text: # -*- coding: utf-8 -*-
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  
  def ious_calu(boxes_1, boxes_2):
      '''
  
      :param boxes_1: [N, 4] [xmin, ymin, xmax, ymax]
      :param boxes_2: [M, 4] [xmin, ymin. xmax, ymax]
      :return:
      '''
      boxes_1 = tf.cast(boxes_1, tf.float32)
      boxes_2 = tf.cast(boxes_2, tf.float32)
      xmin_1, ymin_1, xmax_1, ymax_1 = tf.split(boxes_1, 4, axis=1)  # xmin_1 shape is [N, 1]..
      xmin_2, ymin_2, xmax_2, ymax_2 = tf.unstack(boxes_2, axis=1)  # xmin_2 shape is [M, ]..
  
      max_xmin = tf.maximum(xmin_1, xmin_2)
      min_xmax = tf.minimum(xmax_1, xmax_2)
  
      max_ymin = tf.maximum(ymin_1, ymin_2)
      min_ymax = tf.minimum(ymax_1, ymax_2)
  
      overlap_h = tf.maximum(0., min_ymax - max_ymin)  # avoid h < 0
      overlap_w = tf.maximum(0., min_xmax - max_xmin)
  
      overlaps = overlap_h * overlap_w
  
      area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1)  # [N, 1]
      area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2)  # [M, ]
  
      ious = overlaps / (area_1 + area_2 - overlaps)
  
      return ious
  
  
  def clip_boxes_to_img_boundaries(boxes, img_shape):
      '''
  
      :param decode_boxes:
      :return: decode boxes, and already clip to boundaries
      '''
  
      with tf.name_scope('clip_boxes_to_img_boundaries'):
  
          # xmin, ymin, xmax, ymax = tf.unstack(decode_boxes, axis=1)
          xmin = boxes[:, 0]
          ymin = boxes[:, 1]
          xmax = boxes[:, 2]
          ymax = boxes[:, 3]
          img_h, img_w = img_shape[1], img_shape[2]
  
          img_h, img_w = tf.cast(img_h, tf.float32), tf.cast(img_w, tf.float32)
  
          xmin = tf.maximum(tf.minimum(xmin, img_w-1.), 0.)
          ymin = tf.maximum(tf.minimum(ymin, img_h-1.), 0.)
  
          xmax = tf.maximum(tf.minimum(xmax, img_w-1.), 0.)
          ymax = tf.maximum(tf.minimum(ymax, img_h-1.), 0.)
  
          return tf.transpose(tf.stack([xmin, ymin, xmax, ymax]))
  
  
  def filter_outside_boxes(boxes, img_h, img_w):
      '''
      :param anchors:boxes with format [xmin, ymin, xmax, ymax]
      :param img_h: height of image
      :param img_w: width of image
      :return: indices of anchors that inside the image boundary
      '''
  
      with tf.name_scope('filter_outside_boxes'):
          xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1)
  
          xmin_index = tf.greater_equal(xmin, 0)
          ymin_index = tf.greater_equal(ymin, 0)
          xmax_index = tf.less_equal(xmax, tf.cast(img_w, tf.float32))
          ymax_index = tf.less_equal(ymax, tf.cast(img_h, tf.float32))
  
          indices = tf.transpose(tf.stack([xmin_index, ymin_index, xmax_index, ymax_index]))
          indices = tf.cast(indices, dtype=tf.int32)
          indices = tf.reduce_sum(indices, axis=1)
          indices = tf.where(tf.equal(indices, 4))
          # indices = tf.equal(indices, 4)
          return tf.reshape(indices, [-1])
  
  
  def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes):
  
      '''
      num of boxes less than max num of boxes, so it need to pad with zeros[0, 0, 0, 0]
      :param boxes:
      :param scores: [-1]
      :param max_num_of_boxes:
      :return:
      '''
  
      pad_num = tf.cast(max_num_of_boxes, tf.int32) - tf.shape(boxes)[0]
  
      zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype)
      zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype)
  
      final_boxes = tf.concat([boxes, zero_boxes], axis=0)
  
      final_scores = tf.concat([scores, zero_scores], axis=0)
  
      return final_boxes, final_scores