preprocess.py 文件源码

def tf_random_aspect_resize(image, label, low_val=1.0, upper_val=1.5):
  shape = tf.shape(image)
  height = shape[0]
  width = shape[1]

  # 1~1.5
  which_side = tf.to_float(tf.random_uniform([1]))[0]
  multi_val = tf.to_float(tf.random_uniform([1]))[0] * (upper_val - low_val) + low_val

  new_height = tf.cond(which_side > 0.5, lambda: tf.to_float(height), lambda: tf.to_float(height) * multi_val)
  new_width = tf.cond(which_side <= 0.5, lambda: tf.to_float(width), lambda: tf.to_float(width) * multi_val)

  new_height = tf.to_int32(new_height)
  new_width = tf.to_int32(new_width)

  image = tf.expand_dims(image, 0)
  label = tf.expand_dims(label, 0)
  resized_image = tf.image.resize_bilinear(image, [new_height, new_width], align_corners=False)
  resized_image = tf.cast(resized_image, tf.uint8)
  resized_label = tf.image.resize_nearest_neighbor(label, [new_height, new_width], align_corners=False)
  resized_label = tf.cast(resized_label, tf.uint8)
  resized_image = tf.squeeze(resized_image, 0)
  resized_label = tf.squeeze(resized_label, 0)
  return resized_image, resized_label