StitchVideo/stitch/src/Arith_BATask.cpp

#include "Arith_BATask.h"
#include "ceres/ceres.h"
using namespace ceres;

struct CostFunctor {
    template <typename T>
    bool operator()(const T* const x, T* residual) const {
        residual[0] = 10.0 - x[0];
        return true;
    }
};

BA_Task::BA_Task()
{
    // 来个100帧
    _imgVec.reserve(100);
}

BA_Task::~BA_Task()
{
}

SINT32 BA_Task::addFrame(GD_VIDEO_FRAME_S img, FrameInfo para)
{
    // 缓存图像帧
    if (img.enPixelFormat == GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y8)
    {
        _imgVec.emplace_back(Mat(img.u32Height, img.u32Width, CV_8UC1, img.u64VirAddr[0]));
    }



    return _imgVec.size();
}






void BA_Task::Test()
{
    google::InitGoogleLogging("ceres");

    // The variable to solve for with its initial value. It will be
    // mutated in place by the solver.
    double x = 0.5;
    const double initial_x = x;

    // Build the problem.
    ceres::Problem problem;

    // Set up the only cost function (also known as residual). This uses
    // auto-differentiation to obtain the derivative (jacobian).
    ceres::CostFunction* cost_function =
        new ceres::AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
    problem.AddResidualBlock(cost_function, nullptr, &x);

    // Run the solver!
    ceres::Solver::Options options;
    options.minimizer_progress_to_stdout = true;
    ceres::Solver::Summary summary;
    ceres::Solve(options, &problem, &summary);

    std::cout << summary.BriefReport() << "\n";
    std::cout << "x : " << initial_x << " -> " << x << "\n";
}