def filter_loss(target, prediction, adv_):
prediction = tf.image.rgb_to_grayscale(Getfilter(19, prediction))
target = tf.image.rgb_to_grayscale(Getfilter(19, target))
enhanced = tf.reshape(prediction, [-1, PATCH_WIDTH * PATCH_HEIGHT])
dslr = tf.reshape(target, [-1, PATCH_WIDTH * PATCH_HEIGHT])
adversarial_ = tf.multiply(enhanced, 1 - adv_) + tf.multiply(dslr, adv_)
adversarial_image = tf.reshape(adversarial_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 1])
discrim_predictions = models.adversarial(adversarial_image)
discrim_target = tf.concat([adv_, 1 - adv_], 1)
loss_filter = -tf.reduce_sum(discrim_target * tf.log(
tf.clip_by_value(discrim_predictions, 1e-10, 1.0)))
correct_predictions = tf.equal(tf.argmax(discrim_predictions, 1),
tf.argmax(discrim_target, 1))
discim_accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
return -loss_filter, discim_accuracy
# transform both dslr and enhanced images to grayscale
enhanced_gray = tf.reshape(tf.image.rgb_to_grayscale(enhanced),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
dslr_gray = tf.reshape(tf.image.rgb_to_grayscale(dslr_image),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
# push randomly the enhanced or dslr image to an adversarial CNN-discriminator
adversarial_ = tf.multiply(enhanced_gray, 1 - adv_) + tf.multiply(
dslr_gray, adv_)
adversarial_image = tf.reshape(adversarial_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 1])
discrim_predictions = models.adversarial(adversarial_image)
# losses
# 1) texture (adversarial) loss
discrim_target = tf.concat([adv_, 1 - adv_], 1)
loss_discrim = -tf.reduce_sum(discrim_target * tf.log(
tf.clip_by_value(discrim_predictions, 1e-10, 1.0)))
loss_texture = -loss_discrim
correct_predictions = tf.equal(tf.argmax(discrim_predictions, 1),
tf.argmax(discrim_target, 1))
discim_accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
# 2) content loss
training_steps = 350
generator_model = None
adversarial_model = None
discriminator_model = None
batch_size = 128
if GAN:
for i in range(training_steps):
x = X[i * batch_size:(i + 1) * batch_size, :, :]
size = x.shape[0]
if not discriminator_model:
discriminator_model = discriminator()
if not generator_model:
generator_model = generator()
if not adversarial_model:
adversarial_model = adversarial(
generator_model, discriminator_model)
noise = np.random.uniform(0,
1.0,
size=[batch_size, 100])
fakes = generator_model.predict(noise)
x = np.concatenate((x, fakes))
x = np.expand_dims(x, axis=3)
y = np.zeros([2 * batch_size, 1])
y[:batch_size, :] = 1
d_loss = discriminator_model.train_on_batch(x, y)
y = np.ones([batch_size, 1])
noise = np.random.uniform(0,
1.0,
size=[batch_size, 100])
[-1, PATCH_WIDTH * PATCH_HEIGHT * 3])
dslr_blur = tf.reshape(utils.blur(dslr_image),
[-1, PATCH_WIDTH * PATCH_HEIGHT * 3])
# push randomly the enhanced or dslr image to an adversarial CNN-discriminator
adversarial_ = tf.multiply(enhanced_gray, 1 - adv_) + tf.multiply(
dslr_gray, adv_)
adversarial_image = tf.reshape(adversarial_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 1])
adversarial_color_ = tf.multiply(
enhanced_blur, 1 - adv_color_) + tf.multiply(dslr_blur, adv_color_)
adversarial_color_image = tf.reshape(adversarial_color_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 3])
discrim_predictions = models.adversarial(adversarial_image)
discrim_predictions_color = models.adversarial(adversarial_color_image)
# losses
# 1) texture (adversarial) loss
discrim_target = tf.concat([adv_, 1 - adv_], 1)
loss_discrim = -tf.reduce_sum(discrim_target * tf.log(
tf.clip_by_value(discrim_predictions, 1e-10, 1.0)))
loss_texture = -loss_discrim
correct_predictions = tf.equal(tf.argmax(discrim_predictions, 1),
tf.argmax(discrim_target, 1))
discim_accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
