Updated fov_estimator for filtering out isolated points #41
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| @@ -97,6 +99,8 @@ def __call__( | |||
| z_min=self.z_min, | |||
| z_max=self.z_max, | |||
| ) | |||
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| self._eliminate_isolated_pts(pc_bev, 10, 30) | |||
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Can you make 10 and 30 input parameters to the model so we could tune them if needed? Also provide a description of what they are in the docstring.
| @@ -152,6 +156,24 @@ def _estimate_fov_from_polar_lidar( | |||
| ) -> "Polygon": | |||
| """To be implemented in subclass""" | |||
| raise NotImplementedError | |||
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| def _eliminate_isolated_pts(self, pc_bev, m_away, num_pts): | |||
| ptMap = defaultdict(int) | |||
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Python prefers snake case for variables, i.e. pt_map
| def _eliminate_isolated_pts(self, pc_bev, m_away, num_pts): | ||
| ptMap = defaultdict(int) | ||
| usable_pts = [] | ||
| for p1 in pc_bev.data.x: |
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You don't actually need the loops to do the distance. You can do broadcasting in numpy to make this much faster. See e.g., https://sparrow.dev/pairwise-distance-in-numpy/
Also note that the distance is commutative, meaning dist(a, b) = dist(b, a) so even if you did need loops, you could make the first loop for i in range(0, len(pc_bev.data.x)) and the second loop for j in range(i, len(pc_bev.data.x)) which would cut out half of the evaluations.
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