如何使用python opencv测量同一图像中两条线之间的角度？

Question

我使用霍夫变换检测到一条不直的车道边界线，然后分别提取该线。然后与另一个具有直线的图像混合。现在我需要计算这两条线之间的角度，但我不知道这些线的坐标。所以我尝试使用给出垂直线坐标的代码，但它无法具体识别这些坐标。有没有办法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 cvimport numpy as npsrc = cv.imread("blended2.png", cv.IMREAD_COLOR)if len(src.shape) != 2:    gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)else:    gray = srcgray = 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 = 100maxLineGap = 200lines = 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得每条线的角度。然后可以通过减去两条线之间的差来找到两条线之间的角度。

我们首先使用算术平均来np.mean对导致此结果的图像进行阈值处理。

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

# Compute arithmetic mean

image = np.mean(image, axis=2)

现在我们执行skimage.transform.hough_line检测线

# 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异以获得我们的结果

# 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)