public DnnDetectedObject[][] ClassifyObjects(IEnumerable <Mat> images)
{
if (images is null)
{
throw new ArgumentNullException(nameof(images));
}
var imageList = new List <Mat>(images);
foreach (var image in imageList)
{
if (image?.Empty() == true)
{
throw new ArgumentNullException(nameof(images), "One of the images is not initialized");
}
}
using var blob = CvDnn.BlobFromImages(imageList, 1.0 / 255, new Size(320, 320), crop: false);
nnet.SetInput(blob);
//forward model
nnet.Forward(outs, _outNames);
if (imageList.Count == 1)
{
return(ExtractYolo3SingleResults(outs, imageList[0], threshold, nmsThreshold));
}
else
{
return(ExtractYolo3BatchedResults(outs, images, threshold, nmsThreshold));
}
}
private IList <DnnDetectedObject[]> InternalClassifyObjects(IList <Mat> images, float detectionThreshold)
{
using var blob = CvDnn.BlobFromImages(images, scaleFactor, scaleSize, crop: false);
nnet.SetInput(blob);
//forward model
nnet.Forward(outs, _outNames);
if (images.Count == 1)
{
return(ExtractYoloSingleResults(outs, images[0], detectionThreshold, nmsThreshold));
}
else
{
return(ExtractYoloBatchedResults(outs, images, detectionThreshold, nmsThreshold));
}
}
static void Main()
{
var file = "best-supporting-actress.jpg";
//var file = "oscars-2017.jpg";
var prototxt = "VGG_FACE_deploy.prototxt";
var model = "VGG_FACE.caffemodel";
var labeltxt = "names.txt";
var cascade = "haarcascade_frontalface_default.xml";
var org = Cv2.ImRead(file);
//get face using haarcascades , https://github.com/opencv/opencv/tree/master/data/haarcascades
var faceCascade = new CascadeClassifier();
faceCascade.Load(cascade);
var faces = faceCascade.DetectMultiScale(org, 1.1, 6, HaarDetectionType.DoRoughSearch, new Size(60, 60));
var faceList = new List <Mat>();
foreach (var rect in faces)
{
Cv2.Rectangle(org, rect, Scalar.Red);
faceList.Add(org[rect]);
}
//read all names
var labels = ReadLabels(labeltxt);
var blob = CvDnn.BlobFromImages(faceList, 1, new Size(224, 224));
var net = CvDnn.ReadNetFromCaffe(prototxt, model);
net.SetInput(blob, "data");
Stopwatch sw = new Stopwatch();
sw.Start();
//forward model
var prob = net.Forward("prob");
sw.Stop();
Console.WriteLine($"Runtime:{sw.ElapsedMilliseconds} ms");
for (int n = 0; n < prob.Height; n++)
{
//convert result to list
var probList = new Dictionary <int, float>();
for (int i = 0; i < prob.Width; i++)
{
probList.Add(i, prob.At <float>(n, i));
}
//get top 1
var top1 = probList.OrderByDescending(x => x.Value).First();
var label = $"{labels[top1.Key]}:{top1.Value * 100:0.00}%";
Console.WriteLine(label);
//show if confidence > 50%
if (top1.Value > 0.5)
{
var textsize = Cv2.GetTextSize(label, HersheyFonts.HersheyTriplex, 0.5, 1, out var baseline);
var y = faces[n].TopLeft.Y - textsize.Height - baseline <= 0
? faces[n].BottomRight.Y + textsize.Height + baseline : faces[n].TopLeft.Y - baseline;
//draw result
org.PutText(label, new Point(faces[n].TopLeft.X, y), HersheyFonts.HersheyTriplex, 0.5, Scalar.OrangeRed);
}
}
using (new Window("image", org))
{
Cv2.WaitKey();
}
}