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
  #
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  # 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.
  # ==============================================================================
  
  """Operations for np_box_mask_list.BoxMaskList.
  
  Example box operations that are supported:
    * Areas: compute bounding box areas
    * IOU: pairwise intersection-over-union scores
  """
  import numpy as np
  
  from . import np_box_list_ops, np_mask_ops, np_box_mask_list
  
  
  def box_list_to_box_mask_list(boxlist):
    """Converts a BoxList containing 'masks' into a BoxMaskList.
  
    Args:
      boxlist: An np_box_list.BoxList object.
  
    Returns:
      An np_box_mask_list.BoxMaskList object.
  
    Raises:
      ValueError: If boxlist does not contain `masks` as a field.
    """
    if not boxlist.has_field('masks'):
      raise ValueError('boxlist does not contain mask field.')
    box_mask_list = np_box_mask_list.BoxMaskList(
        box_data=boxlist.get(),
        mask_data=boxlist.get_field('masks'))
    extra_fields = boxlist.get_extra_fields()
    for key in extra_fields:
      if key != 'masks':
        box_mask_list.data[key] = boxlist.get_field(key)
    return box_mask_list
  
  
  def area(box_mask_list):
    """Computes area of masks.
  
    Args:
      box_mask_list: np_box_mask_list.BoxMaskList holding N boxes and masks
  
    Returns:
      a numpy array with shape [N*1] representing mask areas
    """
    return np_mask_ops.area(box_mask_list.get_masks())
  
  
  def intersection(box_mask_list1, box_mask_list2):
    """Compute pairwise intersection areas between masks.
  
    Args:
      box_mask_list1: BoxMaskList holding N boxes and masks
      box_mask_list2: BoxMaskList holding M boxes and masks
  
    Returns:
      a numpy array with shape [N*M] representing pairwise intersection area
    """
    return np_mask_ops.intersection(box_mask_list1.get_masks(),
                                    box_mask_list2.get_masks())
  
  
  def iou(box_mask_list1, box_mask_list2):
    """Computes pairwise intersection-over-union between box and mask collections.
  
    Args:
      box_mask_list1: BoxMaskList holding N boxes and masks
      box_mask_list2: BoxMaskList holding M boxes and masks
  
    Returns:
      a numpy array with shape [N, M] representing pairwise iou scores.
    """
    return np_mask_ops.iou(box_mask_list1.get_masks(),
                           box_mask_list2.get_masks())
  
  
  def ioa(box_mask_list1, box_mask_list2):
    """Computes pairwise intersection-over-area between box and mask collections.
  
    Intersection-over-area (ioa) between two masks mask1 and mask2 is defined as
    their intersection area over mask2's area. Note that ioa is not symmetric,
    that is, IOA(mask1, mask2) != IOA(mask2, mask1).
  
    Args:
      box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks
      box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks
  
    Returns:
      a numpy array with shape [N, M] representing pairwise ioa scores.
    """
    return np_mask_ops.ioa(box_mask_list1.get_masks(), box_mask_list2.get_masks())
  
  
  def gather(box_mask_list, indices, fields=None):
    """Gather boxes from np_box_mask_list.BoxMaskList according to indices.
  
    By default, gather returns boxes corresponding to the input index list, as
    well as all additional fields stored in the box_mask_list (indexing into the
    first dimension).  However one can optionally only gather from a
    subset of fields.
  
    Args:
      box_mask_list: np_box_mask_list.BoxMaskList holding N boxes
      indices: a 1-d numpy array of type int_
      fields: (optional) list of fields to also gather from.  If None (default),
          all fields are gathered from.  Pass an empty fields list to only gather
          the box coordinates.
  
    Returns:
      subbox_mask_list: a np_box_mask_list.BoxMaskList corresponding to the subset
          of the input box_mask_list specified by indices
  
    Raises:
      ValueError: if specified field is not contained in box_mask_list or if the
          indices are not of type int_
    """
    if fields is not None:
      if 'masks' not in fields:
        fields.append('masks')
    return box_list_to_box_mask_list(
        np_box_list_ops.gather(
            boxlist=box_mask_list, indices=indices, fields=fields))
  
  
  def sort_by_field(box_mask_list, field,
                    order=np_box_list_ops.SortOrder.DESCEND):
    """Sort boxes and associated fields according to a scalar field.
  
    A common use case is reordering the boxes according to descending scores.
  
    Args:
      box_mask_list: BoxMaskList holding N boxes.
      field: A BoxMaskList field for sorting and reordering the BoxMaskList.
      order: (Optional) 'descend' or 'ascend'. Default is descend.
  
    Returns:
      sorted_box_mask_list: A sorted BoxMaskList with the field in the specified
        order.
    """
    return box_list_to_box_mask_list(
        np_box_list_ops.sort_by_field(
            boxlist=box_mask_list, field=field, order=order))
  
  
  def non_max_suppression(box_mask_list,
                          max_output_size=10000,
                          iou_threshold=1.0,
                          score_threshold=-10.0):
    """Non maximum suppression.
  
    This op greedily selects a subset of detection bounding boxes, pruning
    away boxes that have high IOU (intersection over union) overlap (> thresh)
    with already selected boxes. In each iteration, the detected bounding box with
    highest score in the available pool is selected.
  
    Args:
      box_mask_list: np_box_mask_list.BoxMaskList holding N boxes.  Must contain
        a 'scores' field representing detection scores. All scores belong to the
        same class.
      max_output_size: maximum number of retained boxes
      iou_threshold: intersection over union threshold.
      score_threshold: minimum score threshold. Remove the boxes with scores
                       less than this value. Default value is set to -10. A very
                       low threshold to pass pretty much all the boxes, unless
                       the user sets a different score threshold.
  
    Returns:
      an np_box_mask_list.BoxMaskList holding M boxes where M <= max_output_size
  
    Raises:
      ValueError: if 'scores' field does not exist
      ValueError: if threshold is not in [0, 1]
      ValueError: if max_output_size < 0
    """
    if not box_mask_list.has_field('scores'):
      raise ValueError('Field scores does not exist')
    if iou_threshold < 0. or iou_threshold > 1.0:
      raise ValueError('IOU threshold must be in [0, 1]')
    if max_output_size < 0:
      raise ValueError('max_output_size must be bigger than 0.')
  
    box_mask_list = filter_scores_greater_than(box_mask_list, score_threshold)
    if box_mask_list.num_boxes() == 0:
      return box_mask_list
  
    box_mask_list = sort_by_field(box_mask_list, 'scores')
  
    # Prevent further computation if NMS is disabled.
    if iou_threshold == 1.0:
      if box_mask_list.num_boxes() > max_output_size:
        selected_indices = np.arange(max_output_size)
        return gather(box_mask_list, selected_indices)
      else:
        return box_mask_list
  
    masks = box_mask_list.get_masks()
    num_masks = box_mask_list.num_boxes()
  
    # is_index_valid is True only for all remaining valid boxes,
    is_index_valid = np.full(num_masks, 1, dtype=bool)
    selected_indices = []
    num_output = 0
    for i in range(num_masks):
      if num_output < max_output_size:
        if is_index_valid[i]:
          num_output += 1
          selected_indices.append(i)
          is_index_valid[i] = False
          valid_indices = np.where(is_index_valid)[0]
          if valid_indices.size == 0:
            break
  
          intersect_over_union = np_mask_ops.iou(
              np.expand_dims(masks[i], axis=0), masks[valid_indices])
          intersect_over_union = np.squeeze(intersect_over_union, axis=0)
          is_index_valid[valid_indices] = np.logical_and(
              is_index_valid[valid_indices],
              intersect_over_union <= iou_threshold)
    return gather(box_mask_list, np.array(selected_indices))
  
  
  def multi_class_non_max_suppression(box_mask_list, score_thresh, iou_thresh,
                                      max_output_size):
    """Multi-class version of non maximum suppression.
  
    This op greedily selects a subset of detection bounding boxes, pruning
    away boxes that have high IOU (intersection over union) overlap (> thresh)
    with already selected boxes.  It operates independently for each class for
    which scores are provided (via the scores field of the input box_list),
    pruning boxes with score less than a provided threshold prior to
    applying NMS.
  
    Args:
      box_mask_list: np_box_mask_list.BoxMaskList holding N boxes.  Must contain a
        'scores' field representing detection scores.  This scores field is a
        tensor that can be 1 dimensional (in the case of a single class) or
        2-dimensional, in which case we assume that it takes the
        shape [num_boxes, num_classes]. We further assume that this rank is known
        statically and that scores.shape[1] is also known (i.e., the number of
        classes is fixed and known at graph construction time).
      score_thresh: scalar threshold for score (low scoring boxes are removed).
      iou_thresh: scalar threshold for IOU (boxes that that high IOU overlap
        with previously selected boxes are removed).
      max_output_size: maximum number of retained boxes per class.
  
    Returns:
      a box_mask_list holding M boxes with a rank-1 scores field representing
        corresponding scores for each box with scores sorted in decreasing order
        and a rank-1 classes field representing a class label for each box.
    Raises:
      ValueError: if iou_thresh is not in [0, 1] or if input box_mask_list does
        not have a valid scores field.
    """
    if not 0 <= iou_thresh <= 1.0:
      raise ValueError('thresh must be between 0 and 1')
    if not isinstance(box_mask_list, np_box_mask_list.BoxMaskList):
      raise ValueError('box_mask_list must be a box_mask_list')
    if not box_mask_list.has_field('scores'):
      raise ValueError('input box_mask_list must have \'scores\' field')
    scores = box_mask_list.get_field('scores')
    if len(scores.shape) == 1:
      scores = np.reshape(scores, [-1, 1])
    elif len(scores.shape) == 2:
      if scores.shape[1] is None:
        raise ValueError('scores field must have statically defined second '
                         'dimension')
    else:
      raise ValueError('scores field must be of rank 1 or 2')
  
    num_boxes = box_mask_list.num_boxes()
    num_scores = scores.shape[0]
    num_classes = scores.shape[1]
  
    if num_boxes != num_scores:
      raise ValueError('Incorrect scores field length: actual vs expected.')
  
    selected_boxes_list = []
    for class_idx in range(num_classes):
      box_mask_list_and_class_scores = np_box_mask_list.BoxMaskList(
          box_data=box_mask_list.get(),
          mask_data=box_mask_list.get_masks())
      class_scores = np.reshape(scores[0:num_scores, class_idx], [-1])
      box_mask_list_and_class_scores.add_field('scores', class_scores)
      box_mask_list_filt = filter_scores_greater_than(
          box_mask_list_and_class_scores, score_thresh)
      nms_result = non_max_suppression(
          box_mask_list_filt,
          max_output_size=max_output_size,
          iou_threshold=iou_thresh,
          score_threshold=score_thresh)
      nms_result.add_field(
          'classes',
          np.zeros_like(nms_result.get_field('scores')) + class_idx)
      selected_boxes_list.append(nms_result)
    selected_boxes = np_box_list_ops.concatenate(selected_boxes_list)
    sorted_boxes = np_box_list_ops.sort_by_field(selected_boxes, 'scores')
    return box_list_to_box_mask_list(boxlist=sorted_boxes)
  
  
  def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0):
    """Prunes the boxes in list1 that overlap less than thresh with list2.
  
    For each mask in box_mask_list1, we want its IOA to be more than minoverlap
    with at least one of the masks in box_mask_list2. If it does not, we remove
    it. If the masks are not full size image, we do the pruning based on boxes.
  
    Args:
      box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks.
      box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks.
      minoverlap: Minimum required overlap between boxes, to count them as
                  overlapping.
  
    Returns:
      A pruned box_mask_list with size [N', 4].
    """
    intersection_over_area = ioa(box_mask_list2, box_mask_list1)  # [M, N] tensor
    intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
    keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
    keep_inds = np.nonzero(keep_bool)[0]
    new_box_mask_list1 = gather(box_mask_list1, keep_inds)
    return new_box_mask_list1
  
  
  def concatenate(box_mask_lists, fields=None):
    """Concatenate list of box_mask_lists.
  
    This op concatenates a list of input box_mask_lists into a larger
    box_mask_list.  It also
    handles concatenation of box_mask_list fields as long as the field tensor
    shapes are equal except for the first dimension.
  
    Args:
      box_mask_lists: list of np_box_mask_list.BoxMaskList objects
      fields: optional list of fields to also concatenate.  By default, all
        fields from the first BoxMaskList in the list are included in the
        concatenation.
  
    Returns:
      a box_mask_list with number of boxes equal to
        sum([box_mask_list.num_boxes() for box_mask_list in box_mask_list])
    Raises:
      ValueError: if box_mask_lists is invalid (i.e., is not a list, is empty, or
        contains non box_mask_list objects), or if requested fields are not
        contained in all box_mask_lists
    """
    if fields is not None:
      if 'masks' not in fields:
        fields.append('masks')
    return box_list_to_box_mask_list(
        np_box_list_ops.concatenate(boxlists=box_mask_lists, fields=fields))
  
  
  def filter_scores_greater_than(box_mask_list, thresh):
    """Filter to keep only boxes and masks with score exceeding a given threshold.
  
    This op keeps the collection of boxes and masks whose corresponding scores are
    greater than the input threshold.
  
    Args:
      box_mask_list: BoxMaskList holding N boxes and masks.  Must contain a
        'scores' field representing detection scores.
      thresh: scalar threshold
  
    Returns:
      a BoxMaskList holding M boxes and masks where M <= N
  
    Raises:
      ValueError: if box_mask_list not a np_box_mask_list.BoxMaskList object or
        if it does not have a scores field
    """
    if not isinstance(box_mask_list, np_box_mask_list.BoxMaskList):
      raise ValueError('box_mask_list must be a BoxMaskList')
    if not box_mask_list.has_field('scores'):
      raise ValueError('input box_mask_list must have \'scores\' field')
    scores = box_mask_list.get_field('scores')
    if len(scores.shape) > 2:
      raise ValueError('Scores should have rank 1 or 2')
    if len(scores.shape) == 2 and scores.shape[1] != 1:
      raise ValueError('Scores should have rank 1 or have shape '
                       'consistent with [None, 1]')
    high_score_indices = np.reshape(np.where(np.greater(scores, thresh)),
                                    [-1]).astype(np.int32)
    return gather(box_mask_list, high_score_indices)
  