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import gradio as gr | |
import cv2 | |
import numpy as np | |
import face_recognition | |
import os | |
import sys | |
from pathlib import Path | |
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 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.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) | |
iface = gr.Interface( | |
fn=greet, | |
inputs=gr.Video(source="webcam", format="mp4", streaming="True"), | |
outputs="image" | |
) | |
iface.launch() | |