davisinteractive.metrics.jaccard
batched_jaccard
batched_jaccard(y_true,
y_pred,
average_over_objects=True,
nb_objects=None)
Jaccard similarity over two subsets of binary elements A and B:
\mathcal{J} = \frac{A \cap B}{A \cup B}
Arguments
- y_true: Numpy Array. Array of shape (B x H x W) and type integer giving the ground truth of the object instance segmentation.
- y_pred: Numpy Array. Array of shape (B x H x W) and type integer giving the prediction of the object segmentation.
- average_over_objects: Boolean. Weather or not to average the jaccard over all the objects in the sequence. Default True.
- nb_objects: Integer. Number of objects in the ground truth mask. If
None
the value will be infered fromy_true
. Setting this value will speed up the computation.
Returns
ndarray
: Returns an array of shape (B) with the average jaccard for
all instances at each frame if average_over_objects=True
. If
average_over_objects=False
returns an array of shape (B x nObj)
with nObj being the number of objects on y_true
.
batched_f_measure
batched_f_measure(y_true,
y_pred,
average_over_objects=True,
nb_objects=None,
bound_th=0.008)
Arguments
- y_true: Numpy Array. Array of shape (B x H x W) and type integer giving the ground truth of the object instance segmentation.
- y_pred: Numpy Array. Array of shape (B x H x W) and type integer giving the prediction of the object segmentation.
- average_over_objects: Boolean. Weather or not to average the F-measure over all the objects in the sequence. Default True.
- nb_objects: Integer. Number of objects in the ground truth mask. If
None
the value will be infered fromy_true
. Setting this value will speed up the computation.
Returns
ndarray
: Returns an array of shape (B) with the average F-measure for
all instances at each frame if average_over_objects=True
. If
average_over_objects=False
returns an array of shape (B x nObj)
with nObj being the number of objects on y_true
.