def test_different_iou_threshold(self):
boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80],
[200, 200, 210, 300], [200, 200, 210, 250]],
dtype=float)
boxlist = np_box_list.BoxList(boxes)
boxlist.add_field('scores', np.array([0.9, 0.8, 0.7, 0.6]))
max_output_size = 4
iou_threshold = .4
expected_boxes = np.array([
[0, 0, 20, 100],
[200, 200, 210, 300],
],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
iou_threshold = .5
expected_boxes = np.array(
[[0, 0, 20, 100], [200, 200, 210, 300], [200, 200, 210, 250]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
iou_threshold = .8
expected_boxes = np.array([[0, 0, 20, 100], [0, 0, 20, 80],
[200, 200, 210, 300], [200, 200, 210, 250]],
dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_with_no_scores_field(self):
boxlist = np_box_list.BoxList(self._boxes)
max_output_size = 3
iou_threshold = 0.5
with self.assertRaises(ValueError):
np_box_list_ops.non_max_suppression(boxlist, max_output_size,
iou_threshold)
def _compute_tp_fp_for_single_class(self, detected_boxes, detected_scores,
groundtruth_boxes,
groundtruth_is_difficult_list):
"""Labels boxes detected with the same class from the same image as tp/fp.
Args:
detected_boxes: A numpy array of shape [N, 4] representing detected box
coordinates
detected_scores: A 1-d numpy array of length N representing classification
score
groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
box coordinates
groundtruth_is_difficult_list: A boolean numpy array of length M denoting
whether a ground truth box is a difficult instance or not
Returns:
scores: A numpy array representing the detection scores
tp_fp_labels: a boolean numpy array indicating whether a detection is a
true positive.
"""
if groundtruth_boxes.size == 0:
is_gt_box_detected = np.array([], dtype=bool)
else:
is_gt_box_detected = np.zeros(groundtruth_boxes.shape[0],
dtype=bool)
if detected_boxes.size == 0:
return np.array([], dtype=float), np.array(
[], dtype=bool), is_gt_box_detected
detected_boxlist = np_box_list.BoxList(detected_boxes)
detected_boxlist.add_field('scores', detected_scores)
detected_boxlist = np_box_list_ops.non_max_suppression(
detected_boxlist, self.nms_max_output_boxes,
self.nms_iou_threshold)
scores = detected_boxlist.get_field('scores')
if groundtruth_boxes.size == 0:
return scores, np.zeros(detected_boxlist.num_boxes(),
dtype=bool), is_gt_box_detected
gt_boxlist = np_box_list.BoxList(groundtruth_boxes)
iou = np_box_list_ops.iou(detected_boxlist, gt_boxlist)
max_overlap_gt_ids = np.argmax(iou, axis=1)
tp_fp_labels = np.zeros(detected_boxlist.num_boxes(), dtype=bool)
is_matched_to_difficult_box = np.zeros(detected_boxlist.num_boxes(),
dtype=bool)
for i in range(detected_boxlist.num_boxes()):
gt_id = max_overlap_gt_ids[i]
if iou[i, gt_id] >= self.matching_iou_threshold:
if not groundtruth_is_difficult_list[gt_id]:
if not is_gt_box_detected[gt_id]:
tp_fp_labels[i] = True
is_gt_box_detected[gt_id] = True
else:
is_matched_to_difficult_box[i] = True
return scores[~is_matched_to_difficult_box], tp_fp_labels[
~is_matched_to_difficult_box], is_gt_box_detected
def test_select_from_ten_indentical_boxes(self):
boxes = np.array(10 * [[0, 0, 1, 1]], dtype=float)
boxlist = np_box_list.BoxList(boxes)
boxlist.add_field('scores', np.array(10 * [0.8]))
iou_threshold = .5
max_output_size = 3
expected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_select_at_most_two_from_three_clusters(self):
boxlist = np_box_list.BoxList(self._boxes)
boxlist.add_field('scores',
np.array([.9, .75, .6, .95, .5, .3], dtype=float))
max_output_size = 2
iou_threshold = 0.5
expected_boxes = np.array([[0, 10, 1, 11], [0, 0, 1, 1]], dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def test_nms_disabled_max_output_size_equals_three(self):
boxlist = np_box_list.BoxList(self._boxes)
boxlist.add_field('scores',
np.array([.9, .75, .6, .95, .2, .3], dtype=float))
max_output_size = 3
iou_threshold = 1. # No NMS
expected_boxes = np.array(
[[0, 10, 1, 11], [0, 0, 1, 1], [0, 0.1, 1, 1.1]], dtype=float)
nms_boxlist = np_box_list_ops.non_max_suppression(
boxlist, max_output_size, iou_threshold)
self.assertAllClose(nms_boxlist.get(), expected_boxes)
def _get_overlaps_and_scores_box_mode(self, detected_boxes,
detected_scores, groundtruth_boxes,
groundtruth_is_group_of_list):
"""Computes overlaps and scores between detected and groudntruth boxes.
Args:
detected_boxes: A numpy array of shape [N, 4] representing detected box
coordinates
detected_scores: A 1-d numpy array of length N representing classification
score
groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
box coordinates
groundtruth_is_group_of_list: A boolean numpy array of length M denoting
whether a ground truth box has group-of tag. If a groundtruth box
is group-of box, every detection matching this box is ignored.
Returns:
iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
gt_group_of_boxlist.num_boxes() == 0 it will be None.
scores: The score of the detected boxlist.
num_boxes: Number of non-maximum suppressed detected boxes.
"""
detected_boxlist = np_box_list.BoxList(detected_boxes)
detected_boxlist.add_field('scores', detected_scores)
detected_boxlist = np_box_list_ops.non_max_suppression(
detected_boxlist, self.nms_max_output_boxes,
self.nms_iou_threshold)
gt_non_group_of_boxlist = np_box_list.BoxList(
groundtruth_boxes[~groundtruth_is_group_of_list])
gt_group_of_boxlist = np_box_list.BoxList(
groundtruth_boxes[groundtruth_is_group_of_list])
iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
ioa = np.transpose(
np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist))
scores = detected_boxlist.get_field('scores')
num_boxes = detected_boxlist.num_boxes()
return iou, ioa, scores, num_boxes
