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parallelized hypotheses evaluation cycle in gpu::solvePnpRansac

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pull/13383/head
Alexey Spizhevoy 14 years ago
parent cae59a7caf
commit eb8c0b8b4b
3 changed files with 67 additions and 38 deletions
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  1. 2
      modules/gpu/include/opencv2/gpu/gpu.hpp
  2. 102
      modules/gpu/src/calib3d.cpp
  3. 1
      modules/gpu/src/precomp.hpp

2
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -874,13 +874,11 @@ namespace cv
use_extrinsic_guess(false),
num_iters(100),
max_dist(2.f),
min_num_inliers(-1),
inliers(NULL) {}
int subset_size;
bool use_extrinsic_guess;
int num_iters;
float max_dist;
int min_num_inliers;
vector<int>* inliers;
};

102
modules/gpu/src/calib3d.cpp
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@ -173,13 +173,71 @@ namespace
} while (was);
}
}
// Computes rotation, translation pair for small subsets if the input data
class TransformHypothesesGenerator
{
public:
TransformHypothesesGenerator(const Mat& object_, const Mat& image_, const Mat& camera_mat_,
int num_points_, int subset_size_, Mat rot_matrices_, Mat transl_vectors_)
: object(&object_), image(&image_), camera_mat(&camera_mat_), num_points(num_points_),
subset_size(subset_size_), rot_matrices(rot_matrices_), transl_vectors(transl_vectors_) {}
void operator()(const BlockedRange& range) const
{
// We assume that input is undistorted
Mat empty_dist_coef;
// Input data for generation of the current hypothesis
vector<int> subset_indices(subset_size);
Mat_<Point3f> object_subset(1, subset_size);
Mat_<Point2f> image_subset(1, subset_size);
// Current hypothesis data
Mat rot_vec(1, 3, CV_64F);
Mat rot_mat(3, 3, CV_64F);
Mat transl_vec(1, 3, CV_64F);
for (int iter = range.begin(); iter < range.end(); ++iter)
{
selectRandom(subset_size, num_points, subset_indices);
for (int i = 0; i < subset_size; ++i)
{
object_subset(0, i) = object->at<Point3f>(subset_indices[i]);
image_subset(0, i) = image->at<Point2f>(subset_indices[i]);
}
solvePnP(object_subset, image_subset, *camera_mat, empty_dist_coef, rot_vec, transl_vec);
// Remember translation vector
Mat transl_vec_ = transl_vectors.colRange(iter * 3, (iter + 1) * 3);
transl_vec = transl_vec.reshape(0, 1);
transl_vec.convertTo(transl_vec_, CV_32F);
// Remember rotation matrix
Rodrigues(rot_vec, rot_mat);
Mat rot_mat_ = rot_matrices.colRange(iter * 9, (iter + 1) * 9).reshape(0, 3);
rot_mat.convertTo(rot_mat_, CV_32F);
}
}
const Mat* object;
const Mat* image;
const Mat* camera_mat;
int num_points;
int subset_size;
// Hypotheses storage (global)
Mat rot_matrices;
Mat transl_vectors;
};
}
void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
const Mat& dist_coef, Mat& rvec, Mat& tvec, SolvePnpRansacParams params)
{
CV_Assert(object.rows == 1 && object.cols > 0 && object.type() == CV_32FC3);
CV_Assert(image.rows == 1 && image.cols > 1 && image.type() == CV_32FC2);
CV_Assert(image.rows == 1 && image.cols > 0 && image.type() == CV_32FC2);
CV_Assert(object.cols == image.cols);
CV_Assert(camera_mat.size() == Size(3, 3) && camera_mat.type() == CV_32F);
CV_Assert(dist_coef.empty()); // We don't support undistortion for now
@ -187,43 +245,14 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam
const int num_points = object.cols;
// Current hypothesis input
vector<int> subset_indices(params.subset_size);
Mat_<Point3f> object_subset(1, params.subset_size);
Mat_<Point2f> image_subset(1, params.subset_size);
// Current hypothesis result
Mat rot_vec(1, 3, CV_64F);
Mat rot_mat(3, 3, CV_64F);
Mat transl_vec(1, 3, CV_64F);
// All hypotheses results
// Hypotheses storage (global)
Mat rot_matrices(1, params.num_iters * 9, CV_32F);
Mat transl_vectors(1, params.num_iters * 3, CV_32F);
// Generate set of (rotation, translation) hypotheses using small subsets
// of the input data
for (int iter = 0; iter < params.num_iters; ++iter) // TODO TBB?
{
selectRandom(params.subset_size, num_points, subset_indices);
for (int i = 0; i < params.subset_size; ++i)
{
object_subset(0, i) = object.at<Point3f>(subset_indices[i]);
image_subset(0, i) = image.at<Point2f>(subset_indices[i]);
}
solvePnP(object_subset, image_subset, camera_mat, dist_coef, rot_vec, transl_vec);
// Remember translation vector
Mat transl_vec_ = transl_vectors.colRange(iter * 3, (iter + 1) * 3);
transl_vec = transl_vec.reshape(0, 1);
transl_vec.convertTo(transl_vec_, CV_32F);
// Remember rotation matrix
Rodrigues(rot_vec, rot_mat);
Mat rot_mat_ = rot_matrices.colRange(iter * 9, (iter + 1) * 9).reshape(0, 3);
rot_mat.convertTo(rot_mat_, CV_32F);
}
// Generate set of hypotheses using small subsets of the input data
TransformHypothesesGenerator body(object, image, camera_mat, num_points,
params.subset_size, rot_matrices, transl_vectors);
parallel_for(BlockedRange(0, params.num_iters), body);
// Compute scores (i.e. number of inliers) for each hypothesis
GpuMat d_object(object);
@ -241,7 +270,7 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam
int num_inliers = static_cast<int>(best_score);
// Extract the best hypothesis data
rot_mat = rot_matrices.colRange(best_idx.x * 9, (best_idx.x + 1) * 9).reshape(0, 3);
Mat rot_mat = rot_matrices.colRange(best_idx.x * 9, (best_idx.x + 1) * 9).reshape(0, 3);
Rodrigues(rot_mat, rvec);
rvec = rvec.reshape(0, 1);
tvec = transl_vectors.colRange(best_idx.x * 3, (best_idx.x + 1) * 3).clone();
@ -278,3 +307,4 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam
#endif

1
modules/gpu/src/precomp.hpp
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@ -62,6 +62,7 @@
#include "opencv2/gpu/gpu.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/core/internal.hpp"
#if defined(HAVE_CUDA)

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