sleap.nn.data.identity
sleap.nn.data.identity#
Utilities for generating data for track identity models.
- class sleap.nn.data.identity.ClassMapGenerator(sigma: float = 2.0, output_stride: int = 1, centroids: bool = False, class_map_threshold: float = 0.2)[source]#
Transformer to generate class maps from track indices.
- sigma#
Standard deviation of the 2D Gaussian distribution sampled to generate confidence maps for masking the identity maps. This defines the spread in units of the input image’s grid, i.e., it does not take scaling in previous steps into account.
- Type
float
- output_stride#
Relative stride of the generated maps. This is effectively the reciprocal of the output scale, i.e., increase this to generate maps that are smaller than the input images.
- Type
int
- centroids#
If
True
, generate masking confidence maps for centroids rather than instance points.- Type
bool
- class_map_threshold#
Minimum confidence map value below which map values will be replaced with zeros.
- Type
float
- property input_keys: List[str]#
Return the keys that incoming elements are expected to have.
- property output_keys: List[str]#
Return the keys that outgoing elements will have.
- transform_dataset(input_ds: tensorflow.python.data.ops.dataset_ops.DatasetV2) tensorflow.python.data.ops.dataset_ops.DatasetV2 [source]#
Create a dataset that contains the generated class identity maps.
- Parameters
input_ds – A dataset with elements that contain the keys
"image"
,"track_inds"
,"n_tracks"
and either"instances"
or"centroids"
depending on whether thecentroids
attribute is set toTrue
.- Returns
A
tf.data.Dataset
with the same keys as the input, as well as a"class_maps"
key containing the generated class maps.
- class sleap.nn.data.identity.ClassVectorGenerator[source]#
Transformer to generate class probability vectors from track indices.
- property input_keys: List[str]#
Return the keys that incoming elements are expected to have.
- property output_keys: List[str]#
Return the keys that outgoing elements will have.
- transform_dataset(input_ds: tensorflow.python.data.ops.dataset_ops.DatasetV2) tensorflow.python.data.ops.dataset_ops.DatasetV2 [source]#
Create a dataset that contains the generated class identity vectors.
- Parameters
input_ds – A dataset with elements that contain the keys`”track_inds”` and
"n_tracks"
.- Returns
A
tf.data.Dataset
with the same keys as the input, as well as a"class"
key containing the generated class vectors.
- sleap.nn.data.identity.make_class_maps(confmaps: tensorflow.python.framework.ops.Tensor, class_inds: tensorflow.python.framework.ops.Tensor, n_classes: int, threshold: float = 0.2) tensorflow.python.framework.ops.Tensor [source]#
Generate identity class maps using instance-wise confidence maps.
This is useful for making class maps defined on local neighborhoods around the peaks.
- Parameters
confmaps – Confidence maps for the same points as the offset maps as a
tf.Tensor
of shape(grid_height, grid_width, n_instances)
and dtypetf.float32
. This can be generated bysleap.nn.data.confidence_maps.make_confmaps
.class_inds – Class indices as
tf.int32
tensor of shape(n_instances)
.n_classes – Integer number of maximum classes.
threshold – Minimum confidence map value below which map values will be replaced with zeros.
- Returns
The class maps with shape
(grid_height, grid_width, n_classes)
and dtypetf.float32
where each channel will be a binary mask with 1 where the instance confidence maps were higher than the threshold.
Notes
Pixels that have confidence map values from more than one animal will have the class vectors weighed by the relative contribution of each instance.
See also: make_class_vectors, sleap.nn.data.confidence_maps.make_confmaps
- sleap.nn.data.identity.make_class_vectors(class_inds: tensorflow.python.framework.ops.Tensor, n_classes: int) tensorflow.python.framework.ops.Tensor [source]#
Make a binary class vectors from class indices.
- Parameters
class_inds – Class indices as
tf.Tensor
of dtypetf.int32
and shape(n_instances,)
. Indices of-1
will be interpreted as having no class.n_classes – Integer number of maximum classes.
- Returns
A tensor with binary class vectors of shape
(n_instances, n_classes)
of dtypetf.int32
. Instances with no class will have all zeros in their row.
Notes: A class index can be used to represent a track index.