如何使用python opencv测量同一图像中2条线之间的角度？

Question

我已经使用霍夫变换检测到了一条不是笔直的车道边界线，然后分别提取了该线。然后与另一条具有直线的图像融合。现在，我需要计算这两条线之间的角度，但是我不知道这些线的坐标。因此，我尝试使用给出垂直线坐标的代码，但无法明确标识这些坐标。有没有一种方法可以测量这些线之间的角度？这是我的坐标计算代码和两行混合图像

import cv2 as cv
import numpy as np

src = cv.imread("blended2.png", cv.IMREAD_COLOR)

if len(src.shape) != 2:
    gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
else:
    gray = src

gray = cv.bitwise_not(gray)
bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)

horizontal = np.copy(bw)
vertical = np.copy(bw)

cols = horizontal.shape[1]
horizontal_size = int(cols / 30)

horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))
horizontal = cv.erode(horizontal, horizontalStructure)
horizontal = cv.dilate(horizontal, horizontalStructure)

cv.imwrite("img_horizontal8.png", horizontal)

h_transpose = np.transpose(np.nonzero(horizontal))
print("h_transpose")
print(h_transpose[:100])

rows = vertical.shape[0]
verticalsize = int(rows / 30)
verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))
vertical = cv.erode(vertical, verticalStructure)
vertical = cv.dilate(vertical, verticalStructure)

cv.imwrite("img_vertical8.png", vertical)

v_transpose = np.transpose(np.nonzero(vertical))

print("v_transpose")
print(v_transpose[:100])

img = src.copy()

# edges = cv.Canny(vertical,50,150,apertureSize = 3)
minLineLength = 100
maxLineGap = 200
lines = cv.HoughLinesP(vertical,1,np.pi/180,100,minLineLength,maxLineGap)
for line in lines:
    for x1,y1,x2,y2 in line:
        cv.line(img,(x1,y1),(x2,y2),(0,255,0),2)

cv.imshow('houghlinesP_vert', img)
cv.waitKey(0)

Answer 1

一种方法是使用霍夫变换来检测线并获取每条线的角度。然后，通过减去两条线之间的差可以找到两条线之间的角度。

我们首先使用np.mean进行算术平均，从本质上将产生此结果的图像阈值化。

image = cv2.imread('2.png')

# Compute arithmetic mean
image = np.mean(image, axis=2)

现在我们执行skimage.transform.hough_line来检测行

# Perform Hough Transformation to detect lines
hspace, angles, distances = hough_line(image)

# Find angle
angle=[]
for _, a , distances in zip(*hough_line_peaks(hspace, angles, distances)):
    angle.append(a)

接下来，我们获得每条线的角度，并找到差值以获得结果

# Obtain angle for each line
angles = [a*180/np.pi for a in angle]

# Compute difference between the two lines
angle_difference = np.max(angles) - np.min(angles)
print(angle_difference)

  

16.08938547486033

完整代码

from skimage.transform import (hough_line, hough_line_peaks)
import numpy as np
import cv2

image = cv2.imread('2.png')

# Compute arithmetic mean
image = np.mean(image, axis=2)

# Perform Hough Transformation to detect lines
hspace, angles, distances = hough_line(image)

# Find angle
angle=[]
for _, a , distances in zip(*hough_line_peaks(hspace, angles, distances)):
    angle.append(a)

# Obtain angle for each line
angles = [a*180/np.pi for a in angle]

# Compute difference between the two lines
angle_difference = np.max(angles) - np.min(angles)
print(angle_difference)