public char Recoginatinon(SVM svm, Mat img_character)
{
List <float> feature = EmguCVExtension.calculate_feature(img_character);
// Open CV3.1
Mat m = new Mat(1, 32, DepthType.Cv32F, 1);
for (int i = 0; i < feature.Count(); ++i)
{
float temp = feature[i];
m.SetValue(0, i, temp);
}
char c = '*';
int ri = (int)(svm.Predict(m)); // Open CV 3.1
/*int ri = int(svmNew.predict(m));*/
if (ri >= 0 && ri <= 9)
{
c = (char)(ri + 48); //ma ascii 0 = 48
}
if (ri >= 10 && ri < 18)
{
c = (char)(ri + 55); //ma accii A = 5, --> tu A-H
}
if (ri >= 18 && ri < 22)
{
c = (char)(ri + 55 + 2); //K-N, bo I,J
}
if (ri == 22)
{
c = 'P';
}
if (ri == 23)
{
c = 'S';
}
if (ri >= 24 && ri < 27)
{
c = (char)(ri + 60); //T-V,
}
if (ri >= 27 && ri < 30)
{
c = (char)(ri + 61); //X-Z
}
return(c);
}
/// <summary>
/// Phương thức được sửa dụng để xử lý hình ảnh và xuất ra thông tin của biển số xe bao gồm processedGray, List<Rect> và List<Mat>
/// </summary>
/// <param name="colorImage">Source color image.</param>
/// <param name="processedGray">Resulting gray image.</param>
/// <param name="listRect">Kết quả xuất ra List<Rectagle> của biển số xe</param>
/// <param name="listMat">Kết quả xuất ra List<Mat> của biển số xe</param>
public int IdentifyContours(Bitmap colorImage, out Bitmap processedGray, out List <Rectangle> listRect, out List <Mat> listMat)
{
#region Xác định và cắt các vùng của từng chữ số
Image <Gray, byte> grayImage = new Image <Gray, byte>(colorImage);
Image <Bgr, byte> bgrImageBackup = new Image <Bgr, byte>(colorImage);
Image <Gray, byte> grayImageBackup = new Image <Gray, byte>(colorImage);
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
//grayImage = grayImage.Resize(400, 400, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
Mat binary = new Image <Gray, byte>(grayImage.Width, grayImage.Height).Mat;
Image <Bgr, byte> color = new Image <Bgr, byte>(colorImage);
CvInvoke.AdaptiveThreshold(grayImage, binary, 255, AdaptiveThresholdType.MeanC, ThresholdType.Binary, 55, 5);
//Mat gray, binary;
// CvInvoke.Imshow("binary",binary);
List <Mat> cb = new List <Mat>();
Mat or_binary = binary.Clone();
Mat _plate = binary.Clone();
Mat hierachy = new Mat();
Mat element = CvInvoke.GetStructuringElement(ElementShape.Cross, new Size(3, 3), new Point(0, 0));
CvInvoke.Erode(binary, binary, element, new Point(0, 0), 1, BorderType.Constant, new MCvScalar());
CvInvoke.Dilate(binary, binary, element, new Point(0, 0), 1, BorderType.Constant, new MCvScalar());
CvInvoke.FindContours(binary, contours, hierachy, RetrType.Tree, ChainApproxMethod.ChainApproxSimple, new Point(0, 0));
List <Mat> c = new List <Mat>();
List <Rectangle> r_characters = new List <Rectangle>();
for (int j = 0; j < contours.Size; ++j)
{
Rectangle sub_r = CvInvoke.BoundingRectangle(contours[j]);
if (sub_r.Width > 20 && sub_r.Width < 160 &&
sub_r.Height > 80 && sub_r.Height < 160 && sub_r.Y > 0 && sub_r.X > 0)
{
Mat cj = new Mat(_plate, sub_r);
double ratio = (double)EmguCVExtension.count_pixel(cj) / (cj.Cols * cj.Rows);
if (ratio > 0.2 && ratio < 0.7)
{
int X = sub_r.X - MARGIN_RECT > 0 ? sub_r.X - MARGIN_RECT : sub_r.X;
int Y = sub_r.Y - MARGIN_RECT > 0 ? sub_r.Y - MARGIN_RECT : sub_r.Y;
r_characters.Add(new Rectangle(X, Y, sub_r.Width + MARGIN_RECT * 2, sub_r.Height + MARGIN_RECT * 2));
}
}
}
#endregion
#region Sap xep va xoa bo vung den du thua
if (r_characters.Count >= 7)
{
#region Sap xep
List <Rectangle> listUp = new List <Rectangle>();
List <Rectangle> listDown = new List <Rectangle>();
bool r0 = false;
for (int i = 0; i < r_characters.Count - 1; ++i)
{
if (r_characters[0].Y <= r_characters[i + 1].Y + 30 && r_characters[0].Y + 30 > r_characters[i + 1].Y)
{
listUp.Add(r_characters[i + 1]);
if (!r0)
{
listUp.Add(r_characters[0]);
r0 = true;
}
}
else
{
listDown.Add(r_characters[i + 1]);
if (!r0)
{
listDown.Add(r_characters[0]);
r0 = true;
}
}
}
for (int i = 0; i < listDown.Count - 1; i++)
{
for (int j = i + 1; j < listDown.Count; ++j)
{
Rectangle temp;
if (listDown[j].X > listDown[i].X)
{
temp = listDown[j];
listDown[j] = listDown[i];
listDown[i] = temp;
}
}
}
for (int i = 0; i < listUp.Count - 1; i++)
{
for (int j = i + 1; j < listUp.Count; ++j)
{
Rectangle temp;
if (listUp[j].X > listUp[i].X)
{
temp = listUp[j];
listUp[j] = listUp[i];
listUp[i] = temp;
}
}
}
r_characters.Clear();
r_characters.AddRange(listUp);
r_characters.AddRange(listDown);
for (int i = 0; i < r_characters.Count; ++i)
{
Mat cj = new Mat(_plate, r_characters[i]);
c.Add(cj);
}
#endregion
#region Xoa bo cac vung den nhi phan du thua
foreach (var item in c)
{
try
{
double maxArea = 0.0;
VectorOfVectorOfPoint contours2 = new VectorOfVectorOfPoint();
Mat hierachy2 = new Mat();
CvInvoke.FindContours(item, contours2, hierachy2, RetrType.External, ChainApproxMethod.ChainApproxSimple, new Point(0, 0));
Mat result = new Mat();
int savedContour = -1;
for (int i = 0; i < contours2.Size; i++)
{
double area = CvInvoke.ContourArea(contours2[i]);
if (area > maxArea)
{
maxArea = area;
savedContour = i;
}
}
// Create mask
CvInvoke.DrawContours(item, contours2, savedContour, new MCvScalar(255));
}
catch { }
// apply the mask:
cb.Add(item);
}
for (int f = 0; f < cb.Count; ++f)
{
CvInvoke.Imwrite(f.ToString() + ".jpg", cb[f]);
}
#endregion
}
#endregion
listRect = r_characters;
processedGray = grayImage.ToBitmap();
listMat = cb;
return(1);
}
public static bool TrainSVM(string savepath, string trainImgpath)
{
const int number_of_class = 30;
const int number_of_sample = 10;
const int number_of_feature = 32;
//Train SVM OpenCV 3.1
SVM svm = new SVM();
svm.Type = SVM.SvmType.CSvc;
svm.SetKernel(SVM.SvmKernelType.Rbf);
svm.Gamma = 0.5;
svm.C = 16;
svm.TermCriteria = new MCvTermCriteria();
List <string> folders = List_folder(trainImgpath);
if (folders.Count <= 0)
{
//do something
return(false);
}
if (number_of_class != folders.Count || number_of_sample <= 0 || number_of_class <= 0)
{
//do something
return(false);
}
Mat src;
Mat data = new Mat(number_of_sample * number_of_class, number_of_feature, Emgu.CV.CvEnum.DepthType.Cv32F, 1);
Mat label = new Mat(number_of_sample * number_of_class, 1, Emgu.CV.CvEnum.DepthType.Cv32F, 1);
int index = 0;
for (int i = 0; i < folders.Count; ++i)
{
List <string> files = List_file(folders[i]);
if (files.Count <= 0 || files.Count != number_of_sample)
{
return(false);
}
string folder_path = folders[i];
string label_folder = folder_path.Substring(folder_path.Length - 1);
for (int j = 0; j < files.Count; ++j)
{
src = CvInvoke.Imread(files[j]);
if (src.IsEmpty)
{
return(false);
}
List <float> feature = EmguCVExtension.calculate_feature(src);
// Open CV3.1
Mat m = new Mat(1, 32, DepthType.Cv32F, 1);
for (int k = 0; k < feature.Count(); ++k)
{
data.SetValue(index, k, feature[i]);
}
label.SetValue(index, 0, i);
index++;
}
}
// SVM Train OpenCV 3.1
svm.TrainAuto(new TrainData(data, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, label));
svm.Save(savepath);
return(true);
}
