def iou(box_mask_list1, box_mask_list2):
"""Computes pairwise intersection-over-union between box and mask collections.
Args:
box_mask_list1: BoxMaskList holding N boxes and masks
box_mask_list2: BoxMaskList holding M boxes and masks
Returns:
a numpy array with shape [N, M] representing pairwise iou scores.
"""
return np_mask_ops.iou(box_mask_list1.get_masks(),
box_mask_list2.get_masks())
def testIOU(self):
iou = np_mask_ops.iou(self.masks1, self.masks2)
expected_iou = np.array(
[[1.0, 0.0, 8.0 / 25.0], [0.0, 9.0 / 16.0, 7.0 / 28.0]],
dtype=np.float32)
self.assertAllClose(iou, expected_iou)
def non_max_suppression(box_mask_list,
max_output_size=10000,
iou_threshold=1.0,
score_threshold=-10.0):
"""Non maximum suppression.
This op greedily selects a subset of detection bounding boxes, pruning
away boxes that have high IOU (intersection over union) overlap (> thresh)
with already selected boxes. In each iteration, the detected bounding box with
highest score in the available pool is selected.
Args:
box_mask_list: np_box_mask_list.BoxMaskList holding N boxes. Must contain
a 'scores' field representing detection scores. All scores belong to the
same class.
max_output_size: maximum number of retained boxes
iou_threshold: intersection over union threshold.
score_threshold: minimum score threshold. Remove the boxes with scores
less than this value. Default value is set to -10. A very
low threshold to pass pretty much all the boxes, unless
the user sets a different score threshold.
Returns:
an np_box_mask_list.BoxMaskList holding M boxes where M <= max_output_size
Raises:
ValueError: if 'scores' field does not exist
ValueError: if threshold is not in [0, 1]
ValueError: if max_output_size < 0
"""
if not box_mask_list.has_field('scores'):
raise ValueError('Field scores does not exist')
if iou_threshold < 0. or iou_threshold > 1.0:
raise ValueError('IOU threshold must be in [0, 1]')
if max_output_size < 0:
raise ValueError('max_output_size must be bigger than 0.')
box_mask_list = filter_scores_greater_than(box_mask_list, score_threshold)
if box_mask_list.num_boxes() == 0:
return box_mask_list
box_mask_list = sort_by_field(box_mask_list, 'scores')
# Prevent further computation if NMS is disabled.
if iou_threshold == 1.0:
if box_mask_list.num_boxes() > max_output_size:
selected_indices = np.arange(max_output_size)
return gather(box_mask_list, selected_indices)
else:
return box_mask_list
masks = box_mask_list.get_masks()
num_masks = box_mask_list.num_boxes()
# is_index_valid is True only for all remaining valid boxes,
is_index_valid = np.full(num_masks, 1, dtype=bool)
selected_indices = []
num_output = 0
for i in range(num_masks):
if num_output < max_output_size:
if is_index_valid[i]:
num_output += 1
selected_indices.append(i)
is_index_valid[i] = False
valid_indices = np.where(is_index_valid)[0]
if valid_indices.size == 0:
break
intersect_over_union = np_mask_ops.iou(
np.expand_dims(masks[i], axis=0), masks[valid_indices])
intersect_over_union = np.squeeze(intersect_over_union, axis=0)
is_index_valid[valid_indices] = np.logical_and(
is_index_valid[valid_indices],
intersect_over_union <= iou_threshold)
return gather(box_mask_list, np.array(selected_indices))
def testIOU(self):
iou = np_mask_ops.iou(self.masks1, self.masks2)
expected_iou = np.array(
[[1.0, 0.0, 8.0/25.0], [0.0, 9.0 / 16.0, 7.0 / 28.0]], dtype=np.float32)
self.assertAllClose(iou, expected_iou)
