def run():
"""Runs training steps with a mesh summary."""
# Mesh summaries only work on TensorFlow 2.x.
if int(tf.__version__.split('.')[0]) < 1:
raise ImportError('TensorFlow 2.x is required to run this demo.')
# Flag mesh_path is required.
if FLAGS.mesh_path is None:
raise ValueError(
'Flag --mesh_path is required and must contain path to PLY file.')
# Camera and scene configuration.
config_dict = {
'camera': {'cls': 'PerspectiveCamera', 'fov': 75}
}
# Read sample PLY file.
vertices, colors, faces = demo_utils.read_ascii_ply(FLAGS.mesh_path)
# Add batch dimension.
vertices = np.expand_dims(vertices, 0)
faces = np.expand_dims(faces, 0)
colors = np.expand_dims(colors, 0)
# Create summary writer.
writer = tf.summary.create_file_writer(FLAGS.logdir)
with writer.as_default():
for step in range(_MAX_STEPS):
train_step(vertices, faces, colors, config_dict, step)
def test_read_ascii_ply(self):
"""Tests end-to-end PLY file reading and parsing."""
test_ply = os.path.join(os.path.dirname(os.environ['TEST_BINARY']),
'test_data', 'icosphere.ply')
vertices, colors, faces = demo_utils.read_ascii_ply(test_ply)
self.assertEqual(len(vertices), 82)
self.assertEqual(len(vertices), len(colors))
self.assertEqual(len(faces), 80)
def run():
"""Runs session with a mesh summary."""
# Mesh summaries only work on TensorFlow 1.x.
if int(tf.__version__.split(".")[0]) > 1:
raise ImportError("TensorFlow 1.x is required to run this demo.")
# Flag mesh_path is required.
if FLAGS.mesh_path is None:
raise ValueError(
"Flag --mesh_path is required and must contain path to PLY file."
)
# Camera and scene configuration.
config_dict = {"camera": {"cls": "PerspectiveCamera", "fov": 75}}
# Read sample PLY file.
vertices, colors, faces = demo_utils.read_ascii_ply(FLAGS.mesh_path)
# Add batch dimension.
vertices = np.expand_dims(vertices, 0)
faces = np.expand_dims(faces, 0)
colors = np.expand_dims(colors, 0)
# Create placeholders for tensors representing the mesh.
step = tf.placeholder(tf.int32, ())
vertices_tensor = tf.placeholder(tf.float32, vertices.shape)
faces_tensor = tf.placeholder(tf.int32, faces.shape)
colors_tensor = tf.placeholder(tf.int32, colors.shape)
# Change colors over time.
t = tf.cast(step, tf.float32) / _MAX_STEPS
transformed_colors = t * (255 - colors) + (1 - t) * colors
meshes_summary = mesh_summary.op(
"mesh_color_tensor",
vertices=vertices_tensor,
faces=faces_tensor,
colors=transformed_colors,
config_dict=config_dict,
)
# Create summary writer and session.
writer = tf.summary.FileWriter(FLAGS.logdir)
sess = tf.Session()
for i in range(_MAX_STEPS):
summary = sess.run(
meshes_summary,
feed_dict={
vertices_tensor: vertices,
faces_tensor: faces,
colors_tensor: colors,
step: i,
},
)
writer.add_summary(summary, global_step=i)
def run():
"""Runs training steps with a mesh summary."""
# Camera and scene configuration.
config_dict = {"camera": {"cls": "PerspectiveCamera", "fov": 75}}
# Read sample PLY file.
vertices, colors, faces = demo_utils.read_ascii_ply(DEMO_PLY_MESH_PATH)
# Add batch dimension.
vertices = np.expand_dims(vertices, 0)
faces = np.expand_dims(faces, 0)
colors = np.expand_dims(colors, 0)
# Create summary writer.
writer = tf.summary.create_file_writer(FLAGS.logdir)
with writer.as_default():
for step in range(_MAX_STEPS):
train_step(vertices, faces, colors, config_dict, step)