static void Main(string[] args)
{
var mL = new MLContext();
try
{
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mL.Data.LoadFromEnumerable(images);
var modelScorer = new OnnxModelScorer(imagesFolder, modelPath, mL);
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes = probabilities
.Select(x => parser.ParseOutputs(x))
.Select(x => parser.FilterBoundingBoxes(x, 5, .5f));
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());
}
}
public static List <(string ImageFilename, List <(string Label, float Score)> TagList)> GetObjectList(string imagesFolder, string modelFilePath)
{
List <(string ImageFilename, List <(string Label, float Score)> TagList)> result = new List <(string ImageFilename, List <(string Label, float Score)> TagList)>();
MLContext mlContext = new MLContext();
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++)
{
string imageFileName = images.ElementAt(i).Label;
IList <YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);
result.Add((imageFileName, ExtractDetectedObjects(detectedObjects)));
}
return(result);
}
public static IList <BoundingBox> ObtainBoundingBoxes(string base64Image)
{
var opencvImage = Image.ConvertFromBase64ToMat(base64Image);
var tensor = new DenseTensor <float>(new int[] { channels, opencvImage.Height, opencvImage.Width });
using (var mat = new MatOfByte3(opencvImage))
{
var indexer = mat.GetIndexer();
for (int y = 0; y < opencvImage.Height; y++)
{
for (int x = 0; x < opencvImage.Width; x++)
{
Vec3b color = indexer[y, x];
tensor[0, y, x] = (float)color.Item2;
tensor[1, y, x] = (float)color.Item1;
tensor[2, y, x] = (float)color.Item0;
}
}
}
var transform = tensor.Reshape(new ReadOnlySpan <int>(new[] { channels *opencvImage.Height *opencvImage.Width }));
var yoloParser = new YoloOutputParser();
var yoloModel = YoloModel.Instance;
var results = yoloModel.Evaluate(new[] { transform });
return(yoloParser.ParseOutputs(results.First().ToArray()));
}
public IDictionary <string, IList <YoloBoundingBox> > DetectObjectsFromImages(List <string> imageFilePaths,
string uploadFolderPath, string outputFolderPath)
{
MLContext mlContext = new MLContext();
IDictionary <string, IList <YoloBoundingBox> > detectedObjectsDict = new Dictionary <string, IList <YoloBoundingBox> >();
try
{
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFiles(imageFilePaths);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
var modelScorer = new OnnxModelScorer(this.modelFilePath, mlContext);
YoloOutputParser parser = new YoloOutputParser();
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView); // Use model to score data
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .7F));
for (var i = 0; i < images.Count(); i++)
{
string imageFileName = images.ElementAt(i).Label;
string imageFilePath = images.ElementAt(i).ImagePath;
IList <YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);
detectedObjectsDict.Add(imageFilePath, detectedObjects);
DrawBoundingBox(imageFilePath, outputFolderPath, imageFileName, detectedObjects);
}
return(detectedObjectsDict);
}
catch
{
throw;
}
}
public JsonResult CheckImage()
{
string isPersonOnImage = "no";
var file = Request.Files[0];
var currentPath = System.Web.Hosting.HostingEnvironment.ApplicationPhysicalPath;
var modelFilePath = currentPath + @"\Content\Model\TinyYolo2_model.onnx";
//var modelFilePath = @"C:\Users\Ragnus\Desktop\120819\praca\Thesis\Thesis\Content\Model\TinyYolo2_model.onnx";
var sourceImage = Image.FromStream(file.InputStream);
var fileExt = Path.GetExtension(file.FileName);
var filename = Guid.NewGuid() + fileExt; // Unikalny identyfikator + rozszerzenie
var outputFolder = currentPath + @"\Content\ImageToReturn\";
//var outputFolder = @"C:\Users\Ragnus\Desktop\120819\praca\Thesis\Thesis\Content\ImageToReturn\";
var path = Path.Combine(Server.MapPath(AppConfig.ImagesUserFolder), filename);
sourceImage.Save(path);
MLContext mlContext = new MLContext();
IEnumerable <ImageNetData> images = ImageNetData.ReadFrom1File(path);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
var modelScorer = new OnnxModelScorer(path, modelFilePath, mlContext);
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));
// 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);
if (detectedObjects.Count > 0 && detectedObjects[0].Label == "person")
{
isPersonOnImage = "yes";
}
DrawBoundingBox(path, outputFolder, imageFileName, detectedObjects);
LogDetectedObjects(imageFileName, detectedObjects);
}
var img = Image.FromFile(outputFolder + filename);
System.GC.Collect();
System.GC.WaitForPendingFinalizers();
System.IO.File.Delete(path);
img.Dispose();
System.IO.File.Delete(outputFolder + filename);
return(Json(new { isPerson = isPersonOnImage }
, JsonRequestBehavior.AllowGet));
}
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)
.ToList(); // remove!!!
// Post-process model output
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F))
.ToList(); // remove!!!
// 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();
}
public string Detect()
{
List <DetectionResult> result = new List <DetectionResult>();
// 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
YoloOutputParser parser = new YoloOutputParser();
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);
DetectionResult detectionResult = new DetectionResult(imageFileName);
foreach (var detObject in detectedObjects)
{
detectionResult.DetectedObjects.Add(new DetectedObjects(detObject.Label, detObject.Confidence));
}
LogDetectedObjects(imageFileName, detectedObjects);
result.Add(detectionResult);
}
return(JsonConvert.SerializeObject(result));
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
return(null);
}
}
static void Main(string[] args)
{
Console.WriteLine("Hello World!");
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");
//Create the Machine Learning context
MLContext mlContext = new MLContext();
try
{
//Load the data into an IDataView
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
//Score the model
var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);
// Use model to score data
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
//Post-processing - understanding the results
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++)
{
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();
}
public List <List <Predict> > Pipe(string imgPath, IWebHostEnvironment _env)
{
string assetsPath = GetAbsolutePath("assets");
var modelFilePath = Path.Combine(assetsPath, "Model", "Model.onnx");
var absolutePath = Path.Combine(_env.WebRootPath, "assets", imgPath);
var imagesFolder = Path.Combine(assetsPath, "images");
var outputFolder = Path.Combine(assetsPath, "images", "output");
var predicted = new List <List <Predict> >();
MLContext mlContext = new MLContext();
try
{
ImageNetData[] img = new ImageNetData[] { new ImageNetData {
ImagePath = absolutePath, Label = imgPath
} };
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(img);
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++)
{
string imageFileName = images.ElementAt(i).Label;
IList <YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);
//DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);
predicted.Add(LogDetectedObjects(imageFileName, detectedObjects));
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
Console.WriteLine("========= End of Process..Hit any Key ========");
//Console.ReadLine();
return(predicted);
}
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);
}
static void Main(string[] args)
{
try
{
var relativePath = @"../../../assets";
var filehelper = new FileHelper(relativePath);
var images = ImageNetData.ReadFromFile(filehelper.ImagesFolder);
var mlContext = new MLContext();
// Загруэаем данные
var imageDataView = mlContext.Data.LoadFromEnumerable(images);
// Создаем экземпляр OnnxModelScorer и используем его для оценки загруженных данных
var modelScorer = new OnnxModelScorer(filehelper.ImagesFolder, filehelper.ModelFilePath, mlContext);
var probabilities = modelScorer.Score(imageDataView);
// Создаем экземпляр YoloOutputParser и используем его для обработки выходных данных модели
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++)
{
string imageFileName = images.ElementAt(i).Label;
var detectedObjects = boundingBoxes.ElementAt(i);
var imageWithLabels = ImageHelper.DrawBoundingBox(filehelper.ImagesFolder, filehelper.OutputFolder, imageFileName, detectedObjects);
if (!Directory.Exists(filehelper.OutputFolder))
{
Directory.CreateDirectory(filehelper.OutputFolder);
}
imageWithLabels.Save(Path.Combine(filehelper.OutputFolder, imageFileName));
LogDetectedObjects(imageFileName, detectedObjects);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
Console.WriteLine("========= End of Process..Hit any Key ========");
Console.ReadLine();
}
public byte[] Classify(string imagePath, string imageName)
{
var modelFilePath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "..\\..\\..", "assets", "Model", "TinyYolo2_model.onnx");
MLContext mlContext = new MLContext();
try
{
// Load Data
ImageNetData data = new ImageNetData()
{
ImagePath = Path.Combine(imagePath, imageName),
Label = imageName
};
IEnumerable <ImageNetData> image = new List <ImageNetData>()
{
data
};
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(image);
// Create instance of model scorer
var modelScorer = new OnnxModelScorer(modelFilePath, mlContext);
// Use model to score data
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
// Post-process model output
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F)).First();
// Draw bounding boxes for detected objects in each of the images
IList <YoloBoundingBox> detectedObjects = boundingBoxes;
return(DrawBoundingBox(imagePath, imageName, detectedObjects));
}
catch (Exception)
{
return(null); //TODO
}
}
internal static void Start()
{
//var assetsRelativePath = @"../../../assets";
//string assetsPath = GetAbsolutePath(assetsRelativePath);
var modelFilePath = Path.Combine(projectDirectory, "Model", "TinyYolo2_model.onnx");
var imagesFolder = Path.Combine(projectDirectory, "images");
var outputFolder = Path.Combine(projectDirectory, "images", "output");
MLContext mlContext = new MLContext();
try
{
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
//Затем создайте экземпляр OnnxModelScorer и используйте его для оценки загруженных данных.
var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);
// Use model to score data
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
/*
* Теперь пора выполнить шаг постобработки. Создайте экземпляр YoloOutputParser и используйте его для обработки выходных данных модели.
*/
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++)
{
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());
}
}
public void TestYoloOutputParserCorrectlyNamesCat()
{
var cat_jpg = File.ReadAllBytes("assets/001-cat.jpg");
var result = scorer.Score(new[] { cat_jpg });
Assert.NotNull(result);
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes =
result
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));
Assert.NotNull(boundingBoxes);
var first_setOfBoxes = boundingBoxes.First();
Assert.NotNull(first_setOfBoxes);
Assert.AreEqual(first_setOfBoxes[0].Label, "cat");
}
public byte[] Predict(string fileName)
{
var imageDataView = MlContext.Data.LoadFromEnumerable(new List <ImageDataInputItem> {
new ImageDataInputItem {
ImagePath = fileName
}
});
var scoredData = _model.Transform(imageDataView);
var probabilities = scoredData.GetColumn <float[]>(TinyYoloModelSettings.ModelOutput);
var parser = new YoloOutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));
return(DrawBoundingBox(fileName, boundingBoxes.FirstOrDefault()));
}
static void Main(string[] args)
{
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");
MLContext mlContext = new MLContext();
try
{
IEnumerable <ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);
YoloOutputParser parser = new YoloOutputParser();
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));
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("Does it work? ... Hit Any Key ======");
Console.ReadLine();
}
public CameraFrameProcessingResult Process(byte[] image)
{
if (image == null || image.Length == 0)
{
return(null);
}
try
{
// This is a list of float arrays, one per image.
// We are set up to only process 1 image at a time.
IEnumerable <float[]> probabilities = scorer.Score(new[] { image });
// This is the minimum confidence we consider returning for any bounding box.
var thresholdConfidence = 0.5f;
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxSets =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 10, thresholdConfidence));
FramesProcessed++;
return(new CameraFrameProcessingResult()
{
Processor = this, // the processor that generated this result.
Image = image, // the original image for reference.
Boxes = boundingBoxSets.First() // bounding boxes for the first (only) image.
});
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
return(null);
}
}
private void btnStart_Click(object sender, EventArgs e)
{
// Frame image buffer
Mat image = new Mat();
_isRunning = true;
btnStart.Enabled = false;
var mlContext = new MLContext();
// Create instance of model scorer
var modelScorer = new OnnxModelScorer(modelFilePath, mlContext);
// Load model only once
var model = modelScorer.LoadModel();
while (_isRunning)
{
_capture.Read(image); // read frame from webcam
if (image.Empty())
{
break;
}
// Store frame as in-memory source for ML.NET
ImageNetData[] inMemoryCollection = new ImageNetData[]
{
new ImageNetData
{
InputImage = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image),
Label = "",
}
};
var imageDataView = mlContext.Data.LoadFromEnumerable <ImageNetData>(inMemoryCollection);
// Make another copy of the frame. We will use it to draw bounding boxes on it
_mySharpImage = (Image)OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image);
// Use model to score data
IEnumerable <float[]> probabilities = modelScorer.Score(model, imageDataView);
// Post-process model output
YoloOutputParser parser = new YoloOutputParser();
var boundingBoxes =
probabilities
.Select(probability => parser.ParseOutputs(probability))
.Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));
// Since we only have a single frame, it is OK to have i = 0. Otherwise we would need
// to iterate through images.
var i = 0;
IList <YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);
DrawBoundingBox(ref _mySharpImage, detectedObjects);
pictureBox1.SizeMode = PictureBoxSizeMode.StretchImage;
pictureBox1.Image = _mySharpImage;
Cv2.WaitKey(1);
_mySharpImage.Dispose();
inMemoryCollection[0].InputImage.Dispose();
}
btnStart.Enabled = true;
}
public RecognitionStage(ObjectRecognizer objectRecognizer, YoloOutputParser outputParser)
{
_objectRecognizer = objectRecognizer ?? throw new ArgumentNullException(nameof(objectRecognizer));
_outputParser = outputParser ?? throw new ArgumentNullException(nameof(outputParser));
}
