车道线检测识别 [园区道路线检测识别]（开源github,欢迎stared）

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车道线检测识别 [园区道路线检测识别]（开源github,欢迎stared）(https://github.com/ZubinHuang/StartUpHub_RoadLaneDetect)

主要实现道路线区域覆盖

依赖库 Python3.6 OpenCV3 numpy moviepy matplotlib

#主要代码 python3 pipeline.py
import os
import cv2
import utils
import matplotlib.pyplot as plt
import numpy as np
from moviepy.editor import VideoFileClip
import line

def thresholding(img):
    #setting all sorts of thresholds
    x_thresh = utils.abs_sobel_thresh(img, orient='x', thresh_min=10 ,thresh_max=230)
    mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 150))
    dir_thresh = utils.dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
    hls_thresh = utils.hls_select(img, thresh=(120, 255))
    lab_thresh = utils.lab_select(img, thresh=(155, 200))
    luv_thresh = utils.luv_select(img, thresh=(225, 255))

    #Thresholding combination
    threshholded = np.zeros_like(x_thresh)
    threshholded[((x_thresh == 1) & (mag_thresh == 1)) | ((dir_thresh == 1) & (hls_thresh == 1)) | (lab_thresh == 1) | (luv_thresh == 1)] = 1

    return threshholded


def processing(img,object_points,img_points,M,Minv,left_line,right_line):
    #camera calibration, image distortion correction
    undist = utils.cal_undistort(img,object_points,img_points)
    #get the thresholded binary image
    thresholded = thresholding(undist)
    #perform perspective  transform
    thresholded_wraped = cv2.warpPerspective(thresholded, M, img.shape[1::-1], flags=cv2.INTER_LINEAR)

    #perform detection
    if left_line.detected and right_line.detected:
        left_fit, right_fit, left_lane_inds, right_lane_inds = utils.find_line_by_previous(thresholded_wraped,left_line.current_fit,right_line.current_fit)
    else:
        left_fit, right_fit, left_lane_inds, right_lane_inds = utils.find_line(thresholded_wraped)
    left_line.update(left_fit)
    right_line.update(right_fit)

    #draw the detected laneline and the information
    area_img = utils.draw_area(undist,thresholded_wraped,Minv,left_fit, right_fit)
    curvature,pos_from_center = utils.calculate_curv_and_pos(thresholded_wraped,left_fit, right_fit)
    result = utils.draw_values(area_img,curvature,pos_from_center)

    return result
#
#
left_line = line.Line()
right_line = line.Line()
cal_imgs = utils.get_images_by_dir('camera_cal')
object_points,img_points = utils.calibrate(cal_imgs,grid=(9,6))
M,Minv = utils.get_M_Minv()

##draw the processed video
project_outpath = 'vedio_out/project_video_out2.mp4'
#project_video_clip = VideoFileClip("project_video.mp4")
project_video_clip = VideoFileClip("14s.mp4")
project_video_out_clip = project_video_clip.fl_image(lambda clip: processing(clip,object_points,img_points,M,Minv,left_line,right_line))
project_video_out_clip.write_videofile(project_outpath, audio=False)



##draw the processed test image
test_imgs = utils.get_images_by_dir('test_images')
undistorted = []
for img in test_imgs:
   img = utils.cal_undistort(img,object_points,img_points)
   undistorted.append(img)

result=[]
for img in undistorted:
   res = processing(img,object_points,img_points,M,Minv,left_line,right_line)
   result.append(res)

plt.figure(figsize=(10,10))
for i in range(len(result)):

   plt.subplot(len(result),1,i+1)
   plt.title('thresholded_wraped image')
   plt.imshow(result[i][:,:,::-1])
   plt.show()

demo gif动图

[1]:https://github.com/ZubinHuang/StartUpHub_RoadLaneDetect