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)
del dense_pcd
sparse_prob = np.array(pd_prob_collector).astype(float).reshape(
-1, classes_in_model) # (B*N, num_classes)
pd_probs_path = os.path.join(output_dir, file_prefix + ".prob")
np.savetxt(pd_probs_path, sparse_prob, fmt="%f")
print("Exported sparse probs to {}".format(pd_probs_path))
# save original labels and visulize them with from_dataset labels
sparse_labels = np.array(pd_labels_collector).astype(
int).flatten() # (B*N,)
pcd_ori = open3d.geometry.PointCloud()
pcd_ori.points = open3d.utility.Vector3dVector(sparse_points)
pcd_ori.colors = open3d.utility.Vector3dVector(
_label_to_colors_by_name(sparse_labels, flags.from_dataset))
pcd_ori_path = os.path.join(
output_dir, file_prefix + '_' + flags.from_dataset + ".pcd")
open3d.io.write_point_cloud(pcd_ori_path, pcd_ori)
print("Exported sparse pcd to {}".format(pcd_ori_path))
pd_ori_labels_path = os.path.join(
output_dir, file_prefix + '_' + flags.from_dataset + ".labels")
np.savetxt(pd_ori_labels_path, sparse_labels, fmt="%d")
print("Exported sparse labels to {}".format(pd_ori_labels_path))
print("the following is the result of common class:")
common_cm.print_metrics()
print("#" * 100)
if flags.from_dataset == flags.to_dataset:
print("the following is the result of original class:")
cm.print_metrics()
