Opencv Hilditch 细化算法

将gazo.png进行Hilditch细化算法处理吧！算法如下：

1. 从左上角开始进行光栅扫描；
2. $$x_0(x,y)=0$$的话、不进行处理。$$x_0(x,y)=1$$的话，下面五个条件都满足的时候令$$x_0=-1$$：
   • 当前像素的4-近邻中有一个以上0；
   • $$x_0$$的$$8-$$连接数为$$1$$；
   • $$x_1$$至$$x_8$$绝对值之和大于$$2$$；
   • $$8-$$近邻像素的取值有一个以上为$$1$$；
   • 对所有x_n(n\in [1,8])以下任一项成立：
     • $$x_n$$不是$$-1$$；
     • 当x_n为0时，x_0的8-连接数为1。
3. 将每个像素的-1更改为0；
4. 重复进行光栅扫描，直到某一次光栅扫描中步骤3的变化数变为0。

python实现：

import cv2
import numpy as np
import matplotlib.pyplot as plt


# hilditch thining
def hilditch(img):
    # get shape
    H, W, C = img.shape

    # prepare out image
    out = np.zeros((H, W), dtype=np.int)
    out[img[..., 0] > 0] = 1

    # inverse pixel value
    tmp = out.copy()
    _tmp = 1 - tmp

    count = 1
    while count > 0:
        count = 0
        tmp = out.copy()
        _tmp = 1 - tmp

        tmp2 = out.copy()
        _tmp2 = 1 - tmp2

        # each pixel
        for y in range(H):
            for x in range(W):
                # skip black pixel
                if out[y, x] < 1:
                    continue

                judge = 0

                ## condition 1
                if (tmp[y, min(x+1, W-1)] * tmp[max(y-1,0 ), x] * tmp[y, max(x-1, 0)] * tmp[min(y+1, H-1), x]) == 0:
                    judge += 1

                ## condition 2
                c = 0
                c += (_tmp[y, min(x+1, W-1)] - _tmp[y, min(x+1, W-1)] * _tmp[max(y-1, 0), min(x+1, W-1)] * _tmp[max(y-1, 0), x])
                c += (_tmp[max(y-1, 0), x] - _tmp[max(y-1, 0), x] * _tmp[max(y-1, 0), max(x-1, 0)] * _tmp[y, max(x-1, 0)])
                c += (_tmp[y, max(x-1, 0)] - _tmp[y, max(x-1, 0)] * _tmp[min(y+1, H-1), max(x-1, 0)] * _tmp[min(y+1, H-1), x])
                c += (_tmp[min(y+1, H-1), x] - _tmp[min(y+1, H-1), x] * _tmp[min(y+1, H-1), min(x+1, W-1)] * _tmp[y, min(x+1, W-1)])
                if c == 1:
                    judge += 1

                ## condition 3
                if np.sum(tmp[max(y-1, 0) : min(y+2, H), max(x-1, 0) : min(x+2, W)]) >= 3:
                    judge += 1

                ## condition 4
                if np.sum(out[max(y-1, 0) : min(y+2, H), max(x-1, 0) : min(x+2, W)]) >= 2:
                    judge += 1

                ## condition 5
                _tmp2 = 1 - out

                c = 0
                c += (_tmp2[y, min(x+1, W-1)] - _tmp2[y, min(x+1, W-1)] * _tmp2[max(y-1, 0), min(x+1, W-1)] * _tmp2[max(y-1, 0), x])
                c += (_tmp2[max(y-1, 0), x] - _tmp2[max(y-1, 0), x] * (1 - tmp[max(y-1, 0), max(x-1, 0)]) * _tmp2[y, max(x-1, 0)])
                c += (_tmp2[y, max(x-1, 0)] - _tmp2[y, max(x-1, 0)] * _tmp2[min(y+1, H-1), max(x-1, 0)] * _tmp2[min(y+1, H-1), x])
                c += (_tmp2[min(y+1, H-1), x] - _tmp2[min(y+1, H-1), x] * _tmp2[min(y+1, H-1), min(x+1, W-1)] * _tmp2[y, min(x+1, W-1)])
                if c == 1 or (out[max(y-1, 0), max(x-1,0 )] != tmp[max(y-1, 0), max(x-1, 0)]):
                    judge += 1

                c = 0
                c += (_tmp2[y, min(x+1, W-1)] - _tmp2[y, min(x+1, W-1)] * _tmp2[max(y-1, 0), min(x+1, W-1)] * (1 - tmp[max(y-1, 0), x]))
                c += ((1-tmp[max(y-1, 0), x]) - (1 - tmp[max(y-1, 0), x]) * _tmp2[max(y-1, 0), max(x-1, 0)] * _tmp2[y, max(x-1, 0)])
                c += (_tmp2[y, max(x-1,0 )] - _tmp2[y, max(x-1,0 )] * _tmp2[min(y+1, H-1), max(x-1, 0)] * _tmp2[min(y+1, H-1), x])
                c += (_tmp2[min(y+1, H-1), x] - _tmp2[min(y+1, H-1), x] * _tmp2[min(y+1, H-1), min(x+1, W-1)] * _tmp2[y, min(x+1, W-1)])
                if c == 1 or (out[max(y-1, 0), x] != tmp[max(y-1, 0), x]):
                    judge += 1

                c = 0
                c += (_tmp2[y, min(x+1, W-1)] - _tmp2[y, min(x+1, W-1)] * (1 - tmp[max(y-1, 0), min(x+1, W-1)]) * _tmp2[max(y-1, 0), x])
                c += (_tmp2[max(y-1, 0), x] - _tmp2[max(y-1, 0), x] * _tmp2[max(y-1, 0), max(x-1, 0)] * _tmp2[y, max(x-1, 0)])
                c += (_tmp2[y, max(x-1, 0)] - _tmp2[y, max(x-1, 0)] * _tmp2[min(y+1, H-1), max(x-1, 0)] * _tmp2[min(y+1, H-1), x])
                c += (_tmp2[min(y+1, H-1), x] - _tmp2[min(y+1, H-1), x] * _tmp2[min(y+1, H-1), min(x+1, W-1)] * _tmp2[y, min(x+1, W-1)])
                if c == 1 or (out[max(y-1, 0), min(x+1, W-1)] != tmp[max(y-1, 0), min(x+1, W-1)]):
                    judge += 1

                c = 0
                c += (_tmp2[y, min(x+1, W-1)] - _tmp2[y, min(x+1, W-1)] * _tmp2[max(y-1, 0), min(x+1, W-1)] * _tmp2[max(y-1, 0), x])
                c += (_tmp2[max(y-1, 0), x] - _tmp2[max(y-1, 0), x] * _tmp2[max(y-1, 0), max(x-1, 0)] * (1 - tmp[y, max(x-1, 0)]))
                c += ((1 - tmp[y, max(x-1, 0)]) - (1 - tmp[y, max(x-1, 0)]) * _tmp2[min(y+1, H-1), max(x-1, 0)] * _tmp2[min(y+1, H-1), x])
                c += (_tmp2[min(y+1, H-1), x] - _tmp2[min(y+1, H-1), x] * _tmp2[min(y+1, H-1), min(x+1, W-1)] * _tmp2[y, min(x+1, W-1)])
                if c == 1 or (out[y, max(x-1, 0)] != tmp[y, max(x-1, 0)]):
                    judge += 1

                if judge >= 8:
                    out[y, x] = 0
                    count += 1

    out = out.astype(np.uint8) * 255

    return out


# Read image
img = cv2.imread("gazo.png").astype(np.float32)

# hilditch thining
out = hilditch(img)

# Save result
cv2.imwrite("out.png", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()

输入(gazo.png)：

输出：