keypoint_ops.py 文件源码

def to_normalized_coordinates(keypoints, height, width,
                              check_range=True, scope=None):
  """Converts absolute keypoint coordinates to normalized coordinates in [0, 1].

  Usually one uses the dynamic shape of the image or conv-layer tensor:
    keypoints = keypoint_ops.to_normalized_coordinates(keypoints,
                                                       tf.shape(images)[1],
                                                       tf.shape(images)[2]),

  This function raises an assertion failed error at graph execution time when
  the maximum coordinate is smaller than 1.01 (which means that coordinates are
  already normalized). The value 1.01 is to deal with small rounding errors.

  Args:
    keypoints: A tensor of shape [num_instances, num_keypoints, 2].
    height: Maximum value for y coordinate of absolute keypoint coordinates.
    width: Maximum value for x coordinate of absolute keypoint coordinates.
    check_range: If True, checks if the coordinates are normalized.
    scope: name scope.

  Returns:
    tensor of shape [num_instances, num_keypoints, 2] with normalized
    coordinates in [0, 1].
  """
  with tf.name_scope(scope, 'ToNormalizedCoordinates'):
    height = tf.cast(height, tf.float32)
    width = tf.cast(width, tf.float32)

    if check_range:
      max_val = tf.reduce_max(keypoints)
      max_assert = tf.Assert(tf.greater(max_val, 1.01),
                             ['max value is lower than 1.01: ', max_val])
      with tf.control_dependencies([max_assert]):
        width = tf.identity(width)

    return scale(keypoints, 1.0 / height, 1.0 / width)