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import numpy as np
import cv2
import requests
import face_recognition
import os
from datetime import datetime
import streamlit as st
Images = []
classnames = []
myList = os.listdir()
for cls in myList:
if os.path.splitext(cls)[1] == ".jpg":
curImg = cv2.imread(f'{cls}')
Images.append(curImg)
classnames.append(os.path.splitext(cls)[0])
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
encodeListknown = findEncodings(Images)
st.write('Encoding Complete')
cap = cv2.VideoCapture(0) # Use 0 for the default camera
while True:
ret, frame = cap.read()
frame = cv2.flip(frame, 1) # Flip horizontally
st.image(frame)
if ret:
imgS = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
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]
st.write(name)
y1, x2, y2, x1 = faceLoc
y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(frame, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
cv2.putText(frame, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
# Sending data to a URL
url = "https://kiwi-whispering-plier.glitch.me/update"
data1 = {'name': name}
response = requests.post(url, data=data1)
if response.status_code == 200:
st.write("Data updated on: " + url)
else:
st.write("Data not updated")
st.image(image)
if bytes_data is None:
st.stop()
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