A Python IoT framework to easily integrate sensors of any kind into your projects. We also have sample clients for standard web sites and Adobe AEM Screens.

• About
  • Communication
  • Events
  • TO DO
  • Notes
• Installation
• Configure
• Examples
  • Data collection
  • Javascript integration
  • MQTT
  • Logging
• Contributing
  • Issues
  • Pull requests
  • Coding style
  • Code of conduct
• License

The number one goal of this effort was to make it dead simple to use sensors. So, we've isolated and simplified everything other than data collection. In SimpleSensor's basic use, just collect data and put it in the queue. Everything else is handled by the other threads, which we have developed for you. There may be a bit of configuration that needs to be done to turn on and connect to your communication channels, but that’s it. You could also extend SimpleSensor and add your own modules and communication channels; we'll gladly take pull requests that follow the contribution guidelines.

The application is broken down into the following domains

1. Communication channels
2. Logging
3. Data Collection

For examples on how to use these domains, including things you can do with a collection point, an Adobe AEM client, AEM Screens, or plain old Javascipt, see the Examples section.

For printable camera enclosures, see the enclosures subdirectory, and feel free to contribute your own!

Today we have two communications channels built into the platform. We will add more over time.

1. Local websocket
2. MQTT

Azure IoT Hub is currently under development

• Greater platform support (stable Linux and macOS installation instructions)
• Wider range of sensors (taking suggestions, open an issue!)
• Greater camera support
• Handle localOnly flags
• Target detection zone - specify where in camera feed detections should be triggered using grid system
• Communication from sensors to SimpleSensor (code is there, events are not handled yet)

Today we have two support for two types of cameras in our facedetect module:

• IDS XS
• Standard usb webcams

Currently tested platforms:

• Windows 10
• AEM 6.3 w/ AEM Screens
• Python 3.6 w/ Anaconda
• OpenCV 3.2

To shutdown, press ESC while the console has focus.

Get Anaconda, from command prompt or Anaconda Shell run:

• conda create -n ss python=3.6 anaconda
• activate ss or source activate ss
• pip install -r requirements.txt

NOTE: See also Paho MQTT

NOTE: These are folder names, not file/class/module names. When adding new modules keep the folder names unique.

NOTE: Save this file as secrets.conf

In config/base.conf, add/remove the folder names of the modules you wish to use. For example, to use both websocket and MQTT messages, set your communication_modules to ["websocketServer", "mqttClient"]

1. Duplicate and rename the /collection_modules/baseCollectionPoint directory
2. Add/replace collection_modules array in config/base.conf with the name of your new collection point's folder
3. Write your initialization logic in the __init__() function
4. Write your collection loop logic in the run() function
5. Optionally import constants through a module configuration file, use camCollectionPoint as an example

NOTE: For details on the included camera/face detection module, see the module's README

These are some samples of things you can do within the run() function of your collectionPoint.py

_cpType = 'elite_sensor'
_cpid = 'sensor_1337'
_topic = 'Detected'
_coords = (47.6062, 122.3321)
_localOnly = False
msg = (_cpid, _cpType, _topic, extendedData={'location':_coords}, _localOnly)

self.outQueue.put(msg)

self.mTracker = MultiTracker("KCF", self.moduleConfig, self.loggingQueue)

ok = self.mmTracker.add(bbox={'x':x,'y':y,'w':w,'h':h}, frame=frame)

A sample Javascript client connection via websocket can be found here

<div id="displayEventsElement"></div>
<script src="websocketClient.js" type="text/javascript"></script>
document.addEventListener("DOMContentLoaded", function(event) {
  var _displayEventsElement = document.getElementById('displayEventsElement');
  var cecWebsocketClient = new WebsocketClient("127.0.0.1", 13254); // Connect to localhost at port 13254
});

websocketClient.addEventListener("message", function(eventData){
  // Do something with the event data
  displayEventsElement.append(JSON.stringify(eventData));
});

Publishing messages to MQTT requires an MQTT broker (Adafruit IO, for example) to be configured and enabled in /config/base.conf. There are two ways to publish MQTT messages currently.

The first method is to publish the entire JSON string dump of the message, if your broker supports it. This is enabled by setting mqtt_publish_json to True in /config/base.conf. The MQTTClient dumps the JSON into utf8 encoding and publishes it like so:

_payload = json.dumps(_msg.__dict__).encode('utf8')
self._client.publish('{0}/feeds/{1}'.format(self._username, _feedName), payload=_payload)

The second method is to publish the values of each key in the extended data to separate feeds. To use this method, set both mqtt_send_json and mqtt_publish_face_values to False in /config/base.conf. The MQTTClient will now publish each field of the extendedData dictionary message to it's own MQTT feed.

For example, the message created by this code:

_cpType = 'elite_sensor'
_cpid = 'sensor_1337'
_topic = 'Detected'
_localOnly = False
_extendedData = {
    "age": 23,
    "gender": 0,
    "glasses": 1,
    "beard": 0.7
}
msg = (_cpid, _cpType, _topic, extendedData=_extendedData, _localOnly)

Would result in 4 publish events:

1. username/feeds/age would receive an update value of 23
2. username/feeds/gender would receive an update value of 0
3. username/feeds/glasses would receive an update value of 1
4. username/feeds/beard would receive an update value of 0.7

Note A modified version of this function is also in mqttClient.py, which can be used in conjunction with the Azure Face API (or another prediction engine with the same output structure) and the sample camCollectionPoint. It only publishes prediction data, and flattens a dictionary of values to an average.

self.logger.info("Starting a thing")

self.logger.debug("Made it here!1112")

self.logger.warning("This could be bad")

self.logger.warn("This also could be bad")

self.logger.critical("This *is* bad")

self.logger.error("Failed to do the thing: %s"%error)

Make a cool collection module that you want to share? Or fix up a bug? Maybe you made SimpleSensor work on a new platform? We're open for contributions, we just ask that they follow some guidelines to keep things clean and efficient.

If you're looking for a place to jump in with contributing, check out our TO DO

All submissions should come in the form of pull requests and need to be reviewed by project contributors. Read GitHub's pull request documentation for more information on sending pull requests.

Issues should either include a proposal for a feature or, in the case of bugs, include the expected behavior, the actual behavior, your environment details, and ideally steps to reproduce. They should also ideally address actual issues with the code, not issues with setting up the environment. Please follow the issue template for consistency.

Pull requests should include references to the title of the issue, and changes proposed in the pull request. Please follow the pull request template for consistency.

Try to make your code readable first and foremost. Then match the code that surrounds it. Otherwise follow these current styles:

• mixedCase function/variable/object names
• mixedCase file and folder names
• _mixedCase constant names
• lowercase_with_underscores config file settings, ie. some_prop = 1337
• CamelCase config object variable, ie. self.config['SomeProp']
• docstrings for all functions, ie. """ Function summary """

This project adheres to the Adobe code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to FILLINEMAILHERE.