如何使用 opencv 霍夫线算法得到线的交点？

这是我的代码:

import cv2
import numpy as np
import imutils

im = cv2.imread('../data/test1.jpg')
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 60, 150, apertureSize=3)

img = im.copy()
lines = cv2.HoughLines(edges,1,np.pi/180,200)

for line in lines:
    for rho,theta in line:
        a = np.cos(theta)
        b = np.sin(theta)
        x0 = a*rho
        y0 = b*rho
        x1 = int(x0 + 3000*(-b))
        y1 = int(y0 + 3000*(a))
        x2 = int(x0 - 3000*(-b))
        y2 = int(y0 - 3000*(a))
        cv2.line(img,(x1,y1),(x2,y2),(0,255,0),10)

cv2.imshow('houghlines',imutils.resize(img, height=650))
cv2.waitKey(0)
cv2.destroyAllWindows()

最佳答案

你不想得到平行线的交点；只有垂直线与水平线的交点。此外，由于您有垂直线，计算斜率可能会导致爆炸或 inf 斜率，因此您不应该使用 y = mx+b方程。你需要做两件事:

import cv2
import numpy as np

img = cv2.imread('sudoku.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 5)
adapt_type = cv2.ADAPTIVE_THRESH_GAUSSIAN_C
thresh_type = cv2.THRESH_BINARY_INV
bin_img = cv2.adaptiveThreshold(blur, 255, adapt_type, thresh_type, 11, 2)

rho, theta, thresh = 2, np.pi/180, 400
lines = cv2.HoughLines(bin_img, rho, theta, thresh)

from collections import defaultdict
def segment_by_angle_kmeans(lines, k=2, **kwargs):
    """Groups lines based on angle with k-means.

    Uses k-means on the coordinates of the angle on the unit circle 
    to segment `k` angles inside `lines`.
    """

    # Define criteria = (type, max_iter, epsilon)
    default_criteria_type = cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER
    criteria = kwargs.get('criteria', (default_criteria_type, 10, 1.0))
    flags = kwargs.get('flags', cv2.KMEANS_RANDOM_CENTERS)
    attempts = kwargs.get('attempts', 10)

    # returns angles in [0, pi] in radians
    angles = np.array([line[0][1] for line in lines])
    # multiply the angles by two and find coordinates of that angle
    pts = np.array([[np.cos(2*angle), np.sin(2*angle)]
                    for angle in angles], dtype=np.float32)

    # run kmeans on the coords
    labels, centers = cv2.kmeans(pts, k, None, criteria, attempts, flags)[1:]
    labels = labels.reshape(-1)  # transpose to row vec

    # segment lines based on their kmeans label
    segmented = defaultdict(list)
    for i, line in enumerate(lines):
        segmented[labels[i]].append(line)
    segmented = list(segmented.values())
    return segmented

segmented = segment_by_angle_kmeans(lines)

def intersection(line1, line2):
    """Finds the intersection of two lines given in Hesse normal form.

    Returns closest integer pixel locations.
    See https://stackoverflow.com/a/383527/5087436
    """
    rho1, theta1 = line1[0]
    rho2, theta2 = line2[0]
    A = np.array([
        [np.cos(theta1), np.sin(theta1)],
        [np.cos(theta2), np.sin(theta2)]
    ])
    b = np.array([[rho1], [rho2]])
    x0, y0 = np.linalg.solve(A, b)
    x0, y0 = int(np.round(x0)), int(np.round(y0))
    return [[x0, y0]]


def segmented_intersections(lines):
    """Finds the intersections between groups of lines."""

    intersections = []
    for i, group in enumerate(lines[:-1]):
        for next_group in lines[i+1:]:
            for line1 in group:
                for line2 in next_group:
                    intersections.append(intersection(line1, line2)) 

    return intersections

intersections = segmented_intersections(segmented)

关于python - 找到使用 houghlines opencv 绘制的两条线的交点，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46565975/