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face_attendance.py
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face_attendance.py
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import mysql.connector
import face_recognition
import cv2
import numpy as np
from datetime import date, datetime
#Opening connection to database.
attend = mysql.connector.connect(user='root', password='qwertyui', host='127.0.0.1', database='attendancedb')
#Connecting to database
cur = attend.cursor(buffered=True)
#For adding name and time to attendance
def register(name):
#Getting today's time and date
time = datetime.now().strftime("%H:%M:%S")
datenow = datetime.now().strftime("%Y/%m/%d")
query = "SELECT id FROM staff WHERE name=%s"
where = (name,)
cur.execute(query, where)
staff_id = cur.fetchone()
staff_id = staff_id[0]
query = "SELECT staff_id FROM attended WHERE staff_id=%s and date=%s"
where = (staff_id, datenow)
cur.execute(query, where)
cstaff = cur.fetchone()
if cstaff == None:
cur.execute("INSERT INTO attended (staff_id, name, time, date) VALUES (%s, %s, %s, %s)", (staff_id, name, time, datenow))
attend.commit()
#Getting access to webcam. 0 for main cam
video_capture = cv2.VideoCapture(0)
#creating arrays of faces and names(staff id's or student id's)
known_face_encodings = []
staff_ids = []
known_names = []
#querying face_encodings and corresponding ids from database
cur.execute("SELECT img_name, name FROM Staff")
encs = cur.fetchall()
for row in encs:
#Loading sample pic and learning to recognize
face = face_recognition.load_image_file(row[0])
encoding = face_recognition.face_encodings(face)[0]
known_face_encodings.append(encoding)
known_names.append(row[1])
#initializing some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
#Grab a frame from video
ret, frame = video_capture.read()
#Resizing video frame to 1/4 for faster recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
#Convert image from BGR color(OpenCV) to RGB color(face_recognition compatible)
rgb_small_frame = small_frame[:, :, ::-1]
if process_this_frame:
#Find all faces and encodings in current video frame
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
#If there is a match for known faces
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
#Timestamping
#If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_names[first_match_index]
face_names.append(name)
register(name)
process_this_frame = not 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
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#Closing database connection
attend.close()
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()