Python np_box_mask_list_ops.non_max_suppression方法代碼示例

# 需要導入模塊: from object_detection.utils import np_box_mask_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_mask_list_ops import non_max_suppression [as 別名]
def test_with_no_scores_field(self):
    box_mask_list = np_box_mask_list.BoxMaskList(
        box_data=self.boxes1, mask_data=self.masks1)
    max_output_size = 3
    iou_threshold = 0.5

    with self.assertRaises(ValueError):
      np_box_mask_list_ops.non_max_suppression(
          box_mask_list, max_output_size, iou_threshold) 

# 需要導入模塊: from object_detection.utils import np_box_mask_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_mask_list_ops import non_max_suppression [as 別名]
def test_nms_disabled_max_output_size_equals_one(self):
    box_mask_list = np_box_mask_list.BoxMaskList(
        box_data=self.boxes2, mask_data=self.masks2)
    box_mask_list.add_field('scores',
                            np.array([.9, .75, .6], dtype=float))
    max_output_size = 1
    iou_threshold = 1.  # No NMS
    expected_boxes = np.array([[3.0, 4.0, 6.0, 8.0]], dtype=float)
    expected_masks = np.array(
        [[[0, 1, 0], [1, 1, 1], [0, 0, 0]]], dtype=np.uint8)
    nms_box_mask_list = np_box_mask_list_ops.non_max_suppression(
        box_mask_list, max_output_size, iou_threshold)
    self.assertAllClose(nms_box_mask_list.get(), expected_boxes)
    self.assertAllClose(nms_box_mask_list.get_masks(), expected_masks) 

# 需要導入模塊: from object_detection.utils import np_box_mask_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_mask_list_ops import non_max_suppression [as 別名]
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 

# 需要導入模塊: from object_detection.utils import np_box_mask_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_mask_list_ops import non_max_suppression [as 別名]
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 

# 需要導入模塊: from object_detection.utils import np_box_mask_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_mask_list_ops import non_max_suppression [as 別名]
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