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testing.py
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from PIL import Image
import tensorflow as tf
import numpy as np
from matplotlib import image
from matplotlib import pyplot as plt
import os
import time
import tensorflow as tf
IMG_SIZE = 128
PATH = os.path.dirname(__file__)
def load_test():
master_dir = PATH + '\\test_image'
x = []
y = []
for image_file in os.listdir( master_dir )[ 0 : 3 ]:
rgb_image = Image.open( os.path.join( master_dir , image_file ) ).resize( ( IMG_SIZE , IMG_SIZE ) )
rgb_img_array = (np.asarray( rgb_image ) ) / 255
gray_image = rgb_image.convert('L')
gray_img_array = ( np.asarray( gray_image ).reshape( ( IMG_SIZE , IMG_SIZE , 1 ) ) ) / 255
x.append(gray_img_array)
y.append(rgb_img_array)
return np.array(x), np.array(y)
def get_generator_model():
inputs = tf.keras.layers.Input( shape=( IMG_SIZE , IMG_SIZE , 1 ) )
conv1 = tf.keras.layers.Conv2D( 16 , kernel_size=( 5 , 5 ) , strides=1 )( inputs )
conv1 = tf.keras.layers.LeakyReLU()( conv1 )
conv1 = tf.keras.layers.Conv2D( 32 , kernel_size=( 3 , 3 ) , strides=1)( conv1 )
conv1 = tf.keras.layers.LeakyReLU()( conv1 )
conv1 = tf.keras.layers.Conv2D( 32 , kernel_size=( 3 , 3 ) , strides=1)( conv1 )
conv1 = tf.keras.layers.LeakyReLU()( conv1 )
conv2 = tf.keras.layers.Conv2D( 32 , kernel_size=( 5 , 5 ) , strides=1)( conv1 )
conv2 = tf.keras.layers.LeakyReLU()( conv2 )
conv2 = tf.keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 )( conv2 )
conv2 = tf.keras.layers.LeakyReLU()( conv2 )
conv2 = tf.keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 )( conv2 )
conv2 = tf.keras.layers.LeakyReLU()( conv2 )
conv3 = tf.keras.layers.Conv2D( 64 , kernel_size=( 5 , 5 ) , strides=1 )( conv2 )
conv3 = tf.keras.layers.LeakyReLU()( conv3 )
conv3 = tf.keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1 )( conv3 )
conv3 = tf.keras.layers.LeakyReLU()( conv3 )
conv3 = tf.keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1 )( conv3 )
conv3 = tf.keras.layers.LeakyReLU()( conv3 )
bottleneck = tf.keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1 , activation='tanh' , padding='same' )( conv3 )
concat_1 = tf.keras.layers.Concatenate()( [ bottleneck , conv3 ] )
conv_up_3 = tf.keras.layers.Conv2DTranspose( 128 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' )( concat_1 )
conv_up_3 = tf.keras.layers.Conv2DTranspose( 128 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' )( conv_up_3 )
conv_up_3 = tf.keras.layers.Conv2DTranspose( 64 , kernel_size=( 5 , 5 ) , strides=1 , activation='relu' )( conv_up_3 )
concat_2 = tf.keras.layers.Concatenate()( [ conv_up_3 , conv2 ] )
conv_up_2 = tf.keras.layers.Conv2DTranspose( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' )( concat_2 )
conv_up_2 = tf.keras.layers.Conv2DTranspose( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' )( conv_up_2 )
conv_up_2 = tf.keras.layers.Conv2DTranspose( 32 , kernel_size=( 5 , 5 ) , strides=1 , activation='relu' )( conv_up_2 )
concat_3 = tf.keras.layers.Concatenate()( [ conv_up_2 , conv1 ] )
conv_up_1 = tf.keras.layers.Conv2DTranspose( 32 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu')( concat_3 )
conv_up_1 = tf.keras.layers.Conv2DTranspose( 32 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu')( conv_up_1 )
conv_up_1 = tf.keras.layers.Conv2DTranspose( 3 , kernel_size=( 5 , 5 ) , strides=1 , activation='relu')( conv_up_1 )
model = tf.keras.models.Model( inputs , conv_up_1 )
return model
def get_discriminator_model():
layers = [
tf.keras.layers.Conv2D( 32 , kernel_size=( 7 , 7 ) , strides=1 , activation='relu' , input_shape=( IMG_SIZE , IMG_SIZE , 3 ) ),
tf.keras.layers.Conv2D( 32 , kernel_size=( 7, 7 ) , strides=1, activation='relu' ),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D( 64 , kernel_size=( 5 , 5 ) , strides=1, activation='relu' ),
tf.keras.layers.Conv2D( 64 , kernel_size=( 5 , 5 ) , strides=1, activation='relu' ),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1, activation='relu' ),
tf.keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1, activation='relu' ),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D( 256 , kernel_size=( 3 , 3 ) , strides=1, activation='relu' ),
tf.keras.layers.Conv2D( 256 , kernel_size=( 3 , 3 ) , strides=1, activation='relu' ),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense( 512, activation='relu' ) ,
tf.keras.layers.Dense( 128 , activation='relu' ) ,
tf.keras.layers.Dense( 16 , activation='relu' ) ,
tf.keras.layers.Dense( 1 , activation='sigmoid' )
]
model = tf.keras.models.Sequential( layers )
return model
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output) - tf.random.uniform( shape=real_output.shape , maxval=0.1 ) , real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output) + tf.random.uniform( shape=fake_output.shape , maxval=0.1 ) , fake_output)
total_loss = real_loss + fake_loss
return total_loss
def generator_loss(fake_output , real_y):
real_y = tf.cast( real_y , 'float32' )
return mse( fake_output , real_y )
opt = tf.keras.optimizers.Adam( 0.0005 )
generator = get_generator_model()
discriminator = get_discriminator_model()
cross_entropy = tf.keras.losses.BinaryCrossentropy()
mse = tf.keras.losses.MeanSquaredError()
generator.compile(optimizer=opt, loss=generator_loss, metrics=['accuracy'])
discriminator.compile(optimizer=opt, loss=discriminator_loss, metrics=['accuracy'])
checkpoint_dir = PATH + '\\training_checkpoints\\training_checkpoints4h'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=opt,
discriminator_optimizer=opt,
generator=generator,
discriminator=discriminator)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
test = load_test()
# generated_image = g_model(test[0]).numpy()
print(test[0].shape)
generated_image = generator.predict(test[0])[0,:,:,:]
plt.figure(figsize=(10,10))
or_image = plt.subplot(3,3,1)
or_image.set_title('Grayscale Input', fontsize=16)
plt.imshow( test[0][0,:,:,:] , cmap='gray' )
in_image = plt.subplot(3,3,2)
image = Image.fromarray((generated_image * 255).astype( 'uint8' )).resize(( 256 , 256 ))
image.save(PATH + "\\results\\result.jpg")
image = np.asarray( image )
in_image.set_title('Colorized Output', fontsize=16)
plt.imshow( image )
ou_image = plt.subplot(3,3,3)
image = Image.fromarray( ( test[1][0,:,:,:] * 255 ).astype( 'uint8' ) ).resize( ( 1024 , 1024 ) )
ou_image.set_title('Ground Truth', fontsize=16)
plt.imshow( image )
plt.savefig(PATH + "\\results\\plot.jpg")
plt.show()