FaceRcognizer/FaceRecognizer/MainForm.cs

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using System.IO;

using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.UI;
using Emgu.Util;
using Emgu.CV.Face;

namespace FaceRecognizer
{
    public partial class MainForm : Form
    {
        LBPHFaceRecognizer reconizer;
        CascadeClassifier faceCascade;
        Image<Gray, byte> temp;
        List<Image<Gray, byte>> imageList = new List<Image<Gray, byte>>();
        List<string> imageLabels = new List<string>();
        List<int> imageIndicies = new List<int>();
        List<int> predictions = new List<int>();
        Rectangle[] detectedFaces;
        string trainImagesFolder = Application.StartupPath + "\\Train";
        string reconizeImagesFolder = Application.StartupPath + "\\photo";


        public MainForm()
        {
            InitializeComponent();
            console.AppendText("Main OK!");            
        }

        void train(string personName)
        {            
            faceCascade = new CascadeClassifier("haarcascade_frontalface_alt_tree.xml");
            reconizer = new LBPHFaceRecognizer();
            int j = 0;
            foreach (var file in Directory.GetFiles(trainImagesFolder))
            {                
                if (file.EndsWith("jpg"))
                {
                    temp = new Image<Gray, byte>(file);
                    temp._EqualizeHist();
                    var detected_faces = faceCascade.DetectMultiScale(temp, 1.1, 2, new Size(24, 24), Size.Empty);
                    if (detected_faces.Length == 0) { continue; }
                    temp.ROI = detected_faces[0];                        
                    temp = temp.Copy();
                    temp = temp.Resize(24, 24, Inter.Cubic);
                    imageList.Add(temp);
                    imageLabels.Add(Path.GetFileNameWithoutExtension(file));
                }
                j++;
            }

            for (int i = 0; i < imageList.Count; i++)
            {
                imageIndicies.Add(i);
            }

            try
            {
                reconizer.Train(imageList.ToArray(), imageIndicies.ToArray());
                reconizer.Write(personName);
                //console.AppendText("Training done!\nFile Name: " + personName);                
                reconizer.Dispose();
                imageList.Clear();
                faceCascade.Dispose();
                temp.Dispose();
            }
            catch (Exception e) { } //console.AppendText("Error: " + e.Message); }                            
        }

        void recognize(string personName)
        {
            int i = 0;
            try
            {
                //reconizer = new LBPHFaceRecognizer(1, 8, 8, 9, 65);
                reconizer = new LBPHFaceRecognizer();
                faceCascade = new CascadeClassifier("haarcascade_frontalface_alt_tree.xml");
                predictions.Clear();
                //reconizer = new LBPHFaceRecognizer();
                reconizer.Read(personName);

                foreach (var file in Directory.GetFiles(reconizeImagesFolder))
                {
                    if (file.EndsWith("jpg"))
                    {
                        temp = new Image<Gray, byte>(file);
                        temp._EqualizeHist();
                        
                        var faces = faceCascade.DetectMultiScale(temp, 1.1, 2, new Size(24, 24), Size.Empty);
                        if (faces.Length == 0) { continue; }
                        temp.ROI = faces[0];
                        temp = temp.Copy();
                        temp.Resize(24, 24, Inter.Cubic);
                        temp.ToBitmap().Save(reconizeImagesFolder + "\\" + i + ".jpg");
                        var result = reconizer.Predict(temp);
                        predictions.Add(result.Label);
                    }
                    i++;
                }
                reconizer.Dispose();
                imageList.Clear();
                faceCascade.Dispose();
                temp.Dispose();
            }
            catch (Exception e){ throw e; }
        }

        private void TrainBtn_Click(object sender, EventArgs e)
        {
            trainBackWorker.RunWorkerAsync();
            console.AppendText("Training started for " + nameTxtBx.Text);
        }

        private void RecconizeBtn_Click(object sender, EventArgs e)
        {
            recognizeBackWorker.RunWorkerAsync();
            console.AppendText("Started recognition");
        }

        private void console_TextChanged(object sender, EventArgs e)
        {
            console.AppendText("\n");
            console.SelectionStart = console.Text.Length;
            console.ScrollToCaret();
        }

        private void trainBackWorker_DoWork(object sender, DoWorkEventArgs e)
        {
            train(nameTxtBx.Text);
        }       

        private void trainBackWorker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e)
        {
            console.AppendText("Finished training: " + nameTxtBx.Text);
        }

        private void recognizeBackWorker_DoWork(object sender, DoWorkEventArgs e)
        {
            recognize(nameTxtBx.Text);
        }

        private void recognizeBackWorker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e)
        {
            console.AppendText("Recognition finished\nResults:");
            printResults();
        }

        void printResults()
        {
            foreach(var number in predictions)
            {
                console.AppendText(number.ToString());
            }
        }
    }
}