一、首先进行canny边缘检测，为获取车道线边缘做准备
import cv2
gray_img = cv2.imread('img.jpg',cv2.IMREAD_GRAYSCALE)
canny_img = cv2.Canny(gray_img,50,100)
cv2.imwrite('canny_img.jpg',canny_img)
cv2.imshow('canny',canny_img)
cv2.waitKey(0)
二、进行ROI提取获取确切的车道线边缘（红色线内部）
方法：在图像中，黑色表示0，白色为1，那么要保留矩形内的白色线，就使用逻辑与，当然前提是图像矩形外也是0，那么就采用创建一个全0图像，然后在矩形内全1，之后与之前的canny图像进行与操作，即可得到需要的车道线边缘。
import cv2
import numpy as np
canny_img = cv2.imread('canny_img.jpg',cv2.IMREAD_GRAYSCALE)
roi = np.zeros_like(canny_img)
roi = cv2.fillPoly(roi,np.array([[[0, 368],[300, 210], [340, 210], [640, 368]]]),color=255)
roi_img = cv2.bitwise_and(canny_img, roi)
cv2.imwrite('roi_img.jpg',roi_img)
cv2.imshow('roi_img',roi_img)
cv2.waitKey(0)
三、利用概率霍夫变换获取直线，并将斜率正数和复数的线段给分割开来
TIPs:使用霍夫变换需要将图像先二值化
概率霍夫变换函数：
- lines=cv2.HoughLinesP(image, rho,theta,threshold,minLineLength, maxLineGap)
- image:图像，必须是8位单通道二值图像
- rho:以像素为单位的距离r的精度，一般情况下是使用1
- theta:表示搜索可能的角度，使用的精度是np.pi/180
- threshold:阈值，该值越小，判定的直线越多，相反则直线越少
- minLineLength:默认为0，控制接受直线的最小长度
- maxLineGap:控制接受共线线段的最小间隔，如果两点间隔超过了参数，就认为两点不在同一直线上，默认为0
- lines:返回值由numpy.ndarray构成，每一对都是一对浮点数，表示线段的两个端点
import cv2
import numpy as np
#计算斜率
def calculate_slope(line):
x_1, y_1, x_2, y_2 = line[0]
return (y_2 - y_1) / (x_2 - x_1)
edge_img = cv2.imread('masked_edge_img.jpg', cv2.IMREAD_GRAYSCALE)
#霍夫变换获取所有线段
lines = cv2.HoughLinesP(edge_img, 1, np.pi / 180, 15, minLineLength=40,
maxLineGap=20)
#利用斜率划分线段
left_lines = [line for line in lines if calculate_slope(line) < 0]
right_lines = [line for line in lines if calculate_slope(line) > 0]四、离群值过滤，剔除斜率相差过大的线段
流程：
- 获取所有的线段的斜率，然后计算斜率的平均值
- 遍历所有斜率，计算和平均斜率的差值，寻找最大的那个斜率对应的直线，如果差值大于阈值，那么就从列表中剔除对应的线段和斜率
- 循环执行操作，直到剩下的全部都是小于阈值的线段
def reject_abnormal_lines(lines, threshold):
slopes = [calculate_slope(line) for line in lines]
while len(lines) > 0:
mean = np.mean(slopes)
diff = [abs(s - mean) for s in slopes]
idx = np.argmax(diff)
if diff[idx] > threshold:
slopes.pop(idx)
lines.pop(idx)
else:
break
return lines
reject_abnormal_lines(left_lines, threshold=0.2)
reject_abnormal_lines(right_lines, threshold=0.2)五、最小二乘拟合，实现将左边和右边的线段互相拟合成一条直线，形成车道线
流程：
- 取出所有的直线的x和y坐标，组成列表，利用np.ravel进行将高维转一维数组
- 利用np.polyfit进行直线的拟合，最终得到拟合后的直线的斜率和截距，类似y=kx+b的（k,b）
- 最终要返回(x_min,y_min,x_max,y_max）的一个np.array的数据，那么就是用np.polyval求多项式的值，举个example，np.polyval([3,0,1], 5) # 3 * 5**2 + 0 * 5**1 + 1，即可以获得对应x坐标的y坐标。
def least_squares_fit(lines):
# 1. 取出所有坐标点
x_coords = np.ravel([[line[0][0], line[0][2]] for line in lines])
y_coords = np.ravel([[line[0][1], line[0][3]] for line in lines])
# 2. 进行直线拟合.得到多项式系数
poly = np.polyfit(x_coords, y_coords, deg=1)
print(poly)
# 3. 根据多项式系数,计算两个直线上的点,用于唯一确定这条直线
point_min = (np.min(x_coords), np.polyval(poly, np.min(x_coords)))
point_max = (np.max(x_coords), np.polyval(poly, np.max(x_coords)))
return np.array([point_min, point_max], dtype=np.int)
print("left lane")
print(least_squares_fit(left_lines))
print("right lane")
print(least_squares_fit(right_lines))六、绘制线段
cv2.line(img, tuple(left_line[0]), tuple(left_line[1]), color=(0, 255, 255), thickness=5)
cv2.line(img, tuple(right_line[0]), tuple(right_line[1]), color=(0, 255, 255), thickness=5)全部代码（视频显示）
import cv2
import numpy as np
def get_edge_img(color_img, gaussian_ksize=5, gaussian_sigmax=1,
canny_threshold1=50, canny_threshold2=100):
"""
灰度化,模糊,canny变换,提取边缘
:param color_img: 彩色图,channels=3
"""
gaussian = cv2.GaussianBlur(color_img, (gaussian_ksize, gaussian_ksize),
gaussian_sigmax)
gray_img = cv2.cvtColor(gaussian, cv2.COLOR_BGR2GRAY)
edges_img = cv2.Canny(gray_img, canny_threshold1, canny_threshold2)
return edges_img
def roi_mask(gray_img):
"""
对gray_img进行掩膜
:param gray_img: 灰度图,channels=1
"""
poly_pts = np.array([[[0, 368], [300, 210], [340, 210], [640, 368]]])
mask = np.zeros_like(gray_img)
mask = cv2.fillPoly(mask, pts=poly_pts, color=255)
img_mask = cv2.bitwise_and(gray_img, mask)
return img_mask
def get_lines(edge_img):
"""
获取edge_img中的所有线段
:param edge_img: 标记边缘的灰度图
"""
def calculate_slope(line):
"""
计算线段line的斜率
:param line: np.array([[x_1, y_1, x_2, y_2]])
:return:
"""
x_1, y_1, x_2, y_2 = line[0]
return (y_2 - y_1) / (x_2 - x_1)
def reject_abnormal_lines(lines, threshold=0.2):
"""
剔除斜率不一致的线段
:param lines: 线段集合, [np.array([[x_1, y_1, x_2, y_2]]),np.array([[x_1, y_1, x_2, y_2]]),...,np.array([[x_1, y_1, x_2, y_2]])]
"""
slopes = [calculate_slope(line) for line in lines]
while len(lines) > 0:
mean = np.mean(slopes)
diff = [abs(s - mean) for s in slopes]
idx = np.argmax(diff)
if diff[idx] > threshold:
slopes.pop(idx)
lines.pop(idx)
else:
break
return lines
def least_squares_fit(lines):
"""
将lines中的线段拟合成一条线段
:param lines: 线段集合, [np.array([[x_1, y_1, x_2, y_2]]),np.array([[x_1, y_1, x_2, y_2]]),...,np.array([[x_1, y_1, x_2, y_2]])]
:return: 线段上的两点,np.array([[xmin, ymin], [xmax, ymax]])
"""
x_coords = np.ravel([[line[0][0], line[0][2]] for line in lines])
y_coords = np.ravel([[line[0][1], line[0][3]] for line in lines])
poly = np.polyfit(x_coords, y_coords, deg=1)
point_min = (np.min(x_coords), np.polyval(poly, np.min(x_coords)))
point_max = (np.max(x_coords), np.polyval(poly, np.max(x_coords)))
return np.array([point_min, point_max], dtype=np.int)
# 获取所有线段
lines = cv2.HoughLinesP(edge_img, 1, np.pi / 180, 15, minLineLength=40,
maxLineGap=20)
# 按照斜率分成车道线
left_lines = [line for line in lines if calculate_slope(line) > 0]
right_lines = [line for line in lines if calculate_slope(line) < 0]
# 剔除离群线段
left_lines = reject_abnormal_lines(left_lines)
right_lines = reject_abnormal_lines(right_lines)
return least_squares_fit(left_lines), least_squares_fit(right_lines)
def draw_lines(img, lines):
left_line, right_line = lines
cv2.line(img, tuple(left_line[0]), tuple(left_line[1]), color=(0, 255, 255),
thickness=5)
cv2.line(img, tuple(right_line[0]), tuple(right_line[1]),
color=(0, 255, 255), thickness=5)
def show_lane(color_img):
edge_img = get_edge_img(color_img)
mask_gray_img = roi_mask(edge_img)
lines = get_lines(mask_gray_img)
draw_lines(color_img, lines)
return color_img
capture = cv2.VideoCapture('video.mp4')
while True:
ret, frame = capture.read()
if not ret:
break
frame = show_lane(frame)
cv2.imshow('frame', frame)
cv2.waitKey(10)总结
到此这篇关于OpenCV实战案例之车道线识别详解的文章就介绍到这了,更多相关OpenCV车道线识别内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！
