/// <summary>
/// 颜色法 筛选
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void tbtnlFilter_Click(object sender, EventArgs e)
{
if (this.srcImg == null)
{
return;
}
if (this.srcImg.Empty())
{
return;
}
rectList.Clear();
PicMatrois.Clear();
tsColorChangeText_TextChanged(sender, e);
int minWidth = int.Parse(this.txtMinWidth.Text);
int minHeight = int.Parse(this.txtMinHeight.Text);
int maxWidth = int.Parse(this.txtMaxWidth.Text);
int maxHeight = int.Parse(this.txtMaxHeight.Text);
float WhScale = float.Parse(this.txtWHScale.Text);
this.panSegmentList.Controls.Clear();
this.panChars.Controls.Clear();
// rects 集合
foreach (var item in this.rectList)
{
if (
item.Width >= minWidth &&
item.Width <= maxWidth &&
item.Height >= minHeight &&
item.Height <= maxHeight &&
item.Width / item.Height > 0.8 &&
item.Width / item.Height < WhScale
)
{
//SubMat 对矩形区域进行截取
Mat matRoi = this.srcImg.SubMat(item);
PlateCar plateCar = Plate_Car_SVM.TestCar(matRoi);
if (PlateCar.车牌 == plateCar)
{
PicMatrois.Add(matRoi);
}
this.GenerateROI(matRoi, this.panSegmentList);
}
}
}
/// <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();
}
}
}
}
//图片处理
private void ProImg()
{
int FsIndex = 0;
string imagePath = @"C:\test-imgs";
if (!Directory.Exists(imagePath))
{
MessageBox.Show("图片测试文件夹路径需为:C: test-imgs");
return;
}
string[] subDirecotries = Directory.GetDirectories(imagePath);
DirectoryInfo di = new DirectoryInfo(imagePath);
if (subDirecotries.Length == 0)
{
FileInfo[] FileList = di.GetFiles();
ArrayList FAL = new ArrayList();
//筛选图片
foreach (FileInfo Files in FileList)
{
String FilesName = (Files.Name).ToUpper();
if (FilesName.EndsWith(".JPG") || FilesName.EndsWith(".PNG") || FilesName.EndsWith(".BMP"))
{
FAL.Add(Files);
}
}
//清空数组
Array.Clear(FileList, 0, FileList.Length);
//重新赋值
FileList = (FileInfo[])(FAL.ToArray(typeof(FileInfo)));
//根据文件名排序
Array.Sort(FileList, new FileNameSort());
//把文件名存进数组
for (int i = 0; i < FileList.Length; i++)
{
fileNames.Add(FileList[i].FullName);
}
//--------------获取图片处理所需的参数和阈值----------------------
//加载Datatable数据表
LoadDT();
//模糊
int blurSize = int.Parse(this.blurSize.Text);
// 边缘
int cannyThreshold1 = int.Parse(this.canny1.Text);
int cannyThreshold2 = int.Parse(this.canny2.Text);
// 膨胀的 element
int ksizex = int.Parse(this.txDaliteX.Text);
int ksizey = int.Parse(this.txDaliteY.Text);
int minWidth = int.Parse(this.txtMinWidth.Text);
int minHeight = int.Parse(this.txtMinHeight.Text);
int maxWidth = int.Parse(this.txtMaxWidth.Text);
int maxHeight = int.Parse(this.txtMaxHeight.Text);
float WhScale = float.Parse(this.txtWHScale.Text);
for (int index = 0; index < fileNames.Count; index++)
{
dtMatANDRect.Clear();
//清空PicMatrois图片结果集
PicMatrois = new List <Mat>();
//rectList.Clear();
int MatIndex = 0;
FileNameAndMat.Clear();
string fileName = fileNames[index];
//调用识别方法
// 读取原图
this.srcImg = Cv2.ImRead(fileName);
// 归一化
if (srcImg.Cols > 2000)
{
Cv2.Resize(srcImg, srcImg, new OpenCvSharp.Size(srcImg.Cols * 0.3, srcImg.Rows * 0.3));
}
//原图变化
this.picSrc.Image = this.srcImg.ToBitmap();
//获取HSV均衡图
this.HSVEvenimg = my_Method.ColorMethod(this.srcImg, blurSize, cannyThreshold1, cannyThreshold2, this.HSVEvenimg);
// 膨胀的 element
Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(ksizex, ksizey));
// 定义点的二维数组,颜色法筛选开始
if (this.srcImg == null)
{
return;
}
if (this.srcImg.Empty())
{
return;
}
Point[][] contours = null;
HierarchyIndex[] hierarchies = null;
contours = my_Method.ColorRange(element, contours, hierarchies, this.srcImg, this.HSVEvenimg, 3);
for (int i = 0; i < contours.Length; i++)
{
// 轮廓的最小矩形
Rect rect = Cv2.BoundingRect(contours[i]);
if (rect.Width >= minWidth &&
rect.Width <= maxWidth &&
rect.Height >= minHeight &&
rect.Height <= maxHeight &&
rect.Width / rect.Height > 0.8 &&
rect.Width / rect.Height < WhScale
)
{
Mat matRoi = this.srcImg.SubMat(rect);
PlateCar plateCar = Plate_Car_SVM.TestCar(matRoi);
if (PlateCar.车牌 == plateCar)
{
MatIndex++;
FileNameAndMat.Add(MatIndex, matRoi);
Console.WriteLine(dtMatANDRect.Columns.Count);
dtMatANDRect.Rows.Add(MatIndex, rect.X, rect.Y);
}
}
}
//判断颜色法是否筛选出符合要求的车牌，如果有添加进集合，结束筛选，开始下张图片
if (MatIndex != 0)
{
DataView dv = dtMatANDRect.DefaultView;
dv.Sort = "X ASC";
//dtMatANDRect.Clear();
dtMatANDRect = dv.ToTable();
for (int i = 0; i < dtMatANDRect.Rows.Count; i++)
{
int Mindex = int.Parse(dtMatANDRect.Rows[i]["MatIndex"].ToString());
Console.WriteLine(dtMatANDRect.Rows[i]["X"]);
if (FileNameAndMat.ContainsKey(Mindex))
{
Mat resMat;
FileNameAndMat.TryGetValue(Mindex, out resMat);
PicMatrois.Add(resMat);
}
}
}
//如果没，调用Sobel法
else
{
//调用Sobel法
this.SobelMethod();
if (this.srcImg == null)
{
return;
}
if (this.srcImg.Empty())
{
return;
}
//找轮廓
Point[][] contours_sobel = null;
HierarchyIndex[] hierarchyIndex = null;
Cv2.FindContours(threshold_Erode, out contours_sobel, out hierarchyIndex,
RetrievalModes.External, ContourApproximationModes.ApproxSimple);
// Sobel法筛选。对轮廓求最小外接矩形，然后验证，不满足条件的淘汰。
this.panSegment_Sobel.Controls.Clear();
int minWidth_sobel = int.Parse(this.txtMInWidth_sobel.Text);
int minHeight_sobel = int.Parse(this.txtMInHeight_sobel.Text);
int maxWidth_sobel = int.Parse(this.txtMaxWidth_sobel.Text);
int maxHeight_sobel = int.Parse(this.txtMaxHeight_sobel.Text);
float WhScale_sobel = float.Parse(this.txtWHScale_sobel.Text);
//List<Mat> SobelMat = new List<Mat>();
for (int i = 0; i < contours_sobel.Length; i++)
{
Rect rect = Cv2.BoundingRect(contours_sobel[i]);
if (rect.Width >= minWidth_sobel &&
rect.Width <= maxWidth_sobel &&
rect.Height >= minHeight_sobel &&
rect.Height <= maxHeight_sobel &&
rect.Width / rect.Height >= 0.8 &&
rect.Width / rect.Height <= WhScale_sobel)
{
Mat matROI = this.srcImg.SubMat(rect);
PlateCar plateCar = Plate_Car_SVM.TestCar(matROI);
if (PlateCar.车牌 == plateCar)
{
MatIndex++;
FileNameAndMat.Add(MatIndex, matROI);
dtMatANDRect.Rows.Add(MatIndex, rect.X, rect.Y);
}
}
}
if (MatIndex != 0)
{
DataView dv = dtMatANDRect.DefaultView;
dv.Sort = "X ASC";
//dtMatANDRect.Clear();
dtMatANDRect = dv.ToTable();
for (int i = 0; i < dtMatANDRect.Rows.Count; i++)
{
int Mindex = int.Parse(dtMatANDRect.Rows[i]["MatIndex"].ToString());
if (FileNameAndMat.ContainsKey(Mindex))
{
Mat resMat;
FileNameAndMat.TryGetValue(Mindex, out resMat);
PicMatrois.Add(resMat);
}
}
}
}
// 图片名称 和 得到的矩形存在Map中
FileNameAndResult.Add(fileNames[index], PicMatrois);
}
//当一张图片有多张车牌时用于判断
String FileName = null;
foreach (KeyValuePair <String, List <Mat> > fvr in FileNameAndResult)
{
string fileName = fvr.Key;
List <Mat> listChar = fvr.Value;
string shortFileName = fileName.Substring(fileName.LastIndexOf(@"\") + 1);
foreach (Mat value in listChar)
{
String res = this.CharsSplit(value);
//Console.WriteLine(this.CharsSplit(value));
///////////////////////////////////////////////////// 效率 shortFileName 存的值
if (res != null && res != "" && res.Length > 4)
{
if (shortFileName == FileName)
{
file.Write("\r\n" + res);
}
else
{
FsIndex++;
if (FsIndex == 1)
{
file.Write(shortFileName + "\r\n" + res);
}
else
{
file.Write("\r\n" + shortFileName + "\r\n" + res);
}
}
Console.WriteLine("=========================" + res);
FileName = shortFileName;
}
}
}
file.Close();
PicMatrois.Clear();
}
}