Merge pull request #338 from patricksnape:msvc_python_fixes

modules/bioinspired/src/transientareassegmentationmodule.cpp

@@ -454,7 +454,7 @@ void TransientAreasSegmentationModuleImpl::_run(const std::valarray<float> &inpu
 
     // first square the input in order to increase the signal to noise ratio
     // get motion local energy
-    _squaringSpatiotemporalLPfilter(&inputToSegment[channelIndex*getNBpixels()], &_localMotion[0]);
+    _squaringSpatiotemporalLPfilter(&const_cast<std::valarray<float>&>(inputToSegment)[channelIndex*getNBpixels()], &_localMotion[0]);
 
     // second low pass filter: access to the neighborhood motion energy
     _spatiotemporalLPfilter(&_localMotion[0], &_neighborhoodMotion[0], 1);

modules/line_descriptor/src/LSDDetector.cpp

@@ -179,7 +179,7 @@ void LSDDetector::detectImpl( const Mat& imageSrc, std::vector<KeyLine>& keyline
       kl.sPointInOctaveY = (float) extremes[1];
       kl.ePointInOctaveX = (float) extremes[2];
       kl.ePointInOctaveY = (float) extremes[3];
-      kl.lineLength = (float) sqrt( pow( extremes[0] - extremes[2], 2 ) + pow( extremes[1] - extremes[3], 2 ) );
+      kl.lineLength = (float) sqrt( pow( (float) extremes[0] - extremes[2], 2 ) + pow( (float) extremes[1] - extremes[3], 2 ) );
 
       /* compute number of pixels covered by line */
       LineIterator li( gaussianPyrs[j], Point( extremes[0], extremes[1] ), Point( extremes[2], extremes[3] ) );

modules/line_descriptor/src/binary_descriptor.cpp

@@ -653,7 +653,7 @@ void BinaryDescriptor::computeImpl( const Mat& imageSrc, std::vector<KeyLine>& k
         uchar* pointerToRow = descriptors.ptr( originalIndex );
 
         /* get LBD data */
-        float* desVec = sl[k][lineC].descriptor.data();
+        float* desVec = &sl[k][lineC].descriptor.front();
 
         /* fill current row with binary descriptor */
         for ( int comb = 0; comb < 32; comb++ )
@@ -692,7 +692,7 @@ int BinaryDescriptor::OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines )
   /* sigma values and reduction factor used in Gaussian pyramids */
   float preSigma2 = 0;  //orignal image is not blurred, has zero sigma;
   float curSigma2 = 1.0;  //[sqrt(2)]^0=1;
-  double factor = sqrt( 2 );  //the down sample factor between connective two octave images
+  double factor = sqrt( 2.0 );  //the down sample factor between connective two octave images
 
   /* loop over number of octaves */
   for ( int octaveCount = 0; octaveCount < params.numOfOctave_; octaveCount++ )
@@ -1241,7 +1241,7 @@ int BinaryDescriptor::computeLBD( ScaleLines &keyLines, bool useDetectionData )
 
       /* construct line descriptor */
       pSingleLine->descriptor.resize( descriptor_size );
-      desVec = pSingleLine->descriptor.data();
+      desVec = &pSingleLine->descriptor.front();
 
       short desID;
 
@@ -1280,7 +1280,7 @@ int BinaryDescriptor::computeLBD( ScaleLines &keyLines, bool useDetectionData )
       float tempM, tempS;
       tempM = 0;
       tempS = 0;
-      desVec = pSingleLine->descriptor.data();
+      desVec = &pSingleLine->descriptor.front();
 
       int base = 0;
       for ( short i = 0; i < (short) ( NUM_OF_BANDS * 8 ); ++base, i = (short) ( base * 8 ) )
@@ -1297,7 +1297,7 @@ int BinaryDescriptor::computeLBD( ScaleLines &keyLines, bool useDetectionData )
 
       tempM = 1 / sqrt( tempM );
       tempS = 1 / sqrt( tempS );
-      desVec = pSingleLine->descriptor.data();
+      desVec = &pSingleLine->descriptor.front();
       base = 0;
       for ( short i = 0; i < (short) ( NUM_OF_BANDS * 8 ); ++base, i = (short) ( base * 8 ) )
       {
@@ -1315,7 +1315,7 @@ int BinaryDescriptor::computeLBD( ScaleLines &keyLines, bool useDetectionData )
        * a threshold is used to limit the value of element in the unit feature
        * vector no larger than this threshold. In Z.Wang's work, a value of 0.4 is found
        * empirically to be a proper threshold.*/
-      desVec = pSingleLine->descriptor.data();
+      desVec = &pSingleLine->descriptor.front();
       for ( short i = 0; i < descriptor_size; i++ )
       {
         if( desVec[i] > 0.4 )
@@ -1344,7 +1344,7 @@ int BinaryDescriptor::computeLBD( ScaleLines &keyLines, bool useDetectionData )
     for ( int g = 0; g < 32; g++ )
     {
       /* get LBD data */
-      float* des_Vec = keyLines[lineIDInScaleVec][0].descriptor.data();
+      float* des_Vec = &keyLines[lineIDInScaleVec][0].descriptor.front();
       *pointerToRow = des_Vec[g];
       pointerToRow++;
 
@@ -2204,9 +2204,9 @@ int BinaryDescriptor::EDLineDetector::EdgeDrawing( cv::Mat &image, EdgeChains &e
   edgeChains.xCors.resize( offsetPFirst + offsetPSecond );
   edgeChains.yCors.resize( offsetPFirst + offsetPSecond );
   edgeChains.sId.resize( offsetPS + 1 );
-  unsigned int *pxCors = edgeChains.xCors.data();
-  unsigned int *pyCors = edgeChains.yCors.data();
-  unsigned int *psId = edgeChains.sId.data();
+  unsigned int *pxCors = &edgeChains.xCors.front();
+  unsigned int *pyCors = &edgeChains.yCors.front();
+  unsigned int *psId = &edgeChains.sId.front();
   offsetPFirst = 0;
   offsetPSecond = 0;
   unsigned int indexInCors = 0;
@@ -2252,12 +2252,12 @@ int BinaryDescriptor::EDLineDetector::EDline( cv::Mat &image, LineChains &lines
   lines.xCors.resize( linePixelID );
   lines.yCors.resize( linePixelID );
   lines.sId.resize( 5 * edges.numOfEdges );
-  unsigned int *pEdgeXCors = edges.xCors.data();
-  unsigned int *pEdgeYCors = edges.yCors.data();
-  unsigned int *pEdgeSID = edges.sId.data();
-  unsigned int *pLineXCors = lines.xCors.data();
-  unsigned int *pLineYCors = lines.yCors.data();
-  unsigned int *pLineSID = lines.sId.data();
+  unsigned int *pEdgeXCors = &edges.xCors.front();
+  unsigned int *pEdgeYCors = &edges.yCors.front();
+  unsigned int *pEdgeSID = &edges.sId.front();
+  unsigned int *pLineXCors = &lines.xCors.front();
+  unsigned int *pLineYCors = &lines.yCors.front();
+  unsigned int *pLineSID = &lines.sId.front();
   logNT_ = 2.0 * ( log10( (double) imageWidth ) + log10( (double) imageHeight ) );
   double lineFitErr = 0;    //the line fit error;
   std::vector<double> lineEquation( 2, 0 );
@@ -2732,9 +2732,9 @@ int BinaryDescriptor::EDLineDetector::EDline( cv::Mat &image )
   lineSalience_.resize( lines_.numOfLines );
   unsigned char *pgImg = gImgWO_.ptr();
   unsigned int indexInLineArray;
-  unsigned int *pXCor = lines_.xCors.data();
-  unsigned int *pYCor = lines_.yCors.data();
-  unsigned int *pSID = lines_.sId.data();
+  unsigned int *pXCor = &lines_.xCors.front();
+  unsigned int *pYCor = &lines_.yCors.front();
+  unsigned int *pSID = &lines_.sId.front();
   for ( unsigned int i = 0; i < lineSalience_.size(); i++ )
   {
     int salience = 0;

modules/saliency/src/motionSaliencyBinWangApr2014.cpp

@@ -39,6 +39,7 @@
  //
  //M*/
 
+#include <limits>
 #include "precomp.hpp"
 //TODO delete highgui include
 //#include <opencv2/highgui.hpp>
@@ -81,7 +82,7 @@ bool MotionSaliencyBinWangApr2014::init()
   // Since data is even, the median is estimated using two values ​​that occupy
   // the position (n / 2) and ((n / 2) +1) (choose their arithmetic mean).
 
-  potentialBackground = Mat( imgSize.height, imgSize.width, CV_32FC2, Scalar( NAN, 0 ) );
+  potentialBackground = Mat( imgSize.height, imgSize.width, CV_32FC2, Scalar( std::numeric_limits<float>::quiet_NaN(), 0 ) );
 
   backgroundModel.resize( K + 1 );
 
@@ -89,7 +90,7 @@ bool MotionSaliencyBinWangApr2014::init()
   {
     Mat* tmpm = new Mat;
     tmpm->create( imgSize.height, imgSize.width, CV_32FC2 );
-    tmpm->setTo( Scalar( NAN, 0 ) );
+    tmpm->setTo( Scalar( std::numeric_limits<float>::quiet_NaN(), 0 ) );
     Ptr<Mat> tmp = Ptr<Mat>( tmpm );
     backgroundModel[i] = tmp;
   }
@@ -418,50 +419,50 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
               if( i > 0 && j > 0 && i < ( backgroundModel[z]->rows - 1 ) && j < ( backgroundModel[z]->cols - 1 ) )
               {
                 split( *backgroundModel[z], mv );
-                backgroundModelROI = mv[0]( Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), roiSize, roiSize ) );
+                backgroundModelROI = mv[0]( Rect( j - (int) floor((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), roiSize, roiSize ) );
               }
               else if( i == 0 && j == 0 )  // upper leftt
               {
                 split( *backgroundModel[z], mv );
-                backgroundModelROI = mv[0]( Rect( j, i, (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
+                backgroundModelROI = mv[0]( Rect( j, i, (int) ceil((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ) ) );
               }
               else if( j == 0 && i > 0 && i < ( backgroundModel[z]->rows - 1 ) )  // middle left
               {
                 split( *backgroundModel[z], mv );
-                backgroundModelROI = mv[0]( Rect( j, i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), roiSize ) );
+                backgroundModelROI = mv[0]( Rect( j, i - (int) floor((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ), roiSize ) );
               }
               else if( i == ( backgroundModel[z]->rows - 1 ) && j == 0 )  //down left
               {
                 split( *backgroundModel[z], mv );
-                backgroundModelROI = mv[0]( Rect( j, i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
+                backgroundModelROI = mv[0]( Rect( j, i - (int) floor((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ) ) );
               }
               else if( i == 0 && j > 0 && j < ( backgroundModel[z]->cols - 1 ) )  // upper - middle
               {
                 split( *backgroundModel[z], mv );
-                backgroundModelROI = mv[0]( Rect( ( j - (int) floor( roiSize / 2 ) ), i, roiSize, (int) ceil( roiSize / 2 ) ) );
+                backgroundModelROI = mv[0]( Rect( ( j - (int) floor((float) roiSize / 2 ) ), i, roiSize, (int) ceil((float) roiSize / 2 ) ) );
               }
               else if( i == ( backgroundModel[z]->rows - 1 ) && j > 0 && j < ( backgroundModel[z]->cols - 1 ) )  //down middle
               {
                 split( *backgroundModel[z], mv );
                 backgroundModelROI = mv[0](
-                    Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), roiSize, (int) ceil( roiSize / 2 ) ) );
+                    Rect( j - (int) floor((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), roiSize, (int) ceil((float) roiSize / 2 ) ) );
               }
               else if( i == 0 && j == ( backgroundModel[z]->cols - 1 ) )  // upper right
               {
                 split( *backgroundModel[z], mv );
-                backgroundModelROI = mv[0]( Rect( j - (int) floor( roiSize / 2 ), i, (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
+                backgroundModelROI = mv[0]( Rect( j - (int) floor((float) roiSize / 2 ), i, (int) ceil((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ) ) );
               }
               else if( j == ( backgroundModel[z]->cols - 1 ) && i > 0 && i < ( backgroundModel[z]->rows - 1 ) )  // middle - right
               {
                 split( *backgroundModel[z], mv );
                 backgroundModelROI = mv[0](
-                    Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), roiSize ) );
+                    Rect( j - (int) floor((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ), roiSize ) );
               }
               else if( i == ( backgroundModel[z]->rows - 1 ) && j == ( backgroundModel[z]->cols - 1 ) )  // down right
               {
                 split( *backgroundModel[z], mv );
                 backgroundModelROI = mv[0](
-                    Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
+                    Rect( j - (int) floor((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ) ) );
               }
 
               /* Check if the value of current pixel BA in potentialBackground model is already contained in at least one of its neighbors'
@@ -479,7 +480,7 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
                 /////////////////// REPLACEMENT of backgroundModel template ///////////////////
                 //replace TA with current TK
                 backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
-                potentialBackground.at<Vec2f>( i, j )[0] = (float)NAN;
+                potentialBackground.at<Vec2f>( i, j )[0] = std::numeric_limits<float>::quiet_NaN();
                 potentialBackground.at<Vec2f>( i, j )[1] = 0;
 
                 break;
@@ -489,7 +490,7 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
           else
           {
             backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
-            potentialBackground.at<Vec2f>( i, j )[0] = (float)NAN;
+            potentialBackground.at<Vec2f>( i, j )[0] = std::numeric_limits<float>::quiet_NaN();
             potentialBackground.at<Vec2f>( i, j )[1] = 0;
           }
         }  // close if of EVALUATION