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
def processing(img, object_points, img_points, M, Minv, left_line, right_line):
undist = utils.cal_undistort(img, object_points, img_points)
thresholded = thresholding(undist)
thresholded_wraped = cv2.warpPerspective(thresholded,
M,
img.shape[1::-1],
flags=cv2.INTER_LINEAR)
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)
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
import cv2
import utils
import matplotlib.pyplot as plt
import numpy as np
#script mainly use for drawing the demo picture
cal_imgs = utils.get_images_by_dir('camera_cal')
object_points,img_points = utils.calibrate(cal_imgs,grid=(9,6))
#test_imgs = utils.get_images_by_dir('test_images')
test_imgs = utils.get_images_by_dir('new_test')
undistorted = []
for img in test_imgs:
img = utils.cal_undistort(img,object_points,img_points)
undistorted.append(img)
trans_on_test=[]
for img in undistorted:
src = np.float32([[(203, 720), (585, 460), (695, 460), (1127, 720)]])
dst = np.float32([[(320, 720), (320, 0), (960, 0), (960, 720)]])
M = cv2.getPerspectiveTransform(src, dst)
trans = cv2.warpPerspective(img, M, img.shape[1::-1], flags=cv2.INTER_LINEAR)
trans_on_test.append(trans)
thresh = []
binary_wrapeds = []
histogram = []
for img in undistorted:
x_thresh = utils.abs_sobel_thresh(img, orient='x', thresh_min=55, thresh_max=100)
import cv2
import utils
import matplotlib.pyplot as plt
import numpy as np
#script mainly use for drawing the demo picture
cal_imgs = utils.get_images_by_dir('camera_cal')
object_points, img_points = utils.calibrate(cal_imgs, grid=(9, 6))
test_imgs = utils.get_images_by_dir('test_images1')
#test_imgs = utils.get_images_by_dir('new_test')
undistorted = []
for img in test_imgs:
img = utils.cal_undistort(img, object_points, img_points)
undistorted.append(img)
trans_on_test = []
for img in undistorted:
src = np.float32([[(203, 720), (585, 460), (695, 460), (1127, 720)]])
dst = np.float32([[(320, 720), (320, 0), (960, 0), (960, 720)]])
M = cv2.getPerspectiveTransform(src, dst)
trans = cv2.warpPerspective(img,
M,
img.shape[1::-1],
flags=cv2.INTER_LINEAR)
trans_on_test.append(trans)
thresh = []
binary_wrapeds = []