def cp(args):
path = "/gaitlab/input/input.mp4"
# remove the old file
os.system('rm {}'.format(path))
# save the new file
url = args["video_url"]
with urllib.request.urlopen(url) as response, open(path, 'wb') as out_file:
shutil.copyfileobj(response, out_file)
# run predictions
result, video = predict(path)
files = {'file': video}
# store results
r = requests.post("http://www/annotation/{}/".format(args["annotation_id"]),
files = files,
data = {"result": json.dumps(result)})
return None
import requests
import threading
import csv
import demo
import img_process
import time
plt.rcParams.update({'figure.max_open_warning': 0}) #fix the memory error
Chrome_driver_location = 'get_image/2/webdriver/chromedriver78'
destination_url_student = 'https://irs.thsrc.com.tw/IMINT/?student=university'
destination_url_default = 'https://irs.thsrc.com.tw/IMINT/'
train_number = 434 #火車班次號碼
dir = '/Users/garys/Desktop/2.jpg'
img_process.process(dir) #process
passcode = demo.predict(dir) #predict
print(passcode)
driver = webdriver.Chrome(Chrome_driver_location)
driver.get(destination_url_default) # 輸入範例網址，交給瀏覽器
cookie = driver.get_cookies() #get cookie
pageSource = driver.page_source # 取得網頁原始碼
#driver.find_element_by_id("bookingMethod2").click()
#driver.find_element_by_name("toTrainIDInputField").send_keys(train_number)
driver.find_element_by_id('btn-confirm').click()
啟程站 = Select(driver.find_element_by_name('selectStartStation'))
啟程站.select_by_value("1")
終點站 = Select(driver.find_element_by_name('selectDestinationStation'))
def surveillance():
station = "None"
cam1 = "../static/images/Camnormal.png"
cam2 = "../static/images/Camnormal.png"
cam3 = "../static/images/Camnormal.png"
if session['stage'] == 0:
target = os.path.join(APP_ROOT, 'static/videos')
print(target)
if not os.path.isdir(target):
os.mkdir(target)
session['filenames'] = []
for file in request.files.getlist("file"):
print(file)
session['filenames'].append(file.filename)
destination = "/".join([target, file.filename])
print(destination)
file.save(destination)
text = urllib.parse.quote_plus("where is the nearest police station")
url = 'https://google.com/search?q=' + text
response = requests.get(url)
soup = BeautifulSoup(response.text,'lxml')
for g in soup.find_all(class_='kR1eme rllt__wrap-on-expand'):
station = g.span.text
break
# model can be one of lstm, lrcn, mlp, conv_3d, c3d.
model = 'lrcn'
# Must be a weights file.
saved_model = 'static/videos/lrcn-images.114-2.304.hdf5'
# Sequence length must match the lengh used during training.
seq_length = 40
# Limit must match that used during training.
class_limit = None
if model in ['conv_3d', 'c3d', 'lrcn']:
data_type = 'images'
image_shape = (80, 80, 3)
elif model in ['lstm', 'mlp']:
data_type = 'features'
image_shape = None
else:
raise ValueError("Invalid model. See train.py for options.")
if session['stage'] <3:
prediction = predict(data_type, seq_length, saved_model, image_shape, os.path.splitext(session['filenames'][session['stage']])[0], class_limit)
session['stage'] += 1
if session['stage'] == 1:
session['prediction1'] = prediction
if session['stage'] == 2:
session['prediction2'] = prediction
if session['stage'] == 3:
session['prediction3'] = prediction
if "Crime Detected" in prediction:
if session['stage'] == 1:
cam1 = "../static/images/camalert.gif"
if session['stage'] == 2:
cam2 = "../static/images/camalert.gif"
if session['stage'] == 3:
cam3 = "../static/images/camalert.gif"
return render_template("surveillance.html", video0="../static/videos/" + session['filenames'][0],
video1="../static/videos/" + session['filenames'][1], video2="../static/videos/" + session['filenames'][2],
station=station, prediction1=session['prediction1'], prediction2=session['prediction2'],
prediction3=session['prediction3'], stage = session['stage'], cam1=cam1, cam2=cam2, cam3=cam3)