def from_instance_masks(instance_masks,
instance_ids=None,
instance_labels=None):
from datumaro.util.mask_tools import merge_masks
if instance_ids is not None:
assert len(instance_ids) == len(instance_masks)
else:
instance_ids = [None] * len(instance_masks)
if instance_labels is not None:
assert len(instance_labels) == len(instance_masks)
else:
instance_labels = [None] * len(instance_masks)
instance_masks = sorted(zip(instance_masks, instance_ids,
instance_labels),
key=lambda m: m[0].z_order)
instance_mask = [
m.as_instance_mask(id if id is not None else 1 + idx)
for idx, (m, id, _) in enumerate(instance_masks)
]
instance_mask = merge_masks(instance_mask)
cls_mask = [m.as_class_mask(c) for m, _, c in instance_masks]
cls_mask = merge_masks(cls_mask)
return __class__(class_mask=cls_mask, instance_mask=instance_mask)
def crop_segments(cls, instances, img_width, img_height):
instances = sorted(instances, key=lambda x: x[0].z_order)
segment_map = []
segments = []
for inst_idx, (_, polygons, mask, _) in enumerate(instances):
if polygons:
segment_map.extend(inst_idx for p in polygons)
segments.extend(polygons)
elif mask is not None:
segment_map.append(inst_idx)
segments.append(mask)
segments = mask_tools.crop_covered_segments(segments, img_width,
img_height)
for inst_idx, inst in enumerate(instances):
new_segments = [
s for si_id, s in zip(segment_map, segments)
if si_id == inst_idx
]
if not new_segments:
inst[1] = []
inst[2] = None
continue
if inst[1]:
inst[1] = sum(new_segments, [])
else:
mask = mask_tools.merge_masks(new_segments)
inst[2] = mask_tools.mask_to_rle(mask)
return instances
def find_instance_parts(self, group, img_width, img_height):
boxes = [a for a in group if a.type == AnnotationType.bbox]
polygons = [a for a in group if a.type == AnnotationType.polygon]
masks = [a for a in group if a.type == AnnotationType.mask]
anns = boxes + polygons + masks
leader = anno_tools.find_group_leader(anns)
bbox = anno_tools.max_bbox(anns)
mask = None
polygons = [p.points for p in polygons]
if self._context._segmentation_mode == SegmentationMode.guess:
use_masks = True == leader.attributes.get(
'is_crowd',
find(masks, lambda x: x.label == leader.label) is not None)
elif self._context._segmentation_mode == SegmentationMode.polygons:
use_masks = False
elif self._context._segmentation_mode == SegmentationMode.mask:
use_masks = True
else:
raise NotImplementedError("Unexpected segmentation mode '%s'" % \
self._context._segmentation_mode)
if use_masks:
if polygons:
mask = mask_tools.rles_to_mask(polygons, img_width, img_height)
if masks:
masks = (m.image for m in masks)
if mask is not None:
masks += chain(masks, [mask])
mask = mask_tools.merge_masks(masks)
if mask is not None:
mask = mask_tools.mask_to_rle(mask)
polygons = []
else:
if masks:
mask = mask_tools.merge_masks(m.image for m in masks)
polygons += mask_tools.mask_to_polygons(mask)
mask = None
return [leader, polygons, mask, bbox]
def _find_instance_parts(self, group, img_width, img_height):
boxes = [a for a in group if a.type == AnnotationType.bbox]
masks = [a for a in group if a.type == AnnotationType.mask]
anns = boxes + masks
leader = find_group_leader(anns)
bbox = max_bbox(anns)
mask = None
if self._save_masks:
mask = merge_masks([m.image for m in masks])
return [leader, mask, bbox]
def test_can_merge_masks(self):
masks = [
np.array([0, 2, 4, 0, 0, 1]),
np.array([0, 1, 1, 0, 2, 0]),
np.array([0, 0, 2, 3, 0, 0]),
]
expected = \
np.array([0, 1, 2, 3, 2, 1])
actual = mask_tools.merge_masks(masks)
self.assertTrue(np.array_equal(expected, actual),
'%s\nvs.\n%s' % (expected, actual))
def _save_annotations(self, item, anno_dir):
masks = [a for a in item.annotations if a.type == AnnotationType.mask]
if not masks:
return
instance_ids = [int(a.attributes['track_id']) for a in masks]
masks = sorted(zip(masks, instance_ids), key=lambda e: e[0].z_order)
mask = merge_masks(
(m.image, MotsPath.MAX_INSTANCES * (1 + m.label) + id)
for m, id in masks)
save_image(osp.join(anno_dir, item.id + '.png'),
mask,
create_dir=True,
dtype=np.uint16)
def merge_segments(cls,
instance,
img_width,
img_height,
include_polygons=False):
polygons = [a for a in instance if a.type == AnnotationType.polygon]
masks = [a for a in instance if a.type == AnnotationType.mask]
if not polygons and not masks:
return []
if not polygons and len(masks) == 1:
return masks
leader = find_group_leader(polygons + masks)
instance = []
# Build the resulting mask
mask = None
if include_polygons and polygons:
polygons = [p.points for p in polygons]
mask = mask_tools.rles_to_mask(polygons, img_width, img_height)
else:
instance += polygons # keep unused polygons
if masks:
masks = (m.image for m in masks)
if mask is not None:
masks = chain(masks, [mask])
mask = mask_tools.merge_masks(masks)
if mask is None:
return instance
mask = mask_tools.mask_to_rle(mask)
mask = mask_utils.frPyObjects(mask, *mask['size'])
instance.append(
RleMask(rle=mask,
label=leader.label,
z_order=leader.z_order,
id=leader.id,
attributes=leader.attributes,
group=leader.group))
return instance
def save_annotations(self, item):
if not item.has_image:
return
ann_filename = item.id + CocoPath.PANOPTIC_EXT
segments_info = list()
masks = []
next_id = self._min_ann_id
for ann in item.annotations:
if ann.type != AnnotationType.mask:
continue
if not ann.id:
ann.id = next_id
next_id += 1
segment_info = {}
segment_info['id'] = ann.id
segment_info['category_id'] = cast(ann.label, int, -1) + 1
segment_info['area'] = float(ann.get_area())
segment_info['bbox'] = [float(p) for p in ann.get_bbox()]
segment_info['iscrowd'] = cast(ann.attributes.get("is_crowd"), int,
0)
segments_info.append(segment_info)
masks.append(ann)
if not masks:
return
pan_format = mask_tools.merge_masks((m.image, m.id) for m in masks)
save_image(osp.join(self._context._segmentation_dir, ann_filename),
mask_tools.index2bgr(pan_format),
create_dir=True)
elem = {
'image_id': self._get_image_id(item),
'file_name': ann_filename,
'segments_info': segments_info
}
self.annotations.append(elem)