public static void DisplayImage(Image img, PictureBox pb, bool stretch, bool invert)
{
Bitmap bm;
if (img is Int32Image)
{
Int32Image disp = new Int32Image((Int32Image)img);
if (invert)
{
VisionLab.Not((Int32Image)disp);
}
if (stretch)
{
VisionLab.Multiply((Int32Image)disp, 255);
}
bm = VisionLabEx.JLToBitmap(disp);
disp.Dispose();
}
else
{
bm = VisionLabEx.JLToBitmap(img);
}
if (pb.Image != null)
{
pb.Image.Dispose();
}
pb.Image = bm;
}
/* Description:
* Find the largest license plate in the image
* - Segment using ThresholdHSVchannels
* - Remove blobs which are not license plates
* Input:
* //Original image
* RGB888Image plateImage
* Output:
* //Segmented license plate
* ref Int32Image binaryPlateImage
* Return:
* //License plate found?
* bool
*/
public static bool FindPlate(RGB888Image plateImage, ref Int32Image binaryPlateImage)
{
//Constants
const int c_threshold_h_min = 21;
const int c_threshold_h_max = 50;
const int c_threshold_s_min = 100;
const int c_threshold_s_max = 255;
const int c_threshold_v_min = 100;
const int c_threshold_v_max = 255;
const int c_remove_blobs_min = 1;
const int c_remove_blobs_max = 5000;
//*******************************//
//** Exercise: **//
//** adjust licenseplate **//
//** segmentation **//
//*******************************//
//Find licenseplate
HSV888Image plateImageHSV = new HSV888Image();
//Convert to RGB to HSV
VisionLab.FastRGBToHSV(plateImage, plateImageHSV);
//Threshold HSV image
VisionLab.Threshold3Channels(plateImageHSV, binaryPlateImage, c_threshold_h_min, c_threshold_h_max, c_threshold_s_min, c_threshold_s_max, c_threshold_v_min, c_threshold_v_max);
//Remove blobs with small areas
VisionLab.RemoveBlobs(binaryPlateImage, Connected.EightConnected, BlobAnalyse.BA_Area, c_remove_blobs_min, c_remove_blobs_max);
plateImageHSV.Dispose();
//GC.Collect();
//Return true, if pixels found
return(VisionLab.SumIntPixels(binaryPlateImage) > 0);
}
private void frmMain_Load(object sender, EventArgs e)
{
VisionLab.InitVisionLib();
lexicon = null;
blobMatcher = null;
LoadFiles();
}
public static void GetBlobsInfo(Image binaryImage, vector_BlobAnalyse analysis, ref vector_Blob blobs)
{
Int32Image labeledBinaryImage = new Int32Image();
VisionLab.Convert(binaryImage, labeledBinaryImage);
int maxlabel = VisionLab.LabelBlobs(labeledBinaryImage, Connected.EightConnected);
VisionLab.BlobAnalysis(labeledBinaryImage, VisionLab.VectorToSet_BlobAnalyse(analysis), maxlabel, blobs);
labeledBinaryImage.Dispose();
//GC.Collect();
}
/* Description:
* Locates the characters of the license plate
* - Warp image (Rectify)
* - Segment characters
* - Remove blobs which are to small (Lines between characters)
* Input:
* //Original image
* RGB888Image plateImage
* //Segmented license plate
* Int32Image binaryPlateImage
* Output:
* //Image containing binary six characters
* ref Int32Image binaryCharacterImage
* Return:
* //Function executed successfully
* bool
*/
public static bool FindCharacters(RGB888Image plateImage, Int32Image binaryPlateImage, ref Int32Image binaryCharacterImage)
{
//Constants
const int c_height = 100;
const int c_width = 470;
const int c_remove_blobs_min = 1;
const int c_remove_blobs_max = 400;
XYCoord leftTop = new XYCoord();
XYCoord rightTop = new XYCoord();
XYCoord leftBottom = new XYCoord();
XYCoord rightBottom = new XYCoord();
//Find licenseplate
VisionLab.FindCornersRectangle(binaryPlateImage, Connected.EightConnected, 0.5, Orientation.Landscape, leftTop, rightTop, leftBottom, rightBottom);
Int32Image plateImageGray = new Int32Image();
VisionLab.Convert(plateImage, plateImageGray);
try
{
//Rectify plate
VisionLab.Warp(plateImageGray, binaryCharacterImage, TransformDirection.ForwardT, new Coord2D(leftTop), new Coord2D(rightTop), new Coord2D(leftBottom), new Coord2D(rightBottom), c_height, c_width, 0);
}
catch (Exception)
{
//Warp, 3 coords on one line
return(false);
}
plateImageGray.Dispose();
//*******************************//
//** Exercise: **//
//** adjust licenseplate **//
//** segmentation **//
//*******************************//
//Find dark text on bright licenseplate using ThresholdISOData
VisionLab.ThresholdIsoData(binaryCharacterImage, ObjectBrightness.DarkObject);
//Remove blobs connected to the border
VisionLab.RemoveBorderBlobs(binaryCharacterImage, Connected.EightConnected, Border.AllBorders);
//Remove small blobs
VisionLab.RemoveBlobs(binaryCharacterImage, Connected.EightConnected, BlobAnalyse.BA_Area, c_remove_blobs_min, c_remove_blobs_max);
leftTop.Dispose();
rightTop.Dispose();
leftBottom.Dispose();
rightBottom.Dispose();
//GC.Collect();
return(true);
}
/*
* Description:
* Read the license plate
* Input:
* //Rectified license plate image containing six characters
* Int32Image labeledRectifiedPlateImage
* Output:
* //Result by the blob matcher
* ref LicensePlate result
* Return:
* //six characters found
* bool
*/
public static bool MatchPlate(Int32Image binaryCharacterImage, ref LicensePlate result)
{
try
{
VisionLab.SetInt32Image(cmdInt, "binaryCharacterImage", binaryCharacterImage);
String plateStr = StripTime(cmdInt.ExecRequest("icall MatchPlate binaryCharacterImage"));
if (plateStr.Substring(0, 5) != "false")
{
string[] plateResult = plateStr.Split(' ');
string plateResultChars = plateResult[0];
result.confidence = double.Parse(plateResult[1], CultureInfo.InvariantCulture);
for (int c = 0; c < 6; c++)
{
result.characters.Add(new LicenseCharacter(plateResultChars[c].ToString(), double.Parse(plateResult[2 + c], CultureInfo.InvariantCulture), result.confidence));
}
bool[] types = new bool[6];
for (int i = 0; i < 6; i++)
{
types[i] = '0' <= result.characters[i].character[0] && result.characters[i].character[0] <= '9';
}
if (types[0] && types[1] && !types[2] && !types[3] && !types[4] && !types[5])
{
return(true);
}
if (!types[0] && !types[1] && types[2] && types[3] && !types[4] && !types[5])
{
return(true);
}
if (!types[0] && !types[1] && !types[2] && !types[3] && types[4] && types[5])
{
return(true);
}
if (types[0] && !types[1] && !types[2] && !types[3] && types[4] && types[5])
{
return(true);
}
if (types[0] && types[1] && !types[2] && !types[3] && !types[4] && types[5])
{
return(true);
}
return(false);
}
else
{
return(false);
}
}
catch (System.Exception ex)
{
throw new Exception("MatchPlate: " + ex.Message);
}
}
/*
* Description:
* Find the largest license plate in the image
* - Segment using ThresholdHSVchannels
* - Remove blobs which are not license plates
* Input:
* //Original image
* RGB888Image plateImage
* Output:
* //Segmented license plate
* ref Int32Image binaryPlateImage
* Return:
* //License plate found?
* bool
*/
public static bool FindPlate(RGB888Image plateImage, ref Int32Image binaryPlateImage)
{
try
{
//Constants
const int c_threshold_h_min = 14;
const int c_threshold_h_max = 50;
const int c_threshold_s_min = 100;
const int c_threshold_s_max = 255;
const int c_threshold_v_min = 46;
const int c_threshold_v_max = 255;
//*******************************//
//** Exercise: **//
//** adjust licenseplate **//
//** segmentation **//
//*******************************//
//Find licenseplate
HSV888Image plateImageHSV = new HSV888Image();
//Convert to RGB to HSV
VisionLab.FastRGBToHSV(plateImage, plateImageHSV);
//Threshold HSV image
VisionLab.Threshold3Channels(plateImageHSV, binaryPlateImage, c_threshold_h_min, c_threshold_h_max, c_threshold_s_min, c_threshold_s_max, c_threshold_v_min, c_threshold_v_max);
VisionLab.LabelBlobs(binaryPlateImage, Connected.EightConnected);
VisionLab.RemoveBlobs(binaryPlateImage, Connected.EightConnected, BlobAnalyse.BA_LengthBreadthRatio, 0.0, 2.50);
VisionLab.RemoveBlobs(binaryPlateImage, Connected.EightConnected, BlobAnalyse.BA_LengthBreadthRatio, 10.0, 9999.0);
//Remove blobs with small areas
VisionLab.RemoveBlobs(binaryPlateImage, Connected.EightConnected, BlobAnalyse.BA_Area, 1, 1000, UseXOrY.UseX);
//VisionLab.RemoveBlobs(binaryPlateImage, Connected.EightConnected, BlobAnalyse.BA_Area, 80000, 120000);
//Fill up characters
VisionLab.FillHoles(binaryPlateImage, Connected.EightConnected);
plateImageHSV.Dispose();
//Return true, if pixels found
return(VisionLab.SumIntPixels(binaryPlateImage) > 0);
}
catch (System.Exception ex)
{
throw new Exception("FindPlate: " + ex.Message);
}
}
/*
* Description:
* Find the largest license plate in the image
* - Segment using ThresholdHSVchannels
* - Remove blobs which are not license plates
* Input:
* //Original image
* RGB888Image plateImage
* Output:
* //Segmented license plate
* ref Int32Image binaryPlateImage
* Return:
* //License plate found?
* bool
*/
public static bool FindPlate(RGB888Image plateImage, ref Int32Image binaryPlateImage)
{
try {
VisionLab.SetRGB888Image(cmdInt, "plateImage", plateImage);
String result = StripTime(cmdInt.ExecRequest("icall FindPlate plateImage binaryPlateImage"));
if (result == "true")
{
VisionLab.GetInt32Image(cmdInt, "binaryPlateImage", binaryPlateImage);
}
return(result == "true");
}
catch (System.Exception ex)
{
throw new Exception("FindPlate: " + ex.Message);
}
}
public static Bitmap JLToBitmap(Image img)
{
Bitmap bm = new Bitmap(img.GetWidth(), img.GetHeight(), PixelFormat.Format32bppRgb);
BitmapData bmdata = bm.LockBits(new Rectangle(0, 0, bm.Width, bm.Height), ImageLockMode.ReadOnly, PixelFormat.Format32bppRgb);
try
{
RGB888Image vlimage = new RGB888Image();
VisionLab.Convert(img, vlimage);
CopyMemory(bmdata.Scan0, vlimage.GetBufPtr(), vlimage.GetWidth() * vlimage.GetHeight() * 4 /*RGB8*/);
vlimage.Dispose();
}
finally
{
bm.UnlockBits(bmdata);
}
return(bm);
}
/*
* Description:
* - Initialize command interpreter
* - add BlobMatcher
* - Add Scripts
*/
public static void Init()
{
try
{
if (cmdInt == null)
{
cmdInt = VisionLab.VisLibCmdIntCreate(100000, EchoMode.EchoOff, false);
}
String exePath = Path.GetDirectoryName(System.Diagnostics.Process.GetCurrentProcess().MainModule.FileName);
String result;
result = cmdInt.ExecRequest("PM_ReadFromFile PatternMatcher " + exePath + "\\..\\..\\..\\..\\..\\..\\VL\\lic_fonts.pm");
result = cmdInt.ExecRequest("AddScript FindPlate " + exePath + "\\..\\..\\..\\..\\..\\..\\VL\\find_plate.jls");
result = cmdInt.ExecRequest("AddScript FindCharacters " + exePath + "\\..\\..\\..\\..\\..\\..\\VL\\find_characters.jls");
result = cmdInt.ExecRequest("AddScript MatchPlate " + exePath + "\\..\\..\\..\\..\\..\\..\\VL\\match_plate.jls");
result = cmdInt.ExecRequest("SetPrecision 6");
}
catch (System.Exception ex)
{
throw new Exception("Init: " + ex.Message);
}
}
/*
* Description:
* Locates the characters of the license plate
* - Warp image (Rectify)
* - Segment characters
* - Remove blobs which are to small (Lines between characters)
* Input:
* //Original image
* RGB888Image plateImage
* //Segmented license plate
* Int32Image binaryPlateImage
* Output:
* //Image containing binary six characters
* ref Int32Image binaryCharacterImage
* Return:
* //Function executed successfully
* bool
*/
public static bool FindCharacters(RGB888Image plateImage, Int32Image binaryPlateImage, ref Int32Image binaryCharacterImage)
{
try
{
VisionLab.SetRGB888Image(cmdInt, "plateImage", plateImage);
VisionLab.SetInt32Image(cmdInt, "binaryPlateImage", binaryPlateImage);
String result = StripTime(cmdInt.ExecRequest("icall FindCharacters plateImage binaryPlateImage binaryCharacterImage"));
if (result == "true")
{
VisionLab.GetInt32Image(cmdInt, "binaryCharacterImage", binaryCharacterImage);
}
return(result == "true");
}
catch (System.Exception ex)
{
//if (ex.Message.StartsWith("[DefaultLUTForImage]"))
// return false;
////if (ex.Message.Substring(0, 6) == "[DefaultLUTForImage]")
//// return false;
throw new Exception("FindCharacters: " + ex.Message);
}
}
/* Description:
* Find the largest license plate in the image
* - Segment using ThresholdHSVchannels
* - Remove blobs which are not license plates
* Input:
* //Original image
* RGB888Image plateImage
* Output:
* //Segmented license plate
* ref Int16Image binaryPlateImage
* Return:
* //License plate found?
* bool
*/
public static bool FindPlate(RGB888Image plateImage, ref Int16Image binaryPlateImage, TresholdConditions state)
{
//Constants
int c_threshold_h_min = 0;
int c_threshold_h_max = 0;
int c_threshold_s_min = 0;
int c_threshold_s_max = 0;
int c_threshold_v_min = 0;
int c_threshold_v_max = 0;
int c_remove_blobs_min = 0;
int c_remove_blobs_max = 500;
switch (state)
{
case (TresholdConditions.NORMAAL):
c_threshold_h_min = 21;
c_threshold_h_max = 50;
c_threshold_s_min = 100;
c_threshold_s_max = 255;
c_threshold_v_min = 100;
c_threshold_v_max = 255;
break;
case (TresholdConditions.ONDERBELICHT):
c_threshold_h_min = 11;
c_threshold_h_max = 119;
c_threshold_s_min = 23;
c_threshold_s_max = 255;
c_threshold_v_min = 56;
c_threshold_v_max = 176;
break;
case (TresholdConditions.OVERBELICHT):
c_threshold_h_min = 0;
c_threshold_h_max = 241;
c_threshold_s_min = 29;
c_threshold_s_max = 241;
c_threshold_v_min = 249;
c_threshold_v_max = 255;
break;
}
//*******************************//
//** Exercise: **//
//** adjust licenseplate **//
//** segmentation **//
//*******************************//
//Find licenseplate
HSV888Image plateImageHSV = new HSV888Image();
//Convert to RGB to HSV
VisionLab.FastRGBToHSV(plateImage, plateImageHSV);
//Threshold HSV image
VisionLab.Threshold3Channels(plateImageHSV, binaryPlateImage, c_threshold_h_min, c_threshold_h_max, c_threshold_s_min, c_threshold_s_max, c_threshold_v_min, c_threshold_v_max);
//Convert to a 32 bit format
Int32Image binaryPlateImage32 = new Int32Image();
VisionLab.Convert(binaryPlateImage, binaryPlateImage32);
//Remove blobs with small areas
VisionLab.RemoveBlobs(binaryPlateImage32, Connected.EightConnected, BlobAnalyse.BA_Area, c_remove_blobs_min, c_remove_blobs_max);
//Remove border blobs
VisionLab.RemoveBorderBlobs(binaryPlateImage32, Connected.EightConnected, Border.AllBorders);
//Length Breath Ratio
VisionLab.RemoveBlobs(binaryPlateImage32, Connected.EightConnected, BlobAnalyse.BA_LengthBreadthRatio, 0, 2.5);
VisionLab.RemoveBlobs(binaryPlateImage32, Connected.EightConnected, BlobAnalyse.BA_LengthBreadthRatio, 6.7, 10);
// Remove blobs that have to less holes
VisionLab.RemoveBlobs(binaryPlateImage32, Connected.EightConnected, BlobAnalyse.BA_NrOfHoles, 0, 5);
// And remove blobs that have a to small area for the holes
VisionLab.RemoveBlobs(binaryPlateImage32, Connected.EightConnected, BlobAnalyse.BA_AreaHoles, 0, 200);
//Convert back to a 16 bit format
VisionLab.Convert(binaryPlateImage32, binaryPlateImage);
//binPlateImage32.Dispose();
binaryPlateImage32.Dispose();
plateImageHSV.Dispose();
GC.Collect();
//Return true, if pixels found
return(VisionLab.SumIntPixels(binaryPlateImage) > 0);
//return VisionLab.LabelBlobs(binaryPlateImage, Connected.EightConnected) == 1;
}
/*
* Description:
* Read the license plate
* Input:
* //Rectified license plate image containing six characters
* Int32Image labeledRectifiedPlateImage
* Output:
* //Result by the blob matcher
* ref LicensePlate result
* Return:
* //six characters found
* bool
*/
public static bool MatchPlate(Int32Image binaryCharacterImage, BlobMatcher_Int32 matcher, ClassLexicon lexicon, ref LicensePlate result, ref LicensePlate lexiconResult)
{
try
{
//Check if 6 characters/blobs have been found and label image.
if (VisionLab.LabelBlobs(binaryCharacterImage, Connected.EightConnected) != 6)
{
return(false);
}
//Calculate dimensions and locations of all characters/blobs.
vector_BlobAnalyse ba_vec = new vector_BlobAnalyse();
ba_vec.Add(BlobAnalyse.BA_TopLeft);
ba_vec.Add(BlobAnalyse.BA_Height);
ba_vec.Add(BlobAnalyse.BA_Width);
vector_Blob returnBlobs = new vector_Blob();
VisionLab.BlobAnalysis(binaryCharacterImage, VisionLab.VectorToSet_BlobAnalyse(ba_vec), VisionLab.MaxPixel(binaryCharacterImage), returnBlobs, SortOrder.SortDown, BlobAnalyse.BA_TopLeft, UseXOrY.UseX);
ba_vec.Dispose();
Int32Image binaryCharacter = new Int32Image();
//Create data structure for lexicon.
vector_vector_LetterMatch match = new vector_vector_LetterMatch();
//Process each character/blob.
foreach (Blob b in returnBlobs)
{
//Cut out character
VisionLab.ROI(binaryCharacterImage, binaryCharacter, b.TopLeft(), new HeightWidth(b.Height(), b.Width()));
//Convert ROI result to binary
VisionLab.ClipPixelValue(binaryCharacter, 0, 1);
//Calculate character's classification for all classes.
vector_PatternMatchResult returnMatches = new vector_PatternMatchResult();
float conf = matcher.AllMatches(binaryCharacter, (float)-0.5, (float)0.5, returnMatches);
float err = returnMatches[0].error;
int id = returnMatches[0].id;
string chr = matcher.PatternName(id);
//Fill datastructure for lexicon.
match.Add(VisionLabEx.PatternMatchResultToLetterMatch(returnMatches));
//Store best match in result
result.characters.Add(new LicenseCharacter(chr, err, conf));
}
//Validate match with lexicon.
vector_int bestWord = new vector_int();
lexiconResult.confidence = lexicon.FindBestWord(match, bestWord, Optimize.OptimizeForMinimum);
for (int i = 0; i < bestWord.Count; i++)
{
string character = matcher.PatternName(bestWord[i]);
//Store lexicon result
lexiconResult.characters.Add(new LicenseCharacter(character));
}
binaryCharacter.Dispose();
returnBlobs.Dispose();
match.Dispose();
bestWord.Dispose();
bool[] types = new bool[6];
for (int i = 0; i < 6; i++)
{
types[i] = '0' <= result.characters[i].character[0] && result.characters[i].character[0] <= '9';
}
if (types[0] && types[1] && !types[2] && !types[3] && !types[4] && !types[5])
{
return(true);
}
if (!types[0] && !types[1] && types[2] && types[3] && !types[4] && !types[5])
{
return(true);
}
if (!types[0] && !types[1] && !types[2] && !types[3] && types[4] && types[5])
{
return(true);
}
if (types[0] && !types[1] && !types[2] && !types[3] && types[4] && types[5])
{
return(true);
}
if (types[0] && types[1] && !types[2] && !types[3] && !types[4] && types[5])
{
return(true);
}
return(false);
}
catch (System.Exception ex)
{
throw new Exception("MatchPlate: " + ex.Message);
}
}
private void MatchImage(String Filename, bool add)
{
ErrorTB.Text = "";
lblExpected.Text = Filename.Substring(0, 6);
//*************************************//
//** load and display original image **//
//*************************************//
Bitmap bm;
bm = new Bitmap(Filename);
RGB888Image plateImage = VisionLabEx.BitmapToJL(bm);
DisplayImage(plateImage, imgOrig);
//****************//
//** Find plate **//
//****************//
Int16Image binaryPlateImage = new Int16Image();
if (!LicensePlateMatcher.FindPlate(plateImage, ref binaryPlateImage, TresholdConditions.NORMAAL) &&
!LicensePlateMatcher.FindPlate(plateImage, ref binaryPlateImage, TresholdConditions.OVERBELICHT) &&
!LicensePlateMatcher.FindPlate(plateImage, ref binaryPlateImage, TresholdConditions.ONDERBELICHT))
{
DisplayImage(binaryPlateImage, imgPlateBin, true, true);
lblLexiconResult.Text = "";
if (add)
{
lstFindPlateErr.Items.Add(Filename);
lblFindPlateErrCount.Text = lstFindPlateErr.Items.Count.ToString();
}
ClearResultLabels();
return;
}
DisplayImage(binaryPlateImage, imgPlateBin, true, true);
//*******************//
//** Rectify plate **//
//*******************//
Int16Image binaryRectifiedImage = new Int16Image();
do
{
// Check if we can find the plate
if (LicensePlateMatcher.FindCharacters(plateImage, binaryPlateImage, ref binaryRectifiedImage))
{
// if so we are done
break;
}
else
{
// ************************************
// ** Find the biggest blob in the **
// ** image and remove that one **
// ************************************
VisionLab.LabelBlobs(binaryPlateImage, Connected.EightConnected);
vector_BlobAnalyse ba_vec = new vector_BlobAnalyse();
ba_vec.Add(BlobAnalyse.BA_Area);
vector_Blob blobs = new vector_Blob();
VisionLab.BlobAnalysis(binaryPlateImage, VisionLab.VectorToSet_BlobAnalyse(ba_vec), VisionLab.MaxPixel(binaryPlateImage), blobs);
int biggestArea = 0;
foreach (Blob ba in blobs)
{
if (ba.Area() > biggestArea)
{
biggestArea = ba.Area();
}
}
VisionLab.RemoveBlobs(binaryPlateImage, Connected.EightConnected, BlobAnalyse.BA_Area, biggestArea - 1, biggestArea + 1);
}
} // Repeat this until there is nothing left
while (VisionLab.SumIntPixels(binaryPlateImage) > 0);
// If that is so it wasn't found
if (VisionLab.SumIntPixels(binaryPlateImage) == 0)
{
// So do all this
if (imgRectifiedPlate.Image != null)
{
imgRectifiedPlate.Image.Dispose();
}
imgRectifiedPlate.Image = null;
lblLexiconResult.Text = "";
if (add)
{
lstRectifyPlateErr.Items.Add(Filename);
lblRectfyPlateErrCount.Text = lstRectifyPlateErr.Items.Count.ToString();
}
ClearResultLabels();
return;
}
DisplayImage(binaryRectifiedImage, imgRectifiedPlate, true, true);
//*****************//
//** Match Plate **//
//*****************//
LicensePlate result = new LicensePlate();
LicensePlate lexiconResult = new LicensePlate();
if (!LicensePlateMatcher.MatchPlate(binaryRectifiedImage, blobMatcher, lexicon, ref result, ref lexiconResult, false))
{
lblLexiconResult.Text = "";
if (add)
{
lstMatchPlateErr.Items.Add(Filename);
lblMatchPlateErrCount.Text = lstMatchPlateErr.Items.Count.ToString();
}
ClearResultLabels();
return;
}
// Extra way to up confidence perhaps
if (result.characters != null && result.confidence < (double)nupConfidence.Value / 100)
{
LicensePlateMatcher.MatchPlate(binaryRectifiedImage, blobMatcher, lexicon, ref result, ref lexiconResult, true);
}
//*********************//
//** Process results **//
//*********************//
ProcessResults(result, lexiconResult, Filename, (double)nupConfidence.Value / 100, add);
bm.Dispose();
plateImage.Dispose();
binaryPlateImage.Dispose();
binaryRectifiedImage.Dispose();
//Force a garbage collect to prevens malloc errors from unmanaged code.
GC.Collect();
}
/*
* Description:
* Read the license plate
* Input:
* //Rectified license plate image containing six characters
* Int16Image labeledRectifiedPlateImage
* BlobMatcher_Int16 matcher //initialized blobmatcher
* ClassLexicon lexicon //initialized lexicon
* Output:
* //Result by the blob matcher
* ref LicensePlate result
* //Result by the lexicon
* ref LicensePlate lexiconResult
* Return:
* //six characters found
* bool
*/
public static bool MatchPlate(Int16Image binaryCharacterImage, BlobMatcher_Int16 matcher,
ClassLexicon lexicon, ref LicensePlate result, ref LicensePlate lexiconResult, bool dilate)
{
// NIEUW
// 2de optie voor aanroep als eerste low confidence levert
if (dilate)
{
Int16Image temp = new Int16Image();
VisionLab.Dilation(binaryCharacterImage, temp, new Mask_Int32(3, 3, 1));
binaryCharacterImage = new Int16Image(temp);
temp.Dispose();
}
if (VisionLab.LabelBlobs(binaryCharacterImage, Connected.EightConnected) != 6)
{
return(false);
}
//Calculate dimensions and locations of all characters/blobs.
vector_BlobAnalyse ba_vec = new vector_BlobAnalyse();
ba_vec.Add(BlobAnalyse.BA_TopLeft);
ba_vec.Add(BlobAnalyse.BA_Height);
ba_vec.Add(BlobAnalyse.BA_Width);
vector_Blob returnBlobs = new vector_Blob();
VisionLab.BlobAnalysis(binaryCharacterImage, VisionLab.VectorToSet_BlobAnalyse(ba_vec), VisionLab.MaxPixel(binaryCharacterImage), returnBlobs, SortOrder.SortDown, BlobAnalyse.BA_TopLeft, UseXOrY.UseX);
ba_vec.Dispose();
Int16Image binaryCharacter = new Int16Image();
//Create data structure for lexicon.
vector_vector_LetterMatch match = new vector_vector_LetterMatch();
// NIEUW
// Change the matcher params
matcher.ChangeParams(60, 10, 64, 0);
//Process each character/blob.
foreach (Blob b in returnBlobs)
{
//Cut out character
VisionLab.ROI(binaryCharacterImage, binaryCharacter, b.TopLeft(), new HeightWidth(b.Height(), b.Width()));
//Convert ROI result to binary
VisionLab.ClipPixelValue(binaryCharacter, 0, 1);
//Calculate character's classification for all classes.
vector_PatternMatchResult returnMatches = new vector_PatternMatchResult();
float conf = matcher.AllMatches(binaryCharacter, (float)-0.5, (float)0.5, returnMatches);
float err = returnMatches[0].error;
int id = returnMatches[0].id;
string chr = matcher.PatternName(id);
// NIEUW
// If error to big decrease the confidence
if (err > 0.20f)
{
conf -= 0.2f;
}
//Fill datastructure for lexicon.
match.Add(VisionLabEx.PatternMatchResultToLetterMatch(returnMatches));
//Store best match in result
result.characters.Add(
new LicenseCharacter(
chr,
err,
conf,
// NIEUW
// Extra param: The middle of a character
// (used for matching patterns)
b.TopLeft().x + ((b.TopRight().x - b.TopLeft().x) / 2),
// NIEUW
// All other results that we're found
// So we can switch between em
returnMatches
));
}
//Validate match with lexicon.
vector_int bestWord = new vector_int();
lexiconResult.confidence = lexicon.FindBestWord(match, bestWord, Optimize.OptimizeForMinimum);
for (int i = 0; i < bestWord.Count; i++)
{
string character = matcher.PatternName(bestWord[i]);
//Store lexicon result
lexiconResult.characters.Add(new LicenseCharacter(character));
}
// NIEUW
// Create the best match with the aid of the pattern matcher
result.CalculateBestMatch(matcher);
binaryCharacter.Dispose();
returnBlobs.Dispose();
match.Dispose();
bestWord.Dispose();
GC.Collect();
return(true);
}
/*
* Description:
* Locates the characters of the license plate
* - Warp image (Rectify)
* - Segment characters
* - Remove blobs which are to small (Lines between characters)
* Input:
* //Original image
* RGB888Image plateImage
* //Segmented license plate
* Int32Image binaryPlateImage
* Output:
* //Image containing binary six characters
* ref Int32Image binaryCharacterImage
* Return:
* //Function executed successfully
* bool
*/
public static bool FindCharacters(RGB888Image plateImage, Int32Image binaryPlateImage, ref Int32Image binaryCharacterImage)
{
try
{
//Constants
const int c_height = 100;
const int c_width = 470;
const int c_remove_blobs_min = 1;
const int c_remove_blobs_max = 450;
XYCoord leftTop = new XYCoord();
XYCoord rightTop = new XYCoord();
XYCoord leftBottom = new XYCoord();
XYCoord rightBottom = new XYCoord();
//Find licenseplate
VisionLab.FindCornersRectangle(binaryPlateImage, Connected.EightConnected, 0.5, Orientation.Landscape, leftTop, rightTop, leftBottom, rightBottom);
if (!VisionLab.WarpCoordsValid(new Coord2D(leftTop), new Coord2D(rightTop), new Coord2D(leftBottom), new Coord2D(rightBottom)))
{
VisionLab.FindCornersRectangleSq(binaryPlateImage, Connected.EightConnected, leftTop, rightTop, leftBottom, rightBottom);
if (!VisionLab.WarpCoordsValid(new Coord2D(leftTop), new Coord2D(rightTop), new Coord2D(leftBottom), new Coord2D(rightBottom)))
{
return(false);
}
}
Int32Image plateImageGray = new Int32Image();
VisionLab.Convert(plateImage, plateImageGray);
//Rectify plate
VisionLab.Warp(plateImageGray, binaryCharacterImage, TransformDirection.ForwardT, new Coord2D(leftTop), new Coord2D(rightTop), new Coord2D(leftBottom), new Coord2D(rightBottom), c_height, c_width, 0);
plateImageGray.Dispose();
//*******************************//
//** Exercise: **//
//** adjust licenseplate **//
//** segmentation **//
//*******************************//
//Find dark text on bright licenseplate using ThresholdISOData
VisionLab.ThresholdIsoData(binaryCharacterImage, ObjectBrightness.DarkObject);
//Remove small blobs and noise
Int32Image binaryCharacterImageCopy = new Int32Image(binaryCharacterImage);
VisionLab.Opening(binaryCharacterImageCopy, binaryCharacterImage, new Mask_Int32(5, 1, 1));
//Remove blobs connected to the border
VisionLab.RemoveBorderBlobs(binaryCharacterImage, Connected.EightConnected, Border.AllBorders);
//Remove small blobs
VisionLab.RemoveBlobs(binaryCharacterImage, Connected.EightConnected, BlobAnalyse.BA_Area, c_remove_blobs_min, c_remove_blobs_max);
leftTop.Dispose();
rightTop.Dispose();
leftBottom.Dispose();
rightBottom.Dispose();
return(true);
}
catch (System.Exception ex)
{
throw new Exception("FindCharacters: " + ex.Message);
}
}
/* Description:
* Locates the characters of the license plate
* - Warp image (Rectify)
* - Segment characters
* - Remove blobs which are to small (Lines between characters)
* Input:
* //Original image
* RGB888Image plateImage
* //Segmented license plate
* Int16Image binaryPlateImage
* Output:
* //Image containing binary six characters
* ref Int16Image binaryCharacterImage
* Return:
* //Function executed successfully
* bool
*/
public static bool FindCharacters(RGB888Image plateImage, Int16Image binaryPlateImage, ref Int16Image binaryCharacterImage)
{
//Constants
const int c_height = 100;
const int c_width = 470;
const int c_remove_blobs_min = 0;
const int c_remove_blobs_max = 400;
XYCoord leftTop = new XYCoord();
XYCoord rightTop = new XYCoord();
XYCoord leftBottom = new XYCoord();
XYCoord rightBottom = new XYCoord();
// 2de set coordinaten:
// NIEUW
XYCoord leftTop2 = new XYCoord();
XYCoord rightTop2 = new XYCoord();
XYCoord leftBottom2 = new XYCoord();
XYCoord rightBottom2 = new XYCoord();
//Find licenseplate
Int32Image binaryPlateImage32 = new Int32Image();
VisionLab.Convert(binaryPlateImage, binaryPlateImage32);
VisionLab.FindCornersRectangle(
binaryPlateImage32,
Connected.EightConnected,
0.5,
Orientation.Landscape,
leftTop,
rightTop,
leftBottom,
rightBottom
);
// NIEUW
// Coordinaten bepalen voor deze functie
VisionLab.FindCornersRectangleSq(
binaryPlateImage32,
Connected.EightConnected,
leftTop2,
rightTop2,
leftBottom2,
rightBottom2
);
binaryPlateImage32.Dispose();
Int16Image plateImageGray = new Int16Image();
VisionLab.Convert(plateImage, plateImageGray);
binaryCharacterImage.Assign_Op(plateImageGray);
// Eerst de standaard wrap proberen
try
{
//Rectify plate
VisionLab.Warp(
plateImageGray,
binaryCharacterImage,
TransformDirection.ForwardT,
new Coord2D(leftTop),
new Coord2D(rightTop),
new Coord2D(leftBottom),
new Coord2D(rightBottom),
c_height,
c_width,
0
);
}
catch (Exception)
{
// NIEUW
// Als dat mislukt dan de andere gebruiken
try
{
VisionLab.Warp(plateImageGray,
binaryCharacterImage,
TransformDirection.ForwardT,
new Coord2D(leftTop2),
new Coord2D(rightTop2),
new Coord2D(leftBottom2),
new Coord2D(rightBottom2),
c_height,
c_width,
0
);
}
catch (Exception)
{
return(false);
}
}
plateImageGray.Dispose();
//*******************************//
//** Exercise: **//
//** adjust licenseplate **//
//** segmentation **//
//*******************************//
// NIEUW
Int16Image MaxMin = new Int16Image();
Int16Image MaxMin2 = new Int16Image();
// NIEUW
//2 x max rondje ding
//dan 2 x min rondje ding
//dan van elkaar aftrekken
//(zoeken op heldere object)
Mask_Int32 mask = new Mask_Int32(11, 11, 5, 5);
VisionLab.MaximumFilter(binaryCharacterImage, MaxMin, FixEdge.EdgeExtend, mask);
VisionLab.MaximumFilter(MaxMin, MaxMin2, FixEdge.EdgeExtend, mask);
VisionLab.MinimumFilter(MaxMin2, MaxMin, FixEdge.EdgeExtend, mask);
VisionLab.MinimumFilter(MaxMin, MaxMin2, FixEdge.EdgeExtend, mask);
// Maxmin2 holds the result now of the filter oparations
// Get the difference between both
VisionLab.Subtract(binaryCharacterImage, MaxMin2);
MaxMin2.Dispose();
MaxMin.Dispose();
//Find dark text on bright licenseplate using ThresholdISOData
VisionLab.ThresholdIsoData(binaryCharacterImage, ObjectBrightness.DarkObject);
Int16Image bin = new Int16Image();
// NIEUW
// Recreate images that are corralated / deformed
VisionLab.Opening(binaryCharacterImage, bin, new Mask_Int32(2, 2, 1));
// Convert to a 32 bit format
Int32Image binaryCharacterImage32 = new Int32Image();
// Int32Image binCharImg32 = new Int32Image();
VisionLab.Convert(bin, binaryCharacterImage32);
bin.Dispose();
// Remove blobs connected to the border
VisionLab.RemoveBorderBlobs(binaryCharacterImage32, Connected.EightConnected, Border.AllBorders);
// Remove small blobs
VisionLab.RemoveBlobs(binaryCharacterImage32, Connected.EightConnected, BlobAnalyse.BA_Area, c_remove_blobs_min, c_remove_blobs_max);
//Convert to a 16 bit format
VisionLab.Convert(binaryCharacterImage32, binaryCharacterImage);
binaryCharacterImage32.Dispose();
leftTop.Dispose();
rightTop.Dispose();
leftBottom.Dispose();
rightBottom.Dispose();
GC.Collect();
// NIEUW
// Check if 6 characters/blobs have been found and label image.
if (VisionLab.LabelBlobs(binaryCharacterImage, Connected.EightConnected) != 6)
{
return(false);
}
return(true);
}
