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face_recog.py
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face_recog.py
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import face_recognition
import cv2
from openpyxl import Workbook
import datetime
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Create a woorksheet
book=Workbook()
sheet=book.active
# Load images.
image_1 = face_recognition.load_image_file("1.jpg")
image_1_face_encoding = face_recognition.face_encodings(image_1)[0]
image_5 = face_recognition.load_image_file("5.jpg")
image_5_face_encoding = face_recognition.face_encodings(image_5)[0]
image_7 = face_recognition.load_image_file("7.jpg")
image_7_face_encoding = face_recognition.face_encodings(image_7)[0]
image_3 = face_recognition.load_image_file("3.jpg")
image_3_face_encoding = face_recognition.face_encodings(image_3)[0]
image_4 = face_recognition.load_image_file("4.jpg")
image_4_face_encoding = face_recognition.face_encodings(image_4)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
image_1_face_encoding,
image_5_face_encoding,
image_7_face_encoding,
image_3_face_encoding,
image_4_face_encoding
]
known_face_names = [
"1",
"5",
"7",
"3",
"4"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# Load present date and time
now= datetime.datetime.now()
today=now.day
month=now.month
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
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:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# 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_face_names[first_match_index]
# Assign attendance
if int(name) in range(1,61):
sheet.cell(row=int(name), column=int(today)).value = "Present"
else:
pass
face_names.append(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)
# Save Woorksheet as present month
book.save(str(month)+'.xlsx')
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()