/// <summary>
/// 字符分割 -- 保存 到 txt
/// </summary>
/// <param name="matPlate"></param>
private String CharsSplit(Mat matPlate)
{
List <Mat> ListMat = my_Method.carCharSplit(matPlate);
List <PlateCategory> category = Plate_SVM.TestList(ListMat);
TextBox tx = new TextBox();
for (int index = 0; index < category.Count; index++)
{
if (category[index] == 0)
{
tx.AppendText(string.Format("{0}", ""));
}
else
{
tx.AppendText(string.Format("{0}", category[index]));
}
}
string res = tx.Text.Replace("_", "");
if (res != null && res != "" && res.Length > 4)
{
res = this.SubChar(res);
}
return(res);
}
//创建存放结果集的文件夹与文件
private void CreatFile()
{
//启动加载xml 在debug下 xml
Plate_SVM.Load(Application.StartupPath + @"\MyTestChar.xml");
Plate_Car_SVM.Load(Application.StartupPath + @"\MyTestCar.xml");
String DirectoryPath = @"C:\test-results";
if (!Directory.Exists(DirectoryPath))
{
Directory.CreateDirectory(DirectoryPath);
}
if (File.Exists(DirectoryPath + @"\No14866mresults.txt"))
{
File.Delete(DirectoryPath + @"\No14866mresults.txt");
FileStream fs = new FileStream(DirectoryPath + @"\No14866mresults.txt", FileMode.Create, FileAccess.ReadWrite);//创建写入文件
file = new StreamWriter(fs);
}
else
{
FileStream fs = new FileStream(DirectoryPath + @"\No14866mresults.txt", FileMode.Create, FileAccess.ReadWrite);//创建写入文件
file = new StreamWriter(fs);
}
}
private void OpenXML_Click(object sender, EventArgs e)
{
OpenFileDialog openDlg = new OpenFileDialog();
openDlg.Filter = "XML文件|*.xml";
DialogResult dlgResult = openDlg.ShowDialog();
if (dlgResult == DialogResult.OK)
{
Plate_SVM.Load(openDlg.FileName);
}
}
/// <summary>
/// 保存训练数据XML
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void btnSaveXML_Click(object sender, EventArgs e)
{
SaveFileDialog saveDlg = new SaveFileDialog();
saveDlg.Filter = "XML文件|*.xml";
DialogResult dlgResult = saveDlg.ShowDialog();
if (dlgResult == DialogResult.OK)
{
Plate_SVM.Save(saveDlg.FileName);
this.txtinfo.AppendText("保存-训练数据XML成功 \r\n ");
}
}
/// <summary>
/// 显示lable 点击切割
/// </summary>
/// <param name="matPlate"></param>
/// <param name="panel"></param>
private void CharsSplit(Mat matPlate, Panel panel)
{
panel.Controls.Clear();//清空
Console.WriteLine(panel.Name);
List <Mat> ListMat = my_Method.carCharSplit(matPlate);
//添加灰度图
this.GenerateROI(my_Method.matGray_car, panel, false);
//添加二值图
this.GenerateROI(my_Method.matshoid, panel, false);
//膨胀图
this.GenerateROI(my_Method.matDilate_car, panel, false);
//
this.GenerateROI(my_Method.matWithFalg_car, panel, false);
//矩形
this.GenerateROI(my_Method.matWithRects, panel, false);
// 分割出来的每个字符图
foreach (var item in ListMat)
{
this.GenerateROI(item, panel, false);
}
this.GenerateROI(my_Method.matWithRects, panel, false);
List <PlateCategory> category = Plate_SVM.TestList(ListMat);
TextBox tx = new TextBox();
for (int index = 0; index < category.Count; index++)
{
if (category[index] == 0)
{
tx.AppendText(string.Format("{0}", ""));
}
else
{
tx.AppendText(string.Format("{0}", category[index]));
}
}
string res = tx.Text.Replace("_", "");
//for (int i = 0; i < res.Length; i++)
//{
if (res != null && res != "" && res.Length > 4)
{
res = this.SubChar(res);
}
// 显示识别结果
this.lblCarText.Text = res;
//}
}
private void tvTest_AfterSelect(object sender, TreeViewEventArgs e)
{
if (e.Node.Level == 0)
{
return;
}
string fileName = e.Node.Tag as string;
if (fileName == null)
{
return;
}
Mat matPreview = Cv2.ImRead(fileName);
this.picImage.Image = matPreview.ToBitmap();
PlateCategory category = Plate_SVM.Test(matPreview);
this.txtinfo.AppendText(string.Format("\r\n预测结果：{0}\r\n", category));
}
/// <summary>
/// 开始训练
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void btnStart_Click(object sender, EventArgs e)
{
if (this.samplePath == "")
{
return;
}
List <PlateInfo> plateSamples = new List <PlateInfo>();
string[] subDirectories = Directory.GetDirectories(this.samplePath);
for (int index = 0; index < subDirectories.Length; index++)
{
string subDirectory = subDirectories[index];
string shortDirectory = subDirectory.Substring(subDirectory.LastIndexOf(@"\") + 1);
PlateCategory category = (PlateCategory)Enum.Parse(typeof(PlateCategory), shortDirectory);
string[] fileNames = Directory.GetFiles(subDirectory, "*.jpg");
for (int i = 0; i < fileNames.Length; i++)
{
string fileName = fileNames[i];
this.txtinfo.AppendText(string.Format("{0} 类型：{1}\r\n", fileName, category));
Mat matPlate = Cv2.ImRead(fileName);
PlateInfo plateInfo = new PlateInfo();
plateInfo.PlateMat = matPlate;
plateInfo.Category = category;
plateSamples.Add(plateInfo);
}
}
//将样本的元数据转化为SVM的样品数据和标签数据
Mat trainData = new Mat();
Mat trainLabel = new Mat(plateSamples.Count, 1, MatType.CV_32SC1);
for (int index = 0; index < plateSamples.Count; index++)
{
PlateInfo plateInfo = plateSamples[index];
float[] hog = Plate_SVM.GetHog(plateInfo.PlateMat);
if (index == 0)
{
trainData = Mat.Zeros(plateSamples.Count, hog.Length, MatType.CV_32FC1);
}
for (int colIndex = 0; colIndex < hog.Length; colIndex++)
{
trainData.Set <float>(index, colIndex, hog[colIndex]);
}
trainLabel.Set <int>(index, 0, (int)plateInfo.Category);
this.txtinfo.AppendText(hog.Length.ToString() + "\r\n");
}
Plate_SVM.Train(trainData, trainLabel);
this.txtinfo.AppendText("训练已完成\r\n");
}
/// <summary>
/// 颜色交换
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void toolStripComboBox1_TextChanged(object sender, EventArgs e)
{
// 颜色法
this.HSVEvenimg = my_Method.ColorMethod(this.frameClone, 5, 50, 255, this.HSVEvenimg);
this.picColorM.Image = this.HSVEvenimg.ToBitmap();
// this.GenerateROI(this.HSVEvenimg, this.panelColor, false);
// 颜色区域
Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(11, 2));
// 定义点的二维数组
OpenCvSharp.Point[][] contours = null;
HierarchyIndex[] hierarchies = null;
contours = my_Method.ColorRange(element, contours, hierarchies, frameClone, this.HSVEvenimg, 5);
string s = this.lblText.Text.Trim();
//图像显示识别结果
if (this.lblText.Text.Trim() != null && this.lblText.Text.Trim() != "")
{
String rest = this.lblText.Text.Trim();
if ((int)rest[0] < 127)
{
rest = rest.Remove(0, 1);
}
if (my_Method.bluenum > my_Method.greennum)
{
if (rest.Length > 7)
{
rest.Remove(7);
}
}
if (my_Method.bluenum > my_Method.greennum)
{
if (rest.Length > 8)
{
rest.Remove(8);
}
}
Regex reg = new Regex("[\u4e00-\u9fa5]+");
foreach (Match Chinese in reg.Matches(rest))
{
int indexof = rest.IndexOf(Chinese.Value);
rest = rest.Remove(indexof, 1);
int has = Form_Main.hashRes.Count;
if (Form_Main.hashRes.ContainsKey(Chinese.Value))
{
rest = rest.Insert(indexof, Form_Main.hashRes[Chinese.Value].ToString());
}
}
my_Method.matWithFlag.PutText("result: " + rest, new OpenCvSharp.Point(50, 100), HersheyFonts.HersheyTriplex, 0.8, Scalar.Yellow, 2);
}
// 显示查找的轮廓
this.picMatWithFlag.Image = my_Method.matWithFlag.ToBitmap();
this.rectList.Clear();
for (int i = 0; i < contours.Length; i++)
{
// 轮廓的最小矩形
Rect rect = Cv2.BoundingRect(contours[i]);
this.rectList.Add(rect); // 添加到集合
}
// 遍历找到的矩形 然后截取出来
foreach (var item in this.rectList)
{
if (item.Width >= 40 &&
item.Width <= 2000 &&
item.Height >= 20 &&
item.Height <= 2000 &&
item.Width / item.Height > 0.8 &&
item.Width / item.Height < 5)
{
//SubMat 对矩形区域进行截取
matRoi = this.frameClone.SubMat(item);
if (Plate_Car_SVM.TestCar(matRoi) == PlateCar.车牌)
{
// 显示截取的矩形
this.picRect.Image = matRoi.ToBitmap();
// 进行分割
List <Mat> ListMat = my_Method.carCharSplit(matRoi);
List <PlateCategory> category = Plate_SVM.TestList(ListMat);
TextBox tx = new TextBox();
for (int index = 0; index < category.Count; index++)
{
if (category[index] == 0)
{
tx.AppendText(string.Format("{0}", ""));
}
else
{
tx.AppendText(string.Format("{0}", category[index]));
}
}
string res = tx.Text.Replace("_", "");
Console.WriteLine(res.Length + " : 第一个");
for (int i = 0; i < res.Length; i++)
{
// 判断第一个是不是汉字 不是就删除
// 不是汉字
if ((int)res[0] < 127 && res.Length > 1 && res != null && res != "")
{
res = res.Substring(1);
// 显示识别结果
this.lblText.Text = res;
}
else
{
// 显示识别结果
this.lblText.Text = res;
}
}
GC.Collect();
}
}
}
}
public static List <Mat> carCharSplit(Mat matPlate)
{
Mat hsvmat = matPlate.CvtColor(ColorConversionCodes.BGR2HSV);
Mat[] hsv = hsvmat.Split();
Mat h = hsv[0];
Mat s = hsv[1];
Mat v = hsv[2];
for (int i = 0; i < hsvmat.Rows; i++)
{
for (int j = 0; j < hsvmat.Cols; j++)
{
byte hvalue = h.At <byte>(i, j); //////////////////h通道/-/////////////////////
// Console.WriteLine(hvalue + ":h");
byte svalue = s.At <byte>(i, j); //////////////////s通道/-/////////////////////
byte vvalue = v.At <byte>(i, j); //////////////////v通道/-/////////////////////
// printf("x=%d,y=%d,HSV: H=%d,S=%d,V=%d\n",i,j,hvalue,svalue,vvalue);
if ((hvalue > 80 && hvalue < 130) && (svalue > 43 && svalue < 220) && (vvalue > 46 && vvalue < 255)) //hsv图像在蓝色区域里
{
//蓝色+1
bluenum++;
}
else if ((hvalue > 11 && hvalue < 34) && (svalue > 43 && svalue < 255) && (vvalue > 46 && vvalue < 255))//hsv在黄色区域里
{
//黄色+1
yellownum++;
}
else if ((hvalue > 0 && hvalue < 180) && (svalue > 0 && svalue < 255) && (vvalue > 0 && vvalue < 46))//hsv在黑色区域里
{
blacknum++;
//黑色+1
}
else if ((hvalue > 0 && hvalue < 180) && (svalue > 0 && svalue < 30) && (vvalue > 46 && vvalue < 220))//hsv在白色区域里
{
//白色+1
whitenum++;
}
else if ((hvalue > 60 && hvalue < 90) && (svalue > 43 && svalue < 255) && (vvalue > 46 && vvalue < 255))//hsv在绿色区域里
{
//绿色+1
greennum++;
}
else
{
//其他颜色
othernum++;
}
}
}
Console.WriteLine(greennum + ":绿色");
Console.WriteLine(hsvmat.Rows * hsvmat.Cols + ":总");
Console.WriteLine(yellownum + ":黄色");
Console.WriteLine(bluenum + ":蓝色");
// 灰度
matGray_car = matPlate.CvtColor(ColorConversionCodes.BGR2GRAY);
//二值化
Mat matshoid_th = matGray_car.Threshold(0, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary);
matshoid = new Mat();
// 判断 取反 否
if (bluenum < yellownum && yellownum > greennum)
{
// 取反
matshoid = 255 - matshoid_th;
}
else if (bluenum > yellownum && bluenum > greennum)
{
matshoid = matGray_car.Threshold(0, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary);
}
else if (greennum > yellownum && greennum > bluenum)
{
// 取反
matshoid = 255 - matshoid_th;
}
else
{
matshoid = matGray_car.Threshold(0, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary);
}
// 车牌高度大于20的去除边框和柳钉
Mat clearBorder = new Mat();
Mat clearMoDing = new Mat();
if (matshoid.Height > 22)
{
// 去除边框
clearBorder = ClearBorder(matshoid);
// 去除柳钉
clearMoDing = ClearMaoding(matshoid, 4);
}
else if (matshoid.Height <= 22 && matshoid.Height > 20)
{
// 去除边框
clearBorder = ClearBorder(matshoid);
// 去除柳钉
clearMoDing = ClearMaoding(matshoid, 3);
}
else
{
clearMoDing = matshoid;
}
// 膨胀
Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(1, 1));
matDilate_car = clearMoDing.MorphologyEx(MorphTypes.Dilate, element);
//点的二维数组
OpenCvSharp.Point[][] contours = null;
HierarchyIndex[] hierarchyIndexs = null;
matDilate_car.FindContours(out contours, out hierarchyIndexs, RetrievalModes.External, ContourApproximationModes.ApproxSimple);
matWithFalg_car = matPlate.Clone();
matWithFalg_car.DrawContours(contours, -1, Scalar.Red);
matWithRects = matPlate.Clone();
// 创建集合 存储分割后的图片 -- 用于 识别
List <Mat> ListMat = new List <Mat>();
// 切割的矩形 图
List <Rect> ListRect = new List <Rect>();
for (int index = 0; index < contours.Length; index++)
{
Rect rect = Cv2.BoundingRect(contours[index]);
ListRect.Add(rect);
matWithRects.Rectangle(rect, Scalar.Red);
}
// 对分割的字符排序
SortRectsByLeft_ASC(ListRect);
foreach (var rect in ListRect)
{
Mat matchar = matPlate.SubMat(rect); // 切割矩形
Console.WriteLine("========= 宽:" + matchar.Width + " 高:" + matchar.Height);
// 判断 太小的不要
if (matchar.Width >= 3 && matchar.Height >= 10)
{
PlateCategory plateCate = Plate_SVM.Test(matchar);
ListMat.Add(matchar);
Console.WriteLine(ListMat.Count + " 个");
}
Console.WriteLine("========= 宽:" + rect.Width + " 高:" + rect.Height + " X:" + rect.X + " Y:" + rect.Y);
}
return(ListMat);
}
