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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('Class Attendance-Face RECOGNITION') | |
| index = st.sidebar.selectbox( | |
| 'Toma lista', | |
| (0, 1, 2) | |
| ) | |
| lista = ["./Video/Josue.mp4", | |
| "./Video/rudy.mp4", "./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}") | |
| st.success(name) | |
| cv2.imshow('Webcam', img) | |
| cv2.waitKey() | |