python+opencv车道线检测（简易实现），供大家参考，具体内容如下

技术栈：python+opencv

实现思路：

1、canny边缘检测获取图中的边缘信息；
2、霍夫变换寻找图中直线；
3、绘制梯形感兴趣区域获得车前范围；
4、得到并绘制车道线；

效果展示：

代码实现：

import cv2
import numpy as np


def canny():
 gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)
 #高斯滤波
 blur = cv2.GaussianBlur(gray, (5, 5), 0)
 #边缘检测
 canny_img = cv2.Canny(blur, 50, 150)
 return canny_img


def region_of_interest(r_image):
 h = r_image.shape[0]
 w = r_image.shape[1]
 # 这个区域不稳定，需要根据图片更换
 poly = np.array([
 [(100, h), (500, h), (290, 180), (250, 180)]
 ])
 mask = np.zeros_like(r_image)
 # 绘制掩膜图像
 cv2.fillPoly(mask, poly, 255)
 # 获得ROI区域
 masked_image = cv2.bitwise_and(r_image, mask)
 return masked_image


if __name__ == '__main__':
 image = cv2.imread('test.jpg')
 lane_image = np.copy(image)
 canny = canny()
 cropped_image = region_of_interest(canny)
 cv2.imshow("result", cropped_image)
 cv2.waitKey(0)

霍夫变换加线性拟合改良：

效果图：

代码实现：

主要增加了根据斜率作线性拟合过滤无用点后连线的操作；

import cv2
import numpy as np


def canny():
 gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)
 blur = cv2.GaussianBlur(gray, (5, 5), 0)

 canny_img = cv2.Canny(blur, 50, 150)
 return canny_img


def region_of_interest(r_image):
 h = r_image.shape[0]
 w = r_image.shape[1]

 poly = np.array([
 [(100, h), (500, h), (280, 180), (250, 180)]
 ])
 mask = np.zeros_like(r_image)
 cv2.fillPoly(mask, poly, 255)
 masked_image = cv2.bitwise_and(r_image, mask)
 return masked_image


def get_lines(img_lines):
 if img_lines is not None:
 for line in lines:
 for x1, y1, x2, y2 in line:
 # 分左右车道
 k = (y2 - y1) / (x2 - x1)
 if k < 0:
  lefts.append(line)
 else:
  rights.append(line)


def choose_lines(after_lines, slo_th): # 过滤斜率差别较大的点
 slope = [(y2 - y1) / (x2 - x1) for line in after_lines for x1, x2, y1, y2 in line] # 获得斜率数组
 while len(after_lines) > 0:
 mean = np.mean(slope) # 计算平均斜率
 diff = [abs(s - mean) for s in slope] # 每条线斜率与平均斜率的差距
 idx = np.argmax(diff) # 找到最大斜率的索引
 if diff[idx] > slo_th: # 大于预设的阈值选取
 slope.pop(idx)
 after_lines.pop(idx)
 else:
 break

 return after_lines


def clac_edgepoints(points, y_min, y_max):
 x = [p[0] for p in points]
 y = [p[1] for p in points]

 k = np.polyfit(y, x, 1) # 曲线拟合的函数，找到xy的拟合关系斜率
 func = np.poly1d(k) # 斜率代入可以得到一个y=kx的函数

 x_min = int(func(y_min)) # y_min = 325其实是近似找了一个
 x_max = int(func(y_max))

 return [(x_min, y_min), (x_max, y_max)]


if __name__ == '__main__':
 image = cv2.imread('F:\\A_javaPro\\test.jpg')
 lane_image = np.copy(image)
 canny_img = canny()
 cropped_image = region_of_interest(canny_img)
 lefts = []
 rights = []
 lines = cv2.HoughLinesP(cropped_image, 1, np.pi / 180, 15, np.array([]), minLineLength=40, maxLineGap=20)
 get_lines(lines) # 分别得到左右车道线的图片

 good_leftlines = choose_lines(lefts, 0.1) # 处理后的点
 good_rightlines = choose_lines(rights, 0.1)

 leftpoints = [(x1, y1) for left in good_leftlines for x1, y1, x2, y2 in left]
 leftpoints = leftpoints + [(x2, y2) for left in good_leftlines for x1, y1, x2, y2 in left]

 rightpoints = [(x1, y1) for right in good_rightlines for x1, y1, x2, y2 in right]
 rightpoints = rightpoints + [(x2, y2) for right in good_rightlines for x1, y1, x2, y2 in right]

 lefttop = clac_edgepoints(leftpoints, 180, image.shape[0]) # 要画左右车道线的端点
 righttop = clac_edgepoints(rightpoints, 180, image.shape[0])

 src = np.zeros_like(image)

 cv2.line(src, lefttop[0], lefttop[1], (255, 255, 0), 7)
 cv2.line(src, righttop[0], righttop[1], (255, 255, 0), 7)

 cv2.imshow('line Image', src)
 src_2 = cv2.addWeighted(image, 0.8, src, 1, 0)
 cv2.imshow('Finally Image', src_2)

 cv2.waitKey(0)

待改进：

代码实用性差，几乎不能用于实际，但是可以作为初学者的练手项目；
斑马线检测思路：获取车前感兴趣区域，判断白色像素点比例即可实现；
行人检测思路：opencv有内置行人检测函数，基于内置的训练好的数据集；

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持python博客。