Merge pull request #12116 from luzpaz:misc-typos

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)
         {

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)

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 "")

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)

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

doc/js_tutorials/js_setup/js_usage/js_usage.markdown

@@ -129,7 +129,7 @@ function onOpenCvReady() {
 </html>
 @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
 ------

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

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.

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

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 );

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_;
     }

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
 {

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 );

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);

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<cols; j+=blockDim.x){
             for(int i=initStartRow; i<initStopRow; i++){
                     int pos=::min(i,rows-1);

modules/cudaimgproc/src/mssegmentation.cpp

@@ -342,7 +342,7 @@ void cv::cuda::meanShiftSegmentation(InputArray _src, OutputArray _dst, int sp,
         }
     }
 
-    // Sort all graph's edges connecting different components (in asceding order)
+    // Sort all graph's edges connecting different components (in ascending order)
     std::sort(edges.begin(), edges.end());
 
     // Exclude small components (starting from the nearest couple)

modules/cudawarping/test/test_warp_affine.cpp

@@ -48,7 +48,7 @@ namespace opencv_test { namespace {
 
 namespace
 {
-    cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
+    cv::Mat createTransformMatrix(cv::Size srcSize, double angle)
     {
         cv::Mat M(2, 3, CV_64FC1);
 
@@ -80,7 +80,7 @@ PARAM_TEST_CASE(BuildWarpAffineMaps, cv::cuda::DeviceInfo, cv::Size, Inverse)
 
 CUDA_TEST_P(BuildWarpAffineMaps, Accuracy)
 {
-    cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
+    cv::Mat M = createTransformMatrix(size, CV_PI / 4);
     cv::Mat src = randomMat(randomSize(200, 400), CV_8UC1);
 
     cv::cuda::GpuMat xmap, ymap;
@@ -207,7 +207,7 @@ PARAM_TEST_CASE(WarpAffine, cv::cuda::DeviceInfo, cv::Size, MatType, Inverse, In
 CUDA_TEST_P(WarpAffine, Accuracy)
 {
     cv::Mat src = randomMat(size, type);
-    cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
+    cv::Mat M = createTransformMatrix(size, CV_PI / 3);
     int flags = interpolation;
     if (inverse)
         flags |= cv::WARP_INVERSE_MAP;
@@ -257,7 +257,7 @@ CUDA_TEST_P(WarpAffineNPP, Accuracy)
     cv::Mat src = readImageType("stereobp/aloe-L.png", type);
     ASSERT_FALSE(src.empty());
 
-    cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
+    cv::Mat M = createTransformMatrix(src.size(), CV_PI / 4);
     int flags = interpolation;
     if (inverse)
         flags |= cv::WARP_INVERSE_MAP;

modules/cudawarping/test/test_warp_perspective.cpp

@@ -48,7 +48,7 @@ namespace opencv_test { namespace {
 
 namespace
 {
-    cv::Mat createTransfomMatrix(cv::Size srcSize, double angle)
+    cv::Mat createTransformMatrix(cv::Size srcSize, double angle)
     {
         cv::Mat M(3, 3, CV_64FC1);
 
@@ -81,7 +81,7 @@ PARAM_TEST_CASE(BuildWarpPerspectiveMaps, cv::cuda::DeviceInfo, cv::Size, Invers
 
 CUDA_TEST_P(BuildWarpPerspectiveMaps, Accuracy)
 {
-    cv::Mat M = createTransfomMatrix(size, CV_PI / 4);
+    cv::Mat M = createTransformMatrix(size, CV_PI / 4);
 
     cv::cuda::GpuMat xmap, ymap;
     cv::cuda::buildWarpPerspectiveMaps(M, inverse, size, xmap, ymap);
@@ -210,7 +210,7 @@ PARAM_TEST_CASE(WarpPerspective, cv::cuda::DeviceInfo, cv::Size, MatType, Invers
 CUDA_TEST_P(WarpPerspective, Accuracy)
 {
     cv::Mat src = randomMat(size, type);
-    cv::Mat M = createTransfomMatrix(size, CV_PI / 3);
+    cv::Mat M = createTransformMatrix(size, CV_PI / 3);
     int flags = interpolation;
     if (inverse)
         flags |= cv::WARP_INVERSE_MAP;
@@ -260,7 +260,7 @@ CUDA_TEST_P(WarpPerspectiveNPP, Accuracy)
     cv::Mat src = readImageType("stereobp/aloe-L.png", type);
     ASSERT_FALSE(src.empty());
 
-    cv::Mat M = createTransfomMatrix(src.size(), CV_PI / 4);
+    cv::Mat M = createTransformMatrix(src.size(), CV_PI / 4);
     int flags = interpolation;
     if (inverse)
         flags |= cv::WARP_INVERSE_MAP;

modules/cudev/test/test_warp.cu

@@ -199,7 +199,7 @@ TEST(Resize, Downscale)
 
 // warpAffine & warpPerspective
 
-Mat createAffineTransfomMatrix(Size srcSize, float angle, bool perspective)
+Mat createAffineTransformMatrix(Size srcSize, float angle, bool perspective)
 {
     cv::Mat M(perspective ? 3 : 2, 3, CV_32FC1);
 
@@ -220,7 +220,7 @@ TEST(WarpAffine, Rotation)
     const Size size = randomSize(100, 400);
 
     Mat src = randomMat(size, CV_32FC1, 0, 1);
-    Mat M = createAffineTransfomMatrix(size, static_cast<float>(CV_PI / 4), false);
+    Mat M = createAffineTransformMatrix(size, static_cast<float>(CV_PI / 4), false);
 
     GpuMat_<float> d_src(src);
     GpuMat_<float> d_M;
@@ -240,7 +240,7 @@ TEST(WarpPerspective, Rotation)
     const Size size = randomSize(100, 400);
 
     Mat src = randomMat(size, CV_32FC1, 0, 1);
-    Mat M = createAffineTransfomMatrix(size, static_cast<float>(CV_PI / 4), true);
+    Mat M = createAffineTransformMatrix(size, static_cast<float>(CV_PI / 4), true);
 
     GpuMat_<float> d_src(src);
     GpuMat_<float> d_M;

modules/features2d/doc/read_file_nondiff32.pl

@@ -131,7 +131,7 @@ my $success_structured;
                   }
                   close $in2 or die "Can't close $filein: $!";
                 }
-                #find next else and interprete it
+                #find next else and interpret it
                 open(my $in3,  "<",  $filein)  or die "Can't open $filein: $!";
         $i3=1;
         $ifcount3=0;

modules/features2d/doc/read_file_score32.pl

@@ -119,7 +119,7 @@ my $is_a_corner;
                   }
                   close $in2 or die "Can't close $filein: $!";
                 }
-                #find next else and interprete it
+                #find next else and interpret it
                 open(my $in3,  "<",  $filein)  or die "Can't open $filein: $!";
         $i3=1;
         $ifcount3=0;

modules/ml/src/svm.cpp

@@ -2048,7 +2048,7 @@ public:
             svmType == NU_SVC ? "NU_SVC" :
             svmType == ONE_CLASS ? "ONE_CLASS" :
             svmType == EPS_SVR ? "EPS_SVR" :
-            svmType == NU_SVR ? "NU_SVR" : format("Uknown_%d", svmType);
+            svmType == NU_SVR ? "NU_SVR" : format("Unknown_%d", svmType);
         String kernel_type_str =
             kernelType == LINEAR ? "LINEAR" :
             kernelType == POLY ? "POLY" :

modules/objdetect/src/hog.cpp

@@ -255,8 +255,8 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
     Mat_<float> _lut(1, 256);
     const float* const lut = &_lut(0,0);
 #if CV_SSE2
-    const int indeces[] = { 0, 1, 2, 3 };
-    __m128i idx = _mm_loadu_si128((const __m128i*)indeces);
+    const int indices[] = { 0, 1, 2, 3 };
+    __m128i idx = _mm_loadu_si128((const __m128i*)indices);
     __m128i ifour = _mm_set1_epi32(4);
 
     float* const _data = &_lut(0, 0);
@@ -273,8 +273,8 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
             idx = _mm_add_epi32(idx, ifour);
         }
 #elif CV_NEON
-    const int indeces[] = { 0, 1, 2, 3 };
-    uint32x4_t idx = *(uint32x4_t*)indeces;
+    const int indices[] = { 0, 1, 2, 3 };
+    uint32x4_t idx = *(uint32x4_t*)indices;
     uint32x4_t ifour = vdupq_n_u32(4);
 
     float* const _data = &_lut(0, 0);

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

@@ -9013,7 +9013,7 @@ class NativeArray {
 
 // Implements Boolean test assertions such as EXPECT_TRUE. expression can be
 // either a boolean expression or an AssertionResult. text is a textual
-// represenation of expression as it was passed into the EXPECT_TRUE.
+// representation of expression as it was passed into the EXPECT_TRUE.
 #define GTEST_TEST_BOOLEAN_(expression, text, actual, expected, fail) \
   GTEST_AMBIGUOUS_ELSE_BLOCKER_ \
   if (const ::testing::AssertionResult gtest_ar_ = \

samples/cpp/train_HOG.cpp

@@ -204,7 +204,7 @@ int main( int argc, char** argv )
     const char* keys =
     {
         "{help h|     | show help message}"
-        "{pd    |     | path of directory contains possitive images}"
+        "{pd    |     | path of directory contains positive images}"
         "{nd    |     | path of directory contains negative images}"
         "{td    |     | path of directory contains test images}"
         "{tv    |     | test video file name}"

samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp

@@ -1,6 +1,6 @@
 /**
  * @file introduction_to_pca.cpp
- * @brief This program demonstrates how to use OpenCV PCA to extract the orienation of an object
+ * @brief This program demonstrates how to use OpenCV PCA to extract the orientation of an object
  * @author OpenCV team
  */
 

samples/cpp/warpPerspective_demo.cpp

@@ -26,7 +26,7 @@ static void help(char** argv)
          "\tESC, q - quit the program\n"
          "\tr - change order of points to rotate transformation\n"
          "\tc - delete selected points\n"
-         "\ti - change order of points to invers transformation \n"
+         "\ti - change order of points to inverse transformation \n"
          "\nUse your mouse to select a point and move it to see transformation changes" << endl;
 }
 

samples/dnn/custom_layers.hpp

@@ -198,7 +198,7 @@ private:
 //! [ResizeBilinearLayer]
 
 //
-// The folowing code is used only to generate tutorials documentation.
+// The following code is used only to generate tutorials documentation.
 //
 
 //! [A custom layer interface]

samples/winrt/OcvImageProcessing/OcvImageProcessing/Common/StandardStyles.xaml

@@ -1091,7 +1091,7 @@ Style x:Key="SkipBackAppBarButtonStyle" TargetType="ButtonBase" BasedOn="{Static
     </Style>
     <Style x:Key="PermissionsAppBarButtonStyle" TargetType="ButtonBase" BasedOn="{StaticResource AppBarButtonStyle}">
         <Setter Property="AutomationProperties.AutomationId" Value="PermissionsAppBarButton"/>
-        <Setter Property="AutomationProperties.Name" Value="Permisions"/>
+        <Setter Property="AutomationProperties.Name" Value="Permissions"/>
         <Setter Property="Content" Value="&#xE192;"/>
     </Style>
     <Style x:Key="HighlightAppBarButtonStyle" TargetType="ButtonBase" BasedOn="{StaticResource AppBarButtonStyle}">