def get_ds(cat_desc, regime='e'):
view_index = 5
edge_length_threshold = 0.02
cat_id = cat_desc_to_id(cat_desc)
model_id = '%s_%s' % (regime, cat_desc)
image_ds = RenderConfig().get_dataset(cat_id, view_index)
cloud_ds = get_cloud_manager(
model_id, pre_sampled=True, n_samples=n_samples).get_lazy_dataset()
mesh_ds = get_inferred_mesh_dataset(
model_id, edge_length_threshold=edge_length_threshold)
gt_mesh_ds = get_mesh_dataset(cat_id)
voxel_ds = get_voxel_dataset(
model_id, edge_length_threshold=edge_length_threshold, filled=False)
selected_template_ds = get_selected_template_idx_dataset(model_id)
template_meshes = []
with gt_mesh_ds:
for template_id in get_template_ids(cat_id):
mesh = gt_mesh_ds[template_id]
template_meshes.append(
{k: np.array(mesh[k]) for k in ('vertices', 'faces')})
template_mesh_ds = selected_template_ds.map(lambda i: template_meshes[i])
return Dataset.zip(
image_ds, gt_mesh_ds, cloud_ds, mesh_ds, voxel_ds, template_mesh_ds)
def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def get_ds(cat_desc, regime='e'):
view_index = 5
edge_length_threshold = 0.02
cat_id = cat_desc_to_id(cat_desc)
model_id = '%s_%s' % (regime, cat_desc)
image_ds = RenderConfig().get_dataset(cat_id, view_index)
cloud_ds = get_cloud_manager(
model_id, pre_sampled=True, n_samples=n_samples).get_lazy_dataset()
mesh_ds = get_inferred_mesh_dataset(
model_id, edge_length_threshold=edge_length_threshold)
gt_mesh_ds = get_mesh_dataset(cat_id)
voxel_ds = get_voxel_dataset(
model_id, edge_length_threshold=edge_length_threshold, filled=False)
selected_template_ds = get_selected_template_idx_dataset(model_id)
template_meshes = []
with gt_mesh_ds:
for template_id in get_template_ids(cat_id):
mesh = gt_mesh_ds[template_id]
template_meshes.append(
{k: np.array(mesh[k]) for k in ('vertices', 'faces')})
template_mesh_ds = selected_template_ds.map(lambda i: template_meshes[i])
return Dataset.zip(
image_ds, gt_mesh_ds, cloud_ds, mesh_ds, voxel_ds, template_mesh_ds)
def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def clear_results(model_id, actual=False):
import os
import shutil
from template_ffd.inference.predictions import get_predictions_dataset
from template_ffd.inference.meshes import InferredMeshManager
from template_ffd.inference.clouds import get_cloud_manager
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.eval.chamfer import \
get_chamfer_manager, get_template_chamfer_manager
from template_ffd.eval.iou import IouAutoSavingManager
def maybe_remove(path):
if os.path.isfile(path):
print('Removing file %s' % path)
if actual:
os.remove(path)
elif os.path.isdir(path):
print('Removing subdir %s' % path)
if actual:
shutil.rmtree(path)
predictions_dataset = get_predictions_dataset(model_id)
maybe_remove(predictions_dataset.path)
maybe_remove(get_cloud_manager(model_id, pre_sampled=True).path)
maybe_remove(get_chamfer_manager(model_id, pre_sampled=True).path)
for elt in (None, 0.1, 0.05, 0.02, 0.01):
maybe_remove(InferredMeshManager(model_id, elt).path)
maybe_remove(
get_cloud_manager(model_id,
pre_sampled=False,
edge_length_threshold=elt).path)
for filled in (True, False):
dataset = get_voxel_dataset(model_id,
elt,
filled=filled,
auto_save=False)
maybe_remove(dataset.root_dir)
maybe_remove(IouAutoSavingManager(model_id, elt, filled).path)
maybe_remove(
get_chamfer_manager(model_id,
pre_sampled=False,
edge_length_threshold=elt).path)
maybe_remove(get_template_chamfer_manager(model_id).path)
if not actual:
print('NOTE: this was a dry run. Files not actually removed')
print('Use -a for actual run.')
def get_lazy_dataset(self):
cat_id = cat_desc_to_id(load_params(self._model_id)['cat_desc'])
example_ids = get_example_ids(cat_id, 'eval')
inferred_dataset = get_voxel_dataset(
self._model_id, self._edge_length_threshold, self._voxel_config,
filled=self._filled, example_ids=example_ids)
gt_dataset = get_gt_voxel_dataset(cat_id, filled=self._filled)
voxel_datasets = Dataset.zip(inferred_dataset, gt_dataset)
voxel_datasets = voxel_datasets.subset(example_ids)
def map_fn(v):
return intersection_over_union(v[0].data, v[1].data)
iou_dataset = voxel_datasets.map(map_fn)
return iou_dataset
def get_lazy_dataset(self):
cat_id = cat_desc_to_id(load_params(self._model_id)['cat_desc'])
example_ids = get_example_ids(cat_id, 'eval')
inferred_dataset = get_voxel_dataset(self._model_id,
self._edge_length_threshold,
self._voxel_config,
filled=self._filled,
example_ids=example_ids)
gt_dataset = get_gt_voxel_dataset(cat_id, filled=self._filled)
voxel_datasets = Dataset.zip(inferred_dataset, gt_dataset)
voxel_datasets = voxel_datasets.subset(example_ids)
def map_fn(v):
return intersection_over_union(v[0].data, v[1].data)
iou_dataset = voxel_datasets.map(map_fn)
return iou_dataset
def get_lazy_dataset(self):
cat_id = cat_desc_to_id(load_params(self._model_id)['cat_desc'])
if not isinstance(cat_id, (list, tuple)):
cat_id = [cat_id]
inferred_dataset = get_voxel_dataset(self._model_id,
self._edge_length_threshold,
self._voxel_config,
filled=self._filled)
gt_dataset = get_gt_voxel_dataset(cat_id, filled=self._filled)
gt_dataset = gt_dataset.map_keys(lambda key: key[:2])
with inferred_dataset:
keys = tuple(inferred_dataset.keys())
voxel_datasets = Dataset.zip(inferred_dataset, gt_dataset)
voxel_datasets = voxel_datasets.subset(keys)
def map_fn(v):
return intersection_over_union(v[0].dense_data(),
v[1].dense_data())
iou_dataset = voxel_datasets.map(map_fn)
return iou_dataset