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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.")
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