Fix spelling typos

apps/createsamples/utility.cpp

@@ -1078,8 +1078,8 @@ void cvCreateTrainingSamples( const char* filename,
                 icvPlaceDistortedSample( sample, inverse, maxintensitydev,
                     maxxangle, maxyangle, maxzangle,
                     0   /* nonzero means placing image without cut offs */,
-                    0.0 /* nozero adds random shifting                  */,
-                    0.0 /* nozero adds random scaling                   */,
+                    0.0 /* nonzero adds random shifting                  */,
+                    0.0 /* nonzero adds random scaling                   */,
                     &data );
 
                 if( showsamples )

apps/traincascade/HOGfeatures.h

@@ -45,7 +45,7 @@ class CvHOGEvaluator : public CvFeatureEvaluator
     };
     std::vector<Feature> features;
 
-    cv::Mat normSum; //for nomalization calculation (L1 or L2)
+    cv::Mat normSum; //for normalization calculation (L1 or L2)
     std::vector<cv::Mat> hist;
 };
 
@@ -70,7 +70,7 @@ inline float CvHOGEvaluator::Feature::calc( const std::vector<cv::Mat>& _hists,
 
     const float *pnormSum = _normSum.ptr<float>((int)y);
     normFactor = (float)(pnormSum[fastRect[0].p0] - pnormSum[fastRect[1].p1] - pnormSum[fastRect[2].p2] + pnormSum[fastRect[3].p3]);
-    res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which apper due to floating precision
+    res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which appear due to floating precision
 
     return res;
 }

doc/js_tutorials/js_imgproc/js_contours/js_contours_hierarchy/js_contours_hierarchy.markdown

@@ -145,7 +145,7 @@ no child, parent is contour-3. So array is [-1,-1,-1,3].
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
 
-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.
 

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -17,7 +17,7 @@ In short, we found locations of some parts of an object in another cluttered ima
 is sufficient to find the object exactly on the trainImage.
 
 For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set
-of points from both the images, it will find the perpective transformation of that object. Then we
+of points from both the images, it will find the perspective transformation of that object. Then we
 can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to
 find the transformation.
 
@@ -68,7 +68,7 @@ Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are
 find the object. Otherwise simply show a message saying not enough matches are present.
 
 If enough matches are found, we extract the locations of matched keypoints in both the images. They
-are passed to find the perpective transformation. Once we get this 3x3 transformation matrix, we use
+are passed to find the perspective transformation. Once we get this 3x3 transformation matrix, we use
 it to transform the corners of queryImage to corresponding points in trainImage. Then we draw it.
 @code{.py}
 if len(good)>MIN_MATCH_COUNT:

doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown

@@ -28,7 +28,7 @@ If it is a greater than a threshold value, it is considered as a corner. If we p
 ![image](images/shitomasi_space.png)
 
 From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
-\f$\lambda_{min}\f$, it is conidered as a corner(green region).
+\f$\lambda_{min}\f$, it is considered as a corner(green region).
 
 Code
 ----

doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown

@@ -144,7 +144,7 @@ cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
 ### 7.b. Rotated Rectangle
 
 Here, bounding rectangle is drawn with minimum area, so it considers the rotation also. The function
-used is **cv.minAreaRect()**. It returns a Box2D structure which contains following detals - (
+used is **cv.minAreaRect()**. It returns a Box2D structure which contains following details - (
 center (x,y), (width, height), angle of rotation ). But to draw this rectangle, we need 4 corners of
 the rectangle. It is obtained by the function **cv.boxPoints()**
 @code{.py}

doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/py_contours_hierarchy.markdown

@@ -185,7 +185,7 @@ array([[[ 3, -1,  1, -1],
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
 
-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.
 

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -381,7 +381,7 @@ Here is explained in detail the code for the real time application:
     as not, there are false correspondences or also called *outliers*. The [Random Sample
     Consensus](http://en.wikipedia.org/wiki/RANSAC) or *Ransac* is a non-deterministic iterative
     method which estimate parameters of a mathematical model from observed data producing an
-    approximate result as the number of iterations increase. After appyling *Ransac* all the *outliers*
+    approximate result as the number of iterations increase. After applying *Ransac* all the *outliers*
     will be eliminated to then estimate the camera pose with a certain probability to obtain a good
     solution.
 

doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown

@@ -153,7 +153,7 @@ file name before running the application, e.g.:
 
     $ GRAPH_DUMP_PATH=segm.dot ./bin/example_tutorial_porting_anisotropic_image_segmentation_gapi
 
-Now this file can be visalized with a `dot` command like this:
+Now this file can be visualized with a `dot` command like this:
 
     $ dot segm.dot -Tpng -o segm.png
 
@@ -368,7 +368,7 @@ visualization like this:
 
 ![Anisotropic image segmentation graph with OpenCV & Fluid kernels](pics/segm_fluid.gif)
 
-This graph doesn't differ structually from its previous version (in
+This graph doesn't differ structurally from its previous version (in
 terms of operations and data objects), though a changed layout (on the
 left side of the dump) is easily noticeable.
 

doc/tutorials/gapi/face_beautification/face_beautification.markdown

@@ -427,7 +427,7 @@ the ROI, which will lead to accuracy improvement.
 Unfortunately, another problem occurs if we do that:
 if the rectangular ROI is near the border, a describing square will probably go
 out of the frame --- that leads to errors of the landmarks detector.
-To aviod such a mistake, we have to implement an algorithm that, firstly,
+To avoid such a mistake, we have to implement an algorithm that, firstly,
 describes every rectangle by a square, then counts the farthest coordinates
 turned up to be outside of the frame and, finally, pads the source image by
 borders (e.g. single-colored) with the size counted. It will be safe to take

doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown

@@ -145,7 +145,7 @@ description requires three parameters:
    regular "functions" which take and return data. Here network
    `Faces` (a detector) takes a cv::GMat and returns a cv::GMat, while
    network `AgeGender` is known to provide two outputs (age and gender
-   blobs, respecitvely) -- so its has a `std::tuple<>` as a return
+   blobs, respectively) -- so its has a `std::tuple<>` as a return
    type.
 3. A topology name -- can be any non-empty string, G-API is using
    these names to distinguish networks inside. Names should be unique

doc/tutorials/introduction/clojure_dev_intro/clojure_dev_intro.markdown

@@ -499,7 +499,7 @@ using the following OpenCV methods:
 -   the imwrite static method from the Highgui class to write an image to a file
 -   the GaussianBlur static method from the Imgproc class to apply to blur the original image
 
-We're also going to use the Mat class which is returned from the imread method and accpeted as the
+We're also going to use the Mat class which is returned from the imread method and accepted as the
 main argument to both the GaussianBlur and the imwrite methods.
 
 ### Add an image to the project

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown

@@ -10,7 +10,7 @@ In this tutorial,
 -   We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers
 -   We will use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
     will use the functions:
-    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
+    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifier
     -   @ref cv::CascadeClassifier::detectMultiScale to perform the detection.
 
 Theory

doc/tutorials/objdetect/traincascade.markdown

@@ -168,7 +168,7 @@ Command line arguments of opencv_traincascade application grouped by purposes:
   - `-w <sampleWidth>` :  Width of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
   - `-h <sampleHeight>` : Height of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
 
-- Boosted classifer parameters:
+- Boosted classifier parameters:
   - `-bt <{DAB, RAB, LB, GAB(default)}>` : Type of boosted classifiers: DAB - Discrete AdaBoost, RAB - Real AdaBoost, LB - LogitBoost, GAB - Gentle AdaBoost.
   - `-minHitRate <min_hit_rate>` : Minimal desired hit rate for each stage of the classifier. Overall hit rate may be estimated as (min_hit_rate ^ number_of_stages), @cite Viola04 §4.1.
   - `-maxFalseAlarmRate <max_false_alarm_rate>` : Maximal desired false alarm rate for each stage of the classifier. Overall false alarm rate may be estimated as (max_false_alarm_rate ^ number_of_stages), @cite Viola04 §4.1.

doc/tutorials/videoio/kinect_openni.markdown

@@ -43,7 +43,7 @@ VideoCapture can retrieve the following data:
     -   CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)
     -   CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)
     -   CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)
-    -   CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not ocluded, not shaded etc.)
+    -   CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not occluded, not shaded etc.)
         (CV_8UC1)
 
 -#  data given from BGR image generator:

modules/calib3d/include/opencv2/calib3d.hpp

@@ -1321,7 +1321,7 @@ struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters
     GridType gridType;
 
     CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
-    CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING.
+    CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from prediction. Used by CALIB_CB_CLUSTERING.
 };
 
 #ifndef DISABLE_OPENCV_3_COMPATIBILITY

modules/calib3d/src/calibration.cpp

@@ -48,7 +48,7 @@
 #include <iterator>
 
 /*
-    This is stright-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
+    This is straight-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
     that is (in a large extent) based on the paper:
     Z. Zhang. "A flexible new technique for camera calibration".
     IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.

modules/calib3d/src/chessboard.cpp

@@ -2474,7 +2474,7 @@ int Chessboard::Board::validateCorners(const cv::Mat &data,cv::flann::Index &fla
     std::vector<cv::Point2f>::const_iterator iter1 = points.begin();
     for(;iter1 != points.end();++iter1)
     {
-        // we do not have to check for NaN because of getCorners(flase)
+        // we do not have to check for NaN because of getCorners(false)
         std::vector<cv::Point2f>::const_iterator iter2 = iter1+1;
         for(;iter2 != points.end();++iter2)
             if(*iter1 == *iter2)
@@ -3007,7 +3007,7 @@ Chessboard::Board Chessboard::detectImpl(const Mat& gray,std::vector<cv::Mat> &f
     if(keypoints_seed.empty())
         return Chessboard::Board();
 
-    // check how many points are likely a checkerbord corner
+    // check how many points are likely a checkerboard corner
     float response = fabs(keypoints_seed.front().response*MIN_RESPONSE_RATIO);
     std::vector<KeyPoint>::const_iterator seed_iter = keypoints_seed.begin();
     int count = 0;

modules/calib3d/src/chessboard.hpp

@@ -650,7 +650,7 @@ class Chessboard: public cv::Feature2D
                         bool top(bool check_empty=false);     // moves one corner to the top or returns false
                         bool checkCorner()const;              // returns true if the current corner belongs to at least one
                                                               // none empty cell
-                        bool isNaN()const;                    // returns true if the currnet corner is NaN
+                        bool isNaN()const;                    // returns true if the current corner is NaN
 
                         const cv::Point2f* operator*() const;  // current corner coordinate
                         cv::Point2f* operator*();              // current corner coordinate

modules/calib3d/test/test_chesscorners_badarg.cpp

@@ -94,7 +94,7 @@ void CV_ChessboardDetectorBadArgTest::run( int /*start_from */)
     img = cb.clone();
     initArgs();
     pattern_size = Size(2,2);
-    errors += run_test_case( Error::StsOutOfRange, "Invlid pattern size" );
+    errors += run_test_case( Error::StsOutOfRange, "Invalid pattern size" );
     pattern_size = cbg.cornersSize();
 
     cb.convertTo(img, CV_32F);

modules/core/include/opencv2/core/core_c.h

@@ -1309,7 +1309,7 @@ CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order,
                              const CvArr* delta CV_DEFAULT(NULL),
                              double scale CV_DEFAULT(1.) );
 
-/** Tranposes matrix. Square matrices can be transposed in-place */
+/** Transposes matrix. Square matrices can be transposed in-place */
 CVAPI(void)  cvTranspose( const CvArr* src, CvArr* dst );
 #define cvT cvTranspose
 

modules/core/include/opencv2/core/hal/intrin_avx.hpp

@@ -569,7 +569,7 @@ inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b)
 { return v_int64x4(v256_blend<m>(v_uint64x4(a.val), v_uint64x4(b.val)).val); }
 
 // shuffle
-// todo: emluate 64bit
+// todo: emulate 64bit
 #define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin)  \
     template<int m>                                  \
     inline _Tpvec v256_shuffle(const _Tpvec& a)      \

modules/core/include/opencv2/core/hal/intrin_cpp.hpp

@@ -73,7 +73,7 @@ implemented as a structure based on a one SIMD register.
 
 - cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char
 - cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short
-- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int
+- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsigned/signed) - int
 - cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64
 - cv::v_float32x4: four 32-bit floating point values (signed) - float
 - cv::v_float64x2: two 64-bit floating point values (signed) - double

modules/core/include/opencv2/core/hal/intrin_msa.hpp

@@ -1805,7 +1805,7 @@ inline v_float32x4 v_broadcast_element(const v_float32x4& a)
     return v_setall_f32(v_extract_n<i>(a));
 }
 
-////// FP16 suport ///////
+////// FP16 support ///////
 #if CV_FP16
 inline v_float32x4 v_load_expand(const float16_t* ptr)
 {

modules/core/include/opencv2/core/hal/intrin_wasm.hpp

@@ -94,7 +94,7 @@ struct v_uint16x8
     }
     ushort get0() const
     {
-        return (ushort)wasm_i16x8_extract_lane(val, 0);    // wasm_u16x8_extract_lane() unimplemeted yet
+        return (ushort)wasm_i16x8_extract_lane(val, 0);    // wasm_u16x8_extract_lane() unimplemented yet
     }
 
     v128_t val;

modules/core/include/opencv2/core/hal/msa_macros.h

@@ -50,7 +50,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
 #define msa_ld1q_f32(__a) ((v4f32)__builtin_msa_ld_w(__a, 0))
 #define msa_ld1q_f64(__a) ((v2f64)__builtin_msa_ld_d(__a, 0))
 
-/* Store 64bits vector elments values to the given memory address. */
+/* Store 64bits vector elements values to the given memory address. */
 #define msa_st1_s8(__a, __b)  (*((v8i8*)(__a)) = __b)
 #define msa_st1_s16(__a, __b) (*((v4i16*)(__a)) = __b)
 #define msa_st1_s32(__a, __b) (*((v2i32*)(__a)) = __b)
@@ -377,7 +377,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
 })
 
 /* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector.
-   Input is signed and outpus is unsigned. */
+   Input is signed and output is unsigned. */
 #define msa_qrshrun_n_s16(__a, __b) \
 ({ \
   v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__b)); \

modules/core/include/opencv2/core/opencl/opencl_info.hpp

@@ -62,7 +62,7 @@ static String getDeviceTypeString(const cv::ocl::Device& device)
         }
     }
 
-    return "unkown";
+    return "unknown";
 }
 } // namespace
 

modules/core/include/opencv2/core/optim.hpp

@@ -165,7 +165,7 @@ class CV_EXPORTS DownhillSolver : public MinProblemSolver
 
     /** @brief Sets the initial step that will be used in downhill simplex algorithm.
 
-    Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional
+    Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional
     vectors that are used to determine the shape of initial simplex. Roughly said, initial point
     determines the position of a simplex (it will become simplex's centroid), while step determines the
     spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are

modules/core/include/opencv2/core/vsx_utils.hpp

@@ -317,7 +317,7 @@ VSX_IMPL_1RG(vec_udword2, wi, vec_float4,  wf, xvcvspuxds, vec_ctulo)
  * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837
  *
  * So we're not able to use inline asm and only use built-in functions that CLANG supports
- * and use __builtin_convertvector if clang missng any of vector conversions built-in functions
+ * and use __builtin_convertvector if clang missing any of vector conversions built-in functions
  *
  * todo: clang asm template bug is fixed, need to reconsider the current workarounds.
 */
@@ -491,7 +491,7 @@ VSX_IMPL_CONV_EVEN_2_4(vec_uint4,  vec_double2, vec_ctu, vec_ctuo)
 // Only for Eigen!
 /*
  * changing behavior of conversion intrinsics for gcc has effect on Eigen
- * so we redfine old behavior again only on gcc, clang
+ * so we redefine old behavior again only on gcc, clang
 */
 #if !defined(__clang__) || __clang_major__ > 4
     // ignoring second arg since Eigen only truncates toward zero

modules/core/src/array.cpp

@@ -250,7 +250,7 @@ cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes,
     for( int i = dims - 1; i >= 0; i-- )
     {
         if( sizes[i] < 0 )
-            CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
+            CV_Error( CV_StsBadSize, "one of dimension sizes is non-positive" );
         mat->dim[i].size = sizes[i];
         if( step > INT_MAX )
             CV_Error( CV_StsOutOfRange, "The array is too big" );
@@ -545,7 +545,7 @@ cvCreateSparseMat( int dims, const int* sizes, int type )
     for( i = 0; i < dims; i++ )
     {
         if( sizes[i] <= 0 )
-            CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
+            CV_Error( CV_StsBadSize, "one of dimension sizes is non-positive" );
     }
 
     CvSparseMat* arr = (CvSparseMat*)cvAlloc(sizeof(*arr)+MAX(0,dims-CV_MAX_DIM)*sizeof(arr->size[0]));

modules/core/src/convert_scale.simd.hpp

@@ -53,7 +53,7 @@ cvtabs_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
     }
 }
 
-// variant for convrsions 16f <-> ... w/o unrolling
+// variant for conversions 16f <-> ... w/o unrolling
 template<typename _Ts, typename _Td> inline void
 cvtabs1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
              Size size, float a, float b )
@@ -123,7 +123,7 @@ cvt_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
     }
 }
 
-// variant for convrsions 16f <-> ... w/o unrolling
+// variant for conversions 16f <-> ... w/o unrolling
 template<typename _Ts, typename _Td> inline void
 cvt1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
           Size size, float a, float b )

modules/core/src/downhill_simplex.cpp

@@ -77,7 +77,7 @@ Replaced y(1,ndim,0.0) ------> y(1,ndim+1,0.0)
 
 ***********************************************************************************************************************************
 
-The code below was used in tesing the source code.
+The code below was used in testing the source code.
 Created by @SareeAlnaghy
 
 #include <iostream>