STYLE

Applications/Projections/otbGridBasedImageResampling.cxx

@@ -54,7 +54,7 @@ public:
   typedef otb::Image<DeformationType>   DeformationFieldType;
 
   typedef itk::VectorCastImageFilter
-  <FloatVectorImageType, 
+  <FloatVectorImageType,
    DeformationFieldType>                DeformationFieldCastFilterType;
   
 
@@ -64,7 +64,7 @@ public:
    DeformationFieldType>                WarpFilterType;
   typedef otb::MultiToMonoChannelExtractROI
   <FloatVectorImageType::InternalPixelType,
-   FloatVectorImageType::InternalPixelType> 
+   FloatVectorImageType::InternalPixelType>
                                         ExtractFilterType;
 
   typedef otb::BandMathImageFilter

Applications/Segmentation/otbHooverCompareSegmentation.cxx

@@ -63,7 +63,7 @@ public:
     
     typename TInput::ValueType max = 0.0;
     unsigned int index=0;
-    for (unsigned int i=0 ; i<m_ScoreColors.size() ; i++)
+    for (unsigned int i=0; i<m_ScoreColors.size(); i++)
       {
       if (A[i] > max)
         {
@@ -123,8 +123,8 @@ public:
   typedef otb::LabelMapToAttributeImageFilter
       <LabelMapType, FloatVectorImageType>          AttributeImageFilterType;
   typedef otb::UnaryFunctorImageFilter
-      <FloatVectorImageType, 
-       Int16VectorImageType, 
+      <FloatVectorImageType,
+       Int16VectorImageType,
        Functor::HooverColorMapping
         <FloatPixelType, Int16PixelType> >          HooverColorFilterType;
 

Code/DisparityMap/otbStereorectificationDeformationFieldSource.h

@@ -32,30 +32,30 @@ namespace otb
  *  the displacement related to the elevation between the two images
  *  of the pair always occurs along lines that are called epipolar
  *  lines.
- * 
+ *
  *  In the case of conic acqusition, these lines are perfectly
  *  parallel by definition, while in push-broom geometry, these lines
  *  might not be exactly parallel due to perturbations during
  *  acquisition, especially when considering a large field of view.
- * 
+ *
  *  The purpose of stereo-rectification is to warp both images of the
  *  pairs so that the displacement related to the elevation only
  *  occurs in the horizontal direction (i.e. epipolar lines are
  *  horizontal). This operation is useful for mainly two reasons: it
  *  allows to search for disparities in one direction only, and it
  *  allows to derives anaglyph for 3D viewing with 3D glasses.
- * 
+ *
  *  This filter allows you to compute the deformation fields up to the
  *  sensor model precision needed to warp a pair of stereo images into
  *  epipolar geometry. Warping can be done using the
  *  otb::StreamingWarpImageFilter.
- * 
+ *
  *  Since lines might not be perfectly regular, the algorithm
  *  performed by this filter uses the otb::GenericRSTransform
  *  capabilities to compute the local epipolar lines, and iteratively
  *  build a resampling grid by propagating along these locally
  *  estimated lines.
- * 
+ *
  *  Epipolar images will have a null origin and a size as given by the
  *  GetRectifiedImageSize() method. The deformation fields and size
  *  are derived to produce epipolar images covering the whole extent
@@ -68,7 +68,7 @@ namespace otb
  *  displacement). The SetElevationOffset() method allows to tune the
  *  elevation offset which is only used for local epipolar lines
  *  estimation. The default value of 50 meters should do.
- * 
+ *
  *  Additionnaly, the SetScale() method allows to derive deformation
  *  fields and images size at a coarser (scale > 1) or finer (scale <
  *  1) resolution. The SetGridStep() allows to tune the step of the
@@ -195,7 +195,7 @@ private:
   double m_Scale;
 
   /** Controls the step of the resampling grid (in pixels). A finer
-   * step will lead to more memory consumption. 
+   * step will lead to more memory consumption.
    */
   double m_GridStep;
 

Code/DisparityMap/otbStereorectificationDeformationFieldSource.txx

@@ -53,7 +53,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 const typename StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
-::OutputImageType * 
+::OutputImageType *
 StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 ::GetLeftDeformationFieldOutput() const
 {
@@ -66,7 +66,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 typename StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
-::OutputImageType * 
+::OutputImageType *
 StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 ::GetLeftDeformationFieldOutput()
 {
@@ -79,7 +79,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 const typename StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
-::OutputImageType * 
+::OutputImageType *
 StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 ::GetRightDeformationFieldOutput() const
 {
@@ -92,7 +92,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 typename StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
-::OutputImageType * 
+::OutputImageType *
 StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
 ::GetRightDeformationFieldOutput()
 {
@@ -169,7 +169,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
   // (do not forget that the y axis is flip in our case)
   // TODO: Add some division by zero check here (but this would only
   // happen in case the images are almost epipolar already)
-  double a = (leftEpiLineEnd[1] - leftEpiLineStart[1]) 
+  double a = (leftEpiLineEnd[1] - leftEpiLineStart[1])
            / (leftEpiLineEnd[0] - leftEpiLineStart[0]);
   double b = leftEpiLineStart[1] - a * leftEpiLineStart[0];
 
@@ -194,9 +194,9 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
   double ury = vx * m_LeftImage->GetLargestPossibleRegion().GetSize()[0];
   double llx = uy * m_LeftImage->GetLargestPossibleRegion().GetSize()[1];
   double lly = vy * m_LeftImage->GetLargestPossibleRegion().GetSize()[1];
-  double lrx = ux * m_LeftImage->GetLargestPossibleRegion().GetSize()[0] 
+  double lrx = ux * m_LeftImage->GetLargestPossibleRegion().GetSize()[0]
              + uy * m_LeftImage->GetLargestPossibleRegion().GetSize()[1];
-  double lry = vx * m_LeftImage->GetLargestPossibleRegion().GetSize()[0] 
+  double lry = vx * m_LeftImage->GetLargestPossibleRegion().GetSize()[0]
              + vy * m_LeftImage->GetLargestPossibleRegion().GetSize()[1];
 
   // Bounding box (this time we do not omit ulx)
@@ -342,7 +342,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
     endLine1 = m_RightToLeftTransform->TransformPoint(epiPoint2);
 
     // Estimate the local baseline ratio
-    double localBaselineRatio = 2*m_ElevationOffset 
+    double localBaselineRatio = 2*m_ElevationOffset
                                 / vcl_sqrt((endLine1[0] - startLine1[0])
                                 *  (endLine1[0] - startLine1[0])
                                 +  (endLine1[1] - startLine1[1])
@@ -393,7 +393,7 @@ StereorectificationDeformationFieldSource<TInputImage, TOutputImage>
     // And we compute the equivalent displacement in right image
     endLine2 = m_LeftToRightTransform->TransformPoint(currentPoint1);
     
-    double iscale = vcl_sqrt((endLine2[0]-startLine2[0])*(endLine2[0]-startLine2[0]) 
+    double iscale = vcl_sqrt((endLine2[0]-startLine2[0])*(endLine2[0]-startLine2[0])
                              +
                              (endLine2[1]-startLine2[1])*(endLine2[1]-startLine2[1]));
 

Examples/OBIA/HooverMetricsEstimation.cxx

@@ -29,27 +29,27 @@
 //
 // The following example shows how to compare two segmentations, using Hoover
 // metrics. For instance, it can be used to compare a segmentation produced
-// by your algorithm against a partial ground truth segmentation. In this 
+// by your algorithm against a partial ground truth segmentation. In this
 // example, the ground truth segmentation will be refered by the letters GT
 // whereas the machine segmentation will be refered by MS.
 //
-// The estimation of Hoover metrics is done with two filters : 
+// The estimation of Hoover metrics is done with two filters :
 // \doxygen{otb}{HooverMatrixFilter} and \doxygen{otb}{HooverInstanceFilter}.
 // The first one produces a matrix containing the number of overlapping pixels
 // between MS regions and GT regions. The second one classifies each region among
 // four types (called Hoover instances):
 // \begin{itemize}
-// \item Correct detection : a region is matched with an other one in the 
+// \item Correct detection : a region is matched with an other one in the
 // opposite segmentation, because they cover nearly the same area.
-// \item Over-segmentation : a GT region is matched with a group of MS 
+// \item Over-segmentation : a GT region is matched with a group of MS
 // regions because they cover nearly the same area.
 // \item Under-segmentation : a MS region is matched with a group of GT
 // regions because they cover nearly the same area.
-// \item Missed detection (for GT regions) or Noise (for MS region) : 
+// \item Missed detection (for GT regions) or Noise (for MS region) :
 // un-matched regions.
 // \end{itemize}
 // Note that a region can be tagged with two types. When the Hoover instance
-// have been found, the instance filter computes overall scores for each 
+// have been found, the instance filter computes overall scores for each
 // category : they are the Hoover metrics \footnote{see http://www.trop.mips.uha.fr/pdf/ORASIS-2009.pdf}.
 //
 // Software Guide : EndLatex
@@ -80,11 +80,11 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   // The filters \doxygen{otb}{HooverMatrixFilter} and \doxygen{otb}{HooverInstanceFilter}
   // are designed to handle \doxygen{itk}{LabelMap} images. This is a useful data structure
-  // to represent labelled images. A label map is a set of label objects. Each label object 
-  // stands for a region with a unique label value. The extent of the region is coded as a 
-  // set of line segments, run-length encoded. 
+  // to represent labelled images. A label map is a set of label objects. Each label object
+  // stands for a region with a unique label value. The extent of the region is coded as a
+  // set of line segments, run-length encoded.
   // To be able to add information to these label objects, we use \doxygen{otb}{AttributesMapLabelObject}.
-  // Each region can store a set of attributes. In this case, Hoover instances and metrics 
+  // Each region can store a set of attributes. In this case, Hoover instances and metrics
   // will be stored in these attributes.
   // Software Guide : EndLatex
 
@@ -114,7 +114,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   // The first step is to convert the images from the classical "pixel array" representation
   // to label map: we use \doxygen{itk}{LabelImageToLabelMapFilter}. The background value sets
-  // the label value of regions considered as background: there is no label object for the 
+  // the label value of regions considered as background: there is no label object for the
   // background region.
   // Software Guide : EndLatex
   
@@ -142,11 +142,11 @@ int main(int argc, char* argv[])
   
   // Software Guide : BeginLatex
   // The instance filter computes the Hoover metrics for each region. These metrics
-  // are stored as attributes in each label object. The threshold parameter 
+  // are stored as attributes in each label object. The threshold parameter
   // corresponds to the overlapping ratio above which two regions can be matched.
   // The extended attributes can be used if the user want to keep a trace of the
-  // associations between MS and GT regions : i.e. if a GT region has been matched 
-  // as a correct detection, it will carry an attribute containing the label value 
+  // associations between MS and GT regions : i.e. if a GT region has been matched
+  // as a correct detection, it will carry an attribute containing the label value
   // of the associated MS region (the same principle goes for other types of instance).
   // Software Guide : EndLatex
   
@@ -181,7 +181,7 @@ int main(int argc, char* argv[])
   
   // Software Guide : BeginLatex
   // The output image contains for each GT region its correct detection score ("RC", band 1),
-  // its over-segmentation score ("RF", band 2), its under-segmentation score ("RA", band 3) 
+  // its over-segmentation score ("RF", band 2), its under-segmentation score ("RA", band 3)
   // and its missed detection score ("RM", band 4).
   // Software Guide : EndLatex
   
@@ -194,7 +194,7 @@ int main(int argc, char* argv[])
   // Software Guide : EndCodeSnippet
   
   // Software Guide : BeginLatex
-  // The Hoover scores are also computed for the whole segmentations. Here is some explanation about the score names : 
+  // The Hoover scores are also computed for the whole segmentations. Here is some explanation about the score names :
   // C = correct, F = fragmentation, A = aggregation, M = missed, N = noise.
   // Software Guide : EndLatex