void cv::gpu::projectPoints(const GpuMat&, const Mat&, const Mat&,
const Mat&, const Mat&, GpuMat&, const Stream&) { throw_nogpu(); }
+void cv::gpu::solvePnpRansac(const Mat&, const Mat&, const Mat&, const Mat&,
+ Mat&, Mat&, SolvePnpRansacParams) { throw_nogpu(); }
+
#else
using namespace cv;
const float* proj, DevMem2D_<float2> dst, cudaStream_t stream);
}}}
+
namespace
{
void projectPointsCaller(const GpuMat& src, const Mat& rvec, const Mat& tvec,
::projectPointsCaller(src, rvec, tvec, camera_mat, dist_coef, dst, StreamAccessor::getStream(stream));
}
+
+namespace cv { namespace gpu { namespace solve_pnp_ransac
+{
+ void computeHypothesisScores(
+ const int num_hypotheses, const int num_points, const float* rot_matrices,
+ const float3* transl_vectors, const float3* object, const float2* image,
+ const float3* camera_mat, const float dist_threshold, int* hypothesis_scores);
+}}}
+
+namespace
+{
+ // Selects subset_size random different points from [0, num_points - 1] range
+ void selectRandom(int subset_size, int num_points, vector<int>& subset)
+ {
+ subset.resize(subset_size);
+ for (int i = 0; i < subset_size; ++i)
+ {
+ bool was;
+ do
+ {
+ subset[i] = rand() % num_points;
+ was = false;
+ for (int j = 0; j < i; ++j)
+ if (subset[j] == subset[i])
+ {
+ was = true;
+ break;
+ }
+ } while (was);
+ }
+ }
+}
+
+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(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
+ CV_Assert(!params.use_extrinsic_guess); // We don't support initial guess for now
+
+ 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
+ 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);
+ }
+
+ // Compute scores (i.e. number of inliers) for each hypothesis
+ GpuMat d_object(object);
+ GpuMat d_image(image);
+ GpuMat d_hypothesis_scores(1, params.num_iters, CV_32S);
+ solve_pnp_ransac::computeHypothesisScores(
+ params.num_iters, num_points, rot_matrices.ptr<float>(), transl_vectors.ptr<float3>(),
+ d_object.ptr<float3>(), d_image.ptr<float2>(), camera_mat.ptr<float3>(),
+ params.max_dist * params.max_dist, d_hypothesis_scores.ptr<int>());
+
+ // Find the best hypothesis index
+ Point best_idx;
+ double best_score;
+ minMaxLoc(d_hypothesis_scores, NULL, &best_score, NULL, &best_idx);
+ 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);
+ Rodrigues(rot_mat, rvec);
+ rvec = rvec.reshape(0, 1);
+ tvec = transl_vectors.colRange(best_idx.x * 3, (best_idx.x + 1) * 3).clone();
+ tvec = tvec.reshape(0, 1);
+
+ // Build vector of inlier indices
+ if (params.inliers != NULL)
+ {
+ params.inliers->resize(num_inliers);
+
+ Point3f p;
+ Point3f p_transf;
+ Point2f p_proj;
+ const float* rot = rot_mat.ptr<float>();
+ const float* transl = tvec.ptr<float>();
+ int inlier_id = 0;
+
+ for (int i = 0; i < num_points; ++i)
+ {
+ p = object.at<Point3f>(0, i);
+ p_transf.x = rot[0] * p.x + rot[1] * p.y + rot[2] * p.z + transl[0];
+ p_transf.y = rot[3] * p.x + rot[4] * p.y + rot[5] * p.z + transl[1];
+ p_transf.z = rot[6] * p.x + rot[7] * p.y + rot[8] * p.z + transl[2];
+ if (p_transf.z > 0.f)
+ {
+ p_proj.x = camera_mat.at<float>(0, 0) * p_transf.x / p_transf.z + camera_mat.at<float>(0, 2);
+ p_proj.y = camera_mat.at<float>(1, 1) * p_transf.x / p_transf.z + camera_mat.at<float>(1, 2);
+ if (norm(p_proj - image.at<Point2f>(0, i)) < params.max_dist)
+ (*params.inliers)[inlier_id++] = i;
+ }
+ }
+ }
+}
+
#endif
+
#include "internal_shared.hpp"
#include "opencv2/gpu/device/transform.hpp"
+#define SOLVE_PNP_RANSAC_NUM_ITERS 200
+
namespace cv { namespace gpu
{
namespace transform_points
}
} // namespace transform_points
+
namespace project_points
{
__constant__ float3 crot0;
}
} // namespace project_points
+
+ namespace solve_pnp_ransac
+ {
+ __constant__ float3 crot_matrices[SOLVE_PNP_RANSAC_NUM_ITERS * 3];
+ __constant__ float3 ctransl_vectors[SOLVE_PNP_RANSAC_NUM_ITERS];
+ __constant__ float3 ccamera_mat[2];
+
+ __device__ float sqr(float x)
+ {
+ return x * x;
+ }
+
+ __global__ void computeHypothesisScoresKernel(
+ const int num_points, const float3* object, const float2* image,
+ const float dist_threshold, int* g_num_inliers)
+ {
+ const float3* const &rot_mat = crot_matrices + blockIdx.x * 3;
+ const float3 &transl_vec = ctransl_vectors[blockIdx.x];
+ int num_inliers = 0;
+
+ for (int i = threadIdx.x; i < num_points; i += blockDim.x)
+ {
+ float3 p = object[i];
+ p = make_float3(
+ rot_mat[0].x * p.x + rot_mat[0].y * p.y + rot_mat[0].z * p.z + transl_vec.x,
+ rot_mat[1].x * p.x + rot_mat[1].y * p.y + rot_mat[1].z * p.z + transl_vec.y,
+ rot_mat[2].x * p.x + rot_mat[2].y * p.y + rot_mat[2].z * p.z + transl_vec.z);
+ if (p.z > 0)
+ {
+ p.x = ccamera_mat[0].x * p.x / p.z + ccamera_mat[0].z;
+ p.y = ccamera_mat[1].y * p.y / p.z + ccamera_mat[1].z;
+ float2 image_p = image[i];
+ if (sqr(p.x - image_p.x) + sqr(p.y - image_p.y) < dist_threshold)
+ ++num_inliers;
+ }
+ }
+
+ extern __shared__ float s_num_inliers[];
+ s_num_inliers[threadIdx.x] = num_inliers;
+ __syncthreads();
+
+ for (int step = blockDim.x / 2; step > 0; step >>= 1)
+ {
+ if (threadIdx.x < step)
+ s_num_inliers[threadIdx.x] += s_num_inliers[threadIdx.x + step];
+ __syncthreads();
+ }
+
+ if (threadIdx.x == 0)
+ g_num_inliers[blockIdx.x] = s_num_inliers[0];
+ }
+
+ void computeHypothesisScores(
+ const int num_hypotheses, const int num_points, const float* rot_matrices,
+ const float3* transl_vectors, const float3* object, const float2* image,
+ const float3* camera_mat, const float dist_threshold, int* hypothesis_scores)
+ {
+ cudaSafeCall(cudaMemcpyToSymbol(crot_matrices, rot_matrices, num_hypotheses * 3 * sizeof(float3)));
+ cudaSafeCall(cudaMemcpyToSymbol(ctransl_vectors, transl_vectors, num_hypotheses * sizeof(float3)));
+ cudaSafeCall(cudaMemcpyToSymbol(ccamera_mat, camera_mat, 2 * sizeof(float3)));
+
+ dim3 threads(256);
+ dim3 grid(num_hypotheses);
+ int smem_size = threads.x * sizeof(float);
+
+ computeHypothesisScoresKernel<<<grid, threads, smem_size>>>(
+ num_points, object, image, dist_threshold, hypothesis_scores);
+ cudaSafeCall(cudaThreadSynchronize());
+ }
+ } // namespace solvepnp_ransac
+
}} // namespace cv { namespace gpu
ASSERT_LT(err.dot(err) / res_gold.dot(res_gold), 1e-3f);
}
}
+
+
+TEST(solvePnpRansac, accuracy)
+{
+ RNG& rng = TS::ptr()->get_rng();
+
+ const int num_points = 5000;
+ Mat object = randomMat(rng, Size(num_points, 1), CV_32FC3, 0, 100, false);
+ Mat camera_mat = randomMat(rng, Size(3, 3), CV_32F, 1, 1, false);
+ camera_mat.at<float>(0, 1) = 0.f;
+ camera_mat.at<float>(1, 0) = 0.f;
+ camera_mat.at<float>(2, 0) = 0.f;
+ camera_mat.at<float>(2, 1) = 0.f;
+
+ Mat rvec_gold = randomMat(rng, Size(3, 1), CV_32F, 0, 1, false);
+ Mat tvec_gold = randomMat(rng, Size(3, 1), CV_32F, 0, 1, false);
+
+ vector<Point2f> image_vec;
+ projectPoints(object, rvec_gold, tvec_gold, camera_mat, Mat(), image_vec);
+ Mat image(1, image_vec.size(), CV_32FC2, &image_vec[0]);
+
+ Mat rvec;
+ Mat tvec;
+ solvePnpRansac(object, image, camera_mat, Mat(), rvec, tvec, SolvePnpRansacParams());
+
+ ASSERT_LE(norm(rvec - rvec_gold), 1e-3f);
+ ASSERT_LE(norm(tvec - tvec_gold), 1e-3f);
+}
projects / profile / ivi / opencv.git / commitdiff