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| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| import requests | |
| import face_recognition | |
| import os | |
| from datetime import datetime | |
| import streamlit as st | |
| # Set page title and description | |
| st.set_page_config( | |
| page_title="Attendance System Using Face Recognition", | |
| page_icon="📷", | |
| layout="centered", | |
| initial_sidebar_state="collapsed" | |
| ) | |
| st.title("Attendance System Using Face Recognition 📷") | |
| st.markdown("This app recognizes faces in an image and updates attendance records with current timestamp & Location.") | |
| # Load images for face recognition | |
| Images = [] | |
| classnames = [] | |
| directory = "photos" | |
| myList = os.listdir(directory) | |
| for cls in myList: | |
| if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]: | |
| img_path = os.path.join(directory, cls) | |
| curImg = cv2.imread(img_path) | |
| Images.append(curImg) | |
| classnames.append(os.path.splitext(cls)[0]) | |
| 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 | |
| encodeListknown = findEncodings(Images) | |
| # Take picture using the camera | |
| img_file_buffer = st.camera_input("Take a picture") | |
| if img_file_buffer is not None: | |
| test_image = Image.open(img_file_buffer) | |
| image = np.asarray(test_image) | |
| imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25) | |
| imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB) | |
| facesCurFrame = face_recognition.face_locations(imgS) | |
| encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame) | |
| name = "Unknown" # Default name for unknown faces | |
| if len(encodesCurFrame) > 0: | |
| 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(image, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED) | |
| cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2) | |
| if name != "Unknown": | |
| url = "https://mrvishal7705.000webhostapp.com" | |
| url1 = "/update.php" | |
| data1 = {'name': name} | |
| response = requests.post(url + url1, data=data1) | |
| if response.status_code == 200: | |
| st.success("Data updated on: " + url) | |
| else: | |
| st.warning("Data not updated") | |
| # Apply styling with CSS | |
| st.markdown('<style>img { animation: pulse 2s infinite; }</style>', unsafe_allow_html=True) | |
| st.image(image, use_column_width=True, output_format="PNG") | |
| if name == "Unknown": | |
| st.info("Face not detected. Please try again.") |