CODEWITHSUNDEEP
pythonphysics
hvlife
hvlife

Reputation: 11

Python - generate spline from XY point list pymunk

I have a XY points list (trackP[][]) for car track, and i want to "smooth" it. Because in pymunk (and other physics engines) there is no more complex shapes than polys (or segments), in my opinion I need to interpolate data points, to remove sharp corners... How can I do that? I will need that knowledge to generate "Hill climb racing" track too (smooth track from set of points). My track at the moment (it is procedural generated from seed):track

EDIT: I've found an answer, it's chaikin's corner cutting algorithm. Below is my "fork" of code from here

def chaikins_corner_cutting(coords, refinements=5):
coords.insert(0,coords[len(coords)-1])
coords.append(coords[1])
coords = np.array(coords)

for _ in range(refinements):
    L = coords.repeat(2, axis=0)
    R = np.empty_like(L)
    R[0] = L[0]
    R[2::2] = L[1:-1:2]
    R[1:-1:2] = L[2::2]
    R[-1] = L[-1]
    coords = L * 0.75 + R * 0.25
coords2 = coords.tolist()

for i in range(2**refinements): 
    coords2.pop(0)
    i+=1
for i in range(2**refinements): 
    coords2.pop(len(coords2)-1)
return coords2

And here is final effect after some angle fixing: Track

Upvotes: 1

Views: 372

Answers (1)

Sunday
Sunday

Reputation: 811

I am sure a lot of implementations exist for Splines in Python. The one I know best is availably in SciPy:

https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.interpolate.spline.html

>>> import scipy.interpolate as spi
>>> x = [0,1,2,3]
>>> y = [1,5,3,4]
>>> fsmooth = spi.InterpolatedUnivariateSpline(x, y)
>>> fsmooth([0.5, 0.9, 1.1, 1.5])
array([4.3125, 5.0185, 4.9215, 4.1875])

However, this works for functions y=f(x) in a mathematical sense where for each x value only one y value is visited. Your track is not a function in this sense. So you would need to find a different representation first.

Upvotes: 1

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