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Use openmp to speed up optimize_lines #53

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11 changes: 11 additions & 0 deletions line_refinement/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,13 @@ if(${CERES_VERSION} VERSION_LESS "2.2.0")
add_definitions("-DCERES_PARAMETERIZATION_ENABLED")
endif()

FIND_PACKAGE(OpenMP)
if (OPENMP_FOUND)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
endif (OPENMP_FOUND)

add_subdirectory(pybind11)
include_directories(../third_party/progressive-x/graph-cut-ransac/src/pygcransac/include
../third_party/progressive-x/src/pyprogressivex/include
Expand Down Expand Up @@ -39,3 +46,7 @@ pybind11_add_module(line_refinement line_vp_optim.cpp cost_functions.h vp_det.h)
target_link_libraries(line_refinement PUBLIC ceres stdc++fs
${OpenCV_LIBS} "${GraphCutRANSAC_LIB}"
"${ProgressiveX_LIB}")

if(OpenMP_CXX_FOUND)
target_link_libraries(line_refinement PUBLIC OpenMP::OpenMP_CXX)
endif()
23 changes: 18 additions & 5 deletions line_refinement/line_vp_optim.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,6 @@ std::tuple<std::vector<std::array<double, 4>>, std::vector<int>, std::vector<std
std::vector<std::array<double, 4>> x(num_lines);
for(int i = 0; i < num_lines; i++)
x[i] = {lines[i][0], lines[i][1], lines[i][2], lines[i][3]};
double c_x, c_y, ori, len, perp_dist;

// Extract VPs
std::vector<int> vp_labels;
Expand All @@ -121,19 +120,29 @@ std::tuple<std::vector<std::array<double, 4>>, std::vector<int>, std::vector<std
Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;
options.minimizer_progress_to_stdout = verbose;
options.num_threads = 4;

#ifdef _OPENMP
options.num_threads = 1;
#else
options.num_threads = 4;
#endif

options.max_num_iterations = 20;
if(!optimize_vps)
{
num_iter = 1;
options.max_num_iterations = 100;
}
Solver::Summary summary;

for(int it=0; it < num_iter; it++)
{
// Optimize the lines independently using the current VPs
#pragma omp parallel for
for(int i=0; i < num_lines; i++)
{
{
double c_x, c_y, ori, len, perp_dist;

Solver::Summary summary;
/* Optimization with fixed line length and no tangential translation (2 DOFs) */
c_x = (x[i][0] + x[i][2]) / 2.;
c_y = (x[i][1] + x[i][3]) / 2.;
Expand Down Expand Up @@ -188,8 +197,12 @@ std::tuple<std::vector<std::array<double, 4>>, std::vector<int>, std::vector<std
assign_vps(x, vps, vp_labels, threshold);

// Optimize VPs based on the lines
#pragma omp parallel for
for(int i=0; i < vps.size(); i++)
{
double c_x, c_y, len;

Solver::Summary summary;
// Define the problem
Problem problem;

Expand All @@ -201,7 +214,7 @@ std::tuple<std::vector<std::array<double, 4>>, std::vector<int>, std::vector<std
// Get the middle point
c_x = (x[j][0] + x[j][2]) / 2;
c_y = (x[j][1] + x[j][3]) / 2;
len = std::sqrt(pow(x[i][2] - x[i][0], 2) + pow(x[i][3] - x[i][1], 2));
len = std::sqrt(pow(x[j][2] - x[j][0], 2) + pow(x[j][3] - x[j][1], 2));
LossFunction* vp_loss = new ScaledLoss(
new CauchyLoss(0.5), len, ceres::TAKE_OWNERSHIP);
ceres::CostFunction* vp_cost_function = VpCostFunctor::Create(
Expand Down