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Update path to model files when restoring to be relative #13

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Update path to model files when restoring to be relative #13

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22 changes: 11 additions & 11 deletions predict_1.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright 2016 Niek Temme.
# 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.
Expand Down Expand Up @@ -34,7 +34,7 @@ def predictint(imvalue):
This function returns the predicted integer.
The input 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]))
Expand All @@ -43,7 +43,7 @@ def predictint(imvalue):

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

"""
Load the model.ckpt file
file is stored in the same directory as this python script is started
Expand All @@ -54,9 +54,9 @@ def predictint(imvalue):
"""
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "model.ckpt")
saver.restore(sess, "./model.ckpt")
#print ("Model restored.")

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

Expand All @@ -70,7 +70,7 @@ def imageprepare(argv):
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
Expand All @@ -81,21 +81,21 @@ def imageprepare(argv):
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.
#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]
tva = [ (255-x)*1.0/255.0 for x in tv]
return tva
#print(tva)

Expand All @@ -106,6 +106,6 @@ def main(argv):
imvalue = imageprepare(argv)
predint = predictint(imvalue)
print (predint[0]) #first value in list

if __name__ == "__main__":
main(sys.argv[1])
54 changes: 27 additions & 27 deletions predict_2.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright 2016 Niek Temme.
# 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.
Expand Down Expand Up @@ -34,56 +34,56 @@ 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')
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()
saver = tf.train.Saver()

"""
Load the model2.ckpt file
file is stored in the same directory as this python script is started
Expand All @@ -94,9 +94,9 @@ def max_pool_2x2(x):
"""
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "model2.ckpt")
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)

Expand All @@ -110,32 +110,32 @@ def imageprepare(argv):
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
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.
#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]
tva = [ (255-x)*1.0/255.0 for x in tv]
return tva
#print(tva)

Expand All @@ -146,6 +146,6 @@ def main(argv):
imvalue = imageprepare(argv)
predint = predictint(imvalue)
print (predint[0]) #first value in list

if __name__ == "__main__":
main(sys.argv[1])