mobilenetdet.py 文件源码

def batch_iou_fast(anchors, bboxes):
  """ Compute iou of two batch of boxes. Box format '[y_min, x_min, y_max, x_max]'.
  Args:
    anchors: know shape
    bboxes: dynamic shape
  Return:
    ious: 2-D with shape '[num_bboxes, num_anchors]'
    indices: [num_bboxes, 1]
  """
  num_anchors = anchors.get_shape().as_list()[0]
  tensor_num_bboxes = tf.shape(bboxes)[0]
  indices = tf.reshape(tf.range(tensor_num_bboxes), shape=[-1, 1])
  indices = tf.reshape(tf.stack([indices]*num_anchors, axis=1), shape=[-1, 1])
  bboxes_m = tf.gather_nd(bboxes, indices)

  anchors_m = tf.tile(anchors, [tensor_num_bboxes, 1])

  lr = tf.maximum(
    tf.minimum(bboxes_m[:, 3], anchors_m[:, 3]) -
    tf.maximum(bboxes_m[:, 1], anchors_m[:, 1]),
    0
  )
  tb = tf.maximum(
    tf.minimum(bboxes_m[:, 2], anchors_m[:, 2]) -
    tf.maximum(bboxes_m[:, 0], anchors_m[:, 0]),
    0
  )
  intersection = tf.multiply(tb, lr)
  union = tf.subtract(
    tf.multiply((bboxes_m[:, 3] - bboxes_m[:, 1]), (bboxes_m[:, 2] - bboxes_m[:, 0])) +
    tf.multiply((anchors_m[:, 3] - anchors_m[:, 1]), (anchors_m[:, 2] - anchors_m[:, 0])),
    intersection
  )
  ious = tf.div(intersection, union)

  ious = tf.reshape(ious, shape=[tensor_num_bboxes, num_anchors])

  indices = tf.arg_max(ious, dimension=1)

  return ious, indices