Category Archives: Azure

Windows 10 IoT Core Cognitive Services Custom Vision API

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This application was inspired by one of teachers I work with wanting to count ducks in the stream on the school grounds. The school was having problems with water quality and the they wanted to see if the number of ducks was a factor. (Manually counting the ducks several times a day would be impractical).

I didn’t have a source of training images so built an image classifier using my son’s Lego for testing. In a future post I will build an object detection model once I have some sample images of the stream captured by my Windows 10 IoT Core time lapse camera application.

To start with I added the Azure Cognitive Services Custom Vision API NuGet packages to a new Visual Studio 2017 Windows IoT Core project.

Azure Custom Vision Service NuGet packacges

Then I initialised the Computer Vision API client

try
{
	this.customVisionClient = new CustomVisionPredictionClient(new System.Net.Http.DelegatingHandler[] { })
	{
		ApiKey = this.azureCognitiveServicesSubscriptionKey,
		Endpoint = this.azureCognitiveServicesEndpoint,
	};
}
catch (Exception ex)
{
	this.logging.LogMessage("Azure Cognitive Services Custom Vision Client configuration failed " + ex.Message, LoggingLevel.Error);
	return;
}

Every time the digital input is strobed by the infra red proximity sensor or touch button an image is captured, uploaded for processing, and results displayed in the debug output.

For testing I have used a simple multiclass classifier that I trained with a selection of my son’s Lego. I tagged the brick size height x width x length (1x2x3, smallest of width/height first) and colour (red, green, blue etc.)

Azure Cognitive Services Classifier project creation
Custom vision projects
Lego classifier project properties

The projectID, AzureCognitiveServicesSubscriptionKey (PredictionKey) and PublishedName (From the Performance tab in project) in the app.settings file come from the custom vision project properties.

{
  "InterruptPinNumber": 24,
  "interruptTriggerOn": "RisingEdge",
  "DisplayPinNumber": 35,
  "AzureCognitiveServicesEndpoint": "https://australiaeast.api.cognitive.microsoft.com",
  "AzureCognitiveServicesSubscriptionKey": "41234567890123456789012345678901s,
  "DebounceTimeout": "00:00:30",
  "PublishedName": "LegoBrickClassifierV3",
  "TriggerTag": "1x2x4",
  "TriggerThreshold": "0.4",
  "ProjectID": "c1234567-abcdefghijklmn-1234567890ab"
}

The sample application only supports one trigger tag + probability and if this condition satisfied the Light Emitting Diode (LED) is turned on 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 my small model was pretty good at tagging images of Lego bricks as long as the ambient lighting was consistent and the background fairly plain.

When tagging many bricks my ability to distinguish pearl light grey, light grey, sand blue and grey bricks was a problem. I should have started with a limited palette (red, green, blue) of colours and shapes for my models while evaluating different tagging approaches.

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
Prediction count 54
 Tag:Lime 0.529844046
 Tag:1x1x2 0.4441353
 Tag:Green 0.252290249
 Tag:1x1x3 0.1790101
 Tag:1x2x3 0.132092983
 Tag:Turquoise 0.128928885
 Tag:DarkGreen 0.09383947
 Tag:DarkTurquoise 0.08993266
 Tag:1x2x2 0.08145093
 Tag:1x2x4 0.060960535
 Tag:LightBlue 0.0525473
 Tag:MediumAzure 0.04958712
 Tag:Violet 0.04894981
 Tag:SandGreen 0.048463434
 Tag:LightOrange 0.044860106
 Tag:1X1X1 0.0426577441
 Tag:Azure 0.0416654423
 Tag:Aqua 0.0400410332
 Tag:OliveGreen 0.0387720577
 Tag:Blue 0.035169173
 Tag:White 0.03497391
 Tag:Pink 0.0321456343
 Tag:Transparent 0.0246597622
 Tag:MediumBlue 0.0245670844
 Tag:BrightPink 0.0223842952
 Tag:Flesh 0.0221406389
 Tag:Magenta 0.0208457354
 Tag:Purple 0.0188888311
 Tag:DarkPurple 0.0187285
 Tag:MaerskBlue 0.017609369
 Tag:DarkPink 0.0173041821
 Tag:Lavender 0.0162359159
 Tag:PearlLightGrey 0.0152829709
 Tag:1x1x4 0.0133710662
 Tag:Red 0.0122602312
 Tag:Yellow 0.0118704
 Tag:Clear 0.0114340987
 Tag:LightYellow 0.009903331
 Tag:Black 0.00877647
 Tag:BrightLightYellow 0.00871937349
 Tag:Mediumorange 0.0078356415
 Tag:Tan 0.00738664949
 Tag:Sand 0.00713921571
 Tag:Grey 0.00710422
 Tag:Orange 0.00624707434
 Tag:SandBlue 0.006215865
 Tag:DarkGrey 0.00613187673
 Tag:DarkBlue 0.00578308525
 Tag:DarkOrange 0.003790971
 Tag:DarkTan 0.00348462746
 Tag:LightGrey 0.00321317
 Tag:ReddishBrown 0.00304117263
 Tag:LightBluishGrey 0.00273489812
 Tag:Brown 0.00199119

I’m going to run this application repeatedly, adding more images and retraining the model to see how it performs. Once the model is working wll I’ll try downloading it and running it on a device

Custom Vision Test Harness running on my desk

This sample could be used as a basis for projects like this cat door which stops your pet bringing in dead or wounded animals. The model could be trained with tags to indicate whether the cat is carrying a “present” for their human and locking the door if it is.

STM32 Blue Pill LoRaWAN node

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A few weeks ago I ordered an STM32 Blue Pill LoRaWAN node from the M2M Shop on Tindie for evaluation. I have bought a few M2M client devices including a Low power LoRaWan Node Model A328, and Low power LoRaWan Node Model B1284 for projects and they have worked well. This one looked interesting as I had never used a maple like device before.

Bill of materials (Prices as at July 2019)

  • STM32 Blue Pill LoRaWAN node USD21
  • Grove – Temperature&Humidity Sensor USD11.5
  • Grove – 4 pin Female Jumper to Grove 4 pin Conversion Cable USD3.90

The two sockets on the main board aren’t Grove compatible so I used the 4 pin female to Grove 4 pin conversion cable to connect the temperature and humidity sensor.

STM32 Blue Pill LoRaWAN node test rig

I used a modified version of my Arduino client code which worked after I got the pin reset pin sorted and the female sockets in the right order. 

/*
  Copyright ® 2019 July devMobile Software, All Rights Reserved

  THIS CODE AND INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY
  KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
  IMPLIED WARRANTIES OF MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR
  PURPOSE.
  
  Adapted from LoRa Duplex communication with Sync Word

  Sends temperature & humidity data from Seeedstudio 

  https://www.seeedstudio.com/Grove-Temperature-Humidity-Sensor-High-Accuracy-Min-p-1921.html

  To my Windows 10 IoT Core RFM 9X library

  https://blog.devmobile.co.nz/2018/09/03/rfm9x-iotcore-payload-addressing/
*/
#include <itoa.h>     
#include <SPI.h>     
#include <LoRa.h>

#include <TH02_dev.h>

#define DEBUG
//#define DEBUG_TELEMETRY
//#define DEBUG_LORA

// LoRa field gateway configuration (these settings must match your field gateway)
const char DeviceAddress[] = {"BLUEPILL"};

// Azure IoT Hub FieldGateway
const char FieldGatewayAddress[] = {"LoRaIoT1"}; 
const float FieldGatewayFrequency =  915000000.0;
const byte FieldGatewaySyncWord = 0x12 ;

// Bluepill hardware configuration
const int ChipSelectPin = PA4;
const int InterruptPin = PA0;
const int ResetPin = -1;

// LoRa radio payload configuration
const byte SensorIdValueSeperator = ' ' ;
const byte SensorReadingSeperator = ',' ;
const byte PayloadSizeMaximum = 64 ;
byte payload[PayloadSizeMaximum];
byte payloadLength = 0 ;

const int LoopDelaySeconds = 300 ;

// Sensor configuration
const char SensorIdTemperature[] = {"t"};
const char SensorIdHumidity[] = {"h"};


void setup()
{
  Serial.begin(9600);
#ifdef DEBUG
  while (!Serial);
#endif
  Serial.println("Setup called");

  Serial.println("LoRa setup start");

  // override the default chip select and reset pins
  LoRa.setPins(ChipSelectPin, ResetPin, InterruptPin);
  if (!LoRa.begin(FieldGatewayFrequency))
  {
    Serial.println("LoRa begin failed");
    while (true); // Drop into endless loop requiring restart
  }

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

#ifdef DEBUG_LORA
  LoRa.dumpRegisters(Serial);
#endif
  Serial.println("LoRa setup done.");

  PayloadHeader((byte*)FieldGatewayAddress, strlen(FieldGatewayAddress), (byte*)DeviceAddress, strlen(DeviceAddress));

 // Configure the Seeedstudio TH02 temperature & humidity sensor
  Serial.println("TH02 setup");
  TH02.begin();
  delay(100);
  Serial.println("TH02 Setup done");  

  Serial.println("Setup done");
}

void loop() {
  // read the value from the sensor:
  double temperature = TH02.ReadTemperature();
  double humidity = TH02.ReadHumidity();

  Serial.print("Humidity: ");
  Serial.print(humidity, 0);
  Serial.print(" %\t");
  Serial.print("Temperature: ");
  Serial.print(temperature, 1);
  Serial.println(" *C");

  PayloadReset();

  PayloadAdd(SensorIdHumidity, humidity, 0) ;
  PayloadAdd(SensorIdTemperature, temperature, 1) ;

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

  Serial.println("Loop done");

  delay(LoopDelaySeconds * 1000);
}


void PayloadHeader( byte *to, byte toAddressLength, byte *from, byte fromAddressLength)
{
  byte addressesLength = toAddressLength + fromAddressLength ;

#ifdef DEBUG_TELEMETRY
  Serial.println("PayloadHeader- ");
  Serial.print( "To Address len:");
  Serial.print( toAddressLength );
  Serial.print( " From Address len:");
  Serial.print( fromAddressLength );
  Serial.print( " Addresses length:");
  Serial.print( addressesLength );
  Serial.println( );
#endif

  payloadLength = 0 ;

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

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

  // Copy the "From" into payload
  memcpy(&payload[payloadLength], from, fromAddressLength);
  payloadLength += fromAddressLength ;
}


void PayloadAdd( const char *sensorId, float value, byte decimalPlaces)
{
  byte sensorIdLength = strlen( sensorId ) ;

#ifdef DEBUG_TELEMETRY
  Serial.println("PayloadAdd-float ");
  Serial.print( "SensorId:");
  Serial.print( sensorId );
  Serial.print( " sensorIdLen:");
  Serial.print( sensorIdLength );
  Serial.print( " Value:");
  Serial.print( value, decimalPlaces );
  Serial.print( " payloadLength:");
  Serial.print( payloadLength);
#endif

  memcpy( &payload[payloadLength], sensorId,  sensorIdLength) ;
  payloadLength += sensorIdLength ;
  payload[ payloadLength] = SensorIdValueSeperator;
  payloadLength += 1 ;
  payloadLength += strlen( dtostrf(value, -1, decimalPlaces, (char *)&payload[payloadLength]));
  payload[ payloadLength] = SensorReadingSeperator;
  payloadLength += 1 ;

#ifdef DEBUG_TELEMETRY
  Serial.print( " payloadLength:");
  Serial.print( payloadLength);
  Serial.println( );
#endif
}


void PayloadAdd( const char *sensorId, int value )
{
  byte sensorIdLength = strlen( sensorId ) ;

#ifdef DEBUG_TELEMETRY
  Serial.println("PayloadAdd-int ");
  Serial.print( "SensorId:");
  Serial.print( sensorId );
  Serial.print( " sensorIdLen:");
  Serial.print( sensorIdLength );
  Serial.print( " Value:");
  Serial.print( value );
  Serial.print( " payloadLength:");
  Serial.print( payloadLength);
#endif

  memcpy( &payload[payloadLength], sensorId,  sensorIdLength) ;
  payloadLength += sensorIdLength ;
  payload[ payloadLength] = SensorIdValueSeperator;
  payloadLength += 1 ;
  payloadLength += strlen( itoa( value, (char *)&payload[payloadLength], 10));
  payload[ payloadLength] = SensorReadingSeperator;
  payloadLength += 1 ;

#ifdef DEBUG_TELEMETRY
  Serial.print( " payloadLength:");
  Serial.print( payloadLength);
  Serial.println( );
#endif
}

void PayloadAdd( const char *sensorId, unsigned int value )
{
  byte sensorIdLength = strlen( sensorId ) ;

#ifdef DEBUG_TELEMETRY
  Serial.println("PayloadAdd-unsigned int ");
  Serial.print( "SensorId:");
  Serial.print( sensorId );
  Serial.print( " sensorIdLen:");
  Serial.print( sensorIdLength );
  Serial.print( " Value:");
  Serial.print( value );
  Serial.print( " payloadLength:");
  Serial.print( payloadLength);
#endif

  memcpy( &payload[payloadLength], sensorId,  sensorIdLength) ;
  payloadLength += sensorIdLength ;
  payload[ payloadLength] = SensorIdValueSeperator;
  payloadLength += 1 ;
  payloadLength += strlen( utoa( value, (char *)&payload[payloadLength], 10));
  payload[ payloadLength] = SensorReadingSeperator;
  payloadLength += 1 ;

#ifdef DEBUG_TELEMETRY
  Serial.print( " payloadLength:");
  Serial.print( payloadLength);
  Serial.println( );
#endif
}


void PayloadReset()
{
  byte fromAddressLength = payload[0] & 0xf ;
  byte toAddressLength = payload[0] >> 4 ;
  byte addressesLength = toAddressLength + fromAddressLength ;

  payloadLength = addressesLength + 1;

#ifdef DEBUG_TELEMETRY
  Serial.println("PayloadReset- ");
  Serial.print( "To Address len:");
  Serial.print( toAddressLength );
  Serial.print( " From Address len:");
  Serial.print( fromAddressLength );
  Serial.print( " Addresses length:");
  Serial.print( addressesLength );
  Serial.println( );
#endif
}

To get the application to compile I also had to include itoa.h rather than stdlib.h. 

maple_loader v0.1
Resetting to bootloader via DTR pulse
[Reset via USB Serial Failed! Did you select the right serial port?]
Searching for DFU device [1EAF:0003]...
Assuming the board is in perpetual bootloader mode and continuing to attempt dfu programming...

dfu-util - (C) 2007-2008 by OpenMoko Inc.

Initially I had some problems deploying my software because I hadn’t followed the instructions and run the installation batch file.

14:03:56.946 -> Setup called
14:03:56.946 -> LoRa setup start
14:03:56.946 -> LoRa setup done.
14:03:56.946 -> TH02 setup
14:03:57.046 -> TH02 Setup done
14:03:57.046 -> Setup done
14:03:57.115 -> Humidity: 76 %	Temperature: 18.9 *C
14:03:57.182 -> Loop done
14:08:57.226 -> Humidity: 74 %	Temperature: 18.7 *C
14:08:57.295 -> Loop done
14:13:57.360 -> Humidity: 76 %	Temperature: 18.3 *C
14:13:57.430 -> Loop done
14:18:57.475 -> Humidity: 74 %	Temperature: 18.2 *C
14:18:57.544 -> Loop done
14:23:57.593 -> Humidity: 70 %	Temperature: 17.8 *C
14:23:57.662 -> Loop done
14:28:57.733 -> Humidity: 71 %	Temperature: 17.8 *C
14:28:57.802 -> Loop done
14:33:57.883 -> Humidity: 73 %	Temperature: 17.9 *C
14:33:57.952 -> Loop done
14:38:57.997 -> Humidity: 73 %	Temperature: 18.0 *C
14:38:58.066 -> Loop done
14:43:58.138 -> Humidity: 73 %	Temperature: 18.1 *C
14:43:58.208 -> Loop done
14:48:58.262 -> Humidity: 73 %	Temperature: 18.3 *C
14:48:58.331 -> Loop done
14:53:58.374 -> Humidity: 73 %	Temperature: 18.2 *C
14:53:58.444 -> Loop done
14:58:58.509 -> Humidity: 73 %	Temperature: 18.3 *C
14:58:58.578 -> Loop done
15:03:58.624 -> Humidity: 65 %	Temperature: 16.5 *C
15:03:58.694 -> Loop done
15:08:58.766 -> Humidity: 71 %	Temperature: 18.8 *C
15:08:58.836 -> Loop done
15:13:58.893 -> Humidity: 75 %	Temperature: 19.1 *C
15:13:58.963 -> Loop done

I configured the device to upload to my Azure IoT Hub/Azure IoT Central gateway and after getting the device name configuration right it has been running reliably for a couple of days

Azure IoT Central Temperature and humidity

The device was sitting outside on the deck and rapid increase in temperature is me bringing it inside.

Nexus LoRa Radio 915 MHz Payload Addressing client

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This is a demo Ingenuity Micro Nexus client (based on the Netduino example for my RFM9XLoRaNetMF library) that uploads temperature and humidity data to my Azure IoT Hubs/Central or AdaFruit.IO on Raspberry PI field gateways

Bill of materials (Prices June 2019).

// <copyright file="client.cs" company="devMobile Software">
// Copyright ® 2019 Feb devMobile Software, All Rights Reserved
//
//  MIT License
//
//  Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE"
//
// </copyright>
namespace devMobile.IoT.Nexus.FieldGateway
{
	using System;
	using System.Text;
	using System.Threading;
	using Microsoft.SPOT;
	using Microsoft.SPOT.Hardware;

	using devMobile.IoT.NetMF.ISM;
	using devMobile.NetMF.Sensor;
	using IngenuityMicro.Nexus;

	class NexusClient
	{
		private Rfm9XDevice rfm9XDevice;
		private readonly TimeSpan dueTime = new TimeSpan(0, 0, 15);
		private readonly TimeSpan periodTime = new TimeSpan(0, 0, 60);
		private readonly SiliconLabsSI7005 sensor = new SiliconLabsSI7005();
		private readonly Led _led = new Led();
		private readonly byte[] fieldGatewayAddress = Encoding.UTF8.GetBytes("LoRaIoT1");
		private readonly byte[] deviceAddress = Encoding.UTF8.GetBytes("Nexus915");

		public NexusClient()
		{
			rfm9XDevice = new Rfm9XDevice(SPI.SPI_module.SPI3, (Cpu.Pin)28, (Cpu.Pin)15, (Cpu.Pin)26);
			_led.Set(0, 0, 0);
		}

		public void Run()
		{

			rfm9XDevice.Initialise(frequency: 915000000, paBoost: true, rxPayloadCrcOn: true);
			rfm9XDevice.Receive(deviceAddress);

			rfm9XDevice.OnDataReceived += rfm9XDevice_OnDataReceived;
			rfm9XDevice.OnTransmit += rfm9XDevice_OnTransmit;

			Timer humidityAndtemperatureUpdates = new Timer(HumidityAndTemperatureTimerProc, null, dueTime, periodTime);

			Thread.Sleep(Timeout.Infinite);
		}


		private void HumidityAndTemperatureTimerProc(object state)
		{
			_led.Set(0, 128, 0);

			double humidity = sensor.Humidity();
			double temperature = sensor.Temperature();

			Debug.Print(DateTime.UtcNow.ToString("hh:mm:ss") + " H:" + humidity.ToString("F1") + " T:" + temperature.ToString("F1"));

			rfm9XDevice.Send(fieldGatewayAddress, Encoding.UTF8.GetBytes("t " + temperature.ToString("F1") + ",H " + humidity.ToString("F0")));
		}

		void rfm9XDevice_OnTransmit()
		{
			_led.Set(0, 0, 0);

			Debug.Print("Transmit-Done");
		}

		void rfm9XDevice_OnDataReceived(byte[] address, float packetSnr, int packetRssi, int rssi, byte[] data)
		{
			try
			{
				string messageText = new string(UTF8Encoding.UTF8.GetChars(data));
				string addressText = new string(UTF8Encoding.UTF8.GetChars(address));

				Debug.Print(DateTime.UtcNow.ToString("HH:MM:ss") + "-Rfm9X PacketSnr " + packetSnr.ToString("F1") + " Packet RSSI " + packetRssi + "dBm RSSI " + rssi + "dBm = " + data.Length + " byte message " + @"""" + messageText + @"""");
			}
			catch (Exception ex)
			{
				Debug.Print(ex.Message);
			}
		}
	}
}

Overall the development process was good with no modifications to my RFM9X.NetMF library or SI7005 library (bar removing a Netduino I2C work around) required

Nexus device with Seeedstudio Temperature & Humidity Sensors
Nexus Sensor data in Azure IoT Hub Field Gateway ETW Logging
Nexus temperature & humidity data displayed in Azure IoT Central

Windows 10 IoT Core Cognitive Services Computer Vision API

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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

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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.

Windows 10 IoT Core TPM SAS Token Expiry

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This is for people who were searching for why the SAS token issued by the TPM on their Windows 10 IoT Core device is expiring much quicker than expected or might have noticed that something isn’t quite right with the “validity” period. (as at early May 2019). If you want to “follow along at home” the code I used is available on GitHub.

I found the SAS key was expiring in roughly 5 minutes and the validity period in the configuration didn’t appear to have any effect on how long the SAS token was valid.

10:04:16 Application started
...
10:04:27 SAS token needs renewing
10:04:30 SAS token renewed 
 10:04:30.984 AzureIoTHubClient SendEventAsync starting
 10:04:36.709 AzureIoTHubClient SendEventAsync starting
The thread 0x1464 has exited with code 0 (0x0).
 10:04:37.808 AzureIoTHubClient SendEventAsync finished
 10:04:37.808 AzureIoTHubClient SendEventAsync finished
The thread 0xb88 has exited with code 0 (0x0).
The thread 0x1208 has exited with code 0 (0x0).
The thread 0x448 has exited with code 0 (0x0).
The thread 0x540 has exited with code 0 (0x0).
 10:04:46.763 AzureIoTHubClient SendEventAsync starting
 10:04:47.051 AzureIoTHubClient SendEventAsync finished
The thread 0x10d8 has exited with code 0 (0x0).
The thread 0x6e0 has exited with code 0 (0x0).
The thread 0xf7c has exited with code 0 (0x0).
 10:04:56.808 AzureIoTHubClient SendEventAsync starting
 10:04:57.103 AzureIoTHubClient SendEventAsync finished
The thread 0xb8c has exited with code 0 (0x0).
The thread 0xc60 has exited with code 0 (0x0).
 10:05:06.784 AzureIoTHubClient SendEventAsync starting
 10:05:07.057 AzureIoTHubClient SendEventAsync finished
...
The thread 0x4f4 has exited with code 0 (0x0).
The thread 0xe10 has exited with code 0 (0x0).
The thread 0x3c8 has exited with code 0 (0x0).
 10:09:06.773 AzureIoTHubClient SendEventAsync starting
 10:09:07.044 AzureIoTHubClient SendEventAsync finished
The thread 0xf70 has exited with code 0 (0x0).
The thread 0x1214 has exited with code 0 (0x0).
 10:09:16.819 AzureIoTHubClient SendEventAsync starting
 10:09:17.104 AzureIoTHubClient SendEventAsync finished
The thread 0x1358 has exited with code 0 (0x0).
The thread 0x400 has exited with code 0 (0x0).
 10:09:26.802 AzureIoTHubClient SendEventAsync starting
 10:09:27.064 AzureIoTHubClient SendEventAsync finished
The thread 0x920 has exited with code 0 (0x0).
The thread 0x1684 has exited with code 0 (0x0).
The thread 0x4ec has exited with code 0 (0x0).
 10:09:36.759 AzureIoTHubClient SendEventAsync starting
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Net.Requests.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.Net.WebSockets.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
Sending payload to AzureIoTHub failed:CONNECT failed: RefusedNotAuthorized

I went and looked at the NuGet package details and it seemed a bit old.

I have the RedGate Reflector plugin installed on my development box so I quickly disassembled the Microsoft.Devices.TPM assembly to see what was going on. The Reflector code is pretty readable and it wouldn’t take much “refactoring” to get it looking like “human” generated code. 

public string GetSASToken(uint validity = 0xe10)
{
    string deviceId = this.GetDeviceId();
    string hostName = this.GetHostName();
    long num = (DateTime.get_Now().ToUniversalTime().ToFileTime() / 0x98_9680L) - 0x2_b610_9100L;
    string str3 = "";
    if ((hostName.Length > 0) && (deviceId.Length > 0))
    {
        object[] objArray1 = new object[] { hostName, "/devices/", deviceId, "\n", (long) num };
        byte[] bytes = new UTF8Encoding().GetBytes(string.Concat((object[]) objArray1));
        byte[] buffer2 = this.SignHmac(bytes);
        if (buffer2.Length != 0)
        {
            string str5 = this.AzureUrlEncode(Convert.ToBase64String(buffer2));
            object[] objArray2 = new object[] { "SharedAccessSignature sr=", hostName, "/devices/", deviceId, "&sig=", str5, "&se=", (long) num };
            str3 = string.Concat((object[]) objArray2);
        }
    }
    return str3;
}

The validity parameter appears to not used. Below is the current code from the Azure IoT CSharp SDK on GitHub repository and they are different, the validity is used. 

public string GetSASToken(uint validity = 3600)
{
   const long WINDOWS_TICKS_PER_SEC = 10000000;
   const long EPOCH_DIFFERNECE = 11644473600;
   string deviceId = GetDeviceId();
   string hostName = GetHostName();
   long expirationTime = (DateTime.Now.ToUniversalTime().ToFileTime() / WINDOWS_TICKS_PER_SEC) - EPOCH_DIFFERNECE;
   expirationTime += validity;
   string sasToken = "";
   if ((hostName.Length > 0) && (deviceId.Length > 0))
   {
      // Encode the message to sign with the TPM
      UTF8Encoding utf8 = new UTF8Encoding();
      string tokenContent = hostName + "/devices/" + deviceId + "\n" + expirationTime;
      Byte[] encodedBytes = utf8.GetBytes(tokenContent);

      // Sign the message
      Byte[] hmac = SignHmac(encodedBytes);

      // if we got a signature foramt it
      if (hmac.Length > 0)
      {
         // Encode the output and assemble the connection string
         string hmacString = AzureUrlEncode(System.Convert.ToBase64String(hmac));
         sasToken = "SharedAccessSignature sr=" + hostName + "/devices/" + deviceId + "&sig=" + hmacString + "&se=" + expirationTime;
         }
   }
   return sasToken;
}

I went back and look at the Github history and it looks like a patch was applied after the NuGet packages were released in May 2016.

If you read from the TPM and get nothing make sure you’re using the right TPM slot number and have “System Management” checked in the capabilities tab of the application manifest.

I’m still not certain the validity is being applied correctly and will dig into in a future post.

Azure IOT Hub nRF24L01 Windows 10 IoT Core Field Gateway with BorosRF2

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A couple of BorosRF2 Dual nRF24L01 Hats arrived earlier in the week. After some testing with my nRF24L01 Test application I have added compile-time configuration options for the two nRF24L01 sockets to my Azure IoT Hub nRF24L01 Field Gateway.

Boros RF2 with Dual nRF24L01 devices
public sealed class StartupTask : IBackgroundTask
{
   private const string ConfigurationFilename = "config.json";

   private const byte MessageHeaderPosition = 0;
   private const byte MessageHeaderLength = 1;

   // nRF24 Hardware interface configuration
#if CEECH_NRF24L01P_SHIELD
   private const byte RF24ModuleChipEnablePin = 25;
   private const byte RF24ModuleChipSelectPin = 0;
   private const byte RF24ModuleInterruptPin = 17;
#endif

#if BOROS_RF2_SHIELD_RADIO_0
   private const byte RF24ModuleChipEnablePin = 24;
   private const byte RF24ModuleChipSelectPin = 0;
   private const byte RF24ModuleInterruptPin = 27;
#endif

#if BOROS_RF2_SHIELD_RADIO_1
   private const byte RF24ModuleChipEnablePin = 25;
   private const byte RF24ModuleChipSelectPin = 1;
   private const byte RF24ModuleInterruptPin = 22;
#endif

private readonly LoggingChannel logging = new LoggingChannel("devMobile Azure IotHub nRF24L01 Field Gateway", null, new Guid("4bd2826e-54a1-4ba9-bf63-92b73ea1ac4a"));
private readonly RF24 rf24 = new RF24();

This version supports one nRF24L01 device socket active at a time.

Enabling both nRF24L01 device sockets broke outbound message routing in a prototype branch with cloud to device(C2D) messaging support. This functionality is part of an Over The Air(OTA) device provisioning implementation I’m working o.

Windows 10 IoT Core Time-Lapse Camera Azure IoT Hub Storage Revisited

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In my previous post the application uploaded images to an Azure storage account associated with an Azure IoT Hub based on configuration file settings. The application didn’t use any of the Azure IoT Hub device management functionality like device twins and direct methods.

Time-lapse camera setup

In this version only the Azure IoT hub connection string and protocol to use are stored in the JSON configuration file. 

{
  "AzureIoTHubConnectionString": "",
  "TransportType": "Mqtt",
}

On startup the application uploads a selection of properties to the Azure IoT Hub to assist with support, fault finding etc.

// This is from the OS 
reportedProperties["Timezone"] = TimeZoneSettings.CurrentTimeZoneDisplayName;
reportedProperties["OSVersion"] = Environment.OSVersion.VersionString;
reportedProperties["MachineName"] = Environment.MachineName;
reportedProperties["ApplicationDisplayName"] = package.DisplayName;
reportedProperties["ApplicationName"] = packageId.Name;
reportedProperties["ApplicationVersion"] = string.Format($"{version.Major}.{version.Minor}.{version.Build}.{version.Revision}");

// Unique identifier from the hardware
SystemIdentificationInfo systemIdentificationInfo = SystemIdentification.GetSystemIdForPublisher();
using (DataReader reader = DataReader.FromBuffer(systemIdentificationInfo.Id))
{
   byte[] bytes = new byte[systemIdentificationInfo.Id.Length];
   reader.ReadBytes(bytes);
   reportedProperties["SystemId"] = BitConverter.ToString(bytes);
}

Azure Portal Device Properties

The Azure Storage file and folder name formats along with the image capture due and update periods are configured in the DeviceTwin properties. Initially I had some problems with the dynamic property types so had to .ToString and then Timespan.TryParse the periods.

Twin deviceTwin= azureIoTHubClient.GetTwinAsync().Result;

if (!deviceTwin.Properties.Desired.Contains("AzureImageFilenameLatestFormat"))
{
   this.logging.LogMessage("DeviceTwin.Properties AzureImageFilenameLatestFormat setting missing", LoggingLevel.Warning);
   return;
}
…
if (!deviceTwin.Properties.Desired.Contains("ImageUpdateDue") || !TimeSpan.TryParse(deviceTwin.Properties.Desired["ImageUpdateDue"].Value.ToString(), out imageUpdateDue))
{
   this.logging.LogMessage("DeviceTwin.Properties ImageUpdateDue setting missing or invalid format", LoggingLevel.Warning);
   return;
}
Azure Portal Device Settings

The application also supports two commands “ImageCapture’ and “DeviceReboot”. For testing I used Azure Device Explorer

After running the installer (available from GitHub) the application will create a default configuration file in 

\User Folders\LocalAppData\PhotoTimerTriggerAzureIoTHubStorage-uwp_1.2.0.0_arm__nmn3tag1rpsaw\LocalState\

Which can be downloaded, modified then uploaded using the portal file explorer application. If you want to make the application run on device start-up the radio button below needs to be selected.

Windows 10 IoT Core Time-Lapse Camera Azure IoT Hub Storage

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After building a couple of time lapse camera applications for Windows 10 IoT Core I built a version which uploads the images to the Azure storage account associated with an Azure IoT Hub.

I really wanted to be able to do a time-lapse video of a storm coming up the Canterbury Plains to Christchurch and combine it with the wind direction, windspeed, temperature and humidity data from my weather station which uploads data to Azure through my Azure IoT Hub LoRa field gateway.

Time-lapse camera setup

The application captures images with a configurable period after configurable start-up delay. The Azure storage root folder name is based on the device name in the Azure IoT Hub connection string. The folder(s) where the historic images are stored are configurable and the images can optionally be in monthly, daily, hourly etc. folders. The current image is stored in the root folder for the device and it’s name is configurable.

{
  "AzureIoTHubConnectionString": "",
  "TransportType": "Mqtt",
  "AzureImageFilenameFormatLatest": "latest.jpg",
  "AzureImageFilenameFormatHistory": "{0:yyMMdd}/{0:yyMMddHHmmss}.jpg",
  "ImageUpdateDueSeconds": 30,
  "ImageUpdatePeriodSeconds": 300
}

With the above setup I have a folder for each device in the historic fiolder and the most recent image i.e. “latest.jpg” in the root folder. The file and folder names are assembled with a parameterised string.format . The parameter {0} is the current UTC time

Pay attention to your folder/file name formatting, I was tripped up by

  • mm – minutes vs. MM – months
  • hh – 12 hour clock vs. HH -24 hour clock

With 12 images every hour

The application logs events on start-up and every time a picture is taken

After running the installer (available from GitHub) the application will create a default configuration file in

User Folders\LocalAppData\PhotoTimerTriggerAzureIoTHubStorage-uwp_1.0.0.0_arm__nmn3tag1rpsaw\LocalState\

Which can be downloaded, modified then uploaded using the portal file explorer application. If you want to make the application run on device start-up the radio button below needs to be selected.

/*
    Copyright ® 2019 March devMobile Software, All Rights Reserved
 
    MIT License

…
*/
namespace devMobile.Windows10IotCore.IoT.PhotoTimerTriggerAzureIoTHubStorage
{
	using System;
	using System.IO;
	using System.Diagnostics;
	using System.Threading;

	using Microsoft.Azure.Devices.Client;
	using Microsoft.Extensions.Configuration;

	using Windows.ApplicationModel;
	using Windows.ApplicationModel.Background;
	using Windows.Foundation.Diagnostics;
	using Windows.Media.Capture;
	using Windows.Media.MediaProperties;
	using Windows.Storage;
	using Windows.System;
	
	public sealed class StartupTask : IBackgroundTask
	{
		private BackgroundTaskDeferral backgroundTaskDeferral = null;
		private readonly LoggingChannel logging = new LoggingChannel("devMobile Photo Timer Azure IoT Hub Storage", null, new Guid("4bd2826e-54a1-4ba9-bf63-92b73ea1ac4a"));
		private DeviceClient azureIoTHubClient = null;
		private const string ConfigurationFilename = "appsettings.json";
		private Timer ImageUpdatetimer;
		private MediaCapture mediaCapture;
		private string azureIoTHubConnectionString;
		private TransportType transportType;
		private string azureStorageimageFilenameLatestFormat;
		private string azureStorageImageFilenameHistoryFormat;
		private const string ImageFilenameLocal = "latest.jpg";
		private volatile bool cameraBusy = false;

		public void Run(IBackgroundTaskInstance taskInstance)
		{
			StorageFolder localFolder = ApplicationData.Current.LocalFolder;
			int imageUpdateDueSeconds;
			int imageUpdatePeriodSeconds;

			this.logging.LogEvent("Application starting");

			// Log the Application build, OS version information etc.
			LoggingFields startupInformation = new LoggingFields();
			startupInformation.AddString("Timezone", TimeZoneSettings.CurrentTimeZoneDisplayName);
			startupInformation.AddString("OSVersion", Environment.OSVersion.VersionString);
			startupInformation.AddString("MachineName", Environment.MachineName);

			// This is from the application manifest 
			Package package = Package.Current;
			PackageId packageId = package.Id;
			PackageVersion version = packageId.Version;
			startupInformation.AddString("ApplicationVersion", string.Format($"{version.Major}.{version.Minor}.{version.Build}.{version.Revision}"));

			try
			{
				// see if the configuration file is present if not copy minimal sample one from application directory
				if (localFolder.TryGetItemAsync(ConfigurationFilename).AsTask().Result == null)
				{
					StorageFile templateConfigurationfile = Package.Current.InstalledLocation.GetFileAsync(ConfigurationFilename).AsTask().Result;
					templateConfigurationfile.CopyAsync(localFolder, ConfigurationFilename).AsTask();

					this.logging.LogMessage("JSON configuration file missing, templated created", LoggingLevel.Warning);
					return;
				}

				IConfiguration configuration = new ConfigurationBuilder().AddJsonFile(Path.Combine(localFolder.Path, ConfigurationFilename), false, true).Build();

				azureIoTHubConnectionString = configuration.GetSection("AzureIoTHubConnectionString").Value;
				startupInformation.AddString("AzureIoTHubConnectionString", azureIoTHubConnectionString);

				transportType = (TransportType)Enum.Parse( typeof(TransportType), configuration.GetSection("TransportType").Value);
				startupInformation.AddString("TransportType", transportType.ToString());

				azureStorageimageFilenameLatestFormat = configuration.GetSection("AzureImageFilenameFormatLatest").Value;
				startupInformation.AddString("ImageFilenameLatestFormat", azureStorageimageFilenameLatestFormat);

				azureStorageImageFilenameHistoryFormat = configuration.GetSection("AzureImageFilenameFormatHistory").Value;
				startupInformation.AddString("ImageFilenameHistoryFormat", azureStorageImageFilenameHistoryFormat);

				imageUpdateDueSeconds = int.Parse(configuration.GetSection("ImageUpdateDueSeconds").Value);
				startupInformation.AddInt32("ImageUpdateDueSeconds", imageUpdateDueSeconds);

				imageUpdatePeriodSeconds = int.Parse(configuration.GetSection("ImageUpdatePeriodSeconds").Value);
				startupInformation.AddInt32("ImageUpdatePeriodSeconds", imageUpdatePeriodSeconds);
			}
			catch (Exception ex)
			{
				this.logging.LogMessage("JSON configuration file load or settings retrieval failed " + ex.Message, LoggingLevel.Error);
				return;
			}

			try
			{
				azureIoTHubClient = DeviceClient.CreateFromConnectionString(azureIoTHubConnectionString, transportType);
			}
			catch (Exception ex)
			{
				this.logging.LogMessage("AzureIOT Hub connection failed " + ex.Message, LoggingLevel.Error);
				return;
			}

			try
			{
				mediaCapture = new MediaCapture();
				mediaCapture.InitializeAsync().AsTask().Wait();
			}
			catch (Exception ex)
			{
				this.logging.LogMessage("Camera configuration failed " + ex.Message, LoggingLevel.Error);
				return;
			}

			ImageUpdatetimer = new Timer(ImageUpdateTimerCallback, null, new TimeSpan(0, 0, imageUpdateDueSeconds), new TimeSpan(0, 0, imageUpdatePeriodSeconds));

			this.logging.LogEvent("Application started", startupInformation);

			//enable task to continue running in background
			backgroundTaskDeferral = taskInstance.GetDeferral();
		}

		private async void ImageUpdateTimerCallback(object state)
		{
			DateTime currentTime = DateTime.UtcNow;
			Debug.WriteLine($"{DateTime.UtcNow.ToLongTimeString()} Timer triggered");

			// Just incase - stop code being called while photo already in progress
			if (cameraBusy)
			{
				return;
			}
			cameraBusy = true;

			try
			{
				using (Windows.Storage.Streams.InMemoryRandomAccessStream captureStream = new Windows.Storage.Streams.InMemoryRandomAccessStream())
				{
					await mediaCapture.CapturePhotoToStreamAsync(ImageEncodingProperties.CreateJpeg(), captureStream);
					await captureStream.FlushAsync();
#if DEBUG
					IStorageFile photoFile = await KnownFolders.PicturesLibrary.CreateFileAsync(ImageFilenameLocal, CreationCollisionOption.ReplaceExisting);
					ImageEncodingProperties imageProperties = ImageEncodingProperties.CreateJpeg();
					await mediaCapture.CapturePhotoToStorageFileAsync(imageProperties, photoFile);
#endif

					string azureFilenameLatest = string.Format(azureStorageimageFilenameLatestFormat, currentTime);
					string azureFilenameHistory = string.Format(azureStorageImageFilenameHistoryFormat, currentTime);

					LoggingFields imageInformation = new LoggingFields();
					imageInformation.AddDateTime("TakenAtUTC", currentTime);
#if DEBUG
					imageInformation.AddString("LocalFilename", photoFile.Path);
#endif
					imageInformation.AddString("AzureFilenameLatest", azureFilenameLatest);
					imageInformation.AddString("AzureFilenameHistory", azureFilenameHistory);
					this.logging.LogEvent("Saving image(s) to Azure storage", imageInformation);

					// Update the latest image in storage
					if (!string.IsNullOrWhiteSpace(azureFilenameLatest))
					{
						captureStream.Seek(0);
						Debug.WriteLine("AzureIoT Hub latest image upload start");
						await azureIoTHubClient.UploadToBlobAsync(azureFilenameLatest, captureStream.AsStreamForRead());
						Debug.WriteLine("AzureIoT Hub latest image upload done");
					}

					// Upload the historic image to storage
					if (!string.IsNullOrWhiteSpace(azureFilenameHistory))
					{
						captureStream.Seek(0);
						Debug.WriteLine("AzureIoT Hub historic image upload start");
						await azureIoTHubClient.UploadToBlobAsync(azureFilenameHistory, captureStream.AsStreamForRead());
						Debug.WriteLine("AzureIoT Hub historic image upload done");
					}
				}
			}
			catch (Exception ex)
			{
				this.logging.LogMessage("Camera photo save or AzureIoTHub storage upload failed " + ex.Message, LoggingLevel.Error);
			}
			finally
			{
				cameraBusy = false;
			}
		}
	}
}

The images in Azure Storage could then be assembled into a video using a tool like Time Lapse Creator or processed with Azure Custom Vision Service.

Azure Function Log4Net configuration

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This post was inspired by the couple of hours lost from my life yesterday while I figured out how to get Apache Log4Net and Azure Application Insights working in an Azure Function built with .Net Core 2.X.

After extensive searching I found a couple of relevant blog posts but these had complex approaches and I wanted to keep the churn in the codebase I was working on to an absolute minimum.

With the different versions of the libraries involved (Late March 2019) this was what worked for me so YMMV. To provide the simplest possible example I have created a TimerTrigger which logs information via Log4Net to Azure Application Insights.

Initially the Log4Net configuration wasn’t loaded because its location is usually configured in the AssemblyInfo.cs file and .Net Core 2.x code doesn’t have one.

// You can specify all the values or you can default the Build and Revision Numbers
// by using the '*' as shown below:
// [assembly: AssemblyVersion("1.0.*")]
[assembly: AssemblyVersion("1.0.0.0")]
[assembly: AssemblyFileVersion("1.0.0.0")]
[assembly: log4net.Config.XmlConfigurator]

I figured I would have to manually load the Log4Net configuration and had to look at the file system of machine running the function to figure out where the Log4Net XML configuration file was getting copied to.

The “Copy to output directory” setting is important

Then I had to get the Dependency Injection (DI) framework to build an ExecutionContext for me so I could get the FunctionAppDirectory to combine with the Log4Net config file name. I used Path.Combine which is more robust and secure than manually concatenating segments of a path together.

/*
    Copyright ® 2019 March devMobile Software, All Rights Reserved
 
    MIT License
...
*/
namespace ApplicationInsightsAzureFunctionLog4NetClient
{
	using System;
	using System.IO;
	using System.Reflection;
	using log4net;
	using log4net.Config;
	using Microsoft.ApplicationInsights.Extensibility;
	using Microsoft.Azure.WebJobs;

	public static class ApplicationInsightsTimer
	{
		[FunctionName("ApplicationInsightsTimerLog4Net")]
		public static void Run([TimerTrigger("0 */1 * * * *")]TimerInfo myTimer, ExecutionContext executionContext)
		{
			ILog log = log4net.LogManager.GetLogger(System.Reflection.MethodBase.GetCurrentMethod().DeclaringType);

			TelemetryConfiguration.Active.InstrumentationKey = Environment.GetEnvironmentVariable("InstrumentationKey", EnvironmentVariableTarget.Process);

			var logRepository = LogManager.GetRepository(Assembly.GetEntryAssembly());
			XmlConfigurator.Configure(logRepository, new FileInfo(Path.Combine(executionContext.FunctionAppDirectory, "log4net.config")));

			log.Debug("This is a Log4Net Debug message");
			log.Info("This is a Log4Net Info message");
			log.Warn("This is a Log4Net Warning message");
			log.Error("This is a Log4Net Error message");
			log.Fatal("This is a Log4Net Fatal message");

			TelemetryConfiguration.Active.TelemetryChannel.Flush();
		}
	}
}
Log4Net logging in Azure Application Insights

The latest code for my Azure Function Log4net to Applications Insights sample along with some samples for other logging platforms is available on GitHub.