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updated solvePnpRansac performance test

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This commit is contained in:
Alexey Spizhevoy 2011-03-07 13:36:47 +00:00
parent 673061fb17
commit 1d62fddd31
3 changed files with 38 additions and 26 deletions
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3
modules/gpu/src/calib3d.cpp
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@ -145,6 +145,8 @@ void cv::gpu::projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
namespace cv { namespace gpu { namespace solve_pnp_ransac
{
int maxNumIters();
void computeHypothesisScores(
const int num_hypotheses, const int num_points, const float* rot_matrices,
const float3* transl_vectors, const float3* object, const float2* image,
@ -241,6 +243,7 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam
CV_Assert(object.cols == image.cols);
CV_Assert(camera_mat.size() == Size(3, 3) && camera_mat.type() == CV_32F);
CV_Assert(!params.use_extrinsic_guess); // We don't support initial guess for now
CV_Assert(params.num_iters <= solve_pnp_ransac::maxNumIters());
const int num_points = object.cols;
CV_Assert(num_points >= params.subset_size);

11
modules/gpu/src/cuda/calib3d.cu
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@ -43,7 +43,7 @@
#include "internal_shared.hpp"
#include "opencv2/gpu/device/transform.hpp"
#define SOLVE_PNP_RANSAC_NUM_ITERS 200
#define SOLVE_PNP_RANSAC_MAX_NUM_ITERS 200
namespace cv { namespace gpu
{
@ -120,8 +120,13 @@ namespace cv { namespace gpu
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 crot_matrices[SOLVE_PNP_RANSAC_MAX_NUM_ITERS * 3];
__constant__ float3 ctransl_vectors[SOLVE_PNP_RANSAC_MAX_NUM_ITERS];
int maxNumIters()
{
return SOLVE_PNP_RANSAC_MAX_NUM_ITERS;
}
__device__ float sqr(float x)
{

50
samples/gpu/performance/tests.cpp
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@ -792,36 +792,40 @@ void InitSolvePnpRansac()
}
// It's not very correct test as solvePnP and solvePnpRansac use different algorithms internally
// TODO add proper test after CPU solvePnpRansac being added
TEST(solvePnpRansac)
{
InitSolvePnpRansac();
int num_points = 1000000;
for (int num_points = 5000; num_points <= 300000; num_points = int(num_points * 3.76))
{
SUBTEST << "num_points " << num_points;
Mat object; gen(object, 1, num_points, CV_32FC3, Scalar::all(0), Scalar::all(100));
Mat camera_mat; gen(camera_mat, 3, 3, CV_32F, 0.5, 1);
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 object; gen(object, 1, num_points, CV_32FC3, Scalar::all(10), Scalar::all(100));
Mat image; gen(image, 1, num_points, CV_32FC2, Scalar::all(10), Scalar::all(100));
Mat camera_mat; gen(camera_mat, 3, 3, CV_32F, 0.5, 1);
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; gen(rvec_gold, 1, 3, CV_32F, 0, 1);
Mat tvec_gold; gen(tvec_gold, 1, 3, CV_32F, 0, 1);
Mat rvec, tvec;
const int num_iters = 200;
const float max_dist = 2.0f;
vector<int> inliers_cpu;
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]);
CPU_ON;
solvePnPRansac(object, image, camera_mat, Mat(), rvec, tvec, false, num_iters,
max_dist, int(num_points * 0.05), &inliers_cpu);
CPU_OFF;
Mat rvec, tvec;
gpu::SolvePnpRansacParams params;
params.num_iters = num_iters;
params.max_dist = max_dist;
vector<int> inliers_gpu;
params.inliers = &inliers_gpu;
CPU_ON;
solvePnP(object, image, camera_mat, Mat(), rvec, tvec);
CPU_OFF;
GPU_ON;
gpu::SolvePnpRansacParams params;
gpu::solvePnpRansac(object, image, camera_mat, Mat(), rvec, tvec, params);
GPU_OFF;
GPU_ON;
gpu::solvePnpRansac(object, image, camera_mat, Mat(), rvec, tvec, params);
GPU_OFF;
}
}