这不是一个非常有效的答案，但它对严重的噪声具有鲁棒性，并且内核过滤思想对于单尺度匹配（它不处理尺度变化）任务很有用。
使用原始图像（ksize = 201，thresh = 150）
x1c 0d1x的数据
最后一张图像（ksize = 51，thresh = 200）

的
基本的想法是创建一个与你想要的图像/形状匹配的内核。然后你可以在图像上卷积它并寻找热点。最后我对蒙版进行去重化以去除粗线（在蒙版上运行hough-lines希望只得到一条线而不是一堆线）。

import cv2
import math
import numpy as np
# rescale image
def scale(img, scale_factor):
    h,w = img.shape[:2];
    h = int(h*scale_factor);
    w = int(w*scale_factor);
    return cv2.resize(img, (w,h));
# chops padding
def unpad(img, padsize):
    h,w = img.shape[:2];
    img = img[padsize:h-padsize, padsize:w-padsize];
    return img;
# WARNING: be aware of the direction of the zero (positive vs negative)
def zero_catch(value):
    zeroish = 0.00001
    if value <= zeroish:
        return zeroish
    return value
# get line endpoints
def get_coords(x, y, angle, imwidth, imheight):
    x1_length = (x-imwidth) / zero_catch(math.cos(math.radians(angle)));
    y1_length = (y-imheight) / zero_catch(math.sin(math.radians(angle)));
    length = max(abs(x1_length), abs(y1_length));
    endx1 = x + length * math.cos(math.radians(angle));
    endy1 = y + length * math.sin(math.radians(angle));
    x2_length = (x-imwidth) / zero_catch(math.cos(math.radians(angle+180)));
    y2_length = (y-imheight) / zero_catch(math.sin(math.radians(angle+180)));
    length = max(abs(x2_length), abs(y2_length));
    endx2 = x + length * math.cos(math.radians(angle+180));
    endy2 = y + length * math.sin(math.radians(angle+180));
    return int(endx1), int(endy1), int(endx2), int(endy2);
# skinny up the image
def skeletonize(img):
    size = np.size(img)
    skel = np.zeros(img.shape, np.uint8)
    element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
    done = False
    while( not done):
        eroded = cv2.erode(img,element)
        temp = cv2.dilate(eroded,element)
        temp = cv2.subtract(img,temp)
        skel = cv2.bitwise_or(skel,temp)
        img = eroded;
        zeros = size - cv2.countNonZero(img)
        if zeros==size:
            done = True
    return skel;
# load image
filepath = "noisy_lines.png";
img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE);
orig_height, orig_width = img.shape[:2];
# pad to avoid edge dropoff
pad = int(orig_width/2);
img = cv2.copyMakeBorder(img, pad, pad, pad, pad, cv2.BORDER_REFLECT, None)
# get mask
_, mask = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU);
height,width = mask.shape;
# background canvas
canvas = np.zeros_like(mask);
# super brute force solution
ksize = 51; # tweakable scale setting
center = int(ksize / 2);
kernel = np.zeros((ksize, ksize), np.uint8);
# draw line
start_angle = -60;
angle = start_angle; # start offset so that we can see the 0 degree get drawn
while angle < start_angle + 180:
    kernel = np.zeros((ksize,ksize), np.float32);
    sx, sy, ex, ey = get_coords(center, center, angle, ksize, ksize);
    kernel = cv2.line(kernel, (sx,sy), (ex,ey), 1.0 / ksize, 1);
    angle += 1;
    # convolve
    conv = cv2.filter2D(mask, -1, kernel, borderType=cv2.BORDER_CONSTANT);
    # paint onto canvas
    _, linemask = cv2.threshold(conv, 200, 255, cv2.THRESH_BINARY); # tweakable sensitivity setting
    canvas = cv2.bitwise_or(canvas, linemask);
    # chop off padding
    display_canvas = unpad(canvas, pad);
    display_conv = unpad(conv, pad);
    # stack the images
    sbs = np.hstack((display_canvas, display_conv));
    sbs = scale(sbs, 0.5);
    # show
    cv2.imshow("side-by-side", sbs);
    key = cv2.waitKey(1);
    if key == ord('q'):
        break;
# show image
cv2.destroyAllWindows();
display_canvas = unpad(canvas, pad);
display_canvas = skeletonize(display_canvas);
orig_img = cv2.imread(filepath);
cv2.imshow("Image", orig_img);
cv2.imshow("Canvas", display_canvas);
cv2.waitKey(0);

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