1
2 //Multiple face detection and recognition in real time
3 //Using EmguCV cross platform .Net wrapper to the Intel OpenCV image processing library for C#.Net
4 //Writed by Sergio Andrés Guitérrez Rojas
5 //"Serg3ant" for the delveloper comunity
6 // Sergiogut1805@hotmail.com
7 //Regards from Bucaramanga-Colombia ;)
8
9 using System;
10 using System.Collections.Generic;
11 using System.Drawing;
12 using System.Windows.Forms;
13 using Emgu.CV;
14 using Emgu.CV.Structure;
15 using Emgu.CV.CvEnum;
16 using System.IO;
17 using System.Diagnostics;
18
19 namespace MultiFaceRec
20 {
21 public partial class FrmPrincipal : Form
22 {
23 //Declararation of all variables, vectors and haarcascades
24 Image<Bgr, Byte> currentFrame;
25 Capture grabber;
26 HaarCascade face;
27 HaarCascade eye;
28 MCvFont font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5d, 0.5d);
29 Image<Gray, byte> result, TrainedFace = null;
30 Image<Gray, byte> gray = null;
31 List<Image<Gray, byte>> trainingImages = new List<Image<Gray, byte>>();
32 List<string> labels= new List<string>();
33 List<string> NamePersons = new List<string>();
34 int ContTrain, NumLabels, t;
35 string name, names = null;
36
37
38 public FrmPrincipal()
39 {
40 InitializeComponent();
41 //Load haarcascades for face detection
42 face = new HaarCascade("haarcascade_frontalface_default.xml");
43 //eye = new HaarCascade("haarcascade_eye.xml");
44 try
45 {
46 //Load of previus trainned faces and labels for each image
47 string Labelsinfo = File.ReadAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt");
48 string[] Labels = Labelsinfo.Split('%');
49 NumLabels = Convert.ToInt16(Labels[0]);
50 ContTrain = NumLabels;
51 string LoadFaces;
52
53 for (int tf = 1; tf < NumLabels+1; tf++)
54 {
55 LoadFaces = "face" + tf + ".bmp";
56 trainingImages.Add(new Image<Gray, byte>(Application.StartupPath + "/TrainedFaces/" + LoadFaces));
57 labels.Add(Labels[tf]);
58 }
59
60 }
61 catch(Exception e)
62 {
63 //MessageBox.Show(e.ToString());
64 MessageBox.Show("Nothing in binary database, please add at least a face(Simply train the prototype with the Add Face Button).", "Triained faces load", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
65 }
66
67 }
68
69
70 private void button1_Click(object sender, EventArgs e)
71 {
72 //Initialize the capture device
73 grabber = new Capture();
74 grabber.QueryFrame();
75 //Initialize the FrameGraber event
76 Application.Idle += new EventHandler(FrameGrabber);
77 button1.Enabled = false;
78 }
79
80
81 private void button2_Click(object sender, System.EventArgs e)
82 {
83 try
84 {
85 //Trained face counter
86 ContTrain = ContTrain + 1;
87
88 //Get a gray frame from capture device
89 gray = grabber.QueryGrayFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
90
91 //Face Detector
92 MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
93 face,
94 1.2,
95 10,
96 Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
97 new Size(20, 20));
98
99 //Action for each element detected
100 foreach (MCvAvgComp f in facesDetected[0])
101 {
102 TrainedFace = currentFrame.Copy(f.rect).Convert<Gray, byte>();
103 break;
104 }
105
106 //resize face detected image for force to compare the same size with the
107 //test image with cubic interpolation type method
108 TrainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
109 trainingImages.Add(TrainedFace);
110 labels.Add(textBox1.Text);
111
112 //Show face added in gray scale
113 imageBox1.Image = TrainedFace;
114
115 //Write the number of triained faces in a file text for further load
116 File.WriteAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt", trainingImages.ToArray().Length.ToString() + "%");
117
118 //Write the labels of triained faces in a file text for further load
119 for (int i = 1; i < trainingImages.ToArray().Length + 1; i++)
120 {
121 trainingImages.ToArray()[i - 1].Save(Application.StartupPath + "/TrainedFaces/face" + i + ".bmp");
122 File.AppendAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt", labels.ToArray()[i - 1] + "%");
123 }
124
125 MessageBox.Show(textBox1.Text + "´s face detected and added :)", "Training OK", MessageBoxButtons.OK, MessageBoxIcon.Information);
126 }
127 catch
128 {
129 MessageBox.Show("Enable the face detection first", "Training Fail", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
130 }
131 }
132
133
134 void FrameGrabber(object sender, EventArgs e)
135 {
136 label3.Text = "0";
137 //label4.Text = "";
138 NamePersons.Add("");
139
140
141 //Get the current frame form capture device
142 currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
143
144 //Convert it to Grayscale
145 gray = currentFrame.Convert<Gray, Byte>();
146
147 //Face Detector
148 MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
149 face,
150 1.2,
151 10,
152 Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
153 new Size(20, 20));
154
155 //Action for each element detected
156 foreach (MCvAvgComp f in facesDetected[0])
157 {
158 t = t + 1;
159 result = currentFrame.Copy(f.rect).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
160 //draw the face detected in the 0th (gray) channel with blue color
161 currentFrame.Draw(f.rect, new Bgr(Color.Red), 2);
162
163
164 if (trainingImages.ToArray().Length != 0)
165 {
166 //TermCriteria for face recognition with numbers of trained images like maxIteration
167 MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
168
169 //Eigen face recognizer
170 EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
171 trainingImages.ToArray(),
172 labels.ToArray(),
173 3000,
174 ref termCrit);
175
176 name = recognizer.Recognize(result);
177
178 //Draw the label for each face detected and recognized
179 currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));
180
181 }
182
183 NamePersons[t-1] = name;
184 NamePersons.Add("");
185
186
187 //Set the number of faces detected on the scene
188 label3.Text = facesDetected[0].Length.ToString();
189
190 /*
191 //Set the region of interest on the faces
192
193 gray.ROI = f.rect;
194 MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
195 eye,
196 1.1,
197 10,
198 Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
199 new Size(20, 20));
200 gray.ROI = Rectangle.Empty;
201
202 foreach (MCvAvgComp ey in eyesDetected[0])
203 {
204 Rectangle eyeRect = ey.rect;
205 eyeRect.Offset(f.rect.X, f.rect.Y);
206 currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
207 }
208 */
209
210 }
211 t = 0;
212
213 //Names concatenation of persons recognized
214 for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
215 {
216 names = names + NamePersons[nnn] + ", ";
217 }
218 //Show the faces procesed and recognized
219 imageBoxFrameGrabber.Image = currentFrame;
220 label4.Text = names;
221 names = "";
222 //Clear the list(vector) of names
223 NamePersons.Clear();
224
225 }
226
227
228
229
230
231
232 }
233 }
