Windows 10 IoT Core Cognitive Services Computer Vision API

This application was inspired by one of teachers I work with wanting to check occupancy of different areas in the school library. I had been using the Computer Vision service to try and identify objects around my home and office which had been moderately successful but not terribly useful or accurate.

I added the Azure Cognitive Services Computer Vision API NuGet packages to my Visual Studio 2017 Windows IoT Core project.

Azure Cognitive Services Computer Vision API library

Then I initialised the Computer Vision API client

try
{
	this.computerVisionClient = new ComputerVisionClient(
			 new Microsoft.Azure.CognitiveServices.Vision.ComputerVision.ApiKeyServiceClientCredentials(this.azureCognitiveServicesSubscriptionKey),
			 new System.Net.Http.DelegatingHandler[] { })
	{
		Endpoint = this.azureCognitiveServicesEndpoint,
	};
}
catch (Exception ex)
{
	this.logging.LogMessage("Azure Cognitive Services Computer Vision client configuration failed " + ex.Message, LoggingLevel.Error);
	return;
}

Every time the digital input is strobed by the passive infra red motion detector an image is captured, then uploaded for processing, and finally results displayed. For this sample I’m looking for categories which indicate the image is of a group of people (The categories are configured in the appsettings file)

{
  "InterruptPinNumber": 24,
  "interruptTriggerOn": "RisingEdge",
  "DisplayPinNumber": 35,
  "AzureCognitiveServicesEndpoint": "https://australiaeast.api.cognitive.microsoft.com/",
  "AzureCognitiveServicesSubscriptionKey": "1234567890abcdefghijklmnopqrstuv",
  "ComputerVisionCategoryNames":"people_group,people_many",
  "LocalImageFilenameFormatLatest": "{0}.jpg",
  "LocalImageFilenameFormatHistoric": "{1:yyMMddHHmmss}.jpg",
  "DebounceTimeout": "00:00:30"
} 

If any of the specified categories are identified in the image I illuminate a Light Emitting Diode (LED) for 5 seconds, if an image is being processed or the minimum period between images has not passed the LED is illuminated for 5 milliseconds .

		private async void InterruptGpioPin_ValueChanged(GpioPin sender, GpioPinValueChangedEventArgs args)
		{
			DateTime currentTime = DateTime.UtcNow;
			Debug.WriteLine($"Digital Input Interrupt {sender.PinNumber} triggered {args.Edge}");

			if (args.Edge != this.interruptTriggerOn)
			{
				return;
			}

			// Check that enough time has passed for picture to be taken
			if ((currentTime - this.imageLastCapturedAtUtc) < this.debounceTimeout)
			{
				this.displayGpioPin.Write(GpioPinValue.High);
				this.displayOffTimer.Change(this.timerPeriodDetectIlluminated, this.timerPeriodInfinite);
				return;
			}

			this.imageLastCapturedAtUtc = currentTime;

			// Just incase - stop code being called while photo already in progress
			if (this.cameraBusy)
			{
				this.displayGpioPin.Write(GpioPinValue.High);
				this.displayOffTimer.Change(this.timerPeriodDetectIlluminated, this.timerPeriodInfinite);
				return;
			}

			this.cameraBusy = true;

			try
			{
				using (Windows.Storage.Streams.InMemoryRandomAccessStream captureStream = new Windows.Storage.Streams.InMemoryRandomAccessStream())
				{
					this.mediaCapture.CapturePhotoToStreamAsync(ImageEncodingProperties.CreateJpeg(), captureStream).AsTask().Wait();
					captureStream.FlushAsync().AsTask().Wait();
					captureStream.Seek(0);

					IStorageFile photoFile = await KnownFolders.PicturesLibrary.CreateFileAsync(ImageFilename, CreationCollisionOption.ReplaceExisting);
					ImageEncodingProperties imageProperties = ImageEncodingProperties.CreateJpeg();
					await this.mediaCapture.CapturePhotoToStorageFileAsync(imageProperties, photoFile);

					ImageAnalysis imageAnalysis = await this.computerVisionClient.AnalyzeImageInStreamAsync(captureStream.AsStreamForRead());

					Debug.WriteLine($"Tag count {imageAnalysis.Categories.Count}");

					if (imageAnalysis.Categories.Intersect(this.categoryList, new CategoryComparer()).Any())
					{
						this.displayGpioPin.Write(GpioPinValue.High);

						// Start the timer to turn the LED off
						this.displayOffTimer.Change(this.timerPeriodFaceIlluminated, this.timerPeriodInfinite);
					}

					LoggingFields imageInformation = new LoggingFields();

					imageInformation.AddDateTime("TakenAtUTC", currentTime);
					imageInformation.AddInt32("Pin", sender.PinNumber);
					Debug.WriteLine($"Categories:{imageAnalysis.Categories.Count}");
					imageInformation.AddInt32("Categories", imageAnalysis.Categories.Count);
					foreach (Category category in imageAnalysis.Categories)
					{
						Debug.WriteLine($" Category:{category.Name} {category.Score}");
						imageInformation.AddDouble($"Category:{category.Name}", category.Score);
					}

					this.logging.LogEvent("Captured image processed by Cognitive Services", imageInformation);
				}
			}
			catch (Exception ex)
			{
				this.logging.LogMessage("Camera photo or save failed " + ex.Message, LoggingLevel.Error);
			}
			finally
			{
				this.cameraBusy = false;
			}
		}

		private void TimerCallback(object state)
		{
			this.displayGpioPin.Write(GpioPinValue.Low);
		}

		internal class CategoryComparer : IEqualityComparer<Category>
		{
			public bool Equals(Category x, Category y)
			{
				if (string.Equals(x.Name, y.Name, StringComparison.OrdinalIgnoreCase))
				{
					return true;
				}

				return false;
			}

			public int GetHashCode(Category obj)
			{
				return obj.Name.GetHashCode();
			}
		}

I found that the Computer vision service was pretty good at categorising photos of images like this displayed on my second monitor as containing a group of people.

The debugging output of the application includes the different categories identified in the captured image.

Digital Input Interrupt 24 triggered RisingEdge
Digital Input Interrupt 24 triggered FallingEdge
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Diagnostics.DiagnosticSource.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Collections.NonGeneric.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Runtime.Serialization.Formatters.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Diagnostics.TraceSource.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Collections.Specialized.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Drawing.Primitives.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Runtime.Serialization.Primitives.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Data.Common.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Xml.ReaderWriter.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Private.Xml.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'Anonymously Hosted DynamicMethods Assembly'. 
Tag count 1
Categories:1
 Category:people_group 0.8671875
The thread 0x634 has exited with code 0 (0x0).

I used an infrared motion sensor to trigger capture and processing of an image to simulate a application for detecting if there is a group of people in an area of the school library.

I’m going to run this application alongside one of my time-lapse applications to record a days worth of images and manually check the accuracy of the image categorisation. I think that camera location maybe important as well so I’ll try a selection of different USB cameras and locations.

Trial PIR triggered computer vision client

I also found the small PIR motion detector didn’t work very well in a larger space so I’m going to trial a configurable sensor and a repurposed burglar alarm sensor.

Windows 10 IoT Core Cognitive Services Face API

After building a series of Windows 10 IoT Core applications to capture images and store them

I figured some sample applications which used Azure Cognitive Services Vision Services to process captured images would be interesting.

This application was inspired by one of my students who has been looking at an Arduino based LoRa wireless connected sensor for monitoring Ultraviolet(UV) light levels and wanted to check that juniors at the school were wearing their hats on sunny days before going outside.

First I needed create a Cognitive Services instance and get the subscription key and endpoint.

Azure Cognitive Services Instance Creation

Then I added the Azure Cognitive Services Face API NuGet packages into my Visual Studio Windows IoT Core project

Azure Cognitive Services Vision Face API library

Then initialise the Face API client

try
{
	this.faceClient = new FaceClient(
			 new Microsoft.Azure.CognitiveServices.Vision.Face.ApiKeyServiceClientCredentials(this.azureCognitiveServicesSubscriptionKey),
											 new System.Net.Http.DelegatingHandler[] { })
	{
		Endpoint = this.azureCognitiveServicesEndpoint,
	};
}
catch (Exception ex)
{
	this.logging.LogMessage("Azure Cognitive Services Face Client configuration failed " + ex.Message, LoggingLevel.Error);
	return;
}

Then every time a digital input is strobed and image is captured, then uploaded for processing, and finally results displayed. The interrupt handler has code to stop re-entrancy and contactor bounce causing issues. I also requested that the Face service include age and gender attributes with associated confidence values.

If a face is found in the image I illuminate a Light Emitting Diode (LED) for 5 seconds, if an image is being processed or the minimum period between images has not passed the LED is illuminated for 5 milliseconds .

private async void InterruptGpioPin_ValueChanged(GpioPin sender, GpioPinValueChangedEventArgs args)
{
	DateTime currentTime = DateTime.UtcNow;
	Debug.WriteLine($"Digital Input Interrupt {sender.PinNumber} triggered {args.Edge}");

	if (args.Edge != this.interruptTriggerOn)
	{
		return;
	}

	// Check that enough time has passed for picture to be taken
	if ((currentTime - this.imageLastCapturedAtUtc) < this.debounceTimeout)
	{
		this.displayGpioPin.Write(GpioPinValue.High);
		this.displayOffTimer.Change(this.timerPeriodDetectIlluminated, this.timerPeriodInfinite);
		return;
	}

	this.imageLastCapturedAtUtc = currentTime;

	// Just incase - stop code being called while photo already in progress
	if (this.cameraBusy)
	{
		this.displayGpioPin.Write(GpioPinValue.High);
		this.displayOffTimer.Change(this.timerPeriodDetectIlluminated, this.timerPeriodInfinite);
		return;
	}

	this.cameraBusy = true;

	try
	{
		using (Windows.Storage.Streams.InMemoryRandomAccessStream captureStream = new Windows.Storage.Streams.InMemoryRandomAccessStream())
		{
			this.mediaCapture.CapturePhotoToStreamAsync(ImageEncodingProperties.CreateJpeg(), captureStream).AsTask().Wait();
			captureStream.FlushAsync().AsTask().Wait();
			captureStream.Seek(0);
			IStorageFile photoFile = await KnownFolders.PicturesLibrary.CreateFileAsync(ImageFilename, CreationCollisionOption.ReplaceExisting);
			ImageEncodingProperties imageProperties = ImageEncodingProperties.CreateJpeg();
			await this.mediaCapture.CapturePhotoToStorageFileAsync(imageProperties, photoFile);

			IList<FaceAttributeType> returnfaceAttributes = new List<FaceAttributeType>();
			returnfaceAttributes.Add(FaceAttributeType.Gender);
			returnfaceAttributes.Add(FaceAttributeType.Age);

			IList<DetectedFace> detectedFaces = await this.faceClient.Face.DetectWithStreamAsync(captureStream.AsStreamForRead(), returnFaceAttributes: returnfaceAttributes);

			Debug.WriteLine($"Count {detectedFaces.Count}");

			if (detectedFaces.Count > 0)
			{
				this.displayGpioPin.Write(GpioPinValue.High);

						// Start the timer to turn the LED off
				this.displayOffTimer.Change(this.timerPeriodFaceIlluminated, this.timerPeriodInfinite);
			}

			LoggingFields imageInformation = new LoggingFields();
			imageInformation.AddDateTime("TakenAtUTC", currentTime);
			imageInformation.AddInt32("Pin", sender.PinNumber);
			imageInformation.AddInt32("Faces", detectedFaces.Count);
			foreach (DetectedFace detectedFace in detectedFaces)
			{
				Debug.WriteLine("Face");
				if (detectedFace.FaceId.HasValue)
				{
					imageInformation.AddGuid("FaceId", detectedFace.FaceId.Value);
					Debug.WriteLine($" Id:{detectedFace.FaceId.Value}");
				}
				imageInformation.AddInt32("Left", detectedFace.FaceRectangle.Left);
				imageInformation.AddInt32("Width", detectedFace.FaceRectangle.Width);
				imageInformation.AddInt32("Top", detectedFace.FaceRectangle.Top);
				imageInformation.AddInt32("Height", detectedFace.FaceRectangle.Height);
				Debug.WriteLine($" L:{detectedFace.FaceRectangle.Left} W:{detectedFace.FaceRectangle.Width} T:{detectedFace.FaceRectangle.Top} H:{detectedFace.FaceRectangle.Height}");
				if (detectedFace.FaceAttributes != null)
				{
					if (detectedFace.FaceAttributes.Gender.HasValue)
					{
						imageInformation.AddString("Gender", detectedFace.FaceAttributes.Gender.Value.ToString());
						Debug.WriteLine($" Gender:{detectedFace.FaceAttributes.Gender.ToString()}");
					}

					if (detectedFace.FaceAttributes.Age.HasValue)
					{
						imageInformation.AddDouble("Age", detectedFace.FaceAttributes.Age.Value);
						Debug.WriteLine($" Age:{detectedFace.FaceAttributes.Age.Value.ToString("F1")}");
					}
				}
			}

			this.logging.LogEvent("Captured image processed by Cognitive Services", imageInformation);
		}
	}
	catch (Exception ex)
	{
		this.logging.LogMessage("Camera photo or save failed " + ex.Message, LoggingLevel.Error);
	}
	finally
	{
		this.cameraBusy = false;
	}
}

private void TimerCallback(object state)
{
	this.displayGpioPin.Write(GpioPinValue.Low);
}

This is the image uploaded to the Cognitive Services Vision Face API from my DragonBoard 410C

Which was a photo of this sample image displayed on my second monitor

The debugging output of the application includes the bounding box, gender, age and unique identifier of each detected face.

Digital Input Interrupt 24 triggered RisingEdge
Digital Input Interrupt 24 triggered FallingEdge
Count 13
Face
 Id:41ab8a38-180e-4b63-ab47-d502b8534467
 L:12 W:51 T:129 H:51
 Gender:Female
 Age:24.0
Face
 Id:554f7557-2b78-4392-9c73-5e51fedf0300
 L:115 W:48 T:146 H:48
 Gender:Female
 Age:19.0
Face
 Id:f67ae4cc-1129-46a8-8c5b-0e79f350cbaa
 L:547 W:46 T:162 H:46
 Gender:Female
 Age:56.0
Face
 Id:fad453fb-0923-4ae2-8c9d-73c9d89eaaf4
 L:585 W:45 T:116 H:45
 Gender:Female
 Age:25.0
Face
 Id:c2d2ca4e-faa6-49e8-8cd9-8d21abfc374c
 L:410 W:44 T:154 H:44
 Gender:Female
 Age:23.0
Face
 Id:6fb75edb-654c-47ff-baf0-847a31d2fd85
 L:70 W:44 T:57 H:44
 Gender:Male
 Age:37.0
Face
 Id:d6c97a9a-c49f-4d9c-8eac-eb2fbc03abc1
 L:469 W:44 T:122 H:44
 Gender:Female
 Age:38.0
Face
 Id:e193bf15-6d8c-4c30-adb5-4ca5fb0f0271
 L:206 W:44 T:117 H:44
 Gender:Male
 Age:33.0
Face
 Id:d1ba5a42-0475-4b65-afc8-0651439e1f1e
 L:293 W:44 T:74 H:44
 Gender:Male
 Age:59.0
Face
 Id:b6a7c551-bdad-4e38-8976-923b568d2721
 L:282 W:43 T:144 H:43
 Gender:Female
 Age:28.0
Face
 Id:8be87f6d-7350-4bc3-87f5-3415894b8fac
 L:513 W:42 T:78 H:42
 Gender:Male
 Age:36.0
Face
 Id:e73bd4d7-81a4-403c-aa73-1408ae1068c0
 L:163 W:36 T:94 H:36
 Gender:Female
 Age:44.0
Face
 Id:462a6948-a05e-4fea-918d-23d8289e0401
 L:407 W:36 T:73 H:36
 Gender:Male
 Age:27.0
The thread 0x8e0 has exited with code 0 (0x0).

I used a simple infrared proximity sensor trigger the image capture to simulate an application for monitoring the number of people in or people entering a room.

Infrared Proximity Sensor triggered Face API test client

Overall I found that with not a lot of code I could capture an image, upload it to Azure Cognitive Services Face API for processing and the algorithm would reasonably reliably detect faces and features.

Elecrow 32u4 with Lora RFM95 IOT Board Payload Addressing Client

This is a demo Elecrow 32u4 with Lora RFM95 IOT Board-868MHz/915MHz client (based on one of the examples from Arduino-LoRa) that uploads telemetry data to my Windows 10 IoT Core on Raspberry PI field gateway proof of concept(PoC).

The elecrow wiki had sample code based on the RadioHead library which was useful.

Bill of materials (Prices Sep 2018)

  • 32u4 with Lora RFM95 IOT Board-868MHz/915MHz USD22.50
  • Seeedstudio LightLevel Sensor USD2.90
  • Elecrow Crowtail to Grove 4 pin Conversion Cable USD1.00

The code is pretty basic, it reads a value from the light sensor, scales it, then packs the payload and sets the necessary RFM9X/SX127X LoRa module configuration, has no power conservation, advanced wireless configuration etc.

Elecrow32u4LoRa

/*
  Adapted from LoRa Duplex communication with Sync Word

  Sends Light data from Seeedstudio 

   https://www.seeedstudio.com/Grove-Light-Sensor-v1-2-p-2727.html

  To my Windows 10 IoT Core RFM 9X library

  https://blog.devmobile.co.nz/2018/09/03/rfm9x-iotcore-payload-addressing/

*/
#include               // include libraries
#include
const int csPin = 10;          // LoRa radio chip select
const int resetPin = 9;       // LoRa radio reset
const int irqPin = 2;         // change for your board; must be a hardware interrupt pin

// Field gateway configuration
const char FieldGatewayAddress[] = "LoRaIoT1";
const float FieldGatewayFrequency =  915000000.0;
const byte FieldGatewaySyncWord = 0x12 ;

// Payload configuration
const int PayloadSizeMaximum = 64 ;
byte payload[PayloadSizeMaximum] = "";
const byte SensorReadingSeperator = ',' ;

// Manual serial number configuration
const char DeviceId[] = {"Elecrow32u4"};

const int analogInPin = A0;
const int LoopSleepDelaySeconds = 60 ;

void setup() {
  Serial.begin(9600);

  Serial.println("LoRa Setup");

  // override the default CS, reset, and IRQ pins (optional)
  LoRa.setPins(csPin, resetPin, irqPin);// set CS, reset, IRQ pin

  if (!LoRa.begin(FieldGatewayFrequency))
  {
    Serial.println("LoRa init failed. Check your connections.");
    while (true);
  }

  // Need to do this so field gateways pays attention to messages from this device
  LoRa.enableCrc();
  LoRa.setSyncWord(FieldGatewaySyncWord);  

  LoRa.dumpRegisters(Serial);
  Serial.println("LoRa Setup done.");

  Serial.println("Setup done");
}

void loop()
{
  int payloadLength = 0 ;
  int sensorValue = 0;
  int outputValue = 0; 

  Serial.println("Loop called");
  memset(payload, 0, sizeof(payload));

  // Scale the sensor value to a %
  sensorValue = analogRead(analogInPin);
  outputValue = map(sensorValue, 0, 1023, 0, 100);  

  // prepare the payload header with "To" Address length (top nibble) and "From" address length (bottom nibble)
  payload[0] = (strlen(FieldGatewayAddress) << 4) | strlen( DeviceId ) ;
  payloadLength += 1;

  // Copy the "To" address into payload
  memcpy(&payload[payloadLength], FieldGatewayAddress, strlen(FieldGatewayAddress));
  payloadLength += strlen(FieldGatewayAddress) ;

  // Copy the "From" into payload
  memcpy(&payload[payloadLength], DeviceId, strlen(DeviceId));
  payloadLength += strlen(DeviceId) ;

  Serial.println("Loop called 5");

  Serial.print("L:");
  Serial.print( outputValue ) ;
  Serial.println( "%" ) ;

  // Copy the temperature into the payload
  payload[ payloadLength] = 'l';
  payloadLength += 1 ;
  payload[ payloadLength] = ' ';
  payloadLength += 1 ;
  payloadLength += strlen( itoa(outputValue, &payload[payloadLength],10 ));  

  // display info about payload then send it (No ACK) with LoRa unlike nRF24L01
  Serial.print( "RFM9X/SX127X Payload length:");
  Serial.print( payloadLength );
  Serial.println( " bytes" );

  LoRa.beginPacket();
  LoRa.write( payload, payloadLength );
  LoRa.endPacket();      

  Serial.println("Loop done");

  delay(LoopSleepDelaySeconds * 1000l);
}

In the debug output window the messages from the device looked like this

14:06:38-RX From Elecrow32u4 PacketSnr 9.8 Packet RSSI -88dBm RSSI -110dBm = 4 byte message "l 85"
Sensor Elecrow32u4l Value 85
AzureIoTHubClient SendEventAsync start
AzureIoTHubClient SendEventAsync finish
The thread 0x930 has exited with code 0 (0x0).
The thread 0xb74 has exited with code 0 (0x0).
The thread 0x3c8 has exited with code 0 (0x0).
The thread 0x984 has exited with code 0 (0x0).
14:07:01-RX From IoTMCU915 PacketSnr 9.3 Packet RSSI -87dBm RSSI -110dBm = 12 byte message "t 13.7,h 113"
Sensor IoTMCU915t Value 13.7
Sensor IoTMCU915h Value 113
AzureIoTHubClient SendEventAsync start
AzureIoTHubClient SendEventAsync finish
The thread 0x6e8 has exited with code 0 (0x0).
The thread 0x7b4 has exited with code 0 (0x0).
The thread 0xe9c has exited with code 0 (0x0).

My battery is a bit of an overkill and to reduce power consumption I would disconnect/remove the light emitting diode(LED)

Azure IoT Hubs LoRa Windows 10 IoT Core Field Gateway

This project is now live on github.com, sample Arduino with Dragino LoRa Shield for Arduino, MakerFabs Maduino, Dragino LoRa Mini Dev, M2M Low power Node and Netduino with Elecrow LoRa RFM95 Shield clients uploaded in the next couple of days.

AzureIOTHubExplorerScreenGrab20180912

The bare minimum configuration is

{
  "AzureIoTHubDeviceConnectionString": "HostName=qwertyuiop.azure-devices.net;DeviceId=LoRaGateway;SharedAccessKey=1234567890qwertyuiop987654321qwertyuiop1234g=",
  "AzureIoTHubTransportType": "Amqp",
  "SensorIDIsDeviceIDSensorID": true,
  "Address": "LoRaIoT1",
  "Frequency": 915000000.0
}

So far battery life and wireless communications range for the Arduino clients is looking pretty good. CRC presence checking and validation is turned so have a look at one of the sample clients.

ArduinoUnoR3DraginoLoRa
It took a bit longer than expected as upgrading to the latest version (v1.18.0 as at 12 Sep 2018) of Microsoft.Azure.Devices.Client (from 1.6.3) broke my field gateway with timeouts and exceptions.

I’ll be doing some more testing over the next couple of weeks so it is a work in progress.

AdaFruit.IO LoRa Windows 10 IoT Core Field Gateway

This project is now live on github.com, sample Arduino with Dragino LoRa Shield for Arduino, MakerFabs Maduino, Dragino LoRa Mini Dev, M2M Low power Node and Netduino with Elecrow LoRa RFM95 Shield clients uploaded in the next couple of days.

AdaFruit.IO.LoRaScreenShot
While building this AdaFruit.IO LoRa field gateway, and sample clients I revisited my RFM9XLoRa-Net library a couple of times adding functionality and renaming constants to make it more consistent. I made many of the default values public so they could be used in the field gateway config file.
The bare minimum configuration is

{
“AdaFruitIOUserName”: “——“,
“AdaFruitIOApiKey”: “——“,
“AdaFruitIOGroupName”: “——”
“Address”: “——“,
“Frequency”: 915000000.0
}

So far battery life and wireless communications range for the Arduino clients is looking pretty good.

ArduinoUnoR3DraginoLoRa

Rfm9xLoRaDevice NetMF SNR and RSSI

The signal to noise Ratio (SNR) and Received Signal Strength Indication(RSSI) for inbound messages required reading values from three registers
•RegPktSnrValue
•RegPktRssiValue
•RegRssiValue

I had to modify the OnDataRecievedHandler method signature so the values could be returned

 public delegate void OnDataRecievedHandler(float packetSnr, int packetRssi, int rssi, byte[] data);

I was inspired by the RSSI adjustment approach used in the Arduino-LoRa library

// Get the RSSI HF vs. LF port adjustment section 5.5.5 RSSI and SNR in LoRa Mode
float packetSnr = this.Rfm9XLoraModem.ReadByte((byte)Registers.RegPktSnrValue) * 0.25f;

int rssi = this.Rfm9XLoraModem.ReadByte((byte)Registers.RegRssiValue);
if (Frequency > RFMidBandThreshold)
{
  rssi = RssiAdjustmentHF + rssi;
}
else
{
  rssi = RssiAdjustmentLF + rssi;
}

int packetRssi = this.Rfm9XLoraModem.ReadByte((byte)Registers.RegPktRssiValue);
if (Frequency > RFMidBandThreshold)
{
  packetRssi = RssiAdjustmentHF + packetRssi;
}
else
{
  packetRssi = RssiAdjustmentLF + packetRssi;
}

OnDataReceived?.Invoke( packetSnr, packetRssi, rssi, messageBytes);

The values displayed in the Rfm9xLoRaDeviceClient application looked reasonable, but will need further checking

00:06:14-Rfm9X PacketSnr 9.8 Packet RSSI -47dBm RSSI -111dBm = 28 byte message "Hello W10 IoT Core LoRa! 182"
Sending 20 bytes message Hello NetMF LoRa! 38
Transmit-Done
00:06:24-Rfm9X PacketSnr 9.8 Packet RSSI -48dBm RSSI -111dBm = 28 byte message "Hello W10 IoT Core LoRa! 181"
Sending 20 bytes message Hello NetMF LoRa! 39
Transmit-Done
00:06:34-Rfm9X PacketSnr 9.8 Packet RSSI -47dBm RSSI -112dBm = 28 byte message "Hello W10 IoT Core LoRa! 180"
Sending 20 bytes message Hello NetMF LoRa! 40
Transmit-Done
00:06:44-Rfm9X PacketSnr 10.0 Packet RSSI -48dBm RSSI -111dBm = 28 byte message "Hello W10 IoT Core LoRa! 179"

 

RFM9X.NetMF on Github

After a month of posts the source code of V0.9 of my RFM9X/SX127X library is on GitHub. I included all of the source for my test harness and proof of concept(PoC) applications so other people can follow along with “my learning experience”.

I need to trial with some more hardware, frequency bands, variety of clients, initialisation configurations and backport the last round of fixes from my .Net library.

The simplest possible application .NetMF using the new library

/---------------------------------------------------------------------------------
// Copyright (c) August 2018, devMobile Software
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
//---------------------------------------------------------------------------------
namespace devMobile.IoT.NetMF.Rfm9X.Client
{
   using System;
   using System.Text;
   using System.Threading;
   using devMobile.IoT.NetMF.ISM;
   using Microsoft.SPOT;
   using SecretLabs.NETMF.Hardware.Netduino;

   public class Program
   {
      public static void Main()
      {
         Rfm9XDevice rfm9XDevice = new Rfm9XDevice(Pins.GPIO_PIN_D10, Pins.GPIO_PIN_D9, Pins.GPIO_PIN_D2);
         byte MessageCount = Byte.MinValue;

         rfm9XDevice.Initialise( Rfm9XDevice.RegOpModeMode.ReceiveContinuous, 915000000, paBoost: true, rxPayloadCrcOn: true);
         rfm9XDevice.OnDataReceived += rfm9XDevice_OnDataReceived;
         rfm9XDevice.OnTransmit += rfm9XDevice_OnTransmit;

         while (true)
         {
            string messageText = "Hello NetMF LoRa! " + MessageCount.ToString();
            MessageCount += 1;
            byte[] messageBytes = UTF8Encoding.UTF8.GetBytes(messageText);
            Debug.Print("Sending " + messageBytes.Length + " bytes message " + messageText);
            rfm9XDevice.SendMessage(messageBytes);

            Thread.Sleep(10000);
         }
      }

      static void rfm9XDevice_OnTransmit()
      {
         Debug.Print("Transmit-Done");
      }

      static void rfm9XDevice_OnDataReceived(byte[] data)
      {
         try
         {
            string messageText = new string(UTF8Encoding.UTF8.GetChars(data));

            Debug.Print("Received " + data.Length.ToString() + " byte message " + messageText);
         }
         catch (Exception ex)
         {
            Debug.Print(ex.Message);
         }
      }
   }
}

// Dirty hack for Rosyln 
namespace System.Diagnostics
{
   public enum DebuggerBrowsableState
   {
      Never = 0,
      Collapsed = 2,
      RootHidden = 3
   }
}

I need to do more testing (especially of the initialisation options) and will add basic device addressing soon so my field gateway will only see messages which it is interested in.