Merge pull request #23109 from seanm:misc-warnings

* Fixed clang -Wnewline-eof warnings
* Fixed all trivial clang -Wextra-semi and -Wc++98-compat-extra-semi warnings
* Removed trailing semi from various macros
* Fixed various -Wunused-macros warnings
* Fixed some trivial -Wdocumentation warnings
* Fixed some -Wdocumentation-deprecated-sync warnings
* Fixed incorrect indentation
* Suppressed some clang warnings in 3rd party code
* Fixed QRCodeEncoder::Params documentation.

---------

Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>

modules/features2d/src/hal_replacement.hpp

@@ -64,9 +64,12 @@
 //! @{
 /**
    @brief Detects corners using the FAST algorithm, returns mask.
-   @param src_data,src_step Source image
-   @param dst_data,dst_step Destination mask
-   @param width,height Source image dimensions
+   @param src_data Source image data
+   @param src_step Source image step
+   @param dst_data Destination mask data
+   @param dst_step Destination mask step
+   @param width Source image width
+   @param height Source image height
    @param type FAST type
 */
 inline int hal_ni_FAST_dense(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height, cv::FastFeatureDetector::DetectorType type) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
@@ -89,8 +92,10 @@ inline int hal_ni_FAST_NMS(const uchar* src_data, size_t src_step, uchar* dst_da
 
 /**
    @brief Detects corners using the FAST algorithm.
-   @param src_data,src_step Source image
-   @param width,height Source image dimensions
+   @param src_data Source image data
+   @param src_step Source image step
+   @param width Source image width
+   @param height Source image height
    @param keypoints_data Pointer to keypoints
    @param keypoints_count Count of keypoints
    @param threshold Threshold for keypoint

modules/features2d/src/kaze/nldiffusion_functions.cpp

@@ -86,9 +86,9 @@ void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int
 /**
  * @brief This function computes the Perona and Malik conductivity coefficient g1
  * g1 = exp(-|dL|^2/k^2)
- * @param Lx First order image derivative in X-direction (horizontal)
- * @param Ly First order image derivative in Y-direction (vertical)
- * @param dst Output image
+ * @param _Lx First order image derivative in X-direction (horizontal)
+ * @param _Ly First order image derivative in Y-direction (vertical)
+ * @param _dst Output image
  * @param k Contrast factor parameter
  */
 void pm_g1(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
@@ -117,9 +117,9 @@ void pm_g1(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
 /**
  * @brief This function computes the Perona and Malik conductivity coefficient g2
  * g2 = 1 / (1 + dL^2 / k^2)
- * @param Lx First order image derivative in X-direction (horizontal)
- * @param Ly First order image derivative in Y-direction (vertical)
- * @param dst Output image
+ * @param _Lx First order image derivative in X-direction (horizontal)
+ * @param _Ly First order image derivative in Y-direction (vertical)
+ * @param _dst Output image
  * @param k Contrast factor parameter
  */
 void pm_g2(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
@@ -146,9 +146,9 @@ void pm_g2(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
 /* ************************************************************************* */
 /**
  * @brief This function computes Weickert conductivity coefficient gw
- * @param Lx First order image derivative in X-direction (horizontal)
- * @param Ly First order image derivative in Y-direction (vertical)
- * @param dst Output image
+ * @param _Lx First order image derivative in X-direction (horizontal)
+ * @param _Ly First order image derivative in Y-direction (vertical)
+ * @param _dst Output image
  * @param k Contrast factor parameter
  * @note For more information check the following paper: J. Weickert
  * Applications of nonlinear diffusion in image processing and computer vision,
@@ -183,9 +183,9 @@ void weickert_diffusivity(InputArray _Lx, InputArray _Ly, OutputArray _dst, floa
 /**
 * @brief This function computes Charbonnier conductivity coefficient gc
 * gc = 1 / sqrt(1 + dL^2 / k^2)
-* @param Lx First order image derivative in X-direction (horizontal)
-* @param Ly First order image derivative in Y-direction (vertical)
-* @param dst Output image
+* @param _Lx First order image derivative in X-direction (horizontal)
+* @param _Ly First order image derivative in Y-direction (vertical)
+* @param _dst Output image
 * @param k Contrast factor parameter
 * @note For more information check the following paper: J. Weickert
 * Applications of nonlinear diffusion in image processing and computer vision,
@@ -323,7 +323,7 @@ void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, in
  * @param _ky Vertical kernel values
  * @param dx Derivative order in X-direction (horizontal)
  * @param dy Derivative order in Y-direction (vertical)
- * @param scale_ Scale factor or derivative size
+ * @param scale Scale factor or derivative size
  */
 void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
     CV_INSTRUMENT_REGION();
@@ -415,7 +415,7 @@ private:
 /* ************************************************************************* */
 /**
 * @brief This function performs a scalar non-linear diffusion step
-* @param Ld2 Output image in the evolution
+* @param Ld Output image in the evolution
 * @param c Conductivity image
 * @param Lstep Previous image in the evolution
 * @param stepsize The step size in time units
@@ -490,7 +490,7 @@ void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsi
 /* ************************************************************************* */
 /**
 * @brief This function downsamples the input image using OpenCV resize
-* @param img Input image to be downsampled
+* @param src Input image to be downsampled
 * @param dst Output image with half of the resolution of the input image
 */
 void halfsample_image(const cv::Mat& src, cv::Mat& dst) {

modules/features2d/src/kaze/utils.h

@@ -6,7 +6,7 @@
  * @brief This function computes the value of a 2D Gaussian function
  * @param x X Position
  * @param y Y Position
- * @param sig Standard Deviation
+ * @param sigma Standard Deviation
  */
 inline float gaussian(float x, float y, float sigma) {
   return expf(-(x*x + y*y) / (2.0f*sigma*sigma));