//测试
public static PlateChar Test(Mat matTest)
{
try
{
if (IsReady == false || svm == null)
{
throw new Exception("训练数据为空，请重新训练字符类型识别或加载数据");
}
PlateChar result = PlateChar.非字符;
if (IsReady == false || svm == null)
{
return(result);
}
float[] descriptor = ComputeHogDescriptors(matTest);
Mat testDescriptor = TypeConvert.Float2Mat(descriptor);
float predict = svm.Predict(testDescriptor);
result = (PlateChar)((int)predict);
return(result);
}
catch (Exception ex)
{
throw ex;
}
}
public CharImage(string fileName, string name, PlateChar plateChar, OpenCvSharp.Size matSize)
{
this.FileName = fileName;
this.Name = name;
this.PlateChar = plateChar;
this.MatSize = matSize;
}
public static void SaveCharSample(Mat charMat, PlateChar plateChar, string libPath)
{
CharInfo charInfo = new CharInfo();
charInfo.OriginalMat = charMat;
charInfo.PlateChar = plateChar;
SaveCharSample(charInfo, libPath);
}
public static void SaveCharSample(Mat charMat, PlateChar plateChar, string libPath, string
shortFileNameNoExt)
{
string fileName = string.Format(@"{0}\chars\{1}\{2}.jpg", libPath, plateChar, shortFileNameNoExt);
charMat = charMat.Resize(HOGWinSize);
charMat.SaveImage(fileName);
}
public CharInfo(PlateChar plateChar, Mat originalMat, Rect originalRect, PlateLocateMethod plateLocateMethod, CharSplitMethod charSplitMethod)
{
this.PlateChar = plateChar;
this.OriginalMat = originalMat;
this.OriginalRect = originalRect;
this.PlateLocateMethod = plateLocateMethod;
this.CharSplitMethod = charSplitMethod;
}
//
public static PlateInfo GetPlateInfo(PlateInfo plateInfo, PlateColor plateColor, CharSplitMethod
splitMethod)
{
PlateInfo result = new PlateInfo();
result.PlateCategory = plateInfo.PlateCategory;
result.OriginalMat = plateInfo.OriginalMat;
result.OriginalRect = plateInfo.OriginalRect;
result.PlateLocateMethod = plateInfo.PlateLocateMethod;
result.PlateColor = plateColor;
List <CharInfo> charInfos = new List <CharInfo>();
switch (splitMethod)
{
case CharSplitMethod.伽马:
charInfos = CharSegment_V3.SplitePlateByGammaTransform(plateInfo.OriginalMat,
plateColor);
break;
case CharSplitMethod.指数:
charInfos = CharSegment_V3.SplitePlateByIndexTransform(plateInfo.OriginalMat, plateColor);
break;
case CharSplitMethod.对数:
charInfos = CharSegment_V3.SplitePlateByLogTransform(plateInfo.OriginalMat, plateColor);
break;
case CharSplitMethod.原图:
default:
charInfos = CharSegment_V3.SplitePlateByOriginal(plateInfo.OriginalMat,
plateInfo.OriginalMat, plateColor);
break;
}
//启动SVM识别字符并将其中的非字符去除
for (int index = charInfos.Count - 1; index >= 0; index--)
{
CharInfo charInfo = charInfos[index];
PlateChar plateChar = PlateChar_SVM.Test(charInfo.OriginalMat);
if (plateChar == PlateChar.非字符)
{
charInfos.RemoveAt(index);
}
charInfo.PlateChar = plateChar;
}
result.CharInfos = charInfos;
//检测
CheckLeftAndRightToRemove(result);
CheckPlateColor(result);
return(result);
}
int yCount = 0; //��������
#endregion Fields
#region Constructors
public FormPlate(FormFrame f)
{
InitializeComponent();
formFrame = f;
ucButtons = new UCButtons(f, this.pnRight);
ucButtons.SetAckVisible(true);
ucButtons.RegisterBtnEvent(ClickUp, ClickDown, ClickAck, ClickReturn,null);
this.pnRight.Controls.Add(ucButtons);
plateCharList = new List<PlateChar>();
plateCharList.AddRange(formFrame.configManage.cfg.plateCharList);
for (int i = 0; i <= 9; i++)
{
PlateChar plateChar = new PlateChar();
plateChar.display = i.ToString();
plateChar.value = i.ToString();
plateCharList.Add(plateChar);
}
for (char i = 'A'; i <= 'Z'; i++)
{
PlateChar plateChar = new PlateChar();
plateChar.display = i.ToString();
plateChar.value = i.ToString();
plateCharList.Add(plateChar);
}
yCount = plateCharList.Count / xCount;
if (plateCharList.Count % xCount != 0) yCount++;
listScreen = new List<string>(xCount * yCount);
for (int i = 0; i < plateCharList.Count; i++)
{
listScreen.Add(plateCharList[i].display);
}
listScreen.Add("<- ");
listScreen.Add("CE");
}
//
public static PlateChar Test(Mat matTest)
{
//Mat matTest = mat.CvtColor(ColorConversionCodes.BayerRG2BGR);
//try
//{
if (IsReady == false || svm == null)
{
throw new Exception("训练数据为空，请重新训练字符或加载数据");
}
PlateChar result = PlateChar.非字符;
//$$$$$$$$$$$$$$$$$$$$$$$$
Mat matTestGray = matTest;
if (matTest.Channels() == 3)
{
matTestGray = matTest.CvtColor(ColorConversionCodes.BGR2GRAY);
}
//之后改为1-255
Mat matTestDst = matTestGray.Threshold(0, 225, ThresholdTypes.Otsu | ThresholdTypes.Binary);
float[] descriptor = ComputeHogDescriptors(matTestDst);
Mat testDescriptor = Mat.Zeros(1, descriptor.Length, MatType.CV_32FC1);
for (int index = 0; index < descriptor.Length; index++)
{
testDescriptor.Set <float>(0, index, descriptor[index]);
}
float predict = svm.Predict(testDescriptor);
result = (PlateChar)((int)predict);
return(result);
//}
//catch (Exception ex)
//{
// throw ex;
//}
}
//对字符进行训练
public static void TrainSVMDataForCharRecog(string path)
{
Console.WriteLine("preparing for training data!!!!");
List <TrainStruct> svmData = new List <TrainStruct>();
List <List <string> > ImgFiles = new List <List <string> >();
//for(int index=0;index<10;index++)
//{
// string label = "_" + index.ToString();
// string filePath = @"C:\Users\faiz\Desktop\AI\车牌-字符样本\车牌-字符样本\chars\" + label;
// Console.WriteLine("{0}", filePath);
// List<string> files = getSampleFiles(filePath);
// ImgFiles.Add(files);
//}
for (int index = (int)PlateChar.A; index <= (int)PlateChar._9; index++)
{
if (index >= 28 && index <= 37)
{
string labelForNumber = "_" + (index - 28).ToString();
string filePath = path + labelForNumber;//@"C:\Users\faiz\Desktop\AI\车牌-字符样本\车牌-字符样本\chars\"
Console.WriteLine("{0}", filePath);
List <string> files = getSampleFiles(filePath);
ImgFiles.Add(files);
}
else
{
PlateChar plateChar = (PlateChar)index;
string labelForWord = plateChar.ToString();
string filePath = path + labelForWord;//@"C:\Users\faiz\Desktop\AI\车牌-字符样本\车牌-字符样本\chars\"
Console.WriteLine("{0}", filePath);
List <string> files = getSampleFiles(filePath);
ImgFiles.Add(files);
}
}
//现在主要是要识别广东车牌
List <string> exFiles = getSampleFiles(path + PlateChar.粤.ToString());
ImgFiles.Add(exFiles);
for (int index1 = 0; index1 < ImgFiles.Count - 1; index1++)
{
for (int index2 = 0; index2 < ImgFiles.ElementAt(index1).Count; index2++)
{
TrainStruct trainData;
trainData.file = ImgFiles.ElementAt(index1).ElementAt(index2);
trainData.label = index1 + 2;
svmData.Add(trainData);
}
}
//最后再添加东的数据
for (int index = 0; index < ImgFiles.ElementAt(ImgFiles.Count - 1).Count; index++)
{
TrainStruct trainData;
trainData.file = ImgFiles.ElementAt(ImgFiles.Count - 1).ElementAt(index);
trainData.label = (int)PlateChar.粤;
svmData.Add(trainData);
}
Mat samples = new Mat();
Mat responses = new Mat();
//读取数据并进行处理
for (int index = 0; index < svmData.Count; index++)
{
//以灰度的方式读取图片
Mat img = Cv2.ImRead(svmData[index].file, ImreadModes.Grayscale);
//剔除无用数据
if (img.Data == null)
{
Console.WriteLine("failed to load image {0}", svmData[index].file);
}
//对图片进行二值化
Mat dst = new Mat();
Cv2.Threshold(img, dst, 1, 255, ThresholdTypes.Otsu | ThresholdTypes.Binary);
Mat feature = GetCharSvmHOGFeatures(dst);
//获取HOG特征
feature = feature.Reshape(1, 1);
samples.PushBack(feature);
responses.PushBack(Int2Mat(svmData[index].label));
}
// 训练数据的格式，OpenCV规定 samples 中的数据都是需要32位浮点型
// 因为TrainData::create 第一个参数是规定死的要cv_32F
samples.ConvertTo(samples, MatType.CV_32F);
// samples 将图片和样本标签合并成为一个训练集数据
// 第二个参数的原因是，我们的samples 中的每个图片数据的排列都是一行
if (PlateChar_SVM.Train(samples, responses))
{
Console.WriteLine("Trained success!!!");
PlateChar_SVM.Save(@"E:\工作文件夹（workplace）\VSworkplace\PlateRecog\charRecog.xml");
Console.WriteLine("\".xml\"has been written!!1");
}
else
{
Console.WriteLine("failed to train data");
}
}