Emgu CV EigenObjectRecognizer不能正常工作

Question

我試過編碼人臉識別程序，需要社區的幫助。 下面發佈的代碼編譯沒有錯誤，但識別器似乎不工作？ 基本上，target.jpg包含一個pic1.jpg（3人以內）的人物，因此識別器應該能夠更容易地檢測到它。

下面的代碼運行時沒有錯誤，但是pic1.jpg中的所有3人都是裝箱的，並且所有3個面的GetEigenDistances都是0.只有在pic1.jpg（person in target.jpg）中的人員應該是盒裝。

任何想法，我哪裏出錯了？提前致謝。

我使用emgu CV 2.4用C＃2010速成

using System; 
using System.Collections.Generic; 
using System.ComponentModel; 
using System.Data; 
using System.Drawing; 
using System.Linq; 
using System.Text; 
using System.Windows.Forms; 
using Emgu.CV; 
using Emgu.Util; 
using Emgu.CV.Structure; 
using Emgu.CV.UI; 
using Emgu.CV.CvEnum; 


namespace FaceReco 
{ 
    public partial class Form1 : Form 
    { 
     private HaarCascade haar; 
     List<Image<Gray, byte>> trainingImages = new List<Image<Gray, byte>>(); 
     Image<Gray, byte> TrainedFace, UnknownFace = null; 
     MCvFont font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5d, 0.5d); 


     public Form1() 
     { 
      InitializeComponent(); 
     } 

     private void Form1_Load(object sender, EventArgs e) 
     { 
      // adjust path to find your XML file 
      haar = new HaarCascade("haarcascade_frontalface_alt_tree.xml"); 

      //Read an target image 
      Image TargetImg = Image.FromFile(Environment.CurrentDirectory + "\\target\\target.jpg"); 
      Image<Bgr, byte> TargetFrame = new Image<Bgr, byte>(new Bitmap(TargetImg)); 

      //FACE DETECTION FOR TARGET FACE 
      if (TargetImg != null) // confirm that image is valid 
      { 
       //convert the image to gray scale 
       Image<Gray, byte> grayframe = TargetFrame.Convert<Gray, byte>(); 
       var faces = grayframe.DetectHaarCascade(haar, 1.4, 4, 
             HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, 
             new Size(25, 25))[0]; 
       foreach (var face in faces) 
       { 
        //add into training array 
        TrainedFace = TargetFrame.Copy(face.rect).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC); 
        trainingImages.Add(TrainedFace); 
        break; 
       } 
       TargetImageBox.Image = TrainedFace; 
      } 


      //Read an unknown image 
      Image UnknownImg = Image.FromFile(Environment.CurrentDirectory + "\\img\\pic1.jpg"); 
      Image<Bgr, byte> UnknownFrame = new Image<Bgr, byte>(new Bitmap(UnknownImg)); 

      //FACE DETECTION PROCESS 
      if (UnknownFrame != null) // confirm that image is valid 
      { 
       //convert the image to gray scale 
       Image<Gray, byte> grayframe = UnknownFrame.Convert<Gray, byte>(); 

       //Detect faces from the gray-scale image and store into an array of type 'var',i.e 'MCvAvgComp[]' 
       var faces = grayframe.DetectHaarCascade(haar, 1.4, 4, 
             HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, 
             new Size(25, 25))[0]; 

       //draw a green rectangle on each detected face in image 
       foreach (var face in faces) 
       { 
        UnknownFace = UnknownFrame.Copy(face.rect).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC); 
        MCvTermCriteria termCrit = new MCvTermCriteria(16, 0.001); 
        //Eigen face recognizer 
        EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), ref termCrit); 

        // if recognise face, draw green box 
        if (recognizer.Recognize(UnknownFace) != null) 
        { 
         UnknownFrame.Draw(face.rect, new Bgr(Color.Green), 3); 
        } 

        float f = recognizer.GetEigenDistances(UnknownFace)[0]; 
        // display threshold 
        UnknownFrame.Draw(f.ToString("R"), ref font, new Point(face.rect.X - 3, face.rect.Y - 3), new Bgr(Color.Red)); 

       } 

       //Display the image 
       CamImageBox.Image = UnknownFrame; 
      } 
     } 
    } 
} 

Answer 1

這個區域還沒有我的專業，但如果我能幫助我會盡力。這是我正在使用和它的工作相當好。

試着用GPU來做所有的工作，它比CPU做得更快！

List<Rectangle> faces = new List<Rectangle>(); 

List<Rectangle> eyes = new List<Rectangle>(); 

RightCameraImage = RightCameraImageCapture.QueryFrame().Resize(480, 360, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC); //Read the files as an 8-bit Bgr image 

//Emgu.CV.GPU.GpuInvoke.HasCuda 
if (GpuInvoke.HasCuda) 
{ 
Video.DetectFace.UsingGPU(RightCameraImage, Main.FaceGpuCascadeClassifier, Main.EyeGpuCascadeClassifier, faces, eyes, out detectionTime); 
} 
else 
{ 
Video.DetectFace.UsingCPU(RightCameraImage, Main.FaceCascadeClassifier, Main.EyeCascadeClassifier, faces, eyes, out detectionTime); 
} 

string PersonsName = string.Empty; 

Image<Gray, byte> GreyScaleFaceImage; 

foreach (Rectangle face in faces) 
{ 
RightCameraImage.Draw(face, new Bgr(Color.Red), 2); 

GreyScaleFaceImage = RightCameraImage.Copy(face).Convert<Gray, byte>().Resize(200, 200, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC); 

if (KnownFacesList.Count > 0) 
{ 
// MCvTermCriteria for face recognition... 
MCvTermCriteria mCvTermCriteria = new MCvTermCriteria(KnownFacesList.Count, 0.001); 

// Recognize Known Faces with Eigen Object Recognizer... 
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(KnownFacesList.ToArray(), KnownNamesList.ToArray(), eigenDistanceThreashhold, ref mCvTermCriteria); 

EigenObjectRecognizer.RecognitionResult recognitionResult = recognizer.Recognize(GreyScaleFaceImage); 

if (recognitionResult != null) 
{ 
// Set the Persons Name... 
PersonsName = recognitionResult.Label; 

// Draw the label for each face detected and recognized... 
RightCameraImage.Draw(PersonsName, ref mCvFont, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen)); 
} 
else 
{ 
// Draw the label for each face NOT Detected... 
RightCameraImage.Draw(FaceUnknown, ref mCvFont, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen)); 
} 
} 
} 

我的代碼在類：Video.DetectFace：

using System; 
using Emgu.CV; 
using Emgu.CV.GPU; 
using System.Drawing; 
using Emgu.CV.Structure; 
using System.Diagnostics; 
using System.Collections.Generic; 

namespace Video 
{ 
//----------------------------------------------------------------------------------- 
// Copyright (C) 2004-2012 by EMGU. All rights reserved. Modified by Chris Sykes. 
//----------------------------------------------------------------------------------- 

public static class DetectFace 
{ 
// Use me like this: 
/* 
//Emgu.CV.GPU.GpuInvoke.HasCuda 
if (GpuInvoke.HasCuda) 
{ 
DetectUsingGPU(...); 
} 
else 
{ 
DetectUsingCPU(...); 
} 
*/ 

private static Stopwatch watch; 

public static void UsingGPU(Image<Bgr, Byte> image, GpuCascadeClassifier face, GpuCascadeClassifier eye, List<Rectangle> faces, List<Rectangle> eyes, out long detectionTime) 
{ 
watch = Stopwatch.StartNew(); 

using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image)) 
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>()) 
{ 
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty); 
faces.AddRange(faceRegion); 
foreach (Rectangle f in faceRegion) 
{ 
using (GpuImage<Gray, Byte> faceImg = gpuGray.GetSubRect(f)) 
{ 
//For some reason a clone is required. 
//Might be a bug of GpuCascadeClassifier in opencv 
using (GpuImage<Gray, Byte> clone = faceImg.Clone()) 
{ 
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty); 

foreach (Rectangle e in eyeRegion) 
{ 
Rectangle eyeRect = e; 
eyeRect.Offset(f.X, f.Y); 
eyes.Add(eyeRect); 
} 
} 
} 
} 
} 
watch.Stop(); 
detectionTime = watch.ElapsedMilliseconds; 
} 
public static void UsingCPU(Image<Bgr, Byte> image, CascadeClassifier face, CascadeClassifier eye, List<Rectangle> faces, List<Rectangle> eyes, out long detectionTime) 
{ 
watch = Stopwatch.StartNew(); 
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale 
{ 
//normalizes brightness and increases contrast of the image 
gray._EqualizeHist(); 

//Detect the faces from the gray scale image and store the locations as rectangle 
//The first dimensional is the channel 
//The second dimension is the index of the rectangle in the specific channel 
Rectangle[] facesDetected = face.DetectMultiScale(gray, 1.1, 10, new Size(20, 20), Size.Empty); 
faces.AddRange(facesDetected); 

foreach (Rectangle f in facesDetected) 
{ 
//Set the region of interest on the faces 
gray.ROI = f; 
Rectangle[] eyesDetected = eye.DetectMultiScale(gray, 1.1, 10, new Size(20, 20), Size.Empty); 
gray.ROI = Rectangle.Empty; 

foreach (Rectangle e in eyesDetected) 
{ 
Rectangle eyeRect = e; 
eyeRect.Offset(f.X, f.Y); 
eyes.Add(eyeRect); 
} 
} 
} 
watch.Stop(); 
detectionTime = watch.ElapsedMilliseconds; 
} 
} // END of CLASS... 
}// END of NAMESPACE...