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Merge pull request #28362 from vrabaud:lsh

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Replace pow with std::pow
This commit is contained in:
Alexander Smorkalov
2026-01-05 16:10:22 +03:00
committed by GitHub
parent 10589eb2c8 5622958189
commit f3758f40ae
47 changed files with 131 additions and 140 deletions
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apps/traincascade/boost.cpp
+1 -1
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@@ -942,7 +942,7 @@ void CvCascadeBoostTree::write( FileStorage &fs, const Mat& featureMap )
int maxCatCount = ((CvCascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount();
int subsetN = (maxCatCount + 31)/32;
queue<CvDTreeNode*> internalNodesQueue;
int size = (int)pow( 2.f, (float)ensemble->get_params().max_depth);
int size = (int)std::pow( 2, ensemble->get_params().max_depth);
std::vector<float> leafVals(size);
int leafValIdx = 0;
int internalNodeIdx = 1;
apps/traincascade/cascadeclassifier.cpp
+1 -1
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@@ -198,7 +198,7 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
else if ( startNumStages == 1)
cout << endl << "Stage 0 is loaded" << endl;
double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
double requiredLeafFARate = std::pow( stageParams->maxFalseAlarm, numStages ) /
(double)stageParams->max_depth;
double tempLeafFARate;
doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown
+6 -6
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@@ -631,9 +631,9 @@ Here is explained in detail the code for the real time application:
KF.transitionMatrix.at<double>(3,6) = dt;
KF.transitionMatrix.at<double>(4,7) = dt;
KF.transitionMatrix.at<double>(5,8) = dt;
KF.transitionMatrix.at<double>(0,6) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(1,7) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(2,8) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(0,6) = 0.5*std::pow(dt,2);
KF.transitionMatrix.at<double>(1,7) = 0.5*std::pow(dt,2);
KF.transitionMatrix.at<double>(2,8) = 0.5*std::pow(dt,2);
// orientation
KF.transitionMatrix.at<double>(9,12) = dt;
@@ -642,9 +642,9 @@ Here is explained in detail the code for the real time application:
KF.transitionMatrix.at<double>(12,15) = dt;
KF.transitionMatrix.at<double>(13,16) = dt;
KF.transitionMatrix.at<double>(14,17) = dt;
KF.transitionMatrix.at<double>(9,15) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(10,16) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(11,17) = 0.5*pow(dt,2);
KF.transitionMatrix.at<double>(9,15) = 0.5*std::pow(dt,2);
KF.transitionMatrix.at<double>(10,16) = 0.5*std::pow(dt,2);
KF.transitionMatrix.at<double>(11,17) = 0.5*std::pow(dt,2);
/* MEASUREMENT MODEL */
modules/calib3d/src/chessboard.cpp
+1 -1
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@@ -725,7 +725,7 @@ void FastX::detectImpl(const cv::Mat& _gray_image,
// calc images
// for each angle step
int scale_id = scale-parameters.min_scale;
int scale_size = int(pow(2.0,scale+1+super_res));
int scale_size = int(std::pow(2,scale+1+super_res));
int scale_size2 = int((scale_size/7)*2+1);
std::vector<cv::UMat> images;
images.resize(2*num);
modules/calib3d/src/dls.h
+1 -1
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@@ -162,7 +162,7 @@ private:
int n1 = nn - 1;
int low = 0;
int high = nn - 1;
double eps = std::pow(2.0, -52.0);
double eps = std::pow(2, -52);
double exshift = 0.0;
double p = 0, q = 0, r = 0, s = 0, z = 0, t, w, x, y;
modules/calib3d/src/rho.cpp
+1 -1
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@@ -1744,7 +1744,7 @@ static inline unsigned sacCalcIterBound(double confidence,
* \[ k = \frac{\log{(1-confidence)}}{\log{(1-inlierRate**sampleSize)}} \]
*/
double atLeastOneOutlierProbability = 1.-pow(inlierRate, (double)sampleSize);
double atLeastOneOutlierProbability = 1.-std::pow(inlierRate, (double)sampleSize);
/**
* There are two special cases: When argument to log() is 0 and when it is 1.
modules/calib3d/src/triangulate.cpp
+2 -2
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@@ -319,9 +319,9 @@ icvCorrectMatches(CvMat *F_, CvMat *points1_, CvMat *points2_, CvMat *new_points
x1 = tmp31_2->data.db[0];
y1 = tmp31_2->data.db[1];
tmp31->data.db[0] = f2*pow(c*t_min+d,2);
tmp31->data.db[0] = f2*std::pow(c*t_min+d,2);
tmp31->data.db[1] = -(a*t_min+b)*(c*t_min+d);
tmp31->data.db[2] = f2*f2*pow(c*t_min+d,2) + pow(a*t_min+b,2);
tmp31->data.db[2] = f2*f2*std::pow(c*t_min+d,2) + std::pow(a*t_min+b,2);
tmp31->data.db[0] /= tmp31->data.db[2];
tmp31->data.db[1] /= tmp31->data.db[2];
tmp31->data.db[2] /= tmp31->data.db[2];
modules/calib3d/src/undistort.dispatch.cpp
+1 -1
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@@ -516,7 +516,7 @@ static void cvUndistortPointsInternal( const CvMat* _src, CvMat* _dst, const CvM
double x_proj = xd*fx + cx;
double y_proj = yd*fy + cy;
error = sqrt( pow(x_proj - u, 2) + pow(y_proj - v, 2) );
error = sqrt( std::pow(x_proj - u, 2) + std::pow(y_proj - v, 2) );
}
if (error > prevError) {
alpha *= .5;
modules/calib3d/src/upnp.cpp
+37 -37
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@@ -411,18 +411,18 @@ Mat upnp::compute_constraint_distance_2param_6eq_2unk_f_unk(const Mat& M1)
double m[13];
for (int i = 1; i < 13; ++i) m[i] = *M1.ptr<double>(i-1);
double t1 = pow( m[4], 2 );
double t4 = pow( m[1], 2 );
double t5 = pow( m[5], 2 );
double t8 = pow( m[2], 2 );
double t10 = pow( m[6], 2 );
double t13 = pow( m[3], 2 );
double t15 = pow( m[7], 2 );
double t18 = pow( m[8], 2 );
double t22 = pow( m[9], 2 );
double t26 = pow( m[10], 2 );
double t29 = pow( m[11], 2 );
double t33 = pow( m[12], 2 );
double t1 = std::pow( m[4], 2 );
double t4 = std::pow( m[1], 2 );
double t5 = std::pow( m[5], 2 );
double t8 = std::pow( m[2], 2 );
double t10 = std::pow( m[6], 2 );
double t13 = std::pow( m[3], 2 );
double t15 = std::pow( m[7], 2 );
double t18 = std::pow( m[8], 2 );
double t22 = std::pow( m[9], 2 );
double t26 = std::pow( m[10], 2 );
double t29 = std::pow( m[11], 2 );
double t33 = std::pow( m[12], 2 );
*P.ptr<double>(0,0) = t1 - 2 * m[4] * m[1] + t4 + t5 - 2 * m[5] * m[2] + t8;
*P.ptr<double>(0,1) = t10 - 2 * m[6] * m[3] + t13;
@@ -451,42 +451,42 @@ Mat upnp::compute_constraint_distance_3param_6eq_6unk_f_unk(const Mat& M1, const
m[2][i] = *M2.ptr<double>(i-1);
}
double t1 = pow( m[1][4], 2 );
double t2 = pow( m[1][1], 2 );
double t7 = pow( m[1][5], 2 );
double t8 = pow( m[1][2], 2 );
double t1 = std::pow( m[1][4], 2 );
double t2 = std::pow( m[1][1], 2 );
double t7 = std::pow( m[1][5], 2 );
double t8 = std::pow( m[1][2], 2 );
double t11 = m[1][1] * m[2][1];
double t12 = m[1][5] * m[2][5];
double t15 = m[1][2] * m[2][2];
double t16 = m[1][4] * m[2][4];
double t19 = pow( m[2][4], 2 );
double t22 = pow( m[2][2], 2 );
double t23 = pow( m[2][1], 2 );
double t24 = pow( m[2][5], 2 );
double t28 = pow( m[1][6], 2 );
double t29 = pow( m[1][3], 2 );
double t34 = pow( m[1][3], 2 );
double t19 = std::pow( m[2][4], 2 );
double t22 = std::pow( m[2][2], 2 );
double t23 = std::pow( m[2][1], 2 );
double t24 = std::pow( m[2][5], 2 );
double t28 = std::pow( m[1][6], 2 );
double t29 = std::pow( m[1][3], 2 );
double t34 = std::pow( m[1][3], 2 );
double t36 = m[1][6] * m[2][6];
double t40 = pow( m[2][6], 2 );
double t41 = pow( m[2][3], 2 );
double t47 = pow( m[1][7], 2 );
double t48 = pow( m[1][8], 2 );
double t40 = std::pow( m[2][6], 2 );
double t41 = std::pow( m[2][3], 2 );
double t47 = std::pow( m[1][7], 2 );
double t48 = std::pow( m[1][8], 2 );
double t52 = m[1][7] * m[2][7];
double t55 = m[1][8] * m[2][8];
double t59 = pow( m[2][8], 2 );
double t62 = pow( m[2][7], 2 );
double t64 = pow( m[1][9], 2 );
double t59 = std::pow( m[2][8], 2 );
double t62 = std::pow( m[2][7], 2 );
double t64 = std::pow( m[1][9], 2 );
double t68 = m[1][9] * m[2][9];
double t74 = pow( m[2][9], 2 );
double t78 = pow( m[1][10], 2 );
double t79 = pow( m[1][11], 2 );
double t74 = std::pow( m[2][9], 2 );
double t78 = std::pow( m[1][10], 2 );
double t79 = std::pow( m[1][11], 2 );
double t84 = m[1][10] * m[2][10];
double t87 = m[1][11] * m[2][11];
double t90 = pow( m[2][10], 2 );
double t95 = pow( m[2][11], 2 );
double t99 = pow( m[1][12], 2 );
double t90 = std::pow( m[2][10], 2 );
double t95 = std::pow( m[2][11], 2 );
double t99 = std::pow( m[1][12], 2 );
double t101 = m[1][12] * m[2][12];
double t105 = pow( m[2][12], 2 );
double t105 = std::pow( m[2][12], 2 );
*P.ptr<double>(0,0) = t1 + t2 - 2 * m[1][4] * m[1][1] - 2 * m[1][5] * m[1][2] + t7 + t8;
*P.ptr<double>(0,1) = -2 * m[2][4] * m[1][1] + 2 * t11 + 2 * t12 - 2 * m[1][4] * m[2][1] - 2 * m[2][5] * m[1][2] + 2 * t15 + 2 * t16 - 2 * m[1][5] * m[2][2];
modules/calib3d/src/usac/degeneracy.cpp
+1 -1
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@@ -435,7 +435,7 @@ public:
const int num_f_inliers_of_h_outliers = getNonPlanarSupport(Mat(F), num_models_used_so_far >= MAX_MODELS_TO_TEST, non_planar_support);
if (non_planar_support < num_f_inliers_of_h_outliers) {
non_planar_support = num_f_inliers_of_h_outliers;
const double predicted_iters = log_conf / log(1 - pow(static_cast<double>
const double predicted_iters = log_conf / log(1 - std::pow(static_cast<double>
(getNonPlanarSupport(Mat(F), non_planar_pts, num_non_planar_pts)) / num_non_planar_pts, 2));
if (use_preemptive && ! std::isinf(predicted_iters) && predicted_iters < max_iters)
max_iters = static_cast<int>(predicted_iters);
modules/calib3d/src/usac/local_optimization.cpp
+2 -2
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@@ -454,7 +454,7 @@ public:
DoF = DoF_; C = C_;
max_sigma = max_sigma_;
squared_sigma_max_2 = max_sigma * max_sigma * 2.0;
one_over_sigma = C * pow(2.0, (DoF - 1.0) * 0.5) / max_sigma;
one_over_sigma = C * std::pow(2, (DoF - 1.0) * 0.5) / max_sigma;
max_sigma_sqr = squared_sigma_max_2 * 0.5;
rescale_err = scale_of_stored_gammas / squared_sigma_max_2;
}
@@ -464,7 +464,7 @@ public:
int getInliersWeights (const std::vector<float> &errors, std::vector<int> &inliers, std::vector<double> &weights, double thr_sqr) const override {
const auto _max_sigma = thr_sqr;
const auto _squared_sigma_max_2 = _max_sigma * _max_sigma * 2.0;
const auto _one_over_sigma = C * pow(2.0, (DoF - 1.0) * 0.5) / _max_sigma;
const auto _one_over_sigma = C * std::pow(2, (DoF - 1.0) * 0.5) / _max_sigma;
const auto _max_sigma_sqr = _squared_sigma_max_2 * 0.5;
const auto _rescale_err = scale_of_stored_gammas / _squared_sigma_max_2;
return getInliersWeights(errors, inliers, weights, _one_over_sigma, _rescale_err, _max_sigma_sqr);
modules/calib3d/src/usac/quality.cpp
+1 -1
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@@ -197,7 +197,7 @@ public:
const auto maximum_sigma_2 = (float) (maximum_sigma * maximum_sigma);
maximum_sigma_2_per_2 = maximum_sigma_2 / 2.f;
const auto maximum_sigma_2_times_2 = maximum_sigma_2 * 2.f;
two_ad_dof_plus_one_per_maximum_sigma = pow(2.0, (DoF + 1.0)*.5)/maximum_sigma;
two_ad_dof_plus_one_per_maximum_sigma = std::pow(2, (DoF + 1.0)*.5)/maximum_sigma;
rescale_err = gamma_generator->getScaleOfGammaCompleteValues() / maximum_sigma_2_times_2;
stored_incomplete_gamma_number_min1 = static_cast<unsigned int>(gamma_generator->getTableSize()-1);
modules/calib3d/src/usac/ransac_solvers.cpp
+2 -2
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@@ -1014,14 +1014,14 @@ public:
_quality->getInliers(best_model, temp_inliers));
// quick test on lambda from all inliers (= upper bound of independent inliers)
// if model with independent inliers is not random for Poisson with all inliers then it is not random using independent inliers too
if (pow(Utils::getPoissonCDF(lambda_non_random_all_inliers, non_random_inls_best_model), num_total_tested_models) < 0.9999) {
if (std::pow(Utils::getPoissonCDF(lambda_non_random_all_inliers, non_random_inls_best_model), num_total_tested_models) < 0.9999) {
std::vector<int> inliers_list(models_for_random_test.size());
for (int m = 0; m < (int)models_for_random_test.size(); m++)
inliers_list[m] = getIndependentInliers(models_for_random_test[m], samples_for_random_test[m],
temp_inliers, _quality->getInliers(models_for_random_test[m], temp_inliers));
int min_non_rand_inliers;
const double lambda = getLambda(inliers_list, 1.644, points_size, sample_size, true, min_non_rand_inliers);
const double cdf_lambda = Utils::getPoissonCDF(lambda, non_random_inls_best_model), cdf_N = pow(cdf_lambda, num_total_tested_models);
const double cdf_lambda = Utils::getPoissonCDF(lambda, non_random_inls_best_model), cdf_N = std::pow(cdf_lambda, num_total_tested_models);
model_conf = cdf_N < 0.9999 ? ModelConfidence ::RANDOM : ModelConfidence ::NON_RANDOM;
} else model_conf = ModelConfidence ::NON_RANDOM;
}
modules/calib3d/src/usac/termination.cpp
+2 -2
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@@ -42,7 +42,7 @@ public:
}
static int getMaxIterations (int inlier_number, int sample_size, int points_size, double conf) {
const double pred_iters = log(1 - conf) / log(1 - pow(static_cast<double>(inlier_number)/points_size, sample_size));
const double pred_iters = log(1 - conf) / log(1 - std::pow(static_cast<double>(inlier_number)/points_size, sample_size));
if (std::isinf(pred_iters))
return INT_MAX;
return (int) pred_iters + 1;
@@ -322,7 +322,7 @@ public:
continue;
// add 1 to termination length since num_inliers_under_termination_len is updated
const double new_max_samples = log_conf/log(1-pow(static_cast<double>(num_inliers_under_termination_len)
const double new_max_samples = log_conf/log(1-std::pow(static_cast<double>(num_inliers_under_termination_len)
/ (termination_len+1), sample_size));
if (! std::isinf(new_max_samples) && predicted_iterations > new_max_samples) {
modules/calib3d/test/test_undistort_points.cpp
+1 -1
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@@ -194,7 +194,7 @@ TEST_F(UndistortPointsTest, stop_criteria)
projectPoints(pt_undist_vec_homogeneous, -rVec,
Mat::zeros(3,1,CV_64F), cameraMatrix, distCoeffs, pt_redistorted_vec);
const double obtainedError = sqrt( pow(pt_distorted.x - pt_redistorted_vec[0].x, 2) + pow(pt_distorted.y - pt_redistorted_vec[0].y, 2) );
const double obtainedError = sqrt( std::pow(pt_distorted.x - pt_redistorted_vec[0].x, 2) + std::pow(pt_distorted.y - pt_redistorted_vec[0].y, 2) );
ASSERT_LE(obtainedError, maxError);
}
modules/calib3d/test/test_usac.cpp
+7 -7
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@@ -182,8 +182,8 @@ static double getError (TestSolver test_case, int pt_idx, const cv::Mat &pts1, c
cv::Mat l2 = model * pt1;
cv::Mat l1 = model.t() * pt2;
// Sampson error
return fabs(pt2.dot(l2)) / sqrt(pow(l1.at<double>(0), 2) + pow(l1.at<double>(1), 2) +
pow(l2.at<double>(0), 2) + pow(l2.at<double>(1), 2));
return fabs(pt2.dot(l2)) / sqrt(std::pow(l1.at<double>(0), 2) + std::pow(l1.at<double>(1), 2) +
std::pow(l2.at<double>(0), 2) + std::pow(l2.at<double>(1), 2));
} else
if (test_case == TestSolver::PnP) { // PnP, reprojection error
cv::Mat img_pt = model * pt2; img_pt /= img_pt.at<double>(2);
@@ -233,7 +233,7 @@ TEST(usac_Homography, accuracy) {
pts_size, TestSolver ::Homogr, inl_ratio/*inl ratio*/, 0.1 /*noise std*/, gt_inliers);
// compute max_iters with standard upper bound rule for RANSAC with 1.5x tolerance
const double conf = 0.99, thr = 2., max_iters = 1.3 * log(1 - conf) /
log(1 - pow(inl_ratio, 4 /* sample size */));
log(1 - std::pow(inl_ratio, 4 /* sample size */));
for (auto flag : flags) {
cv::Mat mask, H = cv::findHomography(pts1, pts2,flag, thr, mask,
int(max_iters), conf);
@@ -257,7 +257,7 @@ TEST(usac_Fundamental, accuracy) {
for (auto flag : flags) {
const int sample_size = flag == USAC_FM_8PTS ? 8 : 7;
const double max_iters = 1.25 * log(1 - conf) /
log(1 - pow(inl_ratio, sample_size));
log(1 - std::pow(inl_ratio, sample_size));
cv::Mat mask, F = cv::findFundamentalMat(pts1, pts2,flag, thr, conf,
int(max_iters), mask);
checkInliersMask(TestSolver::Fundam, inl_size, thr, pts1, pts2, F, mask);
@@ -374,7 +374,7 @@ TEST(usac_P3P, accuracy) {
int inl_size = generatePoints(rng, img_pts, obj_pts, K1, K2, false /*two calib*/,
pts_size, TestSolver ::PnP, inl_ratio, 0.15 /*noise std*/, gt_inliers);
const double conf = 0.99, thr = 2., max_iters = 1.3 * log(1 - conf) /
log(1 - pow(inl_ratio, 3 /* sample size */));
log(1 - std::pow(inl_ratio, 3 /* sample size */));
for (auto flag : flags) {
std::vector<int> inliers;
@@ -402,7 +402,7 @@ TEST (usac_Affine2D, accuracy) {
int inl_size = generatePoints(rng, pts1, pts2, K1, K2, false /*two calib*/,
pts_size, TestSolver ::Affine, inl_ratio, 0.15 /*noise std*/, gt_inliers);
const double conf = 0.99, thr = 2., max_iters = 1.3 * log(1 - conf) /
log(1 - pow(inl_ratio, 3 /* sample size */));
log(1 - std::pow(inl_ratio, 3 /* sample size */));
for (auto flag : flags) {
cv::Mat mask, A = cv::estimateAffine2D(pts1, pts2, mask, flag, thr, (size_t)max_iters, conf, 0);
cv::vconcat(A, cv::Mat(cv::Matx13d(0,0,1)), A);
@@ -493,7 +493,7 @@ TEST(usac_solvePnPRansac, regression_21105) {
generatePoints(rng, img_pts, obj_pts, K1, K2, false /*two calib*/,
pts_size, TestSolver ::PnP, inl_ratio, 0.15 /*noise std*/, gt_inliers);
const double conf = 0.99, thr = 2., max_iters = 1.3 * log(1 - conf) /
log(1 - pow(inl_ratio, 3 /* sample size */));
log(1 - std::pow(inl_ratio, 3 /* sample size */));
const int flag = USAC_DEFAULT;
std::vector<int> inliers;
cv::Matx31d rvec, tvec;
modules/core/src/mathfuncs.cpp
+7 -16
View File
@@ -1675,16 +1675,8 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots )
}
else if( d == 0 )
{
if(R >= 0)
{
x0 = -2*pow(R, 1./3) - a1/3;
x1 = pow(R, 1./3) - a1/3;
}
else
{
x0 = 2*pow(-R, 1./3) - a1/3;
x1 = -pow(-R, 1./3) - a1/3;
}
x0 = -2*std::cbrt(R) - a1/3;
x1 = std::cbrt(R) - a1/3;
x2 = 0;
n = x0 == x1 ? 1 : 2;
x1 = x0 == x1 ? 0 : x1;
@@ -1693,7 +1685,7 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots )
{
double e;
d = sqrt(-d);
e = pow(d + fabs(R), 1./3);
e = std::cbrt(d + fabs(R));
if( R > 0 )
e = -e;
x0 = (e + Q / e) - a1 * (1./3);
@@ -1808,15 +1800,14 @@ double cv::solvePoly( InputArray _coeffs0, OutputArray _roots0, int maxIters )
if( num_same_root % 2 != 0){
Mat cube_coefs(4, 1, CV_64FC1);
Mat cube_roots(3, 1, CV_64FC2);
cube_coefs.at<double>(3) = -(pow(old_num_re, 3));
cube_coefs.at<double>(2) = -(15*pow(old_num_re, 2) + 27*pow(old_num_im, 2));
cube_coefs.at<double>(3) = -(std::pow(old_num_re, 3));
cube_coefs.at<double>(2) = -(15*std::pow(old_num_re, 2) + 27*std::pow(old_num_im, 2));
cube_coefs.at<double>(1) = -48*old_num_re;
cube_coefs.at<double>(0) = 64;
solveCubic(cube_coefs, cube_roots);
if(cube_roots.at<double>(0) >= 0) num.re = pow(cube_roots.at<double>(0), 1./3);
else num.re = -pow(-cube_roots.at<double>(0), 1./3);
num.im = sqrt(pow(num.re, 2) / 3 - old_num_re / (3*num.re));
num.re = std::cbrt(cube_roots.at<double>(0));
num.im = sqrt(std::pow(num.re, 2) / 3 - old_num_re / (3*num.re));
}
}
roots[i] = p - num;
modules/core/test/test_countnonzero.cpp
+1 -1
View File
@@ -265,7 +265,7 @@ TEST_P (CountNonZeroND, ndim)
data = 0;
EXPECT_EQ(0, cv::countNonZero(data));
data = Scalar::all(1);
int expected = static_cast<int>(pow(static_cast<float>(ONE_SIZE), dims));
int expected = static_cast<int>(std::pow(static_cast<float>(ONE_SIZE), dims));
EXPECT_EQ(expected, cv::countNonZero(data));
}
modules/core/test/test_eigen.cpp
+1 -1
View File
@@ -367,7 +367,7 @@ bool Core_EigenTest::check_full(int type)
for (int i = 0; i < ntests; ++i)
{
int src_size = (int)(std::pow(2.0, (rng.uniform(0, MAX_DEGREE) + 1.)));
int src_size = (int)(std::pow(2, (rng.uniform(0, MAX_DEGREE) + 1.)));
cv::Mat src(src_size, src_size, type);
modules/core/test/test_math.cpp
+4 -4
View File
@@ -146,7 +146,7 @@ void Core_PowTest::get_minmax_bounds( int /*i*/, int /*j*/, int type, Scalar& lo
if( power > 0 )
{
double mval = cvtest::getMaxVal(type);
double u1 = pow(mval,1./power)*2;
double u1 = std::pow(mval,1./power)*2;
u = MIN(u,u1);
}
@@ -322,7 +322,7 @@ void Core_PowTest::prepare_to_validation( int /*test_case_idx*/ )
for( j = 0; j < ncols; j++ )
{
double val = ((float*)a_data)[j];
val = pow( fabs(val), power );
val = std::pow( fabs(val), power );
((float*)b_data)[j] = (float)val;
}
else
@@ -340,7 +340,7 @@ void Core_PowTest::prepare_to_validation( int /*test_case_idx*/ )
for( j = 0; j < ncols; j++ )
{
double val = ((double*)a_data)[j];
val = pow( fabs(val), power );
val = std::pow( fabs(val), power );
((double*)b_data)[j] = (double)val;
}
else
@@ -2394,7 +2394,7 @@ void Core_SolvePolyTest::run( int )
for (int j = 0; j < n; ++j)
{
s += fabs(r[j].real()) + fabs(r[j].imag());
div += sqrt(pow(r[j].real() - ar[j].real(), 2) + pow(r[j].imag() - ar[j].imag(), 2));
div += sqrt(std::pow(r[j].real() - ar[j].real(), 2) + std::pow(r[j].imag() - ar[j].imag(), 2));
}
div /= s;
pass = pass && div < err_eps;
modules/dnn/src/layers/elementwise_layers.cpp
+2 -2
View File
@@ -2619,7 +2619,7 @@ struct PowerFunctor : public BaseFunctor
for( int i = 0; i < len; i++ )
{
float x = srcptr[i];
dstptr[i] = pow(a*x + b, p);
dstptr[i] = std::pow(a*x + b, p);
}
}
}
@@ -2674,7 +2674,7 @@ struct PowerFunctor : public BaseFunctor
}
if (power != 1.0f)
{
topExpr = pow(topExpr, power);
topExpr = std::pow(topExpr, power);
}
top(x, y, c, n) = topExpr;
}
modules/dnn/src/layers/lrn_layer.cpp
+1 -1
View File
@@ -396,7 +396,7 @@ public:
base = alphaSize * sum(padded_sq(x + r.x, y + r.y, c, n));
}
base += static_cast<float>(bias);
top(x, y, c, n) = inputBuffer(x, y, c, n) / pow(base, beta);
top(x, y, c, n) = inputBuffer(x, y, c, n) / std::pow(base, beta);
return Ptr<BackendNode>(new HalideBackendNode({ padded_sq, top }));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
modules/dnn/src/layers/normalize_bbox_layer.cpp
+2 -2
View File
@@ -147,7 +147,7 @@ public:
{
// add eps to avoid overflow
float absSum = sum(buffer)[0] + epsilon;
float norm = pow(absSum, 1.0f / pnorm);
float norm = std::pow(absSum, 1.0f / pnorm);
multiply(src, 1.0f / norm, dst);
}
else
@@ -229,7 +229,7 @@ public:
{
// add eps to avoid overflow
float absSum = sum(buffer)[0] + epsilon;
float norm = pow(absSum, 1.0f / pnorm);
float norm = std::pow(absSum, 1.0f / pnorm);
multiply(src, 1.0f / norm, dst);
}
else
modules/dnn/src/opencl/activations.cl
+1 -1
View File
@@ -141,7 +141,7 @@ __kernel void PowForward(const int n, __global const T* in, __global T* out,
{
int index = get_global_id(0);
if (index < n)
out[index] = pow(shift + scale * in[index], power);
out[index] = std::pow(shift + scale * in[index], power);
}
__kernel void ELUForward(const int n, __global const T* in, __global T* out,
modules/dnn/src/opencl/conv_layer_spatial.cl
+1 -1
View File
@@ -55,7 +55,7 @@
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (negative_slope[c])))
#define FUSED_ARG __global const KERNEL_ARG_DTYPE* negative_slope,
#elif defined(FUSED_CONV_POWER)
#define ACTIVATION_RELU_FUNCTION(x, c) pow(x, (Dtype)power)
#define ACTIVATION_RELU_FUNCTION(x, c) std::pow(x, (Dtype)power)
#define FUSED_ARG KERNEL_ARG_DTYPE power,
#elif defined(FUSED_CONV_TANH)
#define ACTIVATION_RELU_FUNCTION(x, c) tanh(x)
modules/dnn/src/opencl/lrn.cl
+1 -1
View File
@@ -28,7 +28,7 @@ __kernel void LRNComputeOutput(const int nthreads, __global T* in, __global T* s
int index = get_global_id(0);
int tmp = get_global_size(0);
for(index; index < nthreads; index += tmp)
out[index] = in[index] * pow(scale[index], negative_beta);
out[index] = in[index] * std::pow(scale[index], negative_beta);
}
__kernel void LRNFillScale(const int nthreads, __global T* in, const int num, const int channels, const int height, const int width, const int size, const T alpha_over_size, const T k, __global T* scale) {
modules/dnn/test/test_common.impl.hpp
+1 -1
View File
@@ -508,7 +508,7 @@ size_t DNNTestLayer::getTopMemoryUsageMB()
#ifdef _WIN32
PROCESS_MEMORY_COUNTERS proc;
GetProcessMemoryInfo(GetCurrentProcess(), &proc, sizeof(proc));
return proc.PeakWorkingSetSize / pow(1024, 2); // bytes to megabytes
return proc.PeakWorkingSetSize / std::pow(1024, 2); // bytes to megabytes
#else
std::ifstream status("/proc/self/status");
std::string line, title;
modules/features2d/src/brisk.cpp
+1 -1
View File
@@ -446,7 +446,7 @@ BRISK_Impl::generateKernel(const std::vector<float> &radiusList,
const float sigma_scale = 1.3f;
for (unsigned int scale = 0; scale < scales_; ++scale) {
scaleList_[scale] = (float) std::pow((double) 2.0, (double) (scale * lb_scale_step));
scaleList_[scale] = (float) std::pow(2, scale * lb_scale_step);
sizeList_[scale] = 0;
BriskPatternPoint *patternIteratorOuter = patternPoints_ + (scale * n_rot_ * points_);
// generate the pattern points look-up
modules/features2d/src/kaze/AKAZEFeatures.cpp
+1 -1
View File
@@ -73,7 +73,7 @@ void AKAZEFeatures::Allocate_Memory_Evolution(void) {
for (int j = 0; j < options_.nsublevels; j++) {
MEvolution step;
step.size = Size(level_width, level_height);
step.esigma = options_.soffset*pow(2.f, (float)(j) / (float)(options_.nsublevels) + i);
step.esigma = (float)(options_.soffset*std::pow(2, (float)(j) / (float)(options_.nsublevels) + i));
step.sigma_size = cvRound(step.esigma * options_.derivative_factor / power); // In fact sigma_size only depends on j
step.etime = 0.5f * (step.esigma * step.esigma);
step.octave = i;
modules/features2d/src/kaze/KAZEFeatures.cpp
+2 -2
View File
@@ -66,7 +66,7 @@ void KAZEFeatures::Allocate_Memory_Evolution(void) {
aux.Lt = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Lsmooth = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.Ldet = Mat::zeros(options_.img_height, options_.img_width, CV_32F);
aux.esigma = options_.soffset*pow((float)2.0f, (float)(j) / (float)(options_.nsublevels)+i);
aux.esigma = (float)(options_.soffset*std::pow(2, (float)(j) / (float)(options_.nsublevels)+i));
aux.etime = 0.5f*(aux.esigma*aux.esigma);
aux.sigma_size = cvRound(aux.esigma);
aux.octave = i;
@@ -472,7 +472,7 @@ void KAZEFeatures::Do_Subpixel_Refinement(std::vector<KeyPoint> &kpts) {
dsc = kpts_[i].octave + (kpts_[i].angle + *(dst.ptr<float>(2))) / ((float)(options_.nsublevels));
// In OpenCV the size of a keypoint is the diameter!!
kpts_[i].size = 2.0f*options_.soffset*pow((float)2.0f, dsc);
kpts_[i].size = (float)(2*options_.soffset*std::pow(2, dsc));
kpts_[i].angle = 0.0;
}
// Set the points to be deleted after the for loop
modules/features2d/src/sift.dispatch.cpp
+2 -2
View File
@@ -231,10 +231,10 @@ void SIFT_Impl::buildGaussianPyramid( const Mat& base, std::vector<Mat>& pyr, in
// precompute Gaussian sigmas using the following formula:
// \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
sig[0] = sigma;
double k = std::pow( 2., 1. / nOctaveLayers );
double k = std::pow( 2, 1. / nOctaveLayers );
for( int i = 1; i < nOctaveLayers + 3; i++ )
{
double sig_prev = std::pow(k, (double)(i-1))*sigma;
double sig_prev = (double)std::pow(k, i-1)*sigma;
double sig_total = sig_prev*k;
sig[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);
}
modules/flann/include/opencv2/flann/dist.h
+3 -3
View File
@@ -397,7 +397,7 @@ struct MinkowskiDistance
diff1 = (ResultType)abs(a[1] - b[1]);
diff2 = (ResultType)abs(a[2] - b[2]);
diff3 = (ResultType)abs(a[3] - b[3]);
result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order);
result += std::pow(diff0,order) + std::pow(diff1,order) + std::pow(diff2,order) + std::pow(diff3,order);
a += 4;
b += 4;
@@ -408,7 +408,7 @@ struct MinkowskiDistance
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
while (a < last) {
diff0 = (ResultType)abs(*a++ - *b++);
result += pow(diff0,order);
result += std::pow(diff0,order);
}
return result;
}
@@ -419,7 +419,7 @@ struct MinkowskiDistance
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return pow(static_cast<ResultType>(abs(a-b)),order);
return std::pow(static_cast<ResultType>(abs(a-b)),order);
}
};
modules/gapi/src/3rdparty/vasot/src/components/ot/mtt/rgb_histogram.cpp
Vendored
+1 -1
View File
@@ -11,7 +11,7 @@ namespace ot {
RgbHistogram::RgbHistogram(int32_t rgb_bin_size)
: rgb_bin_size_(rgb_bin_size), rgb_num_bins_(256 / rgb_bin_size),
rgb_hist_size_(static_cast<int32_t>(pow(rgb_num_bins_, 3))) {
rgb_hist_size_(static_cast<int32_t>(std::pow(rgb_num_bins_, 3))) {
}
RgbHistogram::~RgbHistogram(void) {
modules/imgproc/src/corner.cpp
+1 -1
View File
@@ -598,7 +598,7 @@ static bool ipp_cornerHarris( Mat &src, Mat &dst, int blockSize, int ksize, doub
scale *= 2.0;
if (depth == CV_8U)
scale *= 255.0;
scale = std::pow(scale, -4.0);
scale = std::pow(scale, -4);
if (ippiHarrisCornerGetBufferSize(roisize, masksize, blockSize, datatype, cn, &bufsize) >= 0)
{
modules/imgproc/src/lsd.cpp
+3 -3
View File
@@ -138,7 +138,7 @@ inline double get_limit(cv::Point2d p, int row, double slope) {
inline double log_gamma_windschitl(const double& x)
{
return 0.918938533204673 + (x-0.5)*log(x) - x
+ 0.5*x*log(x*sinh(1/x) + 1/(810.0*pow(x, 6.0)));
+ 0.5*x*log(x*sinh(1/x) + 1/(810.0*std::pow(x, 6)));
}
/**
@@ -156,7 +156,7 @@ inline double log_gamma_lanczos(const double& x)
for(int n = 0; n < 7; ++n)
{
a -= log(x + double(n));
b += q[n] * pow(x, double(n));
b += q[n] * std::pow(x, n);
}
return a + log(b);
}
@@ -1037,7 +1037,7 @@ double LineSegmentDetectorImpl::nfa(const int& n, const int& k, const double& p)
bin_tail += term;
if(bin_term < 1)
{
double err = term * ((1 - pow(mult_term, double(n-i+1))) / (1 - mult_term) - 1);
double err = term * ((1 - std::pow(mult_term, double(n-i+1))) / (1 - mult_term) - 1);
if(err < tolerance * fabs(-log10(bin_tail) - LOG_NT) * bin_tail) break;
}
modules/imgproc/test/test_drawing.cpp
+3 -3
View File
@@ -554,9 +554,9 @@ TEST(Drawing, _914)
line(img, Point(-5, 20), Point(260, 20), Scalar(0), 2, 4);
line(img, Point(10, 0), Point(10, 255), Scalar(0), 2, 4);
double x0 = 0.0/pow(2.0, -2.0);
double x1 = 255.0/pow(2.0, -2.0);
double y = 30.5/pow(2.0, -2.0);
double x0 = 0.0/std::pow(2, -2);
double x1 = 255.0/std::pow(2, -2);
double y = 30.5/std::pow(2, -2);
line(img, Point(int(x0), int(y)), Point(int(x1), int(y)), Scalar(0), 2, 4, 2);
modules/imgproc/test/test_histograms.cpp
+3 -3
View File
@@ -142,7 +142,7 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ )
cdims = cvtest::randInt(rng) % max_cdims + 1;
hist_size = exp(cvtest::randReal(rng)*max_log_size*CV_LOG2);
max_dim_size = cvRound(pow(hist_size,1./cdims));
max_dim_size = cvRound(std::pow(hist_size,1./cdims));
total_size = 1;
uniform = cvtest::randInt(rng) % 2;
hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY;
@@ -208,7 +208,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
for( j = 0; j < 10; j++ )
{
q = 1. + (j+1)*0.1;
if( (pow(q,(double)n)-1)/(q-1.) >= _high-_low )
if( (std::pow(q,n)-1)/(q-1.) >= _high-_low )
break;
}
@@ -220,7 +220,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
else
{
q = 1 + j*0.1;
delta = cvFloor((_high-_low)*(q-1)/(pow(q,(double)n) - 1));
delta = cvFloor((_high-_low)*(q-1)/(std::pow(q,n) - 1));
delta = MAX(delta, 1.);
}
val = _low;
modules/ml/src/ann_mlp.cpp
+1 -1
View File
@@ -276,7 +276,7 @@ public:
{
int n1 = layer_sizes[i-1];
int n2 = layer_sizes[i];
double val = 0, G = n2 > 2 ? 0.7*pow((double)n1,1./(n2-1)) : 1.;
double val = 0, G = n2 > 2 ? 0.7*std::pow(n1,1./(n2-1)) : 1.;
double* w = weights[i].ptr<double>();
// initialize weights using Nguyen-Widrow algorithm
modules/photo/src/contrast_preserve.cpp
+1 -1
View File
@@ -102,7 +102,7 @@ void cv::decolor(InputArray _src, OutputArray _dst, OutputArray _color_boost)
vector <double> temp2(EXPsum.size());
vector <double> wei1(polyGrad.size());
while(sqrt(pow(E-pre_E,2)) > tol)
while(std::abs(E-pre_E) > tol)
{
iterCount +=1;
pre_E = E;
modules/photo/src/contrast_preserve.hpp
+6 -6
View File
@@ -91,7 +91,7 @@ double Decolor::energyCalcu(const vector <double> &Cg, const vector < vector <do
}
for(size_t i=0;i<polyGrad[0].size();i++)
energy[i] = -1.0*log(exp(-1.0*pow(temp[i],2)/sigma) + exp(-1.0*pow(temp1[i],2)/sigma));
energy[i] = -1.0*log(exp(-1.0*std::pow(temp[i],2)/sigma) + exp(-1.0*std::pow(temp1[i],2)/sigma));
double sum = 0.0;
for(size_t i=0;i<polyGrad[0].size();i++)
@@ -187,7 +187,7 @@ void Decolor::colorGrad(const Mat &img, vector <double> &Cg) const
Cg.resize(ImL.size());
for(size_t i=0;i<ImL.size();i++)
{
const double res = sqrt(pow(ImL[i],2) + pow(Ima[i],2) + pow(Imb[i],2))/100;
const double res = sqrt(std::pow(ImL[i],2) + std::pow(Ima[i],2) + std::pow(Imb[i],2))/100;
Cg[i] = res;
}
}
@@ -288,8 +288,8 @@ void Decolor::grad_system(const Mat &im, vector < vector < double > > &polyGrad,
for(int i = 0;i<h;i++)
for(int j=0;j<w;j++)
curIm.at<float>(i,j)=static_cast<float>(
pow(rgb_channel[2].at<float>(i,j),r)*pow(rgb_channel[1].at<float>(i,j),g)*
pow(rgb_channel[0].at<float>(i,j),b));
std::pow(rgb_channel[2].at<float>(i,j),r)*std::pow(rgb_channel[1].at<float>(i,j),g)*
std::pow(rgb_channel[0].at<float>(i,j),b));
vector <double> curGrad;
gradvector(curIm,curGrad);
add_to_vector_poly(polyGrad,curGrad,idx1);
@@ -366,8 +366,8 @@ void Decolor::grayImContruct(vector <double> &wei, const Mat &img, Mat &Gray) co
for(int i = 0;i<h;i++)
for(int j=0;j<w;j++)
Gray.at<float>(i,j)=static_cast<float>(Gray.at<float>(i,j) +
static_cast<float>(wei[kk])*pow(rgb_channel[2].at<float>(i,j),r)*pow(rgb_channel[1].at<float>(i,j),g)*
pow(rgb_channel[0].at<float>(i,j),b));
static_cast<float>(wei[kk])*std::pow(rgb_channel[2].at<float>(i,j),r)*std::pow(rgb_channel[1].at<float>(i,j),g)*
std::pow(rgb_channel[0].at<float>(i,j),b));
kk=kk+1;
}
modules/photo/src/npr.hpp
+4 -4
View File
@@ -184,7 +184,7 @@ void Domain_Filter::compute_Rfilter(Mat &output, Mat &hz, float sigma_h)
for(int i=0;i<h;i++)
for(int j=0;j<w;j++)
V.at<float>(i,j) = pow(a,hz.at<float>(i,j));
V.at<float>(i,j) = std::pow(a,hz.at<float>(i,j));
for(int i=0; i<h; i++)
{
@@ -490,7 +490,7 @@ void Domain_Filter::filter(const Mat &img, Mat &res, float sigma_s = 60, float s
for(int i=0;i<no_of_iter;i++)
{
sigma_h = (float) (sigma_s * sqrt(3.0) * pow(2.0,(no_of_iter - (i+1))) / sqrt(pow(4.0,no_of_iter) -1));
sigma_h = (float) (sigma_s * sqrt(3.0) * std::pow(2,(no_of_iter - (i+1))) / sqrt(std::pow(4,no_of_iter) -1));
compute_Rfilter(O, horiz, sigma_h);
@@ -513,7 +513,7 @@ void Domain_Filter::filter(const Mat &img, Mat &res, float sigma_s = 60, float s
for(int i=0;i<no_of_iter;i++)
{
sigma_h = (float) (sigma_s * sqrt(3.0) * pow(2.0,(no_of_iter - (i+1))) / sqrt(pow(4.0,no_of_iter) -1));
sigma_h = (float) (sigma_s * sqrt(3.0) * std::pow(2,(no_of_iter - (i+1))) / sqrt(std::pow(4,no_of_iter) -1));
radius = (float) sqrt(3.0) * sigma_h;
@@ -560,7 +560,7 @@ void Domain_Filter::pencil_sketch(const Mat &img, Mat &sketch, Mat &color_res, f
for(int i=0;i<no_of_iter;i++)
{
sigma_h = (float) (sigma_s * sqrt(3.0) * pow(2.0,(no_of_iter - (i+1))) / sqrt(pow(4.0,no_of_iter) -1));
sigma_h = (float) (sigma_s * sqrt(3.0) * std::pow(2,(no_of_iter - (i+1))) / sqrt(std::pow(4,no_of_iter) -1));
radius = (float) sqrt(3.0) * sigma_h;
modules/photo/src/tonemap.cpp
+1 -1
View File
@@ -232,7 +232,7 @@ public:
log_img.release();
float key = (log_max - log_mean) / (log_max - log_min);
float map_key = 0.3f + 0.7f * pow(key, 1.4f);
float map_key = 0.3f + 0.7f * std::pow(key, 1.4f);
intensity = exp(-intensity);
Scalar chan_mean = mean(img);
float gray_mean = static_cast<float>(mean(gray_img)[0]);
modules/stitching/src/autocalib.cpp
+1 -1
View File
@@ -156,7 +156,7 @@ bool calibrateRotatingCamera(const std::vector<Mat> &Hs, Mat &K)
for (int i = 0; i < m; ++i)
{
CV_Assert(Hs[i].size() == Size(3, 3) && Hs[i].type() == CV_64F);
Hs_[i] = Hs[i] / std::pow(determinant(Hs[i]), 1./3.);
Hs_[i] = Hs[i] / std::cbrt(determinant(Hs[i]));
}
const int idx_map[3][3] = {{0, 1, 2}, {1, 3, 4}, {2, 4, 5}};
modules/video/src/ecc.cpp
+1 -1
View File
@@ -217,7 +217,7 @@ static void project_onto_jacobian_ECC(const Mat& src1, const Mat& src2, Mat& dst
Mat mat;
for (int i = 0; i < dst.rows; i++) {
mat = Mat(src1.colRange(i * w, (i + 1) * w));
dstPtr[i * (dst.rows + 1)] = (float)pow(norm(mat), 2); // diagonal elements
dstPtr[i * (dst.rows + 1)] = (float)std::pow(norm(mat), 2); // diagonal elements
for (int j = i + 1; j < dst.cols; j++) { // j starts from i+1
dstPtr[i * dst.cols + j] = (float)mat.dot(src2.colRange(j * w, (j + 1) * w));
modules/video/src/tracking/detail/tracking_online_mil.cpp
+4 -4
View File
@@ -286,8 +286,8 @@ void ClfOnlineStump::update(const Mat& posx, const Mat& negx, const Mat_<float>&
_q = (_mu1 - _mu0) / 2;
_s = sign(_mu1 - _mu0);
_log_n0 = std::log(float(1.0f / pow(_sig0, 0.5f)));
_log_n1 = std::log(float(1.0f / pow(_sig1, 0.5f)));
_log_n0 = std::log(float(1.0f / std::pow(_sig0, 0.5f)));
_log_n1 = std::log(float(1.0f / std::pow(_sig1, 0.5f)));
//_e1 = -1.0f/(2.0f*_sig1+1e-99f);
//_e0 = -1.0f/(2.0f*_sig0+1e-99f);
_e1 = -1.0f / (2.0f * _sig1 + std::numeric_limits<float>::min());
@@ -314,8 +314,8 @@ void ClfOnlineStump::update(const Mat& posx, const Mat& negx, const Mat_<float>&
_q = (_mu1 - _mu0) / 2;
_s = sign(_mu1 - _mu0);
_log_n0 = std::log(float(1.0f / pow(_sig0, 0.5f)));
_log_n1 = std::log(float(1.0f / pow(_sig1, 0.5f)));
_log_n0 = std::log(float(1.0f / std::pow(_sig0, 0.5f)));
_log_n1 = std::log(float(1.0f / std::pow(_sig1, 0.5f)));
//_e1 = -1.0f/(2.0f*_sig1+1e-99f);
//_e0 = -1.0f/(2.0f*_sig0+1e-99f);
_e1 = -1.0f / (2.0f * _sig1 + std::numeric_limits<float>::min());
modules/videoio/src/cap_aravis.cpp
+1 -1
View File
@@ -383,7 +383,7 @@ void CvCaptureCAM_Aravis::autoExposureControl(const Mat & image)
midGrey = brightness;
double maxe = 1e6 / fps;
double ne = CLIP( ( exposure * d ) / ( dmid * pow(sqrt(2), -2 * exposureCompensation) ), exposureMin, maxe);
double ne = CLIP( ( exposure * d ) / ( dmid * std::pow(sqrt(2), -2 * exposureCompensation) ), exposureMin, maxe);
// if change of value requires intervention
if(std::fabs(d-dmid) > 5) {
modules/videoio/src/cap_mfx_writer.cpp
+2 -2
View File
@@ -21,11 +21,11 @@ static float estimateBitrate(int codecId, size_t pixelNum, float fps)
}
else if (codecId == MFX_CODEC_AVC)
{
bitrate = (mp * 140 + 19) * pow(fps, 0.60f);
bitrate = (mp * 140 + 19) * std::pow(fps, 0.60f);
}
else if (codecId == MFX_CODEC_HEVC)
{
bitrate = (mp * 63 + 45) * pow(fps, 0.60f);
bitrate = (mp * 63 + 45) * std::pow(fps, 0.60f);
}
else
{