content: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  import numpy as np
  
  def unique_boxes(boxes, scale=1.0):
      """Return indices of unique boxes."""
      v = np.array([1, 1e3, 1e6, 1e9])
      hashes = np.round(boxes * scale).dot(v)
      _, index = np.unique(hashes, return_index=True)
      return np.sort(index)
  
  def xywh_to_xyxy(boxes):
      """Convert [x y w h] box format to [x1 y1 x2 y2] format."""
      return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))
  
  def xyxy_to_xywh(boxes):
      """Convert [x1 y1 x2 y2] box format to [x y w h] format."""
      return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))
  
  def validate_boxes(boxes, width=0, height=0):
      """Check that a set of boxes are valid."""
      x1 = boxes[:, 0]
      y1 = boxes[:, 1]
      x2 = boxes[:, 2]
      y2 = boxes[:, 3]
      assert (x1 >= 0).all()
      assert (y1 >= 0).all()
      assert (x2 >= x1).all()
      assert (y2 >= y1).all()
      assert (x2 < width).all()
      assert (y2 < height).all()
  
  def filter_small_boxes(boxes, min_size):
      w = boxes[:, 2] - boxes[:, 0]
      h = boxes[:, 3] - boxes[:, 1]
      keep = np.where((w >= min_size) & (h > min_size))[0]
      return keep
  