Merge pull request opencv#10846 from luzpaz:misc-modules-typos-cont

Author: alalek

doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown

@@ -27,16 +27,16 @@ Explanation / Result
     @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image
     ![](images/source.jpeg)
 
--#  Then if we have an image with a white background, it is good to transform it to black. This will help us to descriminate the foreground objects easier when we will apply the Distance Transform:
+-#  Then if we have an image with a white background, it is good to transform it to black. This will help us to discriminate the foreground objects easier when we will apply the Distance Transform:
     @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg
     ![](images/black_bg.jpeg)
 
--#  Afterwards we will sharp our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative):
+-#  Afterwards we will sharpen our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative):
     @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp
     ![](images/laplace.jpeg)
     ![](images/sharp.jpeg)
 
--#  Now we transfrom our new sharped source image to a grayscale and a binary one, respectively:
+-#  Now we transform our new sharpened source image to a grayscale and a binary one, respectively:
     @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin
     ![](images/bin.jpeg)
 

modules/calib3d/src/calibinit.cpp

@@ -1391,7 +1391,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count,
         }
     }
 
-    // start with a corner that belongs to a quad with a signle neighbor.
+    // start with a corner that belongs to a quad with a single neighbor.
     // if we do not have such, start with a corner of a quad with two neighbors.
     if( !first )
         first = first2;
@@ -2173,7 +2173,7 @@ bool cv::findCirclesGrid2( InputArray _image, Size patternSize,
         boxFinder.getAsymmetricHoles(centers);
         break;
           default:
-            CV_Error(CV_StsBadArg, "Unkown pattern type");
+            CV_Error(CV_StsBadArg, "Unknown pattern type");
         }
 
         if (i != 0)

modules/calib3d/src/circlesgrid.cpp

@@ -619,7 +619,7 @@ bool CirclesGridFinder::findHoles()
     }
 
     default:
-      CV_Error(Error::StsBadArg, "Unkown pattern type");
+      CV_Error(Error::StsBadArg, "Unknown pattern type");
   }
   return (isDetectionCorrect());
   //CV_Error( 0, "Detection is not correct" );

modules/calib3d/src/five-point.cpp

@@ -506,7 +506,7 @@ int cv::recoverPose( InputArray E, InputArray _points1, InputArray _points2,
     // Do the cheirality check.
     // Notice here a threshold dist is used to filter
     // out far away points (i.e. infinite points) since
-    // there depth may vary between postive and negtive.
+    // their depth may vary between positive and negtive.
     std::vector<Mat> allTriangulations(4);
     Mat Q;
 

modules/calib3d/src/opencl/stereobm.cl

@@ -141,7 +141,7 @@ __kernel void stereoBM(__global const uchar * leftptr,
                        __global const uchar * rightptr,
                        __global uchar * dispptr, int disp_step, int disp_offset,
                        int rows, int cols,                                              // rows, cols of left and right images, not disp
-                       int textureTreshold, int uniquenessRatio)
+                       int textureThreshold, int uniquenessRatio)
 {
     int lz = get_local_id(0);
     int gx = get_global_id(1) * BLOCK_SIZE_X;

modules/calib3d/src/p3p.cpp

@@ -192,7 +192,7 @@ int p3p::solve(double R[4][3][3], double t[4][3],
 }
 
 /// Given 3D distances between three points and cosines of 3 angles at the apex, calculates
-/// the lentghs of the line segments connecting projection center (P) and the three 3D points (A, B, C).
+/// the lengths of the line segments connecting projection center (P) and the three 3D points (A, B, C).
 /// Returned distances are for |PA|, |PB|, |PC| respectively.
 /// Only the solution to the main branch.
 /// Reference : X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem"

modules/calib3d/src/polynom_solver.cpp

@@ -32,7 +32,7 @@ int solve_deg3(double a, double b, double c, double d,
                double & x0, double & x1, double & x2)
 {
   if (a == 0) {
-    // Solve second order sytem
+    // Solve second order system
     if (b == 0)	{
       // Solve first order system
       if (c == 0)

modules/calib3d/src/rho.cpp

@@ -443,7 +443,7 @@ static inline void   sacSub8x1            (float*       Hout,
 /**
  * External access to context constructor.
  *
- * @return A pointer to the context if successful; NULL if an error occured.
+ * @return A pointer to the context if successful; NULL if an error occurred.
  */
 
 Ptr<RHO_HEST> rhoInit(void){
@@ -1205,7 +1205,7 @@ inline void   RHO_HEST_REFC::PROSACGoToNextPhase(void){
 
 /**
  * Get a sample according to PROSAC rules. Namely:
- * - If we're past the phase end interation, select randomly 4 out of the first
+ * - If we're past the phase end interaction, select randomly 4 out of the first
  *   phNum matches.
  * - Otherwise, select match phNum-1 and select randomly the 3 others out of
  *   the first phNum-1 matches.
@@ -1742,7 +1742,7 @@ inline void   RHO_HEST_REFC::updateBounds(void){
 }
 
 /**
- * Ouput the best model so far to the output argument.
+ * Output the best model so far to the output argument.
  *
  * Reads    (direct): arg.finalH, best.H, arg.inl, best.inl, arg.N
  * Reads   (callees): arg.finalH, arg.inl, arg.N
@@ -1762,7 +1762,7 @@ inline void   RHO_HEST_REFC::outputModel(void){
 }
 
 /**
- * Ouput a zeroed H to the output argument.
+ * Output a zeroed H to the output argument.
  *
  * Reads    (direct): arg.finalH, arg.inl, arg.N
  * Reads   (callees): None.

modules/calib3d/test/test_cameracalibration_tilt.cpp

@@ -80,7 +80,7 @@ class cameraCalibrationTiltTest : public ::testing::Test {
     static const double m_pointTargetDist;
     static const int m_pointTargetNum;
 
-    /** image distance coresponding to working distance */
+    /** image distance corresponding to working distance */
     double m_imageDistance;
     /** image tilt angle corresponding to the tilt of the object plane */
     double m_imageTiltDegree;
@@ -202,7 +202,7 @@ void cameraCalibrationTiltTest::SetUp()
     double nearFarFactorImage[2] = {
         aperture/(aperture - circleConfusion),
         aperture/(aperture + circleConfusion)};
-    // on the object side - points that determin the field of
+    // on the object side - points that determine the field of
     // view
     std::vector<cv::Vec3d> fovBottomTop(6);
     std::vector<cv::Vec3d>::iterator itFov = fovBottomTop.begin();
@@ -552,7 +552,7 @@ void showVec(const std::string& name, const INPUT& in, const cv::Mat& est)
 For given camera matrix and distortion coefficients
 - project point target in different positions onto the sensor
 - add pixel noise
-- estimate camera modell with noisy measurements
+- estimate camera model with noisy measurements
 - compare result with initial model parameter
 
 Parameter are differently affected by the noise
@@ -623,7 +623,7 @@ TEST_F(cameraCalibrationTiltTest, calibrateCamera)
             cv::TermCriteria::COUNT+cv::TermCriteria::EPS,
             50000,
             1e-14);
-        // modell coice
+        // model choice
         int flag =
             cv::CALIB_FIX_ASPECT_RATIO |
             // cv::CALIB_RATIONAL_MODEL |

modules/calib3d/test/test_posit.cpp

@@ -94,7 +94,7 @@ void CV_POSITTest::run( int start_from )
     const float flFocalLength = 760.f;
     const float flEpsilon = 0.5f;
 
-    /* Initilization */
+    /* Initialization */
     criteria.type = CV_TERMCRIT_EPS|CV_TERMCRIT_ITER;
     criteria.epsilon = flEpsilon;
     criteria.max_iter = 10000;