cv2 python 获取斑马线_python+opencv实现车道线检测

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；

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