def main(_):
import tensorflow as tf
from shape_tfds.shape.shapenet import core
tf.compat.v1.enable_eager_execution()
FLAGS = flags.FLAGS
ids, names = core.load_synset_ids()
name = FLAGS.synset
synset_id = name if name in names else ids[name]
if synset_id not in names:
raise ValueError("Invalid synset_id %s" % synset_id)
renderer = core.Renderer(FLAGS.renderer, resolution=(FLAGS.resolution,) * 2,)
builder = core.ShapenetCore(
config=core.RenderingConfig(
synset_id=synset_id, renderer=renderer, seed=FLAGS.seed
),
from_cache=FLAGS.from_cache,
)
builder.download_and_prepare()
if FLAGS.vis:
def vis(example):
import matplotlib.pyplot as plt
plt.imshow(example["image"].numpy())
plt.show()
dataset = builder.as_dataset(split="train")
for example in dataset:
vis(example)
def main(_):
tf.compat.v1.enable_eager_execution()
FLAGS = flags.FLAGS
ids, names = core.load_synset_ids()
name = FLAGS.synset
synset_id = name if name in names else ids[name]
if synset_id not in names:
raise ValueError("Invalid synset_id %s" % synset_id)
builder = core.ShapenetCore(config=core.FrustumVoxelConfig(
synset_id=synset_id, resolution=FLAGS.resolution, seed=FLAGS.seed))
builder.download_and_prepare()
if FLAGS.vis:
def vis(example):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # pylint: disable=unused-import
ax = plt.gca(projection="3d")
ax.voxels(example["voxels"].numpy())
# ax.axis("square")
plt.show()
dataset = builder.as_dataset(split="train")
for example in dataset:
vis(example)
def main(_):
tf.compat.v1.enable_eager_execution()
FLAGS = flags.FLAGS
ids, names = core.load_synset_ids()
name = FLAGS.synset
seed = FLAGS.seed
synset_id = name if name in names else ids[name]
if synset_id not in names:
raise ValueError("Invalid synset_id %s" % synset_id)
configs = dict(
image=core.TrimeshRenderingConfig(synset_id=synset_id,
resolution=(FLAGS.image_res, ) * 2,
seed=seed),
voxels=core.FrustumVoxelConfig(synset_id=synset_id,
resolution=FLAGS.vox_res,
seed=seed),
)
builders = {
k: core.ShapenetCore(config=config)
for k, config in configs.items()
}
for b in builders.values():
b.download_and_prepare()
if FLAGS.vis:
def vis(example):
import matplotlib.pyplot as plt
image = example["image"].numpy()
voxels = tf.reduce_any(example["voxels"], axis=-1)
voxels = tf.image.resize(
tf.expand_dims(tf.cast(voxels, tf.uint8), axis=-1),
image.shape[:2],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
)
voxels = tf.cast(tf.squeeze(voxels, axis=-1), tf.bool).numpy()
# voxels = voxels.T
# voxels = voxels[:, -1::-1]
image[np.logical_not(voxels)] = 0
plt.imshow(image)
plt.show()
datasets = {
k: b.as_dataset(split="train",
shuffle_files=False).map(lambda x: x[k])
for k, b in builders.items()
}
dataset = tf.data.Dataset.zip(datasets)
for example in dataset:
vis(example)
# ax.axis("square")
plt.show()
synset_id = ids[synset_name]
configs = dict(
image=core.TrimeshRenderingConfig(
synset_id=synset_id,
resolution=resolution,
view_fn=core.views.random_view_fn(seed_offset),
),
voxels=core.VoxelConfig(synset_id, resolution=32),
)
builders = {
k: core.ShapenetCore(config=config)
for k, config in configs.items()
}
for b in builders.values():
b.download_and_prepare()
datasets = {
k: b.as_dataset(split="train", shuffle_files=False).map(lambda x: x[k])
for k, b in builders.items()
}
dataset = tf.data.Dataset.zip(datasets)
for example in dataset:
image = example["image"].numpy()
voxels = example["voxels"].numpy()
import os
import tensorflow as tf
from shape_tfds.shape.shapenet import core
tf.compat.v1.enable_eager_execution()
resolution = 32
split = "train"
names = ("suitcase", "telephone", "table")
ids, _ = core.load_synset_ids()
configs = (core.VoxelConfig(ids[n], resolution) for n in names)
builders = tuple(core.ShapenetCore(config=config) for config in configs)
all_fns = []
for builder in builders:
builder.download_and_prepare()
prefix = "%s-%s" % (builder.name.split("/")[0], split)
data_dir = builder.data_dir
record_fns = tuple(
os.path.join(data_dir, fn) for fn in os.listdir(data_dir)
if fn.startswith(prefix))
all_fns.extend(record_fns)
print(all_fns)
builder = builders[0]
dataset = tf.data.TFRecordDataset(all_fns, num_parallel_reads=len(all_fns))
dataset = dataset.map(builder._file_format_adapter._parser.parse_example)