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%matplotlib inline
import os
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

from ipywidgets import widgets,interact
from IPython.display import clear_output

import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""


    

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dane = "/DATA/shared/datasets/cifar10/cifar10_test.tfrecord"
dane_train = "/DATA/shared/datasets/cifar10/cifar10_train.tfrecord"


    

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label2txt = ["airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck" ]


    

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def read_data(filename_queue):
    reader = tf.TFRecordReader()
    _, se = reader.read(filename_queue)
    f = tf.parse_single_example(se,features={'image/encoded':tf.FixedLenFeature([],tf.string),
                                            'image/class/label':tf.FixedLenFeature([],tf.int64),
                                            'image/height':tf.FixedLenFeature([],tf.int64),
                                            'image/width':tf.FixedLenFeature([],tf.int64)})
    image = tf.image.decode_png(f['image/encoded'],channels=3)
    image.set_shape( (32,32,3) ) 
    return image,f['image/class/label']


    

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fq = tf.train.string_input_producer([dane_train])
image_data, label = read_data(filename_queue=fq)


    

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batch_size = 128
images, sparse_labels = tf.train.shuffle_batch( [image_data,label],batch_size=batch_size,
                                               num_threads=2,
                                               capacity=1000+3*batch_size,
                                               min_after_dequeue=1000
                                               )
images = (tf.cast(images,tf.float32)-128.0)/33.0


    

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config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=config)
tf.global_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)


    

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im,l = sess.run([images, sparse_labels])


    

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@interact(ith=widgets.IntSlider(max=im.shape[0]))
def show_ith(ith):
    clear_output(wait=True)
    plt.imshow((128+33*im[ith,:,:,:]).astype(np.uint8))
    plt.title(label2txt[l[ith]])
    plt.show()


    

    




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