def to_detection(self, frame_size):
"""Returns a :class:`fiftyone.core.labels.Detection` representation of
the object.
Args:
frame_size: the ``(width, height)`` of the image
Returns:
a :class:`fiftyone.core.labels.Detection`
"""
label = self.name
bounding_box = self.bndbox.to_detection_format(frame_size)
detection = fol.Detection(label=label, bounding_box=bounding_box)
if self.pose is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["pose"] = fol.CategoricalAttribute(
value=self.pose)
if self.truncated is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["truncated"] = fol.CategoricalAttribute(
value=self.truncated)
if self.difficult is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["difficult"] = fol.CategoricalAttribute(
value=self.difficult)
if self.occluded is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["occluded"] = fol.CategoricalAttribute(
value=self.occluded)
return detection
def to_detection(self, frame_size):
"""Returns a :class:`fiftyone.core.labels.Detection` representation of
the box.
Args:
frame_size: the ``(width, height)`` of the image
Returns:
a :class:`fiftyone.core.labels.Detection`
"""
label = self.label
width, height = frame_size
bounding_box = [
self.xtl / width,
self.ytl / height,
(self.xbr - self.xtl) / width,
(self.ybr - self.ytl) / height,
]
attributes = {
a.name: fol.CategoricalAttribute(value=a.value)
for a in self.attributes
}
return fol.Detection(
label=label,
bounding_box=bounding_box,
attributes=attributes,
)
def _parse_attribute(self, value):
if etau.is_str(value):
return fol.CategoricalAttribute(value=value)
if isinstance(value, bool):
return fol.BooleanAttribute(value=value)
if etau.is_numeric(value):
return fol.NumericAttribute(value=value)
return fol.Attribute(value=value)
def to_attribute(self):
"""Returns a :class:`fiftyone.core.labels.Attribute` representation of
the attribute.
Returns:
a :class:`fiftyone.core.labels.Attribute`
"""
if isinstance(self.value, bool):
return fol.BooleanAttribute(value=self.value)
if etau.is_numeric(self.value):
return fol.NumericAttribute(value=self.value)
return fol.CategoricalAttribute(value=self.value)
def to_detection(self, frame_size, classes=None, supercategory_map=None):
"""Returns a :class:`fiftyone.core.labels.Detection` representation of
the object.
Args:
frame_size: the ``(width, height)`` of the image
classes (None): the list of classes
supercategory_map (None): a dict mapping class names to
supercategories
Returns:
a :class:`fiftyone.core.labels.Detection`
"""
if classes:
label = classes[self.category_id]
else:
label = str(self.category_id)
width, height = frame_size
x, y, w, h = self.bbox
bounding_box = [x / width, y / height, w / width, h / height]
detection = fol.Detection(label=label, bounding_box=bounding_box)
if supercategory_map is not None:
supercategory = supercategory_map.get(label, None)
else:
supercategory = None
if supercategory is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["supercategory"] = fol.CategoricalAttribute(
value=supercategory)
if self.area is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["area"] = fol.NumericAttribute(
value=self.area)
if self.iscrowd is not None:
# pylint: disable=unsupported-assignment-operation
detection.attributes["iscrowd"] = fol.NumericAttribute(
value=self.iscrowd)
# @todo parse `segmentation`
return detection