interpolated_prob, axis=-1) # (batch_size,)
interpolated_common_label = _2common(interpolated_label,
from_dataset) # (N1,)
interpolated_color = _label_to_colors_by_name(interpolated_label,
from_dataset) # (N1, 3)
interpolated_common_color = _label_to_colors_by_name(
interpolated_common_label, 'common') # (N1, 3)
del interpolated_common_label
# evaluate interpolation result if ground truth is available
raw_label_path = os.path.join(raw_folder, fname_without_ext + '.labels')
if from_dataset == to_dataset and os.path.exists(raw_label_path):
label_gt = load_labels(raw_label_path) # (N1,)
ConfusionMatrix = ConfusionMatrix(num_classes)
ConfusionMatrix.increment_from_list(label_gt.flatten(),
interpolated_label.flatten())
ConfusionMatrix.print_metrics()
label_path = os.path.join(dense_folder, fname_without_ext + '.labels')
np.savetxt(label_path, interpolated_label, fmt="%d")
del interpolated_label
print('writing labels for %s' % fname_without_ext)
# output pcd with from_dataset label
dense_pcd = open3d.geometry.PointCloud()
dense_pcd.points = open3d.utility.Vector3dVector(raw_points)
dense_pcd.colors = open3d.utility.Vector3dVector(interpolated_color)
del interpolated_color
dense_pcd_path = os.path.join(
dense_folder, fname_without_ext + '_' + from_dataset + '.pcd')
open3d.io.write_point_cloud(dense_pcd_path, dense_pcd)
global_step=global_step)
print("Batch size: {}, time: {}".format(current_batch_size,
time.time() - s))
common_gt = _2common(gt_labels, flags.to_dataset) # (B, N)
common_pd = _2common(pd_labels, flags.from_dataset) # (B, N)
# Save to collector for file output
points_collector.extend(points) # (B, N, 3)
pd_labels_collector.extend(pd_labels) # (B, N)
pd_common_labels_collector.extend(common_pd) # (B, N)
pd_prob_collector.extend(pd_prob) # (B, N, num_classes)
# Increment confusion matrix
common_cm.increment_from_list(common_gt.flatten(),
common_pd.flatten())
if flags.from_dataset == flags.to_dataset:
cm.increment_from_list(gt_labels.flatten(),
pd_labels.flatten())
# Save sparse point cloud and predicted labels
file_prefix = os.path.basename(file_data.file_path_without_ext)
sparse_points = np.array(points_collector).reshape((-1, 3)) # (B*N, 3)
sparse_common_labels = np.array(
pd_common_labels_collector).flatten() # (B*N,)
pcd_common = open3d.geometry.PointCloud()
pcd_common.points = open3d.utility.Vector3dVector(sparse_points)
pcd_common.colors = open3d.utility.Vector3dVector(
_label_to_colors_by_name(sparse_common_labels, 'common'))
pcd_path = os.path.join(output_dir, file_prefix + "_common.pcd")
