def vis_clouds(model_id,
pre_sampled=True,
n_samples=1024,
edge_length_threshold=0.1,
shuffle=False):
import random
import numpy as np
from mayavi import mlab
import matplotlib.pyplot as plt
from dids import Dataset
from shapenet.core.blender_renderings.config import RenderConfig
from shapenet.core.meshes import get_mesh_dataset
from util3d.mayavi_vis import vis_point_cloud
from util3d.mayavi_vis import vis_mesh
from template_ffd.data.ids import get_example_ids
from template_ffd.inference.clouds import get_inferred_cloud_dataset
from template_ffd.model import get_builder
builder = get_builder(model_id)
cat_id = builder.cat_id
kwargs = dict(model_id=model_id, n_samples=n_samples)
if not pre_sampled:
kwargs['edge_length_threshold'] = edge_length_threshold
cloud_dataset = get_inferred_cloud_dataset(pre_sampled=pre_sampled,
**kwargs)
image_dataset = RenderConfig().get_dataset(cat_id, builder.view_index)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
random.shuffle(example_ids)
mesh_dataset = get_mesh_dataset(cat_id)
zipped_dataset = Dataset.zip(image_dataset, cloud_dataset, mesh_dataset)
# zipped_dataset = Dataset.zip(image_dataset, cloud_dataset)
with zipped_dataset:
for example_id in example_ids:
image, cloud, mesh = zipped_dataset[example_id]
# image, cloud = zipped_dataset[example_id]
plt.imshow(image)
vis_point_cloud(np.array(cloud),
color=(0, 1, 0),
scale_factor=0.01)
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vis_mesh(v,
f,
color=(0, 0, 1),
opacity=0.1,
include_wireframe=False)
plt.show(block=False)
mlab.show()
plt.close()
def vis_clouds(
model_id, pre_sampled=True, n_samples=1024, edge_length_threshold=0.1,
shuffle=False):
import random
import numpy as np
from mayavi import mlab
import matplotlib.pyplot as plt
from dids import Dataset
from shapenet.core.blender_renderings.config import RenderConfig
from shapenet.core.meshes import get_mesh_dataset
from util3d.mayavi_vis import vis_point_cloud
from util3d.mayavi_vis import vis_mesh
from template_ffd.data.ids import get_example_ids
from template_ffd.inference.clouds import get_inferred_cloud_dataset
from template_ffd.model import get_builder
builder = get_builder(model_id)
cat_id = builder.cat_id
kwargs = dict(model_id=model_id, n_samples=n_samples)
if not pre_sampled:
kwargs['edge_length_threshold'] = edge_length_threshold
cloud_dataset = get_inferred_cloud_dataset(
pre_sampled=pre_sampled, **kwargs)
image_dataset = RenderConfig().get_dataset(cat_id, builder.view_index)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
random.shuffle(example_ids)
mesh_dataset = get_mesh_dataset(cat_id)
zipped_dataset = Dataset.zip(image_dataset, cloud_dataset, mesh_dataset)
# zipped_dataset = Dataset.zip(image_dataset, cloud_dataset)
with zipped_dataset:
for example_id in example_ids:
image, cloud, mesh = zipped_dataset[example_id]
# image, cloud = zipped_dataset[example_id]
plt.imshow(image)
vis_point_cloud(
np.array(cloud), color=(0, 1, 0), scale_factor=0.01)
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vis_mesh(
v, f, color=(0, 0, 1), opacity=0.1, include_wireframe=False)
plt.show(block=False)
mlab.show()
plt.close()
def get_inferred_cloud_dataset(self):
return clouds.get_inferred_cloud_dataset(
pre_sampled=True, model_id=self._model_id,
n_samples=self._n_samples, **self._kwargs)
def get_inferred_cloud_dataset(self):
return clouds.get_inferred_cloud_dataset(
pre_sampled=True, model_id=self._model_id,
n_samples=self._n_samples, **self._kwargs)