// before checkSerialNumber is called, usingSerialNumber() has to be called at least ONCE
int checkSerialNumber(license_id id) {
int featureType, returnValue=LIC_FAIL;
#ifdef WIN32
featureType = getFeatureType(id);
switch(featureType) {
case CIEI:
if(CIsupported() == LIC_SUCCESS || EIsupported() == LIC_SUCCESS) { returnValue=LIC_SUCCESS; }
//msg("case of CIEI ... returning $1") << eoarg << returnValue << endl;
//printf("case of CIEI ... returning %d\n", returnValue);
break;
case CI:
if(CIsupported() == LIC_SUCCESS) { returnValue=LIC_SUCCESS; }
//msg("case of CI ... returning $1") << eoarg << returnValue << endl;
//printf("case of CI ... returning %d\n", returnValue);
break;
case EI:
if(EIsupported() == LIC_SUCCESS) { returnValue=LIC_SUCCESS; }
//msg("case of EI ... returning $1") << eoarg << returnValue << endl;
//printf("case of EI ... returning %d\n", returnValue);
break;
default:
//printf("Failure to find that feature %d\n", featureType);
//msg("Failure to find that feature $1") << featureType << eoarg << eom;
returnValue=LIC_FAIL;
break;
}
#endif
//printf("after switch in checkSerial\n");
return(returnValue);
}
void CascadeClassifier::detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
candidates.clear();
if (!maskGenerator.empty())
maskGenerator->initializeMask(image);
if( maxObjectSize.height == 0 || maxObjectSize.width == 0 )
maxObjectSize = image.size();
Mat grayImage = image;
if( grayImage.channels() > 1 )
{
Mat temp;
cvtColor(grayImage, temp, COLOR_BGR2GRAY);
grayImage = temp;
}
Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U);
for( double factor = 1; ; factor *= scaleFactor )
{
Size originalWindowSize = getOriginalWindowSize();
Size windowSize( cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
Size scaledImageSize( cvRound( grayImage.cols/factor ), cvRound( grayImage.rows/factor ) );
Size processingRectSize( scaledImageSize.width - originalWindowSize.width, scaledImageSize.height - originalWindowSize.height );
if( processingRectSize.width <= 0 || processingRectSize.height <= 0 )
break;
if( windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height )
break;
if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height )
continue;
Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data );
resize( grayImage, scaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
int yStep;
if( getFeatureType() == cv::FeatureEvaluator::HOG )
{
yStep = 4;
}
else
{
yStep = factor > 2. ? 1 : 2;
}
int stripCount, stripSize;
const int PTS_PER_THREAD = 1000;
stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
stripCount = std::min(std::max(stripCount, 1), 100);
stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates,
rejectLevels, levelWeights, outputRejectLevels ) )
break;
}
}