使用 Windows 机器学习 API 在 Windows 应用中部署 TensorFlow 模型
最后一部分将解释如何创建带有 GUI 的简单 UWP 应用，通过使用 Windows ML 评估 YOLO 模型来流式传输网络摄像头和检测物体。
在 Visual Studio 中创建 UWP 应用
- 打开 Visual Studio，选择
Create a new project.搜索 UWP，然后选择Blank App (Universal Windows)。
- 在下一页上，通过为项目指定名称和位置来配置项目设置。 然后选择应用的目标和最低 OS 版本。 要使用 Windows ML API，则必须使用 X，也可选择 NuGet 包以向下支持 X。如果选择使用 NuGet 包，请按照以下说明操作：[link]。
调用 Windows ML API 来评估模型
步骤 1：使用机器学习代码生成器为 Windows ML API 生成包装器类。
步骤 2：在生成的 .cs 文件中修改生成的代码。 最终文件如下所示：
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using Windows.Media;
using Windows.Storage;
using Windows.Storage.Streams;
using Windows.AI.MachineLearning;
namespace yolodemo
{
public sealed class YoloInput
{
public TensorFloat input_100; // shape(-1,3,416,416)
}
public sealed class YoloOutput
{
public TensorFloat concat_1600; // shape(-1,-1,-1)
}
public sealed class YoloModel
{
private LearningModel model;
private LearningModelSession session;
private LearningModelBinding binding;
public static async Task<YoloModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
YoloModel learningModel = new YoloModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return learningModel;
}
public async Task<YoloOutput> EvaluateAsync(YoloInput input)
{
binding.Bind("input_1:0", input.input_100);
var result = await session.EvaluateAsync(binding, "0");
var output = new YoloOutput();
output.concat_1600 = result.Outputs["concat_16:0"] as TensorFloat;
return output;
}
}
}
评估每个视频帧以检测物体并绘制范围框。
- 将以下库添加到 mainPage.xaml.cs。
using System.Threading.Tasks;
using Windows.Devices.Enumeration;
using Windows.Media;
using Windows.Media.Capture;
using Windows.Storage;
using Windows.UI;
using Windows.UI.Xaml.Media.Imaging;
using Windows.UI.Xaml.Shapes;
using Windows.AI.MachineLearning;
- 在
public sealed partial class MainPage : Page中添加以下变量。
private MediaCapture _media_capture;
private LearningModel _model;
private LearningModelSession _session;
private LearningModelBinding _binding;
private readonly SolidColorBrush _fill_brush = new SolidColorBrush(Colors.Transparent);
private readonly SolidColorBrush _line_brush = new SolidColorBrush(Colors.DarkGreen);
private readonly double _line_thickness = 2.0;
private readonly string[] _labels =
{
"<list of labels>"
};
- 创建表现检测结果格式的结构。
internal struct DetectionResult
{
public string label;
public List<float> bbox;
public double prob;
}
- 创建一个 Comparer 对象，用于比较两个 Box 类型的对象。 此类将用于在检测到的物体周围绘制范围框。
class Comparer : IComparer<DetectionResult>
{
public int Compare(DetectionResult x, DetectionResult y)
{
return y.prob.CompareTo(x.prob);
}
}
- 添加以下方法以初始化设备的网络摄像头流，并开始处理每一帧以检测物体。
private async Task InitCameraAsync()
{
if (_media_capture == null || _media_capture.CameraStreamState == Windows.Media.Devices.CameraStreamState.Shutdown || _media_capture.CameraStreamState == Windows.Media.Devices.CameraStreamState.NotStreaming)
{
if (_media_capture != null)
{
_media_capture.Dispose();
}
MediaCaptureInitializationSettings settings = new MediaCaptureInitializationSettings();
var cameras = await DeviceInformation.FindAllAsync(DeviceClass.VideoCapture);
var camera = cameras.FirstOrDefault();
settings.VideoDeviceId = camera.Id;
_media_capture = new MediaCapture();
await _media_capture.InitializeAsync(settings);
WebCam.Source = _media_capture;
}
if (_media_capture.CameraStreamState == Windows.Media.Devices.CameraStreamState.NotStreaming)
{
await _media_capture.StartPreviewAsync();
WebCam.Visibility = Visibility.Visible;
}
ProcessFrame();
}
- 添加以下方法来处理每一帧。 此方法调用 EvaluateFrame 和 DrawBoxes，我们将在后面的步骤中实现它们。
private async Task ProcessFrame()
{
var frame = new VideoFrame(Windows.Graphics.Imaging.BitmapPixelFormat.Bgra8, (int)WebCam.Width, (int)WebCam.Height);
await _media_capture.GetPreviewFrameAsync(frame);
var results = await EvaluateFrame(frame);
await DrawBoxes(results.ToArray(), frame);
ProcessFrame();
}
- 创建新的 Sigmoid 浮点
private float Sigmoid(float val)
{
var x = (float)Math.Exp(val);
return x / (1.0f + x);
}
- 创建用于正确检测物体的阈值。
private float ComputeIOU(DetectionResult DRa, DetectionResult DRb)
{
float ay1 = DRa.bbox[0];
float ax1 = DRa.bbox[1];
float ay2 = DRa.bbox[2];
float ax2 = DRa.bbox[3];
float by1 = DRb.bbox[0];
float bx1 = DRb.bbox[1];
float by2 = DRb.bbox[2];
float bx2 = DRb.bbox[3];
Debug.Assert(ay1 < ay2);
Debug.Assert(ax1 < ax2);
Debug.Assert(by1 < by2);
Debug.Assert(bx1 < bx2);
// determine the coordinates of the intersection rectangle
float x_left = Math.Max(ax1, bx1);
float y_top = Math.Max(ay1, by1);
float x_right = Math.Min(ax2, bx2);
float y_bottom = Math.Min(ay2, by2);
if (x_right < x_left || y_bottom < y_top)
return 0;
float intersection_area = (x_right - x_left) * (y_bottom - y_top);
float bb1_area = (ax2 - ax1) * (ay2 - ay1);
float bb2_area = (bx2 - bx1) * (by2 - by1);
float iou = intersection_area / (bb1_area + bb2_area - intersection_area);
Debug.Assert(iou >= 0 && iou <= 1);
return iou;
}
- 实现以下列表，以跟踪帧中检测到的当前物体。
private List<DetectionResult> NMS(IReadOnlyList<DetectionResult> detections,
float IOU_threshold = 0.45f,
float score_threshold=0.3f)
{
List<DetectionResult> final_detections = new List<DetectionResult>();
for (int i = 0; i < detections.Count; i++)
{
int j = 0;
for (j = 0; j < final_detections.Count; j++)
{
if (ComputeIOU(final_detections[j], detections[i]) > IOU_threshold)
{
break;
}
}
if (j==final_detections.Count)
{
final_detections.Add(detections[i]);
}
}
return final_detections;
}
- 实现以下方法。
private List<DetectionResult> ParseResult(float[] results)
{
int c_values = 84;
int c_boxes = results.Length / c_values;
float confidence_threshold = 0.5f;
List<DetectionResult> detections = new List<DetectionResult>();
this.OverlayCanvas.Children.Clear();
for (int i_box = 0; i_box < c_boxes; i_box++)
{
float max_prob = 0.0f;
int label_index = -1;
for (int j_confidence = 4; j_confidence < c_values; j_confidence++)
{
int index = i_box * c_values + j_confidence;
if (results[index] > max_prob)
{
max_prob = results[index];
label_index = j_confidence - 4;
}
}
if (max_prob > confidence_threshold)
{
List<float> bbox = new List<float>();
bbox.Add(results[i_box * c_values + 0]);
bbox.Add(results[i_box * c_values + 1]);
bbox.Add(results[i_box * c_values + 2]);
bbox.Add(results[i_box * c_values + 3]);
detections.Add(new DetectionResult()
{
label = _labels[label_index],
bbox = bbox,
prob = max_prob
});
}
}
return detections;
}
- 添加以下方法，以在帧中检测到的物体周围绘制框。
private async Task DrawBoxes(float[] results, VideoFrame frame)
{
List<DetectionResult> detections = ParseResult(results);
Comparer cp = new Comparer();
detections.Sort(cp);
IReadOnlyList<DetectionResult> final_detetions = NMS(detections);
for (int i=0; i < final_detetions.Count; ++i)
{
int top = (int)(final_detetions[i].bbox[0] * WebCam.Height);
int left = (int)(final_detetions[i].bbox[1] * WebCam.Width);
int bottom = (int)(final_detetions[i].bbox[2] * WebCam.Height);
int right = (int)(final_detetions[i].bbox[3] * WebCam.Width);
var brush = new ImageBrush();
var bitmap_source = new SoftwareBitmapSource();
await bitmap_source.SetBitmapAsync(frame.SoftwareBitmap);
brush.ImageSource = bitmap_source;
// brush.Stretch = Stretch.Fill;
this.OverlayCanvas.Background = brush;
var r = new Rectangle();
r.Tag = i;
r.Width = right - left;
r.Height = bottom - top;
r.Fill = this._fill_brush;
r.Stroke = this._line_brush;
r.StrokeThickness = this._line_thickness;
r.Margin = new Thickness(left, top, 0, 0);
this.OverlayCanvas.Children.Add(r);
// Default configuration for border
// Render text label
var border = new Border();
var backgroundColorBrush = new SolidColorBrush(Colors.Black);
var foregroundColorBrush = new SolidColorBrush(Colors.SpringGreen);
var textBlock = new TextBlock();
textBlock.Foreground = foregroundColorBrush;
textBlock.FontSize = 18;
textBlock.Text = final_detetions[i].label;
// Hide
textBlock.Visibility = Visibility.Collapsed;
border.Background = backgroundColorBrush;
border.Child = textBlock;
Canvas.SetLeft(border, final_detetions[i].bbox[1] * 416 + 2);
Canvas.SetTop(border, final_detetions[i].bbox[0] * 416 + 2);
textBlock.Visibility = Visibility.Visible;
// Add to canvas
this.OverlayCanvas.Children.Add(border);
}
}
- 现在我们处理了必要的基础结构，接下来合并评估结果。 此方法根据当前帧评估模型，以检测物体。
private async Task<List<float>> EvaluateFrame(VideoFrame frame)
{
_binding.Clear();
_binding.Bind("input_1:0", frame);
var results = await _session.EvaluateAsync(_binding, "");
Debug.Print("output done\n");
TensorFloat result = results.Outputs["Identity:0"] as TensorFloat;
var shape = result.Shape;
var data = result.GetAsVectorView();
return data.ToList<float>();
}
- 应用需要以某种方式启动。 添加一个方法，它在用户按下
Go按钮时开始网络摄像头流和模型评估。
private void button_go_Click(object sender, RoutedEventArgs e)
{
InitModelAsync();
InitCameraAsync();
}
- 添加一个方法，用于调用 Windows ML API 来评估模型。 首先从存储中加载模型，然后创建会话并将其绑定到内存。
private async Task InitModelAsync()
{
var model_file = await StorageFile.GetFileFromApplicationUriAsync(new Uri("ms-appx:///Assets//Yolo.onnx"));
_model = await LearningModel.LoadFromStorageFileAsync(model_file);
var device = new LearningModelDevice(LearningModelDeviceKind.Cpu);
_session = new LearningModelSession(_model, device);
_binding = new LearningModelBinding(_session);
}
启动应用程序
你已成功创建实时物体检测应用程序！ 选择 Visual Studio 顶部栏上的 Run 按钮来启动应用。 该应用应如下所示。
其他资源
若要详细了解本教程中所述的主题，请访问以下资源:
- Windows ML 工具：了解更多工具，例如 Windows ML 仪表板、WinMLRunner 和 mglen Windows ML 代码生成器。
- ONNX 模型：详细了解 ONNX 格式。
- Windows ML 性能和内存：详细了解如何使用 Windows ML 管理应用性能。
- Windows 机器学习 API 参考：详细了解 Windows ML API 的三个方面。
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