Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/datasets/src/or_pascal.cpp

@@ -143,7 +143,6 @@ Ptr<Object> OR_pascalImp::parseAnnotation(const string &path, const string &id)
         case XML_ERROR_FILE_NOT_FOUND:
             error_message = "XML file not found! " + error_message;
             CV_Error(Error::StsParseError, error_message);
-            return annotation;
         default:
             CV_Error(Error::StsParseError, error_message);
             break;

modules/face/src/face_alignment.cpp

@@ -79,8 +79,7 @@ bool FacemarkKazemiImpl::convertToUnit(Rect r,Mat &warp){
 bool FacemarkKazemiImpl::setMeanExtreme(){
     if(meanshape.empty()){
         String error_message = "Model not loaded properly.No mean shape found.Aborting...";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     for(size_t i=0;i<meanshape.size();i++){
         if(meanshape[i].x>maxmeanx)
@@ -98,7 +97,7 @@ bool FacemarkKazemiImpl::calcMeanShape (vector< vector<Point2f> >& trainlandmark
     //clear the loaded meanshape
     if(trainimages.empty()||trainlandmarks.size()!=trainimages.size()) {
         // throw error if no data (or simply return -1?)
-        CV_ErrorNoReturn(Error::StsBadArg, "Number of images is not equal to corresponding landmarks. Aborting...");
+        CV_Error(Error::StsBadArg, "Number of images is not equal to corresponding landmarks. Aborting...");
     }
     meanshape.clear();
     vector<Mat> finalimages;
@@ -165,7 +164,7 @@ bool FacemarkKazemiImpl::scaleData( vector< vector<Point2f> > & trainlandmarks,
 {
     if(trainimages.empty()||trainimages.size()!=trainlandmarks.size()){
         // throw error if no data (or simply return -1?)
-        CV_ErrorNoReturn(Error::StsBadArg, "The data is not loaded properly by train function. Aborting...");
+        CV_Error(Error::StsBadArg, "The data is not loaded properly by train function. Aborting...");
     }
     float scalex,scaley;
     //scale all images and their landmarks according  to input size

modules/face/src/facemark.cpp

@@ -228,8 +228,7 @@ bool loadTrainingData(vector<String> filename,vector< vector<Point2f> >
         f1.open(filename[j].c_str(),ios::in);
         if(!f1.is_open()){
             cout<<filename[j]<<endl;
-            CV_ErrorNoReturn(Error::StsError, "File can't be opened for reading!");
-            return false;
+            CV_Error(Error::StsError, "File can't be opened for reading!");
         }
         //get the path of the image whose landmarks have to be detected
         getline(f1,img);

modules/face/src/lbph_faces.cpp

@@ -307,10 +307,8 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
         case CV_32FC1:
             return histc_(src, minVal, maxVal, normed);
             break;
-        default:
-            CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet."); break;
     }
-    return Mat();
+    CV_Error(Error::StsUnmatchedFormats, "This type is not implemented yet.");
 }
 
 

modules/face/src/regtree.cpp

@@ -113,8 +113,7 @@ bool FacemarkKazemiImpl:: getBestSplit(vector<Point2f> pixel_coordinates, vector
 {
     if(samples[0].shapeResiduals.size()!=samples[0].current_shape.size()){
         String error_message = "Error while generating split.Residuals are not complete.Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     //This vector stores the matrices where each matrix represents
     //sum of the residuals of shapes of samples which go to the left
@@ -222,13 +221,11 @@ bool FacemarkKazemiImpl :: generateSplit(queue<node_info>& curr,vector<Point2f>
 bool FacemarkKazemiImpl :: buildRegtree(regtree& tree,vector<training_sample>& samples,vector<Point2f> pixel_coordinates){
     if(samples.size()==0){
         String error_message = "Error while building regression tree.Empty samples. Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     if(pixel_coordinates.size()==0){
         String error_message = "Error while building regression tree.No pixel coordinates. Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     queue<node_info> curr;
     node_info parent;
@@ -291,8 +288,7 @@ unsigned long FacemarkKazemiImpl::divideSamples (splitr split,vector<training_sa
 {
     if(samples.size()==0){
         String error_message = "Error while dividing samples. Sample array empty. Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return 0;
+        CV_Error(Error::StsBadArg, error_message);
     }
     unsigned long i = start;
     training_sample temp;

modules/face/src/trainFacemark.cpp

@@ -54,8 +54,7 @@ bool FacemarkKazemiImpl::setTrainingParameters(String filename){
     fs.open(filename, FileStorage::READ);
     if (!fs.isOpened())
     {   String error_message = "Error while opening configuration file.Aborting..";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     int cascade_depth_;
     int tree_depth_;
@@ -105,8 +104,7 @@ unsigned long FacemarkKazemiImpl::  getNearestLandmark(Point2f pixel)
     if(meanshape.empty()) {
             // throw error if no data (or simply return -1?)
             String error_message = "The data is not loaded properly by train function. Aborting...";
-            CV_ErrorNoReturn(Error::StsBadArg, error_message);
-            return false;
+            CV_Error(Error::StsBadArg, error_message);
     }
     float dist=float(INT_MAX);
     unsigned long index =0;
@@ -122,8 +120,7 @@ unsigned long FacemarkKazemiImpl::  getNearestLandmark(Point2f pixel)
 bool FacemarkKazemiImpl :: getRelativePixels(vector<Point2f> sample,vector<Point2f>& pixel_coordinates,std::vector<int> nearest){
     if(sample.size()!=meanshape.size()){
         String error_message = "Error while finding relative shape. Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     Mat transform_mat;
     transform_mat = estimateRigidTransform(meanshape,sample,false);
@@ -146,8 +143,7 @@ bool FacemarkKazemiImpl :: getRelativePixels(vector<Point2f> sample,vector<Point
 bool FacemarkKazemiImpl::getPixelIntensities(Mat img,vector<Point2f> pixel_coordinates,vector<int>& pixel_intensities,Rect face){
     if(pixel_coordinates.size()==0){
         String error_message = "No pixel coordinates found. Aborting.....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     Mat transform_mat;
     convertToActual(face,transform_mat);
@@ -259,13 +255,11 @@ bool FacemarkKazemiImpl :: saveModel(String filename){
     ofstream f(filename.c_str(),ios::binary);
     if(!f.is_open()){
         String error_message = "Error while opening file to write model. Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     if(loaded_forests.size()!=loaded_pixel_coordinates.size()){
         String error_message = "Incorrect training data. Aborting....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     string s("cascade_depth");
     uint64_t len = s.size();
@@ -306,14 +300,12 @@ void FacemarkKazemiImpl::training(String imageList, String groundTruth){
     imageList.clear();
     groundTruth.clear();
     String error_message = "Less arguments than required";
-    CV_ErrorNoReturn(Error::StsBadArg, error_message);
-    return ;
+    CV_Error(Error::StsBadArg, error_message);
 }
 bool FacemarkKazemiImpl::training(vector<Mat>& images, vector< vector<Point2f> >& landmarks,string filename,Size scale,string modelFilename){
     if(!setTrainingParameters(filename)){
         String error_message = "Error while loading training parameters";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     vector<Rect> rectangles;
     scaleData(landmarks,images,scale);
@@ -321,8 +313,7 @@ bool FacemarkKazemiImpl::training(vector<Mat>& images, vector< vector<Point2f> >
     if(images.size()!=landmarks.size()){
         // throw error if no data (or simply return -1?)
         String error_message = "The data is not loaded properly. Aborting training function....";
-        CV_ErrorNoReturn(Error::StsBadArg, error_message);
-        return false;
+        CV_Error(Error::StsBadArg, error_message);
     }
     vector<training_sample> samples;
     getTestCoordinates();

modules/line_descriptor/src/binary_descriptor.cpp

@@ -337,13 +337,8 @@ int BinaryDescriptor::descriptorSize() const
 /* power function with error management */
 static inline int get2Pow( int i )
 {
-  if( i >= 0 && i <= 7 )
-    return 1 << i;
-  else
-  {
-    CV_Error( Error::StsBadArg, "Invalid power argument" );
-    return -1;
-  }
+  CV_DbgAssert(i >= 0 && i <= 7);
+  return 1 << i;
 }
 
 /* compute Gaussian pyramids */

modules/rgbd/src/linemod.cpp

@@ -66,7 +66,6 @@ static inline int getLabel(int quantized)
     case 128: return 7;
     default:
       CV_Error(Error::StsBadArg, "Invalid value of quantized parameter");
-      return -1; //avoid warning
   }
 }
 

modules/tracking/src/gtrTracker.cpp

@@ -58,7 +58,7 @@ Ptr<TrackerGOTURN> TrackerGOTURN::create(const TrackerGOTURN::Params &parameters
     return Ptr<gtr::TrackerGOTURNImpl>(new gtr::TrackerGOTURNImpl(parameters));
 #else
     (void)(parameters);
-    CV_ErrorNoReturn(cv::Error::StsNotImplemented , "to use GOTURN, the tracking module needs to be built with opencv_dnn !");
+    CV_Error(cv::Error::StsNotImplemented , "to use GOTURN, the tracking module needs to be built with opencv_dnn !");
 #endif
 }
 Ptr<TrackerGOTURN> TrackerGOTURN::create()

modules/tracking/src/tracker.cpp

@@ -73,7 +73,6 @@ bool Tracker::init( InputArray image, const Rect2d& boundingBox )
   if( model == 0 )
   {
     CV_Error( -1, "The model is not initialized" );
-    return false;
   }
 
   if( initTracker )

modules/tracking/src/trackerBoostingModel.cpp

@@ -89,7 +89,6 @@ void TrackerBoostingModel::responseToConfidenceMap( const std::vector<Mat>& resp
   if( currentSample.empty() )
   {
     CV_Error( -1, "The samples in Model estimation are empty" );
-    return;
   }
 
   for ( size_t i = 0; i < currentSample.size(); i++ )

modules/tracking/src/trackerFeature.cpp

@@ -90,7 +90,6 @@ Ptr<TrackerFeature> TrackerFeature::create( const String& trackerFeatureType )
   }
 
   CV_Error( -1, "Tracker feature type not supported" );
-  return Ptr<TrackerFeature>();
 }
 
 String TrackerFeature::getClassName() const

modules/tracking/src/trackerMILModel.cpp

@@ -67,7 +67,6 @@ void TrackerMILModel::responseToConfidenceMap( const std::vector<Mat>& responses
   if( currentSample.empty() )
   {
     CV_Error( -1, "The samples in Model estimation are empty" );
-    return;
   }
 
   for ( size_t i = 0; i < responses.size(); i++ )

modules/tracking/src/trackerModel.cpp

@@ -112,7 +112,6 @@ bool TrackerModel::runStateEstimator()
   if( stateEstimator == 0 )
   {
     CV_Error( -1, "Tracker state estimator is not setted" );
-    return false;
   }
   Ptr<TrackerTargetState> targetState = stateEstimator->estimate( confidenceMaps );
   if( targetState == 0 )

modules/tracking/src/trackerSamplerAlgorithm.cpp

@@ -83,7 +83,6 @@ Ptr<TrackerSamplerAlgorithm> TrackerSamplerAlgorithm::create( const String& trac
   }
 
   CV_Error( -1, "Tracker sampler algorithm type not supported" );
-  return Ptr<TrackerSamplerAlgorithm>();
 }
 
 String TrackerSamplerAlgorithm::getClassName() const

modules/tracking/src/trackerStateEstimator.cpp

@@ -85,7 +85,6 @@ Ptr<TrackerStateEstimator> TrackerStateEstimator::create( const String& trackeSt
   }
 
   CV_Error( -1, "Tracker state estimator type not supported" );
-  return Ptr<TrackerStateEstimator>();
 }
 
 String TrackerStateEstimator::getClassName() const

modules/xfeatures2d/samples/bagofwords_classification.cpp

@@ -1794,12 +1794,9 @@ bool VocData::getClassifierGroundTruthImage(const string& obj_class, const strin
     {
         //image found, so return corresponding ground truth
         return m_classifier_gt_all_present[std::distance(m_classifier_gt_all_ids.begin(),it)] != 0;
-    } else {
-        string err_msg = "could not find classifier ground truth for image '" + id + "' and class '" + obj_class + "'";
-        CV_Error(Error::StsError,err_msg.c_str());
     }
-
-    return true;
+    string err_msg = "could not find classifier ground truth for image '" + id + "' and class '" + obj_class + "'";
+    CV_Error(Error::StsError,err_msg.c_str());
 }
 
 //-------------------------------------------------------------------

modules/xfeatures2d/src/pct_signatures/distance.hpp

@@ -204,10 +204,8 @@ namespace cv
                     return distanceL5(points1, idx1, points2, idx2);
                 case PCTSignatures::L_INFINITY:
                     return distanceLInfinity(points1, idx1, points2, idx2);
-                default:
-                    CV_Error(Error::StsBadArg, "Distance function not implemented!");
-                    return -1;
                 }
+                CV_Error(Error::StsBadArg, "Distance function not implemented!");
             }
         }
     }

modules/xfeatures2d/src/pct_signatures/similarity.hpp

@@ -117,10 +117,8 @@ namespace cv
                     return gaussianSimilarity(distancefunction, similarityParameter, points1, idx1, points2, idx2);
                 case PCTSignatures::HEURISTIC:
                     return heuristicSimilarity(distancefunction, similarityParameter, points1, idx1, points2, idx2);
-                default:
-                    CV_Error(Error::StsNotImplemented, "Similarity function not implemented!");
-                    return -1;
                 }
+                CV_Error(Error::StsNotImplemented, "Similarity function not implemented!");
             }
         }
     }

modules/xfeatures2d/src/surf.cuda.cpp

@@ -51,7 +51,7 @@ using namespace cv::cuda;
 
 cv::cuda::SURF_CUDA::SURF_CUDA() { throw_no_cuda(); }
 cv::cuda::SURF_CUDA::SURF_CUDA(double, int, int, bool, float, bool) { throw_no_cuda(); }
-int cv::cuda::SURF_CUDA::descriptorSize() const { throw_no_cuda(); return 0;}
+int cv::cuda::SURF_CUDA::descriptorSize() const { throw_no_cuda(); }
 void cv::cuda::SURF_CUDA::uploadKeypoints(const std::vector<KeyPoint>&, GpuMat&) { throw_no_cuda(); }
 void cv::cuda::SURF_CUDA::downloadKeypoints(const GpuMat&, std::vector<KeyPoint>&) { throw_no_cuda(); }
 void cv::cuda::SURF_CUDA::downloadDescriptors(const GpuMat&, std::vector<float>&) { throw_no_cuda(); }

modules/ximgproc/src/disparity_filters.cpp

@@ -480,7 +480,6 @@ Ptr<StereoMatcher> createRightMatcher(Ptr<StereoMatcher> matcher_left)
     else
     {
         CV_Error(Error::StsBadArg, "createRightMatcher supports only StereoBM and StereoSGBM");
-        return Ptr<StereoMatcher>();
     }
 }
 

modules/ximgproc/src/weighted_median_filter.cpp

@@ -56,6 +56,7 @@ void from32FTo32S(Mat &img, Mat &outImg, int nI, float *mapping)
 {
     int rows = img.rows, cols = img.cols;
     size_t alls = (size_t)rows * cols;
+    CV_Assert(alls < INT_MAX);
 
     CV_Assert(img.isContinuous());
     float *imgPtr = img.ptr<float>();
@@ -66,7 +67,7 @@ void from32FTo32S(Mat &img, Mat &outImg, int nI, float *mapping)
     for (size_t i = 0; i < alls; i++)
     {
         pairFI& d = data[i];
-        d.second = i;
+        d.second = (int)i;
         d.first = imgPtr[i];
     }
 
@@ -128,7 +129,7 @@ void from32FTo32S(Mat &img, Mat &outImg, int nI, float *mapping)
             mapping[cnt] = data[(baseI+i-1)>>1].first; //median
             cnt++;
             base = data[i].first;
-            baseI = i;
+            baseI = (int)i;
         }
         retImgPtr[data[i].second] = cnt;
     }