UWP經過機器學習加載ONNX進行表情識別

時間 2020-02-09

標籤 uwp 經過機器學習加載 onnx 進行表情識別简体版

原文原文鏈接

首先咱們先來講說這個ONNX

ONNX是一種針對機器學習所設計的開放式的文件格式，用於存儲訓練好的模型。它使得不一樣的人工智能框架（如Pytorch, MXNet）能夠採用相同格式存儲模型數據並交互。 ONNX的規範及代碼主要由微軟，亞馬遜，Facebook 和 IBM 等公司共同開發，以開放源代碼的方式託管在Github上。目前官方支持加載ONNX模型並進行推理的深度學習框架有： Caffe2, PyTorch, MXNet，ML.NET，TensorRT 和 Microsoft CNTK，而且 TensorFlow 也非官方的支持ONNX。---維基百科git

看了上面的引用你們應該知道了這個實際上是個文件格式用來存儲訓練好的模型，因此我這篇帖子既然是作表情識別那確定是須要有個能識別表情的模型。有了這個模型咱們就能夠根據圖片上的人物，進行表情的識別判斷了。

恰好微軟對機器學習這塊也挺上心的，因此我也趁着疫情比較閒，就來學習學習了。UWP的機器學習的api微軟已經切成正式了，因此你們能夠放心使用。

這就是uwp api文檔開頭就是AI的

我實際上是個小白因此我就直接拿官方的一個demo的簡化版來進行講解了，官方的demo演示以下。

這個app就是經過攝像頭讀取每一幀進行和模型匹配得出結果的

下面是機器學習的微軟的github地址

Emoji8的git地址

我今天要說的就是這個demo的簡化代碼大體運行流程

下面是項目結構圖

我把官方項目簡化了因此只留下了識別後的文本移除了一些依賴的庫

核心代碼在IntelligenceService類裏的Current_SoftwareBitmapFrameCaptured方法裏

private async void Current_SoftwareBitmapFrameCaptured(object sender, SoftwareBitmapEventArgs e)
        {
            Debug.WriteLine("FrameCaptured");
            Debug.WriteLine($"Frame evaluation started {DateTime.Now}" );
            if (e.SoftwareBitmap != null)
            {
                BitmapPixelFormat bpf = e.SoftwareBitmap.BitmapPixelFormat;

                var uncroppedBitmap = SoftwareBitmap.Convert(e.SoftwareBitmap, BitmapPixelFormat.Nv12);
                var faces = await _faceDetector.DetectFacesAsync(uncroppedBitmap);
                if (faces.Count > 0)
                {
                    //crop image to focus on face portion
                    var faceBox = faces[0].FaceBox;
                    VideoFrame inputFrame = VideoFrame.CreateWithSoftwareBitmap(e.SoftwareBitmap);
                    VideoFrame tmp = null;
                    tmp = new VideoFrame(e.SoftwareBitmap.BitmapPixelFormat, (int)(faceBox.Width + faceBox.Width % 2) - 2, (int)(faceBox.Height + faceBox.Height % 2) - 2);
                    await inputFrame.CopyToAsync(tmp, faceBox, null);

                    //crop image to fit model input requirements
                    VideoFrame croppedInputImage = new VideoFrame(BitmapPixelFormat.Gray8, (int)_inputImageDescriptor.Shape[3], (int)_inputImageDescriptor.Shape[2]);
                    var srcBounds = GetCropBounds(
                        tmp.SoftwareBitmap.PixelWidth,
                        tmp.SoftwareBitmap.PixelHeight,
                        croppedInputImage.SoftwareBitmap.PixelWidth,
                        croppedInputImage.SoftwareBitmap.PixelHeight);
                    await tmp.CopyToAsync(croppedInputImage, srcBounds, null);

                    ImageFeatureValue imageTensor = ImageFeatureValue.CreateFromVideoFrame(croppedInputImage);

                    _binding = new LearningModelBinding(_session);

                    TensorFloat outputTensor = TensorFloat.Create(_outputTensorDescriptor.Shape);
                    List<float> _outputVariableList = new List<float>();

                    // Bind inputs + outputs
                    _binding.Bind(_inputImageDescriptor.Name, imageTensor);
                    _binding.Bind(_outputTensorDescriptor.Name, outputTensor);

                    // Evaluate results
                    var results = await _session.EvaluateAsync(_binding, new Guid().ToString());

                    Debug.WriteLine("ResultsEvaluated: " + results.ToString());

                    var outputTensorList = outputTensor.GetAsVectorView();
                    var resultsList = new List<float>(outputTensorList.Count);
                    for (int i = 0; i < outputTensorList.Count; i++)
                    {
                        resultsList.Add(outputTensorList[i]);
                    }

                    var softMaxexOutputs = SoftMax(resultsList);

                    double maxProb = 0;
                    int maxIndex = 0;

                    // Comb through the evaluation results
                    for (int i = 0; i < Constants.POTENTIAL_EMOJI_NAME_LIST.Count(); i++)
                    {
                        // Record the dominant emotion probability & its location
                        if (softMaxexOutputs[i] > maxProb)
                        {
                            maxIndex = i;
                            maxProb = softMaxexOutputs[i];
                        }                      
                    }

                    Debug.WriteLine($"Probability = {maxProb}, Threshold set to = {Constants.CLASSIFICATION_CERTAINTY_THRESHOLD}, Emotion = {Constants.POTENTIAL_EMOJI_NAME_LIST[maxIndex]}");

                    // For evaluations run on the MainPage, update the emoji carousel
                    if (maxProb >= Constants.CLASSIFICATION_CERTAINTY_THRESHOLD)
                    {
                        Debug.WriteLine("first page emoji should start to update");
                        IntelligenceServiceEmotionClassified?.Invoke(this, new ClassifiedEmojiEventArgs(CurrentEmojis._emojis.Emojis[maxIndex]));
                    }

                    // Dispose of resources
                    if (e.SoftwareBitmap != null)
                    {
                        e.SoftwareBitmap.Dispose();
                        e.SoftwareBitmap = null;
                    }
                }
            }
            IntelligenceServiceProcessingCompleted?.Invoke(this, null);
            Debug.WriteLine($"Frame evaluation finished {DateTime.Now}");
        }

        //WinML team function
        private List<float> SoftMax(List<float> inputs)
        {
            List<float> inputsExp = new List<float>();
            float inputsExpSum = 0;
            for (int i = 0; i < inputs.Count; i++)
            {
                var input = inputs[i];
                inputsExp.Add((float)Math.Exp(input));
                inputsExpSum += inputsExp[i];
            }
            inputsExpSum = inputsExpSum == 0 ? 1 : inputsExpSum;
            for (int i = 0; i < inputs.Count; i++)
            {
                inputsExp[i] /= inputsExpSum;
            }
            return inputsExp;
        }

        public static BitmapBounds GetCropBounds(int srcWidth, int srcHeight, int targetWidth, int targetHeight)
        {
            var modelHeight = targetHeight;
            var modelWidth = targetWidth;
            BitmapBounds bounds = new BitmapBounds();
            // we need to recalculate the crop bounds in order to correctly center-crop the input image
            float flRequiredAspectRatio = (float)modelWidth / modelHeight;

            if (flRequiredAspectRatio * srcHeight > (float)srcWidth)
            {
                // clip on the y axis
                bounds.Height = (uint)Math.Min((srcWidth / flRequiredAspectRatio + 0.5f), srcHeight);
                bounds.Width = (uint)srcWidth;
                bounds.X = 0;
                bounds.Y = (uint)(srcHeight - bounds.Height) / 2;
            }
            else // clip on the x axis
            {
                bounds.Width = (uint)Math.Min((flRequiredAspectRatio * srcHeight + 0.5f), srcWidth);
                bounds.Height = (uint)srcHeight;
                bounds.X = (uint)(srcWidth - bounds.Width) / 2; ;
                bounds.Y = 0;
            }
            return bounds;
        }