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from keras.models import load_model
from PIL import Image
import numpy as np
import cv2
import requests
import face_recognition
import os
from datetime import datetime
#the following are to do with this interactive notebook code
from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
import pylab # this allows you to control figure size
pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
import io
import streamlit as st
bytes_data=None
Images = []
classnames = []
myList = os.listdir()
#st.write(myList)
for cls in myList:
if os.path.splitext(cls)[1] == ".jpg" :
curImg = cv2.imread(f'{cls}')
Images.append(curImg)
classnames.append(os.path.splitext(cls)[0])
st.write(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
encodeListknown = findEncodings(Images)
st.write('Encoding Complete')
img_file_buffer=st.camera_input("Take a picture")
if img_file_buffer is not None:
test_image = Image.open(img_file_buffer)
st.image(test_image, use_column_width=True)
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)
for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):
matches = face_recognition.compare_faces(encodeListknown,encodeFace)
faceDis = face_recognition.face_distance(encodeListknown,encodeFace)
#print(faceDis)
matchIndex = np.argmin(faceDis)
if matches[matchIndex]:
name = classnames[matchIndex]
st.write(name)
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)
##############
url = "https://kiwi-whispering-plier.glitch.me/update"
data = {
'name': name,
}
response = requests.get(url, params=data)
if response.status_code == 200 :
st.write(" data updated on : https://kiwi-whispering-plier.glitch.me" )
else : st.write("data not updated ")
##############################
st.image(image)
if bytes_data is None:
st.stop()
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