public void ResizeInMemoryImages()
{
var mlContext = new MLContext(seed: 1);
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var dataObjects = InMemoryImage.LoadFromTsv(mlContext, dataFile, imageFolder);
var dataView = mlContext.Data.LoadFromEnumerable <InMemoryImage>(dataObjects);
var pipeline = mlContext.Transforms.ResizeImages("ResizedImage", 100, 100, nameof(InMemoryImage.LoadedImage));
// Check that the output is resized, and that it didn't resize the original image object
var model = pipeline.Fit(dataView);
var resizedDV = model.Transform(dataView);
var rowView = resizedDV.Preview().RowView;
var resizedImage = (Bitmap)rowView.First().Values.Last().Value;
Assert.Equal(100, resizedImage.Height);
Assert.NotEqual(100, dataObjects[0].LoadedImage.Height);
// Also check usage of prediction Engine
// And that the references to the original image objects aren't lost
var predEngine = mlContext.Model.CreatePredictionEngine <InMemoryImage, InMemoryImageOutput>(model);
for (int i = 0; i < dataObjects.Count(); i++)
{
var prediction = predEngine.Predict(dataObjects[i]);
Assert.Equal(100, prediction.ResizedImage.Height);
Assert.NotEqual(100, prediction.LoadedImage.Height);
Assert.True(prediction.LoadedImage == dataObjects[i].LoadedImage);
Assert.False(prediction.ResizedImage == dataObjects[i].LoadedImage);
}
// Check that the last in-memory image hasn't been disposed
// By running ResizeImageTransformer (see https://github.com/dotnet/machinelearning/issues/4126)
bool disposed = false;
try
{
int i = dataObjects.Last().LoadedImage.Height;
}
catch
{
disposed = true;
}
Assert.False(disposed, "The last in memory image had been disposed by running ResizeImageTransformer");
}
public static IEnumerable <byte[]> DetectAndParseFaces(Byte[] imagedata, int minSize, int marginInPercent)
{
using (Devmasters.Imaging.InMemoryImage image = new InMemoryImage(imagedata))
{
if (marginInPercent > 99 || marginInPercent < 0)
{
throw new ArgumentOutOfRangeException("marginInPercent", "must be between 0 and 100");
}
List <byte[]> facesImg = new List <byte[]>();
CascadeClassifier _cascadeClassifier;
_cascadeClassifier = new CascadeClassifier(Lib.Init.WebAppDataPath + "haarcascade_frontalface_default.xml");
Image <Bgr, byte> img = new Image <Bgr, byte>(image.Image);
Image <Gray, byte> grayframe = img.Convert <Gray, byte>();
var faces = _cascadeClassifier.DetectMultiScale(grayframe, 1.1, 10, Size.Empty); //the actual face detection happens here
foreach (var face in faces)
{
if (face.Width < minSize || face.Height < minSize)
{
continue;
}
int newX = face.X;
int newY = face.Y;
int changeX = (int)Math.Round(face.Width * ((double)marginInPercent / 100D));
int changeY = (int)Math.Round(face.Height * ((double)marginInPercent / 100D));
newX = newX - changeX / 2; newX = newX < 0 ? 0 : newX;
newY = newY - changeY / 2; newY = newY < 0 ? 0 : newY;
int newWidth = face.Width + changeX;
int newHeight = face.Height + changeY;
if (newX + newWidth > image.Image.Width)
{
newWidth = image.Image.Width - newX;
}
if (newY + newHeight > image.Image.Height)
{
newHeight = image.Image.Height - newY;
}
Rectangle newFaceRect = new Rectangle(newX, newY, newWidth, newHeight);
//img.Draw(newFaceRect, new Bgr(Color.BurlyWood), 3); //the detected face(s) is highlighted here using a box that is drawn around it/them
using (System.IO.MemoryStream ms = new System.IO.MemoryStream())
{
image.SaveAsJPEG(ms, 95);
using (InMemoryImage imi = new InMemoryImage(ms.ToArray()))
{
imi.Crop(newFaceRect);
using (System.IO.MemoryStream lms = new System.IO.MemoryStream())
{
imi.SaveAsJPEG(lms, 95);
facesImg.Add(lms.ToArray());
}
}
}
}
return(facesImg);
}
}
