text: """
  Copyright (c) 2018-2021 Intel Corporation
  
  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.
  """
  
  import numpy as np
  from .base_representation import BaseRepresentation
  
  
  class PoseEstimationRepresentation(BaseRepresentation):
      def __init__(self, identifier='', x_values=None, y_values=None, visibility=None, labels=None):
          super().__init__(identifier)
          self.x_values = x_values if np.size(x_values) > 0 else []
          self.y_values = y_values if np.size(y_values) > 0 else []
          self.visibility = visibility if visibility is not None and np.size(visibility) > 0 else [2] * len(self.x_values)
          self.labels = labels if labels is not None else np.array([1]*len(self.x_values))
  
      @property
      def areas(self):
          areas = self.metadata.get('areas')
          if areas:
              return areas
          if self.size == 0:
              return []
          x_mins = np.min(self.x_values, axis=1)
          x_maxs = np.max(self.x_values, axis=1)
          y_mins = np.min(self.y_values, axis=1)
          y_maxs = np.max(self.y_values, axis=1)
          return (x_maxs - x_mins) * (y_maxs - y_mins)
  
      @property
      def bboxes(self):
          rects = self.metadata.get('rects')
          if rects:
              return rects
          if self.size == 0:
              return []
          x_mins = np.min(self.x_values, axis=1)
          x_maxs = np.max(self.x_values, axis=1)
          y_mins = np.min(self.y_values, axis=1)
          y_maxs = np.max(self.y_values, axis=1)
          return [[x_min, y_min, x_max, y_max] for x_min, y_min, x_max, y_max in zip(x_mins, y_mins, x_maxs, y_maxs)]
  
      @property
      def size(self):
          return len(self.x_values)
  
  
  class PoseEstimationAnnotation(PoseEstimationRepresentation):
      pass
  
  
  class PoseEstimationPrediction(PoseEstimationRepresentation):
      def __init__(self, identifier='', x_values=None, y_values=None, visibility=None, scores=None, labels=None):
          super().__init__(identifier, x_values, y_values, visibility, labels)
          self.scores = scores if scores is not None else np.array([])
  
      def to_annotation(self, **kwargs):
          return PoseEstimationAnnotation(self.identifier, self.x_values, self.y_values, self.visibility, self.labels)
  