From 2003eb1b9b095c3538ec165e89fcb9fafbbcb25a Mon Sep 17 00:00:00 2001 From: "luz.paz" Date: Tue, 31 Jul 2018 11:11:25 -0400 Subject: [PATCH] Misc. typos Found via `codespell -q 3 -I ../opencv-whitelist.txt --skip="./3rdparty"` --- .../rotationConverters.cpp | 4 ++-- cmake/FindCUDA.cmake | 4 ++-- cmake/OpenCVPCHSupport.cmake | 2 +- cmake/templates/OpenCVConfig.cmake.in | 2 +- .../js_imgproc/js_grabcut/js_grabcut.markdown | 2 +- .../js_setup/js_usage/js_usage.markdown | 2 +- .../py_imgproc/py_grabcut/py_grabcut.markdown | 2 +- .../real_time_pose/real_time_pose.markdown | 16 ++++++++-------- .../windows_install/windows_install.markdown | 4 ++-- modules/calib3d/test/test_chesscorners.cpp | 2 +- .../calib3d/test/test_chesscorners_timing.cpp | 2 +- modules/core/include/opencv2/core/types_c.h | 2 +- modules/core/src/datastructs.cpp | 2 +- modules/core/src/persistence_c.cpp | 2 +- modules/cudafilters/src/cuda/median_filter.cu | 2 +- modules/cudaimgproc/src/mssegmentation.cpp | 2 +- modules/features2d/doc/read_file_nondiff32.pl | 2 +- modules/features2d/doc/read_file_score32.pl | 2 +- modules/ml/src/svm.cpp | 2 +- modules/ts/include/opencv2/ts/ts_gtest.h | 2 +- samples/cpp/train_HOG.cpp | 2 +- .../introduction_to_pca/introduction_to_pca.cpp | 2 +- samples/cpp/warpPerspective_demo.cpp | 2 +- samples/dnn/custom_layers.hpp | 2 +- .../Common/StandardStyles.xaml | 2 +- 25 files changed, 35 insertions(+), 35 deletions(-) diff --git a/apps/interactive-calibration/rotationConverters.cpp b/apps/interactive-calibration/rotationConverters.cpp index ff31c9e380..421d15a924 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 632b8c8285..37d557a792 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 b4658c604b..f9b1b48b65 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 84262a87b3..fefa359e0a 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 570a490fea..ef71d07aa5 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 72f481df7a..88aba1afd5 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 2c489e2453..7dc22d37aa 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 b974b8bc63..4347d11651 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 e60c846b12..7f491d8fdd 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 8303a8dcd4..e55d069de0 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 4d643a1d45..b89d2e0147 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 7e384a5c6f..81e986fcd1 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 56528fcf69..83c11c1855 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 ed315d0971..ed349cc150 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 f8e02cb039..fe26c7be0e 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 - +