debugged bindings

stgl · May 14, 2019 · 68b40ed · 68b40ed
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commit 68b40ed
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Showing 3 changed files with 44 additions and 30 deletions.
diff --git a/libmcc_lidar/Algorithm.cpp b/libmcc_lidar/Algorithm.cpp
@@ -73,8 +73,8 @@ namespace mcc
 
     // points not yet classified
 
-    UnclassifiedPoints splinePoints(points);
     IUnclassifiedPoints & U = points;
+    UnclassifiedPoints splinePoints(points);
 
     // Locate points that are vertically stacked (at same x,y coordinates), and
     // within each stack, classify all points but the lowest one as non-ground.

diff --git a/libmcc_lidar/src/mcc.cpp b/libmcc_lidar/src/mcc.cpp
@@ -29,11 +29,12 @@ shared_ptr<PointVector> readPoints(double *x,
 
 	for(int i=0; i < n; i++) {
 
-		(*pts)[i].setCoordinates(XyzPoint(x[n], y[n], z[n]));
-		xyExtent.minX = (std::isnan(xyExtent.minX) | (x[n] < xyExtent.minX)) ? x[n] : xyExtent.minX;
-		xyExtent.maxX = (std::isnan(xyExtent.maxX) | (x[n] > xyExtent.maxX)) ? x[n] : xyExtent.maxX;
-		xyExtent.minY = (std::isnan(xyExtent.minY) | (y[n] < xyExtent.minY)) ? y[n] : xyExtent.minY;
-		xyExtent.maxY = (std::isnan(xyExtent.maxY) | (y[n] > xyExtent.maxY)) ? y[n] : xyExtent.maxY;
+		(*pts)[i].setCoordinates(XyzPoint(x[i], y[i], z[i]));
+
+		xyExtent.minX = (std::isnan(xyExtent.minX) | (x[i] < xyExtent.minX)) ? x[i] : xyExtent.minX;
+		xyExtent.maxX = (std::isnan(xyExtent.maxX) | (x[i] > xyExtent.maxX)) ? x[i] : xyExtent.maxX;
+		xyExtent.minY = (std::isnan(xyExtent.minY) | (y[i] < xyExtent.minY)) ? y[i] : xyExtent.minY;
+		xyExtent.maxY = (std::isnan(xyExtent.maxY) | (y[i] > xyExtent.maxY)) ? y[i] : xyExtent.maxY;
 	}
 
 	return pts;

diff --git a/python/pymcc_lidar.pyx b/python/pymcc_lidar.pyx
@@ -3,52 +3,65 @@ cimport pymcc_lidar
 cimport numpy as np
 from libc.stdlib cimport malloc, free
 
-def pymcc_classification(np.ndarray[double, ndim = 1, mode = 'c'] x not None, np.ndarray[double, ndim = 1, mode = 'c'] y not None, np.ndarray[double, ndim = 1, mode = 'c'] z not None, scaleDomain2Spacing not None, curvatureThreshold not None):
+def pymcc_classification(np.ndarray[double, ndim = 2, mode = 'c'] xyz not None, scaleDomain2Spacing not None, curvatureThreshold not None):
 
-  m_x = x.size
-  m_y = y.size
-  m_z = z.size
+  m_xyz = xyz.shape[0]
+  n_xyz = xyz.shape[1]
 
-  assert m_x == m_y
-  assert m_y == m_z
+  assert n_xyz == 3
 
   cdef int * classification;
   cdef double resolution = scaleDomain2Spacing;
   cdef double thresh = curvatureThreshold;
+  cdef int32_t n = m_xyz;
+  cdef double *x = <double *> malloc(n * sizeof(double));
+  cdef double *y = <double *> malloc(n * sizeof(double));
+  cdef double *z = <double *> malloc(n * sizeof(double));
+
+  for i in range(n):
+      x[i] = xyz[i, 0]
+      y[i] = xyz[i, 1]
+      z[i] = xyz[i, 2]
+
 
-  cdef double *xd = &x[0];
-  cdef double *yd = &y[0];
-  cdef double *zd = &z[0];
-  cdef int32_t n = m_x;
 
   with nogil: 
-    classification = pymcc_classify(xd, yd, zd, n, resolution, thresh);
+    classification = pymcc_classify(x, y, z, n, resolution, thresh);
 
-  cdef np.ndarray[np.int32_t, ndim=1] np_classification = np.empty(m_x, dtype=np.int32);
-  for i in range(m_x):
+  cdef np.ndarray[int32_t, ndim=1] np_classification = np.empty(n, dtype=np.int32);
+  for i in range(n):
     np_classification[i] = classification[i]
 
   return np_classification
 
-def pymcc_singlepass(np.ndarray[double, ndim = 1, mode = 'c'] x not None, np.ndarray[double, ndim = 1, mode = 'c'] y not None, np.ndarray[double, ndim = 1, mode = 'c'] z not None, scaleDomainSpacing not None):
-
-  m_x = x.size
-  m_y = y.size
-  m_z = z.size
+def pymcc_singlepass(np.ndarray[double, ndim = 2, mode = 'c'] xyz not None, scaleDomainSpacing not None):
 
-  assert m_x == m_y
-  assert m_y == m_z
+  m_xyz = xyz.shape[0]
+  n_xyz = xyz.shape[1]
+
+  assert n_xyz == 3
 
   cdef double *h;
   cdef double resolution = scaleDomainSpacing;
-  cdef int32_t n = m_x;
+  cdef int32_t n = m_xyz;
+  cdef double *x = <double *> malloc(n * sizeof(double));
+  cdef double *y = <double *> malloc(n * sizeof(double));
+  cdef double *z = <double *> malloc(n * sizeof(double));
+
+  for i in range(n):
+      x[i] = xyz[i, 0]
+      y[i] = xyz[i, 1]
+      z[i] = xyz[i, 2]
 
   with nogil: 
-    h = pymcc_pass(&x[0], &y[0], &z[0], n, resolution);
+    h = pymcc_pass(x, y, z, n, resolution);
 
-  cdef np.ndarray[ndim=1, dtype=np.float] np_h = np.empty(m_x);
-  for i in range(m_x):
+  cdef np.ndarray[double, ndim=1] np_h = np.empty(m_xyz);
+  for i in range(m_xyz):
     np_h[i] = h[i]
+  free(x);
+  free(y);
+  free(z);
   return np_h