78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
"""Represent the lines of the arena"""
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try:
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from ulab import numpy as np
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except ImportError:
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import numpy as np
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boundary_lines = [
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[(0,0), (0, 1500)],
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[(0, 1500), (1500, 1500)],
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[(1500, 1500), (1500, 500)],
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[(1500, 500), (1000, 500)],
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[(1000, 500), (1000, 0)],
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[(1000, 0), (0, 0)],
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]
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width = 1500
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height = 1500
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grid_cell_size = 50
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overscan = 10 # 10 each way
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def get_distance_to_segment(x, y, segment):
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"""Return the distance from the point to the segment.
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Segment -> ((x1, y1), (x2, y2))
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All segments are horizontal or vertical.
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"""
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x1, y1 = segment[0]
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x2, y2 = segment[1]
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# if the segment is horizontal, the point will be closest to the y value of the segment
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if y1 == y2 and x >= min(x1, x2) and x <= max(x1, x2):
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return abs(y - y1)
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# if the segment is vertical, the point will be closest to the x value of the segment
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if x1 == x2 and y >= min(y1, y2) and y <= max(y1, y2):
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return abs(x - x1)
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# the point will be closest to one of the end points
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return np.sqrt(
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min(
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(x - x1) ** 2 + (y - y1) ** 2,
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(x - x2) ** 2 + (y - y2) ** 2
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)
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)
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def get_distance_likelihood(x, y):
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"""Return the distance from the point to the nearest segment as a decay function."""
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min_distance = None
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for segment in boundary_lines:
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distance = get_distance_to_segment(x, y, segment)
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if min_distance is None or distance < min_distance:
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min_distance = distance
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return 1.0 / (1 + min_distance/250) ** 2
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grid_cell_size = 50
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overscan = 10 # 10 each way
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# beam endpoint model
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def make_distance_grid():
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"""Take the boundary lines. With and overscan of 10 cells, and grid cell size of 5cm (50mm),
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make a grid of the distance to the nearest boundary line.
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"""
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grid = np.zeros((
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width // grid_cell_size + 2 * overscan,
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height // grid_cell_size + 2 * overscan
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), dtype=float)
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for x in range(grid.shape[0]):
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column_x = x * grid_cell_size - (overscan * grid_cell_size)
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for y in range(grid.shape[1]):
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row_y = y * grid_cell_size - (overscan * grid_cell_size)
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grid[x, y] = get_distance_likelihood(
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column_x, row_y
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)
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return grid
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distance_grid = make_distance_grid()
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