static void Main(string[] args)
{
int errorCode = 0;
TextRecognizer.InitTextRecognizer();
InitParameters();
//foreach (var file in Directory.EnumerateFiles(@"D:\Imagini ANPR auto\1", "*.jpg"))
foreach (var file in Directory.EnumerateFiles(@"D:\Imagini ANPR auto\5", "2*.png"))
{
Stopwatch watch = Stopwatch.StartNew(); // time the detection process
Mat imgOriginal = new Mat(file);
Mat imgGrayScale = new Mat();
Mat imgThresh = new Mat();
List <KeyValuePair <string, bool> > licensePlateNumber = new List <KeyValuePair <string, bool> >();
ImageProcess.Preprocess(imgOriginal, ref imgGrayScale, ref imgThresh, ref errorCode);
if (Properties.Settings.Default.debug)
{
CvInvoke.Imshow("Original image", imgOriginal);
CvInvoke.Imshow("Threshold", imgThresh);
}
List <KeyValuePair <Mat, Mat> > licensePlates = PlateExtraction.DetectPlate(imgGrayScale, imgThresh, ref errorCode);
foreach (var licensePlate in licensePlates)
{
if (licensePlate.Value != null)
{
licensePlateNumber.Add(TextRecognizer.RecognizeText(licensePlate.Value));
}
}
foreach (KeyValuePair <string, bool> pair in licensePlateNumber)
{
if (pair.Value == true)
{
Console.WriteLine("License plate # " + pair.Key);
break;
}
else
{
Console.WriteLine("License plate # " + LicensePlateRegex.LicensePlateNumberProcess(pair.Key));
}
}
watch.Stop(); //stop the timer
Console.WriteLine(watch.Elapsed);
CvInvoke.WaitKey();
CvInvoke.DestroyAllWindows();
}
Console.ReadLine();
}
public static KeyValuePair <string, bool> RecognizeText(Mat imgInput)
{
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
List <ContoursWithData> listOfContoursWithData = new List <ContoursWithData>(); // declare a list of contours with data
CvInvoke.FindContours(imgInput, contours, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
//imgInput.Save(@"D:\Visual Studio Projects\KNNtrain\Imagini\4\" + i + ".jpg");
//i++;
// populate list of contours with data
for (int i = 0; i <= contours.Size - 1; i++) // for each contour
{
if ((CvInvoke.ContourArea(contours[i]) > MIN_CONTOUR_AREA))
{
ContoursWithData contourWithData = new ContoursWithData(); // declare new contour with data
contourWithData.contour = contours[i]; // populate contour member variable
contourWithData.boundingRectangle = CvInvoke.BoundingRectangle(contourWithData.contour); // calculate bounding rectangle
contourWithData.dblArea = CvInvoke.ContourArea(contourWithData.contour); // calculate area
if (contourWithData.checkIfContourIsValid())
{
listOfContoursWithData.Add(contourWithData);// add to list of contours with data
}
else
{
if (contourWithData.boundingRectangle.Width > Properties.Settings.Default.boundingRectangleWidthMax)
{
Mat imgROItoBeCloned = new Mat(imgInput, contourWithData.boundingRectangle);
Mat imgROI = imgROItoBeCloned.Clone();
Rectangle rectangleFirst = new Rectangle(0, 0, contourWithData.boundingRectangle.Width / 2, contourWithData.boundingRectangle.Height);
Rectangle rectangleSecond = new Rectangle(contourWithData.boundingRectangle.Width / 2, 0, contourWithData.boundingRectangle.Width / 2, contourWithData.boundingRectangle.Height);
Mat firstImage = new Mat(imgROI, rectangleFirst);
Mat secondImage = new Mat(imgROI, rectangleSecond);
VectorOfVectorOfPoint contoursSplitImage = new VectorOfVectorOfPoint();
CvInvoke.FindContours(firstImage, contoursSplitImage, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
for (int j = 0; j <= contoursSplitImage.Size - 1; j++) // for each contour
{
if ((CvInvoke.ContourArea(contoursSplitImage[j]) > MIN_CONTOUR_AREA))
{
ContoursWithData contourWithDataFirstImage = new ContoursWithData(); // declare new contour with data
contourWithDataFirstImage.contour = contoursSplitImage[j]; // populate contour member variable
contourWithDataFirstImage.boundingRectangle = CvInvoke.BoundingRectangle(contourWithDataFirstImage.contour); // calculate bounding rectangle
contourWithDataFirstImage.boundingRectangle.X = contourWithData.boundingRectangle.X;
contourWithDataFirstImage.boundingRectangle.Y = contourWithData.boundingRectangle.Y;
contourWithDataFirstImage.dblArea = CvInvoke.ContourArea(contourWithDataFirstImage.contour); // calculate area
if (contourWithDataFirstImage.checkIfContourIsValid())
{
listOfContoursWithData.Add(contourWithDataFirstImage);// add to list of contours with data
}
}
}
contoursSplitImage = new VectorOfVectorOfPoint();
CvInvoke.FindContours(secondImage, contoursSplitImage, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
for (int j = 0; j <= contoursSplitImage.Size - 1; j++) // for each contour
{
if ((CvInvoke.ContourArea(contoursSplitImage[j]) > MIN_CONTOUR_AREA))
{
ContoursWithData contourWithDataSecondImage = new ContoursWithData(); // declare new contour with data
contourWithDataSecondImage.contour = contoursSplitImage[j]; // populate contour member variable
contourWithDataSecondImage.boundingRectangle = CvInvoke.BoundingRectangle(contourWithDataSecondImage.contour); // calculate bounding rectangle
contourWithDataSecondImage.boundingRectangle.X = contourWithData.boundingRectangle.X + contourWithData.boundingRectangle.Width / 2;
contourWithDataSecondImage.boundingRectangle.Y = contourWithData.boundingRectangle.Y;
contourWithDataSecondImage.dblArea = CvInvoke.ContourArea(contourWithDataSecondImage.contour); // calculate area
if (contourWithDataSecondImage.checkIfContourIsValid())
{
listOfContoursWithData.Add(contourWithDataSecondImage);// add to list of contours with data
}
}
}
}
}
}
}
float averageLocationY = 0;
float sumLocationY = 0;
foreach (var item in listOfContoursWithData)
{
sumLocationY += item.boundingRectangle.Y + item.boundingRectangle.Height / 2;
}
averageLocationY = sumLocationY / listOfContoursWithData.Count;
float minLocationY = averageLocationY - 0.15f * averageLocationY;
float maxLocationY = averageLocationY + 0.15f * averageLocationY;
listOfContoursWithData.RemoveAll(x => minLocationY > x.boundingRectangle.Y + x.boundingRectangle.Height / 2 || maxLocationY < x.boundingRectangle.Y + x.boundingRectangle.Height / 2);
// sort contours with data from left to right
listOfContoursWithData.Sort((oneContourWithData, otherContourWithData) => oneContourWithData.boundingRectangle.X.CompareTo(otherContourWithData.boundingRectangle.X));
string strFinalString = ""; // declare final string, this will have the final number sequence by the end of the program
foreach (ContoursWithData contourWithData in listOfContoursWithData) // for each contour in list of valid contours
{
CvInvoke.Rectangle(imgInput, contourWithData.boundingRectangle, new MCvScalar(200, 0.0, 0.0), 2); // draw green rect around the current char
Mat imgROItoBeCloned = new Mat(imgInput, contourWithData.boundingRectangle); // get ROI image of bounding rect
Mat imgROI = imgROItoBeCloned.Clone(); // clone ROI image so we don't change original when we resize
Mat imgROIResized = new Mat();
// resize image, this is necessary for char recognition
CvInvoke.Resize(imgROI, imgROIResized, new System.Drawing.Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT));
// declare a Matrix of the same dimensions as the Image we are adding to the data structure of training images
Matrix <float> mtxTemp = new Matrix <float>(imgROIResized.Size);
// declare a flattened (only 1 row) matrix of the same total size
Matrix <float> mtxTempReshaped = new Matrix <float>(1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT);
imgROIResized.ConvertTo(mtxTemp, DepthType.Cv32F); // convert Image to a Matrix of Singles with the same dimensions
for (int intRow = 0; intRow <= RESIZED_IMAGE_HEIGHT - 1; intRow++) // flatten Matrix into one row by RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT number of columns
{
for (int intCol = 0; intCol <= RESIZED_IMAGE_WIDTH - 1; intCol++)
{
mtxTempReshaped[0, (intRow * RESIZED_IMAGE_WIDTH) + intCol] = mtxTemp[intRow, intCol];
}
}
float sngCurrentChar;
sngCurrentChar = kNearest.Predict(mtxTempReshaped); // finally we can call Predict !!!
strFinalString = strFinalString + (char)(Convert.ToInt32(sngCurrentChar)); // append current char to full string of chars
}
bool licensePlateRegex = LicensePlateRegex.MatchRegex(strFinalString);
return(new KeyValuePair <string, bool>(strFinalString, licensePlateRegex));
}
