=================================================== imagenode: Capture and Send Images and Sensor Data

Introduction

imagenode enables Raspberry Pi computers to capture images with the PiCamera, perform image transformations and send them to a central imagehub for further processing. It can also send other sensor data such as temperature data and GPIO data. The processing power of the Raspberry Pi is used to detect events (like the water meter flowing or a coyote crossing the back yard), and then send a limited number of images of the event. It also works on other types of (non Raspberry Pi) computers with USB cams or webcams.

Here are a couple of screenshots showing images sent by a Raspberry Pi PiCamera and displayed on a Mac. In the top screenshot, a ballpoint pen hanging from a string is still. In the bottom screenshot, the ballpoint pen is swinging back and forth. The largest image in each screenshot is the full frame sent by the PiCamera. The smaller windows are showing the imagenode motion detector parameter tuning displays including the detected motion state of "still" and "moving":

.. image:: docs/images/still_and_moving.png

.. contents::

Overview

imagenode is the image capture and sending portion of a computer vision pipeline that is typically run on multiple computers. For example, a Raspberry Pi computer runs imagenode to capture images with a PiCamera and perform some simple image processing. The images are transferred by imageZMQ (see reference) to a hub computer running imagehub (often a Mac) for further image processing. The real benefit of imagenode is that it can use the the processing power of the Raspberry Pi to:

• Continuously capture images (around 10 frames a second is typical)

• Analyze the images to detect events (e.g., water meter started flowing)

• When a detected event occurs:

  • Send an event message about the event to the imagehub
  • Send a select few "detected state change" images to the imagehub

So, instead of 36,000 images an hour being sent from our water meter cam to our imagehub, only about 20 images are sent each time the water starts flowing or stops flowing. Instead of many thousands of images an hour showing a mostly unmoving farm area, our critter cams spot coyotes, raccoons and rabbits and only send event messages and images when something is actually seen moving about.

imagenode provides image capture, event detection and transmission services as part of a distributed computer vision system that includes multiple computers with cameras, sensors, database hubs and communication links. See Using imagenode in distributed computer vision projects <docs/imagenode-uses.rst>_ for a more detailed explanation of the overall project design. See the Yin Yang Ranch project <https://github.com/jeffbass/yin-yang-ranch>_ for more details about the architecture of the imagenode <--> imageZMQ <--> imagehub system.

Imagenode Capabilities

• Continuously captures images using PiCameras or USB webcams.
• Performs image transformation and motion, light or color detection.
• Sends detected events and relevant images to an image hub using imageZMQ.
• Can capture and send other sensor data gathered using the GPIO pins.
• Can control lighting (e.g., white LED or Infrared LED area lights).
• Sends event messages (e.g., water is flowing) as well as images.

Dependencies and Installation

imagenode has been tested with:

• Python 3.6 and newer

• OpenCV 3.3 and 4.0 and newer

• Raspberry Pi OS Buster, Raspbian Stretch and Raspbian Jessie

  • NOT yet tested with Raspberry Pi OS Bullseye. Waiting for a production replacement for the Python PiCamera module.

• PyZMQ 16.0 and newer

• RPi.GPIO 0.6 and newer (imported only if using GPIO pins)

• picamera 1.13 (imported only if using PiCamera)

• imageZMQ 1.1.1 and newer

• imutils 0.4.3 and newer (used get to images from PiCamera)

• psutil 5.7.2 and newer

• PyYAML 5.3 and newer

• w1thermsensor 1.3 (if using DS18S20 temperature sensor)

  • NOT yet compatible with w1thermsensor version 2 which uses a new API

• adafruit-circuitpython-dht 3.4.2 and newer (if using DHT11 or DHT22 sensor)

imagenode captures images and uses imageZMQ to transfer the images. It is best to install and test imageZMQ before installing imagenode. The instructions for installing and testing imageZMQ are in the imageZMQ GitHub repository <https://github.com/jeffbass/imagezmq.git>_.

imagenode is still in early development, so it is not yet in PyPI. Get it by cloning the GitHub repository::

git clone https://github.com/jeffbass/imagenode.git

Once you have cloned imagenode to a directory on your local machine, you can run the tests using the instructions below. The instructions assume you have cloned imagehub to the user home directory.

Imagenode settings via YAML files

imagenode requires a LOT of settings: settings for the camera, settings for the GPIO pins, settings for each detector and each ROI, etc. The settings are kept in a YAML file and are changed to "tune" the image capture, ROIs, motion detection and computer vision parameters. An example YAML file is included in the "yaml" directory. An explanation of the yaml file and how to adjust the settings is in imagenode Settings and YAML files <docs/settings-yaml.rst>_.

Running the Tests

imagenode should be tested in stages, with each stage testing a little more functionality. The tests are numbered in the order in which they should be run to determine if imagenode is running correctly on your systems.

Test imagenode in the same virtualenv in which you tested imagenZMQ. For the imageZMQ testing and for the imagenode testing, my virtualenv is called py3cv3.

imagenode requires imageZMQ be installed and working. Before running any tests with imagenode, be sure you have successfully installed imageZMQ and run all of its tests. The imageZMQ tests must run successfully on every computer you will be using imagenode on. You can use pip to install imageZMQ.

Directory Structure for running the tests

imagenode is not far enough along in development to be pip installable. So it should both be git-cloned to any computer that it will be running on. I have done all testing at the user home directory of every computer. Here is a simplified directory layout::

~ # user home directory +--- imagenode.yaml # copied from one of the imagenode yaml files & edited | +--- imagenode # the git-cloned directory for imagenode +--- sub directories include docs, imagenode, tests, yaml

This directory arrangement, including docs, imagenode code, tests, etc. is a common development directory arrangement on GitHub. Using git clone from your user home directory (either on a Mac, a RPi or other Linux computer) will put the imagenode directories in the right place for testing. Each test described below requires you to copy the appropriate testN.yaml file to imagenode.yaml in the user home directory as shown in the above directory diagram. The receive_test.py program acts as the image hub test receiver for each imagenode test. It must be started and running before running imagenode.py.

Test 1: Running imagenode and imageZMQ together on a Mac

The first test runs both the sending program imagenode and the receiving program receive_test.py (acting as a test hub) on a Mac (or linux computer) with a webcam. It tests that the imagenode software is installed correctly and that the imagenode.yaml file has been copied and edited in a way that works. It uses the webcam on the Mac for testing. It uses a "lighted" versus "dark" detector applied to a specified ROI.

Test 2: Sending a light detector stream of images from RPi PiCamera to a Mac

The second test runs imagenode on a Raspberry Pi, using receive_test.py (acting as a test hub) on a Mac (or Linux computer). It tests that the imagenode software is installed correctly on the RPi and that the imagenode.yaml file has been copied and edited in a way that works. It tests that the imageZMQ communication is working between the Raspberry Pi and the Mac. It also tests the Picamera. It uses a "lighted" versus "dark" detector applied to a specified ROI.

Test 3: Sending a motion detector stream of images from RPi PiCamera to a Mac

The third test runs imagenode on a Raspberry Pi, using receive_test.py (acting as a test hub) on a Mac (or Linux computer). It is very similar to Test 2, except that it uses a "moving" versus "still" motion detector applied to a specified ROI.

Test 4: Sending temperature readings from RPi temperature sensor to a Mac

The fourth test runs imagenode on a Raspberry Pi, using receive_test.py (acting as a test hub) on a Mac (or Linux computer). It allows testing of the temperature sensor capabilities of imagenode. It requires setting up a DS18B20 temperature sensor and connecting it appropriately to RPi GPIO pin 4.

The details of running the 4 tests are here <docs/testing.rst>_.

Running imagenode in production

Running the test programs requires that you leave a terminal window open, which is helpful for testing, but not for production runs. I use systemctl / systemd to start imagenode in production. I have provided an example imagenode.service unit configuration file that shows how I start imagenode for the production programs observing my small farm. I have found the systemctl / systemd system to be best way to start / stop / restart and check the running status of imagenode over several years of testing. For those who prefer using a shell script to start imagenode, I have included an example imagenode.sh. It is important to run imagenode in the right virtualenv in production, regardless of your choice of program startup tools.

In production, you would want to set the test options used to print settings to False; they are only helpful during testing. All errors and imagenode event messages are saved in the file imagehub.log which defaults to the same directory as imagenode.py. You might want the log to be in a different directory for production; the log file location can be set by changing it in the logging function at the bottom of the imagenode.py program file.

Additional Documentation

• More details on running the tests <docs/testing.rst>_.
• How imagenode works <docs/imagenode-details.rst>_.
• How imagenode is used in a larger project <docs/imagenode-uses.rst>_.
• Version History and Changelog <HISTORY.md>_.
• Research and Development Roadmap <docs/research-roadmap.rst>_.
• The imageZMQ classes that allow transfer of images <https://github.com/jeffbass/imagezmq>_.
• The imagehub software that saves events and images <https://github.com/jeffbass/imagehub>_.
• The larger farm automation / computer vision project <https://github.com/jeffbass/yin-yang-ranch>_. This project shows the overall system architecture. It also contains links to my PyCon 2020 talk video and slides explaining the project.

Contributing

imagenode is in early development and testing. I welcome open issues and pull requests, but because the programs are still rapidly evolving, it is best to open an issue for some discussion before submitting pull requests. We can exchange ideas about your potential pull request and how to best test your code.

Contributors

Thanks for all contributions big and small. Some significant ones:

+--------------------------+-----------------+----------------------------------------------+ | Contribution | Name | GitHub | +--------------------------+-----------------+----------------------------------------------+ | Initial code & docs | Jeff Bass | @jeffbass <https://github.com/jeffbass>_ | +--------------------------+-----------------+----------------------------------------------+ | Added code and | | | | documentation for | | | | PiCamera settings | Stephen Kirby | @sbkirby <https://github.com/sbkirby>_ | +--------------------------+-----------------+----------------------------------------------+ | Added DHT11 & DHT22 | | | | sensor capability | Stephen Kirby | @sbkirby <https://github.com/sbkirby>_ | +--------------------------+-----------------+----------------------------------------------+ | Added multiple detectors | | | | per camera capability | Stephen Kirby | @sbkirby <https://github.com/sbkirby>_ | +--------------------------+-----------------+----------------------------------------------+

Acknowledgments

• ZeroMQ is a great messaging library with great documentation at ZeroMQ.org <http://zeromq.org/>_.
• PyZMQ serialization examples provided a starting point for imageZMQ. See the PyZMQ documentation <https://pyzmq.readthedocs.io/en/latest/index.html>_.
• OpenCV and its Python bindings provide great scaffolding for computer vision projects large or small: OpenCV.org <https://opencv.org/>_.
• imutils is a collection of Python classes and methods that allow computer vision programs using OpenCV to be cleaner and more compact. It has a very helpful threaded image reader for Raspberry PiCamera modules or webcams. It allowed me to shorten my camera reading programs on the Raspberry Pi by half: imutils on GitHub <https://github.com/jrosebr1/imutils>_. imutils is an open source project authored by Adrian Rosebrock.
• The motion detection function detect_motion() borrowed a lot of helpful code from a motion detector tutorial post <https://www.pyimagesearch.com/2015/06/01/home-surveillance-and-motion-detection-with-the-raspberry-pi-python-and-opencv/>_ by Adrian Rosebrock of PyImageSearch.com.