Merge pull request #28300 from raimbekovm:fix-array-typo

docs: fix spelling errors in documentation and comments

modules/calib3d/include/opencv2/calib3d.hpp

@@ -1411,7 +1411,7 @@ CV_EXPORTS_W bool checkChessboard(InputArray img, Size size);
 -   @ref CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description).
 -   @ref CALIB_CB_MARKER The detected pattern must have a marker (see description).
 This should be used if an accurate camera calibration is required.
-@param meta Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
+@param meta Optional output array of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
 Each entry stands for one corner of the pattern and can have one of the following values:
 -   0 = no meta data attached
 -   1 = left-top corner of a black cell

modules/core/include/opencv2/core/quaternion.hpp

@@ -57,7 +57,7 @@ class QuatEnum
 public:
     /** @brief Enum of Euler angles type.
      *
-     * Without considering the possibility of using two different convertions for the definition of the rotation axes ,
+     * Without considering the possibility of using two different conversions for the definition of the rotation axes ,
      * there exists twelve possible sequences of rotation axes, divided into two groups:
      * - Proper Euler angles (Z-X-Z, X-Y-X, Y-Z-Y, Z-Y-Z, X-Z-X, Y-X-Y)
      * - Tait–Bryan angles (X-Y-Z, Y-Z-X, Z-X-Y, X-Z-Y, Z-Y-X, Y-X-Z).
@@ -273,7 +273,7 @@ public:
      * where \f$ q_{X, \theta_1} \f$ is created from @ref createFromXRot,  \f$ q_{Y, \theta_2} \f$ is created from @ref createFromYRot,
      *  \f$ q_{Z, \theta_3} \f$ is created from @ref createFromZRot.
      * @param angles the Euler angles in a vector of length 3
-     * @param eulerAnglesType the convertion Euler angles type
+     * @param eulerAnglesType the conversion Euler angles type
      */
     static Quat<_Tp> createFromEulerAngles(const Vec<_Tp, 3> &angles, QuatEnum::EulerAnglesType eulerAnglesType);
 
@@ -1610,7 +1610,7 @@ public:
      * EXT_ZXZ| \f$ \theta_1 = \arctan2(m_{31},m_{32}) \\\theta_2 = \arccos(m_{33}) \\\theta_3 = \arctan2(-m_{13},m_{23})\f$| \f$ \theta_1=0\\ \theta_2=0\\ \theta_3=\arctan2(m_{21},m_{22}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3=\arctan2(m_{21},m_{11}) \f$
      * EXT_ZYZ| \f$ \theta_1 = \arctan2(m_{32},-m_{31})\\\theta_2 = \arccos(m_{33}) \\\theta_3 = \arctan2(m_{23},m_{13}) \f$| \f$ \theta_1=0\\ \theta_2=0\\ \theta_3=\arctan2(m_{21},m_{11}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3=\arctan2(m_{21},m_{11}) \f$
      *
-     * @param eulerAnglesType the convertion Euler angles type
+     * @param eulerAnglesType the conversion Euler angles type
      */
 
     Vec<_Tp, 3> toEulerAngles(QuatEnum::EulerAnglesType eulerAnglesType);

modules/dnn/src/layers/instance_norm_layer.cpp

@@ -221,7 +221,7 @@ public:
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE {
-        // onnx to openvino convertion: https://github.com/openvinotoolkit/openvino/blob/2023.1.0/src/frontends/onnx/frontend/src/op/instance_norm.cpp
+        // onnx to openvino conversion: https://github.com/openvinotoolkit/openvino/blob/2023.1.0/src/frontends/onnx/frontend/src/op/instance_norm.cpp
 
         auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
         const auto &input_shape = ieInpNode.get_shape();

modules/dnn/src/layers/matmul_layer.cpp

@@ -423,7 +423,7 @@ class MatMulLayerImpl CV_FINAL : public MatMulLayer {
         op->set_input_x1_by_name(*input_A_node, input_A_wrapper->name.c_str());
         op->update_input_desc_x1(*input_A_desc);
         // set inputs : x2
-        if (blobs.empty()) { // varaible input B
+        if (blobs.empty()) { // variable input B
             auto input_B_wrapper = inputs[1].dynamicCast<CannBackendWrapper>();
             auto input_B_desc = input_B_wrapper->getTensorDesc();
             auto input_B_node = nodes[1].dynamicCast<CannBackendNode>()->getOp();

modules/dnn/src/layers/nary_eltwise_layers.cpp

@@ -1036,7 +1036,7 @@ public:
         else if (op == OPERATION::LESS_EQUAL)
             node = std::make_shared<ov::op::v1::LessEqual>(inp0, inp1);
         // Ideally we should do this but int32 internal blobs are converted to float32 data type in inference.
-        // TODO: Remove data type convertion when we have type inference.
+        // TODO: Remove data type conversion when we have type inference.
         else if (op == OPERATION::MOD) {
             auto inp0_i64 = std::make_shared<ov::op::v0::Convert>(inp0, ov::element::i64);
             auto inp1_i64 = std::make_shared<ov::op::v0::Convert>(inp1, ov::element::i64);

modules/gapi/samples/onevpl_infer_with_advanced_device_selection.cpp

@@ -434,7 +434,7 @@ int main(int argc, char *argv[]) {
     //
     // - you should pass such wrappers as constructor arguments for each component in pipeline:
     //      a) use extended constructor for `onevpl::GSource` for activating predefined device & context
-    //      b) use `cfgContextParams` method of `cv::gapi::ie::Params` to enable `PreprocesingEngine`
+    //      b) use `cfgContextParams` method of `cv::gapi::ie::Params` to enable `PreprocessingEngine`
     // for predefined device & context
     //      c) use `InferenceEngine::ParamMap` to activate remote ctx in Inference Engine for given
     // device & context
@@ -577,14 +577,14 @@ int main(int argc, char *argv[]) {
     }
 #endif // HAVE_INF_ENGINE
 
-    // turn on VPP PreprocesingEngine if available & requested
+    // turn on VPP PreprocessingEngine if available & requested
     if (flow_settings->vpl_preproc_enable) {
         if (is_gpu(preproc_device)) {
-            // activate VPP PreprocesingEngine on GPU
+            // activate VPP PreprocessingEngine on GPU
             face_net.cfgPreprocessingParams(gpu_accel_device.value(),
                                             gpu_accel_ctx.value());
         } else {
-            // activate VPP PreprocesingEngine on CPU
+            // activate VPP PreprocessingEngine on CPU
             face_net.cfgPreprocessingParams(cpu_accel_device,
                                             cpu_accel_ctx);
         }

modules/imgcodecs/src/grfmt_tiff.cpp

@@ -988,7 +988,7 @@ bool  TiffDecoder::readData( Mat& img )
                                         break;
 
                                     default:
-                                        CV_LOG_ONCE_ERROR(NULL, "OpenCV TIFF(line " << __LINE__ << "): Unsupported convertion :"
+                                        CV_LOG_ONCE_ERROR(NULL, "OpenCV TIFF(line " << __LINE__ << "): Unsupported conversion :"
                                                                << " bpp = " << bpp << " ncn = " << (int)ncn
                                                                << " wanted_channels =" << wanted_channels  );
                                         break;