Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/aruco/README.md

@@ -7,6 +7,6 @@ ArUco markers are easy to detect pattern grids that yield up to 1024 different p
 
 **ChArUco**
 
-ArUco markers were improved by interspersing them inside a checkerboard called ChArUco. Checkerboard corner intersectionsa provide more stable corners because the edge location bias on one square is countered by the opposite edge orientation in the connecting square. By interspersing ArUco markers inside the checkerboard, each checkerboard corner gets a label which enables it to be used in complex calibration or pose scenarios where you cannot see all the corners of the checkerboard.
+ArUco markers were improved by interspersing them inside a checkerboard called ChArUco. Checkerboard corner intersections provide more stable corners because the edge location bias on one square is countered by the opposite edge orientation in the connecting square. By interspersing ArUco markers inside the checkerboard, each checkerboard corner gets a label which enables it to be used in complex calibration or pose scenarios where you cannot see all the corners of the checkerboard.
 
 The smallest ChArUco board is 5 checkers and 4 markers called a "Diamond Marker".

modules/aruco/misc/python/test/test_aruco.py

@@ -13,19 +13,19 @@ class aruco_test(NewOpenCVTests):
 
     def test_idsAccessibility(self):
 
-        ids = np.array([[elem] for elem in range(17)])
+        ids = np.arange(17)
-        rev_ids = np.array(list(reversed(ids)))
+        rev_ids = ids[::-1]
 
         aruco_dict  = cv.aruco.Dictionary_get(cv.aruco.DICT_5X5_250)
         board = cv.aruco.CharucoBoard_create(7, 5, 1, 0.5, aruco_dict)
 
-        self.assertTrue(np.equal(board.ids, ids).all())
+        np.testing.assert_array_equal(board.ids.squeeze(), ids)
 
         board.ids = rev_ids
-        self.assertTrue(np.equal(board.ids, rev_ids).all())
+        np.testing.assert_array_equal(board.ids.squeeze(), rev_ids)
 
         board.setIds(ids)
-        self.assertTrue(np.equal(board.ids, ids).all())
+        np.testing.assert_array_equal(board.ids.squeeze(), ids)
 
         with self.assertRaises(cv.error):
             board.setIds(np.array([0]))

modules/aruco/samples/detector_params.yml

@@ -8,7 +8,7 @@ adaptiveThreshConstant: 7
 minMarkerPerimeterRate: 0.03
 maxMarkerPerimeterRate: 4.0
 polygonalApproxAccuracyRate: 0.05
-minCornerDistance: 10.0
+minCornerDistanceRate: 0.05
 minDistanceToBorder: 3
 minMarkerDistance: 10.0
 minMarkerDistanceRate: 0.05

modules/line_descriptor/src/LSDDetector.cpp

@@ -170,7 +170,7 @@ void LSDDetector::detectImpl( const Mat& imageSrc, std::vector<KeyLine>& keyline
   int class_counter = -1;
   for ( int octaveIdx = 0; octaveIdx < (int) lines_lsd.size(); octaveIdx++ )
   {
-    float octaveScale = pow( (float)scale, octaveIdx );
+    float octaveScale = std::pow((float)scale, (float)octaveIdx);
     for ( int k = 0; k < (int) lines_lsd[octaveIdx].size(); k++ )
     {
       KeyLine kl;

modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp

@@ -455,7 +455,7 @@ private:
   inline static float LoG( float x, float y, float delta )
   {
     float d = - ( x * x + y * y ) / ( 2 * delta * delta );
-    return -1.0f / ( (float) ( CV_PI ) * pow( delta, 4 ) ) * ( 1 + d ) * exp( d );
+    return -1.0f / ( (float) ( CV_PI ) * (delta*delta*delta*delta) ) * ( 1 + d ) * exp( d );
   }  // Laplacian of Gaussian
 
 // Read matrix from binary file

modules/superres/src/btv_l1.cpp

@@ -356,7 +356,7 @@ namespace
         for (int m = 0, ind = 0; m <= ksize; ++m)
         {
             for (int l = ksize; l + m >= 0; --l, ++ind)
-                btvWeights[ind] = pow(alpha_f, std::abs(m) + std::abs(l));
+                btvWeights[ind] = static_cast<float>(pow(alpha_f, std::abs(m) + std::abs(l)));
         }
     }
 

modules/tracking/src/trackerCSRT.cpp

@@ -511,7 +511,7 @@ void TrackerCSRTImpl::init(InputArray image_, const Rect& boundingBox)
             sqrt(original_target_size.width * original_target_size.height)));
     template_size.width = template_size.height =
         (template_size.width + template_size.height) / 2.0f;
-    rescale_ratio = sqrt(pow(params.template_size,2) / (template_size.width * template_size.height));
+    rescale_ratio = sqrt((params.template_size * params.template_size) / (template_size.width * template_size.height));
     if(rescale_ratio > 1)  {
         rescale_ratio = 1;
     }

modules/tracking/src/trackerCSRTScaleEstimation.cpp

@@ -91,10 +91,10 @@ DSST::DSST(const Mat &image,
 
     scale_sigma = static_cast<float>(sqrt(scales_count) * sigma_factor);
 
-    min_scale_factor = pow(scale_step,
+    min_scale_factor = static_cast<float>(pow(scale_step,
-            cvCeil(log(max(5.0 / template_size.width, 5.0 / template_size.height)) / log(scale_step)));
+            cvCeil(log(max(5.0 / template_size.width, 5.0 / template_size.height)) / log(scale_step))));
-    max_scale_factor = powf(scale_step,
+    max_scale_factor = static_cast<float>(pow(scale_step,
-            static_cast<float>(cvFloor(log(min((float)image.rows / (float)bounding_box.width,
+            cvFloor(log(min((float)image.rows / (float)bounding_box.width,
             (float)image.cols / (float)bounding_box.height)) / log(scale_step))));
     ys = Mat(1, scales_count, CV_32FC1);
     float ss, sf;

modules/xfeatures2d/src/msd.cpp

@@ -303,7 +303,7 @@ namespace cv
                 p_res.y = j + xr + 0.5f;
             } else
             {
-                float effectiveScaleFactor = std::pow(m_scale_factor, scale);
+                float effectiveScaleFactor = std::pow(m_scale_factor, (float)scale);
                 p_res.x = (i + xc + 0.5f) * effectiveScaleFactor;
                 p_res.y = (j + xr + 0.5f) * effectiveScaleFactor;
 
@@ -691,7 +691,7 @@ namespace cv
                                     continue;
                             }
                             kp_temp.response = saliency[r][j * cW + i];
-                            kp_temp.size = (m_patch_radius * 2.0f + 1) * std::pow(m_scale_factor, r);
+                            kp_temp.size = (m_patch_radius * 2.0f + 1) * std::pow(m_scale_factor, (float)r);
                             kp_temp.octave = r;
                             if (m_compute_orientation)
                                 kp_temp.angle = computeOrientation(m_scaleSpace[r], i, j, orientPoints);

modules/ximgproc/src/graphsegmentation.cpp

@@ -194,7 +194,8 @@ namespace cv {
                                     float tmp_total = 0;
 
                                     for ( int channel = 0; channel < nb_channels; channel++) {
-                                        tmp_total += pow(p[j * nb_channels + channel] - p2[j2 * nb_channels + channel], 2);
+                                        float tmp_diff = p[j * nb_channels + channel] - p2[j2 * nb_channels + channel];
+                                        tmp_total += tmp_diff * tmp_diff;
                                     }
 
                                     float diff = 0;