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基于opencv-python的车道线检测（高级）
发布于2021-07-25 06:47 阅读(482) 评论(0) 点赞(18) 收藏(4)
运行环境：python3.8， numpy=1.19.5，opencv-python=4.5.1
注意：以下内容只提供了代码与运行结果，具体原理与细节见https://zhuanlan.zhihu.com/p/54866418
效果展示：
一、文章目录
1、相机标定
2、视频畸形修正
3、透视变换
4、提取车道线
5、矩形滑窗
6、跟踪车道线
7、求取曲率与偏移量
8、逆投影到原图
9、视频车道线检测
二、实操
注意：后一节代码是在上一节代码的基础上进行修改（排版比较繁琐，可以选择章节进行运行）
1、相机标定
import cv2
import glob
import numpy as np
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# Read distorted chessboard image，测试
test_distort_image = cv2.imread('./camera_cal/calibration4.jpg')
# Do undistortion
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# 显示
cv2.imshow('img_0', test_distort_image)
cv2.imshow('img', test_undistort_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
2、视频畸形修正
import cv2
import glob
import numpy as np
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 视频输入
video_input = 'challenge.mp4'
cap = cv2.VideoCapture(video_input)
count = 1
while True:
ret, image = cap.read()
if ret:
undistort_image = undistortImage(image, mtx, dist)
cv2.imwrite('original_image/' + str(count) + '.jpg', undistort_image)
count += 1
else:
break
cap.release()
未修正：
修正后：
3、透视变换
“透视”是图像成像时，物体距离摄像机越远，看起来越小的一种现象。在真实世界中，左右互相平行的车道线，会在图像的最远处交汇成一个点。这个现象就是“透视成像”的原理造成的。
目的：矫正为真实世界的形状
效果：投影成鸟瞰图
import cv2
import glob
import numpy as np
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取图片
test_distort_image = cv2.imread('test_img/test2.jpg')
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 460], [700, 460], [1096, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# 显示
cv2.imshow('img', test_warp_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
透视变换后：
4、提取车道线
方法：根据HSL模型中的L（亮度）通道来分割出图像中的白色车道线，同时可以根据Lab模型中的b（蓝黄）通道来分割出图像中的黄色车道线，再将两次的分割结果，取合集，叠加到一幅图上，得到两条完整的车道线。
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
# Step 4 : Create a thresholded binary image
# 亮度划分函数
def hlsLSelect(img, thresh=(220, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
l_channel = hls[:, :, 1]
l_channel = l_channel*(255/np.max(l_channel))
binary_output = np.zeros_like(l_channel)
binary_output[(l_channel > thresh[0]) & (l_channel <= thresh[1])] = 255
return binary_output
# 亮度划分函数
def hlsSSelect(img, thresh=(125, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
s_channel = s_channel*(255/np.max(s_channel))
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 255
return binary_output
def dirThreshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction_sobelxy = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction_sobelxy)
binary_output[(direction_sobelxy >= thresh[0]) & (direction_sobelxy <= thresh[1])] = 1
# 6) Return the binary image
return binary_output
def magThreshold(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, sobel_kernel)
# 3) Calculate the magnitude
# abs_sobelx = np.absolute(sobelx)
# abs_sobely = np.absolute(sobely)
abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(abs_sobelxy/np.max(abs_sobelxy) * 255)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy >= mag_thresh[0]) & (scaled_sobelxy <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def absSobelThreshold(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
# binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
# binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return scaled_sobel
# Lab蓝黄通道划分函数
def labBSelect(img, thresh=(215, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
lab_b = lab[:, :, 2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 100:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 255
# 3) Return a binary image of threshold result
return binary_output
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取图片
test_distort_image = cv2.imread('test_img/test3.jpg')
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 440], [700, 440], [1100, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换，得到变换后的结果图，类似俯视图
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# Step 4 提取车道线
# 提取1：sobel算子提取
# min_thresh = 30
# max_thresh = 100
# Result_sobelAbs = absSobelThreshold(test_warp_image, 'x', min_thresh, max_thresh)
# 提取2：hls方法提取，保留黄色和白色的车道线
# min_thresh = 150
# max_thresh = 255
# Result_hlsS = hlsSSelect(test_warp_image, (min_thresh, max_thresh))
# 提取3：hls方法提取，只保留白色的车道线
# min_thresh = 215
# max_thresh = 255
# Result_hlsL = hlsLSelect(test_warp_image, (min_thresh, max_thresh))
# 提取4：lab方法提取，只保留黄色的车道线
# min_thresh = 195
# max_thresh = 255
# Result_labB = labBSelect(test_warp_image, (min_thresh, max_thresh))
# 提取5：方法1-4结合
# sx_binary = absSobelThreshold(test_warp_image, 'x', 30, 150)
# hlsL_binary = hlsLSelect(test_warp_image)
# labB_binary = labBSelect(test_warp_image)
# combined_binary = np.zeros_like(sx_binary)
# combined_binary[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 提取6：只要求hsl与lab即可，结果与5一致，省略Sobel算子
hlsL_binary = hlsLSelect(test_warp_image)
labB_binary = labBSelect(test_warp_image)
combined_line_img = np.zeros_like(hlsL_binary)
combined_line_img[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 显示
cv2.imshow('img_0', combined_line_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
提取车道线结果：
5、矩形滑窗
方法：利用直方图统计每列点的个数，取最大的列数为滑窗（边缘）中点得到初始滑窗的位置，然后统计滑窗内所有点横坐标，求取均值作为下一个滑窗，循环往复得到左右线所有滑窗。
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
#################################################################
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
#################################################################
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
# Step 4 : Create a thresholded binary image
#################################################################
# 亮度划分函数
def hlsLSelect(img, thresh=(220, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
l_channel = hls[:, :, 1]
l_channel = l_channel*(255/np.max(l_channel))
binary_output = np.zeros_like(l_channel)
binary_output[(l_channel > thresh[0]) & (l_channel <= thresh[1])] = 255
return binary_output
# 亮度划分函数
def hlsSSelect(img, thresh=(125, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
s_channel = s_channel*(255/np.max(s_channel))
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 255
return binary_output
def dirThreshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction_sobelxy = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction_sobelxy)
binary_output[(direction_sobelxy >= thresh[0]) & (direction_sobelxy <= thresh[1])] = 1
# 6) Return the binary image
return binary_output
def magThreshold(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, sobel_kernel)
# 3) Calculate the magnitude
# abs_sobelx = np.absolute(sobelx)
# abs_sobely = np.absolute(sobely)
abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(abs_sobelxy/np.max(abs_sobelxy) * 255)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy >= mag_thresh[0]) & (scaled_sobelxy <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def absSobelThreshold(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
# binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
# binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return scaled_sobel
# Lab蓝黄通道划分函数
def labBSelect(img, thresh=(215, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
lab_b = lab[:, :, 2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 100:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 255
# 3) Return a binary image of threshold result
return binary_output
# Step 5 : 矩形滑窗 Detect lane lines through moving window
# Step 5 : Detect lane lines through moving window
#################################################################
# 画矩形滑窗
def find_lane_pixels(binary_warped, nwindows, margin, minpix):
# 按列进行直方图统计
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:, :], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# 确定图片中非零值的坐标
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# 第一个窗口位置
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# 依次确定每个窗口的位置
for window in range(nwindows):
# 确定窗口左下和右上点的坐标
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low),
(win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low),
(win_xright_high, win_y_high), (0, 255, 0), 2)
# 确定当前框中非零值的坐标，其中nonzero()[0]是指非零元素的横坐标
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# 根据当前框坐标值确定下一个框
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
# 将矩形滑窗中的点改变颜色
def fit_polynomial(binary_warped, nwindows=9, margin=100, minpix=50):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(
binary_warped, nwindows, margin, minpix)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
# Visualization #
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, ploty
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取图片
test_distort_image = cv2.imread('test_img/test3.jpg')
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 440], [700, 440], [1100, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换，得到变换后的结果图，类似俯视图
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# Step 4 提取车道线
# 提取1：sobel算子提取
# min_thresh = 30
# max_thresh = 100
# Result_sobelAbs = absSobelThreshold(test_warp_image, 'x', min_thresh, max_thresh)
# 提取2：hls方法提取，保留黄色和白色的车道线
# min_thresh = 150
# max_thresh = 255
# Result_hlsS = hlsSSelect(test_warp_image, (min_thresh, max_thresh))
# 提取3：hls方法提取，只保留白色的车道线
# min_thresh = 215
# max_thresh = 255
# Result_hlsL = hlsLSelect(test_warp_image, (min_thresh, max_thresh))
# 提取4：lab方法提取，只保留黄色的车道线
# min_thresh = 195
# max_thresh = 255
# Result_labB = labBSelect(test_warp_image, (min_thresh, max_thresh))
# 提取5：方法1-4结合
# sx_binary = absSobelThreshold(test_warp_image, 'x', 30, 150)
# hlsL_binary = hlsLSelect(test_warp_image)
# labB_binary = labBSelect(test_warp_image)
# combined_binary = np.zeros_like(sx_binary)
# combined_binary[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 提取6：只要求hsl与lab即可，结果与5一致，省略Sobel算子
hlsL_binary = hlsLSelect(test_warp_image)
labB_binary = labBSelect(test_warp_image)
combined_line_img = np.zeros_like(hlsL_binary)
combined_line_img[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# Step 5 矩形滑窗
out_img, left_fit, right_fit, ploty = fit_polynomial(combined_line_img, nwindows=9, margin=80, minpix=40)
# 显示
cv2.imshow('img_0', out_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
矩形滑窗结果：
6、跟踪车道线
方法：对左右车道线的点进行曲线拟合得到左右两端的直线，然后对车道线拟合曲线设定一个左右偏移量，得到4条曲线，然后利用cv2的填充函数分别进行填充，得到左右2个阴影轨道。
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
#################################################################
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
#################################################################
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
# Step 4 : Create a thresholded binary image
#################################################################
# 亮度划分函数
def hlsLSelect(img, thresh=(220, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
l_channel = hls[:, :, 1]
l_channel = l_channel*(255/np.max(l_channel))
binary_output = np.zeros_like(l_channel)
binary_output[(l_channel > thresh[0]) & (l_channel <= thresh[1])] = 255
return binary_output
# 亮度划分函数
def hlsSSelect(img, thresh=(125, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
s_channel = s_channel*(255/np.max(s_channel))
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 255
return binary_output
def dirThreshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction_sobelxy = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction_sobelxy)
binary_output[(direction_sobelxy >= thresh[0]) & (direction_sobelxy <= thresh[1])] = 1
# 6) Return the binary image
return binary_output
def magThreshold(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, sobel_kernel)
# 3) Calculate the magnitude
# abs_sobelx = np.absolute(sobelx)
# abs_sobely = np.absolute(sobely)
abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(abs_sobelxy/np.max(abs_sobelxy) * 255)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy >= mag_thresh[0]) & (scaled_sobelxy <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def absSobelThreshold(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
# binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
# binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return scaled_sobel
# Lab蓝黄通道划分函数
def labBSelect(img, thresh=(215, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
lab_b = lab[:, :, 2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 100:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 255
# 3) Return a binary image of threshold result
return binary_output
# Step 5 : 矩形滑窗 Detect lane lines through moving window
# Step 5 : Detect lane lines through moving window
#################################################################
def find_lane_pixels(binary_warped, nwindows, margin, minpix):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:, :], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low),
(win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low),
(win_xright_high, win_y_high), (0, 255, 0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped, nwindows=9, margin=100, minpix=50):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(
binary_warped, nwindows, margin, minpix)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
# Visualization #
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, ploty
# Step 6 : Track lane lines based latest lane line result
#################################################################
def fit_poly(img_shape, leftx, lefty, rightx, righty):
# ## TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
# ## TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty, left_fit, right_fit
def search_around_poly(binary_warped, left_fit, right_fit):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 60
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# ## TO-DO: Set the area of search based on activated x-values ###
# ## within the +/- margin of our polynomial function ###
# ## Hint: consider the window areas for the similarly named variables ###
# ## in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty, left_fit, right_fit = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
# # Visualization # #
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# 根据每条拟合曲线偏移得到左右两条拟合曲线，随后进行填充 right_line_pts维度为（1，1440，2）
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# 将曲线围成的区域画出来,window_img为绿色阴影
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# 绘制拟合曲线 Plot the polynomial lines onto the image
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.show()
# # End visualization steps # #
return result, left_fit, right_fit, ploty
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取图片
test_distort_image = cv2.imread('test_img/test3.jpg')
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 440], [700, 440], [1100, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换，得到变换后的结果图，类似俯视图
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# Step 4 提取车道线
# 提取1：sobel算子提取
# min_thresh = 30
# max_thresh = 100
# Result_sobelAbs = absSobelThreshold(test_warp_image, 'x', min_thresh, max_thresh)
# 提取2：hls方法提取，保留黄色和白色的车道线
# min_thresh = 150
# max_thresh = 255
# Result_hlsS = hlsSSelect(test_warp_image, (min_thresh, max_thresh))
# 提取3：hls方法提取，只保留白色的车道线
# min_thresh = 215
# max_thresh = 255
# Result_hlsL = hlsLSelect(test_warp_image, (min_thresh, max_thresh))
# 提取4：lab方法提取，只保留黄色的车道线
# min_thresh = 195
# max_thresh = 255
# Result_labB = labBSelect(test_warp_image, (min_thresh, max_thresh))
# 提取5：方法1-4结合
# sx_binary = absSobelThreshold(test_warp_image, 'x', 30, 150)
# hlsL_binary = hlsLSelect(test_warp_image)
# labB_binary = labBSelect(test_warp_image)
# combined_binary = np.zeros_like(sx_binary)
# combined_binary[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 提取6：只要求hsl与lab即可，结果与5一致，省略Sobel算子
hlsL_binary = hlsLSelect(test_warp_image)
labB_binary = labBSelect(test_warp_image)
combined_line_img = np.zeros_like(hlsL_binary)
combined_line_img[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# Step 5 矩形滑窗
out_img, left_fit, right_fit, ploty = fit_polynomial(combined_line_img, nwindows=9, margin=80, minpix=40)
# Step 6 跟踪车道线
track_result, track_left_fit, track_right_fit, ploty, = search_around_poly(combined_line_img, left_fit, right_fit)
# 显示
cv2.imshow('img_0', track_result)
cv2.waitKey(0)
cv2.destroyAllWindows()
跟踪车道线结果：
7、求取曲率与偏移量
偏移量：相对于左右车道线中心线的偏移
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
#################################################################
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
#################################################################
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
# Step 4 : Create a thresholded binary image
#################################################################
# 亮度划分函数
def hlsLSelect(img, thresh=(220, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
l_channel = hls[:, :, 1]
l_channel = l_channel*(255/np.max(l_channel))
binary_output = np.zeros_like(l_channel)
binary_output[(l_channel > thresh[0]) & (l_channel <= thresh[1])] = 255
return binary_output
# 亮度划分函数
def hlsSSelect(img, thresh=(125, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
s_channel = s_channel*(255/np.max(s_channel))
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 255
return binary_output
def dirThreshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction_sobelxy = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction_sobelxy)
binary_output[(direction_sobelxy >= thresh[0]) & (direction_sobelxy <= thresh[1])] = 1
# 6) Return the binary image
return binary_output
def magThreshold(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, sobel_kernel)
# 3) Calculate the magnitude
# abs_sobelx = np.absolute(sobelx)
# abs_sobely = np.absolute(sobely)
abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(abs_sobelxy/np.max(abs_sobelxy) * 255)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy >= mag_thresh[0]) & (scaled_sobelxy <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def absSobelThreshold(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
# binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
# binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return scaled_sobel
# Lab蓝黄通道划分函数
def labBSelect(img, thresh=(215, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
lab_b = lab[:, :, 2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 100:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 255
# 3) Return a binary image of threshold result
return binary_output
# Step 5 : 矩形滑窗 Detect lane lines through moving window
# Step 5 : Detect lane lines through moving window
#################################################################
def find_lane_pixels(binary_warped, nwindows, margin, minpix):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:, :], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low),
(win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low),
(win_xright_high, win_y_high), (0, 255, 0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped, nwindows=9, margin=100, minpix=50):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(
binary_warped, nwindows, margin, minpix)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
# Visualization #
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, ploty
# Step 6 : Track lane lines based latest lane line result
#################################################################
def fit_poly(img_shape, leftx, lefty, rightx, righty):
# ## TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
# ## TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty, left_fit, right_fit
def search_around_poly(binary_warped, left_fit, right_fit):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 60
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# ## TO-DO: Set the area of search based on activated x-values ###
# ## within the +/- margin of our polynomial function ###
# ## Hint: consider the window areas for the similarly named variables ###
# ## in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty, left_fit, right_fit = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
# # Visualization # #
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# 根据每条拟合曲线偏移得到左右两条拟合曲线，随后进行填充 right_line_pts维度为（1，1440，2）
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# 将曲线围成的区域画出来,window_img为绿色阴影
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# 绘制拟合曲线 Plot the polynomial lines onto the image
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.show()
# # End visualization steps # #
return result, left_fit, right_fit, ploty
# Step 7 : 曲率与偏移量计算
#################################################################
def measure_curvature_real(
left_fit_cr, right_fit_cr, ploty,
ym_per_pix=30/720, xm_per_pix=3.7/700):
'''
Calculates the curvature of polynomial functions in meters.
'''
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Calculation of R_curve (radius of curvature)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
left_position = left_fit_cr[0]*720 + left_fit_cr[1]*720 + left_fit_cr[2]
right_position = right_fit_cr[0]*720 + right_fit_cr[1]*720 + right_fit_cr[2]
midpoint = 1280/2
lane_center =(left_position + right_position)/2
offset = (midpoint - lane_center) * xm_per_pix
return left_curverad, right_curverad, offset
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取图片
test_distort_image = cv2.imread('test_img/test3.jpg')
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 440], [700, 440], [1100, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换，得到变换后的结果图，类似俯视图
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# Step 4 提取车道线
# 提取1：sobel算子提取
# min_thresh = 30
# max_thresh = 100
# Result_sobelAbs = absSobelThreshold(test_warp_image, 'x', min_thresh, max_thresh)
# 提取2：hls方法提取，保留黄色和白色的车道线
# min_thresh = 150
# max_thresh = 255
# Result_hlsS = hlsSSelect(test_warp_image, (min_thresh, max_thresh))
# 提取3：hls方法提取，只保留白色的车道线
# min_thresh = 215
# max_thresh = 255
# Result_hlsL = hlsLSelect(test_warp_image, (min_thresh, max_thresh))
# 提取4：lab方法提取，只保留黄色的车道线
# min_thresh = 195
# max_thresh = 255
# Result_labB = labBSelect(test_warp_image, (min_thresh, max_thresh))
# 提取5：方法1-4结合
# sx_binary = absSobelThreshold(test_warp_image, 'x', 30, 150)
# hlsL_binary = hlsLSelect(test_warp_image)
# labB_binary = labBSelect(test_warp_image)
# combined_binary = np.zeros_like(sx_binary)
# combined_binary[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 提取6：只要求hsl与lab即可，结果与5一致，省略Sobel算子
hlsL_binary = hlsLSelect(test_warp_image)
labB_binary = labBSelect(test_warp_image)
combined_line_img = np.zeros_like(hlsL_binary)
combined_line_img[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# Step 5 矩形滑窗
out_img, left_fit, right_fit, ploty = fit_polynomial(combined_line_img, nwindows=9, margin=80, minpix=40)
# Step 6 跟踪车道线
track_result, track_left_fit, track_right_fit, plotys, = search_around_poly(combined_line_img, left_fit, right_fit)
# Step 7 求取曲率与偏移量
left_curverad, right_curverad, offset = measure_curvature_real(track_left_fit, track_right_fit, plotys)
average_curverad = (left_curverad + right_curverad)/2
print(left_curverad, 'm', right_curverad, 'm', average_curverad, 'm')
print('offset : ', offset, 'm')
# 显示s
# cv2.imshow('img_0', track_result)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
曲率与偏移量计算结果：
8、逆投影到原图
方法：首先求得左右两条车道线的拟合曲线，并进行填充，绘制在“鸟瞰图”上，随后使用逆透视变换矩阵反投到原图上，即可实现在原图上的可视化效果。（步骤3：计算透视变换矩阵得到的两个矩阵M和Minv，使用M能够实现透视变换，使用Minv能够实现逆透视变换。）
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
#################################################################
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
#################################################################
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
# Step 4 : Create a thresholded binary image
#################################################################
# 亮度划分函数
def hlsLSelect(img, thresh=(220, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
l_channel = hls[:, :, 1]
l_channel = l_channel*(255/np.max(l_channel))
binary_output = np.zeros_like(l_channel)
binary_output[(l_channel > thresh[0]) & (l_channel <= thresh[1])] = 255
return binary_output
# 亮度划分函数
def hlsSSelect(img, thresh=(125, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
s_channel = s_channel*(255/np.max(s_channel))
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 255
return binary_output
def dirThreshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction_sobelxy = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction_sobelxy)
binary_output[(direction_sobelxy >= thresh[0]) & (direction_sobelxy <= thresh[1])] = 1
# 6) Return the binary image
return binary_output
def magThreshold(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, sobel_kernel)
# 3) Calculate the magnitude
# abs_sobelx = np.absolute(sobelx)
# abs_sobely = np.absolute(sobely)
abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(abs_sobelxy/np.max(abs_sobelxy) * 255)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy >= mag_thresh[0]) & (scaled_sobelxy <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def absSobelThreshold(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
# binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
# binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return scaled_sobel
# Lab蓝黄通道划分函数
def labBSelect(img, thresh=(215, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
lab_b = lab[:, :, 2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 100:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 255
# 3) Return a binary image of threshold result
return binary_output
# Step 5 : 矩形滑窗 Detect lane lines through moving window
# Step 5 : Detect lane lines through moving window
#################################################################
def find_lane_pixels(binary_warped, nwindows, margin, minpix):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:, :], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low),
(win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low),
(win_xright_high, win_y_high), (0, 255, 0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped, nwindows=9, margin=100, minpix=50):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(
binary_warped, nwindows, margin, minpix)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
# Visualization #
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, ploty
# Step 6 : Track lane lines based latest lane line result
#################################################################
def fit_poly(img_shape, leftx, lefty, rightx, righty):
# ## TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
# ## TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty, left_fit, right_fit
def search_around_poly(binary_warped, left_fit, right_fit):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 60
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# ## TO-DO: Set the area of search based on activated x-values ###
# ## within the +/- margin of our polynomial function ###
# ## Hint: consider the window areas for the similarly named variables ###
# ## in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty, left_fit, right_fit = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
# # Visualization # #
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# 根据每条拟合曲线偏移得到左右两条拟合曲线，随后进行填充 right_line_pts维度为（1，1440，2）
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# 将曲线围成的区域画出来,window_img为绿色阴影
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# 绘制拟合曲线 Plot the polynomial lines onto the image
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.show()
# # End visualization steps # #
return result, left_fit, right_fit, ploty
# Step 7 : 曲率与偏移量计算
#################################################################
def measure_curvature_real(
left_fit_cr, right_fit_cr, ploty,
ym_per_pix=30/720, xm_per_pix=3.7/700):
'''
Calculates the curvature of polynomial functions in meters.
'''
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Calculation of R_curve (radius of curvature)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
left_position = left_fit_cr[0]*720 + left_fit_cr[1]*720 + left_fit_cr[2]
right_position = right_fit_cr[0]*720 + right_fit_cr[1]*720 + right_fit_cr[2]
midpoint = 1280/2
lane_center =(left_position + right_position)/2
offset = (midpoint - lane_center) * xm_per_pix
return left_curverad, right_curverad, offset
# Step 8 : Draw lane line result on undistorted image
#################################################################
# 不同于step7的左右线2个填充，这里只有一个填充，并逆投影到原图
def drawing(undist, bin_warped, color_warp, left_fitx, right_fitx):
# Create an image to draw the lines on
warp_zero = np.zeros_like(bin_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (undist.shape[1], undist.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
return result
def draw_text(image, curverad, offset):
result = np.copy(image)
font = cv2.FONT_HERSHEY_SIMPLEX # 使用默认字体
text = 'Curve Radius : ' + '{:04.2f}'.format(curverad) + 'm'
cv2.putText(result, text, (20, 50), font, 1.2, (255, 255, 255), 2)
if offset > 0:
text = 'right of center : ' + '{:04.3f}'.format(abs(offset)) + 'm '
else:
text = 'left of center : ' + '{:04.3f}'.format(abs(offset)) + 'm '
cv2.putText(result, text, (20, 100), font, 1.2, (255, 255, 255), 2)
return result
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取图片
test_distort_image = cv2.imread('test_img/test3.jpg')
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 440], [700, 440], [1100, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换，得到变换后的结果图，类似俯视图
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# Step 4 提取车道线
# 提取1：sobel算子提取
# min_thresh = 30
# max_thresh = 100
# Result_sobelAbs = absSobelThreshold(test_warp_image, 'x', min_thresh, max_thresh)
# 提取2：hls方法提取，保留黄色和白色的车道线
# min_thresh = 150
# max_thresh = 255
# Result_hlsS = hlsSSelect(test_warp_image, (min_thresh, max_thresh))
# 提取3：hls方法提取，只保留白色的车道线
# min_thresh = 215
# max_thresh = 255
# Result_hlsL = hlsLSelect(test_warp_image, (min_thresh, max_thresh))
# 提取4：lab方法提取，只保留黄色的车道线
# min_thresh = 195
# max_thresh = 255
# Result_labB = labBSelect(test_warp_image, (min_thresh, max_thresh))
# 提取5：方法1-4结合
# sx_binary = absSobelThreshold(test_warp_image, 'x', 30, 150)
# hlsL_binary = hlsLSelect(test_warp_image)
# labB_binary = labBSelect(test_warp_image)
# combined_binary = np.zeros_like(sx_binary)
# combined_binary[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 提取6：只要求hsl与lab即可，结果与5一致，省略Sobel算子
hlsL_binary = hlsLSelect(test_warp_image)
labB_binary = labBSelect(test_warp_image)
combined_line_img = np.zeros_like(hlsL_binary)
combined_line_img[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# Step 5 矩形滑窗
out_img, left_fit, right_fit, ploty = fit_polynomial(combined_line_img, nwindows=9, margin=80, minpix=40)
# Step 6 跟踪车道线
track_result, track_left_fit, track_right_fit, plotys, = search_around_poly(combined_line_img, left_fit, right_fit)
# Step 7 求取曲率与偏移量
left_curverad, right_curverad, offset = measure_curvature_real(track_left_fit, track_right_fit, plotys)
average_curverad = (left_curverad + right_curverad)/2
# print(left_curverad, 'm', right_curverad, 'm', average_curverad, 'm')
# print('offset : ', offset, 'm')
# Step 8 逆投影到原图
left_fitx = track_left_fit[0]*ploty**2 + track_left_fit[1]*ploty + track_left_fit[2]
right_fitx = track_right_fit[0]*ploty**2 + track_right_fit[1]*ploty + track_right_fit[2]
result = drawing(test_undistort_image, combined_line_img, test_warp_image, left_fitx, right_fitx)
text_result = draw_text(result, average_curverad, offset)
# 显示
cv2.imshow('img_0', text_result)
cv2.waitKey(0)
cv2.destroyAllWindows()
逆投影到原图结果：
9、视频车道线检测
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
# Step 1 读入图片、预处理图片、检测交点、标定相机的一系列操作
#################################################################
def getCameraCalibrationCoefficients(chessboardname, nx, ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(chessboardname)
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Finde the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
print('Do calibration successfully')
return ret, mtx, dist, rvecs, tvecs
# Step 2 传入计算得到的畸变参数，即可将畸变的图像进行畸变修正处理
def undistortImage(distortImage, mtx, dist):
return cv2.undistort(distortImage, mtx, dist, None, mtx)
# Step 3 透视变换 : Warp image based on src_points and dst_points
#################################################################
# The type of src_points & dst_points should be like
# np.float32([ [0,0], [100,200], [200, 300], [300,400]])
def warpImage(image, src_points, dst_points):
image_size = (image.shape[1], image.shape[0])
# rows = img.shape[0] 720
# cols = img.shape[1] 1280
M = cv2.getPerspectiveTransform(src_points, dst_points)
Minv = cv2.getPerspectiveTransform(dst_points, src_points)
warped_image = cv2.warpPerspective(image, M, image_size, flags=cv2.INTER_LINEAR)
return warped_image, M, Minv
# Step 4 : Create a thresholded binary image
#################################################################
# 亮度划分函数
def hlsLSelect(img, thresh=(220, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
l_channel = hls[:, :, 1]
l_channel = l_channel*(255/np.max(l_channel))
binary_output = np.zeros_like(l_channel)
binary_output[(l_channel > thresh[0]) & (l_channel <= thresh[1])] = 255
return binary_output
# 亮度划分函数
def hlsSSelect(img, thresh=(125, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
s_channel = s_channel*(255/np.max(s_channel))
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 255
return binary_output
def dirThreshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction_sobelxy = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction_sobelxy)
binary_output[(direction_sobelxy >= thresh[0]) & (direction_sobelxy <= thresh[1])] = 1
# 6) Return the binary image
return binary_output
def magThreshold(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, sobel_kernel)
# 3) Calculate the magnitude
# abs_sobelx = np.absolute(sobelx)
# abs_sobely = np.absolute(sobely)
abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(abs_sobelxy/np.max(abs_sobelxy) * 255)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy >= mag_thresh[0]) & (scaled_sobelxy <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def absSobelThreshold(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
# binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
# binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return scaled_sobel
# Lab蓝黄通道划分函数
def labBSelect(img, thresh=(215, 255)):
# 1) Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
lab_b = lab[:, :, 2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 100:
lab_b = lab_b*(255/np.max(lab_b))
# 2) Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 255
# 3) Return a binary image of threshold result
return binary_output
# Step 5 : 矩形滑窗 Detect lane lines through moving window
# Step 5 : Detect lane lines through moving window
#################################################################
def find_lane_pixels(binary_warped, nwindows, margin, minpix):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:, :], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low),
(win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low),
(win_xright_high, win_y_high), (0, 255, 0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped, nwindows=9, margin=100, minpix=50):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(
binary_warped, nwindows, margin, minpix)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
# Visualization #
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, ploty
# Step 6 : Track lane lines based latest lane line result
#################################################################
def fit_poly(img_shape, leftx, lefty, rightx, righty):
# ## TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
# ## TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty, left_fit, right_fit
def search_around_poly(binary_warped, left_fit, right_fit):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 60
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# ## TO-DO: Set the area of search based on activated x-values ###
# ## within the +/- margin of our polynomial function ###
# ## Hint: consider the window areas for the similarly named variables ###
# ## in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty, left_fit, right_fit = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
# # Visualization # #
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# 根据每条拟合曲线偏移得到左右两条拟合曲线，随后进行填充 right_line_pts维度为（1，1440，2）
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# 将曲线围成的区域画出来,window_img为绿色阴影
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# 绘制拟合曲线 Plot the polynomial lines onto the image
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.show()
# # End visualization steps # #
return result, left_fit, right_fit, ploty
# Step 7 : 曲率与偏移量计算
#################################################################
def measure_curvature_real(
left_fit_cr, right_fit_cr, ploty,
ym_per_pix=30/720, xm_per_pix=3.7/700):
'''
Calculates the curvature of polynomial functions in meters.
'''
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Calculation of R_curve (radius of curvature)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
left_position = left_fit_cr[0]*720 + left_fit_cr[1]*720 + left_fit_cr[2]
right_position = right_fit_cr[0]*720 + right_fit_cr[1]*720 + right_fit_cr[2]
midpoint = 1280/2
lane_center =(left_position + right_position)/2
offset = (midpoint - lane_center) * xm_per_pix
return left_curverad, right_curverad, offset
# Step 8 : Draw lane line result on undistorted image
#################################################################
# 不同于step7的左右线2个填充，这里只有一个填充，并逆投影到原图
def drawing(undist, bin_warped, color_warp, left_fitx, right_fitx):
# Create an image to draw the lines on
warp_zero = np.zeros_like(bin_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (undist.shape[1], undist.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
return result
def draw_text(image, curverad, offset):
result = np.copy(image)
font = cv2.FONT_HERSHEY_SIMPLEX # 使用默认字体
text = 'Curve Radius : ' + '{:04.2f}'.format(curverad) + 'm'
cv2.putText(result, text, (20, 50), font, 1.2, (255, 255, 255), 2)
if offset > 0:
text = 'right of center : ' + '{:04.3f}'.format(abs(offset)) + 'm '
else:
text = 'left of center : ' + '{:04.3f}'.format(abs(offset)) + 'm '
cv2.putText(result, text, (20, 100), font, 1.2, (255, 255, 255), 2)
return result
if __name__ == "__main__":
nx = 9
ny = 6
# Step 1 获取畸变参数
rets, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)
# 读取视频
cap = cv2.VideoCapture("solidYellowLeft.mp4")
ret, frame = cap.read()
# 结果写入视频
out = cv2.VideoWriter('output1.mp4', cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 25, (frame.shape[1], frame.shape[0]))
while ret:
test_distort_image = frame
# Step 2 畸变修正
test_undistort_image = undistortImage(test_distort_image, mtx, dist)
# Step 3 透视变换
# “不断调整src和dst的值，确保在直线道路上，能够调试出满意的透视变换图像”
# 左图梯形区域的四个端点
src = np.float32([[580, 440], [700, 440], [1100, 720], [200, 720]])
# 右图矩形区域的四个端点
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
# 变换，得到变换后的结果图，类似俯视图
test_warp_image, M, Minv = warpImage(test_undistort_image, src, dst)
# Step 4 提取车道线
# 提取1：sobel算子提取
# min_thresh = 30
# max_thresh = 100
# Result_sobelAbs = absSobelThreshold(test_warp_image, 'x', min_thresh, max_thresh)
# 提取2：hls方法提取，保留黄色和白色的车道线
# min_thresh = 150
# max_thresh = 255
# Result_hlsS = hlsSSelect(test_warp_image, (min_thresh, max_thresh))
# 提取3：hls方法提取，只保留白色的车道线
# min_thresh = 215
# max_thresh = 255
# Result_hlsL = hlsLSelect(test_warp_image, (min_thresh, max_thresh))
# 提取4：lab方法提取，只保留黄色的车道线
# min_thresh = 195
# max_thresh = 255
# Result_labB = labBSelect(test_warp_image, (min_thresh, max_thresh))
# 提取5：方法1-4结合
# sx_binary = absSobelThreshold(test_warp_image, 'x', 30, 150)
# hlsL_binary = hlsLSelect(test_warp_image)
# labB_binary = labBSelect(test_warp_image)
# combined_binary = np.zeros_like(sx_binary)
# combined_binary[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# 提取6：只要求hsl与lab即可，结果与5一致，省略Sobel算子
hlsL_binary = hlsLSelect(test_warp_image)
labB_binary = labBSelect(test_warp_image)
combined_line_img = np.zeros_like(hlsL_binary)
combined_line_img[(hlsL_binary == 255) | (labB_binary == 255)] = 255
# Step 5 矩形滑窗
out_img, left_fit, right_fit, ploty = fit_polynomial(combined_line_img, nwindows=9, margin=80, minpix=40)
# Step 6 跟踪车道线
track_result, track_left_fit, track_right_fit, plotys, = search_around_poly(combined_line_img, left_fit, right_fit)
# Step 7 求取曲率与偏移量
left_curverad, right_curverad, offset = measure_curvature_real(track_left_fit, track_right_fit, plotys)
average_curverad = (left_curverad + right_curverad)/2
# print(left_curverad, 'm', right_curverad, 'm', average_curverad, 'm')
# print('offset : ', offset, 'm')
# Step 8 逆投影到原图
left_fitx = track_left_fit[0]*ploty**2 + track_left_fit[1]*ploty + track_left_fit[2]
right_fitx = track_right_fit[0]*ploty**2 + track_right_fit[1]*ploty + track_right_fit[2]
result = drawing(test_undistort_image, combined_line_img, test_warp_image, left_fitx, right_fitx)
text_result = draw_text(result, average_curverad, offset)
# 显示
cv2.imshow('img_0', text_result)
cv2.waitKey(1)
# 将处理后的帧数写到视频
out.write(text_result)
# 播放结束跳出循环
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()
使用心得：
可以对弯道车道线进行很好的检测，但鲁棒性较差，需要进行调参和重新标定，特别是对透视变换过程中的参数调节，需要不断进行尝试。
以上源代码提取链接：
链接：https://pan.baidu.com/s/12w0Q7SvUGb744gZyP8-ugg
提取码：wqu8
参考资料：
https://zhuanlan.zhihu.com/p/54866418
原文链接:https://blog.csdn.net/weixin_45679938/article/details/119006492
所属网站分类: 技术文章 > 博客
作者：听说你喜欢我
链接：http://www.pythonpdf.com/blog/article/516/9f26895ab9e2c9340489/
来源：编程知识网
任何形式的转载都请注明出处,如有侵权 一经发现 必将追究其法律责任
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