Merge branch 4.x

modules/3d/src/usac/essential_solver.cpp

@@ -142,7 +142,7 @@ public:
             }
 
             std::vector<double> c(11), rs;
-            // filling coefficients of 10-degree polynomial satysfying zero-determinant constraint of essential matrix, ie., det(E) = 0
+            // filling coefficients of 10-degree polynomial satisfying zero-determinant constraint of essential matrix, ie., det(E) = 0
             // based on "An Efficient Solution to the Five-Point Relative Pose Problem" (David Nister)
             // same as in five-point.cpp
             c[10] = (b[0]*b[17]*b[34]+b[26]*b[4]*b[21]-b[26]*b[17]*b[8]-b[13]*b[4]*b[34]-b[0]*b[21]*b[30]+b[13]*b[30]*b[8]);

modules/3d/src/usac/ransac_solvers.cpp

@@ -322,7 +322,7 @@ void UniversalRANSAC::initialize (int state, Ptr<MinimalSolver> &min_solver, Ptr
                 params->getUpperIncompleteOfSigmaQuantile()); break;
         case ScoreMethod::SCORE_METHOD_LMEDS :
             quality = LMedsQuality::create(points_size, threshold, error); break;
-        default: CV_Error(cv::Error::StsNotImplemented, "Score is not imeplemeted!");
+        default: CV_Error(cv::Error::StsNotImplemented, "Score is not implemented!");
     }
 
     const auto is_ge_solver = params->getRansacSolver() == GEM_SOLVER;

modules/3d/src/usac/sampler.cpp

@@ -62,8 +62,8 @@ Ptr<UniformSampler> UniformSampler::create(int state, int sample_size_, int poin
 /////////////////////////////////// PROSAC (SIMPLE) SAMPLER ///////////////////////////////////////
 /*
 * PROSAC (simple) sampler does not use array of precalculated T_n (n is subset size) samples, but computes T_n for
-* specific n directy in generateSample() function.
-* Also, the stopping length (or maximum subset size n*) by default is set to points_size (N) and does not updating
+* specific n directly in generateSample() function.
+* Also, the stopping length (or maximum subset size n*) by default is set to points_size (N) and does not update
 * during computation.
 */
 class ProsacSimpleSamplerImpl : public ProsacSimpleSampler {
@@ -176,7 +176,7 @@ protected:
     // In our experiments, the parameter was set to T_N = 200000
     int growth_max_samples;
 
-    // how many time PROSAC generateSample() was called
+    // how many times PROSAC generateSample() was called
     int kth_sample_number;
     Ptr<UniformRandomGenerator> random_gen;
 public:
@@ -488,7 +488,7 @@ public:
 
         points_large_neighborhood_size = 0;
 
-        // find indicies of points that have sufficient neighborhood (at least sample_size-1)
+        // find indices of points that have sufficient neighborhood (at least sample_size-1)
         for (int pt_idx = 0; pt_idx < points_size; pt_idx++)
             if ((int)neighborhood_graph->getNeighbors(pt_idx).size() >= sample_size-1)
                 points_large_neighborhood[points_large_neighborhood_size++] = pt_idx;

modules/3d/src/usac/termination.cpp

@@ -19,7 +19,7 @@ public:
 
     /*
      * Get upper bound iterations for any sample number
-     * n is points size, w is inlier ratio, p is desired probability, k is expceted number of iterations.
+     * n is points size, w is inlier ratio, p is desired probability, k is expected number of iterations.
      * 1 - p = (1 - w^n)^k,
      * k = log_(1-w^n) (1-p)
      * k = ln (1-p) / ln (1-w^n)

modules/3d/test/test_solvepnp_ransac.cpp

@@ -1531,8 +1531,8 @@ TEST(Calib3d_SolvePnP, generic)
                 }
                 else
                 {
-                    p3f = p3f_;
-                    p2f = p2f_;
+                    p3f = vector<Point3f>(p3f_.begin(), p3f_.end());
+                    p2f = vector<Point2f>(p2f_.begin(), p2f_.end());
                 }
 
                 vector<double> reprojectionErrors;