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from sklearn.neighbors import KNeighborsClassifier | |
import cv2 | |
import pickle | |
import numpy as np | |
import os | |
import csv | |
import time | |
from datetime import datetime | |
from flask import Flask, render_template, request | |
from win32com.client import Dispatch | |
def speak(str1): | |
speak=Dispatch(("SAPI.SpVoice")) | |
speak.Speak(str1) | |
facedetect=cv2.CascadeClassifier('data/haarcascade_frontalface_default.xml') | |
with open('data/names.pkl', 'rb') as w: | |
LABELS=pickle.load(w) | |
with open('data/faces_data.pkl', 'rb') as f: | |
FACES=pickle.load(f) | |
# print('Shape of Faces matrix --> ', FACES.shape) | |
knn=KNeighborsClassifier(n_neighbors=5) | |
knn.fit(FACES, LABELS) | |
COL_NAMES = ['NAME', 'TIME'] | |
# def take_attendence(): | |
# ret,frame=video.read() | |
# gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
# faces=facedetect.detectMultiScale(gray, 1.3 ,5) | |
# for (x,y,w,h) in faces: | |
# crop_img=frame[y:y+h, x:x+w, :] | |
# resized_img=cv2.resize(crop_img, (50,50)).flatten().reshape(1,-1) | |
# output=knn.predict(resized_img) | |
# ts=time.time() | |
# date=datetime.fromtimestamp(ts).strftime("%d-%m-%Y") | |
# timestamp=datetime.fromtimestamp(ts).strftime("%H:%M-%S") | |
# exist=os.path.isfile("Attendance/Attendance_" + date + ".csv") | |
# cv2.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 1) | |
# cv2.rectangle(frame,(x,y),(x+w,y+h),(50,50,255),2) | |
# cv2.rectangle(frame,(x,y-40),(x+w,y),(50,50,255),-1) | |
# cv2.putText(frame, str(output[0]), (x,y-15), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1) | |
# cv2.rectangle(frame, (x,y), (x+w, y+h), (50,50,255), 1) | |
# attendance=[str(output[0]), str(timestamp)] | |
# speak("Attendance Taken..") | |
# if exist: | |
# with open("Attendance/Attendance_" + date + ".csv", "+a") as csvfile: | |
# writer=csv.writer(csvfile) | |
# writer.writerow(attendance) | |
# csvfile.close() | |
# else: | |
# with open("Attendance/Attendance_" + date + ".csv", "+a") as csvfile: | |
# writer=csv.writer(csvfile) | |
# writer.writerow(COL_NAMES) | |
# writer.writerow(attendance) | |
# csvfile.close() | |
# # if k==ord('q'): | |
# # break | |
# # video.release() | |
# # cv2.destroyAllWindows() | |
app = Flask(__name__) | |
def index(): | |
return render_template('index.html') | |
def atten(): | |
return render_template('atten.html') | |
def take_attendance(): | |
video=cv2.VideoCapture(0) | |
ret, frame = video.read() | |
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
faces = facedetect.detectMultiScale(gray, 1.3, 5) | |
for (x,y,w,h) in faces: | |
crop_img=frame[y:y+h, x:x+w, :] | |
resized_img=cv2.resize(crop_img, (50,50)).flatten().reshape(1,-1) | |
output=knn.predict(resized_img) | |
ts=time.time() | |
date=datetime.fromtimestamp(ts).strftime("%d-%m-%Y") | |
timestamp=datetime.fromtimestamp(ts).strftime("%H:%M-%S") | |
exist=os.path.isfile("Attendance/Attendance_" + date + ".csv") | |
cv2.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 1) | |
cv2.rectangle(frame,(x,y),(x+w,y+h),(50,50,255),2) | |
cv2.rectangle(frame,(x,y-40),(x+w,y),(50,50,255),-1) | |
cv2.putText(frame, str(output[0]), (x,y-15), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1) | |
cv2.rectangle(frame, (x,y), (x+w, y+h), (50,50,255), 1) | |
attendance=[str(output[0]), str(timestamp)] | |
speak("Attendance Taken..") | |
if exist: | |
with open("Attendance/Attendance_" + date + ".csv", "+a") as csvfile: | |
writer=csv.writer(csvfile) | |
writer.writerow(attendance) | |
csvfile.close() | |
else: | |
with open("Attendance/Attendance_" + date + ".csv", "+a") as csvfile: | |
writer=csv.writer(csvfile) | |
writer.writerow(COL_NAMES) | |
writer.writerow(attendance) | |
csvfile.close() | |
video.release() | |
return "Attendance taken successfully!" | |
if __name__ == '__main__': | |
app.run(debug=True) |