Merge pull request #10843 from luzpaz:misc-modules-typos

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

@@ -1788,7 +1788,7 @@ CVAPI(int)  cvGraphRemoveVtx( CvGraph* graph, int index );
 CVAPI(int)  cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx );
 
 
-/** Link two vertices specifed by indices or pointers if they
+/** Link two vertices specified by indices or pointers if they
    are not connected or return pointer to already existing edge
    connecting the vertices.
    Functions return 1 if a new edge was created, 0 otherwise */
@@ -2648,7 +2648,7 @@ CVAPI(void) cvSetErrStatus( int status );
 #define CV_ErrModeParent   1   /* Print error and continue */
 #define CV_ErrModeSilent   2   /* Don't print and continue */
 
-/** Retrives current error processing mode */
+/** Retrieves current error processing mode */
 CVAPI(int)  cvGetErrMode( void );
 
 /** Sets error processing mode, returns previously used mode */
@@ -2738,7 +2738,7 @@ static char cvFuncName[] = Name
 /**
  CV_CALL macro calls CV (or IPL) function, checks error status and
  signals a error if the function failed. Useful in "parent node"
- error procesing mode
+ error processing mode
  */
 #define CV_CALL( Func )                                             \
 {                                                                   \

modules/core/include/opencv2/core/cuda.hpp

@@ -56,7 +56,7 @@
   @{
     @defgroup cudacore Core part
     @{
-      @defgroup cudacore_init Initalization and Information
+      @defgroup cudacore_init Initialization and Information
       @defgroup cudacore_struct Data Structures
     @}
   @}

modules/core/include/opencv2/core/mat.hpp

@@ -2184,7 +2184,7 @@ public:
     Mat_(int _ndims, const int* _sizes);
     //! n-dim array constructor that sets each matrix element to specified value
     Mat_(int _ndims, const int* _sizes, const _Tp& value);
-    //! copy/conversion contructor. If m is of different type, it's converted
+    //! copy/conversion constructor. If m is of different type, it's converted
     Mat_(const Mat& m);
     //! copy constructor
     Mat_(const Mat_& m);
@@ -2275,7 +2275,7 @@ public:
     static MatExpr eye(int rows, int cols);
     static MatExpr eye(Size size);
 
-    //! some more overriden methods
+    //! some more overridden methods
     Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright );
     Mat_ operator()( const Range& rowRange, const Range& colRange ) const;
     Mat_ operator()( const Rect& roi ) const;
@@ -2943,7 +2943,7 @@ public:
 
     //! the default constructor
     SparseMat_();
-    //! the full constructor equivelent to SparseMat(dims, _sizes, DataType<_Tp>::type)
+    //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type)
     SparseMat_(int dims, const int* _sizes);
     //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted
     SparseMat_(const SparseMat& m);

modules/core/include/opencv2/core/matx.hpp

@@ -92,7 +92,7 @@ Except of the plain constructor which takes a list of elements, Matx can be init
     float values[] = { 1, 2, 3};
     Matx31f m(values);
 @endcode
-In case if C++11 features are avaliable, std::initializer_list can be also used to initialize Matx:
+In case if C++11 features are available, std::initializer_list can be also used to initialize Matx:
 @code{.cpp}
     Matx31f m = { 1, 2, 3};
 @endcode

modules/core/include/opencv2/core/opengl.hpp

@@ -245,7 +245,7 @@ public:
 
     /** @brief Maps OpenGL buffer to CUDA device memory.
 
-    This operatation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time.
+    This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time.
 
     A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used.
      */

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

@@ -115,7 +115,7 @@ public:
     always sensible) will be used.
 
     @param x The initial point, that will become a centroid of an initial simplex. After the algorithm
-    will terminate, it will be setted to the point where the algorithm stops, the point of possible
+    will terminate, it will be set to the point where the algorithm stops, the point of possible
     minimum.
     @return The value of a function at the point found.
      */
@@ -288,7 +288,7 @@ Bland's rule <http://en.wikipedia.org/wiki/Bland%27s_rule> is used to prevent cy
 contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted,
 in the latter case it is understood to correspond to \f$c^T\f$.
 @param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above
-and the remaining to \f$A\f$. It should containt 32- or 64-bit floating point numbers.
+and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
 @param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the
 formulation above. It will contain 64-bit floating point numbers.
 @return One of cv::SolveLPResult

modules/core/include/opencv2/core/softfloat.hpp

@@ -82,7 +82,7 @@ namespace cv
   Both types support the following:
   - Construction from signed and unsigned 32-bit and 64 integers,
   float/double or raw binary representation
-  - Conversions betweeen each other, to float or double and to int
+  - Conversions between each other, to float or double and to int
   using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of
   saturate_cast functions
   - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision

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

@@ -555,7 +555,7 @@ VSX_IMPL_CONV_EVEN_2_4(vec_uint4,  vec_double2, vec_ctu, vec_ctuo)
 // vector population count
 #define vec_popcntu vec_popcnt
 
-// overload and redirect wih setting second arg to zero
+// overload and redirect with setting second arg to zero
 // since we only support conversions without the second arg
 #define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \
 VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); }
@@ -610,7 +610,7 @@ VSX_IMPL_CONV_ODD_2_4(vec_uint4,  vec_double2, vec_ctuo, vec_ctu)
 
 #endif // XLC VSX compatibility
 
-// ignore GCC warning that casued by -Wunused-but-set-variable in rare cases
+// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases
 #if defined(__GNUG__) && !defined(__clang__)
 #   define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__))
 #else // CLANG, XLC
@@ -736,7 +736,7 @@ VSX_IMPL_LOAD_L8(vec_double2, double)
 #   define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b))
 #endif
 
-// absoulte difference
+// absolute difference
 #ifndef vec_absd
 #   define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b))
 #endif

modules/core/include/opencv2/core/wimage.hpp

@@ -289,7 +289,7 @@ protected:
 };
 
 /** Image class which owns the data, so it can be allocated and is always
-freed.  It cannot be copied but can be explicity cloned.
+freed.  It cannot be copied but can be explicitly cloned.
 */
 template<typename T>
 class WImageBuffer : public WImage<T>

modules/core/src/array.cpp

@@ -1914,7 +1914,7 @@ cvPtrND( const CvArr* arr, const int* idx, int* _type,
 }
 
 
-// Returns specifed element of n-D array given linear index
+// Returns specified element of n-D array given linear index
 CV_IMPL  CvScalar
 cvGet1D( const CvArr* arr, int idx )
 {
@@ -1949,7 +1949,7 @@ cvGet1D( const CvArr* arr, int idx )
 }
 
 
-// Returns specifed element of 2D array
+// Returns specified element of 2D array
 CV_IMPL  CvScalar
 cvGet2D( const CvArr* arr, int y, int x )
 {
@@ -1983,7 +1983,7 @@ cvGet2D( const CvArr* arr, int y, int x )
 }
 
 
-// Returns specifed element of 3D array
+// Returns specified element of 3D array
 CV_IMPL  CvScalar
 cvGet3D( const CvArr* arr, int z, int y, int x )
 {
@@ -2005,7 +2005,7 @@ cvGet3D( const CvArr* arr, int z, int y, int x )
 }
 
 
-// Returns specifed element of nD array
+// Returns specified element of nD array
 CV_IMPL  CvScalar
 cvGetND( const CvArr* arr, const int* idx )
 {
@@ -2025,7 +2025,7 @@ cvGetND( const CvArr* arr, const int* idx )
 }
 
 
-// Returns specifed element of n-D array given linear index
+// Returns specified element of n-D array given linear index
 CV_IMPL  double
 cvGetReal1D( const CvArr* arr, int idx )
 {
@@ -2064,7 +2064,7 @@ cvGetReal1D( const CvArr* arr, int idx )
 }
 
 
-// Returns specifed element of 2D array
+// Returns specified element of 2D array
 CV_IMPL  double
 cvGetReal2D( const CvArr* arr, int y, int x )
 {
@@ -2103,7 +2103,7 @@ cvGetReal2D( const CvArr* arr, int y, int x )
 }
 
 
-// Returns specifed element of 3D array
+// Returns specified element of 3D array
 CV_IMPL  double
 cvGetReal3D( const CvArr* arr, int z, int y, int x )
 {
@@ -2131,7 +2131,7 @@ cvGetReal3D( const CvArr* arr, int z, int y, int x )
 }
 
 
-// Returns specifed element of nD array
+// Returns specified element of nD array
 CV_IMPL  double
 cvGetRealND( const CvArr* arr, const int* idx )
 {
@@ -2156,7 +2156,7 @@ cvGetRealND( const CvArr* arr, const int* idx )
 }
 
 
-// Assigns new value to specifed element of nD array given linear index
+// Assigns new value to specified element of nD array given linear index
 CV_IMPL  void
 cvSet1D( CvArr* arr, int idx, CvScalar scalar )
 {
@@ -2187,7 +2187,7 @@ cvSet1D( CvArr* arr, int idx, CvScalar scalar )
 }
 
 
-// Assigns new value to specifed element of 2D array
+// Assigns new value to specified element of 2D array
 CV_IMPL  void
 cvSet2D( CvArr* arr, int y, int x, CvScalar scalar )
 {
@@ -2216,7 +2216,7 @@ cvSet2D( CvArr* arr, int y, int x, CvScalar scalar )
 }
 
 
-// Assigns new value to specifed element of 3D array
+// Assigns new value to specified element of 3D array
 CV_IMPL  void
 cvSet3D( CvArr* arr, int z, int y, int x, CvScalar scalar )
 {
@@ -2234,7 +2234,7 @@ cvSet3D( CvArr* arr, int z, int y, int x, CvScalar scalar )
 }
 
 
-// Assigns new value to specifed element of nD array
+// Assigns new value to specified element of nD array
 CV_IMPL  void
 cvSetND( CvArr* arr, const int* idx, CvScalar scalar )
 {

modules/core/src/conjugate_gradient.cpp

@@ -150,7 +150,7 @@ namespace cv
         d*=-1.0;
         d.copyTo(r);
 
-        //here everything goes. check that everything is setted properly
+        //here everything goes. check that everything is set properly
         dprintf(("proxy_x\n"));print_matrix(proxy_x);
         dprintf(("d first time\n"));print_matrix(d);
         dprintf(("r\n"));print_matrix(r);

modules/core/src/cuda_info.cpp

@@ -932,7 +932,7 @@ namespace
     {
         // Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
         typedef struct {
-            int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
+            int SM; // 0xMm (hexadecimal notation), M = SM Major version, and m = SM minor version
             int Cores;
         } SMtoCores;
 

modules/core/src/downhill_simplex.cpp

@@ -129,7 +129,7 @@ system("pause");
 return 0;
 }
 
-****Suggesttion for imporving Simplex implentation***************************************************************************************
+****Suggestion for improving Simplex implementation***************************************************************************************
 
 Currently the downhilll simplex method outputs the function value that is minimized. It should also return the coordinate points where the
 function is minimized. This is very useful in many applications such as using back projection methods to find a point of intersection of

modules/core/src/opengl.cpp

@@ -1630,7 +1630,7 @@ Context& initializeContextFromGL()
 
     for (int i = 0; i < (int)numPlatforms; i++)
     {
-        // query platform extension: presence of "cl_khr_gl_sharing" extension is requred
+        // query platform extension: presence of "cl_khr_gl_sharing" extension is required
         {
             AutoBuffer<char> extensionStr;
 

modules/core/src/parallel_impl.cpp

@@ -730,7 +730,7 @@ void ThreadPool::setNumOfThreads(unsigned n)
     {
         num_threads = n;
         if (n == 1)
-           if (job == NULL) reconfigure(0);  // stop worker threads immediatelly
+           if (job == NULL) reconfigure(0);  // stop worker threads immediately
     }
 }
 

modules/core/src/persistence_base64.cpp

@@ -470,7 +470,7 @@ public:
 
     /*
      * a convertor must provide :
-     * - `operator >> (uchar * & dst)` for writting current binary data to `dst` and moving to next data.
+     * - `operator >> (uchar * & dst)` for writing current binary data to `dst` and moving to next data.
      * - `operator bool` for checking if current loaction is valid and not the end.
      */
     template<typename _to_binary_convertor_t> inline
@@ -493,7 +493,7 @@ public:
 
     bool flush()
     {
-        /* controll line width, so on. */
+        /* control line width, so on. */
         size_t len = base64_encode(src_beg, base64_buffer.data(), 0U, src_cur - src_beg);
         if (len == 0U)
             return false;

modules/core/src/persistence_c.cpp

@@ -259,7 +259,7 @@ cvOpenFileStorage( const char* query, CvMemStorage* dststorage, int flags, const
                 xml_buf_size = MIN(xml_buf_size, int(file_size));
                 fseek( fs->file, -xml_buf_size, SEEK_END );
                 char* xml_buf = (char*)cvAlloc( xml_buf_size+2 );
-                // find the last occurence of </opencv_storage>
+                // find the last occurrence of </opencv_storage>
                 for(;;)
                 {
                     int line_offset = (int)ftell( fs->file );

modules/core/src/persistence_types.cpp

@@ -1230,7 +1230,7 @@ static void* icvReadGraph( CvFileStorage* fs, CvFileNode* node )
                     vtx_buf[vtx1], vtx_buf[vtx2], 0, &edge );
 
                 if( result == 0 )
-                    CV_Error( CV_StsBadArg, "Duplicated edge has occured" );
+                    CV_Error( CV_StsBadArg, "Duplicated edge has occurred" );
 
                 edge->weight = *(float*)(dst_ptr + sizeof(int)*2);
                 if( elem_size > (int)sizeof(CvGraphEdge) )

modules/core/src/system.cpp

@@ -481,7 +481,7 @@ struct HWFeatures
         have[CV_CPU_NEON] = (features & ANDROID_CPU_ARM_FEATURE_NEON) != 0;
         have[CV_CPU_FP16] = (features & ANDROID_CPU_ARM_FEATURE_VFP_FP16) != 0;
       #else
-        __android_log_print(ANDROID_LOG_INFO, "OpenCV", "cpufeatures library is not avaialble for CPU detection");
+        __android_log_print(ANDROID_LOG_INFO, "OpenCV", "cpufeatures library is not available for CPU detection");
         #if CV_NEON
         __android_log_print(ANDROID_LOG_INFO, "OpenCV", "- NEON instructions is enabled via build flags");
         have[CV_CPU_NEON] = true;

modules/core/test/ocl/test_dft.cpp

@@ -112,7 +112,7 @@ OCL_TEST_P(Dft, Mat)
     OCL_OFF(cv::dft(src, dst, dft_flags, nonzero_rows));
     OCL_ON(cv::dft(usrc, udst, dft_flags, nonzero_rows));
 
-    // In case forward R2C 1d tranform dst contains only half of output
+    // In case forward R2C 1d transform dst contains only half of output
     // without complex conjugate
     if (dft_type == R2C && is1d && (dft_flags & cv::DFT_INVERSE) == 0)
     {

modules/core/test/test_downhill_simplex.cpp

@@ -51,7 +51,7 @@ static void mytest(cv::Ptr<cv::DownhillSolver> solver,cv::Ptr<cv::MinProblemSolv
     solver->getInitStep(settedStep);
     ASSERT_TRUE(settedStep.rows==1 && settedStep.cols==ndim);
     ASSERT_TRUE(std::equal(step.begin<double>(),step.end<double>(),settedStep.begin<double>()));
-    std::cout<<"step setted:\n\t"<<step<<std::endl;
+    std::cout<<"step set:\n\t"<<step<<std::endl;
     double res=solver->minimize(x);
     std::cout<<"res:\n\t"<<res<<std::endl;
     std::cout<<"x:\n\t"<<x<<std::endl;

modules/core/test/test_operations.cpp

@@ -466,7 +466,7 @@ bool CV_OperationsTest::TestSubMatAccess()
         Vec3f ydir(1.f, 0.f, 1.f);
         Vec3f fpt(0.1f, 0.7f, 0.2f);
         T_bs.setTo(0);
-        T_bs(Range(0,3),Range(2,3)) = 1.0*Mat(cdir); // wierd OpenCV stuff, need to do multiply
+        T_bs(Range(0,3),Range(2,3)) = 1.0*Mat(cdir); // weird OpenCV stuff, need to do multiply
         T_bs(Range(0,3),Range(1,2)) = 1.0*Mat(ydir);
         T_bs(Range(0,3),Range(0,1)) = 1.0*Mat(cdir.cross(ydir));
         T_bs(Range(0,3),Range(3,4)) = 1.0*Mat(fpt);

modules/core/test/test_umat.cpp

@@ -1192,7 +1192,7 @@ OCL_TEST(UMat, DISABLED_OCL_ThreadSafe_CleanupCallback_1_VeryLongTest)
     }
 }
 
-// Case 2: concurent deallocation of UMatData between UMat and Mat deallocators. Hard to catch!
+// Case 2: concurrent deallocation of UMatData between UMat and Mat deallocators. Hard to catch!
 OCL_TEST(UMat, DISABLED_OCL_ThreadSafe_CleanupCallback_2_VeryLongTest)
 {
     if (!cv::ocl::useOpenCL())

modules/dnn/CMakeLists.txt

@@ -41,7 +41,7 @@ endif()
 
 add_definitions(-DHAVE_PROTOBUF=1)
 
-#supress warnings in autogenerated caffe.pb.* files
+#suppress warnings in autogenerated caffe.pb.* files
 ocv_warnings_disable(CMAKE_CXX_FLAGS
     -Wunused-parameter -Wundef -Wignored-qualifiers -Wno-enum-compare
     -Wdeprecated-declarations

modules/dnn/include/opencv2/dnn.hpp

@@ -53,7 +53,7 @@
         - API for new layers creation, layers are building bricks of neural networks;
         - set of built-in most-useful Layers;
         - API to constuct and modify comprehensive neural networks from layers;
-        - functionality for loading serialized networks models from differnet frameworks.
+        - functionality for loading serialized networks models from different frameworks.
 
     Functionality of this module is designed only for forward pass computations (i. e. network testing).
     A network training is in principle not supported.

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

@@ -51,13 +51,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
 
 /** @defgroup dnnLayerList Partial List of Implemented Layers
   @{
-  This subsection of dnn module contains information about bult-in layers and their descriptions.
+  This subsection of dnn module contains information about built-in layers and their descriptions.
 
-  Classes listed here, in fact, provides C++ API for creating intances of bult-in layers.
+  Classes listed here, in fact, provides C++ API for creating instances of built-in layers.
   In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
-  You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
+  You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
 
-  Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
+  Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
   In partuclar, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
   - Convolution
   - Deconvolution
@@ -125,12 +125,12 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
 
         /** @deprecated Use flag `produce_cell_output` in LayerParams.
-          * @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample.
+          * @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample.
           *
-          * If flag is set to true then shape of input blob will be interpeted as [`T`, `N`, `[data dims]`] where `T` specifies number of timpestamps, `N` is number of independent streams.
+          * 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.
           *
-          * If flag is set to false then shape of input blob will be interpeted as [`N`, `[data dims]`].
+          * If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`].
           * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
           */
         CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
@@ -146,7 +146,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true).
          *
          * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`],
-         * where `T` specifies number of timpestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
+         * where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
          *
          * If setUseTimstampsDim() is set to fase then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
          * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]).
@@ -328,7 +328,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      * @param begin Vector of start indices
      * @param size Vector of sizes
      *
-     * More convinient numpy-like slice. One and only output blob
+     * More convenient numpy-like slice. One and only output blob
      * is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]`
      *
      * 3. Torch mode

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

@@ -691,7 +691,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @param swapRB flag which indicates that swap first and last channels
      *  in 3-channel image is necessary.
      *  @param crop flag which indicates whether image will be cropped after resize or not
-     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponing
+     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
      *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
      *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
      *  @returns 4-dimansional Mat with NCHW dimensions order.
@@ -719,7 +719,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @param swapRB flag which indicates that swap first and last channels
      *  in 3-channel image is necessary.
      *  @param crop flag which indicates whether image will be cropped after resize or not
-     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponing
+     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
      *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
      *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
      *  @returns 4-dimansional Mat with NCHW dimensions order.

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

@@ -131,7 +131,7 @@ message PriorBoxParameter {
   // Variance for adjusting the prior bboxes.
   repeated float variance = 6;
   // By default, we calculate img_height, img_width, step_x, step_y based on
-  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitely
+  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitly
   // provided.
   // Explicitly provide the img_size.
   optional uint32 img_size = 7;

modules/dnn/src/dnn.cpp

@@ -58,7 +58,7 @@ namespace cv {
 namespace dnn {
 CV__DNN_EXPERIMENTAL_NS_BEGIN
 
-// this option is usefull to run valgrind memory errors detection
+// this option is useful to run valgrind memory errors detection
 static bool DNN_DISABLE_MEMORY_OPTIMIZATIONS = utils::getConfigurationParameterBool("OPENCV_DNN_DISABLE_MEMORY_OPTIMIZATIONS", false);
 
 using std::vector;
@@ -911,7 +911,7 @@ struct Net::Impl
         int id = getLayerId(layerName);
 
         if (id < 0)
-            CV_Error(Error::StsError, "Requsted layer \"" + layerName + "\" not found");
+            CV_Error(Error::StsError, "Requested layer \"" + layerName + "\" not found");
 
         return getLayerData(id);
     }
@@ -1897,7 +1897,7 @@ struct Net::Impl
         if ((size_t)pin.oid >= ld.outputBlobs.size())
         {
             CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %d outputs, "
-                                           "the #%d was requsted", ld.name.c_str(),
+                                           "the #%d was requested", ld.name.c_str(),
                                            ld.outputBlobs.size(), pin.oid));
         }
         if (preferableTarget != DNN_TARGET_CPU)

modules/dnn/src/layers/concat_layer.cpp

@@ -88,7 +88,7 @@ public:
                 for (int curAxis = 0; curAxis < outputs[0].size(); curAxis++)
                 {
                     if (curAxis != cAxis && outputs[0][curAxis] != curShape[curAxis])
-                        CV_Error(Error::StsBadSize, "Inconsitent shape for ConcatLayer");
+                        CV_Error(Error::StsBadSize, "Inconsistent shape for ConcatLayer");
                 }
             }
 

modules/dnn/src/ocl4dnn/src/math_functions.cpp

@@ -185,7 +185,7 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
     int blockC_height = blocksize;
 
     int use_buffer_indicator = 8;
-    // To fix the edge problem casued by the sub group block read.
+    // To fix the edge problem caused by the sub group block read.
     // we have to pad the image if it's not multiple of tile.
     // just padding one line is enough as the sub group block read
     // will clamp to edge according to the spec.

modules/dnn/src/opencl/conv_layer_spatial.cl

@@ -188,7 +188,7 @@ __kernel void ConvolveBasic(
 #define VLOAD4(_v, _p) do { _v = vload4(0, _p); } while(0)
 
 // Each work-item computes a OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT region of one output map.
-// Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the imput image.
+// Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the input image.
 // NDRange:  (output_width+pad)/ OUT_BLOCK_WIDTH, (output_height+pad)/OUT_BLOCK_HEIGHT, NUM_FILTERS/OUT_BLOCK_DEPTH
 
 // NOTE: for beignet this reqd_work_group_size does not guarantee that SIMD16 mode will be used, the compiler could choose to use two SIMD8 threads, and if that happens the code will break.
@@ -220,7 +220,7 @@ convolve_simd(
 
   int in_addr;
 
-  // find weights adress of given neuron (lid is index)
+  // find weights address of given neuron (lid is index)
   unsigned int weight_addr = (fmg % (ALIGNED_NUM_FILTERS/SIMD_SIZE)) * INPUT_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT * SIMD_SIZE + lid;
 
   for(int i=0;i<OUT_BLOCK_SIZE;i++) {

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -1096,9 +1096,9 @@ void TFImporter::populateNet(Net dstNet)
             dstNet.setInputsNames(netInputs);
         }
         else if (type == "Split") {
-            // TODO: determing axis index remapping by input dimensions order of input blob
+            // TODO: determining axis index remapping by input dimensions order of input blob
             // TODO: slicing input may be Const op
-            // TODO: slicing kernels for convolutions - in current implenmentation it is impossible
+            // TODO: slicing kernels for convolutions - in current implementation it is impossible
             // TODO: add parsing num of slices parameter
             CV_Assert(layer.input_size() == 2);
             // num_split

modules/dnn/test/cityscapes_semsegm_test_enet.py

@@ -8,11 +8,11 @@ try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 try:
     import torch
 except ImportError:
-    raise ImportError('Can\'t find pytorch. Please intall it by following instructions on the official site')
+    raise ImportError('Can\'t find pytorch. Please install it by following instructions on the official site')
 
 from torch.utils.serialization import load_lua
 from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation

modules/dnn/test/imagenet_cls_test_alexnet.py

@@ -9,12 +9,12 @@ try:
     import caffe
 except ImportError:
     raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "git/caffe/python" directory')
+                      'configure environment variable PYTHONPATH to "git/caffe/python" directory')
 try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 
 
 class DataFetch(object):

modules/dnn/test/imagenet_cls_test_googlenet.py

@@ -7,12 +7,12 @@ try:
     import caffe
 except ImportError:
     raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "git/caffe/python" directory')
+                      'configure environment variable PYTHONPATH to "git/caffe/python" directory')
 try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

modules/dnn/test/imagenet_cls_test_inception.py

@@ -9,10 +9,10 @@ try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 
 # If you've got an exception "Cannot load libmkl_avx.so or libmkl_def.so" or similar, try to export next variable
-# before runnigng the script:
+# before running the script:
 # LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so
 
 

modules/dnn/test/pascal_semsegm_test_fcn.py

@@ -9,7 +9,7 @@ try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 
 
 def get_metrics(conf_mat):

modules/viz/include/opencv2/viz/vizcore.hpp

@@ -73,7 +73,7 @@ namespace cv
         CV_EXPORTS Affine3d makeTransformToGlobal(const Vec3d& axis_x, const Vec3d& axis_y, const Vec3d& axis_z, const Vec3d& origin = Vec3d::all(0));
 
         /** @brief Constructs camera pose from position, focal_point and up_vector (see gluLookAt() for more
-        infromation).
+        information).
 
         @param position Position of the camera in global coordinate frame.
         @param focal_point Focal point of the camera in global coordinate frame.

modules/viz/src/vtk/vtkCocoaInteractorFix.mm

@@ -151,7 +151,7 @@ namespace cv { namespace viz {
         {
             [self breakEventLoop];
 
-            // The NSWindow is closing, so prevent anyone from accidently using it
+            // The NSWindow is closing, so prevent anyone from accidentally using it
             renWin->SetRootWindow(NULL);
         }
     }