erickdsama

with open('Downloads/problem_1_data.txt', 'r') as content_file:
    import re
    content = content_file.read()
    data = []
    init = 0
    while True:
        person = {}
        person["name"] = content[init:init+10].strip()
        person["middlename"] = content[init+10:init+20].strip()
        init_street = init+50

erickdsama

# coding=utf-8
import json
import time 

from subprocess import check_output, CalledProcessError

class WHO:
    FROM_ME = "key_from_me == 1"
    OTHERS = "key_from_me != 1"
    ALL = ""

erickdsama

import cv2 
from pylibdmtx.pylibdmtx import decode
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import imutils
cap = cv2.VideoCapture(0) 
id = 1

def filtros_imagen(img):

erickdsama

import json

from django import forms
from django.db import models


class JsonField(models.TextField):

    # object ready to python
    def to_python(self, value):

erickdsama

<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
  <Document>
    <name>ViveBus</name>
    <Style id="icon-1899-DB4436-nodesc-normal">
      <IconStyle>
        <color>ff3644db</color>
        <scale>1</scale>
        <Icon>
          <href>http://www.gstatic.com/mapspro/images/stock/503-wht-blank_maps.png</href>

erickdsama

#!/usr/bin/python

# Import the required modules
import cv2, os
import numpy as np
from PIL import Image
import time
# For face detection we will use the Haar Cascade provided by OpenCV.
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)