Update README.md

Add MQTT connection error handling

.github/FUNDING.yml

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

Dockerfile

@@ -1,59 +1,70 @@
-FROM ubuntu:18.04
+FROM ubuntu:16.04
 
-# Install packages for apt repo
+# Install system packages
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
+RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y python3 \ 
-    apt-transport-https \
+ python3-dev \
-    ca-certificates \
+ python-pil \
-    curl \
+ python-lxml \
-    wget \
+ python-tk \
-    gnupg-agent \
-    dirmngr \
-    software-properties-common
-
-RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys D986B59D
-
-RUN echo "deb http://deb.odroid.in/5422-s bionic main" > /etc/apt/sources.list.d/odroid.list
-
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y \
- python3 \ 
- # OpenCV dependencies
- ffmpeg \
  build-essential \
- cmake \
+ cmake \ 
- unzip \
+ git \ 
- pkg-config \
+ libgtk2.0-dev \ 
+ pkg-config \ 
+ libavcodec-dev \ 
+ libavformat-dev \ 
+ libswscale-dev \ 
+ libtbb2 \
+ libtbb-dev \ 
  libjpeg-dev \
  libpng-dev \
  libtiff-dev \
- libavcodec-dev \
+ libjasper-dev \
- libavformat-dev \
+ libdc1394-22-dev \
- libswscale-dev \
+ x11-apps \
- libv4l-dev \
+ wget \
- libxvidcore-dev \
+ vim \
- libx264-dev \
+ ffmpeg \
- libgtk-3-dev \
+ unzip \
- libatlas-base-dev \
+ libusb-1.0-0-dev \
- gfortran \
+ python3-setuptools \
- python3-dev \
- # Coral USB Python API Dependencies
- libusb-1.0-0 \
- python3-pip \
- python3-pil \
  python3-numpy \
- libc++1 \
+ zlib1g-dev \
- libc++abi1 \
+ libgoogle-glog-dev \
- libunwind8 \
+ swig \
- libgcc1 \
+ libunwind-dev \
+ libc++-dev \
+ libc++abi-dev \
+ build-essential \
  && 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 \
+ pillow \
+ matplotlib \
+ notebook \
  Flask \
+ imutils \
  paho-mqtt \
  PyYAML
 
+# Install tensorflow models object detection
+RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
+RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
+
+# Download & build protobuf-python
+RUN cd /usr/local/src/ \
+ && tar xf protobuf-python-3.5.1.tar.gz \
+ && rm protobuf-python-3.5.1.tar.gz \
+ && cd /usr/local/src/protobuf-3.5.1/ \
+ && ./configure \
+ && make \
+ && make install \
+ && ldconfig \
+ && rm -rf /usr/local/src/protobuf-3.5.1/
+
 # Download & build OpenCV
 RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
 RUN cd /usr/local/src/ \
@@ -65,31 +76,30 @@ RUN cd /usr/local/src/ \
  && 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
+# Download and install EdgeTPU libraries
-RUN wget https://dl.google.com/coral/edgetpu_api/edgetpu_api_latest.tar.gz -O edgetpu_api.tar.gz --trust-server-names
+RUN wget -q -O edgetpu_api.tar.gz --no-check-certificate http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz
 
 RUN tar xzf edgetpu_api.tar.gz \
-  && cd edgetpu_api \
+  && cd python-tflite-source \
-  && cp -p libedgetpu/libedgetpu_arm32.so /usr/lib/arm-linux-gnueabihf/libedgetpu.so.1.0 \
+  && cp -p libedgetpu/libedgetpu_x86_64.so /lib/x86_64-linux-gnu/libedgetpu.so \
-  && ldconfig \
+  && cp edgetpu/swig/compiled_so/_edgetpu_cpp_wrapper_x86_64.so edgetpu/swig/_edgetpu_cpp_wrapper.so \
-  && python3 -m pip install --no-deps "$(ls edgetpu-*-py3-none-any.whl 2>/dev/null)"
+  && cp edgetpu/swig/compiled_so/edgetpu_cpp_wrapper.py edgetpu/swig/ \
-
+  && python3 setup.py develop --user
-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)
 
+# symlink the model and labels
+RUN ln -s /python-tflite-source/edgetpu/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
+RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /label_map.pbtext
+
+# Set TF object detection available
+ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
+RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
+
 WORKDIR /opt/frigate/
 ADD frigate frigate/
 COPY detect_objects.py .

README.md

@@ -1,3 +1,5 @@
+<a href='https://ko-fi.com/P5P7XGO9' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://az743702.vo.msecnd.net/cdn/kofi4.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
+
 # Frigate - Realtime Object Detection for RTSP Cameras
 **Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
 

detect_objects.py

@@ -25,6 +25,15 @@ def main():
     # connect to mqtt and setup last will
     def on_connect(client, userdata, flags, rc):
         print("On connect called")
+        if rc != 0:
+            if rc == 3:
+                print ("MQTT Server unavailable")
+            elif rc == 4:
+                print ("MQTT Bad username or password")
+            elif rc == 5:
+                print ("MQTT Not authorized")
+            else:
+                print ("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
         # publish a message to signal that the service is running
         client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
     client = mqtt.Client()
@@ -87,4 +96,4 @@ def main():
     camera.join()
 
 if __name__ == '__main__':
-    main()
+    main()

frigate/objects.py

@@ -2,6 +2,7 @@ import time
 import datetime
 import threading
 import cv2
+from object_detection.utils import visualization_utils as vis_util
 
 class ObjectCleaner(threading.Thread):
     def __init__(self, objects_parsed, detected_objects):
@@ -81,10 +82,15 @@ class BestPersonFrame(threading.Thread):
                 best_frame = recent_frames[self.best_person['frame_time']]
                 best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
                 # draw the bounding box on the frame
-                color = (255,0,0)
+                vis_util.draw_bounding_box_on_image_array(best_frame,
-                cv2.rectangle(best_frame, (self.best_person['xmin'], self.best_person['ymin']), 
+                    self.best_person['ymin'],
-                    (self.best_person['xmax'], self.best_person['ymax']), 
+                    self.best_person['xmin'],
-                    color, 2)
+                    self.best_person['ymax'],
+                    self.best_person['xmax'],
+                    color='red',
+                    thickness=2,
+                    display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))],
+                    use_normalized_coordinates=False)
 
                 # convert back to BGR
                 self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)

frigate/video.py

@@ -6,6 +6,7 @@ import threading
 import ctypes
 import multiprocessing as mp
 import numpy as np
+from object_detection.utils import visualization_utils as vis_util
 from . util import tonumpyarray
 from . object_detection import FramePrepper
 from . objects import ObjectCleaner, BestPersonFrame
@@ -282,10 +283,15 @@ class Camera:
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         # draw the bounding boxes on the screen
         for obj in detected_objects:
-            color = (255,0,0)
+            vis_util.draw_bounding_box_on_image_array(frame,
-            cv2.rectangle(frame, (obj['xmin'], obj['ymin']), 
+                obj['ymin'],
-                (obj['xmax'], obj['ymax']), 
+                obj['xmin'],
-                color, 2)
+                obj['ymax'],
+                obj['xmax'],
+                color='red',
+                thickness=2,
+                display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
+                use_normalized_coordinates=False)
 
         for region in self.regions:
             color = (255,255,255)