int to proper enum type

apps/interactive-calibration/calibController.cpp

@@ -221,7 +221,7 @@ void calib::calibDataController::filterFrames()
             mCalibData->allCharucoIds.erase(mCalibData->allCharucoIds.begin() + worstElemIndex);
         }
 
-        cv::Mat newErrorsVec = cv::Mat((int)numberOfFrames - 1, 1, CV_64F);
+        cv::Mat newErrorsVec = cv::Mat((int)numberOfFrames - 1, 1, CV_64FC1);
         std::copy(mCalibData->perViewErrors.ptr<double>(0),
                   mCalibData->perViewErrors.ptr<double>((int)worstElemIndex), newErrorsVec.ptr<double>(0));
         if((int)worstElemIndex < (int)numberOfFrames-1) {

apps/interactive-calibration/frameProcessor.cpp

@@ -212,7 +212,7 @@ bool CalibProcessor::checkLastFrame()
     const double badAngleThresh = 40;
 
     if(!mCalibData->cameraMatrix.total()) {
-        tmpCamMatrix = cv::Mat::eye(3, 3, CV_64F);
+        tmpCamMatrix = cv::Mat::eye(3, 3, CV_64FC1);
         tmpCamMatrix.at<double>(0,0) = 20000;
         tmpCamMatrix.at<double>(1,1) = 20000;
         tmpCamMatrix.at<double>(0,2) = mCalibData->imageSize.height/2;

apps/interactive-calibration/rotationConverters.cpp

@@ -17,7 +17,7 @@ void calib::Euler(const cv::Mat& src, cv::Mat& dst, int argType)
     if((src.rows == 3) && (src.cols == 3))
     {
         //convert rotation matrix to 3 angles (pitch, yaw, roll)
-        dst = cv::Mat(3, 1, CV_64F);
+        dst = cv::Mat(3, 1, CV_64FC1);
         double pitch, yaw, roll;
 
         if(src.at<double>(0,2) < -0.998)
@@ -78,9 +78,9 @@ void calib::Euler(const cv::Mat& src, cv::Mat& dst, int argType)
         else if(argType != CALIB_RADIANS)
             CV_Error(cv::Error::StsBadFlag, "Invalid argument type");
 
-        dst = cv::Mat(3, 3, CV_64F);
-        cv::Mat M(3, 3, CV_64F);
-        cv::Mat i = cv::Mat::eye(3, 3, CV_64F);
+        dst = cv::Mat(3, 3, CV_64FC1);
+        cv::Mat M(3, 3, CV_64FC1);
+        cv::Mat i = cv::Mat::eye(3, 3, CV_64FC1);
         i.copyTo(dst);
         i.copyTo(M);
 

apps/traincascade/HOGfeatures.cpp

@@ -156,13 +156,13 @@ void CvHOGEvaluator::Feature::write(FileStorage &fs, int featComponentIdx) const
 
 void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const
 {
-    CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
+    CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC3 );
     int x, y, binIdx;
 
     Size gradSize(img.size());
     Size histSize(histogram[0].size());
-    Mat grad(gradSize, CV_32F);
-    Mat qangle(gradSize, CV_8U);
+    Mat grad(gradSize, CV_32FC1);
+    Mat qangle(gradSize, CV_8UC1);
 
     AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
     int* xmap = mapbuf.data() + 1;
@@ -178,10 +178,10 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
     int width = gradSize.width;
     AutoBuffer<float> _dbuf(width*4);
     float* dbuf = _dbuf.data();
-    Mat Dx(1, width, CV_32F, dbuf);
-    Mat Dy(1, width, CV_32F, dbuf + width);
-    Mat Mag(1, width, CV_32F, dbuf + width*2);
-    Mat Angle(1, width, CV_32F, dbuf + width*3);
+    Mat Dx(1, width, CV_32FC1, dbuf);
+    Mat Dy(1, width, CV_32FC1, dbuf + width);
+    Mat Mag(1, width, CV_32FC1, dbuf + width*2);
+    Mat Angle(1, width, CV_32FC1, dbuf + width*3);
 
     float angleScale = (float)(nbins/CV_PI);
 

apps/traincascade/boost.cpp

@@ -80,7 +80,8 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
 
     __CV_BEGIN__;
 
-    int i, idx_total, idx_selected = 0, step, type, prev = INT_MIN, is_sorted = 1;
+    int i, idx_total, idx_selected = 0, step, prev = INT_MIN, is_sorted = 1;
+    ElemType type;
     uchar* srcb = 0;
     int* srci = 0;
     int* dsti;
@@ -658,7 +659,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
         (MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
     split_heap = cvCreateSet( 0, sizeof(*split_heap), maxSplitSize, tree_storage );
 
-    priors = cvCreateMat( 1, get_num_classes(), CV_64F );
+    priors = cvCreateMat( 1, get_num_classes(), CV_64FC1);
     cvSet(priors, cvScalar(1));
     priors_mult = cvCloneMat( priors );
     counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
@@ -1423,11 +1424,11 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
         cvReleaseMat( &subsample_mask );
         cvReleaseMat( &weights );
 
-        orig_response = cvCreateMat( 1, n, CV_32S );
-        weak_eval = cvCreateMat( 1, n, CV_64F );
-        subsample_mask = cvCreateMat( 1, n, CV_8U );
-        weights = cvCreateMat( 1, n, CV_64F );
-        subtree_weights = cvCreateMat( 1, n + 2, CV_64F );
+        orig_response = cvCreateMat( 1, n, CV_32SC1);
+        weak_eval = cvCreateMat( 1, n, CV_64FC1);
+        subsample_mask = cvCreateMat( 1, n, CV_8UC1);
+        weights = cvCreateMat( 1, n, CV_64FC1);
+        subtree_weights = cvCreateMat( 1, n + 2, CV_64FC1);
 
         if (data->is_buf_16u)
         {
@@ -1464,7 +1465,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
 
         if( params.boost_type == LOGIT )
         {
-            sum_response = cvCreateMat( 1, n, CV_64F );
+            sum_response = cvCreateMat( 1, n, CV_64FC1);
 
             for( int i = 0; i < n; i++ )
             {

apps/traincascade/old_ml_boost.cpp

@@ -1179,11 +1179,11 @@ CvBoost::update_weights( CvBoostTree* tree )
         cvReleaseMat( &weights );
         cvReleaseMat( &subtree_weights );
 
-        CV_CALL( orig_response = cvCreateMat( 1, n, CV_32S ));
-        CV_CALL( weak_eval = cvCreateMat( 1, n, CV_64F ));
-        CV_CALL( subsample_mask = cvCreateMat( 1, n, CV_8U ));
-        CV_CALL( weights = cvCreateMat( 1, n, CV_64F ));
-        CV_CALL( subtree_weights = cvCreateMat( 1, n + 2, CV_64F ));
+        CV_CALL( orig_response = cvCreateMat( 1, n, CV_32SC1));
+        CV_CALL( weak_eval = cvCreateMat( 1, n, CV_64FC1));
+        CV_CALL( subsample_mask = cvCreateMat( 1, n, CV_8UC1));
+        CV_CALL( weights = cvCreateMat( 1, n, CV_64FC1));
+        CV_CALL( subtree_weights = cvCreateMat( 1, n + 2, CV_64FC1));
 
         if( data->have_priors )
         {
@@ -1237,7 +1237,7 @@ CvBoost::update_weights( CvBoostTree* tree )
 
         if( params.boost_type == LOGIT )
         {
-            CV_CALL( sum_response = cvCreateMat( 1, n, CV_64F ));
+            CV_CALL( sum_response = cvCreateMat( 1, n, CV_64FC1));
 
             for( i = 0; i < n; i++ )
             {
@@ -1274,8 +1274,8 @@ CvBoost::update_weights( CvBoostTree* tree )
             cvXorS( subsample_mask, cvScalar(1.), subsample_mask );
             data->get_vectors( subsample_mask, values, missing, 0 );
 
-            _sample = cvMat( 1, data->var_count, CV_32F );
-            _mask = cvMat( 1, data->var_count, CV_8U );
+            _sample = cvMat( 1, data->var_count, CV_32FC1);
+            _mask = cvMat( 1, data->var_count, CV_8UC1);
 
             // run tree through all the non-processed samples
             for( i = 0; i < n; i++ )
@@ -1492,8 +1492,8 @@ CvBoost::get_active_vars( bool absolute_idx )
         const CvDTreeNode* node;
 
         assert(!active_vars && !active_vars_abs);
-        mask = cvCreateMat( 1, data->var_count, CV_8U );
-        inv_map = cvCreateMat( 1, data->var_count, CV_32S );
+        mask = cvCreateMat( 1, data->var_count, CV_8UC1);
+        inv_map = cvCreateMat( 1, data->var_count, CV_32SC1);
         cvZero( mask );
         cvSet( inv_map, cvScalar(-1) );
 
@@ -1533,8 +1533,8 @@ CvBoost::get_active_vars( bool absolute_idx )
 
         //if ( nactive_vars > 0 )
         {
-            active_vars = cvCreateMat( 1, nactive_vars, CV_32S );
-            active_vars_abs = cvCreateMat( 1, nactive_vars, CV_32S );
+            active_vars = cvCreateMat( 1, nactive_vars, CV_32SC1);
+            active_vars_abs = cvCreateMat( 1, nactive_vars, CV_32SC1);
 
             have_active_cat_vars = false;
 

apps/traincascade/old_ml_data.cpp

@@ -213,7 +213,7 @@ int CvMLData::read_csv(const char* filename)
     storage = cvCreateMemStorage();
     seq = cvCreateSeq( 0, sizeof(*seq), cols_count*sizeof(float), storage );
 
-    var_types = cvCreateMat( 1, cols_count, CV_8U );
+    var_types = cvCreateMat( 1, cols_count, CV_8UC1);
     cvZero( var_types );
     var_types_ptr = var_types->data.ptr;
 
@@ -245,7 +245,7 @@ int CvMLData::read_csv(const char* filename)
     fclose(file);
 
     values = cvCreateMat( seq->total, cols_count, CV_32FC1 );
-    missing = cvCreateMat( seq->total, cols_count, CV_8U );
+    missing = cvCreateMat( seq->total, cols_count, CV_8UC1);
     var_idx_mask = cvCreateMat( 1, values->cols, CV_8UC1 );
     cvSet( var_idx_mask, cvRealScalar(1) );
     train_sample_count = seq->total;

apps/traincascade/old_ml_inner_functions.cpp

@@ -206,7 +206,8 @@ cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_
 
     __BEGIN__;
 
-    int i, idx_total, idx_selected = 0, step, type, prev = INT_MIN, is_sorted = 1;
+    int i, idx_total, idx_selected = 0, step, prev = INT_MIN, is_sorted = 1;
+    ElemType type;
     uchar* srcb = 0;
     int* srci = 0;
     int* dsti;
@@ -1062,7 +1063,7 @@ cvPreparePredictData( const CvArr* _sample, int dims_all,
 
 
 static void
-icvConvertDataToSparse( const uchar* src, int src_step, int src_type,
+icvConvertDataToSparse(const uchar* src, int src_step, int src_type,
                         uchar* dst, int dst_step, int dst_type,
                         CvSize size, int* idx )
 {

apps/traincascade/old_ml_tree.cpp

@@ -647,7 +647,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
     {
         int m = get_num_classes();
         double sum = 0;
-        CV_CALL( priors = cvCreateMat( 1, m, CV_64F ));
+        CV_CALL( priors = cvCreateMat( 1, m, CV_64FC1));
         for( i = 0; i < m; i++ )
         {
             double val = have_priors ? params.priors[i] : 1.;
@@ -3390,7 +3390,7 @@ void CvDTree::prune_cv()
     double min_err = 0, min_err_se = 0;
     int min_idx = -1;
 
-    CV_CALL( ab = cvCreateMat( 1, 256, CV_64F ));
+    CV_CALL( ab = cvCreateMat( 1, 256, CV_64FC1));
 
     // build the main tree sequence, calculate alpha's
     for(;;tree_count++)
@@ -3401,7 +3401,7 @@ void CvDTree::prune_cv()
 
         if( ab->cols <= tree_count )
         {
-            CV_CALL( temp = cvCreateMat( 1, ab->cols*3/2, CV_64F ));
+            CV_CALL( temp = cvCreateMat( 1, ab->cols*3/2, CV_64FC1));
             for( ti = 0; ti < ab->cols; ti++ )
                 temp->data.db[ti] = ab->data.db[ti];
             cvReleaseMat( &ab );
@@ -3420,7 +3420,7 @@ void CvDTree::prune_cv()
             ab->data.db[ti] = sqrt(ab->data.db[ti]*ab->data.db[ti+1]);
         ab->data.db[tree_count-1] = DBL_MAX*0.5;
 
-        CV_CALL( err_jk = cvCreateMat( cv_n, tree_count, CV_64F ));
+        CV_CALL( err_jk = cvCreateMat( cv_n, tree_count, CV_64FC1));
         err = err_jk->data.db;
 
         for( j = 0; j < cv_n; j++ )
@@ -3744,7 +3744,7 @@ const CvMat* CvDTree::get_var_importance()
         double* importance;
         if( !node )
             return 0;
-        var_importance = cvCreateMat( 1, data->var_count, CV_64F );
+        var_importance = cvCreateMat( 1, data->var_count, CV_64FC1);
         cvZero( var_importance );
         importance = var_importance->data.db;
 

modules/calib3d/include/opencv2/calib3d.hpp

@@ -1613,7 +1613,7 @@ points1 and points2 are the same input for findEssentialMat. :
     }
 
     // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
-    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
+    Mat cameraMatrix = Mat::eye(3, 3, CV_64FC1);
 
     Mat E, R, t, mask;
 
@@ -1799,7 +1799,7 @@ perspectiveTransform .
 CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity,
                                       OutputArray _3dImage, InputArray Q,
                                       bool handleMissingValues = false,
-                                      int ddepth = -1 );
+                                      ElemDepth ddepth = CV_DEPTH_AUTO);
 
 /** @brief Calculates the Sampson Distance between two points.
 
@@ -2310,7 +2310,7 @@ namespace fisheye
     @param map2 The second output map.
      */
     CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P,
-        const cv::Size& size, int m1type, OutputArray map1, OutputArray map2);
+        const cv::Size& size, ElemType m1type, OutputArray map1, OutputArray map2);
 
     /** @brief Transforms an image to compensate for fisheye lens distortion.
 

modules/calib3d/perf/perf_affine2d.cpp

@@ -61,7 +61,7 @@ static Mat rngPartialAffMat() {
     double ty = rngIn(-2, 2);
     double aff[2*3] = { std::cos(theta) * scale, -std::sin(theta) * scale, tx,
                         std::sin(theta) * scale,  std::cos(theta) * scale, ty };
-    return Mat(2, 3, CV_64F, aff).clone();
+    return Mat(2, 3, CV_64FC1, aff).clone();
 }
 
 PERF_TEST_P( EstimateAffine, EstimateAffine2D, ESTIMATE_PARAMS )
@@ -72,7 +72,7 @@ PERF_TEST_P( EstimateAffine, EstimateAffine2D, ESTIMATE_PARAMS )
     const int method = get<2>(params);
     const size_t refining = get<3>(params);
 
-    Mat aff(2, 3, CV_64F);
+    Mat aff(2, 3, CV_64FC1);
     cv::randu(aff, -2., 2.);
 
     // LMEDS can't handle more than 50% outliers (by design)

modules/calib3d/perf/perf_pnp.cpp

@@ -85,7 +85,7 @@ PERF_TEST_P(PointsNum_Algo, solvePnPSmallPoints,
     warmup(tvec, WARMUP_RNG);
 
     // normalize Rodrigues vector
-    Mat rvec_tmp = Mat::eye(3, 3, CV_32F);
+    Mat rvec_tmp = Mat::eye(3, 3, CV_32FC1);
     cv::Rodrigues(rvec, rvec_tmp);
     cv::Rodrigues(rvec_tmp, rvec);
 
@@ -122,7 +122,7 @@ PERF_TEST_P(PointsNum, DISABLED_SolvePnPRansac, testing::Values(5, 3*9, 7*13))
     camera_mat.at<float>(2, 0) = 0.f;
     camera_mat.at<float>(2, 1) = 0.f;
 
-    Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
+    Mat dist_coef(1, 8, CV_32FC1, cv::Scalar::all(0));
 
     vector<cv::Point2f> image_vec;
 

modules/calib3d/perf/perf_stereosgbm.cpp

@@ -63,7 +63,7 @@ PERF_TEST_P( TestStereoCorresp, DISABLED_TooLongInDebug_SGBM, Combine(Values(Siz
 
     Mat src_left(sz, CV_8UC3);
     Mat src_right(sz, CV_8UC3);
-    Mat dst(sz, CV_16S);
+    Mat dst(sz, CV_16SC1);
 
     MakeArtificialExample(rng,src_left,src_right);
 

modules/calib3d/src/ap3p.cpp

@@ -315,8 +315,8 @@ bool ap3p::solve(cv::Mat &R, cv::Mat &tvec, const cv::Mat &opoints, const cv::Ma
                         points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13],
                         points[14],
                         points[15], points[16], points[17], points[18], points[19]);
-    cv::Mat(3, 1, CV_64F, translation).copyTo(tvec);
-    cv::Mat(3, 3, CV_64F, rotation_matrix).copyTo(R);
+    cv::Mat(3, 1, CV_64FC1, translation).copyTo(tvec);
+    cv::Mat(3, 3, CV_64FC1, rotation_matrix).copyTo(R);
     return result;
 }
 
@@ -342,8 +342,8 @@ int ap3p::solve(std::vector<cv::Mat> &Rs, std::vector<cv::Mat> &tvecs, const cv:
 
     for (int i = 0; i < solutions; i++) {
         cv::Mat R, tvec;
-        cv::Mat(3, 1, CV_64F, translation[i]).copyTo(tvec);
-        cv::Mat(3, 3, CV_64F, rotation_matrix[i]).copyTo(R);
+        cv::Mat(3, 1, CV_64FC1, translation[i]).copyTo(tvec);
+        cv::Mat(3, 3, CV_64FC1, rotation_matrix[i]).copyTo(R);
 
         Rs.push_back(R);
         tvecs.push_back(tvec);