Fix some typos in modules/.

modules/core/include/opencv2/core/mat.hpp

@@ -2290,7 +2290,7 @@ public:
     const_iterator begin() const;
     const_iterator end() const;
 
-    //! template methods for for operation over all matrix elements.
+    //! template methods for operation over all matrix elements.
     // the operations take care of skipping gaps in the end of rows (if any)
     template<typename Functor> void forEach(const Functor& operation);
     template<typename Functor> void forEach(const Functor& operation) const;

modules/cudafilters/include/opencv2/cudafilters.hpp

@@ -318,7 +318,7 @@ CV_EXPORTS Ptr<Filter> createColumnSumFilter(int srcType, int dstType, int ksize
 
 /** @brief Performs median filtering for each point of the source image.
 
-@param srcType type of of source image. Only CV_8UC1 images are supported for now.
+@param srcType type of source image. Only CV_8UC1 images are supported for now.
 @param windowSize Size of the kernerl used for the filtering. Uses a (windowSize x windowSize) filter.
 @param partition Specifies the parallel granularity of the workload. This parameter should be used GPU experts when optimizing performance.
 

modules/cudafilters/src/cuda/median_filter.cu

@@ -235,7 +235,7 @@ namespace cv { namespace cuda { namespace device
         }
         __syncthreads();
 
-        // For 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 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/dnn/src/darknet/darknet_io.cpp

@@ -791,7 +791,7 @@ namespace cv {
                             if (layers_vec.size() > 1)
                             {
                                 // layer ids in layers_vec - inputs of Slice layers
-                                // after adding offset to layers_vec: layer ids - ouputs of Slice layers
+                                // after adding offset to layers_vec: layer ids - outputs of Slice layers
                                 for (size_t k = 0; k < layers_vec.size(); ++k)
                                     layers_vec[k] += layers_vec.size();
 

modules/dnn/src/onnx/opencv-onnx.proto

@@ -61,7 +61,7 @@ enum Version {
   // The version field is always serialized and we will use it to store the
   // version that the  graph is generated from. This helps us set up version
   // control.
-  // For the IR, we are using simple numbers starting with with 0x00000001,
+  // For the IR, we are using simple numbers starting with 0x00000001,
   // which was the version we published on Oct 10, 2017.
   IR_VERSION_2017_10_10 = 0x0000000000000001;
 

modules/imgproc/include/opencv2/imgproc.hpp

@@ -1537,7 +1537,7 @@ CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
 For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
 pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
 
-The unnormalized square box filter can be useful in computing local image statistics such as the the local
+The unnormalized square box filter can be useful in computing local image statistics such as the local
 variance and standard deviation around the neighborhood of a pixel.
 
 @param src input image

modules/imgproc/src/opencl/medianFilter.cl

@@ -43,7 +43,7 @@
 
 #ifdef USE_4OPT
 
-//Utility macros for for 1,2,4 channel images:
+//Utility macros for 1,2,4 channel images:
 //  - LOAD4/STORE4 - load/store 4-pixel groups from/to global memory
 
 //  - SHUFFLE4_3/SHUFFLE4_5 - rearrange scattered border/central pixels into regular 4-pixel variables

modules/imgproc/test/test_convhull.cpp

@@ -1015,7 +1015,7 @@ int CV_MinCircleTest::validate_test_results( int test_case_idx )
     if( point_count >= 2 && (j < 2 || (j == 2 && cvTsDist(v[0],v[1]) < (radius-1)*2/eps)) )
     {
         ts->printf( cvtest::TS::LOG,
-            "There should be at at least 3 points near the circle boundary or 2 points on the diameter\n" );
+            "There should be at least 3 points near the circle boundary or 2 points on the diameter\n" );
         code = cvtest::TS::FAIL_BAD_ACCURACY;
         goto _exit_;
     }

modules/ml/src/lr.cpp

@@ -126,7 +126,7 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
     int num_classes = (int) this->forward_mapper.size();
     if(num_classes < 2)
     {
-        CV_Error( CV_StsBadArg, "data should have atleast 2 classes" );
+        CV_Error( CV_StsBadArg, "data should have at least 2 classes" );
     }
 
     // add a column of ones to the data (bias/intercept term)

modules/videoio/src/cap_gstreamer.cpp

@@ -334,7 +334,7 @@ GStreamerCapture::~GStreamerCapture()
 /*!
  * \brief CvCapture_GStreamer::grabFrame
  * \return
- * Grabs a sample from the pipeline, awaiting consumation by retreiveFrame.
+ * Grabs a sample from the pipeline, awaiting consumation by retrieveFrame.
  * The pipeline is started if it was not running yet
  */
 bool GStreamerCapture::grabFrame()

modules/videoio/src/cap_intelperc.cpp

@@ -507,21 +507,21 @@ bool IntelPerCStreamDepth::setProperty(int propIdx, double propVal)
 }
 bool IntelPerCStreamDepth::retrieveDepthAsOutputArray(cv::OutputArray image)
 {
-    return retriveFrame(CV_16SC1, 0, image);
+    return retrieveFrame(CV_16SC1, 0, image);
 }
 bool IntelPerCStreamDepth::retrieveIRAsOutputArray(cv::OutputArray image)
 {
-    return retriveFrame(CV_16SC1, 1, image);
+    return retrieveFrame(CV_16SC1, 1, image);
 }
 bool IntelPerCStreamDepth::retrieveUVAsOutputArray(cv::OutputArray image)
 {
-    return retriveFrame(CV_32FC2, 2, image);
+    return retrieveFrame(CV_32FC2, 2, image);
 }
 bool IntelPerCStreamDepth::validProfile(const PXCCapture::VideoStream::ProfileInfo& pinfo)
 {
     return (PXCImage::COLOR_FORMAT_DEPTH == pinfo.imageInfo.format);
 }
-bool IntelPerCStreamDepth::retriveFrame(int type, int planeIdx, cv::OutputArray frame)
+bool IntelPerCStreamDepth::retrieveFrame(int type, int planeIdx, cv::OutputArray frame)
 {
     if (!m_pxcImage.IsValid())
         return false;

modules/videoio/src/cap_intelperc.hpp

@@ -85,7 +85,7 @@ public:
 protected:
     virtual bool validProfile(const PXCCapture::VideoStream::ProfileInfo& pinfo);
 protected:
-    bool retriveFrame(int type, int planeIdx, OutputArray frame);
+    bool retrieveFrame(int type, int planeIdx, OutputArray frame);
 };
 
 class VideoCapture_IntelPerC : public IVideoCapture

modules/videoio/src/cap_libv4l.cpp

@@ -1858,7 +1858,7 @@ static int icvSetPropertyCAM_V4L(CvCaptureCAM_V4L* capture, int property_id, dou
         retval = icvSetControl(capture, property_id, value);
     }
 
-    /* return the the status */
+    /* return the status */
     return retval;
 }
 

modules/videoio/test/test_video_io.cpp

@@ -261,7 +261,7 @@ public:
             if (cvtest::debugLevel > 0)
                 std::cout << "i = " << i << ": timestamp = " << timestamp << std::endl;
             const double frame_period = 1000.f/bunny_param.getFps();
-            // NOTE: eps == frame_period, because videoCapture returns frame begining timestamp or frame end
+            // NOTE: eps == frame_period, because videoCapture returns frame beginning timestamp or frame end
             // timestamp depending on codec and back-end. So the first frame has timestamp 0 or frame_period.
             EXPECT_NEAR(timestamp, i*frame_period, frame_period) << "i=" << i;
         }