updated to version 0.0.3

added vision daemon which runs in the background
added more documentation to vision module
now including opencv dependencies in complib package

build_deb.sh

@@ -9,8 +9,25 @@ fpm -s python --python-bin python3 --python-pip pip3 --python-package-name-prefi
     --after-install postinstall.sh \
     --deb-generate-changes \
     --deb-priority "optional" \
+    --deb-systemd "complib.service" \
     -d "python3-pip" \
-    -v 0.0.2-8 -t deb setup.py
+    -d "nginx" \
+    -d "libnginx-mod-rtmp" \
+    -d "libsystemd-dev" \
+    -d "python3-systemd" \
+    -d "gstreamer1.0-tools" \
+    -d "gstreamer1.0-plugins-bad" \
+    -d "gstreamer1.0-plugins-base" \
+    -d "gstreamer1.0-plugins-good" \
+    -d "gstreamer1.0-omx-rpi" \
+    -d "gstreamer1.0-omx-rpi-config" \
+    -d "opencv-dev" \
+    -d "opencv-libs" \
+    -d "opencv-licenses" \
+    -d "opencv-main" \
+    -d "opencv-python" \
+    -d "opencv-scripts" \
+    -v 0.0.3-3 -t deb setup.py
 
 #    --deb-changelog changelog \
 #    --deb-upstream-changelog changelog \

compLib/Vision.py

@@ -6,6 +6,7 @@ from flask import Flask, Response
 
 RTMP_SERVER = os.getenv("RTMP_SERVER", "rtmp://localhost/live/stream")
 SERVE_VIDEO = os.getenv("SERVER_SRC", "/live")
+BUILDING_DOCS = os.getenv("BUILDING_DOCS", "false")
 
 app = Flask(__name__)
 
@@ -27,7 +28,12 @@ HTML = HTML.replace("{{ VIDEO_DST }}", SERVE_VIDEO)
 
 class __Streaming:
     """
-    Private class for opencv stuff.
+    Class that handles rtmp streaming for opencv.
+
+    DO NOT CREATE AN INSTANCE OF THIS CLASS YOURSELF!
+
+    This is automatically done when importing this module. Use Vision.Streaming which is
+    an instance of this class!
 
     grab frames -> do your own processing -> publish frame -> view on http server
     """
@@ -40,7 +46,7 @@ class __Streaming:
         create an object of this class. (There can (SHOULD!) only be one VideCapture)
         """
         self.__camera_stream = cv2.VideoCapture(RTMP_SERVER)
-        #self.__camera_stream = cv2.VideoCapture(0)
+        # self.__camera_stream = cv2.VideoCapture(0)
         self.__newest_frame = None
         self.__lock = threading.Lock()
 
@@ -79,7 +85,7 @@ class __Streaming:
                 buffer_frame = self.__newest_frame.copy()
 
             # encode frame for jpeg stream
-            (flag, encodedImage) = cv2.imencode(".jpg", buffer_frame)
+            (flag, encoded_image) = cv2.imencode(".jpg", buffer_frame)
 
             # if there was an error try again with the next frame
             if not flag:
@@ -87,15 +93,17 @@ class __Streaming:
 
             # else yield encoded frame with mimetype image/jpeg
             yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' +
-                   bytearray(encodedImage) + b'\r\n')
+                   bytearray(encoded_image) + b'\r\n')
 
 
-# instantiate private class __Streaming
-Streaming = __Streaming()
+Streaming = None
+if BUILDING_DOCS == "false":
+    # instantiate private class __Streaming
+    Streaming = __Streaming()
 
 
 @app.route("/live")
-def video_feed():
+def __video_feed():
     """
     Define route for serving jpeg stream.
 
@@ -124,12 +132,13 @@ def __start_flask():
     app.run(host="0.0.0.0", port=9898, debug=True, threaded=True, use_reloader=False)
 
 
-# start flask service in the background
-__webserver_thread = threading.Thread(target=__start_flask)
-__webserver_thread.start()
+if BUILDING_DOCS == "false":
+    # start flask service in the background
+    __webserver_thread = threading.Thread(target=__start_flask)
+    __webserver_thread.start()
 
 # for debugging and testing start processing frames and detecting a 6 by 9 calibration chessboard
-if __name__ == '__main__':
+if __name__ == '__main__' and BUILDING_DOCS == "false":
     while True:
         frame = Streaming.get_frame()
         criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
@@ -140,5 +149,5 @@ if __name__ == '__main__':
         # find the chessboard corners
         ret, corners = cv2.findChessboardCorners(gray, (6, 9), None)
 
-        cv2.drawChessboardCorners(frame,  (6, 9), corners, ret)
+        cv2.drawChessboardCorners(frame, (6, 9), corners, ret)
         Streaming.publish_frame(frame)

compLib/VisionDaemon.py

@@ -0,0 +1,20 @@
+import os
+
+import systemd.daemon
+import LogstashLogging
+from LogstashLogging import logstash_logger
+import logging
+
+__run = """raspivid -t 0 -b 10000000 -w 1920 -h 1080 -fps 30 -n -o - | gst-launch-1.0 fdsrc ! video/x-h264,width=1280,height=720,framerate=30/1,noise-reduction=1,profile=high,stream-format=byte-stream  ! h264parse ! queue ! flvmux streamable=true ! rtmpsink location=\"rtmp://localhost/live/stream\""""
+
+
+if __name__ == '__main__':
+    try:
+        systemd.daemon.notify(systemd.daemon.Notification.READY)
+    except:
+        logstash_logger.warning("Warning, old systemd version detected")
+        systemd.daemon.notify('READY=1')
+
+    logging.info("starting gstreamer background process")
+    os.system(__run)
+    logstash_logger.error("gstreamer stopped...")

compLib/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.0.2"
+__version__ = "0.0.3"
 
 import compLib.LogstashLogging
 import logging

complib.service

@@ -0,0 +1,11 @@
+[Unit]
+Description=Monitoring service
+[Service]
+ExecStart=/usr/bin/python3 /usr/local/lib/python3.7/dist-packages/compLib/VisionDaemon.py
+Environment="debug=False"
+Restart=always
+RestartSec=5
+Type=notify
+[Install]
+Alias=complib
+WantedBy=default.target

docs/source/lib/Vision.rst

@@ -0,0 +1,102 @@
+.. _lib_vision:
+
+Vision
+=====
+
+This module provides an interface for grabbing an rtmp stream and using the images to do some processing in opencv.
+
+How do I use this module?
+
+1. Get frames from the raspberry pi camera
+2. -- here comes your own processing --
+3. Publish the processed frames on an http server
+4. You can view the http stream of your processed images in a web browser
+
+Opencv Stream
+*************
+
+Because of the rtmp stream needing to buffer some frames and waiting for P-Frames, importing this module might take up
+to 5 Seconds.
+
+.. autoclass:: compLib.Vision.__Streaming
+   :members:
+
+Examples
+*********
+
+Using the Vision Module
+---------------------
+
+.. code-block:: python
+
+    import cv2
+    from compLib import Vision
+
+    # get newest opencv frame from camera
+    frame = Vision.Streaming.get_frame()
+
+    # do some processing with the frame.....
+
+    # publish frame to streaming server
+    Vision.Streaming.publish_frame(frame)
+
+Connect the raspberry pi to your internet and view the stream at: "http://your_raspi_ip:9898/". This should display
+your raspberry pi camera. Note: the stream will lag a little bit BUT the processing of the image will be done in
+realtime.
+
+The output on the website should show whatever your raspberry pi cam records:
+
+.. image:: images/opencv_http_stream.png
+  :width: 680
+  :alt: Processed frames from opencv
+
+
+Chessboard Detection
+------------------------------------------
+
+In this example we process the captured stream of images and want to detect chessboards. Run this example and
+point your raspberry pi camera to a chessboard and it should be detected.
+
+For testing you can point it at this image:
+
+.. image:: images/chessboard.jpg
+  :width: 680
+  :alt: Chessboard for opencv processing
+
+.. code-block:: python
+
+    import cv2
+    from compLib import Vision
+
+    # get newest opencv frame from camera
+    frame = Vision.Streaming.get_frame()
+
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+    # convert image to grayscale image
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # find the chessboard corners
+    ret, corners = cv2.findChessboardCorners(gray, (6, 9), None)
+
+    # draw detected chessboard position onto the image
+    cv2.drawChessboardCorners(frame,  (6, 9), corners, ret)
+
+    # publish frame to streaming server
+    Vision.Streaming.publish_frame(frame)
+
+Connect the raspberry pi to your internet and view the stream at: "http://your_raspi_ip:9898/".
+
+The output image should look like this:
+
+.. image:: images/chessboard_detected.jpg
+  :width: 680
+  :alt: Processed frames from opencv
+
+Here is a screenshot of the stream website while viewing the chessboard in this documentation.
+
+.. image:: images/opencv_processed.png
+  :width: 680
+  :alt: Processed frames from opencv
+
+

postinstall.sh

@@ -11,4 +11,28 @@ install_package() {
 
 install_package "smbus"
 install_package "requests"
-install_package "python-logstash-async"
+install_package "python-logstash-async"
+
+echo "Setting up opencv4"
+pkg-config --modversion opencv4
+
+echo "Setting up nginx rtmp server"
+sudo /etc/init.d/nginx start
+
+sudo tee /etc/nginx/nginx.conf > /dev/null <<EOT
+load_module "modules/ngx_rtmp_module.so";
+worker_processes auto;
+rtmp_auto_push on;
+events {}
+rtmp {
+    server {
+        listen 1935;
+        listen [::]:1935 ipv6only=on;
+
+        application live {
+            live on;
+            record off;
+        }
+    }
+}
+EOT

sphinx_to_github.sh

@@ -1,3 +1,4 @@
+export BUILDING_DOCS=true
 cd docs || exit
 rm -rf build
 rm -rf gh-pages
@@ -18,4 +19,5 @@ git push origin gh-pages
 cd ..
 rm -rf gh-pages
 
-cd ..
+cd ..
+export BUILDING_DOCS=false