Python np_box_list_ops.iou方法代碼示例

# 需要導入模塊: from object_detection.utils import np_box_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_list_ops import iou [as 別名]
def _compute_is_aclass_correctly_detected_in_image(
      self, detected_boxes, detected_scores, groundtruth_boxes):
    """Compute CorLoc score for a single class.

    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

    Returns:
      is_class_correctly_detected_in_image: An integer 1 or 0 denoting whether a
          class is correctly detected in the image or not
    """
    if detected_boxes.size > 0:
      if groundtruth_boxes.size > 0:
        max_score_id = np.argmax(detected_scores)
        detected_boxlist = np_box_list.BoxList(
            np.expand_dims(detected_boxes[max_score_id, :], axis=0))
        gt_boxlist = np_box_list.BoxList(groundtruth_boxes)
        iou = np_box_list_ops.iou(detected_boxlist, gt_boxlist)
        if np.max(iou) >= self.matching_iou_threshold:
          return 1
    return 0 

# 需要導入模塊: from object_detection.utils import np_box_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_list_ops import iou [as 別名]
def test_iou(self):
    iou = np_box_list_ops.iou(self.boxlist1, self.boxlist2)
    expected_iou = np.array([[2.0 / 16.0, 0.0, 6.0 / 400.0],
                             [1.0 / 16.0, 0.0, 5.0 / 400.0]],
                            dtype=float)
    self.assertAllClose(iou, expected_iou) 

# 需要導入模塊: from object_detection.utils import np_box_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_list_ops import iou [as 別名]
def _get_overlaps_and_scores_relation_tuples(self, detected_box_tuples,
                                               groundtruth_box_tuples):
    """Computes overlaps and scores between detected and groundtruth tuples.

    Both detections and groundtruth boxes have the same class tuples.

    Args:
      detected_box_tuples: A numpy array of structures with shape [N,],
          representing N tuples, each tuple containing the same number of named
          bounding boxes.
          Each box is of the format [y_min, x_min, y_max, x_max]
      groundtruth_box_tuples: A float numpy array of structures with the shape
          [M,], representing M tuples, each tuple containing the same number
          of named bounding boxes.
          Each box is of the format [y_min, x_min, y_max, x_max]

    Returns:
      result_iou: A float numpy array of size
        [num_detected_tuples, num_gt_box_tuples].
    """

    result_iou = np.ones(
        (detected_box_tuples.shape[0], groundtruth_box_tuples.shape[0]),
        dtype=float)
    for field in detected_box_tuples.dtype.fields:
      detected_boxlist_field = np_box_list.BoxList(detected_box_tuples[field])
      gt_boxlist_field = np_box_list.BoxList(groundtruth_box_tuples[field])
      iou_field = np_box_list_ops.iou(detected_boxlist_field, gt_boxlist_field)
      result_iou = np.minimum(iou_field, result_iou)
    return result_iou 

# 需要導入模塊: from object_detection.utils import np_box_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_list_ops import iou [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_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_list_ops import iou [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_list_ops [as 別名]
# 或者: from object_detection.utils.np_box_list_ops import iou [as 別名]
def _get_overlaps_and_scores_relation_tuples(
      self, detected_box_tuples, detected_scores, groundtruth_box_tuples):
    """Computes overlaps and scores between detected and groundtruth tuples.

    Both detections and groundtruth boxes have the same class tuples.

    Args:
      detected_box_tuples: A numpy array of structures with shape [N,],
          representing N tuples, each tuple containing the same number of named
          bounding boxes.
          Each box is of the format [y_min, x_min, y_max, x_max]
      detected_scores: A float numpy array of shape [N,], representing
          the confidence scores of the detected N object instances.
      groundtruth_box_tuples: A float numpy array of structures with the shape
          [M,], representing M tuples, each tuple containing the same number
          of named bounding boxes.
          Each box is of the format [y_min, x_min, y_max, x_max]

    Returns:
      result_iou: A float numpy array of size
        [num_detected_tuples, num_gt_box_tuples].
      scores: The score of the detected boxlist.
    """

    result_iou = np.ones(
        (detected_box_tuples.shape[0], groundtruth_box_tuples.shape[0]),
        dtype=float)
    for field in detected_box_tuples.dtype.fields:
      detected_boxlist_field = np_box_list.BoxList(detected_box_tuples[field])
      detected_boxlist_field.add_field('scores', detected_scores)
      detected_boxlist_field = np_box_list_ops.sort_by_field(
          detected_boxlist_field, 'scores')
      gt_boxlist_field = np_box_list.BoxList(groundtruth_box_tuples[field])
      iou_field = np_box_list_ops.iou(detected_boxlist_field, gt_boxlist_field)
      result_iou = np.minimum(iou_field, result_iou)
    scores = detected_boxlist_field.get_field('scores')
    return result_iou, scores