Program Listing for File model_fit.h
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#ifndef OPENCV_UTIL_MODEL_FIT_H_
#define OPENCV_UTIL_MODEL_FIT_H_
#include <vector>
#include <opencv2/core/core.hpp>
#include <swri_math_util/random.h>
#include <swri_math_util/ransac.h>
#include <swri_opencv_util/models.h>
namespace swri_opencv_util
{
template <class Model>
cv::Mat FindModel2d(
const cv::Mat& points1,
const cv::Mat& points2,
cv::Mat& inliers1,
cv::Mat& inliers2,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr())
{
cv::Mat model;
// Put data into the expected format.
cv::Mat correspondences;
if (!ZipCorrespondences(points1, points2, correspondences))
{
return model;
}
// Run RANSAC to robustly fit a rigid transform model to the set of
// corresponding points.
swri_math_util::Ransac<Model> ransac(rng);
Model fit_model(correspondences);
model = ransac.FitModel(
fit_model, max_error, confidence, 1, max_iterations, good_points, iterations);
if (good_points.empty())
{
return model;
}
// Populate output data.
bool row_order = points1.rows > 1;
if (row_order)
{
inliers1 = cv::Mat(good_points.size(), 1, CV_32FC2);
inliers2 = cv::Mat(good_points.size(), 1, CV_32FC2);
for (size_t i = 0; i < good_points.size(); ++i)
{
inliers1.at<cv::Vec2f>(i, 0) = points1.at<cv::Vec2f>(good_points[i], 0);
inliers2.at<cv::Vec2f>(i, 0) = points2.at<cv::Vec2f>(good_points[i], 0);
}
}
else
{
inliers1 = cv::Mat(1, good_points.size(), CV_32FC2);
inliers2 = cv::Mat(1, good_points.size(), CV_32FC2);
for (size_t i = 0; i < good_points.size(); ++i)
{
inliers1.at<cv::Vec2f>(0, i) = points1.at<cv::Vec2f>(0, good_points[i]);
inliers2.at<cv::Vec2f>(0, i) = points2.at<cv::Vec2f>(0, good_points[i]);
}
}
return model;
}
cv::Mat FindTranslation2d(
const cv::Mat& points1,
const cv::Mat& points2,
cv::Mat& inliers1,
cv::Mat& inliers2,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
cv::Mat FindRigidTransform2d(
const cv::Mat& points1,
const cv::Mat& points2,
cv::Mat& inliers1,
cv::Mat& inliers2,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
// Returns a 2x3 transform that can be applied to points1 to align them to
// points2.
cv::Mat FitRigidTransform2d(const cv::Mat& points1, const cv::Mat& points2);
// Calculate 3x3 rotation matrix that can be applied to points1 to align them
// to points2.
cv::Mat FitRotation3d(const cv::Mat& points1, const cv::Mat& points2);
cv::Mat FindAffineTransform2d(
const cv::Mat& points1,
const cv::Mat& points2,
cv::Mat& inliers1,
cv::Mat& inliers2,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
cv::Mat FitAffineTransform2d(const cv::Mat& points1, const cv::Mat& points2);
cv::Mat FindHomography(
const cv::Mat& points1,
const cv::Mat& points2,
cv::Mat& inliers1,
cv::Mat& inliers2,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
PlaneModel FindPerpendicularPlaneWithPoint(
const cv::Vec3f& point_on_plane,
const cv::Vec3f& perp_axis,
double max_angle_from_perp,
const cv::Mat& points,
cv::Mat& inliers,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error,
double confidence,
int32_t min_iterations,
int32_t max_iterations,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
PlaneModel FindPlane(
const cv::Mat& points,
cv::Mat& inliers,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t min_iterations = 1,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
PlaneModel FitPlane(const cv::Mat& points);
LineModel3d FindLine3d(
const cv::Mat& points,
cv::Mat& inliers,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t min_iterations = 1,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
LineModel3d FindOrthoLine3d(
const cv::Mat& points,
const LineModel3d& ortho,
cv::Mat& inliers,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t min_iterations = 1,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
LineModel3d FitLine3d(const cv::Mat& points);
CrossModel3d FindCross3d(
const cv::Mat& points,
cv::Mat& inliers,
std::vector<uint32_t> &good_points,
int32_t& iterations,
double max_error = 1.0,
double confidence = 0.9,
int32_t min_iterations = 1,
int32_t max_iterations = 1000,
swri_math_util::RandomGeneratorPtr rng = swri_math_util::RandomGeneratorPtr());
}
#endif // OPENCV_UTIL_MODEL_FIT_H_