labeled_frame
sleap_io.model.labeled_frame
¶
Data structures for data contained within a single video frame.
The LabeledFrame class is a data structure that contains Instances and
PredictedInstances that are associated with a single frame within a video.
LabeledFrame
¶
Labeled data for a single frame of a video.
Attributes:
| Name | Type | Description |
|---|---|---|
video |
Video
|
The |
frame_idx |
int
|
The index of the |
instances |
list[Union[Instance, PredictedInstance]]
|
List of |
Notes
Instances of this class are hashed by identity, not by value. This means that
two LabeledFrame instances with the same attributes will NOT be considered
equal in a set or dict.
Source code in sleap_io/model/labeled_frame.py
@define(eq=False)
class LabeledFrame:
"""Labeled data for a single frame of a video.
Attributes:
video: The `Video` associated with this `LabeledFrame`.
frame_idx: The index of the `LabeledFrame` in the `Video`.
instances: List of `Instance` objects associated with this `LabeledFrame`.
Notes:
Instances of this class are hashed by identity, not by value. This means that
two `LabeledFrame` instances with the same attributes will NOT be considered
equal in a set or dict.
"""
video: Video
frame_idx: int
instances: list[Union[Instance, PredictedInstance]] = field(factory=list)
def __len__(self) -> int:
"""Return the number of instances in the frame."""
return len(self.instances)
def __getitem__(self, key: int) -> Union[Instance, PredictedInstance]:
"""Return the `Instance` at `key` index in the `instances` list."""
return self.instances[key]
def __iter__(self):
"""Iterate over `Instance`s in `instances` list."""
return iter(self.instances)
@property
def user_instances(self) -> list[Instance]:
"""Frame instances that are user-labeled (`Instance` objects)."""
return [inst for inst in self.instances if type(inst) == Instance]
@property
def has_user_instances(self) -> bool:
"""Return True if the frame has any user-labeled instances."""
for inst in self.instances:
if type(inst) == Instance:
return True
return False
@property
def predicted_instances(self) -> list[Instance]:
"""Frame instances that are predicted by a model (`PredictedInstance` objects)."""
return [inst for inst in self.instances if type(inst) == PredictedInstance]
@property
def has_predicted_instances(self) -> bool:
"""Return True if the frame has any predicted instances."""
for inst in self.instances:
if type(inst) == PredictedInstance:
return True
return False
def numpy(self) -> np.ndarray:
"""Return all instances in the frame as a numpy array.
Returns:
Points as a numpy array of shape `(n_instances, n_nodes, 2)`.
Note that the order of the instances is arbitrary.
"""
n_instances = len(self.instances)
n_nodes = len(self.instances[0]) if n_instances > 0 else 0
pts = np.full((n_instances, n_nodes, 2), np.nan)
for i, inst in enumerate(self.instances):
pts[i] = inst.numpy()[:, 0:2]
return pts
@property
def image(self) -> np.ndarray:
"""Return the image of the frame as a numpy array."""
return self.video[self.frame_idx]
@property
def unused_predictions(self) -> list[Instance]:
"""Return a list of "unused" `PredictedInstance` objects in frame.
This is all of the `PredictedInstance` objects which do not have a corresponding
`Instance` in the same track in the same frame.
"""
unused_predictions = []
any_tracks = [inst.track for inst in self.instances if inst.track is not None]
if len(any_tracks):
# Use tracks to determine which predicted instances have been used
used_tracks = [
inst.track
for inst in self.instances
if type(inst) == Instance and inst.track is not None
]
unused_predictions = [
inst
for inst in self.instances
if inst.track not in used_tracks and type(inst) == PredictedInstance
]
else:
# Use from_predicted to determine which predicted instances have been used
# TODO: should we always do this instead of using tracks?
used_instances = [
inst.from_predicted
for inst in self.instances
if inst.from_predicted is not None
]
unused_predictions = [
inst
for inst in self.instances
if type(inst) == PredictedInstance and inst not in used_instances
]
return unused_predictions
def remove_predictions(self):
"""Remove all `PredictedInstance` objects from the frame."""
self.instances = [inst for inst in self.instances if type(inst) == Instance]
def remove_empty_instances(self):
"""Remove all instances with no visible points."""
self.instances = [inst for inst in self.instances if not inst.is_empty]
has_predicted_instances: bool
property
¶
Return True if the frame has any predicted instances.
has_user_instances: bool
property
¶
Return True if the frame has any user-labeled instances.
image: np.ndarray
property
¶
Return the image of the frame as a numpy array.
predicted_instances: list[Instance]
property
¶
Frame instances that are predicted by a model (PredictedInstance objects).
unused_predictions: list[Instance]
property
¶
Return a list of "unused" PredictedInstance objects in frame.
This is all of the PredictedInstance objects which do not have a corresponding
Instance in the same track in the same frame.
user_instances: list[Instance]
property
¶
Frame instances that are user-labeled (Instance objects).
__getitem__(key)
¶
__iter__()
¶
__len__()
¶
numpy()
¶
Return all instances in the frame as a numpy array.
Returns:
| Type | Description |
|---|---|
ndarray
|
Points as a numpy array of shape Note that the order of the instances is arbitrary. |
Source code in sleap_io/model/labeled_frame.py
def numpy(self) -> np.ndarray:
"""Return all instances in the frame as a numpy array.
Returns:
Points as a numpy array of shape `(n_instances, n_nodes, 2)`.
Note that the order of the instances is arbitrary.
"""
n_instances = len(self.instances)
n_nodes = len(self.instances[0]) if n_instances > 0 else 0
pts = np.full((n_instances, n_nodes, 2), np.nan)
for i, inst in enumerate(self.instances):
pts[i] = inst.numpy()[:, 0:2]
return pts