protected void PredictDataUsingModel(string imagesFolder, PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
Console.WriteLine($"Images location: {imagesFolder}");
Console.WriteLine("");
Console.WriteLine("=====Identify the objects in the images=====");
Console.WriteLine("");
var testData = GetImagesData(imagesFolder);
foreach (var sample in testData)
{
var probs = model.Predict(sample).PredictedLabels;
_boundingBoxes = _parser.ParseOutputs(probs);
var filteredBoxes = _parser.FilterBoundingBoxes(_boundingBoxes, 5, .5F);
var outputDirectory = Path.Combine(Directory.GetParent(sample.ImagePath).FullName, "output");
var filename = new FileInfo(sample.ImagePath).Name;
DrawBoundingBox(imagesFolder, outputDirectory, filename, filteredBoxes);
Console.WriteLine(".....The objects in the image {0} are detected as below....", sample.Label);
foreach (var box in filteredBoxes)
{
Console.WriteLine(box.Label + " and its Confidence score: " + box.Confidence);
}
Console.WriteLine("");
}
}
protected void PredictDataUsingModel(string imagesLocation,
string imagesFolder,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
Console.WriteLine($"Tags file location: {imagesLocation}");
Console.WriteLine("");
Console.WriteLine("=====Identify the objects in the images=====");
Console.WriteLine("");
var testData = ImageNetData.ReadFromCsv(imagesLocation, imagesFolder);
foreach (var sample in testData)
{
var probs = model.Predict(sample).PredictedLabels;
IList <YoloBoundingBox> boundingBoxes = _parser.ParseOutputs(probs);
var filteredBoxes = _parser.NonMaxSuppress(boundingBoxes, 5, .5F);
Console.WriteLine(".....The objects in the image {0} are detected as below....", sample.Label);
foreach (var box in filteredBoxes)
{
Console.WriteLine(box.Label + " and its Confidence score: " + box.Confidence);
}
Console.WriteLine("");
}
}
public static void Main()
{
var assetsRelativePath = @"../../../assets";
string assetsPath = GetAbsolutePath(assetsRelativePath);
var modelFilePath = Path.Combine(assetsPath, "Model", "TinyYolo2_model.onnx");
var imagesFolder = Path.Combine(assetsPath, "images");
var outputFolder = Path.Combine(assetsPath, "images", "output");
// Initialize MLContext
MLContext mlContext = new MLContext();
try
{
// Load Data
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
// Create instance of model scorer
var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);
// Use model to score data
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
// Post-process model output
YoloWinMlParser parser = new YoloWinMlParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));
// Draw bounding boxes for detected objects in each of the images
for (var i = 0; i < images.Count(); i++)
{
string imageFileName = images.ElementAt(i).Label;
IList <YoloBoundingBox> 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();
}
