在openCV和python中计算二进制图像中的曲线、Angular 和直线

您可以将findContours与选项CHAIN_APPROX_SIMPLE一起使用.

• Angular 小于某个阈值的点是角点.
• Angular 大于某个阈值的点位于直线上，应删除.
• 距离超过某个阈值的两个相邻点是直线的端点.
• 被识别为角的两个相邻点是直线的端点.
• 所有其他点都属于曲线细节.

Update:

下面是一些代码，你可以从中开始.它展示了如何平滑直线，如何将多个角点合并为一个，以及如何计算每个点的距离和Angular .为了达到要求的结果，你们还有一些工作要做，但我希望它朝着正确的方向发展.

import numpy as np
import numpy.linalg as la
import cv2


def get_angle(p1, p2, p3):
    v1 = np.subtract(p2, p1)
    v2 = np.subtract(p2, p3)
    cos = np.inner(v1, v2) / la.norm(v1) / la.norm(v2)
    rad = np.arccos(np.clip(cos, -1.0, 1.0))
    return np.rad2deg(rad)


def get_angles(p, d):
    n = len(p)
    return [(p[i], get_angle(p[(i-d) % n], p[i], p[(i+d) % n])) for i in range(n)]


def remove_straight(p):
    angles = get_angles(p, 2)                     # approximate angles at points (two steps in path)
    return [p for (p, a) in angles if a < 170]    # remove points with almost straight angles


def max_corner(p):
    angles = get_angles(p, 1)                     # get angles at points
    j = 0

    while j < len(angles):                        # for each point
        k = (j + 1) % len(angles)                 # and its successor
        (pj, aj) = angles[j]
        (pk, ak) = angles[k]

        if la.norm(np.subtract(pj, pk)) <= 4:     # if points are close
            if aj > ak:                           # remove point with greater angle
                angles.pop(j)
            else:
                angles.pop(k)
        else:
            j += 1

    return [p for (p, a) in angles]


def main():
    img = cv2.imread('abc.png')
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY_INV)

    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    for c in contours:                  # for each contour
        pts = [v[0] for v in c]         # get pts from contour
        pts = remove_straight(pts)      # remove almost straight angles
        pts = max_corner(pts)           # remove nearby points with greater angle
        angles = get_angles(pts, 1)     # get angles at points

        # draw result
        for (p, a) in angles:
            if a < 120:
                cv2.circle(img, p, 3, (0, 0, 255), -1)
            else:
                cv2.circle(img, p, 3, (0, 255, 0), -1)

    cv2.imwrite('out.png', img)
    cv2.destroyAllWindows()


main()