In [6]:

    

!pip install opencv-python wget matplotlib
!apt update && apt install -y libsm6 libxext6 libgtk2.0-dev


    

    




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Requirement already satisfied: wget in /usr/local/lib/python2.7/dist-packages (3.2)
Requirement already satisfied: matplotlib in /usr/local/lib/python2.7/dist-packages (2.2.3)
Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python2.7/dist-packages (from opencv-python) (1.15.2)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2.2.1)
Requirement already satisfied: backports.functools-lru-cache in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.5)
Requirement already satisfied: subprocess32 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (3.5.2)
Requirement already satisfied: pytz in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2018.5)
Requirement already satisfied: six>=1.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.11.0)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2.7.3)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.0.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (0.10.0)
Requirement already satisfied: setuptools in /usr/local/lib/python2.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (39.1.0)
You are using pip version 18.0, however version 18.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Get:1 file:/var/nvinfer-runtime-trt-repo-4.0.1-ga-cuda9.0  InRelease
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Reading package lists... Done[0m
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15 packages can be upgraded. Run 'apt list --upgradable' to see them.
Reading package lists... Done
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libsm6 is already the newest version (2:1.2.2-1).
libxext6 is already the newest version (2:1.3.3-1).
libgtk2.0-dev is already the newest version (2.24.30-1ubuntu1.16.04.2).
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In [7]:

    

import numpy as np
import cv2
import cPickle
import matplotlib.pyplot as plt


def extractImagesAndLabels(path, file):
    f = open(path+file, 'rb')
    dict = cPickle.load(f)
    images = dict['data']
    images = np.reshape(images, (10000, 3, 32, 32))
    labels = dict['labels']
    return images, labels

def extractCategories(path, file):
    f = open(path+file, 'rb')
    dict = cPickle.load(f)
    return dict['label_names']

def saveCifarImage(array, path, file):
    # array is 3x32x32. cv2 needs 32x32x3
    array = array.asnumpy().transpose(1,2,0)
    # array is RGB. cv2 needs BGR
    array = cv2.cvtColor(array, cv2.COLOR_RGB2BGR)
    # save to PNG file
    return cv2.imwrite(path+file+".png", array)

images, labels = extractImagesAndLabels("../data/CIFAR-10/cifar-10-batches-py/", "test_batch")
categories = extractCategories("../data/CIFAR-10/cifar-10-batches-py/", "batches.meta")

def getImage(images, id):
    image = images[id]
    image = image.transpose([1, 2, 0])
    image = image.astype('float32')
    image /= 255
    return image

imgid=35
image = getImage(images, imgid)
%matplotlib inline
imgplot = plt.imshow(image)
categoryid = labels[imgid]
print(categories[categoryid])


#ret=saveCifarImage(imgarray[imgid], "../data/CIFAR-10/", "image"+(str)(imgid))


    

    




bird






    

In [8]:

    

from keras.datasets import cifar10
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_test shape:', X_test.shape)
print(X_test.shape[0], 'testing samples')
image=X_test[0]
image = image.astype('float32')
image /= 255
%matplotlib inline
imgplot = plt.imshow(image)


    

    




('X_test shape:', (10000, 32, 32, 3))
(10000, 'testing samples')






    

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