add a few more metrics to debug

Dockerfile

@@ -1,109 +1,60 @@
 FROM ubuntu:18.04
+LABEL maintainer "blakeb@blakeshome.com"
 
-ARG DEVICE
+ENV DEBIAN_FRONTEND=noninteractive
-
 # Install packages for apt repo
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
+RUN apt -qq update && apt -qq install --no-install-recommends -y \
-    apt-transport-https \
-    ca-certificates \
-    curl \
-    wget \
-    gnupg-agent \
-    dirmngr \
     software-properties-common \
-    && rm -rf /var/lib/apt/lists/*
+    # apt-transport-https ca-certificates \
+    build-essential \
+    gnupg wget unzip \
+    # libcap-dev \
+    && add-apt-repository ppa:deadsnakes/ppa -y \
+    && apt -qq install --no-install-recommends -y \
+        python3.7 \
+        python3.7-dev \
+        python3-pip \
+        ffmpeg \
+        # VAAPI drivers for Intel hardware accel
+        libva-drm2 libva2 i965-va-driver vainfo \
+    && python3.7 -m pip install -U wheel setuptools \
+    && python3.7 -m pip install -U \
+        opencv-python-headless \
+        # python-prctl \
+        numpy \
+        imutils \
+        scipy \
+    && python3.7 -m pip install -U \
+        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)
 
-COPY scripts/install_odroid_repo.sh .
+# get model and labels
-
+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 if [ "$DEVICE" = "odroid" ]; then \
+RUN wget -q https://dl.google.com/coral/canned_models/coco_labels.txt -O /labelmap.txt --trust-server-names
-      sh /install_odroid_repo.sh; \
+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 && \
-    fi
+    unzip /cpu_model.zip detect.tflite -d / && \
-
+    mv /detect.tflite /cpu_model.tflite && \
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
+    rm /cpu_model.zip
- python3 \ 
- # OpenCV dependencies
- ffmpeg \
- build-essential \
- cmake \
- unzip \
- pkg-config \
- libjpeg-dev \
- libpng-dev \
- libtiff-dev \
- libavcodec-dev \
- libavformat-dev \
- libswscale-dev \
- libv4l-dev \
- libxvidcore-dev \
- libx264-dev \
- libgtk-3-dev \
- libatlas-base-dev \
- gfortran \
- python3-dev \
- # Coral USB Python API Dependencies
- libusb-1.0-0 \
- python3-pip \
- python3-pil \
- python3-numpy \
- libc++1 \
- libc++abi1 \
- libunwind8 \
- libgcc1 \
- # VAAPI drivers for Intel hardware accel
- libva-drm2 libva2 i965-va-driver vainfo \
- && rm -rf /var/lib/apt/lists/* 
-
-# Install core packages 
-RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
-RUN  pip install -U pip \
- numpy \
- Flask \
- paho-mqtt \
- PyYAML
-
-# Download & build OpenCV
-# TODO: use multistage build to reduce image size: 
-#   https://medium.com/@denismakogon/pain-and-gain-running-opencv-application-with-golang-and-docker-on-alpine-3-7-435aa11c7aec
-#   https://www.merixstudio.com/blog/docker-multi-stage-builds-python-development/
-RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
-RUN cd /usr/local/src/ \
- && unzip 4.0.1.zip \
- && rm 4.0.1.zip \
- && cd /usr/local/src/opencv-4.0.1/ \
- && mkdir build \
- && cd /usr/local/src/opencv-4.0.1/build \ 
- && cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
- && make -j4 \
- && make install \
- && ldconfig \
- && rm -rf /usr/local/src/opencv-4.0.1
-
-# Download and install EdgeTPU libraries for Coral
-RUN wget https://dl.google.com/coral/edgetpu_api/edgetpu_api_latest.tar.gz -O edgetpu_api.tar.gz --trust-server-names \
-  && tar xzf edgetpu_api.tar.gz
-
-COPY scripts/install_edgetpu_api.sh edgetpu_api/install.sh
-
-RUN cd edgetpu_api \
-  && /bin/bash install.sh
-
-# Copy a python 3.6 version
-RUN cd /usr/local/lib/python3.6/dist-packages/edgetpu/swig/ \
-  && ln -s _edgetpu_cpp_wrapper.cpython-35m-arm-linux-gnueabihf.so _edgetpu_cpp_wrapper.cpython-36m-arm-linux-gnueabihf.so
-
-# symlink the model and labels
-RUN wget https://dl.google.com/coral/canned_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite -O mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite --trust-server-names
-RUN wget https://dl.google.com/coral/canned_models/coco_labels.txt -O coco_labels.txt --trust-server-names
-RUN ln -s mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
-RUN ln -s /coco_labels.txt /label_map.pbtext
-
-# Minimize image size 
-RUN (apt-get autoremove -y; \
-     apt-get autoclean -y)
 
 WORKDIR /opt/frigate/
 ADD frigate frigate/
 COPY detect_objects.py .
 COPY benchmark.py .
 
-CMD ["python3", "-u", "detect_objects.py"]
+CMD ["python3.7", "-u", "detect_objects.py"]

README.md

@@ -1,14 +1,13 @@
 # Frigate - Realtime Object Detection for IP Cameras
-**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
-
 Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
 
-- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
+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.
-- Allows you to define specific regions (squares) in the image to look for objects
+
-- No motion detection (for now)
+- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
-- Object detection with Tensorflow runs in a separate thread
+- Uses a very low overhead motion detection to determine where to run object detection
+- Object detection with Tensorflow runs in a separate process
 - Object info is published over MQTT for integration into HomeAssistant as a binary sensor
-- An endpoint is available to view an MJPEG stream for debugging
+- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
 
 ![Diagram](diagram.png)
 
@@ -22,12 +21,16 @@ Build the container with
 docker build -t frigate .
 ```
 
-The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
+Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
+- CPU Model: `/cpu_model.tflite`
+- EdgeTPU Model: `/edgetpu_model.tflite`
+- Labels: `/labelmap.txt`
 
 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 \
@@ -37,11 +40,12 @@ 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
@@ -55,20 +59,24 @@ Example docker-compose:
 
 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).
 
-Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
+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`
+
+Debug info is available at `http://localhost:5000/debug/stats`
 
 ## Integration with HomeAssistant
 ```
 camera:
   - name: Camera Last Person
     platform: mqtt
-    topic: frigate/<camera_name>/snapshot
+    topic: frigate/<camera_name>/person/snapshot
+  - name: Camera Last Car
+    platform: mqtt
+    topic: frigate/<camera_name>/car/snapshot
 
 binary_sensor:
   - name: Camera Person
     platform: mqtt
-    state_topic: "frigate/<camera_name>/objects"
+    state_topic: "frigate/<camera_name>/person"
-    value_template: '{{ value_json.person }}'
     device_class: motion
     availability_topic: "frigate/available"
 
@@ -89,32 +97,36 @@ automation:
           message: "A person was detected."
           data:
             photo:
-              - url: http://<ip>:5000/<camera_name>/best_person.jpg
+              - 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
-
-## Future improvements
-- [x] Remove motion detection for now
-- [x] Try running object detection in a thread rather than a process
-- [x] Implement min person size again
-- [x] Switch to a config file
-- [x] Handle multiple cameras in the same container
-- [ ] Attempt to figure out coral symlinking
-- [ ] Add object list to config with min scores for mqtt
-- [ ] Move mjpeg encoding to a separate process
-- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
-- [ ] See if motion detection is even worth running
-- [ ] Scan for people across entire image rather than specfic regions
-- [ ] Dynamically resize detection area and follow people
-- [ ] Add ability to turn detection on and off via MQTT
-- [ ] Output movie clips of people for notifications, etc.
-- [ ] Integrate with homeassistant push camera
-- [ ] Merge bounding boxes that span multiple regions
-- [ ] Implement mode to save labeled objects for training
-- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
-- [ ] Look into GPU accelerated decoding of RTSP stream
-- [ ] Send video over a socket and use JSMPEG
-- [x] Look into neural compute stick

benchmark.py

@@ -1,20 +1,18 @@
 import statistics
 import numpy as np
-from edgetpu.detection.engine import DetectionEngine
+import time
+from frigate.edgetpu import ObjectDetector
 
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
+object_detector = ObjectDetector()
-PATH_TO_CKPT = '/frozen_inference_graph.pb'
-
-# Load the edgetpu engine and labels
-engine = DetectionEngine(PATH_TO_CKPT)
 
 frame = np.zeros((300,300,3), np.uint8)
-flattened_frame = np.expand_dims(frame, axis=0).flatten()
+input_frame = np.expand_dims(frame, axis=0)
 
 detection_times = []
 
-for x in range(0, 1000):
+for x in range(0, 100):
-    objects = engine.DetectWithInputTensor(flattened_frame, threshold=0.1, top_k=3)
+    start = time.monotonic()
-    detection_times.append(engine.get_inference_time())
+    object_detector.detect_raw(input_frame)
+    detection_times.append(time.monotonic()-start)
 
-print("Average inference time: " + str(statistics.mean(detection_times)))
+print(f"Average inference time: {statistics.mean(detection_times)*1000:.2f}ms")

config/config.example.yml

@@ -39,13 +39,31 @@ mqtt:
 #     - -use_wallclock_as_timestamps
 #     - '1'
 #   output_args:
-#     - -vf
-#     - mpdecimate
 #     - -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:
@@ -63,12 +81,21 @@ cameras:
       # output_args: []
     
     ################
-    ## Optional mask. Must be the same dimensions as your video feed.
+    ## 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
 
@@ -80,31 +107,26 @@ cameras:
     take_frame: 1
 
     ################
-    # size: size of the region in pixels
+    # The expected framerate for the camera. Frigate will try and ensure it maintains this framerate
-    # x_offset/y_offset: position of the upper left corner of your region (top left of image is 0,0)
+    # by dropping frames as necessary. Setting this lower than the actual framerate will allow frigate
-    # min_person_area (optional): minimum width*height of the bounding box for the detected person
+    # to process every frame at the expense of realtime processing.
-    # max_person_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
-    # Tips: All regions are resized to 300x300 before detection because the model is trained on that size.
-    #       Resizing regions takes CPU power. Ideally, all regions should be as close to 300x300 as possible.
-    #       Defining a region that goes outside the bounds of the image will result in errors.
     ################
-    regions:
+    fps: 5
-      - size: 350
+
-        x_offset: 0
+    ################
-        y_offset: 300
+    # Configuration for the snapshots in the debug view and mqtt
-        min_person_area: 5000
+    ################
-        max_person_area: 100000
+    snapshots:
-        threshold: 0.5
+      show_timestamp: True
-      - size: 400
+
-        x_offset: 350
+    ################
-        y_offset: 250
+    # Camera level object config. This config is merged with the global config above.
-        min_person_area: 2000
+    ################
-        max_person_area: 100000
+    objects:
-        threshold: 0.5
+      track:
-      - size: 400
+        - person
-        x_offset: 750
+      filters:
-        y_offset: 250
+        person:
-        min_person_area: 2000
+          min_area: 5000
-        max_person_area: 100000
+          max_area: 100000
-        threshold: 0.5
+          threshold: 0.5

detect_objects.py

@@ -1,13 +1,20 @@
 import cv2
 import time
+import datetime
 import queue
 import yaml
+import threading
+import multiprocessing as mp
+import subprocess as sp
 import numpy as np
-from flask import Flask, Response, make_response
+import logging
+from flask import Flask, Response, make_response, jsonify
 import paho.mqtt.client as mqtt
 
-from frigate.video import Camera
+from frigate.video import track_camera
-from frigate.object_detection import PreppedQueueProcessor
+from frigate.object_processing import TrackedObjectProcessor
+from frigate.util import EventsPerSecond
+from frigate.edgetpu import EdgeTPUProcess
 
 with open('/config/config.yml') as f:
     CONFIG = yaml.safe_load(f)
@@ -37,14 +44,57 @@ FFMPEG_DEFAULT_CONFIG = {
          '-stimeout', '5000000',
          '-use_wallclock_as_timestamps', '1']),
     'output_args': FFMPEG_CONFIG.get('output_args',
-        ['-vf', 'mpdecimate',
+        ['-f', 'rawvideo',
-         '-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(30)
+
+            for name, camera_process in self.camera_processes.items():
+                process = camera_process['process']
+                if (not self.object_processor.get_current_frame_time(name) is None and 
+                    (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
+                    camera_process['detection_fps'].value = 0.0
+                    self.object_processor.camera_data[name]['current_frame_time'] = None
+                    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):
@@ -68,67 +118,129 @@ def main():
     client.connect(MQTT_HOST, MQTT_PORT, 60)
     client.loop_start()
 
-    # Queue for prepped frames, max size set to (number of cameras * 5)
+    # start plasma store
-    max_queue_size = len(CONFIG['cameras'].items())*5
+    plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
-    prepped_frame_queue = queue.Queue(max_queue_size)
+    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL)
+    time.sleep(1)
+    rc = plasma_process.poll()
+    if rc is not None:
+        raise RuntimeError("plasma_store exited unexpectedly with "
+                            "code %d" % (rc,))
 
-    cameras = {}
+    ##
+    # Setup config defaults for cameras
+    ##
     for name, config in CONFIG['cameras'].items():
-        cameras[name] = Camera(name, FFMPEG_DEFAULT_CONFIG, config, prepped_frame_queue, client, MQTT_TOPIC_PREFIX)
+        config['snapshots'] = {
+            'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True)
+        }
 
-    prepped_queue_processor = PreppedQueueProcessor(
+    # Queue for cameras to push tracked objects to
-        cameras,
+    tracked_objects_queue = mp.Queue()
-        prepped_frame_queue
-    )
-    prepped_queue_processor.start()
     
-    for name, camera in cameras.items():
+    # Start the shared tflite process
-        camera.start()
+    tflite_process = EdgeTPUProcess()
-        print("Capture process for {}: {}".format(name, camera.get_capture_pid()))
+
+    # start the camera processes
+    camera_processes = {}
+    for name, config in CONFIG['cameras'].items():
+        camera_processes[name] = {
+            'fps': mp.Value('d', float(config['fps'])),
+            'skipped_fps': mp.Value('d', 0.0),
+            'detection_fps': mp.Value('d', 0.0)
+        }
+        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('/')
     def ishealthy():
         # return a healh
         return "Frigate is running. Alive and healthy!"
 
-    @app.route('/<camera_name>/best_person.jpg')
+    @app.route('/debug/stats')
-    def best_person(camera_name):
+    def stats():
-        if camera_name in cameras:
+        stats = {}
-            best_person_frame = cameras[camera_name].get_best_person()
+
-            if best_person_frame is None:
+        total_detection_fps = 0
-                best_person_frame = np.zeros((720,1280,3), np.uint8)
+
-            ret, jpg = cv2.imencode('.jpg', best_person_frame)
+        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)
+        }
+
+        rc = plasma_process.poll()
+        stats['plasma_store_rc'] = rc
+
+        stats['tracked_objects_queue'] = tracked_objects_queue.qsize()
+
+        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 f'Camera named {camera_name} not found', 404
+            return "Camera named {} not found".format(camera_name), 404
 
     @app.route('/<camera_name>')
     def mjpeg_feed(camera_name):
-        if camera_name in cameras:
+        if camera_name in CONFIG['cameras']:
             # return a multipart response
             return Response(imagestream(camera_name),
                             mimetype='multipart/x-mixed-replace; boundary=frame')
         else:
-            return f'Camera named {camera_name} not found', 404
+            return "Camera named {} not found".format(camera_name), 404
 
     def imagestream(camera_name):
         while True:
-            # max out at 5 FPS
+            # max out at 1 FPS
-            time.sleep(0.2)
+            time.sleep(1)
-            frame = cameras[camera_name].get_current_frame_with_objects()
+            frame = object_processor.get_current_frame(camera_name)
-            # encode the image into a jpg
+            if frame is None:
+                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.join()
+    camera_watchdog.join()
+    
+    plasma_process.terminate()
 
 if __name__ == '__main__':
     main()

frigate/edgetpu.py

@@ -0,0 +1,136 @@
+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

@@ -0,0 +1,79 @@
+import cv2
+import imutils
+import numpy as np
+
+class MotionDetector():
+    def __init__(self, frame_shape, mask, resize_factor=4):
+        self.resize_factor = resize_factor
+        self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
+        self.avg_frame = np.zeros(self.motion_frame_size, np.float)
+        self.avg_delta = np.zeros(self.motion_frame_size, np.float)
+        self.motion_frame_count = 0
+        self.frame_counter = 0
+        resized_mask = cv2.resize(mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+        self.mask = np.where(resized_mask==[0])
+
+    def detect(self, frame):
+        motion_boxes = []
+
+        # resize frame
+        resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+
+        # convert to grayscale
+        gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+
+        # mask frame
+        gray[self.mask] = [255]
+
+        # it takes ~30 frames to establish a baseline
+        # dont bother looking for motion
+        if self.frame_counter < 30:
+            self.frame_counter += 1
+        else:
+            # compare to average
+            frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg_frame))
+
+            # compute the average delta over the past few frames
+            # the alpha value can be modified to configure how sensitive the motion detection is.
+            # higher values mean the current frame impacts the delta a lot, and a single raindrop may
+            # register as motion, too low and a fast moving person wont be detected as motion
+            # this also assumes that a person is in the same location across more than a single frame
+            cv2.accumulateWeighted(frameDelta, self.avg_delta, 0.2)
+
+            # compute the threshold image for the current frame
+            current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
+
+            # black out everything in the avg_delta where there isnt motion in the current frame
+            avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
+            avg_delta_image[np.where(current_thresh==[0])] = [0]
+
+            # then look for deltas above the threshold, but only in areas where there is a delta
+            # in the current frame. this prevents deltas from previous frames from being included
+            thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
+
+            # dilate the thresholded image to fill in holes, then find contours
+            # on thresholded image
+            thresh = cv2.dilate(thresh, None, iterations=2)
+            cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            cnts = imutils.grab_contours(cnts)
+
+            # loop over the contours
+            for c in cnts:
+                # if the contour is big enough, count it as motion
+                contour_area = cv2.contourArea(c)
+                if contour_area > 100:
+                    x, y, w, h = cv2.boundingRect(c)
+                    motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
+        
+        if len(motion_boxes) > 0:
+            self.motion_frame_count += 1
+            # TODO: this really depends on FPS
+            if self.motion_frame_count >= 10:
+                # only average in the current frame if the difference persists for at least 3 frames
+                cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+        else:
+            # when no motion, just keep averaging the frames together
+            cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+            self.motion_frame_count = 0
+
+        return motion_boxes

frigate/mqtt.py

@@ -1,41 +0,0 @@
-import json
-import cv2
-import threading
-
-class MqttObjectPublisher(threading.Thread):
-    def __init__(self, client, topic_prefix, objects_parsed, detected_objects, best_person_frame):
-        threading.Thread.__init__(self)
-        self.client = client
-        self.topic_prefix = topic_prefix
-        self.objects_parsed = objects_parsed
-        self._detected_objects = detected_objects
-        self.best_person_frame = best_person_frame
-
-    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)
-                # send the snapshot over mqtt as well
-                if not self.best_person_frame.best_frame is None:
-                    ret, jpg = cv2.imencode('.jpg', self.best_person_frame.best_frame)
-                    if ret:
-                        jpg_bytes = jpg.tobytes()
-                        self.client.publish(self.topic_prefix+'/snapshot', jpg_bytes, retain=True)

frigate/object_detection.py

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

frigate/object_processing.py

@@ -0,0 +1,154 @@
+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': None,
+            'current_frame': np.zeros((720,1280,3), np.uint8),
+            'object_id': None
+        })
+        
+    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, timeout_ms=0)
+
+            if not current_frame is plasma.ObjectNotAvailable:
+                # draw the bounding boxes on the frame
+                for obj in tracked_objects.values():
+                    thickness = 2
+                    color = COLOR_MAP[obj['label']]
+                    
+                    if obj['frame_time'] != frame_time:
+                        thickness = 1
+                        color = (255,0,0)
+
+                    # draw the bounding boxes on the frame
+                    box = obj['box']
+                    draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
+                    # draw the regions on the frame
+                    region = obj['region']
+                    cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
+                
+                if 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
+
+                # store the object id, so you can delete it at the next loop
+                previous_object_id = self.camera_data[camera]['object_id']
+                if not previous_object_id is None:
+                    self.plasma_client.delete([previous_object_id])
+                self.camera_data[camera]['object_id'] = object_id
+            
+            ###
+            # Maintain the highest scoring recent object and frame for each label
+            ###
+            for obj in tracked_objects.values():
+                # if the object wasn't seen on the current frame, skip it
+                if obj['frame_time'] != frame_time:
+                    continue
+                if obj['label'] in best_objects:
+                    now = datetime.datetime.now().timestamp()
+                    # if the object is a higher score than the current best score 
+                    # or the current object is more than 1 minute old, use the new object
+                    if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
+                        obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
+                        best_objects[obj['label']] = obj
+                else:
+                    obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
+                    best_objects[obj['label']] = obj
+
+            ###
+            # Report over MQTT
+            ###
+            # count objects with more than 2 entries in history by type
+            obj_counter = Counter()
+            for obj in tracked_objects.values():
+                if len(obj['history']) > 1:
+                    obj_counter[obj['label']] += 1
+                    
+            # report on detected objects
+            for obj_name, count in obj_counter.items():
+                new_status = 'ON' if count > 0 else 'OFF'
+                if new_status != current_object_status[obj_name]:
+                    current_object_status[obj_name] = new_status
+                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
+                    # send the best snapshot over mqtt
+                    best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
+                    ret, jpg = cv2.imencode('.jpg', best_frame)
+                    if ret:
+                        jpg_bytes = jpg.tobytes()
+                        self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
+
+            # expire any objects that are ON and no longer detected
+            expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
+            for obj_name in expired_objects:
+                current_object_status[obj_name] = 'OFF'
+                self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
+                # send updated snapshot over mqtt
+                best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
+                ret, jpg = cv2.imencode('.jpg', best_frame)
+                if ret:
+                    jpg_bytes = jpg.tobytes()
+                    self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)

frigate/objects.py

@@ -2,91 +2,158 @@ import time
 import datetime
 import threading
 import cv2
-from . util import draw_box_with_label
+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 ObjectCleaner(threading.Thread):
+class ObjectTracker():
-    def __init__(self, objects_parsed, detected_objects):
+    def __init__(self, max_disappeared):
-        threading.Thread.__init__(self)
+        self.tracked_objects = {}
-        self._objects_parsed = objects_parsed
+        self.disappeared = {}
-        self._detected_objects = detected_objects
+        self.max_disappeared = max_disappeared
 
-    def run(self):
+    def register(self, index, obj):
-        while True:
+        id = f"{obj['frame_time']}-{index}"
+        obj['id'] = id
+        obj['top_score'] = obj['score']
+        self.add_history(obj)
+        self.tracked_objects[id] = obj
+        self.disappeared[id] = 0
 
-            # wait a bit before checking for expired frames
+    def deregister(self, id):
-            time.sleep(0.2)
+        del self.tracked_objects[id]
+        del self.disappeared[id]
     
-            # expire the objects that are more than 1 second old
+    def update(self, id, new_obj):
-            now = datetime.datetime.now().timestamp()
+        self.disappeared[id] = 0
-            # look for the first object found within the last second
+        self.tracked_objects[id].update(new_obj)
-            # (newest objects are appended to the end)
+        self.add_history(self.tracked_objects[id])
-            detected_objects = self._detected_objects.copy()
+        if self.tracked_objects[id]['score'] > self.tracked_objects[id]['top_score']:
+            self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['score']
     
-            num_to_delete = 0
+    def add_history(self, obj):
-            for obj in detected_objects:
+        entry = {
-                if now-obj['frame_time']<2:
+            'score': obj['score'],
-                    break
+            'box': obj['box'],
-                num_to_delete += 1
+            'region': obj['region'],
-            if num_to_delete > 0:
+            'centroid': obj['centroid'],
-                del self._detected_objects[:num_to_delete]
+            'frame_time': obj['frame_time']
+        }
+        if 'history' in obj:
+            obj['history'].append(entry)
+        else:
+            obj['history'] = [entry]
 
-                # notify that parsed objects were changed
+    def match_and_update(self, frame_time, new_objects):
-                with self._objects_parsed:
+        # group by name
-                    self._objects_parsed.notify_all()
+        new_object_groups = defaultdict(lambda: [])
+        for obj in new_objects:
+            new_object_groups[obj[0]].append({
+                'label': obj[0],
+                'score': obj[1],
+                'box': obj[2],
+                'area': obj[3],
+                'region': obj[4],
+                'frame_time': frame_time
+            })
         
+        # update any tracked objects with labels that are not
+        # seen in the current objects and deregister if needed
+        for obj in list(self.tracked_objects.values()):
+            if not obj['label'] in new_object_groups:
+                if self.disappeared[obj['id']] >= self.max_disappeared:
+                    self.deregister(obj['id'])
+                else:
+                    self.disappeared[obj['id']] += 1
         
-# Maintains the frame and person with the highest score from the most recent
+        if len(new_objects) == 0:
-# motion event
+            return
-class BestPersonFrame(threading.Thread):
-    def __init__(self, objects_parsed, recent_frames, detected_objects):
-        threading.Thread.__init__(self)
-        self.objects_parsed = objects_parsed
-        self.recent_frames = recent_frames
-        self.detected_objects = detected_objects
-        self.best_person = None
-        self.best_frame = None
         
-    def run(self):
+        # track objects for each label type
-        while True:
+        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])
 
-            # wait until objects have been parsed
+            # compute centroids of new objects
-            with self.objects_parsed:
+            for obj in group:
-                self.objects_parsed.wait()
+                centroid_x = int((obj['box'][0]+obj['box'][2]) / 2.0)
+                centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0)
+                obj['centroid'] = (centroid_x, centroid_y)
 
-            # make a copy of detected objects
+            if len(current_objects) == 0:
-            detected_objects = self.detected_objects.copy()
+                for index, obj in enumerate(group):
-            detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
+                    self.register(index, obj)
+                return
             
-            # get the highest scoring person
+            new_centroids = np.array([o['centroid'] for o in group])
-            new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
 
-            # if there isnt a person, continue
+            # compute the distance between each pair of tracked
-            if new_best_person is None:
+            # centroids and new centroids, respectively -- our
-                continue
+            # goal will be to match each new centroid to an existing
+            # object centroid
+            D = dist.cdist(current_centroids, new_centroids)
 
-            # if there is no current best_person
+            # in order to perform this matching we must (1) find the
-            if self.best_person is None:
+            # smallest value in each row and then (2) sort the row
-                self.best_person = new_best_person
+            # indexes based on their minimum values so that the row
-            # if there is already a best_person
+            # with the smallest value is at the *front* of the index
+            # list
+            rows = D.min(axis=1).argsort()
+
+            # next, we perform a similar process on the columns by
+            # finding the smallest value in each column and then
+            # sorting using the previously computed row index list
+            cols = D.argmin(axis=1)[rows]
+
+            # in order to determine if we need to update, register,
+            # or deregister an object we need to keep track of which
+            # of the rows and column indexes we have already examined
+            usedRows = set()
+            usedCols = set()
+
+            # loop over the combination of the (row, column) index
+            # tuples
+            for (row, col) in zip(rows, cols):
+                # if we have already examined either the row or
+                # column value before, ignore it
+                if row in usedRows or col in usedCols:
+                    continue
+
+                # otherwise, grab the object ID for the current row,
+                # set its new centroid, and reset the disappeared
+                # counter
+                objectID = current_ids[row]
+                self.update(objectID, group[col])
+
+                # indicate that we have examined each of the row and
+                # column indexes, respectively
+                usedRows.add(row)
+                usedCols.add(col)
+
+            # compute the column index we have NOT yet examined
+            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
+            unusedCols = set(range(0, D.shape[1])).difference(usedCols)
+
+            # in the event that the number of object centroids is
+			# equal or greater than the number of input centroids
+			# we need to check and see if some of these objects have
+			# potentially disappeared
+            if D.shape[0] >= D.shape[1]:
+                for row in unusedRows:
+                    id = current_ids[row]
+
+                    if self.disappeared[id] >= self.max_disappeared:
+                        self.deregister(id)
+                    else:
+                        self.disappeared[id] += 1
+            # if the number of input centroids is greater
+            # than the number of existing object centroids we need to
+            # register each new input centroid as a trackable object
             else:
-                now = datetime.datetime.now().timestamp()
+                for col in unusedCols:
-                # if the new best person is a higher score than the current best person 
+                    self.register(col, group[col])
-                # or the current person is more than 1 minute old, use the new best person
-                if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
-                    self.best_person = new_best_person
-            
-            # make a copy of the recent frames
-            recent_frames = self.recent_frames.copy()
-            
-            if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
-                best_frame = recent_frames[self.best_person['frame_time']]
-
-                label = "{}: {}% {}".format(self.best_person['name'],int(self.best_person['score']*100),int(self.best_person['area']))
-                draw_box_with_label(best_frame, self.best_person['xmin'], self.best_person['ymin'], 
-                    self.best_person['xmax'], self.best_person['ymax'], label)
-                
-                # print a timestamp
-                time_to_show = datetime.datetime.fromtimestamp(self.best_person['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
-                cv2.putText(best_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
-                
-                self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)

frigate/util.py

@@ -1,26 +1,129 @@
+import datetime
+import collections
 import numpy as np
 import cv2
+import threading
+import matplotlib.pyplot as plt
 
-# convert shared memory array into numpy array
+def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
-def tonumpyarray(mp_arr):
+    if color is None:
-    return np.frombuffer(mp_arr.get_obj(), dtype=np.uint8)
+        color = (0,0,255)
-
+    display_text = "{}: {}".format(label, info)
-def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label):
+    cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
-    color = (255,0,0)
-    cv2.rectangle(frame, (x_min, y_min), 
-        (x_max, y_max), 
-        color, 2)
     font_scale = 0.5
     font = cv2.FONT_HERSHEY_SIMPLEX
     # get the width and height of the text box
-    size = cv2.getTextSize(label, font, fontScale=font_scale, thickness=2)
+    size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2)
     text_width = size[0][0]
     text_height = size[0][1]
     line_height = text_height + size[1]
     # set the text start position
-    text_offset_x = x_min
+    if position == 'ul':
-    text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+        text_offset_x = x_min
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+    elif position == 'ur':
+        text_offset_x = x_max - (text_width+8)
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+    elif position == 'bl':
+        text_offset_x = x_min
+        text_offset_y = y_max
+    elif position == 'br':
+        text_offset_x = x_max - (text_width+8)
+        text_offset_y = y_max
     # make the coords of the box with a small padding of two pixels
     textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
     cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
-    cv2.putText(frame, label, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
+    cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
+
+def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):    
+    # size is larger than longest edge
+    size = int(max(xmax-xmin, ymax-ymin)*multiplier)
+    # 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

@@ -2,53 +2,48 @@ 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
-from . util import tonumpyarray, draw_box_with_label
+import hashlib
-from . object_detection import FramePrepper
+import pyarrow.plasma as plasma
-from . objects import ObjectCleaner, BestPersonFrame
+import SharedArray as sa
-from . mqtt import MqttObjectPublisher
+import copy
-
+import itertools
-# Stores 2 seconds worth of frames when motion is detected so they can be used for other threads
+import json
-class FrameTracker(threading.Thread):
+from collections import defaultdict
-    def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames):
+from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond
-        threading.Thread.__init__(self)
+from frigate.objects import ObjectTracker
-        self.shared_frame = shared_frame
+from frigate.edgetpu import RemoteObjectDetector
-        self.frame_time = frame_time
+from frigate.motion import MotionDetector
-        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(source):
-    # capture a single frame and check the frame shape so the correct array
+    ffprobe_cmd = " ".join([
-    # size can be allocated in memory
+        'ffprobe',
+        '-v',
+        'panic',
+        '-show_error',
+        '-show_streams',
+        '-of',
+        'json',
+        '"'+source+'"'
+    ])
+    print(ffprobe_cmd)
+    p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+    (output, err) = p.communicate()
+    p_status = p.wait()
+    info = json.loads(output)
+    print(info)
+
+    video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
+
+    if video_info['height'] != 0 and video_info['width'] != 0:
+        return (video_info['height'], video_info['width'], 3)
+    
+    # fallback to using opencv if ffprobe didnt succeed
     video = cv2.VideoCapture(source)
     ret, frame = video.read()
     frame_shape = frame.shape
@@ -59,270 +54,302 @@ def get_ffmpeg_input(ffmpeg_input):
     frigate_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
     return ffmpeg_input.format(**frigate_vars)
 
-class CameraWatchdog(threading.Thread):
+def filtered(obj, objects_to_track, object_filters, mask):
-    def __init__(self, camera):
+    object_name = obj[0]
-        threading.Thread.__init__(self)
-        self.camera = camera
 
-    def run(self):
+    if not object_name in objects_to_track:
+        return True
     
-        while True:
+    if object_name in object_filters:
-            # wait a bit before checking
+        obj_settings = object_filters[object_name]
-            time.sleep(10)
 
-            if (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 10:
+        # if the min area is larger than the
-                print("last frame is more than 10 seconds old, restarting camera capture...")
+        # detected object, don't add it to detected objects
-                self.camera.start_or_restart_capture()
+        if obj_settings.get('min_area',-1) > obj[3]:
-                time.sleep(5)
+            return True
         
-# Thread to read the stdout of the ffmpeg process and update the current frame
+        # if the detected object is larger than the
-class CameraCapture(threading.Thread):
+        # max area, don't add it to detected objects
-    def __init__(self, camera):
+        if obj_settings.get('max_area', 24000000) < obj[3]:
-        threading.Thread.__init__(self)
+            return True
-        self.camera = camera
 
-    def run(self):
+        # if the score is lower than the threshold, skip
-        frame_num = 0
+        if obj_settings.get('threshold', 0) > obj[1]:
-        while True:
+            return True
-            if self.camera.ffmpeg_process.poll() != None:
-                print("ffmpeg process is not running. exiting capture thread...")
-                break
     
-            raw_image = self.camera.ffmpeg_process.stdout.read(self.camera.frame_size)
+        # 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 len(raw_image) == 0:
+        # if the object is in a masked location, don't add it to detected objects
-                print("ffmpeg didnt return a frame. something is wrong. exiting capture thread...")
+        if mask[y_location][x_location] == [0]:
-                break
+            return True
         
-            frame_num += 1
+        return False
-            if (frame_num % self.camera.take_frame) != 0:
-                continue
 
-            with self.camera.frame_lock:
+def create_tensor_input(frame, region):
-                self.camera.frame_time.value = datetime.datetime.now().timestamp()
+    cropped_frame = frame[region[1]:region[3], region[0]:region[2]]
 
-                self.camera.current_frame[:] = (
+    # Resize to 300x300 if needed
-                    np
+    if cropped_frame.shape != (300, 300, 3):
-                    .frombuffer(raw_image, np.uint8)
+        cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
-                    .reshape(self.camera.frame_shape)
-                )
-            # Notify with the condition that a new frame is ready
-            with self.camera.frame_ready:
-                self.camera.frame_ready.notify_all()
     
-class Camera:
+    # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
-    def __init__(self, name, ffmpeg_config, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
+    return np.expand_dims(cropped_frame, axis=0)
-        self.name = name
-        self.config = config
-        self.detected_objects = []
-        self.recent_frames = {}
 
-        self.ffmpeg = config.get('ffmpeg', {})
+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):
-        self.ffmpeg_input = get_ffmpeg_input(self.ffmpeg['input'])
+    print(f"Starting process for {name}: {os.getpid()}")
-        self.ffmpeg_global_args = self.ffmpeg.get('global_args', ffmpeg_config['global_args'])
-        self.ffmpeg_hwaccel_args = self.ffmpeg.get('hwaccel_args', ffmpeg_config['hwaccel_args'])
-        self.ffmpeg_input_args = self.ffmpeg.get('input_args', ffmpeg_config['input_args'])
-        self.ffmpeg_output_args = self.ffmpeg.get('output_args', ffmpeg_config['output_args'])
 
-        self.take_frame = self.config.get('take_frame', 1)
+    # Merge the ffmpeg config with the global config
-        self.regions = self.config['regions']
+    ffmpeg = config.get('ffmpeg', {})
-        self.frame_shape = get_frame_shape(self.ffmpeg_input)
+    ffmpeg_input = get_ffmpeg_input(ffmpeg['input'])
-        self.frame_size = self.frame_shape[0] * self.frame_shape[1] * self.frame_shape[2]
+    ffmpeg_global_args = ffmpeg.get('global_args', ffmpeg_global_config['global_args'])
-        self.mqtt_client = mqtt_client
+    ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', ffmpeg_global_config['hwaccel_args'])
-        self.mqtt_topic_prefix = '{}/{}'.format(mqtt_prefix, self.name)
+    ffmpeg_input_args = ffmpeg.get('input_args', ffmpeg_global_config['input_args'])
+    ffmpeg_output_args = ffmpeg.get('output_args', ffmpeg_global_config['output_args'])
 
-        # create a numpy array for the current frame in initialize to zeros
+    # Merge the tracked object config with the global config
-        self.current_frame = np.zeros(self.frame_shape, np.uint8)
+    camera_objects_config = config.get('objects', {})    
-        # create shared value for storing the frame_time
+    # combine tracked objects lists
-        self.frame_time = mp.Value('d', 0.0)
+    objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', []))
-        # Lock to control access to the frame
+    # merge object filters
-        self.frame_lock = mp.Lock()
+    global_object_filters = global_objects_config.get('filters', {})
-        # Condition for notifying that a new frame is ready
+    camera_object_filters = camera_objects_config.get('filters', {})
-        self.frame_ready = mp.Condition()
+    objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys())
-        # Condition for notifying that objects were parsed
+    object_filters = {}
-        self.objects_parsed = mp.Condition()
+    for obj in objects_with_config:
+        object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
 
-        self.ffmpeg_process = None
+    expected_fps = config['fps']
-        self.capture_thread = None
+    take_frame = config.get('take_frame', 1)
 
-        # for each region, create a separate thread to resize the region and prep for detection
+    if 'width' in config and 'height' in config:
-        self.detection_prep_threads = []
+        frame_shape = (config['height'], config['width'], 3)
-        for region in self.config['regions']:
+    else:
-            # set a default threshold of 0.5 if not defined
+        frame_shape = get_frame_shape(ffmpeg_input)
-            if not 'threshold' in region:
-                region['threshold'] = 0.5
-            if not isinstance(region['threshold'], float):
-                print('Threshold is not a float. Setting to 0.5 default.')
-                region['threshold'] = 0.5
-            self.detection_prep_threads.append(FramePrepper(
-                self.name,
-                self.current_frame,
-                self.frame_time,
-                self.frame_ready,
-                self.frame_lock,
-                region['size'], region['x_offset'], region['y_offset'], region['threshold'],
-                prepped_frame_queue
-            ))
 
-        # start a thread to store recent motion frames for processing
+    frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
-        self.frame_tracker = FrameTracker(self.current_frame, self.frame_time, 
-            self.frame_ready, self.frame_lock, self.recent_frames)
-        self.frame_tracker.start()
 
-        # start a thread to store the highest scoring recent person frame
+    try:
-        self.best_person_frame = BestPersonFrame(self.objects_parsed, self.recent_frames, self.detected_objects)
+        sa.delete(name)
-        self.best_person_frame.start()
+    except:
+        pass
 
-        # start a thread to expire objects from the detected objects list
+    frame = sa.create(name, shape=frame_shape, dtype=np.uint8)
-        self.object_cleaner = ObjectCleaner(self.objects_parsed, self.detected_objects)
-        self.object_cleaner.start()
 
-        # start a thread to publish object scores (currently only person)
+    # load in the mask for object detection
-        mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects, self.best_person_frame)
+    if 'mask' in config:
-        mqtt_publisher.start()
+        mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE)
+    else:
+        mask = None
 
-        # create a watchdog thread for capture process
+    if mask is None:
-        self.watchdog = CameraWatchdog(self)
+        mask = np.zeros((frame_shape[0], frame_shape[1], 1), np.uint8)
+        mask[:] = 255
 
-        # load in the mask for person detection
+    motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
-        if 'mask' in self.config:
+    object_detector = RemoteObjectDetector('/labelmap.txt', detect_lock, detect_ready, frame_ready)
-            self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
-        else:
-            self.mask = None
 
-        if self.mask is None:
+    object_tracker = ObjectTracker(10)
-            self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
-            self.mask[:] = 255
 
-
+    ffmpeg_cmd = (['ffmpeg'] +
-    def start_or_restart_capture(self):
+            ffmpeg_global_args +
-        if not self.ffmpeg_process is None:
+            ffmpeg_hwaccel_args +
-            print("Terminating the existing ffmpeg process...")
+            ffmpeg_input_args +
-            self.ffmpeg_process.terminate()
+            ['-i', ffmpeg_input] +
-            try:
+            ffmpeg_output_args +
-                print("Waiting for ffmpeg to exit gracefully...")
-                self.ffmpeg_process.wait(timeout=30)
-            except sp.TimeoutExpired:
-                print("FFmpeg didnt exit. Force killing...")
-                self.ffmpeg_process.kill()
-                self.ffmpeg_process.wait()
-
-            print("Waiting for the capture thread to exit...")
-            self.capture_thread.join()
-            self.ffmpeg_process = None
-            self.capture_thread = None
-            
-        # create the process to capture frames from the input stream and store in a shared array
-        print("Creating a new ffmpeg process...")
-        self.start_ffmpeg()
-        
-        print("Creating a new capture thread...")
-        self.capture_thread = CameraCapture(self)
-        print("Starting a new capture thread...")
-        self.capture_thread.start()
-    
-    def start_ffmpeg(self):
-        ffmpeg_cmd = (['ffmpeg'] +
-            self.ffmpeg_global_args +
-            self.ffmpeg_hwaccel_args +
-            self.ffmpeg_input_args +
-            ['-i', self.ffmpeg_input] +
-            self.ffmpeg_output_args +
             ['pipe:'])
 
-        print(" ".join(ffmpeg_cmd))
+    print(" ".join(ffmpeg_cmd))
     
-        self.ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=self.frame_size)
+    ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=frame_size)
     
-    def start(self):
+    plasma_client = plasma.connect("/tmp/plasma")
-        self.start_or_restart_capture()
+    frame_num = 0
-        # start the object detection prep threads
+    avg_wait = 0.0
-        for detection_prep_thread in self.detection_prep_threads:
+    fps_tracker = EventsPerSecond()
-            detection_prep_thread.start()
+    skipped_fps_tracker = EventsPerSecond()
-        self.watchdog.start()
+    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
 
-    def join(self):
+        if not frame_bytes:
-        self.capture_thread.join()
+            break
 
-    def get_capture_pid(self):
+        # limit frame rate
-        return self.ffmpeg_process.pid
+        frame_num += 1
+        if (frame_num % take_frame) != 0:
+            continue
 
-    def add_objects(self, objects):
+        fps_tracker.update()
-        if len(objects) == 0:
+        fps.value = fps_tracker.eps()
-            return
+        detection_fps.value = object_detector.fps.eps()
 
-        for obj in objects:
+        frame_time = datetime.datetime.now().timestamp()
-            # Store object area to use in bounding box labels
-            obj['area'] = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
         
-            if obj['name'] == 'person':
+        # Store frame in numpy array
-                # find the matching region
+        frame[:] = (np
-                region = None
+                    .frombuffer(frame_bytes, np.uint8)
-                for r in self.regions:
+                    .reshape(frame_shape))
-                    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
+        # look for motion
-                # detected person, don't add it to detected objects
+        motion_boxes = motion_detector.detect(frame)
-                if region and 'min_person_area' in region and region['min_person_area'] > obj['area']:
+
+        # 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
+            max_indices = None
+            region_indices = range(len(merged_regions))
+            for a, b in itertools.combinations(region_indices, 2):
+                iou = intersection_over_union(merged_regions[a], merged_regions[b])
+                if iou > max_iou:
+                    max_iou = iou
+                    max_indices = (a, b)
+            if max_iou > 0.1:
+                a = merged_regions[max_indices[0]]
+                b = merged_regions[max_indices[1]]
+                merged_regions.append(calculate_region(frame_shape,
+                    min(a[0], b[0]),
+                    min(a[1], b[1]),
+                    max(a[2], b[2]),
+                    max(a[3], b[3]),
+                    1
+                ))
+                del merged_regions[max(max_indices[0], max_indices[1])]
+                del merged_regions[min(max_indices[0], max_indices[1])]
+            else:
+                break
+
+        # resize regions and detect
+        detections = []
+        for region in merged_regions:
+
+            tensor_input = create_tensor_input(frame, region)
+
+            region_detections = object_detector.detect(tensor_input)
+
+            for d in region_detections:
+                box = d[2]
+                size = region[2]-region[0]
+                x_min = int((box[1] * size) + region[0])
+                y_min = int((box[0] * size) + region[1])
+                x_max = int((box[3] * size) + region[0])
+                y_max = int((box[2] * size) + region[1])
+                det = (d[0],
+                    d[1],
+                    (x_min, y_min, x_max, y_max),
+                    (x_max-x_min)*(y_max-y_min),
+                    region)
+                if filtered(det, objects_to_track, object_filters, mask):
                     continue
+                detections.append(det)
 
-                # if the detected person is larger than the
+        #########
-                # max person area, don't add it to detected objects
+        # merge objects, check for clipped objects and look again up to N times
-                if region and 'max_person_area' in region and region['max_person_area'] < obj['area']:
+        #########
-                    continue
+        refining = True
+        refine_count = 0
+        while refining and refine_count < 4:
+            refining = False
 
-                # compute the coordinates of the person and make sure
+            # group by name
-                # the location isnt outside the bounds of the image (can happen from rounding)
+            detected_object_groups = defaultdict(lambda: [])
-                y_location = min(int(obj['ymax']), len(self.mask)-1)
+            for detection in detections:
-                x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1)
+                detected_object_groups[detection[0]].append(detection)
 
-                # if the person is in a masked location, continue
+            selected_objects = []
-                if self.mask[y_location][x_location] == [0]:
+            for group in detected_object_groups.values():
-                    continue
 
-            self.detected_objects.append(obj)
+                # apply non-maxima suppression to suppress weak, overlapping bounding boxes
+                boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
+                    for o in group]
+                confidences = [o[1] for o in group]
+                idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
 
-        with self.objects_parsed:
+                for index in idxs:
-            self.objects_parsed.notify_all()
+                    obj = group[index[0]]
+                    if clipped(obj, frame_shape): #obj['clipped']:
+                        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])
                         
-    def get_best_person(self):
+                        tensor_input = create_tensor_input(frame, region)
-        return self.best_person_frame.best_frame
+                        # 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)
 
-    def get_current_frame_with_objects(self):
+                        refining = True
-        # make a copy of the current detected objects
+                    else:
-        detected_objects = self.detected_objects.copy()
+                        selected_objects.append(obj)
-        # lock and make a copy of the current frame
-        with self.frame_lock:
-            frame = self.current_frame.copy()
-            frame_time = self.frame_time.value
-
-        # draw the bounding boxes on the screen
-        for obj in detected_objects:
-            label = "{}: {}% {}".format(obj['name'],int(obj['score']*100),int(obj['area']))
-            draw_box_with_label(frame, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'], label)
-
-        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)
-        
-        # print a timestamp
-        time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
-        cv2.putText(frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
-
-        # convert to BGR
-        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
-        return frame
                 
+            # set the detections list to only include top, complete objects
+            # and new detections
+            detections = selected_objects
 
+            if refining:
+                refine_count += 1
         
+        # now that we have refined our detections, we need to track objects
+        object_tracker.match_and_update(frame_time, detections)
 
+        # put the frame in the plasma store
+        object_id = hashlib.sha1(str.encode(f"{name}{frame_time}")).digest()
+        plasma_client.put(frame, plasma.ObjectID(object_id))
+        # add to the queue
+        detected_objects_queue.put((name, frame_time, object_tracker.tracked_objects))

scripts/install_edgetpu_api.sh

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

scripts/install_odroid_repo.sh

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