public List <string> GenerateAnalysis(string guidName)
{
List <string> stringsToReturn = new List <string>();
var assetsRelativePath = @"../../../../DemoMLNet.Task.ObjectDetection.Console/assets";
string assetsPath = ProgramService.GetAbsolutePath(assetsRelativePath);
var modelFilePath = Path.Combine(assetsPath, "Model", "TinyYolo2_model.onnx");
var imagesFolder = Path.Combine(assetsPath, "images");
var outputFolder = Path.Combine(assetsPath, "images", "output");
MLContext mlContext = new MLContext();
try
{
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);
// Use model to score data
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));
for (var i = 0; i < images.Count(); i++)
{
if (images.ElementAt(i).Label == guidName + ".jpg")
{
string imageFileName = images.ElementAt(i).Label;
IList <YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);
ProgramService.DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);
stringsToReturn = ProgramService.LogDetectedObjects(imageFileName, detectedObjects);
}
//Console.WriteLine("========= End of Process..Hit any Key ========");
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
return(stringsToReturn);
}
