def report_chamfer_presampled(self):
evaluation_ids = self.data_splitter.get_data(
tf.estimator.ModeKeys.PREDICT)
point_cloud_dataset = get_point_clouds(self.preprocessed_data_path,
self.cat_id,
self.n_ffd_resamples)
point_cloud_dataset = point_cloud_dataset.subset(evaluation_ids)
mesh_dataset = MeshReader(self.preprocessed_data_path).get_dataset(
self.cat_id)
mesh_dataset = mesh_dataset.subset(evaluation_ids)
mesh_dataset.open()
deformed_predictions = []
ground_truth_point_cloud = []
mesh_ground_truth = []
ffd_dataset = get_template_ffd(self.preprocessed_data_path,
self.cat_id,
edge_length_threshold=None)
template_ids, bs, ps = zip(*self.get_ffd_data(ffd_dataset))
with tf.Graph().as_default():
dataset = get_dataset(self.preprocessed_data_path,
self.cat_id,
self.view_angles,
self.cloud_resamples,
evaluation_ids,
False,
False,
batch_size=len(evaluation_ids))
features, targets = dataset.make_one_shot_iterator().get_next()
predictions = self.build_estimator(
features, targets, tf.estimator.ModeKeys.PREDICT).predictions
saver = tf.train.Saver()
with tf.train.MonitoredSession() as sess:
saver.restore(sess, tf.train.latest_checkpoint(self.model_dir))
data = sess.run(predictions)
point_cloud_dataset.open()
for evaluation_id, prediction_tensor in zip(
evaluation_ids, nested_generator(data)):
dp = prediction_tensor['deformed_points']
probs = prediction_tensor['probs']
i = np.argmax(probs)
predicted_vertices = np.matmul(bs[i], ps[i] + dp[i])
deformed_predictions.append(
sample_points(predicted_vertices,
self.n_ffd_resamples))
ground_truth_point_cloud.append(
point_cloud_dataset[evaluation_id])
mesh_ground_truth.append(mesh_dataset[evaluation_id])
chamfer_list, unnorm_chamfer = get_normalized_chamfer(
mesh_ground_truth, ground_truth_point_cloud,
deformed_predictions, self.n_ffd_resamples)
print("The normalized chamfer for test set is " +
str(np.mean(chamfer_list)))
print("The non normalized chamfer for test set is " +
str(np.mean(unnorm_chamfer)))
def _mesh_transformation(self, edge_length_threshold=0.02):
ffd_dataset = get_template_ffd(self.preprocessed_data_path,
self.cat_id, edge_length_threshold)
template_ids, bs, ps = zip(*self.get_ffd_data(ffd_dataset))
mesh_dataset = get_thresholded_template_mesh(
self.preprocessed_data_path, self.cat_id, edge_length_threshold)
with mesh_dataset:
all_faces = []
all_vertices = []
for k in template_ids:
sg = mesh_dataset[k]
all_faces.append(np.array(sg['faces']))
all_vertices.append(np.array(sg['vertices']))
def transform_predictions(probs, dp):
i = np.argmax(probs)
vertices = np.matmul(bs[i], ps[i] + dp[i])
faces = all_faces[i]
original_vertices = all_vertices[i]
return dict(vertices=vertices,
faces=faces,
original_vertices=original_vertices,
attrs=dict(template_id=template_ids[i]))
return transform_predictions
def get_ffd_data(self, ffd_dataset=None):
if ffd_dataset is None:
n_ffd_points = self.n_ffd_samples
ffd_dataset = get_template_ffd(self.preprocessed_data_path,
self.cat_id,
n_samples=n_ffd_points)
with ffd_dataset:
return tuple(self._get_ffd_data(ffd_dataset))
from helper.shapenet.shapenetMapper import desc_to_id
from deformations.FFD import get_template_ffd
from deformations.meshDeformation import get_thresholded_template_mesh
from mayavi import mlab
import numpy as np
from graphicUtils.visualizer.mayaviVisualizer import visualize_mesh, visualize_point_cloud
ds = get_template_ffd("/media/saurabh/e56e40fb-030d-4f7f-9e63-42ed5f7f6c711/preprocessing_new", desc_to_id("pistol"),
edge_length_threshold=None, n_samples=16384)
key = "1f646ff59cabdddcd810dcd63f342aca"
with ds:
b = np.array(ds[key]['b'])
p = np.array(ds[key]['p'])
mesh_dataset = get_thresholded_template_mesh("/media/saurabh/e56e40fb-030d-4f7f-9e63-42ed5f7f6c711/preprocessing_new", desc_to_id("pistol"),
None)
with mesh_dataset:
f = np.array(mesh_dataset[key]['faces'])
v_orignal = np.array(mesh_dataset[key]['vertices'])
# print(b)
# visualize_mesh(v_orignal, f)
# mlab.show()
visualize_mesh(np.matmul(b, p), f)
mlab.show()
from deformations.FFD import get_template_ffd
get_template_ffd("/media/saurabh/e56e40fb-030d-4f7f-9e63" +
"-42ed5f7f6c71/preprocessing_new/",
"03948459",
n_samples=16384)
