kopia lustrzana https://github.com/OpenDroneMap/ODM
33 wiersze
1.5 KiB
Python
Executable File
33 wiersze
1.5 KiB
Python
Executable File
import numpy as np
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from sklearn.neighbors import BallTree
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EPSILON = 0.00001
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def build_grid(bounds, point_cloud, distance):
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"""First, a 2D grid is built with a distance of 'distance' between points, inside the given bounds.
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Then, only points that don't have a point cloud neighbour closer than 'distance' are left. The rest are filtered out."""
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# Generate a grid of 2D points inside the bounds, with a distance of 'distance' between them
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grid = __build_grid(bounds, distance)
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# Filter out grid points outside the bounds (makes sense if bounds are not squared)
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grid_inside = bounds.keep_points_inside(grid)
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# Filter out the grid points that have a neighbor closer than 'distance' from the given point cloud
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return __calculate_lonely_points(grid_inside, point_cloud, distance)
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def __build_grid(bounds, distance):
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x_min, x_max, y_min, y_max = bounds.corners()
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grid = [[x, y] for x in np.arange(x_min, x_max + distance, distance) for y in np.arange(y_min, y_max + distance, distance)]
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return np.array(grid)
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def __calculate_lonely_points(grid, point_cloud, distance):
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# Generate BallTree for point cloud
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ball_tree = BallTree(point_cloud.get_xy(), metric='manhattan')
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# Calculate for each of the points in the grid, the amount of neighbors in the original ground cloud
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count = ball_tree.query_radius(grid, distance - EPSILON, count_only=True)
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# Return only the points in the grid that don't have a neighbor
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return grid[count == 0]
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