text: # 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 object_detection.utils import np_box_list
  from object_detection.utils import np_box_list_ops
  from object_detection.utils import np_box_mask_list
  from object_detection.utils import np_box_mask_list_ops
  
  
  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,
                 group_of_weight=0.0):
      """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.
        group_of_weight: Weight of the group-of boxes.
      """
      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
      self.group_of_weight = group_of_weight
  
    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 weighted 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 weighted 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]. Can have empty masks, i.e. where all values are 0.
  
      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.transpose(
          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.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
  
    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
  
      iou = np.ndarray([0, 0])
      ioa = np.ndarray([0, 0])
      iou_mask = np.ndarray([0, 0])
      ioa_mask = np.ndarray([0, 0])
      if mask_mode:
        # For Instance Segmentation Evaluation on Open Images V5, not all boxed
        # instances have corresponding segmentation annotations. Those boxes that
        # dont have segmentation annotations are represented as empty masks in
        # groundtruth_masks nd array.
        mask_presence_indicator = (np.sum(groundtruth_masks, axis=(1, 2)) > 0)
  
        (iou_mask, ioa_mask, 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[mask_presence_indicator, :],
             groundtruth_masks=groundtruth_masks[mask_presence_indicator, :],
             groundtruth_is_group_of_list=groundtruth_is_group_of_list[
                 mask_presence_indicator])
        if sum(mask_presence_indicator) < len(mask_presence_indicator):
          # Not all masks are present - some masks are empty
          (iou, ioa, _,
           num_detected_boxes) = self._get_overlaps_and_scores_box_mode(
               detected_boxes=detected_boxes,
               detected_scores=detected_scores,
               groundtruth_boxes=groundtruth_boxes[~mask_presence_indicator, :],
               groundtruth_is_group_of_list=groundtruth_is_group_of_list[
                   ~mask_presence_indicator])
        num_detected_boxes = detected_boxes.shape[0]
      else:
        mask_presence_indicator = np.zeros(
            groundtruth_is_group_of_list.shape, dtype=bool)
        (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_box = np.zeros(num_detected_boxes, dtype=bool)
      is_matched_to_difficult = np.zeros(num_detected_boxes, dtype=bool)
      is_matched_to_group_of = np.zeros(num_detected_boxes, dtype=bool)
  
      def compute_match_iou(iou, groundtruth_nongroup_of_is_difficult_list,
                            is_box):
        """Computes TP/FP for non group-of box matching.
  
        The function updates the following local variables:
          tp_fp_labels - if a box is matched to group-of
          is_matched_to_difficult - the detections that were processed at this are
            matched to difficult box.
          is_matched_to_box - the detections that were processed at this stage are
            marked as is_box.
  
        Args:
          iou: intersection-over-union matrix [num_gt_boxes]x[num_det_boxes].
          groundtruth_nongroup_of_is_difficult_list: boolean that specifies if gt
            box is difficult.
          is_box: boolean that specifies if currently boxes or masks are
            processed.
        """
        max_overlap_gt_ids = np.argmax(iou, axis=1)
        is_gt_detected = np.zeros(iou.shape[1], dtype=bool)
        for i in range(num_detected_boxes):
          gt_id = max_overlap_gt_ids[i]
          is_evaluatable = (not tp_fp_labels[i] and
                            not is_matched_to_difficult[i] and
                            iou[i, gt_id] >= self.matching_iou_threshold and
                            not is_matched_to_group_of[i])
          if is_evaluatable:
            if not groundtruth_nongroup_of_is_difficult_list[gt_id]:
              if not is_gt_detected[gt_id]:
                tp_fp_labels[i] = True
                is_gt_detected[gt_id] = True
                is_matched_to_box[i] = is_box
            else:
              is_matched_to_difficult[i] = True
  
      def compute_match_ioa(ioa, is_box):
        """Computes TP/FP for group-of box matching.
  
        The function updates the following local variables:
          is_matched_to_group_of - if a box is matched to group-of
          is_matched_to_box - the detections that were processed at this stage are
            marked as is_box.
  
        Args:
          ioa: intersection-over-area matrix [num_gt_boxes]x[num_det_boxes].
          is_box: boolean that specifies if currently boxes or masks are
            processed.
  
        Returns:
          scores_group_of: of detections matched to group-of boxes
          [num_groupof_matched].
          tp_fp_labels_group_of: boolean array of size [num_groupof_matched], all
            values are True.
        """
        scores_group_of = np.zeros(ioa.shape[1], dtype=float)
        tp_fp_labels_group_of = self.group_of_weight * np.ones(
            ioa.shape[1], dtype=float)
        max_overlap_group_of_gt_ids = np.argmax(ioa, axis=1)
        for i in range(num_detected_boxes):
          gt_id = max_overlap_group_of_gt_ids[i]
          is_evaluatable = (not tp_fp_labels[i] and
                            not is_matched_to_difficult[i] and
                            ioa[i, gt_id] >= self.matching_iou_threshold and
                            not is_matched_to_group_of[i])
          if is_evaluatable:
            is_matched_to_group_of[i] = True
            is_matched_to_box[i] = is_box
            scores_group_of[gt_id] = max(scores_group_of[gt_id], scores[i])
        selector = np.where((scores_group_of > 0) & (tp_fp_labels_group_of > 0))
        scores_group_of = scores_group_of[selector]
        tp_fp_labels_group_of = tp_fp_labels_group_of[selector]
  
        return scores_group_of, tp_fp_labels_group_of
  
      # The evaluation is done in two stages:
      # 1. Evaluate all objects that actually have instance level masks.
      # 2. Evaluate all objects that are not already evaluated as boxes.
      if iou_mask.shape[1] > 0:
        groundtruth_is_difficult_mask_list = groundtruth_is_difficult_list[
            mask_presence_indicator]
        groundtruth_is_group_of_mask_list = groundtruth_is_group_of_list[
            mask_presence_indicator]
        compute_match_iou(
            iou_mask,
            groundtruth_is_difficult_mask_list[
                ~groundtruth_is_group_of_mask_list],
            is_box=False)
  
      scores_mask_group_of = np.ndarray([0], dtype=float)
      tp_fp_labels_mask_group_of = np.ndarray([0], dtype=float)
      if ioa_mask.shape[1] > 0:
        scores_mask_group_of, tp_fp_labels_mask_group_of = compute_match_ioa(
            ioa_mask, is_box=False)
  
      # Tp-fp evaluation for non-group of boxes (if any).
      if iou.shape[1] > 0:
        groundtruth_is_difficult_box_list = groundtruth_is_difficult_list[
            ~mask_presence_indicator]
        groundtruth_is_group_of_box_list = groundtruth_is_group_of_list[
            ~mask_presence_indicator]
        compute_match_iou(
            iou,
            groundtruth_is_difficult_box_list[~groundtruth_is_group_of_box_list],
            is_box=True)
  
      scores_box_group_of = np.ndarray([0], dtype=float)
      tp_fp_labels_box_group_of = np.ndarray([0], dtype=float)
      if ioa.shape[1] > 0:
        scores_box_group_of, tp_fp_labels_box_group_of = compute_match_ioa(
            ioa, is_box=True)
  
      if mask_mode:
        # Note: here crowds are treated as ignore regions.
        valid_entries = (~is_matched_to_difficult & ~is_matched_to_group_of
                         & ~is_matched_to_box)
        return np.concatenate(
            (scores[valid_entries], scores_mask_group_of)), np.concatenate(
                (tp_fp_labels[valid_entries].astype(float),
                 tp_fp_labels_mask_group_of))
      else:
        valid_entries = (~is_matched_to_difficult & ~is_matched_to_group_of)
        return np.concatenate(
            (scores[valid_entries], scores_box_group_of)), np.concatenate(
                (tp_fp_labels[valid_entries].astype(float),
                 tp_fp_labels_box_group_of))
  
    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
      ]
  