carpedm20

import hashlib
import json
import os
import time
from concurrent.futures import Future, ThreadPoolExecutor
from functools import lru_cache
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional

import requests

carpedm20

# {ai/scripts/head/head_reducer.py}

"""
Head size adjustment based on head-to-body ratio

Pipeline
1) Person pose with YOLOv8 to estimate full body height in pixels
2) Head box with MediaPipe Face Detection
3) Compute shrink/enlarge factor from target head count (or ratio)
4) Apply smooth elliptical radial warp

carpedm20

https://docs.google.com/presentation/d/1EgG3jmUwJjZb64_U-x-yWZCJqajXwsQX/edit?usp=sharing&ouid=113640288960877923196&rtpof=true&sd=true

carpedm20

Intercepts HTTPs Traffic with Python & mitmproxy

Introduction

Modern applications usually make use of back-end API servers to provide their services. With a non-transparent HTTPs proxy, which intercepts the communication between clients and servers (aka the man-in-the-middle scheme), you can easily manipulate both API requests and responses.

This manual helps you create your own proxy with Python and mitmproxy/libmproxy. Mitmproxy ships with both a standalone command-line tool (mitmproxy) and a Python library (libmproxy).

carpedm20

#-*- coding: utf-8 -*-
import json
import requests

from tqdm import tqdm


DIST = 0.01

HEADERS = {

carpedm20

#!/bin/sh
image_path=/home/carpedm20/imagenet
for name in $image_path/val/*.JPEG; do
  convert -resize 256x256\! $name $name
done

carpedm20

FROM dockerfile/ubuntu
MAINTAINER Taehoon Kim(carpedm20@gmail.com)

# Run upgrades
RUN apt-get update

# Install basic packages
RUN apt-get -qq -y install git curl build-essential openssl libssl-dev python-software-properties g++ make 

# Install Python

carpedm20

NAF-tensorflow

First, clone the project:

git clone https://github.com/devsisters/NAF-tensorflow/
cd NAF-tensorflow

To train a model for Pendulum:

python main.py --env_name=Pendulum-v0 --is_train=True

carpedm20

DQN-tensorflow

First, clone the project:

git clone https://github.com/devsisters/DQN-tensorflow/
cd DQN-tensorflow

To train a model for Breakout:

python main.py --env_name=Breakout-v0 --is_train=True

carpedm20

import requests

r = requests.get("http://naver.com")
html = r.text

idx = html.find("<title>")
print html[idx:idx+20]