我尝试编写面部识别程序代码并需要社区的帮助。 下面发布的代码编译没有错误，但识别器似乎无法正常工作？ 基本上target.jpg包含pic1.jpg中的人物裁剪（内部3人），因此识别器应该能够更容易地检测到它。
下面的代码运行时没有错误，但pic1.jpg中的所有3个人都被装箱，并且所有3个面的GetEigenDistance都是0.只有pic1.jpg中的人（target.jpg中的人）应该装箱
我知道哪里出错了？提前谢谢。
我正在使用emgu cv 2.4和c＃2010 express
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;
}
}
}
}
答案 0 :(得分：2)
这方面还不是我的专长，但如果我可以提供帮助，我会尝试。这就是我正在使用它并且它的工作非常好。
尝试使用GPU完成所有工作，比执行此操作的CPU快得多！
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));
}
}
}
我在课堂上的代码：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...