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 get_dataset(self, mode):
dataset_ids = self.data_splitter.get_data(mode)
"""For testing purpose
dataset_ids = [ '10640377f4eb9ecdadceecd3bc8bde14', '115aa37af1a07d24a5a88312547ed863', '1a640c8dffc5d01b8fd30d65663cfd42',
'2091ab9f69c77273de2426af5ed9b6a', '2eca5fa283b317c7602717bb378973f1', '383ed236166980209e23d6b6aa619041',
'3a8478c9f2c1c043eb81825856d1297f', '425abc480a0b390d7cc46b39c0cc084b',
'42de9b896d23244fe6fbd395d87e5106', '446e4145b475eb245751d640a4e334']"""
dataset = get_dataset(self.preprocessed_data_path,
self.cat_id,
self.view_angles,
self.cloud_resamples,
dataset_ids,
mode == tf.estimator.ModeKeys.TRAIN,
mode == tf.estimator.ModeKeys.TRAIN,
batch_size=self.batch_size)
return dataset
def visualize_predicted_pointclouds(self):
evaluation_ids = self.data_splitter.get_data(
tf.estimator.ModeKeys.PREDICT)
template_ids, bs, ps = zip(*self.get_ffd_data())
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([features, predictions])
for prediction_tensor in nested_generator(data):
image = prediction_tensor[0]['image']
image -= np.min(image)
image /= np.max(image)
plt.imshow(image)
plt.show()
dp = prediction_tensor[1]['deformed_points']
probs = prediction_tensor[1]['probs']
i = np.argmax(probs)
predicted_vertices = np.matmul(bs[i], ps[i] + dp[i])
visualize_point_cloud(np.matmul(bs[i], ps[i]),
color=(0, 0, 1),
scale_factor=0.01)
mlab.show()
visualize_point_cloud(predicted_vertices,
color=(1, 0, 0),
scale_factor=0.01)
mlab.show()
