Merge pull request #27338 from omahs:patch-1

Fix typos #27338

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

apps/visualisation/opencv_visualisation.cpp

@@ -113,7 +113,7 @@ int main( int argc, const char** argv )
     int timing = 1;
 
     // Value for cols of storing elements
-    int cols_prefered = 5;
+    int cols_preferred = 5;
 
     // Open the XML model
     FileStorage fs;
@@ -218,7 +218,7 @@ int main( int argc, const char** argv )
         for(int sid = 0; sid < (int)stage_features.size(); sid ++){
             if(draw_planes){
                 int features_nmbr = (int)stage_features[sid].size();
-                int cols = cols_prefered;
+                int cols = cols_preferred;
                 int rows = features_nmbr / cols;
                 if( (features_nmbr % cols) > 0){
                     rows++;
@@ -257,7 +257,7 @@ int main( int argc, const char** argv )
                 result_video.write(temp_window);
                 // Copy the feature image if needed
                 if(draw_planes){
-                    single_feature.copyTo(image_plane(Rect(0 + (fid%cols_prefered)*single_feature.cols, 0 + (fid/cols_prefered) * single_feature.rows, single_feature.cols, single_feature.rows)));
+                    single_feature.copyTo(image_plane(Rect(0 + (fid%cols_preferred)*single_feature.cols, 0 + (fid/cols_preferred) * single_feature.rows, single_feature.cols, single_feature.rows)));
                 }
                 putText(temp_metadata, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
                 putText(temp_metadata, meta2.str(), Point(15,40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
@@ -291,7 +291,7 @@ int main( int argc, const char** argv )
         for(int sid = 0; sid < (int)stage_features.size(); sid ++){
             if(draw_planes){
                 int features_nmbr = (int)stage_features[sid].size();
-                int cols = cols_prefered;
+                int cols = cols_preferred;
                 int rows = features_nmbr / cols;
                 if( (features_nmbr % cols) > 0){
                     rows++;
@@ -353,7 +353,7 @@ int main( int argc, const char** argv )
                     // Bottom right
                     rectangle(single_feature, Rect(resized_inner.x + 2*resized_inner.width, resized_inner.y + 2*resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), 1);
 
-                    single_feature.copyTo(image_plane(Rect(0 + (fid%cols_prefered)*single_feature.cols, 0 + (fid/cols_prefered) * single_feature.rows, single_feature.cols, single_feature.rows)));
+                    single_feature.copyTo(image_plane(Rect(0 + (fid%cols_preferred)*single_feature.cols, 0 + (fid/cols_preferred) * single_feature.rows, single_feature.cols, single_feature.rows)));
                 }
 
                 putText(temp_metadata, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -41,7 +41,7 @@ Assuming that we have successfully trained YOLOX model, the subsequent step invo
 running this model with OpenCV. There are several critical considerations to address before
 proceeding with this process. Let's delve into these aspects.
 
-### YOLO's Pre-proccessing & Output
+### YOLO's Pre-processing & Output
 
 Understanding the nature of inputs and outputs associated with YOLO family detectors is pivotal.
 These detectors, akin to most Deep Neural Networks (DNN), typically exhibit variation in input

hal/carotene/include/carotene/functions.hpp

@@ -1040,7 +1040,7 @@ namespace CAROTENE_NS {
                         s32 maxVal, size_t * maxLocPtr, s32 & maxLocCount, s32 maxLocCapacity);
 
     /*
-        Among each pixel `p` within `src` find min and max values and its first occurences
+        Among each pixel `p` within `src` find min and max values and its first occurrences
     */
     void minMaxLoc(const Size2D &size,
                    const s8 * srcBase, ptrdiff_t srcStride,

hal/openvx/include/ivx.hpp

@@ -1535,7 +1535,7 @@ public:
         return prevPtr;
     }
 
-    /// vxSwapImageHandle() wrapper for the case when no new pointers provided and previous ones are not needed (retrive memory back)
+    /// vxSwapImageHandle() wrapper for the case when no new pointers provided and previous ones are not needed (retrieve memory back)
     void swapHandle()
     { IVX_CHECK_STATUS( vxSwapImageHandle(ref, 0, 0, 0) ); }
 

modules/calib3d/src/usac.hpp

@@ -17,7 +17,7 @@ class Error : public Algorithm {
 public:
     // set model to use getError() function
     virtual void setModelParameters (const Mat &model) = 0;
-    // returns error of point wih @point_idx w.r.t. model
+    // returns error of point with @point_idx w.r.t. model
     virtual float getError (int point_idx) const = 0;
     virtual const std::vector<float> &getErrors (const Mat &model) = 0;
 };

modules/calib3d/test/test_fisheye.cpp

@@ -640,7 +640,7 @@ TEST_F(fisheyeTest, CalibrationWithFixedFocalLength)
     cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, theK, theD,
                            cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
 
-    // ensure that CALIB_FIX_FOCAL_LENGTH works and focal lenght has not changed
+    // ensure that CALIB_FIX_FOCAL_LENGTH works and focal length has not changed
     EXPECT_EQ(theK(0,0), K(0,0));
     EXPECT_EQ(theK(1,1), K(1,1));
 

modules/dnn/src/layers/recurrent_layers.cpp

@@ -405,7 +405,7 @@ public:
             //swap axis 0 and 1 input x
             cv::Mat tmp;
             // Since python input is 4 dimentional and C++ input 3 dimentinal
-            // we need to proccess each differently
+            // we need to process each differently
             if (input[0].dims == 4){
                 // here !!!
                 CV_Assert(input[0].size[3] == 1);

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -2645,7 +2645,7 @@ void TFImporter::parsePReLU(tensorflow::GraphDef& net, const tensorflow::NodeDef
     layerParams.blobs.resize(1);
 
     if (scales.dims == 3) {
-        // Considering scales from Keras wih HWC layout;
+        // Considering scales from Keras with HWC layout;
         transposeND(scales, {2, 0, 1}, layerParams.blobs[0]);
     } else {
         layerParams.blobs[0] = scales;

modules/imgcodecs/src/grfmt_jpeg2000_openjpeg.cpp

@@ -146,7 +146,7 @@ public:
         return (ptr_ - other.ptr_) / step_;
     }
 
-    /* Comparision */
+    /* Comparison */
     bool operator==(const ChannelsIterator<Traits>& other) const CV_NOEXCEPT
     {
         return ptr_ == other.ptr_;

modules/imgproc/test/test_drawing.cpp

@@ -789,7 +789,7 @@ TEST(Drawing, fillpoly_fully)
         cv::Mat labelImage(binary.size(), CV_32S);
         cv::Mat labelCentroids;
         int labels = cv::connectedComponents(binary, labelImage, 4);
-        EXPECT_EQ(2, labels) << "artifacts occured";
+        EXPECT_EQ(2, labels) << "artifacts occurred";
     }
 
     // check if filling went over border
@@ -878,7 +878,7 @@ PARAM_TEST_CASE(FillPolyFully, unsigned, unsigned, int, int, Point, cv::LineType
         cv::Mat labelImage(binary.size(), CV_32S);
         cv::Mat labelCentroids;
         int labels = cv::connectedComponents(binary, labelImage, 4);
-        EXPECT_EQ(2, labels) << "artifacts occured";
+        EXPECT_EQ(2, labels) << "artifacts occurred";
     }
 
     void check_filling_over_border(cv::Mat& img, const std::vector<cv::Point>& polygonPoints)

modules/ml/include/opencv2/ml.hpp

@@ -256,7 +256,7 @@ public:
 
     @param filename The input file name
     @param headerLineCount The number of lines in the beginning to skip; besides the header, the
-        function also skips empty lines and lines staring with `#`
+        function also skips empty lines and lines starting with `#`
     @param responseStartIdx Index of the first output variable. If -1, the function considers the
         last variable as the response
     @param responseEndIdx Index of the last output variable + 1. If -1, then there is single

modules/ml/src/gbt.cpp

@@ -256,7 +256,7 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag,
     // inside gbt learning process only regression decision trees are built
     data->is_classifier = false;
 
-    // preproccessing sample indices
+    // preprocessing sample indices
     if (_sample_idx)
     {
         int sample_idx_len = get_len(_sample_idx);

modules/videoio/src/cap_gstreamer.cpp

@@ -1162,7 +1162,7 @@ bool GStreamerCapture::retrieveFrame(int index, OutputArray dst)
         }
     }
 
-    CV_LOG_ERROR(NULL, "GStreamer(retrive): unrecognized index=" << index);
+    CV_LOG_ERROR(NULL, "GStreamer(retrieve): unrecognized index=" << index);
     return false;
 }
 

modules/videoio/src/cap_v4l.cpp

@@ -440,7 +440,7 @@ struct CvCaptureCAM_V4L CV_FINAL : public IVideoCapture
     bool convertableToRgb() const;
     void convertToRgb(const Buffer &currentBuffer);
 
-    bool havePendingFrame;  // true if next .grab() should be noop, .retrive() resets this flag
+    bool havePendingFrame;  // true if next .grab() should be noop, .retrieve() resets this flag
 };
 
 /***********************   Implementations  ***************************************/