def _get_lazy_evaluation_dataset_single(inf_cloud_ds, cat_id, n_samples,
eval_fn):
def sample_fn(cloud):
return sample_points(np.array(cloud), n_samples)
example_ids = get_example_ids(cat_id, 'eval')
normalization_ds = get_normalization_params_dataset(cat_id)
gt_cloud_ds = get_point_cloud_dataset(
cat_id, n_samples, example_ids=example_ids).map(sample_fn)
with inf_cloud_ds:
keys = tuple(inf_cloud_ds.keys())
normalization_ds = normalization_ds.map_keys(lambda key: key[:2])
gt_cloud_ds = gt_cloud_ds.map_keys(lambda key: key[:2])
zipped = Dataset.zip(inf_cloud_ds, gt_cloud_ds,
normalization_ds).subset(keys, check_present=False)
def map_fn(data):
inf_cloud, gt_cloud, norm_params = data
inf_cloud = normalized(inf_cloud, **norm_params)
gt_cloud = normalized(gt_cloud, **norm_params)
return eval_fn(inf_cloud, gt_cloud)
dataset = zipped.map(map_fn)
return dataset
def get_lazy_evaluation_dataset(inf_cloud_ds, cat_id, n_samples, eval_fn):
example_ids = get_example_ids(cat_id, 'eval')
def sample_fn(cloud):
return sample_points(np.array(cloud), n_samples)
normalization_ds = get_normalization_params_dataset(cat_id)
gt_cloud_ds = get_point_cloud_dataset(
cat_id, n_samples, example_ids=example_ids).map(sample_fn)
zipped = Dataset.zip(inf_cloud_ds, gt_cloud_ds, normalization_ds)
def map_fn(data):
inf_cloud, gt_cloud, norm_params = data
inf_cloud = normalized(inf_cloud, **norm_params)
gt_cloud = normalized(gt_cloud, **norm_params)
return eval_fn(inf_cloud, gt_cloud)
return zipped.map(map_fn)
def get_lazy_dataset(self):
from shapenet.core.point_clouds import get_point_cloud_dataset
from util3d.point_cloud import sample_points
from template_ffd.model import get_builder
from template_ffd.inference.predictions import get_predictions_dataset
builder = get_builder(self._model_id)
cat_id = builder.cat_id
template_ids = builder.template_ids
clouds = []
def sample_fn(cloud):
return sample_points(np.array(cloud), self._n_samples)
gt_clouds = get_point_cloud_dataset(
cat_id, builder.n_samples).map(sample_fn)
with gt_clouds:
for example_id in template_ids:
clouds.append(np.array(gt_clouds[example_id]))
predictions = get_predictions_dataset(self._model_id)
inf_cloud_ds = predictions.map(lambda i: clouds[i].copy())
return _get_lazy_emd_dataset(inf_cloud_ds, cat_id, self._n_samples)
def get_lazy_dataset(self):
from shapenet.core.point_clouds import get_point_cloud_dataset
from util3d.point_cloud import sample_points
from template_ffd.model import get_builder
from template_ffd.inference.predictions import get_predictions_dataset
builder = get_builder(self._model_id)
cat_id = builder.cat_id
template_ids = builder.template_ids
clouds = []
def sample_fn(cloud):
return sample_points(np.array(cloud), self._n_samples)
gt_clouds = get_point_cloud_dataset(
cat_id, builder.n_samples).map(sample_fn)
with gt_clouds:
for example_id in template_ids:
clouds.append(np.array(gt_clouds[example_id]))
predictions = get_predictions_dataset(self._model_id)
inf_cloud_ds = predictions.map(lambda i: clouds[i].copy())
return _get_lazy_emd_dataset(inf_cloud_ds, cat_id, self._n_samples)
#!/usr/bin/python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import dids
from shapenet.core.point_clouds import get_point_cloud_dataset
from shapenet.core.meshes import get_mesh_dataset
from shapenet.core import cat_desc_to_id, get_test_train_split
cat_desc = 'plane'
cat_id = cat_desc_to_id(cat_desc)
cloud_ds = get_point_cloud_dataset(cat_id, 1024)
mesh_ds = get_mesh_dataset(cat_id)
zipped_ds = dids.Dataset.zip(mesh_ds, cloud_ds)
def vis(mesh, cloud):
import numpy as np
from util3d.mayavi_vis import vis_mesh, vis_point_cloud
from mayavi import mlab
vertices, faces = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vis_mesh(vertices, faces, color=(0, 0, 1), axis_order='xzy')
vis_point_cloud(np.array(cloud),
color=(0, 1, 0),
scale_factor=0.01,
axis_order='xzy')
mlab.show()
def __init__(self, cat_id, n_samples, n_resamples):
self._cat_id = cat_id
self._n_samples = n_samples
self._n_resamples = n_resamples
self._dataset = get_point_cloud_dataset(cat_id, n_samples)
self._dataset.open()
import numpy as np
from mayavi import mlab
from util3d.mayavi_vis import vis_point_cloud, vis_normals
from shapenet.core.point_clouds import get_point_cloud_dataset
from shapenet.core.point_clouds import get_cloud_normal_dataset
from shapenet.core import cat_desc_to_id
cat_desc = 'plane'
n_points = 16384
cat_id = cat_desc_to_id(cat_desc)
show_normals = True
if vis_normals:
dataset = get_cloud_normal_dataset(cat_id, n_points)
else:
dataset = get_point_cloud_dataset(cat_id, n_points)
with dataset:
for example_id in dataset:
data = dataset[example_id]
s = random.sample(range(n_points), 1024)
if show_normals:
cloud = np.array(data['points'])
normals = np.array(data['normals'])
cloud = cloud[s]
normals = normals[s]
else:
cloud = np.array(data)
normals = None
cloud = cloud[s]
vis_point_cloud(cloud,
def __init__(self, cat_id, n_samples, n_resamples):
self._cat_id = cat_id
self._n_samples = n_samples
self._n_resamples = n_resamples
self._dataset = get_point_cloud_dataset(cat_id, n_samples)
self._dataset.open()
