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#!/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) |
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<?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> |
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import json | |
from django import forms | |
from django.db import models | |
class JsonField(models.TextField): | |
# object ready to python | |
def to_python(self, value): |
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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): |
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# 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 = "" |
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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 |
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# variable de tipo texto | |
a = "texto" | |
# variable de tipo entero | |
b = 1 | |
# variable de tipo doble | |
c = 1.0 | |
# varibale de tipo boleano |
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mensaje = "hola mundo" | |
iteraciones = 10 | |
mensajes = [] | |
pares = True | |
for iteracion in range(iteraciones): | |
par = iteracion % 2 == 0 |
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[ | |
{ | |
"height": 0.7, | |
"weight": 6.9, | |
"attack": 49, | |
"hp": 45, | |
"defense": 49, | |
"speed": 45, | |
"speedAttack": 65, | |
"speedDefense": 65, |
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