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 visualize_input(self, mode=tf.estimator.ModeKeys.TRAIN):
with tf.Graph().as_default():
if mode == tf.estimator.ModeKeys.PREDICT:
features, targets = self.get_predict_inputs()
elif mode == tf.estimator.ModeKeys.TRAIN:
features, targets = self.get_train_inputs()
with tf.train.MonitoredSession() as sess:
while not sess.should_stop():
data = sess.run([features, targets])
for record in nested_generator(data):
self.visualize_data_pointcloud(*record)
def visualize_raw_images(self, path_list):
with tf.Graph().as_default():
features = get_raw_image_dataset(
path_list).make_one_shot_iterator().get_next()
predictions = self.build_estimator(
features, None, tf.estimator.ModeKeys.PREDICT).predictions
targets = None
if targets is not None:
data_tensors.append(targets)
data_tensors = [predictions, features]
saver = tf.train.Saver()
with tf.train.MonitoredSession() as sess:
saver.restore(sess, tf.train.latest_checkpoint(self.model_dir))
while not sess.should_stop():
data = sess.run(data_tensors)
for record in nested_generator(data):
self.visualize_predictions_mesh(*record)
data = sess.run(data_tensors)
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()
def visualize_predictions(self, mode=tf.estimator.ModeKeys.TRAIN):
with tf.Graph().as_default():
if mode == tf.estimator.ModeKeys.PREDICT:
print("Visualizing test set")
features, targets = self.get_predict_inputs()
elif mode == tf.estimator.ModeKeys.TRAIN:
print("Visualizing train set")
features, targets = self.get_train_inputs()
predictions = self.build_estimator(
features, targets, tf.estimator.ModeKeys.PREDICT).predictions
data_tensors = [predictions, features]
if targets is not None:
data_tensors.append(targets)
saver = tf.train.Saver()
with tf.train.MonitoredSession() as sess:
saver.restore(sess, tf.train.latest_checkpoint(self.model_dir))
while not sess.should_stop():
data = sess.run(data_tensors)
for record in nested_generator(data):
self.visualize_predictions_mesh(*record)
