Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #9308 from alalek:akaze_fixes

Browse Source
pull/9299/head
Alexander Alekhin 7 years ago
parent 922ac1a1ec 94dbc35d92
commit 5c961169cc
4 changed files with 289 additions and 279 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 374
      modules/features2d/src/kaze/AKAZEFeatures.cpp
  2. 13
      modules/features2d/src/kaze/AKAZEFeatures.h
  3. 103
      modules/features2d/test/test_descriptors_regression.cpp
  4. 6
      modules/ts/misc/run_long.py

374
modules/features2d/src/kaze/AKAZEFeatures.cpp
Unescape View File

@ -15,6 +15,10 @@
#include <iostream>
#ifdef HAVE_OPENCL // OpenCL is not well supported
#undef HAVE_OPENCL
#endif
// Namespaces
namespace cv
{
@ -251,11 +255,13 @@ private:
#ifdef HAVE_OPENCL
static inline bool
ocl_non_linear_diffusion_step(const UMat& Lt, const UMat& Lf, UMat& Lstep, float step_size)
ocl_non_linear_diffusion_step(InputArray Lt_, InputArray Lf_, OutputArray Lstep_, float step_size)
{
if(!Lt.isContinuous())
if (!Lt_.isContinuous())
return false;
UMat Lt = Lt_.getUMat(), Lf = Lf_.getUMat(), Lstep = Lstep_.getUMat();
size_t globalSize[] = {(size_t)Lt.cols, (size_t)Lt.rows};
ocl::Kernel ker("AKAZE_nld_step_scalar", ocl::features2d::akaze_oclsrc);
@ -266,23 +272,24 @@ ocl_non_linear_diffusion_step(const UMat& Lt, const UMat& Lf, UMat& Lstep, float
ocl::KernelArg::ReadOnly(Lt),
ocl::KernelArg::PtrReadOnly(Lf),
ocl::KernelArg::PtrWriteOnly(Lstep),
step_size).run(2, globalSize, 0, true);
step_size)
.run(2, globalSize, 0, true);
}
#endif // HAVE_OPENCL
static inline void
non_linear_diffusion_step(const UMat& Lt, const UMat& Lf, UMat& Lstep, float step_size)
non_linear_diffusion_step(InputArray Lt, InputArray Lf, OutputArray Lstep, float step_size)
{
CV_INSTRUMENT_REGION()
Lstep.create(Lt.size(), Lt.type());
CV_OCL_RUN(true, ocl_non_linear_diffusion_step(Lt, Lf, Lstep, step_size));
#ifdef HAVE_OPENCL
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(Lstep.isUMat()), ocl_non_linear_diffusion_step(Lt, Lf, Lstep, step_size));
#endif
// when on CPU UMats should be already allocated on CPU so getMat here is basicallly no-op
Mat Mstep = Lstep.getMat(ACCESS_WRITE);
parallel_for_(Range(0, Lt.rows), NonLinearScalarDiffusionStep(Lt.getMat(ACCESS_READ),
Lf.getMat(ACCESS_READ), Mstep, step_size));
Mat Mstep = Lstep.getMat();
parallel_for_(Range(0, Lt.rows()), NonLinearScalarDiffusionStep(Lt.getMat(), Lf.getMat(), Mstep, step_size));
}
/**
@ -347,8 +354,10 @@ compute_kcontrast(const cv::Mat& Lx, const cv::Mat& Ly, float perc, int nbins)
#ifdef HAVE_OPENCL
static inline bool
ocl_pm_g2(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast)
ocl_pm_g2(InputArray Lx_, InputArray Ly_, OutputArray Lflow_, float kcontrast)
{
UMat Lx = Lx_.getUMat(), Ly = Ly_.getUMat(), Lflow = Lflow_.getUMat();
int total = Lx.rows * Lx.cols;
size_t globalSize[] = {(size_t)total};
@ -360,12 +369,13 @@ ocl_pm_g2(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast)
ocl::KernelArg::PtrReadOnly(Lx),
ocl::KernelArg::PtrReadOnly(Ly),
ocl::KernelArg::PtrWriteOnly(Lflow),
kcontrast, total).run(1, globalSize, 0, true);
kcontrast, total)
.run(1, globalSize, 0, true);
}
#endif // HAVE_OPENCL
static inline void
compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast, int diffusivity)
compute_diffusivity(InputArray Lx, InputArray Ly, OutputArray Lflow, float kcontrast, int diffusivity)
{
CV_INSTRUMENT_REGION()
@ -376,7 +386,9 @@ compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast
pm_g1(Lx, Ly, Lflow, kcontrast);
break;
case KAZE::DIFF_PM_G2:
CV_OCL_RUN(true, ocl_pm_g2(Lx, Ly, Lflow, kcontrast));
#ifdef HAVE_OPENCL
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(Lflow.isUMat()), ocl_pm_g2(Lx, Ly, Lflow, kcontrast));
#endif
pm_g2(Lx, Ly, Lflow, kcontrast);
break;
case KAZE::DIFF_WEICKERT:
@ -391,32 +403,6 @@ compute_diffusivity(const UMat& Lx, const UMat& Ly, UMat& Lflow, float kcontrast
}
}
/**
* @brief Fetches pyramid from the gpu.
* @details Setups mapping for matrices that might be probably on the GPU, if the
* code executes with OpenCL. This will setup MLx, MLy, Mdet members in the pyramid with
* mapping to respective UMats. This must be called before CPU-only parts of AKAZE, that work
* only on these Mats.
*
* This prevents mapping/unmapping overhead (and possible uploads/downloads) that would occur, if
* we just create Mats from UMats each time we need it later. This has devastating effects on OCL
* performace.
*
* @param evolution Pyramid to download
*/
static inline void downloadPyramid(std::vector<Evolution>& evolution)
{
CV_INSTRUMENT_REGION()
for (size_t i = 0; i < evolution.size(); ++i) {
Evolution& e = evolution[i];
e.Mx = e.Lx.getMat(ACCESS_READ);
e.My = e.Ly.getMat(ACCESS_READ);
e.Mt = e.Lt.getMat(ACCESS_READ);
e.Mdet = e.Ldet.getMat(ACCESS_READ);
}
}
/**
* @brief This method creates the nonlinear scale space for a given image
* @param img Input image for which the nonlinear scale space needs to be created
@ -435,12 +421,11 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img)
if (evolution_.size() == 1) {
// we don't need to compute kcontrast factor
Compute_Determinant_Hessian_Response();
downloadPyramid(evolution_);
return;
}
// derivatives, flow and diffusion step
UMat Lx, Ly, Lsmooth, Lflow, Lstep;
Mat Lx, Ly, Lsmooth, Lflow, Lstep;
// compute derivatives for computing k contrast
GaussianBlur(img, Lsmooth, Size(5, 5), 1.0f, 1.0f, BORDER_REPLICATE);
@ -448,8 +433,7 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img)
Scharr(Lsmooth, Ly, CV_32F, 0, 1, 1, 0, BORDER_DEFAULT);
Lsmooth.release();
// compute the kcontrast factor
float kcontrast = compute_kcontrast(Lx.getMat(ACCESS_READ), Ly.getMat(ACCESS_READ),
options_.kcontrast_percentile, options_.kcontrast_nbins);
float kcontrast = compute_kcontrast(Lx, Ly, options_.kcontrast_percentile, options_.kcontrast_nbins);
// Now generate the rest of evolution levels
for (size_t i = 1; i < evolution_.size(); i++) {
@ -483,18 +467,16 @@ void AKAZEFeatures::Create_Nonlinear_Scale_Space(InputArray img)
}
Compute_Determinant_Hessian_Response();
downloadPyramid(evolution_);
return;
}
/* ************************************************************************* */
#ifdef HAVE_OPENCL
static inline bool
ocl_compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy,
UMat& Ldet, float sigma)
ocl_compute_determinant(InputArray Lxx_, InputArray Lxy_, InputArray Lyy_, OutputArray Ldet_, float sigma)
{
UMat Lxx = Lxx_.getUMat(), Lxy = Lxy_.getUMat(), Lyy = Lyy_.getUMat(), Ldet = Ldet_.getUMat();
const int total = Lxx.rows * Lxx.cols;
size_t globalSize[] = {(size_t)total};
@ -507,7 +489,8 @@ ocl_compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy,
ocl::KernelArg::PtrReadOnly(Lxy),
ocl::KernelArg::PtrReadOnly(Lyy),
ocl::KernelArg::PtrWriteOnly(Ldet),
sigma, total).run(1, globalSize, 0, true);
sigma, total)
.run(1, globalSize, 0, true);
}
#endif // HAVE_OPENCL
@ -521,27 +504,30 @@ ocl_compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy,
* @param Ldet output determinant
* @param sigma determinant will be scaled by this sigma
*/
static inline void compute_determinant(const UMat& Lxx, const UMat& Lxy, const UMat& Lyy,
UMat& Ldet, float sigma)
static inline void compute_determinant(InputArray Lxx, InputArray Lxy, InputArray Lyy, OutputArray Ldet, float sigma)
{
CV_INSTRUMENT_REGION()
Ldet.create(Lxx.size(), Lxx.type());
CV_OCL_RUN(true, ocl_compute_determinant(Lxx, Lxy, Lyy, Ldet, sigma));
#ifdef HAVE_OPENCL
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(Ldet.isUMat()), ocl_compute_determinant(Lxx, Lxy, Lyy, Ldet, sigma));
#endif
// output determinant
Mat Mxx = Lxx.getMat(ACCESS_READ), Mxy = Lxy.getMat(ACCESS_READ), Myy = Lyy.getMat(ACCESS_READ);
Mat Mdet = Ldet.getMat(ACCESS_WRITE);
float *lxx = Mxx.ptr<float>();
float *lxy = Mxy.ptr<float>();
float *lyy = Myy.ptr<float>();
float *ldet = Mdet.ptr<float>();
const int total = Lxx.cols * Lxx.rows;
for (int j = 0; j < total; j++) {
ldet[j] = (lxx[j] * lyy[j] - lxy[j] * lxy[j]) * sigma;
Mat Mxx = Lxx.getMat(), Mxy = Lxy.getMat(), Myy = Lyy.getMat(), Mdet = Ldet.getMat();
const int W = Mxx.cols, H = Mxx.rows;
for (int y = 0; y < H; y++)
{
float *lxx = Mxx.ptr<float>(y);
float *lxy = Mxy.ptr<float>(y);
float *lyy = Myy.ptr<float>(y);
float *ldet = Mdet.ptr<float>(y);
for (int x = 0; x < W; x++)
{
ldet[x] = (lxx[x] * lyy[x] - lxy[x] * lxy[x]) * sigma;
}
}
}
class DeterminantHessianResponse : public ParallelLoopBody
@ -554,7 +540,7 @@ public:
void operator()(const Range& range) const
{
UMat Lxx, Lxy, Lyy;
Mat Lxx, Lxy, Lyy;
for (int i = range.start; i < range.end; i++)
{
@ -670,16 +656,16 @@ public:
const Evolution &e = (*evolution_)[i];
Mat &kpts = (*keypoints_by_layers_)[i];
// this mask will hold positions of keypoints in this level
kpts = Mat::zeros(e.Mdet.size(), CV_8UC1);
kpts = Mat::zeros(e.Ldet.size(), CV_8UC1);
// if border is too big we shouldn't search any keypoints
if (e.border + 1 >= e.Ldet.rows)
continue;
const float * prev = e.Mdet.ptr<float>(e.border - 1);
const float * curr = e.Mdet.ptr<float>(e.border );
const float * next = e.Mdet.ptr<float>(e.border + 1);
const float * ldet = e.Mdet.ptr<float>();
const float * prev = e.Ldet.ptr<float>(e.border - 1);
const float * curr = e.Ldet.ptr<float>(e.border );
const float * next = e.Ldet.ptr<float>(e.border + 1);
const float * ldet = e.Ldet.ptr<float>();
uchar *mask = kpts.ptr<uchar>();
const int search_radius = e.sigma_size; // size of keypoint in this level
@ -743,8 +729,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_laye
const Mat &keypoints = keypoints_by_layers[i];
const uchar *const kpts = keypoints_by_layers[i].ptr<uchar>();
uchar *const kpts_prev = keypoints_by_layers[i-1].ptr<uchar>();
const float *const ldet = evolution_[i].Mdet.ptr<float>();
const float *const ldet_prev = evolution_[i-1].Mdet.ptr<float>();
const float *const ldet = evolution_[i].Ldet.ptr<float>();
const float *const ldet_prev = evolution_[i-1].Ldet.ptr<float>();
// ratios are just powers of 2
const int diff_ratio = (int)evolution_[i].octave_ratio / (int)evolution_[i-1].octave_ratio;
const int search_radius = evolution_[i].sigma_size * diff_ratio; // size of keypoint in this level
@ -775,8 +761,8 @@ void AKAZEFeatures::Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_laye
const Mat &keypoints = keypoints_by_layers[i];
const uchar *const kpts = keypoints_by_layers[i].ptr<uchar>();
uchar *const kpts_next = keypoints_by_layers[i+1].ptr<uchar>();
const float *const ldet = evolution_[i].Mdet.ptr<float>();
const float *const ldet_next = evolution_[i+1].Mdet.ptr<float>();
const float *const ldet = evolution_[i].Ldet.ptr<float>();
const float *const ldet_next = evolution_[i+1].Ldet.ptr<float>();
// ratios are just powers of 2, i+1 ratio is always greater or equal to i
const int diff_ratio = (int)evolution_[i+1].octave_ratio / (int)evolution_[i].octave_ratio;
const int search_radius = evolution_[i+1].sigma_size; // size of keypoints in upper level
@ -814,7 +800,7 @@ void AKAZEFeatures::Do_Subpixel_Refinement(
for (size_t i = 0; i < keypoints_by_layers.size(); i++) {
const Evolution &e = evolution_[i];
const float * const ldet = e.Mdet.ptr<float>();
const float * const ldet = e.Ldet.ptr<float>();
const float ratio = e.octave_ratio;
const int cols = e.Ldet.cols;
const Mat& keypoints = keypoints_by_layers[i];
@ -1308,7 +1294,7 @@ void Compute_Main_Orientation(KeyPoint& kpt, const std::vector<Evolution>& evolu
// Sample derivatives responses for the points within radius of 6*scale
const int ang_size = 109;
float resX[ang_size], resY[ang_size];
Sample_Derivative_Response_Radius6(e.Mx, e.My, x0, y0, scale, resX, resY);
Sample_Derivative_Response_Radius6(e.Lx, e.Ly, x0, y0, scale, resX, resY);
// Compute the angle of each gradient vector
float Ang[ang_size];
@ -1445,8 +1431,8 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
ratio = (float)(1 << kpt.octave);
scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
const Mat Lx = evolution[level].Lx;
const Mat Ly = evolution[level].Ly;
yf = kpt.pt.y / ratio;
xf = kpt.pt.x / ratio;
@ -1480,25 +1466,28 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
//Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.50f*scale);
y1 = (int)(sample_y - .5f);
x1 = (int)(sample_x - .5f);
y1 = cvFloor(sample_y);
x1 = cvFloor(sample_x);
y2 = (int)(sample_y + .5f);
x2 = (int)(sample_x + .5f);
y2 = y1 + 1;
x2 = x1 + 1;
if (x1 < 0 || y1 < 0 || x2 >= Lx.cols || y2 >= Lx.rows)
continue; // FIXIT Boundaries
fx = sample_x - x1;
fy = sample_y - y1;
res1 = *(Lx.ptr<float>(y1)+x1);
res2 = *(Lx.ptr<float>(y1)+x2);
res3 = *(Lx.ptr<float>(y2)+x1);
res4 = *(Lx.ptr<float>(y2)+x2);
res1 = Lx.at<float>(y1, x1);
res2 = Lx.at<float>(y1, x2);
res3 = Lx.at<float>(y2, x1);
res4 = Lx.at<float>(y2, x2);
rx = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2 + (1.0f - fx)*fy*res3 + fx*fy*res4;
res1 = *(Ly.ptr<float>(y1)+x1);
res2 = *(Ly.ptr<float>(y1)+x2);
res3 = *(Ly.ptr<float>(y2)+x1);
res4 = *(Ly.ptr<float>(y2)+x2);
res1 = Ly.at<float>(y1, x1);
res2 = Ly.at<float>(y1, x2);
res3 = Ly.at<float>(y2, x1);
res4 = Ly.at<float>(y2, x2);
ry = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2 + (1.0f - fx)*fy*res3 + fx*fy*res4;
rx = gauss_s1*rx;
@ -1533,8 +1522,9 @@ void MSURF_Upright_Descriptor_64_Invoker::Get_MSURF_Upright_Descriptor_64(const
// convert to unit vector
len = sqrt(len);
const float len_inv = 1.0f / len;
for (i = 0; i < dsize; i++) {
desc[i] /= len;
desc[i] *= len_inv;
}
}
@ -1575,8 +1565,8 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
scale = cvRound(0.5f*kpt.size / ratio);
angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
const Mat Lx = evolution[level].Lx;
const Mat Ly = evolution[level].Ly;
yf = kpt.pt.y / ratio;
xf = kpt.pt.x / ratio;
co = cos(angle);
@ -1613,34 +1603,28 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
// Get the gaussian weighted x and y responses
gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
y1 = cvRound(sample_y - 0.5f);
x1 = cvRound(sample_x - 0.5f);
y1 = cvFloor(sample_y);
x1 = cvFloor(sample_x);
y2 = cvRound(sample_y + 0.5f);
x2 = cvRound(sample_x + 0.5f);
y2 = y1 + 1;
x2 = x1 + 1;
// fix crash: indexing with out-of-bounds index, this might happen near the edges of image
// clip values so they fit into the image
const MatSize& size = Lx.size;
y1 = min(max(0, y1), size[0] - 1);
x1 = min(max(0, x1), size[1] - 1);
y2 = min(max(0, y2), size[0] - 1);
x2 = min(max(0, x2), size[1] - 1);
CV_DbgAssert(Lx.size == Ly.size);
if (x1 < 0 || y1 < 0 || x2 >= Lx.cols || y2 >= Lx.rows)
continue; // FIXIT Boundaries
fx = sample_x - x1;
fy = sample_y - y1;
res1 = *(Lx.ptr<float>(y1, x1));
res2 = *(Lx.ptr<float>(y1, x2));
res3 = *(Lx.ptr<float>(y2, x1));
res4 = *(Lx.ptr<float>(y2, x2));
res1 = Lx.at<float>(y1, x1);
res2 = Lx.at<float>(y1, x2);
res3 = Lx.at<float>(y2, x1);
res4 = Lx.at<float>(y2, x2);
rx = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2 + (1.0f - fx)*fy*res3 + fx*fy*res4;
res1 = *(Ly.ptr<float>(y1, x1));
res2 = *(Ly.ptr<float>(y1, x2));
res3 = *(Ly.ptr<float>(y2, x1));
res4 = *(Ly.ptr<float>(y2, x2));
res1 = Ly.at<float>(y1, x1);
res2 = Ly.at<float>(y1, x2);
res3 = Ly.at<float>(y2, x1);
res4 = Ly.at<float>(y2, x2);
ry = (1.0f - fx)*(1.0f - fy)*res1 + fx*(1.0f - fy)*res2 + (1.0f - fx)*fy*res3 + fx*fy*res4;
// Get the x and y derivatives on the rotated axis
@ -1675,8 +1659,9 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
// convert to unit vector
len = sqrt(len);
const float len_inv = 1.0f / len;
for (i = 0; i < dsize; i++) {
desc[i] /= len;
desc[i] *= len_inv;
}
}
@ -1689,13 +1674,6 @@ void MSURF_Descriptor_64_Invoker::Get_MSURF_Descriptor_64(const KeyPoint& kpt, f
*/
void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
float di = 0.0, dx = 0.0, dy = 0.0;
float ri = 0.0, rx = 0.0, ry = 0.0, xf = 0.0, yf = 0.0;
float sample_x = 0.0, sample_y = 0.0, ratio = 0.0;
int x1 = 0, y1 = 0;
int nsamples = 0, scale = 0;
int dcount1 = 0, dcount2 = 0;
const AKAZEOptions & options = *options_;
const std::vector<Evolution>& evolution = *evolution_;
@ -1705,14 +1683,14 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
float values[16*max_channels];
// Get the information from the keypoint
ratio = (float)(1 << kpt.octave);
scale = cvRound(0.5f*kpt.size / ratio);
const float ratio = (float)(1 << kpt.octave);
const int scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
Mat Lt = evolution[level].Mt;
yf = kpt.pt.y / ratio;
xf = kpt.pt.x / ratio;
const Mat Lx = evolution[level].Lx;
const Mat Ly = evolution[level].Ly;
const Mat Lt = evolution[level].Lt;
const float yf = kpt.pt.y / ratio;
const float xf = kpt.pt.x / ratio;
// For 2x2 grid, 3x3 grid and 4x4 grid
const int pattern_size = options_->descriptor_pattern_size;
@ -1726,27 +1704,31 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
memset(desc, 0, desc_size);
// For the three grids
int dcount1 = 0;
for (int z = 0; z < 3; z++) {
dcount2 = 0;
int dcount2 = 0;
const int step = sample_step[z];
for (int i = -pattern_size; i < pattern_size; i += step) {
for (int j = -pattern_size; j < pattern_size; j += step) {
di = dx = dy = 0.0;
nsamples = 0;
float di = 0.0, dx = 0.0, dy = 0.0;
for (int k = i; k < i + step; k++) {
for (int l = j; l < j + step; l++) {
int nsamples = 0;
for (int k = 0; k < step; k++) {
for (int l = 0; l < step; l++) {
// Get the coordinates of the sample point
sample_y = yf + l*scale;
sample_x = xf + k*scale;
const float sample_y = yf + (l+j)*scale;
const float sample_x = xf + (k+i)*scale;
y1 = cvRound(sample_y);
x1 = cvRound(sample_x);
const int y1 = cvRound(sample_y);
const int x1 = cvRound(sample_x);
if (y1 < 0 || y1 >= Lt.rows || x1 < 0 || x1 >= Lt.cols)
continue; // Boundaries
ri = *(Lt.ptr<float>(y1)+x1);
rx = *(Lx.ptr<float>(y1)+x1);
ry = *(Ly.ptr<float>(y1)+x1);
const float ri = Lt.at<float>(y1, x1);
const float rx = Lx.at<float>(y1, x1);
const float ry = Ly.at<float>(y1, x1);
di += ri;
dx += rx;
@ -1755,9 +1737,13 @@ void Upright_MLDB_Full_Descriptor_Invoker::Get_Upright_MLDB_Full_Descriptor(cons
}
}
di /= nsamples;
dx /= nsamples;
dy /= nsamples;
if (nsamples > 0)
{
const float nsamples_inv = 1.0f / nsamples;
di *= nsamples_inv;
dx *= nsamples_inv;
dy *= nsamples_inv;
}
float *val = &values[dcount2*max_channels];
*(val) = di;
@ -1794,17 +1780,20 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st
const std::vector<Evolution>& evolution = *evolution_;
int pattern_size = options_->descriptor_pattern_size;
int chan = options_->descriptor_channels;
int valpos = 0;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
Mat Lt = evolution[level].Mt;
const Mat Lx = evolution[level].Lx;
const Mat Ly = evolution[level].Ly;
const Mat Lt = evolution[level].Lt;
const Size size = Lt.size();
CV_Assert(size == Lx.size());
CV_Assert(size == Ly.size());
int valpos = 0;
for (int i = -pattern_size; i < pattern_size; i += sample_step) {
for (int j = -pattern_size; j < pattern_size; j += sample_step) {
float di, dx, dy;
di = dx = dy = 0.0;
int nsamples = 0;
float di = 0.0f, dx = 0.0f, dy = 0.0f;
int nsamples = 0;
for (int k = i; k < i + sample_step; k++) {
for (int l = j; l < j + sample_step; l++) {
float sample_y = yf + (l*co * scale + k*si*scale);
@ -1813,20 +1802,15 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st
int y1 = cvRound(sample_y);
int x1 = cvRound(sample_x);
// fix crash: indexing with out-of-bounds index, this might happen near the edges of image
// clip values so they fit into the image
const MatSize& size = Lt.size;
CV_DbgAssert(size == Lx.size &&
size == Ly.size);
y1 = min(max(0, y1), size[0] - 1);
x1 = min(max(0, x1), size[1] - 1);
if (y1 < 0 || y1 >= Lt.rows || x1 < 0 || x1 >= Lt.cols)
continue; // Boundaries
float ri = *(Lt.ptr<float>(y1, x1));
float ri = Lt.at<float>(y1, x1);
di += ri;
if(chan > 1) {
float rx = *(Lx.ptr<float>(y1, x1));
float ry = *(Ly.ptr<float>(y1, x1));
float rx = Lx.at<float>(y1, x1);
float ry = Ly.at<float>(y1, x1);
if (chan == 2) {
dx += sqrtf(rx*rx + ry*ry);
}
@ -1840,9 +1824,14 @@ void MLDB_Full_Descriptor_Invoker::MLDB_Fill_Values(float* values, int sample_st
nsamples++;
}
}
di /= nsamples;
dx /= nsamples;
dy /= nsamples;
if (nsamples > 0)
{
const float nsamples_inv = 1.0f / nsamples;
di *= nsamples_inv;
dx *= nsamples_inv;
dy *= nsamples_inv;
}
values[valpos] = di;
if (chan > 1) {
@ -1931,10 +1920,8 @@ void MLDB_Full_Descriptor_Invoker::Get_MLDB_Full_Descriptor(const KeyPoint& kpt,
*/
void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint& kpt, unsigned char *desc, int desc_size) const {
float di = 0.f, dx = 0.f, dy = 0.f;
float rx = 0.f, ry = 0.f;
float sample_x = 0.f, sample_y = 0.f;
int x1 = 0, y1 = 0;
const AKAZEOptions & options = *options_;
const std::vector<Evolution>& evolution = *evolution_;
@ -1944,9 +1931,9 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
int scale = cvRound(0.5f*kpt.size / ratio);
float angle = kpt.angle * static_cast<float>(CV_PI / 180.f);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
Mat Lt = evolution[level].Mt;
const Mat Lx = evolution[level].Lx;
const Mat Ly = evolution[level].Ly;
const Mat Lt = evolution[level].Lt;
float yf = kpt.pt.y / ratio;
float xf = kpt.pt.x / ratio;
float co = cos(angle);
@ -1957,7 +1944,7 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
const int max_channels = 3;
const int channels = options.descriptor_channels;
CV_Assert(channels <= max_channels);
float values[(4 + 9 + 16)*max_channels];
float values[(4 + 9 + 16)*max_channels] = { 0 };
// Sample everything, but only do the comparisons
const int pattern_size = options.descriptor_pattern_size;
@ -1972,9 +1959,7 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
const int *coords = descriptorSamples_.ptr<int>(i);
CV_Assert(coords[0] >= 0 && coords[0] < 3);
const int sample_step = sample_steps[coords[0]];
di = 0.0f;
dx = 0.0f;
dy = 0.0f;
float di = 0.f, dx = 0.f, dy = 0.f;
for (int k = coords[1]; k < coords[1] + sample_step; k++) {
for (int l = coords[2]; l < coords[2] + sample_step; l++) {
@ -1983,14 +1968,17 @@ void MLDB_Descriptor_Subset_Invoker::Get_MLDB_Descriptor_Subset(const KeyPoint&
sample_y = yf + (l*scale*co + k*scale*si);
sample_x = xf + (-l*scale*si + k*scale*co);
y1 = cvRound(sample_y);
x1 = cvRound(sample_x);
const int y1 = cvRound(sample_y);
const int x1 = cvRound(sample_x);
di += *(Lt.ptr<float>(y1)+x1);
if (x1 < 0 || y1 < 0 || x1 >= Lt.cols || y1 >= Lt.rows)
continue; // Boundaries
di += Lt.at<float>(y1, x1);
if (options.descriptor_channels > 1) {
rx = *(Lx.ptr<float>(y1)+x1);
ry = *(Ly.ptr<float>(y1)+x1);
rx = Lx.at<float>(y1, x1);
ry = Ly.at<float>(y1, x1);
if (options.descriptor_channels == 2) {
dx += sqrtf(rx*rx + ry*ry);
@ -2051,14 +2039,17 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
float ratio = (float)(1 << kpt.octave);
int scale = cvRound(0.5f*kpt.size / ratio);
const int level = kpt.class_id;
Mat Lx = evolution[level].Mx;
Mat Ly = evolution[level].My;
Mat Lt = evolution[level].Mt;
const Mat Lx = evolution[level].Lx;
const Mat Ly = evolution[level].Ly;
const Mat Lt = evolution[level].Lt;
float yf = kpt.pt.y / ratio;
float xf = kpt.pt.x / ratio;
// Allocate memory for the matrix of values
Mat values ((4 + 9 + 16)*options.descriptor_channels, 1, CV_32FC1);
const int max_channels = 3;
const int channels = options.descriptor_channels;
CV_Assert(channels <= max_channels);
float values[(4 + 9 + 16)*max_channels] = { 0 };
const int pattern_size = options.descriptor_pattern_size;
CV_Assert((pattern_size & 1) == 0);
@ -2083,11 +2074,15 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
y1 = cvRound(sample_y);
x1 = cvRound(sample_x);
di += *(Lt.ptr<float>(y1)+x1);
if (x1 < 0 || y1 < 0 || x1 >= Lt.cols || y1 >= Lt.rows)
continue; // Boundaries
di += Lt.at<float>(y1, x1);
if (options.descriptor_channels > 1) {
rx = *(Lx.ptr<float>(y1)+x1);
ry = *(Ly.ptr<float>(y1)+x1);
rx = Lx.at<float>(y1, x1);
ry = Ly.at<float>(y1, x1);
if (options.descriptor_channels == 2) {
dx += sqrtf(rx*rx + ry*ry);
@ -2100,26 +2095,26 @@ void Upright_MLDB_Descriptor_Subset_Invoker::Get_Upright_MLDB_Descriptor_Subset(
}
}
*(values.ptr<float>(options.descriptor_channels*i)) = di;
float* pValues = &values[channels * i];
pValues[0] = di;
if (options.descriptor_channels == 2) {
*(values.ptr<float>(options.descriptor_channels*i + 1)) = dx;
pValues[1] = dx;
}
else if (options.descriptor_channels == 3) {
*(values.ptr<float>(options.descriptor_channels*i + 1)) = dx;
*(values.ptr<float>(options.descriptor_channels*i + 2)) = dy;
pValues[1] = dx;
pValues[2] = dy;
}
}
// Do the comparisons
const float *vals = values.ptr<float>(0);
const int *comps = descriptorBits_.ptr<int>(0);
CV_Assert(divUp(descriptorBits_.rows, 8) == desc_size);
memset(desc, 0, desc_size);
for (int i = 0; i<descriptorBits_.rows; i++) {
if (vals[comps[2 * i]] > vals[comps[2 * i + 1]]) {
if (values[comps[2 * i]] > values[comps[2 * i + 1]]) {
desc[i / 8] |= (1 << (i % 8));
}
}
@ -2149,7 +2144,8 @@ void generateDescriptorSubsample(Mat& sampleList, Mat& comparisons, int nbits,
}
ssz *= nchannels;
CV_Assert(nbits <= ssz); // Descriptor size can't be bigger than full descriptor
CV_Assert(ssz == 162*nchannels);
CV_Assert(nbits <= ssz && "Descriptor size can't be bigger than full descriptor (486 = 162*3 - 3 channels)");
// Since the full descriptor is usually under 10k elements, we pick
// the selection from the full matrix. We take as many samples per

13
modules/features2d/src/kaze/AKAZEFeatures.h
Unescape View File

@ -29,15 +29,10 @@ struct Evolution
border = 0;
}
UMat Lx, Ly; ///< First order spatial derivatives
UMat Lt; ///< Evolution image
UMat Lsmooth; ///< Smoothed image, used only for computing determinant, released afterwards
UMat Ldet; ///< Detector response
// the same as above, holding CPU mapping to UMats above
Mat Mx, My;
Mat Mt;
Mat Mdet;
Mat Lx, Ly; ///< First order spatial derivatives
Mat Lt; ///< Evolution image
Mat Lsmooth; ///< Smoothed image, used only for computing determinant, released afterwards
Mat Ldet; ///< Detector response
Size size; ///< Size of the layer
float etime; ///< Evolution time

103
modules/features2d/test/test_descriptors_regression.cpp
Unescape View File

@ -43,6 +43,7 @@
using namespace std;
using namespace cv;
using namespace testing;
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
@ -417,68 +418,82 @@ TEST( Features2d_DescriptorExtractor, batch )
}
}
TEST( Features2d_Feature2d, no_crash )
class DescriptorImage : public TestWithParam<std::string>
{
protected:
virtual void SetUp() {
pattern = GetParam();
}
std::string pattern;
};
TEST_P(DescriptorImage, no_crash)
{
const String& pattern = string(cvtest::TS::ptr()->get_data_path() + "shared/*.png");
vector<String> fnames;
glob(pattern, fnames, false);
glob(cvtest::TS::ptr()->get_data_path() + pattern, fnames, false);
sort(fnames.begin(), fnames.end());
Ptr<AKAZE> akaze = AKAZE::create();
Ptr<AKAZE> akaze_mldb = AKAZE::create(AKAZE::DESCRIPTOR_MLDB);
Ptr<AKAZE> akaze_mldb_upright = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT);
Ptr<AKAZE> akaze_mldb_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 256);
Ptr<AKAZE> akaze_mldb_upright_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 256);
Ptr<AKAZE> akaze_kaze = AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
Ptr<AKAZE> akaze_kaze_upright = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
Ptr<ORB> orb = ORB::create();
Ptr<KAZE> kaze = KAZE::create();
Ptr<BRISK> brisk = BRISK::create();
size_t i, n = fnames.size();
size_t n = fnames.size();
vector<KeyPoint> keypoints;
Mat descriptors;
orb->setMaxFeatures(5000);
for( i = 0; i < n; i++ )
for(size_t i = 0; i < n; i++ )
{
printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
if( strstr(fnames[i].c_str(), "MP.png") != 0 )
{
printf("\tskip\n");
continue;
}
bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;
Mat img = imread(fnames[i], -1);
printf("\tAKAZE ... "); fflush(stdout);
akaze->detectAndCompute(img, noArray(), keypoints, descriptors);
printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
if( checkCount )
{
EXPECT_GT((int)keypoints.size(), 0);
}
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
printf("ok\n");
printf("\tKAZE ... "); fflush(stdout);
kaze->detectAndCompute(img, noArray(), keypoints, descriptors);
printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
if( checkCount )
{
EXPECT_GT((int)keypoints.size(), 0);
}
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
printf("ok\n");
printf("\tORB ... "); fflush(stdout);
orb->detectAndCompute(img, noArray(), keypoints, descriptors);
printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
if( checkCount )
{
EXPECT_GT((int)keypoints.size(), 0);
}
printf("\t%dx%d\n", img.cols, img.rows);
#define TEST_DETECTOR(name, descriptor) \
keypoints.clear(); descriptors.release(); \
printf("\t" name "\n"); fflush(stdout); \
descriptor->detectAndCompute(img, noArray(), keypoints, descriptors); \
printf("\t\t\t(%d keypoints, descriptor size = %d)\n", (int)keypoints.size(), descriptors.cols); fflush(stdout); \
if (checkCount) \
{ \
EXPECT_GT((int)keypoints.size(), 0); \
} \
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
printf("ok\n");
printf("\tBRISK ... "); fflush(stdout);
brisk->detectAndCompute(img, noArray(), keypoints, descriptors);
printf("(%d keypoints) ", (int)keypoints.size()); fflush(stdout);
if( checkCount )
{
EXPECT_GT((int)keypoints.size(), 0);
}
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
printf("ok\n");
}
}
TEST_DETECTOR("AKAZE:MLDB", akaze_mldb);
TEST_DETECTOR("AKAZE:MLDB_UPRIGHT", akaze_mldb_upright);
TEST_DETECTOR("AKAZE:MLDB_256", akaze_mldb_256);
TEST_DETECTOR("AKAZE:MLDB_UPRIGHT_256", akaze_mldb_upright_256);
TEST_DETECTOR("AKAZE:KAZE", akaze_kaze);
TEST_DETECTOR("AKAZE:KAZE_UPRIGHT", akaze_kaze_upright);
TEST_DETECTOR("KAZE", kaze);
TEST_DETECTOR("ORB", orb);
TEST_DETECTOR("BRISK", brisk);
}
}
INSTANTIATE_TEST_CASE_P(Features2d, DescriptorImage,
testing::Values(
"shared/lena.png",
"shared/box*.png",
"shared/fruits*.png",
"shared/airplane.png",
"shared/graffiti.png",
"shared/1_itseez-0001*.png",
"shared/pic*.png",
"shared/templ.png"
)
);

6
modules/ts/misc/run_long.py
Unescape View File

@ -8,7 +8,11 @@ from pprint import PrettyPrinter as PP
LONG_TESTS_DEBUG_VALGRIND = [
('calib3d', 'Calib3d_InitUndistortRectifyMap.accuracy', 2017.22),
('dnn', 'Reproducibility*', 1000), # large DNN models
('features2d', 'Features2d_Feature2d.no_crash', 1235.68),
('features2d', 'Features2d/DescriptorImage.no_crash/3', 1000),
('features2d', 'Features2d/DescriptorImage.no_crash/4', 1000),
('features2d', 'Features2d/DescriptorImage.no_crash/5', 1000),
('features2d', 'Features2d/DescriptorImage.no_crash/6', 1000),
('features2d', 'Features2d/DescriptorImage.no_crash/7', 1000),
('imgcodecs', 'Imgcodecs_Png.write_big', 1000), # memory limit
('imgcodecs', 'Imgcodecs_Tiff.decode_tile16384x16384', 1000), # memory limit
('ml', 'ML_RTrees.regression', 1423.47),

Write Preview
Loading…
Cancel
Save