Python input_preprocess.preprocess_image_and_label方法代码示例

# 需要导入模块: from deeplab import input_preprocess [as 别名]
# 或者: from deeplab.input_preprocess import preprocess_image_and_label [as 别名]
def _create_input_tensors():
  """Creates and prepares input tensors for DeepLab model.

  This method creates a 4-D uint8 image tensor 'ImageTensor' with shape
  [1, None, None, 3]. The actual input tensor name to use during inference is
  'ImageTensor:0'.

  Returns:
    image: Preprocessed 4-D float32 tensor with shape [1, crop_height,
      crop_width, 3].
    original_image_size: Original image shape tensor [height, width].
    resized_image_size: Resized image shape tensor [height, width].
  """
  # input_preprocess takes 4-D image tensor as input.
  input_image = tf.placeholder(tf.uint8, [1, None, None, 3], name=_INPUT_NAME)
  original_image_size = tf.shape(input_image)[1:3]

  # Squeeze the dimension in axis=0 since `preprocess_image_and_label` assumes
  # image to be 3-D.
  image = tf.squeeze(input_image, axis=0)
  resized_image, image, _ = input_preprocess.preprocess_image_and_label(
      image,
      label=None,
      crop_height=FLAGS.crop_size[0],
      crop_width=FLAGS.crop_size[1],
      min_resize_value=FLAGS.min_resize_value,
      max_resize_value=FLAGS.max_resize_value,
      resize_factor=FLAGS.resize_factor,
      is_training=False,
      model_variant=FLAGS.model_variant)
  resized_image_size = tf.shape(resized_image)[:2]

  # Expand the dimension in axis=0, since the following operations assume the
  # image to be 4-D.
  image = tf.expand_dims(image, 0)

  return image, original_image_size, resized_image_size 

# 需要导入模块: from deeplab import input_preprocess [as 别名]
# 或者: from deeplab.input_preprocess import preprocess_image_and_label [as 别名]
def _preprocess_image(self, sample):
    """Preprocesses the image and label.

    Args:
      sample: A sample containing image and label.

    Returns:
      sample: Sample with preprocessed image and label.

    Raises:
      ValueError: Ground truth label not provided during training.
    """
    image = sample[common.IMAGE]
    label = sample[common.LABELS_CLASS]

    original_image, image, label = input_preprocess.preprocess_image_and_label(
        image=image,
        label=label,
        crop_height=self.crop_size[0],
        crop_width=self.crop_size[1],
        min_resize_value=self.min_resize_value,
        max_resize_value=self.max_resize_value,
        resize_factor=self.resize_factor,
        min_scale_factor=self.min_scale_factor,
        max_scale_factor=self.max_scale_factor,
        scale_factor_step_size=self.scale_factor_step_size,
        ignore_label=self.ignore_label,
        is_training=self.is_training,
        model_variant=self.model_variant)

    sample[common.IMAGE] = image

    if not self.is_training:
      # Original image is only used during visualization.
      sample[common.ORIGINAL_IMAGE] = original_image

    if label is not None:
      sample[common.LABEL] = label

    # Remove common.LABEL_CLASS key in the sample since it is only used to
    # derive label and not used in training and evaluation.
    sample.pop(common.LABELS_CLASS, None)

    return sample 

# 需要导入模块: from deeplab import input_preprocess [as 别名]
# 或者: from deeplab.input_preprocess import preprocess_image_and_label [as 别名]
def _preprocess_image(self, sample):
    """Preprocesses the image and label.

    Args:
      sample: A sample containing image and label.

    Returns:
      sample: Sample with preprocessed image and label.

    Raises:
      ValueError: Ground truth label not provided during training.
    """
    image = sample[common.IMAGE]
    label = sample[common.LABELS_CLASS]

    # print(self.crop_size)
    original_image, image, label = input_preprocess.preprocess_image_and_label(
        image=image,
        label=label,
        crop_height=self.crop_size[0],
        crop_width=self.crop_size[1],
        min_resize_value=self.min_resize_value,
        max_resize_value=self.max_resize_value,
        resize_factor=self.resize_factor,
        min_scale_factor=self.min_scale_factor,
        max_scale_factor=self.max_scale_factor,
        scale_factor_step_size=self.scale_factor_step_size,
        ignore_label=self.ignore_label,
        is_training=self.is_training,
        model_variant=self.model_variant)

    sample[common.IMAGE] = image

    if not self.is_training:
      # Original image is only used during visualization.
      sample[common.ORIGINAL_IMAGE] = original_image

    if label is not None:
      sample[common.LABEL] = label

    # Remove common.LABEL_CLASS key in the sample since it is only used to
    # derive label and not used in training and evaluation.
    sample.pop(common.LABELS_CLASS, None)

    return sample