本文整理汇总了Python中TensorflowUtils.unprocess_image方法的典型用法代码示例。如果您正苦于以下问题：Python TensorflowUtils.unprocess_image方法的具体用法？Python TensorflowUtils.unprocess_image怎么用？Python TensorflowUtils.unprocess_image使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类TensorflowUtils的用法示例。
在下文中一共展示了TensorflowUtils.unprocess_image方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import unprocess_image [as 别名]
def main(argv=None):
utils.maybe_download_and_extract(FLAGS.model_dir, DATA_URL)
model_data = get_model_data()
invert_image = get_image(FLAGS.image_path)
print invert_image.shape
mean = model_data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
processed_image = utils.process_image(invert_image, mean_pixel).astype(np.float32)
weights = np.squeeze(model_data['layers'])
invert_net = vgg_net(weights, processed_image)
dummy_image = utils.weight_variable(invert_image.shape, stddev=np.std(invert_image) * 0.1)
tf.histogram_summary("Image Output", dummy_image)
image_net = vgg_net(weights, dummy_image)
with tf.Session() as sess:
invert_layer_features = invert_net[INVERT_LAYER].eval()
loss = 2 * tf.nn.l2_loss(image_net[INVERT_LAYER] - invert_layer_features) / invert_layer_features.size
tf.scalar_summary("Loss", loss)
summary_op = tf.merge_all_summaries()
train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)
best_loss = float('inf')
best = None
summary_writer = tf.train.SummaryWriter(FLAGS.log_dir)
sess.run(tf.initialize_all_variables())
for i in range(1, MAX_ITERATIONS):
train_op.run()
if i % 10 == 0 or i == MAX_ITERATIONS - 1:
this_loss = loss.eval()
print('Step %d' % (i)),
print(' total loss: %g' % this_loss)
summary_writer.add_summary(summary_op.eval(), global_step=i)
if this_loss < best_loss:
best_loss = this_loss
best = dummy_image.eval()
output = utils.unprocess_image(best.reshape(invert_image.shape[1:]), mean_pixel)
scipy.misc.imsave("invert_check.png", output)
output = utils.unprocess_image(best.reshape(invert_image.shape[1:]), mean_pixel)
scipy.misc.imsave("output.png", output)示例2: test
# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import unprocess_image [as 别名]
def test(sess, output_image, mean_pixel):
best = sess.run(output_image)
output = utils.unprocess_image(best.reshape(best.shape[1:]), mean_pixel).astype(np.float32)
scipy.misc.imsave("output.jpg", output)示例3: save_image
# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import unprocess_image [as 别名]
def save_image(filename, image, mean_pixel):
output = utils.unprocess_image(image, mean_pixel)
output = np.uint8(np.clip(output, 0, 255))
scipy.misc.imsave(filename, output)
print "Image saved!"示例4: main
# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import unprocess_image [as 别名]
def main(argv=None):
utils.maybe_download_and_extract(FLAGS.model_dir, DATA_URL)
model_data = get_model_data()
mean = model_data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
weights = np.squeeze(model_data['layers'])
content_image = get_image(FLAGS.content_path)
print content_image.shape
processed_content = utils.process_image(content_image, mean_pixel).astype(np.float32)
style_image = get_image(FLAGS.style_path)
processed_style = utils.process_image(style_image, mean_pixel).astype(np.float32)
content_net = vgg_net(weights, processed_content)
style_net = vgg_net(weights, processed_style)
dummy_image = utils.weight_variable(content_image.shape, stddev=np.std(content_image) * 0.1)
image_net = vgg_net(weights, dummy_image)
with tf.Session() as sess:
content_losses = []
for layer in CONTENT_LAYERS:
feature = content_net[layer].eval()
content_losses.append(tf.nn.l2_loss(image_net[layer] - feature))
content_loss = CONTENT_WEIGHT * reduce(tf.add, content_losses)
style_losses = []
for layer in STYLE_LAYERS:
features = style_net[layer].eval()
features = np.reshape(features, (-1, features.shape[3]))
style_gram = np.matmul(features.T, features) / features.size
image_layer = image_net[layer]
_, height, width, number = map(lambda i: i.value, image_layer.get_shape())
size = height * width * number
feats = tf.reshape(image_layer, (-1, number))
image_gram = tf.matmul(tf.transpose(feats), feats) / size
style_losses.append(0.5*tf.nn.l2_loss(image_gram - style_gram))
style_loss = STYLE_WEIGHT * reduce(tf.add, style_losses)
tv_y_size = utils.get_tensor_size(dummy_image[:, 1:, :, :])
tv_x_size = utils.get_tensor_size(dummy_image[:, :, 1:, :])
tv_loss = VARIATION_WEIGHT * (
(tf.nn.l2_loss(dummy_image[:, 1:, :, :] - dummy_image[:, :content_image.shape[1] - 1, :, :]) /
tv_y_size) +
(tf.nn.l2_loss(dummy_image[:, :, 1:, :] - dummy_image[:, :, :content_image.shape[2] - 1, :]) /
tv_x_size))
loss = content_loss + style_loss + tv_loss
train_step = tf.train.MomentumOptimizer(LEARNING_RATE,MOMENTUM).minimize(loss)
best_loss = float('inf')
best = None
sess.run(tf.initialize_all_variables())
for i in range(1, MAX_ITERATIONS):
train_step.run()
if i % 10 == 0 or i == MAX_ITERATIONS - 1:
this_loss = loss.eval()
print('Step %d' % (i)),
print(' total loss: %g' % this_loss)
if this_loss < best_loss:
best_loss = this_loss
best = dummy_image.eval()
output = utils.unprocess_image(best.reshape(content_image.shape[1:]), mean_pixel)
scipy.misc.imsave("output_check.png", output)
if i % 100 == 0 or i == MAX_ITERATIONS - 1:
print(' content loss: %g' % content_loss.eval()),
print(' style loss: %g' % style_loss.eval()),
print(' tv loss: %g' % tv_loss.eval())
output = utils.unprocess_image(best.reshape(content_image.shape[1:]), mean_pixel)
scipy.misc.imsave("output.png", output)