def predict(video_path):
polyfit = Polyfit()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
loader = tf.train.import_meta_graph(
'./model_test/lane-conv--490--299.meta')
loader.restore(sess, './model_test/lane-conv--490--299')
y = tf.get_collection('pred_network')[0]
graph = tf.get_default_graph()
input_x = graph.get_operation_by_name('x_input').outputs[0]
keep_prob = graph.get_operation_by_name('prob').outputs[0]
cap = cv2.VideoCapture(video_path)
ret, frame = cap.read()
while ret:
ret, frame = cap.read()
# cv2.imshow("img",frame)
img_arr = cv2.resize(frame, dsize=(107, 60))
img_arr = cv2.cvtColor(img_arr, cv2.COLOR_BGR2GRAY)
imgarr = np.reshape(img_arr, newshape=[1, 60, 107, 1])
predict = sess.run(y, feed_dict={
input_x: imgarr,
keep_prob: 1.0
})[0]
predict = np.uint8(np.where(predict == 0, 0, 255))
predict = cv2.resize(predict, dsize=(320, 180))
warped = warp(predict)
left_fit, right_fit, vars = polyfit.poly_fit_slide(warped)
print(left_fit)
result, offset, Radius, k_error = draw(
cv2.resize(frame, dsize=(320, 180)), warped, left_fit,
right_fit)
cv2.imshow('warped', warped)
cv2.imshow('res', result)
cv2.waitKey(1)
# cv2.waitKey(0) # Video process
# VideoProcee(input_path='./test5.avi',output_path='./lane_detect.avi',timeF=1,fps=30)
# train()
# prediction()
# data_read()
sess = tf.Session()
polyfit = Polyfit()
sess.run(tf.global_variables_initializer())
loader = tf.train.import_meta_graph('./model_test/lane-conv---99.meta')
loader.restore(sess, './model_test/lane-conv---99')
y = tf.get_collection('pred_network')[0]
graph = tf.get_default_graph()
input_x = graph.get_operation_by_name('x_input').outputs[0]
keep_prob = graph.get_operation_by_name('prob').outputs[0]
cap = cv2.VideoCapture('./test5.avi')
ret, frame = cap.read()
while ret:
s = time.time()
ret, frame = cap.read()
imgarr = cv2.resize(frame, dsize=(107, 60))
predict = cnn_lane(sess, imgarr)
predict = np.uint8(np.where(predict == 0, 0, 255))
predict = cv2.resize(predict, dsize=(320, 180))
warped = warp(predict)
left_fit, right_fit, vars = polyfit.poly_fit_slide(warped)
result, offset, Radius, k_error = draw(
cv2.resize(frame, dsize=(320, 180)), warped, left_fit, right_fit)
print('Total;', time.time() - s)
cv2.imshow('warped', warped)
cv2.imshow('res', result)
cv2.waitKey(1)
def auto_lane(img):
global cur_mode
global last_mode
global counter
start = time.time()
img = cv2.resize(img, (320, 180))
GreenLight, RedLight, SpeedLimit, HireSign = recongise(img)
## GreenLight = circle_dec(img)
polyfit = Polyfit()
#light recongise
## GreenLight = recongise_light(img)
frame = warp(img)
mask = hsv(frame)
cv2.imshow('warpedmask', frame)
## mask = canny(frame,50,150)
zebracrossing = dec_lane(mask)
print '......................0.0.0.13212', zebracrossing
#mask = cv2.GaussianBlur(frame,(5,5),0)
## kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(2,2))
## mask = cv2.erode(mask,kernel)
## mask = cv2.dilate(mask,kernel)
## cv2.imshow('mask',mask)
cv2.waitKey(1)
try:
left_fit, right_fit, vars = polyfit.poly_fit_slide(mask)
print '---------------------left,right', left_fit, right_fit
if left_fit == 'None' and right_fit == 'None':
print '11111circle'
result, offset, Radius, k_error = img, 'None', 10000, 'None'
## cv2.imshow('re', cv2.resize(img, dsize=(640, 320)))
## cv2.waitKey(1)
else:
print '2222circle'
result, offset, Radius, k_error = draw(img, mask, left_fit,
right_fit)
## cv2.imshow('re',cv2.resize(result,dsize=(640,320)))
## cv2.waitKey(1)
except:
print '---------unexcept error------------'
result, offset, Radius, k_error = img, 0, 10000, 'None'
'''--------------------------lanetype--------------------------'''
'''zhixian:1..wandao:2...zhijiaowan: 3'''
print "........kerror:...", k_error
if k_error == 'None':
cur_mode = last_mode #keeping
else:
if abs(k_error) < 0.06:
cur_mode = 1
if abs(k_error) >= 0.06:
cur_mode = 2
'''cornerflage'''
print '--------', cur_mode, '------', last_mode
corner_flag = corner_det(mask, img)
if corner_flag == 0:
pass
if corner_flag == 1:
cur_mode = 3
last_mode = cur_mode
Lane_type = cur_mode
'''-----------SpeedLimit----------'''
SpeedLimit = 0
'''-----------RedLight------------'''
GreenLight = 0
'''-----------HireSign------------'''
'''-----------StopSign------------'''
StopSign = 0
print 'Corner .....', Lane_type
cv2.imshow('result', result)
cv2.waitKey(1)
return result, offset, Radius, Lane_type, SpeedLimit, RedLight, GreenLight, HireSign, StopSign, zebracrossing