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 async Task <TinyYoloV2O1ModelOutput> EvaluateAsync(TinyYoloV2O1ModelInput input)
{
TinyYoloV2O1ModelOutput output = new TinyYoloV2O1ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("image", input.image);
binding.Bind("grid", output.grid);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
