increase queue size and add ability to take every nth frame

.dockerignore

@@ -1,6 +1 @@
-README.md
-diagram.png
-.gitignore
-debug
-config/
-*.pyc
+README.md

.github/FUNDING.yml

@@ -1 +0,0 @@
-github: blakeblackshear

.gitignore

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

Dockerfile

@@ -1,60 +1,107 @@
-FROM ubuntu:18.04
-LABEL maintainer "blakeb@blakeshome.com"
+FROM ubuntu:16.04
 
-ENV DEBIAN_FRONTEND=noninteractive
-# Install packages for apt repo
-RUN apt -qq update && apt -qq install --no-install-recommends -y \
-    software-properties-common \
-    # apt-transport-https ca-certificates \
-    build-essential \
-    gnupg wget unzip tzdata \
-    # libcap-dev \
-    && add-apt-repository ppa:deadsnakes/ppa -y \
-    && apt -qq install --no-install-recommends -y \
-        python3.7 \
-        python3.7-dev \
-        python3-pip \
-        ffmpeg \
-        # VAAPI drivers for Intel hardware accel
-        libva-drm2 libva2 i965-va-driver vainfo \
-    && python3.7 -m pip install -U wheel setuptools \
-    && python3.7 -m pip install -U \
-        opencv-python-headless \
-        # python-prctl \
-        numpy \
-        imutils \
-        scipy \
-        psutil \
-    && python3.7 -m pip install -U \
-        Flask \
-        paho-mqtt \
-        PyYAML \
-        matplotlib \
-        pyarrow \
-    && echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
-    && wget -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
-    && apt -qq update \
-    && echo "libedgetpu1-max libedgetpu/accepted-eula boolean true" | debconf-set-selections \
-    && apt -qq install --no-install-recommends -y \
-        libedgetpu1-max \
-    ## Tensorflow lite (python 3.7 only)
-    && wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && python3.7 -m pip install tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && rm tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && rm -rf /var/lib/apt/lists/* \
-    && (apt-get autoremove -y; apt-get autoclean -y)
+# Install system packages
+RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y python3 \ 
+ python3-dev \
+ python-pil \
+ python-lxml \
+ python-tk \
+ build-essential \
+ cmake \ 
+ git \ 
+ libgtk2.0-dev \ 
+ pkg-config \ 
+ libavcodec-dev \ 
+ libavformat-dev \ 
+ libswscale-dev \ 
+ libtbb2 \
+ libtbb-dev \ 
+ libjpeg-dev \
+ libpng-dev \
+ libtiff-dev \
+ libjasper-dev \
+ libdc1394-22-dev \
+ x11-apps \
+ wget \
+ vim \
+ ffmpeg \
+ unzip \
+ libusb-1.0-0-dev \
+ python3-setuptools \
+ python3-numpy \
+ zlib1g-dev \
+ libgoogle-glog-dev \
+ swig \
+ libunwind-dev \
+ libc++-dev \
+ libc++abi-dev \
+ build-essential \
+ && rm -rf /var/lib/apt/lists/* 
 
-# get model and labels
-RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite -O /edgetpu_model.tflite --trust-server-names
-RUN wget -q https://dl.google.com/coral/canned_models/coco_labels.txt -O /labelmap.txt --trust-server-names
-RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite -O /cpu_model.tflite 
+# Install core packages 
+RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
+RUN  pip install -U pip \
+ numpy \
+ pillow \
+ matplotlib \
+ notebook \
+ Flask \
+ imutils \
+ paho-mqtt \
+ PyYAML
 
+# Install tensorflow models object detection
+RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
+RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
 
-RUN mkdir /cache /clips
+# Download & build protobuf-python
+RUN cd /usr/local/src/ \
+ && tar xf protobuf-python-3.5.1.tar.gz \
+ && rm protobuf-python-3.5.1.tar.gz \
+ && cd /usr/local/src/protobuf-3.5.1/ \
+ && ./configure \
+ && make \
+ && make install \
+ && ldconfig \
+ && rm -rf /usr/local/src/protobuf-3.5.1/
+
+# Download & build OpenCV
+RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
+RUN cd /usr/local/src/ \
+ && unzip 4.0.1.zip \
+ && rm 4.0.1.zip \
+ && cd /usr/local/src/opencv-4.0.1/ \
+ && mkdir build \
+ && cd /usr/local/src/opencv-4.0.1/build \ 
+ && cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
+ && make -j4 \
+ && make install \
+ && rm -rf /usr/local/src/opencv-4.0.1
+
+# Download and install EdgeTPU libraries
+RUN wget -q -O edgetpu_api.tar.gz --no-check-certificate http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz
+
+RUN tar xzf edgetpu_api.tar.gz \
+  && cd python-tflite-source \
+  && cp -p libedgetpu/libedgetpu_x86_64.so /lib/x86_64-linux-gnu/libedgetpu.so \
+  && cp edgetpu/swig/compiled_so/_edgetpu_cpp_wrapper_x86_64.so edgetpu/swig/_edgetpu_cpp_wrapper.so \
+  && cp edgetpu/swig/compiled_so/edgetpu_cpp_wrapper.py edgetpu/swig/ \
+  && python3 setup.py develop --user
+
+# Minimize image size 
+RUN (apt-get autoremove -y; \
+     apt-get autoclean -y)
+
+# symlink the model and labels
+RUN ln -s /python-tflite-source/edgetpu/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
+RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /label_map.pbtext
+
+# Set TF object detection available
+ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
+RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
 
 WORKDIR /opt/frigate/
 ADD frigate frigate/
 COPY detect_objects.py .
-COPY benchmark.py .
 
-CMD ["python3.7", "-u", "detect_objects.py"]
+CMD ["python3", "-u", "detect_objects.py"]

LICENSE

@@ -1,21 +1,661 @@
-The MIT License
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
 
-Copyright (c) 2020 Blake Blackshear
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
 
-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:
+                            Preamble
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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-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.
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
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+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
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README.md

@@ -1,13 +1,14 @@
-# Frigate - 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.
+# Frigate - Realtime Object Detection for RTSP Cameras
+**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
 
-Use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/) is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
+Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP cameras. Designed for integration with HomeAssistant or others via MQTT.
 
-- 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
+- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
+- Allows you to define specific regions (squares) in the image to look for objects
+- No motion detection (for now)
+- Object detection with Tensorflow runs in a separate thread
 - Object info is published over MQTT for integration into HomeAssistant as a binary sensor
-- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
+- An endpoint is available to view an MJPEG stream for debugging
 
 ![Diagram](diagram.png)
 
@@ -16,250 +17,82 @@ You see multiple bounding boxes because it draws bounding boxes from all frames
 [![](http://img.youtube.com/vi/nqHbCtyo4dY/0.jpg)](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
 
 ## Getting Started
+Build the container with
+```
+docker build -t frigate .
+```
+
+The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
+
 Run the container with
-```bash
+```
 docker run --rm \
--name frigate \
 --privileged \
---shm-size=512m \ # should work for a 2-3 cameras
 -v /dev/bus/usb:/dev/bus/usb \
 -v <path_to_config_dir>:/config:ro \
--v /etc/localtime:/etc/localtime:ro \
 -p 5000:5000 \
--e FRIGATE_RTSP_PASSWORD='password' \
-blakeblackshear/frigate:stable
+-e RTSP_PASSWORD='password' \
+frigate:latest
 ```
 
 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
+    image: frigate:latest
     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"
+      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).
+A `config.yml` file must exist in the `config` directory. See example [here](config/config.yml).
 
-## 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.
-
-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.
+Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
 
 ## Integration with HomeAssistant
-
-Setup a the camera, binary_sensor, sensor and optionally automation as shown for each camera you define in frigate. Replace <camera_name> with the camera name as defined in the frigate `config.yml` (The `frigate_coral_fps` and `frigate_coral_inference` sensors only need to be defined once)
-
 ```
 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"
+  - name: Camera Last Person
+    platform: generic
+    still_image_url: http://<ip>:5000/<camera_name>/best_person.jpg
 
 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.        
+  - name: Camera Person
+    platform: mqtt
+    state_topic: "frigate/<camera_name>/objects"
+    value_template: '{{ value_json.person }}'
+    device_class: moving
+    availability_topic: "frigate/available"
 ```
-## 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`
-The best snapshot for any object type. It is a full resolution image by default. You can change the size of the image by appending `h=height-in-pixels` to the endpoint.
-
-### `/<camera_name>/latest.jpg`
-The most recent frame that frigate has finished processing. It is a full resolution image by default. You can change the size of the image by appending `h=height-in-pixels` to the endpoint.
-
-### `/debug/stats`
-Contains some granular debug info that can be used for sensors in HomeAssistant.
-
-## 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.
-
-### frigate/<camera_name>/events/start
-Message published at the start of any tracked object. JSON looks as follows:
-```json
-{
-    "label": "person",
-    "score": 0.7890625,
-    "box": [
-        468,
-        446,
-        550,
-        592
-    ],
-    "area": 11972,
-    "region": [
-        403,
-        395,
-        613,
-        605
-    ],
-    "frame_time": 1594298020.819046,
-    "centroid": [
-        509,
-        519
-    ],
-    "id": "1594298020.819046-0",
-    "start_time": 1594298020.819046,
-    "top_score": 0.7890625,
-    "history": [
-        {
-            "score": 0.7890625,
-            "box": [
-                468,
-                446,
-                550,
-                592
-            ],
-            "region": [
-                403,
-                395,
-                613,
-                605
-            ],
-            "centroid": [
-                509,
-                519
-            ],
-            "frame_time": 1594298020.819046
-        }
-    ]
-}
-```
-
-### 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.
-
-## 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
-  ```
-
-## Masks and limiting detection to a certain area
-You can create a *bitmap (bmp)* file the same aspect ratio as your camera feed to limit detection to certain areas. The mask works by looking at the bottom center of any bounding box (first image, red dot below) and comparing that to your mask. If that red dot falls on an area of your mask that is black, the detection (and motion) will be ignored. The mask in the second image would limit detection on this camera to only objects that are in the front yard and not the street. 
-
-<img src="docs/example-mask-check-point.png" height="300">
-<img src="docs/example-mask.bmp" height="300">
-<img src="docs/example-mask-overlay.png" height="300">
-
-## 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)
 
 ## 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. 
+- Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
 
+## Future improvements
+- [x] Remove motion detection for now
+- [x] Try running object detection in a thread rather than a process
+- [x] Implement min person size again
+- [x] Switch to a config file
+- [x] Handle multiple cameras in the same container
+- [ ] Attempt to figure out coral symlinking
+- [ ] Add object list to config with min scores for mqtt
+- [ ] Move mjpeg encoding to a separate process
+- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
+- [ ] See if motion detection is even worth running
+- [ ] Scan for people across entire image rather than specfic regions
+- [ ] Dynamically resize detection area and follow people
+- [ ] Add ability to turn detection on and off via MQTT
+- [ ] Output movie clips of people for notifications, etc.
+- [ ] Integrate with homeassistant push camera
+- [ ] Merge bounding boxes that span multiple regions
+- [ ] Implement mode to save labeled objects for training
+- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
+- [ ] Look into GPU accelerated decoding of RTSP stream
+- [ ] Send video over a socket and use JSMPEG
+- [x] Look into neural compute stick

benchmark.py

@@ -1,79 +0,0 @@
-import os
-from statistics import mean
-import multiprocessing as mp
-import numpy as np
-import datetime
-from frigate.edgetpu import ObjectDetector, 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.")

config/config.example.yml

@@ -1,189 +0,0 @@
-web_port: 5000
-
-mqtt:
-  host: mqtt.server.com
-  topic_prefix: frigate
-  # client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
-  # user: username # Optional
-  #################
-  ## Environment variables that begin with 'FRIGATE_' may be referenced in {}.
-  ##   password: '{FRIGATE_MQTT_PASSWORD}'
-  #################
-  # password: password # Optional
-
-#################
-# Default ffmpeg args. Optional and can be overwritten per camera.
-# Should work with most RTSP cameras that send h264 video
-# Built from the properties below with:
-# "ffmpeg" + global_args + input_args + "-i" + input + output_args
-#################
-# ffmpeg:
-#   global_args:
-#     - -hide_banner
-#     - -loglevel
-#     - panic
-#   hwaccel_args: []
-#   input_args:
-#     - -avoid_negative_ts
-#     - make_zero
-#     - -fflags
-#     - nobuffer
-#     - -flags
-#     - low_delay
-#     - -strict
-#     - experimental
-#     - -fflags
-#     - +genpts+discardcorrupt
-#     - -vsync
-#     - drop
-#     - -rtsp_transport
-#     - tcp
-#     - -stimeout
-#     - '5000000'
-#     - -use_wallclock_as_timestamps
-#     - '1'
-#   output_args:
-#     - -f
-#     - rawvideo
-#     - -pix_fmt
-#     - rgb24
-
-####################
-# Global object configuration. Applies to all cameras
-# unless overridden at the camera levels.
-# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
-# All labels from the model are reported over MQTT. These values are used to filter out false positives.
-# min_area (optional): minimum width*height of the bounding box for the detected person
-# max_area (optional): maximum width*height of the bounding box for the detected person
-# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
-####################
-objects:
-  track:
-    - person
-    - car
-    - truck
-  filters:
-    person:
-      min_area: 5000
-      max_area: 100000
-      threshold: 0.8
-
-zones:
-  #################
-  # Name of the zone
-  ################
-  front_steps:
-    cameras:
-      front_door:
-        ####################
-        # For each camera, 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 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
-  driveway:
-    cameras:
-      front_door:
-        coordinates: 545,1077,747,939,788,805
-  yard:
-
-cameras:
-  back:
-    ffmpeg:
-      ################
-      # Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
-      # Environment variables that begin with 'FRIGATE_' may be referenced in {}
-      ################
-      input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-      #################
-      # These values will override default values for just this camera
-      #################
-      # global_args: []
-      # hwaccel_args: []
-      # input_args: []
-      # output_args: []
-    
-    ################
-    ## Optionally specify the resolution of the video feed. Frigate will try to auto detect if not specified
-    ################
-    # height: 1280
-    # width: 720
-
-    ################
-    ## Optional mask. Must be the same aspect ratio as your video feed. Value is either the
-    ## name of a file in the config directory or a base64 encoded bmp image prefixed with
-    ## 'base64,' eg. 'base64,asfasdfasdf....'.
-    ## 
-    ## 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
-
-    ################
-    # 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. 30 seconds of video is added to the start of the clip. These are
-    # optimized to capture "false_positive" examples for improving frigate.
-    #
-    # NOTE: This will only work for camera feeds that can be copied into the mp4 container format without
-    # encoding such as h264. I do not expect this to work for mjpeg streams, and it may not work for many other
-    # types of streams.
-    #
-    # WARNING: Videos in /cache are retained until there are no ongoing events. If you are tracking cars or
-    # other objects for long periods of time, the cache will continue to grow indefinitely.
-    ################
-    save_clips:
-      enabled: False
-      #########
-      # Number of seconds before the event to include in the clips
-      #########
-      pre_capture: 30
-
-    ################
-    # Configuration for the snapshots in the debug view and mqtt
-    ################
-    snapshots:
-      show_timestamp: True
-      draw_zones: False
-
-    ################
-    # Camera level object config. This config is merged with the global config above.
-    ################
-    objects:
-      track:
-        - person
-      filters:
-        person:
-          min_area: 5000
-          max_area: 100000
-          threshold: 0.8

config/config.yml

@@ -0,0 +1,42 @@
+web_port: 5000
+
+mqtt:
+  host: mqtt.server.com
+  topic_prefix: frigate
+
+cameras:
+  back:
+    rtsp:
+      user: viewer
+      host: 10.0.10.10
+      port: 554
+      # values that begin with a "$" will be replaced with environment variable
+      password: $RTSP_PASSWORD
+      path: /cam/realmonitor?channel=1&subtype=2
+    regions:
+      - size: 350
+        x_offset: 0
+        y_offset: 300
+      - size: 400
+        x_offset: 350
+        y_offset: 250
+      - size: 400
+        x_offset: 750
+        y_offset: 250
+    mask: back-mask.bmp
+    known_sizes:
+      - y: 300
+        min: 700
+        max: 1800
+      - y: 400
+        min: 3000
+        max: 7200
+      - y: 500
+        min: 8500
+        max: 20400
+      - y: 600
+        min: 10000
+        max: 50000
+      - y: 700
+        min: 10000
+        max: 125000

detect_objects.py

@@ -1,28 +1,13 @@
-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
+from flask import Flask, Response, make_response
 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_')}
+from frigate.video import Camera
+from frigate.object_detection import PreppedQueueProcessor
 
 with open('/config/config.yml') as f:
     CONFIG = yaml.safe_load(f)
@@ -32,405 +17,74 @@ 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')
 
-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], GLOBAL_OBJECT_CONFIG, camera_process['frame_queue'],
-                        camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue, 
-                        camera_process['process_fps'], camera_process['detection_fps'],
-                        camera_process['read_start'], camera_process['detection_frame']))
-                    process.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 > 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 = mqtt.Client()
     client.on_connect = on_connect
     client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
     if not MQTT_USER is None:
         client.username_pw_set(MQTT_USER, password=MQTT_PASS)
     client.connect(MQTT_HOST, MQTT_PORT, 60)
     client.loop_start()
+    
+    # Queue for prepped frames, max size set to (number of cameras * 5)
+    max_queue_size = len(CONFIG['cameras'].items())*10
+    prepped_frame_queue = queue.Queue(max_queue_size)
 
-    plasma_process = start_plasma_store()
-
-    ##
-    # Setup config defaults for cameras
-    ##
+    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)
-        }
+        cameras[name] = Camera(name, config, prepped_frame_queue, client, MQTT_TOPIC_PREFIX, DEBUG)
 
-    # Queue for cameras to push tracked objects to
-    tracked_objects_queue = mp.Queue()
+    prepped_queue_processor = PreppedQueueProcessor(
+        cameras,
+        prepped_frame_queue
+    )
+    prepped_queue_processor.start()
 
-    # Queue for clip processing
-    event_queue = mp.Queue()
-    
-    # Start the shared tflite process
-    tflite_process = EdgeTPUProcess()
-
-    # 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 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
-        }
-
-        camera_process = mp.Process(target=track_camera, args=(name, config, GLOBAL_OBJECT_CONFIG, frame_queue, frame_shape,
-            tflite_process.detection_queue, tracked_objects_queue, camera_processes[name]['process_fps'], 
-            camera_processes[name]['detection_fps'], 
-            camera_processes[name]['read_start'], camera_processes[name]['detection_frame']))
-        camera_process.daemon = True
-        camera_processes[name]['process'] = camera_process
-
-    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['cameras'], camera_processes, '/cache', '/clips', event_queue, stop_event)
-    event_processor.start()
-    
-    object_processor = TrackedObjectProcessor(CONFIG['cameras'], CONFIG.get('zones', {}), 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)
+    for name, camera in cameras.items():
+        camera.start()
+        print("Capture process for {}: {}".format(name, camera.get_capture_pid()))
 
     # create a flask app that encodes frames a mjpeg on demand
     app = Flask(__name__)
-    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,
-                    '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_frame = object_processor.get_best(camera_name, label)
-            if best_frame is None:
-                best_frame = np.zeros((720,1280,3), np.uint8)
-
-            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>/best_person.jpg')
+    def best_person(camera_name):
+        best_person_frame = cameras[camera_name].get_best_person()
+        if best_person_frame is None:
+            best_person_frame = np.zeros((720,1280,3), np.uint8)
+        ret, jpg = cv2.imencode('.jpg', best_person_frame)
+        response = make_response(jpg.tobytes())
+        response.headers['Content-Type'] = 'image/jpg'
+        return response
 
     @app.route('/<camera_name>')
     def mjpeg_feed(camera_name):
-        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)
+        # return a multipart response
+        return Response(imagestream(camera_name),
+                        mimetype='multipart/x-mixed-replace; boundary=frame')
 
-            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):
+    def imagestream(camera_name):
         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)
-
+            # max out at 5 FPS
+            time.sleep(0.2)
+            frame = cameras[camera_name].get_current_frame_with_objects()
+            # encode the image into a jpg
             ret, jpg = cv2.imencode('.jpg', frame)
             yield (b'--frame\r\n'
                 b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
 
     app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
 
-    object_processor.join()
-    
-    plasma_process.terminate()
+    camera.join()
 
 if __name__ == '__main__':
-    main()
+    main()

docs/DEVICES.md

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

frigate/edgetpu.py

@@ -1,142 +0,0 @@
-import os
-import datetime
-import hashlib
-import multiprocessing as mp
-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():
-    def __init__(self):
-        edge_tpu_delegate = None
-        try:
-            edge_tpu_delegate = load_delegate('libedgetpu.so.1.0')
-        except ValueError:
-            print("No EdgeTPU detected. Falling back to CPU.")
-        
-        if 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_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):
-    print(f"Starting detection process: {os.getpid()}")
-    listen()
-    plasma_client = plasma.connect("/tmp/plasma")
-    object_detector = ObjectDetector()
-
-    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):
-        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.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.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

frigate/events.py

@@ -1,158 +0,0 @@
-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()
-        
-        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()
-
-            if event_type == 'start':
-                self.events_in_process[event_data['id']] = event_data
-
-            if event_type == 'end':
-                if self.config[camera].get('save_clips', {}).get('enabled', False) and len(self.cached_clips) > 0:
-                    self.create_clip(camera, event_data, self.config[camera].get('save_clips', {}).get('pre_capture', 30))
-                del self.events_in_process[event_data['id']]
-
-                

frigate/motion.py

@@ -1,79 +0,0 @@
-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

frigate/mqtt.py

@@ -0,0 +1,33 @@
+import json
+import threading
+
+class MqttObjectPublisher(threading.Thread):
+    def __init__(self, client, topic_prefix, objects_parsed, detected_objects):
+        threading.Thread.__init__(self)
+        self.client = client
+        self.topic_prefix = topic_prefix
+        self.objects_parsed = objects_parsed
+        self._detected_objects = detected_objects
+
+    def run(self):
+        last_sent_payload = ""
+        while True:
+
+            # initialize the payload
+            payload = {}
+
+            # wait until objects have been parsed
+            with self.objects_parsed:
+                self.objects_parsed.wait()
+
+            # add all the person scores in detected objects
+            detected_objects = self._detected_objects.copy()
+            person_score = sum([obj['score'] for obj in detected_objects if obj['name'] == 'person'])
+            # if the person score is more than 100, set person to ON
+            payload['person'] = 'ON' if int(person_score*100) > 100 else 'OFF'
+
+            # send message for objects if different
+            new_payload = json.dumps(payload, sort_keys=True)
+            if new_payload != last_sent_payload:
+                last_sent_payload = new_payload
+                self.client.publish(self.topic_prefix+'/objects', new_payload, retain=False)

frigate/object_detection.py

@@ -0,0 +1,110 @@
+import datetime
+import time
+import cv2
+import threading
+import numpy as np
+from edgetpu.detection.engine import DetectionEngine
+from . util import tonumpyarray
+
+# Path to frozen detection graph. This is the actual model that is used for the object detection.
+PATH_TO_CKPT = '/frozen_inference_graph.pb'
+# List of the strings that is used to add correct label for each box.
+PATH_TO_LABELS = '/label_map.pbtext'
+
+# Function to read labels from text files.
+def ReadLabelFile(file_path):
+    with open(file_path, 'r') as f:
+        lines = f.readlines()
+    ret = {}
+    for line in lines:
+        pair = line.strip().split(maxsplit=1)
+        ret[int(pair[0])] = pair[1].strip()
+    return ret
+
+class PreppedQueueProcessor(threading.Thread):
+    def __init__(self, cameras, prepped_frame_queue):
+
+        threading.Thread.__init__(self)
+        self.cameras = cameras
+        self.prepped_frame_queue = prepped_frame_queue
+        
+        # Load the edgetpu engine and labels
+        self.engine = DetectionEngine(PATH_TO_CKPT)
+        self.labels = ReadLabelFile(PATH_TO_LABELS)
+
+    def run(self):
+        # process queue...
+        while True:
+            frame = self.prepped_frame_queue.get()
+
+            # Actual detection.
+            objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.5, top_k=3)
+            # parse and pass detected objects back to the camera
+            parsed_objects = []
+            for obj in objects:
+                box = obj.bounding_box.flatten().tolist()
+                parsed_objects.append({
+                            'frame_time': frame['frame_time'],
+                            'name': str(self.labels[obj.label_id]),
+                            'score': float(obj.score),
+                            'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']),
+                            'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']),
+                            'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']),
+                            'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset'])
+                        })
+            self.cameras[frame['camera_name']].add_objects(parsed_objects)
+
+
+# should this be a region class?
+class FramePrepper(threading.Thread):
+    def __init__(self, camera_name, shared_frame, frame_time, frame_ready, 
+        frame_lock,
+        region_size, region_x_offset, region_y_offset,
+        prepped_frame_queue):
+
+        threading.Thread.__init__(self)
+        self.camera_name = camera_name
+        self.shared_frame = shared_frame
+        self.frame_time = frame_time
+        self.frame_ready = frame_ready
+        self.frame_lock = frame_lock
+        self.region_size = region_size
+        self.region_x_offset = region_x_offset
+        self.region_y_offset = region_y_offset
+        self.prepped_frame_queue = prepped_frame_queue
+
+    def run(self):
+        frame_time = 0.0
+        while True:
+            now = datetime.datetime.now().timestamp()
+
+            with self.frame_ready:
+                # if there isnt a frame ready for processing or it is old, wait for a new frame
+                if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
+                    self.frame_ready.wait()
+            
+            # make a copy of the cropped frame
+            with self.frame_lock:
+                cropped_frame = self.shared_frame[self.region_y_offset:self.region_y_offset+self.region_size, self.region_x_offset:self.region_x_offset+self.region_size].copy()
+                frame_time = self.frame_time.value
+            
+            # convert to RGB
+            cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
+            # Resize to 300x300 if needed
+            if cropped_frame_rgb.shape != (300, 300, 3):
+                cropped_frame_rgb = cv2.resize(cropped_frame_rgb, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
+            # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
+            frame_expanded = np.expand_dims(cropped_frame_rgb, axis=0)
+
+            # add the frame to the queue
+            if not self.prepped_frame_queue.full():
+                self.prepped_frame_queue.put({
+                    'camera_name': self.camera_name,
+                    'frame_time': frame_time,
+                    'frame': frame_expanded.flatten().copy(),
+                    'region_size': self.region_size,
+                    'region_x_offset': self.region_x_offset,
+                    'region_y_offset': self.region_y_offset
+                })
+            else:
+                print("queue full. moving on")

frigate/object_processing.py

@@ -1,277 +0,0 @@
-import json
-import hashlib
-import datetime
-import time
-import copy
-import cv2
-import threading
-import queue
-import numpy as np
-from collections import Counter, defaultdict
-import itertools
-import pyarrow.plasma as plasma
-import matplotlib.pyplot as plt
-from frigate.util import draw_box_with_label, PlasmaManager
-from frigate.edgetpu import load_labels
-
-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 filter_false_positives(event):
-    if len(event['history']) < 2:
-        return True
-    return False
-
-def zone_filtered(obj, object_config):
-    object_name = obj['label']
-    object_filters = object_config.get('filters', {})
-
-    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['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['score']:
-            return True
-        
-    return False
-
-class TrackedObjectProcessor(threading.Thread):
-    def __init__(self, camera_config, zone_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
-        threading.Thread.__init__(self)
-        self.camera_config = camera_config
-        self.zone_config = zone_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_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
-        })
-        self.zone_data = defaultdict(lambda: {
-            'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
-            'contours': {}
-        })
-
-        # create zone contours
-        for name, config in zone_config.items():
-            for camera, camera_zone_config in config.items():
-                coordinates = camera_zone_config['coordinates']
-                if isinstance(coordinates, list):
-                    self.zone_data[name]['contours'][camera] =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
-                elif isinstance(coordinates, str):
-                    points = coordinates.split(',')
-                    self.zone_data[name]['contours'][camera] =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
-                else:
-                    print(f"Unable to parse zone coordinates for {name} - {camera}")
-        
-        # set colors for zones
-        colors = plt.cm.get_cmap('tab10', len(self.zone_data.keys()))
-        for i, zone in enumerate(self.zone_data.values()):
-            zone['color'] = tuple(int(round(255 * c)) for c in colors(i)[:3])
-
-        self.plasma_client = PlasmaManager(self.stop_event)
-        
-    def get_best(self, camera, label):
-        if label in self.camera_data[camera]['best_objects']:
-            return self.camera_data[camera]['best_objects'][label]['frame']
-        else:
-            return None
-    
-    def get_current_frame(self, camera):
-        return self.camera_data[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_config = self.camera_config[camera]
-            best_objects = self.camera_data[camera]['best_objects']
-            current_object_status = self.camera_data[camera]['object_status']
-            tracked_objects = self.camera_data[camera]['tracked_objects']
-
-            current_ids = current_tracked_objects.keys()
-            previous_ids = 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:
-                # only register the object here if we are sure it isnt a false positive
-                if not filter_false_positives(current_tracked_objects[id]):
-                    tracked_objects[id] = current_tracked_objects[id]
-                    # publish events to mqtt
-                    self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(tracked_objects[id]), retain=False)
-                    self.event_queue.put(('start', camera, tracked_objects[id]))
-            
-            for id in updated_ids:
-                tracked_objects[id] = current_tracked_objects[id]
-            
-            for id in removed_ids:
-                # publish events to mqtt
-                tracked_objects[id]['end_time'] = frame_time
-                self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(tracked_objects[id]), retain=False)
-                self.event_queue.put(('end', camera, tracked_objects[id]))
-                del tracked_objects[id]
-
-            self.camera_data[camera]['current_frame_time'] = frame_time
-
-            # build a dict of objects in each zone for current camera
-            current_objects_in_zones = defaultdict(lambda: [])
-            for obj in tracked_objects.values():
-                bottom_center = (obj['centroid'][0], obj['box'][3])
-                # check each zone
-                for name, zone in self.zone_data.items():
-                    current_contour = zone['contours'].get(camera, None)
-                    # if the current camera does not have a contour for this zone, skip
-                    if current_contour is None:
-                        continue
-                    # check if the object is in the zone and not filtered
-                    if (cv2.pointPolygonTest(current_contour, bottom_center, False) >= 0 
-                        and not zone_filtered(obj, self.zone_config[name][camera].get('filters', {}))):
-                        current_objects_in_zones[name].append(obj['label'])
-
-            ###
-            # Draw tracked objects on the frame
-            ###
-            current_frame = self.plasma_client.get(f"{camera}{frame_time}")
-
-            if not current_frame is plasma.ObjectNotAvailable:
-                # draw the bounding boxes on the frame
-                for obj in tracked_objects.values():
-                    thickness = 2
-                    color = COLOR_MAP[obj['label']]
-                    
-                    if obj['frame_time'] != frame_time:
-                        thickness = 1
-                        color = (255,0,0)
-
-                    # draw the bounding boxes on the frame
-                    box = obj['box']
-                    draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
-                    # draw the regions on the frame
-                    region = obj['region']
-                    cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
-                
-                if camera_config['snapshots']['show_timestamp']:
-                    time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-                    cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
-
-                if camera_config['snapshots']['draw_zones']:
-                    for name, zone in self.zone_data.items():
-                        thickness = 2 if len(current_objects_in_zones[name]) == 0 else 8
-                        if camera in zone['contours']:
-                            cv2.drawContours(current_frame, [zone['contours'][camera]], -1, zone['color'], thickness)
-
-                ###
-                # Set the current frame
-                ###
-                self.camera_data[camera]['current_frame'] = current_frame
-
-                # delete the previous frame from the plasma store and update the object id
-                if not self.camera_data[camera]['object_id'] is None:
-                    self.plasma_client.delete(self.camera_data[camera]['object_id'])
-                self.camera_data[camera]['object_id'] = f"{camera}{frame_time}"
-            
-            ###
-            # Maintain the highest scoring recent object and frame for each label
-            ###
-            for obj in tracked_objects.values():
-                # if the object wasn't seen on the current frame, skip it
-                if obj['frame_time'] != frame_time:
-                    continue
-                if obj['label'] in best_objects:
-                    now = datetime.datetime.now().timestamp()
-                    # if the object is a higher score than the current best score 
-                    # or the current object is more than 1 minute old, use the new object
-                    if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
-                        obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
-                        best_objects[obj['label']] = obj
-                        # send updated snapshot over mqtt
-                        best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_RGB2BGR)
-                        ret, jpg = cv2.imencode('.jpg', best_frame)
-                        if ret:
-                            jpg_bytes = jpg.tobytes()
-                            self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", jpg_bytes, retain=True)
-                else:
-                    obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
-                    best_objects[obj['label']] = obj
-
-            ###
-            # Report over MQTT
-            ###
-
-            # get the zones that are relevant for this camera
-            relevant_zones = [zone for zone, config in self.zone_config.items() if camera in config]
-            for zone in relevant_zones:
-                # create the set of labels in the current frame and previously reported
-                labels_for_zone = set(current_objects_in_zones[zone] + list(self.zone_data[zone]['object_status'][camera].keys()))
-                # for each label
-                for label in labels_for_zone:
-                    # compute the current 'ON' vs 'OFF' status by checking if any camera sees the object in the zone
-                    previous_state = any([c[label] == 'ON' for c in self.zone_data[zone]['object_status'].values()])
-                    self.zone_data[zone]['object_status'][camera][label] = 'ON' if label in current_objects_in_zones[zone] else 'OFF'
-                    new_state = any([c[label] == 'ON' for c in self.zone_data[zone]['object_status'].values()])
-                    # 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)
-
-            # count  by type
-            obj_counter = Counter()
-            for obj in tracked_objects.values():
-                obj_counter[obj['label']] += 1
-                    
-            # report on detected objects
-            for obj_name, count in obj_counter.items():
-                new_status = 'ON' if count > 0 else 'OFF'
-                if new_status != current_object_status[obj_name]:
-                    current_object_status[obj_name] = new_status
-                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
-                    # send the best snapshot over mqtt
-                    best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
-                    ret, jpg = cv2.imencode('.jpg', best_frame)
-                    if ret:
-                        jpg_bytes = jpg.tobytes()
-                        self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
-
-            # expire any objects that are ON and no longer detected
-            expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
-            for obj_name in expired_objects:
-                current_object_status[obj_name] = 'OFF'
-                self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
-                # send updated snapshot over mqtt
-                best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
-                ret, jpg = cv2.imencode('.jpg', best_frame)
-                if ret:
-                    jpg_bytes = jpg.tobytes()
-                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)

frigate/objects.py

@@ -2,165 +2,95 @@ 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
+from object_detection.utils import visualization_utils as vis_util
 
-class ObjectTracker():
-    def __init__(self, max_disappeared):
-        self.tracked_objects = {}
-        self.disappeared = {}
-        self.max_disappeared = max_disappeared
+class ObjectCleaner(threading.Thread):
+    def __init__(self, objects_parsed, detected_objects):
+        threading.Thread.__init__(self)
+        self._objects_parsed = objects_parsed
+        self._detected_objects = detected_objects
 
-    def 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.add_history(obj)
-        self.tracked_objects[id] = obj
-        self.disappeared[id] = 0
+    def run(self):
+        while True:
 
-    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)
-        self.add_history(self.tracked_objects[id])
-        if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
-            self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
-    
-    def add_history(self, obj):
-        entry = {
-            'score': obj['score'],
-            'box': obj['box'],
-            'region': obj['region'],
-            'centroid': obj['centroid'],
-            'frame_time': obj['frame_time']
-        }
-        if 'history' in obj:
-            obj['history'].append(entry)
-            # only maintain the last 20 in history
-            if len(obj['history']) > 20:
-                obj['history'] = obj['history'][-20:]
-        else:
-            obj['history'] = [entry]
+            # wait a bit before checking for expired frames
+            time.sleep(0.2)
 
-    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])
+            # expire the objects that are more than 1 second old
+            now = datetime.datetime.now().timestamp()
+            # look for the first object found within the last second
+            # (newest objects are appended to the end)
+            detected_objects = self._detected_objects.copy()
 
-            # 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)
+            num_to_delete = 0
+            for obj in detected_objects:
+                if now-obj['frame_time']<2:
+                    break
+                num_to_delete += 1
+            if num_to_delete > 0:
+                del self._detected_objects[:num_to_delete]
 
-            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])
+                # notify that parsed objects were changed
+                with self._objects_parsed:
+                    self._objects_parsed.notify_all()
 
-            # 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()
+# Maintains the frame and person with the highest score from the most recent
+# motion event
+class BestPersonFrame(threading.Thread):
+    def __init__(self, objects_parsed, recent_frames, detected_objects):
+        threading.Thread.__init__(self)
+        self.objects_parsed = objects_parsed
+        self.recent_frames = recent_frames
+        self.detected_objects = detected_objects
+        self.best_person = None
+        self.best_frame = None
 
-            # 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]
+    def run(self):
+        while True:
 
-            # 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()
+            # wait until objects have been parsed
+            with self.objects_parsed:
+                self.objects_parsed.wait()
 
-            # 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
+            # make a copy of detected objects
+            detected_objects = self.detected_objects.copy()
+            detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
 
-                # otherwise, grab the object ID for the current row,
-                # set its new centroid, and reset the disappeared
-                # counter
-                objectID = current_ids[row]
-                self.update(objectID, group[col])
+            # get the highest scoring person
+            new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
 
-                # indicate that we have examined each of the row and
-                # column indexes, respectively
-                usedRows.add(row)
-                usedCols.add(col)
+            # if there isnt a person, continue
+            if new_best_person is None:
+                continue
 
-            # 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
+            # if there is no current best_person
+            if self.best_person is None:
+                self.best_person = new_best_person
+            # if there is already a best_person
             else:
-                for col in unusedCols:
-                    self.register(col, group[col])
+                now = datetime.datetime.now().timestamp()
+                # if the new best person is a higher score than the current best person 
+                # or the current person is more than 1 minute old, use the new best person
+                if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
+                    self.best_person = new_best_person
+            
+            # make a copy of the recent frames
+            recent_frames = self.recent_frames.copy()
+            
+            if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
+                best_frame = recent_frames[self.best_person['frame_time']]
+                best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
+                # draw the bounding box on the frame
+                vis_util.draw_bounding_box_on_image_array(best_frame,
+                    self.best_person['ymin'],
+                    self.best_person['xmin'],
+                    self.best_person['ymax'],
+                    self.best_person['xmax'],
+                    color='red',
+                    thickness=2,
+                    display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))],
+                    use_normalized_coordinates=False)
+
+                # convert back to BGR
+                self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)

frigate/util.py

@@ -1,192 +1,5 @@
-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)
-    # 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):
-        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):
-		# 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 PlasmaManager:
-    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:
-                return self.plasma_client.get(object_id, timeout_ms=timeout_ms)
-            except:
-                self.connect()
-                time.sleep(1)
-
-    def put(self, name, obj):
-        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(obj, 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)
+# convert shared memory array into numpy array
+def tonumpyarray(mp_arr):
+    return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)

frigate/video.py

@@ -2,383 +2,328 @@ 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 collections import defaultdict
-from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, PlasmaManager
-from frigate.objects import ObjectTracker
-from frigate.edgetpu import RemoteObjectDetector
-from frigate.motion import MotionDetector
+from object_detection.utils import visualization_utils as vis_util
+from . util import tonumpyarray
+from . object_detection import FramePrepper
+from . objects import ObjectCleaner, BestPersonFrame
+from . mqtt import MqttObjectPublisher
 
-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)
+# fetch the frames as fast a possible and store current frame in a shared memory array
+def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_shape, rtsp_url, take_frame=1):
+    # convert shared memory array into numpy and shape into image array
+    arr = tonumpyarray(shared_arr).reshape(frame_shape)
 
-    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
+    # start the video capture
+    video = cv2.VideoCapture()
+    video.open(rtsp_url)
+    # keep the buffer small so we minimize old data
+    video.set(cv2.CAP_PROP_BUFFERSIZE,1)
 
-    if video_info['height'] != 0 and video_info['width'] != 0:
-        return (video_info['height'], video_info['width'], 3)
+    bad_frame_counter = 0
+    frame_num = 0
+    while True:
+        # check if the video stream is still open, and reopen if needed
+        if not video.isOpened():
+            success = video.open(rtsp_url)
+            if not success:
+                time.sleep(1)
+                continue
+        # grab the frame, but dont decode it yet
+        ret = video.grab()
+        # snapshot the time the frame was grabbed
+        frame_time = datetime.datetime.now()
+        if ret:
+            frame_num += 1
+            if (frame_num % take_frame) != 0:
+                continue
+            # go ahead and decode the current frame
+            ret, frame = video.retrieve()
+            if ret:
+                # Lock access and update frame
+                with frame_lock:
+                    arr[:] = frame
+                    shared_frame_time.value = frame_time.timestamp()
+                # Notify with the condition that a new frame is ready
+                with frame_ready:
+                    frame_ready.notify_all()
+                bad_frame_counter = 0
+            else:
+                print("Unable to decode frame")
+                bad_frame_counter += 1
+        else:
+            print("Unable to grab a frame")
+            bad_frame_counter += 1
+        
+        if bad_frame_counter > 100:
+            video.release()
     
-    # fallback to using opencv if ffprobe didnt succeed
-    video = cv2.VideoCapture(source)
+    video.release()
+
+# Stores 2 seconds worth of frames when motion is detected so they can be used for other threads
+class FrameTracker(threading.Thread):
+    def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames):
+        threading.Thread.__init__(self)
+        self.shared_frame = shared_frame
+        self.frame_time = frame_time
+        self.frame_ready = frame_ready
+        self.frame_lock = frame_lock
+        self.recent_frames = recent_frames
+
+    def run(self):
+        frame_time = 0.0
+        while True:
+            now = datetime.datetime.now().timestamp()
+            # wait for a frame
+            with self.frame_ready:
+                # if there isnt a frame ready for processing or it is old, wait for a signal
+                if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
+                    self.frame_ready.wait()
+            
+            # lock and make a copy of the frame
+            with self.frame_lock: 
+                frame = self.shared_frame.copy()
+                frame_time = self.frame_time.value
+            
+            # add the frame to recent frames
+            self.recent_frames[frame_time] = frame
+
+            # delete any old frames
+            stored_frame_times = list(self.recent_frames.keys())
+            for k in stored_frame_times:
+                if (now - k) > 2:
+                    del self.recent_frames[k]
+
+def get_frame_shape(rtsp_url):
+    # capture a single frame and check the frame shape so the correct array
+    # size can be allocated in memory
+    video = cv2.VideoCapture(rtsp_url)
     ret, frame = video.read()
     frame_shape = frame.shape
     video.release()
     return frame_shape
 
-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 get_rtsp_url(rtsp_config):
+    if (rtsp_config['password'].startswith('$')):
+        rtsp_config['password'] = os.getenv(rtsp_config['password'][1:])
+    return 'rtsp://{}:{}@{}:{}{}'.format(rtsp_config['user'], 
+        rtsp_config['password'], rtsp_config['host'], rtsp_config['port'],
+        rtsp_config['path'])
 
-def filtered(obj, objects_to_track, object_filters, mask):
-    object_name = obj[0]
+def compute_sizes(frame_shape, known_sizes, mask):
+    # create a 3 dimensional numpy array to store estimated sizes
+    estimated_sizes = np.zeros((frame_shape[0], frame_shape[1], 2), np.uint32)
 
-    if not object_name in objects_to_track:
-        return True
-    
-    if object_name in object_filters:
-        obj_settings = object_filters[object_name]
+    sorted_positions = sorted(known_sizes, key=lambda s: s['y'])
 
-        # 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
+    last_position = {'y': 0, 'min': 0, 'max': 0}
+    next_position = sorted_positions.pop(0)
+    # if the next position has the same y coordinate, skip
+    while next_position['y'] == last_position['y']:
+        next_position = sorted_positions.pop(0)
+    y_change = next_position['y']-last_position['y']
+    min_size_change = next_position['min']-last_position['min']
+    max_size_change = next_position['max']-last_position['max']
+    min_step_size = min_size_change/y_change
+    max_step_size = max_size_change/y_change
 
-        # if the score is lower than the threshold, skip
-        if obj_settings.get('threshold', 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)
+    min_current_size = 0
+    max_current_size = 0
 
-        # if the object is in a masked location, don't add it to detected objects
-        if mask[y_location][x_location] == [0]:
-            return True
-        
-        return False
+    for y_position in range(frame_shape[0]):
+        # fill the row with the estimated size
+        estimated_sizes[y_position,:] = [min_current_size, max_current_size]
 
-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
-
-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 = PlasmaManager(stop_event)
-        self.ffmpeg_process = ffmpeg_process
-        self.current_frame = 0
-        self.last_frame = 0
-        self.detection_frame = detection_frame
-        self.stop_event = stop_event
-
-    def run(self):
-        frame_num = 0
-        self.skipped_fps.start()
-        while True:
-            if self.stop_event.is_set():
-                print(f"{self.name}: stop event set. exiting capture thread...")
-                break
-
-            if self.ffmpeg_process.poll() != None:
-                print(f"{self.name}: ffmpeg process is not running. exiting capture thread...")
-                break
-
-            frame_bytes = self.ffmpeg_process.stdout.read(self.frame_size)
-            self.current_frame = datetime.datetime.now().timestamp()
-
-            if len(frame_bytes) == 0:
-                print(f"{self.name}: ffmpeg didnt return a frame. something is wrong.")
-                continue
-
-            self.fps.update()
-
-            frame_num += 1
-            if (frame_num % self.take_frame) != 0:
-                self.skipped_fps.update()
-                continue
-
-            # if the detection process is more than 1 second behind, skip this frame
-            if self.detection_frame.value > 0.0 and (self.last_frame - self.detection_frame.value) > 1:
-                self.skipped_fps.update()
-                continue
-
-            # put the frame in the plasma store
-            self.plasma_client.put(f"{self.name}{self.current_frame}",
-                    np
-                        .frombuffer(frame_bytes, np.uint8)
-                        .reshape(self.frame_shape)
-                )
-            # add to the queue
-            self.frame_queue.put(self.current_frame)
-            self.last_frame = self.current_frame
-
-def track_camera(name, config, global_objects_config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame):
-    print(f"Starting process for {name}: {os.getpid()}")
-    listen()
-
-    detection_frame.value = 0.0
-
-    # Merge the tracked object config with the global config
-    camera_objects_config = config.get('objects', {})    
-    # combine tracked objects lists
-    objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', []))
-    # merge object filters
-    global_object_filters = global_objects_config.get('filters', {})
-    camera_object_filters = camera_objects_config.get('filters', {})
-    objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys())
-    object_filters = {}
-    for obj in objects_with_config:
-        object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
-
-    frame = np.zeros(frame_shape, np.uint8)
-
-    # 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)
-        else:
-            mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE)
-    else:
-        mask = None
-
-    if mask is None:
-        mask = np.zeros((frame_shape[0], frame_shape[1], 1), np.uint8)
-        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 = PlasmaManager()
-    avg_wait = 0.0
-    fps_tracker = EventsPerSecond()
-    fps_tracker.start()
-    object_detector.fps.start()
-    while True:
-        read_start.value = datetime.datetime.now().timestamp()
-        frame_time = frame_queue.get()
-        duration = datetime.datetime.now().timestamp()-read_start.value
-        read_start.value = 0.0
-        avg_wait = (avg_wait*99+duration)/100
-        detection_frame.value = frame_time
-        
-        # Get frame from plasma store
-        frame = plasma_client.get(f"{name}{frame_time}")
-
-        if frame is plasma.ObjectNotAvailable:
-            continue
-
-        fps_tracker.update()
-        fps.value = fps_tracker.eps()
-        detection_fps.value = object_detector.fps.eps()
-        
-        # look for motion
-        motion_boxes = motion_detector.detect(frame)
-
-        tracked_objects = object_tracker.tracked_objects.values()
-
-        # merge areas of motion that intersect with a known tracked object into a single area to look at
-        areas_of_interest = []
-        used_motion_boxes = []
-        for obj in tracked_objects:
-            x_min, y_min, x_max, y_max = obj['box']
-            for m_index, motion_box in enumerate(motion_boxes):
-                if intersection_over_union(motion_box, obj['box']) > .2:
-                    used_motion_boxes.append(m_index)
-                    x_min = min(obj['box'][0], motion_box[0])
-                    y_min = min(obj['box'][1], motion_box[1])
-                    x_max = max(obj['box'][2], motion_box[2])
-                    y_max = max(obj['box'][3], motion_box[3])
-            areas_of_interest.append((x_min, y_min, x_max, y_max))
-        unused_motion_boxes = set(range(0, len(motion_boxes))).difference(used_motion_boxes)
-        
-        # compute motion regions
-        motion_regions = [calculate_region(frame_shape, motion_boxes[i][0], motion_boxes[i][1], motion_boxes[i][2], motion_boxes[i][3], 1.2)
-            for i in unused_motion_boxes]
-        
-        # compute tracked object regions
-        object_regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
-            for a in areas_of_interest]
-        
-        # merge regions with high IOU
-        merged_regions = motion_regions+object_regions
-        while True:
-            max_iou = 0.0
-            max_indices = None
-            region_indices = range(len(merged_regions))
-            for a, b in itertools.combinations(region_indices, 2):
-                iou = intersection_over_union(merged_regions[a], merged_regions[b])
-                if iou > max_iou:
-                    max_iou = iou
-                    max_indices = (a, b)
-            if max_iou > 0.1:
-                a = merged_regions[max_indices[0]]
-                b = merged_regions[max_indices[1]]
-                merged_regions.append(calculate_region(frame_shape,
-                    min(a[0], b[0]),
-                    min(a[1], b[1]),
-                    max(a[2], b[2]),
-                    max(a[3], b[3]),
-                    1
-                ))
-                del merged_regions[max(max_indices[0], max_indices[1])]
-                del merged_regions[min(max_indices[0], max_indices[1])]
+        # if you have reached the next size
+        if y_position == next_position['y']:
+            last_position = next_position
+            # if there are still positions left
+            if len(sorted_positions) > 0:
+                next_position = sorted_positions.pop(0)
+                # if the next position has the same y coordinate, skip
+                while next_position['y'] == last_position['y']:
+                    next_position = sorted_positions.pop(0)
+                y_change = next_position['y']-last_position['y']
+                min_size_change = next_position['min']-last_position['min']
+                max_size_change = next_position['max']-last_position['max']
+                min_step_size = min_size_change/y_change
+                max_step_size = max_size_change/y_change
             else:
-                break
-
-        # resize regions and detect
-        detections = []
-        for region in merged_regions:
-
-            tensor_input = create_tensor_input(frame, region)
-
-            region_detections = object_detector.detect(tensor_input)
-
-            for d in region_detections:
-                box = d[2]
-                size = region[2]-region[0]
-                x_min = int((box[1] * size) + region[0])
-                y_min = int((box[0] * size) + region[1])
-                x_max = int((box[3] * size) + region[0])
-                y_max = int((box[2] * size) + region[1])
-                det = (d[0],
-                    d[1],
-                    (x_min, y_min, x_max, y_max),
-                    (x_max-x_min)*(y_max-y_min),
-                    region)
-                if filtered(det, objects_to_track, object_filters, mask):
-                    continue
-                detections.append(det)
-
-        #########
-        # merge objects, check for clipped objects and look again up to N 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])
-                        
-                        tensor_input = create_tensor_input(frame, region)
-                        # run detection on new region
-                        refined_detections = object_detector.detect(tensor_input)
-                        for d in refined_detections:
-                            box = d[2]
-                            size = region[2]-region[0]
-                            x_min = int((box[1] * size) + region[0])
-                            y_min = int((box[0] * size) + region[1])
-                            x_max = int((box[3] * size) + region[0])
-                            y_max = int((box[2] * size) + region[1])
-                            det = (d[0],
-                                d[1],
-                                (x_min, y_min, x_max, y_max),
-                                (x_max-x_min)*(y_max-y_min),
-                                region)
-                            if filtered(det, objects_to_track, object_filters, mask):
-                                continue
-                            selected_objects.append(det)
-
-                        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
+                min_step_size = 0
+                max_step_size = 0
         
-        # now that we have refined our detections, we need to track objects
-        object_tracker.match_and_update(frame_time, detections)
+        min_current_size += min_step_size
+        max_current_size += max_step_size
 
-        # add to the queue
-        detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects))
+    # apply mask by filling 0s for all locations a person could not be standing
+    if mask is not None:
+        pass
 
-    print(f"{name}: exiting subprocess")
+    return estimated_sizes
+
+class Camera:
+    def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix, debug=False):
+        self.name = name
+        self.config = config
+        self.detected_objects = []
+        self.recent_frames = {}
+        self.rtsp_url = get_rtsp_url(self.config['rtsp'])
+        self.take_frame = self.config.get('take_frame', 1)
+        self.regions = self.config['regions']
+        self.frame_shape = get_frame_shape(self.rtsp_url)
+        self.mqtt_client = mqtt_client
+        self.mqtt_topic_prefix = '{}/{}'.format(mqtt_prefix, self.name)
+        self.debug = debug
+
+        # compute the flattened array length from the shape of the frame
+        flat_array_length = self.frame_shape[0] * self.frame_shape[1] * self.frame_shape[2]
+        # create shared array for storing the full frame image data
+        self.shared_frame_array = mp.Array(ctypes.c_uint8, flat_array_length)
+        # create shared value for storing the frame_time
+        self.shared_frame_time = mp.Value('d', 0.0)
+        # Lock to control access to the frame
+        self.frame_lock = mp.Lock()
+        # Condition for notifying that a new frame is ready
+        self.frame_ready = mp.Condition()
+        # Condition for notifying that objects were parsed
+        self.objects_parsed = mp.Condition()
+
+        # shape current frame so it can be treated as a numpy image
+        self.shared_frame_np = tonumpyarray(self.shared_frame_array).reshape(self.frame_shape)
+
+        # create the process to capture frames from the RTSP stream and store in a shared array
+        self.capture_process = mp.Process(target=fetch_frames, args=(self.shared_frame_array, 
+            self.shared_frame_time, self.frame_lock, self.frame_ready, self.frame_shape,
+            self.rtsp_url, self.take_frame))
+        self.capture_process.daemon = True
+
+        # for each region, create a separate thread to resize the region and prep for detection
+        self.detection_prep_threads = []
+        for region in self.config['regions']:
+            self.detection_prep_threads.append(FramePrepper(
+                self.name,
+                self.shared_frame_np,
+                self.shared_frame_time,
+                self.frame_ready,
+                self.frame_lock,
+                region['size'], region['x_offset'], region['y_offset'],
+                prepped_frame_queue
+            ))
+        
+        # start a thread to store recent motion frames for processing
+        self.frame_tracker = FrameTracker(self.shared_frame_np, self.shared_frame_time, 
+            self.frame_ready, self.frame_lock, self.recent_frames)
+        self.frame_tracker.start()
+
+        # start a thread to store the highest scoring recent person frame
+        self.best_person_frame = BestPersonFrame(self.objects_parsed, self.recent_frames, self.detected_objects)
+        self.best_person_frame.start()
+
+        # start a thread to expire objects from the detected objects list
+        self.object_cleaner = ObjectCleaner(self.objects_parsed, self.detected_objects)
+        self.object_cleaner.start()
+
+        # start a thread to publish object scores (currently only person)
+        mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects)
+        mqtt_publisher.start()
+
+        # load in the mask for person detection
+        if 'mask' in self.config:
+            self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
+        else:
+            self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
+            self.mask[:] = 255
+
+        # pre-compute estimated person size for every pixel in the image
+        if 'known_sizes' in self.config:
+            self.calculated_person_sizes = compute_sizes((self.frame_shape[0], self.frame_shape[1]), 
+                self.config['known_sizes'], None)
+        else:
+            self.calculated_person_sizes = None
+    
+    def start(self):
+        self.capture_process.start()
+        # start the object detection prep threads
+        for detection_prep_thread in self.detection_prep_threads:
+            detection_prep_thread.start()
+    
+    def join(self):
+        self.capture_process.join()
+    
+    def get_capture_pid(self):
+        return self.capture_process.pid
+    
+    def add_objects(self, objects):
+        if len(objects) == 0:
+            return
+
+        for obj in objects:
+            if self.debug:
+                # print out the detected objects, scores and locations
+                print(self.name, obj['name'], obj['score'], obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'])
+            
+            location = (int(obj['ymax']), int((obj['xmax']-obj['xmin'])/2))
+
+            # if the person is in a masked location, continue
+            if self.mask[location[0]][location[1]] == [0]:
+                continue
+
+            if self.calculated_person_sizes is not None and obj['name'] == 'person':
+                person_size_range = self.calculated_person_sizes[location[0]][location[1]]
+
+                # if the person isnt on the ground, continue
+                if(person_size_range[0] == 0 and person_size_range[1] == 0):
+                    continue
+
+                person_size = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
+
+                # if the person is not within 20% of the estimated size for that location, continue
+                if person_size < person_size_range[0] or person_size > person_size_range[1]:
+                    continue
+
+            self.detected_objects.append(obj)
+
+        with self.objects_parsed:
+            self.objects_parsed.notify_all()
+
+    def get_best_person(self):
+        return self.best_person_frame.best_frame
+    
+    def get_current_frame_with_objects(self):
+        # make a copy of the current detected objects
+        detected_objects = self.detected_objects.copy()
+        # lock and make a copy of the current frame
+        with self.frame_lock:
+            frame = self.shared_frame_np.copy()
+
+        # convert to RGB for drawing
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # draw the bounding boxes on the screen
+        for obj in detected_objects:
+            vis_util.draw_bounding_box_on_image_array(frame,
+                obj['ymin'],
+                obj['xmin'],
+                obj['ymax'],
+                obj['xmax'],
+                color='red',
+                thickness=2,
+                display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
+                use_normalized_coordinates=False)
+
+        for region in self.regions:
+            color = (255,255,255)
+            cv2.rectangle(frame, (region['x_offset'], region['y_offset']), 
+                (region['x_offset']+region['size'], region['y_offset']+region['size']), 
+                color, 2)
+
+        # convert back to BGR
+        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+        return frame
+
+
+    
+