data_loader.py 文件源码

def _train_preprocess(reshaped_image, args):
  # Image processing for training the network. Note the many random
  # distortions applied to the image.

  # Randomly crop a [height, width] section of the image.
  reshaped_image = tf.random_crop(reshaped_image, [args.crop_size[0], args.crop_size[1], args.num_channels])

  # Randomly flip the image horizontally.
  reshaped_image = tf.image.random_flip_left_right(reshaped_image)

  # Because these operations are not commutative, consider randomizing
  # the order their operation.
  reshaped_image = tf.image.random_brightness(reshaped_image,
                                               max_delta=63)
  # Randomly changing contrast of the image
  reshaped_image = tf.image.random_contrast(reshaped_image,
                                             lower=0.2, upper=1.8)

  # Subtract off the mean and divide by the variance of the pixels.
  reshaped_image = tf.image.per_image_standardization(reshaped_image)

  # Set the shapes of tensors.
  reshaped_image.set_shape([args.crop_size[0], args.crop_size[1], args.num_channels])
  #read_input.label.set_shape([1])
  return reshaped_image