# 毕业设计 - 车道线检测(自动驾驶 机器视觉)

``````ignore_mask_color = 255
# 获取图片尺寸
imshape = img.shape
vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
# 定义Hough 变换的参数
rho = 1
theta = np.pi/180
threshold = 2
min_line_length = 4 # 组成一条线的最小像素数
max_line_gap = 5    # 可连接线段之间的最大像素间距
# 创建一个用于绘制车道线的图片
line_image = np.copy(img)*0
# 对于 canny 边缘检测结果应用 Hough 变换
# 输出“线”是一个数组，其中包含检测到的线段的端点
lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
min_line_length, max_line_gap)
# 遍历“线”的数组来在 line_image 上绘制
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
color_edges = np.dstack((edges, edges, edges)) ``````

import math
import cv2
import numpy as np
"""
Gray Scale
Gaussian Smoothing
Canny Edge Detection
Hough Transform
Draw Lines [Mark Lane Lines with different Color]
"""
class SimpleLaneLineDetector(object):

``````def __init__(self):
pass
def detect(self,img):
# 图像灰度处理
gray_img = self.grayscale(img)
print(gray_img)
#图像高斯平滑处理
smoothed_img = self.gaussian_blur(img = gray_img, kernel_size = 5)
#canny 边缘检测
canny_img = self.canny(img = smoothed_img, low_threshold = 180, high_threshold = 240)
masked_img = self.region_of_interest(img = canny_img, vertices = self.get_vertices(img))
#霍夫变换
houghed_lines = self.hough_lines(img = masked_img, rho = 1, theta = np.pi/180, threshold = 20, min_line_len = 20, max_line_gap = 180)
# 绘制车道线
output = self.weighted_img(img = houghed_lines, initial_img = img, alpha=0.8, beta=1., gamma=0.)
return output
def grayscale(self,img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def canny(self,img, low_threshold, high_threshold):
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(self,img, kernel_size):
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def region_of_interest(self,img, vertices):
if len(img.shape) > 2:
channel_count = img.shape[2]
else:
def draw_lines(self,img, lines, color=[255, 0, 0], thickness=10):
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def slope_lines(self,image,lines):
img = image.copy()
poly_vertices = []
order = [0,1,3,2]
left_lines = []
right_lines = []
for line in lines:
for x1,y1,x2,y2 in line:
if x1 == x2:
pass
else:
m = (y2 - y1) / (x2 - x1)
c = y1 - m * x1
if m < 0:
left_lines.append((m,c))
elif m >= 0:
right_lines.append((m,c))
left_line = np.mean(left_lines, axis=0)
right_line = np.mean(right_lines, axis=0)
for slope, intercept in [left_line, right_line]:
rows, cols = image.shape[:2]
y1= int(rows)
y2= int(rows*0.6)
x1=int((y1-intercept)/slope)
x2=int((y2-intercept)/slope)
poly_vertices.append((x1, y1))
poly_vertices.append((x2, y2))
self.draw_lines(img, np.array([[[x1,y1,x2,y2]]]))
poly_vertices = [poly_vertices[i] for i in order]
cv2.fillPoly(img, pts = np.array([poly_vertices],'int32'), color = (0,255,0))
def hough_lines(self,img, rho, theta, threshold, min_line_len, max_line_gap):
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, [Skrill下载](https://www.gendan5.com/wallet/Skrill.html)maxLineGap=max_line_gap)
line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
line_img = self.slope_lines(line_img,lines)
return line_img
def weighted_img(self,img, initial_img, alpha=0.1, beta=1., gamma=0.):
lines_edges = cv2.addWeighted(initial_img, alpha, img, beta, gamma)
return lines_edges
def get_vertices(self,image):
rows, cols = image.shape[:2]
bottom_left  = [cols*0.15, rows]
top_left     = [cols*0.45, rows*0.6]
bottom_right = [cols*0.95, rows]
top_right    = [cols*0.55, rows*0.6]
ver = np.array([[bottom_left, top_left, top_right, bottom_right]], dtype=np.int32)
return ver``````