class VideoData:
"""
Helper class to present the detected face region, landmarks and emotions.
"""
#-----------------------------------------
def __init__(self):
"""
Class constructor.
"""
self._faceDet = FaceDetector()
'''
The instance of the face detector.
'''
self._bank = GaborBank()
'''
The instance of the bank of Gabor filters.
'''
self._emotionsDet = EmotionsDetector()
'''
The instance of the emotions detector.
'''
self._face = FaceData()
'''
Data of the last face detected.
'''
self._emotions = OrderedDict()
'''
Data of the last emotions detected.
'''
#-----------------------------------------
def detect(self, frame):
"""
Detects a face and the prototypic emotions on the given frame image.
Parameters
----------
frame: numpy.ndarray
Image where to perform the detections from.
Returns
-------
ret: bool
Indication of success or failure.
"""
ret, face = self._faceDet.detect(frame)
if ret:
self._face = face
# Crop just the face region
frame, face = face.crop(frame)
# Filter it with the Gabor bank
responses = self._bank.filter(frame)
# Detect the prototypic emotions based on the filter responses
self._emotions = self._emotionsDet.detect(face, responses)
return True
else:
self._face = None
return False
#-----------------------------------------
def draw(self, frame):
"""
Draws the detected data of the given frame image.
Parameters
----------
frame: numpy.ndarray
Image where to draw the information to.
"""
# Font settings
font = cv2.FONT_HERSHEY_SIMPLEX
scale = 0.5
thick = 1
glow = 3 * thick
# Color settings
black = (0, 0, 0)
white = (255, 255, 255)
yellow = (0, 255, 255)
red = (0, 0, 255)
empty = True
# Plot the face landmarks and face distance
x = 5
y = 0
w = int(frame.shape[1] * 0.2)
try:
face = self._face
empty = face.isEmpty()
face.draw(frame)
except:
pass
# Plot the emotion probabilities
try:
emotions = self._emotions
if empty:
labels = []
values = []
else:
labels = list(emotions.keys())
values = list(emotions.values())
bigger = labels[values.index(max(values))]
fo = open("foo.txt", "wb")
fo.write(bytes(bigger, 'UTF-8'))
fo.close()
print(bigger)
# Draw the header
text = 'emotions'
size, _ = cv2.getTextSize(text, font, scale, thick)
y += size[1] + 20
cv2.putText(frame, text, (x, y), font, scale, black, glow)
cv2.putText(frame, text, (x, y), font, scale, yellow, thick)
y += 5
cv2.line(frame, (x, y), (x + w, y), black, 1)
size, _ = cv2.getTextSize('happiness', font, scale, thick)
t = size[0] + 20
w = 150
h = size[1]
for l, v in zip(labels, values):
lab = '{}:'.format(l)
val = '{:.2f}'.format(v)
size, _ = cv2.getTextSize(l, font, scale, thick)
# Set a red color for the emotion with bigger probability
color = red if l == bigger else yellow
y += size[1] + 15
p1 = (x + t, y - size[1] - 5)
p2 = (x + t + w, y - size[1] + h + 5)
cv2.rectangle(frame, p1, p2, black, 1)
# Draw the filled rectangle proportional to the probability
p2 = (p1[0] + int((p2[0] - p1[0]) * v), p2[1])
cv2.rectangle(frame, p1, p2, color, -1)
cv2.rectangle(frame, p1, p2, black, 1)
# Draw the emotion label
cv2.putText(frame, lab, (x, y), font, scale, black, glow)
cv2.putText(frame, lab, (x, y), font, scale, color, thick)
# Draw the value of the emotion probability
cv2.putText(frame, val, (x + t + 5, y), font, scale, black,
glow)
cv2.putText(frame, val, (x + t + 5, y), font, scale, white,
thick)
except Exception as e:
print(e)
pass
class VideoData:
def __init__(self):
# 얼굴 검출기 인스턴스
self._faceDet = FaceDetector()
# Gabor 인스턴스
self._bank = GaborBank()
# 감정 탐지기
self._emotionsDet = EmotionsDetector()
# 검출 된 마지막 얼굴 데이터
self._face = FaceData()
# 감지 된 마지막 검정 데이터
self._emotions = OrderedDict()
# 주어진 프레임 이미지에서 얼굴과 프로토 타입 감정을 감지
def detect(self, frame):
# 탐지를 수행할 이밎
ret, face = self._faceDet.detect(frame)
if ret:
self._face = face
# 얼굴 영역 잘라내기
frame, face = face.crop(frame)
# Gabor 로 필터링
responses = self._bank.filter(frame)
# 필터 응답을 기반으로 프로토 타입 감정을 감지
self._emotions = self._emotionsDet.detect(face, responses)
return True
else:
self._face = None
return False
# 지정된 프레임 이미지의 감지 된 데이터를 그림
def draw(self, frame):
# 글꼴 설정 , 스케일 , 두께 , 글로우 , count
font = cv2.FONT_HERSHEY_SIMPLEX
scale = 0.5
thick = 1
glow = 3 * thick
global cnt
global neutral_cnt
global happiness_cnt
global sadness_cnt
global ange_cnt
global fear_cnt
global surprise_cnt
global disgust_cnt
black = (0, 0, 0)
white = (255, 255, 255)
yellow = (0, 255, 255)
red = (0, 0, 255)
empty = True
# 얼굴 표식과 얼굴 거리를 그림
x = 5
y = 0
w = int(frame.shape[1] * 0.2)
try:
face = self._face
empty = face.isEmpty()
face.draw(frame)
except:
pass
try:
emotions = self._emotions
if empty:
labels = []
values = []
else:
labels = list(emotions.keys())
values = list(emotions.values())
bigger = labels[values.index(max(values))]
text = 'emotions' # 감정
size, _ = cv2.getTextSize(text, font, scale, thick)
y += size[1] + 20
# 프레임, 텍스트, (x,y), 글꼴, 크기 , 검정 , 광선
cv2.putText(frame, text, (x, y), font, scale, black, glow)
# 프레임 ,텍스트 ,(x,y), 글꼴 , 크기, 노란색 , 두께
cv2.putText(frame, text, (x, y), font, scale, yellow, thick)
y += 5
cv2.line(frame, (x, y), (x + w, y), black, 1)
# 행복 , 글꼴 ,크기 , 두께
size, _ = cv2.getTextSize('happiness', font, scale, thick)
t = size[0] + 20
w = 150
h = size[1]
for l, v in zip(labels, values):
lab = '{}:'.format(l)
val = '{:.2f}'.format(v)
size, _ = cv2.getTextSize(l, font, scale, thick)
# 가장 큰 확률은 red 로 나머진 yellow
color = red if l == bigger else yellow
if color == red:
if l == 'neutral':
neutral_cnt += 1
if l == 'happiness':
happiness_cnt += 1
if l == 'sadness':
sadness_cnt += 1
if l == 'anger':
ange_cnt += 1
if l == 'fear':
fear_cnt += 1
if l == 'surprise':
surprise_cnt += 1
if l == 'disgust':
disgust_cnt += 1
cnt = [
neutral_cnt, happiness_cnt, sadness_cnt, ange_cnt,
fear_cnt, surprise_cnt, disgust_cnt
]
text1 = ' count '
size, _ = cv2.getTextSize(text1, font, scale, thick)
a = 300 # →
b = 33 # ↓
cv2.putText(frame, text1, (a, b), font, scale, black, glow)
cv2.putText(frame, text1, (a, b), font, scale, yellow,
thick)
text2 = '{:d}'.format(cnt[0]) # 무표정
a = 300 # →
b = 66 # ↓
cv2.putText(frame, text2, (a, b), font, scale, black, glow)
cv2.putText(frame, text2, (a, b), font, scale, yellow,
thick)
text3 = '{:d}'.format(cnt[1]) # 행복
a = 300 # →
b = 90 # ↓
cv2.putText(frame, text3, (a, b), font, scale, black, glow)
cv2.putText(frame, text3, (a, b), font, scale, yellow,
thick)
text4 = '{:d}'.format(cnt[2]) # 슬픔
a = 300 # →
b = 111 # ↓
cv2.putText(frame, text4, (a, b), font, scale, black, glow)
cv2.putText(frame, text4, (a, b), font, scale, yellow,
thick)
text5 = '{:d}'.format(cnt[3]) # 분노
a = 300 # →
b = 140 # ↓
cv2.putText(frame, text5, (a, b), font, scale, black, glow)
cv2.putText(frame, text5, (a, b), font, scale, yellow,
thick)
text6 = '{:d}'.format(cnt[4]) # 공포
a = 300 # →
b = 170 # ↓
cv2.putText(frame, text6, (a, b), font, scale, black, glow)
cv2.putText(frame, text6, (a, b), font, scale, yellow,
thick)
text7 = '{:d}'.format(cnt[5]) # 놀라움
a = 300 # →
b = 200 # ↓
cv2.putText(frame, text7, (a, b), font, scale, black, glow)
cv2.putText(frame, text7, (a, b), font, scale, yellow,
thick)
text8 = '{:d}'.format(cnt[6]) # 혐오감
a = 300 # →
b = 230 # ↓
cv2.putText(frame, text8, (a, b), font, scale, black, glow)
cv2.putText(frame, text8, (a, b), font, scale, yellow,
thick)
y += size[1] + 15
p1 = (x + t, y - size[1] - 5)
p2 = (x + t + w, y - size[1] + h + 5)
cv2.rectangle(frame, p1, p2, black, 1)
# 확률에 비례하는 채워진 사각형을 그립니다.
p2 = (p1[0] + int((p2[0] - p1[0]) * v), p2[1])
cv2.rectangle(frame, p1, p2, color, -1)
cv2.rectangle(frame, p1, p2, black, 1)
# 감정 레이블을 그립니다
# 프레임 , 레이블 , 폰트 , 블랙 , 글로우
# 프레임 , 실습 , 글꼴 , 크기 , 색상 , 두께
cv2.putText(frame, lab, (x, y), font, scale, black, glow)
cv2.putText(frame, lab, (x, y), font, scale, color, thick)
# 감정 확률의 값을 그립니다
# 프레임, 값 , 폰트 , 스케일 , 블랙 , 글로우
# 프레임, 값 , 글꼴 , 크기 , 흰색 , 두꺼운
cv2.putText(frame, val, (x + t + 5, y), font, scale, black,
glow)
cv2.putText(frame, val, (x + t + 5, y), font, scale, white,
thick)
except Exception as e:
print(e)
pass
class VideoData:
"""
Helper class to present the detected face region, landmarks and emotions.
"""
#-----------------------------------------
def __init__(self):
"""
Class constructor.
"""
self._faceDet = FaceDetector()
'''
The instance of the face detector.
'''
self._bank = GaborBank()
'''
The instance of the bank of Gabor filters.
'''
self._emotionsDet = EmotionsDetector()
'''
The instance of the emotions detector.
'''
self._face = FaceData()
'''
Data of the last face detected.
'''
self._emotions = OrderedDict()
'''
Data of the last emotions detected.
'''
#-----------------------------------------
def detect(self, frame):
"""
Detects a face and the prototypic emotions on the given frame image.
Parameters
----------
frame: numpy.ndarray
Image where to perform the detections from.
Returns
-------
ret: bool
Indication of success or failure.
"""
ret, face = self._faceDet.detect(frame)
if ret:
self._face = face
# Crop just the face region
frame, face = face.crop(frame)
# Filter it with the Gabor bank
responses = self._bank.filter(frame)
# Detect the prototypic emotions based on the filter responses
self._emotions = self._emotionsDet.detect(face, responses)
return True
else:
self._face = None
return False
#---------------------------------------------
def imprimir_tempo(self, tempo, frame, lab, val, fps):
[ano, mes, dia, hora, minuto, segundo, nano] = tempo.split('/')
nano = int(nano)
segundo = int(segundo)
minuto = int(minuto)
hora = int(hora)
dia = int(dia)
mes = int(mes)
ano = int(ano)
tempoPassado = frame*int(1000000000/fps)
nano += tempoPassado%1000000000
tempoPassado = int(tempoPassado/1000000000)
if nano >= 1000000000:
nano -= 1000000000
tempoPassado += 1
segundo += tempoPassado%60
tempoPassado = int(tempoPassado/60)
if segundo >= 60:
segundo -= 60
tempoPassado += 1
minuto += tempoPassado%60
tempoPassado = int(tempoPassado/60)
if minuto >= 60:
minuto -= 60
tempoPassado += 1
hora += tempoPassado%60
tempoPassado = int(tempoPassado/24)
if hora >= 24:
hora -= 24
tempoPassado += 1
dia += tempoPassado%24
if hora >= 24:
hora -= 24
saida = str(ano) + '/' + str(mes) + '/' + str(dia) + '/' + str(hora) + '/' + str(minuto) + '/' + str(segundo) + '/' + str(nano) + '-' + lab + '-' + val
print(saida)
#-----------------------------------------
def drawFrame(self, frame, labels):
atual = -1
preto = (0,0,0)
amarelo = (0,255,255)
soft = TAM_LINHA
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.line(frame, (COM_X,FIM_Y), (FIM_X, FIM_Y), preto, soft)
y = COM_Y
for l in labels:
atual += 1
lab = '{}:'.format(l)
x = OFFSETLETRA_X
y = OFFSETLETRA_FRAME - atual*TAM_LET
#size, _ = cv2.getTextSize(lab, font, 1, soft)
#maior label tem tamanho de (164,22)
#print (size)
cv2.putText(frame, lab, (x, y+OFFSETLETRA_Y), font, 1, amarelo, soft)
cv2.line(frame, (COM_X,y), (FIM_X, y), preto, soft)
cv2.line(frame, (DIVISOR_X, y), (DIVISOR_X,FIM_Y), preto, soft)
cv2.line(frame, (FIM_X, y), (FIM_X,FIM_Y), preto, soft)
#cv2.line(frame, (600, y), (600,465), cor, soft)
return frame
#-----------------------------------------
def draw(self, frame, tempo, frameNum, vals, fps, processar):
"""
Draws the detected data of the given frame image.
Parameters
----------
frame: numpy.ndarray
Image where to draw the information to.
"""
amarelo = (0, 255, 255)
empty = True
try:
face = self._face
empty = face.isEmpty()
except:
pass
# Plot the emotion probabilities
try:
emotions = self._emotions
atual = 0
labels = ['Neutral', 'Felicidade', 'Tristeza', 'Raiva', 'Medo', 'Surpresa', 'Desgosto']
if empty:
values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
else:
values = list(emotions.values())
bigger = labels[values.index(max(values))]
frame = self.drawFrame(frame, labels)
for l, v in zip(labels, values):
lab = '{}'.format(l)
val = '{:.2f}'.format(v)
vals[atual].rotate(-1)
vals[atual].pop()
vals[atual].append(v)
for i in range(PONTOS-1):
valor1 = int(OFFSETPONTO - vals[atual][i]*RESOL_LINHA_Y - atual*RESOL_LINHA_Y)
valor2 = int(OFFSETPONTO - vals[atual][i+1]*RESOL_LINHA_Y - atual*RESOL_LINHA_Y)
cv2.line(frame, (OFFSETLINHA+RESOL_LINHA_X*i, valor1), (OFFSETLINHA+RESOL_LINHA_X*(i+1), valor2 ), amarelo, TAM_LINHA)
#cv2.putText(frame, val, (5, 20 + atual*25), font, 1, yellow, 1)
#cv2.putText(frame, '{}'.format(vals[atual][199]), (320, 20 + atual*25), font, 1, yellow, 1)
if processar:
self.imprimir_tempo(tempo, frameNum, lab, val, fps)
atual += 1
return frame, vals
except Exception as e:
print(e)
pass
class VideoData:
"""
Helper class to present the detected face region, landmarks and emotions.
"""
#-----------------------------------------
def __init__(self):
"""
Class constructor.
"""
self._faceDet = FaceDetector()
'''
The instance of the face detector.
'''
self._bank = GaborBank()
'''
The instance of the bank of Gabor filters.
'''
self._emotionsDet = EmotionsDetector()
'''
The instance of the emotions detector.
'''
self._face = FaceData()
'''
Data of the last face detected.
'''
self._emotions = OrderedDict()
'''
Data of the last emotions detected.
'''
#-----------------------------------------
def detect(self, frame):
"""
Detects a face and the prototypic emotions on the given frame image.
Parameters
----------
frame: numpy.ndarray
Image where to perform the detections from.
Returns
-------
ret: bool
Indication of success or failure.
"""
ret, face = self._faceDet.detect(frame)
if ret:
self._face = face
# Crop just the face region
frame, face = face.crop(frame)
# Filter it with the Gabor bank
responses = self._bank.filter(frame)
# Detect the prototypic emotions based on the filter responses
self._emotions = self._emotionsDet.detect(face, responses)
return True
else:
self._face = None
return False
#---------------------------------------------
def imprimir_tempo(self, tempo, frame, lab, val, fps):
[ano, mes, dia, hora, minuto, segundo, nano] = tempo.split('/')
nano = int(nano)
segundo = int(segundo)
minuto = int(minuto)
hora = int(hora)
dia = int(dia)
mes = int(mes)
ano = int(ano)
tempoPassado = frame*int(1000000000/fps)
nano += tempoPassado%1000000000
tempoPassado = int(tempoPassado/1000000000)
if nano >= 1000000000:
nano -= 1000000000
tempoPassado += 1
segundo += tempoPassado%60
tempoPassado = int(tempoPassado/60)
if segundo >= 60:
segundo -= 60
tempoPassado += 1
minuto += tempoPassado%60
tempoPassado = int(tempoPassado/60)
if minuto >= 60:
minuto -= 60
tempoPassado += 1
hora += tempoPassado%60
tempoPassado = int(tempoPassado/24)
if hora >= 24:
hora -= 24
tempoPassado += 1
dia += tempoPassado%24
if hora >= 24:
hora -= 24
saida = str(ano) + '/' + str(mes) + '/' + str(dia) + '/' + str(hora) + '/' + str(minuto) + '/' + str(segundo) + '/' + str(nano) + '-' + lab + '-' + val
print(saida)
#-----------------------------------------
def draw(self, frame, tempo, frameNum, fps):
"""
Draws the detected data of the given frame image.
Parameters
----------
frame: numpy.ndarray
Image where to draw the information to.
"""
empty = True
try:
face = self._face
empty = face.isEmpty()
face.draw(frame)
except:
pass
# Plot the emotion probabilities
try:
emotions = self._emotions
if empty:
labels = []
values = []
else:
labels = list(emotions.keys())
values = list(emotions.values())
bigger = labels[values.index(max(values))]
for l, v in zip(labels, values):
lab = '{}'.format(l)
val = '{:.2f}'.format(v)
self.imprimir_tempo(tempo, frameNum, lab, val, fps)
except Exception as e:
print(e)
pass
def main(argv):
"""
Main entry of this script.
Parameters
------
argv: list of str
Arguments received from the command line.
"""
# Parse the command line
args = parseCommandLine(argv)
# Loads the video or starts the webcam
if args.source == 'cam':
## args.source=filedialog.askopenfilename(filetypes = (("video files","*.mp4")))
video = cv2.VideoCapture("IMG_1328.mp4")
## video = cv2.VideoCapture(args.id)
if not video.isOpened():
print('Error opening webcam of id {}'.format(args.id))
sys.exit(-1)
fps = 0
frameCount = 0
sourceName = 'Webcam #{}'.format(args.id)
##
else:
video = cv2.VideoCapture('IPCAM_IPCAM_2019-10-06_21-34-17.mp4')
## video = cv2.VideoCapture(args.file)
if not video.isOpened():
print('Error opening video file {}'.format(args.file))
sys.exit(-1)
fps = int(video.get(cv2.CAP_PROP_FPS))
frameCount = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
sourceName = args.file
# Force HD resolution (if the video was not recorded in this resolution or
# if the camera does not support it, the frames will be stretched to fit it)
# The intention is just to standardize the input (and make the help window
# work as intended)
## video.set(cv2.CAP_PROP_FRAME_WIDTH, 0.55*fps);
## video.set(cv2.CAP_PROP_FRAME_HEIGHT, 0.55*fps);
## video.set(cv2.CAP_PROP_FPS, fps*0.01);
## video.set(cv2.CAP_PROP_POS_MSEC,1000)
# Create the helper class
data = VideoData()
## image = cv2.imread('face1.png')
## frame=image
# Text settings
font = cv2.FONT_HERSHEY_SIMPLEX
scale = 1
thick = 1
glow = 3 * thick
# Color settings
color = (255, 255, 255)
paused = False
frameNum = 0
print("Frame rate : {0}".format(video.get(cv2.CAP_PROP_FPS)))
count = 0
nameslist = []
biggernames = []
listofnames = []
name = []
finalnamelist = []
## # Process the video input
while True:
if not paused:
start = datetime.now()
ret, img = video.read()
if ret:
frame = img.copy()
FACES = FaceDetector()
ret, face = FACES.detect(frame)
print(len(face))
for obj in face:
listofnames = data.classify_face(frame, obj)
listofemotions = data.detect(frame, obj)
bigger = data.draw(frame, obj)
if (len(listofnames) != 0):
nameslist.append(listofnames[0])
name.append(listofnames[0])
nameslist.append(bigger)
## resize = cv2.resize(sourceName, (680, 680))
cv2.imshow(sourceName, frame)
count += 30
video.set(1, count)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cv2.destroyAllWindows()
for i in name:
if i not in finalnamelist:
finalnamelist.append(i)
print(finalnamelist)
video.release()
finalresult = []
count = 0
expressions = []
for i in finalnamelist:
while (count != len(nameslist)):
if (i == nameslist[count]):
expressions.append(nameslist[count + 1])
count += 2
else:
count += 2
neutral = expressions.count("neutral")
happiness = expressions.count("happiness")
sadness = expressions.count("sadness")
anger = expressions.count("anger")
fear = expressions.count("fear")
surprise = expressions.count("surprise")
disgust = expressions.count("disgust")
activeorinactive = max(neutral, happiness, sadness, anger, fear,
surprise, disgust)
if (activeorinactive == neutral):
finalresult.append(i)
finalresult.append("InActive")
elif (activeorinactive == happiness):
finalresult.append(i)
finalresult.append("Active")
elif (activeorinactive == sadness):
finalresult.append(i)
finalresult.append("InActive")
elif (activeorinactive == anger):
finalresult.append(i)
finalresult.append("Active")
elif (activeorinactive == fear):
finalresult.append(i)
finalresult.append("Active")
elif (activeorinactive == surprise):
finalresult.append(i)
finalresult.append("Active")
elif (activeorinactive == disgust):
finalresult.append(i)
finalresult.append("InActive")
print(finalresult)
## conn = pymysql.connect(host="localhost" , user='******', password="******" , db="db_attendance")
#### conn = mysql.connector.connect(host="localhost" , user='******', password="******" , database="db_attendance")
## cur=conn.cursor()
## query=("insert into tbl_registration(reg_firstname,reg_lastname, reg_email ,reg_registration, reg_department,reg_session,reg_section) values (%s, %s,%s,%s,%s,(select ses_ID from tbl_session where ses_name=%s),(select sec_ID from tbl_section where sec_name=%s))")
## data= cur.execute(query,(st_firstname, st_lastname,st_email,st_department,st_registration,self.combosession.currentText(),st_section))
## conn.commit()
conn = pymysql.connect(host="localhost",
user='******',
password="******",
db="db_attendance")
## conn = mysql.connector.connect(host="localhost" , user='******', password="******" , database="db_attendance")
cur = conn.cursor()
query2 = ("select CURDATE()")
cur.execute(query2)
data2 = cur.fetchall()
print(data2)
dblist = []
print(finalnamelist[0])
for i in finalnamelist:
query4 = (
"select reg_ID from tbl_registration where reg_registration=%s")
cur.execute(query4, (i))
data4 = cur.fetchall()
print(data4)
for i in finalnamelist:
dblist.append(i)
finalnamelist.remove(i)
for i in dblist:
query = (
"update tbl_attendance set att_attendance= %s,att_date=(select curdate()),reg_ID=(select reg_ID from tbl_registration where reg_registration=%s),att_productivity=%s order by att_ID desc limit 1"
)
data = cur.execute(query, (True, i, finalresult[1]))
query3 = ("select sub_ID from tbl_attendance order by att_ID desc limit 1")
cur.execute(query3)
data3 = cur.fetchall()
print(data3)
query4 = ("select ses_ID from tbl_attendance order by att_ID desc limit 1")
cur.execute(query4)
data4 = cur.fetchall()
print(data4)
query5 = ("select sec_ID from tbl_attendance order by att_ID desc limit 1")
cur.execute(query5)
data5 = cur.fetchall()
print(data5)
s = 0
for i in finalnamelist:
s += 1
query = (
"insert into tbl_attendance(ses_ID,sec_ID,sub_ID,reg_ID,att_date,att_attendance,att_productivity) values(%s,%s,%s,(select reg_ID from tbl_registration where reg_registration=%s),(select curdate()), %s,%s)"
)
data = cur.execute(query,
(data4, data5, data3, i, True, finalresult[s]))
print(finalnamelist)
conn.commit()