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53 lines
2.2 KiB
Plaintext
53 lines
2.2 KiB
Plaintext
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/*!
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\page shape_proximity_page Shape Proximity
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The Shape Proximity operation calculates maximal of all possible distances between two shapes.
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Considering this case :
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\image html Shape_proximity_0.png
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The proximity of blue shape to the red one is computed like this :
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For each point of blue the distance to the red is computed using perpendicular projection. The proximity value returned is equal to maximal value of all of this distances.
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To do that the implemented algorithm
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1 - sampling shapes by points then calculating the distance from each sample point from one shape to another
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2 - find the position which gives the maximal distance
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3 - improve the proximity value basing on the exact shapes using the points found on step 2 as a start position
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It may happen that for some point of blue shape no distance to red shape exist using perpendicular projection.
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This is typically the case here :
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\image html Shape_proximity_1.png
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In the case of no perpendicular projection of a point on blue shape exists, instead of perpendicular projection the minimal distance to border point of red shape is considered.
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The distance from EndBlue Point is taken using EndRed Point (black line represents the distance for EndBlue).
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This is just a TUI functionality. The provided class
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<pre>
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geompy.ShapeProximity()
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</pre>
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has an interface to compute proximity value with default parameters
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<pre>
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p = geompy.ShapeProximity()
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value = p.proximity(shape1, shape2)
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</pre>
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Moreover, it also provides the functionality to customize the calculation.
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For example, compute coarse proximity value basing on the number of sampling points for each shape,
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or compute the precise value as a refining operation after the coarse value calculation.
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<pre>
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p = geompy.ShapeProximity()
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p.setShapes(shape1, shape2) # customize calculator with input shapes
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p.setSampling(shape1, 100) # assign number of sample points for the first shape
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p.setSampling(shape2, 25) # assign number of sample points for the second shape
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coarse_proximity = p.coarseProximity() # rough proximity value basing on the shape sampling and tessellation
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fine_proximity = p.preciseProximity() # more precise proximity value using exact shapes
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</pre>
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See also a \ref tui_shape_proximity_page "TUI example".
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*/
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