from PIL import Image import numpy as np import cv2 import requests import face_recognition import os import streamlit as st p1 = "sarwan.jpg" p2 = "ravinder.jpeg" st.title("Face Recognition ") Images = [] classnames = [] # read images and train the face_recognition package img1 = cv2.imread(p1) Images.append(img1) classnames.append("Sarwan") img2 = cv2.imread(p2) Images.append(img2) classnames.append("Ravinder") # Load images for face recognition encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images] # take image from user # Take picture using the camera img_file_buffer = st.camera_input("Take a picture") # recognise the face in the uploaded image 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) # run looop to find match in encodeListknown list for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame): # Assuming that encodeListknown is defined and populated in your code 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() # show the name on image to user 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) # display_image_with_overlay(image, name) st.image(image, use_column_width=True, output_format="PNG") else : st.warning("No faces detected in the image.")