我有许多黑白图像，想将它们转换为一组线条，这样我就可以完全或至少接近完全地从线条重建原始图像。换句话说，我试图将图像矢量化为一组线条。
我已经看过了霍夫线变换 https://docs.opencv.org2.4/modules/imgproc/doc/feature_detection.html?highlight=houghlines#houghlines，然而，这并没有覆盖图像的每个部分，更多的是寻找图像中的线条，而不是将图像完全转换为线条表示。此外，线变换不会对线的实际宽度进行编码，让我猜测如何重建图像（我需要这样做，因为这是训练机器学习算法的预处理步骤）。
到目前为止，我使用 houghLineTransform 尝试了以下代码：
import numpy as np
import cv2
MetersPerPixel=0.1
def loadImageGray(path):
img=(cv2.imread(path,0))
return img
def LineTransform(img):
edges = cv2.Canny(img,50,150,apertureSize = 3)
minLineLength = 10
maxLineGap = 20
lines = cv2.HoughLines(edges,1,np.pi/180,100,minLineLength,maxLineGap)
return lines;
def saveLines(liness):
img=np.zeros((2000,2000,3), np.uint8)
for lines in liness:
for x1,y1,x2,y2 in lines:
print(x1,y1,x2,y2)
img=cv2.line(img,(x1,y1),(x2,y2),(0,255,0),3)
cv2.imwrite('houghlines5.jpg',img)
def main():
img=loadImageGray("loadtest.png")
lines=LineTransform(img)
saveLines(lines)
main()
However when tested using the following 
I got this image:
正如您所看到的，它缺少未轴对齐的线，如果仔细观察，甚至检测到的线也被分成两行，它们之间有一些空间。我还必须以预设宽度绘制这些图像，而实际宽度未知。
编辑：根据@MarkSetcell的建议，我使用以下代码尝试了pypotrace，目前它很大程度上忽略了贝塞尔曲线，只是试图表现得像直线一样，我稍后会关注这个问题，但是现在的结果是' t 最优：
def TraceLines(img):
bmp = potrace.Bitmap(bitmap(img))
path=bmp.trace()
lines=[]
i=0
for curve in path:
for segment in curve:
print(repr(segment))
if segment.is_corner:
c_x, c_y = segment.c
c2_x ,c2_y= segment.end_point
lines.append([[int(c_x), int(c_y),int(c2_x) ,int(c2_y)]])
else:
c_x, c_y = segment.c1
c2_x ,c2_y= segment.end_point
i=i+1
return lines
this results in this image 
, which is an improvement, however while the problem with the circle can be addressed at a later point the missing parts of the square and the weird artefacts on the other straight lines are more problematic. Anyone know how to fix them? Any tips on how to get the line widths?
有人对如何更好地解决这个问题有任何建议吗？
edit edit: here is another test image : 
, it includes multiple line widths I would like to capture.
使用 OpenCVfindContours https://docs.opencv.org/4.1.1/d3/dc0/group__imgproc__shape.html#gadf1ad6a0b82947fa1fe3c3d497f260e0 and drawContours https://docs.opencv.org/4.1.1/d6/d6e/group__imgproc__draw.html#ga746c0625f1781f1ffc9056259103edbc可以首先对线条进行矢量化，然后精确地重新创建原始图像：
import numpy as np
import cv2
img = cv2.imread('loadtest.png', 0)
result_fill = np.ones(img.shape, np.uint8) * 255
result_borders = np.zeros(img.shape, np.uint8)
# the '[:-1]' is used to skip the contour at the outer border of the image
contours = cv2.findContours(img, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)[0][:-1]
# fill spaces between contours by setting thickness to -1
cv2.drawContours(result_fill, contours, -1, 0, -1)
cv2.drawContours(result_borders, contours, -1, 255, 1)
# xor the filled result and the borders to recreate the original image
result = result_fill ^ result_borders
# prints True: the result is now exactly the same as the original
print(np.array_equal(result, img))
cv2.imwrite('contours.png', result)
使用 scikit 图像find_contours https://scikit-image.org/docs/0.16.x/api/skimage.measure.html#skimage.measure.find_contours and approximate_polygon https://scikit-image.org/docs/0.16.x/api/skimage.measure.html#skimage.measure.approximate_polygon允许您通过近似多边形来减少线数（基于这个例子 https://scikit-image.org/docs/stable/auto_examples/edges/plot_polygon.html#sphx-glr-auto-examples-edges-plot-polygon-py):
import numpy as np
from skimage.measure import approximate_polygon, find_contours
import cv2
img = cv2.imread('loadtest.png', 0)
contours = find_contours(img, 0)
result_contour = np.zeros(img.shape + (3, ), np.uint8)
result_polygon1 = np.zeros(img.shape + (3, ), np.uint8)
result_polygon2 = np.zeros(img.shape + (3, ), np.uint8)
for contour in contours:
print('Contour shape:', contour.shape)
# reduce the number of lines by approximating polygons
polygon1 = approximate_polygon(contour, tolerance=2.5)
print('Polygon 1 shape:', polygon1.shape)
# increase tolerance to further reduce number of lines
polygon2 = approximate_polygon(contour, tolerance=15)
print('Polygon 2 shape:', polygon2.shape)
contour = contour.astype(np.int).tolist()
polygon1 = polygon1.astype(np.int).tolist()
polygon2 = polygon2.astype(np.int).tolist()
# draw contour lines
for idx, coords in enumerate(contour[:-1]):
y1, x1, y2, x2 = coords + contour[idx + 1]
result_contour = cv2.line(result_contour, (x1, y1), (x2, y2),
(0, 255, 0), 1)
# draw polygon 1 lines
for idx, coords in enumerate(polygon1[:-1]):
y1, x1, y2, x2 = coords + polygon1[idx + 1]
result_polygon1 = cv2.line(result_polygon1, (x1, y1), (x2, y2),
(0, 255, 0), 1)
# draw polygon 2 lines
for idx, coords in enumerate(polygon2[:-1]):
y1, x1, y2, x2 = coords + polygon2[idx + 1]
result_polygon2 = cv2.line(result_polygon2, (x1, y1), (x2, y2),
(0, 255, 0), 1)
cv2.imwrite('contour_lines.png', result_contour)
cv2.imwrite('polygon1_lines.png', result_polygon1)
cv2.imwrite('polygon2_lines.png', result_polygon2)
Python输出：
Contour shape: (849, 2)
Polygon 1 shape: (28, 2)
Polygon 2 shape: (9, 2)
Contour shape: (825, 2)
Polygon 1 shape: (31, 2)
Polygon 2 shape: (9, 2)
Contour shape: (1457, 2)
Polygon 1 shape: (9, 2)
Polygon 2 shape: (8, 2)
Contour shape: (879, 2)
Polygon 1 shape: (5, 2)
Polygon 2 shape: (5, 2)
Contour shape: (973, 2)
Polygon 1 shape: (5, 2)
Polygon 2 shape: (5, 2)
Contour shape: (224, 2)
Polygon 1 shape: (4, 2)
Polygon 2 shape: (4, 2)
Contour shape: (825, 2)
Polygon 1 shape: (13, 2)
Polygon 2 shape: (13, 2)
Contour shape: (781, 2)
Polygon 1 shape: (13, 2)
Polygon 2 shape: (13, 2)
轮廓线.png：
多边形1_线.png：
多边形2_线.png：
然后可以通过将毕达哥拉斯定理应用于坐标来计算线的长度：line_length = math.sqrt(abs(x2 - x1)**2 + abs(y2 - y1)**2)。如果您想以数值形式获取线条的宽度，请查看以下答案“如何确定线的宽度？” https://stackoverflow.com/questions/22081908/how-to-determine-the-width-of-the-lines一些建议的方法。