Initial revision of skeletonization

2026-07-27 11:27:08 +08:00 · 2011-10-12 13:38:36 +01:00
parent 677c768bb1
commit 772c469ec1
1 changed files with 82 additions and 0 deletions
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+"""skeletonize.py - ???
+
+Original author: Neil Yager
+"""
+
+import numpy as np
+from scipy.ndimage import correlate
+
+def skeletonize(image):
+    """
+    Return a single pixel wide skeleton of all connected
+     components in a binary image
+
+    Parameters
+    ----------
+    
+    image:
+    
+    Returns 
+    -------
+    
+    out: ndarray
+        A matrix containing the thinned image
+   
+    References
+    ----------
+    A fast parallel algorithm for thinning digital patterns, 
+    T. Y. ZHANG and C. Y. SUEN, Communications of the ACM,
+    March 1984, Volume 27, Number 3
+
+    
+    Examples
+    --------
+    """
+    
+    # look up table 
+    lut = [ 0,0,0,1,0,0,1,3,0,0,3,1,1,0,1,3,0,0,0,0,0,0,0,0,2,0,2,0,3,0,3,3,
+            0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,3,0,2,2,
+            0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+            2,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,3,0,2,0,
+            0,1,3,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,
+            3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+            2,3,1,3,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+            2,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,2,2,0,0,2,0,0,0]
+    
+    skeleton = image.copy().astype(np.int8)
+    mask = np.array([[  1,  2,  4],
+                     [128,  0,  8],
+                     [ 64, 32, 16]], np.int8)
+
+    pixelRemoved = True
+    while pixelRemoved:
+        pixelRemoved = False;
+
+        # pass 1
+        neighbours = correlate(skeleton, mask, mode='constant')
+        neighbours[skeleton == 0] = 0
+        codes = np.take(lut, neighbours)
+        if np.any(codes == 1): 
+            pixelRemoved = True
+            skeleton[codes == 1] = 0
+        if np.any(codes == 3): 
+            pixelRemoved = True
+            skeleton[codes == 3] = 0
+
+        # pass 2
+        neighbours = correlate(skeleton, mask, mode='constant')
+        neighbours[skeleton == 0] = 0
+        codes = np.take(lut, neighbours)
+        if np.any(codes == 2): 
+            pixelRemoved = True
+            skeleton[codes == 2] = 0
+        if np.any(codes == 3): 
+            pixelRemoved = True
+            skeleton[codes == 3] = 0
+
+
+
+    return skeleton
+
+    
+