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import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime
import gradio as gr
path = 'images'
images = []
personNames = []
myList = os.listdir(path)
unkownEncodings=[]
print(myList)
for cu_img in myList:
current_Img = cv2.imread(f'{path}/{cu_img}')
images.append(current_Img)
personNames.append(os.path.splitext(cu_img)[0])
print(personNames)
def faceEncodings(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 = faceEncodings(images)
print('All Encodings Complete!!!')
def Attandance(video):
cap = cv2.VideoCapture("messi-ronaldo-fb.jpg")
index=1
while True:
#try:
ret, frame = cap.read()
faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
faces = cv2.cvtColor(faces, cv2.COLOR_BGR2RGB)
facesCurrentFrame = face_recognition.face_locations(faces)
encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
# print(faceDis)
matchIndex = np.argmin(faceDis)
if matches[matchIndex]:
name = personNames[matchIndex].upper()
names.append(name)
if cv2.waitKey(1) == 2:
break
return ''.join(name)
demo=gr.Interface(fn=Attandance,
inputs="video",
outputs="text",
title="Face Attendance",
)
demo.launch(debug=True)
print(len(unkownEncodings))
cap.release()
cv2.destroyAllWindows()
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