@
-import statistics
+import os
+from statistics import mean
+import multiprocessing as mp
 import numpy as np
-from edgetpu.detection.engine import DetectionEngine
+import datetime
+from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
 
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
-PATH_TO_CKPT = '/frozen_inference_graph.pb'
+my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
+labels = load_labels('/labelmap.txt')
 
-# Load the edgetpu engine and labels
-engine = DetectionEngine(PATH_TO_CKPT)
+######
+# Minimal same process runner
+######
+# object_detector = LocalObjectDetector()
+# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
 
-frame = np.zeros((300,300,3), np.uint8)
-flattened_frame = np.expand_dims(frame, axis=0).flatten()
+# start = datetime.datetime.now().timestamp()
 
-detection_times = []
+# frame_times = []
+# for x in range(0, 1000):
+#   start_frame = datetime.datetime.now().timestamp()
 
-for x in range(0, 1000):
-    objects = engine.DetectWithInputTensor(flattened_frame, threshold=0.1, top_k=3)
-    detection_times.append(engine.get_inference_time())
+#   tensor_input[:] = my_frame
+#   detections = object_detector.detect_raw(tensor_input)
+#   parsed_detections = []
+#   for d in detections:
+#       if d[1] < 0.4:
+#           break
+#       parsed_detections.append((
+#           labels[int(d[0])],
+#           float(d[1]),
+#           (d[2], d[3], d[4], d[5])
+#       ))
+#   frame_times.append(datetime.datetime.now().timestamp()-start_frame)
 
-print("Average inference time: " + str(statistics.mean(detection_times)))
+# duration = datetime.datetime.now().timestamp()-start
+# print(f"Processed for {duration:.2f} seconds.")
+# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+
+
+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 = []
+  for x in range(0, num_detections):
+    start_frame = datetime.datetime.now().timestamp()
+    detections = object_detector.detect(my_frame)
+    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")
+
+######
+# Separate process runner
+######
+# event = mp.Event()
+# detection_queue = mp.Queue()
+# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
+
+# 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):
+  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_time = datetime.datetime.now().timestamp()
+
+for p in camera_processes:
+  p.start()
+
+for p in camera_processes:
+  p.join()
+
+duration = datetime.datetime.now().timestamp()-start_time
+print(f"Total - Processed for {duration:.2f} seconds.")

config/config.yml

@@ -1,82 +0,0 @@
-web_port: 5000
-
-mqtt:
-  host: mqtt.server.com
-  topic_prefix: frigate
-#  user: username # Optional -- Uncomment for use
-#  password: password # Optional -- Uncomment for use
-
-cameras:
-  back:
-    rtsp:
-      user: viewer
-      host: 10.0.10.10
-      port: 554
-      # values that begin with a "$" will be replaced with environment variable
-      password: $RTSP_PASSWORD
-      path: /cam/realmonitor?channel=1&subtype=2
-
-    ################
-    ## Optional mask. Must be the same dimensions 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.
-    ################
-    # 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
-
-    ################
-    # Optional hardware acceleration parameters for ffmpeg. If your hardware supports it, it can
-    # greatly reduce the CPU power used to decode the video stream. You will need to determine which
-    # parameters work for your specific hardware. These may work for those with Intel hardware that
-    # supports QuickSync.
-    ################
-    # ffmpeg_hwaccel_args:
-    #   - -hwaccel
-    #   - vaapi
-    #   - -hwaccel_device
-    #   - /dev/dri/renderD128
-    #   - -hwaccel_output_format
-    #   - yuv420p
-
-    ################
-    # FFmpeg log level. Default is "panic". https://ffmpeg.org/ffmpeg.html#Generic-options
-    ################
-    # ffmpeg_log_level: panic
-
-    ################
-    # Optional custom input args. Some cameras may need custom ffmpeg params to work reliably. Specifying
-    # these will replace the default input params.
-    ################
-    # ffmpeg_input_args: []
-
-    ################
-    # Optional custom output args. Some cameras may need custom ffmpeg params to work reliably. Specifying
-    # these will replace the default output params.
-    ################
-    # ffmpeg_output_args: []
-    
-    regions:
-      - size: 350
-        x_offset: 0
-        y_offset: 300
-        min_person_area: 5000
-        threshold: 0.5
-      - size: 400
-        x_offset: 350
-        y_offset: 250
-        min_person_area: 2000
-        threshold: 0.5
-      - size: 400
-        x_offset: 750
-        y_offset: 250
-        min_person_area: 2000
-        threshold: 0.5

detect_objects.py

@@ -1,99 +0,0 @@
-import cv2
-import time
-import queue
-import yaml
-import numpy as np
-from flask import Flask, Response, make_response
-import paho.mqtt.client as mqtt
-
-from frigate.video import Camera
-from frigate.object_detection import PreppedQueueProcessor
-
-with open('/config/config.yml') as f:
-    CONFIG = yaml.safe_load(f)
-
-MQTT_HOST = CONFIG['mqtt']['host']
-MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
-MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
-MQTT_USER = CONFIG.get('mqtt', {}).get('user')
-MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
-
-WEB_PORT = CONFIG.get('web_port', 5000)
-DEBUG = (CONFIG.get('debug', '0') == '1')
-
-def main():
-    # connect to mqtt and setup last will
-    def on_connect(client, userdata, flags, rc):
-        print("On connect called")
-        if rc != 0:
-            if rc == 3:
-                print ("MQTT Server unavailable")
-            elif rc == 4:
-                print ("MQTT Bad username or password")
-            elif rc == 5:
-                print ("MQTT Not authorized")
-            else:
-                print ("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
-        # publish a message to signal that the service is running
-        client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
-    client = mqtt.Client(client_id="frigate")
-    client.on_connect = on_connect
-    client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
-    if not MQTT_USER is None:
-        client.username_pw_set(MQTT_USER, password=MQTT_PASS)
-    client.connect(MQTT_HOST, MQTT_PORT, 60)
-    client.loop_start()
-    
-    # Queue for prepped frames, max size set to (number of cameras * 5)
-    max_queue_size = len(CONFIG['cameras'].items())*5
-    prepped_frame_queue = queue.Queue(max_queue_size)
-
-    cameras = {}
-    for name, config in CONFIG['cameras'].items():
-        cameras[name] = Camera(name, config, prepped_frame_queue, client, MQTT_TOPIC_PREFIX)
-
-    prepped_queue_processor = PreppedQueueProcessor(
-        cameras,
-        prepped_frame_queue
-    )
-    prepped_queue_processor.start()
-
-    for name, camera in cameras.items():
-        camera.start()
-        print("Capture process for {}: {}".format(name, camera.get_capture_pid()))
-
-    # create a flask app that encodes frames a mjpeg on demand
-    app = Flask(__name__)
-
-    @app.route('/<camera_name>/best_person.jpg')
-    def best_person(camera_name):
-        best_person_frame = cameras[camera_name].get_best_person()
-        if best_person_frame is None:
-            best_person_frame = np.zeros((720,1280,3), np.uint8)
-        ret, jpg = cv2.imencode('.jpg', best_person_frame)
-        response = make_response(jpg.tobytes())
-        response.headers['Content-Type'] = 'image/jpg'
-        return response
-
-    @app.route('/<camera_name>')
-    def mjpeg_feed(camera_name):
-        # return a multipart response
-        return Response(imagestream(camera_name),
-                        mimetype='multipart/x-mixed-replace; boundary=frame')
-
-    def imagestream(camera_name):
-        while True:
-            # max out at 5 FPS
-            time.sleep(0.2)
-            frame = cameras[camera_name].get_current_frame_with_objects()
-            # encode the image into a jpg
-            ret, jpg = cv2.imencode('.jpg', frame)
-            yield (b'--frame\r\n'
-                b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
-
-    app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
-
-    camera.join()
-
-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://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-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 mesa-va-drivers \
+        ## Tensorflow lite
+    && wget -q https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
+    && python3.8 -m pip install tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
+    && rm tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)

docker/Dockerfile.amd64nvidia

@@ -0,0 +1,47 @@
+FROM frigate-base
+LABEL maintainer "blakeb@blakeshome.com"
+
+# Install packages for apt repo
+RUN apt-get -qq update \
+    && apt-get -qq install --no-install-recommends -y \
+        # ffmpeg dependencies
+        libgomp1 \
+        ## Tensorflow lite
+    && wget -q https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
+    && python3.8 -m pip install tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
+    && rm tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)
+
+
+# nvidia layer (see https://gitlab.com/nvidia/container-images/cuda/blob/master/dist/11.1/ubuntu20.04-x86_64/base/Dockerfile)
+ENV	    NVIDIA_DRIVER_CAPABILITIES compute,utility,video
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gnupg2 curl ca-certificates && \
+    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub | apt-key add - && \
+    echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
+    echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list && \
+    apt-get purge --autoremove -y curl \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV CUDA_VERSION 11.1.1
+
+# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    cuda-cudart-11-1=11.1.74-1 \
+    cuda-compat-11-1 \
+    && ln -s cuda-11.1 /usr/local/cuda && \
+    rm -rf /var/lib/apt/lists/*
+
+# Required for nvidia-docker v1
+RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
+    echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
+
+ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
+ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
+
+# nvidia-container-runtime
+ENV NVIDIA_VISIBLE_DEVICES all
+ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,video
+ENV NVIDIA_REQUIRE_CUDA "cuda>=11.1 brand=tesla,driver>=418,driver<419 brand=tesla,driver>=440,driver<441 brand=tesla,driver>=450,driver<451"

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://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_armv7l.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)

docker/Dockerfile.base

@@ -0,0 +1,53 @@
+ARG ARCH=amd64
+FROM blakeblackshear/frigate-wheels:${ARCH} as wheels
+FROM blakeblackshear/frigate-ffmpeg:1.0.0-${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 FONTCONFIG_PATH=/etc/fonts
+ENV DEBIAN_FRONTEND=noninteractive
+# Install packages for apt repo
+RUN apt-get -qq update \
+    && apt-get upgrade -y \
+    && apt-get -qq install --no-install-recommends -y \
+    gnupg wget unzip tzdata nginx libnginx-mod-rtmp \
+    && 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=15.0 \
+    && rm -rf /var/lib/apt/lists/* /wheels \
+    && (apt-get autoremove -y; apt-get autoclean -y)
+
+RUN pip3 install \
+    peewee \
+    zeroconf \
+    voluptuous
+
+COPY nginx/nginx.conf /etc/nginx/nginx.conf
+
+# 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
+
+WORKDIR /opt/frigate/
+ADD frigate frigate/
+
+COPY run.sh /run.sh
+RUN chmod +x /run.sh
+
+EXPOSE 5000
+EXPOSE 1935
+
+CMD ["/run.sh"]

docker/Dockerfile.ffmpeg.aarch64

@@ -0,0 +1,474 @@
+# 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 \
+            FREETYPE_VERSION=2.5.5 \
+            FRIBIDI_VERSION=0.19.7 \
+            KVAZAAR_VERSION=1.2.0 \
+            LAME_VERSION=3.100 \
+            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 \
+            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         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         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}
+
+## 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}
+
+## 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 ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${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 +1,466 @@
+# 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 \
+            FREETYPE_VERSION=2.5.5 \
+            FRIBIDI_VERSION=0.19.7 \
+            KVAZAAR_VERSION=1.2.0 \
+            LAME_VERSION=3.100 \
+            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 \
+            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         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         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}
+## 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}
+
+## 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 ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${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 mesa-va-drivers && \
+	rm -rf /var/lib/apt/lists/*

docker/Dockerfile.ffmpeg.amd64nvidia

@@ -0,0 +1,549 @@
+# inspired by https://github.com/jrottenberg/ffmpeg/blob/master/docker-images/4.3/ubuntu1804/Dockerfile
+
+# ffmpeg - http://ffmpeg.org/download.html
+#
+# From https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu
+#
+# https://hub.docker.com/r/jrottenberg/ffmpeg/
+#
+#
+
+FROM    nvidia/cuda:11.1-devel-ubuntu20.04 AS devel-base
+
+ENV	    NVIDIA_DRIVER_CAPABILITIES compute,utility,video
+ENV         DEBIAN_FRONTEND=noninteractive
+
+WORKDIR     /tmp/workdir
+
+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       nvidia/cuda:11.1-runtime-ubuntu20.04 AS runtime-base
+
+ENV	    NVIDIA_DRIVER_CAPABILITIES compute,utility,video
+ENV         DEBIAN_FRONTEND=noninteractive
+
+WORKDIR     /tmp/workdir
+
+RUN     apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 libxcb-shape0-dev && \
+        apt-get autoremove -y && \
+        apt-get clean -y
+
+
+FROM  devel-base as build
+
+ENV        NVIDIA_HEADERS_VERSION=9.1.23.1
+
+ENV         FFMPEG_VERSION=4.3.1 \
+            AOM_VERSION=v1.0.0 \
+            FDKAAC_VERSION=0.1.5 \
+            FREETYPE_VERSION=2.5.5 \
+            FRIBIDI_VERSION=0.19.7 \
+            KVAZAAR_VERSION=1.2.0 \
+            LAME_VERSION=3.100 \
+            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 \
+            LIBZMQ_VERSION=4.3.2 \
+            LIBSRT_VERSION=1.4.1 \
+            LIBARIBB24_VERSION=1.0.3 \
+            LIBPNG_VERSION=1.6.9 \
+            SRC=/usr/local
+
+ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
+ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
+ARG         LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
+ARG         OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
+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         LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
+ARG         LIBARIBB24_SHA256SUM="f61560738926e57f9173510389634d8c06cabedfa857db4b28fb7704707ff128 v1.0.3.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"
+
+
+RUN      buildDeps="autoconf \
+                    automake \
+                    cmake \
+                    curl \
+                    bzip2 \
+                    libexpat1-dev \
+                    g++ \
+                    gcc \
+                    git \
+                    gperf \
+                    libtool \
+                    make \
+                    nasm \
+                    perl \
+                    pkg-config \
+                    python \
+                    libssl-dev \
+                    yasm \
+                    zlib1g-dev" && \
+        apt-get -yqq update && \
+        apt-get install -yq --no-install-recommends ${buildDeps}
+
+RUN \
+	DIR=/tmp/nv-codec-headers && \
+	git clone https://github.com/FFmpeg/nv-codec-headers ${DIR} && \
+	cd ${DIR} && \
+	git checkout n${NVIDIA_HEADERS_VERSION} && \
+	make PREFIX="${PREFIX}" && \
+	make install PREFIX="${PREFIX}" && \
+        rm -rf ${DIR}
+
+## 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}
+## 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}
+
+## 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}
+
+## libsrt https://github.com/Haivision/srt
+RUN \
+        DIR=/tmp/srt && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/Haivision/srt/archive/v${LIBSRT_VERSION}.tar.gz && \
+        tar -xz --strip-components=1 -f v${LIBSRT_VERSION}.tar.gz && \
+        cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
+        make && \
+        make install && \
+        rm -rf ${DIR}
+
+## libpng
+RUN \
+        DIR=/tmp/png && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        git clone https://git.code.sf.net/p/libpng/code ${DIR} -b v${LIBPNG_VERSION} --depth 1 && \
+        ./autogen.sh && \
+        ./configure --prefix="${PREFIX}" && \
+        make check && \
+        make install && \
+        rm -rf ${DIR}
+
+## libaribb24
+RUN \
+        DIR=/tmp/b24 && \
+        mkdir -p ${DIR} && \
+        cd ${DIR} && \
+        curl -sLO https://github.com/nkoriyama/aribb24/archive/v${LIBARIBB24_VERSION}.tar.gz && \
+        echo ${LIBARIBB24_SHA256SUM} | sha256sum --check && \
+        tar -xz --strip-components=1 -f v${LIBARIBB24_VERSION}.tar.gz && \
+        autoreconf -fiv && \
+        ./configure CFLAGS="-I${PREFIX}/include -fPIC" --prefix="${PREFIX}" && \
+        make && \
+        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 ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${PREFIX}" \
+        --enable-libopenjpeg \
+        --enable-libkvazaar \
+        --enable-libaom \
+        --extra-libs=-lpthread \
+        --enable-libsrt \
+        --enable-libaribb24 \
+        --enable-nvenc \
+        --enable-cuda \
+        --enable-cuvid \
+        --enable-libnpp \
+        --extra-cflags="-I${PREFIX}/include -I${PREFIX}/include/ffnvcodec -I/usr/local/cuda/include/" \
+        --extra-ldflags="-L${PREFIX}/lib -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib32/" && \
+        make && \
+        make install && \
+        make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
+        make distclean && \
+        hash -r && \
+        cd tools && \
+        make qt-faststart && cp qt-faststart ${PREFIX}/bin/
+
+## cleanup
+RUN \
+        LD_LIBRARY_PATH="${PREFIX}/lib:${PREFIX}/lib64:${LD_LIBRARY_PATH}" 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/* /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; s:/lib64:/lib:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
+        done
+
+
+
+FROM        runtime-base AS release
+
+ENV         LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64
+
+CMD         ["--help"]
+ENTRYPOINT  ["ffmpeg"]
+
+# copy only needed files, without copying nvidia dev files 
+COPY --from=build /usr/local/bin /usr/local/bin/
+COPY --from=build /usr/local/share /usr/local/share/
+COPY --from=build /usr/local/lib /usr/local/lib/
+COPY --from=build /usr/local/include /usr/local/include/
+
+# Let's make sure the app built correctly
+# Convenient to verify on https://hub.docker.com/r/jrottenberg/ffmpeg/builds/ console output

docker/Dockerfile.ffmpeg.armv7

@@ -0,0 +1,490 @@
+# 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 \
+            FREETYPE_VERSION=2.5.5 \
+            FRIBIDI_VERSION=0.19.7 \
+            KVAZAAR_VERSION=1.2.0 \
+            LAME_VERSION=3.100 \
+            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 \
+            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         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         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}
+
+## 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}
+
+## 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 ${DIR} && \
+        ./configure \
+        --disable-debug \
+        --disable-doc \
+        --disable-ffplay \
+        --enable-shared \
+        --enable-avresample \
+        --enable-libopencore-amrnb \
+        --enable-libopencore-amrwb \
+        --enable-gpl \
+        --enable-libfreetype \
+        --enable-libvidstab \
+        --enable-libmp3lame \
+        --enable-libopus \
+        --enable-libtheora \
+        --enable-libvorbis \
+        --enable-libvpx \
+        --enable-libwebp \
+        --enable-libxcb \
+        --enable-libx265 \
+        --enable-libxvid \
+        --enable-libx264 \
+        --enable-nonfree \
+        --enable-openssl \
+        --enable-libfdk_aac \
+        --enable-postproc \
+        --enable-small \
+        --enable-version3 \
+        --enable-libzmq \
+        --extra-libs=-ldl \
+        --prefix="${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/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

docs/notification-examples.md

@@ -0,0 +1,52 @@
+# Notification examples
+
+```yaml
+automation:
+
+  - alias: When a person enters a zone named yard
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ trigger.payload_json["after"]["label"] == 'person' }}"
+      - "{{ 'yard' in trigger.payload_json["after"]["entered_zones"] }}"
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'A {{trigger.payload_json["after"]["label"]}} has entered the yard.'
+          data:
+            image: 'https://url.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}.jpg'
+            tag: '{{trigger.payload_json["after"]["id"]}}'
+
+  - alias: When a person leaves a zone named yard
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ trigger.payload_json["after"]["label"] == 'person' }}"
+      - "{{ 'yard' in trigger.payload_json["before"]["current_zones"] }}"
+      - "{{ not 'yard' in trigger.payload_json["after"]["current_zones"] }}"
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'A {{trigger.payload_json["after"]["label"]}} has left the yard.'
+          data:
+            image: 'https://url.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}.jpg'
+            tag: '{{trigger.payload_json["after"]["id"]}}'
+
+  - alias: Notify for dogs in the front with a high top score
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ trigger.payload_json["after"]["label"] == 'dog' }}"
+      - "{{ trigger.payload_json["after"]["camera"] == 'front' }}"
+      - "{{ trigger.payload_json["after"]["top_score"] > 0.98 }}"
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'High confidence dog detection.'
+          data:
+            image: 'https://url.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}.jpg'
+            tag: '{{trigger.payload_json["after"]["id"]}}'
+```

docs/nvdec.md

@@ -0,0 +1,105 @@
+# nVidia hardware decoder (NVDEC)
+
+Certain nvidia cards include a hardware decoder, which can greatly improve the
+performance of video decoding. In order to use NVDEC, a special build of 
+ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvidia'
+includes this support for amd64 platforms. An aarch64 for the Jetson, which 
+also includes NVDEC may be added in the future.
+
+## Docker setup
+
+### Requirements
+[nVidia closed source driver](https://www.nvidia.com/en-us/drivers/unix/) required to access NVDEC.
+[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) required to pass NVDEC to docker.
+
+### Setting up docker-compose
+
+In order to pass NVDEC, the docker engine must be set to `nvidia` and the environment variables
+`NVIDIA_VISIBLE_DEVICES=all` and `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` must be set.
+
+In a docker compose file, these lines need to be set:
+```
+services:
+  frigate:
+    ...
+    image: blakeblackshear/frigate:stable-amd64nvidia
+    runtime: nvidia
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all
+      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video 
+```
+
+### Setting up the configuration file
+
+In your frigate config.yml, you'll need to set ffmpeg to use the hardware decoder. 
+The decoder you choose will depend on the input video. 
+
+A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
+```
+ V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
+ V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
+ V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
+ V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
+ V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
+ V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
+ V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
+ V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
+ V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
+ V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)
+ ```
+
+For example, for H265 video (hevc), you'll select `hevc_cuvid`. Add
+`-c:v hevc_covid` to your ffmpeg input arguments:
+
+```
+ffmpeg:
+  input_args:
+    ...
+    - -c:v 
+    - hevc_cuvid
+```
+
+If everything is working correctly, you should see a significant improvement in performance.
+Verify that hardware decoding is working by running `nvidia-smi`, which should show the ffmpeg
+processes:
+
+```
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 455.38       Driver Version: 455.38       CUDA Version: 11.1     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  GeForce GTX 166...  Off  | 00000000:03:00.0 Off |                  N/A |
+| 38%   41C    P2    36W / 125W |   2082MiB /  5942MiB |      5%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+                                                                               
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|    0   N/A  N/A     12737      C   ffmpeg                            249MiB |
+|    0   N/A  N/A     12751      C   ffmpeg                            249MiB |
+|    0   N/A  N/A     12772      C   ffmpeg                            249MiB |
+|    0   N/A  N/A     12775      C   ffmpeg                            249MiB |
+|    0   N/A  N/A     12800      C   ffmpeg                            249MiB |
+|    0   N/A  N/A     12811      C   ffmpeg                            417MiB |
+|    0   N/A  N/A     12827      C   ffmpeg                            417MiB |
++-----------------------------------------------------------------------------+
+```
+
+To further improve performance, you can set ffmpeg to skip frames in the output,
+using the fps filter:
+
+```
+  output_args:
+    - -filter:v 
+    - fps=fps=5
+```
+
+This setting, for example, allows Frigate to consume my 10-15fps camera streams on
+my relatively low powered Haswell machine with relatively low cpu usage.
+

frigate/__main__.py

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

frigate/app.py

@@ -0,0 +1,207 @@
+import json
+import logging
+import multiprocessing as mp
+import os
+from logging.handlers import QueueHandler
+from typing import Dict, List
+import sys
+import signal
+
+import yaml
+from playhouse.sqlite_ext import SqliteExtDatabase
+
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.edgetpu import EdgeTPUProcess
+from frigate.events import EventProcessor, EventCleanup
+from frigate.http import create_app
+from frigate.log import log_process, root_configurer
+from frigate.models import Event
+from frigate.mqtt import create_mqtt_client
+from frigate.object_processing import TrackedObjectProcessor
+from frigate.record import RecordingMaintainer
+from frigate.video import capture_camera, track_camera
+from frigate.watchdog import FrigateWatchdog
+from frigate.zeroconf import broadcast_zeroconf
+
+logger = logging.getLogger(__name__)
+
+class FrigateApp():
+    def __init__(self):
+        self.stop_event = mp.Event()
+        self.config: FrigateConfig = None
+        self.detection_queue = mp.Queue()
+        self.detectors: Dict[str, EdgeTPUProcess] = {}
+        self.detection_out_events: Dict[str, mp.Event] = {}
+        self.detection_shms: List[mp.shared_memory.SharedMemory] = []
+        self.log_queue = mp.Queue()
+        self.camera_metrics = {}
+
+    def ensure_dirs(self):
+        for d in [RECORD_DIR, CLIPS_DIR, CACHE_DIR]:
+            if not os.path.exists(d) and not os.path.islink(d):
+                logger.info(f"Creating directory: {d}")
+                os.makedirs(d)
+            else:
+                logger.debug(f"Skipping directory: {d}")
+    
+    def init_logger(self):
+        self.log_process = mp.Process(target=log_process, args=(self.log_queue,), name='log_process')
+        self.log_process.daemon = True
+        self.log_process.start()
+        root_configurer(self.log_queue)
+    
+    def init_config(self):
+        config_file = os.environ.get('CONFIG_FILE', '/config/config.yml')
+        self.config = FrigateConfig(config_file=config_file)
+
+        for camera_name in self.config.cameras.keys():
+            # create camera_metrics
+            self.camera_metrics[camera_name] = {
+                'camera_fps': mp.Value('d', 0.0),
+                'skipped_fps': mp.Value('d', 0.0),
+                'process_fps': mp.Value('d', 0.0),
+                'detection_fps': mp.Value('d', 0.0),
+                'detection_frame': mp.Value('d', 0.0),
+                'read_start': mp.Value('d', 0.0),
+                'ffmpeg_pid': mp.Value('i', 0),
+                'frame_queue': mp.Queue(maxsize=2)
+            }
+    
+    def set_log_levels(self):
+        logging.getLogger().setLevel(self.config.logger.default)
+        for log, level in self.config.logger.logs.items():
+            logging.getLogger(log).setLevel(level)
+        
+        if not 'werkzeug' in self.config.logger.logs:
+            logging.getLogger('werkzeug').setLevel('ERROR')
+
+    def init_queues(self):
+        # Queues for clip processing
+        self.event_queue = mp.Queue()
+        self.event_processed_queue = mp.Queue()
+
+        # Queue for cameras to push tracked objects to
+        self.detected_frames_queue = mp.Queue(maxsize=len(self.config.cameras.keys())*2)
+
+    def init_database(self):
+        self.db = SqliteExtDatabase(f"/{os.path.join(CLIPS_DIR, 'frigate.db')}")
+        models = [Event]
+        self.db.bind(models)
+        self.db.create_tables(models, safe=True)
+
+    def init_web_server(self):
+        self.flask_app = create_app(self.config, self.db, self.camera_metrics, self.detectors, self.detected_frames_processor)
+
+    def init_mqtt(self):
+        self.mqtt_client = create_mqtt_client(self.config.mqtt)
+
+    def start_detectors(self):
+        for name in self.config.cameras.keys():
+            self.detection_out_events[name] = mp.Event()
+            shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=300*300*3)
+            shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
+            self.detection_shms.append(shm_in)
+            self.detection_shms.append(shm_out)
+
+        for name, detector in self.config.detectors.items():
+            if detector.type == 'cpu':
+                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, out_events=self.detection_out_events, tf_device='cpu')
+            if detector.type == 'edgetpu':
+                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, out_events=self.detection_out_events, tf_device=detector.device)
+
+    def start_detected_frames_processor(self):
+        self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix, 
+            self.detected_frames_queue, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.detected_frames_processor.start()
+
+    def start_camera_processors(self):
+        for name, config in self.config.cameras.items():
+            camera_process = mp.Process(target=track_camera, name=f"camera_processor:{name}", args=(name, config,
+                self.detection_queue, self.detection_out_events[name], self.detected_frames_queue, 
+                self.camera_metrics[name]))
+            camera_process.daemon = True
+            self.camera_metrics[name]['process'] = camera_process
+            camera_process.start()
+            logger.info(f"Camera processor started for {name}: {camera_process.pid}")
+
+    def start_camera_capture_processes(self):
+        for name, config in self.config.cameras.items():
+            capture_process = mp.Process(target=capture_camera, name=f"camera_capture:{name}", args=(name, config,
+                self.camera_metrics[name]))
+            capture_process.daemon = True
+            self.camera_metrics[name]['capture_process'] = capture_process
+            capture_process.start()
+            logger.info(f"Capture process started for {name}: {capture_process.pid}")
+    
+    def start_event_processor(self):
+        self.event_processor = EventProcessor(self.config, self.camera_metrics, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.event_processor.start()
+    
+    def start_event_cleanup(self):
+        self.event_cleanup = EventCleanup(self.config, self.stop_event)
+        self.event_cleanup.start()
+    
+    def start_recording_maintainer(self):
+        self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
+        self.recording_maintainer.start()
+
+    def start_watchdog(self):
+        self.frigate_watchdog = FrigateWatchdog(self.detectors, self.stop_event)
+        self.frigate_watchdog.start()
+
+    def start(self):
+        self.init_logger()
+        try:
+            self.ensure_dirs()
+            try:
+                self.init_config()
+            except Exception as e:
+                logger.error(f"Error parsing config: {e}")
+                self.log_process.terminate()
+                sys.exit(1)
+            self.set_log_levels()
+            self.init_queues()
+            self.init_database()
+            self.init_mqtt()
+        except Exception as e:
+            logger.error(e)
+            self.log_process.terminate()
+            sys.exit(1)
+        self.start_detectors()
+        self.start_detected_frames_processor()
+        self.start_camera_processors()
+        self.start_camera_capture_processes()
+        self.init_web_server()
+        self.start_event_processor()
+        self.start_event_cleanup()
+        self.start_recording_maintainer()
+        self.start_watchdog()
+        # self.zeroconf = broadcast_zeroconf(self.config.mqtt.client_id)
+
+        def receiveSignal(signalNumber, frame):
+            self.stop()
+            sys.exit()
+        
+        signal.signal(signal.SIGTERM, receiveSignal)
+
+        self.flask_app.run(host='127.0.0.1', port=5001, debug=False)
+        self.stop()
+    
+    def stop(self):
+        logger.info(f"Stopping...")
+        self.stop_event.set()
+
+        self.detected_frames_processor.join()
+        self.event_processor.join()
+        self.event_cleanup.join()
+        self.recording_maintainer.join()
+        self.frigate_watchdog.join()
+
+        for detector in self.detectors.values():
+            detector.stop()
+
+        while len(self.detection_shms) > 0:
+            shm = self.detection_shms.pop()
+            shm.close()
+            shm.unlink()

frigate/config.py

@@ -0,0 +1,806 @@
+import base64
+import json
+import os
+from typing import Dict
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import voluptuous as vol
+import yaml
+
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+
+DETECTORS_SCHEMA = vol.Schema(
+    {
+        vol.Required(str): {
+            vol.Required('type', default='edgetpu'): vol.In(['cpu', 'edgetpu']), 
+            vol.Optional('device', default='usb'): str
+        }
+    }
+)
+
+DEFAULT_DETECTORS = {
+    'coral': {
+        'type': 'edgetpu',
+        'device': 'usb'
+    }
+}
+
+MQTT_SCHEMA = vol.Schema(
+    {
+        vol.Required('host'): str,
+        vol.Optional('port', default=1883): int,
+        vol.Optional('topic_prefix', default='frigate'): str,
+        vol.Optional('client_id', default='frigate'): str,
+        'user': str,
+        'password': str
+    }
+)
+
+SAVE_CLIPS_RETAIN_SCHEMA = vol.Schema(
+    {
+        vol.Required('default',default=10): int,
+        'objects': {
+            str: int
+        }
+    }
+)
+
+SAVE_CLIPS_SCHEMA = vol.Schema(
+    {
+        vol.Optional('max_seconds', default=300): int,
+        vol.Optional('retain', default={}): SAVE_CLIPS_RETAIN_SCHEMA
+    }
+)
+
+FFMPEG_GLOBAL_ARGS_DEFAULT = ['-hide_banner','-loglevel','fatal']
+FFMPEG_INPUT_ARGS_DEFAULT = ['-avoid_negative_ts', 'make_zero',
+    '-fflags', '+genpts+discardcorrupt',
+    '-rtsp_transport', 'tcp',
+    '-stimeout', '5000000',
+    '-use_wallclock_as_timestamps', '1']
+DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT = ['-f', 'rawvideo',
+    '-pix_fmt', 'yuv420p']
+RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-c", "copy", "-f", "flv"]
+SAVE_CLIPS_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "segment", "-segment_time",
+    "10", "-segment_format", "mp4", "-reset_timestamps", "1", "-strftime",
+    "1", "-c", "copy", "-an"]
+RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "segment", "-segment_time",
+    "60", "-segment_format", "mp4", "-reset_timestamps", "1", "-strftime",
+    "1", "-c", "copy", "-an"]
+
+GLOBAL_FFMPEG_SCHEMA = vol.Schema(
+    { 
+        vol.Optional('global_args', default=FFMPEG_GLOBAL_ARGS_DEFAULT):  vol.Any(str, [str]),
+        vol.Optional('hwaccel_args', default=[]): vol.Any(str, [str]),
+        vol.Optional('input_args', default=FFMPEG_INPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+        vol.Optional('output_args', default={}): {
+            vol.Optional('detect', default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('record', default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('clips', default=SAVE_CLIPS_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('rtmp', default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+        }
+    }
+)
+
+FILTER_SCHEMA = vol.Schema(
+    { 
+        str: {
+                vol.Optional('min_area', default=0): int,
+                vol.Optional('max_area', default=24000000): int,
+                vol.Optional('threshold', default=0.85): float
+            }
+    }
+)
+
+def filters_for_all_tracked_objects(object_config):
+    for tracked_object in object_config.get('track', ['person']):
+        if not 'filters' in object_config:
+            object_config['filters'] = {}
+        if not tracked_object in object_config['filters']:
+            object_config['filters'][tracked_object] = {}
+    return object_config
+
+OBJECTS_SCHEMA = vol.Schema(vol.All(filters_for_all_tracked_objects,
+    {
+        vol.Optional('track', default=['person']): [str],
+        vol.Optional('filters', default = {}): FILTER_SCHEMA.extend({ str: {vol.Optional('min_score', default=0.5): float}})
+    }
+))
+
+DEFAULT_CAMERA_SAVE_CLIPS = {
+    'enabled': False
+}
+DEFAULT_CAMERA_SNAPSHOTS = {
+    'show_timestamp': True,
+    'draw_zones': False,
+    'draw_bounding_boxes': True,
+    'crop_to_region': True
+}
+
+def each_role_used_once(inputs):
+    roles = [role for i in inputs for role in i['roles']]
+    roles_set = set(roles)
+    if len(roles) > len(roles_set):
+        raise ValueError
+    return inputs
+
+CAMERA_FFMPEG_SCHEMA = vol.Schema(
+    { 
+        vol.Required('inputs'): vol.All([{
+            vol.Required('path'): str,
+            vol.Required('roles'): ['detect', 'clips', 'record', 'rtmp'],
+            'global_args':  vol.Any(str, [str]),
+            'hwaccel_args': vol.Any(str, [str]),
+            'input_args': vol.Any(str, [str]),
+        }], vol.Msg(each_role_used_once, msg="Each input role may only be used once")), 
+        'output_args': {
+            vol.Optional('detect', default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('record', default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('clips', default=SAVE_CLIPS_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+            vol.Optional('rtmp', default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT): vol.Any(str, [str]),
+        }
+    }
+)
+
+def ensure_zones_and_cameras_have_different_names(cameras):
+    zones = [zone for camera in cameras.values() for zone in camera['zones'].keys()]
+    for zone in zones:
+        if zone in cameras.keys():
+            raise ValueError
+    return cameras
+
+CAMERAS_SCHEMA = vol.Schema(vol.All(
+    {
+        str: {
+            vol.Required('ffmpeg'): CAMERA_FFMPEG_SCHEMA,
+            vol.Required('height'): int,
+            vol.Required('width'): int,
+            'fps': int,
+            'mask': str,
+            vol.Optional('best_image_timeout', default=60): int,
+            vol.Optional('zones', default={}):  {
+                str: {
+                    vol.Required('coordinates'): vol.Any(str, [str]),
+                    vol.Optional('filters', default={}): FILTER_SCHEMA
+                }
+            },
+            vol.Optional('save_clips', default=DEFAULT_CAMERA_SAVE_CLIPS): {
+                vol.Optional('enabled', default=False): bool,
+                vol.Optional('pre_capture', default=30): int,
+                'objects': [str],
+                vol.Optional('retain', default={}): SAVE_CLIPS_RETAIN_SCHEMA,
+            },
+            vol.Optional('record', default={}): {
+                'enabled': bool,
+                'retain_days': int,
+            },
+            vol.Optional('rtmp', default={}): {
+                vol.Required('enabled', default=True): bool,
+            },
+            vol.Optional('snapshots', default=DEFAULT_CAMERA_SNAPSHOTS): {
+                vol.Optional('show_timestamp', default=True): bool,
+                vol.Optional('draw_zones', default=False): bool,
+                vol.Optional('draw_bounding_boxes', default=True): bool,
+                vol.Optional('crop_to_region', default=True): bool,
+                vol.Optional('height', default=175): int
+            },
+            'objects': OBJECTS_SCHEMA
+        }
+    }, vol.Msg(ensure_zones_and_cameras_have_different_names, msg='Zones cannot share names with cameras'))
+)
+
+FRIGATE_CONFIG_SCHEMA = vol.Schema(
+    {
+        vol.Optional('detectors', default=DEFAULT_DETECTORS): DETECTORS_SCHEMA,
+        'mqtt': MQTT_SCHEMA,
+        vol.Optional('logger', default={'default': 'info', 'logs': {}}): {
+            vol.Optional('default', default='info'): vol.In(['info', 'debug', 'warning', 'error', 'critical']),
+            vol.Optional('logs', default={}): {str: vol.In(['info', 'debug', 'warning', 'error', 'critical']) }
+        },
+        vol.Optional('save_clips', default={}): SAVE_CLIPS_SCHEMA,
+        vol.Optional('record', default={}): {
+            vol.Optional('enabled', default=False): bool,
+            vol.Optional('retain_days', default=30): int,
+        },
+        vol.Optional('ffmpeg', default={}): GLOBAL_FFMPEG_SCHEMA,
+        vol.Optional('objects', default={}): OBJECTS_SCHEMA,
+        vol.Required('cameras', default={}): CAMERAS_SCHEMA
+    }
+)
+
+class DetectorConfig():
+    def __init__(self, config):
+        self._type = config['type']
+        self._device = config['device']
+    
+    @property
+    def type(self):
+        return self._type
+    
+    @property
+    def device(self):
+        return self._device
+    
+    def to_dict(self):
+        return {
+            'type': self.type,
+            'device': self.device
+        }
+
+class LoggerConfig():
+    def __init__(self, config):
+        self._default = config['default'].upper()
+        self._logs = {k: v.upper() for k, v in config['logs'].items()}
+    
+    @property
+    def default(self):
+        return self._default
+    
+    @property
+    def logs(self):
+        return self._logs
+    
+    def to_dict(self):
+        return {
+            'default': self.default,
+            'logs': self.logs
+        }
+
+class MqttConfig():
+    def __init__(self, config):
+        self._host = config['host']
+        self._port = config['port']
+        self._topic_prefix = config['topic_prefix']
+        self._client_id = config['client_id']
+        self._user = config.get('user')
+        self._password = config.get('password')
+    
+    @property
+    def host(self):
+        return self._host
+    
+    @property
+    def port(self):
+        return self._port
+    
+    @property
+    def topic_prefix(self):
+        return self._topic_prefix
+    
+    @property
+    def client_id(self):
+        return self._client_id
+    
+    @property
+    def user(self):
+        return self._user
+    
+    @property
+    def password(self):
+        return self._password
+
+    def to_dict(self):
+        return {
+            'host': self.host,
+            'port': self.port,
+            'topic_prefix': self.topic_prefix,
+            'client_id': self.client_id,
+            'user': self.user
+        }
+
+class CameraInput():
+    def __init__(self, global_config, ffmpeg_input):
+        self._path = ffmpeg_input['path']
+        self._roles = ffmpeg_input['roles']
+        self._global_args = ffmpeg_input.get('global_args', global_config['global_args'])
+        self._hwaccel_args = ffmpeg_input.get('hwaccel_args', global_config['hwaccel_args'])
+        self._input_args = ffmpeg_input.get('input_args', global_config['input_args'])
+    
+    @property
+    def path(self):
+        return self._path
+    
+    @property
+    def roles(self):
+        return self._roles
+    
+    @property
+    def global_args(self):
+        return self._global_args if isinstance(self._global_args, list) else self._global_args.split(' ')
+    
+    @property
+    def hwaccel_args(self):
+        return self._hwaccel_args if isinstance(self._hwaccel_args, list) else self._hwaccel_args.split(' ')
+
+    @property
+    def input_args(self):
+        return self._input_args if isinstance(self._input_args, list) else self._input_args.split(' ')
+
+class CameraFfmpegConfig():
+    def __init__(self, global_config, config):
+        self._inputs = [CameraInput(global_config, i) for i in config['inputs']]
+        self._output_args = config.get('output_args', global_config['output_args'])
+    
+    @property
+    def inputs(self):
+        return self._inputs
+    
+    @property
+    def output_args(self):
+        return {k: v if isinstance(v, list) else v.split(' ') for k, v in self._output_args.items()}
+
+class SaveClipsRetainConfig():
+    def __init__(self, global_config, config):
+        self._default = config.get('default', global_config.get('default'))
+        self._objects = config.get('objects', global_config.get('objects', {}))
+    
+    @property
+    def default(self):
+        return self._default
+    
+    @property
+    def objects(self):
+        return self._objects
+    
+    def to_dict(self):
+        return {
+            'default': self.default,
+            'objects': self.objects
+        }
+
+class SaveClipsConfig():
+    def __init__(self, config):
+        self._max_seconds = config['max_seconds']
+        self._retain = SaveClipsRetainConfig(config['retain'], config['retain'])
+    
+    @property
+    def max_seconds(self):
+        return self._max_seconds
+
+    @property
+    def retain(self):
+        return self._retain
+    
+    def to_dict(self):
+        return {
+            'max_seconds': self.max_seconds,
+            'retain': self.retain.to_dict()
+        }
+
+class RecordConfig():
+    def __init__(self, global_config, config):
+        self._enabled = config.get('enabled', global_config['enabled'])
+        self._retain_days = config.get('retain_days', global_config['retain_days'])
+
+    @property
+    def enabled(self):
+        return self._enabled
+    
+    @property
+    def retain_days(self):
+        return self._retain_days
+    
+    def to_dict(self):
+        return {
+            'enabled': self.enabled,
+            'retain_days': self.retain_days,
+        }
+
+class FilterConfig():
+    def __init__(self, config):
+        self._min_area = config['min_area']
+        self._max_area = config['max_area']
+        self._threshold = config['threshold']
+        self._min_score = config.get('min_score')
+    
+    @property
+    def min_area(self):
+        return self._min_area
+
+    @property
+    def max_area(self):
+        return self._max_area
+
+    @property
+    def threshold(self):
+        return self._threshold
+    
+    @property
+    def min_score(self):
+        return self._min_score
+    
+    def to_dict(self):
+        return {
+            'min_area': self.min_area,
+            'max_area': self.max_area,
+            'threshold': self.threshold,
+            'min_score': self.min_score
+        }
+
+class ObjectConfig():
+    def __init__(self, global_config, config):
+        self._track = config.get('track', global_config['track'])
+        if 'filters' in config:
+            self._filters = { name: FilterConfig(c) for name, c in config['filters'].items() }
+        else:
+            self._filters = { name: FilterConfig(c) for name, c in global_config['filters'].items() }
+    
+    @property
+    def track(self):
+        return self._track
+    
+    @property
+    def filters(self) -> Dict[str, FilterConfig]:
+        return self._filters
+
+    def to_dict(self):
+        return {
+            'track': self.track,
+            'filters': { k: f.to_dict() for k, f in self.filters.items() }
+        }
+
+class CameraSnapshotsConfig():
+    def __init__(self, config):
+        self._show_timestamp = config['show_timestamp']
+        self._draw_zones = config['draw_zones']
+        self._draw_bounding_boxes = config['draw_bounding_boxes']
+        self._crop_to_region = config['crop_to_region']
+        self._height = config.get('height')
+    
+    @property
+    def show_timestamp(self):
+        return self._show_timestamp
+    
+    @property
+    def draw_zones(self):
+        return self._draw_zones
+
+    @property
+    def draw_bounding_boxes(self):
+        return self._draw_bounding_boxes
+
+    @property
+    def crop_to_region(self):
+        return self._crop_to_region
+
+    @property
+    def height(self):
+        return self._height
+    
+    def to_dict(self):
+        return {
+            'show_timestamp': self.show_timestamp,
+            'draw_zones': self.draw_zones,
+            'draw_bounding_boxes': self.draw_bounding_boxes,
+            'crop_to_region': self.crop_to_region,
+            'height': self.height
+        }
+
+class CameraSaveClipsConfig():
+    def __init__(self, global_config, config):
+        self._enabled = config['enabled']
+        self._pre_capture = config['pre_capture']
+        self._objects = config.get('objects', global_config['objects']['track'])
+        self._retain = SaveClipsRetainConfig(global_config['save_clips']['retain'], config['retain'])
+    
+    @property
+    def enabled(self):
+        return self._enabled
+    
+    @property
+    def pre_capture(self):
+        return self._pre_capture
+
+    @property
+    def objects(self):
+        return self._objects
+    
+    @property
+    def retain(self):
+        return self._retain
+    
+    def to_dict(self):
+        return {
+            'enabled': self.enabled,
+            'pre_capture': self.pre_capture,
+            'objects': self.objects,
+            'retain': self.retain.to_dict()
+        }
+
+class CameraRtmpConfig():
+    def __init__(self, global_config, config):
+        self._enabled = config['enabled']
+    
+    @property
+    def enabled(self):
+        return self._enabled
+    
+    def to_dict(self):
+        return {
+            'enabled': self.enabled,
+        }
+
+class ZoneConfig():
+    def __init__(self, name, config):
+        self._coordinates = config['coordinates']
+        self._filters = { name: FilterConfig(c) for name, c in config['filters'].items() }
+
+        if isinstance(self._coordinates, list):
+            self._contour =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in self._coordinates])
+        elif isinstance(self._coordinates, str):
+            points = self._coordinates.split(',')
+            self._contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+        else:
+            print(f"Unable to parse zone coordinates for {name}")
+            self._contour = np.array([])
+        
+        self._color = (0,0,0)
+    
+    @property
+    def coordinates(self):
+        return self._coordinates
+    
+    @property
+    def contour(self):
+        return self._contour
+    
+    @contour.setter
+    def contour(self, val):
+        self._contour = val
+    
+    @property
+    def color(self):
+        return self._color
+    
+    @color.setter
+    def color(self, val):
+        self._color = val
+    
+    @property
+    def filters(self):
+        return self._filters
+    
+    def to_dict(self):
+        return {
+            'filters': {k: f.to_dict() for k, f in self.filters.items()}
+        }
+
+class CameraConfig():
+    def __init__(self, name, config, global_config):
+        self._name = name
+        self._ffmpeg = CameraFfmpegConfig(global_config['ffmpeg'], config['ffmpeg'])
+        self._height = config.get('height')
+        self._width = config.get('width')
+        self._frame_shape = (self._height, self._width)
+        self._frame_shape_yuv = (self._frame_shape[0]*3//2, self._frame_shape[1])
+        self._fps = config.get('fps')
+        self._mask = self._create_mask(config.get('mask'))
+        self._best_image_timeout = config['best_image_timeout']
+        self._zones = { name: ZoneConfig(name, z) for name, z in config['zones'].items() }
+        self._save_clips = CameraSaveClipsConfig(global_config, config['save_clips'])
+        self._record = RecordConfig(global_config['record'], config['record'])
+        self._rtmp = CameraRtmpConfig(global_config, config['rtmp'])
+        self._snapshots = CameraSnapshotsConfig(config['snapshots'])
+        self._objects = ObjectConfig(global_config['objects'], config.get('objects', {}))
+
+        self._ffmpeg_cmds = []
+        for ffmpeg_input in self._ffmpeg.inputs:
+            self._ffmpeg_cmds.append({
+                'roles': ffmpeg_input.roles,
+                'cmd': self._get_ffmpeg_cmd(ffmpeg_input)
+            })
+
+
+        self._set_zone_colors(self._zones)
+
+    def _create_mask(self, mask):
+        if mask:
+            if mask.startswith('base64,'):
+                img = base64.b64decode(mask[7:]) 
+                np_img = np.fromstring(img, dtype=np.uint8)
+                mask_img = cv2.imdecode(np_img, cv2.IMREAD_GRAYSCALE)
+            elif mask.startswith('poly,'):
+                points = mask.split(',')[1:]
+                contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+                mask_img = np.zeros(self.frame_shape, np.uint8)
+                mask_img[:] = 255
+                cv2.fillPoly(mask_img, pts=[contour], color=(0))
+            else:
+                mask_img = cv2.imread(f"/config/{mask}", cv2.IMREAD_GRAYSCALE)
+        else:
+            mask_img = None
+
+        if mask_img is None or mask_img.size == 0:
+            mask_img = np.zeros(self.frame_shape, np.uint8)
+            mask_img[:] = 255
+        
+        return mask_img
+
+    def _get_ffmpeg_cmd(self, ffmpeg_input):
+        ffmpeg_output_args = []
+        if 'detect' in ffmpeg_input.roles:
+            ffmpeg_output_args = self.ffmpeg.output_args['detect'] + ffmpeg_output_args + ['pipe:']
+            if self.fps:
+                ffmpeg_output_args = ["-r", str(self.fps)] + ffmpeg_output_args
+        if 'rtmp' in ffmpeg_input.roles and self.rtmp.enabled:
+            ffmpeg_output_args = self.ffmpeg.output_args['rtmp'] + [
+                f"rtmp://127.0.0.1/live/{self.name}"
+            ] + ffmpeg_output_args
+        if 'clips' in ffmpeg_input.roles and self.save_clips.enabled:
+            ffmpeg_output_args = self.ffmpeg.output_args['clips'] + [
+                f"{os.path.join(CACHE_DIR, self.name)}-%Y%m%d%H%M%S.mp4"
+            ] + ffmpeg_output_args
+        if 'record' in ffmpeg_input.roles and self.record.enabled:
+            ffmpeg_output_args = self.ffmpeg.output_args['record'] + [
+                f"{os.path.join(RECORD_DIR, self.name)}-%Y%m%d%H%M%S.mp4"
+            ] + ffmpeg_output_args
+        return (['ffmpeg'] +
+                ffmpeg_input.global_args +
+                ffmpeg_input.hwaccel_args +
+                ffmpeg_input.input_args +
+                ['-i', ffmpeg_input.path] +
+                ffmpeg_output_args)
+    
+    def _set_zone_colors(self, zones: Dict[str, ZoneConfig]):
+        # set colors for zones
+        all_zone_names = zones.keys()
+        zone_colors = {}
+        colors = plt.cm.get_cmap('tab10', len(all_zone_names))
+        for i, zone in enumerate(all_zone_names):
+            zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
+        
+        for name, zone in zones.items():
+            zone.color = zone_colors[name]
+    
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def ffmpeg(self):
+        return self._ffmpeg
+    
+    @property
+    def height(self):
+        return self._height
+    
+    @property
+    def width(self):
+        return self._width
+    
+    @property
+    def fps(self):
+        return self._fps
+    
+    @property
+    def mask(self):
+        return self._mask
+    
+    @property
+    def best_image_timeout(self):
+        return self._best_image_timeout
+    
+    @property
+    def zones(self)-> Dict[str, ZoneConfig]:
+        return self._zones
+    
+    @property
+    def save_clips(self):
+        return self._save_clips
+    
+    @property
+    def record(self):
+        return self._record
+    
+    @property
+    def rtmp(self):
+        return self._rtmp
+    
+    @property
+    def snapshots(self):
+        return self._snapshots
+    
+    @property
+    def objects(self):
+        return self._objects
+
+    @property
+    def frame_shape(self):
+        return self._frame_shape
+
+    @property
+    def frame_shape_yuv(self):
+        return self._frame_shape_yuv
+
+    @property
+    def ffmpeg_cmds(self):
+        return self._ffmpeg_cmds
+
+    def to_dict(self):
+        return {
+            'name': self.name,
+            'height': self.height,
+            'width': self.width,
+            'fps': self.fps,
+            'best_image_timeout': self.best_image_timeout,
+            'zones': {k: z.to_dict() for k, z in self.zones.items()},
+            'save_clips': self.save_clips.to_dict(),
+            'record': self.record.to_dict(),
+            'rtmp': self.rtmp.to_dict(),
+            'snapshots': self.snapshots.to_dict(),
+            'objects': self.objects.to_dict(),
+            'frame_shape': self.frame_shape,
+            'ffmpeg_cmds': [{'roles': c['roles'], 'cmd': ' '.join(c['cmd'])} for c in self.ffmpeg_cmds],
+        }
+
+
+class FrigateConfig():
+    def __init__(self, config_file=None, config=None):
+        if config is None and config_file is None:
+            raise ValueError('config or config_file must be defined')
+        elif not config_file is None:
+            config = self._load_file(config_file)
+        
+        config = FRIGATE_CONFIG_SCHEMA(config)
+
+        config = self._sub_env_vars(config)
+
+        self._detectors = { name: DetectorConfig(d) for name, d in config['detectors'].items() }
+        self._mqtt = MqttConfig(config['mqtt'])
+        self._save_clips = SaveClipsConfig(config['save_clips'])
+        self._cameras = { name: CameraConfig(name, c, config) for name, c in config['cameras'].items() }
+        self._logger = LoggerConfig(config['logger'])
+
+    def _sub_env_vars(self, config):
+        frigate_env_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
+
+        if 'password' in config['mqtt']:
+            config['mqtt']['password'] = config['mqtt']['password'].format(**frigate_env_vars) 
+        
+        for camera in config['cameras'].values():
+            for i in camera['ffmpeg']['inputs']:
+                i['path'] = i['path'].format(**frigate_env_vars)
+        
+        return config
+
+    def _load_file(self, config_file):
+        with open(config_file) as f:
+            raw_config = f.read()
+        
+        if config_file.endswith(".yml"):    
+            config = yaml.safe_load(raw_config)
+        elif config_file.endswith(".json"):
+            config = json.loads(raw_config)
+        
+        return config
+    
+    def to_dict(self):
+        return {
+            'detectors': {k: d.to_dict() for k, d in self.detectors.items()},
+            'mqtt': self.mqtt.to_dict(),
+            'save_clips': self.save_clips.to_dict(),
+            'cameras': {k: c.to_dict() for k, c in self.cameras.items()},
+            'logger': self.logger.to_dict()
+        }
+    
+    @property
+    def detectors(self) -> Dict[str, DetectorConfig]:
+        return self._detectors
+    
+    @property
+    def logger(self):
+        return self._logger
+
+    @property
+    def mqtt(self):
+        return self._mqtt
+    
+    @property
+    def save_clips(self):
+        return self._save_clips
+
+    @property
+    def cameras(self) -> Dict[str, CameraConfig]:
+        return self._cameras

frigate/const.py

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

frigate/edgetpu.py

@@ -0,0 +1,223 @@
+import datetime
+import hashlib
+import logging
+import multiprocessing as mp
+import os
+import queue
+import threading
+import signal
+from abc import ABC, abstractmethod
+from multiprocessing.connection import Connection
+from typing import Dict
+
+import numpy as np
+import tflite_runtime.interpreter as tflite
+from tflite_runtime.interpreter import load_delegate
+
+from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
+
+logger = logging.getLogger(__name__)
+
+def load_labels(path, encoding='utf-8'):
+  """Loads labels from file (with or without index numbers).
+  Args:
+    path: path to label file.
+    encoding: label file encoding.
+  Returns:
+    Dictionary mapping indices to labels.
+  """
+  with open(path, 'r', encoding=encoding) as f:
+    lines = f.readlines()
+    if not lines:
+        return {}
+
+    if lines[0].split(' ', maxsplit=1)[0].isdigit():
+        pairs = [line.split(' ', maxsplit=1) for line in lines]
+        return {int(index): label.strip() for index, label in pairs}
+    else:
+        return {index: line.strip() for index, line in enumerate(lines)}
+
+class ObjectDetector(ABC):
+    @abstractmethod
+    def detect(self, tensor_input, threshold = .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
+
+        if tf_device != 'cpu':
+            try:
+                logger.info(f"Attempting to load TPU as {device_config['device']}")
+                edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
+                logger.info("TPU found")
+            except ValueError:
+                logger.info("No EdgeTPU detected. Falling back to CPU.")
+        
+        if edge_tpu_delegate is None:
+            self.interpreter = tflite.Interpreter(
+                model_path='/cpu_model.tflite')
+        else:
+            self.interpreter = tflite.Interpreter(
+                model_path='/edgetpu_model.tflite',
+                experimental_delegates=[edge_tpu_delegate])
+        
+        self.interpreter.allocate_tensors()
+
+        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()
+        boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
+        label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
+        scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
+
+        detections = np.zeros((20,6), np.float32)
+        for i, score in enumerate(scores):
+            detections[i] = [label_codes[i], score, boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]]
+        
+        return detections
+
+def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.Event], avg_speed, start, tf_device):
+    threading.current_thread().name = f"detector:{name}"
+    logger = logging.getLogger(f"detector.{name}")
+    logger.info(f"Starting detection process: {os.getpid()}")
+    listen()
+
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
+    
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
+    frame_manager = SharedMemoryFrameManager()
+    object_detector = LocalObjectDetector(tf_device=tf_device)
+
+    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:
+        if stop_event.is_set():
+            break
+
+        try:
+            connection_id = detection_queue.get(timeout=5)
+        except queue.Empty:
+            continue
+        input_frame = frame_manager.get(connection_id, (1,300,300,3))
+
+        if input_frame is None:
+            continue
+
+        # detect and send the output
+        start.value = datetime.datetime.now().timestamp()
+        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, name, detection_queue, out_events, tf_device=None):
+        self.name = name
+        self.out_events = out_events
+        self.detection_queue = detection_queue
+        self.avg_inference_speed = mp.Value('d', 0.01)
+        self.detection_start = mp.Value('d', 0.0)
+        self.detect_process = None
+        self.tf_device = tf_device
+        self.start_or_restart()
+    
+    def stop(self):
+        self.detect_process.terminate()
+        logging.info("Waiting for detection process to exit gracefully...")
+        self.detect_process.join(timeout=30)
+        if self.detect_process.exitcode is None:
+            logging.info("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.stop()
+        self.detect_process = mp.Process(target=run_detector, name=f"detector:{self.name}", args=(self.name, self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.tf_device))
+        self.detect_process.daemon = True
+        self.detect_process.start()
+
+class RemoteObjectDetector():
+    def __init__(self, name, labels, detection_queue, event):
+        self.labels = load_labels(labels)
+        self.name = name
+        self.fps = EventsPerSecond()
+        self.detection_queue = detection_queue
+        self.event = event
+        self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
+        self.np_shm = np.ndarray((1,300,300,3), dtype=np.uint8, buffer=self.shm.buf)
+        self.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 = []
+
+        # 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 it timed out
+        if result is None:
+            return detections
+
+        for d in self.out_np_shm:
+            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 cleanup(self):
+        self.shm.unlink()
+        self.out_shm.unlink()

frigate/events.py

@@ -0,0 +1,286 @@
+import datetime
+import json
+import logging
+import os
+import queue
+import subprocess as sp
+import threading
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import psutil
+
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.models import Event
+
+from peewee import fn
+
+logger = logging.getLogger(__name__)
+
+class EventProcessor(threading.Thread):
+    def __init__(self, config, camera_processes, event_queue, event_processed_queue, stop_event):
+        threading.Thread.__init__(self)
+        self.name = 'event_processor'
+        self.config = config
+        self.camera_processes = camera_processes
+        self.cached_clips = {}
+        self.event_queue = event_queue
+        self.event_processed_queue = event_processed_queue
+        self.events_in_process = {}
+        self.stop_event = stop_event
+    
+    def refresh_cache(self):
+        cached_files = os.listdir(CACHE_DIR)
+
+        files_in_use = []
+        for process in psutil.process_iter():
+            if process.name() != 'ffmpeg':
+                continue
+            try:
+                flist = process.open_files()
+                if flist:
+                    for nt in flist:
+                        if nt.path.startswith(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(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:
+                logger.info(f"bad file: {f}")
+                os.remove(os.path.join(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.save_clips.max_seconds
+        if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
+            earliest_event = datetime.datetime.now().timestamp()-max_seconds
+        
+        for f, data in list(self.cached_clips.items()):
+            if earliest_event-90 > data['start_time']+data['duration']:
+                del self.cached_clips[f]
+                os.remove(os.path.join(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(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(CLIPS_DIR, clip_name)}.mp4"
+        ]
+
+        p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
+        if p.returncode != 0:
+            logger.error(p.stderr)
+            return
+
+    def run(self):
+        while True:
+            if self.stop_event.is_set():
+                logger.info(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].save_clips
+
+            # if save clips is not enabled for this camera, just continue
+            if not save_clips_config.enabled:
+                if event_type == 'end':
+                    self.event_processed_queue.put((event_data['id'], camera))
+                continue
+
+            # if specific objects are listed for this camera, only save clips for them
+            if not event_data['label'] in save_clips_config.objects:
+                if event_type == 'end':
+                    self.event_processed_queue.put((event_data['id'], camera))
+                continue
+
+            if event_type == 'start':
+                self.events_in_process[event_data['id']] = event_data
+
+            if event_type == 'end':
+                if len(self.cached_clips) > 0 and not event_data['false_positive']:
+                    self.create_clip(camera, event_data, save_clips_config.pre_capture)
+                    Event.create(
+                        id=event_data['id'],
+                        label=event_data['label'],
+                        camera=camera,
+                        start_time=event_data['start_time'],
+                        end_time=event_data['end_time'],
+                        top_score=event_data['top_score'],
+                        false_positive=event_data['false_positive'],
+                        zones=list(event_data['entered_zones']),
+                        thumbnail=event_data['thumbnail']
+                    )
+                del self.events_in_process[event_data['id']]
+                self.event_processed_queue.put((event_data['id'], camera))
+
+class EventCleanup(threading.Thread):
+    def __init__(self, config: FrigateConfig, stop_event):
+        threading.Thread.__init__(self)
+        self.name = 'event_cleanup'
+        self.config = config
+        self.stop_event = stop_event
+
+    def run(self):
+        counter = 0
+        while(True):
+            if self.stop_event.is_set():
+                logger.info(f"Exiting event cleanup...")
+                break
+
+            # only expire events every 10 minutes, but check for stop events every 10 seconds
+            time.sleep(10)
+            counter = counter + 1
+            if counter < 60:
+                continue
+            counter = 0
+
+            camera_keys = list(self.config.cameras.keys())
+
+            # Expire events from unlisted cameras based on the global config
+            retain_config = self.config.save_clips.retain
+            
+            distinct_labels = (Event.select(Event.label)
+                        .where(Event.camera.not_in(camera_keys))
+                        .distinct())
+            
+            # loop over object types in db
+            for l in distinct_labels:
+                # get expiration time for this label
+                expire_days = retain_config.objects.get(l.label, retain_config.default)
+                expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+                # grab all events after specific time
+                expired_events = (
+                    Event.select()
+                        .where(Event.camera.not_in(camera_keys), 
+                            Event.start_time < expire_after, 
+                            Event.label == l.label)
+                )
+                # delete the grabbed clips from disk
+                for event in expired_events:
+                    clip_name = f"{event.camera}-{event.id}"
+                    clip = Path(f"{os.path.join(CLIPS_DIR, clip_name)}.mp4")
+                    clip.unlink(missing_ok=True)
+                # delete the event for this type from the db
+                delete_query = (
+                    Event.delete()
+                        .where(Event.camera.not_in(camera_keys), 
+                            Event.start_time < expire_after, 
+                            Event.label == l.label)
+                )
+                delete_query.execute()
+
+            # Expire events from cameras based on the camera config
+            for name, camera in self.config.cameras.items():
+                retain_config = camera.save_clips.retain
+                # get distinct objects in database for this camera
+                distinct_labels = (Event.select(Event.label)
+                        .where(Event.camera == name)
+                        .distinct())
+
+                # loop over object types in db
+                for l in distinct_labels:
+                    # get expiration time for this label
+                    expire_days = retain_config.objects.get(l.label, retain_config.default)
+                    expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+                    # grab all events after specific time
+                    expired_events = (
+                        Event.select()
+                            .where(Event.camera == name, 
+                                Event.start_time < expire_after, 
+                                Event.label == l.label)
+                    )
+                    # delete the grabbed clips from disk
+                    for event in expired_events:
+                        clip_name = f"{event.camera}-{event.id}"
+                        clip = Path(f"{os.path.join(CLIPS_DIR, clip_name)}.mp4")
+                        clip.unlink(missing_ok=True)
+                    # delete the event for this type from the db
+                    delete_query = (
+                        Event.delete()
+                            .where( Event.camera == name, 
+                                Event.start_time < expire_after, 
+                                Event.label == l.label)
+                    )
+                    delete_query.execute()

frigate/http.py

@@ -0,0 +1,246 @@
+import base64
+import datetime
+import logging
+import os
+import time
+from functools import reduce
+
+import cv2
+import numpy as np
+from flask import (Blueprint, Flask, Response, current_app, jsonify,
+                   make_response, request)
+from peewee import SqliteDatabase, operator, fn, DoesNotExist
+from playhouse.shortcuts import model_to_dict
+
+from frigate.models import Event
+
+logger = logging.getLogger(__name__)
+
+bp = Blueprint('frigate', __name__)
+
+def create_app(frigate_config, database: SqliteDatabase, camera_metrics, detectors, detected_frames_processor):
+    app = Flask(__name__)
+
+    @app.before_request
+    def _db_connect():
+        database.connect()
+
+    @app.teardown_request
+    def _db_close(exc):
+        if not database.is_closed():
+            database.close()
+
+    app.frigate_config = frigate_config
+    app.camera_metrics = camera_metrics
+    app.detectors = detectors
+    app.detected_frames_processor = detected_frames_processor
+    
+    app.register_blueprint(bp)
+
+    return app
+
+@bp.route('/')
+def is_healthy():
+    return "Frigate is running. Alive and healthy!"
+
+@bp.route('/events/summary')
+def events_summary():
+    groups = (
+        Event
+            .select(
+                Event.camera, 
+                Event.label, 
+                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')).alias('day'), 
+                Event.zones,
+                fn.COUNT(Event.id).alias('count')
+            )
+            .group_by(
+                Event.camera, 
+                Event.label, 
+                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')),
+                Event.zones
+            )
+        )
+
+    return jsonify([e for e in groups.dicts()])
+
+@bp.route('/events/<id>')
+def event(id):
+    try:
+        return model_to_dict(Event.get(Event.id == id))
+    except DoesNotExist:
+        return "Event not found", 404
+
+@bp.route('/events/<id>/snapshot.jpg')
+def event_snapshot(id):
+    format = request.args.get('format', 'ios')
+    thumbnail_bytes = None
+    try:
+        event = Event.get(Event.id == id)
+        thumbnail_bytes = base64.b64decode(event.thumbnail)
+    except DoesNotExist:
+        # see if the object is currently being tracked
+        try:
+            for camera_state in current_app.detected_frames_processor.camera_states.values():
+                if id in camera_state.tracked_objects:
+                    tracked_obj = camera_state.tracked_objects.get(id)
+                    if not tracked_obj is None:
+                        thumbnail_bytes = tracked_obj.get_jpg_bytes()
+        except:
+            return "Event not found", 404
+    
+    if thumbnail_bytes is None:
+        return "Event not found", 404
+    
+    # android notifications prefer a 2:1 ratio
+    if format == 'android':
+        jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
+        img = cv2.imdecode(jpg_as_np, flags=1)
+        thumbnail = cv2.copyMakeBorder(img, 0, 0, int(img.shape[1]*0.5), int(img.shape[1]*0.5), cv2.BORDER_CONSTANT, (0,0,0))
+        ret, jpg = cv2.imencode('.jpg', thumbnail)    
+        thumbnail_bytes = jpg.tobytes()
+    
+    response = make_response(thumbnail_bytes)
+    response.headers['Content-Type'] = 'image/jpg'
+    return response
+
+@bp.route('/events')
+def events():
+    limit = request.args.get('limit', 100)
+    camera = request.args.get('camera')
+    label = request.args.get('label')
+    zone = request.args.get('zone')
+    after = request.args.get('after', type=int)
+    before = request.args.get('before', type=int)
+
+    clauses = []
+    
+    if camera:
+        clauses.append((Event.camera == camera))
+    
+    if label:
+        clauses.append((Event.label == label))
+    
+    if zone:
+        clauses.append((Event.zones.cast('text') % f"*\"{zone}\"*"))
+    
+    if after:
+        clauses.append((Event.start_time >= after))
+    
+    if before:
+        clauses.append((Event.start_time <= before))
+
+    if len(clauses) == 0:
+        clauses.append((1 == 1))
+
+    events =    (Event.select()
+                .where(reduce(operator.and_, clauses))
+                .order_by(Event.start_time.desc())
+                .limit(limit))
+
+    return jsonify([model_to_dict(e) for e in events])
+
+@bp.route('/config')
+def config():
+    return jsonify(current_app.frigate_config.to_dict())
+
+@bp.route('/stats')
+def stats():
+    camera_metrics = current_app.camera_metrics
+    stats = {}
+
+    total_detection_fps = 0
+
+    for name, camera_stats in camera_metrics.items():
+        total_detection_fps += camera_stats['detection_fps'].value
+        stats[name] = {
+            'camera_fps': round(camera_stats['camera_fps'].value, 2),
+            'process_fps': round(camera_stats['process_fps'].value, 2),
+            'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
+            'detection_fps': round(camera_stats['detection_fps'].value, 2),
+            'pid': camera_stats['process'].pid,
+            'capture_pid': camera_stats['capture_process'].pid
+        }
+    
+    stats['detectors'] = {}
+    for name, detector in current_app.detectors.items():
+        stats['detectors'][name] = {
+            'inference_speed': round(detector.avg_inference_speed.value*1000, 2),
+            'detection_start': detector.detection_start.value,
+            'pid': detector.detect_process.pid
+        }
+    stats['detection_fps'] = round(total_detection_fps, 2)
+
+    return jsonify(stats)
+
+@bp.route('/<camera_name>/<label>/best.jpg')
+def best(camera_name, label):
+    if camera_name in current_app.frigate_config.cameras:
+        best_object = current_app.detected_frames_processor.get_best(camera_name, label)
+        best_frame = best_object.get('frame')
+        if best_frame is None:
+            best_frame = np.zeros((720,1280,3), np.uint8)
+        else:
+            best_frame = cv2.cvtColor(best_frame, cv2.COLOR_YUV2BGR_I420)
+        
+        crop = bool(request.args.get('crop', 0, type=int))
+        if crop:
+            region = best_object.get('region', [0,0,300,300])
+            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+        
+        height = int(request.args.get('h', str(best_frame.shape[0])))
+        width = int(height*best_frame.shape[1]/best_frame.shape[0])
+
+        best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+        ret, jpg = cv2.imencode('.jpg', best_frame)
+        response = make_response(jpg.tobytes())
+        response.headers['Content-Type'] = 'image/jpg'
+        return response
+    else:
+        return "Camera named {} not found".format(camera_name), 404
+
+@bp.route('/<camera_name>')
+def mjpeg_feed(camera_name):
+    fps = int(request.args.get('fps', '3'))
+    height = int(request.args.get('h', '360'))
+    if camera_name in current_app.frigate_config.cameras:
+        # return a multipart response
+        return Response(imagestream(current_app.detected_frames_processor, camera_name, fps, height),
+                        mimetype='multipart/x-mixed-replace; boundary=frame')
+    else:
+        return "Camera named {} not found".format(camera_name), 404
+
+@bp.route('/<camera_name>/latest.jpg')
+def latest_frame(camera_name):
+    if camera_name in current_app.frigate_config.cameras:
+        # max out at specified FPS
+        frame = current_app.detected_frames_processor.get_current_frame(camera_name)
+        if frame is None:
+            frame = np.zeros((720,1280,3), np.uint8)
+
+        height = int(request.args.get('h', str(frame.shape[0])))
+        width = int(height*frame.shape[1]/frame.shape[0])
+
+        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+
+        ret, jpg = cv2.imencode('.jpg', frame)
+        response = make_response(jpg.tobytes())
+        response.headers['Content-Type'] = 'image/jpg'
+        return response
+    else:
+        return "Camera named {} not found".format(camera_name), 404
+        
+def imagestream(detected_frames_processor, camera_name, fps, height):
+    while True:
+        # max out at specified FPS
+        time.sleep(1/fps)
+        frame = detected_frames_processor.get_current_frame(camera_name, draw=True)
+        if frame is None:
+            frame = np.zeros((height,int(height*16/9),3), np.uint8)
+
+        width = int(height*frame.shape[1]/frame.shape[0])
+        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
+
+        ret, jpg = cv2.imencode('.jpg', frame)
+        yield (b'--frame\r\n'
+            b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')

frigate/log.py

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

frigate/models.py

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

frigate/motion.py

@@ -0,0 +1,83 @@
+import cv2
+import imutils
+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)
+        self.avg_delta = np.zeros(self.motion_frame_size, np.float)
+        self.motion_frame_count = 0
+        self.frame_counter = 0
+        resized_mask = cv2.resize(mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+        self.mask = np.where(resized_mask==[0])
+
+    def detect(self, frame):
+        motion_boxes = []
+
+        gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
+
+        # resize frame
+        resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+
+        # convert to grayscale
+        # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+
+        # mask frame
+        resized_frame[self.mask] = [255]
+
+        # it takes ~30 frames to establish a baseline
+        # dont bother looking for motion
+        if self.frame_counter < 30:
+            self.frame_counter += 1
+        else:
+            # compare to average
+            frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
+
+            # compute the average delta over the past few frames
+            # the alpha value can be modified to configure how sensitive the motion detection is.
+            # higher values mean the current frame impacts the delta a lot, and a single raindrop may
+            # register as motion, too low and a fast moving person wont be detected as motion
+            # this also assumes that a person is in the same location across more than a single frame
+            cv2.accumulateWeighted(frameDelta, self.avg_delta, 0.2)
+
+            # compute the threshold image for the current frame
+            current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
+
+            # black out everything in the avg_delta where there isnt motion in the current frame
+            avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
+            avg_delta_image = cv2.bitwise_and(avg_delta_image, current_thresh)
+
+            # then look for deltas above the threshold, but only in areas where there is a delta
+            # in the current frame. this prevents deltas from previous frames from being included
+            thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
+
+            # dilate the thresholded image to fill in holes, then find contours
+            # on thresholded image
+            thresh = cv2.dilate(thresh, None, iterations=2)
+            cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            cnts = imutils.grab_contours(cnts)
+
+            # loop over the contours
+            for c in cnts:
+                # if the contour is big enough, count it as motion
+                contour_area = cv2.contourArea(c)
+                if contour_area > 100:
+                    x, y, w, h = cv2.boundingRect(c)
+                    motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
+        
+        if len(motion_boxes) > 0:
+            self.motion_frame_count += 1
+            # TODO: this really depends on FPS
+            if self.motion_frame_count >= 10:
+                # only average in the current frame if the difference persists for at least 3 frames
+                cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
+        else:
+            # when no motion, just keep averaging the frames together
+            cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
+            self.motion_frame_count = 0
+
+        return motion_boxes

frigate/mqtt.py

@@ -1,41 +1,36 @@
-import json
-import cv2
+import logging
 import threading
 
-class MqttObjectPublisher(threading.Thread):
-    def __init__(self, client, topic_prefix, objects_parsed, detected_objects, best_person_frame):
-        threading.Thread.__init__(self)
-        self.client = client
-        self.topic_prefix = topic_prefix
-        self.objects_parsed = objects_parsed
-        self._detected_objects = detected_objects
-        self.best_person_frame = best_person_frame
+import paho.mqtt.client as mqtt
 
-    def run(self):
-        last_sent_payload = ""
-        while True:
+from frigate.config import MqttConfig
 
-            # initialize the payload
-            payload = {}
+logger = logging.getLogger(__name__)
 
-            # wait until objects have been parsed
-            with self.objects_parsed:
-                self.objects_parsed.wait()
+def create_mqtt_client(config: MqttConfig):
+    client = mqtt.Client(client_id=config.client_id)
+    def on_connect(client, userdata, flags, rc):
+        threading.current_thread().name = "mqtt"
+        if rc != 0:
+            if rc == 3:
+                logger.error("MQTT Server unavailable")
+            elif rc == 4:
+                logger.error("MQTT Bad username or password")
+            elif rc == 5:
+                logger.error("MQTT Not authorized")
+            else:
+                logger.error("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
             
-            # add all the person scores in detected objects
-            detected_objects = self._detected_objects.copy()
-            person_score = sum([obj['score'] for obj in detected_objects if obj['name'] == 'person'])
-            # if the person score is more than 100, set person to ON
-            payload['person'] = 'ON' if int(person_score*100) > 100 else 'OFF'
-
-            # send message for objects if different
-            new_payload = json.dumps(payload, sort_keys=True)
-            if new_payload != last_sent_payload:
-                last_sent_payload = new_payload
-                self.client.publish(self.topic_prefix+'/objects', new_payload, retain=False)
-                # send the snapshot over mqtt as well
-                if not self.best_person_frame.best_frame is None:
-                    ret, jpg = cv2.imencode('.jpg', self.best_person_frame.best_frame)
-                    if ret:
-                        jpg_bytes = jpg.tobytes()
-                        self.client.publish(self.topic_prefix+'/snapshot', jpg_bytes, retain=True)
+        logger.info("MQTT connected")
+        client.publish(config.topic_prefix+'/available', 'online', retain=True)       
+    client.on_connect = on_connect
+    client.will_set(config.topic_prefix+'/available', payload='offline', qos=1, retain=True)
+    if not config.user is None:
+        client.username_pw_set(config.user, password=config.password)
+    try:
+        client.connect(config.host, config.port, 60)
+    except Exception as e:
+        logger.error(f"Unable to connect to MQTT server: {e}")
+        raise
+    client.loop_start()
+    return client

frigate/object_detection.py

@@ -1,112 +0,0 @@
-import datetime
-import time
-import cv2
-import threading
-import numpy as np
-from edgetpu.detection.engine import DetectionEngine
-from . util import tonumpyarray
-
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
-PATH_TO_CKPT = '/frozen_inference_graph.pb'
-# List of the strings that is used to add correct label for each box.
-PATH_TO_LABELS = '/label_map.pbtext'
-
-# Function to read labels from text files.
-def ReadLabelFile(file_path):
-    with open(file_path, 'r') as f:
-        lines = f.readlines()
-    ret = {}
-    for line in lines:
-        pair = line.strip().split(maxsplit=1)
-        ret[int(pair[0])] = pair[1].strip()
-    return ret
-
-class PreppedQueueProcessor(threading.Thread):
-    def __init__(self, cameras, prepped_frame_queue):
-
-        threading.Thread.__init__(self)
-        self.cameras = cameras
-        self.prepped_frame_queue = prepped_frame_queue
-        
-        # Load the edgetpu engine and labels
-        self.engine = DetectionEngine(PATH_TO_CKPT)
-        self.labels = ReadLabelFile(PATH_TO_LABELS)
-
-    def run(self):
-        # process queue...
-        while True:
-            frame = self.prepped_frame_queue.get()
-
-            # Actual detection.
-            objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=frame['region_threshold'], top_k=3)
-            # print(self.engine.get_inference_time())
-
-            # parse and pass detected objects back to the camera
-            parsed_objects = []
-            for obj in objects:
-                box = obj.bounding_box.flatten().tolist()
-                parsed_objects.append({
-                            'frame_time': frame['frame_time'],
-                            'name': str(self.labels[obj.label_id]),
-                            'score': float(obj.score),
-                            'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']),
-                            'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']),
-                            'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']),
-                            'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset'])
-                        })
-            self.cameras[frame['camera_name']].add_objects(parsed_objects)
-
-
-# should this be a region class?
-class FramePrepper(threading.Thread):
-    def __init__(self, camera_name, shared_frame, frame_time, frame_ready, 
-        frame_lock,
-        region_size, region_x_offset, region_y_offset, region_threshold,
-        prepped_frame_queue):
-
-        threading.Thread.__init__(self)
-        self.camera_name = camera_name
-        self.shared_frame = shared_frame
-        self.frame_time = frame_time
-        self.frame_ready = frame_ready
-        self.frame_lock = frame_lock
-        self.region_size = region_size
-        self.region_x_offset = region_x_offset
-        self.region_y_offset = region_y_offset
-        self.region_threshold = region_threshold
-        self.prepped_frame_queue = prepped_frame_queue
-
-    def run(self):
-        frame_time = 0.0
-        while True:
-            now = datetime.datetime.now().timestamp()
-
-            with self.frame_ready:
-                # if there isnt a frame ready for processing or it is old, wait for a new frame
-                if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
-                    self.frame_ready.wait()
-            
-            # make a copy of the cropped frame
-            with self.frame_lock:
-                cropped_frame = self.shared_frame[self.region_y_offset:self.region_y_offset+self.region_size, self.region_x_offset:self.region_x_offset+self.region_size].copy()
-                frame_time = self.frame_time.value
-            
-            # Resize to 300x300 if needed
-            if cropped_frame.shape != (300, 300, 3):
-                cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
-            # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
-            frame_expanded = np.expand_dims(cropped_frame, axis=0)
-
-            # add the frame to the queue
-            if not self.prepped_frame_queue.full():
-                self.prepped_frame_queue.put({
-                    'camera_name': self.camera_name,
-                    'frame_time': frame_time,
-                    'frame': frame_expanded.flatten().copy(),
-                    'region_size': self.region_size,
-                    'region_threshold': self.region_threshold,
-                    'region_x_offset': self.region_x_offset,
-                    'region_y_offset': self.region_y_offset
-                })
-            else:
-                print("queue full. moving on")

frigate/object_processing.py

@@ -0,0 +1,502 @@
+import copy
+import base64
+import datetime
+import hashlib
+import itertools
+import json
+import logging
+import os
+import queue
+import threading
+import time
+from collections import Counter, defaultdict
+from statistics import mean, median
+from typing import Callable, Dict
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+from frigate.config import FrigateConfig, CameraConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.edgetpu import load_labels
+from frigate.util import SharedMemoryFrameManager, draw_box_with_label
+
+logger = logging.getLogger(__name__)
+
+PATH_TO_LABELS = '/labelmap.txt'
+
+LABELS = load_labels(PATH_TO_LABELS)
+cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
+
+COLOR_MAP = {}
+for key, val in LABELS.items():
+    COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
+
+def on_edge(box, frame_shape):
+    if (
+        box[0] == 0 or
+        box[1] == 0 or
+        box[2] == frame_shape[1]-1 or
+        box[3] == frame_shape[0]-1
+    ):
+        return True
+
+def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
+    # larger is better
+    # cutoff images are less ideal, but they should also be smaller?
+    # better scores are obviously better too
+
+    # if the new_thumb is on an edge, and the current thumb is not
+    if on_edge(new_obj['box'], frame_shape) and not on_edge(current_thumb['box'], frame_shape):
+        return False
+
+    # if the score is better by more than 5%
+    if new_obj['score'] > current_thumb['score']+.05:
+        return True
+    
+    # if the area is 10% larger
+    if new_obj['area'] > current_thumb['area']*1.1:
+        return True
+    
+    return False
+
+class TrackedObject():
+    def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
+        self.obj_data = obj_data
+        self.camera = camera
+        self.camera_config = camera_config
+        self.frame_cache = frame_cache
+        self.current_zones = []
+        self.entered_zones = set()
+        self.false_positive = True
+        self.top_score = self.computed_score = 0.0
+        self.thumbnail_data = None
+        self.frame = None
+        self.previous = None
+        self._snapshot_jpg_time = 0
+        ret, jpg = cv2.imencode('.jpg', np.zeros((300,300,3), np.uint8))
+        self._snapshot_jpg = jpg.tobytes()
+
+        # start the score history
+        self.score_history = [self.obj_data['score']]
+
+    def _is_false_positive(self):
+        # once a true positive, always a true positive
+        if not self.false_positive:
+            return False
+
+        threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
+        if self.computed_score < threshold:
+            return True
+        return False
+
+    def compute_score(self):
+        scores = self.score_history[:]
+        # pad with zeros if you dont have at least 3 scores
+        if len(scores) < 3:
+            scores += [0.0]*(3 - len(scores))
+        return median(scores)
+    
+    def update(self, current_frame_time, obj_data):
+        previous = self.to_dict()
+        self.obj_data.update(obj_data)
+        # if the object is not in the current frame, add a 0.0 to the score history
+        if self.obj_data['frame_time'] != current_frame_time:
+            self.score_history.append(0.0)
+        else:
+            self.score_history.append(self.obj_data['score'])
+        # only keep the last 10 scores
+        if len(self.score_history) > 10:
+            self.score_history = self.score_history[-10:]
+
+        # calculate if this is a false positive
+        self.computed_score = self.compute_score()
+        if self.computed_score > self.top_score:
+            self.top_score = self.computed_score
+        self.false_positive = self._is_false_positive()
+
+        if not self.false_positive:
+            # determine if this frame is a better thumbnail
+            if (
+                self.thumbnail_data is None 
+                or is_better_thumbnail(self.thumbnail_data, self.obj_data, self.camera_config.frame_shape)
+            ):
+                self.thumbnail_data = {
+                    'frame_time': self.obj_data['frame_time'],
+                    'box': self.obj_data['box'],
+                    'area': self.obj_data['area'],
+                    'region': self.obj_data['region'],
+                    'score': self.obj_data['score']
+                }
+                self.previous = previous
+        
+        # check zones
+        current_zones = []
+        bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
+        # check each zone
+        for name, zone in self.camera_config.zones.items():
+            contour = zone.contour
+            # check if the object is in the zone
+            if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
+                # if the object passed the filters once, dont apply again
+                if name in self.current_zones or not zone_filtered(self, zone.filters):
+                    current_zones.append(name)
+                    self.entered_zones.add(name)
+                
+        self.current_zones = current_zones
+    
+    def to_dict(self, include_thumbnail: bool = False):
+        return {
+            'id': self.obj_data['id'],
+            'camera': self.camera,
+            'frame_time': self.obj_data['frame_time'],
+            'label': self.obj_data['label'],
+            'top_score': self.top_score,
+            'false_positive': self.false_positive,
+            'start_time': self.obj_data['start_time'],
+            'end_time': self.obj_data.get('end_time', None),
+            'score': self.obj_data['score'],
+            'box': self.obj_data['box'],
+            'area': self.obj_data['area'],
+            'region': self.obj_data['region'],
+            'current_zones': self.current_zones.copy(),
+            'entered_zones': list(self.entered_zones).copy(),
+            'thumbnail': base64.b64encode(self.get_jpg_bytes()).decode('utf-8') if include_thumbnail else None
+        }
+    
+    def get_jpg_bytes(self):
+        if self.thumbnail_data is None or self._snapshot_jpg_time == self.thumbnail_data['frame_time']:
+            return self._snapshot_jpg
+        
+        if not self.thumbnail_data['frame_time'] in self.frame_cache:
+            logger.error(f"Unable to create thumbnail for {self.obj_data['id']}")
+            logger.error(f"Looking for frame_time of {self.thumbnail_data['frame_time']}")
+            logger.error(f"Thumbnail frames: {','.join([str(k) for k in self.frame_cache.keys()])}")
+            return self._snapshot_jpg
+
+        # TODO: crop first to avoid converting the entire frame?
+        snapshot_config = self.camera_config.snapshots
+        best_frame = cv2.cvtColor(self.frame_cache[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
+
+        if snapshot_config.draw_bounding_boxes:
+            thickness = 2
+            color = COLOR_MAP[self.obj_data['label']]
+            box = self.thumbnail_data['box']
+            draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], self.obj_data['label'], 
+                f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color)
+            
+        if snapshot_config.crop_to_region:
+            region = self.thumbnail_data['region']
+            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+
+        if snapshot_config.height: 
+            height = snapshot_config.height
+            width = int(height*best_frame.shape[1]/best_frame.shape[0])
+            best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+        
+        if snapshot_config.show_timestamp:
+            time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
+            size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
+            text_width = size[0][0]
+            desired_size = max(150, 0.33*best_frame.shape[1])
+            font_scale = desired_size/text_width
+            cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX, 
+                fontScale=font_scale, color=(255, 255, 255), thickness=2)
+
+        ret, jpg = cv2.imencode('.jpg', best_frame)
+        if ret:
+            self._snapshot_jpg = jpg.tobytes()
+        
+        return self._snapshot_jpg
+
+def zone_filtered(obj: TrackedObject, object_config):
+    object_name = obj.obj_data['label']
+
+    if object_name in object_config:
+        obj_settings = object_config[object_name]
+
+        # if the min area is larger than the
+        # detected object, don't add it to detected objects
+        if obj_settings.min_area > obj.obj_data['area']:
+            return True
+        
+        # if the detected object is larger than the
+        # max area, don't add it to detected objects
+        if obj_settings.max_area < obj.obj_data['area']:
+            return True
+
+        # if the score is lower than the threshold, skip
+        if obj_settings.threshold > 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.camera_config = config.cameras[name]
+        self.frame_manager = frame_manager
+        self.best_objects: Dict[str, TrackedObject] = {}
+        self.object_counts = defaultdict(lambda: 0)
+        self.tracked_objects: Dict[str, TrackedObject] = {}
+        self.frame_cache = {}
+        self.zone_objects = defaultdict(lambda: [])
+        self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
+        self.current_frame_lock = threading.Lock()
+        self.current_frame_time = 0.0
+        self.previous_frame_id = None
+        self.callbacks = defaultdict(lambda: [])
+
+    def get_current_frame(self, draw=False):
+        with self.current_frame_lock:
+            frame_copy = np.copy(self._current_frame)
+            frame_time = self.current_frame_time
+            tracked_objects = {k: v.to_dict() for k,v in self.tracked_objects.items()}
+        
+        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.camera_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.camera_config.snapshots.draw_zones:
+                for name, zone in self.camera_config.zones.items():
+                    thickness = 8 if any([name in obj['current_zones'] for obj in tracked_objects.values()]) else 2
+                    cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
+        
+        return frame_copy
+
+    def finished(self, obj_id):
+        del self.tracked_objects[obj_id]
+
+    def on(self, event_type: str, callback: Callable[[Dict], None]):
+        self.callbacks[event_type].append(callback)
+
+    def update(self, frame_time, current_detections):
+        self.current_frame_time = frame_time
+        # get the new frame
+        frame_id = f"{self.name}{frame_time}"
+        current_frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape_yuv)
+
+        current_ids = current_detections.keys()
+        previous_ids = self.tracked_objects.keys()
+        removed_ids = list(set(previous_ids).difference(current_ids))
+        new_ids = list(set(current_ids).difference(previous_ids))
+        updated_ids = list(set(current_ids).intersection(previous_ids))
+
+        for id in new_ids:
+            new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.frame_cache, current_detections[id])
+
+            # call event handlers
+            for c in self.callbacks['start']:
+                c(self.name, new_obj, frame_time)
+        
+        for id in updated_ids:
+            updated_obj = self.tracked_objects[id]
+            updated_obj.update(frame_time, current_detections[id])
+
+            if (not updated_obj.false_positive 
+                and updated_obj.thumbnail_data['frame_time'] == frame_time 
+                and frame_time not in self.frame_cache):
+                self.frame_cache[frame_time] = np.copy(current_frame)
+
+            # call event handlers
+            for c in self.callbacks['update']:
+                c(self.name, updated_obj, frame_time)
+        
+        for id in removed_ids:
+            # publish events to mqtt
+            removed_obj = self.tracked_objects[id]
+            if not 'end_time' in removed_obj.obj_data:
+                removed_obj.obj_data['end_time'] = frame_time
+                for c in self.callbacks['end']:
+                    c(self.name, removed_obj, frame_time)
+
+        # TODO: can i switch to looking this up and only changing when an event ends?
+        # maintain best objects
+        for obj in self.tracked_objects.values():
+            object_type = obj.obj_data['label']
+            # if the object's thumbnail is not from the current frame
+            if obj.false_positive or obj.thumbnail_data['frame_time'] != self.current_frame_time:
+                continue
+            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 (is_better_thumbnail(current_best.thumbnail_data, obj.thumbnail_data, self.camera_config.frame_shape) 
+                    or (now - current_best.thumbnail_data['frame_time']) > self.camera_config.best_image_timeout):
+                    self.best_objects[object_type] = obj
+                    for c in self.callbacks['snapshot']:
+                        c(self.name, self.best_objects[object_type], frame_time)
+            else:
+                self.best_objects[object_type] = obj
+                for c in self.callbacks['snapshot']:
+                    c(self.name, self.best_objects[object_type], frame_time)
+        
+        # update overall camera state for each object type
+        obj_counter = Counter()
+        for obj in self.tracked_objects.values():
+            if not obj.false_positive:
+                obj_counter[obj.obj_data['label']] += 1
+                
+        # report on detected objects
+        for obj_name, count in obj_counter.items():
+            if count != self.object_counts[obj_name]:
+                self.object_counts[obj_name] = count
+                for c in self.callbacks['object_status']:
+                    c(self.name, obj_name, count)
+
+        # expire any objects that are >0 and no longer detected
+        expired_objects = [obj_name for obj_name, count in self.object_counts.items() if count > 0 and not obj_name in obj_counter]
+        for obj_name in expired_objects:
+            self.object_counts[obj_name] = 0
+            for c in self.callbacks['object_status']:
+                c(self.name, obj_name, 0)
+            for c in self.callbacks['snapshot']:
+                c(self.name, self.best_objects[obj_name], frame_time)
+        
+        # cleanup thumbnail frame cache
+        current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj.false_positive])
+        current_best_frames = set([obj.thumbnail_data['frame_time'] for obj in self.best_objects.values()])
+        thumb_frames_to_delete = [t for t in self.frame_cache.keys() if not t in current_thumb_frames and not t in current_best_frames]
+        for t in thumb_frames_to_delete:
+            del self.frame_cache[t]
+        
+        with self.current_frame_lock:
+            self._current_frame = current_frame
+            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, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, event_processed_queue, stop_event):
+        threading.Thread.__init__(self)
+        self.name = "detected_frames_processor"
+        self.config = config
+        self.client = client
+        self.topic_prefix = topic_prefix
+        self.tracked_objects_queue = tracked_objects_queue
+        self.event_queue = event_queue
+        self.event_processed_queue = event_processed_queue
+        self.stop_event = stop_event
+        self.camera_states: Dict[str, CameraState] = {}
+        self.frame_manager = SharedMemoryFrameManager()
+
+        def start(camera, obj: TrackedObject, current_frame_time):
+            self.event_queue.put(('start', camera, obj.to_dict()))
+
+        def update(camera, obj: TrackedObject, current_frame_time):
+            if not obj.thumbnail_data is None and obj.thumbnail_data['frame_time'] == current_frame_time:
+                message = { 'before': obj.previous, 'after': obj.to_dict() }
+                self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
+
+        def end(camera, obj: TrackedObject, current_frame_time):
+            if not obj.false_positive:
+                message = { 'before': obj.previous, 'after': obj.to_dict() }
+                self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
+            self.event_queue.put(('end', camera, obj.to_dict(include_thumbnail=True)))
+        
+        def snapshot(camera, obj: TrackedObject, current_frame_time):
+            self.client.publish(f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot", obj.get_jpg_bytes(), retain=True)
+        
+        def object_status(camera, object_name, status):
+            self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
+
+        for camera in self.config.cameras.keys():
+            camera_state = CameraState(camera, self.config, self.frame_manager)
+            camera_state.on('start', start)
+            camera_state.on('update', update)
+            camera_state.on('end', end)
+            camera_state.on('snapshot', snapshot)
+            camera_state.on('object_status', object_status)
+            self.camera_states[camera] = camera_state
+
+        # {
+        #   'zone_name': {
+        #       'person': {
+        #           'camera_1': 2,
+        #           'camera_2': 1
+        #       }
+        #   }
+        # }
+        self.zone_data = defaultdict(lambda: defaultdict(lambda: {}))
+        
+    def get_best(self, camera, label):
+        # TODO: need a lock here
+        camera_state = self.camera_states[camera]
+        if label in camera_state.best_objects:
+            best_obj = camera_state.best_objects[label]
+            best = best_obj.to_dict()
+            best['frame'] = camera_state.frame_cache[best_obj.thumbnail_data['frame_time']]
+            return best
+        else:
+            return {}
+    
+    def get_current_frame(self, camera, draw=False):
+        return self.camera_states[camera].get_current_frame(draw)
+
+    def run(self):
+        while True:
+            if self.stop_event.is_set():
+                logger.info(f"Exiting object processor...")
+                break
+
+            try:
+                camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
+            except queue.Empty:
+                continue
+
+            camera_state = self.camera_states[camera]
+
+            camera_state.update(frame_time, current_tracked_objects)
+
+            # update zone counts for each label
+            # for each zone in the current camera
+            for zone in self.config.cameras[camera].zones.keys():
+                # count labels for the camera in the zone
+                obj_counter = Counter()
+                for obj in camera_state.tracked_objects.values():
+                    if zone in obj.current_zones and not obj.false_positive:
+                        obj_counter[obj.obj_data['label']] += 1
+                
+                # update counts and publish status
+                for label in set(list(self.zone_data[zone].keys()) + list(obj_counter.keys())):
+                    # if we have previously published a count for this zone/label
+                    zone_label = self.zone_data[zone][label]
+                    if camera in zone_label:
+                        current_count = sum(zone_label.values())
+                        zone_label[camera] = obj_counter[label] if label in obj_counter else 0
+                        new_count = sum(zone_label.values())
+                        if new_count != current_count:
+                            self.client.publish(f"{self.topic_prefix}/{zone}/{label}", new_count, retain=False)
+                    # if this is a new zone/label combo for this camera
+                    else:
+                        if label in obj_counter:
+                            zone_label[camera] = obj_counter[label]
+                            self.client.publish(f"{self.topic_prefix}/{zone}/{label}", obj_counter[label], retain=False)
+
+            # cleanup event finished queue
+            while not self.event_processed_queue.empty():
+                event_id, camera = self.event_processed_queue.get()
+                self.camera_states[camera].finished(event_id)

frigate/objects.py

@@ -1,88 +1,148 @@
-import time
+import copy
 import datetime
+import itertools
+import multiprocessing as mp
+import random
+import string
 import threading
+import time
+from collections import defaultdict
+
 import cv2
-from . util import draw_box_with_label
+import numpy as np
+from scipy.spatial import distance as dist
 
-class ObjectCleaner(threading.Thread):
-    def __init__(self, objects_parsed, detected_objects):
-        threading.Thread.__init__(self)
-        self._objects_parsed = objects_parsed
-        self._detected_objects = detected_objects
-
-    def run(self):
-        while True:
-
-            # wait a bit before checking for expired frames
-            time.sleep(0.2)
-
-            # expire the objects that are more than 1 second old
-            now = datetime.datetime.now().timestamp()
-            # look for the first object found within the last second
-            # (newest objects are appended to the end)
-            detected_objects = self._detected_objects.copy()
-
-            num_to_delete = 0
-            for obj in detected_objects:
-                if now-obj['frame_time']<2:
-                    break
-                num_to_delete += 1
-            if num_to_delete > 0:
-                del self._detected_objects[:num_to_delete]
-
-                # notify that parsed objects were changed
-                with self._objects_parsed:
-                    self._objects_parsed.notify_all()
+from frigate.util import calculate_region, draw_box_with_label
 
 
-# Maintains the frame and person with the highest score from the most recent
-# motion event
-class BestPersonFrame(threading.Thread):
-    def __init__(self, objects_parsed, recent_frames, detected_objects):
-        threading.Thread.__init__(self)
-        self.objects_parsed = objects_parsed
-        self.recent_frames = recent_frames
-        self.detected_objects = detected_objects
-        self.best_person = None
-        self.best_frame = None
+class ObjectTracker():
+    def __init__(self, max_disappeared):
+        self.tracked_objects = {}
+        self.disappeared = {}
+        self.max_disappeared = max_disappeared
 
-    def run(self):
-        while True:
+    def register(self, index, obj):
+        rand_id = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6))
+        id = f"{obj['frame_time']}-{rand_id}"
+        obj['id'] = id
+        obj['start_time'] = obj['frame_time']
+        self.tracked_objects[id] = obj
+        self.disappeared[id] = 0
 
-            # wait until objects have been parsed
-            with self.objects_parsed:
-                self.objects_parsed.wait()
+    def deregister(self, id):
+        del self.tracked_objects[id]
+        del self.disappeared[id]
     
-            # make a copy of detected objects
-            detected_objects = self.detected_objects.copy()
-            detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
+    def update(self, id, new_obj):
+        self.disappeared[id] = 0
+        self.tracked_objects[id].update(new_obj)
 
-            # get the highest scoring person
-            new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
+    def match_and_update(self, frame_time, new_objects):
+        # group by name
+        new_object_groups = defaultdict(lambda: [])
+        for obj in new_objects:
+            new_object_groups[obj[0]].append({
+                'label': obj[0],
+                'score': obj[1],
+                'box': obj[2],
+                'area': obj[3],
+                'region': obj[4],
+                'frame_time': frame_time
+            })
         
-            # if there isnt a person, continue
-            if new_best_person is None:
+        # update any tracked objects with labels that are not
+        # seen in the current objects and deregister if needed
+        for obj in list(self.tracked_objects.values()):
+            if not obj['label'] in new_object_groups:
+                if self.disappeared[obj['id']] >= self.max_disappeared:
+                    self.deregister(obj['id'])
+                else:
+                    self.disappeared[obj['id']] += 1
+        
+        if len(new_objects) == 0:
+            return
+        
+        # track objects for each label type
+        for label, group in new_object_groups.items():
+            current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]
+            current_ids = [o['id'] for o in current_objects]
+            current_centroids = np.array([o['centroid'] for o in current_objects])
+
+            # compute centroids of new objects
+            for obj in group:
+                centroid_x = int((obj['box'][0]+obj['box'][2]) / 2.0)
+                centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0)
+                obj['centroid'] = (centroid_x, centroid_y)
+
+            if len(current_objects) == 0:
+                for index, obj in enumerate(group):
+                    self.register(index, obj)
+                return
+            
+            new_centroids = np.array([o['centroid'] for o in group])
+
+            # compute the distance between each pair of tracked
+            # centroids and new centroids, respectively -- our
+            # goal will be to match each new centroid to an existing
+            # object centroid
+            D = dist.cdist(current_centroids, new_centroids)
+
+            # in order to perform this matching we must (1) find the
+            # smallest value in each row and then (2) sort the row
+            # indexes based on their minimum values so that the row
+            # with the smallest value is at the *front* of the index
+            # list
+            rows = D.min(axis=1).argsort()
+
+            # next, we perform a similar process on the columns by
+            # finding the smallest value in each column and then
+            # sorting using the previously computed row index list
+            cols = D.argmin(axis=1)[rows]
+
+            # in order to determine if we need to update, register,
+            # or deregister an object we need to keep track of which
+            # of the rows and column indexes we have already examined
+            usedRows = set()
+            usedCols = set()
+
+            # loop over the combination of the (row, column) index
+            # tuples
+            for (row, col) in zip(rows, cols):
+                # if we have already examined either the row or
+                # column value before, ignore it
+                if row in usedRows or col in usedCols:
                     continue
 
-            # if there is no current best_person
-            if self.best_person is None:
-                self.best_person = new_best_person
-            # if there is already a best_person
+                # otherwise, grab the object ID for the current row,
+                # set its new centroid, and reset the disappeared
+                # counter
+                objectID = current_ids[row]
+                self.update(objectID, group[col])
+
+                # indicate that we have examined each of the row and
+                # column indexes, respectively
+                usedRows.add(row)
+                usedCols.add(col)
+
+            # compute the column index we have NOT yet examined
+            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
+            unusedCols = set(range(0, D.shape[1])).difference(usedCols)
+
+            # in the event that the number of object centroids is
+			# equal or greater than the number of input centroids
+			# we need to check and see if some of these objects have
+			# potentially disappeared
+            if D.shape[0] >= D.shape[1]:
+                for row in unusedRows:
+                    id = current_ids[row]
+
+                    if self.disappeared[id] >= self.max_disappeared:
+                        self.deregister(id)
                     else:
-                now = datetime.datetime.now().timestamp()
-                # if the new best person is a higher score than the current best person 
-                # or the current person is more than 1 minute old, use the new best person
-                if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
-                    self.best_person = new_best_person
-            
-            # make a copy of the recent frames
-            recent_frames = self.recent_frames.copy()
-            
-            if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
-                best_frame = recent_frames[self.best_person['frame_time']]
-
-                label = "{}: {}% {}".format(self.best_person['name'],int(self.best_person['score']*100),int(self.best_person['area']))
-                draw_box_with_label(best_frame, self.best_person['xmin'], self.best_person['ymin'], 
-                    self.best_person['xmax'], self.best_person['ymax'], label)
-                
-                self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
+                        self.disappeared[id] += 1
+            # if the number of input centroids is greater
+            # than the number of existing object centroids we need to
+            # register each new input centroid as a trackable object
+            else:
+                for col in unusedCols:
+                    self.register(col, group[col])

frigate/record.py

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

frigate/test/test_config.py

@@ -0,0 +1,319 @@
+import json
+from unittest import TestCase, main
+import voluptuous as vol
+from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
+
+class TestConfig(TestCase):
+    def setUp(self):
+        self.minimal = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+    def test_empty(self):
+        FRIGATE_CONFIG_SCHEMA({})
+
+    def test_minimal(self):
+        FRIGATE_CONFIG_SCHEMA(self.minimal)
+    
+    def test_config_class(self):
+        FrigateConfig(config=self.minimal)
+    
+    def test_inherit_tracked_objects(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.track)
+    
+    def test_override_tracked_objects(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'objects': {
+                        'track': ['cat']
+                    }
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('cat' in frigate_config.cameras['back'].objects.track)
+    
+    def test_default_object_filters(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.filters)
+    
+    def test_inherit_object_filters(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'objects': {
+                'track': ['person', 'dog'],
+                'filters': {
+                    'dog': {
+                        'threshold': 0.7
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.filters)
+        assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
+    
+    def test_override_object_filters(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'objects': {
+                        'track': ['person', 'dog'],
+                        'filters': {
+                            'dog': {
+                                'threshold': 0.7
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('dog' in frigate_config.cameras['back'].objects.filters)
+        assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
+    
+    def test_ffmpeg_params(self):
+        config = {
+            'ffmpeg': {
+                'input_args': ['-re']
+            },
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'objects': {
+                        'track': ['person', 'dog'],
+                        'filters': {
+                            'dog': {
+                                'threshold': 0.7
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
+    
+    def test_inherit_save_clips_retention(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        frigate_config = FrigateConfig(config=config)
+        assert(frigate_config.cameras['back'].save_clips.retain.objects['person'] == 30)
+    
+    def test_roles_listed_twice_throws_error(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] },
+                            { 'path': 'rtsp://10.0.0.1:554/video2', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920
+                }
+            }
+        }
+        self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
+    
+    def test_zone_matching_camera_name_throws_error(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'zones': {
+                        'back': {
+                            'coordinates': '1,1,1,1,1,1'
+                        }
+                    }
+                }
+            }
+        }
+        self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
+    
+    def test_save_clips_should_default_to_global_objects(self):
+        config = {
+            'mqtt': {
+                'host': 'mqtt'
+            },
+            'save_clips': {
+                'retain': {
+                    'default': 20,
+                    'objects': {
+                        'person': 30
+                    }
+                }
+            },
+            'objects': {
+                'track': ['person', 'dog']
+            },
+            'cameras': {
+                'back': {
+                    'ffmpeg': {
+                        'inputs': [
+                            { 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
+                        ]
+                    },
+                    'height': 1080,
+                    'width': 1920,
+                    'save_clips': {
+                        'enabled': True
+                    }
+                }
+            }
+        }
+        config = FrigateConfig(config=config)
+        assert(len(config.cameras['back'].save_clips.objects) == 2)
+        assert('dog' in config.cameras['back'].save_clips.objects)
+        assert('person' in config.cameras['back'].save_clips.objects)
+
+if __name__ == '__main__':
+    main(verbosity=2)

frigate/util.py

@@ -1,26 +1,248 @@
-import numpy as np
+import collections
+import datetime
+import hashlib
+import json
+import signal
+import subprocess as sp
+import threading
+import time
+import traceback
+from abc import ABC, abstractmethod
+from multiprocessing import shared_memory
+from typing import AnyStr
+
 import cv2
+import matplotlib.pyplot as plt
+import numpy as np
 
-# convert shared memory array into numpy array
-def tonumpyarray(mp_arr):
-    return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
 
-def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label):
-    color = (255,0,0)
-    cv2.rectangle(frame, (x_min, y_min), 
-        (x_max, y_max), 
-        color, 2)
+def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
+    if color is None:
+        color = (0,0,255)
+    display_text = "{}: {}".format(label, info)
+    cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
     font_scale = 0.5
     font = cv2.FONT_HERSHEY_SIMPLEX
     # get the width and height of the text box
-    size = cv2.getTextSize(label, font, fontScale=font_scale, thickness=2)
+    size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2)
     text_width = size[0][0]
     text_height = size[0][1]
     line_height = text_height + size[1]
     # set the text start position
+    if position == 'ul':
         text_offset_x = x_min
         text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+    elif position == 'ur':
+        text_offset_x = x_max - (text_width+8)
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+    elif position == 'bl':
+        text_offset_x = x_min
+        text_offset_y = y_max
+    elif position == 'br':
+        text_offset_x = x_max - (text_width+8)
+        text_offset_y = y_max
     # make the coords of the box with a small padding of two pixels
     textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
     cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
-    cv2.putText(frame, label, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
+    cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
+
+def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):    
+    # size is larger than longest edge
+    size = int(max(xmax-xmin, ymax-ymin)*multiplier)
+    # dont go any smaller than 300
+    if size < 300:
+        size = 300
+    # if the size is too big to fit in the frame
+    if size > min(frame_shape[0], frame_shape[1]):
+        size = min(frame_shape[0], frame_shape[1])
+
+    # x_offset is midpoint of bounding box minus half the size
+    x_offset = int((xmax-xmin)/2.0+xmin-size/2.0)
+    # if outside the image
+    if x_offset < 0:
+        x_offset = 0
+    elif x_offset > (frame_shape[1]-size):
+        x_offset = (frame_shape[1]-size)
+
+    # y_offset is midpoint of bounding box minus half the size
+    y_offset = int((ymax-ymin)/2.0+ymin-size/2.0)
+    # if outside the image
+    if y_offset < 0:
+        y_offset = 0
+    elif y_offset > (frame_shape[0]-size):
+        y_offset = (frame_shape[0]-size)
+
+    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]),
+        max(box_a[1], box_b[1]),
+        min(box_a[2], box_b[2]),
+        min(box_a[3], box_b[3])
+    )
+
+def area(box):
+    return (box[2]-box[0] + 1)*(box[3]-box[1] + 1)
+    
+def intersection_over_union(box_a, box_b):
+    # determine the (x, y)-coordinates of the intersection rectangle
+    intersect = intersection(box_a, box_b)
+
+    # compute the area of intersection rectangle
+    inter_area = max(0, intersect[2] - intersect[0] + 1) * max(0, intersect[3] - intersect[1] + 1)
+
+    if inter_area == 0:
+        return 0.0
+    
+    # compute the area of both the prediction and ground-truth
+    # rectangles
+    box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
+    box_b_area = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)
+
+    # compute the intersection over union by taking the intersection
+    # area and dividing it by the sum of prediction + ground-truth
+    # areas - the interesection area
+    iou = inter_area / float(box_a_area + box_b_area - inter_area)
+
+    # return the intersection over union value
+    return iou
+
+def clipped(obj, frame_shape):
+    # if the object is within 5 pixels of the region border, and the region is not on the edge
+    # consider the object to be clipped
+    box = obj[2]
+    region = obj[4]
+    if ((region[0] > 5 and box[0]-region[0] <= 5) or 
+        (region[1] > 5 and box[1]-region[1] <= 5) or
+        (frame_shape[1]-region[2] > 5 and region[2]-box[2] <= 5) or
+        (frame_shape[0]-region[3] > 5 and region[3]-box[3] <= 5)):
+        return True
+    else:
+        return False
+
+class EventsPerSecond:
+    def __init__(self, max_events=1000):
+        self._start = None
+        self._max_events = max_events
+        self._timestamps = []
+    
+    def start(self):
+        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)
+        return len([t for t in self._timestamps if t > (now-last_n_seconds)]) / seconds
+
+def print_stack(sig, frame):
+    traceback.print_stack(frame)
+
+def listen():
+    signal.signal(signal.SIGUSR1, print_stack)
+
+class FrameManager(ABC):
+    @abstractmethod
+    def create(self, name, size) -> AnyStr:
+        pass
+
+    @abstractmethod
+    def get(self, name, timeout_ms=0):
+        pass
+
+    @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):
+        if name in self.shm_store:
+            self.shm_store[name].close()
+            self.shm_store[name].unlink()
+            del self.shm_store[name]

frigate/video.py

@@ -1,335 +1,417 @@
-import os
-import time
-import datetime
-import cv2
-import threading
+import base64
+import copy
 import ctypes
+import datetime
+import itertools
+import json
+import logging
 import multiprocessing as mp
+import os
+import queue
 import subprocess as sp
+import signal
+import threading
+import time
+from collections import defaultdict
+from typing import Dict, List
+
+import cv2
 import numpy as np
-from . util import tonumpyarray, draw_box_with_label
-from . object_detection import FramePrepper
-from . objects import ObjectCleaner, BestPersonFrame
-from . mqtt import MqttObjectPublisher
 
-# Stores 2 seconds worth of frames when motion is detected so they can be used for other threads
-class FrameTracker(threading.Thread):
-    def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames):
-        threading.Thread.__init__(self)
-        self.shared_frame = shared_frame
-        self.frame_time = frame_time
-        self.frame_ready = frame_ready
-        self.frame_lock = frame_lock
-        self.recent_frames = recent_frames
+from frigate.config import CameraConfig
+from frigate.edgetpu import RemoteObjectDetector
+from frigate.log import LogPipe
+from frigate.motion import MotionDetector
+from frigate.objects import ObjectTracker
+from frigate.util import (EventsPerSecond, FrameManager,
+                          SharedMemoryFrameManager, area, calculate_region,
+                          clipped, draw_box_with_label, intersection,
+                          intersection_over_union, listen, yuv_region_2_rgb)
 
-    def run(self):
-        frame_time = 0.0
+logger = logging.getLogger(__name__)
+
+def filtered(obj, objects_to_track, object_filters, mask=None):
+    object_name = obj[0]
+
+    if not object_name in objects_to_track:
+        return True
+    
+    if object_name in object_filters:
+        obj_settings = object_filters[object_name]
+
+        # if the min area is larger than the
+        # detected object, don't add it to detected objects
+        if obj_settings.min_area > obj[3]:
+            return True
+        
+        # if the detected object is larger than the
+        # max area, don't add it to detected objects
+        if obj_settings.max_area < obj[3]:
+            return True
+
+        # if the score is lower than the min_score, skip
+        if obj_settings.min_score > obj[1]:
+            return True
+    
+        # compute the coordinates of the object and make sure
+        # the location isnt outside the bounds of the image (can happen from rounding)
+        y_location = min(int(obj[2][3]), len(mask)-1)
+        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 (not mask is None) and (mask[y_location][x_location] == 0):
+            return True
+        
+    return False
+
+def create_tensor_input(frame, region):
+    cropped_frame = yuv_region_2_rgb(frame, region)
+
+    # Resize to 300x300 if needed
+    if cropped_frame.shape != (300, 300, 3):
+        cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
+    
+    # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
+    return np.expand_dims(cropped_frame, axis=0)
+
+def stop_ffmpeg(ffmpeg_process, logger):
+    logger.info("Terminating the existing ffmpeg process...")
+    ffmpeg_process.terminate()
+    try:
+        logger.info("Waiting for ffmpeg to exit gracefully...")
+        ffmpeg_process.communicate(timeout=30)
+    except sp.TimeoutExpired:
+        logger.info("FFmpeg didnt exit. Force killing...")
+        ffmpeg_process.kill()
+        ffmpeg_process.communicate()
+    ffmpeg_process = None
+
+def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None):
+    if not ffmpeg_process is None:
+        stop_ffmpeg(ffmpeg_process, logger)
+
+    if frame_size is None:
+        process = sp.Popen(ffmpeg_cmd, stdout = sp.DEVNULL, stderr=logpipe, stdin = sp.DEVNULL, start_new_session=True)
+    else:
+        process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stderr=logpipe, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
+    return process
+
+def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, 
+    frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value):
+
+    frame_size = frame_shape[0] * frame_shape[1]
+    frame_rate = EventsPerSecond()
+    frame_rate.start()
+    skipped_eps = EventsPerSecond()
+    skipped_eps.start()
     while True:
-            now = datetime.datetime.now().timestamp()
-            # wait for a frame
-            with self.frame_ready:
-                # if there isnt a frame ready for processing or it is old, wait for a signal
-                if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
-                    self.frame_ready.wait()
+        fps.value = frame_rate.eps()
+        skipped_fps = skipped_eps.eps()
 
-            # lock and make a copy of the frame
-            with self.frame_lock: 
-                frame = self.shared_frame.copy()
-                frame_time = self.frame_time.value
+        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:
+          logger.info(f"{camera_name}: ffmpeg sent a broken frame. something is wrong.")
 
-            # add the frame to recent frames
-            self.recent_frames[frame_time] = frame
+          if ffmpeg_process.poll() != None:
+              logger.info(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
+              frame_manager.delete(frame_name)
+              break
           
-            # delete any old frames
-            stored_frame_times = list(self.recent_frames.keys())
-            for k in stored_frame_times:
-                if (now - k) > 2:
-                    del self.recent_frames[k]
+          continue
 
-def get_frame_shape(rtsp_url):
-    # capture a single frame and check the frame shape so the correct array
-    # size can be allocated in memory
-    video = cv2.VideoCapture(rtsp_url)
-    ret, frame = video.read()
-    frame_shape = frame.shape
-    video.release()
-    return frame_shape
+        frame_rate.update()
 
-def get_rtsp_url(rtsp_config):
-    if (rtsp_config['password'].startswith('$')):
-        rtsp_config['password'] = os.getenv(rtsp_config['password'][1:])
-    return 'rtsp://{}:{}@{}:{}{}'.format(rtsp_config['user'], 
-        rtsp_config['password'], rtsp_config['host'], rtsp_config['port'],
-        rtsp_config['path'])
+        # 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, camera):
+    def __init__(self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
         threading.Thread.__init__(self)
-        self.camera = camera
+        self.logger = logging.getLogger(f"watchdog.{camera_name}")
+        self.camera_name = camera_name
+        self.config = config
+        self.capture_thread = None
+        self.ffmpeg_detect_process = None
+        self.logpipe = LogPipe(f"ffmpeg.{self.camera_name}.detect", logging.ERROR)
+        self.ffmpeg_other_processes = []
+        self.camera_fps = camera_fps
+        self.ffmpeg_pid = ffmpeg_pid
+        self.frame_queue = frame_queue
+        self.frame_shape = self.config.frame_shape_yuv
+        self.frame_size = self.frame_shape[0] * self.frame_shape[1]
+        self.stop_event = stop_event
 
     def run(self):
+        self.start_ffmpeg_detect()
 
+        for c in self.config.ffmpeg_cmds:
+            if 'detect' in c['roles']:
+                continue
+            logpipe = LogPipe(f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}", logging.ERROR)
+            self.ffmpeg_other_processes.append({
+                'cmd': c['cmd'],
+                'logpipe': logpipe,
+                'process': start_or_restart_ffmpeg(c['cmd'], self.logger, logpipe)
+            })
+        
+        time.sleep(10)
         while True:
-            # wait a bit before checking
+            if self.stop_event.is_set():
+                stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
+                for p in self.ffmpeg_other_processes:
+                    stop_ffmpeg(p['process'], self.logger)
+                    p['logpipe'].close()
+                self.logpipe.close()
+                break
+
+            now = datetime.datetime.now().timestamp()
+
+            if not self.capture_thread.is_alive():
+                self.start_ffmpeg_detect()
+            elif now - self.capture_thread.current_frame.value > 20:
+                self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...")
+                self.ffmpeg_detect_process.terminate()
+                try:
+                    self.logger.info("Waiting for ffmpeg to exit gracefully...")
+                    self.ffmpeg_detect_process.communicate(timeout=30)
+                except sp.TimeoutExpired:
+                    self.logger.info("FFmpeg didnt exit. Force killing...")
+                    self.ffmpeg_detect_process.kill()
+                    self.ffmpeg_detect_process.communicate()
+            
+            for p in self.ffmpeg_other_processes:
+                poll = p['process'].poll()
+                if poll == None:
+                    continue
+                p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process'])
+            
+            # wait a bit before checking again
             time.sleep(10)
     
-            if (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 10:
-                print("last frame is more than 10 seconds old, restarting camera capture...")
-                self.camera.start_or_restart_capture()
-                time.sleep(5)
-
-# Thread to read the stdout of the ffmpeg process and update the current frame
-class CameraCapture(threading.Thread):
-    def __init__(self, camera):
-        threading.Thread.__init__(self)
-        self.camera = camera
-
-    def run(self):
-        frame_num = 0
-        while True:
-            if self.camera.ffmpeg_process.poll() != None:
-                print("ffmpeg process is not running. exiting capture thread...")
-                break
-
-            raw_image = self.camera.ffmpeg_process.stdout.read(self.camera.frame_size)
-
-            if len(raw_image) == 0:
-                print("ffmpeg didnt return a frame. something is wrong. exiting capture thread...")
-                break
-
-            frame_num += 1
-            if (frame_num % self.camera.take_frame) != 0:
-                continue
-
-            with self.camera.frame_lock:
-                self.camera.frame_time.value = datetime.datetime.now().timestamp()
-                
-                self.camera.current_frame[:] = (
-                    np
-                    .frombuffer(raw_image, np.uint8)
-                    .reshape(self.camera.frame_shape)
-                )
-            # Notify with the condition that a new frame is ready
-            with self.camera.frame_ready:
-                self.camera.frame_ready.notify_all()
-
-class Camera:
-    def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
-        self.name = name
-        self.config = config
-        self.detected_objects = []
-        self.recent_frames = {}
-        self.rtsp_url = get_rtsp_url(self.config['rtsp'])
-        self.take_frame = self.config.get('take_frame', 1)
-        self.ffmpeg_log_level = self.config.get('ffmpeg_log_level', 'panic')
-        self.ffmpeg_hwaccel_args = self.config.get('ffmpeg_hwaccel_args', [])
-        self.ffmpeg_input_args = self.config.get('ffmpeg_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'
-        ])
-        self.ffmpeg_output_args = self.config.get('ffmpeg_output_args', [
-            '-f', 'rawvideo',
-            '-pix_fmt', 'rgb24'
-        ])
-        self.regions = self.config['regions']
-        self.frame_shape = get_frame_shape(self.rtsp_url)
-        self.frame_size = self.frame_shape[0] * self.frame_shape[1] * self.frame_shape[2]
-        self.mqtt_client = mqtt_client
-        self.mqtt_topic_prefix = '{}/{}'.format(mqtt_prefix, self.name)
-
-        # create a numpy array for the current frame in initialize to zeros
-        self.current_frame = np.zeros(self.frame_shape, np.uint8)
-        # create shared value for storing the frame_time
-        self.frame_time = mp.Value('d', 0.0)
-        # Lock to control access to the frame
-        self.frame_lock = mp.Lock()
-        # Condition for notifying that a new frame is ready
-        self.frame_ready = mp.Condition()
-        # Condition for notifying that objects were parsed
-        self.objects_parsed = mp.Condition()
-
-        self.ffmpeg_process = None
-        self.capture_thread = None
-
-        # for each region, create a separate thread to resize the region and prep for detection
-        self.detection_prep_threads = []
-        for region in self.config['regions']:
-            # set a default threshold of 0.5 if not defined
-            if not 'threshold' in region:
-                region['threshold'] = 0.5
-            if not isinstance(region['threshold'], float):
-                print('Threshold is not a float. Setting to 0.5 default.')
-                region['threshold'] = 0.5
-            self.detection_prep_threads.append(FramePrepper(
-                self.name,
-                self.current_frame,
-                self.frame_time,
-                self.frame_ready,
-                self.frame_lock,
-                region['size'], region['x_offset'], region['y_offset'], region['threshold'],
-                prepped_frame_queue
-            ))
-        
-        # start a thread to store recent motion frames for processing
-        self.frame_tracker = FrameTracker(self.current_frame, self.frame_time, 
-            self.frame_ready, self.frame_lock, self.recent_frames)
-        self.frame_tracker.start()
-
-        # start a thread to store the highest scoring recent person frame
-        self.best_person_frame = BestPersonFrame(self.objects_parsed, self.recent_frames, self.detected_objects)
-        self.best_person_frame.start()
-
-        # start a thread to expire objects from the detected objects list
-        self.object_cleaner = ObjectCleaner(self.objects_parsed, self.detected_objects)
-        self.object_cleaner.start()
-
-        # start a thread to publish object scores (currently only person)
-        mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects, self.best_person_frame)
-        mqtt_publisher.start()
-
-        # create a watchdog thread for capture process
-        self.watchdog = CameraWatchdog(self)
-
-        # load in the mask for person detection
-        if 'mask' in self.config:
-            self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
-        else:
-            self.mask = None
-
-        if self.mask is None:
-            self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
-            self.mask[:] = 255
-
-
-    def start_or_restart_capture(self):
-        if not self.ffmpeg_process is None:
-            print("Terminating the existing ffmpeg process...")
-            self.ffmpeg_process.terminate()
-            try:
-                print("Waiting for ffmpeg to exit gracefully...")
-                self.ffmpeg_process.wait(timeout=30)
-            except sp.TimeoutExpired:
-                print("FFmpeg didnt exit. Force killing...")
-                self.ffmpeg_process.kill()
-                self.ffmpeg_process.wait()
-
-            print("Waiting for the capture thread to exit...")
-            self.capture_thread.join()
-            self.ffmpeg_process = None
-            self.capture_thread = None
-            
-        # create the process to capture frames from the RTSP stream and store in a shared array
-        print("Creating a new ffmpeg process...")
-        self.start_ffmpeg()
-        
-        print("Creating a new capture thread...")
-        self.capture_thread = CameraCapture(self)
-        print("Starting a new capture thread...")
+    def start_ffmpeg_detect(self):
+        ffmpeg_cmd = [c['cmd'] for c in self.config.ffmpeg_cmds if 'detect' in c['roles']][0]
+        self.ffmpeg_detect_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.logger, self.logpipe, self.frame_size)
+        self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid
+        self.capture_thread = CameraCapture(self.camera_name, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue, 
+            self.camera_fps)
         self.capture_thread.start()
 
-    def start_ffmpeg(self):
-        ffmpeg_global_args = [
-            '-hide_banner', '-loglevel', self.ffmpeg_log_level
-        ]
+class CameraCapture(threading.Thread):
+    def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
+        threading.Thread.__init__(self)
+        self.name = f"capture:{camera_name}"
+        self.camera_name = camera_name
+        self.frame_shape = frame_shape
+        self.frame_queue = frame_queue
+        self.fps = fps
+        self.skipped_fps = EventsPerSecond()
+        self.frame_manager = SharedMemoryFrameManager()
+        self.ffmpeg_process = ffmpeg_process
+        self.current_frame = mp.Value('d', 0.0)
+        self.last_frame = 0
 
-        ffmpeg_cmd = (['ffmpeg'] +
-            ffmpeg_global_args +
-            self.ffmpeg_hwaccel_args +
-            self.ffmpeg_input_args +
-            ['-i', self.rtsp_url] +
-            self.ffmpeg_output_args +
-            ['pipe:'])
+    def run(self):
+        self.skipped_fps.start()
+        capture_frames(self.ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue,
+            self.fps, self.skipped_fps, self.current_frame)
 
-        print(" ".join(ffmpeg_cmd))
+def capture_camera(name, config: CameraConfig, process_info):
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
     
-        self.ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=self.frame_size)
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
 
-    def start(self):
-        self.start_or_restart_capture()
-        # start the object detection prep threads
-        for detection_prep_thread in self.detection_prep_threads:
-            detection_prep_thread.start()
-        self.watchdog.start()
+    frame_queue = process_info['frame_queue']
+    camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'], stop_event)
+    camera_watchdog.start()
+    camera_watchdog.join()
 
-    def join(self):
-        self.capture_thread.join()
+def track_camera(name, config: CameraConfig, detection_queue, result_connection, detected_objects_queue, process_info):
+    stop_event = mp.Event()
+    def receiveSignal(signalNumber, frame):
+        stop_event.set()
     
-    def get_capture_pid(self):
-        return self.ffmpeg_process.pid
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
 
-    def add_objects(self, objects):
-        if len(objects) == 0:
-            return
+    threading.current_thread().name = f"process:{name}"
+    listen()
 
-        for obj in objects:
-            # Store object area to use in bounding box labels
-            obj['area'] = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
+    frame_queue = process_info['frame_queue']
 
-            if obj['name'] == 'person':
-                # find the matching region
-                region = None
-                for r in self.regions:
-                    if (
-                            obj['xmin'] >= r['x_offset'] and
-                            obj['ymin'] >= r['y_offset'] and
-                            obj['xmax'] <= r['x_offset']+r['size'] and
-                            obj['ymax'] <= r['y_offset']+r['size']
-                        ): 
-                        region = r
+    frame_shape = config.frame_shape
+    objects_to_track = config.objects.track
+    object_filters = config.objects.filters
+    mask = config.mask
+
+    motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
+    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection)
+
+    object_tracker = ObjectTracker(10)
+
+    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)
+
+    logger.info(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, mask, stop_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()
+
+    while True:
+        if stop_event.is_set():
             break
 
-                # if the min person area is larger than the
-                # detected person, don't add it to detected objects
-                if region and 'min_person_area' in region and region['min_person_area'] > obj['area']:
+        if exit_on_empty and frame_queue.empty():
+            logger.info(f"Exiting track_objects...")
+            break
+
+        try:
+            frame_time = frame_queue.get(True, 10)
+        except queue.Empty:
             continue
 
-                # compute the coordinates of the person and make sure
-                # the location isnt outside the bounds of the image (can happen from rounding)
-                y_location = min(int(obj['ymax']), len(self.mask)-1)
-                x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1)
+        current_frame_time.value = frame_time
 
-                # if the person is in a masked location, continue
-                if self.mask[y_location][x_location] == [0]:
+        frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
+
+        if frame is None:
+            logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
             continue
 
-            self.detected_objects.append(obj)
+        # look for motion
+        motion_boxes = motion_detector.detect(frame)
 
-        with self.objects_parsed:
-            self.objects_parsed.notify_all()
+        tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values()]
 
-    def get_best_person(self):
-        return self.best_person_frame.best_frame
+        # combine motion boxes with known locations of existing objects
+        combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
 
-    def get_current_frame_with_objects(self):
-        # make a copy of the current detected objects
-        detected_objects = self.detected_objects.copy()
-        # lock and make a copy of the current frame
-        with self.frame_lock:
-            frame = self.current_frame.copy()
+        # compute regions
+        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
+            for a in combined_boxes]
 
-        # draw the bounding boxes on the screen
-        for obj in detected_objects:
-            label = "{}: {}% {}".format(obj['name'],int(obj['score']*100),int(obj['area']))
-            draw_box_with_label(frame, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'], label)
+        # combine overlapping regions
+        combined_regions = reduce_boxes(regions)
 
-        for region in self.regions:
-            color = (255,255,255)
-            cv2.rectangle(frame, (region['x_offset'], region['y_offset']), 
-                (region['x_offset']+region['size'], region['y_offset']+region['size']), 
-                color, 2)
+        # re-compute regions
+        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
+            for a in combined_regions]
 
-        # convert to BGR
-        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+        # resize regions and detect
+        detections = []
+        for region in regions:
+            detections.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
         
-        return frame
+        #########
+        # merge objects, check for clipped objects and look again up to 4 times
+        #########
+        refining = True
+        refine_count = 0
+        while refining and refine_count < 4:
+            refining = False
 
+            # group by name
+            detected_object_groups = defaultdict(lambda: [])
+            for detection in detections:
+                detected_object_groups[detection[0]].append(detection)
 
+            selected_objects = []
+            for group in detected_object_groups.values():
 
+                # apply non-maxima suppression to suppress weak, overlapping bounding boxes
+                boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
+                    for o in group]
+                confidences = [o[1] for o in group]
+                idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
 
+                for index in idxs:
+                    obj = group[index[0]]
+                    if clipped(obj, frame_shape):
+                        box = obj[2]
+                        # calculate a new region that will hopefully get the entire object
+                        region = calculate_region(frame_shape, 
+                            box[0], box[1],
+                            box[2], box[3])
+                        
+                        selected_objects.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+
+                        refining = True
+                    else:
+                        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 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}")

frigate/watchdog.py

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

frigate/zeroconf.py

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

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

nginx/nginx.conf

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

process_clip.py

@@ -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()

run.sh

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

scripts/install_edgetpu_api.sh

@@ -1,50 +0,0 @@
-#!/bin/bash
-set -e
-
-CPU_ARCH=$(uname -m)
-OS_VERSION=$(uname -v)
-
-echo "CPU_ARCH ${CPU_ARCH}"
-echo "OS_VERSION ${OS_VERSION}"
-
-if [[ "${CPU_ARCH}" == "x86_64" ]]; then
-  echo "Recognized as Linux on x86_64."
-  LIBEDGETPU_SUFFIX=x86_64
-  HOST_GNU_TYPE=x86_64-linux-gnu
-elif [[ "${CPU_ARCH}" == "armv7l" ]]; then
-  echo "Recognized as Linux on ARM32 platform."
-  LIBEDGETPU_SUFFIX=arm32
-  HOST_GNU_TYPE=arm-linux-gnueabihf
-elif [[ "${CPU_ARCH}" == "aarch64" ]]; then
-  echo "Recognized as generic ARM64 platform."
-  LIBEDGETPU_SUFFIX=arm64
-  HOST_GNU_TYPE=aarch64-linux-gnu
-fi
-
-if [[ -z "${HOST_GNU_TYPE}" ]]; then
-  echo "Your platform is not supported."
-  exit 1
-fi
-
-echo "Using maximum operating frequency."
-LIBEDGETPU_SRC="libedgetpu/libedgetpu_${LIBEDGETPU_SUFFIX}.so"
-LIBEDGETPU_DST="/usr/lib/${HOST_GNU_TYPE}/libedgetpu.so.1.0"
-
-# Runtime library.
-echo "Installing Edge TPU runtime library [${LIBEDGETPU_DST}]..."
-if [[ -f "${LIBEDGETPU_DST}" ]]; then
-  echo "File already exists. Replacing it..."
-  rm -f "${LIBEDGETPU_DST}"
-fi
-
-cp -p "${LIBEDGETPU_SRC}" "${LIBEDGETPU_DST}"
-ldconfig
-echo "Done."
-
-# Python API.
-WHEEL=$(ls edgetpu-*-py3-none-any.whl 2>/dev/null)
-if [[ $? == 0 ]]; then
-  echo "Installing Edge TPU Python API..."
-  python3 -m pip install --no-deps "${WHEEL}"
-  echo "Done."
-fi

scripts/install_odroid_repo.sh

@@ -1,5 +0,0 @@
-#!/bin/bash
-
-apt-key adv --keyserver keyserver.ubuntu.com --recv-keys D986B59D
-
-echo "deb http://deb.odroid.in/5422-s bionic main" > /etc/apt/sources.list.d/odroid.list