public static void Main()
{
var assetsRelativePath = @"assets";
string assetsPath = GetAbsolutePath(assetsRelativePath);
var tagsTsv = Path.Combine(assetsPath, "images", "tags.tsv");
var modelFilePath = Path.Combine(assetsPath, "resnet152v2", "resnet152v2.onnx");
var labelsTxt = Path.Combine(assetsPath, "resnet152v2", "synset_text.txt");
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(tagsTsv, imagesFolder, modelFilePath, labelsTxt);
// Use model to score data
modelScorer.Score();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
Console.WriteLine("========= End of Process..Hit any Key ========");
Console.ReadLine();
}
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 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
}
}
