diff --git a/apps/interactive-calibration/rotationConverters.cpp b/apps/interactive-calibration/rotationConverters.cpp index ff31c9e380caf576857f3c7587f01d4b4f28bbea..421d15a924d2c462afadaaec47be135daf5d2d6b 100644 --- a/apps/interactive-calibration/rotationConverters.cpp +++ b/apps/interactive-calibration/rotationConverters.cpp @@ -16,7 +16,7 @@ void calib::Euler(const cv::Mat& src, cv::Mat& dst, int argType) { if((src.rows == 3) && (src.cols == 3)) { - //convert rotaion matrix to 3 angles (pitch, yaw, roll) + //convert rotation matrix to 3 angles (pitch, yaw, roll) dst = cv::Mat(3, 1, CV_64F); double pitch, yaw, roll; @@ -55,7 +55,7 @@ void calib::Euler(const cv::Mat& src, cv::Mat& dst, int argType) else if( (src.cols == 1 && src.rows == 3) || (src.cols == 3 && src.rows == 1 ) ) { - //convert vector which contains 3 angles (pitch, yaw, roll) to rotaion matrix + //convert vector which contains 3 angles (pitch, yaw, roll) to rotation matrix double pitch, yaw, roll; if(src.cols == 1 && src.rows == 3) { diff --git a/cmake/FindCUDA.cmake b/cmake/FindCUDA.cmake index 632b8c8285104f3d6acc86a31275c001357958d5..37d557a792e1b1dc13dbba497e761171c2d81f14 100644 --- a/cmake/FindCUDA.cmake +++ b/cmake/FindCUDA.cmake @@ -141,7 +141,7 @@ # -- Same as CUDA_ADD_EXECUTABLE except that a library is created. # # CUDA_BUILD_CLEAN_TARGET() -# -- Creates a convience target that deletes all the dependency files +# -- Creates a convenience target that deletes all the dependency files # generated. You should make clean after running this target to ensure the # dependency files get regenerated. # @@ -473,7 +473,7 @@ else() endif() # Propagate the host flags to the host compiler via -Xcompiler -option(CUDA_PROPAGATE_HOST_FLAGS "Propage C/CXX_FLAGS and friends to the host compiler via -Xcompile" ON) +option(CUDA_PROPAGATE_HOST_FLAGS "Propagate C/CXX_FLAGS and friends to the host compiler via -Xcompile" ON) # Enable CUDA_SEPARABLE_COMPILATION option(CUDA_SEPARABLE_COMPILATION "Compile CUDA objects with separable compilation enabled. Requires CUDA 5.0+" OFF) diff --git a/cmake/OpenCVPCHSupport.cmake b/cmake/OpenCVPCHSupport.cmake index b4658c604bc60715c676d43c4fd22c43b14638e3..f9b1b48b658a96afda893832cbcc41965dd2e115 100644 --- a/cmake/OpenCVPCHSupport.cmake +++ b/cmake/OpenCVPCHSupport.cmake @@ -362,7 +362,7 @@ MACRO(ADD_NATIVE_PRECOMPILED_HEADER _targetName _input) endif() endforeach() - #also inlude ${oldProps} to have the same compile options + #also include ${oldProps} to have the same compile options GET_TARGET_PROPERTY(oldProps ${_targetName} COMPILE_FLAGS) if (oldProps MATCHES NOTFOUND) SET(oldProps "") diff --git a/cmake/templates/OpenCVConfig.cmake.in b/cmake/templates/OpenCVConfig.cmake.in index 84262a87b30f358b92e2a3f5138314b328a6cbc6..fefa359e0a9d8b82e69766ad54c8bd66f9f5cc56 100644 --- a/cmake/templates/OpenCVConfig.cmake.in +++ b/cmake/templates/OpenCVConfig.cmake.in @@ -260,7 +260,7 @@ endif() set(OpenCV_LIBRARIES ${OpenCV_LIBS}) # -# Some macroses for samples +# Some macros for samples # macro(ocv_check_dependencies) set(OCV_DEPENDENCIES_FOUND TRUE) diff --git a/doc/js_tutorials/js_imgproc/js_grabcut/js_grabcut.markdown b/doc/js_tutorials/js_imgproc/js_grabcut/js_grabcut.markdown index 570a490fea216028ec68e8aa68e8787789803264..ef71d07aa5e0cc652e8cee87c6508d911e96600d 100644 --- a/doc/js_tutorials/js_imgproc/js_grabcut/js_grabcut.markdown +++ b/doc/js_tutorials/js_imgproc/js_grabcut/js_grabcut.markdown @@ -29,7 +29,7 @@ What happens in background ? objects). Everything inside rectangle is unknown. Similarly any user input specifying foreground and background are considered as hard-labelling which means they won't change in the process. -- Computer does an initial labelling depeding on the data we gave. It labels the foreground and +- Computer does an initial labelling depending on the data we gave. It labels the foreground and background pixels (or it hard-labels) - Now a Gaussian Mixture Model(GMM) is used to model the foreground and background. - Depending on the data we gave, GMM learns and create new pixel distribution. That is, the diff --git a/doc/js_tutorials/js_setup/js_usage/js_usage.markdown b/doc/js_tutorials/js_setup/js_usage/js_usage.markdown index 72f481df7a90a76e3fc822a67beda1250a96caa6..88aba1afd5525272b497f50824c99becbd6959a4 100644 --- a/doc/js_tutorials/js_setup/js_usage/js_usage.markdown +++ b/doc/js_tutorials/js_setup/js_usage/js_usage.markdown @@ -129,7 +129,7 @@ function onOpenCvReady() { @endcode -@note You have to call delete method of cv.Mat to free memory allocated in Emscripten's heap. Please refer to [Memeory management of Emscripten](https://kripken.github.io/emscripten-site/docs/porting/connecting_cpp_and_javascript/embind.html#memory-management) for details. +@note You have to call delete method of cv.Mat to free memory allocated in Emscripten's heap. Please refer to [Memory management of Emscripten](https://kripken.github.io/emscripten-site/docs/porting/connecting_cpp_and_javascript/embind.html#memory-management) for details. Try it ------ diff --git a/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown b/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown index 2c489e2453a59e8eef12c40881ed1b2ae8a37f69..7dc22d37aad57de9536debe46487cf894f451e20 100644 --- a/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown +++ b/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown @@ -37,7 +37,7 @@ So what happens in background ? objects). Everything inside rectangle is unknown. Similarly any user input specifying foreground and background are considered as hard-labelling which means they won't change in the process. -- Computer does an initial labelling depeding on the data we gave. It labels the foreground and +- Computer does an initial labelling depending on the data we gave. It labels the foreground and background pixels (or it hard-labels) - Now a Gaussian Mixture Model(GMM) is used to model the foreground and background. - Depending on the data we gave, GMM learns and create new pixel distribution. That is, the diff --git a/doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown b/doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown index b974b8bc63273155f443678dcb0bb10b2f0c25fe..4347d116510b922884bfc69b6c180bd93171193f 100644 --- a/doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown +++ b/doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown @@ -16,7 +16,7 @@ In this tutorial is explained how to build a real time application to estimate t order to track a textured object with six degrees of freedom given a 2D image and its 3D textured model. -The application will have the followings parts: +The application will have the following parts: - Read 3D textured object model and object mesh. - Take input from Camera or Video. @@ -426,16 +426,16 @@ Here is explained in detail the code for the real time application: @endcode OpenCV provides four PnP methods: ITERATIVE, EPNP, P3P and DLS. Depending on the application type, the estimation method will be different. In the case that we want to make a real time application, - the more suitable methods are EPNP and P3P due to that are faster than ITERATIVE and DLS at + the more suitable methods are EPNP and P3P since they are faster than ITERATIVE and DLS at finding an optimal solution. However, EPNP and P3P are not especially robust in front of planar - surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this this - tutorial is used ITERATIVE method due to the object to be detected has planar surfaces. + surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this + tutorial an ITERATIVE method is used due to the object to be detected has planar surfaces. - The OpenCV RANSAC implementation wants you to provide three parameters: the maximum number of - iterations until stop the algorithm, the maximum allowed distance between the observed and - computed point projections to consider it an inlier and the confidence to obtain a good result. + The OpenCV RANSAC implementation wants you to provide three parameters: 1) the maximum number of + iterations until the algorithm stops, 2) the maximum allowed distance between the observed and + computed point projections to consider it an inlier and 3) the confidence to obtain a good result. You can tune these parameters in order to improve your algorithm performance. Increasing the - number of iterations you will have a more accurate solution, but will take more time to find a + number of iterations will have a more accurate solution, but will take more time to find a solution. Increasing the reprojection error will reduce the computation time, but your solution will be unaccurate. Decreasing the confidence your algorithm will be faster, but the obtained solution will be unaccurate. diff --git a/doc/tutorials/introduction/windows_install/windows_install.markdown b/doc/tutorials/introduction/windows_install/windows_install.markdown index e60c846b12e8f9edeb62e40cb62c56a7112ed8bb..7f491d8fdd7df6be9dbafbc5b25678037c545d61 100644 --- a/doc/tutorials/introduction/windows_install/windows_install.markdown +++ b/doc/tutorials/introduction/windows_install/windows_install.markdown @@ -46,7 +46,7 @@ cd /c/lib myRepo=$(pwd) CMAKE_CONFIG_GENERATOR="Visual Studio 14 2015 Win64" if [ ! -d "$myRepo/opencv" ]; then - echo "clonning opencv" + echo "cloning opencv" git clone https://github.com/opencv/opencv.git mkdir Build mkdir Build/opencv @@ -58,7 +58,7 @@ else cd .. fi if [ ! -d "$myRepo/opencv_contrib" ]; then - echo "clonning opencv_contrib" + echo "cloning opencv_contrib" git clone https://github.com/opencv/opencv_contrib.git mkdir Build mkdir Build/opencv_contrib diff --git a/modules/calib3d/test/test_chesscorners.cpp b/modules/calib3d/test/test_chesscorners.cpp index 8303a8dcd424a123d83f8e22547359b798fef1b3..e55d069de0596f760d2a98a35aca641d09ba2269 100644 --- a/modules/calib3d/test/test_chesscorners.cpp +++ b/modules/calib3d/test/test_chesscorners.cpp @@ -198,7 +198,7 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 ) { - ts->printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() ); + ts->printf( cvtest::TS::LOG, "%s can not be read or is not valid\n", (folder + filename).c_str() ); ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n", fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2); ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); diff --git a/modules/calib3d/test/test_chesscorners_timing.cpp b/modules/calib3d/test/test_chesscorners_timing.cpp index 4d643a1d4514bb9b1c95dd1ccd6f292bf3e5232a..b89d2e01478505a444e0542a9da28855f76fba07 100644 --- a/modules/calib3d/test/test_chesscorners_timing.cpp +++ b/modules/calib3d/test/test_chesscorners_timing.cpp @@ -85,7 +85,7 @@ void CV_ChessboardDetectorTimingTest::run( int start_from ) if( !fs || !board_list || !CV_NODE_IS_SEQ(board_list->tag) || board_list->data.seq->total % 4 != 0 ) { - ts->printf( cvtest::TS::LOG, "chessboard_timing_list.dat can not be readed or is not valid" ); + ts->printf( cvtest::TS::LOG, "chessboard_timing_list.dat can not be read or is not valid" ); code = cvtest::TS::FAIL_MISSING_TEST_DATA; goto _exit_; } diff --git a/modules/core/include/opencv2/core/types_c.h b/modules/core/include/opencv2/core/types_c.h index 7e384a5c6f7d493a0370141de7677e091550d59e..81e986fcd15c17ba189aba2eef926f4ab32a6196 100644 --- a/modules/core/include/opencv2/core/types_c.h +++ b/modules/core/include/opencv2/core/types_c.h @@ -1764,7 +1764,7 @@ typedef struct CvString } CvString; -/** All the keys (names) of elements in the readed file storage +/** All the keys (names) of elements in the read file storage are stored in the hash to speed up the lookup operations: */ typedef struct CvStringHashNode { diff --git a/modules/core/src/datastructs.cpp b/modules/core/src/datastructs.cpp index 56528fcf699ac2002f4e01ad58c1817776b7f547..83c11c18555d119a836edf74e2206f2a59ef2f3e 100644 --- a/modules/core/src/datastructs.cpp +++ b/modules/core/src/datastructs.cpp @@ -2779,7 +2779,7 @@ cvGraphAddEdgeByPtr( CvGraph* graph, if( start_vtx == end_vtx ) CV_Error( start_vtx ? CV_StsBadArg : CV_StsNullPtr, - "vertex pointers coinside (or set to NULL)" ); + "vertex pointers coincide (or set to NULL)" ); edge = (CvGraphEdge*)cvSetNew( (CvSet*)(graph->edges) ); assert( edge->flags >= 0 ); diff --git a/modules/core/src/persistence_c.cpp b/modules/core/src/persistence_c.cpp index ed315d0971bf1ed9f6e67cac3f1cbbbfc301f691..ed349cc15052bd42beb78828d02c89973ea25b38 100644 --- a/modules/core/src/persistence_c.cpp +++ b/modules/core/src/persistence_c.cpp @@ -1063,7 +1063,7 @@ cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, CV_Error( CV_StsNullPtr, "Null pointer to reader or destination array" ); if( !reader->seq && len != 1 ) - CV_Error( CV_StsBadSize, "The readed sequence is a scalar, thus len must be 1" ); + CV_Error( CV_StsBadSize, "The read sequence is a scalar, thus len must be 1" ); fmt_pair_count = icvDecodeFormat( dt, fmt_pairs, CV_FS_MAX_FMT_PAIRS ); size_t step = ::icvCalcStructSize(dt, 0); diff --git a/modules/cudafilters/src/cuda/median_filter.cu b/modules/cudafilters/src/cuda/median_filter.cu index f8e02cb039072d857e93a62d1949877a358ab600..fe26c7be0e33155f0ea5611c7cea12823a5fb335 100644 --- a/modules/cudafilters/src/cuda/median_filter.cu +++ b/modules/cudafilters/src/cuda/median_filter.cu @@ -246,7 +246,7 @@ namespace cv { namespace cuda { namespace device } __syncthreads(); - // Fot all remaining rows in the median filter, add the values to the the histogram + // For all remaining rows in the median filter, add the values to the the histogram for (int j=threadIdx.x; j - +