C# 通过Emgu CV 人脸检测

1、Emgu CV使用opencv人脸检测，C#使用代码（转载于Emgu CV Example）：

using System;

using System.Collections.Generic;

using System.Diagnostics;

using System.Drawing;

using Emgu.CV;

using Emgu.CV.Structure;

#if !IOS

using Emgu.CV.Cuda;

#endif



namespace 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、参数说明，人脸检测耗时影响，精度影响

Rectangle[] facesDetected = face.DetectMultiScale(

                     ugray, //灰度图像，单通道图片

                     1.1,   //scaleFactor 1.1~1.5 越大耗时越低、检测精度越低

                     10,    //minNeighbors 3~15 越高耗时越低

                     new Size(20, 20)); //最小脸部大小？ //最大脸部大小，  越大耗时越低

 

DetectMultiScale支持多线程，

加载人脸识别模型可以全局初始化，减小耗时。

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