// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: keir@google.com (Keir Mierle)
// sameeragarwal@google.com (Sameer Agarwal)
//
// End-to-end bundle adjustment tests for Ceres. It uses a bundle
// adjustment problem with 16 cameras and two thousand points.
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <string>
#include "ceres/internal/port.h"
#include "ceres/autodiff_cost_function.h"
#include "ceres/ordered_groups.h"
#include "ceres/problem.h"
#include "ceres/rotation.h"
#include "ceres/solver.h"
#include "ceres/stringprintf.h"
#include "ceres/test_util.h"
#include "ceres/types.h"
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
using std::string;
using std::vector;
const bool kAutomaticOrdering = true;
const bool kUserOrdering = false;
// This class implements the SystemTestProblem interface and provides
// access to a bundle adjustment problem. It is based on
// examples/bundle_adjustment_example.cc. Currently a small 16 camera
// problem is hard coded in the constructor.
class BundleAdjustmentProblem {
public:
BundleAdjustmentProblem() {
const string input_file = TestFileAbsolutePath("problem-16-22106-pre.txt");
ReadData(input_file);
BuildProblem();
}
~BundleAdjustmentProblem() {
delete []point_index_;
delete []camera_index_;
delete []observations_;
delete []parameters_;
}
Problem* mutable_problem() { return &problem_; }
Solver::Options* mutable_solver_options() { return &options_; }
int num_cameras() const { return num_cameras_; }
int num_points() const { return num_points_; }
int num_observations() const { return num_observations_; }
const int* point_index() const { return point_index_; }
const int* camera_index() const { return camera_index_; }
const double* observations() const { return observations_; }
double* mutable_cameras() { return parameters_; }
double* mutable_points() { return parameters_ + 9 * num_cameras_; }
static double kResidualTolerance;
private:
void ReadData(const string& filename) {
FILE * fptr = fopen(filename.c_str(), "r");
if (!fptr) {
LOG(FATAL) << "File Error: unable to open file " << filename;
}
// This will die horribly on invalid files. Them's the breaks.
FscanfOrDie(fptr, "%d", &num_cameras_);
FscanfOrDie(fptr, "%d", &num_points_);
FscanfOrDie(fptr, "%d", &num_observations_);
VLOG(1) << "Header: " << num_cameras_
<< " " << num_points_
<< " " << 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);
}
}
void BuildProblem() {
double* points = mutable_points();
double* cameras = mutable_cameras();
for (int i = 0; i < num_observations(); ++i) {
// Each Residual block takes a point and a camera as input and
// outputs a 2 dimensional residual.
CostFunction* cost_function =
new AutoDiffCostFunction<BundlerResidual, 2, 9, 3>(
new BundlerResidual(observations_[2*i + 0],
observations_[2*i + 1]));
// Each observation correponds to a pair of a camera and a point
// which are identified by camera_index()[i] and
// point_index()[i] respectively.
double* camera = cameras + 9 * camera_index_[i];
double* point = points + 3 * point_index()[i];
problem_.AddResidualBlock(cost_function, NULL, camera, point);
}
options_.linear_solver_ordering.reset(new ParameterBlockOrdering);
// The points come before the cameras.
for (int i = 0; i < num_points_; ++i) {
options_.linear_solver_ordering->AddElementToGroup(points + 3 * i, 0);
}
for (int i = 0; i < num_cameras_; ++i) {
options_.linear_solver_ordering->AddElementToGroup(cameras + 9 * i, 1);
}
options_.linear_solver_type = DENSE_SCHUR;
options_.max_num_iterations = 25;
options_.function_tolerance = 1e-10;
options_.gradient_tolerance = 1e-10;
options_.parameter_tolerance = 1e-10;
}
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.";
}
}
// Templated pinhole camera model. 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 to be located at the image
// center).
struct BundlerResidual {
// (u, v): the position of the observation with respect to the image
// center point.
BundlerResidual(double u, double v): u(u), v(v) {}
template <typename T>
bool operator()(const T* const camera,
const T* const point,
T* residuals) const {
T p[3];
AngleAxisRotatePoint(camera, point, p);
// Add the translation vector
p[0] += camera[3];
p[1] += camera[4];
p[2] += camera[5];
const T& focal = camera[6];
const T& l1 = camera[7];
const T& l2 = camera[8];
// 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 = - focal * p[0] / p[2];
T yp = - focal * p[1] / p[2];
// Apply second and fourth order radial distortion.
T r2 = xp*xp + yp*yp;
T distortion = T(1.0) + r2 * (l1 + l2 * r2);
residuals[0] = distortion * xp - u;
residuals[1] = distortion * yp - v;
return true;
}
double u;
double v;
};
Problem problem_;
Solver::Options options_;
int num_cameras_;
int num_points_;
int num_observations_;
int num_parameters_;
int* point_index_;
int* camera_index_;
double* observations_;
// The parameter vector is laid out as follows
// [camera_1, ..., camera_n, point_1, ..., point_m]
double* parameters_;
};
double BundleAdjustmentProblem::kResidualTolerance = 1e-4;
typedef SystemTest<BundleAdjustmentProblem> BundleAdjustmentTest;
TEST_F(BundleAdjustmentTest, DenseSchurWithAutomaticOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(DENSE_SCHUR, NO_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest, DenseSchurWithUserOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(DENSE_SCHUR, NO_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest, IterativeSchurWithJacobiAndAutomaticOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kAutomaticOrdering, JACOBI));
}
TEST_F(BundleAdjustmentTest, IterativeSchurWithJacobiAndUserOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kUserOrdering, JACOBI));
}
TEST_F(BundleAdjustmentTest,
IterativeSchurWithSchurJacobiAndAutomaticOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR,
NO_SPARSE,
kAutomaticOrdering,
SCHUR_JACOBI));
}
TEST_F(BundleAdjustmentTest, IterativeSchurWithSchurJacobiAndUserOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kUserOrdering, SCHUR_JACOBI));
}
#ifndef CERES_NO_SUITESPARSE
TEST_F(BundleAdjustmentTest,
SparseNormalCholeskyWithAutomaticOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
SparseNormalCholeskyWithUserOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
SparseSchurWithAutomaticOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_SCHUR, SUITE_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest, SparseSchurWithUserOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_SCHUR, SUITE_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
IterativeSchurWithClusterJacobiAndAutomaticOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kAutomaticOrdering,
CLUSTER_JACOBI));
}
TEST_F(BundleAdjustmentTest,
IterativeSchurWithClusterJacobiAndUserOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kUserOrdering,
CLUSTER_JACOBI));
}
TEST_F(BundleAdjustmentTest,
IterativeSchurWithClusterTridiagonalAndAutomaticOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kAutomaticOrdering,
CLUSTER_TRIDIAGONAL));
}
TEST_F(BundleAdjustmentTest,
IterativeSchurWithClusterTridiagonalAndUserOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kUserOrdering,
CLUSTER_TRIDIAGONAL));
}
#endif // CERES_NO_SUITESPARSE
#ifndef CERES_NO_CXSPARSE
TEST_F(BundleAdjustmentTest,
SparseNormalCholeskyWithAutomaticOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
SparseNormalCholeskyWithUserOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest, SparseSchurWithAutomaticOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_SCHUR, CX_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest, SparseSchurWithUserOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_SCHUR, CX_SPARSE, kUserOrdering));
}
#endif // CERES_NO_CXSPARSE
#ifdef CERES_USE_EIGEN_SPARSE
TEST_F(BundleAdjustmentTest,
SparseNormalCholeskyWithAutomaticOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_NORMAL_CHOLESKY, EIGEN_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
SparseNormalCholeskyWithUserOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_NORMAL_CHOLESKY, EIGEN_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
SparseSchurWithAutomaticOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest, SparseSchurWithUserOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
SolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kUserOrdering));
}
#endif // CERES_USE_EIGEN_SPARSE
#ifdef CERES_USE_OPENMP
TEST_F(BundleAdjustmentTest, MultiThreadedDenseSchurWithAutomaticOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(DENSE_SCHUR, NO_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest, MultiThreadedDenseSchurWithUserOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(DENSE_SCHUR, NO_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithJacobiAndAutomaticOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
NO_SPARSE,
kAutomaticOrdering,
JACOBI));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithJacobiAndUserOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR, NO_SPARSE, kUserOrdering, JACOBI));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithSchurJacobiAndAutomaticOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
NO_SPARSE,
kAutomaticOrdering,
SCHUR_JACOBI));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithSchurJacobiAndUserOrdering) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
NO_SPARSE,
kUserOrdering,
SCHUR_JACOBI));
}
#ifndef CERES_NO_SUITESPARSE
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseNormalCholeskyWithAutomaticOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
SUITE_SPARSE,
kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseNormalCholeskyWithUserOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
SUITE_SPARSE,
kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseSchurWithAutomaticOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_SCHUR,
SUITE_SPARSE,
kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseSchurWithUserOrderingUsingSuiteSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_SCHUR, SUITE_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithClusterJacobiAndAutomaticOrderingUsingSuiteSparse) { // NOLINT
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kAutomaticOrdering,
CLUSTER_JACOBI));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithClusterJacobiAndUserOrderingUsingSuiteSparse) { // NOLINT
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kUserOrdering,
CLUSTER_JACOBI));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithClusterTridiagonalAndAutomaticOrderingUsingSuiteSparse) { // NOLINT
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kAutomaticOrdering,
CLUSTER_TRIDIAGONAL));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedIterativeSchurWithClusterTridiagonalAndUserOrderingUsingSuiteSparse) { // NOTLINT
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(ITERATIVE_SCHUR,
SUITE_SPARSE,
kUserOrdering,
CLUSTER_TRIDIAGONAL));
}
#endif // CERES_NO_SUITESPARSE
#ifndef CERES_NO_CXSPARSE
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseNormalCholeskyWithAutomaticOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
CX_SPARSE,
kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseNormalCholeskyWithUserOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY, CX_SPARSE, kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseSchurWithAutomaticOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_SCHUR, CX_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseSchurWithUserOrderingUsingCXSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_SCHUR, CX_SPARSE, kUserOrdering));
}
#endif // CERES_NO_CXSPARSE
#ifdef CERES_USE_EIGEN_SPARSE
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseNormalCholeskyWithAutomaticOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
EIGEN_SPARSE,
kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseNormalCholeskyWithUserOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_NORMAL_CHOLESKY,
EIGEN_SPARSE,
kUserOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseSchurWithAutomaticOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kAutomaticOrdering));
}
TEST_F(BundleAdjustmentTest,
MultiThreadedSparseSchurWithUserOrderingUsingEigenSparse) {
RunSolverForConfigAndExpectResidualsMatch(
ThreadedSolverConfig(SPARSE_SCHUR, EIGEN_SPARSE, kUserOrdering));
}
#endif // CERES_USE_EIGEN_SPARSE
#endif // CERES_USE_OPENMP
} // namespace internal
} // namespace ceres