skeleton.py

from scipy import weave
import numpy as np
import cv2
import sys
import imutils

def _thinningIteration(im, iter):
	I, M = im, np.zeros(im.shape, np.uint8)
	expr = """
	for (int i = 1; i < NI[0]-1; i++) {
		for (int j = 1; j < NI[1]-1; j++) {
			int p2 = I2(i-1, j);
			int p3 = I2(i-1, j+1);
			int p4 = I2(i, j+1);
			int p5 = I2(i+1, j+1);
			int p6 = I2(i+1, j);
			int p7 = I2(i+1, j-1);
			int p8 = I2(i, j-1);
			int p9 = I2(i-1, j-1);
			int A  = (p2 == 0 && p3 == 1) + (p3 == 0 && p4 == 1) +
			         (p4 == 0 && p5 == 1) + (p5 == 0 && p6 == 1) +
			         (p6 == 0 && p7 == 1) + (p7 == 0 && p8 == 1) +
			         (p8 == 0 && p9 == 1) + (p9 == 0 && p2 == 1);
			int B  = p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9;
			int m1 = iter == 0 ? (p2 * p4 * p6) : (p2 * p4 * p8);
			int m2 = iter == 0 ? (p4 * p6 * p8) : (p2 * p6 * p8);
			if (A == 1 && B >= 2 && B <= 6 && m1 == 0 && m2 == 0) {
				M2(i,j) = 1;
			}
		}
	}
	"""

	weave.inline(expr, ["I", "iter", "M"])
	return (I & ~M)

def thinning(src):
	dst = src.copy() / 255
	prev = np.zeros(src.shape[:2], np.uint8)
	diff = None

	while True:
		dst = _thinningIteration(dst, 0)
		dst = _thinningIteration(dst, 1)
		diff = np.absolute(dst - prev)
		prev = dst.copy()
		if np.sum(diff) == 0:
			break

	return dst * 255

def skeleton_endpoints(skel):
    # make out input nice, possibly necessary
    skel = skel.copy()
    skel[skel!=0] = 1
    skel = np.uint8(skel)

    # apply the convolution
    kernel = np.uint8([[1,  1, 1],
                       [1, 10, 1],
                       [1,  1, 1]])

    filtered = cv2.filter2D(skel,-1,kernel)

    out = np.zeros_like(skel)
    out[np.where(filtered == 11)] = 255
    return out

if __name__ == "__main__":

	src = cv2.imread("circuit6.jpg")
	src = imutils.resize(src,width=640)
	gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)

	img = cv2.GaussianBlur(gray,(9,9),0)
	th = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
			cv2.THRESH_BINARY_INV,11,2)
	bw = thinning(th)

	ends = skeleton_endpoints(bw)
	cv2.imshow("thinning", bw)
	cv2.imshow("ends", ends)
	cv2.waitKey()