我正在使用EMGUCV进行面部检测和识别,使用的版本是'3.3.0',使用的IP摄像机是CP Plus子弹型摄像机。
我能够进行检测和识别,但是识别后的实时视频非常慢,比实际流慢了10-15秒。不确定代码中是否包含任何内容。
下面是我到目前为止完成的代码。不确定如何加快速度。
Form_Load()
{
_videoCapture = new VideoCapture("rtsp://uname:[email protected]:554");
Application.Idle += new EventHandler(FrameGrabber_Standard);
}
private void FrameGrabber_Standard(object sender, EventArgs e)
{
try
{
Image<Bgr, Byte> currentFrame = _videoCapture.QueryFrame().ToImage<Bgr, Byte>().Resize(640, 480, Inter.Cubic);
if (currentFrame != null)
{
Image<Gray, byte> gray_frame = currentFrame.Convert<Gray, Byte>();
List<Rectangle> listFaces = new List<Rectangle>();
detectfacensobj.DetectFaces(currentFrame, listFaces); //the actual face detection happens here
foreach (Rectangle face in listFaces)
{
faceResult = currentFrame.Convert<Bgr, byte>();
if (Eigenfaces.IsTrained)
{
Image<Gray, byte> grayFaceResult = faceResult.Convert<Gray, byte>();
var result = Eigenfaces.eigenFaceRecognizer.Predict(grayFaceResult.Resize(200, 200, Inter.Cubic));
if (result.Label != -1)
{
CvInvoke.PutText(currentFrame, Eigenfaces.eigenlabels[result.Label], new System.Drawing.Point(face.X - 2, face.Y - 2), FontFace.HersheyComplex, 1.0, new Bgr(Color.Orange).MCvScalar);
}
}
currentFrame.Draw(face, new Bgr(Color.Red), 2);
}
captureImageBox.Image = currentFrame.Bitmap;
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
public bool DetectFaces(IInputArray image, List<Rectangle> listfaces)
{
bool isDetect = false;
try
{
using (UMat grayImage = new UMat())
{
CvInvoke.CvtColor(image, grayImage, ColorConversion.Bgr2Gray);
CvInvoke.EqualizeHist(grayImage, grayImage);
Rectangle[] facesDetected = faceCascadeClass.DetectMultiScale(grayImage, 1.1, 3, new Size(20, 20));
if (facesDetected.Length == 0)
{
isDetect = false;
}
else
{
isDetect = true;
listfaces.AddRange(facesDetected);
}
}
}
catch (Exception ex)
{
System.Windows.Forms.MessageBox.Show("Erro" + ex.Message);
}
return isDetect;
}
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[如果您想更快地进行人脸检测,请查看此视频https://www.youtube.com/watch?v=tnhemcKciNY
使用Xailient人脸检测器,他们可以使用单核在Raspberry Pi 3b +上获得24fps。