Merge pull request #108 from eEcoLiDAR/pulse_penetration_104

Pulse penetration 104
eEcoLiDAR · Apr 16, 2018 · 9b79530 · 9b79530
2 parents e3ac7df + 4e6c865
commit 9b79530
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Showing 3 changed files with 7 additions and 5 deletions.
diff --git a/laserchicken/compute_neighbors.py b/laserchicken/compute_neighbors.py
@@ -41,9 +41,9 @@ def compute_cylinder_neighborhood(environment_pc, target_pc, radius):
     if cyl_size > mem_size * MEMORY_THRESHOLD:
         y = target_pc[point]['y']['data']
 
-        num_points = math.floor(mem_size * MEMORY_THRESHOLD) / \
-            (avg_points_cyl * sys.getsizeof(int))
-        print("Number of points: %d" % num_points)
+        num_points = math.floor(mem_size * MEMORY_THRESHOLD / \
+            (avg_points_cyl * sys.getsizeof(int)))
+        print("Number of points: %f" % num_points)
 
         env_tree = kd_tree.get_kdtree_for_pc(environment_pc)
 

diff --git a/laserchicken/feature_extractor/__init__.py b/laserchicken/feature_extractor/__init__.py
@@ -98,6 +98,8 @@ def _add_or_update_feature(env_point_cloud, neighborhoods, target_point_cloud, e
     n_features = len(provided_features)
     feature_values = [np.empty(n_targets, dtype=np.float64)
                       for i in range(n_features)]
+
+    print("The number of targets %d and number of features %d for extraction!!!" % (n_targets, n_features))
     for target_index in range(n_targets):
         point_values = extractor.extract(env_point_cloud, neighborhoods[target_index], target_point_cloud,
                                          target_index, volume)

diff --git a/laserchicken/feature_extractor/pulse_penetration_feature_extractor.py b/laserchicken/feature_extractor/pulse_penetration_feature_extractor.py
@@ -52,8 +52,8 @@ def extract(self, point_cloud, neighborhood, target_point_cloud, target_index, v
         :param volume_description: volume object that describes the shape and size of the search volume
         :return: feature value
         """
-        class_neighbors = np.array(point_cloud[point]['raw_classification']["data"])[
-            neighborhood]
+        class_neighbors = [point_cloud[point]['raw_classification']["data"][n] for n in neighborhood]
+
         ground_indices = self._get_ground_indices(
             class_neighbors, self.ground_tags)