+ fix compiler warnings

src/Mod/ReverseEngineering/App/AppReverseEngineering.cpp

@@ -48,7 +48,7 @@
 #include "Segmentation.h"
 #include "SampleConsensus.h"
 #if defined(HAVE_PCL_FILTERS)
-#include <pcl/filters/passthrough.h>
+#include <pcl/filters/passthrough.h>
 #include <pcl/filters/voxel_grid.h>
 #include <pcl/point_types.h>
 #endif
@@ -129,7 +129,7 @@ public:
         add_keyword_method("sampleConsensus",&Module::sampleConsensus,
             "sampleConsensus()."
         );
-#endif
+#endif
         initialize("This module is the ReverseEngineering module."); // register with Python
     }
 
@@ -388,7 +388,6 @@ Mesh.show(m)
     Py::Object viewTriangulation(const Py::Tuple& args, const Py::Dict& kwds)
     {
         PyObject *pts;
-        PyObject *vec = 0;
         int width;
         int height;
 
@@ -594,12 +593,12 @@ Mesh.show(m)
             cloud->push_back(pcl::PointXYZ(it->x, it->y, it->z));
         }
 
-        // Create the filtering object
+        // Create the filtering object
         pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downSmpl (new pcl::PointCloud<pcl::PointXYZ>);
-        pcl::VoxelGrid<pcl::PointXYZ> voxG;
-        voxG.setInputCloud (cloud);
-        voxG.setLeafSize (voxDimX, voxDimY, voxDimZ);
-        voxG.filter (*cloud_downSmpl);
+        pcl::VoxelGrid<pcl::PointXYZ> voxG;
+        voxG.setInputCloud (cloud);
+        voxG.setLeafSize (voxDimX, voxDimY, voxDimZ);
+        voxG.filter (*cloud_downSmpl);
 
         Points::PointKernel* points_sample = new Points::PointKernel();
         points_sample->reserve(cloud_downSmpl->size());
@@ -631,12 +630,12 @@ Mesh.show(m)
         estimate.setSearchRadius(searchRadius);
         estimate.perform(normals);
 
-        Py::List list;
-        for (std::vector<Base::Vector3d>::iterator it = normals.begin(); it != normals.end(); ++it) {
-            list.append(Py::Vector(*it));
-        }
-
-        return list;
+        Py::List list;
+        for (std::vector<Base::Vector3d>::iterator it = normals.begin(); it != normals.end(); ++it) {
+            list.append(Py::Vector(*it));
+        }
+
+        return list;
     }
 #endif
 #if defined(HAVE_PCL_SEGMENTATION)
@@ -669,17 +668,17 @@ Mesh.show(m)
         else {
             segm.perform(ksearch);
         }
-
-        Py::List lists;
-        for (std::list<std::vector<int> >::iterator it = clusters.begin(); it != clusters.end(); ++it) {
-            Py::Tuple tuple(it->size());
-            for (std::size_t i = 0; i < it->size(); i++) {
-                tuple.setItem(i, Py::Long((*it)[i]));
-            }
-            lists.append(tuple);
-        }
-
-        return lists;
+
+        Py::List lists;
+        for (std::list<std::vector<int> >::iterator it = clusters.begin(); it != clusters.end(); ++it) {
+            Py::Tuple tuple(it->size());
+            for (std::size_t i = 0; i < it->size(); i++) {
+                tuple.setItem(i, Py::Long((*it)[i]));
+            }
+            lists.append(tuple);
+        }
+
+        return lists;
     }
     Py::Object featureSegmentation(const Py::Tuple& args, const Py::Dict& kwds)
     {
@@ -697,16 +696,16 @@ Mesh.show(m)
         Segmentation segm(*points, clusters);
         segm.perform(ksearch);
 
-        Py::List lists;
-        for (std::list<std::vector<int> >::iterator it = clusters.begin(); it != clusters.end(); ++it) {
-            Py::Tuple tuple(it->size());
-            for (std::size_t i = 0; i < it->size(); i++) {
-                tuple.setItem(i, Py::Long((*it)[i]));
-            }
-            lists.append(tuple);
-        }
-
-        return lists;
+        Py::List lists;
+        for (std::list<std::vector<int> >::iterator it = clusters.begin(); it != clusters.end(); ++it) {
+            Py::Tuple tuple(it->size());
+            for (std::size_t i = 0; i < it->size(); i++) {
+                tuple.setItem(i, Py::Long((*it)[i]));
+            }
+            lists.append(tuple);
+        }
+
+        return lists;
     }
 #endif
 #if defined(HAVE_PCL_SAMPLE_CONSENSUS)
@@ -724,17 +723,17 @@ Mesh.show(m)
         std::vector<float> parameters;
         SampleConsensus sample(*points);
         double probability = sample.perform(parameters);
-
-        Py::Dict dict;
-        Py::Tuple tuple(parameters.size());
-        for (std::size_t i = 0; i < parameters.size(); i++)
-            tuple.setItem(i, Py::Float(parameters[i]));
-        dict.setItem(Py::String("Probability"), Py::Float(probability));
-        dict.setItem(Py::String("Parameters"), tuple);
-
-        return dict;
+
+        Py::Dict dict;
+        Py::Tuple tuple(parameters.size());
+        for (std::size_t i = 0; i < parameters.size(); i++)
+            tuple.setItem(i, Py::Float(parameters[i]));
+        dict.setItem(Py::String("Probability"), Py::Float(probability));
+        dict.setItem(Py::String("Parameters"), tuple);
+
+        return dict;
     }
-#endif
+#endif
 };
 } // namespace Reen
 

src/Mod/ReverseEngineering/App/SurfaceTriangulation.cpp

@@ -421,7 +421,7 @@ ImageTriangulation::ImageTriangulation(int width, int height, const Points::Poin
 
 void ImageTriangulation::perform()
 {
-    if (myPoints.size() != width * height)
+    if (myPoints.size() != static_cast<std::size_t>(width * height))
         throw Base::RuntimeError("Number of points doesn't match with given width and height");
 
     //construct dataset