from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf
from random import randint
import numpy
import cv2
from matplotlib import pyplot as plt
from PIL import Image
from numpy import array
import scipy.misc
import webbrowser

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for _ in range(4000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
prediction=tf.argmax(y,1)

num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]
#print ((img))
print ("    ")
print ("    ")
imagePath = 'C:\\Users\\dabes\\Desktop\\TensorFlow\\NIST_data\\0\\hsf_0\\hsf_0_00014.png'
#imagePath='C:\\Users\\dabes\Desktop\\TensorFlow\\TensorFlow#3TestImage.png'
#x_data = cv2.imread(imagePath)
img = Image.open(imagePath).convert('F')
print (img)
print ("    ")
img2 = numpy.array(img)
print (img2)
print ("    ")
x_data = scipy.misc.imresize(img2, (28,28))
print ("    ")
print (x_data)
x_data = x_data.flatten()
print ("    ")
print (x_data)
print ("    ")
print ("predictions", prediction.eval(feed_dict={x: [x_data]}, session=sess))
