This workshop helps you get started with Microsoft Bot Framework and Cognitive Services.
You must perform the following steps to prepare your computer for this lab:
- Install Microsoft Windows 10.
- Install Microsoft Visual Studio 2015.
- Update all VS extensions to their latest versions
- Install the Bot Application Template
- Save in you VS template directory "%USERPROFILE%\Documents\Visual Studio 2015\Templates\ProjectTemplates\Visual C#"
- Install the Bot Framework Emulator
Microsoft Bot Framework is a comprehensive offering to build and deploy high quality bots for your users to enjoy in their favorite conversation experiences. Developers writing bots all face the same problems: bots require basic I/O; they must have language and dialog skills; they must be performant, responsive and scalable; and they must connect to users – ideally in any conversation experience and language the user chooses. Bot Framework provides just what you need to build, connect, manage and publish intelligent bots that interact naturally wherever your users are talking – from text/sms to Skype, Slack, Facebook Messenger, Kik, Office 365 mail and other popular services. This lab will show you how to build a basic bot, connect it to a LUIS model, and get responses back.
Please follow the instructions described in the Word document to get started with Microsoft Bot Framework.
The FaceEmotion sample app provides an example on how to use the Face and Emotion APIs from Cognitive Services.
-
If you haven't already, install Visual Studio 2015.
-
In Visual Studio, on the File menu, click New Project. In the Installed Templates list, select C# as your programming language and choose the Blank Application template. Name the project as you wish and press enter to create it.
-
First let's create the interface. In MainPage.xaml replace the automatically generated Grid with this RelativePanel.
<RelativePanel Background="{ThemeResource ApplicationPageBackgroundThemeBrush}">
<Grid Height="90" x:Name="LogoStackPanel"
Background="#0078D7"
RelativePanel.AlignRightWithPanel="True"
RelativePanel.AlignLeftWithPanel="True">
<StackPanel Orientation="Vertical"
HorizontalAlignment="Left"
Margin="20,4,0,0">
<Image Height="24"
Margin="2,0,0,0"
HorizontalAlignment="Left"
Source="Images/microsoftLogo.png" />
<TextBlock Text="Cognitive Serivces"
FontSize="26"
Foreground="WhiteSmoke"></TextBlock>
<TextBlock Text="Face and Emotion Detection"
FontSize="18"
Foreground="WhiteSmoke"></TextBlock>
</StackPanel>
</Grid>
<Grid RelativePanel.AlignLeftWith="LogoStackPanel"
RelativePanel.Below="LogoStackPanel">
<Image x:Name="FacePhoto"
Width="600"
Height="800"
Margin="20,10,40,44"
Stretch="Uniform"
VerticalAlignment="Top"
HorizontalAlignment="Left" />
<Canvas Name="FacesCanvas"
Margin="20,10,40,44" />
<Button x:Name="BrowseButton"
Margin="20,5,0,10"
Height="32"
VerticalAlignment="Bottom"
Content="Browse..."
Click="BrowseButton_Click" />
</Grid>
</RelativePanel>-
As you can see we have a simple interface with a banner and a button for choosing a picture. For the banner we need the Microsoft logo. Simply create a new folder, call it Images, and copy there the microsoftLogo.png, which you can find in this repo.
-
Let's add now the FaceDetection client libray to our project. In Solution Explorer, right click on References and choose Manage NuGet Packages. Click the Browse tab, search for Microsoft.Project.Oxford.Face, select that package in the list, and click Install.
-
In MainPage.xaml.cs reference the library
using Microsoft.ProjectOxford.Face;- Create a new folder and name it ViewModels. Right click on it and add a New Item. Choose Class from the list and name it MyFaceModel.cs. In the newly created class reference again the Microsoft.ProjectOxford.Face library and add the following Face attributes:
class MyFaceModel
{
public object Age { get; internal set; }
public string FaceId { get; internal set; }
public FaceRectangle FaceRect { get; internal set; }
public object Gender { get; internal set; }
}- Back in the MainWindow class insert the following code:
private readonly IFaceServiceClient faceServiceClient = new FaceServiceClient("Your subscription key"); Note Please replace the text with your subscription key. You can sign up here to get access to it.
- Insert the following code inside the MainWindow class for the Browse button
private async void BrowseButton_Click(object sender, RoutedEventArgs e)
{
//Upload picture
FileOpenPicker openPicker = new FileOpenPicker();
openPicker.ViewMode = PickerViewMode.Thumbnail;
openPicker.SuggestedStartLocation = PickerLocationId.PicturesLibrary;
openPicker.FileTypeFilter.Add(".jpg");
openPicker.FileTypeFilter.Add(".jpeg");
openPicker.FileTypeFilter.Add(".png");
StorageFile file = await openPicker.PickSingleFileAsync();
var bitmapImage = new BitmapImage();
var stream = await file.OpenAsync(FileAccessMode.Read);
bitmapImage.SetSource(stream);
if (file != null)
{
FacePhoto.Source = bitmapImage;
scale = FacePhoto.Width / bitmapImage.PixelWidth;
}
else
{
Debug.WriteLine("Operation cancelled.");
}
//TODO DetectFaces
}- We also need to add a variable for scaling the image so that it is displayed correctly.
private double scale;- Now we can create the method DetectFaces. The most straightforward way to detect faces is by calling the Face - Detect API by uploading the image file directly. When using the client library, this can be done by using an asynchronous method DetectAsync of FaceServiceClient.
private async Task<List<MyFaceModel>> DetectFaces(Stream imageStream)
{
var collection = new List<MyFaceModel>();
var attributes = new List<FaceAttributeType>();
attributes.Add(FaceAttributeType.Gender);
attributes.Add(FaceAttributeType.Age);
try
{
using (var s = imageStream)
{
var faces = await faceServiceClient.DetectAsync(s, true, true, attributes);
foreach (var face in faces)
{
collection.Add(new MyFaceModel()
{
FaceId = face.FaceId.ToString(),
FaceRect = face.FaceRectangle,
Gender = face.FaceAttributes.Gender,
Age = face.FaceAttributes.Age
});
}
}
}
catch (InvalidOperationException e)
{
Debug.WriteLine("Error Message: " + e.Message);
}
return collection;
}- Let's replace now the //TODO comment in the BrowseButton_Click method with the following code snippet. We're basically calling the DetectFaces and the DrawFaces methods after the user chose the picture to analyse.
//Remove any existing rectangles from previous events
FacesCanvas.Children.Clear();
//DetectFaces
var s = await file.OpenAsync(FileAccessMode.Read);
List<MyFaceModel> faces = await DetectFaces(s.AsStream());
var t = await file.OpenAsync(FileAccessMode.Read);
DrawFaces(faces);- We can now create the method DrawFaces to mark the faces in the image
/// <summary>
/// Highlight the faces and display estimated age, gender and emotion.
/// </summary>
/// <param name="faces">The faces detected in the image</param>
/// <param name="emotions">The emotions detected in the image</param>
private void DrawFaces(List<MyFaceModel> faces)
{
//Check if the are any faces in this image
if (faces != null)
{
//For each detected face
for (int i = 0; i < faces.Count; i++)
{
Debug.WriteLine("Age: " + faces[i].Age);
Debug.WriteLine("Gender: " + faces[i].Gender);
Rectangle faceBoundingBox = new Rectangle();
//Set bounding box stroke properties
faceBoundingBox.StrokeThickness = 3;
//Highlight the first face in the set
faceBoundingBox.Stroke = (i == 0 ? new SolidColorBrush(Colors.White) : new SolidColorBrush(Colors.DeepSkyBlue));
if (scale == 0)
{
scale = 1;
}
faceBoundingBox.Margin = new Thickness(faces[i].FaceRect.Left * scale, faces[i].FaceRect.Top * scale, faces[i].FaceRect.Height * scale, faces[i].FaceRect.Width * scale);
faceBoundingBox.Width = faces[i].FaceRect.Width * scale;
faceBoundingBox.Height = faces[i].FaceRect.Height * scale;
TextBlock age = new TextBlock();
var predictedAge = Convert.ToInt32(faces[i].Age);
age.Text = faces[i].Gender + ", " + predictedAge;
age.Margin = new Thickness(faces[i].FaceRect.Left * scale, (faces[i].FaceRect.Top * scale) - 20, faces[i].FaceRect.Height * scale, faces[i].FaceRect.Width * scale);
age.Foreground = (i == 0 ? new SolidColorBrush(Colors.White) : new SolidColorBrush(Colors.DeepSkyBlue));
//Add grid to canvas containing all face UI objects
FacesCanvas.Children.Add(faceBoundingBox);
FacesCanvas.Children.Add(age);
}
}
else
{
Debug.WriteLine("No faces identified");
}
}-
We can run the program and test it. We are able now to browse for a photo and display it in the window. The faces in the picture are automatically detected and marked.
-
Next step is to detect also the emotions of the people in this photo.
-
First of all we need to add the NuGet package for Emotion to the project and add the reference to it in the MainWindow class.
using Microsoft.ProjectOxford.Emotion.Contract;- Insert the following code in the MainWindow class and replace the text with your subscription key.
private readonly EmotionServiceClient emotionServiceClient = new EmotionServiceClient("Your subscription key"); - We add a method DetectEmotions
private async Task<Emotion[]> DetectEmotions(Stream imageStream)
{
try
{
Emotion[] emotionResult = await emotionServiceClient.RecognizeAsync(imageStream);
return emotionResult;
}
catch (Exception exception)
{
Debug.WriteLine(exception.ToString());
return null;
}
}- We only want the emotion with the highest probability, that's why we implement a method GetEmotion which returns the most likely identified emotion.
private Dictionary<float,string> GetEmotions(Emotion emotion)
{
var emotions = new Dictionary<float, string>();
emotions.Add(emotion.Scores.Happiness, "happy");
emotions.Add(emotion.Scores.Neutral, "neutral");
emotions.Add(emotion.Scores.Sadness, "sad");
emotions.Add(emotion.Scores.Surprise, "surprised");
emotions.Add(emotion.Scores.Fear, "scared");
emotions.Add(emotion.Scores.Disgust, "disgusted");
emotions.Add(emotion.Scores.Contempt, "contemptuous");
emotions.Add(emotion.Scores.Anger, "angry");
return emotions;
}- Let's modify the DrawFaces method so it also displays the recognized emotions.
private void DrawFaces(List<MyFaceModel> faces, Emotion[] emotions)
{
//Check if the are any faces in this image
if (faces != null)
{
//For each detected face
for (int i = 0; i < faces.Count; i++)
{
Debug.WriteLine("Age: " + faces[i].Age);
Debug.WriteLine("Gender: " + faces[i].Gender);
Debug.WriteLine("Emotion: " + emotions[i].Scores.Happiness);
Rectangle faceBoundingBox = new Rectangle();
//Set bounding box stroke properties
faceBoundingBox.StrokeThickness = 3;
//Highlight the first face in the set
faceBoundingBox.Stroke = (i == 0 ? new SolidColorBrush(Colors.White) : new SolidColorBrush(Colors.DeepSkyBlue));
if (scale == 0)
{
scale = 1;
}
faceBoundingBox.Margin = new Thickness(faces[i].FaceRect.Left * scale, faces[i].FaceRect.Top * scale, faces[i].FaceRect.Height * scale, faces[i].FaceRect.Width * scale);
faceBoundingBox.Width = faces[i].FaceRect.Width * scale;
faceBoundingBox.Height = faces[i].FaceRect.Height * scale;
TextBlock age = new TextBlock();
var predictedAge = Convert.ToInt32(faces[i].Age);
Dictionary<float, string> detectedEmotion = GetEmotions(emotions[i]);
// Acquire keys and sort them.
var list = detectedEmotion.Keys.ToList();
list.Sort();
// Loop through keys.
//foreach (var key in list)
//{
// Debug.WriteLine("{0}: {1}", key, detectedEmotion[key]);
//}
age.Text = faces[i].Gender + ", " + predictedAge + ", " + detectedEmotion[list.Last()];
age.Margin = new Thickness(faces[i].FaceRect.Left * scale, (faces[i].FaceRect.Top * scale) - 20, faces[i].FaceRect.Height * scale, faces[i].FaceRect.Width * scale);
age.Foreground = (i == 0 ? new SolidColorBrush(Colors.White) : new SolidColorBrush(Colors.DeepSkyBlue));
//Add grid to canvas containing all face UI objects
FacesCanvas.Children.Add(faceBoundingBox);
FacesCanvas.Children.Add(age);
}
}
else
{
Debug.WriteLine("No faces identified");
}
}- Last step, we need to call DetectEmotions in the BrowseButton_Click method.
//Detect emotions
Emotion[] emotions = await DetectEmotions(t.AsStream());
DrawFaces(faces, emotions);