upd

core/imagelib/filters.py

@@ -30,7 +30,7 @@ def apply_random_hsv_shift(img, mask=None, rnd_state=None):
     h, s, v = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
     h = ( h + rnd_state.randint(360) ) % 360
     s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
-    v = np.clip ( v + rnd_state.random()/2-0.25, 0, 1 )                    
+    v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )                    
     
     result = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
     if mask is not None:

core/imagelib/sd/__init__.py

@@ -1 +1,2 @@
-from .draw import *
+from .draw import *
+from .calc import *

core/imagelib/sd/calc.py

@@ -0,0 +1,20 @@
+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 )
+
+    return npla.norm ( pa - ba*h[...,None], axis=1 )
+    
+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