Fixed 44 of 99 unreadVariable hints raised by CppCheck 1.85 (not all,…

… because there are some false positives and ignored hints in tutorials and 3rdParty directories)

apps/3d_rec_framework/tools/apps/src/global_classification.cpp

@@ -103,7 +103,6 @@ segmentAndClassify (typename pcl::rec_3d_framework::GlobalNNPipeline<DistT, Poin
       global.getCategory (categories);
       global.getConfidence (conf);
 
-      std::string category = categories[0];
       Eigen::Vector4f centroid;
       pcl::compute3DCentroid (*xyz_points, indices[i].indices, centroid);
       for (size_t kk = 0; kk < categories.size (); kk++)

apps/src/grabcut_2d.cpp

@@ -471,12 +471,6 @@ int main (int argc, char** argv)
     return (-1);
   }
 
-  std::string object_file = "object.pcd", background_file = "background.pcd";
-  if (parsed_file_indices.size () >= 3)
-    background_file = argv[parsed_file_indices[2]];
-  if (parsed_file_indices.size () >= 2)
-    object_file = argv[parsed_file_indices[1]];
-
   pcl::PCDReader reader;
   // Test the header
   pcl::PCLPointCloud2 dummy;

cuda/apps/src/kinect_normals_cuda.cpp

@@ -121,9 +121,9 @@ class NormalEstimation
       // we got a cloud in device..
 
       boost::shared_ptr<typename Storage<float4>::type> normals;
-      float focallength = 580/2.0;
       {
         ScopeTimeCPU time ("Normal Estimation");
+        float focallength = 580/2.0;
         normals = computePointNormals<Storage, typename PointIterator<Storage,PointXYZRGB>::type > (data->points.begin (), data->points.end (), focallength, data, 0.05, 30);
       }
       go_on = false;
@@ -164,11 +164,11 @@ class NormalEstimation
       d2c.compute<Storage> (depth_image, image, constant, data, false, 1, smoothing_nr_iterations, smoothing_filter_size);
       //d2c.compute<Storage> (depth_image, image, constant, data, true, 2);
 
-      boost::shared_ptr<typename Storage<float4>::type> normals;
-      float focallength = 580/2.0;
+      boost::shared_ptr<typename Storage<float4>::type> normals;      
       {
         ScopeTimeCPU time ("Normal Estimation");
         normals = computeFastPointNormals<Storage> (data);
+        //float focallength = 580/2.0;
         //normals = computePointNormals<Storage, typename PointIterator<Storage,PointXYZRGB>::type > (data->points.begin (), data->points.end (), focallength, data, 0.05, 30);
       }
 

cuda/sample_consensus/src/msac.cpp

@@ -159,7 +159,7 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
 
         // Compute the k parameter (k=log(z)/log(1-w^n))
         float w = (float)((float)n_best_inliers_count / (float)nr_remaining_points);
-        float p_no_outliers = 1.0f - pow (w, 1.0f);
+        float p_no_outliers = 1.0f - w;
         p_no_outliers = (std::max) (std::numeric_limits<float>::epsilon (), p_no_outliers);       // Avoid division by -Inf
         p_no_outliers = (std::min) (1.0f - std::numeric_limits<float>::epsilon (), p_no_outliers);   // Avoid division by 0.
         if (p_no_outliers == 1.0f)
@@ -186,7 +186,7 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
 
           // Compute the k parameter (k=log(z)/log(1-w^n))
           float w = (float)((float)min_nr_in_shape / (float)nr_remaining_points);
-          float p_no_outliers = 1.0f - pow (w, 1.0f);
+          float p_no_outliers = 1.0f - w;
           p_no_outliers = (std::max) (std::numeric_limits<float>::epsilon (), p_no_outliers);       // Avoid division by -Inf
           p_no_outliers = (std::min) (1.0f - std::numeric_limits<float>::epsilon (), p_no_outliers);   // Avoid division by 0.
           if (p_no_outliers != 1.0f)
@@ -227,8 +227,6 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
         n_best_inliers_count = 0;
         k = max_batches_ * iterations_per_batch_;
         // go through all other models, invalidating those whose samples are inliers to the best one
-        int counter_invalid = 0;
-        int counter_inliers = 0;
 
         //for (unsigned int b = 0; b <= cur_batch; b++)
         unsigned int b = cur_batch;  
@@ -240,7 +238,6 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
           if (!hypothesis_valid[b * iterations_per_batch_ + j])
           {
             //std::cerr << "model " << j << " in batch " << b <<" is an invalid model" << std::endl;
-            counter_invalid ++;
             continue;
           }
           if ((b*iterations_per_batch_ + j) == extracted_model)
@@ -252,7 +249,6 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
           if (sac_model_->isSampleInlier (hypotheses_inliers_stencils[extracted_model], h_samples[b], j))
           {
             //std::cerr << "sample point for model " << j << " in batch " << b <<" is inlier to best model " << extracted_model << std::endl;
-            counter_inliers ++;
             hypothesis_valid[b * iterations_per_batch_ + j] = false;
             hypotheses_inlier_count[b*iterations_per_batch_ + j] = 0;
             if (j <= i)
@@ -280,7 +276,7 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
 
               // Compute the k parameter (k=log(z)/log(1-w^n))
               float w = (float)((float)n_best_inliers_count / (float)nr_remaining_points);
-              float p_no_outliers = 1.0f - pow (w, 1.0f);
+              float p_no_outliers = 1.0f - w;
               p_no_outliers = (std::max) (std::numeric_limits<float>::epsilon (), p_no_outliers);       // Avoid division by -Inf
               p_no_outliers = (std::min) (1.0f - std::numeric_limits<float>::epsilon (), p_no_outliers);   // Avoid division by 0.
               if (p_no_outliers == 1.0f)
@@ -291,7 +287,6 @@ pcl_cuda::MultiRandomSampleConsensus<Storage>::computeModel (int debug_verbosity
 
           }
         }
-        //std::cerr << "invalid models: " << counter_invalid << " , inlier models: " << counter_inliers << std::endl;
       }
       }
     }

gpu/kinfu/tools/evaluation.cpp

@@ -85,9 +85,7 @@ Evaluation::Evaluation(const std::string& folder) : folder_(folder), visualizati
   string rgb_file = folder_ + "rgb.txt";
 
   readFile(depth_file, depth_stamps_and_filenames_);
-  readFile(rgb_file, rgb_stamps_and_filenames_);  
-
-  string associated_file = folder_ + "associated.txt";
+  readFile(rgb_file, rgb_stamps_and_filenames_);
 }
 
 void Evaluation::setMatchFile(const std::string& file)

gpu/kinfu/tools/kinfu_app_sim.cpp

@@ -328,8 +328,8 @@ write_rgb_image(const uint8_t* rgb_buffer)
 void
 depthBufferToMM(const float* depth_buffer,unsigned short* depth_img)
 {
-  int npixels = range_likelihood_->getWidth() * range_likelihood_->getHeight();
- // unsigned short * depth_img = new unsigned short[npixels ];
+  //int npixels = range_likelihood_->getWidth() * range_likelihood_->getHeight();
+  //unsigned short * depth_img = new unsigned short[npixels ];
   for (int y = 0; y <  480; ++y)
   {
     for (int x = 0; x < 640; ++x)
@@ -412,7 +412,6 @@ capture (Eigen::Isometry3d pose_in,unsigned short* depth_buffer_mm,const uint8_t
 
   std::vector<Eigen::Isometry3d, Eigen::aligned_allocator<Eigen::Isometry3d> > poses;
   std::vector<float> scores;
-  int n = 1;
   poses.push_back (pose_in);
   // HACK: mfallon modified computeLikelihoods to only call render()  (which is currently private)
   // need to make render public and use it.

gpu/kinfu_large_scale/src/cyclical_buffer.cpp

@@ -132,11 +132,6 @@ pcl::gpu::kinfuLS::CyclicalBuffer::performShift (const TsdfVolume::Ptr volume, c
 
   // retrieve existing data from the world model
   PointCloud<PointXYZI>::Ptr previously_existing_slice (new  PointCloud<PointXYZI>);
-  double min_bound_x  = buffer_.origin_GRID_global.x + buffer_.voxels_size.x - 1;
-  double new_origin_x = buffer_.origin_GRID_global.x + offset_x;
-  double new_origin_y = buffer_.origin_GRID_global.y + offset_y;
-  double new_origin_z = buffer_.origin_GRID_global.z + offset_z;
-
   world_model_.getExistingData (buffer_.origin_GRID_global.x, buffer_.origin_GRID_global.y, buffer_.origin_GRID_global.z,
                                 offset_x, offset_y, offset_z,
                                 buffer_.voxels_size.x - 1, buffer_.voxels_size.y - 1, buffer_.voxels_size.z - 1,

gpu/kinfu_large_scale/tools/evaluation.cpp

@@ -85,9 +85,7 @@ Evaluation::Evaluation(const std::string& folder) : folder_(folder), visualizati
   string rgb_file = folder_ + "rgb.txt";
 
   readFile(depth_file, depth_stamps_and_filenames_);
-  readFile(rgb_file, rgb_stamps_and_filenames_);  
-
-  string associated_file = folder_ + "associated.txt";
+  readFile(rgb_file, rgb_stamps_and_filenames_);
 }
 
 void Evaluation::setMatchFile(const std::string& file)

gpu/kinfu_large_scale/tools/kinfuLS_app.cpp

@@ -419,8 +419,6 @@ struct SceneCloudView
     // create a 5-point visual for each camera
     pcl::PointXYZ p1, p2, p3, p4, p5;
     p1.x=0; p1.y=0; p1.z=0;
-    double angleX = RAD2DEG (2.0 * atan (width / (2.0*focal)));
-    double angleY = RAD2DEG (2.0 * atan (height / (2.0*focal)));
     double dist = 0.75;
     double minX, minY, maxX, maxY;
     maxX = dist*tan (atan (width / (2.0*focal)));

gpu/kinfu_large_scale/tools/kinfu_app_sim.cpp

@@ -326,8 +326,8 @@ write_rgb_image(const uint8_t* rgb_buffer)
 void
 depthBufferToMM(const float* depth_buffer,unsigned short* depth_img)
 {
-  int npixels = range_likelihood_->getWidth() * range_likelihood_->getHeight();
- // unsigned short * depth_img = new unsigned short[npixels ];
+  //int npixels = range_likelihood_->getWidth() * range_likelihood_->getHeight();
+  //unsigned short * depth_img = new unsigned short[npixels ];
   for (int y = 0; y <  480; ++y)
   {
     for (int x = 0; x < 640; ++x)
@@ -410,7 +410,6 @@ capture (Eigen::Isometry3d pose_in,unsigned short* depth_buffer_mm,const uint8_t
 
   std::vector<Eigen::Isometry3d, Eigen::aligned_allocator<Eigen::Isometry3d> > poses;
   std::vector<float> scores;
-  int n = 1;
   poses.push_back (pose_in);
   // HACK: mfallon modified computeLikelihoods to only call render()  (which is currently private)
   // need to make render public and use it.

gpu/kinfu_large_scale/tools/standalone_texture_mapping.cpp

@@ -291,8 +291,6 @@ void showCameras (pcl::texture_mapping::CameraVector cams, pcl::PointCloud<pcl::
     // create a 5-point visual for each camera
     pcl::PointXYZ p1, p2, p3, p4, p5;
     p1.x=0; p1.y=0; p1.z=0;
-    double angleX = RAD2DEG (2.0 * atan (width / (2.0*focal)));
-    double angleY = RAD2DEG (2.0 * atan (height / (2.0*focal)));
     double dist = 0.75;
     double minX, minY, maxX, maxY;
     maxX = dist*tan (atan (width / (2.0*focal)));
@@ -375,7 +373,6 @@ bool readCamPoseFile(std::string filename, pcl::TextureMapping<pcl::PointXYZ>::C
   }
   myReadFile.seekg(ios::beg);
 
-  char current_line[1024];
   double val;
 
   // go to line 2 to read translations
@@ -450,7 +447,6 @@ main (int argc, char** argv)
 
   const boost::filesystem::path base_dir (".");
   std::string extension (".txt");
-  int cpt_cam = 0;
   for (boost::filesystem::directory_iterator it (base_dir); it != boost::filesystem::directory_iterator (); ++it)
   {
     if(boost::filesystem::is_regular_file (it->status ()) && boost::filesystem::extension (it->path ()) == extension)
@@ -459,7 +455,6 @@ main (int argc, char** argv)
       readCamPoseFile(it->path ().string (), cam);
       cam.texture_file = boost::filesystem::basename (it->path ()) + ".png";
       my_cams.push_back (cam);
-      cpt_cam++ ;
     }
   }
   PCL_INFO ("\tLoaded %d textures.\n", my_cams.size ());

gpu/people/src/people_detector.cpp

@@ -181,10 +181,7 @@ pcl::gpu::people::PeopleDetector::process (const pcl::PointCloud<PointTC>::Const
 
 int
 pcl::gpu::people::PeopleDetector::process ()
-{
-  int cols = cloud_device_.cols();
-  int rows = cloud_device_.rows();      
-
+{  
   rdf_detector_->process(depth_device1_, cloud_host_, AREA_THRES);
 
   const RDFBodyPartsDetector::BlobMatrix& sorted = rdf_detector_->getBlobMatrix();
@@ -205,6 +202,7 @@ pcl::gpu::people::PeopleDetector::process ()
       shs5(cloud_host_, seed, &flowermat_host_.points[0]);
     }
 
+    int cols = cloud_device_.cols();
     fg_mask_.upload(flowermat_host_.points, cols);
     device::Dilatation::invoke(fg_mask_, kernelRect5x5_, fg_mask_grown_);
 
@@ -221,18 +219,18 @@ pcl::gpu::people::PeopleDetector::process ()
     {      
       Tree2 t2;
       buildTree(sorted2, cloud_host_, Neck, c, t2);
-      int par = 0;
+      /*int par = 0;
       for(int f = 0; f < NUM_PARTS; f++)
       {
-       /* if(t2.parts_lid[f] == NO_CHILD)
+        if(t2.parts_lid[f] == NO_CHILD)
         {
           cerr << "1;";
           par++;
         }
         else
-           cerr << "0;";*/
-      }
-      static int counter = 0; // TODO move this logging to PeopleApp
+           cerr << "0;";
+      }*/
+      //static int counter = 0; // TODO move this logging to PeopleApp
       //cerr << t2.nr_parts << ";" << par << ";" << t2.total_dist_error << ";" << t2.norm_dist_error << ";" << counter++ << ";" << endl;
       return 2;
     }
@@ -271,10 +269,7 @@ pcl::gpu::people::PeopleDetector::processProb (const pcl::PointCloud<PointTC>::C
 
 int
 pcl::gpu::people::PeopleDetector::processProb ()
-{
-  int cols = cloud_device_.cols();
-  int rows = cloud_device_.rows();
-
+{  
   PCL_DEBUG("[pcl::gpu::people::PeopleDetector::processProb] : (D) : called\n");
 
   // First iteration no tracking can take place
@@ -346,6 +341,7 @@ pcl::gpu::people::PeopleDetector::processProb ()
       shs5(cloud_host_, seed, &flowermat_host_.points[0]);
     }
 
+    int cols = cloud_device_.cols();
     fg_mask_.upload(flowermat_host_.points, cols);
     device::Dilatation::invoke(fg_mask_, kernelRect5x5_, fg_mask_grown_);
 
@@ -374,20 +370,19 @@ pcl::gpu::people::PeopleDetector::processProb ()
     {
       Tree2 t2;
       buildTree(sorted2, cloud_host_, Neck, c, t2, person_attribs_);
-      int par = 0;
+      //int par = 0;
       for(int f = 0; f < NUM_PARTS; f++)
       {
         if(t2.parts_lid[f] == NO_CHILD)
         {
           cerr << "1;";
-          par++;
+          //par++;
         }
         else
            cerr << "0;";
       }
       std::cerr << std::endl;
-      static int counter = 0; // TODO move this logging to PeopleApp
-
+      //static int counter = 0; // TODO move this logging to PeopleApp
       //cerr << t2.nr_parts << ";" << par << ";" << t2.total_dist_error << ";" << t2.norm_dist_error << ";" << counter++ << ";" << endl;
       first_iteration_ = false;
       return 2;

io/src/vlp_grabber.cpp

@@ -101,7 +101,6 @@ pcl::VLPGrabber::getDefaultNetworkAddress ()
 void
 pcl::VLPGrabber::toPointClouds (HDLDataPacket *dataPacket)
 {
-  static uint32_t scan_counter = 0;
   static uint32_t sweep_counter = 0;
   if (sizeof(HDLLaserReturn) != 3)
     return;
@@ -110,8 +109,6 @@ pcl::VLPGrabber::toPointClouds (HDLDataPacket *dataPacket)
   time (&system_time);
   time_t velodyne_time = (system_time & 0x00000000ffffffffl) << 32 | dataPacket->gpsTimestamp;
 
-  scan_counter++;
-
   double interpolated_azimuth_delta;
 
   uint8_t index = 1;

keypoints/src/agast_2d.cpp

@@ -116,7 +116,6 @@ pcl::keypoints::agast::AbstractAgastDetector::applyNonMaxSuppression (
   std::vector<pcl::PointUV, Eigen::aligned_allocator<pcl::PointUV> >::const_iterator curr_corner;
   int lastRowCorner_ind = 0, next_lastRowCorner_ind = 0;
   std::vector<int>::iterator nms_flags_p;
-  std::vector<pcl::PointUV, Eigen::aligned_allocator<pcl::PointUV> >::iterator curr_corner_nms;
   int j;
   int num_corners_all = int (corners_all.size ());
   int n_max_corners = int (corners_nms.capacity ());
@@ -144,7 +143,6 @@ pcl::keypoints::agast::AbstractAgastDetector::applyNonMaxSuppression (
   nms_flags.resize (num_corners_all);
 
   nms_flags_p = nms_flags.begin ();
-  curr_corner_nms = corners_nms.begin ();
 
   // set all flags to MAXIMUM
   for (j = num_corners_all; j > 0; j--)

ml/src/kmeans.cpp

@@ -133,7 +133,6 @@ pcl::Kmeans::kMeans ()
 {
   bool not_converged = true;
   bool move;
-  unsigned int num_iterations = 0;
   PointId pid;
   ClusterId cid, to_cluster;
   float d, min;
@@ -181,7 +180,6 @@ pcl::Kmeans::kMeans ()
         clusters_to_points_[to_cluster].insert(pid);
       }
     }
-    num_iterations++;
   } // end while
 }
 

simulation/src/glsl_shader.cpp

@@ -129,8 +129,6 @@ pcl::simulation::gllib::Program::addShaderFile (const std::string& filename, Sha
   char* text = readTextFile (filename.c_str ());
   if(text == NULL)  return (false);
 
-  std::string source(text);
-
   bool rval = addShaderText (text, shader_type);
   delete [] text;
   return rval;

surface/src/on_nurbs/fitting_curve_2d_apdm.cpp

@@ -239,7 +239,7 @@ FittingCurve2dAPDM::addCPsOnClosestPointViolation (double max_error)
   //  m_data->interior_line_start.clear ();
   //  m_data->interior_line_end.clear ();
 
-  int nknots (0);
+  //int nknots (0);
 
   for (unsigned i = 0; i < elements.size () - 1; i++)
   {
@@ -265,7 +265,7 @@ FittingCurve2dAPDM::addCPsOnClosestPointViolation (double max_error)
         m_nurbs.InsertKnot (xi + 0.5 * dxi, 1);
         //        m_data->interior_line_start.push_back (p2);
         //        m_data->interior_line_end.push_back (p1);
-        nknots++;
+        //nknots++;
         inserted = true;
       }
     }
@@ -289,7 +289,7 @@ FittingCurve2dAPDM::addCPsOnClosestPointViolation (double max_error)
         m_nurbs.InsertKnot (xi, 1);
         //        m_data->interior_line_start.push_back (p2);
         //        m_data->interior_line_end.push_back (p1);
-        nknots++;
+        //nknots++;
       }
     }