// This method is executed by the ThreadPoolTimer, it performs the evaluation on a copy of the VideoFrame
private async void EvaluateVideoFrame(ThreadPoolTimer timer)
{
// If a lock is being held, or WinML isn't fully initialized, return
if (!semaphore.Wait(0) || !modelBindingComplete)
{
return;
}
try
{
using (evaluatableVideoFrame)
{
// ************ WinML Evaluate Frame ************ //
Debug.WriteLine($"RelativeTime in Seconds: {evaluatableVideoFrame.RelativeTime?.Seconds}");
await model.EvaluateAsync(binding, "TinyYOLO");
// Remove overlapping and low confidence bounding boxes
filteredBoxes = parser.NonMaxSuppress(parser.ParseOutputs(outputArray.ToArray()), 5, .5F);
Debug.WriteLine(filteredBoxes.Count <= 0 ? $"No Valid Bounding Boxes" : $"Valid Bounding Boxes: {filteredBoxes.Count}");
}
}
catch (Exception ex)
{
Debug.WriteLine($"EvaluateFrameException: {ex}");
}
finally
{
semaphore.Release();
}
}
private static void PredictImage(string filename, ImageNetPrediction Predicted)
{
//获取对象信息
Console.WriteLine(".....The objects in the image are detected as below....");
YoloWinMlParser _parser = new YoloWinMlParser();
IList <YoloBoundingBox> boundingBoxes = _parser.ParseOutputs(Predicted.PredictedLabels, 0.4f);
foreach (var box in boundingBoxes)
{
Console.WriteLine(box.Label + " and its Confidence score: " + box.Confidence + "Location: " + box.Rect);
}
//过滤
Console.WriteLine(".....The filtered objects as below....");
var filteredBoxes = _parser.NonMaxSuppress(boundingBoxes, 5, 0.6F);
Bitmap bitmapSource = Image.FromFile(filename) as Bitmap;
Bitmap bitmapOut = new Bitmap(bitmapSource, ImageNetSettings.imageWidth, ImageNetSettings.imageHeight);
Graphics graphics = Graphics.FromImage(bitmapOut);
foreach (var box in filteredBoxes)
{
Console.WriteLine(box.Label + " and its Confidence score: " + box.Confidence + "Location: " + box.Rect);
graphics.DrawRectangle(Pens.Red, box.Rect.X, box.Rect.Y, box.Rect.Width, box.Rect.Height);
graphics.DrawString(box.Label, new Font("宋体", 15), Brushes.Red, box.X + 10, box.Y);
}
bitmapOut.Save(filename + "_predicted.png");
bitmapOut?.Dispose();
bitmapSource?.Dispose();
}
public async Task <IList <YoloBoundingBox> > AnalyzeImage(VideoFrame videoFrame)
{
// This appears to be the right way to handle background tasks.
// We return to the main thread as fast as we can, and wait for the next call to the Update()
// to advance our processing
#if SDK_1809
TinyYoloV2O12Input input = new TinyYoloV2O12Input {
image = ImageFeatureValue.CreateFromVideoFrame(videoFrame)
};
#else
TinyYoloV2O1ModelInput input = new TinyYoloV2O1ModelInput {
image = videoFrame
};
#endif
var dims = GetDimensionsFromVideoFrame(videoFrame);
int width = dims.Item1;
int height = dims.Item2;
var predictions = await model.EvaluateAsync(input).ConfigureAwait(false);
#if SDK_1809
var boxes = parser.ParseOutputs(predictions.grid.GetAsVectorView().ToArray(), width, height, DetectionThreshold);
#else
var boxes = parser.ParseOutputs(predictions.grid.ToArray(), width, height, DetectionThreshold);
#endif
boxes = boxes.Where(b => b.Confidence >= DetectionThreshold).ToList();
// normalize coordinates
boxes = parser.NonMaxSuppress(boxes);
return(boxes.ToList());
}
public void DetectObjectsUsingModel(ImageInputData imageInputData)
{
var probs = model.Predict(imageInputData).PredictedLabels;
IList <YoloBoundingBox> boundingBoxes = _parser.ParseOutputs(probs);
filteredBoxes = _parser.NonMaxSuppress(boundingBoxes, 5, .5F);
}
public void DetectObjectsUsingModel(string imagesFilePath)
{
var imageInputData = new ImageNetData {
ImagePath = imagesFilePath
};
var probs = model.Predict(imageInputData).PredictedLabels;
IList <YoloBoundingBox> boundingBoxes = _parser.ParseOutputs(probs);
filteredBoxes = _parser.NonMaxSuppress(boundingBoxes, 5, .5F);
}
private void DrawOverlays(VideoFrame inputImage)
{
YoloCanvas.Children.Clear();
if (_boxes.Count <= 0)
{
return;
}
var filteredBoxes = _parser.NonMaxSuppress(_boxes, 5, .5F);
foreach (var box in filteredBoxes)
{
DrawYoloBoundingBox(box, YoloCanvas);
}
}