/// <summary>
/// Gets bounding boxes/objects from bitmap
/// </summary>
/// <param name="imageInputData"></param>
/// <returns></returns>
private List <BoundingBox> GetObjectsFromModel(ImageInputData imageInputData)
{
var labels = CustomVisionPredictionEngine?.Predict(imageInputData).PredictedLabels ?? TinyYoloPredictionEngine?.Predict(imageInputData).PredictedLabels;
var boundingBoxes = OutputParser.ParseOutputs(labels);
var filteredBoxes = OutputParser.FilterBoundingBoxes(boundingBoxes, 5, 0.5f);
return(filteredBoxes);
}
public static void Main()
{
var assetsRelativePath = @"../../../assets";
var assetsPath = GetAbsolutePath(assetsRelativePath);
var modelFilePath = Path.Combine(assetsPath, "Model", "TinyYolo2_model.onnx");
//var modelFilePath = Path.Combine(assetsPath, "Model", "Yolov3.onnx");
//var modelFilePath = Path.Combine(assetsPath, "Model", "model.onnx");
var imagesFolder = Path.Combine(assetsPath, "people");
var outputFolder = Path.Combine(imagesFolder, "output");
// Initialize MLContext
var mlContext = new MLContext();
try
{
// Load Data
var images = ImageNetData.ReadFromFile(imagesFolder);
var imageDataView = mlContext.Data.LoadFromEnumerable(images);
// Create instance of model scorer
var modelScorer = new Scorer(imagesFolder, modelFilePath, mlContext);
// Use model to score data
var probabilities = modelScorer.Score(imageDataView);
// Post-process model output
var parser = new OutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F))
.ToList()
;
// Draw bounding boxes for detected objects in each of the images
for (var i = 0; i < images.Count(); i++)
{
var imageFileName = images.ElementAt(i).Label;
var detectedObjects = boundingBoxes.ElementAt(i);
DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);
LogDetectedObjects(imageFileName, detectedObjects);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
Console.WriteLine("========= End of Process..Hit any Key ========");
Console.ReadLine();
}
public IEnumerator Detect(Color32[] picture, System.Action <IList <BoundingBox> > callback)
{
// Anonymous block thar uses a tenspr param, this tensor isfrom the TransformInput method
using (var tensor = TransformInput(picture, ImageNetSettings.imageWidth, ImageNetSettings.imageHeight)) {
var inputs = new Dictionary <string, Tensor> ();
inputs.Add(ModelSettings.ModelInput, tensor);
yield return(StartCoroutine(worker.StartManualSchedule(inputs)));
var output = worker.PeekOutput(ModelSettings.ModelOutput);
var results = outputParser.ParseOutputs(output, MINIMUM_CONFIDENCE);
var boxes = outputParser.FilterBoundingBoxes(results, 5, MINIMUM_CONFIDENCE);
callback(boxes);
}
}
