improve doc.

modules/calib3d/include/opencv2/calib3d.hpp

@@ -396,7 +396,7 @@ and a rotation matrix.
 It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
 degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
 sequence of rotations about the three principal axes that results in the same orientation of an
-object, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules
+object, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles
 are only one of the possible solutions.
  */
 CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ,
@@ -583,7 +583,7 @@ projections, as well as the camera matrix and the distortion coefficients.
         it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
         np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
    -   The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
-       unstable and sometimes give completly wrong results. If you pass one of these two
+       unstable and sometimes give completely wrong results. If you pass one of these two
        flags, **SOLVEPNP_EPNP** method will be used instead.
    -   The minimum number of points is 4. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
        methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
@@ -1371,9 +1371,9 @@ be floating-point (single or double precision).
 @param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
 Note that this function assumes that points1 and points2 are feature points from cameras with the
 same camera matrix.
-@param method Method for computing a fundamental matrix.
+@param method Method for computing an essential matrix.
 -   **RANSAC** for the RANSAC algorithm.
--   **MEDS** for the LMedS algorithm.
+-   **LMEDS** for the LMedS algorithm.
 @param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
 confidence (probability) that the estimated matrix is correct.
 @param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
@@ -1655,7 +1655,7 @@ to 3D points with a very large Z value (currently set to 10000).
 depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
 
 The function transforms a single-channel disparity map to a 3-channel image representing a 3D
-surface. That is, for each pixel (x,y) andthe corresponding disparity d=disparity(x,y) , it
+surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
 computes:
 
 \f[\begin{array}{l} [X \; Y \; Z \; W]^T =  \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T  \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f]
@@ -1704,7 +1704,7 @@ CV_EXPORTS_W  int estimateAffine3D(InputArray src, InputArray dst,
 @param from First input 2D point set.
 @param to Second input 2D point set.
 @param inliers Output vector indicating which points are inliers.
-@param method Robust method used to compute tranformation. The following methods are possible:
+@param method Robust method used to compute transformation. The following methods are possible:
 -   cv::RANSAC - RANSAC-based robust method
 -   cv::LMEDS - Least-Median robust method
 RANSAC is the default method.
@@ -1744,7 +1744,7 @@ two 2D point sets.
 @param from First input 2D point set.
 @param to Second input 2D point set.
 @param inliers Output vector indicating which points are inliers.
-@param method Robust method used to compute tranformation. The following methods are possible:
+@param method Robust method used to compute transformation. The following methods are possible:
 -   cv::RANSAC - RANSAC-based robust method
 -   cv::LMEDS - Least-Median robust method
 RANSAC is the default method.
@@ -2043,7 +2043,7 @@ namespace fisheye
     @param alpha The skew coefficient.
     @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
 
-    Note that the function assumes the camera matrix of the undistorted points to be indentity.
+    Note that the function assumes the camera matrix of the undistorted points to be identity.
     This means if you want to transform back points undistorted with undistortPoints() you have to
     multiply them with \f$P^{-1}\f$.
      */

modules/calib3d/src/calibration.cpp

@@ -2997,7 +2997,7 @@ cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ,
         eulerAngles->z = acos(_Qz[0][0]) * (_Qz[0][1] >= 0 ? 1 : -1) * (180.0 / CV_PI);
     }
 
-    /* Calulate orthogonal matrix. */
+    /* Calculate orthogonal matrix. */
     /*
     Q = QzT * QyT * QxT
     */

modules/calib3d/src/fundam.cpp

@@ -171,7 +171,7 @@ public:
             float ww = 1.f/(Hf[6]*M[i].x + Hf[7]*M[i].y + 1.f);
             float dx = (Hf[0]*M[i].x + Hf[1]*M[i].y + Hf[2])*ww - m[i].x;
             float dy = (Hf[3]*M[i].x + Hf[4]*M[i].y + Hf[5])*ww - m[i].y;
-            err[i] = (float)(dx*dx + dy*dy);
+            err[i] = dx*dx + dy*dy;
         }
     }
 };

modules/calib3d/src/ptsetreg.cpp

@@ -594,7 +594,7 @@ public:
     bool checkSubset( InputArray _ms1, InputArray, int count ) const
     {
         Mat ms1 = _ms1.getMat();
-        // check colinearity and also check that points are too close
+        // check collinearity and also check that points are too close
         // only ms1 affects actual estimation stability
         return !haveCollinearPoints(ms1, count);
     }