// Ceres Solver - A fast non-linear least squares minimizer

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// Author: keir@google.com (Keir Mierle)

//

// A minimal, self-contained bundle adjuster using Ceres, that reads

// files from University of Washington' Bundle Adjustment in the Large dataset:

// http://grail.cs.washington.edu/projects/bal

//

// This does not use the best configuration for solving; see the more involved

// bundle_adjuster.cc file for details.

#include <cmath>

#include <cstdio>

#include <iostream>

#include "ceres/ceres.h"

#include "ceres/rotation.h"

// Read a Bundle Adjustment in the Large dataset.

class BALProblem {

 public:

  ~BALProblem() {

    delete[] point_index_;

    delete[] camera_index_;

    delete[] observations_;

    delete[] parameters_;

  }

  int num_observations()       const { return num_observations_;               }

  const double* observations() const { return observations_;                   }

  double* mutable_cameras()          { return parameters_;                     }

  double* mutable_points()           { return parameters_  + 9 * num_cameras_; }

  double* mutable_camera_for_observation(int i) {

    return mutable_cameras() + camera_index_[i] * 9;

  }

  double* mutable_point_for_observation(int i) {

    return mutable_points() + point_index_[i] * 3;

  }

  bool LoadFile(const char* filename) {

    FILE* fptr = fopen(filename, "r");

    if (fptr == NULL) {

      return false;

    };

    FscanfOrDie(fptr, "%d", &num_cameras_);

    FscanfOrDie(fptr, "%d", &num_points_);

    FscanfOrDie(fptr, "%d", &num_observations_);

    point_index_ = new int[num_observations_];

    camera_index_ = new int[num_observations_];

    observations_ = new double[2 * num_observations_];

    num_parameters_ = 9 * num_cameras_ + 3 * num_points_;

    parameters_ = new double[num_parameters_];

    for (int i = 0; i < num_observations_; ++i) {

      FscanfOrDie(fptr, "%d", camera_index_ + i);

      FscanfOrDie(fptr, "%d", point_index_ + i);

      for (int j = 0; j < 2; ++j) {

        FscanfOrDie(fptr, "%lf", observations_ + 2*i + j);

      }

    }

    for (int i = 0; i < num_parameters_; ++i) {

      FscanfOrDie(fptr, "%lf", parameters_ + i);

    }

    return true;

  }

 private:

  template<typename T>

  void FscanfOrDie(FILE *fptr, const char *format, T *value) {

    int num_scanned = fscanf(fptr, format, value);

    if (num_scanned != 1) {

      LOG(FATAL) << "Invalid UW data file.";

    }

  }

  int num_cameras_;

  int num_points_;

  int num_observations_;

  int num_parameters_;

  int* point_index_;

  int* camera_index_;

  double* observations_;

  double* parameters_;

};

// Templated pinhole camera model for used with Ceres.  The camera is

// parameterized using 9 parameters: 3 for rotation, 3 for translation, 1 for

// focal length and 2 for radial distortion. The principal point is not modeled

// (i.e. it is assumed be located at the image center).

struct SnavelyReprojectionError {

  SnavelyReprojectionError(double observed_x, double observed_y)

      : observed_x(observed_x), observed_y(observed_y) {}

  template <typename T>

  bool operator()(const T* const camera,

                  const T* const point,

                  T* residuals) const {

    // camera[0,1,2] are the angle-axis rotation.

    T p[3];

    ceres::AngleAxisRotatePoint(camera, point, p);

    // camera[3,4,5] are the translation.

    p[0] += camera[3];

    p[1] += camera[4];

    p[2] += camera[5];

    // Compute the center of distortion. The sign change comes from

    // the camera model that Noah Snavely's Bundler assumes, whereby

    // the camera coordinate system has a negative z axis.

    T xp = - p[0] / p[2];

    T yp = - p[1] / p[2];

    // Apply second and fourth order radial distortion.

    const T& l1 = camera[7];

    const T& l2 = camera[8];

    T r2 = xp*xp + yp*yp;

    T distortion = 1.0 + r2  * (l1 + l2  * r2);

    // Compute final projected point position.

    const T& focal = camera[6];

    T predicted_x = focal * distortion * xp;

    T predicted_y = focal * distortion * yp;

    // The error is the difference between the predicted and observed position.

    residuals[0] = predicted_x - observed_x;

    residuals[1] = predicted_y - observed_y;

    return true;

  }

  // Factory to hide the construction of the CostFunction object from

  // the client code.

  static ceres::CostFunction* Create(const double observed_x,

                                     const double observed_y) {

    return (new ceres::AutoDiffCostFunction<SnavelyReprojectionError, 2, 9, 3>(

                new SnavelyReprojectionError(observed_x, observed_y)));

  }

  double observed_x;

  double observed_y;

};

int main(int argc, char** argv) {

  google::InitGoogleLogging(argv[0]);

  if (argc != 2) {

    std::cerr << "usage: simple_bundle_adjuster <bal_problem>\n";

    return 1;

  }

  BALProblem bal_problem;

  if (!bal_problem.LoadFile(argv[1])) {

    std::cerr << "ERROR: unable to open file " << argv[1] << "\n";

    return 1;

  }

  const double* observations = bal_problem.observations();

  // Create residuals for each observation in the bundle adjustment problem. The

  // parameters for cameras and points are added automatically.

  ceres::Problem problem;

  for (int i = 0; i < bal_problem.num_observations(); ++i) {

    // Each Residual block takes a point and a camera as input and outputs a 2

    // dimensional residual. Internally, the cost function stores the observed

    // image location and compares the reprojection against the observation.

    ceres::CostFunction* cost_function =

        SnavelyReprojectionError::Create(observations[2 * i + 0],

                                         observations[2 * i + 1]);

    problem.AddResidualBlock(cost_function,

                             NULL /* squared loss */,

                             bal_problem.mutable_camera_for_observation(i),

                             bal_problem.mutable_point_for_observation(i));

  }

  // Make Ceres automatically detect the bundle structure. Note that the

  // standard solver, SPARSE_NORMAL_CHOLESKY, also works fine but it is slower

  // for standard bundle adjustment problems.

  ceres::Solver::Options options;

  options.linear_solver_type = ceres::DENSE_SCHUR;

  options.minimizer_progress_to_stdout = true;

  ceres::Solver::Summary summary;

  ceres::Solve(options, &problem, &summary);

  std::cout << summary.FullReport() << "\n";

  return 0;

}