时间戳

首页 > 解决方案 > 为什么opencv houghline检测到的线条与图像中的真实线条不平行？

python - 为什么opencv houghline检测到的线条与图像中的真实线条不平行？

问题描述

higuys，首先请看下图： 在此处输入图像描述

为什么用 houghline 检测和绘制的线条与足球场的白线并不真正平行？

我需要那个交点准确地出现在图像上

那么它需要在图像中用白线绘制直线平行线谁能帮我解决这个问题

提前致谢

如果您想使用代码，这是原始图像 d.jpg： 在此处输入图像描述

这是我的python代码：

import numpy as np
import cv2
from collections import defaultdict
import sys
import math

img2 = cv2.imread("e:/d.jpg")
edges2 = cv2.Canny(img2, 40, 55)
def segment_by_angle_kmeans(lines, k=2, **kwargs):


 # 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)
 # Get angles in [0, pi] radians
 angles = np.array([line[0][1] for line in lines])
 # Multiply the angles by two and find coordinates of that angle on the Unit Circle
 pts = np.array([[np.cos(2*angle), np.sin(2*angle)] for angle in angles], dtype=np.float32)
 # Run k-means
 if sys.version_info[0] == 2:
    # python 2.x
    ret, labels, centers = cv2.kmeans(pts, k, criteria, attempts, flags)
 else: 
    # python 3.x, syntax has changed.
    labels, centers = cv2.kmeans(pts, k, None, criteria, attempts, flags)[1:]

 labels = labels.reshape(-1) # Transpose to row vector

 # Segment lines based on their label of 0 or 1
 segmented = defaultdict(list)
 for i, line in zip(range(len(lines)), lines):
    segmented[labels[i]].append(line)

 segmented = list(segmented.values())
 print("Segmented lines into two groups: %d, %d" % (len(segmented[0]), len(segmented[1])))

 return segmented


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

 Returns closest integer pixel locations.


 """

 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):
 """
 Find the intersection 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


def drawLines(img, lines, color=(0,0,255)):
 """
 Draw lines on an image
 """
 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 + 1000*(-b))
        y1 = int(y0 + 1000*(a))
        x2 = int(x0 - 1000*(-b))
        y2 = int(y0 - 1000*(a))
        cv2.line(img, (x1,y1), (x2,y2), color, 2)




 # Detect lines
 rho = 2.7
 theta = np.pi/50
 thresh = 410
 lines = cv2.HoughLines(edges2, rho, theta, thresh)
 print("Found lines: %d" % (len(lines)))

 # Draw all Hough lines
 img_with_all_lines = np.copy(2)
 drawLines(img_with_all_lines, lines)

 # Cluster line angles into 2 groups (vertical and horizontal)
 segmented = segment_by_angle_kmeans(lines, 2)

 # Find the intersections of each vertical line with each horizontal line
 intersections = segmented_intersections(segmented)

 img_with_segmented_lines = np.copy(img2)

 # Draw vertical lines 
 vertical_lines = segmented[1]
 img_with_vertical_lines = np.copy(img2)
 drawLines(img_with_segmented_lines, vertical_lines, (255,255,0))

 # Draw horizontal lines 
 horizontal_lines = segmented[0]
 img_with_horizontal_lines = np.copy(img2)
 drawLines(img_with_segmented_lines, horizontal_lines, (0,255,255))

 # Draw intersection points 

 intersections.pop(5)
 intersections.pop(4)

 mn = intersections[3]
 mk = intersections[2]
 intersections = intersections[:2] 
 intersections.append(mn)
 intersections.append(mk)
 print(intersections)
 for point in intersections:
 pt = (point[0][0], point[0][1])
 length = 5

 cv2.circle(img_with_segmented_lines, pt, 5 , (255, 0, 255), -1)

 cv2.imshow("Segmented lines", img_with_segmented_lines)
 cv2.waitKey()

标签： pythonopencvimage-processinghoughlines

解决方案

Hough 使用具有有限分辨率的累加器，该分辨率决定了精度。您可以减少 theta（以运行时间为代价，并可能降低可靠性）。

如果我必须这样做，我可能会添加一些后处理来更精确地检测沿着霍夫线的线，fi 使用方向梯度滤波器和稳健的线拟合。

推荐阅读