work of colin csl allowing to acces normal of point clouds

pcl/_pcl.pyx

@@ -303,6 +303,36 @@ cdef class PointCloud:
         cseg.setInputNormals (normals.makeShared());
 
         return seg
+
+    def calc_normals(self, int ksearch=-1, double searchRadius=-1.0):
+        """
+        Return a numpy 2D array containing the normal of the pointcloud
+        :TODO: using a loop may be slow, maybe with some numpy magic it can be performed at once.
+        """
+<<<<<<< HEAD
+        if ksearch == -1 and searchRadius == -1.0:
+            raise ValueError, "At least one input parameter must be entered"
+
+=======
+>>>>>>> 05f77676c7447e16fd664c45813bfb6497085ecc
+        cdef cpp.PointNormalCloud_t normals
+        mpcl_compute_normals(deref(self.thisptr), ksearch, searchRadius, normals)
+        cdef float x,y,z
+        cdef int n = self.thisptr.size()
+        cdef cnp.ndarray[float, ndim=2] result = np.empty([n,3], dtype=np.float32)
+        cdef int i = 0
+        while i < n:
+            result[i,0] = normals.at(i).normal_x
+            result[i,1] = normals.at(i).normal_y
+            result[i,2] = normals.at(i).normal_z
+            i += 1
+        return result
+
+<<<<<<< HEAD
+
+
+=======
+>>>>>>> 05f77676c7447e16fd664c45813bfb6497085ecc
 
     def make_statistical_outlier_filter(self):
         """

pcl/pcl_defs.pxd

@@ -34,7 +34,15 @@ cdef extern from "pcl/point_types.h" namespace "pcl":
         float y
         float z
     cdef struct Normal:
+<<<<<<< HEAD
+=======
         pass
+>>>>>>> 05f77676c7447e16fd664c45813bfb6497085ecc
+        Normal()
+        float normal_x
+        float normal_y
+        float normal_z
+        float curvature
 
 cdef extern from "pcl/features/normal_3d.h" namespace "pcl":
     cdef cppclass NormalEstimation[T, N]:

tests/test.py

@@ -221,6 +221,16 @@ def testResize(self):
         # better exceptions.
         self.assertRaises(MemoryError, self.p.resize, -1)
 
+class TestCalcNormals(unittest.TestCase):
+    def setUp(self):
+        self.p = pcl.PointCloud()
+        self.p.from_list([[0,0,0],[1,0,0],[0,1,0]])
+    def testCalcNormals(self):
+        normals = self.p.calc_normals(20)
+        truth = np.array([[0,0,1],[0,0,1],[0,0,1]])
+        self.assertEqual(normals, truth)
+        self.assertEqual(normals.size, self.p.size)
+
 class TestSegmenterNormal(unittest.TestCase):
 
     def setUp(self):