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import gradio as gr
import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime
def greet(video):
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 findEncoding(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.writelinbes(f'\n{name},{dtString}')
encodeListKnown = findEncodings(images)
print('Encoding Complete')
cap = cv2.VideoCapture(video)
while True:
succes, img = cap.read()
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.FILED)
cv2.putText(img,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),2)
markAttendance(name)
cv2.imshow('Webcam',img)
cv2.waitKey(1)
return gray
iface = gr.Interface(
fn=greet,
inputs=gr.Video(source = "webcam", format = "mp4", streaming = "True"),
outputs="image"
)
iface.launch()
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