C# 通過Emgu CV 人臉檢測

1、Emgu CV使用opencv人臉檢測，C#使用代碼（轉載于Emgu CV Example）：

using System;using System.Collections.Generic;using System.Diagnostics;using System.Drawing;using Emgu.CV;using Emgu.CV.Structure;#if !IOSusing Emgu.CV.Cuda;#endifnamespace FaceDetection{   public static class DetectFace   {      public static void Detect(        Mat image, String faceFileName, String eyeFileName,         List<Rectangle> faces, List<Rectangle> eyes,         bool tryUseCuda, bool tryUSEOpenCL,        out long detectionTime)      {         Stopwatch watch;                  #if !IOS         if (tryUseCuda && CudaInvoke.HasCuda)         {            using (CudaCascadeClassifier face = new CudaCascadeClassifier(faceFileName))            using (CudaCascadeClassifier eye = new CudaCascadeClassifier(eyeFileName))            {               watch = Stopwatch.StartNew();               using (CudaImage<Bgr, Byte> gpuImage = new CudaImage<Bgr, byte>(image))               using (CudaImage<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 (CudaImage<Gray, Byte> faceImg = gpuGray.GetSubRect(f))                     {                        //For some reason a clone is required.                        //Might be a bug of CudaCascadeClassifier in opencv                        using (CudaImage<Gray, Byte> clone = faceImg.Clone(null))                        {                           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();            }         }         else         #endif         {            //Many opencl functions require opencl compatible gpu devices.             //As of opencv 3.0-alpha, opencv will crash if opencl is enable and only opencv compatible cpu device is PResented            //So we need to call CvInvoke.HaveOpenCLCompatibleGpuDevice instead of CvInvoke.HaveOpenCL (which also returns true on a system that only have cpu opencl devices).            CvInvoke.UseOpenCL = tryUseOpenCL && CvInvoke.HaveOpenCLCompatibleGpuDevice;            //Read the HaarCascade objects            using (CascadeClassifier face = new CascadeClassifier(faceFileName))            using (CascadeClassifier eye = new CascadeClassifier(eyeFileName))            {               watch = Stopwatch.StartNew();               using (UMat ugray = new UMat())               {                  CvInvoke.CvtColor(image, ugray, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);                  //normalizes brightness and increases contrast of the image                  CvInvoke.EqualizeHist(ugray, ugray);                  //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(                     ugray,                     1.1,                     10,                     new Size(20, 20));                                       faces.AddRange(facesDetected);                  foreach (Rectangle f in facesDetected)                  {                     //Get the region of interest on the faces                     using (UMat faceRegion = new UMat(ugray, f))                     {                        Rectangle[] eyesDetected = eye.DetectMultiScale(                           faceRegion,                           1.1,                           10,                           new Size(20, 20));                                                foreach (Rectangle e in eyesDetected)                        {                           Rectangle eyeRect = e;                           eyeRect.Offset(f.X, f.Y);                           eyes.Add(eyeRect);                        }                     }                  }               }               watch.Stop();            }         }         detectionTime = watch.ElapsedMilliseconds;      }   }}

2、參數說明，人臉檢測耗時影響，精度影響

Rectangle[] facesDetected = face.DetectMultiScale(                     ugray, //灰度圖像，單通道圖片                     1.1,   //scaleFactor 1.1~1.5 越大耗時越低、檢測精度越低                     10,    //minNeighbors 3~15 越高耗時越低                     new Size(20, 20)); //最小臉部大??？ //最大臉部大小，  越大耗時越低

DetectMultiScale支持多線程，加載人臉識別模型可以全局初始化，減小耗時。

using (CudaCascadeClassifier face = new CudaCascadeClassifier(faceFileName)) using (CudaCascadeClassifier eye = new CudaCascadeClassifier(eyeFileName))