public void DisplayImage(Image img, PictureBox pb, bool stretch, bool invert)
{
Bitmap bm;
if (img is Int16Image)
{
Int16Image disp = new Int16Image((Int16Image)img);
if (invert)
{
VisionLab.Not((Int16Image)disp);
}
if (stretch)
{
VisionLab.Multiply((Int16Image)disp, 255);
}
bm = VisionLabEx.JLToBitmap(disp);
disp.Dispose();
}
else
{
bm = VisionLabEx.JLToBitmap(img);
}
if (pb.Image != null)
{
pb.Image.Dispose();
}
pb.Image = bm;
}
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();
}
public void DisplayImage(Image img, PictureBox pb, bool stretch, bool invert)
{
Bitmap bm;
if (img is Int16Image)
{
Int16Image disp = new Int16Image((Int16Image)img);
if (invert)
VisionLab.Not((Int16Image)disp);
if (stretch)
VisionLab.Multiply((Int16Image)disp, 255);
bm = VisionLabEx.JLToBitmap(disp);
disp.Dispose();
} else
bm = VisionLabEx.JLToBitmap(img);
if (pb.Image != null)
{
pb.Image.Dispose();
}
pb.Image = bm;
}
/*
* Description:
* Read the license plate
* Input:
* //Rectified license plate image containing six characters
* Int32Image 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(Int32Image binaryCharacterImage, BlobMatcher_Int16 matcher, ClassLexicon lexicon, ref LicensePlate result, ref LicensePlate lexiconResult)
{
//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();
Int16Image binaryCharacter16 = new Int16Image();
//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();
VisionLab.Convert(binaryCharacter, binaryCharacter16);
float conf = matcher.AllMatches(binaryCharacter16, (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();
binaryCharacter16.Dispose();
returnBlobs.Dispose();
match.Dispose();
bestWord.Dispose();
//GC.Collect();
return(true);
}
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
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;
}
/*
* 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
* 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);
}
