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)
's') # 交易日停市
except:
print(key, '日期：', date, '今日停市')
compute_allfund(date)
else:
total = 0
for key in hold_stocks.keys():
if hold_stocks[key] != 0:
tmp = ts.get_hist_data(key, start=date, end=date)
tmp = tmp.reset_index(drop=False)
try:
if (tmp['close'].iloc[0] - buyprice[key]
) / buyprice[key] <= -0.05: # 0.05止损
trading(key, date, hold_stocks[key],
tmp['close'].iloc[0], 's')
print('止损！', date, '股票代码：', key)
else:
total += tmp['close'].iloc[0] * hold_stocks[key]
except:
print(key, '日期：', date, '今日停市')
print(date, '持仓资金为：', round(total, 2))
compute_allfund(date)
if __name__ == '__main__':
setting(50000, 0.003)
main_proc()
# draw_line.draw(date_list, fund_list, '每日资金情况图')
draw_line.draw(date_list, rate_list, '持有期收益率情况图')
info_table.to_csv('个股持有情况分析.csv', encoding='gb18030')
# 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)