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Colombo 2020-03-21 00:01:53 +04:00
parent 79b8b8a7a7
commit a9b23e9851
1 changed files with 20 additions and 15 deletions
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35
core/imagelib/sd/calc.py
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@ -1,20 +1,25 @@
import numpy as np
import numpy.linalg as npla
def dist_to_edges(pts, p):
a = pts[:-1,:]
b = pts[1:,:]
edges = np.concatenate( ( pts[:-1,None,:], pts[1:,None,:] ), axis=-2)
pa = p-a
ba = b-a
h = np.clip( np.einsum('ij,ij->i', pa, ba) / np.einsum('ij,ij->i', ba, ba), 0, 1 )
def dist_to_edges(pts, pt, is_closed=False):
"""
returns array of dist from pt to edge and projection pt to edges
"""
if is_closed:
a = pts
b = np.concatenate( (pts[1:,:], pts[0:1,:]), axis=0 )
else:
a = pts[:-1,:]
b = pts[1:,:]
return npla.norm ( pa - ba*h[...,None], axis=1 )
pa = pt-a
ba = b-a
div = np.einsum('ij,ij->i', ba, ba)
div[div==0]=1
h = np.clip( np.einsum('ij,ij->i', pa, ba) / div, 0, 1 )
x = npla.norm ( pa - ba*h[...,None], axis=1 )
return x, a+ba*h[...,None]
def nearest_edge_id_and_dist(pts, p):
x = dist_to_edges(pts, p)
if len(x) != 0:
return np.argmin(x), np.min(x)
return None, None