Delete IntelligentAPI_EmotionRecognizer directory
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amrutha95
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using Windows.AI.MachineLearning;
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using Windows.Foundation;
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using Windows.Graphics.Imaging;
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using Windows.Media;
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using Windows.Media.FaceAnalysis;
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using Windows.Storage;
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using Windows.Storage.Streams;
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namespace CommunityToolkit.Labs.Intelligent.EmotionRecognition
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{
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public class DetectedEmotion
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{
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public int emotionIndex;
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public string emotion;
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}
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public class EmotionRecognizer
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{
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private LearningModel _model = null;
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private LearningModelSession _session = null;
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private LearningModelBinding _binding = null;
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private static EmotionRecognizer instance = null;
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private static List<string> labels;
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private async Task InitModelAsync()
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{
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// load model file
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var file = await StorageFile.GetFileFromApplicationUriAsync(new Uri("ms-appx:///IntelligentAPI_EmotionRecognizer/Assets/model_emotion.onnx"));
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//Loads the mdoel from the file
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_model = await LearningModel.LoadFromStorageFileAsync(file);
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//Creating a session that binds the model to the device running the model
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_session = new LearningModelSession(_model, new LearningModelDevice(GetDeviceKind()));
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}
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private void LoadLabels()
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{
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labels = new List<string>()
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{
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"Neutral",
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"Happiness",
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"Surprise",
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"Sadness",
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"Anger",
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"Disgust",
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"Fear",
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"Contempt"
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};
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}
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LearningModelDeviceKind GetDeviceKind()
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{
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return LearningModelDeviceKind.Cpu;
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}
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private async static Task<IList<DetectedFace>> DetectFacesInImageAsync(SoftwareBitmap bitmap)
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{
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FaceDetector faceDetector = await FaceDetector.CreateAsync();
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var convertedBitmap = SoftwareBitmap.Convert(bitmap, BitmapPixelFormat.Gray8);
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return await faceDetector.DetectFacesAsync(convertedBitmap);
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}
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public async static Task<DetectedEmotion> DetectEmotion(SoftwareBitmap bitmap)
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{
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if (instance == null)
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{
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instance = new EmotionRecognizer();
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}
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return await instance.EvaluateFrame(bitmap);
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}
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public async Task<DetectedEmotion> EvaluateFrame(SoftwareBitmap softwareBitmap)
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{
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await InitModelAsync();
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LoadLabels();
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DetectedFace detectedFace = await DetectFace(softwareBitmap);
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if (detectedFace != null)
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{
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return await EvaluateEmotionInFace(detectedFace, softwareBitmap);
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}
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return null;
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}
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public async Task<DetectedEmotion> EvaluateEmotionInFace(DetectedFace detectedFace, SoftwareBitmap softwareBitmap)
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{
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var boundingBox = new Rect(detectedFace.FaceBox.X,
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detectedFace.FaceBox.Y,
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detectedFace.FaceBox.Width,
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detectedFace.FaceBox.Height);
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softwareBitmap = SoftwareBitmap.Convert(softwareBitmap, BitmapPixelFormat.Bgra8);
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var croppedFace = await Crop(softwareBitmap, boundingBox);
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LearningModelEvaluationResult emotionResults = await BindAndEvaluateModelAsync(croppedFace);
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// to get percentages, you'd need to run the output through a softmax function
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// we don't need percentages, we just need max value
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TensorFloat emotionIndexTensor = emotionResults.Outputs["Plus692_Output_0"] as TensorFloat;
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var emotionList = emotionIndexTensor.GetAsVectorView().ToList();
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var emotionIndex = emotionList.IndexOf(emotionList.Max());
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return new DetectedEmotion() { emotionIndex = emotionIndex, emotion = labels[emotionIndex] };
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}
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private static async Task<DetectedFace> DetectFace(SoftwareBitmap softwareBitmap)
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{
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var faces = await DetectFacesInImageAsync(softwareBitmap);
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// if there is a face in the frame, evaluate the emotion
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var detectedFace = faces.FirstOrDefault();
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return detectedFace;
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}
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public static async Task<SoftwareBitmap> Crop(SoftwareBitmap softwareBitmap, Rect bounds)
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{
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VideoFrame vid = VideoFrame.CreateWithSoftwareBitmap(softwareBitmap);
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vid = await Crop(vid, bounds);
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return vid.SoftwareBitmap;
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}
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public static async Task<VideoFrame> Crop(VideoFrame videoFrame, Rect bounds)
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{
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BitmapBounds cropBounds = new BitmapBounds()
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{
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Width = (uint)bounds.Width,
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Height = (uint)bounds.Height,
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X = (uint)bounds.X,
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Y = (uint)bounds.Y
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};
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VideoFrame result = new VideoFrame(BitmapPixelFormat.Bgra8,
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(int)cropBounds.Width,
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(int)cropBounds.Height,
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BitmapAlphaMode.Premultiplied);
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await videoFrame.CopyToAsync(result, cropBounds, null);
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return result;
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}
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private async Task<LearningModelEvaluationResult> BindAndEvaluateModelAsync(SoftwareBitmap croppedFace)
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{
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//Create Learning model binding which binds
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_binding = new LearningModelBinding(_session);
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_binding.Bind("Input3", VideoFrame.CreateWithSoftwareBitmap(croppedFace));
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var emotionResults = await _session.EvaluateAsync(_binding, "id");
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return emotionResults;
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}
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}
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}
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<Project Sdk="MSBuild.Sdk.Extras">
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<PropertyGroup>
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<RootNamespace>IntelligentAPI.EmotionRecognition</RootNamespace>
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</PropertyGroup>
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<PropertyGroup>
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<PackageId>CommunityToolkit.Labs.Intelligent.EmotionRecognition2</PackageId>
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<Description>
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This package performs Emotion Recognition on an input image by using the Emotion FERPlus model.
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</Description>
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<Version>0.0.2</Version>
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</PropertyGroup>
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<ItemGroup>
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<EmbeddedResource Include="Properties\IntelligentAPI_EmotionRecognizer.rd.xml" />
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</ItemGroup>
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<ItemGroup>
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<Content Include="Assets\model_emotion.onnx" Pack="True" PackagePath="lib/uap10.0.17763/Assets"/>
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</ItemGroup>
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<ItemGroup>
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<None Include="ImageClassifier.licenseheader" />
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</ItemGroup>
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<Target Name="CustomBeforeBuild" BeforeTargets="BeforeBuild">
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<Exec Command="powershell.exe –NonInteractive –ExecutionPolicy Unrestricted -command "& {.\Scripts\script.ps1 }"" />
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</Target>
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</Project>
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using System.Reflection;
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using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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// General Information about an assembly is controlled through the following
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// set of attributes. Change these attribute values to modify the information
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// associated with an assembly.
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[assembly: AssemblyTrademark("")]
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[assembly: AssemblyCulture("")]
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// Version information for an assembly consists of the following four values:
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//
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// Major Version
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// Minor Version
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// Build Number
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// Revision
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//
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// You can specify all the values or you can default the Build and Revision Numbers
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// by using the '*' as shown below:
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// [assembly: AssemblyVersion("1.0.*")]
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[assembly: ComVisible(false)]
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<?xml version="1.0" encoding="utf-8"?>
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<!--
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This file contains Runtime Directives, specifications about types your application accesses
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through reflection and other dynamic code patterns. Runtime Directives are used to control the
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.NET Native optimizer and ensure that it does not remove code accessed by your library. If your
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library does not do any reflection, then you generally do not need to edit this file. However,
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if your library reflects over types, especially types passed to it or derived from its types,
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then you should write Runtime Directives.
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The most common use of reflection in libraries is to discover information about types passed
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to the library. Runtime Directives have three ways to express requirements on types passed to
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your library.
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1. Parameter, GenericParameter, TypeParameter, TypeEnumerableParameter
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Use these directives to reflect over types passed as a parameter.
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2. SubTypes
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Use a SubTypes directive to reflect over types derived from another type.
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3. AttributeImplies
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Use an AttributeImplies directive to indicate that your library needs to reflect over
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types or methods decorated with an attribute.
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For more information on writing Runtime Directives for libraries, please visit
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https://go.microsoft.com/fwlink/?LinkID=391919
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-->
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<Directives xmlns="http://schemas.microsoft.com/netfx/2013/01/metadata">
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<Library Name="IntelligentAPI_EmotionRecognizer">
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<!-- add directives for your library here -->
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</Library>
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</Directives>
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$ProgressPreference = 'SilentlyContinue'
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$emotionferplusfile = "./Assets/model_emotion.onnx"
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if (-not(Test-Path -Path $emotionferplusfile -PathType Leaf)) {
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try {
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Invoke-WebRequest -URI "https://github.com/onnx/models/raw/master/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-8.onnx" -OutFile $emotionferplusfile
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Write-Host "The file [$emotionferplusfile] has been created."
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}
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catch {
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throw $_.Exception.Message
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}
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}
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else {
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Write-Host "The file [$emotionferplusfile] exists."
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}
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