fix zone filters fixes #218

update config merging and example config
remove affiliate links
2026-07-21 19:29:00 +03:00 · 2020-10-11 11:38:32 -05:00 · 2020-10-11 11:38:32 -05:00 · 2020-10-08 07:26:02 -05:00 · 2020-09-20 20:58:10 -05:00 · 2020-09-18 07:14:44 -05:00
106 changed files with 3142 additions and 949 deletions
@@ -0,0 +1,6 @@
+README.md
+diagram.png
+.gitignore
+debug
+config/
+*.pyc
@@ -0,0 +1 @@
+github: blakeblackshear
@@ -0,0 +1,55 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Version of frigate**
+What version are you using?
+
+**Config file**
+Include your full config file wrapped in back ticks.
+```
+config here
+```
+
+**Logs**
+```
+Include relevant log output here
+```
+
+**Frigate debug stats**
+```
+Output from frigate's /debug/stats endpoint
+```
+
+**FFprobe from your camera**
+
+Run the following command and paste output below
+```
+ffprobe <stream_url>
+```
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Computer Hardware**
+ - OS: [e.g. Ubuntu, Windows]
+ - Virtualization: [e.g. Proxmox, Virtualbox]
+ - Coral Version: [e.g. USB, PCIe, None]
+ - Network Setup: [e.g. Wired, WiFi]
+
+**Camera Info:**
+ - Manufacturer: [e.g. Dahua]
+ - Model: [e.g. IPC-HDW5231R-ZE]
+ - Resolution: [e.g. 720p]
+ - FPS: [e.g. 5]
+
+**Additional context**
+Add any other context about the problem here.
@@ -0,0 +1,4 @@
+*.pyc 
+debug
+.vscode
+config/config.yml
@@ -1,5 +0,0 @@
-/*!
-  Copyright (c) 2017 Jed Watson.
-  Licensed under the MIT License (MIT), see
-  http://jedwatson.github.io/classnames
-*/
@@ -1 +0,0 @@
-/*! algoliasearch-lite.umd.js | 4.8.4 | © Algolia, inc. | https://github.com/algolia/algoliasearch-client-javascript */
@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1,31 +0,0 @@
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-<link rel="alternate" type="application/rss+xml" href="/frigate/blog/rss.xml" title="Frigate Blog RSS Feed">
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-<nav aria-label="Skip navigation links"><button type="button" tabindex="0" class="skipToContent_11B0">Skip to main content</button></nav><nav class="navbar navbar--fixed-top"><div class="navbar__inner"><div class="navbar__items"><div aria-label="Navigation bar toggle" class="navbar__toggle" role="button" tabindex="0"><svg aria-label="Menu" width="30" height="30" viewBox="0 0 30 30" role="img" focusable="false"><title>Menu</title><path stroke="currentColor" stroke-linecap="round" stroke-miterlimit="10" stroke-width="2" d="M4 7h22M4 15h22M4 23h22"></path></svg></div><a class="navbar__brand" href="/frigate/"><img src="/frigate/img/logo.svg" alt="Frigate" class="themedImage_YANc themedImage--light_3CMI navbar__logo"><img src="/frigate/img/logo-dark.svg" alt="Frigate" class="themedImage_YANc themedImage--dark_3ARp navbar__logo"><strong class="navbar__title">Frigate</strong></a><a class="navbar__item navbar__link" href="/frigate/">Docs</a></div><div class="navbar__items navbar__items--right"><a href="https://github.com/blakeblackshear/frigate" target="_blank" rel="noopener noreferrer" class="navbar__item navbar__link">GitHub</a><div class="react-toggle react-toggle--disabled displayOnlyInLargeViewport_2N3Q"><div class="react-toggle-track"><div class="react-toggle-track-check"><span class="toggle_3NWk">🌜</span></div><div class="react-toggle-track-x"><span class="toggle_3NWk">🌞</span></div></div><div class="react-toggle-thumb"></div><input type="checkbox" disabled="" aria-label="Dark mode toggle" class="react-toggle-screenreader-only"></div><button type="button" class="DocSearch DocSearch-Button" aria-label="Search"><div class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></div></button></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div><div class="navbar-sidebar"><div class="navbar-sidebar__brand"><a class="navbar__brand" href="/frigate/"><img src="/frigate/img/logo.svg" alt="Frigate" class="themedImage_YANc themedImage--light_3CMI navbar__logo"><img src="/frigate/img/logo-dark.svg" alt="Frigate" class="themedImage_YANc themedImage--dark_3ARp navbar__logo"><strong class="navbar__title">Frigate</strong></a></div><div class="navbar-sidebar__items"><div class="menu"><ul class="menu__list"><li class="menu__list-item"><a class="menu__link" href="/frigate/">Docs</a></li><li class="menu__list-item"><a href="https://github.com/blakeblackshear/frigate" target="_blank" rel="noopener noreferrer" class="menu__link">GitHub</a></li></ul></div></div></div></nav><div class="main-wrapper"><main class="container margin-vert--xl"><div class="row"><div class="col col--6 col--offset-3"><h1 class="hero__title">Page Not Found</h1><p>We could not find what you were looking for.</p><p>Please contact the owner of the site that linked you to the original URL and let them know their link is broken.</p></div></div></main></div><footer class="footer footer--dark"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">Community</h4><ul class="footer__items"><li class="footer__item"><a href="https://github.com/blakeblackshear/frigate" target="_blank" rel="noopener noreferrer" class="footer__link-item">GitHub</a></li><li class="footer__item"><a href="https://github.com/blakeblackshear/frigate/discussions" target="_blank" rel="noopener noreferrer" class="footer__link-item">Discussions</a></li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright">Copyright © 2021 Blake Blackshear</div></div></div></footer></div>
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@@ -1 +0,0 @@
-docs.frigate.video
@@ -0,0 +1,63 @@
+FROM ubuntu:18.04
+LABEL maintainer "blakeb@blakeshome.com"
+
+ENV DEBIAN_FRONTEND=noninteractive
+# Install packages for apt repo
+RUN apt -qq update && apt -qq install --no-install-recommends -y \
+    software-properties-common \
+    # apt-transport-https ca-certificates \
+    build-essential \
+    gnupg wget unzip tzdata \
+    # libcap-dev \
+    && add-apt-repository ppa:deadsnakes/ppa -y \
+    && apt -qq install --no-install-recommends -y \
+        python3.7 \
+        python3.7-dev \
+        python3-pip \
+        ffmpeg \
+        # VAAPI drivers for Intel hardware accel
+        libva-drm2 libva2 i965-va-driver vainfo \
+    && python3.7 -m pip install -U pip \
+    && python3.7 -m pip install -U wheel setuptools \
+    && python3.7 -m pip install -U \
+        opencv-python-headless \
+        # python-prctl \
+        numpy \
+        imutils \
+        scipy \
+        psutil \
+    && python3.7 -m pip install -U \
+        Flask \
+        paho-mqtt \
+        PyYAML \
+        matplotlib \
+        pyarrow \
+        click \
+    && echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
+    && wget -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
+    && apt -qq update \
+    && echo "libedgetpu1-max libedgetpu/accepted-eula boolean true" | debconf-set-selections \
+    && apt -qq install --no-install-recommends -y \
+        libedgetpu1-max \
+    ## Tensorflow lite (python 3.7 only)
+    && wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
+    && python3.7 -m pip install tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
+    && rm tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
+    && rm -rf /var/lib/apt/lists/* \
+    && (apt-get autoremove -y; apt-get autoclean -y)
+
+# get model and labels
+RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite -O /edgetpu_model.tflite --trust-server-names
+COPY labelmap.txt /labelmap.txt
+RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite -O /cpu_model.tflite 
+
+
+RUN mkdir /cache /clips
+
+WORKDIR /opt/frigate/
+ADD frigate frigate/
+COPY detect_objects.py .
+COPY benchmark.py .
+COPY process_clip.py .
+
+CMD ["python3.7", "-u", "detect_objects.py"]
@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) 2020 Blake Blackshear
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+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.
@@ -0,0 +1,388 @@
+# Frigate - NVR With Realtime Object Detection for IP Cameras
+Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
+
+Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
+
+- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
+- Uses a very low overhead motion detection to determine where to run object detection
+- Object detection with Tensorflow runs in a separate process
+- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
+- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
+
+![Diagram](diagram.png)
+
+## 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")
+
+## Getting Started
+Run the container with
+```bash
+docker run --rm \
+-name blakeblackshear/frigate:stable \
+--privileged \
+--shm-size=512m \ # should work for a 2-3 cameras
+-v /dev/bus/usb:/dev/bus/usb \
+-v <path_to_config_dir>:/config:ro \
+-v /etc/localtime:/etc/localtime:ro \
+-p 5000:5000 \
+-e FRIGATE_RTSP_PASSWORD='password' \
+blakeblackshear/frigate:stable
+```
+
+Example docker-compose:
+```yaml
+  frigate:
+    container_name: frigate
+    restart: unless-stopped
+    privileged: true
+    shm_size: '1g' # should work for 5-7 cameras
+    image: blakeblackshear/frigate:stable
+    volumes:
+      - /dev/bus/usb:/dev/bus/usb
+      - /etc/localtime:/etc/localtime:ro
+      - <path_to_config>:/config
+      - <path_to_directory_for_clips>:/clips
+    ports:
+      - "5000:5000"
+    environment:
+      FRIGATE_RTSP_PASSWORD: "password"
+```
+
+A `config.yml` file must exist in the `config` directory. See example [here](config/config.example.yml) and device specific info can be found [here](docs/DEVICES.md).
+
+## Recommended Hardware
+|Name|Inference Speed|Notes|
+|----|---------------|-----|
+|Atomic Pi|16ms|Best option for a dedicated low power board with a small number of cameras.|
+|Intel NUC NUC7i3BNK|8-10ms|Best possible performance. Can handle 7+ cameras at 5fps depending on typical amounts of motion.|
+|BMAX B2 Plus|10-12ms|Good balance of performance and cost. Also capable of running many other services at the same time as frigate.|
+|Minisforum GK41|9-10ms|Great alternative to a NUC. Easily handiles 4 1080p cameras.|
+
+ARM boards are not officially supported at the moment due to some python dependencies that require modification to work on ARM devices. The Raspberry Pi4 gets about 16ms inference speeds, but the hardware acceleration for ffmpeg does not work for converting yuv420 to rgb24. The Atomic Pi is x86 and much more efficient.
+
+Users have reported varying success in getting frigate to run in a VM. In some cases, the virtualization layer introduces a significant delay in communication with the Coral. If running virtualized in Proxmox, pass the USB card/interface to the virtual machine not the USB ID for faster inference speed.
+
+## Integration with HomeAssistant
+
+Setup a camera, binary_sensor, sensor and optionally automation as shown for each camera you define in frigate. Replace <camera_name> with the camera name as defined in the frigate `config.yml` (The `frigate_coral_fps` and `frigate_coral_inference` sensors only need to be defined once)
+
+```
+camera:
+  - name: <camera_name> Last Person
+    platform: mqtt
+    topic: frigate/<camera_name>/person/snapshot
+  - name: <camera_name> Last Car
+    platform: mqtt
+    topic: frigate/<camera_name>/car/snapshot
+
+binary_sensor:
+  - name: <camera_name> Person
+    platform: mqtt
+    state_topic: "frigate/<camera_name>/person"
+    device_class: motion
+    availability_topic: "frigate/available"
+
+sensor:
+  - platform: rest
+    name: Frigate Debug
+    resource: http://localhost:5000/debug/stats
+    scan_interval: 5
+    json_attributes:
+      - <camera_name>
+      - coral
+    value_template: 'OK'  
+  - platform: template
+    sensors:
+      <camera_name>_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["fps"] }}'
+        unit_of_measurement: 'FPS'
+      <camera_name>_skipped_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["skipped_fps"] }}'
+        unit_of_measurement: 'FPS'
+      <camera_name>_detection_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["<camera_name>"]["detection_fps"] }}'
+        unit_of_measurement: 'FPS'
+      frigate_coral_fps: 
+        value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["fps"] }}'
+        unit_of_measurement: 'FPS'
+      frigate_coral_inference:
+        value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["inference_speed"] }}' 
+        unit_of_measurement: 'ms'
+        
+automation:
+  - alias: Alert me if a person is detected while armed away
+    trigger: 
+      platform: state
+      entity_id: binary_sensor.camera_person
+      from: 'off'
+      to: 'on'
+    condition:
+      - condition: state
+        entity_id: alarm_control_panel.home_alarm
+        state: armed_away
+    action:
+      - service: notify.user_telegram
+        data:
+          message: "A person was detected."
+          data:
+            photo:
+              - url: http://<ip>:5000/<camera_name>/person/best.jpg
+                caption: A person was detected.        
+```
+## 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
+
+### `/debug/stats`
+Contains some granular debug info that can be used for sensors in HomeAssistant. See details below.
+
+## MQTT Messages
+These are the MQTT messages generated by Frigate. The default topic_prefix is `frigate`, but can be changed in the config file.
+
+### frigate/available
+Designed to be used as an availability topic with HomeAssistant. Possible message are:
+"online": published when frigate is running (on startup)
+"offline": published right before frigate stops
+
+### frigate/<camera_name>/<object_name>
+Publishes `ON` or `OFF` and is designed to be used a as a binary sensor in HomeAssistant for whether or not that object type is detected.
+
+### frigate/<camera_name>/<object_name>/snapshot
+Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image
+is published again.
+
+The height and crop of snapshots can be configured as shown in the example config.
+
+### frigate/<camera_name>/events/start
+Message published at the start of any tracked object. JSON looks as follows:
+```json
+{
+    "label": "person",
+    "score": 0.87890625,
+    "box": [
+        95,
+        155,
+        581,
+        1182
+    ],
+    "area": 499122,
+    "region": [
+        0,
+        132,
+        1080,
+        1212
+    ],
+    "frame_time": 1600208805.60284,
+    "centroid": [
+        338,
+        668
+    ],
+    "id": "1600208805.60284-k1l43p",
+    "start_time": 1600208805.60284,
+    "top_score": 0.87890625,
+    "zones": [],
+    "score_history": [
+        0.87890625
+    ],
+    "computed_score": 0.0,
+    "false_positive": true
+}
+```
+
+### frigate/<camera_name>/events/end
+Same as `frigate/<camera_name>/events/start`, but with an `end_time` property as well.
+
+### frigate/<zone_name>/<object_name>
+Publishes `ON` or `OFF` and is designed to be used a as a binary sensor in HomeAssistant for whether or not that object type is detected in the zone.
+
+## Understanding min_score and threshold
+`min_score` defines the minimum score for Frigate to begin tracking a detected object. Any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores 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.
+
+## Using a custom model or labels
+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
+  ```
+
+## 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 90 seconds of video for each camera. The cache files are written to disk at /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 the /clips directory along with a json file containing the current information about the tracked object.
+
+### Global Configuration Options
+- `max_seconds`: This limits the size of the cache when an object is being tracked. If an object is stationary and being tracked for a long time, the cache files will expire and this value will be the maximum clip length for the *end* of the event. For example, if this is set to 300 seconds and an object is being tracked for 600 seconds, the clip will end up being the last 300 seconds. Defaults to 300 seconds.
+
+### Per-camera Configuration Options
+- `pre_capture`: Defines how much time should be included in the clip prior to the beginning of the event. Defaults to 30 seconds.
+- `objects`: List of object types to save clips for. Object types here must be listed for tracking at the camera or global configuration. Defaults to all tracked objects.
+
+## Google Coral Configuration
+Frigate attempts to detect your Coral device automatically. If you have multiple Coral devices or a version that is not detected automatically, you can specify using the `tensorflow_device` config option.
+
+## Masks and limiting detection to a certain area
+The mask works by looking at the bottom center of any bounding box (first image, red dot below) and comparing that to your mask. If that red dot falls on an area of your mask that is black, the detection (and motion) will be ignored. The mask in the second image would limit detection on this camera to only objects that are in the front yard and not the street. 
+
+<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>
+
+The following types of masks are supported:
+- `base64`: Base64 encoded image file
+- `poly`: List of x,y points like zone configuration
+- `image`: Path to an image file in the config directory
+
+`base64` and `image` masks must be the same aspect ratio as your camera. 
+
+## 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. See the sample config for details on how to configure.
+
+During testing, `draw_zones` can be set in the config to tell frigate to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
+
+![Zone Example](docs/zone_example.jpg)
+
+## Debug Info
+```jsonc
+{
+    /* Per Camera Stats */
+    "back": {
+        /***************
+        * Frames per second being consumed from your camera. If this is higher
+        * than it is supposed to be, you should set -r FPS in your input_args.
+        * camera_fps = process_fps + skipped_fps
+        ***************/
+        "camera_fps": 5.0,
+        /***************
+        * Number of times detection is run per second. This can be higher than
+        * your camera FPS because frigate often looks at the same frame multiple times
+        * or in multiple locations
+        ***************/
+        "detection_fps": 1.5,
+        /***************
+        * PID for the ffmpeg process that consumes this camera
+        ***************/
+        "ffmpeg_pid": 27,
+        /***************
+        * Timestamps of frames in various parts of processing
+        ***************/
+        "frame_info": {
+            /***************
+            * Timestamp of the frame frigate is running object detection on.
+            ***************/
+            "detect": 1596994991.91426,
+            /***************
+            * Timestamp of the frame frigate is processing detected objects on.
+            * This is where MQTT messages are sent, zones are checked, etc.
+            ***************/
+            "process": 1596994991.91426,
+            /***************
+            * Timestamp of the frame frigate last read from ffmpeg.
+            ***************/
+            "read": 1596994991.91426
+        },
+        /***************
+        * PID for the process that runs detection for this camera
+        ***************/
+        "pid": 34,
+        /***************
+        * Frames per second being processed by frigate.
+        ***************/
+        "process_fps": 5.1,
+        /***************
+        * Timestamp when the detection process started looking for a frame. If this value stays constant
+        * for a long time, that means there aren't any frames in the frame queue.
+        ***************/
+        "read_start": 1596994991.943814,
+        /***************
+        * Frames per second skip for processing by frigate.
+        ***************/
+        "skipped_fps": 0.0
+    },
+    /* Coral Stats */
+    "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,
+        /***************
+        * Frames per second of the Coral. This should be the sum of all detection_fps values from cameras.
+        ***************/
+        "fps": 6.9,
+        /***************
+        * 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
+    },
+    "plasma_store_rc": null // Return code for the plasma store. This should be null normally.
+}
+```
+
+## Tips
+- Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed. Not as effective, but you can also modify the `take_frame` [configuration](config/config.example.yml) for each camera to only analyze every other frame, or every third frame, etc. 
+- Hard code the resolution of each camera in your config if you are having difficulty starting frigate or if the initial ffprobe for camerea resolution fails or returns incorrect info. Example:
+```
+cameras:
+  back:
+    ffmpeg:
+      input: rtsp://<camera>
+    height: 1080
+    width: 1920
+```
+- Additional logging is available in the docker container - You can view the logs by running `docker logs -t frigate`
+- Object configuration - Tracked objects types, sizes and thresholds can be defined globally and/or on a per camera basis. The global and camera object configuration is *merged*. For example, if you defined tracking person, car, and truck globally but modified your backyard camera to only track person, the global config would merge making the effective list for the backyard camera still contain person, car and truck. If you want precise object tracking per camera, best practice to put a minimal list of objects at the global level and expand objects on a per camera basis. Object threshold and area configuration will be used first from the camera object config (if defined) and then from the global config.  See the [example config](config/config.example.yml) for more information.
+
+## 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
+```
+
@@ -0,0 +1,79 @@
+import os
+from statistics import mean
+import multiprocessing as mp
+import numpy as np
+import datetime
+from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
+
+my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
+labels = load_labels('/labelmap.txt')
+
+######
+# Minimal same process runner
+######
+# object_detector = ObjectDetector()
+# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
+
+# start = datetime.datetime.now().timestamp()
+
+# frame_times = []
+# for x in range(0, 1000):
+#   start_frame = datetime.datetime.now().timestamp()
+
+#   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)
+
+# 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")
+
+######
+# Separate process runner
+######
+def start(id, num_detections, detection_queue):
+  object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue)
+  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
+  print(f"{id} - Processed for {duration:.2f} seconds.")
+  print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
+
+edgetpu_process = EdgeTPUProcess()
+
+# start(1, 1000, edgetpu_process.detect_lock, edgetpu_process.detect_ready, edgetpu_process.frame_ready)
+
+####
+# Multiple camera processes
+####
+camera_processes = []
+for x in range(0, 10):
+  camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue))
+  camera_process.daemon = True
+  camera_processes.append(camera_process)
+
+start = datetime.datetime.now().timestamp()
+
+for p in camera_processes:
+  p.start()
+
+for p in camera_processes:
+  p.join()
+
+duration = datetime.datetime.now().timestamp()-start
+print(f"Total - Processed for {duration:.2f} seconds.")
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+web_port: 5000
+
+################
+## Tell frigate to look for a specific EdgeTPU device. Useful if you want to run multiple instances of frigate
+## on the same machine with multiple EdgeTPUs. https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
+################
+tensorflow_device: usb
+
+mqtt:
+  host: mqtt.server.com
+  topic_prefix: frigate
+  # client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
+  # user: username # Optional
+  #################
+  ## Environment variables that begin with 'FRIGATE_' may be referenced in {}.
+  ##   password: '{FRIGATE_MQTT_PASSWORD}'
+  #################
+  # password: password # Optional
+
+################
+# Global configuration for saving clips
+################
+save_clips:
+  ###########
+  # Maximum length of time to retain video during long events.
+  # 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
+
+#################
+# Default ffmpeg args. Optional and can be overwritten per camera.
+# Should work with most RTSP cameras that send h264 video
+# Built from the properties below with:
+# "ffmpeg" + global_args + input_args + "-i" + input + output_args
+#################
+# ffmpeg:
+#   global_args:
+#     - -hide_banner
+#     - -loglevel
+#     - panic
+#   hwaccel_args: []
+#   input_args:
+#     - -avoid_negative_ts
+#     - make_zero
+#     - -fflags
+#     - nobuffer
+#     - -flags
+#     - low_delay
+#     - -strict
+#     - experimental
+#     - -fflags
+#     - +genpts+discardcorrupt
+#     - -vsync
+#     - drop
+#     - -rtsp_transport
+#     - tcp
+#     - -stimeout
+#     - '5000000'
+#     - -use_wallclock_as_timestamps
+#     - '1'
+#   output_args:
+#     - -f
+#     - rawvideo
+#     - -pix_fmt
+#     - rgb24
+
+####################
+# Global object configuration. Applies to all cameras
+# unless overridden at the camera levels.
+# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
+# All labels from the model are reported over MQTT. These values are used to filter out false positives.
+# min_area (optional): minimum width*height of the bounding box for the detected object
+# max_area (optional): maximum width*height of the bounding box for the detected object
+# min_score (optional): minimum score for the object to initiate tracking
+# threshold (optional): The minimum decimal percentage for tracked object's computed score to considered a true positive
+####################
+objects:
+  track:
+    - person
+  filters:
+    person:
+      min_area: 5000
+      max_area: 100000
+      min_score: 0.5
+      threshold: 0.85
+
+cameras:
+  back:
+    ffmpeg:
+      ################
+      # Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
+      # Environment variables that begin with 'FRIGATE_' may be referenced in {}
+      ################
+      input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+      #################
+      # These values will override default values for just this camera
+      #################
+      # global_args: []
+      # hwaccel_args: []
+      # input_args: []
+      # output_args: []
+    
+    ################
+    ## Optionally specify the resolution of the video feed. Frigate will try to auto detect if not specified
+    ################
+    # height: 1280
+    # width: 720
+
+    ################
+    ## Specify the framerate of your camera
+    ##
+    ## NOTE: This should only be set in the event ffmpeg is unable to determine your camera's framerate
+    ##       on its own and the reported framerate for your camera in frigate is well over what is expected.
+    ################
+    # fps: 5
+
+    ################
+    ## Optional mask. Must be the same aspect ratio as your video feed. Value is any of the following:
+    ##   - name of a file in the config directory
+    ##   - base64 encoded image prefixed with 'base64,' eg. 'base64,asfasdfasdf....'
+    ##   - polygon of x,y coordinates prefixed with 'poly,' eg. 'poly,0,900,1080,900,1080,1920,0,1920'
+    ## 
+    ## The mask works by looking at the bottom center of the bounding box for the detected
+    ## person in the image. If that pixel in the mask is a black pixel, it ignores it as a
+    ## false positive. In my mask, the grass and driveway visible from my backdoor camera 
+    ## are white. The garage doors, sky, and trees (anywhere it would be impossible for a 
+    ## person to stand) are black.
+    ## 
+    ## Masked areas are also ignored for motion detection.
+    ################
+    # mask: back-mask.bmp
+
+    ################
+    # Allows you to limit the framerate within frigate for cameras that do not support
+    # custom framerates. A value of 1 tells frigate to look at every frame, 2 every 2nd frame, 
+    # 3 every 3rd frame, etc.
+    ################
+    take_frame: 1
+
+    ################
+    # The number of seconds to retain the highest scoring image for the best.jpg endpoint before allowing it
+    # to be replaced by a newer image. Defaults to 60 seconds.
+    ################
+    best_image_timeout: 60
+
+    ################
+    # MQTT settings
+    ################
+    # mqtt:
+    #   crop_to_region: True
+    #   snapshot_height: 300
+
+    ################
+    # Zones
+    ################
+    zones:
+      #################
+      # Name of the zone
+      ################
+      front_steps:
+        ####################
+        # A list of x,y coordinates to define the polygon of the zone. The top 
+        # left corner is 0,0. Can also be a comma separated string of all x,y coordinates combined.
+        # The same zone name can exist across multiple cameras if they have overlapping FOVs.
+        # An object is determined to be in the zone based on whether or not the bottom center
+        # of it's bounding box is within the polygon. The polygon must have at least 3 points.
+        # Coordinates can be generated at https://www.image-map.net/
+        ####################
+        coordinates:
+          - 545,1077
+          - 747,939
+          - 788,805
+        ################
+        # Zone level object filters. These are applied in addition to the global and camera filters
+        # and should be more restrictive than the global and camera filters. The global and camera
+        # filters are applied upstream.
+        ################
+        filters:
+          person:
+            min_area: 5000
+            max_area: 100000
+            threshold: 0.8
+
+    ################
+    # This will save a clip for each tracked object by frigate along with a json file that contains
+    # data related to the tracked object. This works by telling ffmpeg to write video segments to /cache
+    # from the video stream without re-encoding. Clips are then created by using ffmpeg to merge segments
+    # without re-encoding. The segments saved are unaltered from what frigate receives to avoid re-encoding.
+    # They do not contain bounding boxes. These are optimized to capture "false_positive" examples for improving frigate.
+    #
+    # NOTE: This feature does not work if you have "-vsync drop" configured 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:
+      enabled: False
+      #########
+      # Number of seconds before the event to include in the clips
+      #########
+      pre_capture: 30
+      #########
+      # Objects to save clips for. Defaults to all tracked object types.
+      #########
+      # objects:
+      #   - person      
+
+    ################
+    # Configuration for the snapshots in the debug view and mqtt
+    ################
+    snapshots:
+      show_timestamp: True
+      draw_zones: False
+
+    ################
+    # Camera level object 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
@@ -1,48 +0,0 @@
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-performance of video decoding. In order to use NVDEC, a special build of
-ffmpeg with NVDEC support is required. The special docker architecture &#x27;amd64nvidia&#x27;
-includes this support for amd64 platforms. An aarch64 for the Jetson, which
-also includes NVDEC may be added in the future.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor enhancedAnchor_prK2" id="docker-setup"></a>Docker setup<a class="hash-link" href="#docker-setup" title="Direct link to heading">#</a></h2><h3><a aria-hidden="true" tabindex="-1" class="anchor enhancedAnchor_prK2" id="requirements"></a>Requirements<a class="hash-link" href="#requirements" title="Direct link to heading">#</a></h3><p><a href="https://www.nvidia.com/en-us/drivers/unix/" target="_blank" rel="noopener noreferrer">nVidia closed source driver</a> required to access NVDEC.
-<a href="https://github.com/NVIDIA/nvidia-docker" target="_blank" rel="noopener noreferrer">nvidia-docker</a> required to pass NVDEC to docker.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor enhancedAnchor_prK2" id="setting-up-docker-compose"></a>Setting up docker-compose<a class="hash-link" href="#setting-up-docker-compose" title="Direct link to heading">#</a></h3><p>In order to pass NVDEC, the docker engine must be set to <code>nvidia</code> and the environment variables
-<code>NVIDIA_VISIBLE_DEVICES=all</code> and <code>NVIDIA_DRIVER_CAPABILITIES=compute,utility,video</code> must be set.</p><p>In a docker compose file, these lines need to be set:</p><div class="mdxCodeBlock_1zKU"><div class="codeBlockContent_actS"><div tabindex="0" class="prism-code language-undefined codeBlock_tuNs thin-scrollbar"><div class="codeBlockLines_3uvA" style="color:#bfc7d5;background-color:#292d3e"><div class="token-line" style="color:#bfc7d5"><span class="token plain">services:</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">  frigate:</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    ...</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    image: blakeblackshear/frigate:stable-amd64nvidia</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    runtime: nvidia</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    environment:</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">      - NVIDIA_VISIBLE_DEVICES=all</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video</span></div></div></div><button type="button" aria-label="Copy code to clipboard" class="copyButton_2GIj">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor enhancedAnchor_prK2" id="setting-up-the-configuration-file"></a>Setting up the configuration file<a class="hash-link" href="#setting-up-the-configuration-file" title="Direct link to heading">#</a></h3><p>In your frigate config.yml, you&#x27;ll need to set ffmpeg to use the hardware decoder.
-The decoder you choose will depend on the input video.</p><p>A list of supported codecs (you can use <code>ffmpeg -decoders | grep cuvid</code> in the container to get a list)</p><div class="mdxCodeBlock_1zKU"><div class="codeBlockContent_actS"><div tabindex="0" class="prism-code language-undefined codeBlock_tuNs thin-scrollbar"><div class="codeBlockLines_3uvA" style="color:#bfc7d5;background-color:#292d3e"><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain"> V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)</span></div></div></div><button type="button" aria-label="Copy code to clipboard" class="copyButton_2GIj">Copy</button></div></div><p>For example, for H265 video (hevc), you&#x27;ll select <code>hevc_cuvid</code>. Add
-<code>-c:v hevc_cuvid</code> to your ffmpeg input arguments:</p><div class="mdxCodeBlock_1zKU"><div class="codeBlockContent_actS"><div tabindex="0" class="prism-code language-undefined codeBlock_tuNs thin-scrollbar"><div class="codeBlockLines_3uvA" style="color:#bfc7d5;background-color:#292d3e"><div class="token-line" style="color:#bfc7d5"><span class="token plain">ffmpeg:</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">  input_args:</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    ...</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    - -c:v</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    - hevc_cuvid</span></div></div></div><button type="button" aria-label="Copy code to clipboard" class="copyButton_2GIj">Copy</button></div></div><p>If everything is working correctly, you should see a significant improvement in performance.
-Verify that hardware decoding is working by running <code>nvidia-smi</code>, which should show the ffmpeg
-processes:</p><div class="mdxCodeBlock_1zKU"><div class="codeBlockContent_actS"><div tabindex="0" class="prism-code language-undefined codeBlock_tuNs thin-scrollbar"><div class="codeBlockLines_3uvA" style="color:#bfc7d5;background-color:#292d3e"><div class="token-line" style="color:#bfc7d5"><span class="token plain">+-----------------------------------------------------------------------------+</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">| NVIDIA-SMI 455.38       Driver Version: 455.38       CUDA Version: 11.1     |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|-------------------------------+----------------------+----------------------+</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|                               |                      |               MIG M. |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|===============================+======================+======================|</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|   0  GeForce GTX 166...  Off  | 00000000:03:00.0 Off |                  N/A |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">| 38%   41C    P2    36W / 125W |   2082MiB /  5942MiB |      5%      Default |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|                               |                      |                  N/A |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">+-------------------------------+----------------------+----------------------+</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain" style="display:inline-block">
-</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">+-----------------------------------------------------------------------------+</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">| Processes:                                                                  |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|        ID   ID                                                   Usage      |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|=============================================================================|</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12737      C   ffmpeg                            249MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12751      C   ffmpeg                            249MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12772      C   ffmpeg                            249MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12775      C   ffmpeg                            249MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12800      C   ffmpeg                            249MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12811      C   ffmpeg                            417MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">|    0   N/A  N/A     12827      C   ffmpeg                            417MiB |</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">+-----------------------------------------------------------------------------+</span></div></div></div><button type="button" aria-label="Copy code to clipboard" class="copyButton_2GIj">Copy</button></div></div><p>To further improve performance, you can set ffmpeg to skip frames in the output,
-using the fps filter:</p><div class="mdxCodeBlock_1zKU"><div class="codeBlockContent_actS"><div tabindex="0" class="prism-code language-undefined codeBlock_tuNs thin-scrollbar"><div class="codeBlockLines_3uvA" style="color:#bfc7d5;background-color:#292d3e"><div class="token-line" style="color:#bfc7d5"><span class="token plain">  output_args:</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    - -filter:v</span></div><div class="token-line" style="color:#bfc7d5"><span class="token plain">    - fps=fps=5</span></div></div></div><button type="button" aria-label="Copy code to clipboard" class="copyButton_2GIj">Copy</button></div></div><p>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.</p></div></article><div class="margin-vert--xl"><div class="row"><div class="col"><a href="https://github.com/blakeblackshear/frigate/edit/master/docs/docs/configuration/nvdec.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="1.2em" width="1.2em" preserveAspectRatio="xMidYMid meet" role="img" viewBox="0 0 40 40" class="iconEdit_2LL7"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div></div></div><div class="margin-vert--lg"><nav class="pagination-nav" aria-label="Blog list page navigation"><div class="pagination-nav__item"></div><div class="pagination-nav__item pagination-nav__item--next"></div></nav></div></div></div><div class="col col--3"><div class="tableOfContents_2xL- thin-scrollbar"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#docker-setup" class="table-of-contents__link">Docker setup</a><ul><li><a href="#requirements" class="table-of-contents__link">Requirements</a></li><li><a href="#setting-up-docker-compose" class="table-of-contents__link">Setting up docker-compose</a></li><li><a href="#setting-up-the-configuration-file" class="table-of-contents__link">Setting up the configuration file</a></li></ul></li></ul></div></div></div></div></main></div></div><footer class="footer footer--dark"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">Community</h4><ul class="footer__items"><li class="footer__item"><a href="https://github.com/blakeblackshear/frigate" target="_blank" rel="noopener noreferrer" class="footer__link-item">GitHub</a></li><li class="footer__item"><a href="https://github.com/blakeblackshear/frigate/discussions" target="_blank" rel="noopener noreferrer" class="footer__link-item">Discussions</a></li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright">Copyright © 2021 Blake Blackshear</div></div></div></footer></div>
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@@ -0,0 +1,455 @@
+import os
+import signal
+import sys
+import traceback
+import signal
+import cv2
+import time
+import datetime
+import queue
+import yaml
+import threading
+import multiprocessing as mp
+import subprocess as sp
+import numpy as np
+import logging
+from flask import Flask, Response, make_response, jsonify, request
+import paho.mqtt.client as mqtt
+
+from frigate.video import track_camera, get_ffmpeg_input, get_frame_shape, CameraCapture, start_or_restart_ffmpeg
+from frigate.object_processing import TrackedObjectProcessor
+from frigate.events import EventProcessor
+from frigate.util import EventsPerSecond
+from frigate.edgetpu import EdgeTPUProcess
+
+FRIGATE_VARS = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
+
+with open('/config/config.yml') as f:
+    CONFIG = yaml.safe_load(f)
+
+MQTT_HOST = CONFIG['mqtt']['host']
+MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
+MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
+MQTT_USER = CONFIG.get('mqtt', {}).get('user')
+MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
+if not MQTT_PASS is None:
+    MQTT_PASS = MQTT_PASS.format(**FRIGATE_VARS)
+MQTT_CLIENT_ID = CONFIG.get('mqtt', {}).get('client_id', 'frigate')
+
+# Set the default FFmpeg config
+FFMPEG_CONFIG = CONFIG.get('ffmpeg', {})
+FFMPEG_DEFAULT_CONFIG = {
+    'global_args': FFMPEG_CONFIG.get('global_args', 
+        ['-hide_banner','-loglevel','panic']),
+    'hwaccel_args': FFMPEG_CONFIG.get('hwaccel_args',
+        []),
+    'input_args': FFMPEG_CONFIG.get('input_args',
+        ['-avoid_negative_ts', 'make_zero',
+         '-fflags', 'nobuffer',
+         '-flags', 'low_delay',
+         '-strict', 'experimental',
+         '-fflags', '+genpts+discardcorrupt',
+         '-rtsp_transport', 'tcp',
+         '-stimeout', '5000000',
+         '-use_wallclock_as_timestamps', '1']),
+    'output_args': FFMPEG_CONFIG.get('output_args',
+        ['-f', 'rawvideo',
+         '-pix_fmt', 'rgb24'])
+}
+
+GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})
+
+WEB_PORT = CONFIG.get('web_port', 5000)
+DEBUG = (CONFIG.get('debug', '0') == '1')
+TENSORFLOW_DEVICE = CONFIG.get('tensorflow_device')
+
+def start_plasma_store():
+    plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
+    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
+    time.sleep(1)
+    rc = plasma_process.poll()
+    if rc is not None:
+        return None
+    return plasma_process
+
+class CameraWatchdog(threading.Thread):
+    def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, plasma_process, stop_event):
+        threading.Thread.__init__(self)
+        self.camera_processes = camera_processes
+        self.config = config
+        self.tflite_process = tflite_process
+        self.tracked_objects_queue = tracked_objects_queue
+        self.plasma_process = plasma_process
+        self.stop_event = stop_event
+
+    def run(self):
+        time.sleep(10)
+        while True:
+            # wait a bit before checking
+            time.sleep(10)
+
+            if self.stop_event.is_set():
+                print(f"Exiting watchdog...")
+                break
+
+            now = datetime.datetime.now().timestamp()
+            
+            # check the plasma process
+            rc = self.plasma_process.poll()
+            if rc != None:
+                print(f"plasma_process exited unexpectedly with {rc}")
+                self.plasma_process = start_plasma_store()
+
+            # check the detection process
+            detection_start = self.tflite_process.detection_start.value
+            if (detection_start > 0.0 and 
+                now - detection_start > 10):
+                print("Detection appears to be stuck. Restarting detection process")
+                self.tflite_process.start_or_restart()
+            elif not self.tflite_process.detect_process.is_alive():
+                print("Detection appears to have stopped. Restarting detection process")
+                self.tflite_process.start_or_restart()
+
+            # check the camera processes
+            for name, camera_process in self.camera_processes.items():
+                process = camera_process['process']
+                if not process.is_alive():
+                    print(f"Track process for {name} is not alive. Starting again...")
+                    camera_process['process_fps'].value = 0.0
+                    camera_process['detection_fps'].value = 0.0
+                    camera_process['read_start'].value = 0.0
+                    process = mp.Process(target=track_camera, args=(name, self.config[name], camera_process['frame_queue'],
+                        camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue, 
+                        camera_process['process_fps'], camera_process['detection_fps'],
+                        camera_process['read_start'], camera_process['detection_frame'], self.stop_event))
+                    process.daemon = True
+                    camera_process['process'] = process
+                    process.start()
+                    print(f"Track process started for {name}: {process.pid}")
+                
+                if not camera_process['capture_thread'].is_alive():
+                    frame_shape = camera_process['frame_shape']
+                    frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
+                    ffmpeg_process = start_or_restart_ffmpeg(camera_process['ffmpeg_cmd'], frame_size)
+                    camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, camera_process['frame_queue'], 
+                        camera_process['take_frame'], camera_process['camera_fps'], camera_process['detection_frame'], self.stop_event)
+                    camera_capture.start()
+                    camera_process['ffmpeg_process'] = ffmpeg_process
+                    camera_process['capture_thread'] = camera_capture
+                elif now - camera_process['capture_thread'].current_frame.value > 5:
+                    print(f"No frames received from {name} in 5 seconds. Exiting ffmpeg...")
+                    ffmpeg_process = camera_process['ffmpeg_process']
+                    ffmpeg_process.terminate()
+                    try:
+                        print("Waiting for ffmpeg to exit gracefully...")
+                        ffmpeg_process.communicate(timeout=30)
+                    except sp.TimeoutExpired:
+                        print("FFmpeg didnt exit. Force killing...")
+                        ffmpeg_process.kill()
+                        ffmpeg_process.communicate()
+
+def main():
+    stop_event = threading.Event()
+    # connect to mqtt and setup last will
+    def on_connect(client, userdata, flags, rc):
+        print("On connect called")
+        if rc != 0:
+            if rc == 3:
+                print ("MQTT Server unavailable")
+            elif rc == 4:
+                print ("MQTT Bad username or password")
+            elif rc == 5:
+                print ("MQTT Not authorized")
+            else:
+                print ("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
+        # publish a message to signal that the service is running
+        client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
+    client = mqtt.Client(client_id=MQTT_CLIENT_ID)
+    client.on_connect = on_connect
+    client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
+    if not MQTT_USER is None:
+        client.username_pw_set(MQTT_USER, password=MQTT_PASS)
+    client.connect(MQTT_HOST, MQTT_PORT, 60)
+    client.loop_start()
+
+    plasma_process = start_plasma_store()
+
+    ##
+    # Setup config defaults for cameras
+    ##
+    for name, config in CONFIG['cameras'].items():
+        config['snapshots'] = {
+            'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True),
+            'draw_zones': config.get('snapshots', {}).get('draw_zones', False)
+        }
+        config['zones'] = config.get('zones', {})
+
+    # Queue for cameras to push tracked objects to
+    tracked_objects_queue = mp.Queue()
+
+    # Queue for clip processing
+    event_queue = mp.Queue()
+    
+    # Start the shared tflite process
+    tflite_process = EdgeTPUProcess(TENSORFLOW_DEVICE)
+
+    # start the camera processes
+    camera_processes = {}
+    for name, config in CONFIG['cameras'].items():
+        # Merge the ffmpeg config with the global config
+        ffmpeg = config.get('ffmpeg', {})
+        ffmpeg_input = get_ffmpeg_input(ffmpeg['input'])
+        ffmpeg_global_args = ffmpeg.get('global_args', FFMPEG_DEFAULT_CONFIG['global_args'])
+        ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', FFMPEG_DEFAULT_CONFIG['hwaccel_args'])
+        ffmpeg_input_args = ffmpeg.get('input_args', FFMPEG_DEFAULT_CONFIG['input_args'])
+        ffmpeg_output_args = ffmpeg.get('output_args', FFMPEG_DEFAULT_CONFIG['output_args'])
+        if not config.get('fps') is None:
+            ffmpeg_output_args = ["-r", str(config.get('fps'))] + ffmpeg_output_args
+        if config.get('save_clips', {}).get('enabled', False):
+            ffmpeg_output_args = [
+                "-f",
+                "segment",
+                "-segment_time",
+                "10",
+                "-segment_format",
+                "mp4",
+                "-reset_timestamps",
+                "1",
+                "-strftime",
+                "1",
+                "-c",
+                "copy",
+                "-an",
+                "-map",
+                "0",
+                f"/cache/{name}-%Y%m%d%H%M%S.mp4"
+            ] + ffmpeg_output_args
+        ffmpeg_cmd = (['ffmpeg'] +
+                ffmpeg_global_args +
+                ffmpeg_hwaccel_args +
+                ffmpeg_input_args +
+                ['-i', ffmpeg_input] +
+                ffmpeg_output_args +
+                ['pipe:'])
+        
+        if 'width' in config and 'height' in config:
+            frame_shape = (config['height'], config['width'], 3)
+        else:
+            frame_shape = get_frame_shape(ffmpeg_input)
+
+        frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
+        take_frame = config.get('take_frame', 1)
+
+        detection_frame = mp.Value('d', 0.0)
+
+        ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size)
+        frame_queue = mp.Queue()
+        camera_fps = EventsPerSecond()
+        camera_fps.start()
+        camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, detection_frame, stop_event)
+        camera_capture.start()
+
+        camera_processes[name] = {
+            'camera_fps': camera_fps,
+            'take_frame': take_frame,
+            'process_fps': mp.Value('d', 0.0),
+            'detection_fps': mp.Value('d', 0.0),
+            'detection_frame': detection_frame,
+            'read_start': mp.Value('d', 0.0),
+            'ffmpeg_process': ffmpeg_process,
+            'ffmpeg_cmd': ffmpeg_cmd,
+            'frame_queue': frame_queue,
+            'frame_shape': frame_shape,
+            'capture_thread': camera_capture
+        }
+
+        # merge global object config into camera object config
+        camera_objects_config = config.get('objects', {})
+        # get objects to track for camera
+        objects_to_track = camera_objects_config.get('track', GLOBAL_OBJECT_CONFIG.get('track', ['person']))
+        # get object filters
+        object_filters = camera_objects_config.get('filters', GLOBAL_OBJECT_CONFIG.get('filters', {}))
+        config['objects'] = {
+            'track': objects_to_track,
+            'filters': object_filters
+        }
+
+        camera_process = mp.Process(target=track_camera, args=(name, config, frame_queue, frame_shape,
+            tflite_process.detection_queue, tracked_objects_queue, camera_processes[name]['process_fps'], 
+            camera_processes[name]['detection_fps'], 
+            camera_processes[name]['read_start'], camera_processes[name]['detection_frame'], stop_event))
+        camera_process.daemon = True
+        camera_processes[name]['process'] = camera_process
+
+    for name, camera_process in camera_processes.items():
+        camera_process['process'].start()
+        print(f"Camera_process started for {name}: {camera_process['process'].pid}")
+
+    event_processor = EventProcessor(CONFIG, camera_processes, '/cache', '/clips', event_queue, stop_event)
+    event_processor.start()
+    
+    object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
+    object_processor.start()
+    
+    camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process, stop_event)
+    camera_watchdog.start()
+
+    def receiveSignal(signalNumber, frame):
+        print('Received:', signalNumber)
+        stop_event.set()
+        event_processor.join()
+        object_processor.join()
+        camera_watchdog.join()
+        for name, camera_process in camera_processes.items():
+            camera_process['capture_thread'].join()
+        rc = camera_watchdog.plasma_process.poll()
+        if rc == None:
+            camera_watchdog.plasma_process.terminate()
+        sys.exit()
+    
+    signal.signal(signal.SIGTERM, receiveSignal)
+    signal.signal(signal.SIGINT, receiveSignal)
+
+    # create a flask app that encodes frames a mjpeg on demand
+    app = Flask(__name__)
+    log = logging.getLogger('werkzeug')
+    log.setLevel(logging.ERROR)
+
+    @app.route('/')
+    def ishealthy():
+        # return a healh
+        return "Frigate is running. Alive and healthy!"
+
+    @app.route('/debug/stack')
+    def processor_stack():
+        frame = sys._current_frames().get(object_processor.ident, None)
+        if frame:
+            return "<br>".join(traceback.format_stack(frame)), 200
+        else:
+            return "no frame found", 200
+
+    @app.route('/debug/print_stack')
+    def print_stack():
+        pid = int(request.args.get('pid', 0))
+        if pid == 0:
+            return "missing pid", 200
+        else:
+            os.kill(pid, signal.SIGUSR1)
+            return "check logs", 200
+
+    @app.route('/debug/stats')
+    def stats():
+        stats = {}
+
+        total_detection_fps = 0
+
+        for name, camera_stats in camera_processes.items():
+            total_detection_fps += camera_stats['detection_fps'].value
+            capture_thread = camera_stats['capture_thread']
+            stats[name] = {
+                'camera_fps': round(capture_thread.fps.eps(), 2),
+                'process_fps': round(camera_stats['process_fps'].value, 2),
+                'skipped_fps': round(capture_thread.skipped_fps.eps(), 2),
+                'detection_fps': round(camera_stats['detection_fps'].value, 2),
+                'read_start': camera_stats['read_start'].value,
+                'pid': camera_stats['process'].pid,
+                'ffmpeg_pid': camera_stats['ffmpeg_process'].pid,
+                'frame_info': {
+                    'read': capture_thread.current_frame.value,
+                    'detect': camera_stats['detection_frame'].value,
+                    'process': object_processor.camera_data[name]['current_frame_time']
+                }
+            }
+        
+        stats['coral'] = {
+            'fps': round(total_detection_fps, 2),
+            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
+            'detection_start': tflite_process.detection_start.value,
+            'pid': tflite_process.detect_process.pid
+        }
+
+        rc = camera_watchdog.plasma_process.poll()
+        stats['plasma_store_rc'] = rc
+
+        return jsonify(stats)
+
+    @app.route('/<camera_name>/<label>/best.jpg')
+    def best(camera_name, label):
+        if camera_name in CONFIG['cameras']:
+            best_object = object_processor.get_best(camera_name, label)
+            best_frame = best_object.get('frame', np.zeros((720,1280,3), np.uint8))
+            
+            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)
+            best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
+            ret, jpg = cv2.imencode('.jpg', best_frame)
+            response = make_response(jpg.tobytes())
+            response.headers['Content-Type'] = 'image/jpg'
+            return response
+        else:
+            return "Camera named {} not found".format(camera_name), 404
+
+    @app.route('/<camera_name>')
+    def mjpeg_feed(camera_name):
+        fps = int(request.args.get('fps', '3'))
+        height = int(request.args.get('h', '360'))
+        if camera_name in CONFIG['cameras']:
+            # return a multipart response
+            return Response(imagestream(camera_name, fps, height),
+                            mimetype='multipart/x-mixed-replace; boundary=frame')
+        else:
+            return "Camera named {} not found".format(camera_name), 404
+    
+    @app.route('/<camera_name>/latest.jpg')
+    def latest_frame(camera_name):
+        if camera_name in CONFIG['cameras']:
+            # max out at specified FPS
+            frame = object_processor.get_current_frame(camera_name)
+            if frame is None:
+                frame = np.zeros((720,1280,3), np.uint8)
+
+            height = int(request.args.get('h', str(frame.shape[0])))
+            width = int(height*frame.shape[1]/frame.shape[0])
+
+            frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+            ret, jpg = cv2.imencode('.jpg', frame)
+            response = make_response(jpg.tobytes())
+            response.headers['Content-Type'] = 'image/jpg'
+            return response
+        else:
+            return "Camera named {} not found".format(camera_name), 404
+            
+    def imagestream(camera_name, fps, height):
+        while True:
+            # max out at specified FPS
+            time.sleep(1/fps)
+            frame = object_processor.get_current_frame(camera_name)
+            if frame is None:
+                frame = np.zeros((height,int(height*16/9),3), np.uint8)
+
+            width = int(height*frame.shape[1]/frame.shape[0])
+
+            frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
+            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+            ret, jpg = cv2.imencode('.jpg', frame)
+            yield (b'--frame\r\n'
+                b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
+
+    app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
+
+    object_processor.join()
+    
+    plasma_process.terminate()
+
+if __name__ == '__main__':
+    main()
@@ -0,0 +1,74 @@
+# Configuration Examples
+
+### Default (most RTSP cameras)
+This is the default ffmpeg command and should work with most RTSP cameras that send h264 video
+```yaml
+ffmpeg:
+  global_args:
+    - -hide_banner
+    - -loglevel
+    - panic
+  hwaccel_args: []
+  input_args:
+    - -avoid_negative_ts
+    - make_zero 
+    - -fflags
+    - nobuffer
+    - -flags
+    - low_delay
+    - -strict
+    - experimental
+    - -fflags
+    - +genpts+discardcorrupt
+    - -vsync
+    - drop
+    - -rtsp_transport
+    - tcp
+    - -stimeout
+    - '5000000' 
+    - -use_wallclock_as_timestamps
+    - '1'
+  output_args:
+    - -vf
+    - mpdecimate
+    - -f
+    - rawvideo
+    - -pix_fmt
+    - rgb24
+```
+
+### RTMP Cameras
+The input parameters need to be adjusted for RTMP cameras
+```yaml
+ffmpeg:
+input_args:
+    - -avoid_negative_ts
+    - make_zero
+    - -fflags
+    - nobuffer
+    - -flags
+    - low_delay
+    - -strict
+    - experimental
+    - -fflags
+    - +genpts+discardcorrupt
+    - -vsync
+    - drop
+    - -use_wallclock_as_timestamps
+    - '1'
+```
+
+
+### Hardware Acceleration
+
+Intel Quicksync
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -hwaccel
+    - vaapi
+    - -hwaccel_device
+    - /dev/dri/renderD128
+    - -hwaccel_output_format
+    - yuv420p
+```
@@ -0,0 +1,182 @@
+import os
+import datetime
+import hashlib
+import multiprocessing as mp
+from abc import ABC, abstractmethod
+import numpy as np
+import pyarrow.plasma as plasma
+import tflite_runtime.interpreter as tflite
+from tflite_runtime.interpreter import load_delegate
+from frigate.util import EventsPerSecond, listen
+
+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
+        try:
+            print(f"Attempting to load TPU as {device_config['device']}")
+            edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
+            print("TPU found")
+        except ValueError:
+            try:
+                print(f"Attempting to load TPU as pci:0")
+                edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', {"device": "pci:0"})
+                print("PCIe TPU found")
+            except ValueError:
+                print("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(detection_queue, avg_speed, start, tf_device):
+    print(f"Starting detection process: {os.getpid()}")
+    listen()
+    plasma_client = plasma.connect("/tmp/plasma")
+    object_detector = LocalObjectDetector(tf_device=tf_device)
+
+    while True:
+        object_id_str = detection_queue.get()
+        object_id_hash = hashlib.sha1(str.encode(object_id_str))
+        object_id = plasma.ObjectID(object_id_hash.digest())
+        object_id_out = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{object_id_str}")).digest())
+        input_frame = plasma_client.get(object_id, timeout_ms=0)
+
+        if input_frame is plasma.ObjectNotAvailable:
+            continue
+
+        # detect and put the output in the plasma store
+        start.value = datetime.datetime.now().timestamp()
+        plasma_client.put(object_detector.detect_raw(input_frame), object_id_out)
+        duration = datetime.datetime.now().timestamp()-start.value
+        start.value = 0.0
+
+        avg_speed.value = (avg_speed.value*9 + duration)/10
+        
+class EdgeTPUProcess():
+    def __init__(self, tf_device=None):
+        self.detection_queue = mp.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 start_or_restart(self):
+        self.detection_start.value = 0.0
+        if (not self.detect_process is None) and self.detect_process.is_alive():
+            self.detect_process.terminate()
+            print("Waiting for detection process to exit gracefully...")
+            self.detect_process.join(timeout=30)
+            if self.detect_process.exitcode is None:
+                print("Detection process didnt exit. Force killing...")
+                self.detect_process.kill()
+                self.detect_process.join()
+        self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.avg_inference_speed, self.detection_start, self.tf_device))
+        self.detect_process.daemon = True
+        self.detect_process.start()
+
+class RemoteObjectDetector():
+    def __init__(self, name, labels, detection_queue):
+        self.labels = load_labels(labels)
+        self.name = name
+        self.fps = EventsPerSecond()
+        self.plasma_client = plasma.connect("/tmp/plasma")
+        self.detection_queue = detection_queue
+    
+    def detect(self, tensor_input, threshold=.4):
+        detections = []
+
+        now = f"{self.name}-{str(datetime.datetime.now().timestamp())}"
+        object_id_frame = plasma.ObjectID(hashlib.sha1(str.encode(now)).digest())
+        object_id_detections = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{now}")).digest())
+        self.plasma_client.put(tensor_input, object_id_frame)
+        self.detection_queue.put(now)
+        raw_detections = self.plasma_client.get(object_id_detections, timeout_ms=10000)
+
+        if raw_detections is plasma.ObjectNotAvailable:
+            self.plasma_client.delete([object_id_frame])
+            return detections
+
+        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.plasma_client.delete([object_id_frame, object_id_detections])
+        self.fps.update()
+        return detections
@@ -0,0 +1,174 @@
+import os
+import time
+import psutil
+import threading
+from collections import defaultdict
+import json
+import datetime
+import subprocess as sp
+import queue
+
+class EventProcessor(threading.Thread):
+    def __init__(self, config, camera_processes, cache_dir, clip_dir, event_queue, stop_event):
+        threading.Thread.__init__(self)
+        self.config = config
+        self.camera_processes = camera_processes
+        self.cache_dir = cache_dir
+        self.clip_dir = clip_dir
+        self.cached_clips = {}
+        self.event_queue = event_queue
+        self.events_in_process = {}
+        self.stop_event = stop_event
+    
+    def refresh_cache(self):
+        cached_files = os.listdir(self.cache_dir)
+
+        files_in_use = []
+        for process_data in self.camera_processes.values():
+            try:
+                ffmpeg_process = psutil.Process(pid=process_data['ffmpeg_process'].pid)
+                flist = ffmpeg_process.open_files()
+                if flist:
+                    for nt in flist:
+                        if nt.path.startswith(self.cache_dir):
+                            files_in_use.append(nt.path.split('/')[-1])
+            except:
+                continue
+
+        for f in cached_files:
+            if f in files_in_use or f in self.cached_clips:
+                continue
+
+            camera = '-'.join(f.split('-')[:-1])
+            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
+        
+            ffprobe_cmd = " ".join([
+                'ffprobe',
+                '-v',
+                'error',
+                '-show_entries',
+                'format=duration',
+                '-of',
+                'default=noprint_wrappers=1:nokey=1',
+                f"{os.path.join(self.cache_dir,f)}"
+            ])
+            p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+            (output, err) = p.communicate()
+            p_status = p.wait()
+            if p_status == 0:
+                duration = float(output.decode('utf-8').strip())
+            else:
+                print(f"bad file: {f}")
+                os.remove(os.path.join(self.cache_dir,f))
+                continue
+
+            self.cached_clips[f] = {
+                'path': f,
+                'camera': camera,
+                'start_time': start_time.timestamp(),
+                'duration': duration
+            }
+
+        if len(self.events_in_process) > 0:
+            earliest_event = min(self.events_in_process.values(), key=lambda x:x['start_time'])['start_time']
+        else:
+            earliest_event = datetime.datetime.now().timestamp()
+
+        # if the earliest event exceeds the max seconds, cap it
+        max_seconds = self.config.get('save_clips', {}).get('max_seconds', 300)
+        if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
+            earliest_event = datetime.datetime.now().timestamp()-max_seconds
+        
+        for f, data in list(self.cached_clips.items()):
+            if earliest_event-90 > data['start_time']+data['duration']:
+                del self.cached_clips[f]
+                os.remove(os.path.join(self.cache_dir,f))
+
+    def create_clip(self, camera, event_data, pre_capture):
+        # get all clips from the camera with the event sorted
+        sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
+
+        while sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']:
+            time.sleep(5)
+            self.refresh_cache()
+            # get all clips from the camera with the event sorted
+            sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
+        
+        playlist_start = event_data['start_time']-pre_capture
+        playlist_end = event_data['end_time']+5
+        playlist_lines = []
+        for clip in sorted_clips:
+            # clip ends before playlist start time, skip
+            if clip['start_time']+clip['duration'] < playlist_start:
+                continue
+            # clip starts after playlist ends, finish
+            if clip['start_time'] > playlist_end:
+                break
+            playlist_lines.append(f"file '{os.path.join(self.cache_dir,clip['path'])}'")
+            # if this is the starting clip, add an inpoint
+            if clip['start_time'] < playlist_start:
+                playlist_lines.append(f"inpoint {int(playlist_start-clip['start_time'])}")
+            # if this is the ending clip, add an outpoint
+            if clip['start_time']+clip['duration'] > playlist_end:
+                playlist_lines.append(f"outpoint {int(playlist_end-clip['start_time'])}")
+
+        clip_name = f"{camera}-{event_data['id']}"
+        ffmpeg_cmd = [
+            'ffmpeg',
+            '-y',
+            '-protocol_whitelist',
+            'pipe,file',
+            '-f',
+            'concat',
+            '-safe',
+            '0',
+            '-i',
+            '-',
+            '-c',
+            'copy',
+            f"{os.path.join(self.clip_dir, clip_name)}.mp4"
+        ]
+
+        p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
+        if p.returncode != 0:
+            print(p.stderr)
+            return
+        
+        with open(f"{os.path.join(self.clip_dir, clip_name)}.json", 'w') as outfile:
+            json.dump(event_data, outfile)
+
+    def run(self):
+        while True:
+            if self.stop_event.is_set():
+                print(f"Exiting event processor...")
+                break
+
+            try:
+                event_type, camera, event_data = self.event_queue.get(timeout=10)
+            except queue.Empty:
+                if not self.stop_event.is_set():
+                    self.refresh_cache()
+                continue
+
+            self.refresh_cache()
+
+            save_clips_config = self.config['cameras'][camera].get('save_clips', {})
+
+            # if save clips is not enabled for this camera, just continue
+            if not save_clips_config.get('enabled', False):
+                continue
+
+            # if specific objects are listed for this camera, only save clips for them
+            if 'objects' in save_clips_config:
+                if not event_data['label'] in save_clips_config['objects']:
+                    continue
+
+            if event_type == 'start':
+                self.events_in_process[event_data['id']] = event_data
+
+            if event_type == 'end':
+                if len(self.cached_clips) > 0 and not event_data['false_positive']:
+                    self.create_clip(camera, event_data, save_clips_config.get('pre_capture', 30))
+                del self.events_in_process[event_data['id']]
+
+                
@@ -0,0 +1,79 @@
+import cv2
+import imutils
+import numpy as np
+
+class MotionDetector():
+    def __init__(self, frame_shape, mask, resize_factor=4):
+        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 = []
+
+        # resize frame
+        resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+
+        # convert to grayscale
+        gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+
+        # mask frame
+        gray[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(gray, 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[np.where(current_thresh==[0])] = [0]
+
+            # 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(gray, self.avg_frame, 0.2)
+        else:
+            # when no motion, just keep averaging the frames together
+            cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+            self.motion_frame_count = 0
+
+        return motion_boxes
@@ -0,0 +1,363 @@
+import json
+import hashlib
+import datetime
+import time
+import copy
+import cv2
+import threading
+import queue
+import copy
+import numpy as np
+from collections import Counter, defaultdict
+import itertools
+import pyarrow.plasma as plasma
+import matplotlib.pyplot as plt
+from frigate.util import draw_box_with_label, PlasmaFrameManager
+from frigate.edgetpu import load_labels
+from typing import Callable, Dict
+from statistics import mean, median
+
+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 zone_filtered(obj, object_config):
+    object_name = obj['label']
+
+    if object_name in object_config:
+        obj_settings = object_config[object_name]
+
+        # if the min area is larger than the
+        # detected object, don't add it to detected objects
+        if obj_settings.get('min_area',-1) > obj['area']:
+            return True
+        
+        # if the detected object is larger than the
+        # max area, don't add it to detected objects
+        if obj_settings.get('max_area', 24000000) < obj['area']:
+            return True
+
+        # if the score is lower than the threshold, skip
+        if obj_settings.get('threshold', 0) > obj['computed_score']:
+            return True
+        
+    return False
+
+# Maintains the state of a camera
+class CameraState():
+    def __init__(self, name, config, frame_manager):
+        self.name = name
+        self.config = config
+        self.frame_manager = frame_manager
+
+        self.best_objects = {}
+        self.object_status = defaultdict(lambda: 'OFF')
+        self.tracked_objects = {}
+        self.zone_objects = defaultdict(lambda: [])
+        self.current_frame = np.zeros((720,1280,3), np.uint8)
+        self.current_frame_time = 0.0
+        self.previous_frame_id = None
+        self.callbacks = defaultdict(lambda: [])
+
+    def false_positive(self, obj):
+        # once a true positive, always a true positive
+        if not obj.get('false_positive', True):
+            return False
+
+        threshold = self.config['objects'].get('filters', {}).get(obj['label'], {}).get('threshold', 0.85)
+        if obj['computed_score'] < threshold:
+            return True
+        return False
+
+    def compute_score(self, obj):
+        scores = obj['score_history'][:]
+        # pad with zeros if you dont have at least 3 scores
+        if len(scores) < 3:
+            scores += [0.0]*(3 - len(scores))
+        return median(scores)
+
+    def on(self, event_type: str, callback: Callable[[Dict], None]):
+        self.callbacks[event_type].append(callback)
+
+    def update(self, frame_time, tracked_objects):
+        self.current_frame_time = frame_time
+        # get the new frame and delete the old frame
+        frame_id = f"{self.name}{frame_time}"
+        self.current_frame = self.frame_manager.get(frame_id)
+        if not self.previous_frame_id is None:
+            self.frame_manager.delete(self.previous_frame_id)
+        self.previous_frame_id = frame_id
+
+        current_ids = tracked_objects.keys()
+        previous_ids = self.tracked_objects.keys()
+        removed_ids = list(set(previous_ids).difference(current_ids))
+        new_ids = list(set(current_ids).difference(previous_ids))
+        updated_ids = list(set(current_ids).intersection(previous_ids))
+
+        for id in new_ids:
+            self.tracked_objects[id] = tracked_objects[id]
+            self.tracked_objects[id]['zones'] = []
+
+            # start the score history
+            self.tracked_objects[id]['score_history'] = [self.tracked_objects[id]['score']]
+
+            # calculate if this is a false positive
+            self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
+            self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
+
+            # call event handlers
+            for c in self.callbacks['start']:
+                c(self.name, tracked_objects[id])
+        
+        for id in updated_ids:
+            self.tracked_objects[id].update(tracked_objects[id])
+
+            # if the object is not in the current frame, add a 0.0 to the score history
+            if self.tracked_objects[id]['frame_time'] != self.current_frame_time:
+                self.tracked_objects[id]['score_history'].append(0.0)
+            else:
+                self.tracked_objects[id]['score_history'].append(self.tracked_objects[id]['score'])
+            # only keep the last 10 scores
+            if len(self.tracked_objects[id]['score_history']) > 10:
+                self.tracked_objects[id]['score_history'] = self.tracked_objects[id]['score_history'][-10:]
+
+            # calculate if this is a false positive
+            self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
+            self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
+
+            # call event handlers
+            for c in self.callbacks['update']:
+                c(self.name, self.tracked_objects[id])
+        
+        for id in removed_ids:
+            # publish events to mqtt
+            self.tracked_objects[id]['end_time'] = frame_time
+            for c in self.callbacks['end']:
+                c(self.name, self.tracked_objects[id])
+            del self.tracked_objects[id]
+
+        # check to see if the objects are in any zones
+        for obj in self.tracked_objects.values():
+            current_zones = []
+            bottom_center = (obj['centroid'][0], obj['box'][3])
+            # check each zone
+            for name, zone in self.config['zones'].items():
+                contour = zone['contour']
+                # check if the object is in the zone and not filtered
+                if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0 
+                    and not zone_filtered(obj, zone.get('filters', {}))):
+                    current_zones.append(name)
+            obj['zones'] = current_zones
+        
+        # draw on the frame
+        if not self.current_frame is None:
+            # draw the bounding boxes on the frame
+            for obj in self.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(self.current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
+                # draw the regions on the frame
+                region = obj['region']
+                cv2.rectangle(self.current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
+            
+            if self.config['snapshots']['show_timestamp']:
+                time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
+                cv2.putText(self.current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
+
+            if self.config['snapshots']['draw_zones']:
+                for name, zone in self.config['zones'].items():
+                    thickness = 8 if any([name in obj['zones'] for obj in self.tracked_objects.values()]) else 2
+                    cv2.drawContours(self.current_frame, [zone['contour']], -1, zone['color'], thickness)
+
+        # maintain best objects
+        for obj in self.tracked_objects.values():
+            object_type = obj['label']
+            # if the object wasn't seen on the current frame, skip it
+            if obj['frame_time'] != self.current_frame_time or obj['false_positive']:
+                continue
+            obj_copy = copy.deepcopy(obj)
+            if object_type in self.best_objects:
+                current_best = self.best_objects[object_type]
+                now = datetime.datetime.now().timestamp()
+                # if the object is a higher score than the current best score 
+                # or the current object is older than desired, use the new object
+                if obj_copy['score'] > current_best['score'] or (now - current_best['frame_time']) > self.config.get('best_image_timeout', 60):
+                    obj_copy['frame'] = np.copy(self.current_frame)
+                    self.best_objects[object_type] = obj_copy
+                    for c in self.callbacks['snapshot']:
+                        c(self.name, self.best_objects[object_type])
+            else:
+                obj_copy['frame'] = np.copy(self.current_frame)
+                self.best_objects[object_type] = obj_copy
+                for c in self.callbacks['snapshot']:
+                    c(self.name, self.best_objects[object_type])
+        
+        # update overall camera state for each object type
+        obj_counter = Counter()
+        for obj in self.tracked_objects.values():
+            if not obj['false_positive']:
+                obj_counter[obj['label']] += 1
+                
+        # report on detected objects
+        for obj_name, count in obj_counter.items():
+            new_status = 'ON' if count > 0 else 'OFF'
+            if new_status != self.object_status[obj_name]:
+                self.object_status[obj_name] = new_status
+                for c in self.callbacks['object_status']:
+                    c(self.name, obj_name, new_status)
+
+        # expire any objects that are ON and no longer detected
+        expired_objects = [obj_name for obj_name, status in self.object_status.items() if status == 'ON' and not obj_name in obj_counter]
+        for obj_name in expired_objects:
+            self.object_status[obj_name] = 'OFF'
+            for c in self.callbacks['object_status']:
+                c(self.name, obj_name, 'OFF')
+            for c in self.callbacks['snapshot']:
+                c(self.name, self.best_objects[obj_name])
+
+
+class TrackedObjectProcessor(threading.Thread):
+    def __init__(self, camera_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
+        threading.Thread.__init__(self)
+        self.camera_config = camera_config
+        self.client = client
+        self.topic_prefix = topic_prefix
+        self.tracked_objects_queue = tracked_objects_queue
+        self.event_queue = event_queue
+        self.stop_event = stop_event
+        self.camera_states: Dict[str, CameraState] = {}
+        self.plasma_client = PlasmaFrameManager(self.stop_event)
+
+        def start(camera, obj):
+            # publish events to mqtt
+            self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(obj), retain=False)
+            self.event_queue.put(('start', camera, obj))
+
+        def update(camera, obj):
+            pass
+
+        def end(camera, obj):
+            self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(obj), retain=False)
+            self.event_queue.put(('end', camera, obj))
+        
+        def snapshot(camera, obj):
+            if not 'frame' in obj:
+                return
+            best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_RGB2BGR)
+            mqtt_config = self.camera_config[camera].get('mqtt', {'crop_to_region': False})
+            if mqtt_config.get('crop_to_region'):
+                region = obj['region']
+                best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+            if 'snapshot_height' in mqtt_config: 
+                height = int(mqtt_config['snapshot_height'])
+                width = int(height*best_frame.shape[1]/best_frame.shape[0])
+                best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+            ret, jpg = cv2.imencode('.jpg', best_frame)
+            if ret:
+                jpg_bytes = jpg.tobytes()
+                self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", jpg_bytes, retain=True)
+        
+        def object_status(camera, object_name, status):
+            self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
+
+        for camera in self.camera_config.keys():
+            camera_state = CameraState(camera, self.camera_config[camera], self.plasma_client)
+            camera_state.on('start', start)
+            camera_state.on('update', update)
+            camera_state.on('end', end)
+            camera_state.on('snapshot', snapshot)
+            camera_state.on('object_status', object_status)
+            self.camera_states[camera] = camera_state
+
+        self.camera_data = defaultdict(lambda: {
+            'best_objects': {},
+            'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
+            'tracked_objects': {},
+            'current_frame': np.zeros((720,1280,3), np.uint8),
+            'current_frame_time': 0.0,
+            'object_id': None
+        })
+        # {
+        #   'zone_name': {
+        #       'person': ['camera_1', 'camera_2']
+        #   }
+        # }
+        self.zone_data = defaultdict(lambda: defaultdict(lambda: set()))
+
+        # set colors for zones
+        all_zone_names = set([zone for config in self.camera_config.values() for zone in config['zones'].keys()])
+        zone_colors = {}
+        colors = plt.cm.get_cmap('tab10', len(all_zone_names))
+        for i, zone in enumerate(all_zone_names):
+            zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
+
+        # create zone contours
+        for camera_config in self.camera_config.values():
+            for zone_name, zone_config in camera_config['zones'].items():
+                zone_config['color'] = zone_colors[zone_name]
+                coordinates = zone_config['coordinates']
+                if isinstance(coordinates, list):
+                    zone_config['contour'] =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
+                elif isinstance(coordinates, str):
+                    points = coordinates.split(',')
+                    zone_config['contour'] =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+                else:
+                    print(f"Unable to parse zone coordinates for {zone_name} - {camera}")
+        
+    def get_best(self, camera, label):
+        best_objects = self.camera_states[camera].best_objects
+        if label in best_objects:
+            return best_objects[label]
+        else:
+            return {}
+    
+    def get_current_frame(self, camera):
+        return self.camera_states[camera].current_frame
+
+    def run(self):
+        while True:
+            if self.stop_event.is_set():
+                print(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 status for each label
+            for zone in camera_state.config['zones'].keys():
+                # get labels for current camera and all labels in current zone
+                labels_for_camera = set([obj['label'] for obj in camera_state.tracked_objects.values() if zone in obj['zones'] and not obj['false_positive']])
+                labels_to_check = labels_for_camera | set(self.zone_data[zone].keys())
+                # for each label in zone
+                for label in labels_to_check:
+                    camera_list = self.zone_data[zone][label]
+                    # remove or add the camera to the list for the current label
+                    previous_state = len(camera_list) > 0
+                    if label in labels_for_camera:
+                        camera_list.add(camera_state.name)
+                    elif camera_state.name in camera_list:
+                        camera_list.remove(camera_state.name)
+                    new_state = len(camera_list) > 0
+                    # if the value is changing, send over MQTT
+                    if previous_state == False and new_state == True:
+                        self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'ON', retain=False)
+                    elif previous_state == True and new_state == False:
+                        self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'OFF', retain=False)
@@ -0,0 +1,148 @@
+import time
+import datetime
+import threading
+import cv2
+import itertools
+import copy
+import numpy as np
+import random
+import string
+import multiprocessing as mp
+from collections import defaultdict
+from scipy.spatial import distance as dist
+from frigate.util import draw_box_with_label, calculate_region
+
+class ObjectTracker():
+    def __init__(self, max_disappeared):
+        self.tracked_objects = {}
+        self.disappeared = {}
+        self.max_disappeared = max_disappeared
+
+    def register(self, index, obj):
+        rand_id = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6))
+        id = f"{obj['frame_time']}-{rand_id}"
+        obj['id'] = id
+        obj['start_time'] = obj['frame_time']
+        obj['top_score'] = obj['score']
+        self.tracked_objects[id] = obj
+        self.disappeared[id] = 0
+
+    def deregister(self, id):
+        del self.tracked_objects[id]
+        del self.disappeared[id]
+    
+    def update(self, id, new_obj):
+        self.disappeared[id] = 0
+        self.tracked_objects[id].update(new_obj)
+        if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
+            self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
+
+    def match_and_update(self, frame_time, new_objects):
+        # group by name
+        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
+            })
+        
+        # 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
+
+                # 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:
+                        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])
@@ -0,0 +1,229 @@
+from abc import ABC, abstractmethod
+import datetime
+import time
+import signal
+import traceback
+import collections
+import numpy as np
+import cv2
+import threading
+import matplotlib.pyplot as plt
+import hashlib
+import pyarrow.plasma as plasma
+
+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(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, 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 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 get(self, name, timeout_ms=0):
+        pass
+
+    @abstractmethod
+    def put(self, name, frame):
+        pass
+
+    @abstractmethod
+    def delete(self, name):
+        pass
+
+class DictFrameManager(FrameManager):
+    def __init__(self):
+        self.frames = {}
+    
+    def get(self, name, timeout_ms=0):
+        return self.frames.get(name)
+    
+    def put(self, name, frame):
+        self.frames[name] = frame
+    
+    def delete(self, name):
+        del self.frames[name]
+
+class PlasmaFrameManager(FrameManager):
+    def __init__(self, stop_event=None):
+        self.stop_event = stop_event
+        self.connect()
+    
+    def connect(self):
+        while True:
+            if self.stop_event != None and self.stop_event.is_set():
+                return
+            try:
+                self.plasma_client = plasma.connect("/tmp/plasma")
+                return
+            except:
+                print(f"TrackedObjectProcessor: unable to connect plasma client")
+                time.sleep(10)
+
+    def get(self, name, timeout_ms=0):
+        object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
+        while True:
+            if self.stop_event != None and self.stop_event.is_set():
+                return
+            try:
+                frame = self.plasma_client.get(object_id, timeout_ms=timeout_ms)
+                if frame is plasma.ObjectNotAvailable:
+                    return None
+                return frame
+            except:
+                self.connect()
+                time.sleep(1)
+
+    def put(self, name, frame):
+        object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
+        while True:
+            if self.stop_event != None and self.stop_event.is_set():
+                return
+            try:
+                self.plasma_client.put(frame, object_id)
+                return
+            except Exception as e:
+                print(f"Failed to put in plasma: {e}")
+                self.connect()
+                time.sleep(1)
+
+    def delete(self, name):
+        object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
+        while True:
+            if self.stop_event != None and self.stop_event.is_set():
+                return
+            try:
+                self.plasma_client.delete([object_id])
+                return
+            except:
+                self.connect()
+                time.sleep(1)
@@ -0,0 +1,366 @@
+import os
+import time
+import datetime
+import cv2
+import queue
+import threading
+import ctypes
+import pyarrow.plasma as plasma
+import multiprocessing as mp
+import subprocess as sp
+import numpy as np
+import copy
+import itertools
+import json
+import base64
+from typing import Dict, List
+from collections import defaultdict
+from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, FrameManager, PlasmaFrameManager
+from frigate.objects import ObjectTracker
+from frigate.edgetpu import RemoteObjectDetector
+from frigate.motion import MotionDetector
+
+def get_frame_shape(source):
+    ffprobe_cmd = " ".join([
+        'ffprobe',
+        '-v',
+        'panic',
+        '-show_error',
+        '-show_streams',
+        '-of',
+        'json',
+        '"'+source+'"'
+    ])
+    print(ffprobe_cmd)
+    p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+    (output, err) = p.communicate()
+    p_status = p.wait()
+    info = json.loads(output)
+    print(info)
+
+    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
+
+    if video_info['height'] != 0 and video_info['width'] != 0:
+        return (video_info['height'], video_info['width'], 3)
+    
+    # fallback to using opencv if ffprobe didnt succeed
+    video = cv2.VideoCapture(source)
+    ret, frame = video.read()
+    frame_shape = frame.shape
+    video.release()
+    return frame_shape
+
+def get_ffmpeg_input(ffmpeg_input):
+    frigate_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
+    return ffmpeg_input.format(**frigate_vars)
+
+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.get('min_area',-1) > obj[3]:
+            return True
+        
+        # if the detected object is larger than the
+        # max area, don't add it to detected objects
+        if obj_settings.get('max_area', 24000000) < obj[3]:
+            return True
+
+        # if the score is lower than the min_score, skip
+        if obj_settings.get('min_score', 0) > obj[1]:
+            return True
+    
+        # compute the coordinates of the object and make sure
+        # 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 = frame[region[1]:region[3], region[0]:region[2]]
+
+    # 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 start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
+    if not ffmpeg_process is None:
+        print("Terminating the existing ffmpeg process...")
+        ffmpeg_process.terminate()
+        try:
+            print("Waiting for ffmpeg to exit gracefully...")
+            ffmpeg_process.communicate(timeout=30)
+        except sp.TimeoutExpired:
+            print("FFmpeg didnt exit. Force killing...")
+            ffmpeg_process.kill()
+            ffmpeg_process.communicate()
+        ffmpeg_process = None
+
+    print("Creating ffmpeg process...")
+    print(" ".join(ffmpeg_cmd))
+    process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
+    return process
+
+def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, 
+    frame_queue, take_frame: int, fps:EventsPerSecond, skipped_fps: EventsPerSecond, 
+    stop_event: mp.Event, detection_frame: mp.Value, current_frame: mp.Value):
+
+    frame_num = 0
+    last_frame = 0
+    frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
+    skipped_fps.start()
+    while True:
+        if stop_event.is_set():
+            print(f"{camera_name}: stop event set. exiting capture thread...")
+            break
+
+        frame_bytes = ffmpeg_process.stdout.read(frame_size)
+        current_frame.value = datetime.datetime.now().timestamp()
+
+        if len(frame_bytes) < frame_size:
+            print(f"{camera_name}: ffmpeg sent a broken frame. something is wrong.")
+
+            if ffmpeg_process.poll() != None:
+                print(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
+                break
+            else:
+                continue
+
+        fps.update()
+
+        frame_num += 1
+        if (frame_num % take_frame) != 0:
+            skipped_fps.update()
+            continue
+
+        # if the detection process is more than 1 second behind, skip this frame
+        if detection_frame.value > 0.0 and (last_frame - detection_frame.value) > 1:
+            skipped_fps.update()
+            continue
+
+        # put the frame in the frame manager
+        frame_manager.put(f"{camera_name}{current_frame.value}",
+                np
+                    .frombuffer(frame_bytes, np.uint8)
+                    .reshape(frame_shape)
+            )
+        # add to the queue
+        frame_queue.put(current_frame.value)
+        last_frame = current_frame.value
+
+class CameraCapture(threading.Thread):
+    def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, detection_frame, stop_event):
+        threading.Thread.__init__(self)
+        self.name = name
+        self.frame_shape = frame_shape
+        self.frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
+        self.frame_queue = frame_queue
+        self.take_frame = take_frame
+        self.fps = fps
+        self.skipped_fps = EventsPerSecond()
+        self.plasma_client = PlasmaFrameManager(stop_event)
+        self.ffmpeg_process = ffmpeg_process
+        self.current_frame = mp.Value('d', 0.0)
+        self.last_frame = 0
+        self.detection_frame = detection_frame
+        self.stop_event = stop_event
+
+    def run(self):
+        self.skipped_fps.start()
+        capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.plasma_client, self.frame_queue, self.take_frame,
+            self.fps, self.skipped_fps, self.stop_event, self.detection_frame, self.current_frame)
+
+def track_camera(name, config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame, stop_event):
+    print(f"Starting process for {name}: {os.getpid()}")
+    listen()
+
+    detection_frame.value = 0.0
+
+    # Merge the tracked object config with the global config
+    camera_objects_config = config.get('objects', {})
+    objects_to_track = camera_objects_config.get('track', [])
+    object_filters = camera_objects_config.get('filters', {})
+
+    # load in the mask for object detection
+    if 'mask' in config:
+        if config['mask'].startswith('base64,'):
+            img = base64.b64decode(config['mask'][7:]) 
+            npimg = np.fromstring(img, dtype=np.uint8)
+            mask = cv2.imdecode(npimg, cv2.IMREAD_GRAYSCALE)
+        elif config['mask'].startswith('poly,'):
+            points = config['mask'].split(',')[1:]
+            contour =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+            mask = np.zeros((frame_shape[0], frame_shape[1]), np.uint8)
+            mask[:] = 255
+            cv2.fillPoly(mask, pts=[contour], color=(0))
+        else:
+            mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE)
+    else:
+        mask = None
+
+    if mask is None or mask.size == 0:
+        mask = np.zeros((frame_shape[0], frame_shape[1]), np.uint8)
+        mask[:] = 255
+
+    motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
+    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue)
+
+    object_tracker = ObjectTracker(10)
+
+    plasma_client = PlasmaFrameManager()
+
+    process_frames(name, frame_queue, frame_shape, plasma_client, motion_detector, object_detector,
+        object_tracker, detected_objects_queue, fps, detection_fps, detection_frame, objects_to_track, object_filters, mask, stop_event)
+
+    print(f"{name}: exiting subprocess")
+
+def reduce_boxes(boxes):
+    if len(boxes) == 0:
+        return []
+    reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0]
+    return [tuple(b) for b in reduced_boxes]
+
+def detect(object_detector, frame, region, objects_to_track, object_filters, mask):
+    tensor_input = create_tensor_input(frame, region)
+
+    detections = []
+    region_detections = object_detector.detect(tensor_input)
+    for d in region_detections:
+        box = d[2]
+        size = region[2]-region[0]
+        x_min = int((box[1] * size) + region[0])
+        y_min = int((box[0] * size) + region[1])
+        x_max = int((box[3] * size) + region[0])
+        y_max = int((box[2] * size) + region[1])
+        det = (d[0],
+            d[1],
+            (x_min, y_min, x_max, y_max),
+            (x_max-x_min)*(y_max-y_min),
+            region)
+        # apply object filters
+        if filtered(det, objects_to_track, object_filters, mask):
+            continue
+        detections.append(det)
+    return detections
+
+def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, 
+    frame_manager: FrameManager, motion_detector: MotionDetector, 
+    object_detector: RemoteObjectDetector, object_tracker: ObjectTracker,
+    detected_objects_queue: mp.Queue, fps: mp.Value, detection_fps: mp.Value, current_frame_time: mp.Value,
+    objects_to_track: List[str], object_filters: Dict, mask, stop_event: mp.Event,
+    exit_on_empty: bool = False):
+    
+    fps_tracker = EventsPerSecond()
+    fps_tracker.start()
+
+    while True:
+        if stop_event.is_set() or (exit_on_empty and frame_queue.empty()):
+                print(f"Exiting track_objects...")
+                break
+
+        try:
+            frame_time = frame_queue.get(True, 10)
+        except queue.Empty:
+            continue
+
+        
+        current_frame_time.value = frame_time
+
+        frame = frame_manager.get(f"{camera_name}{frame_time}")
+
+        if frame is None:
+            print(f"{camera_name}: frame {frame_time} is not in memory store.")
+            continue
+        
+        fps_tracker.update()
+        fps.value = fps_tracker.eps()
+
+        # look for motion
+        motion_boxes = motion_detector.detect(frame)
+
+        tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values()]
+
+        # combine motion boxes with known locations of existing objects
+        combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
+
+        # compute regions
+        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
+            for a in combined_boxes]
+
+        # combine overlapping regions
+        combined_regions = reduce_boxes(regions)
+
+        # re-compute regions
+        regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
+            for a in combined_regions]
+        
+        # resize regions and detect
+        detections = []
+        for region in regions:
+            detections.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+        
+        #########
+        # merge objects, check for clipped objects and look again up to 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
+        detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects))
+
+        detection_fps.value = object_detector.fps.eps()
@@ -1,3 +0,0 @@
-<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M130 446.5C131.6 459.3 145 468 137 470C129 472 94 406.5 86 378.5C78 350.5 73.5 319 75.4999 301C77.4999 283 181 255 181 247.5C181 240 147.5 247 146 241C144.5 235 171.3 238.6 178.5 229C189.75 214 204 216.5 213 208.5C222 200.5 233 170 235 157C237 144 215 129 209 119C203 109 222 102 268 83C314 64 460 22 462 27C464 32 414 53 379 66C344 79 287 104 287 111C287 118 290 123.5 288 139.5C286 155.5 285.76 162.971 282 173.5C279.5 180.5 277 197 282 212C286 224 299 233 305 235C310 235.333 323.8 235.8 339 235C358 234 385 236 385 241C385 246 344 243 344 250C344 257 386 249 385 256C384 263 350 260 332 260C317.6 260 296.333 259.333 287 256L285 263C281.667 263 274.7 265 267.5 265C258.5 265 258 268 241.5 268C225 268 230 267 215 266C200 265 144 308 134 322C124 336 130 370 130 385.5C130 399.428 128 430.5 130 446.5Z" fill="white"/>
-</svg>
@@ -1,3 +0,0 @@
-<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M130 446.5C131.6 459.3 145 468 137 470C129 472 94 406.5 86 378.5C78 350.5 73.5 319 75.5 301C77.4999 283 181 255 181 247.5C181 240 147.5 247 146 241C144.5 235 171.3 238.6 178.5 229C189.75 214 204 216.5 213 208.5C222 200.5 233 170 235 157C237 144 215 129 209 119C203 109 222 102 268 83C314 64 460 22 462 27C464 32 414 53 379 66C344 79 287 104 287 111C287 118 290 123.5 288 139.5C286 155.5 285.76 162.971 282 173.5C279.5 180.5 277 197 282 212C286 224 299 233 305 235C310 235.333 323.8 235.8 339 235C358 234 385 236 385 241C385 246 344 243 344 250C344 257 386 249 385 256C384 263 350 260 332 260C317.6 260 296.333 259.333 287 256L285 263C281.667 263 274.7 265 267.5 265C258.5 265 258 268 241.5 268C225 268 230 267 215 266C200 265 144 308 134 322C124 336 130 370 130 385.5C130 399.428 128 430.5 130 446.5Z" fill="black"/>
-</svg>
@@ -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
@@ -1,44 +0,0 @@
-/*
-object-assign
-(c) Sindre Sorhus
-@license MIT
-*/
-
-/* NProgress, (c) 2013, 2014 Rico Sta. Cruz - http://ricostacruz.com/nprogress
- * @license MIT */
-
-/** @license React v0.19.1
- * scheduler.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-/** @license React v16.13.1
- * react-is.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-/** @license React v16.14.0
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-/** @license React v16.14.0
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<OpenSearchDescription xmlns="http://a9.com/-/spec/opensearch/1.1/"
-                       xmlns:moz="http://www.mozilla.org/2006/browser/search/">
-  <ShortName>Frigate</ShortName>
-  <Description>Search Frigate</Description>
-  <InputEncoding>UTF-8</InputEncoding>
-  <Image width="16" height="16" type="image/x-icon">https://blakeblackshear.github.io/img/favicon.ico</Image>
-  <Url type="text/html" method="get" template="https://blakeblackshear.github.io/search?q={searchTerms}"/>
-  <Url type="application/opensearchdescription+xml" rel="self" template="https://blakeblackshear.github.io/opensearch.xml" />
-  <moz:SearchForm>https://blakeblackshear.github.io</moz:SearchForm>
-</OpenSearchDescription>
@@ -0,0 +1,148 @@
+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, 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_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']}")
+        
+        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}")
+        # 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)
+        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()
@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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