Merge pull request #3123 from sturkmen72:legacy_constants

Author: alalek

modules/cudaarithm/include/opencv2/cudaarithm.hpp

@@ -662,11 +662,11 @@ CV_EXPORTS_W void countNonZero(InputArray src, OutputArray dst, Stream& stream =
 @param dim Dimension index along which the matrix is reduced. 0 means that the matrix is reduced
 to a single row. 1 means that the matrix is reduced to a single column.
 @param reduceOp Reduction operation that could be one of the following:
--   **CV_REDUCE_SUM** The output is the sum of all rows/columns of the matrix.
--   **CV_REDUCE_AVG** The output is the mean vector of all rows/columns of the matrix.
--   **CV_REDUCE_MAX** The output is the maximum (column/row-wise) of all rows/columns of the
+-   **REDUCE_SUM** The output is the sum of all rows/columns of the matrix.
+-   **REDUCE_AVG** The output is the mean vector of all rows/columns of the matrix.
+-   **REDUCE_MAX** The output is the maximum (column/row-wise) of all rows/columns of the
 matrix.
--   **CV_REDUCE_MIN** The output is the minimum (column/row-wise) of all rows/columns of the
+-   **REDUCE_MIN** The output is the minimum (column/row-wise) of all rows/columns of the
 matrix.
 @param dtype When it is negative, the destination vector will have the same type as the source
 matrix. Otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels()) .
@@ -675,7 +675,7 @@ matrix. Otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channe
 The function reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
 1D vectors and performing the specified operation on the vectors until a single row/column is
 obtained. For example, the function can be used to compute horizontal and vertical projections of a
-raster image. In case of CV_REDUCE_SUM and CV_REDUCE_AVG , the output may have a larger element
+raster image. In case of REDUCE_SUM and REDUCE_AVG , the output may have a larger element
 bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction
 modes.
 

modules/xobjdetect/src/lbpfeatures.cpp

@@ -93,7 +93,7 @@ void CvLBPEvaluator::generateFeatures()
 
 CvLBPEvaluator::Feature::Feature()
 {
-    rect = cvRect(0, 0, 0, 0);
+    rect = Rect(0, 0, 0, 0);
 }
 
 CvLBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
@@ -108,7 +108,7 @@ CvLBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int
 
 void CvLBPEvaluator::Feature::calcPoints(int offset)
 {
-    Rect tr = rect = cvRect(x_, y_, block_w_, block_h_);
+    Rect tr = rect = Rect(x_, y_, block_w_, block_h_);
     CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
     tr.x += 2*rect.width;
     CV_SUM_OFFSETS( p[2], p[3], p[6], p[7], tr, offset )

modules/xobjdetect/src/precomp.hpp

@@ -46,14 +46,9 @@ the use of this software, even if advised of the possibility of such damage.
 #define __OPENCV_XOBJDETECT_PRECOMP_HPP__
 
 #include <opencv2/xobjdetect.hpp>
-
 #include <opencv2/core/utility.hpp>
-
 #include <opencv2/imgproc.hpp>
-#include <opencv2/imgproc/types_c.h>
-
 #include <opencv2/core.hpp>
-
 #include <opencv2/imgcodecs.hpp>
 #include <opencv2/objdetect.hpp>
 

modules/xobjdetect/src/waldboost.cpp

@@ -73,14 +73,14 @@ static void compute_min_step(const Mat &data_pos, const Mat &data_neg, size_t n_
 
     Mat reduced_pos, reduced_neg;
 
-    reduce(data_pos, reduced_pos, 1, CV_REDUCE_MIN);
-    reduce(data_neg, reduced_neg, 1, CV_REDUCE_MIN);
+    reduce(data_pos, reduced_pos, 1, REDUCE_MIN);
+    reduce(data_neg, reduced_neg, 1, REDUCE_MIN);
     min(reduced_pos, reduced_neg, data_min);
     data_min -= 0.01;
 
     Mat data_max;
-    reduce(data_pos, reduced_pos, 1, CV_REDUCE_MAX);
-    reduce(data_neg, reduced_neg, 1, CV_REDUCE_MAX);
+    reduce(data_pos, reduced_pos, 1, REDUCE_MAX);
+    reduce(data_neg, reduced_neg, 1, REDUCE_MAX);
     max(reduced_pos, reduced_neg, data_max);
     data_max += 0.01;
 

modules/xobjdetect/src/wbdetector.cpp

@@ -108,8 +108,8 @@ void WBDetectorImpl::train(
     vector<Mat> pos_imgs = read_imgs(pos_samples_path);
     vector<Mat> neg_imgs = sample_patches(neg_imgs_path, 24, 24, pos_imgs.size() * 10);
 
-    assert(pos_imgs.size());
-    assert(neg_imgs.size());
+    CV_Assert(pos_imgs.size());
+    CV_Assert(neg_imgs.size());
 
     int n_features;
     Mat pos_data, neg_data;
@@ -173,7 +173,7 @@ void WBDetectorImpl::train(
             if (confidences.rows > 0) {
                 Mat1i indices;
                 sortIdx(confidences, indices,
-                        CV_SORT_EVERY_COLUMN + CV_SORT_DESCENDING);
+                        SORT_EVERY_COLUMN + SORT_DESCENDING);
 
                 int win_count = min(max_per_image, confidences.rows);
                 win_count = min(win_count, stage_neg - bootstrap_count);
@@ -209,7 +209,7 @@ void WBDetectorImpl::detect(
     Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create();
     eval->init(params, 1, Size(24, 24));
     boost_.detect(eval, img, scales, bboxes, confidences);
-    assert(confidences.size() == bboxes.size());
+    CV_Assert(confidences.size() == bboxes.size());
 }
 
 Ptr<WBDetector>