一個小小工程師的心情抒發天地

終於完成了影像辨識的第一部分了，

在這邊先放幾個比較重要的Function就好，

接下來我會在github那邊弄一個帳號，

以後如果有source，就丟往那裡囉，省得每次都要找半天。

 

        #region 處理影像的動作

        #endregion
        private void ProcessFrameAsync(object sender, EventArgs e)
        {
            try
            {
                using (Mat objMat = new Mat())
                {
                    // Get image from OpenCV , and Analysis face is exist.
                    objVideoCapture.Retrieve(objMat);
                    OpenCVResult result = this.CaptureFace(objMat);
                    // 在影像上進行框線的繪圖
                    for (int f = 0; f < result.faces.Count; f++)
                        CvInvoke.Rectangle(objMat, result.faces[f], new Bgr(Color.Red).MCvScalar, 2);

                    for (int y = 0; y < result.eyes.Count; y++)
                        CvInvoke.Rectangle(objMat, result.eyes[y], new Bgr(Color.Yellow).MCvScalar, 1);

                    // 將圖片放到PictureBox之中
                    if (picRender.Image != null)
                        picRender.Image.Dispose();

                    IntPtr gdibitmap = objMat.Bitmap.GetHbitmap();
                    picRender.Image = Image.FromHbitmap(gdibitmap);
                    DeleteObject(gdibitmap);

                    Thread.Sleep(5);
                }
            }
            catch (Exception E)
            {
                MessageBox.Show(E.Message);
            }
        }

 

 #region 透過OpenCV 進行人臉判斷，是否存在
        #endregion
        private OpenCVResult CaptureFace(Mat objMat)
        {
            long detectionTime;
            List faces = new List();
            List eyes = new List();
            DetectFace.Detect(
                 objMat, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
                 faces, eyes,
                 out detectionTime);

            // 重新計算比例 
            // decimal : 28-29 個有效數字
            decimal diWidth = decimal.Parse(picRender.Width.ToString()) / decimal.Parse(objMat.Bitmap.Width.ToString());
            decimal diHeight = decimal.Parse(picRender.Height.ToString()) / decimal.Parse(objMat.Bitmap.Height.ToString());

            List objDraw = new List();

            for (int i = 0; i < faces.Count; i++)
            {
                objDraw.Add(new Rectangle(
                    (int)(faces[i].X * diWidth),
                    (int)(faces[i].Y * diHeight),
                    (int)(faces[i].Width * diWidth),
                    (int)(faces[i].Height * diHeight)
                    ));
            }
            OpenCVResult result = new OpenCVResult()
            {
                eyes = eyes,
                faces = faces,
            };
            return result;
        }

 

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

using System.Diagnostics;
using System.Drawing;
using Emgu.CV;
using Emgu.CV.Structure;
#if !(__IOS__ || NETFX_CORE)
using Emgu.CV.Cuda;
#endif




namespace FaceDetect_EmguCV
{
    public static class DetectFace
    {
        public static void Detect
        (
            IInputArray         image               , 
            String              faceFileName        ,    
            String              eyeFileName         ,
            List     faces               , 
            List     eyes                ,
            out long            detectionTime

        ) 
        {
            // timer control
            Stopwatch watch;
            using (InputArray iaImage = image.GetInputArray())
            {
#if !(__IOS__ || NETFX_CORE)

                if (iaImage.Kind == InputArray.Type.CudaGpuMat && CudaInvoke.HasCuda)
                {
                    using (CudaCascadeClassifier face = new CudaCascadeClassifier(faceFileName))
                    using (CudaCascadeClassifier eye = new CudaCascadeClassifier(eyeFileName))
                    {
                        face.ScaleFactor = 1.1;
                        face.MinNeighbors = 10;
                        face.MinObjectSize = Size.Empty;

                        eye.ScaleFactor = 1.1;
                        eye.MinNeighbors = 10;
                        eye.MinObjectSize = Size.Empty;

                        watch = Stopwatch.StartNew();
                        using (CudaImage gpuImage = new CudaImage(image))
                        using (CudaImage gpuGray = gpuImage.Convert())
                        using (GpuMat region = new GpuMat())
                        {
                            face.DetectMultiScale(gpuGray, region);
                            Rectangle[] faceRegion = face.Convert(region);
                            faces.AddRange(faceRegion);
                            foreach (Rectangle f in faceRegion)
                            {
                                using (CudaImage faceImg = gpuGray.GetSubRect(f))
                                {
                                    // For some reason a clone is required.
                                    // Might be a bug of CudaCasscadeClassifier in opencv
                                    using (CudaImage clone = faceImg.Clone(null))
                                    using (GpuMat eyeRegionMat = new GpuMat())
                                    {
                                        eye.DetectMultiScale(clone, eyeRegionMat);
                                        Rectangle[] eyeRegion = eye.Convert(eyeRegionMat);
                                        foreach (Rectangle e in eyeRegion)
                                        {
                                            Rectangle eyeRect = e;
                                            eyeRect.Offset(f.X, f.Y);
                                            eyes.Add(eyeRect);
                                        }
                                    }
                                }
                            }
                        }
                        watch.Stop();
                    }
                }
                else
#endif
                {
                    //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;
            }
        }
    }
}