Fix typos

modules/aruco/include/opencv2/aruco.hpp

@@ -354,7 +354,7 @@ class CV_EXPORTS_W GridBoard : public Board {
     // number of markers in X and Y directions
     int _markersX, _markersY;
 
-    // marker side lenght (normally in meters)
+    // marker side length (normally in meters)
     float _markerLength;
 
     // separation between markers in the grid

modules/aruco/src/apriltag_quad_thresh.cpp

@@ -661,7 +661,7 @@ int fit_quad(const Ptr<DetectorParameters> &_params, const Mat im, zarray_t *clu
     if (dot < 0)
         return 0;
 
-    // we now sort the points according to theta. This is a prepatory
+    // we now sort the points according to theta. This is a preparatory
     // step for segmenting them into four lines.
     if (1) {
         //        zarray_sort(cluster, pt_compare_theta);

modules/aruco/src/charuco.cpp

@@ -574,7 +574,7 @@ void drawDetectedCornersCharuco(InputOutputArray _image, InputArray _charucoCorn
 
 /**
   * Check if a set of 3d points are enough for calibration. Z coordinate is ignored.
-  * Only axis paralel lines are considered
+  * Only axis parallel lines are considered
   */
 static bool _arePointsEnoughForPoseEstimation(const vector< Point3f > &points) {
 

modules/aruco/src/dictionary.cpp

@@ -122,7 +122,7 @@ bool Dictionary::identify(const Mat &onlyBits, int &idx, int &rotation,
             }
         }
 
-        // if maxCorrection is fullfilled, return this one
+        // if maxCorrection is fulfilled, return this one
         if(currentMinDistance <= maxCorrectionRecalculed) {
             idx = m;
             rotation = currentRotation;

modules/aruco/tutorials/charuco_detection/charuco_detection.markdown

@@ -32,7 +32,7 @@ This class, as the rest of ChArUco functionalities, are defined in:
     #include <opencv2/aruco/charuco.hpp>
 @endcode
 
-To define a ```CharucoBoard```, it is necesary:
+To define a ```CharucoBoard```, it is necessary:
 
 - Number of chessboard squares in X direction.
 - Number of chessboard squares in Y direction.

modules/aruco/tutorials/charuco_diamond_detection/charuco_diamond_detection.markdown

@@ -102,7 +102,7 @@ function and, for each diamond, the corners are represented in the same order th
 starting with the top-left corner. The second returned parameter, ```diamondIds```, contains all the ids of the returned
 diamond corners in ```diamondCorners```. Each id is actually an array of 4 integers that can be represented with ```Vec4i```.
 
-The detected diamond can be visualized using the function ```drawDetectedDiamonds()``` which simply recieves the image and the diamond
+The detected diamond can be visualized using the function ```drawDetectedDiamonds()``` which simply receives the image and the diamond
 corners and ids:
 
 @code{.cpp}

modules/bioinspired/doc/retina.markdown

@@ -134,7 +134,7 @@ For more information, refer to the following papers :
 
 -   Please have a look at the reference work of Jeanny Herault that you can read in his book : @cite Herault2010
 
-This retina filter code includes the research contributions of phd/research collegues from which
+This retina filter code includes the research contributions of phd/research colleagues from which
 code has been redrawn by the author :
 
 -   take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color

modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp

@@ -75,7 +75,7 @@ static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, con
 		cvtColor(rgbIntImg, intGrayImage, cv::COLOR_BGR2GRAY);
 	}
 
-	// get histogram density probability in order to cut values under above edges limits (here 5-95%)... usefull for HDR pixel errors cancellation
+	// get histogram density probability in order to cut values under above edges limits (here 5-95%)... useful for HDR pixel errors cancellation
 	cv::Mat dst, hist;
 	int histSize = 256;
 	calcHist(&intGrayImage, 1, 0, cv::Mat(), hist, 1, &histSize, 0);
@@ -233,7 +233,7 @@ int main(int argc, char* argv[])
 		// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
 		retina->write("RetinaDefaultParameters.xml");
 
-		// desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
+		// desactivate Magnocellular pathway processing (motion information extraction) since it is not useful here
 		retina->activateMovingContoursProcessing(false);
 
 		// declare retina output buffers

modules/bioinspired/samples/cpp/OpenEXRimages_HDR_Retina_toneMapping.cpp

@@ -74,7 +74,7 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
          cvtColor(rgbIntImg, intGrayImage, cv::COLOR_BGR2GRAY);
      }
 
-     // get histogram density probability in order to cut values under above edges limits (here 5-95%)... usefull for HDR pixel errors cancellation
+     // get histogram density probability in order to cut values under above edges limits (here 5-95%)... useful for HDR pixel errors cancellation
      cv::Mat dst, hist;
      int histSize = 256;
      calcHist(&intGrayImage, 1, 0, cv::Mat(), hist, 1, &histSize, 0);
@@ -231,7 +231,7 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
          // save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
          retina->write("RetinaDefaultParameters.xml");
 
-         // desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
+         // desactivate Magnocellular pathway processing (motion information extraction) since it is not useful here
          retina->activateMovingContoursProcessing(false);
 
          // declare retina output buffers

modules/bioinspired/src/retina_ocl.cpp

@@ -1367,7 +1367,7 @@ void RetinaFilter::setGlobalParameters(const float OPLspatialResponse1, const fl
     _normalizeMagnoOutput_0_maxOutputValue = normalizeMagnoOutput_0_maxOutputValue;
     _maxOutputValue = maxOutputValue;
     _photoreceptorsPrefilter.setV0CompressionParameter(0.9f, maxInputValue, meanValue);
-    _photoreceptorsPrefilter.setLPfilterParameters(0, 0, 10, 3); // keeps low pass filter with low cut frequency in memory (usefull for the tone mapping function)
+    _photoreceptorsPrefilter.setLPfilterParameters(0, 0, 10, 3); // keeps low pass filter with low cut frequency in memory (useful for the tone mapping function)
     _ParvoRetinaFilter.setOPLandParvoFiltersParameters(0, OPLtemporalresponse1, OPLspatialResponse1, OPLassymetryGain, OPLtemporalresponse2, OPLspatialResponse2);
     _ParvoRetinaFilter.setV0CompressionParameter(0.9f, maxInputValue, meanValue);
     _MagnoRetinaFilter.setCoefficientsTable(LPfilterGain, LPfilterTemporalresponse, LPfilterSpatialResponse, MovingContoursExtractorCoefficient, 0, 2.0f * LPfilterSpatialResponse);
@@ -1433,7 +1433,7 @@ bool RetinaFilter::runFilter(const UMat &imageInput, const bool useAdaptiveFilte
 
     if (_useParvoOutput)
     {
-        _ParvoRetinaFilter.normalizeGrayOutputCentredSigmoide(); // models the saturation of the cells, usefull for visualisation of the ON-OFF Parvo Output, Bipolar cells outputs do not change !!!
+        _ParvoRetinaFilter.normalizeGrayOutputCentredSigmoide(); // models the saturation of the cells, useful for visualisation of the ON-OFF Parvo Output, Bipolar cells outputs do not change !!!
         _ParvoRetinaFilter.centerReductImageLuminance(); // best for further spectrum analysis
 
         if (_normalizeParvoOutput_0_maxOutputValue)

modules/ccalib/tutorials/omnidir_tutorial.markdown

@@ -153,7 +153,7 @@ The first step of stereo reconstruction is stereo rectification so that epipolar
 
 The API of stereo reconstruction for omnidrectional camera is ```omnidir::stereoReconstruct```. Here we use an example to show how it works.
 
-First, calibrate a stereo pair of cameras as describe above and get parameters like ```K1```, ```D1```, ```xi1```, ```K2```, ```D2```, ```xi2```, ```rvec```, ```tvec```. Then read two images from the first and second camera respectively, for instance, ```image1``` and ```image2```, which are shown below.
+First, calibrate a stereo pair of cameras as described above and get parameters like ```K1```, ```D1```, ```xi1```, ```K2```, ```D2```, ```xi2```, ```rvec```, ```tvec```. Then read two images from the first and second camera respectively, for instance, ```image1``` and ```image2```, which are shown below.
 
 ![image](img/imgs.jpg)
 

modules/cvv/src/controller/view_controller.hpp

@@ -191,13 +191,13 @@ class ViewController
 	void showCallTab(size_t tabId);
 
 	/**
-	 * @brief Shows the tab and opens it if neccessary.
+	 * @brief Shows the tab and opens it if necessary.
 	 * @param tabId id of the tab
 	 */
 	void showAndOpenCallTab(size_t tabId);
 
 	/**
-	 * @brief Opens the tab it if neccessary.
+	 * @brief Opens the tab if necessary.
 	 * @param tabId id of the tab
 	 */
 	void openCallTab(size_t tabId);

modules/cvv/src/extension_api/api.hpp

@@ -43,7 +43,7 @@ template <class MView> void addMatchView(const QString name)
 using TabFactory = controller::TabFactory;
 /**
  * @brief Introduces a new call-type.
- * @param factory A function that recieves a reference to a call and should
+ * @param factory A function that receives a reference to a call and should
  * return the appropriate
  * window.
  */

modules/cvv/src/gui/overview_group_subtable.hpp

@@ -72,7 +72,7 @@ class OverviewGroupSubtable : public QWidget
 	/**
 	 * @brief Set the displayed rows.
 	 * @note This method does some optimisations to only fully rebuild all
-	 * rows if neccessary.
+	 * rows if necessary.
 	 * @param newGroup new group of rows that will be displayed
 	 */
 	void setRowGroup(stfl::ElementGroup<OverviewTableRow> &newGroup);

modules/cvv/src/qtutil/matchview/cvvkeypoint.hpp

@@ -93,7 +93,7 @@ class CVVKeyPoint : public QGraphicsObject,public cv::KeyPoint
 
 signals:
 	/**
-	 * @brief this signal will be emited when the imagepoint in the scene
+	 * @brief this signal will be emitted when the imagepoint in the scene
 	 * has changed
 	 * @param visible it is true if this keypoint is in the visibleArea
 	 */

modules/cvv/src/qtutil/matchview/keypointmanagement.hpp

@@ -112,7 +112,7 @@ public slots:
 signals:
 
 	/**
-	 * @brief this signal will be emited when the selection was changed.
+	 * @brief this signal will be emitted when the selection was changed.
 	 * it can be used for syncronisation with other selector
 	 */
 	void updateSelection(const std::vector<cv::KeyPoint> &selection);

modules/cvv/src/qtutil/matchview/keypointselectionselector.hpp

@@ -11,7 +11,7 @@
 namespace cvv{ namespace qtutil{
 
 /**
- * @brief this class can use diffrent KeyPointSelection
+ * @brief this class can use different KeyPointSelection
  * you can register functions which take a std::vector<cv::KeyPoint> as argument.
  */
 class KeyPointSelectionSelector:public KeyPointSelection,public RegisterHelper<KeyPointSelection,std::vector<cv::KeyPoint>>{

modules/cvv/src/qtutil/matchview/keypointsettingsselector.hpp

@@ -11,7 +11,7 @@
 namespace cvv{ namespace qtutil{
 
 /**
- * @brief this class can use diffrent KeyPointSettings
+ * @brief this class can use different KeyPointSettings
  * you can register functios which take a std::vector<cv::DMatch> as argument.
  */
 class KeyPointSettingsSelector:public KeyPointSettings, public RegisterHelper<KeyPointSettings,std::vector<cv::KeyPoint>>{

modules/cvv/src/qtutil/matchview/keypointvaluechooser.hpp

@@ -11,7 +11,7 @@ namespace cvv{ namespace qtutil{
 
 /**
  * @brief this widget contains a combobox with the attributes of an keypoint as entry.
- * you cann call the method getChoosenValue which return the choosen value of the given keypoint
+ * you can call the method getChoosenValue which return the chosen value of the given keypoint
  */
 class KeyPointValueChooser:public QWidget{
 
@@ -25,8 +25,8 @@ class KeyPointValueChooser:public QWidget{
 	KeyPointValueChooser(QWidget *parent=nullptr);
 
 	/**
-	 * @brief returns the choosen value of the given keypoint
-	 * @return the choosen value of the given keypoint
+	 * @brief returns the chosen value of the given keypoint
+	 * @return the chosen value of the given keypoint
 	 */
 	double getChoosenValue(cv::KeyPoint keypoint);
 

modules/cvv/src/qtutil/matchview/matchmanagement.hpp

@@ -110,7 +110,7 @@ public slots:
 signals:
 
 	/**
-	 * @brief this signal will be emited when the selection was changed.
+	 * @brief this signal will be emitted when the selection was changed.
 	 * it can be used for syncronisation with other selector
 	 */
 	void updateSelection(const std::vector<cv::DMatch> &selection);

modules/cvv/src/qtutil/matchview/matchselectionselector.hpp

@@ -11,7 +11,7 @@
 namespace cvv{ namespace qtutil{
 
 /**
- * @brief this class can use diffrent MatchSelection
+ * @brief this class can use different MatchSelection
  * you can register functions which take a std::vector<cv::DMatch> as argument.
  */
 class MatchSelectionSelector:public MatchSelection,public RegisterHelper<MatchSelection,std::vector<cv::DMatch>>{

modules/cvv/src/qtutil/matchview/matchsettingsselector.hpp

@@ -11,7 +11,7 @@
 namespace cvv{ namespace qtutil{
 
 /**
- * @brief this class can use diffrent MatchSettings
+ * @brief this class can use different MatchSettings
  * you can register functios which take a std::vector<cv::DMatch> as argument.
  */
 class MatchSettingsSelector:public MatchSettings, public RegisterHelper<MatchSettings,std::vector<cv::DMatch>>{

modules/cvv/src/qtutil/matchview/singlecolorkeypointpen.hpp

@@ -13,7 +13,7 @@ namespace qtutil
 
 /**
  * This KeyPointPen return for all CVVKeyPoints the same Color,
- * the Color can be choosen by an QColorDialog
+ * the Color can be chosen by an QColorDialog
  */
 
 class SingleColorKeyPen : public KeyPointSettings

modules/cvv/src/qtutil/matchview/singlecolormatchpen.hpp

@@ -16,7 +16,7 @@ namespace qtutil
 
 /**
  * This MatchPen return for all CVVMatches the same Color,
- * the Color can be choosen by an QColorDialog
+ * the Color can be chosen by an QColorDialog
  */
 
 class SingleColorMatchPen : public MatchSettings

modules/cvv/src/qtutil/signalslot.hpp

@@ -44,7 +44,7 @@ class Signal : public QObject
 	}
 signals:
 	/**
-	 * @brief The signal emited by emitSignal.
+	 * @brief The signal emitted by emitSignal.
 	 */
 	void signal() const;
 };
@@ -158,7 +158,7 @@ class SignalMatRef : public QObject
 	}
 signals:
 	/**
-	 * @brief The signal emited by emitSignal.
+	 * @brief The signal emitted by emitSignal.
 	 */
 	void signal(cv::Mat &mat) const;
 };

modules/cvv/src/qtutil/util.hpp

@@ -73,9 +73,9 @@ QSet<QString> createStringSet(QString string);
 std::pair<bool, QString> typeToQString(const cv::Mat &mat);
 
 /**
- * @brief Returns a string descripton to a image conversion result.
+ * @brief Returns a string description to a image conversion result.
  * @param result The image conversion result.
- * @return The descripton.
+ * @return The description.
  */
 QString conversionResultToString(const ImageConversionResult &result);
 

modules/cvv/src/stfl/stfl_engine.hpp

@@ -47,7 +47,7 @@ template <typename Element> class STFLEngine
 	/**
 	 * @brief Constructs (and initializes) a new engine.
 	 * 
-	 * Use this constructor only if you want to have controll about everything.
+	 * Use this constructor only if you want to have control about everything.
 	 * Consider using the simple constructor in combination with the add*
 	 * commands instead.
 	 *

modules/datasets/samples/tr_icdar_benchmark.cpp

@@ -280,7 +280,7 @@ int main(int argc, char *argv[])
             ocr->run(grey, group_img, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
 
             output.erase(remove(output.begin(), output.end(), '\n'), output.end());
-            //cout << "OCR output = \"" << output << "\" lenght = " << output.size() << endl;
+            //cout << "OCR output = \"" << output << "\" length = " << output.size() << endl;
 
             if (output.size() < 3)
                 continue;

modules/datasets/samples/tr_svt_benchmark.cpp

@@ -233,7 +233,7 @@ int main(int argc, char *argv[])
             ocr->run(group_img, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
 
             output.erase(remove(output.begin(), output.end(), '\n'), output.end());
-            //cout << "OCR output = \"" << output << "\" lenght = " << output.size() << endl;
+            //cout << "OCR output = \"" << output << "\" length = " << output.size() << endl;
             if (output.size() < 3)
                 continue;
 

modules/face/include/opencv2/face.hpp

@@ -112,7 +112,7 @@ Here is an example of setting a threshold for the Eigenfaces method, when creati
 int num_components = 10;
 double threshold = 10.0;
 // Then if you want to have a cv::FaceRecognizer with a confidence threshold,
-// create the concrete implementation with the appropiate parameters:
+// create the concrete implementation with the appropriate parameters:
 Ptr<FaceRecognizer> model = EigenFaceRecognizer::create(num_components, threshold);
 @endcode
 

modules/face/include/opencv2/face/face_alignment.hpp

@@ -23,7 +23,7 @@ class CV_EXPORTS_W FacemarkKazemi : public Facemark
         unsigned long tree_depth;
         /// num_trees_per_cascade_level This stores number of trees fit per cascade level.
         unsigned long num_trees_per_cascade_level;
-        /// learning_rate stores the learning rate in gradient boosting, also reffered as shrinkage.
+        /// learning_rate stores the learning rate in gradient boosting, also referred as shrinkage.
         float learning_rate;
         /// oversampling_amount stores number of initialisations used to create training samples.
         unsigned long oversampling_amount;

modules/face/include/opencv2/face/facemark_train.hpp

@@ -249,7 +249,7 @@ Ptr<Facemark> facemark = FacemarkLBF::create();
 
 The typical pipeline for facemark detection is listed as follows:
 - (Non-mandatory) Set a user defined face detection using FacemarkTrain::setFaceDetector.
-  The facemark algorithms are desgined to fit the facial points into a face.
+  The facemark algorithms are designed to fit the facial points into a face.
   Therefore, the face information should be provided to the facemark algorithm.
   Some algorithms might provides a default face recognition function.
   However, the users might prefer to use their own face detector to obtains the best possible detection result.

modules/fuzzy/tutorials/inpainting/inpainting.markdown

@@ -72,7 +72,7 @@ Using the masks, we applied three different kind of corruption on the same input
 
 ![input1, input2 and input3](images/fuzzy_inp_input.jpg)
 
-> Do not forget that in real life usage, images `input1`, `input2` and `input3` are created naturaly and used as the input directly.
+> Do not forget that in real life usage, images `input1`, `input2` and `input3` are created naturally and used as the input directly.
 
 Declaration of output images follows. In the following lines, the method of inpainting is applied. Let me explain three different algorithms one by one.
 

modules/hfs/include/opencv2/hfs.hpp

@@ -100,7 +100,7 @@ CV_WRAP virtual float getSpatialWeight() = 0;
 * above(the SLIC stage). It describes the size of each
 * superpixel when initializing SLIC. Every superpixel
 * approximately has \f$slicSpixelSize \times slicSpixelSize\f$
-* pixels in the begining.
+* pixels in the beginning.
 */
 CV_WRAP virtual void setSlicSpixelSize(int n) = 0;
 CV_WRAP virtual int getSlicSpixelSize() = 0;

modules/line_descriptor/include/opencv2/line_descriptor/descriptor.hpp

@@ -214,7 +214,7 @@ class CV_EXPORTS_W BinaryDescriptor : public Algorithm
   @param parameters configuration parameters BinaryDescriptor::Params
 
   If no argument is provided, constructor sets default values (see comments in the code snippet in
-  previous section). Default values are strongly reccomended.
+  previous section). Default values are strongly recommended.
   */
   BinaryDescriptor( const BinaryDescriptor::Params &parameters = BinaryDescriptor::Params() );
 

modules/line_descriptor/src/binary_descriptor.cpp

@@ -807,7 +807,7 @@ int BinaryDescriptor::OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines )
         float diffNearThreshold = ( tempValue > 6 ) ? ( tempValue ) : 6;
         diffNearThreshold = ( diffNearThreshold < 12 ) ? diffNearThreshold : 12;
 
-        /* compute scaled lenght of current line */
+        /* compute scaled length of current line */
         dx = fabs( edLineVec_[octaveCount]->lineEndpoints_[lineCurId][0] - edLineVec_[octaveCount]->lineEndpoints_[lineCurId][2] );  //x1-x2
         dy = fabs( edLineVec_[octaveCount]->lineEndpoints_[lineCurId][1] - edLineVec_[octaveCount]->lineEndpoints_[lineCurId][3] );  //y1-y2
         length = scale[octaveCount] * sqrt( dx * dx + dy * dy );

modules/matlab/generator/filters.py

@@ -18,7 +18,7 @@ def ninputs(fun):
 def outputs(args):
     '''Determines whether any of the given arguments is an output
     reference, and returns a list of only those elements.
-    In OpenCV, output references are preceeded by CV_OUT or has *OutputArray* type
+    In OpenCV, output references are preceded by CV_OUT or has *OutputArray* type
     '''
     try:
       return [arg for arg in args['only'] if arg.O and not arg.I]

modules/rgbd/include/opencv2/rgbd/dynafu.hpp

@@ -189,7 +189,7 @@ class CV_EXPORTS_W DynaFu
       Input image is converted to CV_32F internally if has another type.
 
     @param depth one-channel image which size and depth scale is described in algorithm's parameters
-    @return true if succeded to align new frame with current scene, false if opposite
+    @return true if succeeded to align new frame with current scene, false if opposite
     */
     CV_WRAP virtual bool update(InputArray depth) = 0;