Steve Spencer's Blog

Blogging on Azure Stuff

Raspberry Pi2 , Iot Core and Azure Service Bus

Using Raspberry Pi2 on Windows 10 IoT core has a number of challenges mainly due to the limitations of both the universal app APIs and also the lack of APIs that currently run on the platform. I specifically wanted to utilise Azure Service Bus Topics to send/receive messages on my Raspberry Pi2. After a bit of searching around I decided that the easiest way to achieve this was to use the Service Bus REST API. There are a number of samples included in the documentation:

Receiving a message: https://msdn.microsoft.com/en-us/library/azure/hh690923.aspx

Sending a message: https://msdn.microsoft.com/en-us/library/azure/hh690922.aspx

The full code for the sample uses WebClient but I needed to use HttpClient so I converted the samples accordingly.

[EDIT] The above links don't work anymore so I've published my code on GitHub https://github.com/sdspencer-mvp/RaspberryPi2-UnlockTheDoor/blob/master/UnlockTheDoor/MainPage.xaml.cs 

Sending a message to the service bus requires a POST and receive and delete requires a DELETE. The following code shows how this was achieved using HttpClient

private async void SendMessage(string baseAddress, string queueTopicName, string token, string body, IDictionary<string, string> properties)

{

    string fullAddress = baseAddress + queueTopicName + "/messages" + "?timeout=60&api-version=2013-08 ";

    await SendViaHttp(token, body, properties, fullAddress, HttpMethod.Post);

}

 

 

 

// Receives and deletes the next message from the given resource (queue, topic, or subscription)

// using the resourceName and an HTTP DELETE request.

private static async System.Threading.Tasks.Task <string> ReceiveAndDeleteMessageFromSubscription(string baseAddress, string topic, string subscription, string token, IDictionary<string, string> properties)

{

    string fullAddress = baseAddress + topic + "/Subscriptions/" + subscription + "/messages/head" + "?timeout=60";

    HttpResponseMessage response = await SendViaHttp(token, "", properties, fullAddress, HttpMethod.Delete);

    string content = "";

    if (response.IsSuccessStatusCode)

    {

        // we should have retrieved a message

        content = await response.Content.ReadAsStringAsync();

    }

    return content;

}

 

 

 

private static async System.Threading.Tasks.Task<HttpResponseMessage> SendViaHttp(string token, string body, IDictionary<string, string> properties, string fullAddress, HttpMethod httpMethod )

{

    HttpClient webClient = new HttpClient();

    HttpRequestMessage request = new HttpRequestMessage()

    {

        RequestUri = new Uri(fullAddress),

        Method = httpMethod ,

 

    };

    webClient.DefaultRequestHeaders.Add("Authorization", token);

 

    if (properties != null)

    {

        foreach (string property in properties.Keys)

        {

            request.Headers.Add(property, properties[property]);

        }

    }

    request.Content = new FormUrlEncodedContent(new[] { new KeyValuePair<string, string>("", body) });

    HttpResponseMessage response = await webClient.SendAsync(request);

    if (!response.IsSuccessStatusCode)

    {

        string error = string.Format("{0} : {1}", response.StatusCode, response.ReasonPhrase);

        throw new Exception(error);

    }

    return response;

}

 

There was an issue with the GetSASToken method as some of the encryption classes weren't supported on the Universal App so I converted it to the following:

private string GetSASToken(string baseAddress, string SASKeyName, string SASKeyValue)

{

    TimeSpan fromEpochStart = DateTime.UtcNow - new DateTime(1970, 1, 1);

    string expiry = Convert.ToString((int)fromEpochStart.TotalSeconds + 3600);

    string stringToSign = WebUtility.UrlEncode(baseAddress) + "\n" + expiry;

    string hmac = GetSHA256Key(Encoding.UTF8.GetBytes(SASKeyValue), stringToSign);

    string hash = HmacSha256(SASKeyValue, stringToSign);

    string sasToken = String.Format(CultureInfo.InvariantCulture, "SharedAccessSignature sr={0}&sig={1}&se={2}&skn={3}",

        WebUtility.UrlEncode(baseAddress), WebUtility.UrlEncode(hash), expiry, SASKeyName);

    return sasToken;

}

 

 

public string HmacSha256(string secretKey, string value)

{

    // Move strings to buffers.

    var key = CryptographicBuffer.ConvertStringToBinary(secretKey, BinaryStringEncoding.Utf8);

    var msg = CryptographicBuffer.ConvertStringToBinary(value, BinaryStringEncoding.Utf8);

 

    // Create HMAC.

    var objMacProv = MacAlgorithmProvider.OpenAlgorithm(MacAlgorithmNames.HmacSha256);

    var hash = objMacProv.CreateHash(key);

    hash.Append(msg);

    return CryptographicBuffer.EncodeToBase64String(hash.GetValueAndReset());

}

 

This allowed me to send and receive messages on my Raspberry Pi2 using IoT core. I created the subscriptions for the topic using a separate app using the .NET SDK which is cheating I guess, but I’ll get around to converting it at some point.

 

In order to use this the following parameters are used:

 

SendMessage( BaseAddress, Username, Token, MessageBody, MessageProperties)

 

BaseAddress is “https://<yournamespace>.servicebus.windows.net/”

 

Token is the return value from the GetSASToken method. using the same base address as above and the KeyName and Key are obtained from the Azure portal and is of the format

 

Endpoint=sb://<yournamespace>.servicebus.windows.net/;SharedAccessKeyName=<KeyName>;SharedAccessKey=<Key>.

 

MessageBody – This is the string value of the message body

 

MessageProperties are a Dictionary containing name/value pairs that will get added to the Request headers. For example I have set the message properties when I press the door bell button on my Raspberry PI2

 

Dictionary<string, string> properties = new Dictionary<string, string>();

properties.Add("Priority", "High");

properties.Add("MessageType", "Command");

properties.Add("Command", "BingBong");

 

These are added to the service bus message and allow me to have subscriptions that filer on Command message types as well as the specific command of BingBong

 

Receiving messages are a bit trickier as we need to create a separate task that is continually running. Once the message is received we need to get back to the main tread to execute the action for the message

await Task.Run(async () =>

{

.

.

.

string message = await ReceiveAndDeleteMessageFromSubscription(_BaseAddress

,_TopicName

, _SubscriptionName

, token, null);

 if (message.Contains("Unlock"))

{

   await Windows.ApplicationModel.Core.CoreApplication.MainView.CoreWindow.Dispatcher.RunAsync(

      CoreDispatcherPriority.Normal,

      () =>

      {

          SwitchLED(false);

     });

}

 

.

.

}

 

You may want to put a delay in this if receiving the messages causes the app to slow down due to the message loop hogging all the resources. There’s a default timeout in the call to SendAsync and this will automatically slow the thread down.

 

I now have a working Raspberry PI2 that can send and receive message to the Azure Service bus. I’ve created a test win forms app that allows me to send messages to the Service bus and it allows me to control the Raspberry Pi2 remotely. The next phase is to build a workflow engine that hooks up to the service bus and allows me to automatically control the Raspberry Pi. 

Making My Azure ML Project Oxford Sample Application More Visual

Following on from my last post where I introduce Project Oxford I’ve done a bit more work to take the project that was built and make it more visual. To summarise, Project Oxford is a set of APIs that build on top of Azure ML to provide Face, Speech, Computer Vision and Language Understanding Intelligence Service (LUIS). There was a good video from Build 2015 that I watched to provide an overview of each of the APIs.

I used the tutorials to build an application that would identify a number of people from a known list in a photograph and highlight the ones that were unknown. The Face API requires people to be trained with a set of photos first, before identification can be made. This was done by using the code in the samples. I created a folder for each person that I wanted to be trained and added different photos of each person with and without hats, and sunglasses and also with different expressions. Then each set of folders was passed to the training API. Once trained you can then use the rest of the Face API to firstly identify faces in a picture and then take each face that is found and see if they are known.

One useful tip I’ve found is to have Fiddler running whilst you are debugging as it is far easier to see any errors in the body of the response message than in the exceptions that are thrown. Details of the errors can be seen in the Face API documentation.

The process for training is as follows (Note the terminology is based around the SDK methods, but I’ve linked to the API page as this gives details about the errors etc):

  1. Create a Person Group
  2. Create a Face list for each person using Face Detect
  3. Create a Person one for each person you want to identify with the person group id and face list
  4. Train the Person Group

Note: The training does not last forever and you will need to redo it periodically. If you try and detect a person when training has expired then you will get an error response saying that the person group is unknown.

To Identify each individual in a photograph:

  1. Stream the photograph into Detect. This will return a list of faces with face ids
  2. Iterate around each Face and call Identify 
  3. Use the Identify Results to extract the names by calling Get Person.

This is where I got to with the previous post, but this wasn’t very visual and as I was working with photographs I thought it would be useful to use the data returned to draw a box around the faces that were identified and add the name of the person underneath. This was also useful to know which person was identified incorrectly. On the project Oxford web site there was the following image

I wanted to emulate this and also to take it one step further. The data returned from the face detection API provides details about gender, age, the area (face rectangle) in the picture where the face was found, face landmarks, and head pose. What the detection API did not do was to tie the name of the person to the face. We do already have this information as it was returned from the Identify API and Get Person. The attribute that links them is the face id. Using the results of the Identify API I called get person for each face identified to return the person’s name and stored this in a Dictionary along with the face ID. This then allowed me to load the original photograph into memory draw the rectangles for each face and add the text below each using the face id to extract the rectangle and match the name from the Dictionary, This could then be scaled shown in the app.

Face Recognition with Azure ML and Project Oxford

I’ve wanted to use Azure Machine Learning for a while but didn’t know where to start. Microsoft have released some gallery applications for Azure ML to take away some of the complexity and make it easy for developers to use the service. One item in the gallery that will be useful is Project Oxford. Project Oxford offers a number of features and the one I am going to talk about here is the Face API.

With the Face API you can train Azure ML with pictures of a number of people and then use the matching api to see whether any of the trained people appear in the image.

This is easy to setup and there is a good tutorial here: http://www.projectoxford.ai/doc/face/How-To/identifyperson

Firstly you will need to sign up and get a subscription key http://www.projectoxford.ai/doc/general/subscription-key-mgmt

Login to Azure portal with an Azure subscription, The link should open market place. Scroll down to find Face APIs and then click through to the purchase button and purchase. This api is currently free.

Your face api service will now be created. Once complete you need to extract the keys for use in your app. Click on your face api service then click the Manage Button

clip_image001

Click on show to view your key and copy it into your application

clip_image002

Download the face api from https://www.projectoxford.ai/sdk unzip and add to your project, then add a reference in your application.

Follow the code here: http://www.projectoxford.ai/doc/face/How-To/identifyperson

Be aware that when this is run you may get a bad request error (I used fiddler to see the error) when creating a Person Group. This seems to be due to case sensitivity and when I made the parameters lower case it worked! The sample code above is mixed case but the service seems to want all lowercase. Details of the error messages can be found here: https://dev.projectoxford.ai/docs/services/54d85c1d5eefd00dc474a0ef/operations/54f0387249c3f70a50e79b84 The body of the response contains the exact details of the error.

There are limitations on file size so I ended up editing mine down to below 4MB

Once trained you can detect multiple people in one photo graph and will identify those that it knows

I've trained it with a number of people especially as my daughter was identified as her mum :-)

Now I've added her into the training files she is not mistaken.

You might need to play around with the training files especially to take into account hats and glasses.

Enjoy

Introducing the Azure App Service

Last month Microsoft announced the Azure App Service (http://azure.microsoft.com/blog/2015/03/24/announcing-azure-app-service/). The App Service incorporates Web (sites) Apps and Mobile apps and introduces two new services: API Apps and Logic Apps.

API Apps allows you to build small RESTful services that can be combined together with Web, Mobile and/or Logic apps to build your application.

There is new tooling for Visual Studio (http://blogs.msdn.com/b/visualstudio/archive/2015/03/24/introducing-the-azure-api-apps-tools-for-visual-studio-2013.aspx) to help you build API apps, as well as providing the ability to debug your API App when it is deployed in Azure (http://azure.microsoft.com/en-us/documentation/articles/app-service-dotnet-remotely-debug-api-app/).

API Apps are documented using Swagger (http://swagger.io/) and there is a UI in the portal to allow you to run the app with sample data. To access the Swagger UI click the API App URL in the portal and add \swagger to the end. Click on the API method you are interested in and then click the Action button (POST in my example below).

image

This expands out to allow you to exercise the API.

An example API is documented here: https://azure.microsoft.com/en-us/documentation/articles/app-service-dotnet-create-api-app/

There is also a market place for API Apps which include API connectors for Office 365, Service Bus, OneDrive, Drop Box and various others. You need to install them as API apps before they can be used in other apps. All authentication to the services is done in the API app creation process and this therefore makes it easier to wire them together as the authentication is handled for you. Connectors can be used to trigger Logic Apps and also as Actions. Details of this along with the list of available connectors is here (http://azure.microsoft.com/en-us/documentation/articles/app-service-logic-use-biztalk-connectors/)

I'm going to blog in more detail about logic apps later, but for now here are a couple of tips for API Apps:

  1. In order to enable swagger and to ensure that your APIs that return data are documented correctly there is some additional code that needs to be added. This is documented here: http://blogs.msdn.com/b/hosamshobak/archive/2015/03/31/logic-app-with-simple-api-app-with-inputs-and-outputs.aspx
  2. When you create an API app, especially if you created it from the market place (e.g. Azure Storage Blob Connector, Service Bus Connector etc) you are asked for configuration at the time of creation. Once it is created, it is not obvious where to find the configuration. In the new Azure Portal, Browse to API Apps and click on the one you want to reconfigure. In the Essentials panel that appears click on the API app host link. Click the settings Icon followed by Application Settings. Scroll down and any settings for the API App will be visible and can be changed. This is useful if you need to remember which service bus topic and subscription are configured for example.

Azure Storage Version Changes

If you are unaware, older versions of the Azure Storage API will be turned off in December 2015. This means that any of your applications that use these older versions will stop working. If you are accessing the Storage API through an SDK then you most likely just need to rebuild with a newer supported version. If you are accessing the REST API directly then you will need to ensure that the code changes to support the newer API versions.

Full details of the changes can be found at: http://azure.microsoft.com/blog/2014/08/04/microsoft-azure-storage-service-version-removal/

Changing Website Settings through the Azure Portal

When using configuration in Microsoft Azure websites, ensure that you put configuration that you are likely to change often in AppSettings. This allows you to make configuration changes in the Management portal of Azure rather than having to edit the web.config file directly. An example of where you might like to do this include settings that allow you to disable site features temporarily such as during an upgrade or routine maintenance.

App settings in the web config file are names/value pairs and are accessed as follows:

System.Configuration.ConfigurationManager.AppSettings["StevesSetting"]

Which can be seen in the web.config as follows:

<configuration>
  .
  <appSettings>
      <add key="StevesSetting" value="Webconfig setting" />
  </appSettings>
  .
</configuration>

In order to manage this configuration in the portal you need to navigate to your website and click the configure tab (in the old portal)

image

and scroll down to app settings, then add in the setting you wish to change

image

Or in the old portal navigate to the website and click settings then applications settings and scroll down to the app settings section

image

Setting Custom Domain for Traffic Manager and Azure Websites

Recently I’ve been looking at using traffic manager to front up websites hosted in Azure Websites. I needed to setup a custom domain name instead of using mydomain.trafficmanager.net.

In order to use Traffic Manager with an Azure website the website needs to be setup using a Standard Hosting Plan.

Each website you want to be included in the traffic manager routing will need to be added as an endpoint in the traffic manager portal.

Once you have this setup you will need to add the DNS CNAME record for your domain. This needs to be configured at your Domain provider. You set the CNAME to point to mydomain.trafficmanager.net

In order for the traffic to be routed to your Azure hosted website(s), each website setup as an endpoint in traffic manager will need to have your mapped domain e.g. www.mydomain.com  configured. This is done under settings->Custom Domains and SSL in the new portal and under the configure tab –> manage domains (or click the Manage Domains button)

If you don’t add this then you will see this 404 error page whenever you try to navigate to the site through the traffic manager custom domain name:

image

Azure Websites: Blocking access to the azurewebsites.net url

I’ve been setting up one of our services as the backend service for Azure API management. Part of this process we have mapped DNS to point to the service. As the service is hosted in Azure Websites there are now two urls that exist which can be used to access the service. I wanted to stop a user from accessing the site using the azurewebsites.net url and only access it via the mapped domain. This is easy to achieve and can be configured in the web.config file of the service.

In the <system.webServer> section add the following configuration

<rewrite>
    <rules>
        <rule name="Block traffic to the raw azurewebsites url"  patternSyntax="Wildcard" stopProcessing="true">
          <match url="*" />
          <conditions>
            <add input="{HTTP_HOST}" pattern="*azurewebsites.net*" />
          </conditions>
          <action type="CustomResponse" statusCode="403" statusReason="Forbidden"
          statusDescription="Site is not accessible" />
        </rule>
    </rules>
</rewrite>

Now if I try and access my site through the azurewebsites.net url, I get a 403 error, but accessing through the mapped domain is fine.

Azure Media Services Live Media Streaming General Availability

Yesterday Scott Guthrie announced a number of enhancements to Microsoft Azure. One of the enhancements is the General Availability of Azure Media Services Live Media Streaming. This gives us the ability to stream live events on a service that has already been used to deliver big events such as the 2014 Sochi Winter Olympics and the 2014 FIFA World Cup.

I’ve look at this for a couple of our projects and found it relatively fast and easy to set up a live media event even from my laptop using its built in camera. There’s a good blob post that walks you through the process of setting up the Live Streaming service. I used this post and was quickly streaming both audio and video from my laptop.

The main piece of software that you need to install is a Video/Audio Encode the supports Smooth Streaming or RTMP. I used the WireCast encoder as specified in the post. You can try out the encoder for 2 weeks as long as you don’t mind seeing the Wirecast Logo on your video (which is removed if you buy a license). Media services pricing can be found here

The Media Services team have provided a MPEG-DASH player to help you test your live streams.

It appears that once you have created a stream that is is still accessible on demand after the event has completed.Also there is around a 20s delay when you receive the stream on your player.

Azure Websites Slots and Configuration

One of the conundrums we have with deploying sites to test means that there is often a lot of configuration that is needed on a test site that is different to a live site. There is also the time and risk of deploying a new instance into the production sites once testing has completed.

Azure websites has introduced deployments slots which allows you to have multiple deployments and swap between them in a similar way you could do with the production and staging slots in cloud services. Websites has the added advantage that you can have more than two slots and you can call them whatever you want.

One approach we are looking at to ensure consistency with what is deployed is to configure up a number of slots on the website for a variety of uses e.g. Production, Staging, UAT. The issue with having multiple slots is that there are often sets of configurations that are required to ensure that each slot will work with the correct backend. By default all configuration stored in the appsettings in web.config will move with the slot. Details of the exact configuration settings that move with the deployment can be found here (http://azure.microsoft.com/en-gb/documentation/articles/web-sites-staged-publishing/)

For example, in my web.config file I have the following setting

<appSettings>

<add key="about" value="This is the web.config text" />

</appSettings>

This setting can be overridden in the Azure portal(s) and these by default will follow the deployment and not stay with the slot.

image

So in this example the "about" config will be set to "This is Now the Staging slot" and when the staging slot is swapped to be production, the new production configuration will also be "This is Now the Staging slot"

This is not necessarily what you want on production. Websites has a feature, that is currently unsupported by the management portal(s), which allows specific configuration items to become sticky i.e. they stay with the slot. There is a powershell cmdlet which allows individual appsettings to be marked as sticky and remain with the slot regardless of the deployment that is in the slot and they will also remain in the slot when the slots are swapped.

This can be set for both Appsettings and connection strings by running the following commands

Set-AzureWebsite -Name somesite -SlotStickyAppSettingNames @("about", "another_config_key")

Set-AzureWebsite -Name somesite -SlotStickyConnectionStringNames @("a_connection_string", "some_other_connectionstring")

After running the commands the example above will still have the configuration setting above, but once the deployment is swapped from the staging slot to production the configuration will remain on the staging slot.

This approach should now allow us to deploy to a UAT slot with UAT configuration and allow the customer to test, when they are happy we can move the same deployment that has just been tested to the staging slot with production configuration and be tested in isolation to live to ensure that it works. When you are happy that the staging slot is working this can then be swapped out to production.

For a more detailed introduction to slots and configuration see:

http://azure.microsoft.com/en-gb/documentation/articles/web-sites-staged-publishing/

http://blog.amitapple.com/post/2014/11/azure-websites-slots/#.VG22ik1yaAg