本文整理汇总了Python中object_detection.utils.np_box_list_ops.ioa方法的典型用法代码示例。如果您正苦于以下问题：Python np_box_list_ops.ioa方法的具体用法？Python np_box_list_ops.ioa怎么用？Python np_box_list_ops.ioa使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.utils.np_box_list_ops的用法示例。
在下文中一共展示了np_box_list_ops.ioa方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_ioa
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import ioa [as 别名]
def test_ioa(self):
boxlist1 = np_box_list.BoxList(
np.array(
[[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=
np.float32))
boxlist2 = np_box_list.BoxList(
np.array(
[[0.5, 0.25, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype=np.float32))
ioa21 = np_box_list_ops.ioa(boxlist2, boxlist1)
expected_ioa21 = np.array([[0.5, 0.0],
[1.0, 1.0]],
dtype=np.float32)
self.assertAllClose(ioa21, expected_ioa21) 示例2: _get_overlaps_and_scores_box_mode
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import ioa [as 别名]
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 示例3: _get_overlaps_and_scores_box_mode
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import ioa [as 别名]
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_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 示例4: _get_overlaps_and_scores_box_mode
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import ioa [as 别名]
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 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:40，代码来源:per_image_evaluation.py