Wanna use tf-pose-estimation?
- install conda
- Run
conda create -n tf15 python tensorflow=1.15 - Follow the guide from: https://github.com/ildoonet/tf-pose-estimation
pip3 install opencv-python==3.4.2.17
| #Recommended minimum configuration: | |
| acl to_localhost dst 127.0.0.0/8 | |
| acl localnet src 0.0.0.0/8 192.168.100.0/24 192.168.101.0/24 | |
| acl SSL_ports port 443 | |
| acl Safe_ports port 80 # http | |
| acl Safe_ports port 21 # ftp | |
| acl Safe_ports port 443 # https | |
| acl Safe_ports port 70 # gopher | |
| acl Safe_ports port 210 # wais | |
| acl Safe_ports port 1025-65535 # unregistered ports |
| $hWnd = WinWait("S207 - Chân Long 2016 - Google Chrome") | |
| WinActivate($hWnd) | |
| ;ControlClick("S207 - Chân Long 2016 - Google Chrome", "", "Chrome_WidgetWin_1", "left", 1, 1047, 563) | |
| $i = 1 |
Wanna use tf-pose-estimation?
conda create -n tf15 python tensorflow=1.15pip3 install opencv-python==3.4.2.17| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>DBMS</title> | |
| <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" integrity="sha384-JcKb8q3iqJ61gNV9KGb8thSsNjpSL0n8PARn9HuZOnIxN0hoP+VmmDGMN5t9UJ0Z" crossorigin="anonymous"> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js" integrity="sha512-bLT0Qm9VnAYZDflyKcBaQ2gg0hSYNQrJ8RilYldYQ1FxQYoCLtUjuuRuZo+fjqhx/qtq/1itJ0C2ejDxltZVFg==" crossorigin="anonymous"></script> | |
| <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js" integrity="sha384-B4gt1jrGC7Jh4AgTPSdUtOBvfO8shuf57BaghqFfPlYxofvL8/KUEfYiJOMMV+rV" crossorigin="anonymous"></script> | |
| </head> | |
| <body> | |
| <div class="container"> |
| # This file may be used to create an environment using: | |
| # $ conda create --name <env> --file <this file> | |
| # platform: linux-64 | |
| _libgcc_mutex=0.1=main | |
| argon2-cffi=20.1.0=pypi_0 | |
| async-generator=1.10=pypi_0 | |
| attrs=20.3.0=pypi_0 | |
| backcall=0.2.0=pyhd3eb1b0_0 | |
| bleach=3.3.0=pypi_0 | |
| ca-certificates=2021.1.19=h06a4308_0 |
| import numpy as np | |
| Allocation = np.array([ [0, 1, 0], | |
| [2, 0, 0], | |
| [3, 0, 2], | |
| [2, 1, 1], | |
| [0, 0, 2]] | |
| ) | |
| Max = np.array([[7, 5, 3], | |
| [3, 2, 2], |
| <script>alert("it's me!")</script> |
| import numpy as np | |
| import sys | |
| import PySimpleGUI as sg | |
| # Tutorial: https://www.jigsawacademy.com/blogs/cyber-security/hill-cipher/ | |
| def matrix_cofactor(matrix): | |
| return np.linalg.inv(matrix).T * np.linalg.det(matrix) | |
| def egcd(a, b): |
| p = '111000' | |
| # 19b, 21b, 22b | |
| k = '1100110110101101011011001010110001101110011100010011100011101111'#1100110110101101011. 0110010101100011011100.11100010011100011101111 | |
| x = k[:19] | |
| y = k[19:41] | |
| z = k[41:] | |
| s = '' | |
| # print(x, y, z) |
| N = 10 | |
| S = [0]*256 | |
| T = [0]*256 | |
| K = [10, 20, 25, 15, 40, 60, 30, 70, 75, 90] | |
| P = [1, 2, 2, 2] | |
| C = [] | |
| for i in range(0, 256): | |
| S[i] = i | |
| T[i] = K[i % N] |