def __call__(self, img):
undistorted = camera.undistort(img, self.mtx, self.dist)
thresh_img = processImage(undistorted)
warped, Minv = perspective.warp(thresh_img)
leftx_poly, rightx_poly = self.lanefinder.findLanes(warped)
polygon = vis.drawLaneWithPolygon(warped, leftx_poly, rightx_poly,
self.ploty)
polygon = perspective.unwarp(polygon, Minv)
combined = cv2.addWeighted(undistorted, 1.0, polygon, 0.3, 0)
curvature = self.lanefinder.getCurvature()
car_offset = self.lanefinder.getCarOffset()
cv2.putText(combined,
"Curvature Radius:" + "{:5.2f}km".format(curvature),
(20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1.0, [255, 255, 255],
2, cv2.LINE_AA)
cv2.putText(
combined,
"Distance from Center:" + '{:5.2f}cm'.format(car_offset * 100),
(20, 80), cv2.FONT_HERSHEY_SIMPLEX, 1.0, [255, 255, 255], 2,
cv2.LINE_AA)
return combined
def draw(orign,predict_img, left_fit, right_fit):
M,Minv = perspective()
warped = warp(np.uint8(predict_img * 255))
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
plot_y = np.linspace(0, warped.shape[0] - 1, warped.shape[0])
left_fit_x = left_fit[0]*plot_y**2 + left_fit[1]*plot_y + left_fit[2]
right_fit_x = right_fit[0]*plot_y**2 + right_fit[1]*plot_y + right_fit[2]
middle_fit_x = (left_fit_x+right_fit_x)/2
dy = 2*left_fit[0]*710 + left_fit[1]
ddy = 2*left_fit[0]
R = ((1+dy**2)**(3/2))/ddy*2.87/584
pts_left = np.array([np.transpose(np.vstack([left_fit_x, plot_y]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fit_x, plot_y])))])
pts_middle = np.array([np.flipud(np.transpose(np.vstack([middle_fit_x, plot_y])))])
PTS =np.hstack(pts_middle)
pts = np.hstack((pts_left, pts_right))
try:
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0),)
cv2.polylines(color_warp,np.int_([PTS]),isClosed=False,color=(0,0,255),thickness=5)
cv2.polylines(color_warp, np.int_([pts]), isClosed=False, color=(255, 150,0),thickness=8)
print(color_warp.shape)
except:
pass
newwarp = cv2.warpPerspective(color_warp, Minv, (880, 246))
newwarp = cv2.copyMakeBorder(newwarp,420,54,200,200, cv2.BORDER_CONSTANT,value=[0,0,0])
result = cv2.addWeighted(orign, 1, newwarp, 0.3, 0)
try:
cv2.putText(result, str('Radius of curvature:') + str(round(R, 2)), (60, 100), cv2.FONT_HERSHEY_SIMPLEX, 1,(255, 0, 5), 1)
pass
except:
pass
return result
def find_lane_xyaxis(img):
img = warp(np.uint8(img * 255))
expected_x_starts = [500, 1254] # #[520,1264]���峵��ͼ����������ʼ����
xyaxis = [[], []] #����һ����list������������������ߵ�����
search_range = int(248) # #������Χ���Գ����߿�ȵ�����֮һ��������
y_iters = int(12) # #���峵��ͼ����������������
lane_pixels = img.nonzero() #ȡ�������߶�ֵͼ���з�������꣬������������
lane_pixels_y = np.array(lane_pixels[0]) #ȡ����������������
lane_pixels_x = np.array(lane_pixels[1]) #ȡ�������ߺ�������
for lane_idx in range(2):
start_x = expected_x_starts[lane_idx]
flag = False #����flag�������ж��Ƿ���box�д��ڳ�����
for y_idx in range(y_iters): #�ֱ���ɶ�������������������ȡ
y_mid = int((y_iters - y_idx) * 5951 /
y_iters) #������������ֵ��0 - 5951��û��496������ȡһ��
'''����box���ĸ���������'''
y_min = y_mid - search_range
y_max = y_mid + search_range
x_min = start_x - search_range
x_max = start_x + search_range
found_indices = (
(lane_pixels_x >= x_min) & (lane_pixels_x <= x_max) &
(lane_pixels_y >= y_min) &
(lane_pixels_y <= y_max)).nonzero()[0] #ȡ�������ߺ��������λ��
found_x = lane_pixels_x[found_indices] #ȡ�������ߺ�������
if len(found_x) > 1:
start_x = int(
np.mean(found_x)) #ȥ�����ߺ��������ƽ��ֵ��Ϊʵ�ʺ�������
flag = True #box�д��ڳ�����
if flag:
xyaxis[lane_idx].append([start_x,
y_mid]) #��������������ӵ�list��
else:
pass
return xyaxis
def find_lane_xyaxis(img):
img = warp(np.uint8(img * 255))
expected_x_starts = [500, 1254] # #[520,1264]定义车道图像矩阵横向起始坐标
xyaxis = [[], []]#定义一个空list用来存放左右两车道线的坐标
search_range = int(248) # #搜索范围，以车道线宽度的三分之一长度搜索
y_iters = int(12) # #定义车道图像矩阵纵向坐标个数
lane_pixels = img.nonzero()#取出车道线二值图像中非零点坐标，即车道线坐标
lane_pixels_y = np.array(lane_pixels[0])#取出车道线纵向坐标
lane_pixels_x = np.array(lane_pixels[1])#取出车道线横向坐标
for lane_idx in range(2):
start_x = expected_x_starts[lane_idx]
flag = False #定义flag，用以判断是否在box中存在车道线
for y_idx in range(y_iters):#分别完成对左右两车道线坐标提取
y_mid = int((y_iters - y_idx) * 5951 / y_iters)#定义纵向坐标值从0 - 5951，没隔496个像素取一个
'''定义box的四个顶点坐标'''
y_min = y_mid - search_range
y_max = y_mid + search_range
x_min = start_x - search_range
x_max = start_x + search_range
found_indices = ((lane_pixels_x >= x_min) & (lane_pixels_x <= x_max) & (lane_pixels_y >= y_min) & (
lane_pixels_y <= y_max)).nonzero()[0]#取出车道线横向坐标的位置
found_x = lane_pixels_x[found_indices]#取出车道线横向坐标
if len(found_x) > 1:
start_x = int(np.mean(found_x))#去车道线横向坐标的平均值作为实际横向坐标
flag = True#box中存在车道线
if flag:
xyaxis[lane_idx].append([start_x, y_mid])#将车道线坐标添加到list中
else:
pass
return xyaxis
def draw(orign,predict_img, left_fit, right_fit):
M,Minv = perspective()
warped = warp(np.uint8(predict_img * 255))
cv2.imwrite('./newwarp.jpg', warped)
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
plot_y = np.linspace(0, warped.shape[0] - 1, warped.shape[0])
left_fit_x = left_fit[0]*plot_y**2 + left_fit[1]*plot_y + left_fit[2]
right_fit_x = right_fit[0]*plot_y**2 + right_fit[1]*plot_y + right_fit[2]
middle_fit_x = (left_fit_x+right_fit_x)/2
dy = 2*left_fit[0]*710 + left_fit[1]
ddy = 2*left_fit[0]
R = ((1+dy**2)**(3/2))/ddy*2.87/584
pts_left = np.array([np.transpose(np.vstack([left_fit_x, plot_y]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fit_x, plot_y])))])
pts_middle = np.array([np.flipud(np.transpose(np.vstack([middle_fit_x, plot_y])))])
PTS =np.hstack(pts_middle)
pts = np.hstack((pts_left, pts_right))
try:
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0),)
cv2.polylines(color_warp,np.int_([PTS]),isClosed=False,color=(0,0,255),thickness=5)
cv2.polylines(color_warp, np.int_([pts]), isClosed=False, color=(255, 150,0),thickness=8)
except:
pass
cv2.imwrite('./newwarp0.jpg', color_warp)
newwarp = cv2.warpPerspective(color_warp, Minv, (880, 246))
cv2.imwrite('./newwarp1.jpg',newwarp)
newwarp = cv2.copyMakeBorder(newwarp,420,54,200,200, cv2.BORDER_CONSTANT,value=[0,0,0])
result = cv2.addWeighted(orign, 1, newwarp, 0.3, 0)
cv2.imwrite('./result.jpg',result)
try:
cv2.putText(result, str('Radius of curvature:') + str(round(R, 2)), (60, 100), cv2.FONT_HERSHEY_SIMPLEX, 1,(255, 0, 5), 1)
print( str('Radius of curvature:') + str(round(R, 2))+'m')
except:
pass
#offset center
left_point = left_fit[0] * 700 ** 2 + left_fit[1] * 700 + left_fit[2]
right_point = right_fit[0] * 700 ** 2 + right_fit[1] * 700 + right_fit[2]
lane_center = (left_point + right_point) / 2
off_center = (result.shape[1] / 2 - lane_center) * (3 / 734)
off_center = round(off_center, 3)
if off_center < 0:
print("Offset to right:", abs(off_center), str("m"))
center_messages = "Offset to right: : " + str(abs(off_center)) + str("m")
if off_center > 0:
print("Offset to left:", abs(off_center), str("m"))
center_messages = "Offset to left:" + str(abs(off_center)) + str("m")
if off_center == 0:
print("On the center:", abs(off_center), str("m"))
cv2.putText(result, center_messages, (60, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1)
return result
