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('Face RECOGNITION') index = st.sidebar.selectbox( 'Toma lista', (0, 1, 2) ) lista = ["/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/Josue.mp4", "/Users/hectorgonzalez/Documents/CLOUD/streamlit/Video/rudy.mp4", "/Users/hectorgonzalez/Documents/CLOUD/streamlit/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}", icon="🚨") st.success(name, icon="✅") cv2.imshow('Webcam', img) cv2.waitKey()