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4692a32 aa24660 4692a32 3392900 4692a32 3392900 4692a32 62e52f7 4692a32 3392900 4692a32 3392900 4692a32 3392900 eb619e5 b9a8fc5 eb619e5 b9a8fc5 4692a32 3392900 4692a32 62e52f7 4692a32 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | ####### Section 1 ###################
from PIL import Image
import numpy as np
import cv2
import requests
import dlib
import face_recognition
import os
import streamlit as st
from urllib import request
####### Section 2 ###################
p1 = "sarwan.jpg"
p2 = "rattantata.png"
p3 = "Ravinder.jpg"
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("RattanTata")
img3 = cv2.imread(p3)
Images.append(img3)
classnames.append("RavinderKaur")
# directory = "facerecognition"
# myList = os.listdir(directory)
# for cls in myList:
# if os.path.splitext(cls)[1] in [".jpg", ".jpeg",".png"]:
# img_path = os.path.join(directory, cls)
# curImg = cv2.imread(img_path)
# Images.append(curImg)
# classnames.append(os.path.splitext(cls)[0])
# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
####### Section 3 ###################
# Take picture using the camera
img_file_buffer = st.camera_input("Take Your 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)
image = image.copy()
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)
faceMatchedflag = 0
# 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()
#st.write (name)
# 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)
########## update website
# # using Get Method
# url = "https://fc11.glitch.me/submit?email=pm&message=faceReco&name="
# url = url + name
# st.write(url)
# res = urllib.request.urlopen(url)
# response = requests.post(url )
# # using post method
#url = "https://aimljul24f.glitch.me/save"
# url1 = "/save"
#data = {'rollno': '99','name': name, 'email': 'p@g.com','pwd': '**' }
# response = requests.post(url +url1 , data=data)
# Post Method is invoked if data != None
#req = request.Request(url , method="POST", data=data)
# Response
# resp = request.urlopen(req)
# if response.status_code == 200:
# st.success("Data updated on: " + "https://aimljul24f.glitch.me/")
# else:
# st.warning("Data not updated")
########### end update website
faceMatchedflag = 1
st.image(image , use_column_width=True, output_format="PNG")
if(faceMatchedflag == 0) :
st.warning("No faces detected in the image.")
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