radezet

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

radezet

import os
from glob import glob
from datetime import datetime
from shutil import copyfile

import imgaug as ia
from imgaug import augmenters as iaa
from scipy.misc import imsave, imread

INPUT = 'test'

radezet

import os
import random
from glob import glob
from PIL import Image

INPUT = 'flower_split'
VAL_NUMBER = 4

def get_files(path):
    dirs = [x[0] for x in os.walk(path)][1:]

radezet

import os
from glob import glob
from datetime import datetime
from shutil import copyfile

import imgaug as ia
from imgaug import augmenters as iaa
from scipy.misc import imsave, imread

INPUT = 'flower_split'

radezet

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter

train=pd.read_csv('run_train,tag_accuracy_1.csv', sep=',', header=0)
val=pd.read_csv('run_validation,tag_accuracy_1.csv', sep=',', header=0)


def annot_max(x, y, ax=None):

radezet

import os
import csv

from PIL import Image

output = 'flower_split'

with open('flower_labels.csv') as csvfile:
    label_reader = csv.reader(csvfile, delimiter=',')
    next(label_reader)  # skipping header

radezet

import numpy as np
from PIL import Image

for i in range(100):
    noise = np.random.rand(28, 28, 3) * 255
    noise_img = Image.fromarray(noise.astype('uint8'))
    noise_img.save(str(i) + '.jpg')