我使用霍夫變換檢測到一條不直的車道邊界線，然后分別提取該線。然后與另一個具有直線的圖像混合?，F在我需要計算這兩條線之間的角度，但我不知道這些線的坐標。所以我嘗試使用給出垂直線坐標的代碼，但它無法具體識別這些坐標。有沒有辦法測量這些線之間的角度？這是我的坐標計算代碼和兩行混合圖像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)
1 回答
桃花長相依
TA貢獻1860條經驗 獲得超8個贊
一種方法是使用霍夫變換來檢測線并獲得每條線的角度。然后可以通過減去兩條線之間的差來找到兩條線之間的角度。
我們首先使用算術平均來np.mean對導致此結果的圖像進行閾值處理。
image = cv2.imread('2.png')
# Compute arithmetic mean
image = np.mean(image, axis=2)
現在我們執行skimage.transform.hough_line檢測線
# 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)
接下來我們獲得每條線的角度并找到差異以獲得我們的結果
# 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
完整代碼
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)
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