def __init__(self):
"""create a solved cube object"""
#INDEX: 0 1 2 3 4 5
self.sides = ['F','R','B','L','U','D'] #just for convience
self.colors = ['R','B','O','G','W','Y']
self.faces_list = []
#makes a face of each color
for color in self.colors:
self.faces_list.append(face(color))
#Hash table hashes to lists, easy handle
self.faces = {'F': self.faces_list[0],'R': self.faces_list[1],
'B': self.faces_list[2],'L': self.faces_list[3],
'U': self.faces_list[4],'D': self.faces_list[5]}
#this hashes characters to the member functions
self.rotations ={'F': self.F, 'R': self.R, 'B': self.B,
'L': self.L, 'U': self.U, 'D': self.D}
#this hashes characters to 4-direction tuples NESW
self.directions = {'F': ('U','R','D','L'), 'R': ('U','B','D','F'),
'B': ('U','L','D','R'), 'L': ('U','F','D','B'),
'U': ('B','R','F','L'), 'D': ('F','R','B','L')}
def s(self):
global trail_face
print(trail_face)
if trail_face==1:
self.txt2=('.......Trail 2.......')
img=face()
record=database()
record.connection()
prediction=img.prediction(keypairs(record.get_profile()))
print('prediction:',prediction)
self.txt2='Done'
print('user:'******'Authorized'
trail_face=0
self.manager.current='tier2'
break
else:
self.txt2='Failed! Try Again'
trail_face+=1
self.manager.current='tier1'
break
if trail_face>1:
trail_face=0
self.txt2='**No Access**'
self.manager.current='first'
def process(self):
resize.resize(self.path)
temp_path = self.path
while (temp_path[-1]!='\\'):
temp_path = temp_path[:-1]
img = fr.api.load_image_file(temp_path+'___temp.jpg')
location = fr.api.face_locations(img, number_of_times_to_upsample=1, model='hog')
encoding = fr.api.face_encodings(img, known_face_locations=location, num_jitters=1)
for i in range(len(encoding)) :
self.faces.append(face(self.path, location[i], encoding[i]))
def initFaces(self):
self.faces = [None for i in range(6)]
self.faces[FACE_TOP] = face.face(self.Depth, self.Width, self.Wall)
self.faces[FACE_TOP].setTabType(face.FHEIGHT, SLOTS)
self.faces[FACE_TOP].setTabType(face.FWIDTH, SLOTS)
self.faces[FACE_BOTTOM] = face.face(self.Depth, self.Width, self.Wall)
self.faces[FACE_BOTTOM].setTabType(face.FHEIGHT, SLOTS)
self.faces[FACE_BOTTOM].setTabType(face.FWIDTH, SLOTS)
self.faces[FACE_LEFT] = face.face(self.Height, self.Depth, self.Wall)
self.faces[FACE_LEFT].setTabType(face.FHEIGHT, SLOTS)
self.faces[FACE_LEFT].setTabType(face.FWIDTH, TABS)
self.faces[FACE_RIGHT] = face.face(self.Height, self.Depth, self.Wall)
self.faces[FACE_RIGHT].setTabType(face.FHEIGHT, SLOTS)
self.faces[FACE_RIGHT].setTabType(face.FWIDTH, TABS)
self.faces[FACE_FRONT] = face.face(self.Height, self.Width, self.Wall)
self.faces[FACE_FRONT].setTabType(face.FWIDTH, TABS)
self.faces[FACE_FRONT].setTabType(face.FWIDTH, TABS)
self.faces[FACE_BACK] = face.face(self.Height, self.Width, self.Wall)
self.faces[FACE_BACK].setTabType(face.FWIDTH, TABS)
self.faces[FACE_BACK].setTabType(face.FWIDTH, TABS)
for fc in self.faces:
fc.setNoRelief()
def __init__(self):
"""create a solved cube object"""
#INDEX: 0 1 2 3 4 5
self.sides = ['F', 'R', 'B', 'L', 'U', 'D'] #just for convience
self.colors = ['R', 'B', 'O', 'G', 'W', 'Y']
self.faces_list = []
#makes a face of each color
for color in self.colors:
self.faces_list.append(face(color))
#Hash table hashes to lists, easy handle
self.faces = {
'F': self.faces_list[0],
'R': self.faces_list[1],
'B': self.faces_list[2],
'L': self.faces_list[3],
'U': self.faces_list[4],
'D': self.faces_list[5]
}
#this hashes characters to the member functions
self.rotations = {
'F': self.F,
'R': self.R,
'B': self.B,
'L': self.L,
'U': self.U,
'D': self.D
}
#this hashes characters to 4-direction tuples NESW
self.directions = {
'F': ('U', 'R', 'D', 'L'),
'R': ('U', 'B', 'D', 'F'),
'B': ('U', 'L', 'D', 'R'),
'L': ('U', 'F', 'D', 'B'),
'U': ('B', 'R', 'F', 'L'),
'D': ('F', 'R', 'B', 'L')
}
def collecting():
data = urlopen(base_url).read()
soup = BeautifulSoup(data, "html.parser")
dd = datetime.today()
collect_time = str(dd.year) + "," + str(dd.month) + "," + str(dd.day)
patter = '[^\w\s]'
co = []
ll = []
for i in soup.find_all('div',
{'class': 'hdline_flick_item'}): #헤드라인 사진포함된 것 추출
a = i.find('a')
ll.append(base_url + a.get('href'))
for i in soup.find_all('dd'): #대표기사들 추출
b = i.find('a')
ll.append(b.get('href'))
for k in soup.find_all('div', 'hdline_article_tit'): #헤드라인 추출
c = k.find('a')
ll.append(base_url + c.get('href'))
for data in soup.find_all('div', 'mtype_list_wide'): #나머지 기사 추출
try:
for a in data.find_all('a'):
link = a.get('href') # for getting link
ll.append(link)
except OSError:
break
for i in soup.find_all('ul',
{'class': 'section_list_ranking'}): #가장많이본 뉴스 추출
for j in i.find_all('a'):
link = j.get('href')
ll.append(base_url + link)
for i in ll:
cs = []
article_body, title = parse(i)
press_1 = press(i)
good, nice, sad, angry, wanted, recommand = face(i)
dic = {
'title': title,
'press': press_1,
'good': good,
'nice': nice,
'sad': sad,
'angry': angry,
'wanted': wanted,
'recommand': recommand
}
cs.append('naver_news')
cs.append(title)
cs.append(article_body)
cs.append(collect_time)
cs.append(i)
cs.append(good)
cs.append(nice)
cs.append(sad)
cs.append(angry)
cs.append(wanted)
cs.append(recommand)
cs.append(press_1)
try:
save('naver_news', title, article_body, collect_time, i, good,
nice, sad, angry, wanted, recommand, press_1)
except: #헤드라인 뉴스와 분야별 순위에 같이 포함되면 기본키 중복으로 삽입 거절당하기 때문에 그것을 방지하기 위한 예외처리
pass
co.append(cs)
panda(co)
def train(self):
s=face()
record.connection()
s.train2(keypairs(record.get_profile()))
self.label='**Training Completed**'
self.manager.current='training'
def get_frame(self):
fn = None
self.frames = open("stream.jpg", 'wb+')
self.ret, self.frame = self.cap.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(self.frame, (0, 0), fx=0.25, fy=0.25)
# Only process every other frame of video to save time
if self.process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(small_frame)
face_encodings = face_recognition.face_encodings(
small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
# results = face_recognition.compare_faces(self.known_faces_encodings, face_encoding)
face_distances = face_recognition.face_distance(
self.known_faces_encodings, face_encoding)
# print(face_distances)
# print()
# print(results)
# import ipdb; ipdb.set_trace();
if min(face_distances) < 0.5:
# uid = uids[results.index(True)]
uid = self.uids[np.argmin(face_distances)]
name = uid
else:
name = uuid.uuid4().hex
self.uids.append(name)
self.known_faces_encodings.append(face_encoding)
# name = "Unknown"
face_names.append(name)
process_this_frame = not self.process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations,
face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
crop_face = self.frame[top:bottom, left:right]
fn = 'data/new_faces/{}.png'.format(name)
if not os.path.isfile(fn):
cv2.imwrite(fn, crop_face)
cv2.imwrite('static/{}.png'.format(name), crop_face)
# Draw a box around the face
cv2.rectangle(self.frame, (left, top), (right, bottom),
(0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(self.frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(self.frame, str(name), (left + 6, bottom - 6), font,
1.0, (255, 255, 255), 1)
if self.ret: # frame captures without errors...
cv2.imwrite("stream.jpg", self.frame) # Save image...
with open('result.html', 'w') as f:
result = pd.DataFrame()
result['uid'] = face_names
fns = []
vks = []
genders = []
for uid in face_names:
if uid in df.index:
print("&&&&&&&&&&&&&&&&")
kind = 'familiar'
fns.append('/static/{}.jpg'.format(uid))
vks.append('https://vk.com/id{}'.format(uid))
# import ipdb; ipdb.set_trace()
genders.append(df.loc[uid]['sex'])
else:
print(self.prev_fn)
print(fn)
res = {}
if self.prev_fn != fn:
res = face(fn)
print(res)
if 'error' not in res:
try:
res = res[0].get('faceAttributes')
except:
print(res)
res = {}
self.prev_fn = fn
kind = res.get('gender', 'None')
print(kind)
genders.append(kind)
fns.append('/static/{}.png'.format(uid))
vks.append(None)
if uid == '144144243':
kind = 'friend'
print(kind)
if face_names:
create_html(face_names[0], kind)
else:
create_html('0', 'empty')
result['photo'] = fns
result['vk'] = vks
result['gender'] = genders
# print(result)
f.write(template.render({'x': result}))
return self.frames.read()
def take_photo(albumPath):
img = 0
display = 0
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cv2.namedWindow('camera', cv2.WINDOW_NORMAL)
cv2.setWindowProperty('camera', cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
cv2.setWindowProperty('camera', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_NORMAL)
# ii = Image.open('background.png')
# ret, camera = cap.read()
# print(camera.shape)
# im = ii.resize((ii.size[0]//3,ii.size[1]//3),Image.NEAREST)
white = cv2.imread('../../../photo_email/background.png')
faces = []
phrase = 0
while (True):
# Capture camera-by-camera
ret, camera = cap.read()
display = camera.copy()
location = fr.api.face_locations(display)
if len(location) > 0:
for fc in location:
display = cv2.rectangle(display, (fc[3], fc[0]),
(fc[1], fc[2]), (0, 255, 0), 4)
if phrase == 0:
display = np.concatenate((display[0:480, 140:500], white, white),
axis=1)
elif phrase == 1:
display = np.concatenate(
(faces[0], display[0:480, 140:500], white), axis=1)
else:
display = np.concatenate(
(faces[0], faces[1], display[0:480, 140:500]), axis=1)
cv2.imshow('camera', display)
key = cv2.waitKey(1)
if key == ord('q'):
cap.release()
cv2.destroyAllWindows()
break
elif key == ord('c') and len(location) > 0:
faces.append(camera[0:480, 140:500])
if phrase < 2:
phrase += 1
else:
cap.release()
cv2.destroyAllWindows()
break
photo = []
for f in faces:
location = fr.api.face_locations(f)
encoding = fr.api.face_encodings(f, known_face_locations=location)
# print(encoding)
photo.append(face(0, location, encoding))
alb = album.load(albumPath)
ppp = alb.match(photo, 0.8)
# print(r"""C:\Users\4E14ChuYatHong\Desktop\20190909_Prizegiving_ceremony\_DSC7317.JPG 0.4951498138713914
# C:\Users\4E14ChuYatHong\Desktop\20190909_Prizegiving_ceremony\_DSC7318.JPG 0.502228817791267""")
# sys.exit()
for p in ppp:
print(p[0].path, p[1][0])
def process(frame, count=0):
cv2.imwrite('donga.jpg', frame)
if face('donga.jpg') == 1:
print("Good")
else:
email('donga.jpg')
def post(self):
str_xml = self.request.body #获得post来的数据
xml = etree.fromstring(str_xml)#进行XML解析
msgType=xml.find("MsgType").text
fromUser=xml.find("FromUserName").text
toUser=xml.find("ToUserName").text
respContent = ""
if msgType == "text":
content=xml.find("Content").text#获得用户所输入的内容
if content.startswith("翻译"):
#拆分以翻译开头
reinfo = re.compile("^翻译")
#首先content值类型是unicode，所以要先转换为str在拆分,strip去除左右空格
args = reinfo.sub('', content.encode("utf-8")).strip()
if not args:
respContent = promptMsg.getFanYiMsg()
else:
respContent = translate.Translate().baiduFanYi(args)
elif content.startswith("歌曲"):
reinfo = re.compile("^歌曲")
args = reinfo.sub('', content.encode("utf-8")).strip()
argslist = args.split("@")
des = "来自百度音乐"
if not argslist[0]:
respContent = promptMsg.getMusicMsg()
elif len(argslist) == 1:
respContent = music.Music().baiduMusic(argslist[0],"")
else:
des = argslist[1]
respContent = music.Music().baiduMusic(argslist[0],argslist[1])
if argslist[0]:
self.finish(weixinResult.result_music(fromUser, toUser, int(time.time()), argslist[0],des,respContent[0],respContent[1]))
elif content.startswith("天气"):
reinfo = re.compile("^天气")
args = reinfo.sub('', content.encode("utf-8")).strip()
if not args:
respContent = promptMsg.getWeather()
else:
newsMsg = weather.Weather().baiduWeather(args, fromUser, toUser)
self.finish(newsMsg)
elif content.startswith("ip"):
reinfo = re.compile("^ip")
args = reinfo.sub('', content.encode("utf-8")).strip()
if not args:
respContent = promptMsg.getIp()
else:
respContent = ip.IP().taobaoIP(args)
elif content.startswith("手机"):
reinfo = re.compile("^手机")
args = reinfo.sub('', content.encode("utf-8")).strip()
if not args:
respContent = promptMsg.getPhone()
else:
respContent = phone.Phone().tenpayPhone(args).encode("utf-8")
elif content.startswith("苹果"):
reinfo = re.compile("^苹果")
args = reinfo.sub('', content.encode("utf-8")).strip()
if not args:
respContent = promptMsg.getApple()
else:
result, code = imei.IMEI().appleIMEI(args, toUser, fromUser)
if code == 0:
self.finish(result)
elif code == -1:
respContent = result
elif content.encode("utf-8").strip() == "历史上的今天" or content.encode("utf-8").strip() == "lssdjt":
respContent = todayonhistory.History().rijiben()
elif len(content.replace("—","-").replace("-","-").split("-")) == 3:
s = content.replace("—","-").replace("-","-").split("-")
region = s[0]
start = s[1]
end = s[2]
newsMsg = navigation.Navigation().baiduNavigation(toUser, fromUser, region, start, end)
self.finish(newsMsg)
else:
respContent = promptMsg.getTextMsg()
elif msgType == "image":
#取得图片地址
picUrl = xml.find("PicUrl").text
respContent = face.face(picUrl)
elif msgType == "location":
respContent = xml.find("Location_X").text
elif msgType == "event":
#事件类型
eventType = xml.find("Event").text
#订阅
if eventType == "subscribe":
respContent = "谢谢您关注娟子服装。"
#取消订阅
#elif eventType == "unsubscribe":
#取消订阅后用户再收不到公众号发送的消息，因此不需要回复消息
#自定义菜单点击事件
elif eventType == "CLICK":
#事件KEY值，与创建自定义菜单时指定的KEY值对应
eventKey = xml.find("EventKey").text
#TODO
self.finish(weixinResult.result_text(fromUser, toUser, int(time.time()), respContent))
continue
# frameDelta = cv2.absdiff(firstFrame, gray)
consecDelta = cv2.absdiff(prevFrame, gray)
# thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
consecThresh = cv2.threshold(consecDelta, 25, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
# pyautogui.confirm('Shall I confirm?')
# thresh = cv2.dilate(thresh, None, iterations=2)
consecThresh = cv2.dilate(consecThresh, None, iterations=2)
# _, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
_, f**k, _ = cv2.findContours(consecThresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
detect_face = face(frame)
for x in f**k:
y = cv2.contourArea(x)
if cv2.contourArea(x) < 3000 and not detect_face:
text = "Unoccupied"
continue
else:
text = "Occupied"
if text == "Occupied":
if detect_face:
print("good")
count = 0
else:
count += 1
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
# pyautogui.confirm('Shall I confirm?')
thresh = cv2.dilate(thresh, None, iterations=2)
_, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < args["min_area"]:
continue
text = "Occupied"
if text == "Occupied":
if face(frame) is 1:
print("good")
else:
print('bad')
cv2.imwrite('donga.jpg', frame)
Process(target=email, args=('donga.jpg', )).start()
# process(text,frame)
# cv2.putText(frame, "Room Status: {}".format(text), (10, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# For testing purposes show these videos
# Process(cv2.imshow("Security Feed", frame)).start()
# cv2.imshow("Thresh", thresh)
# cv2.imshow("Frame Delta", frameDelta)
def secret_cam():
global time
# start program from command line
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
ap.add_argument("-t",default=False)
args = vars(ap.parse_args())
#Video of thief
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
#(h, w) = face_recognition.load_image_file('donga.jpg').shape[:2]
# Webcam
if args.get("video", None) is None:
cam = cv2.VideoCapture(0)
time.sleep(0.25)
# otherwise, we are reading from a video file
else:
cam = cv2.VideoCapture(args["video"])
# initialize the first frame in the video stream
firstFrame = None
count,vid_no,x=0,0,True
time_now = datetime.datetime.now()
while True:
w, h = cam.get(3), cam.get(4)
if x is True:
time_now = datetime.datetime.now()
video=cv2.VideoWriter('videos/'+str(time_now)+'.avi',fourcc,15.0,(int(w),int(h)),True)
x=False
# grab the current frame and initialize the occupied/unoccupied text
(grabbed, frame) = cam.read()
text = "Unoccupied"
# resize the frame, convert it to grayscale, and blur it
# frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the first frame is None, initialize it
if firstFrame is None:
firstFrame = gray
prevFrame = gray
continue
consecDelta = cv2.absdiff(prevFrame,gray)
consecThresh = cv2.threshold(consecDelta,25,255,cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
# pyautogui.confirm('Shall I confirm?')
consecThresh=cv2.dilate(consecThresh,None,iterations=2)
_, f**k, _ = cv2.findContours(consecThresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
detect_face=face(frame)
for dam in f**k:
# if the contour is too small, ignore it
if cv2.contourArea(dam) < 11000 and not detect_face:
continue
(x, y, w, h) = cv2.boundingRect(dam)
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
if text=="Occupied":
if detect_face is 1:
print("good")
count=0
else:
count+=1
print('bad')
video.write(frame)
if count >= 50:
Process(target=drive,args=('videos/'+str(time_now)+'.avi',)).start()
vid_no+=1
count=0
x=True
prevFrame = gray
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow("Security Feed", frame)
# cv2.imshow('gray',gray)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
# cleanup the cam and close any open windows
try:
video.release()
except:
pass
cam.release()
cv2.destroyAllWindows()
return frame
