def main(unused_argv):
if not FLAGS.stack_folder:
raise ValueError("stack_folder was not defined")
(pano_stack, alignment_params) = stack_io.read_stack(FLAGS.stack_folder)
unused_azimuth, lighting_context = stack_io.load_sample_illuminations()
lighting_context_factors = tf.constant(lighting_context, dtype=tf.float32)
# [0, 1]-ranged panoramas of shape [384, 960].
pano_stack = tf.constant(pano_stack, dtype=tf.float32)
alignment_params = tf.constant(alignment_params, dtype=tf.float32)
# Align images using parameters.
alignment_module = image_alignment.ImageAlignment(regularization=0.3)
aligned_stack = alignment_module.align_images(pano_stack, alignment_params)
factorize_model = network.FactorizeEncoderDecoder(
{
"lighting_dim": 32,
"permanent_dim": 16
}, is_training=False)
stack_factors = factorize_model.compute_decomposition(
aligned_stack, single_image_decomposition=False, average_stack=True)
recon = network.recomposite_from_log_components(
stack_factors["log_reflectance"], stack_factors["log_shading"])
rotate_shading_image = factorize_model.generate_sun_rotation(
stack_factors["permanent_factor"][:1],
lighting_context_factors[FLAGS.lighting_context_index:FLAGS.
lighting_context_index + 1],
FLAGS.azimuth_frame_rate)
results = network.recomposite_from_log_components(
stack_factors["log_reflectance"], rotate_shading_image)
# Restore factorization network weights from ckpt.
tf.train.init_from_checkpoint(
"./factorize_a_city/ckpt/factorize_model.ckpt",
{"decomp_internal/": "decomp_internal/"})
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
out = sess.run(results)
stack_io.write_stack_images(FLAGS.output_dir, out / 255.)
def main(unused_argv):
if not FLAGS.stack_folder:
raise ValueError("stack_folder was not defined")
# Load example stacks. Each panorama has shape [384, 960, 3] and has values
# between [0, 1].
(permanent_stack,
alignment_params) = stack_io.read_stack(FLAGS.stack_folder)
# Load example azimuth and illumination samples.
azimuth, lighting_context = stack_io.load_sample_illuminations()
permanent_stack = tf.constant(permanent_stack, dtype=tf.float32)
azimuth_factors = tf.constant(azimuth, dtype=tf.float32)
lighting_context_factors = tf.constant(lighting_context, dtype=tf.float32)
# Align images using learnt parameters.
alignment_module = image_alignment.ImageAlignment(regularization=0.3)
aligned_stack = alignment_module.align_images(permanent_stack,
alignment_params)
factorize_model = network.FactorizeEncoderDecoder(
{
"lighting_dim": 32,
"permanent_dim": 16
}, is_training=False)
stack_factors = factorize_model.compute_decomposition(aligned_stack)
permanent_factor = stack_factors["permanent_factor"]
permanent_factor = tf.tile(permanent_factor[:1],
[azimuth.shape[0], 1, 1, 1])
shading_image = factorize_model.generate_shading_image(
permanent_factor, lighting_context_factors, azimuth_factors)
relit_results = network.recomposite_from_log_components(
stack_factors["log_reflectance"], shading_image)
# Restore factorization network weights from ckpt.
tf.train.init_from_checkpoint(
"./factorize_a_city/ckpt/factorize_model.ckpt",
{"decomp_internal/": "decomp_internal/"})
sess = tf.Session()
sess.run(tf.global_variables_initializer())
out = sess.run(relit_results)
stack_io.write_stack_images(FLAGS.output_dir, out / 255.)