diff --git a/modules/core/include/opencv2/core/mat.hpp b/modules/core/include/opencv2/core/mat.hpp index dc9d526b97..78f7487e51 100644 --- a/modules/core/include/opencv2/core/mat.hpp +++ b/modules/core/include/opencv2/core/mat.hpp @@ -1366,15 +1366,15 @@ public: /** @overload * @param cn New number of channels. If the parameter is 0, the number of channels remains the same. - * @param newndims New number of dimentions. - * @param newsz Array with new matrix size by all dimentions. If some sizes are zero, + * @param newndims New number of dimensions. + * @param newsz Array with new matrix size by all dimensions. If some sizes are zero, * the original sizes in those dimensions are presumed. */ Mat reshape(int cn, int newndims, const int* newsz) const; /** @overload * @param cn New number of channels. If the parameter is 0, the number of channels remains the same. - * @param newshape Vector with new matrix size by all dimentions. If some sizes are zero, + * @param newshape Vector with new matrix size by all dimensions. If some sizes are zero, * the original sizes in those dimensions are presumed. */ Mat reshape(int cn, const std::vector& newshape) const; diff --git a/modules/dnn/src/layers/recurrent_layers.cpp b/modules/dnn/src/layers/recurrent_layers.cpp index 3d960012e7..020bf6b1bd 100644 --- a/modules/dnn/src/layers/recurrent_layers.cpp +++ b/modules/dnn/src/layers/recurrent_layers.cpp @@ -416,7 +416,7 @@ public: if (layout == BATCH_SEQ_HID){ //swap axis 0 and 1 input x cv::Mat tmp; - // Since python input is 4 dimentional and C++ input 3 dimentinal + // Since python input is 4 dimensional and C++ input 3 dimensional // we need to process each differently if (input[0].dims == 4){ // here !!! diff --git a/modules/gapi/include/opencv2/gapi/gmat.hpp b/modules/gapi/include/opencv2/gapi/gmat.hpp index 6d6f74ff7f..69cbd094ea 100644 --- a/modules/gapi/include/opencv2/gapi/gmat.hpp +++ b/modules/gapi/include/opencv2/gapi/gmat.hpp @@ -245,7 +245,7 @@ struct GAPI_EXPORTS_W_SIMPLE GMatDesc static inline GMatDesc empty_gmat_desc() { return GMatDesc{-1,-1,{-1,-1}}; } namespace gapi { namespace detail { -/** Checks GMatDesc fields if the passed matrix is a set of n-dimentional points. +/** Checks GMatDesc fields if the passed matrix is a set of n-dimensional points. @param in GMatDesc to check. @param n expected dimensionality. @return the amount of points. In case input matrix can't be described as vector of points diff --git a/modules/gapi/include/opencv2/gapi/imgproc.hpp b/modules/gapi/include/opencv2/gapi/imgproc.hpp index 96aaa5a447..689c42c82b 100644 --- a/modules/gapi/include/opencv2/gapi/imgproc.hpp +++ b/modules/gapi/include/opencv2/gapi/imgproc.hpp @@ -217,7 +217,7 @@ namespace imgproc { GAPI_Assert (in.depth == CV_32S || in.depth == CV_32F); int amount = detail::checkVector(in, 2u); GAPI_Assert(amount != -1 && - "Input Mat can't be described as vector of 2-dimentional points"); + "Input Mat can't be described as vector of 2-dimensional points"); } return empty_gopaque_desc(); } @@ -242,7 +242,7 @@ namespace imgproc { static GOpaqueDesc outMeta(GMatDesc in,DistanceTypes,double,double,double) { int amount = detail::checkVector(in, 2u); GAPI_Assert(amount != -1 && - "Input Mat can't be described as vector of 2-dimentional points"); + "Input Mat can't be described as vector of 2-dimensional points"); return empty_gopaque_desc(); } }; @@ -276,7 +276,7 @@ namespace imgproc { static GOpaqueDesc outMeta(GMatDesc in,int,double,double,double) { int amount = detail::checkVector(in, 3u); GAPI_Assert(amount != -1 && - "Input Mat can't be described as vector of 3-dimentional points"); + "Input Mat can't be described as vector of 3-dimensional points"); return empty_gopaque_desc(); } }; @@ -1235,7 +1235,7 @@ weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . @note - Function textual ID is "org.opencv.imgproc.shape.fitLine2DMat" - - In case of an N-dimentional points' set given, Mat should be 2-dimensional, have a single row + - In case of an N-dimensional points' set given, Mat should be 2-dimensional, have a single row or column if there are N channels, or have N columns if there is a single channel. @param src Input set of 2D points stored in one of possible containers: Mat, @@ -1307,7 +1307,7 @@ weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . @note - Function textual ID is "org.opencv.imgproc.shape.fitLine3DMat" - - In case of an N-dimentional points' set given, Mat should be 2-dimensional, have a single row + - In case of an N-dimensional points' set given, Mat should be 2-dimensional, have a single row or column if there are N channels, or have N columns if there is a single channel. @param src Input set of 3D points stored in one of possible containers: Mat, diff --git a/modules/objdetect/src/aruco/aruco_detector.cpp b/modules/objdetect/src/aruco/aruco_detector.cpp index fefaecbfef..19343bf1c2 100644 --- a/modules/objdetect/src/aruco/aruco_detector.cpp +++ b/modules/objdetect/src/aruco/aruco_detector.cpp @@ -125,7 +125,7 @@ static void _threshold(InputArray _in, OutputArray _out, int winSize, double con /** - * @brief Given a tresholded image, find the contours, calculate their polygonal approximation + * @brief Given a thresholded image, find the contours, calculate their polygonal approximation * and take those that accomplish some conditions */ static void _findMarkerContours(const Mat &in, vector > &candidates, diff --git a/modules/video/include/opencv2/video/background_segm.hpp b/modules/video/include/opencv2/video/background_segm.hpp index 73409f27d4..2d3ea823b3 100644 --- a/modules/video/include/opencv2/video/background_segm.hpp +++ b/modules/video/include/opencv2/video/background_segm.hpp @@ -117,7 +117,7 @@ public: CV_WRAP virtual int getNMixtures() const = 0; /** @brief Sets the number of gaussian components in the background model. - The model needs to be reinitalized to reserve memory. + The model needs to be reinitialized to reserve memory. */ CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization! @@ -268,7 +268,7 @@ public: CV_WRAP virtual int getNSamples() const = 0; /** @brief Sets the number of data samples in the background model. - The model needs to be reinitalized to reserve memory. + The model needs to be reinitialized to reserve memory. */ CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!