fixed typo in opencv_stitching
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0ec452c152
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@ -38,80 +38,80 @@
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// or tort (including negligence or otherwise) arising in any way out of
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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// the use of this software, even if advised of the possibility of such damage.
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//
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//
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//M*/
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//M*/
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#include "autocalib.hpp"
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#include "autocalib.hpp"
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#include "util.hpp"
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#include "util.hpp"
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using namespace std;
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using namespace std;
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using namespace cv;
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using namespace cv;
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void focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok, bool &f1_ok)
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void focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok, bool &f1_ok)
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{
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{
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CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3));
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CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3));
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const double* h = reinterpret_cast<const double*>(H.data);
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const double* h = reinterpret_cast<const double*>(H.data);
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double d1, d2; // Denominators
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double d1, d2; // Denominators
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double v1, v2; // Focal squares value candidates
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double v1, v2; // Focal squares value candidates
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f1_ok = true;
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f1_ok = true;
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d1 = h[6] * h[7];
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d1 = h[6] * h[7];
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d2 = (h[7] - h[6]) * (h[7] + h[6]);
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d2 = (h[7] - h[6]) * (h[7] + h[6]);
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v1 = -(h[0] * h[1] + h[3] * h[4]) / d1;
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v1 = -(h[0] * h[1] + h[3] * h[4]) / d1;
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v2 = (h[0] * h[0] + h[3] * h[3] - h[1] * h[1] - h[4] * h[4]) / d2;
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v2 = (h[0] * h[0] + h[3] * h[3] - h[1] * h[1] - h[4] * h[4]) / d2;
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if (v1 < v2) swap(v1, v2);
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if (v1 < v2) swap(v1, v2);
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if (v1 > 0 && v2 > 0) f1 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
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if (v1 > 0 && v2 > 0) f1 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
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else if (v1 > 0) f1 = sqrt(v1);
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else if (v1 > 0) f1 = sqrt(v1);
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else f1_ok = false;
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else f1_ok = false;
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f0_ok = true;
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f0_ok = true;
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d1 = h[0] * h[3] + h[1] * h[4];
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d1 = h[0] * h[3] + h[1] * h[4];
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d2 = h[0] * h[0] + h[1] * h[1] - h[3] * h[3] - h[4] * h[4];
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d2 = h[0] * h[0] + h[1] * h[1] - h[3] * h[3] - h[4] * h[4];
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v1 = -h[2] * h[5] / d1;
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v1 = -h[2] * h[5] / d1;
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v2 = (h[5] * h[5] - h[2] * h[2]) / d2;
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v2 = (h[5] * h[5] - h[2] * h[2]) / d2;
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if (v1 < v2) swap(v1, v2);
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if (v1 < v2) swap(v1, v2);
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if (v1 > 0 && v2 > 0) f0 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
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if (v1 > 0 && v2 > 0) f0 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
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else if (v1 > 0) f0 = sqrt(v1);
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else if (v1 > 0) f0 = sqrt(v1);
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else f0_ok = false;
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else f0_ok = false;
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}
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}
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void estimateFocal(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
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void estimateFocal(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
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vector<double> &focals)
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vector<double> &focals)
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{
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{
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const int num_images = static_cast<int>(features.size());
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const int num_images = static_cast<int>(features.size());
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focals.resize(num_images);
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focals.resize(num_images);
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vector<double> all_focals;
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vector<double> all_focals;
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for (int i = 0; i < num_images; ++i)
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for (int i = 0; i < num_images; ++i)
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{
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{
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for (int j = 0; j < num_images; ++j)
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for (int j = 0; j < num_images; ++j)
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{
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{
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const MatchesInfo &m = pairwise_matches[i*num_images + j];
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const MatchesInfo &m = pairwise_matches[i*num_images + j];
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if (m.H.empty())
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if (m.H.empty())
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continue;
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continue;
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double f0, f1;
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double f0, f1;
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bool f0ok, f1ok;
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bool f0ok, f1ok;
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focalsFromHomography(m.H, f0, f1, f0ok, f1ok);
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focalsFromHomography(m.H, f0, f1, f0ok, f1ok);
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if (f0ok && f1ok)
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if (f0ok && f1ok)
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all_focals.push_back(sqrt(f0 * f1));
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all_focals.push_back(sqrt(f0 * f1));
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}
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}
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}
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}
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if (static_cast<int>(all_focals.size()) < num_images - 1)
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if (static_cast<int>(all_focals.size()) >= num_images - 1)
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{
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{
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LOGLN("Can't estimate focal length, will use anaive approach");
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nth_element(all_focals.begin(), all_focals.begin() + all_focals.size()/2, all_focals.end());
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double focals_sum = 0;
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for (int i = 0; i < num_images; ++i)
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for (int i = 0; i < num_images; ++i)
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focals[i] = all_focals[all_focals.size()/2];
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focals_sum += features[i].img_size.width + features[i].img_size.height;
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}
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for (int i = 0; i < num_images; ++i)
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else
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focals[i] = focals_sum / num_images;
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{
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}
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LOGLN("Can't estimate focal length, will use naive approach");
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else
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double focals_sum = 0;
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{
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for (int i = 0; i < num_images; ++i)
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nth_element(all_focals.begin(), all_focals.begin() + all_focals.size()/2, all_focals.end());
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focals_sum += features[i].img_size.width + features[i].img_size.height;
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for (int i = 0; i < num_images; ++i)
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for (int i = 0; i < num_images; ++i)
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focals[i] = all_focals[all_focals.size()/2];
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focals[i] = focals_sum / num_images;
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}
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}
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}
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}
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