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.
  """
  from __future__ import (
      absolute_import,
      division,
      print_function,
      unicode_literals,
  )
  import numpy as np
  
  from . import (
      np_box_list,
      np_box_list_ops,
      np_box_mask_list,
      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):
          """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
          """
          self.matching_iou_threshold = matching_iou_threshold
          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
          """
          (
              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,
          )
  
          return scores, tp_fp_labels
  
      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_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)
          gt_non_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)
          scores = detected_boxlist.get_field("scores")
          num_boxes = detected_boxlist.num_boxes()
          return iou, None, 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)
  
          (
              iou,
              _,
              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
  
          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,
          ]
  