python - 将黑白图像完全转换为一组线(也称为仅使用线进行矢量化)

我有许多黑白图像，想将它们转换为一组线，这样我就可以从这些线完全或至少接近完全重建原始图像。换句话说，我正在尝试将图像矢量化为一组线。

我已经看过HoughLinesTransform，但是它并没有覆盖图像的每个部分，而是更多关于在图像中查找线条，而不是将图像完全转换为线条表示。另外，线变换不对线的实际宽度进行编码，这让我猜测如何重新构造图像(我需要这样做，因为这是训练机器学习算法的前一步)。

到目前为止，我已经使用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()

但是，当使用以下方法进行测试时

我得到了这张图片:


如您所见，它丢失了未与轴对齐的线，并且如果您仔细观察，即使检测到的线也被分成了两行，并且它们之间有一定间隔。我还必须以预设的宽度绘制这些图像，而实际宽度未知。

编辑:根据@MarkSetchell的建议，我通过使用以下代码尝试了pypotrace，目前它在很大程度上忽略了贝塞尔曲线，只是试图像它们是直线一样工作，稍后我将重点关注该问题，但是现在结果不正确。 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

这会产生图像，这是一种改进，但是可以在稍后的位置解决圆的问题，而正方形的缺失部分和其他直线上的怪异伪像会更成问题。有人知道如何解决它们吗？关于如何获得线宽的任何提示？

有人对如何更好地解决此问题有任何建议吗？

编辑编辑:这是另一个测试图像:，它包含多个我要捕获的线宽。

最佳答案

OpenCV的

使用OpenCV的 findContours 和 drawContours ，可以首先矢量化线条，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)

结果



Scikit图片

使用scikit-image的 find_contours 和 approximate_polygon 可以通过近似多边形(基于this example)来减少行数:

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)

结果

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)

outline_lines.png:



polygon1_lines.png:



polygon2_lines.png:



然后可以通过将毕达哥拉斯定理应用于坐标line_length = math.sqrt(abs(x2 - x1)**2 + abs(y2 - y1)**2)来计算线的长度。如果要将线的宽度作为数值获取，请查看"How to determine the width of the lines?"的一些建议方法的答案。

关于python - 将黑白图像完全转换为一组线(也称为仅使用线进行矢量化)，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/58340068/