double AutoCorr::getMinimalPattern(unsigned int &sX, unsigned int &sY, unsigned int &sizeX, unsigned int &sizeY)
{
const float epsilon = 0.00005;
float *rho_x = 0;
float *rho_y = 0;
getACX(m_autocorr, rho_x);
getACY(m_autocorr, rho_y);
float stdDevX = 0;
float stdDevY = 0;
int* xPeaks = new int[m_autocorr.cols];
int* yPeaks = new int[m_autocorr.rows];
int peaksX = countPeaks(rho_x, stdDevX, m_autocorr.cols, xPeaks);
int peaksY = countPeaks(rho_y, stdDevY, m_autocorr.rows, yPeaks);
std::vector<int> high_xPeaks;
float meanPeakHeight = 0;
for (int i = 0; i < peaksX; i++)
{
meanPeakHeight += calcPeakHeight(xPeaks[i], rho_x, m_autocorr.cols);
}
meanPeakHeight /= peaksX;
for (int i = 0; i < peaksX; i++)
{
if (calcPeakHeight(xPeaks[i], rho_x, m_autocorr.cols) > meanPeakHeight)
{
high_xPeaks.push_back(xPeaks[i]);
}
}
std::vector<int> high_yPeaks;
meanPeakHeight = 0;
for (int i = 0; i < peaksY; i++)
{
meanPeakHeight += calcPeakHeight(yPeaks[i], rho_y, m_autocorr.rows);
}
meanPeakHeight /= peaksY;
for (int i = 0; i < peaksY; i++)
{
if (calcPeakHeight(yPeaks[i], rho_y, m_autocorr.rows) > meanPeakHeight)
{
high_yPeaks.push_back(yPeaks[i]);
}
}
//x direction
float x_highest_correlation = -FLT_MAX;
for (int x = 0; x < high_xPeaks.size() / 2; x++)
{
for (int x2 = x + 1; x2 < high_xPeaks.size(); x2++)
{
//choose size for subrect
int width = high_xPeaks[x2] - high_xPeaks[x] + 1;
int height = m_image.rows;
//choose center for the subrect
int cx = high_xPeaks[x] + width / 2;
int cy = height / 2;
//extract the subrect
cv::Mat dst;
cv::getRectSubPix(m_image, cv::Size(width, height), cv::Point2f(cx, cy), dst);
//calculate cross correlation between dst and m_image
CrossCorr* cc = new CrossCorr(m_image, dst);
float correlation = 0;
for (int i = 0; i < m_image.cols / width; i++)
{
correlation += fabs(cc->at((high_xPeaks[x] + i * width) % m_image.cols, 0));
}
if (correlation > x_highest_correlation)
{
x_highest_correlation = correlation;
sizeX = width;
sX = high_xPeaks[x];
}
}
}
//Could not find enough peaks
if (x_highest_correlation == -FLT_MAX)
{
sizeX = m_image.cols;
sX = 0;
}
//y direction
float y_highest_correlation = -FLT_MAX;
for (int y = 0; y < high_yPeaks.size(); y++)
{
for (int y2 = y + 1; y2 < high_yPeaks.size(); y2++)
{
//choose size for subrect
int width = sizeX;
int height = high_yPeaks[y2] - high_yPeaks[y] + 1;
//choose center for the subrect
int cx = sX;
int cy = high_yPeaks[y] + width / 2;
//extract the subrect
cv::Mat dst;
cv::getRectSubPix(m_image, cv::Size(width, height), cv::Point2f(cx, cy), dst);
//calculate cross correlation between dst and m_image
CrossCorr* cc = new CrossCorr(m_image, dst);
float correlation = 0;
for (int i = 0; i < m_image.rows / height; i++)
{
for (int j = 0; j < m_image.cols / width; j++)
{
correlation += fabs(cc->at((sX + j * width) % m_image.cols, (high_yPeaks[y] + i * height) % m_image.rows));
}
}
if (correlation > y_highest_correlation)
{
y_highest_correlation = correlation;
sizeY = height;
sY = high_yPeaks[y];
}
}
}
//Could not find enough peaks
if (y_highest_correlation == -FLT_MAX)
{
sizeY = m_image.rows;
sY = 0;
}
if (y_highest_correlation == -FLT_MAX == x_highest_correlation || stdDevX < 0.00001 && stdDevY < 0.00001 || peaksX < 0.00001 || peaksY < 0.00001)
{
//Texture is aperiodic
return 0;
}
else
{
return 1.0f / std::min(stdDevX/(m_autocorr.cols / peaksX), stdDevY/(m_autocorr.rows / peaksY));
}
}
double AutoCorr::getMinimalPattern(unsigned int &sX, unsigned int &sY, unsigned int &sizeX, unsigned int &sizeY, const int minimalPatternSize = 10)
{
const float epsilon = 0.00005;
float *rho_x = 0;
float *rho_y = 0;
getACX(m_autocorr, rho_x);
getACY(m_autocorr, rho_y);
float stdDevX = 0;
float stdDevY = 0;
int* xPeaks = new int[m_autocorr.cols];
int* yPeaks = new int[m_autocorr.rows];
int peaksX = countPeaks(rho_x, stdDevX, m_autocorr.cols, xPeaks);
int peaksY = countPeaks(rho_y, stdDevY, m_autocorr.rows, yPeaks);
// std::cout<<"Peaks x:"<<peaksX<<"\t\t StdDev rho_x: "<<stdDevX/(m_autocorr.cols / peaksX)<<std::endl;
// std::cout<<"Peaks y:"<<peaksY<<"\t\t StdDev rho_y: "<<stdDevY/(m_autocorr.rows / peaksY)<<std::endl;
for (int i = 0; i < m_autocorr.cols; i++)
{
std::cerr<<i<<" "<<rho_x[i]<<std::endl;
}
// for (int i = 0; i < peaksX; i++)
// {
// std::cout<<xPeaks[i]<<" "<<0<<std::endl;
// }
std::vector<int> high_xPeaks;
for (int i = 0; i < peaksX; i++)
{
if (/*calcPeakHeight(xPeaks[i], rho_x, m_autocorr.cols) > minimalPatternSize &&*/ rho_x[xPeaks[i]] > 0)
{
high_xPeaks.push_back(xPeaks[i]);
// std::cout<<high_xPeaks.back()<<" "<<0<<std::endl;
}
}
std::vector<int> high_yPeaks;
for (int i = 0; i < peaksY; i++)
{
if (/*calcPeakHeight(yPeaks[i], rho_y, m_autocorr.rows) > minimalPatternSize &&*/ rho_y[yPeaks[i]] > 0)
{
high_yPeaks.push_back(yPeaks[i]);
}
}
std::priority_queue<patternDim, std::vector<patternDim>, patternDim> x_patterns;
//x direction
for (int x = 0; x < high_xPeaks.size() / 2; x++)
{
for (int x2 = x + 1; x2 < high_xPeaks.size() / 2; x2++)
{
patternDim p;
float dist = fabs(calcPeakHeight(high_xPeaks[x], rho_x, m_autocorr.cols) - calcPeakHeight(high_xPeaks[x2], rho_x, m_autocorr.cols));
float d1 = fabs(fabs(rho_x[high_xPeaks[x]] - rho_x[high_xPeaks[x] - 1]) - fabs(rho_x[high_xPeaks[x2]] - rho_x[high_xPeaks[x2] - 1]));
float d2 = fabs(fabs(rho_x[high_xPeaks[x]] - rho_x[high_xPeaks[x] + 1]) - fabs(rho_x[high_xPeaks[x2]] - rho_x[high_xPeaks[x2] + 1]));
p.size = high_xPeaks[x2] - high_xPeaks[x];
p.s = high_xPeaks[x];
p.fit = dist + d1 + d2;
x_patterns.push(p);
}
}
if (x_patterns.empty())
{
sizeX = m_image.cols;
sX = 0;
}
else
{
//Test the first elements of the priority queuer by cross correlation
float x_highest_correlation = -FLT_MAX;
for (int x = 0; x < std::min((size_t)10, x_patterns.size()); x++)
{
//choose size for subrect
int width = x_patterns.top().size;
int height = m_image.rows;
//choose center for the subrect
int cx = x_patterns.top().s + width / 2;
int cy = height / 2;
//extract the subrect
cv::Mat dst;
cv::getRectSubPix(m_image, cv::Size(width, height), cv::Point2f(cx, cy), dst);
//calculate cross correlation between dst and m_image
CrossCorr* cc = new CrossCorr(m_image, dst);
float correlation = 0;
for (int i = 0; i < m_image.cols / width; i++)
{
correlation += cc->at((x_patterns.top().s + i * width) % m_image.cols, 0);
}
// std::cout<<"x: "<<x<<" "<<correlation<<std::endl;
if (correlation > x_highest_correlation)
{
x_highest_correlation = correlation;
sizeX = width;
sX = x_patterns.top().s;
}
x_patterns.pop();
}
}
//y direction
std::priority_queue<patternDim, std::vector<patternDim>, patternDim> y_patterns;
for (int y = 0; y < high_yPeaks.size() / 2; y++)
{
for (int y2 = y + 1; y2 < high_yPeaks.size() / 2; y2++)
{
patternDim p;
float dist = fabs(calcPeakHeight(high_yPeaks[y], rho_y, m_autocorr.rows) - calcPeakHeight(high_yPeaks[y2], rho_y, m_autocorr.rows));
float d1 = fabs(fabs(rho_y[high_yPeaks[y]] - rho_y[high_yPeaks[y] - 1]) - fabs(rho_y[high_yPeaks[y2]] - rho_y[high_yPeaks[y2] - 1]));
float d2 = fabs(fabs(rho_y[high_yPeaks[y]] - rho_y[high_yPeaks[y] + 1]) - fabs(rho_y[high_yPeaks[y2]] - rho_y[high_yPeaks[y2] + 1]));
p.size = high_yPeaks[y2] - high_yPeaks[y];
p.s = high_yPeaks[y];
p.fit = dist + d1 + d2;
y_patterns.push(p);
}
}
//Could not find more than one peak
if (y_patterns.empty())
{
sizeY = m_image.rows;
sY = 0;
}
else
{
//Test the first elements of the priority queuer by cross correlation
float y_highest_correlation = -FLT_MAX;
for (int y = 0; y < std::min((size_t)10, y_patterns.size()); y++)
{
//choose size for subrect
int width = m_image.cols;
int height = y_patterns.top().size;
//choose center for the subrect
int cx = width / 2;
int cy = y_patterns.top().s + width / 2;
//extract the subrect
cv::Mat dst;
cv::getRectSubPix(m_image, cv::Size(width, height), cv::Point2f(cx, cy), dst);
//calculate cross correlation between dst and m_image
CrossCorr* cc = new CrossCorr(m_image, dst);
float correlation = 0;
for (int i = 0; i < m_image.rows / height; i++)
{
correlation += cc->at(0, (y_patterns.top().s + i * height) % m_image.rows);
}
// std::cout<<"y: "<<y<<" "<<correlation<<std::endl;
if (correlation > y_highest_correlation)
{
y_highest_correlation = correlation;
sizeY = height;
sY = y_patterns.top().s;
}
y_patterns.pop();
}
}
return 0; //TODO
}
