def filter_bad_area(boxes: tf.Tensor, labels: tf.Tensor) -> tf.Tensor:
"""Remove all the boxes that have an area less or equal to 0.
Arguments:
- *boxes*: A tensor of shape [num_boxes, (y1, x1, y2, x2)]
- *labels*: A tensor of shape [num_boxes, ]
Returns:
- *boxes*: A tensor of shape [N <= num_boxes, (y1, x1, y2, x2)]
- *labels*: A tensor of shape [N <= num_boxes, ]
"""
area = compute_area(boxes)
return tf.gather_nd(boxes, tf.where(area > 0)), tf.gather_nd(labels, tf.where(area > 0))
def filter_bad_area(
groundtruths: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]:
"""Remove all the boxes that have an area less or equal to 0.
Arguments:
- *groundtruths*: A dict with the following keys:
1. bbox: A tensor of shape [num_boxes, (y1, x1, y2, x2)]
2. label: A tensor of shape [num_boxes, ]
Returns:
- *groundtruths*: All the groundtruths which match have not been filtered.
"""
area = compute_area(groundtruths[BoxField.BOXES])
filter_area = tf.where(area > 0)
return _filter(groundtruths, filter_area)
def assign_pyramid_level_to_boxes(boxes, num_level, level_target=2):
"""Compute the pyramid level of an RoI
Arguments:
- *boxes*: A tensor of shape
[nb_batches * nb_boxes, 4]
- *num_level*: Assign all the boxes mapped to a superior level to the num_level.
level_target: Will affect all the boxes of area 224^2 to the level_target of the pyramid.
Returns:
A 2-D tensor of type int32 and shape [nb_batches * nb_boxes]
corresponding to the target level of the pyramid.
"""
denominator = tf.constant(224, dtype=boxes.dtype)
area = compute_area(boxes)
k = level_target + tf.math.log(tf.sqrt(area) / denominator + K.epsilon(
)) * tf.cast(1. / tf.math.log(2.0), dtype=boxes.dtype)
k = tf.cast(k, tf.int32)
k = tf.clip_by_value(k, 0, num_level - 1)
k = tf.reshape(k, [-1])
return k
def test_compute_area():
boxes = tf.constant([[0.0, 0.0, 10.0, 20.0], [1.0, 2.0, 3.0, 4.0]])
exp_output = [200.0, 4.0]
areas_output = box_ops.compute_area(boxes)
np.testing.assert_allclose(areas_output, exp_output)