Merge pull request #3969 from Dikay900:master_to_2_4

doc/tutorials/core/adding_images/adding_images.rst

@@ -26,7 +26,7 @@ From our previous tutorial, we know already a bit of *Pixel operators*. An inter
 
    g(x) = (1 - \alpha)f_{0}(x) + \alpha f_{1}(x)
 
-By varying :math:`\alpha` from :math:`0 \rightarrow 1` this operator can be used to perform a temporal *cross-disolve* between two images or videos, as seen in slide shows and film productions (cool, eh?)
+By varying :math:`\alpha` from :math:`0 \rightarrow 1` this operator can be used to perform a temporal *cross-dissolve* between two images or videos, as seen in slide shows and film productions (cool, eh?)
 
 Code
 =====

doc/tutorials/core/basic_geometric_drawing/basic_geometric_drawing.rst

@@ -167,7 +167,7 @@ Explanation
         * The ellipse center is located in the point **(w/2.0, w/2.0)** and is enclosed in a box of size **(w/4.0, w/16.0)**
         * The ellipse is rotated **angle** degrees
         * The ellipse extends an arc between **0** and **360** degrees
-        * The color of the figure will be **Scalar( 255, 255, 0)** which means blue in RGB value.
+        * The color of the figure will be **Scalar( 255, 0, 0)** which means blue in BGR value.
         * The ellipse's **thickness** is 2.
 
 

doc/tutorials/core/random_generator_and_text/random_generator_and_text.rst

@@ -116,7 +116,7 @@ Explanation
         pt1.x = rng.uniform( x_1, x_2 );
         pt1.y = rng.uniform( y_1, y_2 );
 
-     * We know that **rng** is a *Random number generator* object. In the code above we are calling **rng.uniform(a,b)**. This generates a radombly uniformed distribution between the values **a** and **b** (inclusive in **a**, exclusive in **b**).
+     * We know that **rng** is a *Random number generator* object. In the code above we are calling **rng.uniform(a,b)**. This generates a randomly uniformed distribution between the values **a** and **b** (inclusive in **a**, exclusive in **b**).
 
      * From the explanation above, we deduce that the extremes *pt1* and *pt2* will be random values, so the lines positions will be quite impredictable, giving a nice visual effect (check out the Result section below).
 
@@ -138,7 +138,7 @@ Explanation
 
        As we can see, the return value is an *Scalar* with 3 randomly initialized values, which are used as the *R*, *G* and *B* parameters for the line color. Hence, the color of the lines will be random too!
 
-#. The explanation above applies for the other functions generating circles, ellipses, polygones, etc. The parameters such as *center* and *vertices* are also generated randomly.
+#. The explanation above applies for the other functions generating circles, ellipses, polygons, etc. The parameters such as *center* and *vertices* are also generated randomly.
 
 #. Before finishing, we also should take a look at the functions *Display_Random_Text* and *Displaying_Big_End*, since they both have a few interesting features:
 

modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst

@@ -128,11 +128,11 @@ Finds the camera intrinsic and extrinsic parameters from several views of a cali
 
 .. ocv:pyoldfunction:: cv.CalibrateCamera2(objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, flags=0)-> None
 
-    :param objectPoints: In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space. The outer vector contains as many elements as the number of the pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns, or even different patterns in different views. Then, the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0.
+    :param objectPoints: In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer vector contains as many elements as the number of the pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns, or even different patterns in different views. Then, the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0.
 
         In the old interface all the vectors of object points from different views are concatenated together.
 
-    :param imagePoints: In the new interface it is a vector of vectors of the projections of calibration pattern points. ``imagePoints.size()`` and ``objectPoints.size()`` and ``imagePoints[i].size()`` must be equal to ``objectPoints[i].size()`` for each ``i``.
+    :param imagePoints: In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). ``imagePoints.size()`` and ``objectPoints.size()`` and ``imagePoints[i].size()`` must be equal to ``objectPoints[i].size()`` for each ``i``.
 
         In the old interface all the vectors of object points from different views are concatenated together.
 
@@ -144,7 +144,7 @@ Finds the camera intrinsic and extrinsic parameters from several views of a cali
 
     :param distCoeffs: Output vector of distortion coefficients  :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])`  of 4, 5, or 8 elements.
 
-    :param rvecs: Output  vector  of rotation vectors (see  :ocv:func:`Rodrigues` ) estimated for each pattern view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
+    :param rvecs: Output  vector  of rotation vectors (see  :ocv:func:`Rodrigues` ) estimated for each pattern view (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
 
     :param tvecs: Output vector of translation vectors estimated for each pattern view.