对摄像头拍摄到的多赛道进行检测并输出每条赛道的角度和中点坐标

运行结果:

下面的代码都有注释

下面函数是先对整个画面进行扫描，选取合适的色块并排列得到他们的面积和对应的索引

def FindRoad():#在这里面寻找面积最大的4目标没有意义，这里我们主要用到的是没有找到目标时返回的结果area2 = []b = []Inf = -1if len(contour1) >= 1:                             #找到目标至少为一for i in range(len(contour1)):if cv2.contourArea(contour1[i])>50:area2.append(cv2.contourArea(contour1[i]))  #将找到目标面积储存到area2if len(area2)!=0:for j in range(4):   #只对前4个最大的面积进行操作b.append(area2.index(max(area2)))           #把目标按照面积大小从大到小存入列表barea2[area2.index(max(area2))] = Inf        #把刚刚存入列表的面积重新赋值为-1else:return 1b.sort()   #因为目标赛道检测到的面积有可能并不是按照顺序排列，所以在这里进行排列return belse:return 1

设置判断赛道之间的间距，根据的是得到的赛道边缘的横坐标

def JudgeRoad(n):s = []try:if n[2]-n[1]!=n[1]-n[0]:s.append(n[1])                    #列表s储存赛道边缘位置横坐标else:s.append(min(n))try:for i in range(len(n)):if n[i + 1] - n[i] >= 50:  #赛道的间距>50s.append(n[i])s.append(n[i + 1])if i==len(n)-2:if n[len(n)-1] - n[len(n)-2]<5:s.append(n[len(n)-1])breakelse:s.append(n[len(n)-2])breakexcept:passreturn s                   #返回赛道横坐标列表except Exception :return 1                   #错误时一定要返回值pass

计算赛道的角度

def calc_angle(x1,y1,x2,y2):angle = 0k = 0h = 0y = y1-y2x = x1-x2if x == 0:passelse:k = y/xh = math.atan(k)angle = 90 - abs(math.degrees(h))return angle

完整代码：

import cv2
import numpy as np
import math# cap = cv2.VideoCapture('C:/Users/K2095/Desktop/777.mp4')(这是我提前拍摄的模拟小车运行时摄像头的画面)
cap = cv2.VideoCapture(0)def FindRoad():area2 = []b = []Inf = -1if len(contour1) >= 1:                             #找到目标至少为一for i in range(len(contour1)):if cv2.contourArea(contour1[i])>50:area2.append(cv2.contourArea(contour1[i]))  #将找到目标面积储存到area2if len(area2)!=0:for j in range(4):   #只对前4个最大的面积进行操作b.append(area2.index(max(area2)))           #把目标按照面积大小从大到小存入列表barea2[area2.index(max(area2))] = Inf        #把刚刚存入列表的面积重新赋值为-1else:return 1b.sort()   #因为目标赛道检测到的面积有可能并不是按照顺序排列，所以在这里进行排列return belse:return 1def JudgeRoad(n):s = []try:if n[2]-n[1]!=n[1]-n[0]:s.append(n[1])                    #列表s储存赛道边缘位置横坐标else:s.append(min(n))try:for i in range(len(n)):if n[i + 1] - n[i] >= 50:  #赛道的间距>50s.append(n[i])s.append(n[i + 1])if i==len(n)-2:if n[len(n)-1] - n[len(n)-2]<5:s.append(n[len(n)-1])breakelse:s.append(n[len(n)-2])breakexcept:passreturn s                   #返回赛道横坐标列表except Exception :return 1                   #错误时一定要返回值passdef calc_angle(x1,y1,x2,y2):angle = 0k = 0h = 0y = y1-y2x = x1-x2if x == 0:passelse:k = y/xh = math.atan(k)angle = 90 - abs(math.degrees(h))return anglewhile True:success , img = cap.read()img = cv2.resize(img, (640, 480))imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)lowerblack = np.array([0, 0, 0])upperblack = np.array([255, 255, 120])maskblack = cv2.inRange(imgHSV, lowerblack, upperblack)contour1, hierarchy1 = cv2.findContours(maskblack, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)color = maskblack[380]                          #遍历掩膜第380行全部像素值color2 = maskblack[420]blackindex = np.where(color == 255)                      #储存像素为255的索引值blackindex2 = np.where(color2 == 255)npindex = np.array(blackindex)npindex2 = np.array(blackindex2)a = npindex.ravel()                                 #将数组维度拉成一维数组c = npindex2.ravel()FindRoad()o = FindRoad()  #为赛道的索引b = JudgeRoad(a)  #赛道边缘的横坐标，为偶数个b1 = []   #储存赛道中点横坐标blackindex2 = JudgeRoad(c)npindex2 = []if o == 1:print('未找到赛道')passelse:if len(str(b)) <= 1:       #b类型转换成字符串(防止报错)b1.append(1)else:for i in range(len(b)):if i % 2 == 0:b1.append((b[i] + b[i + 1]) / 2)else:passif len(str(blackindex2)) <= 1:npindex2.append(1)else:for i in range(len(blackindex2)):if i % 2 == 0:npindex2.append((blackindex2[i] + blackindex2[i + 1]) / 2)else:passif len(b1)==len(npindex2):for i in range(len(b1)):angle = calc_angle(npindex2[i], 420, b1[i], 380)cv2.circle(img, (int(npindex2[i]), 420), 3, 255, -1)cv2.circle(img, (int(b1[i]), 380), 3, 255, -1)print('赛道{}角度:{},中点横坐标:{}'.format(i+1,angle,b1[i]))else:passcv2.imshow('s',maskblack)cv2.imshow('s1', img)if cv2.waitKey(30) & 0xFF == 27:  # 按Esc关闭break