This commit is contained in:
deeppomf 2018-02-10 22:19:48 -05:00
parent 485d76e710
commit 704ff80bba
10 changed files with 638 additions and 0 deletions

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decensor.py Normal file
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import numpy as np
import tensorflow as tf
from PIL import Image
import tqdm
import os
import matplotlib.pyplot as plt
import sys
sys.path.append('..')
from model import Model
IMAGE_SIZE = 128
LOCAL_SIZE = 64
HOLE_MIN = 24
HOLE_MAX = 48
BATCH_SIZE = 16
image_path = './lfw.npy'
def test():
x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
is_training = tf.placeholder(tf.bool, [])
model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
sess = tf.Session()
init_op = tf.global_variables_initializer()
sess.run(init_op)
saver = tf.train.Saver()
saver.restore(sess, './models/latest')
x_test = np.load(test_npy)
np.random.shuffle(x_test)
x_test = np.array([a / 127.5 - 1 for a in x_test])
step_num = int(len(x_test) / BATCH_SIZE)
cnt = 0
for i in tqdm.tqdm(range(step_num)):
x_batch = x_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
_, mask_batch = get_points()
completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
for i in range(BATCH_SIZE):
cnt += 1
raw = x_batch[i]
raw = np.array((raw + 1) * 127.5, dtype=np.uint8)
masked = raw * (1 - mask_batch[i]) + np.ones_like(raw) * mask_batch[i] * 255
img = completion[i]
img = np.array((img + 1) * 127.5, dtype=np.uint8)
dst = './output/{}.jpg'.format("{0:06d}".format(cnt))
output_image([['Input', masked], ['Output', img], ['Ground Truth', raw]], dst)
def get_points():
points = []
mask = []
for i in range(BATCH_SIZE):
x1, y1 = np.random.randint(0, IMAGE_SIZE - LOCAL_SIZE + 1, 2)
x2, y2 = np.array([x1, y1]) + LOCAL_SIZE
points.append([x1, y1, x2, y2])
w, h = np.random.randint(HOLE_MIN, HOLE_MAX + 1, 2)
p1 = x1 + np.random.randint(0, LOCAL_SIZE - w)
q1 = y1 + np.random.randint(0, LOCAL_SIZE - h)
p2 = p1 + w
q2 = q1 + h
m = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 1), dtype=np.uint8)
m[q1:q2 + 1, p1:p2 + 1] = 1
mask.append(m)
return np.array(points), np.array(mask)
def output_image(images, dst):
fig = plt.figure()
for i, image in enumerate(images):
text, img = image
fig.add_subplot(1, 3, i + 1)
plt.imshow(img)
plt.tick_params(labelbottom='off')
plt.tick_params(labelleft='off')
plt.gca().get_xaxis().set_ticks_position('none')
plt.gca().get_yaxis().set_ticks_position('none')
plt.xlabel(text)
plt.savefig(dst)
plt.close()
if __name__ == '__main__':
test()

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layer.py Normal file
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import tensorflow as tf
def conv_layer(x, filter_shape, stride):
filters = tf.get_variable(
name='weight',
shape=filter_shape,
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),
trainable=True)
return tf.nn.conv2d(x, filters, [1, stride, stride, 1], padding='SAME')
def dilated_conv_layer(x, filter_shape, dilation):
filters = tf.get_variable(
name='weight',
shape=filter_shape,
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),
trainable=True)
return tf.nn.atrous_conv2d(x, filters, dilation, padding='SAME')
def deconv_layer(x, filter_shape, output_shape, stride):
filters = tf.get_variable(
name='weight',
shape=filter_shape,
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),
trainable=True)
return tf.nn.conv2d_transpose(x, filters, output_shape, [1, stride, stride, 1])
def batch_normalize(x, is_training, decay=0.99, epsilon=0.001):
def bn_train():
batch_mean, batch_var = tf.nn.moments(x, axes=[0, 1, 2])
train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))
train_var = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay))
with tf.control_dependencies([train_mean, train_var]):
return tf.nn.batch_normalization(x, batch_mean, batch_var, beta, scale, epsilon)
def bn_inference():
return tf.nn.batch_normalization(x, pop_mean, pop_var, beta, scale, epsilon)
dim = x.get_shape().as_list()[-1]
beta = tf.get_variable(
name='beta',
shape=[dim],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.0),
trainable=True)
scale = tf.get_variable(
name='scale',
shape=[dim],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1),
trainable=True)
pop_mean = tf.get_variable(
name='pop_mean',
shape=[dim],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0),
trainable=False)
pop_var = tf.get_variable(
name='pop_var',
shape=[dim],
dtype=tf.float32,
initializer=tf.constant_initializer(1.0),
trainable=False)
return tf.cond(is_training, bn_train, bn_inference)
def flatten_layer(x):
input_shape = x.get_shape().as_list()
dim = input_shape[1] * input_shape[2] * input_shape[3]
transposed = tf.transpose(x, (0, 3, 1, 2))
return tf.reshape(transposed, [-1, dim])
def full_connection_layer(x, out_dim):
in_dim = x.get_shape().as_list()[-1]
W = tf.get_variable(
name='weight',
shape=[in_dim, out_dim],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1),
trainable=True)
b = tf.get_variable(
name='bias',
shape=[out_dim],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0),
trainable=True)
return tf.add(tf.matmul(x, W), b)

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load.py Normal file
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import os
import numpy as np
def load(dir_='./training_data/npy'):
x_train = np.load(os.path.join(dir_, 'x_train.npy'))
x_test = np.load(os.path.join(dir_, 'x_test.npy'))
return x_train, x_test
if __name__ == '__main__':
x_train, x_test = load()
print(x_train.shape)
print(x_test.shape)

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model.py Normal file
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from layer import *
class Model:
def __init__(self, x, mask, local_x, global_completion, local_completion, is_training, batch_size):
self.batch_size = batch_size
self.imitation = self.generator(x * (1 - mask), is_training)
self.completion = self.imitation * mask + x * (1 - mask)
self.real = self.discriminator(x, local_x, reuse=False)
self.fake = self.discriminator(global_completion, local_completion, reuse=True)
self.g_loss = self.calc_g_loss(x, self.completion)
self.d_loss = self.calc_d_loss(self.real, self.fake)
self.g_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
self.d_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
def generator(self, x, is_training):
with tf.variable_scope('generator'):
with tf.variable_scope('conv1'):
x = conv_layer(x, [5, 5, 3, 64], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv2'):
x = conv_layer(x, [3, 3, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv3'):
x = conv_layer(x, [3, 3, 128, 128], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv4'):
x = conv_layer(x, [3, 3, 128, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv5'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv6'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated1'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated2'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 4)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated3'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 8)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('dilated4'):
x = dilated_conv_layer(x, [3, 3, 256, 256], 16)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv7'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv8'):
x = conv_layer(x, [3, 3, 256, 256], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('deconv1'):
x = deconv_layer(x, [4, 4, 128, 256], [self.batch_size, 64, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv9'):
x = conv_layer(x, [3, 3, 128, 128], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('deconv2'):
x = deconv_layer(x, [4, 4, 64, 128], [self.batch_size, 128, 128, 64], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv10'):
x = conv_layer(x, [3, 3, 64, 32], 1)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv11'):
x = conv_layer(x, [3, 3, 32, 3], 1)
x = tf.nn.tanh(x)
return x
def discriminator(self, global_x, local_x, reuse):
def global_discriminator(x):
is_training = tf.constant(True)
with tf.variable_scope('global'):
with tf.variable_scope('conv1'):
x = conv_layer(x, [5, 5, 3, 64], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv2'):
x = conv_layer(x, [5, 5, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv3'):
x = conv_layer(x, [5, 5, 128, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv4'):
x = conv_layer(x, [5, 5, 256, 512], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv5'):
x = conv_layer(x, [5, 5, 512, 512], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('fc'):
x = flatten_layer(x)
x = full_connection_layer(x, 1024)
return x
def local_discriminator(x):
is_training = tf.constant(True)
with tf.variable_scope('local'):
with tf.variable_scope('conv1'):
x = conv_layer(x, [5, 5, 3, 64], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv2'):
x = conv_layer(x, [5, 5, 64, 128], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv3'):
x = conv_layer(x, [5, 5, 128, 256], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('conv4'):
x = conv_layer(x, [5, 5, 256, 512], 2)
x = batch_normalize(x, is_training)
x = tf.nn.relu(x)
with tf.variable_scope('fc'):
x = flatten_layer(x)
x = full_connection_layer(x, 1024)
return x
with tf.variable_scope('discriminator', reuse=reuse):
global_output = global_discriminator(global_x)
local_output = local_discriminator(local_x)
with tf.variable_scope('concatenation'):
output = tf.concat((global_output, local_output), 1)
output = full_connection_layer(output, 1)
return output
def calc_g_loss(self, x, completion):
loss = tf.nn.l2_loss(x - completion)
return tf.reduce_mean(loss)
def calc_d_loss(self, real, fake):
alpha = 4e-4
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=real, labels=tf.ones_like(real)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=fake, labels=tf.zeros_like(fake)))
return tf.add(d_loss_real, d_loss_fake) * alpha

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import numpy as np
import tensorflow as tf
from PIL import Image
import tqdm
import os
import matplotlib.pyplot as plt
import sys
sys.path.append('..')
from model import Model
IMAGE_SIZE = 128
LOCAL_SIZE = 64
HOLE_MIN = 24
HOLE_MAX = 48
BATCH_SIZE = 16
test_npy = './lfw.npy'
def test():
x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
is_training = tf.placeholder(tf.bool, [])
model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
sess = tf.Session()
init_op = tf.global_variables_initializer()
sess.run(init_op)
saver = tf.train.Saver()
saver.restore(sess, './models/latest')
x_test = np.load(test_npy)
np.random.shuffle(x_test)
x_test = np.array([a / 127.5 - 1 for a in x_test])
step_num = int(len(x_test) / BATCH_SIZE)
cnt = 0
for i in tqdm.tqdm(range(step_num)):
x_batch = x_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
_, mask_batch = get_points()
completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
for i in range(BATCH_SIZE):
cnt += 1
raw = x_batch[i]
raw = np.array((raw + 1) * 127.5, dtype=np.uint8)
masked = raw * (1 - mask_batch[i]) + np.ones_like(raw) * mask_batch[i] * 255
img = completion[i]
img = np.array((img + 1) * 127.5, dtype=np.uint8)
dst = './testing_output_images/{}.jpg'.format("{0:06d}".format(cnt))
output_image([['Input', masked], ['Output', img], ['Ground Truth', raw]], dst)
def get_points():
points = []
mask = []
for i in range(BATCH_SIZE):
x1, y1 = np.random.randint(0, IMAGE_SIZE - LOCAL_SIZE + 1, 2)
x2, y2 = np.array([x1, y1]) + LOCAL_SIZE
points.append([x1, y1, x2, y2])
w, h = np.random.randint(HOLE_MIN, HOLE_MAX + 1, 2)
p1 = x1 + np.random.randint(0, LOCAL_SIZE - w)
q1 = y1 + np.random.randint(0, LOCAL_SIZE - h)
p2 = p1 + w
q2 = q1 + h
m = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 1), dtype=np.uint8)
m[q1:q2 + 1, p1:p2 + 1] = 1
mask.append(m)
return np.array(points), np.array(mask)
def output_image(images, dst):
fig = plt.figure()
for i, image in enumerate(images):
text, img = image
fig.add_subplot(1, 3, i + 1)
plt.imshow(img)
plt.tick_params(labelbottom='off')
plt.tick_params(labelleft='off')
plt.gca().get_xaxis().set_ticks_position('none')
plt.gca().get_yaxis().set_ticks_position('none')
plt.xlabel(text)
plt.savefig(dst)
plt.close()
if __name__ == '__main__':
test()

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import numpy as np
import tensorflow as tf
from PIL import Image
import tqdm
from model import Model
import load
IMAGE_SIZE = 128
LOCAL_SIZE = 64
HOLE_MIN = 24
HOLE_MAX = 48
LEARNING_RATE = 1e-3
BATCH_SIZE = 16
PRETRAIN_EPOCH = 100
def train():
x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
is_training = tf.placeholder(tf.bool, [])
model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
sess = tf.Session()
global_step = tf.Variable(0, name='global_step', trainable=False)
epoch = tf.Variable(0, name='epoch', trainable=False)
opt = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
g_train_op = opt.minimize(model.g_loss, global_step=global_step, var_list=model.g_variables)
d_train_op = opt.minimize(model.d_loss, global_step=global_step, var_list=model.d_variables)
init_op = tf.global_variables_initializer()
sess.run(init_op)
if tf.train.get_checkpoint_state('./models'):
saver = tf.train.Saver()
saver.restore(sess, './models/latest')
x_train, x_test = load.load()
x_train = np.array([a / 127.5 - 1 for a in x_train])
x_test = np.array([a / 127.5 - 1 for a in x_test])
step_num = int(len(x_train) / BATCH_SIZE)
while True:
sess.run(tf.assign(epoch, tf.add(epoch, 1)))
print('epoch: {}'.format(sess.run(epoch)))
np.random.shuffle(x_train)
# Completion
if sess.run(epoch) <= PRETRAIN_EPOCH:
g_loss_value = 0
for i in tqdm.tqdm(range(step_num)):
x_batch = x_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
points_batch, mask_batch = get_points()
_, g_loss = sess.run([g_train_op, model.g_loss], feed_dict={x: x_batch, mask: mask_batch, is_training: True})
g_loss_value += g_loss
print('Completion loss: {}'.format(g_loss_value))
np.random.shuffle(x_test)
x_batch = x_test[:BATCH_SIZE]
completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
sample = np.array((completion[0] + 1) * 127.5, dtype=np.uint8)
result = Image.fromarray(sample)
result.save('./training_output_images/{}.jpg'.format("{0:06d}".format(sess.run(epoch))))
saver = tf.train.Saver()
saver.save(sess, './models/latest', write_meta_graph=False)
if sess.run(epoch) == PRETRAIN_EPOCH:
saver.save(sess, './models/pretrained', write_meta_graph=False)
# Discrimitation
else:
g_loss_value = 0
d_loss_value = 0
for i in tqdm.tqdm(range(step_num)):
x_batch = x_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
points_batch, mask_batch = get_points()
_, g_loss, completion = sess.run([g_train_op, model.g_loss, model.completion], feed_dict={x: x_batch, mask: mask_batch, is_training: True})
g_loss_value += g_loss
local_x_batch = []
local_completion_batch = []
for i in range(BATCH_SIZE):
x1, y1, x2, y2 = points_batch[i]
local_x_batch.append(x_batch[i][y1:y2, x1:x2, :])
local_completion_batch.append(completion[i][y1:y2, x1:x2, :])
local_x_batch = np.array(local_x_batch)
local_completion_batch = np.array(local_completion_batch)
_, d_loss = sess.run(
[d_train_op, model.d_loss],
feed_dict={x: x_batch, mask: mask_batch, local_x: local_x_batch, global_completion: completion, local_completion: local_completion_batch, is_training: True})
d_loss_value += d_loss
print('Completion loss: {}'.format(g_loss_value))
print('Discriminator loss: {}'.format(d_loss_value))
np.random.shuffle(x_test)
x_batch = x_test[:BATCH_SIZE]
completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
sample = np.array((completion[0] + 1) * 127.5, dtype=np.uint8)
result = Image.fromarray(sample)
result.save('./training_output_images/{}.jpg'.format("{0:06d}".format(sess.run(epoch))))
saver = tf.train.Saver()
saver.save(sess, './models/latest', write_meta_graph=False)
def get_points():
points = []
mask = []
for i in range(BATCH_SIZE):
x1, y1 = np.random.randint(0, IMAGE_SIZE - LOCAL_SIZE + 1, 2)
x2, y2 = np.array([x1, y1]) + LOCAL_SIZE
points.append([x1, y1, x2, y2])
w, h = np.random.randint(HOLE_MIN, HOLE_MAX + 1, 2)
p1 = x1 + np.random.randint(0, LOCAL_SIZE - w)
q1 = y1 + np.random.randint(0, LOCAL_SIZE - h)
p2 = p1 + w
q2 = q1 + h
m = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 1), dtype=np.uint8)
m[q1:q2 + 1, p1:p2 + 1] = 1
mask.append(m)
return np.array(points), np.array(mask)
if __name__ == '__main__':
train()

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images/*
npy/*
!.gitkeep

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training_data/to_npy.py Normal file
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import glob
import os
#import cv2
from PIL import Image
import numpy as np
ratio = 0.95
image_size = 128
x = []
paths = glob.glob('images/*')
for path in paths:
#img = cv2.imread(path)
#img = Image.open(path)
#img = cv2.resize(img, (image_size, image_size))
#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#x.append(img)
temp = Image.open(path)
keep = temp.copy()
keep = np.array(keep)
x.append(keep)
temp.close()
x = np.array(x, dtype=np.uint8)
#np.random.shuffle(x)
p = int(ratio * len(x))
x_train = x[:p]
x_test = x[p:]
if not os.path.exists('./npy'):
os.mkdir('./npy')
np.save('./npy/x_train.npy', x_train)
np.save('./npy/x_test.npy', x_test)

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