working odroid build, still needs hwaccel

.dockerignore

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

.github/FUNDING.yml

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

.gitignore

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

Dockerfile

@@ -1,60 +1,97 @@
 FROM ubuntu:18.04
-LABEL maintainer "blakeb@blakeshome.com"
 
-ENV DEBIAN_FRONTEND=noninteractive
 # Install packages for apt repo
-RUN apt -qq update && apt -qq install --no-install-recommends -y \
+RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
-    software-properties-common \
+    apt-transport-https \
-    # apt-transport-https ca-certificates \
+    ca-certificates \
-    build-essential \
+    curl \
-    gnupg wget unzip \
+    wget \
-    # libcap-dev \
+    gnupg-agent \
-    && add-apt-repository ppa:deadsnakes/ppa -y \
+    dirmngr \
-    && apt -qq install --no-install-recommends -y \
+    software-properties-common
-        python3.7 \
-        python3.7-dev \
-        python3-pip \
-        ffmpeg \
-        # VAAPI drivers for Intel hardware accel
-        libva-drm2 libva2 i965-va-driver vainfo \
-    && python3.7 -m pip install -U wheel setuptools \
-    && python3.7 -m pip install -U \
-        opencv-python-headless \
-        # python-prctl \
-        numpy \
-        imutils \
-        scipy \
-    && python3.7 -m pip install -U \
-        SharedArray \
-        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-cp37-cp37m-linux_x86_64.whl \
-    && python3.7 -m pip install tflite_runtime-2.1.0-cp37-cp37m-linux_x86_64.whl \
-    && rm tflite_runtime-2.1.0-cp37-cp37m-linux_x86_64.whl \
-    && rm -rf /var/lib/apt/lists/* \
-    && (apt-get autoremove -y; apt-get autoclean -y)
 
-# get model and labels
+RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys D986B59D
-RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/mobilenet_ssd_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 echo "deb http://deb.odroid.in/5422-s bionic main" > /etc/apt/sources.list.d/odroid.list
-RUN wget -q https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip -O /cpu_model.zip && \
+
-    unzip /cpu_model.zip detect.tflite -d / && \
+RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
-    mv /detect.tflite /cpu_model.tflite && \
+ python3 \ 
-    rm /cpu_model.zip
+ # OpenCV dependencies
+ ffmpeg \
+ build-essential \
+ cmake \
+ unzip \
+ pkg-config \
+ libjpeg-dev \
+ libpng-dev \
+ libtiff-dev \
+ libavcodec-dev \
+ libavformat-dev \
+ libswscale-dev \
+ libv4l-dev \
+ libxvidcore-dev \
+ libx264-dev \
+ libgtk-3-dev \
+ libatlas-base-dev \
+ gfortran \
+ python3-dev \
+ # Coral USB Python API Dependencies
+ libusb-1.0-0 \
+ python3-pip \
+ python3-pil \
+ python3-numpy \
+ libc++1 \
+ libc++abi1 \
+ libunwind8 \
+ libgcc1 \
+ && rm -rf /var/lib/apt/lists/* 
+
+# Install core packages 
+RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
+RUN  pip install -U pip \
+ numpy \
+ Flask \
+ paho-mqtt \
+ PyYAML
+
+# Download & build OpenCV
+RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
+RUN cd /usr/local/src/ \
+ && unzip 4.0.1.zip \
+ && rm 4.0.1.zip \
+ && cd /usr/local/src/opencv-4.0.1/ \
+ && mkdir build \
+ && cd /usr/local/src/opencv-4.0.1/build \ 
+ && cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
+ && make -j4 \
+ && make install \
+ && ldconfig \
+ && rm -rf /usr/local/src/opencv-4.0.1
+
+# Download and install EdgeTPU libraries for Coral
+RUN wget https://dl.google.com/coral/edgetpu_api/edgetpu_api_latest.tar.gz -O edgetpu_api.tar.gz --trust-server-names
+
+RUN tar xzf edgetpu_api.tar.gz \
+  && cd edgetpu_api \
+  && cp -p libedgetpu/libedgetpu_arm32.so /usr/lib/arm-linux-gnueabihf/libedgetpu.so.1.0 \
+  && ldconfig \
+  && python3 -m pip install --no-deps "$(ls edgetpu-*-py3-none-any.whl 2>/dev/null)"
+
+RUN cd /usr/local/lib/python3.6/dist-packages/edgetpu/swig/ \
+  && ln -s _edgetpu_cpp_wrapper.cpython-35m-arm-linux-gnueabihf.so _edgetpu_cpp_wrapper.cpython-36m-arm-linux-gnueabihf.so
+
+# symlink the model and labels
+RUN wget https://dl.google.com/coral/canned_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite -O mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite --trust-server-names
+RUN wget https://dl.google.com/coral/canned_models/coco_labels.txt -O coco_labels.txt --trust-server-names
+RUN ln -s mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
+RUN ln -s /coco_labels.txt /label_map.pbtext
+
+# Minimize image size 
+RUN (apt-get autoremove -y; \
+     apt-get autoclean -y)
 
 WORKDIR /opt/frigate/
 ADD frigate frigate/
 COPY detect_objects.py .
-COPY benchmark.py .
 
-CMD ["python3.7", "-u", "detect_objects.py"]
+CMD ["python3", "-u", "detect_objects.py"]

README.md

@@ -1,13 +1,14 @@
-# Frigate - Realtime Object Detection for IP Cameras
+# Frigate - Realtime Object Detection for RTSP Cameras
-Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
+**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
+- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
-- Uses a very low overhead motion detection to determine where to run object detection
+- Allows you to define specific regions (squares) in the image to look for objects
-- Object detection with Tensorflow runs in a separate process
+- 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)
 
@@ -21,112 +22,77 @@ Build the container with
 docker build -t frigate .
 ```
 
-Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
+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/).
-- CPU Model: `/cpu_model.tflite`
-- EdgeTPU Model: `/edgetpu_model.tflite`
-- Labels: `/labelmap.txt`
 
 Run the container with
-```bash
+```
 docker run --rm \
 --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' \
+-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: frigate:latest
     volumes:
       - /dev/bus/usb:/dev/bus/usb
-      - /etc/localtime:/etc/localtime:ro
       - <path_to_config>:/config
     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).
 
-Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best snapshot for any object type with at `http://localhost:5000/<camera_name>/<object_name>/best.jpg`
+Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
-
-Debug info is available at `http://localhost:5000/debug/stats`
 
 ## Integration with HomeAssistant
 ```
 camera:
   - name: Camera Last Person
-    platform: mqtt
+    platform: generic
-    topic: frigate/<camera_name>/person/snapshot
+    still_image_url: http://<ip>:5000/<camera_name>/best_person.jpg
-  - name: Camera Last Car
-    platform: mqtt
-    topic: frigate/<camera_name>/car/snapshot
 
 binary_sensor:
   - name: Camera Person
     platform: mqtt
-    state_topic: "frigate/<camera_name>/person"
+    state_topic: "frigate/<camera_name>/objects"
+    value_template: '{{ value_json.person }}'
     device_class: motion
     availability_topic: "frigate/available"
-
-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.
-
-sensor:
-  - platform: rest
-    name: Frigate Debug
-    resource: http://localhost:5000/debug/stats
-    scan_interval: 5
-    json_attributes:
-      - back
-      - coral
-    value_template: 'OK'  
-  - platform: template
-    sensors:
-      back_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["back"]["fps"] }}'
-        unit_of_measurement: 'FPS'
-      back_skipped_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["back"]["skipped_fps"] }}'
-        unit_of_measurement: 'FPS'
-      back_detection_fps: 
-        value_template: '{{ states.sensor.frigate_debug.attributes["back"]["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'
 ```
 
 ## Tips
-- Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed
+- 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

@@ -1,18 +0,0 @@
-import statistics
-import numpy as np
-import time
-from frigate.edgetpu import ObjectDetector
-
-object_detector = ObjectDetector()
-
-frame = np.zeros((300,300,3), np.uint8)
-input_frame = np.expand_dims(frame, axis=0)
-
-detection_times = []
-
-for x in range(0, 100):
-    start = time.monotonic()
-    object_detector.detect_raw(input_frame)
-    detection_times.append(time.monotonic()-start)
-
-print(f"Average inference time: {statistics.mean(detection_times)*1000:.2f}ms")

config/config.example.yml

@@ -1,132 +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 -- Uncomment for use
-#  password: password # Optional -- Uncomment for use
-
-#################
-# Default ffmpeg args. Optional and can be overwritten per camera.
-# Should work with most RTSP cameras that send h264 video
-# Built from the properties below with:
-# "ffmpeg" + global_args + input_args + "-i" + input + output_args
-#################
-# ffmpeg:
-#   global_args:
-#     - -hide_banner
-#     - -loglevel
-#     - panic
-#   hwaccel_args: []
-#   input_args:
-#     - -avoid_negative_ts
-#     - make_zero
-#     - -fflags
-#     - nobuffer
-#     - -flags
-#     - low_delay
-#     - -strict
-#     - experimental
-#     - -fflags
-#     - +genpts+discardcorrupt
-#     - -vsync
-#     - drop
-#     - -rtsp_transport
-#     - tcp
-#     - -stimeout
-#     - '5000000'
-#     - -use_wallclock_as_timestamps
-#     - '1'
-#   output_args:
-#     - -f
-#     - rawvideo
-#     - -pix_fmt
-#     - rgb24
-
-####################
-# Global object configuration. Applies to all cameras
-# unless overridden at the camera levels.
-# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
-# All labels from the model are reported over MQTT. These values are used to filter out false positives.
-# min_area (optional): minimum width*height of the bounding box for the detected person
-# max_area (optional): maximum width*height of the bounding box for the detected person
-# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
-####################
-objects:
-  track:
-    - person
-    - car
-    - truck
-  filters:
-    person:
-      min_area: 5000
-      max_area: 100000
-      threshold: 0.5
-
-cameras:
-  back:
-    ffmpeg:
-      ################
-      # Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
-      # Environment variables that begin with 'FRIGATE_' may be referenced in {}
-      ################
-      input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-      #################
-      # These values will override default values for just this camera
-      #################
-      # global_args: []
-      # hwaccel_args: []
-      # input_args: []
-      # output_args: []
-    
-    ################
-    ## Optionally specify the resolution of the video feed. Frigate will try to auto detect if not specified
-    ################
-    # height: 1280
-    # width: 720
-
-    ################
-    ## Optional mask. Must be the same aspect ratio as your video feed.
-    ## 
-    ## The mask works by looking at the bottom center of the bounding box for the detected
-    ## person in the image. If that pixel in the mask is a black pixel, it ignores it as a
-    ## false positive. In my mask, the grass and driveway visible from my backdoor camera 
-    ## are white. The garage doors, sky, and trees (anywhere it would be impossible for a 
-    ## person to stand) are black.
-    ## 
-    ## Masked areas are also ignored for motion detection.
-    ################
-    # mask: back-mask.bmp
-
-    ################
-    # Allows you to limit the framerate within frigate for cameras that do not support
-    # custom framerates. A value of 1 tells frigate to look at every frame, 2 every 2nd frame, 
-    # 3 every 3rd frame, etc.
-    ################
-    take_frame: 1
-
-    ################
-    # The expected framerate for the camera. Frigate will try and ensure it maintains this framerate
-    # by dropping frames as necessary. Setting this lower than the actual framerate will allow frigate
-    # to process every frame at the expense of realtime processing.
-    ################
-    fps: 5
-
-    ################
-    # Configuration for the snapshots in the debug view and mqtt
-    ################
-    snapshots:
-      show_timestamp: True
-
-    ################
-    # Camera level object config. This config is merged with the global config above.
-    ################
-    objects:
-      track:
-        - person
-      filters:
-        person:
-          min_area: 5000
-          max_area: 100000
-          threshold: 0.5

config/config.yml

@@ -0,0 +1,34 @@
+web_port: 5000
+
+mqtt:
+  host: mqtt.server.com
+  topic_prefix: frigate
+#  user: username # Optional -- Uncomment for use
+#  password: password # Optional -- Uncomment for use
+
+cameras:
+  back:
+    rtsp:
+      user: viewer
+      host: 10.0.10.10
+      port: 554
+      # values that begin with a "$" will be replaced with environment variable
+      password: $RTSP_PASSWORD
+      path: /cam/realmonitor?channel=1&subtype=2
+    mask: back-mask.bmp
+    regions:
+      - size: 350
+        x_offset: 0
+        y_offset: 300
+        min_person_area: 5000
+        threshold: 0.5
+      - size: 400
+        x_offset: 350
+        y_offset: 250
+        min_person_area: 2000
+        threshold: 0.5
+      - size: 400
+        x_offset: 750
+        y_offset: 250
+        min_person_area: 2000
+        threshold: 0.5

detect_objects.py

@@ -1,20 +1,13 @@
 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
-from flask import Flask, Response, make_response, jsonify
 import paho.mqtt.client as mqtt
 
-from frigate.video import track_camera
+from frigate.video import Camera
-from frigate.object_processing import TrackedObjectProcessor
+from frigate.object_detection import PreppedQueueProcessor
-from frigate.util import EventsPerSecond
-from frigate.edgetpu import EdgeTPUProcess
 
 with open('/config/config.yml') as f:
     CONFIG = yaml.safe_load(f)
@@ -24,216 +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')
-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',
-         '-vsync', 'drop',
-         '-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')
 
-class CameraWatchdog(threading.Thread):
-    def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, object_processor):
-        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.object_processor = object_processor
-
-    def run(self):
-        time.sleep(10)
-        while True:
-            # wait a bit before checking
-            time.sleep(10)
-
-            for name, camera_process in self.camera_processes.items():
-                process = camera_process['process']
-                if (datetime.datetime.now().timestamp() - self.object_processor.get_current_frame_time(name)) > 30:
-                    print(f"Last frame for {name} is more than 30 seconds old...")
-                    if process.is_alive():
-                        process.terminate()
-                        print("Waiting for process to exit gracefully...")
-                        process.join(timeout=30)
-                        if process.exitcode is None:
-                            print("Process didnt exit. Force killing...")
-                            process.kill()
-                            process.join()
-                if not process.is_alive():
-                    print(f"Process for {name} is not alive. Starting again...")
-                    camera_process['fps'].value = float(self.config[name]['fps'])
-                    camera_process['skipped_fps'].value = 0.0
-                    process = mp.Process(target=track_camera, args=(name, self.config[name], FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
-                        self.tflite_process.detect_lock, self.tflite_process.detect_ready, self.tflite_process.frame_ready, self.tracked_objects_queue, 
-                        camera_process['fps'], camera_process['skipped_fps'], camera_process['detection_fps']))
-                    process.daemon = True
-                    camera_process['process'] = process
-                    process.start()
-                    print(f"Camera_process started for {name}: {process.pid}")
-
 def main():
     # connect to mqtt and setup last will
     def on_connect(client, userdata, flags, rc):
         print("On connect called")
-        if rc != 0:
-            if rc == 3:
-                print ("MQTT Server unavailable")
-            elif rc == 4:
-                print ("MQTT Bad username or password")
-            elif rc == 5:
-                print ("MQTT Not authorized")
-            else:
-                print ("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
         # publish a message to signal that the service is running
         client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
-    client = mqtt.Client(client_id=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())*5
+    prepped_frame_queue = queue.Queue(max_queue_size)
 
-    # start plasma store
+    cameras = {}
-    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:
-        raise RuntimeError("plasma_store exited unexpectedly with "
-                            "code %d" % (rc,))
-
-    ##
-    # Setup config defaults for cameras
-    ##
     for name, config in CONFIG['cameras'].items():
-        config['snapshots'] = {
+        cameras[name] = Camera(name, config, prepped_frame_queue, client, MQTT_TOPIC_PREFIX)
-            'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True)
-        }
 
-    # Queue for cameras to push tracked objects to
+    prepped_queue_processor = PreppedQueueProcessor(
-    tracked_objects_queue = mp.Queue()
+        cameras,
-    
+        prepped_frame_queue
-    # Start the shared tflite process
+    )
-    tflite_process = EdgeTPUProcess()
+    prepped_queue_processor.start()
 
-    # start the camera processes
+    for name, camera in cameras.items():
-    camera_processes = {}
+        camera.start()
-    for name, config in CONFIG['cameras'].items():
+        print("Capture process for {}: {}".format(name, camera.get_capture_pid()))
-        camera_processes[name] = {
-            'fps': mp.Value('d', float(config['fps'])),
-            'skipped_fps': mp.Value('d', 0.0),
-            'detection_fps': mp.Value('d', 0.0),
-            'last_frame': datetime.datetime.now().timestamp()
-        }
-        camera_process = mp.Process(target=track_camera, args=(name, config, FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
-            tflite_process.detect_lock, tflite_process.detect_ready, tflite_process.frame_ready, tracked_objects_queue, 
-            camera_processes[name]['fps'], camera_processes[name]['skipped_fps'], camera_processes[name]['detection_fps']))
-        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}")
-    
-    object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue)
-    object_processor.start()
-    
-    camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, object_processor)
-    camera_watchdog.start()
 
     # create a flask app that encodes frames a mjpeg on demand
     app = Flask(__name__)
-    log = logging.getLogger('werkzeug')
-    log.setLevel(logging.ERROR)
 
-    @app.route('/')
+    @app.route('/<camera_name>/best_person.jpg')
-    def ishealthy():
+    def best_person(camera_name):
-        # return a healh
+        best_person_frame = cameras[camera_name].get_best_person()
-        return "Frigate is running. Alive and healthy!"
+        if best_person_frame is None:
-
+            best_person_frame = np.zeros((720,1280,3), np.uint8)
-    @app.route('/debug/stats')
+        ret, jpg = cv2.imencode('.jpg', best_person_frame)
-    def stats():
+        response = make_response(jpg.tobytes())
-        stats = {}
+        response.headers['Content-Type'] = 'image/jpg'
-
+        return response
-        total_detection_fps = 0
-
-        for name, camera_stats in camera_processes.items():
-            total_detection_fps += camera_stats['detection_fps'].value
-            stats[name] = {
-                'fps': camera_stats['fps'].value,
-                'skipped_fps': camera_stats['skipped_fps'].value,
-                'detection_fps': camera_stats['detection_fps'].value
-            }
-        
-        stats['coral'] = {
-            'fps': total_detection_fps,
-            'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2)
-        }
-
-        return jsonify(stats)
-
-    @app.route('/<camera_name>/<label>/best.jpg')
-    def best(camera_name, label):
-        if camera_name in CONFIG['cameras']:
-            best_frame = object_processor.get_best(camera_name, label)
-            if best_frame is None:
-                best_frame = np.zeros((720,1280,3), np.uint8)
-            best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
-            ret, jpg = cv2.imencode('.jpg', best_frame)
-            response = make_response(jpg.tobytes())
-            response.headers['Content-Type'] = 'image/jpg'
-            return response
-        else:
-            return "Camera named {} not found".format(camera_name), 404
 
     @app.route('/<camera_name>')
     def mjpeg_feed(camera_name):
-        if camera_name in CONFIG['cameras']:
+        # return a multipart response
-            # return a multipart response
+        return Response(imagestream(camera_name),
-            return Response(imagestream(camera_name),
+                        mimetype='multipart/x-mixed-replace; boundary=frame')
-                            mimetype='multipart/x-mixed-replace; boundary=frame')
-        else:
-            return "Camera named {} not found".format(camera_name), 404
 
     def imagestream(camera_name):
         while True:
-            # max out at 1 FPS
+            # max out at 5 FPS
-            time.sleep(1)
+            time.sleep(0.2)
-            frame = object_processor.get_current_frame(camera_name)
+            frame = cameras[camera_name].get_current_frame_with_objects()
-            if frame is None:
+            # encode the image into a jpg
-                frame = np.zeros((720,1280,3), np.uint8)
-            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
             ret, jpg = cv2.imencode('.jpg', frame)
             yield (b'--frame\r\n'
                 b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
 
     app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
 
-    camera_watchdog.join()
+    camera.join()
-    
-    plasma_process.terminate()
 
 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

@@ -1,136 +0,0 @@
-import os
-import datetime
-import multiprocessing as mp
-import numpy as np
-import SharedArray as sa
-import tflite_runtime.interpreter as tflite
-from tflite_runtime.interpreter import load_delegate
-from frigate.util import EventsPerSecond
-
-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
-
-class EdgeTPUProcess():
-    def __init__(self):
-        # TODO: see if we can use the plasma store with a queue and maintain the same speeds
-        try:
-            sa.delete("frame")
-        except:
-            pass
-        try:
-            sa.delete("detections")
-        except:
-            pass
-
-        self.input_frame = sa.create("frame", shape=(1,300,300,3), dtype=np.uint8)
-        self.detections = sa.create("detections", shape=(20,6), dtype=np.float32)
-
-        self.detect_lock = mp.Lock()
-        self.detect_ready = mp.Event()
-        self.frame_ready = mp.Event()
-        self.avg_inference_speed = mp.Value('d', 0.01)
-
-        def run_detector(detect_ready, frame_ready, avg_speed):
-            print(f"Starting detection process: {os.getpid()}")
-            object_detector = ObjectDetector()
-            input_frame = sa.attach("frame")
-            detections = sa.attach("detections")
-
-            while True:
-                # wait until a frame is ready
-                frame_ready.wait()
-                start = datetime.datetime.now().timestamp()
-                # signal that the process is busy
-                frame_ready.clear()
-                detections[:] = object_detector.detect_raw(input_frame)
-                # signal that the process is ready to detect
-                detect_ready.set()
-                duration = datetime.datetime.now().timestamp()-start
-                avg_speed.value = (avg_speed.value*9 + duration)/10
-
-        self.detect_process = mp.Process(target=run_detector, args=(self.detect_ready, self.frame_ready, self.avg_inference_speed))
-        self.detect_process.daemon = True
-        self.detect_process.start()
-
-class RemoteObjectDetector():
-    def __init__(self, labels, detect_lock, detect_ready, frame_ready):
-        self.labels = load_labels(labels)
-
-        self.input_frame = sa.attach("frame")
-        self.detections = sa.attach("detections")
-
-        self.fps = EventsPerSecond()
-
-        self.detect_lock = detect_lock
-        self.detect_ready = detect_ready
-        self.frame_ready = frame_ready
-    
-    def detect(self, tensor_input, threshold=.4):
-        detections = []
-        with self.detect_lock:
-            self.input_frame[:] = tensor_input
-            # unset detections and signal that a frame is ready
-            self.detect_ready.clear()
-            self.frame_ready.set()
-            # wait until the detection process is finished,
-            self.detect_ready.wait()
-            for d in self.detections:
-                if d[1] < threshold:
-                    break
-                detections.append((
-                    self.labels[int(d[0])],
-                    float(d[1]),
-                    (d[2], d[3], d[4], d[5])
-                ))
-        self.fps.update()
-        return detections

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 +1,112 @@
+import datetime
+import time
+import cv2
+import threading
+import numpy as np
+from edgetpu.detection.engine import DetectionEngine
+from . util import tonumpyarray
+
+# Path to frozen detection graph. This is the actual model that is used for the object detection.
+PATH_TO_CKPT = '/frozen_inference_graph.pb'
+# List of the strings that is used to add correct label for each box.
+PATH_TO_LABELS = '/label_map.pbtext'
+
+# Function to read labels from text files.
+def ReadLabelFile(file_path):
+    with open(file_path, 'r') as f:
+        lines = f.readlines()
+    ret = {}
+    for line in lines:
+        pair = line.strip().split(maxsplit=1)
+        ret[int(pair[0])] = pair[1].strip()
+    return ret
+
+class PreppedQueueProcessor(threading.Thread):
+    def __init__(self, cameras, prepped_frame_queue):
+
+        threading.Thread.__init__(self)
+        self.cameras = cameras
+        self.prepped_frame_queue = prepped_frame_queue
+        
+        # Load the edgetpu engine and labels
+        self.engine = DetectionEngine(PATH_TO_CKPT)
+        self.labels = ReadLabelFile(PATH_TO_LABELS)
+
+    def run(self):
+        # process queue...
+        while True:
+            frame = self.prepped_frame_queue.get()
+
+            # Actual detection.
+            objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=frame['region_threshold'], top_k=3)
+            # parse and pass detected objects back to the camera
+            parsed_objects = []
+            for obj in objects:
+                box = obj.bounding_box.flatten().tolist()
+                parsed_objects.append({
+                            'frame_time': frame['frame_time'],
+                            'name': str(self.labels[obj.label_id]),
+                            'score': float(obj.score),
+                            'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']),
+                            'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']),
+                            'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']),
+                            'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset'])
+                        })
+            self.cameras[frame['camera_name']].add_objects(parsed_objects)
+
+
+# should this be a region class?
+class FramePrepper(threading.Thread):
+    def __init__(self, camera_name, shared_frame, frame_time, frame_ready, 
+        frame_lock,
+        region_size, region_x_offset, region_y_offset, region_threshold,
+        prepped_frame_queue):
+
+        threading.Thread.__init__(self)
+        self.camera_name = camera_name
+        self.shared_frame = shared_frame
+        self.frame_time = frame_time
+        self.frame_ready = frame_ready
+        self.frame_lock = frame_lock
+        self.region_size = region_size
+        self.region_x_offset = region_x_offset
+        self.region_y_offset = region_y_offset
+        self.region_threshold = region_threshold
+        self.prepped_frame_queue = prepped_frame_queue
+
+    def run(self):
+        frame_time = 0.0
+        while True:
+            now = datetime.datetime.now().timestamp()
+
+            with self.frame_ready:
+                # if there isnt a frame ready for processing or it is old, wait for a new frame
+                if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
+                    self.frame_ready.wait()
+            
+            # make a copy of the cropped frame
+            with self.frame_lock:
+                cropped_frame = self.shared_frame[self.region_y_offset:self.region_y_offset+self.region_size, self.region_x_offset:self.region_x_offset+self.region_size].copy()
+                frame_time = self.frame_time.value
+            
+            # 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_threshold': self.region_threshold,
+                    'region_x_offset': self.region_x_offset,
+                    'region_y_offset': self.region_y_offset
+                })
+            else:
+                print("queue full. moving on")

frigate/object_processing.py

@@ -1,145 +0,0 @@
-import json
-import hashlib
-import datetime
-import copy
-import cv2
-import threading
-import numpy as np
-from collections import Counter, defaultdict
-import itertools
-import pyarrow.plasma as plasma
-import SharedArray as sa
-import matplotlib.pyplot as plt
-from frigate.util import draw_box_with_label
-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])
-
-class TrackedObjectProcessor(threading.Thread):
-    def __init__(self, config, client, topic_prefix, tracked_objects_queue):
-        threading.Thread.__init__(self)
-        self.config = config
-        self.client = client
-        self.topic_prefix = topic_prefix
-        self.tracked_objects_queue = tracked_objects_queue
-        self.plasma_client = plasma.connect("/tmp/plasma")
-        self.camera_data = defaultdict(lambda: {
-            'best_objects': {},
-            'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
-            'tracked_objects': {},
-            'current_frame_time': datetime.datetime.now().timestamp()
-        })
-        
-    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 get_current_frame_time(self, camera):
-        return self.camera_data[camera]['current_frame_time']
-
-    def run(self):
-        while True:
-            camera, frame_time, tracked_objects = self.tracked_objects_queue.get()
-
-            config = self.config[camera]
-            best_objects = self.camera_data[camera]['best_objects']
-            current_object_status = self.camera_data[camera]['object_status']
-            self.camera_data[camera]['tracked_objects'] = tracked_objects
-
-            ###
-            # Draw tracked objects on the frame
-            ###
-            object_id_hash = hashlib.sha1(str.encode(f"{camera}{frame_time}"))
-            object_id_bytes = object_id_hash.digest()
-            object_id = plasma.ObjectID(object_id_bytes)
-            current_frame = self.plasma_client.get(object_id)
-
-            # 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 config['snapshots']['show_timestamp']:
-                time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-                cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
-
-            ###
-            # Set the current frame as ready
-            ###
-            self.camera_data[camera]['current_frame'] = current_frame
-            self.camera_data[camera]['current_frame_time'] = 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(current_frame)
-                        best_objects[obj['label']] = obj
-                else:
-                    obj['frame'] = np.copy(current_frame)
-                    best_objects[obj['label']] = obj
-
-            ###
-            # Report over MQTT
-            ###
-            # count objects with more than 2 entries in history by type
-            obj_counter = Counter()
-            for obj in tracked_objects.values():
-                if len(obj['history']) > 1:
-                    obj_counter[obj['label']] += 1
-                    
-            # report on detected objects
-            for obj_name, count in obj_counter.items():
-                new_status = 'ON' if count > 0 else 'OFF'
-                if new_status != current_object_status[obj_name]:
-                    current_object_status[obj_name] = new_status
-                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
-                    # send the best snapshot over mqtt
-                    best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
-                    ret, jpg = cv2.imencode('.jpg', best_frame)
-                    if ret:
-                        jpg_bytes = jpg.tobytes()
-                        self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
-
-            # expire any objects that are ON and no longer detected
-            expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
-            for obj_name in expired_objects:
-                current_object_status[obj_name] = 'OFF'
-                self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
-                # send updated snapshot over mqtt
-                best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
-                ret, jpg = cv2.imencode('.jpg', best_frame)
-                if ret:
-                    jpg_bytes = jpg.tobytes()
-                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)

frigate/objects.py

@@ -2,154 +2,89 @@ import time
 import datetime
 import threading
 import cv2
-import itertools
-import copy
-import numpy as np
-import multiprocessing as mp
-from collections import defaultdict
-from scipy.spatial import distance as dist
-from frigate.util import draw_box_with_label, calculate_region
 
-class ObjectTracker():
+class ObjectCleaner(threading.Thread):
-    def __init__(self, max_disappeared):
+    def __init__(self, objects_parsed, detected_objects):
-        self.tracked_objects = {}
+        threading.Thread.__init__(self)
-        self.disappeared = {}
+        self._objects_parsed = objects_parsed
-        self.max_disappeared = max_disappeared
+        self._detected_objects = detected_objects
 
-    def register(self, index, obj):
+    def run(self):
-        id = f"{obj['frame_time']}-{index}"
+        while True:
-        obj['id'] = id
-        obj['top_score'] = obj['score']
-        self.add_history(obj)
-        self.tracked_objects[id] = obj
-        self.disappeared[id] = 0
 
-    def deregister(self, id):
+            # wait a bit before checking for expired frames
-        del self.tracked_objects[id]
+            time.sleep(0.2)
-        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)
-        else:
-            obj['history'] = [entry]
 
-    def match_and_update(self, frame_time, new_objects):
+            # expire the objects that are more than 1 second old
-        if len(new_objects) == 0:
+            now = datetime.datetime.now().timestamp()
-            for id in list(self.tracked_objects.keys()):
+            # look for the first object found within the last second
-                if self.disappeared[id] >= self.max_disappeared:
+            # (newest objects are appended to the end)
-                    self.deregister(id)
+            detected_objects = self._detected_objects.copy()
-                else:
-                    self.disappeared[id] += 1
-            return
-            
-        # 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
-            })
-        
-        # track objects for each label type
-        for label, group in new_object_groups.items():
-            current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]
-            current_ids = [o['id'] for o in current_objects]
-            current_centroids = np.array([o['centroid'] for o in current_objects])
 
-            # compute centroids of new objects
+            num_to_delete = 0
-            for obj in group:
+            for obj in detected_objects:
-                centroid_x = int((obj['box'][0]+obj['box'][2]) / 2.0)
+                if now-obj['frame_time']<2:
-                centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0)
+                    break
-                obj['centroid'] = (centroid_x, centroid_y)
+                num_to_delete += 1
+            if num_to_delete > 0:
+                del self._detected_objects[:num_to_delete]
 
-            if len(current_objects) == 0:
+                # notify that parsed objects were changed
-                for index, obj in enumerate(group):
+                with self._objects_parsed:
-                    self.register(index, obj)
+                    self._objects_parsed.notify_all()
-                return
-            
-            new_centroids = np.array([o['centroid'] for o in group])
 
-            # compute the distance between each pair of tracked
-            # centroids and new centroids, respectively -- our
-            # goal will be to match each new centroid to an existing
-            # object centroid
-            D = dist.cdist(current_centroids, new_centroids)
 
-            # in order to perform this matching we must (1) find the
+# Maintains the frame and person with the highest score from the most recent
-            # smallest value in each row and then (2) sort the row
+# motion event
-            # indexes based on their minimum values so that the row
+class BestPersonFrame(threading.Thread):
-            # with the smallest value is at the *front* of the index
+    def __init__(self, objects_parsed, recent_frames, detected_objects):
-            # list
+        threading.Thread.__init__(self)
-            rows = D.min(axis=1).argsort()
+        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
+    def run(self):
-            # finding the smallest value in each column and then
+        while True:
-            # sorting using the previously computed row index list
-            cols = D.argmin(axis=1)[rows]
 
-            # in order to determine if we need to update, register,
+            # wait until objects have been parsed
-            # or deregister an object we need to keep track of which
+            with self.objects_parsed:
-            # of the rows and column indexes we have already examined
+                self.objects_parsed.wait()
-            usedRows = set()
-            usedCols = set()
 
-            # loop over the combination of the (row, column) index
+            # make a copy of detected objects
-            # tuples
+            detected_objects = self.detected_objects.copy()
-            for (row, col) in zip(rows, cols):
+            detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
-                # if we have already examined either the row or
-                # column value before, ignore it
-                if row in usedRows or col in usedCols:
-                    continue
 
-                # otherwise, grab the object ID for the current row,
+            # get the highest scoring person
-                # set its new centroid, and reset the disappeared
+            new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
-                # counter
-                objectID = current_ids[row]
-                self.update(objectID, group[col])
 
-                # indicate that we have examined each of the row and
+            # if there isnt a person, continue
-                # column indexes, respectively
+            if new_best_person is None:
-                usedRows.add(row)
+                continue
-                usedCols.add(col)
 
-            # compute the column index we have NOT yet examined
+            # if there is no current best_person
-            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
+            if self.best_person is None:
-            unusedCols = set(range(0, D.shape[1])).difference(usedCols)
+                self.best_person = new_best_person
-
+            # if there is already a best_person
-            # in the event that the number of object centroids is
-			# equal or greater than the number of input centroids
-			# we need to check and see if some of these objects have
-			# potentially disappeared
-            if D.shape[0] >= D.shape[1]:
-                for row in unusedRows:
-                    id = current_ids[row]
-
-                    if self.disappeared[id] >= self.max_disappeared:
-                        self.deregister(id)
-                    else:
-                        self.disappeared[id] += 1
-            # if the number of input centroids is greater
-            # than the number of existing object centroids we need to
-            # register each new input centroid as a trackable object
             else:
-                for col in unusedCols:
+                now = datetime.datetime.now().timestamp()
-                    self.register(col, group[col])
+                # 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
+                color = (255,0,0)
+                cv2.rectangle(best_frame, (self.best_person['xmin'], self.best_person['ymin']), 
+                    (self.best_person['xmax'], self.best_person['ymax']), 
+                    color, 2)
+
+                # convert back to BGR
+                self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)

frigate/util.py

@@ -1,129 +1,5 @@
-import datetime
-import collections
 import numpy as np
-import cv2
-import threading
-import matplotlib.pyplot as plt
 
-def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
+# convert shared memory array into numpy array
-    if color is None:
+def tonumpyarray(mp_arr):
-        color = (0,0,255)
+    return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
-    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

frigate/video.py

@@ -2,351 +2,302 @@ import os
 import time
 import datetime
 import cv2
-import queue
 import threading
 import ctypes
 import multiprocessing as mp
-import subprocess as sp
 import numpy as np
-import hashlib
+from . util import tonumpyarray
-import pyarrow.plasma as plasma
+from . object_detection import FramePrepper
-import SharedArray as sa
+from . objects import ObjectCleaner, BestPersonFrame
-import copy
+from . mqtt import MqttObjectPublisher
-import itertools
-import json
-from collections import defaultdict
-from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond
-from frigate.objects import ObjectTracker
-from frigate.edgetpu import RemoteObjectDetector
-from frigate.motion import MotionDetector
 
-# TODO: add back opencv fallback
+# fetch the frames as fast a possible and store current frame in a shared memory array
-def get_frame_shape(source):
+def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_shape, rtsp_url):
-    ffprobe_cmd = " ".join([
+    # convert shared memory array into numpy and shape into image array
-        'ffprobe',
+    arr = tonumpyarray(shared_arr).reshape(frame_shape)
-        '-v',
-        'panic',
-        '-show_error',
-        '-show_streams',
-        '-of',
-        'json',
-        '"'+source+'"'
-    ])
-    print(ffprobe_cmd)
-    p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
-    (output, err) = p.communicate()
-    p_status = p.wait()
-    info = json.loads(output)
-    print(info)
 
-    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
+    # start the video capture
+    video = cv2.VideoCapture()
+    video.open(rtsp_url)
+    print("Opening the 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:
+    bad_frame_counter = 0
-        return (video_info['height'], video_info['width'], 3)
+    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:
+            # 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.release()
-    video = cv2.VideoCapture(source)
+
+# 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):
+def get_rtsp_url(rtsp_config):
-    frigate_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
+    if (rtsp_config['password'].startswith('$')):
-    return ffmpeg_input.format(**frigate_vars)
+        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):
+class CameraWatchdog(threading.Thread):
-    object_name = obj[0]
+    def __init__(self, camera):
+        threading.Thread.__init__(self)
+        self.camera = camera
 
-    if not object_name in objects_to_track:
+    def run(self):
-        return True
-    
-    if object_name in object_filters:
-        obj_settings = object_filters[object_name]
 
-        # if the min area is larger than the
-        # detected object, don't add it to detected objects
-        if obj_settings.get('min_area',-1) > obj[3]:
-            return True
-        
-        # if the detected object is larger than the
-        # max area, don't add it to detected objects
-        if obj_settings.get('max_area', 24000000) < obj[3]:
-            return True
-
-        # if the score is lower than the 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)
-
-        # 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
-
-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 track_camera(name, config, ffmpeg_global_config, global_objects_config, detect_lock, detect_ready, frame_ready, detected_objects_queue, fps, skipped_fps, detection_fps):
-    print(f"Starting process for {name}: {os.getpid()}")
-
-    # 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_global_config['global_args'])
-    ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', ffmpeg_global_config['hwaccel_args'])
-    ffmpeg_input_args = ffmpeg.get('input_args', ffmpeg_global_config['input_args'])
-    ffmpeg_output_args = ffmpeg.get('output_args', ffmpeg_global_config['output_args'])
-
-    # 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, {})}
-
-    expected_fps = config['fps']
-    take_frame = config.get('take_frame', 1)
-
-    frame_shape = get_frame_shape(ffmpeg_input)
-    frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
-
-    try:
-        sa.delete(name)
-    except:
-        pass
-
-    frame = sa.create(name, shape=frame_shape, dtype=np.uint8)
-
-    # load in the mask for object detection
-    if 'mask' in config:
-        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('/labelmap.txt', detect_lock, detect_ready, frame_ready)
-
-    object_tracker = ObjectTracker(10)
-
-    ffmpeg_cmd = (['ffmpeg'] +
-            ffmpeg_global_args +
-            ffmpeg_hwaccel_args +
-            ffmpeg_input_args +
-            ['-i', ffmpeg_input] +
-            ffmpeg_output_args +
-            ['pipe:'])
-
-    print(" ".join(ffmpeg_cmd))
-    
-    ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size)
-    
-    plasma_client = plasma.connect("/tmp/plasma")
-    frame_num = 0
-    avg_wait = 0.0
-    fps_tracker = EventsPerSecond()
-    skipped_fps_tracker = EventsPerSecond()
-    fps_tracker.start()
-    skipped_fps_tracker.start()
-    object_detector.fps.start()
-    while True:
-        start = datetime.datetime.now().timestamp()
-        frame_bytes = ffmpeg_process.stdout.read(frame_size)
-        duration = datetime.datetime.now().timestamp()-start
-        avg_wait = (avg_wait*99+duration)/100
-
-        if not frame_bytes:
-            break
-
-        # limit frame rate
-        frame_num += 1
-        if (frame_num % take_frame) != 0:
-            continue
-
-        fps_tracker.update()
-        fps.value = fps_tracker.eps()
-        detection_fps.value = object_detector.fps.eps()
-
-        frame_time = datetime.datetime.now().timestamp()
-        
-        # Store frame in numpy array
-        frame[:] = (np
-                    .frombuffer(frame_bytes, np.uint8)
-                    .reshape(frame_shape))
-        
-        # look for motion
-        motion_boxes = motion_detector.detect(frame)
-
-        # skip object detection if we are below the min_fps and wait time is less than half the average
-        if frame_num > 100 and fps.value < expected_fps-1 and duration < 0.5*avg_wait:
-            skipped_fps_tracker.update()
-            skipped_fps.value = skipped_fps_tracker.eps()
-            continue
-        
-        skipped_fps.value = skipped_fps_tracker.eps()
-
-        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 area(intersection(obj['box'], motion_box))/area(motion_box) > .5:
-                    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
+            # wait a bit before checking
-            max_indices = None
+            time.sleep(60)
-            region_indices = range(len(merged_regions))
-            for a, b in itertools.combinations(region_indices, 2):
-                iou = intersection_over_union(merged_regions[a], merged_regions[b])
-                if iou > max_iou:
-                    max_iou = iou
-                    max_indices = (a, b)
-            if max_iou > 0.1:
-                a = merged_regions[max_indices[0]]
-                b = merged_regions[max_indices[1]]
-                merged_regions.append(calculate_region(frame_shape,
-                    min(a[0], b[0]),
-                    min(a[1], b[1]),
-                    max(a[2], b[2]),
-                    max(a[3], b[3]),
-                    1
-                ))
-                del merged_regions[max(max_indices[0], max_indices[1])]
-                del merged_regions[min(max_indices[0], max_indices[1])]
-            else:
-                break
 
-        # resize regions and detect
+            if (datetime.datetime.now().timestamp() - self.camera.shared_frame_time.value) > 2:
-        detections = []
+                print("last frame is more than 2 seconds old, restarting camera capture...")
-        for region in merged_regions:
+                self.camera.start_or_restart_capture()
+                time.sleep(5)
 
-            tensor_input = create_tensor_input(frame, region)
+class Camera:
+    def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
+        self.name = name
+        self.config = config
+        self.detected_objects = []
+        self.recent_frames = {}
+        self.rtsp_url = get_rtsp_url(self.config['rtsp'])
+        self.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)
 
-            region_detections = object_detector.detect(tensor_input)
+        # 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()
 
-            for d in region_detections:
+        # shape current frame so it can be treated as a numpy image
-                box = d[2]
+        self.shared_frame_np = tonumpyarray(self.shared_frame_array).reshape(self.frame_shape)
-                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)
 
-        #########
+        self.capture_process = None
-        # 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
+        # for each region, create a separate thread to resize the region and prep for detection
-            detected_object_groups = defaultdict(lambda: [])
+        self.detection_prep_threads = []
-            for detection in detections:
+        for region in self.config['regions']:
-                detected_object_groups[detection[0]].append(detection)
+            # set a default threshold of 0.5 if not defined
-
+            if not 'threshold' in region:
-            selected_objects = []
+                region['threshold'] = 0.5
-            for group in detected_object_groups.values():
+            if not isinstance(region['threshold'], float):
-
+                print('Threshold is not a float. Setting to 0.5 default.')
-                # apply non-maxima suppression to suppress weak, overlapping bounding boxes
+                region['threshold'] = 0.5
-                boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
+            self.detection_prep_threads.append(FramePrepper(
-                    for o in group]
+                self.name,
-                confidences = [o[1] for o in group]
+                self.shared_frame_np,
-                idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+                self.shared_frame_time,
-
+                self.frame_ready,
-                for index in idxs:
+                self.frame_lock,
-                    obj = group[index[0]]
+                region['size'], region['x_offset'], region['y_offset'], region['threshold'],
-                    if clipped(obj, frame_shape): #obj['clipped']:
+                prepped_frame_queue
-                        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
         
-        # now that we have refined our detections, we need to track objects
+        # start a thread to store recent motion frames for processing
-        object_tracker.match_and_update(frame_time, detections)
+        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()
 
-        # put the frame in the plasma store
+        # start a thread to store the highest scoring recent person frame
-        object_id = hashlib.sha1(str.encode(f"{name}{frame_time}")).digest()
+        self.best_person_frame = BestPersonFrame(self.objects_parsed, self.recent_frames, self.detected_objects)
-        plasma_client.put(frame, plasma.ObjectID(object_id))
+        self.best_person_frame.start()
-        # add to the queue
+
-        detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects))
+        # 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()
+
+        # create a watchdog thread for capture process
+        self.watchdog = CameraWatchdog(self)
+
+        # load in the mask for person detection
+        if 'mask' in self.config:
+            self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
+        else:
+            self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
+            self.mask[:] = 255
+
+
+    def start_or_restart_capture(self):
+        if not self.capture_process is None:
+            print("Terminating the existing capture process...")
+            self.capture_process.terminate()
+            del self.capture_process
+            self.capture_process = None
+            
+        # create the process to capture frames from the RTSP stream and store in a shared array
+        print("Creating a new capture process...")
+        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.capture_process.daemon = True
+        print("Starting a new capture process...")
+        self.capture_process.start()
+    
+    def start(self):
+        self.start_or_restart_capture()
+        # start the object detection prep threads
+        for detection_prep_thread in self.detection_prep_threads:
+            detection_prep_thread.start()
+        self.watchdog.start()
+    
+    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 obj['name'] == 'person':
+                person_area = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
+                # find the matching region
+                region = None
+                for r in self.regions:
+                    if (
+                            obj['xmin'] >= r['x_offset'] and
+                            obj['ymin'] >= r['y_offset'] and
+                            obj['xmax'] <= r['x_offset']+r['size'] and
+                            obj['ymax'] <= r['y_offset']+r['size']
+                        ): 
+                        region = r
+                        break
+                
+                # if the min person area is larger than the
+                # detected person, don't add it to detected objects
+                if region and region['min_person_area'] > person_area:
+                    continue
+            
+                # compute the coordinates of the person and make sure
+                # the location isnt outide the bounds of the image (can happen from rounding)
+                y_location = min(int(obj['ymax']), len(self.mask)-1)
+                x_location = min(int((obj['xmax']-obj['xmin'])/2.0), len(self.mask[0])-1)
+
+                # if the person is in a masked location, continue
+                if self.mask[y_location][x_location] == [0]:
+                    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:
+            color = (255,0,0)
+            cv2.rectangle(frame, (obj['xmin'], obj['ymin']), 
+                (obj['xmax'], obj['ymax']), 
+                color, 2)
+
+        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
+
+
+    
+