split pulse penetration ratio and dens abs mean z and norm features

laserchicken/feature_extractor/__init__.py

@@ -29,6 +29,8 @@
 from .entropy_norm_z_feature_extractor import EntropyNormZFeatureExtractor
 from .median_norm_z_feature_extractor import MedianNormZFeatureExtractor
 from .percentile_norm_z_feature_extractor import PercentileNormZFeatureExtractor
+from .density_absolute_mean_z_feature_extractor import DensityAbsoluteMeanZFeatureExtractor
+from .density_absolute_mean_norm_z_feature_extractor import DensityAbsoluteMeanNormZFeatureExtractor
 
 
 def _create_feature_map(module_name=__name__):

laserchicken/feature_extractor/density_absolute_mean_norm_z_feature_extractor.py

@@ -0,0 +1,33 @@
+"""Pulse penetration ratio and density absolute mean calculations.
+
+See https://github.com/eEcoLiDAR/eEcoLiDAR/issues/23.
+"""
+
+import numpy as np
+
+from laserchicken.feature_extractor.abc import AbstractFeatureExtractor
+from laserchicken.feature_extractor.density_absolute_mean_z_feature_extractor import \
+    DensityAbsoluteMeanZFeatureExtractor
+from laserchicken.keys import point, normalized_height
+
+# classification according to
+# http://www.asprs.org/wp-content/uploads/2010/12/LAS_1-4_R6.pdf
+GROUND_TAGS = [2]
+
+
+class DensityAbsoluteMeanNormZFeatureExtractor(DensityAbsoluteMeanZFeatureExtractor):
+    """Feature extractor for the point density."""
+    DATA_KEY = normalized_height
+
+    @classmethod
+    def provides(cls):
+        """
+        Get a list of names of the feature values.
+
+        This will return as many names as the number feature values that will be returned.
+        For instance, if a feature extractor returns the first 3 eigen values, this method
+        should return 3 names, for instance 'eigen_value_1', 'eigen_value_2' and 'eigen_value_3'.
+
+        :return: List of feature names
+        """
+        return ['density_absolute_mean_norm_z']

laserchicken/feature_extractor/density_absolute_mean_z_feature_extractor.py

@@ -0,0 +1,93 @@
+"""Pulse penetration ratio and density absolute mean calculations.
+
+See https://github.com/eEcoLiDAR/eEcoLiDAR/issues/23.
+"""
+
+import numpy as np
+
+from laserchicken.feature_extractor.abc import AbstractFeatureExtractor
+from laserchicken.keys import point, normalized_height
+
+# classification according to
+# http://www.asprs.org/wp-content/uploads/2010/12/LAS_1-4_R6.pdf
+GROUND_TAGS = [2]
+
+
+def _is_ground(i, point_cloud):
+    return point_cloud[point]['raw_classification']["data"][i] in GROUND_TAGS
+
+
+class DensityAbsoluteMeanZFeatureExtractor(AbstractFeatureExtractor):
+    """Feature extractor for the point density."""
+    DATA_KEY = 'z'
+
+    @classmethod
+    def requires(cls):
+        """
+        Get a list of names of the point attributes that are needed for this feature extraction.
+
+        For simple features, this could be just x, y, and z. Other features can build on again
+        other features to have been computed first.
+
+        :return: List of feature names
+        """
+        return []
+
+    @classmethod
+    def provides(cls):
+        """
+        Get a list of names of the feature values.
+
+        This will return as many names as the number feature values that will be returned.
+        For instance, if a feature extractor returns the first 3 eigen values, this method
+        should return 3 names, for instance 'eigen_value_1', 'eigen_value_2' and 'eigen_value_3'.
+
+        :return: List of feature names
+        """
+        return ['density_absolute_mean_z']
+
+    def extract(self, point_cloud, neighborhood, target_point_cloud, target_index, volume_description):
+        """
+        Extract the feature value(s) of the point cloud at location of the target.
+
+        :param point_cloud: environment (search space) point cloud
+        :param neighborhood: array of indices of points within the point_cloud argument
+        :param target_point_cloud: point cloud that contains target point
+        :param target_index: index of the target point in the target point cloud
+        :param volume_description: volume object that describes the shape and size of the search volume
+        :return: feature value
+        """
+        if 'raw_classification' not in point_cloud[point]:
+            raise ValueError(
+                'Missing raw_classification attribute which is necessary for calculating density_absolute_mean.')
+
+        non_ground_indices = [i for i in neighborhood if not _is_ground(i, point_cloud)]
+        density_absolute_mean_z = self._get_density_absolute_mean(non_ground_indices, point_cloud)
+
+        return density_absolute_mean_z
+
+    @staticmethod
+    def _get_ground_indices(point_cloud, ground_tags):
+        index_grd = []
+        for ipt, c in enumerate(point_cloud):
+            if c in ground_tags:
+                index_grd.append(ipt)
+        return index_grd
+
+    def _get_density_absolute_mean(self, non_ground_indices, source_point_cloud):
+        n_non_ground = len(non_ground_indices)
+        z_non_ground = source_point_cloud[point][self.DATA_KEY]["data"][non_ground_indices]
+        if n_non_ground == 0:
+            density_absolute_mean = 0.
+        else:
+            density_absolute_mean = float(
+                len(z_non_ground[z_non_ground > np.mean(z_non_ground)])) / n_non_ground * 100.
+        return density_absolute_mean
+
+    def get_params(self):
+        """
+        Return a tuple of parameters involved in the current feature extractor object.
+
+        Needed for provenance.
+        """
+        return ()

laserchicken/feature_extractor/pulse_penetration_feature_extractor.py

@@ -43,7 +43,7 @@ def provides(cls):
 
         :return: List of feature names
         """
-        return ['pulse_penetration_ratio', 'density_absolute_mean_z', 'density_absolute_mean_norm_z']
+        return ['pulse_penetration_ratio']
 
     def extract(self, point_cloud, neighborhood, target_point_cloud, target_index, volume_description):
         """
@@ -65,12 +65,7 @@ def extract(self, point_cloud, neighborhood, target_point_cloud, target_index, v
         pulse_penetration_ratio = self._get_pulse_penetration_ratio(
             ground_indices, len(neighborhood))
 
-        non_ground_indices = [i for i in neighborhood if not _is_ground(i, point_cloud)]
-        density_absolute_mean_z = self._get_density_absolute_mean(non_ground_indices, point_cloud, 'z')
-        density_absolute_mean_norm_z = self._get_density_absolute_mean(
-            non_ground_indices, point_cloud, normalized_height)
-
-        return pulse_penetration_ratio, density_absolute_mean_z, density_absolute_mean_norm_z
+        return pulse_penetration_ratio
 
     @staticmethod
     def _get_ground_indices(point_cloud, ground_tags):
@@ -86,16 +81,6 @@ def _get_pulse_penetration_ratio(ground_indices, n_total_points):
         n_ground = len(ground_indices)
         return float(n_ground) / n_total
 
-    def _get_density_absolute_mean(self, non_ground_indices, source_point_cloud, height_key):
-        n_non_ground = len(non_ground_indices)
-        z_non_ground = source_point_cloud[point][height_key]["data"][non_ground_indices]
-        if n_non_ground == 0:
-            density_absolute_mean = 0.
-        else:
-            density_absolute_mean = float(
-                len(z_non_ground[z_non_ground > np.mean(z_non_ground)])) / n_non_ground * 100.
-        return density_absolute_mean
-
     def get_params(self):
         """
         Return a tuple of parameters involved in the current feature extractor object.

laserchicken/feature_extractor/test_density_absolute_mean_norm_z_extractor.py

@@ -0,0 +1,32 @@
+import unittest
+
+import numpy as np
+
+from laserchicken.feature_extractor.density_absolute_mean_norm_z_feature_extractor import \
+    DensityAbsoluteMeanNormZFeatureExtractor
+from laserchicken.feature_extractor.density_absolute_mean_z_feature_extractor import \
+    DensityAbsoluteMeanZFeatureExtractor
+from laserchicken.feature_extractor.pulse_penetration_feature_extractor import PulsePenetrationFeatureExtractor
+from laserchicken.keys import point
+from laserchicken.test_tools import create_point_cloud
+
+
+class TestDensityAbsoluteMeanNormZFeatureExtractorArtificialData(unittest.TestCase):
+    def test_simle_case_correct(self):
+        """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
+        ground = 2  # Ground tag
+        veg = 4  # Medium vegetation tag
+        x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
+        point_cloud = create_point_cloud(x, y, np.zeros_like(z), normalized_z=z)
+        point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
+                                                    'type': 'double'}
+        neighborhood = list(range(len(x)))
+
+        extractor = DensityAbsoluteMeanNormZFeatureExtractor()
+        density_absolute_mean = extractor.extract(point_cloud, neighborhood, None, None, None)
+
+        self.assertAlmostEqual(density_absolute_mean, 25)
+
+
+if __name__ == '__main__':
+    unittest.main()

laserchicken/feature_extractor/test_density_absolute_mean_z_extractor.py

@@ -0,0 +1,29 @@
+import unittest
+
+import numpy as np
+
+from laserchicken.feature_extractor.density_absolute_mean_z_feature_extractor import \
+    DensityAbsoluteMeanZFeatureExtractor
+from laserchicken.keys import point
+from laserchicken.test_tools import create_point_cloud
+
+
+class TestDensityAbsoluteMeanZFeatureExtractorArtificialData(unittest.TestCase):
+    def test_simle_case_correct(self):
+        """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
+        ground = 2  # Ground tag
+        veg = 4  # Medium vegetation tag
+        x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
+        point_cloud = create_point_cloud(x, y, z)
+        point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
+                                                    'type': 'double'}
+        neighborhood = list(range(len(x)))
+
+        extractor = DensityAbsoluteMeanZFeatureExtractor()
+        density_absolute_mean = extractor.extract(point_cloud, neighborhood, None, None, None)
+
+        self.assertAlmostEqual(density_absolute_mean, 25)
+
+
+if __name__ == '__main__':
+    unittest.main()

laserchicken/feature_extractor/test_pulse_penetration_extractor.py

@@ -18,8 +18,7 @@ class TestPulsePenetrationFeatureExtractorArtificialData(unittest.TestCase):
     def test_pulse(self):
         """Pulse extractor on artificial data should yield expected feature values."""
         extractor = PulsePenetrationFeatureExtractor()
-        pp_ratio, _ = extractor.extract(
-            self.point_cloud, self.neighborhood, None, None, None)
+        pp_ratio = extractor.extract(self.point_cloud, self.neighborhood, None, None, None)
         self.assertEqual(pp_ratio, self.expected_pp_ratio)
 
     def _set_plane_data(self):
@@ -71,23 +70,6 @@ def setUp(self):
         self.expected_pp_ratio = float(self.points_per_plane) / n_points
 
 
-class TestDensityAbsoluteMeanFeatureExtractorArtificialData(unittest.TestCase):
-    def test_simle_case_correct(self):
-        """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
-        ground = 2  # Ground tag
-        veg = 4  # Medium vegetation tag
-        x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
-        point_cloud = create_point_cloud(x, y, z)
-        point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
-                                                    'type': 'double'}
-        neighborhood = list(range(len(x)))
-
-        extractor = PulsePenetrationFeatureExtractor()
-        _, density_absolute_mean = extractor.extract(point_cloud, neighborhood, None, None, None)
-
-        self.assertAlmostEqual(density_absolute_mean, 25)
-
-
 class TestPulsePenetratioFeatureExtractorRealData(unittest.TestCase):
     """Test the pulse extractor on real data and make sure it doesn't crash."""
     _test_file_name = 'AHN3.las'