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v053 release
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@danielS91
danielS91 committed Apr 4, 2023
1 parent 8060e62 commit 0baf3cf
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4 changes: 4 additions & 0 deletions README.md
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Expand Up @@ -238,6 +238,10 @@ For further details, we refer to the usage in our [EMSANet repository](https://g
The dataset can be used as an iterator (detectron2 usually does this) and can then be mapped with the custom mappers to generate the correct layout of the data.

## Changelog
**Version 0.5.3 (Mar 31, 2023)**
- *no dataset preparation related changes*
- minor changes to `nicr_sa_prepare_labeled_point_clouds` and `nicr_sa_labeled_pc_viewer`

**Version 0.5.2 (Mar 28, 2023)**
- hypersim: change instance encoding: do not view G and B channel as uint16 use bit shifting instead
- add new scripts and update entry points:
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10 changes: 5 additions & 5 deletions nicr_scene_analysis_datasets/scripts/prepare_labeled_point_clouds.py
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Expand Up @@ -82,7 +82,7 @@ def _parse_args():
help="Whether to map semantic classes to benchmark format."
)
parser.add_argument(
'--write-scannet-label',
'--write-scannet-benchmark-ground-truth',
action="store_true",
help="If set, ground-truth label files that can be used for ScanNet "
"benchmarking scripts will be written."
Expand Down Expand Up @@ -209,9 +209,9 @@ def main():
args = _parse_args()

# base output directories
output_path = osp.join(args.output_path, args.dataset + '_ply', args.split)
scannet_output_path = osp.join(args.output_path, args.dataset + '_gt',
'3d', args.split)
output_path = osp.join(args.output_path, args.split, 'ply')
scannet_output_path = osp.join(args.output_path, args.split,
'scannet_benchmark_gt')

# helper function to load dataset
def load_dataset(sample_keys):
Expand Down Expand Up @@ -320,7 +320,7 @@ def load_dataset(sample_keys):
_save_ply(fp, filtered_pc, filtered_attributes, shift=args.shift)

# write ScanNet ground-truth label files
if args.write_scannet_label:
if args.write_scannet_benchmark_ground_truth:
filename = '_'.join(last_identifier[:-1]) + '.txt'

# pure semantic labels
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207 changes: 163 additions & 44 deletions nicr_scene_analysis_datasets/scripts/viewer_labeled_point_cloud.py
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Expand Up @@ -24,12 +24,14 @@

USAGE = inspect.cleandoc(
"""
Use the keys 1-5 to switch between displaying:
1: color,
2: semantic ground-truth annotations,
3: instance ground-truth annotations,
4: predicted semantic labels (see --semantic-label-file`, and
5: predicted instance labels (see --instance-label-file`).
Use the keys 1-7 to switch between displaying:
1: color given in ply file,
2: semantic ground-truth annotations given in label field in ply file,
3: instance ground-truth annotations given in label field in ply file,
4: additional (predicted) semantic labels given by `--semantic-label-filepath`,
5: additional (predicted) instance labels given by `--instance-label-filepath`,
6: additional (predicted) semantic labels given by `--panoptic-label-filepath`,
7: additional (predicted) instance labels given by `--panoptic-label-filepath`.
Press h to show further help and q to quit.
"""
Expand All @@ -53,10 +55,12 @@ def _parse_args():
parser.add_argument(
'--mode',
type=str,
choices=('color',
'semantic', 'instance',
'additional_semantic', 'additional_instance'),
default='color',
choices=(
'ply_color', 'ply_semantic', 'ply_instance',
'additional_semantic', 'additional_instance',
'additional_panoptic_semantic', 'additional_panoptic_instance'
),
default='ply_color',
help='Render mode (use keys to switch)'
)
parser.add_argument(
Expand Down Expand Up @@ -87,12 +91,35 @@ def _parse_args():
help="Path to txt file containing the instance label for each vertex. "
"Useful for visualizing predictions."
)
parser.add_argument(
'--panoptic-label-filepath',
type=str,
default=None,
help="Path to txt file containing the panoptic label as sem*shift+ins "
"for each vertex. See `--use-scannet-format` for 'shift'. Useful "
"for visualizing predictions."
)
parser.add_argument(
'--use-scannet-format',
action='store_true',
help="If specified, labels will be taken as sem*1000+inst instead of "
"our sem*(1<<16)+ins encoding."
)
parser.add_argument(
'--use-panoptic-labels-as-instance-labels',
action='store_true',
help="If specified, panoptic labels will be used as instance labels. "
"This is useful if panoptic labels with 0-based instance ids per "
"semantic class are given, which is common after panoptic "
"merging. Consider using `--enumerate-instances` as well, to get "
"smaller instance ids."
)
parser.add_argument(
'--enumerate-instances',
action='store_true',
help="If specified, instance labels will be enumerated before "
"visualization. This is useful to get smaller instance ids. "
)

# experimental
parser.add_argument(
Expand All @@ -112,11 +139,20 @@ def _parse_args():
return parser.parse_args()


def _get_instance_colors(instance_labels, instance_cmap):
# convert to continuos labels
# instance_labels = np.where(
# np.unique(instance_labels) == instance_labels[:, np.newaxis]
# )[1]
def _get_instance_colors(instance_labels,
instance_cmap,
enumerate_instances=False):

# enumerate instances
if enumerate_instances:
instance_labels_ = np.zeros_like(instance_labels) # 0: no instance
for new_id, old_id in enumerate(np.unique(instance_labels), start=1):
if old_id == 0:
# no instance label
continue
instance_labels_[instance_labels == old_id] = new_id
instance_labels = instance_labels_

if instance_labels.max() >= len(instance_cmap):
warnings.warn(
f"Color map has only {len(instance_cmap)} colors, but the "
Expand All @@ -126,7 +162,51 @@ def _get_instance_colors(instance_labels, instance_cmap):
return instance_cmap[instance_labels % len(instance_cmap)]


def _load_ply(filepath, semantic_cmap, instance_cmap, use_scannet_format=False):
def split_panoptic_labels(panoptic_labels,
use_scannet_format=False,
use_panoptic_as_instance=False):
if use_scannet_format:
# uint16
semantic_labels = panoptic_labels // 1000
instance_labels = panoptic_labels - semantic_labels * 1000
else:
# uint32
semantic_labels = panoptic_labels >> 16
instance_labels = panoptic_labels & 0xFFFF

if not use_panoptic_as_instance:
# check for same instance ids with multiple semantic classes (typically
# happens when merging semantic and instance to panoptic labels)
uniques, indices = np.unique(instance_labels, return_index=True)
for instance_id, mask in zip(uniques, indices):
if instance_id == 0:
# no instance label
continue

semantic_classes = np.unique(semantic_labels[mask])
if len(semantic_classes) > 1:
# multiple semantic labels for the same instance id
warnings.warn(
f"Instance id {instance_id} has multiple semantic labels: "
f"{semantic_classes}. Consider adding "
"`--use-panoptic-labels-as-instance-labels` to get unique "
"instance ids and `--enumerate-instances` to get smaller "
"instance ids."
)
break
else:
# use panoptic labels as instance labels
instance_labels = panoptic_labels

return semantic_labels, instance_labels


def _load_ply(filepath,
semantic_cmap,
instance_cmap,
use_scannet_format=False,
use_panoptic_as_instance=False,
enumerate_instances=False):
plydata = plyfile.PlyData.read(filepath)

num_verts = plydata['vertex'].count
Expand All @@ -150,29 +230,31 @@ def _load_ply(filepath, semantic_cmap, instance_cmap, use_scannet_format=False):
if 'label' in plydata['vertex']:
labels = plydata['vertex'].data['label'] # uint16 / uint32

if use_scannet_format:
# uint16
semantic_labels = labels // 1000
instance_labels = labels - semantic_labels * 1000
else:
# uint32
semantic_labels = labels >> 16
instance_labels = labels & 0xFFFF
# split labels (panoptic / semantic-instance)
semantic_labels, instance_labels = split_panoptic_labels(
panoptic_labels=labels,
use_scannet_format=use_scannet_format,
use_panoptic_as_instance=use_panoptic_as_instance
)

# semantic colors
semantic_colors = semantic_cmap[semantic_labels]

# instance colors
instance_colors = _get_instance_colors(instance_labels, instance_cmap)
instance_colors = _get_instance_colors(
instance_labels=instance_labels,
instance_cmap=instance_cmap,
enumerate_instances=enumerate_instances
)
else:
# we do not have annotations
instance_colors = np.zeros_like(colors)
semantic_colors = np.zeros_like(colors)

labels = {
'color': colors.astype('float64') / 255,
'instance': instance_colors.astype('float64') / 255,
'semantic': semantic_colors.astype('float64') / 255
'ply_color': colors.astype('float64') / 255,
'ply_instance': instance_colors.astype('float64') / 255,
'ply_semantic': semantic_colors.astype('float64') / 255
}

pc.colors = o3d.utility.Vector3dVector(colors)
Expand All @@ -193,7 +275,9 @@ def main():
filepath=args.filepath,
semantic_cmap=semantic_cmap,
instance_cmap=instance_cmap,
use_scannet_format=args.use_scannet_format
use_scannet_format=args.use_scannet_format,
use_panoptic_as_instance=args.use_panoptic_labels_as_instance_labels,
enumerate_instances=args.enumerate_instances
)
pc.colors = o3d.utility.Vector3dVector(labels[args.mode])

Expand All @@ -208,23 +292,41 @@ def main():
# print usage
print_section("Usage", USAGE)

# load additional predicted labels
# load additional (predicted) labels
if args.semantic_label_filepath is not None:
semantic_labels = np.loadtxt(args.semantic_label_filepath,
dtype=np.uint64)

assert len(semantic_labels) == len(np.array(pc.points))

labels['additional_semantic'] = semantic_cmap[semantic_labels] / 255

if args.instance_label_filepath is not None:
instance_labels = np.loadtxt(args.instance_label_filepath,
dtype=np.uint64)

assert len(instance_labels) == len(np.array(pc.points))

labels['additional_instance'] = _get_instance_colors(
instance_labels, instance_cmap
instance_labels=instance_labels,
instance_cmap=instance_cmap,
enumerate_instances=args.enumerate_instances
) / 255

if args.panoptic_label_filepath is not None:
panoptic_labels = np.loadtxt(args.panoptic_label_filepath,
dtype=np.uint64)
assert len(panoptic_labels) == len(np.array(pc.points))

semantic_labels, instance_labels = split_panoptic_labels(
panoptic_labels=panoptic_labels,
use_scannet_format=args.use_scannet_format,
use_panoptic_as_instance=args.use_panoptic_labels_as_instance_labels
)
labels['additional_panoptic_semantic'] = \
semantic_cmap[semantic_labels] / 255
labels['additional_panoptic_instance'] = _get_instance_colors(
instance_labels=instance_labels,
instance_cmap=instance_cmap,
enumerate_instances=args.enumerate_instances
) / 255

visualizer = o3d.visualization.VisualizerWithKeyCallback()
Expand All @@ -248,17 +350,34 @@ def _func(vis):
visualizer.add_geometry(pc)

# add callback for switching between modes
visualizer.register_key_callback(ord('1'),
_change_mode('color'))
visualizer.register_key_callback(ord('2'),
_change_mode('semantic'))
visualizer.register_key_callback(ord('3'),
_change_mode('instance'))
visualizer.register_key_callback(ord('4'),
_change_mode('additional_semantic'))
visualizer.register_key_callback(ord('5'),
_change_mode('additional_instance'))

visualizer.register_key_callback(
ord('1'),
_change_mode('ply_color')
)
visualizer.register_key_callback(
ord('2'),
_change_mode('ply_semantic')
)
visualizer.register_key_callback(
ord('3'),
_change_mode('ply_instance')
)
visualizer.register_key_callback(
ord('4'),
_change_mode('additional_semantic')
)
visualizer.register_key_callback(
ord('5'),
_change_mode('additional_instance')
)
visualizer.register_key_callback(
ord('6'),
_change_mode('additional_panoptic_semantic')
)
visualizer.register_key_callback(
ord('7'),
_change_mode('additional_panoptic_instance')
)
# experimental: visualize correspondences between the (grund-truth) point
# cloud given by args.filepath and a second point cloud given by
# args.second_pc_filepath, e.g., a mapped representation
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2 changes: 1 addition & 1 deletion nicr_scene_analysis_datasets/version.py
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Expand Up @@ -8,7 +8,7 @@

_VERSION_MAJOR = 0
_VERSION_MINOR = 5
_VERSION_MICRO = 2
_VERSION_MICRO = 3


def get_version(with_suffix=False): # pragma no cover
Expand Down

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