public Detector()
{
var model = OnnxModel.FromFile("Models\\facial-lmk-detector.onnx");
var modelOptions = (model as IServiceProvider).GetService(typeof(Microsoft.ML.OnnxRuntime.SessionOptions)) as Microsoft.ML.OnnxRuntime.SessionOptions;
modelOptions.GraphOptimizationLevel = Microsoft.ML.OnnxRuntime.GraphOptimizationLevel.ORT_DISABLE_ALL;
_Session = model.CreateSession();
}
public Anime2SketchFilter()
{
var model = OnnxModel.FromFile("Models\\anime2sketch_512x512.onnx");
var modelOptions = (model as IServiceProvider).GetService(typeof(Microsoft.ML.OnnxRuntime.SessionOptions)) as Microsoft.ML.OnnxRuntime.SessionOptions;
modelOptions.GraphOptimizationLevel = Microsoft.ML.OnnxRuntime.GraphOptimizationLevel.ORT_DISABLE_ALL;
_Session = model.CreateSession();
}
public void TestResnet50(string imagePath, string expectedResult)
{
// https://onnxruntime.ai/docs/tutorials/resnet50_csharp.html
var srcImage = MemoryBitmap.Load(imagePath);
var model = OnnxModel.FromFile("Models\\resnet50-v2-7.onnx");
// image normalization
// https://github.com/onnx/models/tree/master/vision/classification/resnet#preprocessing
var modelXform = MultiplyAdd
.CreateMul(0.229f, 0.224f, 0.225f)
.ConcatAdd(0.485f, 0.456f, 0.406f)
// .GetTransposedZYXW()
.GetInverse();
var imagePreprocessor = new ImageProcessor <Pixel.RGB96F>(modelXform);
using (var session = model.CreateSession())
{
var workingTensor = session
.GetInputTensor <float>(0)
.AsSpanTensor4()
.GetSubTensor(0)
.SetImage(srcImage, imagePreprocessor);
// run
session.Inference();
// get results
var result = session
.GetOutputTensor <float>(0)
.AsSpanTensor2()
.GetSubTensor(0);
result.ApplySoftMax();
var scores = result.ToArray();
var pairs = Labels.resnet50_v2_7_Labels
.Zip(scores, (Label, Score) => (Label, Score))
.OrderByDescending(item => item.Score)
.ToList();
foreach (var pair in pairs)
{
TestContext.WriteLine($"{pair.Label} = {pair.Score}");
}
Assert.AreEqual(expectedResult, pairs[0].Label);
}
}
public void TestYuNet(string imagePath)
{
// https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet
// https://github.com/ShiqiYu/libfacedetection
// https://github.com/Kazuhito00/YuNet-ONNX-TFLite-Sample/blob/main/yunet/yunet_onnx.py
var srcImage = MemoryBitmap.Load(imagePath, GDICodec.Default);
var pmodel = OnnxModel.FromFile("Models\\face_detection_yunet_2021sep.onnx");
var imagePreprocessor = new ImageProcessor <Pixel.RGB96F>();
using (var session = pmodel.CreateSession())
{
var workingTensor = session.UseInputTensor <float>(0, 1, 3, srcImage.Height, srcImage.Width)
.VerifyName(n => n == "input")
.AsSpanTensor4()
.GetSubTensor(0)
.SetImage(srcImage, imagePreprocessor);
const int COUNT = 15040;
var loc = session.UseOutputTensor <float>(0, COUNT, 14).VerifyName(n => n == "loc");
var conf = session.UseOutputTensor <float>(1, COUNT, 2).VerifyName(n => n == "conf");
var iou = session.UseOutputTensor <float>(2, COUNT, 1).VerifyName(n => n == "iou");
// run
session.Inference();
// https://github.com/ShiqiYu/libfacedetection/blob/master/src/facedetectcnn.cpp#L731
var r_loc = loc.AsSpanTensor2().UpCast <YuNetEntry>().Span.ToArray();
var r_conf = conf.AsSpanTensor2().UpCast <System.Numerics.Vector2>().Span.ToArray();
var r_iou = iou.AsSpanTensor2().UpCast <float>().Span;
// softmax1vector2class(mbox_conf);
// clamp1vector(mbox_iou);
// SpanTensor.ApplySoftMax(r_conf);
var sorted = r_conf
.Select(item => item.Y)
.Zip(r_loc)
.OrderByDescending(item => item.First)
.ToArray();
var prior_variance = new [] { 0.1f, 0.1f, 0.2f, 0.2f };
}
}
public void TestMcnnFace(string imagePath)
{
// https://github.com/linxiaohui/mtcnn-opencv/tree/main/mtcnn_cv2
var srcImage = MemoryBitmap.Load(imagePath, GDICodec.Default);
var pmodel = OnnxModel.FromFile("Models\\MCNN\\pnet.onnx");
var imagePreprocessor = new ImageProcessor <Pixel.RGB96F>();
using (var session = pmodel.CreateSession())
{
var workingTensor = session.UseInputTensor <float>(0, 1, srcImage.Height, srcImage.Width, 3)
.VerifyName(n => n == "input_1")
.AsSpanTensor4()
.GetSubTensor(0)
.SetImage(srcImage, imagePreprocessor);
var conv2d = session.UseOutputTensor <float>(0, 1, 251, 251, 4).VerifyName(n => n == "conv2d_4");
var softmax = session.UseOutputTensor <float>(1, 1, 251, 251, 2).VerifyName(n => n == "softmax");
// run
session.Inference();
// get results:
var score = softmax
.AsSpanTensor4()
.GetSubTensor(0)
.UpCast <System.Numerics.Vector2>()
.Span
.ToArray();
var cv2d = conv2d
.AsSpanTensor4()
.GetSubTensor(0)
.UpCast <System.Numerics.Vector4>()
.Span
.ToArray();
var pairs = score
.Select(item => item.Y)
.Zip(cv2d.Select(xyxy => xyxy * new System.Numerics.Vector4(srcImage.Height, srcImage.Width, srcImage.Height, srcImage.Width)))
.OrderByDescending(item => item.First)
.ToArray();
}
}
public void TestArcFace(string imagePath)
{
// https://github.com/onnx/models/tree/master/vision/body_analysis/arcface
// https://github.com/openvinotoolkit/open_model_zoo/tree/2021.4.1/models/public/mtcnn
var srcImage = MemoryBitmap.Load(imagePath, GDICodec.Default);
var srcImagePreprocessor = new ImageProcessor <Pixel.RGB96F>();
var model = OnnxModel.FromFile("Models\\arcfaceresnet100-8.onnx");
using (var session = model.CreateSession())
{
var workingTensor = session.GetInputTensor <float>(0)
.AsSpanTensor4()
.GetSubTensor(0)
.SetImage(srcImage, srcImagePreprocessor);
// run
session.Inference();
// get results
var result = session
.GetOutputTensor <float>(0)
.AsSpanTensor2()
.GetSubTensor(0)
.ToArray();
var resultv4 = System.Runtime.InteropServices.MemoryMarshal.Cast <float, System.Numerics.Vector4>(result);
for (int i = 0; i < resultv4.Length; ++i)
{
TestContext.WriteLine(resultv4[i]);
}
}
}