public BPN(ActivationFunction func, vector_int layerDescrTab, BiasNodes bias, vector_MinMaxInput minMaxInputTab, double minOutput, double maxOutput) : this(VisionLabPINVOKE.new_BPN__SWIG_1((int)func, vector_int.getCPtr(layerDescrTab), (int)bias, vector_MinMaxInput.getCPtr(minMaxInputTab), minOutput, maxOutput), true)
{
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public vector_intEnumerator(vector_int collection)
{
collectionRef = collection;
currentIndex = -1;
currentObject = null;
currentSize = collectionRef.Count;
}
public vector_int(vector_int other) : this(VisionLabPINVOKE.new_vector_int__SWIG_1(vector_int.getCPtr(other)), true)
{
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void GetSelectedSet(vector_vector_double features, vector_int classIds)
{
VisionLabPINVOKE.ClassFeatureSet_YUV16_GetSelectedSet(swigCPtr, vector_vector_double.getCPtr(features), vector_int.getCPtr(classIds));
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void SetWord(int index, vector_int word)
{
VisionLabPINVOKE.ClassLexicon_SetWord(swigCPtr, index, vector_int.getCPtr(word));
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void RemoveWord(vector_int word)
{
VisionLabPINVOKE.ClassLexicon_RemoveWord__SWIG_1(swigCPtr, vector_int.getCPtr(word));
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void GetClassIds(vector_int classIds)
{
VisionLabPINVOKE.ClassFeatureSet_YUV16_GetClassIds(swigCPtr, vector_int.getCPtr(classIds));
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void SetRange(int index, vector_int values)
{
VisionLabPINVOKE.vector_int_SetRange(swigCPtr, index, vector_int.getCPtr(values));
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void Init(ActivationFunction func, vector_int layerDescrTab, BiasNodes bias, vector_MinMaxInput minMaxInputTab, double minOutput, double maxOutput)
{
VisionLabPINVOKE.BPN_Init(swigCPtr, (int)func, vector_int.getCPtr(layerDescrTab), (int)bias, vector_MinMaxInput.getCPtr(minMaxInputTab), minOutput, maxOutput);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public void GetSets(vector_DoubleImage images, vector_int classIds)
{
VisionLabPINVOKE.ClassImageSet_Double_GetSets(swigCPtr, vector_DoubleImage.getCPtr(images), vector_int.getCPtr(classIds));
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
}
public vector_int GetWord(int index)
{
vector_int ret = new vector_int(VisionLabPINVOKE.ClassLexicon_GetWord(swigCPtr, index), true);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public double EvaluateClassSet(vector_vector_double inputs, vector_int classesExp, vector_int classesRes, vector_double confidencies, vector_vector_double outputs, ref double meanError)
{
double ret = VisionLabPINVOKE.BPN_Classifier_EvaluateClassSet(swigCPtr, vector_vector_double.getCPtr(inputs), vector_int.getCPtr(classesExp), vector_int.getCPtr(classesRes), vector_double.getCPtr(confidencies), vector_vector_double.getCPtr(outputs), ref meanError);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public double EvaluateClassImageSet(ClassImageSet_RGB16 cis, vector_int classesRes, vector_double confidencies, vector_vector_double outputs, ref double meanError)
{
double ret = VisionLabPINVOKE.BPN_ImageClassifier_RGB16_EvaluateClassImageSet(swigCPtr, ClassImageSet_RGB16.getCPtr(cis), vector_int.getCPtr(classesRes), vector_double.getCPtr(confidencies), vector_vector_double.getCPtr(outputs), ref meanError);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public double FindBestWord(vector_vector_LetterMatch wmTab, vector_int bestWord, Optimize opt)
{
double ret = VisionLabPINVOKE.ClassLexicon_FindBestWord__SWIG_1(swigCPtr, vector_vector_LetterMatch.getCPtr(wmTab), vector_int.getCPtr(bestWord), (int)opt);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public vector_int GetRange(int index, int count)
{
IntPtr cPtr = VisionLabPINVOKE.vector_int_GetRange(swigCPtr, index, count);
vector_int ret = (cPtr == IntPtr.Zero) ? null : new vector_int(cPtr, true);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public static vector_int Repeat(int value, int count)
{
IntPtr cPtr = VisionLabPINVOKE.vector_int_Repeat(value, count);
vector_int ret = (cPtr == IntPtr.Zero) ? null : new vector_int(cPtr, true);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
/*
* 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);
}
}
public static void LUT(Int32Image image, int minPixel, vector_int tab) {
VisionLabPINVOKE.LUT__SWIG_9(Int32Image.getCPtr(image), minPixel, vector_int.getCPtr(tab));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public void Init(ActivationFunction func, vector_int layerDescrTab, BiasNodes bias, vector_MinMaxInput minMaxInputTab, double minOutput, double maxOutput) {
VisionLabPINVOKE.BPN_Init(swigCPtr, (int)func, vector_int.getCPtr(layerDescrTab), (int)bias, vector_MinMaxInput.getCPtr(minMaxInputTab), minOutput, maxOutput);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public static SWIGTYPE_p_std__mapT_std__string_int_t VectorsToMap_string_int(vector_string key, vector_int value) {
SWIGTYPE_p_std__mapT_std__string_int_t ret = new SWIGTYPE_p_std__mapT_std__string_int_t(VisionLabPINVOKE.VectorsToMap_string_int(vector_string.getCPtr(key), vector_int.getCPtr(value)), true);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
return ret;
}
public static void MapToVectors_string_int(SWIGTYPE_p_std__mapT_std__string_int_t keyvalue, vector_string key, vector_int value) {
VisionLabPINVOKE.MapToVectors_string_int(SWIGTYPE_p_std__mapT_std__string_int_t.getCPtr(keyvalue), vector_string.getCPtr(key), vector_int.getCPtr(value));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public double TrainImageSet(int nrOfEpochs, double learnRate, double momentum, vector_DoubleImage images, vector_int classes, ref double meanError) {
double ret = VisionLabPINVOKE.BPN_ImageClassifier_Double_TrainImageSet(swigCPtr, nrOfEpochs, learnRate, momentum, vector_DoubleImage.getCPtr(images), vector_int.getCPtr(classes), ref meanError);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
return ret;
}
public void SetWord(int index, vector_int word) {
VisionLabPINVOKE.ClassLexicon_SetWord(swigCPtr, index, vector_int.getCPtr(word));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public void GetSelectedSet(vector_vector_double features, vector_int classIds) {
VisionLabPINVOKE.ClassFeatureSet_Float_GetSelectedSet(swigCPtr, vector_vector_double.getCPtr(features), vector_int.getCPtr(classIds));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public void GetClassIds(vector_int classIds) {
VisionLabPINVOKE.ClassImageSet_Float_GetClassIds(swigCPtr, vector_int.getCPtr(classIds));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
/*
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;
}
public BPN(ActivationFunction func, vector_int layerDescrTab, BiasNodes bias, vector_MinMaxInput minMaxInputTab, double minOutput, double maxOutput) : this(VisionLabPINVOKE.new_BPN__SWIG_1((int)func, vector_int.getCPtr(layerDescrTab), (int)bias, vector_MinMaxInput.getCPtr(minMaxInputTab), minOutput, maxOutput), true) {
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public void RemoveWord(vector_int word) {
VisionLabPINVOKE.ClassLexicon_RemoveWord__SWIG_1(swigCPtr, vector_int.getCPtr(word));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public vector_int GetWord(int index) {
vector_int ret = new vector_int(VisionLabPINVOKE.ClassLexicon_GetWord(swigCPtr, index), true);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
return ret;
}
internal static HandleRef getCPtr(vector_int obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
public double TrainImageSet(int nrOfEpochs, double learnRate, double momentum, vector_RGB161616Image images, vector_int classes, ref double meanError)
{
double ret = VisionLabPINVOKE.BPN_ImageClassifier_RGB16_TrainImageSet(swigCPtr, nrOfEpochs, learnRate, momentum, vector_RGB161616Image.getCPtr(images), vector_int.getCPtr(classes), ref meanError);
if (VisionLabPINVOKE.SWIGPendingException.Pending)
{
throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public static void ResampleLine(Int32Image image, Coord2D start, Coord2D end, int nrPixels, vector_Coord2D cTab, vector_int pTab) {
VisionLabPINVOKE.ResampleLine__SWIG_5(Int32Image.getCPtr(image), Coord2D.getCPtr(start), Coord2D.getCPtr(end), nrPixels, vector_Coord2D.getCPtr(cTab), vector_int.getCPtr(pTab));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public void GetSets(vector_Int8Image images, vector_int classIds) {
VisionLabPINVOKE.ClassImageSet_Int8_GetSets(swigCPtr, vector_Int8Image.getCPtr(images), vector_int.getCPtr(classIds));
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
}
public static SWIGTYPE_p_std__setT_int_t VectorToSet_int(vector_int v) {
SWIGTYPE_p_std__setT_int_t ret = new SWIGTYPE_p_std__setT_int_t(VisionLabPINVOKE.VectorToSet_int(vector_int.getCPtr(v)), true);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
return ret;
}
public double EvaluateClassImageSet(ClassImageSet_Double cis, vector_int classesRes, vector_double confidencies, vector_vector_double outputs, ref double meanError) {
double ret = VisionLabPINVOKE.BPN_ImageClassifier_Double_EvaluateClassImageSet(swigCPtr, ClassImageSet_Double.getCPtr(cis), vector_int.getCPtr(classesRes), vector_double.getCPtr(confidencies), vector_vector_double.getCPtr(outputs), ref meanError);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
return ret;
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(vector_int obj)
{
return((obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr);
}
/*
* 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);
}
public double FindBestWord(vector_vector_LetterMatch wmTab, vector_int bestWord, Optimize opt) {
double ret = VisionLabPINVOKE.ClassLexicon_FindBestWord__SWIG_1(swigCPtr, vector_vector_LetterMatch.getCPtr(wmTab), vector_int.getCPtr(bestWord), (int)opt);
if (VisionLabPINVOKE.SWIGPendingException.Pending) throw VisionLabPINVOKE.SWIGPendingException.Retrieve();
return ret;
}