tfm.vision.mask_ops.instance_masks_overlap

Calculates the IoUs and IoAs between the detection masks and the ground truth masks.

tfm.vision.mask_ops.instance_masks_overlap(
    boxes: tf.Tensor,
    masks: tf.Tensor,
    gt_boxes: tf.Tensor,
    gt_masks: tf.Tensor,
    output_size: List[int],
    mask_binarize_threshold: float = 0.5
) -> Tuple[tf.Tensor, tf.Tensor]

IoU: intersection over union. IoA: intersection over the area of the detection masks.

Args
`boxes`	a tensor with a shape of [batch_size, N, 4]. The last dimension is the pixel coordinates in [ymin, xmin, ymax, xmax] form.
`masks`	a float tensor with a shape of [batch_size, N, mask_height, mask_width] representing the instance masks w.r.t. the `boxes`.
`gt_boxes`	a tensor with a shape of [batch_size, M, 4]. The last dimension is the pixel coordinates in [ymin, xmin, ymax, xmax] form.
`gt_masks`	a float tensor with a shape of [batch_size, M, gt_mask_height, gt_mask_width] representing the instance masks w.r.t. the `gt_boxes`.
`output_size`	two integers that represent the height and width of the output masks.
`mask_binarize_threshold`	a float representing the threshold for binarizing mask values. Default value is 0.5.

Returns
`iou`	a tensor with as a shape of [batch_size, N, M].

tfm.vision.mask_ops.instance_masks_overlap

Args

Returns