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# NOTAS USO:

# build
# docker build -t spat_index_r .

# run (abrir entorno docker en modo interactivo)
# se asume que Datos_Andres esta en la carpeta actual y tiene los scripts a ejecutar
# docker run -it -v $(pwd)/Datos_Andres:/Datos_Andres --rm spat_index_r bash

# run script 

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import time
import numpy as np
import random
import struct
import socket
import sys
import os
import sys
from collections import namedtuple

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from sklearn.datasets import load_digits
import tensorflow as tf
import time
import numpy as np
from tensorflow.python import debug as tf_debug

# From http://adventuresinmachinelearning.com/tensorflow-dataset-tutorial/

class timeit:
    def __enter__(self):

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-- pastebin get 39pZ9DBg gistget
-- gistget get https://gist.github.com/aferral/554c1234e21309f985e45ab1f6cd7047
-- floppy debe estar a la izquierda
 
fuelSlot = 4
 
-- Revisa cantidad de cofres, turtle, levers, combustible
leverSlot = 1
turtleSlot = 2
chestSlot = 3

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import sys
import numpy as np
import tensorflow as tf
from datetime import datetime

device_name = sys.argv[1]  # Choose device from cmd line. Options: gpu or cpu
shape = (int(sys.argv[2]), int(sys.argv[2]))
if device_name == "gpu":
    device_name = "/gpu:0"
else:

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import cv2
import numpy as np
import os
import pickle

#----------------------INICIO PARAMETROS

imagesFolder = "C:/Users/andres/Desktop/CrackImages/CleanCracks"
dataOutFolder = "C:/Users/andres/Desktop/CrackImages/saved"
imageTypes = ['jpeg']