# Copyright 2016 Niek Temme.
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Predict a handwritten integer (MNIST expert).

Script requires
1) saved model (model2.ckpt file) in the same location as the script is run from.
(requried a model created in the MNIST expert tutorial)
2) one argument (png file location of a handwritten integer)

Documentation at:
http://niektemme.com/ @@to do
"""
import image
import modules
import sys
import tensorflow as tf
from PIL import Image, ImageFilter
import os
import cv2
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


def predictint(imvalue):
"""
 This function returns the predicted integer.
 The imput is the pixel values from the imageprepare() function.
 """

 # Define the model (same as when creating the model file)
 x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# init_op = tf.initialize_all_variables()
 init_op = tf.global_variables_initializer()
saver = tf.train.Saver()

"""
 Load the model2.ckpt file
 file is stored in the same directory as this python script is started
 Use the model to predict the integer. Integer is returend as list.

 Based on the documentatoin at
 https://www.tensorflow.org/versions/master/how_tos/variables/index.html
 """
 with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "model2.ckpt")
#print("Model restored.")

 prediction = tf.argmax(y_conv, 1)
return prediction.eval(feed_dict={x: [imvalue], keep_prob: 1.0}, session=sess)

def imageprepare(argv):
"""
 This function returns the pixel values.
 The imput is a png file location.
 """
 im = Image.open(argv).convert('L')
width = float(im.size[0])
height = float(im.size[1])
newImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels

 if width > height: # check which dimension is bigger
 # Width is bigger. Width becomes 20 pixels.
 nheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width
 if (nheigth == 0): # rare case but minimum is 1 pixel
 nheigth = 1
 # resize and sharpen
 img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wtop = int(round(((28 - nheight) / 2), 0)) # caculate horizontal pozition
 newImage.paste(img, (4, wtop)) # paste resized image on white canvas
 else:
# Height is bigger. Heigth becomes 20 pixels.
 nwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height
 if (nwidth == 0): # rare case but minimum is 1 pixel
 nwidth = 1
 # resize and sharpen
 img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition
 newImage.paste(img, (wleft, 4)) # paste resized image on white canvas

 # newImage.save("sample.png")

 tv = list(newImage.getdata()) # get pixel values

 # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
 tva = [(255 - x) * 1.0 / 255.0 for x in tv]
return tva



imvalue = imageprepare('1.png')
predint = predictint(imvalue)
print(predint[0])



imvalue = imageprepare('2.png')
predint = predictint(imvalue)
print(predint[0])


..


imvalue = imageprepare('n번째 이미지.png')
predint = predictint(imvalue)
print(predint[0])



이거 왜 오류 나는 걸까요?ㅠ








####












NotFoundError (see above for traceback): Tensor name "Variable_10" not found in checkpoint files model2.ckpt

[[Node: save_1/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save_1/Const_0_0, save_1/RestoreV2/tensor_names, save_1/RestoreV2/shape_and_slices)]]




오류는 이거고


mnist 학습시켜서 숫자 검증하는데 이미지 하나는 되는데 2개이상 돌릴라면 오류뜨는데


한 스크립트에서 어캐 하면 돌릴수 있을까요?