Danny Staple
87 lines
3.1 KiB
Python
87 lines
3.1 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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# 0, 0 is bottom left. Heading 0 is right, with heading increasing anticlockwise. Standard position angles.
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def point_is_inside_arena(x, y):
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"""Return True if the point is inside the arena.
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if the point is inside the rectangle, but not inside the cutout, it's inside the arena.
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"""
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# is it inside the rectangle?
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if x < 0 or x > width \
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or y < 0 or y > height:
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return False
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# is it inside the cutout?
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if x > 1000 and y < 500:
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return False
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return True
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def get_point_distance_to_segment(x, y, segment):
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"""Return the distance squared 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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segment_x1, segment_y1 = segment[0]
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segment_x2, segment_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 segment_y1 == segment_y2 and x >= min(segment_x1, segment_x2) and x <= max(segment_x1, segment_x2):
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return abs(y - segment_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 segment_x1 == segment_x2 and y >= min(segment_y1, segment_y2) and y <= max(segment_y1, segment_y2):
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return abs(x - segment_x1)
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# the point will be closest to one of the end points
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return np.sqrt(min((x - segment_x1) ** 2 + (y - segment_y1) ** 2, (x - segment_x2) ** 2 + (y - segment_y2) ** 2))
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def get_point_decay_from_nearest_segment(segments, x, y):
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"""Return the distance from the point to the nearest segment as a decay function."""
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max_decay = None
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for segment in segments:
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decay = 1.0 / max(1, get_point_distance_to_segment(x, y, segment))
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if max_decay is None or decay > max_decay:
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max_decay = decay
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return max_decay
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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((width // grid_cell_size + 2 * overscan, height // grid_cell_size + 2 * overscan), dtype=np.uint8)
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# 4kb as floats, 1 kb as uint8s.
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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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value = int(get_point_decay_from_nearest_segment(boundary_lines, column_x, y * grid_cell_size - (overscan * grid_cell_size)) * 255)
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grid[x, y] = value
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return grid
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distance_grid = make_distance_grid()
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def get_distance_grid_at_point(x, y):
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"""Return the distance grid value at the given point."""
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grid_x = int(x // grid_cell_size + overscan)
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grid_y = int(y // grid_cell_size + overscan)
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if grid_x < 0 or grid_x >= distance_grid.shape[0] or grid_y < 0 or grid_y >= distance_grid.shape[1]:
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return 0
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return distance_grid[grid_x, grid_y]
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