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amaz_augumentationCustom.py
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import urllib.request
import tarfile
from os import system
import os
import sys
import cv2
import six
import pickle
from tqdm import tqdm
import numpy as np
import random
import amaz_augumentationPicture
augumentation = amaz_augumentationPicture.AugumentationPicture()
class Normalize32(object):
@staticmethod
def train(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(32,36))
res = augumentation.crop_random(res,(32,32))
res = augumentation.normalize(res,value=10.)
res = augumentation.flip_horizontal(res,0.5)
res = augumentation.convert_to_chainerVariable(res)
return res
@staticmethod
def test(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(32,32))
res = augumentation.normalize(res,value=10.)
res = augumentation.convert_to_chainerVariable(res)
return res
class Normalize64(object):
@staticmethod
def train(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(64,68))
res = augumentation.crop_random(res,(64,64))
res = augumentation.normalize(res,value=10.)
res = augumentation.flip_horizontal(res,0.5)
res = augumentation.convert_to_chainerVariable(res)
return res
@staticmethod
def test(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(64,64))
res = augumentation.normalize(res,value=10.)
res = augumentation.convert_to_chainerVariable(res)
return res
class Normalize128(object):
@staticmethod
def train(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(128,132))
res = augumentation.crop_random(res,(128,128))
res = augumentation.normalize(res,value=10.)
res = augumentation.flip_horizontal(res,0.5)
res = augumentation.convert_to_chainerVariable(res)
return res
@staticmethod
def test(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(128,128))
res = augumentation.normalize(res,value=10.)
res = augumentation.convert_to_chainerVariable(res)
return res
class Normalize224(object):
@staticmethod
def train(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(224,230))
res = augumentation.crop_random(res,(224,224))
res = augumentation.normalize(res,value=10.)
res = augumentation.flip_horizontal(res,0.5)
res = augumentation.convert_to_chainerVariable(res)
return res
@staticmethod
def test(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(224,224))
res = augumentation.normalize(res,value=10.)
res = augumentation.convert_to_chainerVariable(res)
return res
class Normalize448(object):
@staticmethod
def train(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(448,452))
res = augumentation.crop_random(res,(448,448))
res = augumentation.normalize(res,value=10.)
res = augumentation.flip_horizontal(res,0.5)
res = augumentation.convert_to_chainerVariable(res)
return res
@staticmethod
def test(X):
res = augumentation.convert_to_imgAry(X)
res = augumentation.resize(res,(448,448))
res = augumentation.normalize(res,value=10.)
res = augumentation.convert_to_chainerVariable(res)
return res