/// <summary>
/// 人脸识别
/// </summary>
/// <param name="emguImage"></param>
/// <returns></returns>
public faceDetectedObj faceRecognize(Mat emguImage)
{
faceDetectedObj fdo = GetFaceRectangle(emguImage);
Image <Gray, byte> tempImg = fdo.originalImg.ToImage <Gray, byte>();
#region 给识别出的所有人脸画矩形框
using (Graphics g = Graphics.FromImage(fdo.originalImg.Bitmap))
{
foreach (Rectangle face in fdo.facesRectangle)
{
g.DrawRectangle(new Pen(Color.Red, 2), face); //给识别出的人脸画矩形框
Image <Gray, byte> GrayFace = tempImg.Copy(face).Resize(100, 100, Emgu.CV.CvEnum.Inter.Cubic);
GrayFace._EqualizeHist(); //得到均衡化人脸的灰度图像
#region 得到匹配姓名，并画出
Emgu.CV.Face.FaceRecognizer.PredictionResult pr = tfr.faceRecognizer.Predict(GrayFace);
string recogniseName = tfr.trainedFileList.trainedFileName[pr.Label].ToString();
name = tfr.trainedFileList.trainedFileName[pr.Label].ToString();
Font font = new Font("宋体", 16, GraphicsUnit.Pixel);
SolidBrush fontLine = new SolidBrush(Color.Yellow);
float xPos = face.X + (face.Width / 2 - (name.Length * 14) / 2);
float yPos = face.Y - 21;
g.DrawString(name, font, fontLine, xPos, yPos);
#endregion
fdo.names.Add(name);
}
}
#endregion
return(fdo);
}
static void RunRecognizer()
{
RandomizeSets(); //re - randomize the set when training again
//train the set
Console.WriteLine("Training set");
GlobalVars.fishFace.Train(GlobalVars.fishFaceImages_training, GlobalVars.emotionForClassifier_training);
int count = 0;
int amountCorrect = 0;
foreach (Image <Gray, Byte> img in GlobalVars.fishFaceImages_prediction) //predict all images in the prediction set
{
Emgu.CV.Face.FaceRecognizer.PredictionResult prediction = GlobalVars.fishFace.Predict(img);
// Console.WriteLine("Predicted emotion is: " + (Emotions)prediction.Label + " With a " + prediction.Distance + " Distance. Actual emotion is " + (Emotions)GlobalVars.emotionForClassifier_prediction[count]);
if (GlobalVars.emotionForClassifier_prediction[count] == prediction.Label) //check to see if the predicted emotion is correct
{
amountCorrect++;
}
else //write the wrong image to disk for inspection, can comment out if this feature is not required
{
CvInvoke.Imwrite(GlobalVars.path + @"\Cohn-Kanade Images\IncorrectImages\" + (Emotions)prediction.Label + (Emotions)GlobalVars.emotionForClassifier_prediction[count] + count + ".png", img);
}
count++;
}
Console.WriteLine("Got " + amountCorrect + " out of " + count + " images"); //inform user the amount that was correctly predicted
}
/// <summary>
/// 人脸识别
/// </summary>
/// <param name="emguImage"></param>
/// <returns></returns>
public faceDetectedObj faceRecognize(Mat emguImage)
{
faceDetectedObj fdo = GetFaceRectangle(emguImage);
Image <Gray, byte> tempImg = fdo.originalImg.ToImage <Gray, byte>();
#region 给识别出的所有人脸画矩形框
using (Graphics g = Graphics.FromImage(fdo.originalImg.Bitmap))
{
foreach (Rectangle face in fdo.facesRectangle)
{
Image <Gray, byte> GrayFace = tempImg.Copy(face).Resize(100, 100, Emgu.CV.CvEnum.Inter.Cubic);
GrayFace._EqualizeHist();//得到均衡化人脸的灰度图像
#region 得到匹配姓名
Emgu.CV.Face.FaceRecognizer.PredictionResult pr = tfr.faceRecognizer.Predict(GrayFace);
string name = "";
//Distance越小表示 越可能是同一个人
if (pr.Distance < 4000)
{
name = tfr.trainedFileList.trainedFileName[pr.Label].ToString();
}
#endregion
fdo.names.Add(name);
}
}
#endregion
return(fdo);
}
/// <summary>
/// 人脸识别
/// </summary>
/// <param name="emguImage"></param>
/// <returns></returns>
public FaceDetectedObj faceRecognize(Mat emguImage)
{
try
{
FaceDetectedObj fdo = GetFaceRectangle(emguImage);
Image <Gray, byte> tempImg = fdo.originalImg.ToImage <Gray, byte>();
#region 给识别出的所有人脸画矩形框
using (Graphics g = Graphics.FromImage(fdo.originalImg.Bitmap))
{
foreach (Rectangle face in fdo.facesRectangle)
{
g.DrawRectangle(new Pen(Color.Red, 2), face);//给识别出的人脸画矩形框
Image <Gray, byte> grayFace = tempImg.Copy(face).Resize(100, 100, Emgu.CV.CvEnum.Inter.Cubic);
grayFace._EqualizeHist(); //得到均衡化人脸的灰度图像
#region 得到匹配姓名，并画出
Emgu.CV.Face.FaceRecognizer.PredictionResult pr = _trainedFaceRecognizer.faceRecognizer.Predict(grayFace);
int label = pr.Label;
if (label < 0)
{
continue;
}
string recogniseName = _trainedFaceRecognizer.trainedFileList.trainedFileName[pr.Label].ToString();
string name = _trainedFaceRecognizer.trainedFileList.trainedFileName[pr.Label].ToString();
Font font = new Font("宋体", 16, GraphicsUnit.Pixel);
SolidBrush fontLine = new SolidBrush(Color.Yellow);
float xPos = face.X + (face.Width / 2 - (name.Length * 14) / 2);
float yPos = face.Y - 21;
g.DrawString(name, font, fontLine, xPos, yPos);
if (_nameCahce != name)
{
_nameCahce = name;
SpeechSynthesizer synth = new SpeechSynthesizer();
synth.SetOutputToDefaultAudioDevice();
synth.SpeakAsync("欢迎您" + name);
}
#endregion
fdo.names.Add(name);
}
}
#endregion
return(fdo);
}
catch (Exception ex)
{
throw ex;
}
}
/// <summary>
/// 人脸识别
/// </summary>
/// <param name="emguImage"></param>
/// <returns></returns>
public faceDetectedObj faceRecognize(Mat emguImage)
{
faceDetectedObj fdo = GetFaceRectangle(emguImage);
Image <Gray, byte> tempImg = fdo.originalImg.ToImage <Gray, byte>();
#region 给识别出的所有人脸画矩形框
using (Graphics g = Graphics.FromImage(fdo.originalImg.Bitmap))
{
foreach (Rectangle face in fdo.facesRectangle)
{
g.DrawRectangle(new Pen(Color.Red, 2), face);//给识别出的人脸画矩形框
Image <Gray, byte> GrayFace = tempImg.Copy(face).Resize(100, 100, Emgu.CV.CvEnum.Inter.Cubic);
GrayFace._EqualizeHist();//得到均衡化人脸的灰度图像
//Mat pMat = new Mat();
//float[] ranges = new float[] { 0.0f, 256.0f };
//using (Emgu.CV.Util.VectorOfMat vm = new Emgu.CV.Util.VectorOfMat())
//{
// vm.Push(GrayFace);
// CvInvoke.CalcHist(vm, new int[] { 0 }, null, pMat, new int[] { 1 }, ranges, false);
//}
#region 得到匹配姓名，并画出
Emgu.CV.Face.FaceRecognizer.PredictionResult pr = tfr.faceRecognizer.Predict(GrayFace);
System.Diagnostics.Debug.WriteLine(pr.Distance + " " + pr.Label);
string recogniseName = tfr.trainedFileList.trainedFileName[pr.Label].ToString();
string name = tfr.trainedFileList.trainedFileName[pr.Label].ToString();
Font font = new Font("宋体", 16, GraphicsUnit.Pixel);
SolidBrush fontLine = new SolidBrush(Color.Yellow);
float xPos = face.X + (face.Width / 2 - (name.Length * 14) / 2);
float yPos = face.Y - 21;
g.DrawString(name, font, fontLine, xPos, yPos);
#endregion
fdo.names.Add(name);
}
}
GC.Collect();
#endregion
return(fdo);
}
/// <summary>
/// 人脸识别
/// </summary>
/// <param name="emguImage"></param>
/// <returns></returns>
public faceDetectedObj faceRecognize(Mat emguImage)
{
//tfr = new TrainedFaceRecognizer();
//tfr.trainedFileList = SetSampleFacesList();
//tfr.faceRecognizer = new Emgu.CV.Face.LBPHFaceRecognizer(1, 8, 8, 8, 100);
faceDetectedObj fdo = GetFaceRectangle(emguImage);
Image <Gray, byte> tempImg = fdo.originalImg.ToImage <Gray, byte>();
#region 给识别出的所有人脸画矩形框
using (Graphics g = Graphics.FromImage(fdo.originalImg.Bitmap))
{
foreach (Rectangle face in fdo.facesRectangle)
{
g.DrawRectangle(new Pen(Color.Red, 2), face);
Image <Gray, byte> GrayFace = tempImg.Copy(face).Resize(100, 100, Emgu.CV.CvEnum.Inter.Cubic);
GrayFace._EqualizeHist();//得到均衡化人脸的灰度图像
#region 得到匹配姓名
//tfr.faceRecognizer.Load("test.xml");
Emgu.CV.Face.FaceRecognizer.PredictionResult pr = tfr.faceRecognizer.Predict(GrayFace);
string name = "";
//Distance越小表示 越可能是同一个人
//if (pr.Distance < 4000)
//{
name = tfr.trainedFileList.trainedFileName[pr.Label].ToString();
// }
Font font = new Font("宋体", 16, GraphicsUnit.Pixel);
SolidBrush fontLine = new SolidBrush(Color.Yellow);
float xPos = face.X + (face.Width / 2 - (name.Length * 14) / 2);
float yPos = face.Y - 21;
g.DrawString(name, font, fontLine, xPos, yPos);
#endregion
fdo.names.Add(name);
}
}
GC.Collect();
#endregion
return(fdo);
}
