目录
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
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
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)
转载：https://blog.csdn.net/weixin_54627824/article/details/127203408
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