sachadee

<script src="https://cdnjs.cloudflare.com/ajax/libs/crypto-js/4.1.1/crypto-js.min.js"></script>
<script>
//JS FIX IV ENCRYPTION CBC
var message = 'I love Medium';
var key ='AAAAAAAAAAAAAAAA'//key used in Python
key = CryptoJS.enc.Utf8.parse(key);
var iv = CryptoJS.enc.Utf8.parse('BBBBBBBBBBBBBBBB')
var encrypted = CryptoJS.AES.encrypt(message, key, { iv: iv, mode: CryptoJS.mode.CBC});
encrypted =encrypted.toString();
console.log('encrypted',encrypted );

sachadee

<script src="https://cdnjs.cloudflare.com/ajax/libs/crypto-js/4.1.1/crypto-js.min.js"></script>
<script>
 var encrypted ='gfp6wzvTH3lN5TO2B37yWQ=='; //python is base64 ECB
 var key ='AAAAAAAAAAAAAAAA'//key used in Python
 key = CryptoJS.enc.Utf8.parse(key); 
 var decrypted =  CryptoJS.AES.decrypt(encrypted, key, {mode:CryptoJS.mode.ECB});
 console.log(decrypted.toString(CryptoJS.enc.Utf8));
</script>

sachadee

<script src="https://cdnjs.cloudflare.com/ajax/libs/crypto-js/4.1.1/crypto-js.min.js"></script>
<script>
//PYTHON FIX IV ENCRYPTION AND PYTHON FIX IV
var Base64CBC ='VEX7Eequ5TM9+jlgrwnkNw==';
var iv = CryptoJS.enc.Utf8.parse('BBBBBBBBBBBBBBBB');
//PYTHON RANDOM IV ENCRYPTION AND PYTHON RANDOM IV
//var Base64CBC ='uJrS9Zp1R5WjOEUkSK9clQ==';
//var iv = CryptoJS.enc.Base64.parse('l5I5Toqn5RoX0JfTLQB9Pw==');
var key ='AAAAAAAAAAAAAAAA'//key used in Python
 key = CryptoJS.enc.Utf8.parse(key);

sachadee

import base64 
from Crypto.Cipher import AES
from Crypto.Util.Padding import pad,unpad

#AES ECB mode without IV

data = 'I love Medium'
key = 'AAAAAAAAAAAAAAAA' #Must Be 16 char for AES128

def encrypt(raw):

sachadee

import base64 
from Crypto.Cipher import AES
from Crypto.Util.Padding import pad,unpad

#CBC with Fix IV

data = 'I love Medium'
key = 'AAAAAAAAAAAAAAAA' #16 char for AES128

#FIX IV

sachadee

import base64
from Crypto.Cipher import AES
from Crypto.Util.Padding import pad,unpad
from Crypto.Random import get_random_bytes

#CBC mode with random IV

data = 'I love Medium'
key = 'AAAAAAAAAAAAAAAA' #16 char for AES128

sachadee

import torch
import numpy as np
import cv2
import torchvision.transforms as transforms
from PIL import Image 

# Read the image with openCV
# image = cv2.imread('sachadee.jpg')

# Convert BGR image to RGB image

sachadee

#Base Image
print("Image Dtype:", np.array(image).dtype)
print("Image shape:", np.array(image).shape)

print('***********')

#Tensor image
print("Tensor Dtype:", tensor.dtype)
print("Tensor shape:", tensor.shape)

sachadee

tensor_to_pil = transforms.ToPILImage()(tensor)
tensor_to_pil.show()

sachadee

# get embedding for each face
    embeddings = np.zeros([len(loader.dataset), embedding_size])
    with torch.no_grad():
        for idx, (image, _) in enumerate(
            tqdm(loader, desc="Create embeddings matrix", total=len(loader)),
        ):
            print(idx)
            embeddings[idx, :] = F.normalize(backbone(image.to(device))).cpu()
            tensor_to_pil = transforms.ToPILImage()(image.squeeze_(0)).convert('RGB')
            tensor_to_pil.show()