int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
Q_UNUSED(a)
QImage inImage("lena.png");
inImage = inImage.convertToFormat(QImage::Format_Grayscale8);
QImage outImage(inImage.size(), inImage.format());
QVector<int> gradient;
QVector<int> direction;
sobel(inImage, gradient, direction);
QVector<int> thinned = thinning(inImage.width(), inImage.height(),
gradient, direction);
QVector<int> thresholded = threshold(75, 150, thinned);
QVector<int> canny = hysteresis(inImage.width(), inImage.height(),
thresholded);
const int *iImg = canny.constData();
quint8 *oImg = outImage.bits();
int size = inImage.width() * inImage.height();
for (int i = 0; i < size; i++)
oImg[i] = qBound(0, iImg[i], 255);
outImage.save("canny.png");
return EXIT_SUCCESS;
}
/**
* This is an example on how to call the thinning function above.
*/
int main()
{
cv::Mat src = cv::imread("../test_image.png");
if (src.empty())
return -1;
cv::Mat bw;
cv::cvtColor(src, bw, CV_BGR2GRAY);
cv::threshold(bw, bw, 10, 255, CV_THRESH_BINARY);
const int N = 30;
int64 time = 0;
for (int i = 0; i < N; ++i)
{
cv::Mat bb = i < N - 1 ? bw.clone() : bw;
int64 t = cvGetTickCount();
thinning(bb, false);
time += (cvGetTickCount() - t);
}
printf("%f", time / cvGetTickFrequency() / N);
//thinning(bw);
cv::imshow("src", src);
cv::imshow("dst", bw);
cv::waitKey(0);
return 0;
}
Mat preProcess(Mat &img){
Mat res = Mat(img.rows, img.cols, CV_8UC1);
blur( img, res, Size( 3, 3 ), Point(-1,-1) );
Mat aux = res.clone();
bilateralFilter ( aux, res, 5, 5*2, 5/2 );
aux = res.clone();
cv::GaussianBlur(aux, res, cv::Size(0, 0), 3);
cv::addWeighted(aux, 1.5, res, -0.5, 0, res);
//Filtro de Wiener
cvWiener2ADP(res, res, 5, 5);
//Binarizacao e Afinamento
threshold(res, res, mediana(res), 255, THRESH_BINARY_INV);
//Esqueletização
thinning(res);
/*namedWindow("Preprocess", CV_WINDOW_AUTOSIZE);
imshow("Preprocess", res);
waitKey(0);*/
return res;
}
/*
* detects straight lines in an image
*
* @param img the image to perform line detection on
*
* @return a vector with the cartesian coordinates of any detected line segments' endpoints
*
*/
cv::vector<cv::Vec4i> ImageProcessor::lineDetection(cv::Mat & src){
//convert image to grayscale if not done already
if(src.channels() > 1){
cv::cvtColor(src, src, CV_RGB2GRAY);
}
//convert image to a binary image
cv::threshold(src, src, 10, 255, CV_THRESH_BINARY_INV);
//thin out the lines
thinning(src);
//detect all straight lines in the image
cv::vector<cv::Vec4i> lines;
cv::HoughLinesP(src, lines, 1, CV_PI/180, 50, 50, 10 );
return lines;
}
bool IPLCanny::processInputData(IPLImage* image , int, bool useOpenCV)
{
// delete previous result
delete _result;
_result = NULL;
delete _binaryImage;
_binaryImage = NULL;
int width = image->width();
int height = image->height();
_result = new IPLImage( image->type(), width, height );
_binaryImage = new IPLImage( IPLData::IMAGE_BW, width, height );
// get properties
int window = getProcessPropertyInt("window");
double sigma = getProcessPropertyDouble("sigma");
double lowThreshold = getProcessPropertyDouble("lowThreshold");
double highThreshold = getProcessPropertyDouble("highThreshold");
std::stringstream s;
s << "Window: ";
s << window;
addInformation(s.str());
//! @todo currently only the opencv implementation works
if(useOpenCV || true)
{
notifyProgressEventHandler(-1);
cv::Mat input;
cv::Mat output;
cvtColor(image->toCvMat(), input, CV_BGR2GRAY);
cv::Canny(input, output, lowThreshold*255, highThreshold*255, window);
delete _result;
_result = new IPLImage(output);
return true;
}
return false;
// Create a Gaussian 1D filter
int N = ceil( sigma * sqrt( 2.0*log( 1.0/0.015 ) ) + 1.0 );
double ssq = sigma*sigma;
double* gau = new double [window];
double* dgau = new double [window];
for( int k = -N; k <= N; ++k )
{
gau[k+N] = gauss ( (double)k, ssq );
dgau[k+N] = dGauss ( (double)k, 0, ssq );
}
// Create a directional derivative of 2D Gaussian (along X-axis)
// Since the result is symmetric along X, we can get the derivative along
// Y-axis simply by transposing the result for X direction.
// DoubleImage* dgau = new DoubleImage( window, window );
// for( int y = -N; y <= N; ++y )
// for( int x = -N; x <= N; ++x )
// dgau->f(x+N, y+N) = dGauss( x, y, ssq );
int progress = 0;
int maxProgress = width * image->getNumberOfPlanes();
int nrOfPlanes = image->getNumberOfPlanes();
//#pragma omp parallel for
for( int planeNr=0; planeNr < nrOfPlanes; planeNr++ )
{
IPLImagePlane* plane = image->plane( planeNr );
IPLImagePlane* newplane = _result->plane( planeNr );
// ******** Gaussian filtering of input image
IPLImagePlane* gI = new IPLImagePlane( width, height );
// horizontal run (normalizing original image)
IPLImagePlane* tmpI = new IPLImagePlane( width, height );
for(int x=0; x<width; x++)
{ // progress
notifyProgressEventHandler(100*progress++/maxProgress);
for(int y=0; y<height; y++)
{
double sum = 0;
int i = 0;
for( int kx=-N; kx<=N; kx++ )
{
double img = (double) plane->bp(x+kx, y);
sum += (img * gau[i++]);
}
tmpI->p(x,y) = (double) (sum);
}
}
// vertiacl run
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
double sum = 0;
int i = 0;
for( int ky=-N; ky<=N; ky++ )
{
double img = tmpI->bp(x, y+ky);
sum += (img * gau[i++]);
}
gI->p(x,y) = sum;
}
}
//delete tmpI;
// ******** Apply directional derivatives ...
// ... in x-direction
IPLImagePlane* dx = new IPLImagePlane( width, height );
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
dx->p(x,y) = 0.0;
for( int k=1; k<N; k++ )
{
dx->p(x,y) += ( gI->bp(x-k,y) - gI->bp(x+k,y) ) * dgau[k];
}
}
}
// double maxVal = 0.0;
// for(int x=0; x<width; x++)
// for(int y=0; y<height; y++)
// if( dx->f(x,y) > maxVal ) maxVal = dx->f(x,y);
// ... in y-direction
IPLImagePlane* dy = new IPLImagePlane( width, height );
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
dy->p(x,y) = 0.0;
for( int k=1; k<N; k++ )
{
dy->p(x,y) += ( gI->bp(x,y-k) - gI->bp(x,y+k) ) * dgau[k];
}
}
}
// ******** Compute magnitude and binarization thresholds
IPLImagePlane* mag = new IPLImagePlane( width, height );
double magMax = 0.0;
double magMin = 999999999.0;
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
double val = sqrt( dx->p(x,y)*dx->p(x,y) + dy->p(x,y)*dy->p(x,y) );
mag->p(x,y) = val;
if( val > magMax ) magMax = val;
if( val < magMin ) magMin = val;
}
}
//// ******** Non-maxima suppression - edge pixels should be a local maximum
_orientedImage = new IPLOrientedImage( width, height );
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
double ix = dx->p(x,y);
double iy = dy->p(x,y);
double g = mag->p(x,y);
// determine 4-neighbor direction of gradient
int dir4 = 0;
if( (iy<=0.0 && ix>-iy) || (iy>=0.0 && ix<-iy) )
dir4 = 1;
else if( (ix>0.0 && -iy>=ix) || (ix<0.0 && -iy<=ix) )
dir4 = 2;
else if( (ix<=0.0 && ix>iy) || (ix>=0.0 && ix<iy) )
dir4 = 3;
else if( (iy<0.0 && ix<=iy) || (iy>0.0 && ix>=iy) )
dir4 = 4;
else
continue;
double gradmag1, gradmag2, d;
switch(dir4)
{
case 1: d = std::fabs(iy/ix);
gradmag1 = mag->bp(x+1,y)*(1-d) + mag->bp(x+1,y-1)*d;
gradmag2 = mag->bp(x-1,y)*(1-d) + mag->bp(x-1,y+1)*d;
break;
case 2: d = std::fabs(ix/iy);
gradmag1 = mag->bp(x,y-1)*(1-d) + mag->bp(x+1,y-1)*d;
gradmag2 = mag->bp(x,y+1)*(1-d) + mag->bp(x-1,y+1)*d;
break;
case 3: d = std::fabs(ix/iy);
gradmag1 = mag->bp(x,y-1)*(1-d) + mag->bp(x-1,y-1)*d;
gradmag2 = mag->bp(x,y+1)*(1-d) + mag->bp(x+1,y+1)*d;
break;
case 4: d = std::fabs(iy/ix);
gradmag1 = mag->bp(x-1,y)*(1-d) + mag->bp(x-1,y-1)*d;
gradmag2 = mag->bp(x+1,y)*(1-d) + mag->bp(x+1,y+1)*d;
break;
}
if( g > gradmag1 && g > gradmag2 )
{
_orientedImage->magnitude(x,y) = g;
_orientedImage->phase(x,y) = atan2(iy,ix);
}
}
}
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
_orientedImage->magnitude(x,y) /= magMax;
double val = _orientedImage->magnitude(x,y)*255.0;
// double val = mag->f(x,y)/magMax*255.0;
if (val > 255.0 ) val = 255.0;
if (val < 0.0 ) val = 0.0;
newplane->p(x,y) = (unsigned char ) val;
}
}
// ******** Binarize with hysteresis threshold
double hist[ 256 ];
for( int i=0; i<256; ++i )
hist[i] = 0;
int pixCount = 0;
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
if( _orientedImage->magnitude(x,y) > 0.0 )
{
int index = floor( _orientedImage->magnitude(x,y)*256.0+0.5 );
++hist[ index ];
++pixCount;
}
}
}
double PercentOfPixelsNotEdges = 0.7*pixCount;
double highThresh = 0.0;
double cumsum = 0.0;
for( int i=0; i<256; ++i )
{
cumsum += hist[i];
if( cumsum > PercentOfPixelsNotEdges )
{
highThresh = (double)i / 256.0;
break;
}
}
double lowThresh = 0.4 * highThresh;
IPLImagePlane* binPlane = _binaryImage->plane( 0 );
for(int x=0; x<width; x++)
{
for(int y=0; y<height; y++)
{
if(_orientedImage->magnitude(x,y) >= highThresh)
trace(x, y, lowThresh, _orientedImage, binPlane);
}
}
//delete dx;
//delete dy;
//delete gI;
thinning(_orientedImage, binPlane, newplane );
}
//delete [] gau;
//delete [] dgau;
return true;
}
