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import streamlit as st
import pandas as pd
import numpy as np
import cv2
import face_recognition
import os
import sys
from pathlib import Path
from datetime import datetime
st.title('Face RECOGNITION')
index = st.sidebar.selectbox(
'Toma lista',
(0, 1, 2)
)
lista = ["/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/Josue.mp4",
"/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/rudy.mp4", "/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/video.mp4"]
st.write(f'You selected: {lista[index]}')
path = "ImagesAttendance"
images = []
classNames = []
myList = os.listdir(path)
print(myList)
for cl in myList:
curImg = cv2.imread(f'{path}/{cl}')
images.append(curImg)
classNames.append(os.path.splitext(cl)[0])
print(classNames)
def findEncodings(images):
encodeList = []
for img in images:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
encode = face_recognition.face_encodings(img)[0]
encodeList.append(encode)
return encodeList
def markAttendance(name):
with open('Attendance.csv', 'r+') as f:
myDataList = f.readlines()
nameList = []
for line in myDataList:
entry = line.split(',')
nameList.append(entry[0])
if name not in nameList:
now = datetime.now()
dtString = now.strftime('%H:%M:%S')
f.writelines(f'\n{name},{dtString}, {now}')
encodeListKnown = findEncodings(images)
print('Encoding Complete')
# Videos sections
# Rudys one /Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/vid.mp4
videoLoaded = (
lista[index])
video_file = open(
videoLoaded, 'rb')
video_bytes = video_file.read()
st.video(video_bytes)
cap = cv2.VideoCapture(videoLoaded)
while True:
success, img = cap.read()
if success == False:
print("No image")
break
imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
#imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2GRAY)
facesCurFrame = face_recognition.face_locations(imgS)
encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
matches = face_recognition.compare_faces(
encodeListKnown, encodeFace)
faceDis = face_recognition.face_distance(
encodeListKnown, encodeFace)
matchIndex = np.argmin(faceDis)
if matches[matchIndex]:
name = classNames[matchIndex].upper()
y1, x2, y2, x1 = faceLoc
y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(img, (x1, y2-35), (x2, y2), (0, 255, 0), cv2.FILLED)
cv2.putText(img, name, (x1+6, y2-6),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
markAttendance(name)
print(name)
st.error(f"Lista de alumnos {classNames}", icon="🚨")
st.success(name, icon="✅")
cv2.imshow('Webcam', img)
cv2.waitKey()
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