content: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Evaluate Object Detection result on a single image.
  
  Annotate each detected result as true positives or false positive according to
  a predefined IOU ratio. Non Maximum Supression is used by default. Multi class
  detection is supported by default.
  Based on the settings, per image evaluation is either performed on boxes or
  on object masks.
  """
  import numpy as np
  
  from . import np_box_list_ops, np_box_mask_list_ops, np_box_list, np_box_mask_list
  
  
  class PerImageEvaluation(object):
    """Evaluate detection result of a single image."""
  
    def __init__(self,
                 num_groundtruth_classes,
                 matching_iou_threshold=0.5,
                 nms_iou_threshold=0.3,
                 nms_max_output_boxes=50):
      """Initialized PerImageEvaluation by evaluation parameters.
  
      Args:
        num_groundtruth_classes: Number of ground truth object classes
        matching_iou_threshold: A ratio of area intersection to union, which is
            the threshold to consider whether a detection is true positive or not
        nms_iou_threshold: IOU threshold used in Non Maximum Suppression.
        nms_max_output_boxes: Number of maximum output boxes in NMS.
      """
      self.matching_iou_threshold = matching_iou_threshold
      self.nms_iou_threshold = nms_iou_threshold
      self.nms_max_output_boxes = nms_max_output_boxes
      self.num_groundtruth_classes = num_groundtruth_classes
  
    def compute_object_detection_metrics(
        self, detected_boxes, detected_scores, detected_class_labels,
        groundtruth_boxes, groundtruth_class_labels,
        groundtruth_is_difficult_list, groundtruth_is_group_of_list,
        detected_masks=None, groundtruth_masks=None):
      """Evaluates detections as being tp, fp or ignored from a single image.
  
      The evaluation is done in two stages:
       1. All detections are matched to non group-of boxes; true positives are
          determined and detections matched to difficult boxes are ignored.
       2. Detections that are determined as false positives are matched against
          group-of boxes and ignored if matched.
  
      Args:
        detected_boxes: A float numpy array of shape [N, 4], representing N
            regions of detected object regions.
            Each row is of the format [y_min, x_min, y_max, x_max]
        detected_scores: A float numpy array of shape [N, 1], representing
            the confidence scores of the detected N object instances.
        detected_class_labels: A integer numpy array of shape [N, 1], repreneting
            the class labels of the detected N object instances.
        groundtruth_boxes: A float numpy array of shape [M, 4], representing M
            regions of object instances in ground truth
        groundtruth_class_labels: An integer numpy array of shape [M, 1],
            representing M class labels of object instances in ground truth
        groundtruth_is_difficult_list: A boolean numpy array of length M denoting
            whether a ground truth box is a difficult instance or not
        groundtruth_is_group_of_list: A boolean numpy array of length M denoting
            whether a ground truth box has group-of tag
        detected_masks: (optional) A uint8 numpy array of shape
          [N, height, width]. If not None, the metrics will be computed based
          on masks.
        groundtruth_masks: (optional) A uint8 numpy array of shape
          [M, height, width].
  
      Returns:
        scores: A list of C float numpy arrays. Each numpy array is of
            shape [K, 1], representing K scores detected with object class
            label c
        tp_fp_labels: A list of C boolean numpy arrays. Each numpy array
            is of shape [K, 1], representing K True/False positive label of
            object instances detected with class label c
        is_class_correctly_detected_in_image: a numpy integer array of
            shape [C, 1], indicating whether the correponding class has a least
            one instance being correctly detected in the image
      """
      detected_boxes, detected_scores, detected_class_labels, detected_masks = (
          self._remove_invalid_boxes(detected_boxes, detected_scores,
                                     detected_class_labels, detected_masks))
      scores, tp_fp_labels = self._compute_tp_fp(
          detected_boxes=detected_boxes,
          detected_scores=detected_scores,
          detected_class_labels=detected_class_labels,
          groundtruth_boxes=groundtruth_boxes,
          groundtruth_class_labels=groundtruth_class_labels,
          groundtruth_is_difficult_list=groundtruth_is_difficult_list,
          groundtruth_is_group_of_list=groundtruth_is_group_of_list,
          detected_masks=detected_masks,
          groundtruth_masks=groundtruth_masks)
  
      is_class_correctly_detected_in_image = self._compute_cor_loc(
          detected_boxes=detected_boxes,
          detected_scores=detected_scores,
          detected_class_labels=detected_class_labels,
          groundtruth_boxes=groundtruth_boxes,
          groundtruth_class_labels=groundtruth_class_labels,
          detected_masks=detected_masks,
          groundtruth_masks=groundtruth_masks)
  
      return scores, tp_fp_labels, is_class_correctly_detected_in_image
  
    def _compute_cor_loc(self, detected_boxes, detected_scores,
                         detected_class_labels, groundtruth_boxes,
                         groundtruth_class_labels, detected_masks=None,
                         groundtruth_masks=None):
      """Compute CorLoc score for object detection result.
  
      Args:
        detected_boxes: A float numpy array of shape [N, 4], representing N
            regions of detected object regions.
            Each row is of the format [y_min, x_min, y_max, x_max]
        detected_scores: A float numpy array of shape [N, 1], representing
            the confidence scores of the detected N object instances.
        detected_class_labels: A integer numpy array of shape [N, 1], repreneting
            the class labels of the detected N object instances.
        groundtruth_boxes: A float numpy array of shape [M, 4], representing M
            regions of object instances in ground truth
        groundtruth_class_labels: An integer numpy array of shape [M, 1],
            representing M class labels of object instances in ground truth
        detected_masks: (optional) A uint8 numpy array of shape
          [N, height, width]. If not None, the scores will be computed based
          on masks.
        groundtruth_masks: (optional) A uint8 numpy array of shape
          [M, height, width].
  
      Returns:
        is_class_correctly_detected_in_image: a numpy integer array of
            shape [C, 1], indicating whether the correponding class has a least
            one instance being correctly detected in the image
  
      Raises:
        ValueError: If detected masks is not None but groundtruth masks are None,
          or the other way around.
      """
      if (detected_masks is not None and
          groundtruth_masks is None) or (detected_masks is None and
                                         groundtruth_masks is not None):
        raise ValueError(
            'If `detected_masks` is provided, then `groundtruth_masks` should '
            'also be provided.'
        )
  
      is_class_correctly_detected_in_image = np.zeros(
          self.num_groundtruth_classes, dtype=int)
      for i in range(self.num_groundtruth_classes):
        (gt_boxes_at_ith_class, gt_masks_at_ith_class,
         detected_boxes_at_ith_class, detected_scores_at_ith_class,
         detected_masks_at_ith_class) = self._get_ith_class_arrays(
             detected_boxes, detected_scores, detected_masks,
             detected_class_labels, groundtruth_boxes, groundtruth_masks,
             groundtruth_class_labels, i)
        is_class_correctly_detected_in_image[i] = (
            self._compute_is_class_correctly_detected_in_image(
                detected_boxes=detected_boxes_at_ith_class,
                detected_scores=detected_scores_at_ith_class,
                groundtruth_boxes=gt_boxes_at_ith_class,
                detected_masks=detected_masks_at_ith_class,
                groundtruth_masks=gt_masks_at_ith_class))
  
      return is_class_correctly_detected_in_image
  
    def _compute_is_class_correctly_detected_in_image(
        self, detected_boxes, detected_scores, groundtruth_boxes,
        detected_masks=None, groundtruth_masks=None):
      """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
        detected_masks: (optional) A np.uint8 numpy array of shape
          [N, height, width]. If not None, the scores will be computed based
          on masks.
        groundtruth_masks: (optional) A np.uint8 numpy array of shape
          [M, height, width].
  
      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)
          mask_mode = False
          if detected_masks is not None and groundtruth_masks is not None:
            mask_mode = True
          if mask_mode:
            detected_boxlist = np_box_mask_list.BoxMaskList(
                box_data=np.expand_dims(detected_boxes[max_score_id], axis=0),
                mask_data=np.expand_dims(detected_masks[max_score_id], axis=0))
            gt_boxlist = np_box_mask_list.BoxMaskList(
                box_data=groundtruth_boxes, mask_data=groundtruth_masks)
            iou = np_box_mask_list_ops.iou(detected_boxlist, gt_boxlist)
          else:
            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
  
    def _compute_tp_fp(self, detected_boxes, detected_scores,
                       detected_class_labels, groundtruth_boxes,
                       groundtruth_class_labels, groundtruth_is_difficult_list,
                       groundtruth_is_group_of_list,
                       detected_masks=None, groundtruth_masks=None):
      """Labels true/false positives of detections of an image across all classes.
  
      Args:
        detected_boxes: A float numpy array of shape [N, 4], representing N
            regions of detected object regions.
            Each row is of the format [y_min, x_min, y_max, x_max]
        detected_scores: A float numpy array of shape [N, 1], representing
            the confidence scores of the detected N object instances.
        detected_class_labels: A integer numpy array of shape [N, 1], repreneting
            the class labels of the detected N object instances.
        groundtruth_boxes: A float numpy array of shape [M, 4], representing M
            regions of object instances in ground truth
        groundtruth_class_labels: An integer numpy array of shape [M, 1],
            representing M class labels of object instances in ground truth
        groundtruth_is_difficult_list: A boolean numpy array of length M denoting
            whether a ground truth box is a difficult instance or not
        groundtruth_is_group_of_list: A boolean numpy array of length M denoting
            whether a ground truth box has group-of tag
        detected_masks: (optional) A np.uint8 numpy array of shape
          [N, height, width]. If not None, the scores will be computed based
          on masks.
        groundtruth_masks: (optional) A np.uint8 numpy array of shape
          [M, height, width].
  
      Returns:
        result_scores: A list of float numpy arrays. Each numpy array is of
            shape [K, 1], representing K scores detected with object class
            label c
        result_tp_fp_labels: A list of boolean numpy array. Each numpy array is of
            shape [K, 1], representing K True/False positive label of object
            instances detected with class label c
  
      Raises:
        ValueError: If detected masks is not None but groundtruth masks are None,
          or the other way around.
      """
      if detected_masks is not None and groundtruth_masks is None:
        raise ValueError(
            'Detected masks is available but groundtruth masks is not.')
      if detected_masks is None and groundtruth_masks is not None:
        raise ValueError(
            'Groundtruth masks is available but detected masks is not.')
  
      result_scores = []
      result_tp_fp_labels = []
      for i in range(self.num_groundtruth_classes):
        groundtruth_is_difficult_list_at_ith_class = (
            groundtruth_is_difficult_list[groundtruth_class_labels == i])
        groundtruth_is_group_of_list_at_ith_class = (
            groundtruth_is_group_of_list[groundtruth_class_labels == i])
        (gt_boxes_at_ith_class, gt_masks_at_ith_class,
         detected_boxes_at_ith_class, detected_scores_at_ith_class,
         detected_masks_at_ith_class) = self._get_ith_class_arrays(
             detected_boxes, detected_scores, detected_masks,
             detected_class_labels, groundtruth_boxes, groundtruth_masks,
             groundtruth_class_labels, i)
        scores, tp_fp_labels = self._compute_tp_fp_for_single_class(
            detected_boxes=detected_boxes_at_ith_class,
            detected_scores=detected_scores_at_ith_class,
            groundtruth_boxes=gt_boxes_at_ith_class,
            groundtruth_is_difficult_list=
            groundtruth_is_difficult_list_at_ith_class,
            groundtruth_is_group_of_list=
            groundtruth_is_group_of_list_at_ith_class,
            detected_masks=detected_masks_at_ith_class,
            groundtruth_masks=gt_masks_at_ith_class)
        result_scores.append(scores)
        result_tp_fp_labels.append(tp_fp_labels)
      return result_scores, result_tp_fp_labels
  
    def _get_overlaps_and_scores_mask_mode(
        self, detected_boxes, detected_scores, detected_masks, groundtruth_boxes,
        groundtruth_masks, groundtruth_is_group_of_list):
      """Computes overlaps and scores between detected and groudntruth masks.
  
      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
        detected_masks: A uint8 numpy array of shape [N, height, width]. If not
            None, the scores will be computed based on masks.
        groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
            box coordinates
        groundtruth_masks: A uint8 numpy array of shape [M, height, width].
        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_mask_list.BoxMaskList(
          box_data=detected_boxes, mask_data=detected_masks)
      detected_boxlist.add_field('scores', detected_scores)
      detected_boxlist = np_box_mask_list_ops.non_max_suppression(
          detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold)
      gt_non_group_of_boxlist = np_box_mask_list.BoxMaskList(
          box_data=groundtruth_boxes[~groundtruth_is_group_of_list],
          mask_data=groundtruth_masks[~groundtruth_is_group_of_list])
      gt_group_of_boxlist = np_box_mask_list.BoxMaskList(
          box_data=groundtruth_boxes[groundtruth_is_group_of_list],
          mask_data=groundtruth_masks[groundtruth_is_group_of_list])
      iou = np_box_mask_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
      ioa = np_box_mask_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
  
    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
  
    def _compute_tp_fp_for_single_class(
        self, detected_boxes, detected_scores, groundtruth_boxes,
        groundtruth_is_difficult_list, groundtruth_is_group_of_list,
        detected_masks=None, groundtruth_masks=None):
      """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. If a
            groundtruth box is difficult, every detection matching this box
            is ignored.
        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.
        detected_masks: (optional) A uint8 numpy array of shape
          [N, height, width]. If not None, the scores will be computed based
          on masks.
        groundtruth_masks: (optional) A uint8 numpy array of shape
          [M, height, width].
  
      Returns:
        Two arrays of the same size, containing all boxes that were evaluated as
        being true positives or false positives; if a box matched to a difficult
        box or to a group-of box, it is ignored.
  
        scores: A numpy array representing the detection scores.
        tp_fp_labels: a boolean numpy array indicating whether a detection is a
            true positive.
      """
      if detected_boxes.size == 0:
        return np.array([], dtype=float), np.array([], dtype=bool)
  
      mask_mode = False
      if detected_masks is not None and groundtruth_masks is not None:
        mask_mode = True
  
      if mask_mode:
        (iou, ioa, scores,
         num_detected_boxes) = self._get_overlaps_and_scores_mask_mode(
             detected_boxes=detected_boxes,
             detected_scores=detected_scores,
             detected_masks=detected_masks,
             groundtruth_boxes=groundtruth_boxes,
             groundtruth_masks=groundtruth_masks,
             groundtruth_is_group_of_list=groundtruth_is_group_of_list)
      else:
        (iou, ioa, scores,
         num_detected_boxes) = self._get_overlaps_and_scores_box_mode(
             detected_boxes=detected_boxes,
             detected_scores=detected_scores,
             groundtruth_boxes=groundtruth_boxes,
             groundtruth_is_group_of_list=groundtruth_is_group_of_list)
  
      if groundtruth_boxes.size == 0:
        return scores, np.zeros(num_detected_boxes, dtype=bool)
  
      tp_fp_labels = np.zeros(num_detected_boxes, dtype=bool)
      is_matched_to_difficult_box = np.zeros(num_detected_boxes, dtype=bool)
      is_matched_to_group_of_box = np.zeros(num_detected_boxes, dtype=bool)
  
      # The evaluation is done in two stages:
      # 1. All detections are matched to non group-of boxes; true positives are
      #    determined and detections matched to difficult boxes are ignored.
      # 2. Detections that are determined as false positives are matched against
      #    group-of boxes and ignored if matched.
  
      # Tp-fp evaluation for non-group of boxes (if any).
      if iou.shape[1] > 0:
        groundtruth_nongroup_of_is_difficult_list = groundtruth_is_difficult_list[
            ~groundtruth_is_group_of_list]
        max_overlap_gt_ids = np.argmax(iou, axis=1)
        is_gt_box_detected = np.zeros(iou.shape[1], dtype=bool)
        for i in range(num_detected_boxes):
          gt_id = max_overlap_gt_ids[i]
          if iou[i, gt_id] >= self.matching_iou_threshold:
            if not groundtruth_nongroup_of_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
  
      # Tp-fp evaluation for group of boxes.
      if ioa.shape[0] > 0:
        max_overlap_group_of_gt = np.max(ioa, axis=0)
        for i in range(num_detected_boxes):
          if (not tp_fp_labels[i] and not is_matched_to_difficult_box[i] and
              max_overlap_group_of_gt[i] >= self.matching_iou_threshold):
            is_matched_to_group_of_box[i] = True
  
      return scores[~is_matched_to_difficult_box
                    & ~is_matched_to_group_of_box], tp_fp_labels[
                        ~is_matched_to_difficult_box
                        & ~is_matched_to_group_of_box]
  
    def _get_ith_class_arrays(self, detected_boxes, detected_scores,
                              detected_masks, detected_class_labels,
                              groundtruth_boxes, groundtruth_masks,
                              groundtruth_class_labels, class_index):
      """Returns numpy arrays belonging to class with index `class_index`.
  
      Args:
        detected_boxes: A numpy array containing detected boxes.
        detected_scores: A numpy array containing detected scores.
        detected_masks: A numpy array containing detected masks.
        detected_class_labels: A numpy array containing detected class labels.
        groundtruth_boxes: A numpy array containing groundtruth boxes.
        groundtruth_masks: A numpy array containing groundtruth masks.
        groundtruth_class_labels: A numpy array containing groundtruth class
          labels.
        class_index: An integer index.
  
      Returns:
        gt_boxes_at_ith_class: A numpy array containing groundtruth boxes labeled
          as ith class.
        gt_masks_at_ith_class: A numpy array containing groundtruth masks labeled
          as ith class.
        detected_boxes_at_ith_class: A numpy array containing detected boxes
          corresponding to the ith class.
        detected_scores_at_ith_class: A numpy array containing detected scores
          corresponding to the ith class.
        detected_masks_at_ith_class: A numpy array containing detected masks
          corresponding to the ith class.
      """
      selected_groundtruth = (groundtruth_class_labels == class_index)
      gt_boxes_at_ith_class = groundtruth_boxes[selected_groundtruth]
      if groundtruth_masks is not None:
        gt_masks_at_ith_class = groundtruth_masks[selected_groundtruth]
      else:
        gt_masks_at_ith_class = None
      selected_detections = (detected_class_labels == class_index)
      detected_boxes_at_ith_class = detected_boxes[selected_detections]
      detected_scores_at_ith_class = detected_scores[selected_detections]
      if detected_masks is not None:
        detected_masks_at_ith_class = detected_masks[selected_detections]
      else:
        detected_masks_at_ith_class = None
      return (gt_boxes_at_ith_class, gt_masks_at_ith_class,
              detected_boxes_at_ith_class, detected_scores_at_ith_class,
              detected_masks_at_ith_class)
  
    def _remove_invalid_boxes(self, detected_boxes, detected_scores,
                              detected_class_labels, detected_masks=None):
      """Removes entries with invalid boxes.
  
      A box is invalid if either its xmax is smaller than its xmin, or its ymax
      is smaller than its ymin.
  
      Args:
        detected_boxes: A float numpy array of size [num_boxes, 4] containing box
          coordinates in [ymin, xmin, ymax, xmax] format.
        detected_scores: A float numpy array of size [num_boxes].
        detected_class_labels: A int32 numpy array of size [num_boxes].
        detected_masks: A uint8 numpy array of size [num_boxes, height, width].
  
      Returns:
        valid_detected_boxes: A float numpy array of size [num_valid_boxes, 4]
          containing box coordinates in [ymin, xmin, ymax, xmax] format.
        valid_detected_scores: A float numpy array of size [num_valid_boxes].
        valid_detected_class_labels: A int32 numpy array of size
          [num_valid_boxes].
        valid_detected_masks: A uint8 numpy array of size
          [num_valid_boxes, height, width].
      """
      valid_indices = np.logical_and(detected_boxes[:, 0] < detected_boxes[:, 2],
                                     detected_boxes[:, 1] < detected_boxes[:, 3])
      detected_boxes = detected_boxes[valid_indices]
      detected_scores = detected_scores[valid_indices]
      detected_class_labels = detected_class_labels[valid_indices]
      if detected_masks is not None:
        detected_masks = detected_masks[valid_indices]
      return [
          detected_boxes, detected_scores, detected_class_labels, detected_masks
      ]
  