private void bWorker_DoWork(object sender, DoWorkEventArgs e)
{
//Loading YOLO config
Darknet darknet = new Darknet(YoloCfgFile, YoloWeightsFile);
var classNames = File.ReadAllLines(YoloNamesFile);
//Capturing frames
for (; ;)
{
Mat originalBackground = this.background.Clone();
Mat frame;
frame = cam.RetrieveMat();
if (frame.Empty())
{
Cv2.WaitKey();
return;
}
Mat inputBlob = CvDnn.BlobFromImage(frame, 1 / 255d, new OpenCvSharp.Size(544, 544), new Scalar(), true, false);
var results = darknet.Detect(frame, 0.5f);
foreach (var result in results)
{
var p1 = new OpenCvSharp.Point(result.x, result.y);
var p2 = new OpenCvSharp.Point(result.x + result.w, result.y + result.h);
if (CheckContoursAmount(contours, binary, p1, p2))
{
//Formatting labels
var label = $"{classNames[result.obj_id]}";
var textSize = Cv2.GetTextSize(label,
HersheyFonts.HersheyTriplex,
0.5,
1,
out var baseline);
Cv2.Rectangle(originalBackground, p1, p2, Colors[result.obj_id], -1);
Cv2.PutText(
originalBackground,
label,
new OpenCvSharp.Point((int)(result.x + (result.w / 2.0) - (textSize.Width / 2.0)), (int)(result.y + (result.h / 2.0))),
HersheyFonts.HersheyTriplex,
0.5,
Scalar.Black);
}
}
bWorker.ReportProgress(0, originalBackground);
}
}
public static void Main(string[] args)
{
// Initialise darknet with official yolov3 model
using (var darknet = new Darknet("data/yolov3.cfg", "data/yolov3.weights"))
{
// Load all classnames to make the relationship between yolov3 model id and real names of objects
var classNames = File.ReadAllLines("data/coco.names");
// Open dog.jpg and convert it to an OpenCV Mat Object
var image = new Mat("data/dog.jpg");
// Try to detect all objects that model know with a thresh of 30%
var results = darknet.Detect(image, 0.3f);
// Print all results found by yolo
foreach (var result in results)
{
Console.WriteLine($"{classNames[result.ObjId]} - {result.Prob * 100}%");
}
Console.WriteLine("Press any key to quit the example :");
Console.ReadLine();
}
}
public static List <NetResult> DetectObjects(Mat image)
{
return(_yoloDnnPicker.Detect(image));
}