Merge pull request #16241 from bwignall:typo

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 @@ protected:
     };
     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 @@ public:
 
     /** @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>

modules/core/src/system.cpp

@@ -1519,7 +1519,7 @@ public:
     {
         TlsAbstraction* tls = getTlsAbstraction();
         if (NULL == tls)
-            return;  // TLS signleton is not available (terminated)
+            return;  // TLS singleton is not available (terminated)
         ThreadData *pTD = tlsValue == NULL ? (ThreadData*)tls->getData() : (ThreadData*)tlsValue;
         if (pTD == NULL)
             return;  // no OpenCV TLS data for this thread
@@ -1610,7 +1610,7 @@ public:
 
         TlsAbstraction* tls = getTlsAbstraction();
         if (NULL == tls)
-            return NULL;  // TLS signleton is not available (terminated)
+            return NULL;  // TLS singleton is not available (terminated)
 
         ThreadData* threadData = (ThreadData*)tls->getData();
         if(threadData && threadData->slots.size() > slotIdx)
@@ -1646,7 +1646,7 @@ public:
 
         TlsAbstraction* tls = getTlsAbstraction();
         if (NULL == tls)
-            return;  // TLS signleton is not available (terminated)
+            return;  // TLS singleton is not available (terminated)
 
         ThreadData* threadData = (ThreadData*)tls->getData();
         if(!threadData)

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -134,7 +134,7 @@ CV__DNN_INLINE_NS_BEGIN
         virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
 
         /** @deprecated Use flag `produce_cell_output` in LayerParams.
-          * @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample.
+          * @brief Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
           *
           * If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
           * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.

modules/dnn/src/cuda/slice.cu

@@ -84,7 +84,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
          * Reasoning:
          * ----------
          * Suppose an item's indices in the output tensor is [o1, o2, ...]. The indices in the input
-         * tensor will be [o1 + off1, o2 + off2, ...]. The rest of the elements in the input are igored.
+         * tensor will be [o1 + off1, o2 + off2, ...]. The rest of the elements in the input are ignored.
          *
          * If the size of the first axis of the input and output tensor is unity, the input and output indices
          * for all the elements will be of the form be [0, o2 + off2, ...] and [0, o2, ...] respectively. Note that

modules/dnn/src/cuda4dnn/csl/cudnn/convolution.hpp

@@ -227,7 +227,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu
                 if (std::is_same<T, half>::value)
                     CUDA4DNN_CHECK_CUDNN(cudnnSetConvolutionMathType(descriptor, CUDNN_TENSOR_OP_MATH));
             } catch (...) {
-                /* cudnnDestroyConvolutionDescriptor will not fail for a valid desriptor object */
+                /* cudnnDestroyConvolutionDescriptor will not fail for a valid descriptor object */
                 CUDA4DNN_CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(descriptor));
                 throw;
             }

modules/dnn/src/cuda4dnn/csl/memory.hpp

@@ -266,7 +266,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
 
         /** page-locks \p size_in_bytes bytes of memory starting from \p ptr_
          *
-         * Pre-conditons:
+         * Pre-conditions:
          * - host memory should be unregistered
          */
         MemoryLockGuard(void* ptr_, std::size_t size_in_bytes) {

modules/dnn/src/cuda4dnn/csl/pointer.hpp

@@ -33,7 +33,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
      *
      * A `DevicePtr<T>` can implicitly convert to `DevicePtr<const T>`.
      *
-     * Specalizations:
+     * Specializations:
      * - DevicePtr<void>/DevicePtr<const void> do not support pointer arithmetic (but relational operators are provided)
      * - any device pointer pointing to mutable memory is implicitly convertible to DevicePtr<void>
      * - any device pointer is implicitly convertible to DevicePtr<const void>

modules/dnn/src/cuda4dnn/csl/tensor.hpp

@@ -67,7 +67,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
      */
     template <class T>
     class Tensor {
-        static_assert(std::is_standard_layout<T>::value, "T must staisfy StandardLayoutType");
+        static_assert(std::is_standard_layout<T>::value, "T must satisfy StandardLayoutType");
 
     public:
         using value_type    = typename ManagedPtr<T>::element_type;
@@ -553,7 +553,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
          * - [start, end) represents a forward range containing the length of the axes in order
          * - the number of axis lengths must be less than or equal to the rank
          * - at most one axis length is allowed for length deduction
-         * - the lengths provided must ensure that the total number of elements remains unchnged
+         * - the lengths provided must ensure that the total number of elements remains unchanged
          *
          * Exception Guarantee: Strong
          */
@@ -898,7 +898,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
          * - [start, end) represents a forward range containing length of the axes in order starting from axis zero
          * - the number of axis lengths must be less than or equal to the tensor rank
          * - at most one axis length is allowed for length deduction
-         * - the lengths provided must ensure that the total number of elements remains unchnged
+         * - the lengths provided must ensure that the total number of elements remains unchanged
          *
          * Exception Guarantee: Strong
          */

modules/dnn/src/cuda4dnn/csl/tensor_ops.hpp

@@ -35,7 +35,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
          * Pre-conditions:
          * - \p dest and \p src must have the same shape
          *
-         * Exception Gaurantee: Basic
+         * Exception Guarantee: Basic
          */
         template <class T> inline
         void copy(const Stream& stream, TensorSpan<T> dest, TensorView<T> src) {
@@ -50,7 +50,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
          * - \p A and \p B must meet the mathematical requirements for matrix multiplication
          * - \p result must be large enough to hold the result
          *
-         * Exception Gaurantee: Basic
+         * Exception Guarantee: Basic
          */
         template <class T> inline
         void gemm(const cublas::Handle& handle, T beta, TensorSpan<T> result, T alpha, bool transa, TensorView<T> A, bool transb, TensorView<T> B) {
@@ -108,7 +108,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
          * Pre-conditions:
          * - \p A and \p result must be compatible tensors
          *
-         * Exception Gaurantee: Basic
+         * Exception Guarantee: Basic
          */
         template <class T> inline
         void softmax(const cudnn::Handle& handle, TensorSpan<T> output, TensorView<T> input, int channel_axis, bool log) {

modules/dnn/src/cuda4dnn/primitives/pooling.hpp

@@ -103,7 +103,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
                 CV_Assert(pooling_order == pads_end.size());
 
                 /* cuDNN rounds down by default; hence, if ceilMode is false, we do nothing
-                 * otherwise, we add extra padding towards the end so that the convolution arithmetic yeilds
+                 * otherwise, we add extra padding towards the end so that the convolution arithmetic yields
                  * the correct output size without having to deal with fancy fractional sizes
                  */
                 auto pads_end_modified = pads_end;

modules/dnn/src/ie_ngraph.cpp

@@ -622,7 +622,7 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
                             try {
                                 wrapper->outProms[processedOutputs].setException(std::current_exception());
                             } catch(...) {
-                                CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
+                                CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
                             }
                         }
                     }
@@ -635,7 +635,7 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
                         try {
                             wrapper->outProms[processedOutputs].setException(e);
                         } catch(...) {
-                            CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
+                            CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
                         }
                     }
                 }

modules/dnn/src/onnx/opencv-onnx.proto

@@ -116,7 +116,7 @@ message AttributeProto {
 
   // The type field MUST be present for this version of the IR.
   // For 0.0.1 versions of the IR, this field was not defined, and
-  // implementations needed to use has_field hueristics to determine
+  // implementations needed to use has_field heuristics to determine
   // which value field was in use.  For IR_VERSION 0.0.2 or later, this
   // field MUST be set and match the f|i|s|t|... field in use.  This
   // change was made to accommodate proto3 implementations.
@@ -323,7 +323,7 @@ message TensorProto {
   // For float and complex64 values
   // Complex64 tensors are encoded as a single array of floats,
   // with the real components appearing in odd numbered positions,
-  // and the corresponding imaginary component apparing in the
+  // and the corresponding imaginary component appearing in the
   // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
   // is encoded as [1.0, 2.0 ,3.0 ,4.0]
   // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
@@ -373,7 +373,7 @@ message TensorProto {
   // For double
   // Complex64 tensors are encoded as a single array of doubles,
   // with the real components appearing in odd numbered positions,
-  // and the corresponding imaginary component apparing in the
+  // and the corresponding imaginary component appearing in the
   // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
   // is encoded as [1.0, 2.0 ,3.0 ,4.0]
   // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128

modules/dnn/src/op_cuda.hpp

@@ -350,7 +350,7 @@ namespace cv { namespace dnn {
 
     private:
         /* The same tensor memory can be reused by different layers whenever possible.
-         * Hence, it is possible for different backend warppers to point to the same memory.
+         * Hence, it is possible for different backend wrappers to point to the same memory.
          * However, it may use only a part of that memory and have a different shape.
          *
          * We store the common information such as device tensor and its corresponding host memory in

modules/dnn/src/vkcom/src/context.cpp

@@ -243,7 +243,7 @@ Context::Context()
     queueCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO;
     queueCreateInfo.queueFamilyIndex = kQueueFamilyIndex;
     queueCreateInfo.queueCount = 1; // create one queue in this family. We don't need more.
-    float queuePriorities = 1.0;  // we only have one queue, so this is not that imporant.
+    float queuePriorities = 1.0;  // we only have one queue, so this is not that important.
     queueCreateInfo.pQueuePriorities = &queuePriorities;
 
     VkDeviceCreateInfo deviceCreateInfo = {};

modules/features2d/include/opencv2/features2d.hpp

@@ -398,7 +398,7 @@ code which is distributed under GPL.
 class CV_EXPORTS_W MSER : public Feature2D
 {
 public:
-    /** @brief Full consturctor for %MSER detector
+    /** @brief Full constructor for %MSER detector
 
     @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
     @param _min_area prune the area which smaller than minArea

modules/features2d/src/kaze/nldiffusion_functions.h

@@ -36,7 +36,7 @@ void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int
 // Nonlinear diffusion filtering scalar step
 void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
 
-// For non-maxima suppresion
+// For non-maxima suppression
 bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
 
 // Image downsampling

modules/features2d/src/mser.cpp

@@ -983,7 +983,7 @@ extractMSER_8uC3( const Mat& src,
                     double s = (double)(lr->size-lr->sizei)/(lr->dt-lr->di);
                     if ( s < lr->s )
                     {
-                        // skip the first one and check stablity
+                        // skip the first one and check stability
                         if ( i > lr->reinit+1 && MSCRStableCheck( lr, params ) )
                         {
                             if ( lr->tmsr == NULL )

modules/flann/include/opencv2/flann/simplex_downhill.h

@@ -131,7 +131,7 @@ float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
         }
 
         if (val_r<vals[0]) {
-            // value is smaller than smalest in simplex
+            // value is smaller than smallest in simplex
 
             // expand some more to see if it drops further
             for (int i=0; i<n; ++i) {

modules/gapi/doc/00-root.markdown

@@ -95,7 +95,7 @@ Internally, cv::GComputation::apply() compiles the captured graph for
 the given input parameters and executes the compiled graph on data
 immediately.
 
-There is a number important concepts can be outlines with this examle:
+There is a number important concepts can be outlines with this example:
 * Graph declaration and graph execution are distinct steps;
 * Graph is built implicitly from a sequence of G-API expressions;
 * G-API supports function-like calls -- e.g. cv::gapi::resize(), and

modules/gapi/doc/01-background.markdown

@@ -36,7 +36,7 @@ software optimization due to diffent costs of memory access on modern
 computer architectures -- the more data is reused in the first level
 cache, the more efficient pipeline is.
 
-Definitely the aforementioned techinques can be applied manually --
+Definitely the aforementioned techniques can be applied manually --
 but it requires extra skills and knowledge of the target platform and
 the algorithm implementation changes irrevocably -- becoming more
 specific, less flexible, and harder to extend and maintain.

modules/gapi/doc/slides/gapi_overview.org

@@ -242,7 +242,7 @@ Graph *protocol* defines what arguments a computation was defined on
 - A type name (every operation is a C++ type);
 - Operation signature (similar to ~std::function<>~);
 - Operation identifier (a string);
-- Metadata callback -- desribe what is the output value format(s),
+- Metadata callback -- describe what is the output value format(s),
   given the input and arguments.
 - Use ~OpType::on(...)~ to use a new kernel ~OpType~ to construct graphs.
 #+LaTeX: {\footnotesize

modules/gapi/include/opencv2/gapi/cpu/gcpukernel.hpp

@@ -124,7 +124,7 @@ protected:
     F m_f;
 };
 
-// FIXME: This is an ugly ad-hoc imlpementation. TODO: refactor
+// FIXME: This is an ugly ad-hoc implementation. TODO: refactor
 
 namespace detail
 {

modules/gapi/include/opencv2/gapi/fluid/gfluidkernel.hpp

@@ -39,7 +39,7 @@ namespace fluid
      */
     GAPI_EXPORTS cv::gapi::GBackend backend();
     /** @} */
-} // namespace flud
+} // namespace fluid
 } // namespace gapi
 
 

modules/gapi/include/opencv2/gapi/gcompiled.hpp

@@ -148,7 +148,7 @@ public:
      * @param outs vector of output cv::Mat objects to produce by the
      * computation.
      *
-     * Numbers of elements in ins/outs vectos must match numbers of
+     * Numbers of elements in ins/outs vectors must match numbers of
      * inputs/outputs which were used to define the source GComputation.
      */
     void operator() (const std::vector<cv::Mat> &ins,            // Compatibility overload

modules/gapi/include/opencv2/gapi/gcomputation.hpp

@@ -314,7 +314,7 @@ public:
      * @param args compilation arguments for underlying compilation
      * process.
      *
-     * Numbers of elements in ins/outs vectos must match numbers of
+     * Numbers of elements in ins/outs vectors must match numbers of
      * inputs/outputs which were used to define this GComputation.
      */
     void apply(const std::vector<cv::Mat>& ins,         // Compatibility overload
@@ -373,7 +373,7 @@ public:
     //     template<typename... Ts>
     //     GCompiled compile(const Ts&... metas, GCompileArgs &&args)
     //
-    // But not all compilers can hande this (and seems they shouldn't be able to).
+    // But not all compilers can handle this (and seems they shouldn't be able to).
     // FIXME: SFINAE looks ugly in the generated documentation
     /**
      * @overload

modules/gapi/include/opencv2/gapi/gtype_traits.hpp

@@ -101,7 +101,7 @@ namespace detail
     template<>           struct GTypeOf<cv::gapi::own::Scalar> { using type = cv::GScalar;   };
     template<typename U> struct GTypeOf<std::vector<U> >       { using type = cv::GArray<U>; };
     // FIXME: This is not quite correct since IStreamSource may produce not only Mat but also Scalar
-    // and vector data. TODO: Extend the type dispatchig on these types too.
+    // and vector data. TODO: Extend the type dispatching on these types too.
     template<>           struct GTypeOf<cv::gapi::wip::IStreamSource::Ptr> { using type = cv::GMat;};
     template<class T> using g_type_of_t = typename GTypeOf<T>::type;
 

modules/gapi/include/opencv2/gapi/ocl/goclkernel.hpp

@@ -94,7 +94,7 @@ protected:
     F m_f;
 };
 
-// FIXME: This is an ugly ad-hoc imlpementation. TODO: refactor
+// FIXME: This is an ugly ad-hoc implementation. TODO: refactor
 
 namespace detail
 {

modules/gapi/include/opencv2/gapi/streaming/cap.hpp

@@ -35,7 +35,7 @@ namespace wip {
  * This class implements IStreamSource interface.
  * Its constructor takes the same parameters as cv::VideoCapture does.
  *
- * Please make sure that videoio OpenCV module is avaiable before using
+ * Please make sure that videoio OpenCV module is available before using
  * this in your application (G-API doesn't depend on it directly).
  *
  * @note stream sources are passed to G-API via shared pointers, so

modules/gapi/samples/draw_example.cpp

@@ -7,7 +7,7 @@
 int main(int argc, char *argv[])
 {
     if (argc < 2) {
-        std::cerr << "Filename requried" << std::endl;
+        std::cerr << "Filename required" << std::endl;
         return 1;
     }
 

modules/gapi/src/api/ft_render.cpp

@@ -61,13 +61,13 @@ cv::Size cv::gapi::wip::draw::FTTextRender::Priv::getTextSize(const std::wstring
     //                                              or decrement (for right-to-left writing) the pen position after a
     //                                              glyph has been rendered when processing text
     //
-    //   widht (bitmap->width)                   - The width of glyph
+    //   width (bitmap->width)                   - The width of glyph
     //
     //
-    //   Algorihm to compute size of the text bounding box:
+    //   Algorithm to compute size of the text bounding box:
     //
     //   1) Go through all symbols and shift pen position and save glyph parameters (left, advance, width)
-    //      If left + pen postion < 0 set left to 0. For example it's maybe happened
+    //      If left + pen position < 0 set left to 0. For example it's maybe happened
     //      if we print first letter 'J' or any other letter with negative 'left'
     //      We want to render glyph in pen position + left, so we must't allow it to be negative
     //

modules/gapi/src/api/render_ocv.cpp

@@ -184,7 +184,7 @@ void drawPrimitivesOCV(cv::Mat& in,
                 cv::Point org(0, mask.rows - baseline);
                 cv::putText(mask, tp.text, org, tp.ff, tp.fs, 255, tp.thick);
 
-                // Org is bottom left point, trasform it to top left point for blendImage
+                // Org is bottom left point, transform it to top left point for blendImage
                 cv::Point tl(tp.org.x, tp.org.y - mask.size().height + baseline);
 
                 blendTextMask(in, mask, tl, tp.color);
@@ -208,7 +208,7 @@ void drawPrimitivesOCV(cv::Mat& in,
                 cv::Point org(0, mask.rows - baseline);
                 ftpr->putText(mask, ftp.text, org, ftp.fh);
 
-                // Org is bottom left point, trasform it to top left point for blendImage
+                // Org is bottom left point, transform it to top left point for blendImage
                 cv::Point tl(ftp.org.x, ftp.org.y - mask.size().height + baseline);
 
                 blendTextMask(in, mask, tl, color);

modules/gapi/src/backends/fluid/gfluidimgproc.cpp

@@ -1823,7 +1823,7 @@ GAPI_FLUID_KERNEL(GFluidBayerGR2RGB, cv::gapi::imgproc::GBayerGR2RGB, false)
     }
 };
 
-} // namespace fliud
+} // namespace fluid
 } // namespace gapi
 } // namespace cv
 

modules/gapi/src/backends/fluid/gfluidimgproc_func.dispatch.cpp

@@ -209,7 +209,7 @@ RUN_MEDBLUR3X3_IMPL( float)
 
 #undef RUN_MEDBLUR3X3_IMPL
 
-} // namespace fliud
+} // namespace fluid
 } // namespace gapi
 } // namespace cv
 

modules/gapi/src/backends/fluid/gfluidutils.hpp

@@ -25,7 +25,7 @@ using cv::gapi::own::rintd;
 
 //--------------------------------
 //
-// Macros for mappig of data types
+// Macros for mapping of data types
 //
 //--------------------------------
 

modules/gapi/src/backends/ie/giebackend.cpp

@@ -185,7 +185,7 @@ struct IEUnit {
         // The practice shows that not all inputs and not all outputs
         // are mandatory to specify in IE model.
         // So what we're concerned here about is:
-        // if opeation's (not topology's) input/output number is
+        // if operation's (not topology's) input/output number is
         // greater than 1, then we do care about input/output layer
         // names. Otherwise, names are picked up automatically.
         // TODO: Probably this check could be done at the API entry point? (gnet)

modules/gapi/src/backends/ocl/goclimgproc.hpp

@@ -15,7 +15,7 @@
 
 namespace cv { namespace gimpl {
 
-// NB: This is what a "Kernel Package" from the origianl Wiki doc should be.
+// NB: This is what a "Kernel Package" from the original Wiki doc should be.
 void loadOCLImgProc(std::map<std::string, cv::GOCLKernel> &kmap);
 
 }}

modules/gapi/src/compiler/passes/islands.cpp

@@ -32,7 +32,7 @@ namespace
         //
         // In this case, Data object is part of Island A if and only if:
         // - Data object's producer is part of Island A,
-        // - AND any of Data obejct's consumers is part of Island A.
+        // - AND any of Data object's consumers is part of Island A.
         //
         //   Op["island0"] --> Data[ ? ] --> Op["island0"]
         //                       :
@@ -147,7 +147,7 @@ void cv::gimpl::passes::checkIslands(ade::passes::PassContext &ctx)
 
         // Run the recursive traversal process as described in 5/a-d.
         // This process is like a flood-fill traversal for island.
-        // If there's to distint successful flood-fills happened for the same island
+        // If there's to distinct successful flood-fills happened for the same island
         // name, there are two islands with this name.
         std::stack<ade::NodeHandle> stack;
         stack.push(tagged_nh);

modules/gapi/src/executor/gstreamingexecutor.cpp

@@ -198,7 +198,7 @@ void sync_data(cv::GRunArgs &results, cv::GRunArgsP &outputs)
 // "Stop" is received.
 //
 // Queue reader is the class which encapsulates all this logic and
-// provies threads with a managed storage and an easy API to obtain
+// provides threads with a managed storage and an easy API to obtain
 // data.
 class QueueReader
 {

modules/gapi/test/common/gapi_core_tests.hpp

@@ -67,7 +67,7 @@ inline std::ostream& operator<<(std::ostream& os, bitwiseOp op)
 // initMatsRandU - function that is used to initialize input/output data
 // FIXTURE_API(mathOp,bool,double,bool) - test-specific parameters (types)
 // 4 - number of test-specific parameters
-// opType, testWithScalar, scale, doReverseOp - test-spcific parameters (names)
+// opType, testWithScalar, scale, doReverseOp - test-specific parameters (names)
 //
 // We get:
 // 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function

modules/gapi/test/common/gapi_core_tests_inl.hpp

@@ -294,7 +294,7 @@ TEST_P(Polar2CartTest, AccuracyTest)
         // expect of single-precision elementary functions implementation.
         //
         // However, good idea is making such threshold configurable: parameter
-        // of this test - which a specific test istantiation could setup.
+        // of this test - which a specific test instantiation could setup.
         //
         // Note that test instantiation for the OpenCV back-end could even let
         // the threshold equal to zero, as CV back-end calls the same kernel.
@@ -340,7 +340,7 @@ TEST_P(Cart2PolarTest, AccuracyTest)
         // expect of single-precision elementary functions implementation.
         //
         // However, good idea is making such threshold configurable: parameter
-        // of this test - which a specific test istantiation could setup.
+        // of this test - which a specific test instantiation could setup.
         //
         // Note that test instantiation for the OpenCV back-end could even let
         // the threshold equal to zero, as CV back-end calls the same kernel.

modules/gapi/test/common/gapi_imgproc_tests.hpp

@@ -19,7 +19,7 @@ namespace opencv_test
 // initMatrixRandN - function that is used to initialize input/output data
 // FIXTURE_API(CompareMats,int,int) - test-specific parameters (types)
 // 3 - number of test-specific parameters
-// cmpF, kernSize, borderType - test-spcific parameters (names)
+// cmpF, kernSize, borderType - test-specific parameters (names)
 //
 // We get:
 // 1. Default parameters: int type, cv::Size sz, int dtype, getCompileArgs() function

modules/gapi/test/gapi_async_test.cpp

@@ -426,7 +426,7 @@ struct output_args_lifetime : ::testing::Test{
     static constexpr const int num_of_requests = 20;
 };
 TYPED_TEST_CASE_P(output_args_lifetime);
-//There are intentionaly no actual checks (asserts and verify) in output_args_lifetime tests.
+//There are intentionally no actual checks (asserts and verify) in output_args_lifetime tests.
 //They are more of example use-cases than real tests. (ASAN/valgrind can still catch issues here)
 TYPED_TEST_P(output_args_lifetime, callback){
 

modules/gapi/test/gapi_smoke_test.cpp

@@ -64,7 +64,7 @@ TEST(GAPI, Mat_Recreate)
     EXPECT_EQ(m3.at<uchar>(0, 0), m4.at<uchar>(0, 0));
 
     // cv::Mat::create must be NOOP if we don't change the meta,
-    // even if the origianl mat is created from handle.
+    // even if the original mat is created from handle.
     m4.create(3, 3, CV_8U);
     EXPECT_EQ(m3.rows, m4.rows);
     EXPECT_EQ(m3.cols, m4.cols);

modules/imgproc/include/opencv2/imgproc/imgproc_c.h

@@ -1151,7 +1151,7 @@ CVAPI(CvScalar)  cvColorToScalar( double packed_color, int arrtype );
 /** @brief Returns the polygon points which make up the given ellipse.
 
 The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
-sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than
+sweep of the ellipse arc can be done by specifying arc_start and arc_end to be something other than
 0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
 number of points stored into 'pts' is returned by this function.
 @see cv::ellipse2Poly

modules/imgproc/src/approx.cpp

@@ -630,7 +630,7 @@ approxPolyDP_( const Point_<T>* src_contour, int count0, Point_<T>* dst_contour,
         WRITE_PT( src_contour[count-1] );
 
     // last stage: do final clean-up of the approximated contour -
-    // remove extra points on the [almost] stright lines.
+    // remove extra points on the [almost] straight lines.
     is_closed = is_closed0;
     count = new_count;
     pos = is_closed ? count - 1 : 0;

modules/imgproc/src/shapedescr.cpp

@@ -776,7 +776,7 @@ cv::RotatedRect cv::fitEllipseDirect( InputArray _points )
 namespace cv
 {
 
-// Calculates bounding rectagnle of a point set or retrieves already calculated
+// Calculates bounding rectangle of a point set or retrieves already calculated
 static Rect pointSetBoundingRect( const Mat& points )
 {
     int npoints = points.checkVector(2);
@@ -1392,7 +1392,7 @@ cvFitEllipse2( const CvArr* array )
     return cvBox2D(cv::fitEllipse(points));
 }
 
-/* Calculates bounding rectagnle of a point set or retrieves already calculated */
+/* Calculates bounding rectangle of a point set or retrieves already calculated */
 CV_IMPL  CvRect
 cvBoundingRect( CvArr* array, int update )
 {

modules/imgproc/test/test_approxpoly.cpp

@@ -325,7 +325,7 @@ void CV_ApproxPolyTest::run( int /*start_from*/ )
             if( DstSeq == NULL )
             {
                 ts->printf( cvtest::TS::LOG,
-                    "cvApproxPoly returned NULL for contour #%d, espilon = %g\n", i, Eps );
+                    "cvApproxPoly returned NULL for contour #%d, epsilon = %g\n", i, Eps );
                 code = cvtest::TS::FAIL_INVALID_OUTPUT;
                 goto _exit_;
             } // if( DstSeq == NULL )

modules/imgproc/test/test_intersection.cpp

@@ -60,7 +60,7 @@ namespace opencv_test { namespace {
 // 6 - partial intersection, rectangle on top of different size
 // 7 - full intersection, rectangle fully enclosed in the other
 // 8 - partial intersection, rectangle corner just touching. point contact
-// 9 - partial intersetion. rectangle side by side, line contact
+// 9 - partial intersection. rectangle side by side, line contact
 
 static void compare(const std::vector<Point2f>& test, const std::vector<Point2f>& target)
 {

modules/java/android_sdk/CMakeLists.txt

@@ -44,7 +44,7 @@ foreach(file ${seed_project_files_rel})
 endforeach()
 
 list(APPEND depends gen_opencv_java_source "${OPENCV_DEPHELPER}/gen_opencv_java_source")
-ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Andoid SDK) source files" ${depends})
+ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Android SDK) source files" ${depends})
 file(REMOVE "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")  # force rebuild after CMake run
 
 set(depends ${the_module}_android_source_copy "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")
@@ -134,7 +134,7 @@ foreach(file ${__files_rel})
 endforeach()
 
 list(APPEND depends gen_opencv_java_source "${OPENCV_DEPHELPER}/gen_opencv_java_source")
-ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Andoid SDK) source files" ${depends})
+ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Android SDK) source files" ${depends})
 file(REMOVE "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")  # force rebuild after CMake run
 
 set(depends ${the_module}_android_source_copy "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")

modules/java/generator/android/java/org/opencv/android/CameraBridgeViewBase.java

@@ -248,7 +248,7 @@ public abstract class CameraBridgeViewBase extends SurfaceView implements Surfac
 
     /**
      * This method is provided for clients, so they can disable camera connection and stop
-     * the delivery of frames even though the surface view itself is not destroyed and still stays on the scren
+     * the delivery of frames even though the surface view itself is not destroyed and still stays on the screen
      */
     public void disableView() {
         synchronized(mSyncObject) {

modules/js/perf/README.md

@@ -32,4 +32,4 @@ To run performance tests, please launch a local web server in <build_dir>/bin fo
 
 Navigate the web browser to the kernel page you want to test, like http://localhost:8080/perf/imgproc/cvtcolor.html.
 
-You can input the paramater, and then click the `Run` button to run the specific case, or it will run all the cases.
+You can input the parameter, and then click the `Run` button to run the specific case, or it will run all the cases.

modules/ml/include/opencv2/ml.hpp

@@ -1683,7 +1683,7 @@ public:
 
     /** @brief This function returns the trained parameters arranged across rows.
 
-    For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of
+    For a two class classification problem, it returns a row matrix. It returns learnt parameters of
     the Logistic Regression as a matrix of type CV_32F.
      */
     CV_WRAP virtual Mat get_learnt_thetas() const = 0;

modules/python/test/test_algorithm_rw.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-"""Algorithm serializaion test."""
+"""Algorithm serialization test."""
 import tempfile
 import os
 import cv2 as cv

modules/python/test/test_cuda.py

@@ -181,7 +181,7 @@ class cuda_test(NewOpenCVTests):
             self.assertTrue('GpuMat' in str(type(gpu_mat)), msg=type(gpu_mat))
             #TODO: print(cv.utils.dumpInputArray(gpu_mat)) # - no support for GpuMat
 
-            # not checking output, therefore sepearate tests for different signatures is unecessary
+            # not checking output, therefore sepearate tests for different signatures is unnecessary
             ret, _gpu_mat2 = reader.nextFrame(gpu_mat)
             #TODO: self.assertTrue(gpu_mat == gpu_mat2)
             self.assertTrue(ret)

modules/python/test/test_persistence.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-""""Core serializaion tests."""
+""""Core serialization tests."""
 import tempfile
 import os
 import cv2 as cv

modules/stitching/include/opencv2/stitching/detail/matchers.hpp

@@ -215,14 +215,14 @@ finds two best matches for each feature and leaves the best one only if the
 ratio between descriptor distances is greater than the threshold match_conf.
 
 Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
-transformation (affine trasformation estimate will be placed in matches_info).
+transformation (affine transformation estimate will be placed in matches_info).
 
 @sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
  */
 class CV_EXPORTS_W AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
 {
 public:
-    /** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
+    /** @brief Constructs a "best of 2 nearest" matcher that expects affine transformation
     between images
 
     @param full_affine whether to use full affine transformation with 6 degress of freedom or reduced

modules/ts/include/opencv2/ts/ts_gtest.h

@@ -11367,7 +11367,7 @@ void UniversalTersePrint(const T& value, ::std::ostream* os) {
 // NUL-terminated string.
 template <typename T>
 void UniversalPrint(const T& value, ::std::ostream* os) {
-  // A workarond for the bug in VC++ 7.1 that prevents us from instantiating
+  // A workaround for the bug in VC++ 7.1 that prevents us from instantiating
   // UniversalPrinter with T directly.
   typedef T T1;
   UniversalPrinter<T1>::Print(value, os);

modules/ts/misc/run_android.py

@@ -94,11 +94,11 @@ class Aapt(Tool):
         # get test instrumentation info
         instrumentation_tag = [t for t in tags if t.startswith("instrumentation ")]
         if not instrumentation_tag:
-            raise Err("Can not find instrumentation detials in: %s", exe)
+            raise Err("Can not find instrumentation details in: %s", exe)
         res.pkg_runner = re.search(r"^[ ]+A: android:name\(0x[0-9a-f]{8}\)=\"(?P<runner>.*?)\" \(Raw: \"(?P=runner)\"\)\r?$", instrumentation_tag[0], flags=re.MULTILINE).group("runner")
         res.pkg_target = re.search(r"^[ ]+A: android:targetPackage\(0x[0-9a-f]{8}\)=\"(?P<pkg>.*?)\" \(Raw: \"(?P=pkg)\"\)\r?$", instrumentation_tag[0], flags=re.MULTILINE).group("pkg")
         if not res.pkg_name or not res.pkg_runner or not res.pkg_target:
-            raise Err("Can not find instrumentation detials in: %s", exe)
+            raise Err("Can not find instrumentation details in: %s", exe)
         return res
 
 

modules/ts/src/ts.cpp

@@ -452,7 +452,7 @@ int BadArgTest::run_test_case( int expected_code, const string& _descr )
     {
         thrown = true;
         if (e.code != expected_code &&
-            e.code != cv::Error::StsError && e.code != cv::Error::StsAssert  // Exact error codes support will be dropped. Checks should provide proper text messages intead.
+            e.code != cv::Error::StsError && e.code != cv::Error::StsAssert  // Exact error codes support will be dropped. Checks should provide proper text messages instead.
         )
         {
             ts->printf(TS::LOG, "%s (test case #%d): the error code %d is different from the expected %d\n",

modules/video/src/bgfg_KNN.cpp

@@ -110,7 +110,7 @@ public:
     //set parameters
     // N - the number of samples stored in memory per model
     nN = defaultNsamples;
-    //kNN - k nearest neighbour - number on NN for detcting background - default K=[0.1*nN]
+    //kNN - k nearest neighbour - number on NN for detecting background - default K=[0.1*nN]
     nkNN=MAX(1,cvRound(0.1*nN*3+0.40));
 
     //Tb - Threshold Tb*kernelwidth
@@ -292,7 +292,7 @@ protected:
     //less important parameters - things you might change but be careful
     ////////////////////////
     int nN;//totlal number of samples
-    int nkNN;//number on NN for detcting background - default K=[0.1*nN]
+    int nkNN;//number on NN for detecting background - default K=[0.1*nN]
 
     //shadow detection parameters
     bool bShadowDetection;//default 1 - do shadow detection

modules/video/src/bgfg_gaussmix2.cpp

@@ -181,7 +181,7 @@ public:
     //! computes a background image which are the mean of all background gaussians
     virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
 
-    //! re-initiaization method
+    //! re-initialization method
     void initialize(Size _frameSize, int _frameType)
     {
         frameSize = _frameSize;

modules/videoio/include/opencv2/videoio/legacy/constants_c.h

@@ -225,8 +225,8 @@ enum
     CV_CAP_PROP_XI_COOLING                                      = 466, // Start camera cooling.
     CV_CAP_PROP_XI_TARGET_TEMP                                  = 467, // Set sensor target temperature for cooling.
     CV_CAP_PROP_XI_CHIP_TEMP                                    = 468, // Camera sensor temperature
-    CV_CAP_PROP_XI_HOUS_TEMP                                    = 469, // Camera housing tepmerature
-    CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP                          = 590, // Camera housing back side tepmerature
+    CV_CAP_PROP_XI_HOUS_TEMP                                    = 469, // Camera housing temperature
+    CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP                          = 590, // Camera housing back side temperature
     CV_CAP_PROP_XI_SENSOR_BOARD_TEMP                            = 596, // Camera sensor board temperature
     CV_CAP_PROP_XI_CMS                                          = 470, // Mode of color management system.
     CV_CAP_PROP_XI_APPLY_CMS                                    = 471, // Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).

modules/videoio/src/cap_aravis.cpp

@@ -300,7 +300,7 @@ bool CvCaptureCAM_Aravis::grabFrame()
             size_t buffer_size;
             framebuffer = (void*)arv_buffer_get_data (arv_buffer, &buffer_size);
 
-            // retrieve image size properites
+            // retrieve image size properties
             arv_buffer_get_image_region (arv_buffer, &xoffset, &yoffset, &width, &height);
 
             // retrieve image ID set by camera

modules/videoio/src/cap_avfoundation.mm

@@ -1298,7 +1298,7 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
             colorSpace, kCGImageAlphaLast|kCGBitmapByteOrderDefault,
             provider, NULL, false, kCGRenderingIntentDefault);
 
-    //CGImage -> CVPixelBufferRef coversion
+    //CGImage -> CVPixelBufferRef conversion
     CVPixelBufferRef pixelBuffer = NULL;
     CFDataRef cfData = CGDataProviderCopyData(CGImageGetDataProvider(cgImage));
     int status = CVPixelBufferCreateWithBytes(NULL,

modules/videoio/src/cap_avfoundation_mac.mm

@@ -814,7 +814,7 @@ bool CvCaptureFile::setupReadingAt(CMTime position) {
     if (mMode == CV_CAP_MODE_BGR || mMode == CV_CAP_MODE_RGB) {
         // For CV_CAP_MODE_BGR, read frames as BGRA (AV Foundation's YUV->RGB conversion is slightly faster than OpenCV's CV_YUV2BGR_YV12)
         // kCVPixelFormatType_32ABGR is reportedly faster on OS X, but OpenCV doesn't have a CV_ABGR2BGR conversion.
-        // kCVPixelFormatType_24RGB is significanly slower than kCVPixelFormatType_32BGRA.
+        // kCVPixelFormatType_24RGB is significantly slower than kCVPixelFormatType_32BGRA.
         pixelFormat = kCVPixelFormatType_32BGRA;
         mFormat = CV_8UC3;
     } else if (mMode == CV_CAP_MODE_GRAY) {
@@ -1332,7 +1332,7 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
             colorSpace, kCGImageAlphaLast|kCGBitmapByteOrderDefault,
             provider, NULL, false, kCGRenderingIntentDefault);
 
-    //CGImage -> CVPixelBufferRef coversion
+    //CGImage -> CVPixelBufferRef conversion
     CVPixelBufferRef pixelBuffer = NULL;
     CFDataRef cfData = CGDataProviderCopyData(CGImageGetDataProvider(cgImage));
     int status = CVPixelBufferCreateWithBytes(NULL,

modules/videoio/src/cap_gstreamer.cpp

@@ -953,7 +953,7 @@ bool GStreamerCapture::open(const String &filename_)
  * \return property value
  *
  * There are two ways the properties can be retrieved. For seek-based properties we can query the pipeline.
- * For frame-based properties, we use the caps of the lasst receivef sample. This means that some properties
+ * For frame-based properties, we use the caps of the last receivef sample. This means that some properties
  * are not available until a first frame was received
  */
 double GStreamerCapture::getProperty(int propId) const

platforms/ios/cmake/Modules/Platform/iOS.cmake

@@ -46,7 +46,7 @@ if (APPLE_FRAMEWORK AND BUILD_SHARED_LIBS)
   set (CMAKE_INSTALL_NAME_DIR "@rpath")
 endif()
 
-# Hidden visibilty is required for cxx on iOS
+# Hidden visibility is required for cxx on iOS
 set (no_warn "-Wno-unused-function -Wno-overloaded-virtual")
 set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${no_warn}")
 set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libc++ -fvisibility=hidden -fvisibility-inlines-hidden ${no_warn}")

platforms/linux/mips.toolchain.cmake

@@ -4,7 +4,7 @@
 #  Toolchains with 'img' in the name are for MIPS R6 instruction sets.
 #  It is recommended to use cmake-gui application for build scripts configuration and generation:
 #  1. Run cmake-gui
-#  2. Specifiy toolchain file for cross-compiling, mips32r5el-gnu.toolchian.cmake or mips64r6el-gnu.toolchain.cmake
+#  2. Specify toolchain file for cross-compiling, mips32r5el-gnu.toolchian.cmake or mips64r6el-gnu.toolchain.cmake
 #     can be selected.
 #  3. Configure and Generate makefiles.
 #  4. make -j4 & make install

platforms/linux/mips32r5el-gnu.toolchain.cmake

@@ -4,7 +4,7 @@
 #  Toolchains with 'img' in the name are for MIPS R6 instruction sets.
 #  It is recommended to use cmake-gui for build scripts configuration and generation:
 #  1. Run cmake-gui
-#  2. Specifiy toolchain file mips32r5el-gnu.toolchian.cmake for cross-compiling.
+#  2. Specify toolchain file mips32r5el-gnu.toolchian.cmake for cross-compiling.
 #  3. Configure and Generate makefiles.
 #  4. make -j4 & make install
 # ----------------------------------------------------------------------------------------------