public async Task recognize(uint[] rgb_image, int height, int width)
{
var request = new PredictRequest()
{
ModelSpec = new ModelSpec()
{
Name = "default"
}
};
request.Inputs.Add("inputs", TensorBuilder.CreateTensorFromImage(rgb_image, height, width, 3));
// Run the prediction
var predictResponse = await client.PredictAsync(request);
//float num_classes= TensorProtoDecoder.TensorProtoToFloat(predictResponse.Outputs["num_classes"]);
int num_detections = (int)TensorProtoDecoder.TensorProtoToFloat(predictResponse.Outputs["num_detections"]);
float[] classes = TensorProtoDecoder.TensorProtoToFloatArray(predictResponse.Outputs["detection_classes"]);
float[] bboxes = TensorProtoDecoder.TensorProtoToFloatArray(predictResponse.Outputs["detection_boxes"]);
float[] scores = TensorProtoDecoder.TensorProtoToFloatArray(predictResponse.Outputs["detection_scores"]);
for (var i = 0; i < num_detections; i++)
{
float[] bbox = new float[4];
Array.Copy(bboxes, i * 4, bbox, 0, 4);
detectedObjects.Add(new Detection
{
boundingBox = bbox,
objectClass = label_list[(int)classes[i]],
confidence = scores[i]
});
}
readyForNextFrame = true;
}
public Task <PredictResponse> PredictAsync(PredictRequest predictRequest) => _predictionServiceClient.PredictAsync(predictRequest).ResponseAsync;
public async Task <T> ScoreAsync <T>(IScoringRequest request) where T : class
{
var result = await _client.PredictAsync(request.MakePredictRequest());
return(result.Outputs["output_alias"].Convert <T>());
}