C # Face Detection Through Emgu CV,

C # Face Detection Through Emgu CV,

1. Emgu CV uses opencv face detection, and C # uses code (reproduced in 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. parameter description: Impact of face detection time consumption and precision

Rectangle [] facesDetected = face. DetectMultiScale (ugray, // grayscale image, single-channel image 1.1, // scaleFactor 1.1 ~ The larger the value is, the lower the time consumption and the lower the detection precision is. 10, // minNeighbors 3 ~ 15 The higher the time, the lower the new Size (20, 20); // minimum face Size? // The maximum face size. The larger the face size, the lower the time consumption.

 

DetectMultiScale supports multithreading,

The face recognition model can be loaded to initialize globally to reduce time consumption.

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