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#Face-recognition.py
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from datetime import datetime
import requests
import face_recognition
import cv2
import numpy as np
import pandas as pd
import winsound
from googleapiclient import discovery
from google.oauth2 import service_account
# ------------------------------------------ CONSTANT GOOGLE-API ------------------------------------------ #
# face-recognition-attendance-sy@ekc-elektro-itk.iam.gserviceaccount.com
# 113270042921053675328
SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
SERVICE_ACCOUNT_FILE = 'ekc_elektro_itk.json'
CREDENTIALS = service_account.Credentials.from_service_account_file(
filename=SERVICE_ACCOUNT_FILE, scopes=SCOPES)
SERVICE = discovery.build('sheets', 'v4', credentials=CREDENTIALS)
SPREADSHEET_ID = "1c6uz6mBi5DX0KDEaNtDF-GNmeckyGyP_QGE55RP2fYA"
RANGE_POST = "Record!A1:C1"
INPUT_USER = "USER_ENTERED"
# ------------------------------------------ CONSTANT SHEETY-API ------------------------------------------ #
# Rahasia = "tubes-biomekanika@biomekanika.iam.gserviceaccount.com "
SHEETY_ENDPOINT = "https://api.sheety.co/d71c9673498530a411013b121ea3e53e/ekcAttendanceSystem/ekc"
BEARER_AUTHENTICATION = {
"Authorization": "Bearer fix_dapat_a"
}
# -------------------------------------------- DEFINISI FUNGSI -------------------------------------------- #
FONT = cv2.FONT_HERSHEY_SIMPLEX
def waktu_deteksi():
"""Memberikan waktu saat pendeteksian ini terjadi."""
global tanggal_sekarang, jam_sekarang
date = datetime.today()
tanggal_sekarang = date.strftime("%d/%m/%Y")
jam_sekarang = date.strftime("%H:%M:%S")
def save_to_database_by_sheety():
"""Menyimpan data orang yang telah terdeteksi."""
attendance_parameter = {
"ekc": {
"nama": nama_sekarang,
"jam": jam_sekarang,
"tanggal": tanggal_sekarang,
}
}
requests.post(url=SHEETY_ENDPOINT, json=attendance_parameter, headers=BEARER_AUTHENTICATION)
print(nama_sekarang, jam_sekarang, tanggal_sekarang)
def confirm_bell():
"""Memberikan sebuah bunyi sebagai bentuk konfirmasi data telah tercatat oleh sistem."""
bel = winsound
bel.Beep(frequency, duration)
def save_to_database_by_google_API():
"""Menyimpan data orang yang telah terdeteksi ke GOOGLE SHEETS dengan Google API"""
data = [[nama_sekarang, jam_sekarang, tanggal_sekarang]]
request = SERVICE.spreadsheets().values().append(spreadsheetId=SPREADSHEET_ID, range=RANGE_POST,
valueInputOption=INPUT_USER, insertDataOption="INSERT_ROWS",
body={"values": data})
response = request.execute()
# print(response)
# ------------------------------------------------ VARIABLE ----------------------------------------------- #
df_face_encode = pd.read_csv("data/elektro_face_encodings.csv", delimiter=',', header=None)
known_face_encodings = df_face_encode.to_numpy()
data_name = open("data/elektro-nama.csv", "r")
df_name = data_name.read()
df_name = df_name.split("\n")
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
nama_sebelum = ""
unknown = 0
frequency = 800 # Hz
duration = 200 # millisecond
list_nama_berurut = [""]
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(1)
while True:
# Grab a single frame of video
waktu = datetime.now().replace(microsecond=0)
waktu3 = waktu.strftime(f"%A %d/%m/%Y %H:%M:%S")
ret, frame = video_capture.read()
# Only process every other frame of video to save time
if process_this_frame:
# 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]
# 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 = []
# ----------------------------------- MENCARI WAJAH YANG COCOK ------------------------------------ #
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)
nama_sekarang = "Unknown"
# Use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
nama_sekarang = df_name[best_match_index].replace('"', '')
if nama_sekarang == list_nama_berurut[0]:
list_nama_berurut.append(nama_sekarang)
else:
list_nama_berurut = [nama_sekarang]
nama = list_nama_berurut[0]
jumlah_consecutive = list_nama_berurut.count(nama)
try:
if jumlah_consecutive >= 10:
list_nama_berurut = [""]
if nama == nama_sebelum:
pass
else:
nama_sebelum = nama
# ----------------------- SAVE DATA TERDETEKSI TO GOOGLE SHEET ----------------------- #
waktu_deteksi()
# save_to_database_by_sheety()
save_to_database_by_google_API()
# ------------------------------------ BEL CONFIRMS ---------------------------------- #
confirm_bell()
else:
pass
except:
pass
face_names.append(nama_sekarang)
process_this_frame = not process_this_frame
# ----------------------------------------- DISPLAY DINAMIS ------------------------------------------ #
# Display the results
for (top, right, bottom, left), nama_sekarang 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 - 30), (right, bottom), (0, 0, 255), cv2.FILLED)
cv2.putText(frame, nama_sekarang.split()[0], (left + 6, bottom - 8), FONT, 0.65, (255, 255, 255), 1)
# ------------------------------------------ DISPLAY STATIS ------------------------------------------ #
# Setup status box
cv2.rectangle(frame, (0, 0), (640, 40), (0, 0, 0), -1) #(245, 117, 16)
cv2.putText(frame, (waktu3 + " | EKC - ELEKTRO - ITK"), (18, 27),
FONT, 0.58, (255, 255, 255), 1, cv2.LINE_AA)
# Display the resulting image
cv2.imshow('SMART GOVERNANCE - Face Recognition For Access to Public Service', frame)
# 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()